modelId
string | author
string | last_modified
timestamp[us, tz=UTC] | downloads
int64 | likes
int64 | library_name
string | tags
list | pipeline_tag
string | createdAt
timestamp[us, tz=UTC] | card
string |
|---|---|---|---|---|---|---|---|---|---|
MayBashendy/Arabic_FineTuningAraBERT_AugV4_k60_task2_organization_fold1
|
MayBashendy
| 2024-11-16T23:10:43Z
| 6
| 0
|
transformers
|
[
"transformers",
"safetensors",
"bert",
"text-classification",
"generated_from_trainer",
"base_model:aubmindlab/bert-base-arabertv02",
"base_model:finetune:aubmindlab/bert-base-arabertv02",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2024-11-16T22:25:02Z
|
---
library_name: transformers
base_model: aubmindlab/bert-base-arabertv02
tags:
- generated_from_trainer
model-index:
- name: Arabic_FineTuningAraBERT_AugV4_k60_task2_organization_fold1
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# Arabic_FineTuningAraBERT_AugV4_k60_task2_organization_fold1
This model is a fine-tuned version of [aubmindlab/bert-base-arabertv02](https://huggingface.co/aubmindlab/bert-base-arabertv02) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7022
- Qwk: 0.4167
- Mse: 0.7022
- Rmse: 0.8380
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Qwk | Mse | Rmse |
|:-------------:|:------:|:----:|:---------------:|:-------:|:------:|:------:|
| No log | 0.0023 | 2 | 3.8735 | 0.0 | 3.8735 | 1.9681 |
| No log | 0.0045 | 4 | 1.9022 | -0.1803 | 1.9022 | 1.3792 |
| No log | 0.0068 | 6 | 1.0131 | -0.0563 | 1.0131 | 1.0065 |
| No log | 0.0090 | 8 | 0.6782 | 0.0400 | 0.6782 | 0.8235 |
| No log | 0.0113 | 10 | 1.0019 | 0.0727 | 1.0019 | 1.0009 |
| No log | 0.0135 | 12 | 1.2173 | 0.0 | 1.2173 | 1.1033 |
| No log | 0.0158 | 14 | 1.0573 | -0.0800 | 1.0573 | 1.0282 |
| No log | 0.0180 | 16 | 1.0174 | 0.0 | 1.0174 | 1.0087 |
| No log | 0.0203 | 18 | 0.7626 | 0.0 | 0.7626 | 0.8733 |
| No log | 0.0225 | 20 | 0.7263 | 0.0 | 0.7263 | 0.8522 |
| No log | 0.0248 | 22 | 0.9209 | 0.0 | 0.9209 | 0.9596 |
| No log | 0.0270 | 24 | 0.8496 | 0.0 | 0.8496 | 0.9217 |
| No log | 0.0293 | 26 | 0.8805 | -0.0800 | 0.8805 | 0.9384 |
| No log | 0.0315 | 28 | 0.8506 | -0.0800 | 0.8506 | 0.9223 |
| No log | 0.0338 | 30 | 1.1342 | 0.0800 | 1.1342 | 1.0650 |
| No log | 0.0360 | 32 | 1.2587 | 0.1176 | 1.2587 | 1.1219 |
| No log | 0.0383 | 34 | 1.2015 | 0.1750 | 1.2015 | 1.0961 |
| No log | 0.0405 | 36 | 0.9410 | 0.2000 | 0.9410 | 0.9700 |
| No log | 0.0428 | 38 | 0.8309 | 0.2000 | 0.8309 | 0.9116 |
| No log | 0.0450 | 40 | 0.7230 | 0.1176 | 0.7230 | 0.8503 |
| No log | 0.0473 | 42 | 0.6932 | 0.0769 | 0.6932 | 0.8326 |
| No log | 0.0495 | 44 | 0.8579 | 0.2000 | 0.8579 | 0.9262 |
| No log | 0.0518 | 46 | 1.3608 | 0.1474 | 1.3608 | 1.1665 |
| No log | 0.0541 | 48 | 1.8858 | 0.0 | 1.8858 | 1.3732 |
| No log | 0.0563 | 50 | 1.7641 | 0.0308 | 1.7641 | 1.3282 |
| No log | 0.0586 | 52 | 1.3319 | -0.1647 | 1.3319 | 1.1541 |
| No log | 0.0608 | 54 | 1.3802 | -0.0200 | 1.3802 | 1.1748 |
| No log | 0.0631 | 56 | 1.7351 | 0.0160 | 1.7351 | 1.3172 |
| No log | 0.0653 | 58 | 1.9599 | 0.0308 | 1.9599 | 1.4000 |
| No log | 0.0676 | 60 | 1.3907 | 0.1386 | 1.3907 | 1.1793 |
| No log | 0.0698 | 62 | 0.9536 | 0.1739 | 0.9536 | 0.9765 |
| No log | 0.0721 | 64 | 0.9115 | 0.1739 | 0.9115 | 0.9547 |
| No log | 0.0743 | 66 | 1.3161 | 0.0198 | 1.3161 | 1.1472 |
| No log | 0.0766 | 68 | 2.2699 | 0.0 | 2.2699 | 1.5066 |
| No log | 0.0788 | 70 | 2.4574 | 0.0116 | 2.4574 | 1.5676 |
| No log | 0.0811 | 72 | 2.1857 | 0.0143 | 2.1857 | 1.4784 |
| No log | 0.0833 | 74 | 1.7118 | 0.0308 | 1.7118 | 1.3083 |
| No log | 0.0856 | 76 | 1.2014 | 0.0690 | 1.2014 | 1.0961 |
| No log | 0.0878 | 78 | 1.1535 | 0.1220 | 1.1535 | 1.0740 |
| No log | 0.0901 | 80 | 1.4696 | 0.0 | 1.4696 | 1.2123 |
| No log | 0.0923 | 82 | 2.0174 | 0.0444 | 2.0174 | 1.4204 |
| No log | 0.0946 | 84 | 2.3410 | 0.0444 | 2.3410 | 1.5300 |
| No log | 0.0968 | 86 | 2.1502 | 0.0455 | 2.1502 | 1.4663 |
| No log | 0.0991 | 88 | 1.8731 | -0.0488 | 1.8731 | 1.3686 |
| No log | 0.1014 | 90 | 1.9211 | -0.0488 | 1.9211 | 1.3860 |
| No log | 0.1036 | 92 | 2.2163 | 0.0455 | 2.2163 | 1.4887 |
| No log | 0.1059 | 94 | 2.4187 | 0.0851 | 2.4187 | 1.5552 |
| No log | 0.1081 | 96 | 2.2630 | 0.1060 | 2.2630 | 1.5043 |
| No log | 0.1104 | 98 | 1.7589 | -0.0500 | 1.7589 | 1.3262 |
| No log | 0.1126 | 100 | 1.2555 | 0.0250 | 1.2555 | 1.1205 |
| No log | 0.1149 | 102 | 1.1239 | 0.1316 | 1.1239 | 1.0602 |
| No log | 0.1171 | 104 | 1.3339 | -0.0220 | 1.3339 | 1.1550 |
| No log | 0.1194 | 106 | 1.8954 | 0.0153 | 1.8954 | 1.3767 |
| No log | 0.1216 | 108 | 2.2015 | 0.0838 | 2.2015 | 1.4837 |
| No log | 0.1239 | 110 | 2.0003 | 0.0548 | 2.0003 | 1.4143 |
| No log | 0.1261 | 112 | 1.7633 | 0.1905 | 1.7633 | 1.3279 |
| No log | 0.1284 | 114 | 1.3888 | 0.1818 | 1.3888 | 1.1785 |
| No log | 0.1306 | 116 | 1.0749 | 0.0526 | 1.0749 | 1.0368 |
| No log | 0.1329 | 118 | 1.0236 | 0.0833 | 1.0236 | 1.0118 |
| No log | 0.1351 | 120 | 1.1350 | 0.1220 | 1.1350 | 1.0653 |
| No log | 0.1374 | 122 | 1.4946 | 0.0513 | 1.4946 | 1.2225 |
| No log | 0.1396 | 124 | 2.2271 | 0.0959 | 2.2271 | 1.4923 |
| No log | 0.1419 | 126 | 2.4276 | 0.0838 | 2.4276 | 1.5581 |
| No log | 0.1441 | 128 | 2.1596 | 0.0662 | 2.1596 | 1.4696 |
| No log | 0.1464 | 130 | 1.5280 | 0.0640 | 1.5280 | 1.2361 |
| No log | 0.1486 | 132 | 1.0162 | 0.1481 | 1.0162 | 1.0080 |
| No log | 0.1509 | 134 | 0.9108 | 0.1316 | 0.9108 | 0.9544 |
| No log | 0.1532 | 136 | 1.0440 | 0.1481 | 1.0440 | 1.0218 |
| No log | 0.1554 | 138 | 1.3645 | -0.0220 | 1.3645 | 1.1681 |
| No log | 0.1577 | 140 | 1.8663 | 0.0611 | 1.8663 | 1.3661 |
| No log | 0.1599 | 142 | 2.1222 | 0.0548 | 2.1222 | 1.4568 |
| No log | 0.1622 | 144 | 1.9181 | 0.0548 | 1.9181 | 1.3849 |
| No log | 0.1644 | 146 | 1.4330 | -0.0220 | 1.4330 | 1.1971 |
| No log | 0.1667 | 148 | 1.1058 | 0.1316 | 1.1058 | 1.0516 |
| No log | 0.1689 | 150 | 1.1105 | 0.1316 | 1.1105 | 1.0538 |
| No log | 0.1712 | 152 | 1.0656 | 0.1316 | 1.0656 | 1.0323 |
| No log | 0.1734 | 154 | 1.1524 | 0.1316 | 1.1524 | 1.0735 |
| No log | 0.1757 | 156 | 1.4061 | 0.125 | 1.4061 | 1.1858 |
| No log | 0.1779 | 158 | 1.8569 | 0.1364 | 1.8569 | 1.3627 |
| No log | 0.1802 | 160 | 1.9618 | 0.0160 | 1.9618 | 1.4006 |
| No log | 0.1824 | 162 | 1.7884 | -0.0364 | 1.7884 | 1.3373 |
| No log | 0.1847 | 164 | 1.6763 | -0.0364 | 1.6763 | 1.2947 |
| No log | 0.1869 | 166 | 1.5814 | -0.0364 | 1.5814 | 1.2576 |
| No log | 0.1892 | 168 | 1.3565 | 0.0198 | 1.3565 | 1.1647 |
| No log | 0.1914 | 170 | 1.3351 | 0.0625 | 1.3351 | 1.1555 |
| No log | 0.1937 | 172 | 1.2894 | -0.0220 | 1.2894 | 1.1355 |
| No log | 0.1959 | 174 | 1.2869 | -0.0220 | 1.2869 | 1.1344 |
| No log | 0.1982 | 176 | 1.2182 | 0.1481 | 1.2182 | 1.1037 |
| No log | 0.2005 | 178 | 1.3381 | 0.0625 | 1.3381 | 1.1568 |
| No log | 0.2027 | 180 | 1.7172 | 0.0182 | 1.7172 | 1.3104 |
| No log | 0.2050 | 182 | 2.0050 | 0.0735 | 2.0050 | 1.4160 |
| No log | 0.2072 | 184 | 2.0578 | 0.0426 | 2.0578 | 1.4345 |
| No log | 0.2095 | 186 | 1.6268 | 0.0182 | 1.6268 | 1.2754 |
| No log | 0.2117 | 188 | 1.3308 | 0.1099 | 1.3308 | 1.1536 |
| No log | 0.2140 | 190 | 1.1420 | 0.1628 | 1.1420 | 1.0687 |
| No log | 0.2162 | 192 | 1.0526 | 0.0488 | 1.0526 | 1.0259 |
| No log | 0.2185 | 194 | 1.1564 | 0.1628 | 1.1564 | 1.0753 |
| No log | 0.2207 | 196 | 1.4259 | 0.0727 | 1.4259 | 1.1941 |
| No log | 0.2230 | 198 | 1.7201 | -0.0500 | 1.7201 | 1.3115 |
| No log | 0.2252 | 200 | 1.6371 | -0.0174 | 1.6371 | 1.2795 |
| No log | 0.2275 | 202 | 1.2747 | 0.1176 | 1.2747 | 1.1290 |
| No log | 0.2297 | 204 | 0.9111 | 0.1818 | 0.9111 | 0.9545 |
| No log | 0.2320 | 206 | 0.8065 | 0.0952 | 0.8065 | 0.8981 |
| No log | 0.2342 | 208 | 0.8421 | 0.0625 | 0.8421 | 0.9177 |
| No log | 0.2365 | 210 | 0.9543 | 0.1818 | 0.9543 | 0.9769 |
| No log | 0.2387 | 212 | 1.3155 | 0.1600 | 1.3155 | 1.1469 |
| No log | 0.2410 | 214 | 1.5659 | -0.0174 | 1.5659 | 1.2513 |
| No log | 0.2432 | 216 | 1.5303 | 0.0571 | 1.5303 | 1.2371 |
| No log | 0.2455 | 218 | 1.3153 | -0.0465 | 1.3153 | 1.1468 |
| No log | 0.2477 | 220 | 1.0113 | 0.0506 | 1.0113 | 1.0056 |
| No log | 0.25 | 222 | 0.9995 | 0.0506 | 0.9995 | 0.9997 |
| No log | 0.2523 | 224 | 1.1247 | 0.1316 | 1.1247 | 1.0605 |
| No log | 0.2545 | 226 | 1.3671 | 0.0943 | 1.3671 | 1.1692 |
| No log | 0.2568 | 228 | 1.3733 | 0.1273 | 1.3733 | 1.1719 |
| No log | 0.2590 | 230 | 1.1560 | 0.1714 | 1.1560 | 1.0752 |
| No log | 0.2613 | 232 | 0.7882 | 0.4167 | 0.7882 | 0.8878 |
| No log | 0.2635 | 234 | 0.7635 | 0.4167 | 0.7635 | 0.8738 |
| No log | 0.2658 | 236 | 1.0269 | 0.1750 | 1.0269 | 1.0134 |
| No log | 0.2680 | 238 | 1.4135 | 0.0182 | 1.4135 | 1.1889 |
| No log | 0.2703 | 240 | 1.3877 | -0.0941 | 1.3877 | 1.1780 |
| No log | 0.2725 | 242 | 1.4174 | -0.0941 | 1.4174 | 1.1905 |
| No log | 0.2748 | 244 | 1.6380 | -0.0541 | 1.6380 | 1.2798 |
| No log | 0.2770 | 246 | 2.0154 | -0.0305 | 2.0154 | 1.4197 |
| No log | 0.2793 | 248 | 1.8665 | 0.0 | 1.8665 | 1.3662 |
| No log | 0.2815 | 250 | 1.3963 | -0.0625 | 1.3963 | 1.1816 |
| No log | 0.2838 | 252 | 1.3117 | -0.0941 | 1.3117 | 1.1453 |
| No log | 0.2860 | 254 | 1.3134 | -0.0667 | 1.3134 | 1.1460 |
| No log | 0.2883 | 256 | 1.3521 | -0.0667 | 1.3521 | 1.1628 |
| No log | 0.2905 | 258 | 1.3683 | -0.0396 | 1.3683 | 1.1697 |
| No log | 0.2928 | 260 | 1.4943 | 0.0 | 1.4943 | 1.2224 |
| No log | 0.2950 | 262 | 1.5368 | 0.0 | 1.5368 | 1.2397 |
| No log | 0.2973 | 264 | 1.4786 | -0.0206 | 1.4786 | 1.2160 |
| No log | 0.2995 | 266 | 1.7597 | 0.1951 | 1.7597 | 1.3265 |
| No log | 0.3018 | 268 | 2.1822 | 0.0727 | 2.1822 | 1.4772 |
| No log | 0.3041 | 270 | 2.1098 | 0.1342 | 2.1098 | 1.4525 |
| No log | 0.3063 | 272 | 1.5882 | 0.0748 | 1.5882 | 1.2603 |
| No log | 0.3086 | 274 | 1.1195 | 0.0741 | 1.1195 | 1.0581 |
| No log | 0.3108 | 276 | 1.0986 | 0.0741 | 1.0986 | 1.0481 |
| No log | 0.3131 | 278 | 1.2943 | 0.1980 | 1.2943 | 1.1377 |
| No log | 0.3153 | 280 | 1.4898 | 0.0182 | 1.4898 | 1.2206 |
| No log | 0.3176 | 282 | 1.5650 | 0.0182 | 1.5650 | 1.2510 |
| No log | 0.3198 | 284 | 1.3976 | 0.0571 | 1.3976 | 1.1822 |
| No log | 0.3221 | 286 | 1.0461 | 0.0741 | 1.0461 | 1.0228 |
| No log | 0.3243 | 288 | 0.9370 | 0.1972 | 0.9370 | 0.9680 |
| No log | 0.3266 | 290 | 1.0506 | 0.1481 | 1.0506 | 1.0250 |
| No log | 0.3288 | 292 | 1.2735 | 0.0625 | 1.2735 | 1.1285 |
| No log | 0.3311 | 294 | 1.3468 | 0.1386 | 1.3468 | 1.1605 |
| No log | 0.3333 | 296 | 1.4696 | 0.0571 | 1.4696 | 1.2123 |
| No log | 0.3356 | 298 | 1.4595 | 0.0571 | 1.4595 | 1.2081 |
| No log | 0.3378 | 300 | 1.2115 | 0.1099 | 1.2115 | 1.1007 |
| No log | 0.3401 | 302 | 1.0084 | 0.1481 | 1.0084 | 1.0042 |
| No log | 0.3423 | 304 | 0.8267 | 0.1972 | 0.8267 | 0.9092 |
| No log | 0.3446 | 306 | 0.8740 | 0.1972 | 0.8740 | 0.9349 |
| No log | 0.3468 | 308 | 1.1037 | 0.2326 | 1.1037 | 1.0506 |
| No log | 0.3491 | 310 | 1.3697 | 0.1000 | 1.3697 | 1.1703 |
| No log | 0.3514 | 312 | 1.6262 | 0.0909 | 1.6262 | 1.2752 |
| No log | 0.3536 | 314 | 1.6089 | 0.0909 | 1.6089 | 1.2684 |
| No log | 0.3559 | 316 | 1.4202 | 0.1000 | 1.4202 | 1.1917 |
| No log | 0.3581 | 318 | 1.1641 | 0.2326 | 1.1641 | 1.0790 |
| No log | 0.3604 | 320 | 0.9655 | 0.1481 | 0.9655 | 0.9826 |
| No log | 0.3626 | 322 | 0.8933 | 0.1972 | 0.8933 | 0.9451 |
| No log | 0.3649 | 324 | 0.9566 | 0.1481 | 0.9566 | 0.9780 |
| No log | 0.3671 | 326 | 1.0623 | 0.1481 | 1.0623 | 1.0307 |
| No log | 0.3694 | 328 | 1.0448 | 0.1481 | 1.0448 | 1.0222 |
| No log | 0.3716 | 330 | 1.0198 | 0.1481 | 1.0198 | 1.0099 |
| No log | 0.3739 | 332 | 1.0684 | 0.2326 | 1.0684 | 1.0336 |
| No log | 0.3761 | 334 | 1.1642 | 0.1758 | 1.1642 | 1.0790 |
| No log | 0.3784 | 336 | 1.1406 | 0.1758 | 1.1406 | 1.0680 |
| No log | 0.3806 | 338 | 0.9880 | 0.1972 | 0.9880 | 0.9940 |
| No log | 0.3829 | 340 | 0.9897 | 0.1972 | 0.9897 | 0.9948 |
| No log | 0.3851 | 342 | 1.1712 | 0.1758 | 1.1712 | 1.0822 |
| No log | 0.3874 | 344 | 1.5796 | -0.0174 | 1.5796 | 1.2568 |
| No log | 0.3896 | 346 | 1.7164 | 0.0 | 1.7164 | 1.3101 |
| No log | 0.3919 | 348 | 1.5685 | -0.0174 | 1.5685 | 1.2524 |
| No log | 0.3941 | 350 | 1.2358 | -0.0220 | 1.2358 | 1.1117 |
| No log | 0.3964 | 352 | 1.0855 | 0.1972 | 1.0855 | 1.0419 |
| No log | 0.3986 | 354 | 1.1533 | 0.0 | 1.1533 | 1.0739 |
| No log | 0.4009 | 356 | 1.4334 | -0.1714 | 1.4334 | 1.1972 |
| No log | 0.4032 | 358 | 1.6907 | 0.0476 | 1.6907 | 1.3003 |
| No log | 0.4054 | 360 | 1.7013 | 0.0476 | 1.7013 | 1.3043 |
| No log | 0.4077 | 362 | 1.3543 | -0.0220 | 1.3543 | 1.1637 |
| No log | 0.4099 | 364 | 1.0019 | 0.1316 | 1.0019 | 1.0010 |
| No log | 0.4122 | 366 | 0.9585 | 0.1972 | 0.9585 | 0.9790 |
| No log | 0.4144 | 368 | 1.0945 | 0.0 | 1.0945 | 1.0462 |
| No log | 0.4167 | 370 | 1.4569 | -0.1053 | 1.4569 | 1.2070 |
| No log | 0.4189 | 372 | 1.6714 | -0.0174 | 1.6714 | 1.2928 |
| No log | 0.4212 | 374 | 1.6246 | 0.0571 | 1.6246 | 1.2746 |
| No log | 0.4234 | 376 | 1.4190 | 0.0211 | 1.4190 | 1.1912 |
| No log | 0.4257 | 378 | 1.1709 | -0.0465 | 1.1709 | 1.0821 |
| No log | 0.4279 | 380 | 1.0644 | 0.0 | 1.0644 | 1.0317 |
| No log | 0.4302 | 382 | 1.0703 | 0.0 | 1.0703 | 1.0345 |
| No log | 0.4324 | 384 | 1.2359 | -0.0667 | 1.2359 | 1.1117 |
| No log | 0.4347 | 386 | 1.3058 | -0.0667 | 1.3058 | 1.1427 |
| No log | 0.4369 | 388 | 1.3255 | -0.0667 | 1.3255 | 1.1513 |
| No log | 0.4392 | 390 | 1.2080 | -0.0235 | 1.2080 | 1.0991 |
| No log | 0.4414 | 392 | 1.1565 | 0.0250 | 1.1565 | 1.0754 |
| No log | 0.4437 | 394 | 1.0784 | 0.1600 | 1.0784 | 1.0385 |
| No log | 0.4459 | 396 | 1.0475 | 0.1481 | 1.0475 | 1.0235 |
| No log | 0.4482 | 398 | 1.0839 | 0.0233 | 1.0839 | 1.0411 |
| No log | 0.4505 | 400 | 1.0909 | 0.1220 | 1.0909 | 1.0445 |
| No log | 0.4527 | 402 | 1.2547 | -0.0220 | 1.2547 | 1.1201 |
| No log | 0.4550 | 404 | 1.4274 | -0.0220 | 1.4274 | 1.1947 |
| No log | 0.4572 | 406 | 1.6356 | 0.0690 | 1.6356 | 1.2789 |
| No log | 0.4595 | 408 | 1.5108 | 0.0748 | 1.5108 | 1.2291 |
| No log | 0.4617 | 410 | 1.2979 | 0.0455 | 1.2979 | 1.1392 |
| No log | 0.4640 | 412 | 1.1902 | 0.0455 | 1.1902 | 1.0910 |
| No log | 0.4662 | 414 | 1.1457 | 0.0455 | 1.1457 | 1.0704 |
| No log | 0.4685 | 416 | 1.1638 | 0.0455 | 1.1638 | 1.0788 |
| No log | 0.4707 | 418 | 1.1756 | 0.0930 | 1.1756 | 1.0843 |
| No log | 0.4730 | 420 | 1.2875 | 0.0930 | 1.2875 | 1.1347 |
| No log | 0.4752 | 422 | 1.4025 | 0.0211 | 1.4025 | 1.1843 |
| No log | 0.4775 | 424 | 1.2688 | 0.0930 | 1.2688 | 1.1264 |
| No log | 0.4797 | 426 | 1.0789 | 0.1481 | 1.0789 | 1.0387 |
| No log | 0.4820 | 428 | 1.0956 | 0.1481 | 1.0956 | 1.0467 |
| No log | 0.4842 | 430 | 1.0793 | 0.1481 | 1.0793 | 1.0389 |
| No log | 0.4865 | 432 | 0.9955 | 0.1039 | 0.9955 | 0.9977 |
| No log | 0.4887 | 434 | 1.1104 | 0.1220 | 1.1104 | 1.0538 |
| No log | 0.4910 | 436 | 1.3135 | 0.0625 | 1.3135 | 1.1461 |
| No log | 0.4932 | 438 | 1.2305 | 0.0211 | 1.2305 | 1.1093 |
| No log | 0.4955 | 440 | 1.0118 | 0.1481 | 1.0118 | 1.0059 |
| No log | 0.4977 | 442 | 0.9106 | 0.2817 | 0.9106 | 0.9543 |
| No log | 0.5 | 444 | 0.8962 | 0.2817 | 0.8962 | 0.9467 |
| No log | 0.5023 | 446 | 1.0292 | 0.1220 | 1.0292 | 1.0145 |
| No log | 0.5045 | 448 | 1.1282 | 0.1220 | 1.1282 | 1.0622 |
| No log | 0.5068 | 450 | 1.4115 | 0.1356 | 1.4115 | 1.1881 |
| No log | 0.5090 | 452 | 1.4859 | 0.1429 | 1.4859 | 1.2190 |
| No log | 0.5113 | 454 | 1.3220 | 0.1875 | 1.3220 | 1.1498 |
| No log | 0.5135 | 456 | 1.1570 | 0.0233 | 1.1570 | 1.0756 |
| No log | 0.5158 | 458 | 1.1329 | 0.0233 | 1.1329 | 1.0644 |
| No log | 0.5180 | 460 | 1.1871 | -0.0235 | 1.1871 | 1.0895 |
| No log | 0.5203 | 462 | 1.1875 | -0.0235 | 1.1875 | 1.0897 |
| No log | 0.5225 | 464 | 1.2237 | -0.0235 | 1.2237 | 1.1062 |
| No log | 0.5248 | 466 | 1.4407 | 0.1474 | 1.4407 | 1.2003 |
| No log | 0.5270 | 468 | 1.7126 | 0.0476 | 1.7126 | 1.3087 |
| No log | 0.5293 | 470 | 1.6190 | 0.1081 | 1.6190 | 1.2724 |
| No log | 0.5315 | 472 | 1.2366 | 0.0233 | 1.2366 | 1.1120 |
| No log | 0.5338 | 474 | 0.8526 | 0.2192 | 0.8526 | 0.9234 |
| No log | 0.5360 | 476 | 0.7596 | 0.1639 | 0.7596 | 0.8716 |
| No log | 0.5383 | 478 | 0.7853 | 0.1739 | 0.7853 | 0.8862 |
| No log | 0.5405 | 480 | 0.8901 | 0.1667 | 0.8901 | 0.9434 |
| No log | 0.5428 | 482 | 1.0525 | 0.1481 | 1.0525 | 1.0259 |
| No log | 0.5450 | 484 | 1.2704 | 0.0412 | 1.2704 | 1.1271 |
| No log | 0.5473 | 486 | 1.3196 | 0.0412 | 1.3196 | 1.1487 |
| No log | 0.5495 | 488 | 1.2259 | 0.0412 | 1.2259 | 1.1072 |
| No log | 0.5518 | 490 | 1.0430 | 0.1481 | 1.0430 | 1.0213 |
| No log | 0.5541 | 492 | 1.0362 | 0.1481 | 1.0362 | 1.0179 |
| No log | 0.5563 | 494 | 1.1616 | 0.0233 | 1.1616 | 1.0778 |
| No log | 0.5586 | 496 | 1.4584 | 0.0748 | 1.4584 | 1.2076 |
| No log | 0.5608 | 498 | 1.5687 | 0.0690 | 1.5687 | 1.2525 |
| 0.4646 | 0.5631 | 500 | 1.5754 | 0.0571 | 1.5754 | 1.2552 |
| 0.4646 | 0.5653 | 502 | 1.5079 | 0.0571 | 1.5079 | 1.2280 |
| 0.4646 | 0.5676 | 504 | 1.4752 | 0.0571 | 1.4752 | 1.2146 |
| 0.4646 | 0.5698 | 506 | 1.4625 | 0.0571 | 1.4625 | 1.2093 |
| 0.4646 | 0.5721 | 508 | 1.3374 | -0.0667 | 1.3374 | 1.1565 |
| 0.4646 | 0.5743 | 510 | 1.1851 | -0.0220 | 1.1851 | 1.0886 |
| 0.4646 | 0.5766 | 512 | 1.2687 | -0.0667 | 1.2687 | 1.1264 |
| 0.4646 | 0.5788 | 514 | 1.3643 | 0.0211 | 1.3643 | 1.1680 |
| 0.4646 | 0.5811 | 516 | 1.3794 | 0.0211 | 1.3794 | 1.1745 |
| 0.4646 | 0.5833 | 518 | 1.5097 | 0.0400 | 1.5097 | 1.2287 |
| 0.4646 | 0.5856 | 520 | 1.4861 | 0.0400 | 1.4861 | 1.2191 |
| 0.4646 | 0.5878 | 522 | 1.2973 | -0.0667 | 1.2973 | 1.1390 |
| 0.4646 | 0.5901 | 524 | 1.2334 | -0.0667 | 1.2334 | 1.1106 |
| 0.4646 | 0.5923 | 526 | 1.0877 | 0.1481 | 1.0877 | 1.0429 |
| 0.4646 | 0.5946 | 528 | 0.9115 | 0.1667 | 0.9115 | 0.9547 |
| 0.4646 | 0.5968 | 530 | 0.9346 | 0.1220 | 0.9346 | 0.9667 |
| 0.4646 | 0.5991 | 532 | 1.1656 | 0.1220 | 1.1656 | 1.0796 |
| 0.4646 | 0.6014 | 534 | 1.4293 | -0.0220 | 1.4293 | 1.1955 |
| 0.4646 | 0.6036 | 536 | 1.3771 | -0.0220 | 1.3771 | 1.1735 |
| 0.4646 | 0.6059 | 538 | 1.1516 | 0.1220 | 1.1516 | 1.0731 |
| 0.4646 | 0.6081 | 540 | 0.9295 | 0.2192 | 0.9295 | 0.9641 |
| 0.4646 | 0.6104 | 542 | 0.9517 | 0.2192 | 0.9517 | 0.9756 |
| 0.4646 | 0.6126 | 544 | 1.1760 | 0.1290 | 1.1760 | 1.0844 |
| 0.4646 | 0.6149 | 546 | 1.3236 | 0.1031 | 1.3236 | 1.1505 |
| 0.4646 | 0.6171 | 548 | 1.3910 | 0.0 | 1.3910 | 1.1794 |
| 0.4646 | 0.6194 | 550 | 1.2599 | 0.1522 | 1.2599 | 1.1224 |
| 0.4646 | 0.6216 | 552 | 1.1921 | 0.1522 | 1.1921 | 1.0918 |
| 0.4646 | 0.6239 | 554 | 1.0418 | 0.1538 | 1.0418 | 1.0207 |
| 0.4646 | 0.6261 | 556 | 1.0063 | 0.1266 | 1.0063 | 1.0032 |
| 0.4646 | 0.6284 | 558 | 1.0911 | 0.1333 | 1.0911 | 1.0446 |
| 0.4646 | 0.6306 | 560 | 1.2389 | 0.1522 | 1.2389 | 1.1131 |
| 0.4646 | 0.6329 | 562 | 1.4329 | -0.0396 | 1.4329 | 1.1970 |
| 0.4646 | 0.6351 | 564 | 1.5233 | 0.1026 | 1.5233 | 1.2342 |
| 0.4646 | 0.6374 | 566 | 1.2846 | -0.0235 | 1.2846 | 1.1334 |
| 0.4646 | 0.6396 | 568 | 0.9621 | 0.1972 | 0.9621 | 0.9808 |
| 0.4646 | 0.6419 | 570 | 0.8617 | 0.2727 | 0.8617 | 0.9283 |
| 0.4646 | 0.6441 | 572 | 0.9189 | 0.1316 | 0.9189 | 0.9586 |
| 0.4646 | 0.6464 | 574 | 1.2127 | 0.1176 | 1.2127 | 1.1012 |
| 0.4646 | 0.6486 | 576 | 1.5904 | 0.1639 | 1.5904 | 1.2611 |
| 0.4646 | 0.6509 | 578 | 1.6936 | 0.1639 | 1.6936 | 1.3014 |
| 0.4646 | 0.6532 | 580 | 1.4331 | 0.0377 | 1.4331 | 1.1971 |
| 0.4646 | 0.6554 | 582 | 1.0768 | 0.1882 | 1.0768 | 1.0377 |
| 0.4646 | 0.6577 | 584 | 0.9516 | 0.1316 | 0.9516 | 0.9755 |
| 0.4646 | 0.6599 | 586 | 0.9700 | 0.1000 | 0.9700 | 0.9849 |
| 0.4646 | 0.6622 | 588 | 1.0620 | 0.1882 | 1.0620 | 1.0306 |
| 0.4646 | 0.6644 | 590 | 1.1381 | 0.1882 | 1.1381 | 1.0668 |
| 0.4646 | 0.6667 | 592 | 1.1581 | 0.1875 | 1.1581 | 1.0762 |
| 0.4646 | 0.6689 | 594 | 1.0142 | 0.2268 | 1.0142 | 1.0071 |
| 0.4646 | 0.6712 | 596 | 0.8538 | 0.2308 | 0.8538 | 0.9240 |
| 0.4646 | 0.6734 | 598 | 0.7766 | 0.2895 | 0.7766 | 0.8812 |
| 0.4646 | 0.6757 | 600 | 0.8142 | 0.2895 | 0.8142 | 0.9023 |
| 0.4646 | 0.6779 | 602 | 1.0477 | 0.1522 | 1.0477 | 1.0236 |
| 0.4646 | 0.6802 | 604 | 1.4843 | 0.1429 | 1.4843 | 1.2183 |
| 0.4646 | 0.6824 | 606 | 1.5367 | 0.2051 | 1.5367 | 1.2396 |
| 0.4646 | 0.6847 | 608 | 1.2951 | 0.0667 | 1.2951 | 1.1380 |
| 0.4646 | 0.6869 | 610 | 0.9393 | 0.1600 | 0.9393 | 0.9692 |
| 0.4646 | 0.6892 | 612 | 0.7054 | 0.3226 | 0.7054 | 0.8399 |
| 0.4646 | 0.6914 | 614 | 0.6707 | 0.3226 | 0.6707 | 0.8190 |
| 0.4646 | 0.6937 | 616 | 0.7267 | 0.3377 | 0.7267 | 0.8525 |
| 0.4646 | 0.6959 | 618 | 0.9171 | 0.2500 | 0.9171 | 0.9576 |
| 0.4646 | 0.6982 | 620 | 1.1478 | 0.1273 | 1.1478 | 1.0714 |
| 0.4646 | 0.7005 | 622 | 1.4548 | 0.1429 | 1.4548 | 1.2061 |
| 0.4646 | 0.7027 | 624 | 1.3943 | 0.1770 | 1.3943 | 1.1808 |
| 0.4646 | 0.7050 | 626 | 1.1622 | 0.0606 | 1.1622 | 1.0780 |
| 0.4646 | 0.7072 | 628 | 0.9552 | 0.1882 | 0.9552 | 0.9774 |
| 0.4646 | 0.7095 | 630 | 0.9351 | 0.0714 | 0.9351 | 0.9670 |
| 0.4646 | 0.7117 | 632 | 0.9157 | 0.0964 | 0.9157 | 0.9569 |
| 0.4646 | 0.7140 | 634 | 0.9340 | 0.0488 | 0.9340 | 0.9664 |
| 0.4646 | 0.7162 | 636 | 1.0459 | -0.0465 | 1.0459 | 1.0227 |
| 0.4646 | 0.7185 | 638 | 1.2063 | -0.0235 | 1.2063 | 1.0983 |
| 0.4646 | 0.7207 | 640 | 1.2701 | -0.0235 | 1.2701 | 1.1270 |
| 0.4646 | 0.7230 | 642 | 1.3279 | -0.0396 | 1.3279 | 1.1523 |
| 0.4646 | 0.7252 | 644 | 1.2210 | 0.0455 | 1.2210 | 1.1050 |
| 0.4646 | 0.7275 | 646 | 1.1029 | 0.0455 | 1.1029 | 1.0502 |
| 0.4646 | 0.7297 | 648 | 1.1042 | 0.0455 | 1.1042 | 1.0508 |
| 0.4646 | 0.7320 | 650 | 1.1717 | 0.0455 | 1.1717 | 1.0824 |
| 0.4646 | 0.7342 | 652 | 1.0920 | 0.1429 | 1.0920 | 1.0450 |
| 0.4646 | 0.7365 | 654 | 1.1340 | 0.1429 | 1.1340 | 1.0649 |
| 0.4646 | 0.7387 | 656 | 1.2907 | 0.0 | 1.2907 | 1.1361 |
| 0.4646 | 0.7410 | 658 | 1.2005 | -0.0213 | 1.2005 | 1.0957 |
| 0.4646 | 0.7432 | 660 | 1.1094 | -0.0213 | 1.1094 | 1.0533 |
| 0.4646 | 0.7455 | 662 | 1.0815 | 0.0 | 1.0815 | 1.0399 |
| 0.4646 | 0.7477 | 664 | 1.1667 | 0.0217 | 1.1667 | 1.0801 |
| 0.4646 | 0.75 | 666 | 1.1236 | 0.0217 | 1.1236 | 1.0600 |
| 0.4646 | 0.7523 | 668 | 0.9762 | 0.2308 | 0.9762 | 0.9880 |
| 0.4646 | 0.7545 | 670 | 0.8591 | 0.1892 | 0.8591 | 0.9269 |
| 0.4646 | 0.7568 | 672 | 0.8481 | 0.24 | 0.8481 | 0.9209 |
| 0.4646 | 0.7590 | 674 | 0.9258 | 0.1892 | 0.9258 | 0.9622 |
| 0.4646 | 0.7613 | 676 | 1.0705 | 0.1687 | 1.0705 | 1.0347 |
| 0.4646 | 0.7635 | 678 | 1.3751 | -0.0667 | 1.3751 | 1.1727 |
| 0.4646 | 0.7658 | 680 | 1.3921 | -0.0667 | 1.3921 | 1.1799 |
| 0.4646 | 0.7680 | 682 | 1.1685 | 0.0233 | 1.1685 | 1.0810 |
| 0.4646 | 0.7703 | 684 | 0.9546 | 0.1266 | 0.9546 | 0.9771 |
| 0.4646 | 0.7725 | 686 | 0.8979 | 0.1739 | 0.8979 | 0.9476 |
| 0.4646 | 0.7748 | 688 | 0.9710 | 0.1266 | 0.9710 | 0.9854 |
| 0.4646 | 0.7770 | 690 | 1.0416 | 0.1039 | 1.0416 | 1.0206 |
| 0.4646 | 0.7793 | 692 | 1.2000 | -0.0235 | 1.2000 | 1.0954 |
| 0.4646 | 0.7815 | 694 | 1.1732 | -0.0235 | 1.1732 | 1.0832 |
| 0.4646 | 0.7838 | 696 | 1.0808 | 0.0233 | 1.0808 | 1.0396 |
| 0.4646 | 0.7860 | 698 | 0.9425 | 0.1481 | 0.9425 | 0.9708 |
| 0.4646 | 0.7883 | 700 | 0.9126 | 0.1687 | 0.9126 | 0.9553 |
| 0.4646 | 0.7905 | 702 | 1.0159 | 0.1951 | 1.0159 | 1.0079 |
| 0.4646 | 0.7928 | 704 | 1.0473 | 0.1951 | 1.0473 | 1.0234 |
| 0.4646 | 0.7950 | 706 | 1.0157 | 0.1687 | 1.0157 | 1.0078 |
| 0.4646 | 0.7973 | 708 | 1.0097 | 0.1429 | 1.0097 | 1.0048 |
| 0.4646 | 0.7995 | 710 | 0.9447 | 0.1429 | 0.9447 | 0.9720 |
| 0.4646 | 0.8018 | 712 | 0.9254 | 0.1429 | 0.9254 | 0.9620 |
| 0.4646 | 0.8041 | 714 | 1.1039 | 0.1935 | 1.1039 | 1.0507 |
| 0.4646 | 0.8063 | 716 | 1.6462 | 0.0690 | 1.6462 | 1.2830 |
| 0.4646 | 0.8086 | 718 | 1.9479 | 0.1364 | 1.9479 | 1.3957 |
| 0.4646 | 0.8108 | 720 | 1.7951 | 0.1364 | 1.7951 | 1.3398 |
| 0.4646 | 0.8131 | 722 | 1.3507 | 0.0211 | 1.3507 | 1.1622 |
| 0.4646 | 0.8153 | 724 | 0.9517 | 0.1687 | 0.9517 | 0.9756 |
| 0.4646 | 0.8176 | 726 | 0.8423 | 0.1081 | 0.8423 | 0.9178 |
| 0.4646 | 0.8198 | 728 | 0.8880 | 0.1266 | 0.8880 | 0.9424 |
| 0.4646 | 0.8221 | 730 | 1.0151 | 0.1429 | 1.0151 | 1.0075 |
| 0.4646 | 0.8243 | 732 | 1.2533 | 0.0606 | 1.2533 | 1.1195 |
| 0.4646 | 0.8266 | 734 | 1.4488 | 0.1951 | 1.4488 | 1.2036 |
| 0.4646 | 0.8288 | 736 | 1.2855 | 0.1346 | 1.2855 | 1.1338 |
| 0.4646 | 0.8311 | 738 | 1.0650 | 0.125 | 1.0650 | 1.0320 |
| 0.4646 | 0.8333 | 740 | 0.9247 | 0.1000 | 0.9247 | 0.9616 |
| 0.4646 | 0.8356 | 742 | 0.9314 | 0.1000 | 0.9314 | 0.9651 |
| 0.4646 | 0.8378 | 744 | 1.0776 | 0.1687 | 1.0776 | 1.0381 |
| 0.4646 | 0.8401 | 746 | 1.1427 | 0.0690 | 1.1427 | 1.0690 |
| 0.4646 | 0.8423 | 748 | 1.0248 | 0.0 | 1.0248 | 1.0123 |
| 0.4646 | 0.8446 | 750 | 0.9551 | 0.1429 | 0.9551 | 0.9773 |
| 0.4646 | 0.8468 | 752 | 1.1122 | 0.0667 | 1.1122 | 1.0546 |
| 0.4646 | 0.8491 | 754 | 1.1866 | 0.0667 | 1.1866 | 1.0893 |
| 0.4646 | 0.8514 | 756 | 1.0935 | -0.0235 | 1.0935 | 1.0457 |
| 0.4646 | 0.8536 | 758 | 0.8613 | 0.1538 | 0.8613 | 0.9281 |
| 0.4646 | 0.8559 | 760 | 0.7799 | 0.1600 | 0.7799 | 0.8831 |
| 0.4646 | 0.8581 | 762 | 0.8019 | 0.1220 | 0.8019 | 0.8955 |
| 0.4646 | 0.8604 | 764 | 0.9469 | 0.1429 | 0.9469 | 0.9731 |
| 0.4646 | 0.8626 | 766 | 1.0748 | 0.1935 | 1.0748 | 1.0367 |
| 0.4646 | 0.8649 | 768 | 1.1595 | 0.1935 | 1.1595 | 1.0768 |
| 0.4646 | 0.8671 | 770 | 1.1123 | 0.1522 | 1.1123 | 1.0547 |
| 0.4646 | 0.8694 | 772 | 0.9393 | 0.1687 | 0.9393 | 0.9692 |
| 0.4646 | 0.8716 | 774 | 0.8696 | 0.1687 | 0.8696 | 0.9325 |
| 0.4646 | 0.8739 | 776 | 0.8448 | 0.1687 | 0.8448 | 0.9191 |
| 0.4646 | 0.8761 | 778 | 0.8767 | 0.1951 | 0.8767 | 0.9363 |
| 0.4646 | 0.8784 | 780 | 0.9119 | 0.1951 | 0.9119 | 0.9550 |
| 0.4646 | 0.8806 | 782 | 0.9378 | 0.1951 | 0.9378 | 0.9684 |
| 0.4646 | 0.8829 | 784 | 1.0215 | 0.1481 | 1.0215 | 1.0107 |
| 0.4646 | 0.8851 | 786 | 1.0391 | 0.1481 | 1.0391 | 1.0194 |
| 0.4646 | 0.8874 | 788 | 0.9284 | 0.2105 | 0.9284 | 0.9635 |
| 0.4646 | 0.8896 | 790 | 0.9085 | 0.2308 | 0.9085 | 0.9531 |
| 0.4646 | 0.8919 | 792 | 0.9792 | 0.1818 | 0.9792 | 0.9896 |
| 0.4646 | 0.8941 | 794 | 0.9682 | 0.1220 | 0.9682 | 0.9840 |
| 0.4646 | 0.8964 | 796 | 0.9249 | 0.1687 | 0.9249 | 0.9617 |
| 0.4646 | 0.8986 | 798 | 0.9518 | 0.1687 | 0.9518 | 0.9756 |
| 0.4646 | 0.9009 | 800 | 1.0434 | 0.1687 | 1.0434 | 1.0215 |
| 0.4646 | 0.9032 | 802 | 1.1183 | 0.25 | 1.1183 | 1.0575 |
| 0.4646 | 0.9054 | 804 | 1.3335 | 0.1026 | 1.3335 | 1.1548 |
| 0.4646 | 0.9077 | 806 | 1.5591 | 0.1639 | 1.5591 | 1.2487 |
| 0.4646 | 0.9099 | 808 | 1.4802 | 0.1639 | 1.4802 | 1.2166 |
| 0.4646 | 0.9122 | 810 | 1.1970 | 0.1333 | 1.1970 | 1.0941 |
| 0.4646 | 0.9144 | 812 | 0.9666 | 0.0741 | 0.9666 | 0.9832 |
| 0.4646 | 0.9167 | 814 | 0.8279 | 0.1538 | 0.8279 | 0.9099 |
| 0.4646 | 0.9189 | 816 | 0.7806 | 0.25 | 0.7806 | 0.8835 |
| 0.4646 | 0.9212 | 818 | 0.8351 | 0.1266 | 0.8351 | 0.9138 |
| 0.4646 | 0.9234 | 820 | 0.9649 | 0.1687 | 0.9649 | 0.9823 |
| 0.4646 | 0.9257 | 822 | 1.0246 | 0.1687 | 1.0246 | 1.0122 |
| 0.4646 | 0.9279 | 824 | 1.0580 | 0.1220 | 1.0580 | 1.0286 |
| 0.4646 | 0.9302 | 826 | 1.0284 | 0.1127 | 1.0284 | 1.0141 |
| 0.4646 | 0.9324 | 828 | 1.0387 | 0.1429 | 1.0387 | 1.0192 |
| 0.4646 | 0.9347 | 830 | 1.0169 | 0.1127 | 1.0169 | 1.0084 |
| 0.4646 | 0.9369 | 832 | 0.9218 | 0.1127 | 0.9218 | 0.9601 |
| 0.4646 | 0.9392 | 834 | 0.8920 | 0.2192 | 0.8920 | 0.9445 |
| 0.4646 | 0.9414 | 836 | 0.8440 | 0.2192 | 0.8440 | 0.9187 |
| 0.4646 | 0.9437 | 838 | 0.7875 | 0.2941 | 0.7875 | 0.8874 |
| 0.4646 | 0.9459 | 840 | 0.8641 | 0.2308 | 0.8641 | 0.9295 |
| 0.4646 | 0.9482 | 842 | 0.9556 | 0.0741 | 0.9556 | 0.9775 |
| 0.4646 | 0.9505 | 844 | 0.9574 | 0.0741 | 0.9574 | 0.9785 |
| 0.4646 | 0.9527 | 846 | 0.8726 | 0.1687 | 0.8726 | 0.9341 |
| 0.4646 | 0.9550 | 848 | 0.8124 | 0.3514 | 0.8124 | 0.9013 |
| 0.4646 | 0.9572 | 850 | 0.8763 | 0.1687 | 0.8763 | 0.9361 |
| 0.4646 | 0.9595 | 852 | 1.0261 | 0.1000 | 1.0261 | 1.0130 |
| 0.4646 | 0.9617 | 854 | 1.0167 | 0.1000 | 1.0167 | 1.0083 |
| 0.4646 | 0.9640 | 856 | 0.9372 | 0.1000 | 0.9372 | 0.9681 |
| 0.4646 | 0.9662 | 858 | 0.8982 | 0.1000 | 0.8982 | 0.9478 |
| 0.4646 | 0.9685 | 860 | 0.9277 | 0.1000 | 0.9277 | 0.9632 |
| 0.4646 | 0.9707 | 862 | 0.9200 | 0.1000 | 0.9200 | 0.9592 |
| 0.4646 | 0.9730 | 864 | 0.9007 | 0.0741 | 0.9007 | 0.9491 |
| 0.4646 | 0.9752 | 866 | 0.9176 | 0.1000 | 0.9176 | 0.9579 |
| 0.4646 | 0.9775 | 868 | 0.9553 | 0.1000 | 0.9553 | 0.9774 |
| 0.4646 | 0.9797 | 870 | 1.1264 | 0.1000 | 1.1264 | 1.0613 |
| 0.4646 | 0.9820 | 872 | 1.1992 | 0.1000 | 1.1992 | 1.0951 |
| 0.4646 | 0.9842 | 874 | 1.1301 | 0.1000 | 1.1301 | 1.0631 |
| 0.4646 | 0.9865 | 876 | 1.0290 | 0.1000 | 1.0290 | 1.0144 |
| 0.4646 | 0.9887 | 878 | 0.8702 | 0.1687 | 0.8702 | 0.9329 |
| 0.4646 | 0.9910 | 880 | 0.8183 | 0.1429 | 0.8183 | 0.9046 |
| 0.4646 | 0.9932 | 882 | 0.7929 | 0.2025 | 0.7929 | 0.8904 |
| 0.4646 | 0.9955 | 884 | 0.8469 | 0.1687 | 0.8469 | 0.9203 |
| 0.4646 | 0.9977 | 886 | 0.9339 | 0.1000 | 0.9339 | 0.9664 |
| 0.4646 | 1.0 | 888 | 1.0320 | 0.1000 | 1.0320 | 1.0159 |
| 0.4646 | 1.0023 | 890 | 1.0799 | 0.1000 | 1.0799 | 1.0392 |
| 0.4646 | 1.0045 | 892 | 0.9897 | 0.1000 | 0.9897 | 0.9948 |
| 0.4646 | 1.0068 | 894 | 0.8239 | 0.1818 | 0.8239 | 0.9077 |
| 0.4646 | 1.0090 | 896 | 0.7715 | 0.1892 | 0.7715 | 0.8783 |
| 0.4646 | 1.0113 | 898 | 0.8147 | 0.1667 | 0.8147 | 0.9026 |
| 0.4646 | 1.0135 | 900 | 0.8779 | 0.1316 | 0.8779 | 0.9369 |
| 0.4646 | 1.0158 | 902 | 1.0323 | 0.1000 | 1.0323 | 1.0160 |
| 0.4646 | 1.0180 | 904 | 1.1997 | -0.0235 | 1.1997 | 1.0953 |
| 0.4646 | 1.0203 | 906 | 1.1920 | -0.0235 | 1.1920 | 1.0918 |
| 0.4646 | 1.0225 | 908 | 1.0699 | 0.1000 | 1.0699 | 1.0344 |
| 0.4646 | 1.0248 | 910 | 1.0101 | 0.1000 | 1.0101 | 1.0051 |
| 0.4646 | 1.0270 | 912 | 0.9233 | 0.1127 | 0.9233 | 0.9609 |
| 0.4646 | 1.0293 | 914 | 0.8876 | 0.1667 | 0.8876 | 0.9421 |
| 0.4646 | 1.0315 | 916 | 0.9367 | 0.1667 | 0.9367 | 0.9678 |
| 0.4646 | 1.0338 | 918 | 0.9697 | 0.1750 | 0.9697 | 0.9847 |
| 0.4646 | 1.0360 | 920 | 0.9772 | 0.1750 | 0.9772 | 0.9885 |
| 0.4646 | 1.0383 | 922 | 1.0562 | 0.2143 | 1.0562 | 1.0277 |
| 0.4646 | 1.0405 | 924 | 1.1855 | 0.0204 | 1.1855 | 1.0888 |
| 0.4646 | 1.0428 | 926 | 1.1971 | 0.0233 | 1.1971 | 1.0941 |
| 0.4646 | 1.0450 | 928 | 1.1167 | -0.0235 | 1.1167 | 1.0567 |
| 0.4646 | 1.0473 | 930 | 0.9660 | 0.1481 | 0.9660 | 0.9828 |
| 0.4646 | 1.0495 | 932 | 0.9034 | 0.1818 | 0.9034 | 0.9505 |
| 0.4646 | 1.0518 | 934 | 0.8766 | 0.1818 | 0.8766 | 0.9363 |
| 0.4646 | 1.0541 | 936 | 0.8244 | 0.3143 | 0.8244 | 0.9080 |
| 0.4646 | 1.0563 | 938 | 0.8527 | 0.3143 | 0.8527 | 0.9234 |
| 0.4646 | 1.0586 | 940 | 0.9612 | 0.1882 | 0.9612 | 0.9804 |
| 0.4646 | 1.0608 | 942 | 1.0351 | 0.1429 | 1.0351 | 1.0174 |
| 0.4646 | 1.0631 | 944 | 1.1809 | 0.1481 | 1.1809 | 1.0867 |
| 0.4646 | 1.0653 | 946 | 1.3905 | 0.1386 | 1.3905 | 1.1792 |
| 0.4646 | 1.0676 | 948 | 1.4131 | 0.1081 | 1.4131 | 1.1888 |
| 0.4646 | 1.0698 | 950 | 1.1793 | 0.1000 | 1.1793 | 1.0860 |
| 0.4646 | 1.0721 | 952 | 0.9362 | 0.1481 | 0.9362 | 0.9676 |
| 0.4646 | 1.0743 | 954 | 0.8519 | 0.1429 | 0.8519 | 0.9230 |
| 0.4646 | 1.0766 | 956 | 0.8617 | 0.1429 | 0.8617 | 0.9283 |
| 0.4646 | 1.0788 | 958 | 0.9195 | 0.1481 | 0.9195 | 0.9589 |
| 0.4646 | 1.0811 | 960 | 1.0311 | 0.1481 | 1.0311 | 1.0154 |
| 0.4646 | 1.0833 | 962 | 1.0255 | 0.1481 | 1.0255 | 1.0127 |
| 0.4646 | 1.0856 | 964 | 0.9556 | 0.1687 | 0.9556 | 0.9775 |
| 0.4646 | 1.0878 | 966 | 0.8811 | 0.2025 | 0.8811 | 0.9387 |
| 0.4646 | 1.0901 | 968 | 0.8473 | 0.2025 | 0.8473 | 0.9205 |
| 0.4646 | 1.0923 | 970 | 0.8760 | 0.2025 | 0.8760 | 0.9359 |
| 0.4646 | 1.0946 | 972 | 0.9703 | 0.1000 | 0.9703 | 0.9850 |
| 0.4646 | 1.0968 | 974 | 0.9532 | 0.1000 | 0.9532 | 0.9763 |
| 0.4646 | 1.0991 | 976 | 0.9001 | 0.1000 | 0.9001 | 0.9488 |
| 0.4646 | 1.1014 | 978 | 0.8249 | 0.1429 | 0.8249 | 0.9083 |
| 0.4646 | 1.1036 | 980 | 0.8309 | 0.1429 | 0.8309 | 0.9116 |
| 0.4646 | 1.1059 | 982 | 0.8799 | 0.1429 | 0.8799 | 0.9380 |
| 0.4646 | 1.1081 | 984 | 0.9967 | 0.1429 | 0.9967 | 0.9984 |
| 0.4646 | 1.1104 | 986 | 1.2248 | 0.1875 | 1.2248 | 1.1067 |
| 0.4646 | 1.1126 | 988 | 1.2620 | 0.1875 | 1.2620 | 1.1234 |
| 0.4646 | 1.1149 | 990 | 1.0885 | 0.1481 | 1.0885 | 1.0433 |
| 0.4646 | 1.1171 | 992 | 0.9062 | 0.1429 | 0.9062 | 0.9519 |
| 0.4646 | 1.1194 | 994 | 0.8227 | 0.1481 | 0.8227 | 0.9070 |
| 0.4646 | 1.1216 | 996 | 0.8100 | 0.1481 | 0.8100 | 0.9000 |
| 0.4646 | 1.1239 | 998 | 0.8894 | 0.1429 | 0.8894 | 0.9431 |
| 0.1864 | 1.1261 | 1000 | 1.0488 | 0.1481 | 1.0488 | 1.0241 |
| 0.1864 | 1.1284 | 1002 | 1.1393 | 0.1000 | 1.1393 | 1.0674 |
| 0.1864 | 1.1306 | 1004 | 1.2350 | 0.1000 | 1.2350 | 1.1113 |
| 0.1864 | 1.1329 | 1006 | 1.1916 | 0.1000 | 1.1916 | 1.0916 |
| 0.1864 | 1.1351 | 1008 | 1.0829 | 0.1000 | 1.0829 | 1.0406 |
| 0.1864 | 1.1374 | 1010 | 0.9820 | 0.1220 | 0.9820 | 0.9910 |
| 0.1864 | 1.1396 | 1012 | 0.8580 | 0.1220 | 0.8580 | 0.9263 |
| 0.1864 | 1.1419 | 1014 | 0.8579 | 0.1220 | 0.8579 | 0.9262 |
| 0.1864 | 1.1441 | 1016 | 0.8949 | 0.1429 | 0.8949 | 0.9460 |
| 0.1864 | 1.1464 | 1018 | 0.9751 | 0.1429 | 0.9751 | 0.9875 |
| 0.1864 | 1.1486 | 1020 | 1.0651 | 0.1474 | 1.0651 | 1.0320 |
| 0.1864 | 1.1509 | 1022 | 1.1348 | 0.1064 | 1.1348 | 1.0653 |
| 0.1864 | 1.1532 | 1024 | 1.1460 | 0.1522 | 1.1460 | 1.0705 |
| 0.1864 | 1.1554 | 1026 | 1.1409 | 0.1522 | 1.1409 | 1.0681 |
| 0.1864 | 1.1577 | 1028 | 1.1269 | 0.1481 | 1.1269 | 1.0615 |
| 0.1864 | 1.1599 | 1030 | 1.0689 | 0.1481 | 1.0689 | 1.0339 |
| 0.1864 | 1.1622 | 1032 | 0.9384 | 0.1481 | 0.9384 | 0.9687 |
| 0.1864 | 1.1644 | 1034 | 0.9410 | 0.2105 | 0.9410 | 0.9701 |
| 0.1864 | 1.1667 | 1036 | 1.0559 | 0.1000 | 1.0559 | 1.0276 |
| 0.1864 | 1.1689 | 1038 | 1.2202 | -0.0235 | 1.2202 | 1.1046 |
| 0.1864 | 1.1712 | 1040 | 1.2672 | 0.1882 | 1.2672 | 1.1257 |
| 0.1864 | 1.1734 | 1042 | 1.1196 | 0.1481 | 1.1196 | 1.0581 |
| 0.1864 | 1.1757 | 1044 | 0.9292 | 0.1538 | 0.9292 | 0.9639 |
| 0.1864 | 1.1779 | 1046 | 0.8886 | 0.1538 | 0.8886 | 0.9426 |
| 0.1864 | 1.1802 | 1048 | 0.9478 | 0.1573 | 0.9478 | 0.9735 |
| 0.1864 | 1.1824 | 1050 | 1.0404 | 0.1064 | 1.0404 | 1.0200 |
| 0.1864 | 1.1847 | 1052 | 1.1190 | 0.1522 | 1.1190 | 1.0578 |
| 0.1864 | 1.1869 | 1054 | 1.1259 | 0.1522 | 1.1259 | 1.0611 |
| 0.1864 | 1.1892 | 1056 | 1.0090 | 0.1220 | 1.0090 | 1.0045 |
| 0.1864 | 1.1914 | 1058 | 0.9870 | 0.0964 | 0.9870 | 0.9935 |
| 0.1864 | 1.1937 | 1060 | 1.0751 | 0.1220 | 1.0751 | 1.0369 |
| 0.1864 | 1.1959 | 1062 | 1.0688 | 0.1220 | 1.0688 | 1.0338 |
| 0.1864 | 1.1982 | 1064 | 0.9521 | 0.0964 | 0.9521 | 0.9758 |
| 0.1864 | 1.2005 | 1066 | 0.8687 | 0.2222 | 0.8687 | 0.9320 |
| 0.1864 | 1.2027 | 1068 | 0.8804 | 0.2222 | 0.8804 | 0.9383 |
| 0.1864 | 1.2050 | 1070 | 0.9710 | 0.0964 | 0.9710 | 0.9854 |
| 0.1864 | 1.2072 | 1072 | 1.0768 | 0.1220 | 1.0768 | 1.0377 |
| 0.1864 | 1.2095 | 1074 | 1.0712 | 0.1220 | 1.0712 | 1.0350 |
| 0.1864 | 1.2117 | 1076 | 0.9990 | 0.0964 | 0.9990 | 0.9995 |
| 0.1864 | 1.2140 | 1078 | 0.9318 | 0.1429 | 0.9318 | 0.9653 |
| 0.1864 | 1.2162 | 1080 | 0.9043 | 0.2500 | 0.9043 | 0.9510 |
| 0.1864 | 1.2185 | 1082 | 0.8701 | 0.2500 | 0.8701 | 0.9328 |
| 0.1864 | 1.2207 | 1084 | 0.8982 | 0.1818 | 0.8982 | 0.9477 |
| 0.1864 | 1.2230 | 1086 | 0.9473 | 0.1220 | 0.9473 | 0.9733 |
| 0.1864 | 1.2252 | 1088 | 0.9563 | 0.1220 | 0.9563 | 0.9779 |
| 0.1864 | 1.2275 | 1090 | 0.9006 | 0.1818 | 0.9006 | 0.9490 |
| 0.1864 | 1.2297 | 1092 | 0.9157 | 0.1818 | 0.9157 | 0.9569 |
| 0.1864 | 1.2320 | 1094 | 0.8987 | 0.1818 | 0.8987 | 0.9480 |
| 0.1864 | 1.2342 | 1096 | 0.8358 | 0.1667 | 0.8358 | 0.9142 |
| 0.1864 | 1.2365 | 1098 | 0.7903 | 0.2192 | 0.7903 | 0.8890 |
| 0.1864 | 1.2387 | 1100 | 0.7927 | 0.2941 | 0.7927 | 0.8904 |
| 0.1864 | 1.2410 | 1102 | 0.7813 | 0.2941 | 0.7813 | 0.8839 |
| 0.1864 | 1.2432 | 1104 | 0.8287 | 0.2192 | 0.8287 | 0.9103 |
| 0.1864 | 1.2455 | 1106 | 0.9117 | 0.1220 | 0.9117 | 0.9548 |
| 0.1864 | 1.2477 | 1108 | 0.8917 | 0.1220 | 0.8917 | 0.9443 |
| 0.1864 | 1.25 | 1110 | 0.8356 | 0.2192 | 0.8356 | 0.9141 |
| 0.1864 | 1.2523 | 1112 | 0.7756 | 0.2817 | 0.7756 | 0.8807 |
| 0.1864 | 1.2545 | 1114 | 0.7729 | 0.2817 | 0.7729 | 0.8792 |
| 0.1864 | 1.2568 | 1116 | 0.8199 | 0.2308 | 0.8199 | 0.9055 |
| 0.1864 | 1.2590 | 1118 | 0.8896 | 0.1220 | 0.8896 | 0.9432 |
| 0.1864 | 1.2613 | 1120 | 0.8750 | 0.1818 | 0.8750 | 0.9354 |
| 0.1864 | 1.2635 | 1122 | 0.7801 | 0.3478 | 0.7801 | 0.8832 |
| 0.1864 | 1.2658 | 1124 | 0.7424 | 0.2727 | 0.7424 | 0.8616 |
| 0.1864 | 1.2680 | 1126 | 0.7655 | 0.2817 | 0.7655 | 0.8749 |
| 0.1864 | 1.2703 | 1128 | 0.7973 | 0.2817 | 0.7973 | 0.8929 |
| 0.1864 | 1.2725 | 1130 | 0.8572 | 0.2500 | 0.8572 | 0.9258 |
| 0.1864 | 1.2748 | 1132 | 0.9553 | 0.1687 | 0.9553 | 0.9774 |
| 0.1864 | 1.2770 | 1134 | 1.0734 | 0.1220 | 1.0734 | 1.0361 |
| 0.1864 | 1.2793 | 1136 | 1.0748 | 0.1220 | 1.0748 | 1.0367 |
| 0.1864 | 1.2815 | 1138 | 0.9686 | 0.1220 | 0.9686 | 0.9842 |
| 0.1864 | 1.2838 | 1140 | 0.8635 | 0.1687 | 0.8635 | 0.9292 |
| 0.1864 | 1.2860 | 1142 | 0.7611 | 0.3143 | 0.7611 | 0.8724 |
| 0.1864 | 1.2883 | 1144 | 0.7485 | 0.3143 | 0.7485 | 0.8652 |
| 0.1864 | 1.2905 | 1146 | 0.8189 | 0.2308 | 0.8189 | 0.9049 |
| 0.1864 | 1.2928 | 1148 | 0.9627 | 0.0741 | 0.9627 | 0.9812 |
| 0.1864 | 1.2950 | 1150 | 1.1152 | 0.1882 | 1.1152 | 1.0560 |
| 0.1864 | 1.2973 | 1152 | 1.1281 | 0.1882 | 1.1281 | 1.0621 |
| 0.1864 | 1.2995 | 1154 | 1.0208 | 0.0741 | 1.0208 | 1.0103 |
| 0.1864 | 1.3018 | 1156 | 0.8463 | 0.2308 | 0.8463 | 0.9199 |
| 0.1864 | 1.3041 | 1158 | 0.7752 | 0.3077 | 0.7752 | 0.8805 |
| 0.1864 | 1.3063 | 1160 | 0.7738 | 0.3077 | 0.7738 | 0.8796 |
| 0.1864 | 1.3086 | 1162 | 0.8334 | 0.1892 | 0.8334 | 0.9129 |
| 0.1864 | 1.3108 | 1164 | 0.9200 | 0.1687 | 0.9200 | 0.9591 |
| 0.1864 | 1.3131 | 1166 | 0.9088 | 0.1266 | 0.9088 | 0.9533 |
| 0.1864 | 1.3153 | 1168 | 0.9574 | 0.1687 | 0.9574 | 0.9784 |
| 0.1864 | 1.3176 | 1170 | 0.9825 | 0.1687 | 0.9825 | 0.9912 |
| 0.1864 | 1.3198 | 1172 | 0.9224 | 0.1538 | 0.9224 | 0.9604 |
| 0.1864 | 1.3221 | 1174 | 0.8739 | 0.1892 | 0.8739 | 0.9348 |
| 0.1864 | 1.3243 | 1176 | 0.8699 | 0.1892 | 0.8699 | 0.9327 |
| 0.1864 | 1.3266 | 1178 | 0.9020 | 0.1750 | 0.9020 | 0.9497 |
| 0.1864 | 1.3288 | 1180 | 0.9756 | 0.1429 | 0.9756 | 0.9877 |
| 0.1864 | 1.3311 | 1182 | 1.0950 | 0.1429 | 1.0950 | 1.0464 |
| 0.1864 | 1.3333 | 1184 | 1.1190 | 0.1429 | 1.1190 | 1.0578 |
| 0.1864 | 1.3356 | 1186 | 1.0269 | 0.1429 | 1.0269 | 1.0134 |
| 0.1864 | 1.3378 | 1188 | 0.9120 | 0.1882 | 0.9120 | 0.9550 |
| 0.1864 | 1.3401 | 1190 | 0.8740 | 0.1882 | 0.8740 | 0.9349 |
| 0.1864 | 1.3423 | 1192 | 0.9085 | 0.1429 | 0.9085 | 0.9531 |
| 0.1864 | 1.3446 | 1194 | 0.9299 | 0.1687 | 0.9299 | 0.9643 |
| 0.1864 | 1.3468 | 1196 | 0.8530 | 0.1687 | 0.8530 | 0.9236 |
| 0.1864 | 1.3491 | 1198 | 0.8743 | 0.1687 | 0.8743 | 0.9350 |
| 0.1864 | 1.3514 | 1200 | 0.8987 | 0.1220 | 0.8987 | 0.9480 |
| 0.1864 | 1.3536 | 1202 | 0.9076 | 0.1220 | 0.9076 | 0.9527 |
| 0.1864 | 1.3559 | 1204 | 0.8537 | 0.2308 | 0.8537 | 0.9239 |
| 0.1864 | 1.3581 | 1206 | 0.7835 | 0.2308 | 0.7835 | 0.8851 |
| 0.1864 | 1.3604 | 1208 | 0.7479 | 0.3077 | 0.7479 | 0.8648 |
| 0.1864 | 1.3626 | 1210 | 0.7683 | 0.2857 | 0.7683 | 0.8765 |
| 0.1864 | 1.3649 | 1212 | 0.8813 | 0.1687 | 0.8813 | 0.9388 |
| 0.1864 | 1.3671 | 1214 | 1.0573 | 0.2069 | 1.0573 | 1.0282 |
| 0.1864 | 1.3694 | 1216 | 1.0696 | 0.2069 | 1.0696 | 1.0342 |
| 0.1864 | 1.3716 | 1218 | 0.9557 | 0.1687 | 0.9557 | 0.9776 |
| 0.1864 | 1.3739 | 1220 | 0.8942 | 0.1429 | 0.8942 | 0.9456 |
| 0.1864 | 1.3761 | 1222 | 0.8233 | 0.25 | 0.8233 | 0.9073 |
| 0.1864 | 1.3784 | 1224 | 0.8246 | 0.25 | 0.8246 | 0.9081 |
| 0.1864 | 1.3806 | 1226 | 0.8951 | 0.1882 | 0.8951 | 0.9461 |
| 0.1864 | 1.3829 | 1228 | 0.9677 | 0.1687 | 0.9677 | 0.9837 |
| 0.1864 | 1.3851 | 1230 | 0.9238 | 0.1429 | 0.9238 | 0.9611 |
| 0.1864 | 1.3874 | 1232 | 0.8405 | 0.1750 | 0.8405 | 0.9168 |
| 0.1864 | 1.3896 | 1234 | 0.8225 | 0.1750 | 0.8225 | 0.9069 |
| 0.1864 | 1.3919 | 1236 | 0.8427 | 0.1266 | 0.8427 | 0.9180 |
| 0.1864 | 1.3941 | 1238 | 0.9064 | 0.0964 | 0.9064 | 0.9521 |
| 0.1864 | 1.3964 | 1240 | 1.0477 | 0.1481 | 1.0477 | 1.0236 |
| 0.1864 | 1.3986 | 1242 | 1.1032 | 0.2326 | 1.1032 | 1.0503 |
| 0.1864 | 1.4009 | 1244 | 1.0144 | 0.1220 | 1.0144 | 1.0072 |
| 0.1864 | 1.4032 | 1246 | 0.9258 | 0.0964 | 0.9258 | 0.9622 |
| 0.1864 | 1.4054 | 1248 | 0.8986 | 0.1220 | 0.8986 | 0.9479 |
| 0.1864 | 1.4077 | 1250 | 0.8593 | 0.1220 | 0.8593 | 0.9270 |
| 0.1864 | 1.4099 | 1252 | 0.7987 | 0.0769 | 0.7987 | 0.8937 |
| 0.1864 | 1.4122 | 1254 | 0.8271 | 0.0964 | 0.8271 | 0.9095 |
| 0.1864 | 1.4144 | 1256 | 0.8992 | 0.1481 | 0.8992 | 0.9482 |
| 0.1864 | 1.4167 | 1258 | 0.9634 | 0.1481 | 0.9634 | 0.9815 |
| 0.1864 | 1.4189 | 1260 | 0.9470 | 0.1481 | 0.9470 | 0.9731 |
| 0.1864 | 1.4212 | 1262 | 0.9805 | 0.1481 | 0.9805 | 0.9902 |
| 0.1864 | 1.4234 | 1264 | 0.9270 | 0.1481 | 0.9270 | 0.9628 |
| 0.1864 | 1.4257 | 1266 | 0.7998 | 0.1220 | 0.7998 | 0.8943 |
| 0.1864 | 1.4279 | 1268 | 0.7400 | 0.24 | 0.7400 | 0.8603 |
| 0.1864 | 1.4302 | 1270 | 0.7613 | 0.2500 | 0.7613 | 0.8725 |
| 0.1864 | 1.4324 | 1272 | 0.7964 | 0.2308 | 0.7964 | 0.8924 |
| 0.1864 | 1.4347 | 1274 | 0.9149 | 0.1220 | 0.9149 | 0.9565 |
| 0.1864 | 1.4369 | 1276 | 1.0457 | 0.1481 | 1.0457 | 1.0226 |
| 0.1864 | 1.4392 | 1278 | 1.0255 | 0.1481 | 1.0255 | 1.0127 |
| 0.1864 | 1.4414 | 1280 | 0.9351 | 0.1481 | 0.9351 | 0.9670 |
| 0.1864 | 1.4437 | 1282 | 0.7763 | 0.2703 | 0.7763 | 0.8811 |
| 0.1864 | 1.4459 | 1284 | 0.7020 | 0.3333 | 0.7020 | 0.8378 |
| 0.1864 | 1.4482 | 1286 | 0.6876 | 0.3333 | 0.6876 | 0.8292 |
| 0.1864 | 1.4505 | 1288 | 0.7289 | 0.3143 | 0.7289 | 0.8538 |
| 0.1864 | 1.4527 | 1290 | 0.7846 | 0.2025 | 0.7846 | 0.8858 |
| 0.1864 | 1.4550 | 1292 | 0.8531 | 0.2143 | 0.8531 | 0.9236 |
| 0.1864 | 1.4572 | 1294 | 0.8602 | 0.1882 | 0.8602 | 0.9274 |
| 0.1864 | 1.4595 | 1296 | 0.9228 | 0.1882 | 0.9228 | 0.9606 |
| 0.1864 | 1.4617 | 1298 | 0.9519 | 0.1882 | 0.9519 | 0.9757 |
| 0.1864 | 1.4640 | 1300 | 1.0243 | 0.1687 | 1.0243 | 1.0121 |
| 0.1864 | 1.4662 | 1302 | 1.0289 | 0.0741 | 1.0289 | 1.0144 |
| 0.1864 | 1.4685 | 1304 | 0.9037 | 0.1220 | 0.9037 | 0.9507 |
| 0.1864 | 1.4707 | 1306 | 0.7778 | 0.3478 | 0.7778 | 0.8819 |
| 0.1864 | 1.4730 | 1308 | 0.7220 | 0.3478 | 0.7220 | 0.8497 |
| 0.1864 | 1.4752 | 1310 | 0.7469 | 0.3478 | 0.7469 | 0.8642 |
| 0.1864 | 1.4775 | 1312 | 0.8208 | 0.25 | 0.8208 | 0.9060 |
| 0.1864 | 1.4797 | 1314 | 0.8083 | 0.3014 | 0.8083 | 0.8991 |
| 0.1864 | 1.4820 | 1316 | 0.8085 | 0.3200 | 0.8085 | 0.8992 |
| 0.1864 | 1.4842 | 1318 | 0.7452 | 0.3143 | 0.7452 | 0.8633 |
| 0.1864 | 1.4865 | 1320 | 0.7451 | 0.3662 | 0.7451 | 0.8632 |
| 0.1864 | 1.4887 | 1322 | 0.7506 | 0.3333 | 0.7506 | 0.8664 |
| 0.1864 | 1.4910 | 1324 | 0.7889 | 0.3333 | 0.7889 | 0.8882 |
| 0.1864 | 1.4932 | 1326 | 0.8679 | 0.1882 | 0.8679 | 0.9316 |
| 0.1864 | 1.4955 | 1328 | 0.8739 | 0.1882 | 0.8739 | 0.9348 |
| 0.1864 | 1.4977 | 1330 | 0.8529 | 0.2597 | 0.8529 | 0.9235 |
| 0.1864 | 1.5 | 1332 | 0.8371 | 0.2500 | 0.8371 | 0.9149 |
| 0.1864 | 1.5023 | 1334 | 0.8254 | 0.2105 | 0.8254 | 0.9085 |
| 0.1864 | 1.5045 | 1336 | 0.7971 | 0.2785 | 0.7971 | 0.8928 |
| 0.1864 | 1.5068 | 1338 | 0.8664 | 0.1882 | 0.8664 | 0.9308 |
| 0.1864 | 1.5090 | 1340 | 0.9320 | 0.1687 | 0.9320 | 0.9654 |
| 0.1864 | 1.5113 | 1342 | 0.8793 | 0.1687 | 0.8793 | 0.9377 |
| 0.1864 | 1.5135 | 1344 | 0.8440 | 0.1882 | 0.8440 | 0.9187 |
| 0.1864 | 1.5158 | 1346 | 0.7968 | 0.1750 | 0.7968 | 0.8926 |
| 0.1864 | 1.5180 | 1348 | 0.7418 | 0.24 | 0.7418 | 0.8613 |
| 0.1864 | 1.5203 | 1350 | 0.7448 | 0.24 | 0.7448 | 0.8630 |
| 0.1864 | 1.5225 | 1352 | 0.7679 | 0.24 | 0.7679 | 0.8763 |
| 0.1864 | 1.5248 | 1354 | 0.7874 | 0.24 | 0.7874 | 0.8873 |
| 0.1864 | 1.5270 | 1356 | 0.8812 | 0.2143 | 0.8812 | 0.9387 |
| 0.1864 | 1.5293 | 1358 | 0.9383 | 0.1687 | 0.9383 | 0.9687 |
| 0.1864 | 1.5315 | 1360 | 0.9774 | 0.1951 | 0.9774 | 0.9886 |
| 0.1864 | 1.5338 | 1362 | 0.8652 | 0.2143 | 0.8652 | 0.9302 |
| 0.1864 | 1.5360 | 1364 | 0.7610 | 0.24 | 0.7610 | 0.8724 |
| 0.1864 | 1.5383 | 1366 | 0.7622 | 0.24 | 0.7622 | 0.8731 |
| 0.1864 | 1.5405 | 1368 | 0.7973 | 0.24 | 0.7973 | 0.8929 |
| 0.1864 | 1.5428 | 1370 | 0.8998 | 0.1882 | 0.8998 | 0.9486 |
| 0.1864 | 1.5450 | 1372 | 1.0183 | 0.1481 | 1.0183 | 1.0091 |
| 0.1864 | 1.5473 | 1374 | 0.9639 | 0.1687 | 0.9639 | 0.9818 |
| 0.1864 | 1.5495 | 1376 | 0.8631 | 0.1882 | 0.8631 | 0.9291 |
| 0.1864 | 1.5518 | 1378 | 0.8494 | 0.2500 | 0.8494 | 0.9216 |
| 0.1864 | 1.5541 | 1380 | 0.8660 | 0.1882 | 0.8660 | 0.9306 |
| 0.1864 | 1.5563 | 1382 | 0.8296 | 0.2895 | 0.8296 | 0.9108 |
| 0.1864 | 1.5586 | 1384 | 0.7819 | 0.3014 | 0.7819 | 0.8842 |
| 0.1864 | 1.5608 | 1386 | 0.7604 | 0.2105 | 0.7604 | 0.8720 |
| 0.1864 | 1.5631 | 1388 | 0.7743 | 0.3514 | 0.7743 | 0.8800 |
| 0.1864 | 1.5653 | 1390 | 0.8519 | 0.3200 | 0.8519 | 0.9230 |
| 0.1864 | 1.5676 | 1392 | 1.0225 | 0.1882 | 1.0225 | 1.0112 |
| 0.1864 | 1.5698 | 1394 | 1.0869 | 0.0964 | 1.0869 | 1.0425 |
| 0.1864 | 1.5721 | 1396 | 1.0295 | 0.1882 | 1.0295 | 1.0146 |
| 0.1864 | 1.5743 | 1398 | 0.9070 | 0.1882 | 0.9070 | 0.9524 |
| 0.1864 | 1.5766 | 1400 | 0.8179 | 0.25 | 0.8179 | 0.9044 |
| 0.1864 | 1.5788 | 1402 | 0.7956 | 0.1600 | 0.7956 | 0.8920 |
| 0.1864 | 1.5811 | 1404 | 0.8057 | 0.1600 | 0.8057 | 0.8976 |
| 0.1864 | 1.5833 | 1406 | 0.8772 | 0.1882 | 0.8772 | 0.9366 |
| 0.1864 | 1.5856 | 1408 | 1.0463 | 0.1429 | 1.0463 | 1.0229 |
| 0.1864 | 1.5878 | 1410 | 1.1636 | 0.0645 | 1.1636 | 1.0787 |
| 0.1864 | 1.5901 | 1412 | 1.1170 | 0.0645 | 1.1170 | 1.0569 |
| 0.1864 | 1.5923 | 1414 | 0.9558 | 0.1882 | 0.9558 | 0.9776 |
| 0.1864 | 1.5946 | 1416 | 0.8244 | 0.1750 | 0.8244 | 0.9080 |
| 0.1864 | 1.5968 | 1418 | 0.7573 | 0.3636 | 0.7573 | 0.8702 |
| 0.1864 | 1.5991 | 1420 | 0.7411 | 0.2623 | 0.7411 | 0.8608 |
| 0.1864 | 1.6014 | 1422 | 0.7484 | 0.3077 | 0.7484 | 0.8651 |
| 0.1864 | 1.6036 | 1424 | 0.7678 | 0.3143 | 0.7678 | 0.8763 |
| 0.1864 | 1.6059 | 1426 | 0.7852 | 0.3143 | 0.7852 | 0.8861 |
| 0.1864 | 1.6081 | 1428 | 0.8326 | 0.1882 | 0.8326 | 0.9125 |
| 0.1864 | 1.6104 | 1430 | 0.8956 | 0.1429 | 0.8956 | 0.9463 |
| 0.1864 | 1.6126 | 1432 | 0.9704 | 0.0964 | 0.9704 | 0.9851 |
| 0.1864 | 1.6149 | 1434 | 0.9286 | 0.1429 | 0.9286 | 0.9636 |
| 0.1864 | 1.6171 | 1436 | 0.8210 | 0.3200 | 0.8210 | 0.9061 |
| 0.1864 | 1.6194 | 1438 | 0.7703 | 0.3662 | 0.7703 | 0.8777 |
| 0.1864 | 1.6216 | 1440 | 0.7645 | 0.3662 | 0.7645 | 0.8744 |
| 0.1864 | 1.6239 | 1442 | 0.7679 | 0.3662 | 0.7679 | 0.8763 |
| 0.1864 | 1.6261 | 1444 | 0.7649 | 0.3662 | 0.7649 | 0.8746 |
| 0.1864 | 1.6284 | 1446 | 0.7692 | 0.3200 | 0.7692 | 0.8770 |
| 0.1864 | 1.6306 | 1448 | 0.7870 | 0.3200 | 0.7870 | 0.8871 |
| 0.1864 | 1.6329 | 1450 | 0.8014 | 0.3200 | 0.8014 | 0.8952 |
| 0.1864 | 1.6351 | 1452 | 0.7695 | 0.3200 | 0.7695 | 0.8772 |
| 0.1864 | 1.6374 | 1454 | 0.7711 | 0.3200 | 0.7711 | 0.8781 |
| 0.1864 | 1.6396 | 1456 | 0.7966 | 0.3684 | 0.7966 | 0.8925 |
| 0.1864 | 1.6419 | 1458 | 0.8293 | 0.3684 | 0.8293 | 0.9107 |
| 0.1864 | 1.6441 | 1460 | 0.8148 | 0.3377 | 0.8148 | 0.9027 |
| 0.1864 | 1.6464 | 1462 | 0.7954 | 0.3200 | 0.7954 | 0.8919 |
| 0.1864 | 1.6486 | 1464 | 0.7841 | 0.3684 | 0.7841 | 0.8855 |
| 0.1864 | 1.6509 | 1466 | 0.7827 | 0.4 | 0.7827 | 0.8847 |
| 0.1864 | 1.6532 | 1468 | 0.8168 | 0.3684 | 0.8168 | 0.9038 |
| 0.1864 | 1.6554 | 1470 | 0.8933 | 0.2326 | 0.8933 | 0.9452 |
| 0.1864 | 1.6577 | 1472 | 0.9625 | 0.0899 | 0.9625 | 0.9811 |
| 0.1864 | 1.6599 | 1474 | 0.9349 | 0.0899 | 0.9349 | 0.9669 |
| 0.1864 | 1.6622 | 1476 | 0.8588 | 0.2500 | 0.8588 | 0.9267 |
| 0.1864 | 1.6644 | 1478 | 0.7987 | 0.3684 | 0.7987 | 0.8937 |
| 0.1864 | 1.6667 | 1480 | 0.7434 | 0.3514 | 0.7434 | 0.8622 |
| 0.1864 | 1.6689 | 1482 | 0.7150 | 0.3684 | 0.7150 | 0.8456 |
| 0.1864 | 1.6712 | 1484 | 0.7052 | 0.3684 | 0.7052 | 0.8397 |
| 0.1864 | 1.6734 | 1486 | 0.7205 | 0.3684 | 0.7205 | 0.8488 |
| 0.1864 | 1.6757 | 1488 | 0.7938 | 0.4156 | 0.7938 | 0.8909 |
| 0.1864 | 1.6779 | 1490 | 0.9552 | 0.1687 | 0.9552 | 0.9773 |
| 0.1864 | 1.6802 | 1492 | 1.1065 | 0.0412 | 1.1065 | 1.0519 |
| 0.1864 | 1.6824 | 1494 | 1.1292 | 0.1176 | 1.1292 | 1.0626 |
| 0.1864 | 1.6847 | 1496 | 1.0220 | 0.0606 | 1.0220 | 1.0110 |
| 0.1864 | 1.6869 | 1498 | 0.8721 | 0.1882 | 0.8721 | 0.9338 |
| 0.1277 | 1.6892 | 1500 | 0.8122 | 0.2683 | 0.8122 | 0.9012 |
| 0.1277 | 1.6914 | 1502 | 0.7849 | 0.3333 | 0.7849 | 0.8859 |
| 0.1277 | 1.6937 | 1504 | 0.8121 | 0.1882 | 0.8121 | 0.9012 |
| 0.1277 | 1.6959 | 1506 | 0.9062 | 0.1220 | 0.9062 | 0.9519 |
| 0.1277 | 1.6982 | 1508 | 0.9931 | 0.0233 | 0.9931 | 0.9965 |
| 0.1277 | 1.7005 | 1510 | 1.0234 | 0.0233 | 1.0234 | 1.0117 |
| 0.1277 | 1.7027 | 1512 | 1.0540 | 0.0233 | 1.0540 | 1.0267 |
| 0.1277 | 1.7050 | 1514 | 1.0162 | 0.0233 | 1.0162 | 1.0080 |
| 0.1277 | 1.7072 | 1516 | 0.9048 | 0.0233 | 0.9048 | 0.9512 |
| 0.1277 | 1.7095 | 1518 | 0.8305 | 0.2727 | 0.8305 | 0.9113 |
| 0.1277 | 1.7117 | 1520 | 0.7816 | 0.2388 | 0.7816 | 0.8841 |
| 0.1277 | 1.7140 | 1522 | 0.7840 | 0.2941 | 0.7840 | 0.8854 |
| 0.1277 | 1.7162 | 1524 | 0.7924 | 0.3662 | 0.7924 | 0.8902 |
| 0.1277 | 1.7185 | 1526 | 0.8269 | 0.3143 | 0.8269 | 0.9093 |
| 0.1277 | 1.7207 | 1528 | 0.8537 | 0.3684 | 0.8537 | 0.9239 |
| 0.1277 | 1.7230 | 1530 | 0.8578 | 0.3684 | 0.8578 | 0.9262 |
| 0.1277 | 1.7252 | 1532 | 0.8226 | 0.3836 | 0.8226 | 0.9070 |
| 0.1277 | 1.7275 | 1534 | 0.7719 | 0.3514 | 0.7719 | 0.8786 |
| 0.1277 | 1.7297 | 1536 | 0.7510 | 0.3836 | 0.7510 | 0.8666 |
| 0.1277 | 1.7320 | 1538 | 0.7330 | 0.3662 | 0.7330 | 0.8562 |
| 0.1277 | 1.7342 | 1540 | 0.7482 | 0.3143 | 0.7482 | 0.8650 |
| 0.1277 | 1.7365 | 1542 | 0.7750 | 0.2609 | 0.7750 | 0.8804 |
| 0.1277 | 1.7387 | 1544 | 0.8738 | 0.2105 | 0.8738 | 0.9348 |
| 0.1277 | 1.7410 | 1546 | 1.0365 | 0.0233 | 1.0365 | 1.0181 |
| 0.1277 | 1.7432 | 1548 | 1.0850 | 0.0233 | 1.0850 | 1.0416 |
| 0.1277 | 1.7455 | 1550 | 1.0098 | 0.0233 | 1.0098 | 1.0049 |
| 0.1277 | 1.7477 | 1552 | 0.8674 | 0.2105 | 0.8674 | 0.9313 |
| 0.1277 | 1.75 | 1554 | 0.7564 | 0.2609 | 0.7564 | 0.8697 |
| 0.1277 | 1.7523 | 1556 | 0.7397 | 0.3662 | 0.7397 | 0.8601 |
| 0.1277 | 1.7545 | 1558 | 0.7619 | 0.2609 | 0.7619 | 0.8729 |
| 0.1277 | 1.7568 | 1560 | 0.7798 | 0.2941 | 0.7798 | 0.8831 |
| 0.1277 | 1.7590 | 1562 | 0.8241 | 0.1667 | 0.8241 | 0.9078 |
| 0.1277 | 1.7613 | 1564 | 0.9239 | 0.1481 | 0.9239 | 0.9612 |
| 0.1277 | 1.7635 | 1566 | 0.9749 | 0.1481 | 0.9749 | 0.9874 |
| 0.1277 | 1.7658 | 1568 | 0.9221 | 0.2105 | 0.9221 | 0.9603 |
| 0.1277 | 1.7680 | 1570 | 0.8056 | 0.1667 | 0.8056 | 0.8976 |
| 0.1277 | 1.7703 | 1572 | 0.7223 | 0.3143 | 0.7223 | 0.8499 |
| 0.1277 | 1.7725 | 1574 | 0.7084 | 0.3662 | 0.7084 | 0.8416 |
| 0.1277 | 1.7748 | 1576 | 0.7355 | 0.2941 | 0.7355 | 0.8576 |
| 0.1277 | 1.7770 | 1578 | 0.8112 | 0.1667 | 0.8112 | 0.9007 |
| 0.1277 | 1.7793 | 1580 | 0.8850 | 0.2105 | 0.8850 | 0.9408 |
| 0.1277 | 1.7815 | 1582 | 0.9289 | 0.2105 | 0.9289 | 0.9638 |
| 0.1277 | 1.7838 | 1584 | 0.8846 | 0.2105 | 0.8846 | 0.9405 |
| 0.1277 | 1.7860 | 1586 | 0.7894 | 0.2192 | 0.7894 | 0.8885 |
| 0.1277 | 1.7883 | 1588 | 0.7065 | 0.3333 | 0.7065 | 0.8405 |
| 0.1277 | 1.7905 | 1590 | 0.6900 | 0.2154 | 0.6900 | 0.8307 |
| 0.1277 | 1.7928 | 1592 | 0.7061 | 0.3200 | 0.7061 | 0.8403 |
| 0.1277 | 1.7950 | 1594 | 0.7485 | 0.3333 | 0.7485 | 0.8651 |
| 0.1277 | 1.7973 | 1596 | 0.8549 | 0.2963 | 0.8549 | 0.9246 |
| 0.1277 | 1.7995 | 1598 | 0.9181 | 0.2597 | 0.9181 | 0.9582 |
| 0.1277 | 1.8018 | 1600 | 0.8734 | 0.3077 | 0.8734 | 0.9346 |
| 0.1277 | 1.8041 | 1602 | 0.7653 | 0.2597 | 0.7653 | 0.8748 |
| 0.1277 | 1.8063 | 1604 | 0.6977 | 0.4324 | 0.6977 | 0.8353 |
| 0.1277 | 1.8086 | 1606 | 0.7037 | 0.25 | 0.7037 | 0.8389 |
| 0.1277 | 1.8108 | 1608 | 0.7263 | 0.25 | 0.7263 | 0.8522 |
| 0.1277 | 1.8131 | 1610 | 0.7392 | 0.2597 | 0.7392 | 0.8598 |
| 0.1277 | 1.8153 | 1612 | 0.7880 | 0.2597 | 0.7880 | 0.8877 |
| 0.1277 | 1.8176 | 1614 | 0.8643 | 0.2597 | 0.8643 | 0.9297 |
| 0.1277 | 1.8198 | 1616 | 0.8738 | 0.24 | 0.8738 | 0.9348 |
| 0.1277 | 1.8221 | 1618 | 0.8119 | 0.2597 | 0.8119 | 0.9010 |
| 0.1277 | 1.8243 | 1620 | 0.7529 | 0.2597 | 0.7529 | 0.8677 |
| 0.1277 | 1.8266 | 1622 | 0.7279 | 0.3836 | 0.7279 | 0.8532 |
| 0.1277 | 1.8288 | 1624 | 0.7373 | 0.3836 | 0.7373 | 0.8587 |
| 0.1277 | 1.8311 | 1626 | 0.7676 | 0.3333 | 0.7676 | 0.8761 |
| 0.1277 | 1.8333 | 1628 | 0.7921 | 0.2597 | 0.7921 | 0.8900 |
| 0.1277 | 1.8356 | 1630 | 0.8129 | 0.2597 | 0.8129 | 0.9016 |
| 0.1277 | 1.8378 | 1632 | 0.8541 | 0.2963 | 0.8541 | 0.9242 |
| 0.1277 | 1.8401 | 1634 | 0.8484 | 0.2963 | 0.8484 | 0.9211 |
| 0.1277 | 1.8423 | 1636 | 0.8133 | 0.2895 | 0.8133 | 0.9019 |
| 0.1277 | 1.8446 | 1638 | 0.7911 | 0.2597 | 0.7911 | 0.8895 |
| 0.1277 | 1.8468 | 1640 | 0.7638 | 0.3333 | 0.7638 | 0.8740 |
| 0.1277 | 1.8491 | 1642 | 0.7642 | 0.3836 | 0.7642 | 0.8742 |
| 0.1277 | 1.8514 | 1644 | 0.7827 | 0.3836 | 0.7827 | 0.8847 |
| 0.1277 | 1.8536 | 1646 | 0.8185 | 0.2597 | 0.8185 | 0.9047 |
| 0.1277 | 1.8559 | 1648 | 0.8586 | 0.2683 | 0.8586 | 0.9266 |
| 0.1277 | 1.8581 | 1650 | 0.8652 | 0.2222 | 0.8652 | 0.9301 |
| 0.1277 | 1.8604 | 1652 | 0.8713 | 0.2222 | 0.8713 | 0.9334 |
| 0.1277 | 1.8626 | 1654 | 0.9049 | 0.2105 | 0.9049 | 0.9513 |
| 0.1277 | 1.8649 | 1656 | 0.9039 | 0.2500 | 0.9039 | 0.9507 |
| 0.1277 | 1.8671 | 1658 | 0.8313 | 0.1600 | 0.8313 | 0.9117 |
| 0.1277 | 1.8694 | 1660 | 0.7296 | 0.2703 | 0.7296 | 0.8541 |
| 0.1277 | 1.8716 | 1662 | 0.6557 | 0.2727 | 0.6557 | 0.8097 |
| 0.1277 | 1.8739 | 1664 | 0.6441 | 0.2258 | 0.6441 | 0.8025 |
| 0.1277 | 1.8761 | 1666 | 0.6616 | 0.2727 | 0.6616 | 0.8134 |
| 0.1277 | 1.8784 | 1668 | 0.7077 | 0.2817 | 0.7077 | 0.8413 |
| 0.1277 | 1.8806 | 1670 | 0.7682 | 0.24 | 0.7682 | 0.8764 |
| 0.1277 | 1.8829 | 1672 | 0.7699 | 0.2817 | 0.7699 | 0.8774 |
| 0.1277 | 1.8851 | 1674 | 0.7347 | 0.3836 | 0.7347 | 0.8571 |
| 0.1277 | 1.8874 | 1676 | 0.7139 | 0.2609 | 0.7139 | 0.8449 |
| 0.1277 | 1.8896 | 1678 | 0.7091 | 0.2609 | 0.7091 | 0.8421 |
| 0.1277 | 1.8919 | 1680 | 0.7316 | 0.3836 | 0.7316 | 0.8553 |
| 0.1277 | 1.8941 | 1682 | 0.7901 | 0.3333 | 0.7901 | 0.8889 |
| 0.1277 | 1.8964 | 1684 | 0.7942 | 0.4 | 0.7942 | 0.8912 |
| 0.1277 | 1.8986 | 1686 | 0.7576 | 0.3333 | 0.7576 | 0.8704 |
| 0.1277 | 1.9009 | 1688 | 0.7462 | 0.3836 | 0.7462 | 0.8638 |
| 0.1277 | 1.9032 | 1690 | 0.7559 | 0.3836 | 0.7559 | 0.8694 |
| 0.1277 | 1.9054 | 1692 | 0.7595 | 0.3514 | 0.7595 | 0.8715 |
| 0.1277 | 1.9077 | 1694 | 0.7695 | 0.3836 | 0.7695 | 0.8772 |
| 0.1277 | 1.9099 | 1696 | 0.7617 | 0.3836 | 0.7617 | 0.8727 |
| 0.1277 | 1.9122 | 1698 | 0.7475 | 0.3836 | 0.7475 | 0.8646 |
| 0.1277 | 1.9144 | 1700 | 0.7235 | 0.3514 | 0.7235 | 0.8506 |
| 0.1277 | 1.9167 | 1702 | 0.7330 | 0.3836 | 0.7330 | 0.8561 |
| 0.1277 | 1.9189 | 1704 | 0.7983 | 0.3662 | 0.7983 | 0.8935 |
| 0.1277 | 1.9212 | 1706 | 0.8749 | 0.2597 | 0.8749 | 0.9354 |
| 0.1277 | 1.9234 | 1708 | 0.8640 | 0.2597 | 0.8640 | 0.9295 |
| 0.1277 | 1.9257 | 1710 | 0.7903 | 0.3143 | 0.7903 | 0.8890 |
| 0.1277 | 1.9279 | 1712 | 0.7194 | 0.3662 | 0.7194 | 0.8482 |
| 0.1277 | 1.9302 | 1714 | 0.6983 | 0.3662 | 0.6983 | 0.8357 |
| 0.1277 | 1.9324 | 1716 | 0.6919 | 0.4167 | 0.6919 | 0.8318 |
| 0.1277 | 1.9347 | 1718 | 0.7065 | 0.3662 | 0.7065 | 0.8405 |
| 0.1277 | 1.9369 | 1720 | 0.7320 | 0.3143 | 0.7320 | 0.8556 |
| 0.1277 | 1.9392 | 1722 | 0.7622 | 0.3143 | 0.7622 | 0.8730 |
| 0.1277 | 1.9414 | 1724 | 0.7983 | 0.3143 | 0.7983 | 0.8935 |
| 0.1277 | 1.9437 | 1726 | 0.7855 | 0.3143 | 0.7855 | 0.8863 |
| 0.1277 | 1.9459 | 1728 | 0.7943 | 0.3143 | 0.7943 | 0.8912 |
| 0.1277 | 1.9482 | 1730 | 0.7858 | 0.3662 | 0.7858 | 0.8864 |
| 0.1277 | 1.9505 | 1732 | 0.7677 | 0.3662 | 0.7677 | 0.8762 |
| 0.1277 | 1.9527 | 1734 | 0.7522 | 0.3836 | 0.7522 | 0.8673 |
| 0.1277 | 1.9550 | 1736 | 0.7374 | 0.3836 | 0.7374 | 0.8587 |
| 0.1277 | 1.9572 | 1738 | 0.7480 | 0.3200 | 0.7480 | 0.8648 |
| 0.1277 | 1.9595 | 1740 | 0.7754 | 0.2105 | 0.7754 | 0.8806 |
| 0.1277 | 1.9617 | 1742 | 0.8142 | 0.2597 | 0.8142 | 0.9023 |
| 0.1277 | 1.9640 | 1744 | 0.8503 | 0.2597 | 0.8503 | 0.9221 |
| 0.1277 | 1.9662 | 1746 | 0.8688 | 0.3200 | 0.8688 | 0.9321 |
| 0.1277 | 1.9685 | 1748 | 0.8896 | 0.2500 | 0.8896 | 0.9432 |
| 0.1277 | 1.9707 | 1750 | 0.8763 | 0.3077 | 0.8763 | 0.9361 |
| 0.1277 | 1.9730 | 1752 | 0.8185 | 0.3200 | 0.8185 | 0.9047 |
| 0.1277 | 1.9752 | 1754 | 0.7764 | 0.3200 | 0.7764 | 0.8811 |
| 0.1277 | 1.9775 | 1756 | 0.7688 | 0.3200 | 0.7688 | 0.8768 |
| 0.1277 | 1.9797 | 1758 | 0.7692 | 0.3836 | 0.7692 | 0.8770 |
| 0.1277 | 1.9820 | 1760 | 0.7820 | 0.3836 | 0.7820 | 0.8843 |
| 0.1277 | 1.9842 | 1762 | 0.8135 | 0.4 | 0.8135 | 0.9019 |
| 0.1277 | 1.9865 | 1764 | 0.8793 | 0.2308 | 0.8793 | 0.9377 |
| 0.1277 | 1.9887 | 1766 | 0.9837 | 0.0 | 0.9837 | 0.9918 |
| 0.1277 | 1.9910 | 1768 | 0.9963 | 0.0 | 0.9963 | 0.9981 |
| 0.1277 | 1.9932 | 1770 | 0.9182 | 0.1818 | 0.9182 | 0.9582 |
| 0.1277 | 1.9955 | 1772 | 0.8186 | 0.24 | 0.8186 | 0.9048 |
| 0.1277 | 1.9977 | 1774 | 0.7863 | 0.2817 | 0.7863 | 0.8867 |
| 0.1277 | 2.0 | 1776 | 0.7806 | 0.2817 | 0.7806 | 0.8835 |
| 0.1277 | 2.0023 | 1778 | 0.7798 | 0.2817 | 0.7798 | 0.8830 |
| 0.1277 | 2.0045 | 1780 | 0.8104 | 0.2703 | 0.8104 | 0.9002 |
| 0.1277 | 2.0068 | 1782 | 0.8612 | 0.2192 | 0.8612 | 0.9280 |
| 0.1277 | 2.0090 | 1784 | 0.9251 | 0.2308 | 0.9251 | 0.9618 |
| 0.1277 | 2.0113 | 1786 | 0.9384 | 0.1220 | 0.9384 | 0.9687 |
| 0.1277 | 2.0135 | 1788 | 0.9143 | 0.2308 | 0.9143 | 0.9562 |
| 0.1277 | 2.0158 | 1790 | 0.8388 | 0.1892 | 0.8388 | 0.9159 |
| 0.1277 | 2.0180 | 1792 | 0.7695 | 0.4167 | 0.7695 | 0.8772 |
| 0.1277 | 2.0203 | 1794 | 0.7647 | 0.4167 | 0.7647 | 0.8745 |
| 0.1277 | 2.0225 | 1796 | 0.7837 | 0.3143 | 0.7837 | 0.8853 |
| 0.1277 | 2.0248 | 1798 | 0.8418 | 0.2192 | 0.8418 | 0.9175 |
| 0.1277 | 2.0270 | 1800 | 0.9044 | 0.1127 | 0.9044 | 0.9510 |
| 0.1277 | 2.0293 | 1802 | 0.8860 | 0.1127 | 0.8860 | 0.9412 |
| 0.1277 | 2.0315 | 1804 | 0.8226 | 0.2192 | 0.8226 | 0.9070 |
| 0.1277 | 2.0338 | 1806 | 0.8099 | 0.2192 | 0.8099 | 0.9000 |
| 0.1277 | 2.0360 | 1808 | 0.8353 | 0.2192 | 0.8353 | 0.9139 |
| 0.1277 | 2.0383 | 1810 | 0.8185 | 0.2192 | 0.8185 | 0.9047 |
| 0.1277 | 2.0405 | 1812 | 0.7821 | 0.1892 | 0.7821 | 0.8843 |
| 0.1277 | 2.0428 | 1814 | 0.8126 | 0.1892 | 0.8126 | 0.9014 |
| 0.1277 | 2.0450 | 1816 | 0.8294 | 0.1892 | 0.8294 | 0.9107 |
| 0.1277 | 2.0473 | 1818 | 0.7956 | 0.2895 | 0.7956 | 0.8920 |
| 0.1277 | 2.0495 | 1820 | 0.7684 | 0.2895 | 0.7684 | 0.8766 |
| 0.1277 | 2.0518 | 1822 | 0.7547 | 0.3514 | 0.7547 | 0.8688 |
| 0.1277 | 2.0541 | 1824 | 0.7668 | 0.2895 | 0.7668 | 0.8757 |
| 0.1277 | 2.0563 | 1826 | 0.7926 | 0.2895 | 0.7926 | 0.8903 |
| 0.1277 | 2.0586 | 1828 | 0.7911 | 0.2895 | 0.7911 | 0.8895 |
| 0.1277 | 2.0608 | 1830 | 0.7741 | 0.3077 | 0.7741 | 0.8798 |
| 0.1277 | 2.0631 | 1832 | 0.7884 | 0.2895 | 0.7884 | 0.8879 |
| 0.1277 | 2.0653 | 1834 | 0.7976 | 0.2222 | 0.7976 | 0.8931 |
| 0.1277 | 2.0676 | 1836 | 0.8248 | 0.2222 | 0.8248 | 0.9082 |
| 0.1277 | 2.0698 | 1838 | 0.8127 | 0.2222 | 0.8127 | 0.9015 |
| 0.1277 | 2.0721 | 1840 | 0.8147 | 0.2222 | 0.8147 | 0.9026 |
| 0.1277 | 2.0743 | 1842 | 0.7755 | 0.2895 | 0.7755 | 0.8806 |
| 0.1277 | 2.0766 | 1844 | 0.7584 | 0.3684 | 0.7584 | 0.8709 |
| 0.1277 | 2.0788 | 1846 | 0.7594 | 0.3684 | 0.7594 | 0.8715 |
| 0.1277 | 2.0811 | 1848 | 0.7879 | 0.2895 | 0.7879 | 0.8876 |
| 0.1277 | 2.0833 | 1850 | 0.8733 | 0.1039 | 0.8733 | 0.9345 |
| 0.1277 | 2.0856 | 1852 | 0.9083 | 0.1039 | 0.9083 | 0.9531 |
| 0.1277 | 2.0878 | 1854 | 0.8623 | 0.2025 | 0.8623 | 0.9286 |
| 0.1277 | 2.0901 | 1856 | 0.7950 | 0.2597 | 0.7950 | 0.8916 |
| 0.1277 | 2.0923 | 1858 | 0.7776 | 0.3514 | 0.7776 | 0.8818 |
| 0.1277 | 2.0946 | 1860 | 0.7770 | 0.2105 | 0.7770 | 0.8815 |
| 0.1277 | 2.0968 | 1862 | 0.7916 | 0.2597 | 0.7916 | 0.8897 |
| 0.1277 | 2.0991 | 1864 | 0.8141 | 0.2105 | 0.8141 | 0.9023 |
| 0.1277 | 2.1014 | 1866 | 0.8301 | 0.1481 | 0.8301 | 0.9111 |
| 0.1277 | 2.1036 | 1868 | 0.8512 | 0.3077 | 0.8512 | 0.9226 |
| 0.1277 | 2.1059 | 1870 | 0.8744 | 0.2895 | 0.8744 | 0.9351 |
| 0.1277 | 2.1081 | 1872 | 0.8570 | 0.2895 | 0.8570 | 0.9257 |
| 0.1277 | 2.1104 | 1874 | 0.8264 | 0.2895 | 0.8264 | 0.9091 |
| 0.1277 | 2.1126 | 1876 | 0.8352 | 0.24 | 0.8352 | 0.9139 |
| 0.1277 | 2.1149 | 1878 | 0.8066 | 0.24 | 0.8066 | 0.8981 |
| 0.1277 | 2.1171 | 1880 | 0.7865 | 0.24 | 0.7865 | 0.8869 |
| 0.1277 | 2.1194 | 1882 | 0.8049 | 0.24 | 0.8049 | 0.8972 |
| 0.1277 | 2.1216 | 1884 | 0.8219 | 0.24 | 0.8219 | 0.9066 |
| 0.1277 | 2.1239 | 1886 | 0.8965 | 0.1667 | 0.8965 | 0.9469 |
| 0.1277 | 2.1261 | 1888 | 0.9070 | 0.1892 | 0.9070 | 0.9524 |
| 0.1277 | 2.1284 | 1890 | 0.9079 | 0.1892 | 0.9079 | 0.9528 |
| 0.1277 | 2.1306 | 1892 | 0.8536 | 0.24 | 0.8536 | 0.9239 |
| 0.1277 | 2.1329 | 1894 | 0.8494 | 0.24 | 0.8494 | 0.9216 |
| 0.1277 | 2.1351 | 1896 | 0.8877 | 0.24 | 0.8877 | 0.9422 |
| 0.1277 | 2.1374 | 1898 | 0.9156 | 0.24 | 0.9156 | 0.9569 |
| 0.1277 | 2.1396 | 1900 | 0.8805 | 0.24 | 0.8805 | 0.9384 |
| 0.1277 | 2.1419 | 1902 | 0.8279 | 0.2105 | 0.8279 | 0.9099 |
| 0.1277 | 2.1441 | 1904 | 0.8325 | 0.2105 | 0.8325 | 0.9124 |
| 0.1277 | 2.1464 | 1906 | 0.8254 | 0.2105 | 0.8254 | 0.9085 |
| 0.1277 | 2.1486 | 1908 | 0.8003 | 0.2597 | 0.8003 | 0.8946 |
| 0.1277 | 2.1509 | 1910 | 0.8187 | 0.2105 | 0.8187 | 0.9048 |
| 0.1277 | 2.1532 | 1912 | 0.8367 | 0.24 | 0.8367 | 0.9147 |
| 0.1277 | 2.1554 | 1914 | 0.8718 | 0.24 | 0.8718 | 0.9337 |
| 0.1277 | 2.1577 | 1916 | 0.8537 | 0.24 | 0.8537 | 0.9240 |
| 0.1277 | 2.1599 | 1918 | 0.8055 | 0.24 | 0.8055 | 0.8975 |
| 0.1277 | 2.1622 | 1920 | 0.8247 | 0.24 | 0.8247 | 0.9081 |
| 0.1277 | 2.1644 | 1922 | 0.8631 | 0.24 | 0.8631 | 0.9290 |
| 0.1277 | 2.1667 | 1924 | 0.8647 | 0.1892 | 0.8647 | 0.9299 |
| 0.1277 | 2.1689 | 1926 | 0.7929 | 0.24 | 0.7929 | 0.8904 |
| 0.1277 | 2.1712 | 1928 | 0.7436 | 0.3143 | 0.7436 | 0.8623 |
| 0.1277 | 2.1734 | 1930 | 0.7228 | 0.3836 | 0.7228 | 0.8502 |
| 0.1277 | 2.1757 | 1932 | 0.7347 | 0.3333 | 0.7347 | 0.8571 |
| 0.1277 | 2.1779 | 1934 | 0.7917 | 0.24 | 0.7917 | 0.8898 |
| 0.1277 | 2.1802 | 1936 | 0.8420 | 0.1892 | 0.8420 | 0.9176 |
| 0.1277 | 2.1824 | 1938 | 0.8643 | 0.2192 | 0.8643 | 0.9297 |
| 0.1277 | 2.1847 | 1940 | 0.8699 | 0.24 | 0.8699 | 0.9327 |
| 0.1277 | 2.1869 | 1942 | 0.8239 | 0.24 | 0.8239 | 0.9077 |
| 0.1277 | 2.1892 | 1944 | 0.7795 | 0.24 | 0.7795 | 0.8829 |
| 0.1277 | 2.1914 | 1946 | 0.7831 | 0.24 | 0.7831 | 0.8849 |
| 0.1277 | 2.1937 | 1948 | 0.8241 | 0.24 | 0.8241 | 0.9078 |
| 0.1277 | 2.1959 | 1950 | 0.8914 | 0.24 | 0.8914 | 0.9441 |
| 0.1277 | 2.1982 | 1952 | 0.9009 | 0.24 | 0.9009 | 0.9492 |
| 0.1277 | 2.2005 | 1954 | 0.8899 | 0.24 | 0.8899 | 0.9434 |
| 0.1277 | 2.2027 | 1956 | 0.8336 | 0.24 | 0.8336 | 0.9130 |
| 0.1277 | 2.2050 | 1958 | 0.8301 | 0.24 | 0.8301 | 0.9111 |
| 0.1277 | 2.2072 | 1960 | 0.8760 | 0.2703 | 0.8760 | 0.9360 |
| 0.1277 | 2.2095 | 1962 | 0.9208 | 0.1972 | 0.9208 | 0.9596 |
| 0.1277 | 2.2117 | 1964 | 0.9288 | 0.1972 | 0.9288 | 0.9638 |
| 0.1277 | 2.2140 | 1966 | 0.8558 | 0.2192 | 0.8558 | 0.9251 |
| 0.1277 | 2.2162 | 1968 | 0.7970 | 0.24 | 0.7970 | 0.8928 |
| 0.1277 | 2.2185 | 1970 | 0.7474 | 0.2597 | 0.7474 | 0.8645 |
| 0.1277 | 2.2207 | 1972 | 0.7350 | 0.3077 | 0.7350 | 0.8573 |
| 0.1277 | 2.2230 | 1974 | 0.7462 | 0.2597 | 0.7462 | 0.8638 |
| 0.1277 | 2.2252 | 1976 | 0.8007 | 0.2703 | 0.8007 | 0.8948 |
| 0.1277 | 2.2275 | 1978 | 0.8227 | 0.1972 | 0.8227 | 0.9071 |
| 0.1277 | 2.2297 | 1980 | 0.8025 | 0.25 | 0.8025 | 0.8958 |
| 0.1277 | 2.2320 | 1982 | 0.7433 | 0.2192 | 0.7433 | 0.8622 |
| 0.1277 | 2.2342 | 1984 | 0.6838 | 0.2597 | 0.6838 | 0.8269 |
| 0.1277 | 2.2365 | 1986 | 0.6911 | 0.2895 | 0.6911 | 0.8313 |
| 0.1277 | 2.2387 | 1988 | 0.7196 | 0.2222 | 0.7196 | 0.8483 |
| 0.1277 | 2.2410 | 1990 | 0.7525 | 0.2308 | 0.7525 | 0.8675 |
| 0.1277 | 2.2432 | 1992 | 0.7951 | 0.2597 | 0.7951 | 0.8917 |
| 0.1277 | 2.2455 | 1994 | 0.8437 | 0.24 | 0.8437 | 0.9185 |
| 0.1277 | 2.2477 | 1996 | 0.8359 | 0.24 | 0.8359 | 0.9143 |
| 0.1277 | 2.25 | 1998 | 0.7923 | 0.2597 | 0.7923 | 0.8901 |
| 0.1023 | 2.2523 | 2000 | 0.7467 | 0.2597 | 0.7467 | 0.8641 |
| 0.1023 | 2.2545 | 2002 | 0.7250 | 0.2895 | 0.7250 | 0.8515 |
| 0.1023 | 2.2568 | 2004 | 0.7136 | 0.2597 | 0.7136 | 0.8447 |
| 0.1023 | 2.2590 | 2006 | 0.6995 | 0.3514 | 0.6995 | 0.8364 |
| 0.1023 | 2.2613 | 2008 | 0.7202 | 0.3077 | 0.7202 | 0.8486 |
| 0.1023 | 2.2635 | 2010 | 0.7596 | 0.2597 | 0.7596 | 0.8716 |
| 0.1023 | 2.2658 | 2012 | 0.7913 | 0.2597 | 0.7913 | 0.8895 |
| 0.1023 | 2.2680 | 2014 | 0.8219 | 0.2105 | 0.8219 | 0.9066 |
| 0.1023 | 2.2703 | 2016 | 0.8847 | 0.24 | 0.8847 | 0.9406 |
| 0.1023 | 2.2725 | 2018 | 0.9474 | 0.2588 | 0.9474 | 0.9734 |
| 0.1023 | 2.2748 | 2020 | 0.9549 | 0.2588 | 0.9549 | 0.9772 |
| 0.1023 | 2.2770 | 2022 | 0.9257 | 0.2588 | 0.9257 | 0.9621 |
| 0.1023 | 2.2793 | 2024 | 0.8211 | 0.2588 | 0.8211 | 0.9061 |
| 0.1023 | 2.2815 | 2026 | 0.7098 | 0.3077 | 0.7098 | 0.8425 |
| 0.1023 | 2.2838 | 2028 | 0.6659 | 0.4 | 0.6659 | 0.8160 |
| 0.1023 | 2.2860 | 2030 | 0.6439 | 0.3143 | 0.6439 | 0.8024 |
| 0.1023 | 2.2883 | 2032 | 0.6392 | 0.3836 | 0.6392 | 0.7995 |
| 0.1023 | 2.2905 | 2034 | 0.6672 | 0.3333 | 0.6672 | 0.8168 |
| 0.1023 | 2.2928 | 2036 | 0.7183 | 0.24 | 0.7183 | 0.8475 |
| 0.1023 | 2.2950 | 2038 | 0.7425 | 0.3250 | 0.7425 | 0.8617 |
| 0.1023 | 2.2973 | 2040 | 0.7585 | 0.2963 | 0.7585 | 0.8709 |
| 0.1023 | 2.2995 | 2042 | 0.7642 | 0.3415 | 0.7642 | 0.8742 |
| 0.1023 | 2.3018 | 2044 | 0.7604 | 0.2785 | 0.7604 | 0.8720 |
| 0.1023 | 2.3041 | 2046 | 0.7364 | 0.3250 | 0.7364 | 0.8581 |
| 0.1023 | 2.3063 | 2048 | 0.7111 | 0.3684 | 0.7111 | 0.8433 |
| 0.1023 | 2.3086 | 2050 | 0.7034 | 0.3684 | 0.7034 | 0.8387 |
| 0.1023 | 2.3108 | 2052 | 0.7168 | 0.3250 | 0.7168 | 0.8466 |
| 0.1023 | 2.3131 | 2054 | 0.7902 | 0.3415 | 0.7902 | 0.8889 |
| 0.1023 | 2.3153 | 2056 | 0.8480 | 0.2143 | 0.8480 | 0.9209 |
| 0.1023 | 2.3176 | 2058 | 0.8280 | 0.2143 | 0.8280 | 0.9099 |
| 0.1023 | 2.3198 | 2060 | 0.7685 | 0.2597 | 0.7685 | 0.8766 |
| 0.1023 | 2.3221 | 2062 | 0.7047 | 0.4 | 0.7047 | 0.8395 |
| 0.1023 | 2.3243 | 2064 | 0.6825 | 0.4 | 0.6825 | 0.8262 |
| 0.1023 | 2.3266 | 2066 | 0.6822 | 0.4 | 0.6822 | 0.8259 |
| 0.1023 | 2.3288 | 2068 | 0.7179 | 0.3143 | 0.7179 | 0.8473 |
| 0.1023 | 2.3311 | 2070 | 0.7702 | 0.1538 | 0.7702 | 0.8776 |
| 0.1023 | 2.3333 | 2072 | 0.7956 | 0.1818 | 0.7956 | 0.8920 |
| 0.1023 | 2.3356 | 2074 | 0.7601 | 0.1538 | 0.7601 | 0.8719 |
| 0.1023 | 2.3378 | 2076 | 0.7380 | 0.2895 | 0.7380 | 0.8591 |
| 0.1023 | 2.3401 | 2078 | 0.7048 | 0.4324 | 0.7048 | 0.8395 |
| 0.1023 | 2.3423 | 2080 | 0.7033 | 0.4 | 0.7033 | 0.8386 |
| 0.1023 | 2.3446 | 2082 | 0.7173 | 0.3684 | 0.7173 | 0.8470 |
| 0.1023 | 2.3468 | 2084 | 0.7400 | 0.3250 | 0.7400 | 0.8602 |
| 0.1023 | 2.3491 | 2086 | 0.7674 | 0.1951 | 0.7674 | 0.8760 |
| 0.1023 | 2.3514 | 2088 | 0.7754 | 0.1951 | 0.7754 | 0.8806 |
| 0.1023 | 2.3536 | 2090 | 0.7563 | 0.3544 | 0.7563 | 0.8697 |
| 0.1023 | 2.3559 | 2092 | 0.7315 | 0.3684 | 0.7315 | 0.8553 |
| 0.1023 | 2.3581 | 2094 | 0.7372 | 0.2597 | 0.7372 | 0.8586 |
| 0.1023 | 2.3604 | 2096 | 0.7412 | 0.2597 | 0.7412 | 0.8610 |
| 0.1023 | 2.3626 | 2098 | 0.7482 | 0.2963 | 0.7482 | 0.8650 |
| 0.1023 | 2.3649 | 2100 | 0.7407 | 0.2963 | 0.7407 | 0.8606 |
| 0.1023 | 2.3671 | 2102 | 0.7410 | 0.2963 | 0.7410 | 0.8608 |
| 0.1023 | 2.3694 | 2104 | 0.7324 | 0.2597 | 0.7324 | 0.8558 |
| 0.1023 | 2.3716 | 2106 | 0.7292 | 0.2597 | 0.7292 | 0.8539 |
| 0.1023 | 2.3739 | 2108 | 0.7313 | 0.2963 | 0.7313 | 0.8552 |
| 0.1023 | 2.3761 | 2110 | 0.7583 | 0.3077 | 0.7583 | 0.8708 |
| 0.1023 | 2.3784 | 2112 | 0.7867 | 0.2895 | 0.7867 | 0.8870 |
| 0.1023 | 2.3806 | 2114 | 0.7732 | 0.2703 | 0.7732 | 0.8793 |
| 0.1023 | 2.3829 | 2116 | 0.7170 | 0.3377 | 0.7170 | 0.8467 |
| 0.1023 | 2.3851 | 2118 | 0.6747 | 0.3836 | 0.6747 | 0.8214 |
| 0.1023 | 2.3874 | 2120 | 0.6550 | 0.3836 | 0.6550 | 0.8093 |
| 0.1023 | 2.3896 | 2122 | 0.6575 | 0.3836 | 0.6575 | 0.8109 |
| 0.1023 | 2.3919 | 2124 | 0.6769 | 0.3836 | 0.6769 | 0.8227 |
| 0.1023 | 2.3941 | 2126 | 0.7059 | 0.3377 | 0.7059 | 0.8402 |
| 0.1023 | 2.3964 | 2128 | 0.7468 | 0.24 | 0.7468 | 0.8642 |
| 0.1023 | 2.3986 | 2130 | 0.7804 | 0.1127 | 0.7804 | 0.8834 |
| 0.1023 | 2.4009 | 2132 | 0.7995 | 0.1429 | 0.7995 | 0.8941 |
| 0.1023 | 2.4032 | 2134 | 0.7629 | 0.1667 | 0.7629 | 0.8734 |
| 0.1023 | 2.4054 | 2136 | 0.7167 | 0.3077 | 0.7167 | 0.8466 |
| 0.1023 | 2.4077 | 2138 | 0.6994 | 0.3077 | 0.6994 | 0.8363 |
| 0.1023 | 2.4099 | 2140 | 0.6943 | 0.3377 | 0.6943 | 0.8333 |
| 0.1023 | 2.4122 | 2142 | 0.6913 | 0.3077 | 0.6913 | 0.8315 |
| 0.1023 | 2.4144 | 2144 | 0.7219 | 0.2895 | 0.7219 | 0.8496 |
| 0.1023 | 2.4167 | 2146 | 0.8015 | 0.1429 | 0.8015 | 0.8953 |
| 0.1023 | 2.4189 | 2148 | 0.9173 | 0.1750 | 0.9173 | 0.9578 |
| 0.1023 | 2.4212 | 2150 | 0.9469 | 0.1882 | 0.9469 | 0.9731 |
| 0.1023 | 2.4234 | 2152 | 0.8971 | 0.0800 | 0.8971 | 0.9471 |
| 0.1023 | 2.4257 | 2154 | 0.8016 | 0.1429 | 0.8016 | 0.8953 |
| 0.1023 | 2.4279 | 2156 | 0.7423 | 0.2703 | 0.7423 | 0.8616 |
| 0.1023 | 2.4302 | 2158 | 0.7022 | 0.3377 | 0.7022 | 0.8380 |
| 0.1023 | 2.4324 | 2160 | 0.7054 | 0.3377 | 0.7054 | 0.8399 |
| 0.1023 | 2.4347 | 2162 | 0.7272 | 0.3377 | 0.7272 | 0.8528 |
| 0.1023 | 2.4369 | 2164 | 0.7341 | 0.3544 | 0.7341 | 0.8568 |
| 0.1023 | 2.4392 | 2166 | 0.7796 | 0.3377 | 0.7796 | 0.8830 |
| 0.1023 | 2.4414 | 2168 | 0.8009 | 0.3077 | 0.8009 | 0.8949 |
| 0.1023 | 2.4437 | 2170 | 0.7960 | 0.3077 | 0.7960 | 0.8922 |
| 0.1023 | 2.4459 | 2172 | 0.7948 | 0.3377 | 0.7948 | 0.8915 |
| 0.1023 | 2.4482 | 2174 | 0.7886 | 0.3377 | 0.7886 | 0.8880 |
| 0.1023 | 2.4505 | 2176 | 0.7708 | 0.3377 | 0.7708 | 0.8780 |
| 0.1023 | 2.4527 | 2178 | 0.7411 | 0.3077 | 0.7411 | 0.8609 |
| 0.1023 | 2.4550 | 2180 | 0.7249 | 0.3077 | 0.7249 | 0.8514 |
| 0.1023 | 2.4572 | 2182 | 0.7155 | 0.3836 | 0.7155 | 0.8459 |
| 0.1023 | 2.4595 | 2184 | 0.7073 | 0.3836 | 0.7073 | 0.8410 |
| 0.1023 | 2.4617 | 2186 | 0.7053 | 0.3836 | 0.7053 | 0.8398 |
| 0.1023 | 2.4640 | 2188 | 0.7178 | 0.3077 | 0.7178 | 0.8472 |
| 0.1023 | 2.4662 | 2190 | 0.7457 | 0.3077 | 0.7457 | 0.8635 |
| 0.1023 | 2.4685 | 2192 | 0.7668 | 0.3377 | 0.7668 | 0.8757 |
| 0.1023 | 2.4707 | 2194 | 0.7747 | 0.3377 | 0.7747 | 0.8802 |
| 0.1023 | 2.4730 | 2196 | 0.8027 | 0.2895 | 0.8027 | 0.8959 |
| 0.1023 | 2.4752 | 2198 | 0.8233 | 0.2895 | 0.8233 | 0.9074 |
| 0.1023 | 2.4775 | 2200 | 0.7923 | 0.2895 | 0.7923 | 0.8901 |
| 0.1023 | 2.4797 | 2202 | 0.7753 | 0.2895 | 0.7753 | 0.8805 |
| 0.1023 | 2.4820 | 2204 | 0.7492 | 0.3662 | 0.7492 | 0.8655 |
| 0.1023 | 2.4842 | 2206 | 0.7494 | 0.3662 | 0.7494 | 0.8657 |
| 0.1023 | 2.4865 | 2208 | 0.7636 | 0.3662 | 0.7636 | 0.8738 |
| 0.1023 | 2.4887 | 2210 | 0.7746 | 0.3662 | 0.7746 | 0.8801 |
| 0.1023 | 2.4910 | 2212 | 0.8146 | 0.2895 | 0.8146 | 0.9026 |
| 0.1023 | 2.4932 | 2214 | 0.8609 | 0.24 | 0.8609 | 0.9279 |
| 0.1023 | 2.4955 | 2216 | 0.8840 | 0.1667 | 0.8840 | 0.9402 |
| 0.1023 | 2.4977 | 2218 | 0.7836 | 0.1127 | 0.7836 | 0.8852 |
| 0.1023 | 2.5 | 2220 | 0.7037 | 0.1667 | 0.7037 | 0.8389 |
| 0.1023 | 2.5023 | 2222 | 0.6620 | 0.25 | 0.6620 | 0.8136 |
| 0.1023 | 2.5045 | 2224 | 0.7397 | 0.24 | 0.7397 | 0.8600 |
| 0.1023 | 2.5068 | 2226 | 0.7919 | 0.2895 | 0.7919 | 0.8899 |
| 0.1023 | 2.5090 | 2228 | 0.8543 | 0.2895 | 0.8543 | 0.9243 |
| 0.1023 | 2.5113 | 2230 | 0.8778 | 0.2895 | 0.8778 | 0.9369 |
| 0.1023 | 2.5135 | 2232 | 0.8850 | 0.24 | 0.8850 | 0.9407 |
| 0.1023 | 2.5158 | 2234 | 0.8553 | 0.2192 | 0.8553 | 0.9248 |
| 0.1023 | 2.5180 | 2236 | 0.7835 | 0.2703 | 0.7835 | 0.8852 |
| 0.1023 | 2.5203 | 2238 | 0.7298 | 0.24 | 0.7298 | 0.8543 |
| 0.1023 | 2.5225 | 2240 | 0.7105 | 0.3662 | 0.7105 | 0.8429 |
| 0.1023 | 2.5248 | 2242 | 0.7233 | 0.3662 | 0.7233 | 0.8505 |
| 0.1023 | 2.5270 | 2244 | 0.7531 | 0.24 | 0.7531 | 0.8678 |
| 0.1023 | 2.5293 | 2246 | 0.8516 | 0.1972 | 0.8516 | 0.9228 |
| 0.1023 | 2.5315 | 2248 | 1.0095 | 0.1429 | 1.0095 | 1.0047 |
| 0.1023 | 2.5338 | 2250 | 1.0591 | 0.1000 | 1.0591 | 1.0291 |
| 0.1023 | 2.5360 | 2252 | 0.9928 | 0.1429 | 0.9928 | 0.9964 |
| 0.1023 | 2.5383 | 2254 | 0.8742 | 0.25 | 0.8742 | 0.9350 |
| 0.1023 | 2.5405 | 2256 | 0.7559 | 0.2703 | 0.7559 | 0.8695 |
| 0.1023 | 2.5428 | 2258 | 0.7140 | 0.3143 | 0.7140 | 0.8450 |
| 0.1023 | 2.5450 | 2260 | 0.7055 | 0.3143 | 0.7055 | 0.8400 |
| 0.1023 | 2.5473 | 2262 | 0.7036 | 0.3662 | 0.7036 | 0.8388 |
| 0.1023 | 2.5495 | 2264 | 0.7342 | 0.2703 | 0.7342 | 0.8568 |
| 0.1023 | 2.5518 | 2266 | 0.7904 | 0.2192 | 0.7904 | 0.8890 |
| 0.1023 | 2.5541 | 2268 | 0.8138 | 0.2192 | 0.8138 | 0.9021 |
| 0.1023 | 2.5563 | 2270 | 0.8248 | 0.2192 | 0.8248 | 0.9082 |
| 0.1023 | 2.5586 | 2272 | 0.8027 | 0.24 | 0.8027 | 0.8959 |
| 0.1023 | 2.5608 | 2274 | 0.7528 | 0.4167 | 0.7528 | 0.8676 |
| 0.1023 | 2.5631 | 2276 | 0.7354 | 0.3836 | 0.7354 | 0.8576 |
| 0.1023 | 2.5653 | 2278 | 0.7440 | 0.3662 | 0.7440 | 0.8625 |
| 0.1023 | 2.5676 | 2280 | 0.7732 | 0.2703 | 0.7732 | 0.8793 |
| 0.1023 | 2.5698 | 2282 | 0.7749 | 0.2703 | 0.7749 | 0.8803 |
| 0.1023 | 2.5721 | 2284 | 0.7686 | 0.2895 | 0.7686 | 0.8767 |
| 0.1023 | 2.5743 | 2286 | 0.7890 | 0.2895 | 0.7890 | 0.8883 |
| 0.1023 | 2.5766 | 2288 | 0.8295 | 0.2703 | 0.8295 | 0.9108 |
| 0.1023 | 2.5788 | 2290 | 0.8269 | 0.2703 | 0.8269 | 0.9093 |
| 0.1023 | 2.5811 | 2292 | 0.8042 | 0.2703 | 0.8042 | 0.8968 |
| 0.1023 | 2.5833 | 2294 | 0.7955 | 0.2895 | 0.7955 | 0.8919 |
| 0.1023 | 2.5856 | 2296 | 0.8039 | 0.2597 | 0.8039 | 0.8966 |
| 0.1023 | 2.5878 | 2298 | 0.8113 | 0.2895 | 0.8113 | 0.9007 |
| 0.1023 | 2.5901 | 2300 | 0.8197 | 0.2895 | 0.8197 | 0.9054 |
| 0.1023 | 2.5923 | 2302 | 0.8578 | 0.2895 | 0.8578 | 0.9261 |
| 0.1023 | 2.5946 | 2304 | 0.8689 | 0.2895 | 0.8689 | 0.9322 |
| 0.1023 | 2.5968 | 2306 | 0.8748 | 0.2222 | 0.8748 | 0.9353 |
| 0.1023 | 2.5991 | 2308 | 0.8637 | 0.3077 | 0.8637 | 0.9293 |
| 0.1023 | 2.6014 | 2310 | 0.8590 | 0.3077 | 0.8590 | 0.9268 |
| 0.1023 | 2.6036 | 2312 | 0.8531 | 0.3077 | 0.8531 | 0.9236 |
| 0.1023 | 2.6059 | 2314 | 0.8577 | 0.2222 | 0.8577 | 0.9261 |
| 0.1023 | 2.6081 | 2316 | 0.8345 | 0.3200 | 0.8345 | 0.9135 |
| 0.1023 | 2.6104 | 2318 | 0.7818 | 0.2597 | 0.7818 | 0.8842 |
| 0.1023 | 2.6126 | 2320 | 0.7586 | 0.3836 | 0.7586 | 0.8710 |
| 0.1023 | 2.6149 | 2322 | 0.7584 | 0.2597 | 0.7584 | 0.8709 |
| 0.1023 | 2.6171 | 2324 | 0.7685 | 0.2895 | 0.7685 | 0.8766 |
| 0.1023 | 2.6194 | 2326 | 0.7790 | 0.3200 | 0.7790 | 0.8826 |
| 0.1023 | 2.6216 | 2328 | 0.8394 | 0.2192 | 0.8394 | 0.9162 |
| 0.1023 | 2.6239 | 2330 | 0.8759 | 0.1538 | 0.8759 | 0.9359 |
| 0.1023 | 2.6261 | 2332 | 0.8574 | 0.2192 | 0.8574 | 0.9260 |
| 0.1023 | 2.6284 | 2334 | 0.8050 | 0.2703 | 0.8050 | 0.8972 |
| 0.1023 | 2.6306 | 2336 | 0.7421 | 0.4167 | 0.7421 | 0.8615 |
| 0.1023 | 2.6329 | 2338 | 0.7174 | 0.2895 | 0.7174 | 0.8470 |
| 0.1023 | 2.6351 | 2340 | 0.7145 | 0.2895 | 0.7145 | 0.8453 |
| 0.1023 | 2.6374 | 2342 | 0.7180 | 0.4324 | 0.7180 | 0.8474 |
| 0.1023 | 2.6396 | 2344 | 0.7492 | 0.2895 | 0.7492 | 0.8656 |
| 0.1023 | 2.6419 | 2346 | 0.7893 | 0.2703 | 0.7893 | 0.8884 |
| 0.1023 | 2.6441 | 2348 | 0.8526 | 0.2192 | 0.8526 | 0.9233 |
| 0.1023 | 2.6464 | 2350 | 0.8713 | 0.1039 | 0.8713 | 0.9334 |
| 0.1023 | 2.6486 | 2352 | 0.8354 | 0.2192 | 0.8354 | 0.9140 |
| 0.1023 | 2.6509 | 2354 | 0.7758 | 0.2895 | 0.7758 | 0.8808 |
| 0.1023 | 2.6532 | 2356 | 0.7444 | 0.2895 | 0.7444 | 0.8628 |
| 0.1023 | 2.6554 | 2358 | 0.7620 | 0.0294 | 0.7620 | 0.8730 |
| 0.1023 | 2.6577 | 2360 | 0.7743 | 0.0294 | 0.7743 | 0.8799 |
| 0.1023 | 2.6599 | 2362 | 0.7609 | 0.0294 | 0.7609 | 0.8723 |
| 0.1023 | 2.6622 | 2364 | 0.7446 | 0.2895 | 0.7446 | 0.8629 |
| 0.1023 | 2.6644 | 2366 | 0.7693 | 0.2895 | 0.7693 | 0.8771 |
| 0.1023 | 2.6667 | 2368 | 0.8230 | 0.1667 | 0.8230 | 0.9072 |
| 0.1023 | 2.6689 | 2370 | 0.8574 | 0.1667 | 0.8574 | 0.9260 |
| 0.1023 | 2.6712 | 2372 | 0.8512 | 0.1127 | 0.8512 | 0.9226 |
| 0.1023 | 2.6734 | 2374 | 0.8550 | 0.1127 | 0.8550 | 0.9247 |
| 0.1023 | 2.6757 | 2376 | 0.8150 | 0.1127 | 0.8150 | 0.9028 |
| 0.1023 | 2.6779 | 2378 | 0.7449 | 0.1667 | 0.7449 | 0.8630 |
| 0.1023 | 2.6802 | 2380 | 0.6859 | 0.4167 | 0.6859 | 0.8282 |
| 0.1023 | 2.6824 | 2382 | 0.6760 | 0.3478 | 0.6760 | 0.8222 |
| 0.1023 | 2.6847 | 2384 | 0.6888 | 0.3514 | 0.6888 | 0.8299 |
| 0.1023 | 2.6869 | 2386 | 0.7174 | 0.4167 | 0.7174 | 0.8470 |
| 0.1023 | 2.6892 | 2388 | 0.7698 | 0.2895 | 0.7698 | 0.8774 |
| 0.1023 | 2.6914 | 2390 | 0.8301 | 0.2192 | 0.8301 | 0.9111 |
| 0.1023 | 2.6937 | 2392 | 0.8536 | 0.1667 | 0.8536 | 0.9239 |
| 0.1023 | 2.6959 | 2394 | 0.8293 | 0.2703 | 0.8293 | 0.9106 |
| 0.1023 | 2.6982 | 2396 | 0.7772 | 0.2895 | 0.7772 | 0.8816 |
| 0.1023 | 2.7005 | 2398 | 0.7463 | 0.3077 | 0.7463 | 0.8639 |
| 0.1023 | 2.7027 | 2400 | 0.7303 | 0.4 | 0.7303 | 0.8546 |
| 0.1023 | 2.7050 | 2402 | 0.7312 | 0.4 | 0.7312 | 0.8551 |
| 0.1023 | 2.7072 | 2404 | 0.7329 | 0.2597 | 0.7329 | 0.8561 |
| 0.1023 | 2.7095 | 2406 | 0.7565 | 0.3200 | 0.7565 | 0.8698 |
| 0.1023 | 2.7117 | 2408 | 0.7697 | 0.2703 | 0.7697 | 0.8773 |
| 0.1023 | 2.7140 | 2410 | 0.7586 | 0.2703 | 0.7586 | 0.8710 |
| 0.1023 | 2.7162 | 2412 | 0.7365 | 0.3200 | 0.7365 | 0.8582 |
| 0.1023 | 2.7185 | 2414 | 0.6940 | 0.3333 | 0.6940 | 0.8331 |
| 0.1023 | 2.7207 | 2416 | 0.6736 | 0.4 | 0.6736 | 0.8207 |
| 0.1023 | 2.7230 | 2418 | 0.6728 | 0.4 | 0.6728 | 0.8202 |
| 0.1023 | 2.7252 | 2420 | 0.6814 | 0.4 | 0.6814 | 0.8255 |
| 0.1023 | 2.7275 | 2422 | 0.7016 | 0.2895 | 0.7016 | 0.8376 |
| 0.1023 | 2.7297 | 2424 | 0.7166 | 0.2895 | 0.7166 | 0.8465 |
| 0.1023 | 2.7320 | 2426 | 0.7581 | 0.2895 | 0.7581 | 0.8707 |
| 0.1023 | 2.7342 | 2428 | 0.7742 | 0.2895 | 0.7742 | 0.8799 |
| 0.1023 | 2.7365 | 2430 | 0.7653 | 0.2597 | 0.7653 | 0.8748 |
| 0.1023 | 2.7387 | 2432 | 0.7495 | 0.2785 | 0.7495 | 0.8658 |
| 0.1023 | 2.7410 | 2434 | 0.7285 | 0.3250 | 0.7285 | 0.8535 |
| 0.1023 | 2.7432 | 2436 | 0.7259 | 0.3684 | 0.7259 | 0.8520 |
| 0.1023 | 2.7455 | 2438 | 0.7241 | 0.2597 | 0.7241 | 0.8510 |
| 0.1023 | 2.7477 | 2440 | 0.7184 | 0.2597 | 0.7184 | 0.8476 |
| 0.1023 | 2.75 | 2442 | 0.7249 | 0.3684 | 0.7249 | 0.8514 |
| 0.1023 | 2.7523 | 2444 | 0.7194 | 0.3684 | 0.7194 | 0.8482 |
| 0.1023 | 2.7545 | 2446 | 0.7233 | 0.3684 | 0.7233 | 0.8505 |
| 0.1023 | 2.7568 | 2448 | 0.7343 | 0.3684 | 0.7343 | 0.8569 |
| 0.1023 | 2.7590 | 2450 | 0.7471 | 0.4 | 0.7471 | 0.8643 |
| 0.1023 | 2.7613 | 2452 | 0.7779 | 0.3250 | 0.7779 | 0.8820 |
| 0.1023 | 2.7635 | 2454 | 0.7892 | 0.3250 | 0.7892 | 0.8884 |
| 0.1023 | 2.7658 | 2456 | 0.7870 | 0.2222 | 0.7870 | 0.8871 |
| 0.1023 | 2.7680 | 2458 | 0.7811 | 0.2222 | 0.7811 | 0.8838 |
| 0.1023 | 2.7703 | 2460 | 0.7810 | 0.3250 | 0.7810 | 0.8837 |
| 0.1023 | 2.7725 | 2462 | 0.7769 | 0.3544 | 0.7769 | 0.8814 |
| 0.1023 | 2.7748 | 2464 | 0.8050 | 0.2895 | 0.8050 | 0.8972 |
| 0.1023 | 2.7770 | 2466 | 0.8251 | 0.1892 | 0.8251 | 0.9084 |
| 0.1023 | 2.7793 | 2468 | 0.8025 | 0.2192 | 0.8025 | 0.8958 |
| 0.1023 | 2.7815 | 2470 | 0.7434 | 0.24 | 0.7434 | 0.8622 |
| 0.1023 | 2.7838 | 2472 | 0.6932 | 0.3333 | 0.6932 | 0.8326 |
| 0.1023 | 2.7860 | 2474 | 0.6707 | 0.4324 | 0.6707 | 0.8190 |
| 0.1023 | 2.7883 | 2476 | 0.6734 | 0.4324 | 0.6734 | 0.8206 |
| 0.1023 | 2.7905 | 2478 | 0.6868 | 0.4324 | 0.6868 | 0.8287 |
| 0.1023 | 2.7928 | 2480 | 0.7438 | 0.1892 | 0.7438 | 0.8624 |
| 0.1023 | 2.7950 | 2482 | 0.8296 | 0.1667 | 0.8296 | 0.9108 |
| 0.1023 | 2.7973 | 2484 | 0.9717 | 0.1600 | 0.9717 | 0.9858 |
| 0.1023 | 2.7995 | 2486 | 1.0040 | 0.1600 | 1.0040 | 1.0020 |
| 0.1023 | 2.8018 | 2488 | 0.9319 | 0.1316 | 0.9319 | 0.9653 |
| 0.1023 | 2.8041 | 2490 | 0.8068 | 0.1127 | 0.8068 | 0.8982 |
| 0.1023 | 2.8063 | 2492 | 0.6917 | 0.3143 | 0.6917 | 0.8317 |
| 0.1023 | 2.8086 | 2494 | 0.6469 | 0.3824 | 0.6469 | 0.8043 |
| 0.1023 | 2.8108 | 2496 | 0.6382 | 0.4375 | 0.6382 | 0.7988 |
| 0.1023 | 2.8131 | 2498 | 0.6455 | 0.3824 | 0.6455 | 0.8034 |
| 0.0919 | 2.8153 | 2500 | 0.6608 | 0.3836 | 0.6608 | 0.8129 |
| 0.0919 | 2.8176 | 2502 | 0.6903 | 0.3836 | 0.6903 | 0.8308 |
| 0.0919 | 2.8198 | 2504 | 0.7563 | 0.2895 | 0.7563 | 0.8697 |
| 0.0919 | 2.8221 | 2506 | 0.7932 | 0.2895 | 0.7932 | 0.8906 |
| 0.0919 | 2.8243 | 2508 | 0.7746 | 0.3077 | 0.7746 | 0.8801 |
| 0.0919 | 2.8266 | 2510 | 0.7376 | 0.3077 | 0.7376 | 0.8588 |
| 0.0919 | 2.8288 | 2512 | 0.7238 | 0.3077 | 0.7238 | 0.8507 |
| 0.0919 | 2.8311 | 2514 | 0.7264 | 0.3077 | 0.7264 | 0.8523 |
| 0.0919 | 2.8333 | 2516 | 0.7367 | 0.3077 | 0.7367 | 0.8583 |
| 0.0919 | 2.8356 | 2518 | 0.7425 | 0.3077 | 0.7425 | 0.8617 |
| 0.0919 | 2.8378 | 2520 | 0.7251 | 0.3077 | 0.7251 | 0.8515 |
| 0.0919 | 2.8401 | 2522 | 0.7269 | 0.3377 | 0.7269 | 0.8526 |
| 0.0919 | 2.8423 | 2524 | 0.7099 | 0.3077 | 0.7099 | 0.8426 |
| 0.0919 | 2.8446 | 2526 | 0.6814 | 0.3544 | 0.6814 | 0.8255 |
| 0.0919 | 2.8468 | 2528 | 0.6659 | 0.3143 | 0.6659 | 0.8160 |
| 0.0919 | 2.8491 | 2530 | 0.6614 | 0.3143 | 0.6614 | 0.8132 |
| 0.0919 | 2.8514 | 2532 | 0.6610 | 0.4324 | 0.6610 | 0.8130 |
| 0.0919 | 2.8536 | 2534 | 0.6749 | 0.3077 | 0.6749 | 0.8215 |
| 0.0919 | 2.8559 | 2536 | 0.6987 | 0.3377 | 0.6987 | 0.8359 |
| 0.0919 | 2.8581 | 2538 | 0.7139 | 0.2895 | 0.7139 | 0.8449 |
| 0.0919 | 2.8604 | 2540 | 0.7153 | 0.2895 | 0.7153 | 0.8457 |
| 0.0919 | 2.8626 | 2542 | 0.7014 | 0.3377 | 0.7014 | 0.8375 |
| 0.0919 | 2.8649 | 2544 | 0.6929 | 0.3077 | 0.6929 | 0.8324 |
| 0.0919 | 2.8671 | 2546 | 0.7038 | 0.2895 | 0.7038 | 0.8389 |
| 0.0919 | 2.8694 | 2548 | 0.6860 | 0.3077 | 0.6860 | 0.8283 |
| 0.0919 | 2.8716 | 2550 | 0.6875 | 0.2895 | 0.6875 | 0.8292 |
| 0.0919 | 2.8739 | 2552 | 0.6865 | 0.2895 | 0.6865 | 0.8285 |
| 0.0919 | 2.8761 | 2554 | 0.6651 | 0.3544 | 0.6651 | 0.8155 |
| 0.0919 | 2.8784 | 2556 | 0.6621 | 0.3544 | 0.6621 | 0.8137 |
| 0.0919 | 2.8806 | 2558 | 0.6765 | 0.3544 | 0.6765 | 0.8225 |
| 0.0919 | 2.8829 | 2560 | 0.7109 | 0.3544 | 0.7109 | 0.8431 |
| 0.0919 | 2.8851 | 2562 | 0.7635 | 0.24 | 0.7635 | 0.8738 |
| 0.0919 | 2.8874 | 2564 | 0.7757 | 0.2703 | 0.7757 | 0.8807 |
| 0.0919 | 2.8896 | 2566 | 0.7661 | 0.2105 | 0.7661 | 0.8753 |
| 0.0919 | 2.8919 | 2568 | 0.7260 | 0.3077 | 0.7260 | 0.8521 |
| 0.0919 | 2.8941 | 2570 | 0.7160 | 0.3544 | 0.7160 | 0.8462 |
| 0.0919 | 2.8964 | 2572 | 0.7326 | 0.3544 | 0.7326 | 0.8559 |
| 0.0919 | 2.8986 | 2574 | 0.7460 | 0.3544 | 0.7460 | 0.8637 |
| 0.0919 | 2.9009 | 2576 | 0.7395 | 0.3544 | 0.7395 | 0.8600 |
| 0.0919 | 2.9032 | 2578 | 0.7268 | 0.3544 | 0.7268 | 0.8525 |
| 0.0919 | 2.9054 | 2580 | 0.7122 | 0.3544 | 0.7122 | 0.8439 |
| 0.0919 | 2.9077 | 2582 | 0.7051 | 0.3077 | 0.7051 | 0.8397 |
| 0.0919 | 2.9099 | 2584 | 0.7010 | 0.3077 | 0.7010 | 0.8372 |
| 0.0919 | 2.9122 | 2586 | 0.7180 | 0.2703 | 0.7180 | 0.8474 |
| 0.0919 | 2.9144 | 2588 | 0.7369 | 0.2192 | 0.7369 | 0.8584 |
| 0.0919 | 2.9167 | 2590 | 0.7191 | 0.2192 | 0.7191 | 0.8480 |
| 0.0919 | 2.9189 | 2592 | 0.6693 | 0.24 | 0.6693 | 0.8181 |
| 0.0919 | 2.9212 | 2594 | 0.6521 | 0.3836 | 0.6521 | 0.8075 |
| 0.0919 | 2.9234 | 2596 | 0.6539 | 0.3143 | 0.6539 | 0.8087 |
| 0.0919 | 2.9257 | 2598 | 0.6645 | 0.24 | 0.6645 | 0.8152 |
| 0.0919 | 2.9279 | 2600 | 0.7096 | 0.2703 | 0.7096 | 0.8424 |
| 0.0919 | 2.9302 | 2602 | 0.7711 | 0.1127 | 0.7711 | 0.8781 |
| 0.0919 | 2.9324 | 2604 | 0.7904 | 0.1127 | 0.7904 | 0.8890 |
| 0.0919 | 2.9347 | 2606 | 0.7516 | 0.2703 | 0.7516 | 0.8670 |
| 0.0919 | 2.9369 | 2608 | 0.6916 | 0.3077 | 0.6916 | 0.8316 |
| 0.0919 | 2.9392 | 2610 | 0.6792 | 0.3544 | 0.6792 | 0.8241 |
| 0.0919 | 2.9414 | 2612 | 0.6891 | 0.3544 | 0.6891 | 0.8301 |
| 0.0919 | 2.9437 | 2614 | 0.6975 | 0.3544 | 0.6975 | 0.8352 |
| 0.0919 | 2.9459 | 2616 | 0.7118 | 0.3544 | 0.7118 | 0.8437 |
| 0.0919 | 2.9482 | 2618 | 0.7216 | 0.3077 | 0.7216 | 0.8495 |
| 0.0919 | 2.9505 | 2620 | 0.7217 | 0.3077 | 0.7217 | 0.8495 |
| 0.0919 | 2.9527 | 2622 | 0.7059 | 0.3077 | 0.7059 | 0.8402 |
| 0.0919 | 2.9550 | 2624 | 0.7123 | 0.3077 | 0.7123 | 0.8440 |
| 0.0919 | 2.9572 | 2626 | 0.7202 | 0.2597 | 0.7202 | 0.8486 |
| 0.0919 | 2.9595 | 2628 | 0.6986 | 0.2597 | 0.6986 | 0.8359 |
| 0.0919 | 2.9617 | 2630 | 0.6570 | 0.3836 | 0.6570 | 0.8106 |
| 0.0919 | 2.9640 | 2632 | 0.6392 | 0.3438 | 0.6392 | 0.7995 |
| 0.0919 | 2.9662 | 2634 | 0.6403 | 0.3438 | 0.6403 | 0.8002 |
| 0.0919 | 2.9685 | 2636 | 0.6495 | 0.2727 | 0.6495 | 0.8059 |
| 0.0919 | 2.9707 | 2638 | 0.6640 | 0.1493 | 0.6640 | 0.8149 |
| 0.0919 | 2.9730 | 2640 | 0.6828 | 0.3662 | 0.6828 | 0.8263 |
| 0.0919 | 2.9752 | 2642 | 0.7151 | 0.3544 | 0.7151 | 0.8457 |
| 0.0919 | 2.9775 | 2644 | 0.7520 | 0.2597 | 0.7520 | 0.8672 |
| 0.0919 | 2.9797 | 2646 | 0.7583 | 0.2597 | 0.7583 | 0.8708 |
| 0.0919 | 2.9820 | 2648 | 0.7324 | 0.2597 | 0.7324 | 0.8558 |
| 0.0919 | 2.9842 | 2650 | 0.6955 | 0.3077 | 0.6955 | 0.8340 |
| 0.0919 | 2.9865 | 2652 | 0.6693 | 0.4348 | 0.6693 | 0.8181 |
| 0.0919 | 2.9887 | 2654 | 0.6669 | 0.4348 | 0.6669 | 0.8166 |
| 0.0919 | 2.9910 | 2656 | 0.6625 | 0.3824 | 0.6625 | 0.8139 |
| 0.0919 | 2.9932 | 2658 | 0.6827 | 0.3143 | 0.6827 | 0.8263 |
| 0.0919 | 2.9955 | 2660 | 0.7152 | 0.2703 | 0.7152 | 0.8457 |
| 0.0919 | 2.9977 | 2662 | 0.7155 | 0.2703 | 0.7155 | 0.8459 |
| 0.0919 | 3.0 | 2664 | 0.7047 | 0.3077 | 0.7047 | 0.8394 |
| 0.0919 | 3.0023 | 2666 | 0.6955 | 0.4 | 0.6955 | 0.8339 |
| 0.0919 | 3.0045 | 2668 | 0.6991 | 0.4 | 0.6991 | 0.8361 |
| 0.0919 | 3.0068 | 2670 | 0.7077 | 0.3684 | 0.7077 | 0.8412 |
| 0.0919 | 3.0090 | 2672 | 0.7197 | 0.3684 | 0.7197 | 0.8483 |
| 0.0919 | 3.0113 | 2674 | 0.7192 | 0.3684 | 0.7192 | 0.8480 |
| 0.0919 | 3.0135 | 2676 | 0.7323 | 0.3544 | 0.7323 | 0.8558 |
| 0.0919 | 3.0158 | 2678 | 0.7424 | 0.3077 | 0.7424 | 0.8616 |
| 0.0919 | 3.0180 | 2680 | 0.7585 | 0.3077 | 0.7585 | 0.8709 |
| 0.0919 | 3.0203 | 2682 | 0.7389 | 0.3077 | 0.7389 | 0.8596 |
| 0.0919 | 3.0225 | 2684 | 0.7262 | 0.3077 | 0.7262 | 0.8522 |
| 0.0919 | 3.0248 | 2686 | 0.7214 | 0.2597 | 0.7214 | 0.8493 |
| 0.0919 | 3.0270 | 2688 | 0.7041 | 0.2597 | 0.7041 | 0.8391 |
| 0.0919 | 3.0293 | 2690 | 0.7021 | 0.2597 | 0.7021 | 0.8379 |
| 0.0919 | 3.0315 | 2692 | 0.7217 | 0.2597 | 0.7217 | 0.8495 |
| 0.0919 | 3.0338 | 2694 | 0.7297 | 0.3200 | 0.7297 | 0.8542 |
| 0.0919 | 3.0360 | 2696 | 0.7197 | 0.3200 | 0.7197 | 0.8483 |
| 0.0919 | 3.0383 | 2698 | 0.7293 | 0.24 | 0.7293 | 0.8540 |
| 0.0919 | 3.0405 | 2700 | 0.7487 | 0.24 | 0.7487 | 0.8653 |
| 0.0919 | 3.0428 | 2702 | 0.7243 | 0.3077 | 0.7243 | 0.8510 |
| 0.0919 | 3.0450 | 2704 | 0.6921 | 0.3544 | 0.6921 | 0.8319 |
| 0.0919 | 3.0473 | 2706 | 0.6835 | 0.4 | 0.6835 | 0.8267 |
| 0.0919 | 3.0495 | 2708 | 0.6910 | 0.2817 | 0.6910 | 0.8313 |
| 0.0919 | 3.0518 | 2710 | 0.7054 | 0.4 | 0.7054 | 0.8399 |
| 0.0919 | 3.0541 | 2712 | 0.7378 | 0.3544 | 0.7378 | 0.8590 |
| 0.0919 | 3.0563 | 2714 | 0.7599 | 0.3077 | 0.7599 | 0.8717 |
| 0.0919 | 3.0586 | 2716 | 0.7671 | 0.3077 | 0.7671 | 0.8758 |
| 0.0919 | 3.0608 | 2718 | 0.7508 | 0.3544 | 0.7508 | 0.8665 |
| 0.0919 | 3.0631 | 2720 | 0.7296 | 0.3544 | 0.7296 | 0.8541 |
| 0.0919 | 3.0653 | 2722 | 0.7065 | 0.3544 | 0.7065 | 0.8406 |
| 0.0919 | 3.0676 | 2724 | 0.6940 | 0.4 | 0.6940 | 0.8331 |
| 0.0919 | 3.0698 | 2726 | 0.6881 | 0.4324 | 0.6881 | 0.8295 |
| 0.0919 | 3.0721 | 2728 | 0.6914 | 0.3077 | 0.6914 | 0.8315 |
| 0.0919 | 3.0743 | 2730 | 0.7122 | 0.3077 | 0.7122 | 0.8439 |
| 0.0919 | 3.0766 | 2732 | 0.7128 | 0.3077 | 0.7128 | 0.8443 |
| 0.0919 | 3.0788 | 2734 | 0.6961 | 0.3077 | 0.6961 | 0.8343 |
| 0.0919 | 3.0811 | 2736 | 0.6918 | 0.3077 | 0.6918 | 0.8318 |
| 0.0919 | 3.0833 | 2738 | 0.6869 | 0.3836 | 0.6869 | 0.8288 |
| 0.0919 | 3.0856 | 2740 | 0.6920 | 0.3836 | 0.6920 | 0.8319 |
| 0.0919 | 3.0878 | 2742 | 0.6970 | 0.4324 | 0.6970 | 0.8348 |
| 0.0919 | 3.0901 | 2744 | 0.7125 | 0.4324 | 0.7125 | 0.8441 |
| 0.0919 | 3.0923 | 2746 | 0.7412 | 0.3077 | 0.7412 | 0.8609 |
| 0.0919 | 3.0946 | 2748 | 0.7617 | 0.3077 | 0.7617 | 0.8727 |
| 0.0919 | 3.0968 | 2750 | 0.7552 | 0.3077 | 0.7552 | 0.8690 |
| 0.0919 | 3.0991 | 2752 | 0.7305 | 0.3077 | 0.7305 | 0.8547 |
| 0.0919 | 3.1014 | 2754 | 0.7005 | 0.4324 | 0.7005 | 0.8370 |
| 0.0919 | 3.1036 | 2756 | 0.6902 | 0.4324 | 0.6902 | 0.8308 |
| 0.0919 | 3.1059 | 2758 | 0.6952 | 0.3836 | 0.6952 | 0.8338 |
| 0.0919 | 3.1081 | 2760 | 0.7286 | 0.2597 | 0.7286 | 0.8536 |
| 0.0919 | 3.1104 | 2762 | 0.7596 | 0.2703 | 0.7596 | 0.8716 |
| 0.0919 | 3.1126 | 2764 | 0.7624 | 0.2703 | 0.7624 | 0.8731 |
| 0.0919 | 3.1149 | 2766 | 0.7597 | 0.2597 | 0.7597 | 0.8716 |
| 0.0919 | 3.1171 | 2768 | 0.7385 | 0.3544 | 0.7385 | 0.8594 |
| 0.0919 | 3.1194 | 2770 | 0.7364 | 0.4 | 0.7364 | 0.8581 |
| 0.0919 | 3.1216 | 2772 | 0.7614 | 0.2817 | 0.7614 | 0.8726 |
| 0.0919 | 3.1239 | 2774 | 0.7695 | 0.4167 | 0.7695 | 0.8772 |
| 0.0919 | 3.1261 | 2776 | 0.7461 | 0.2817 | 0.7461 | 0.8637 |
| 0.0919 | 3.1284 | 2778 | 0.7275 | 0.4324 | 0.7275 | 0.8529 |
| 0.0919 | 3.1306 | 2780 | 0.7277 | 0.3077 | 0.7277 | 0.8531 |
| 0.0919 | 3.1329 | 2782 | 0.7300 | 0.3077 | 0.7300 | 0.8544 |
| 0.0919 | 3.1351 | 2784 | 0.7374 | 0.2895 | 0.7374 | 0.8587 |
| 0.0919 | 3.1374 | 2786 | 0.7408 | 0.24 | 0.7408 | 0.8607 |
| 0.0919 | 3.1396 | 2788 | 0.7049 | 0.24 | 0.7049 | 0.8396 |
| 0.0919 | 3.1419 | 2790 | 0.6578 | 0.3143 | 0.6578 | 0.8111 |
| 0.0919 | 3.1441 | 2792 | 0.6458 | 0.2941 | 0.6458 | 0.8036 |
| 0.0919 | 3.1464 | 2794 | 0.6488 | 0.2941 | 0.6488 | 0.8055 |
| 0.0919 | 3.1486 | 2796 | 0.6692 | 0.2941 | 0.6692 | 0.8180 |
| 0.0919 | 3.1509 | 2798 | 0.6990 | 0.3836 | 0.6990 | 0.8360 |
| 0.0919 | 3.1532 | 2800 | 0.7616 | 0.24 | 0.7616 | 0.8727 |
| 0.0919 | 3.1554 | 2802 | 0.8147 | 0.24 | 0.8147 | 0.9026 |
| 0.0919 | 3.1577 | 2804 | 0.8182 | 0.24 | 0.8182 | 0.9046 |
| 0.0919 | 3.1599 | 2806 | 0.7880 | 0.24 | 0.7880 | 0.8877 |
| 0.0919 | 3.1622 | 2808 | 0.7428 | 0.3077 | 0.7428 | 0.8619 |
| 0.0919 | 3.1644 | 2810 | 0.7291 | 0.3544 | 0.7291 | 0.8539 |
| 0.0919 | 3.1667 | 2812 | 0.7255 | 0.4324 | 0.7255 | 0.8518 |
| 0.0919 | 3.1689 | 2814 | 0.7159 | 0.4324 | 0.7159 | 0.8461 |
| 0.0919 | 3.1712 | 2816 | 0.7047 | 0.3544 | 0.7047 | 0.8394 |
| 0.0919 | 3.1734 | 2818 | 0.7262 | 0.2597 | 0.7262 | 0.8521 |
| 0.0919 | 3.1757 | 2820 | 0.7593 | 0.24 | 0.7593 | 0.8714 |
| 0.0919 | 3.1779 | 2822 | 0.7530 | 0.24 | 0.7530 | 0.8677 |
| 0.0919 | 3.1802 | 2824 | 0.7125 | 0.24 | 0.7125 | 0.8441 |
| 0.0919 | 3.1824 | 2826 | 0.6714 | 0.3544 | 0.6714 | 0.8194 |
| 0.0919 | 3.1847 | 2828 | 0.6619 | 0.4324 | 0.6619 | 0.8136 |
| 0.0919 | 3.1869 | 2830 | 0.6735 | 0.4324 | 0.6735 | 0.8207 |
| 0.0919 | 3.1892 | 2832 | 0.6877 | 0.3544 | 0.6877 | 0.8293 |
| 0.0919 | 3.1914 | 2834 | 0.7014 | 0.3544 | 0.7014 | 0.8375 |
| 0.0919 | 3.1937 | 2836 | 0.7146 | 0.3077 | 0.7146 | 0.8453 |
| 0.0919 | 3.1959 | 2838 | 0.7332 | 0.3077 | 0.7332 | 0.8563 |
| 0.0919 | 3.1982 | 2840 | 0.7519 | 0.2105 | 0.7519 | 0.8671 |
| 0.0919 | 3.2005 | 2842 | 0.7425 | 0.24 | 0.7425 | 0.8617 |
| 0.0919 | 3.2027 | 2844 | 0.7116 | 0.2895 | 0.7116 | 0.8436 |
| 0.0919 | 3.2050 | 2846 | 0.7008 | 0.24 | 0.7008 | 0.8372 |
| 0.0919 | 3.2072 | 2848 | 0.6913 | 0.24 | 0.6913 | 0.8314 |
| 0.0919 | 3.2095 | 2850 | 0.6764 | 0.3377 | 0.6764 | 0.8224 |
| 0.0919 | 3.2117 | 2852 | 0.6626 | 0.3544 | 0.6626 | 0.8140 |
| 0.0919 | 3.2140 | 2854 | 0.6703 | 0.4324 | 0.6703 | 0.8187 |
| 0.0919 | 3.2162 | 2856 | 0.6891 | 0.4324 | 0.6891 | 0.8301 |
| 0.0919 | 3.2185 | 2858 | 0.7155 | 0.3544 | 0.7155 | 0.8458 |
| 0.0919 | 3.2207 | 2860 | 0.7448 | 0.3544 | 0.7448 | 0.8630 |
| 0.0919 | 3.2230 | 2862 | 0.7690 | 0.3077 | 0.7690 | 0.8769 |
| 0.0919 | 3.2252 | 2864 | 0.7977 | 0.3077 | 0.7977 | 0.8931 |
| 0.0919 | 3.2275 | 2866 | 0.7805 | 0.3077 | 0.7805 | 0.8834 |
| 0.0919 | 3.2297 | 2868 | 0.7616 | 0.3077 | 0.7616 | 0.8727 |
| 0.0919 | 3.2320 | 2870 | 0.7416 | 0.3077 | 0.7416 | 0.8612 |
| 0.0919 | 3.2342 | 2872 | 0.7067 | 0.4324 | 0.7067 | 0.8406 |
| 0.0919 | 3.2365 | 2874 | 0.6849 | 0.4324 | 0.6849 | 0.8276 |
| 0.0919 | 3.2387 | 2876 | 0.6872 | 0.4324 | 0.6872 | 0.8290 |
| 0.0919 | 3.2410 | 2878 | 0.6894 | 0.4324 | 0.6894 | 0.8303 |
| 0.0919 | 3.2432 | 2880 | 0.7086 | 0.3662 | 0.7086 | 0.8418 |
| 0.0919 | 3.2455 | 2882 | 0.7503 | 0.2703 | 0.7503 | 0.8662 |
| 0.0919 | 3.2477 | 2884 | 0.7642 | 0.2703 | 0.7642 | 0.8742 |
| 0.0919 | 3.25 | 2886 | 0.7549 | 0.2895 | 0.7549 | 0.8689 |
| 0.0919 | 3.2523 | 2888 | 0.7490 | 0.2597 | 0.7490 | 0.8655 |
| 0.0919 | 3.2545 | 2890 | 0.7425 | 0.3077 | 0.7425 | 0.8617 |
| 0.0919 | 3.2568 | 2892 | 0.7331 | 0.3544 | 0.7331 | 0.8562 |
| 0.0919 | 3.2590 | 2894 | 0.7281 | 0.3544 | 0.7281 | 0.8533 |
| 0.0919 | 3.2613 | 2896 | 0.7250 | 0.3544 | 0.7250 | 0.8514 |
| 0.0919 | 3.2635 | 2898 | 0.7158 | 0.4324 | 0.7158 | 0.8461 |
| 0.0919 | 3.2658 | 2900 | 0.7066 | 0.4324 | 0.7066 | 0.8406 |
| 0.0919 | 3.2680 | 2902 | 0.6966 | 0.4324 | 0.6966 | 0.8346 |
| 0.0919 | 3.2703 | 2904 | 0.6847 | 0.4324 | 0.6847 | 0.8274 |
| 0.0919 | 3.2725 | 2906 | 0.6835 | 0.4324 | 0.6835 | 0.8267 |
| 0.0919 | 3.2748 | 2908 | 0.6941 | 0.4324 | 0.6941 | 0.8331 |
| 0.0919 | 3.2770 | 2910 | 0.7143 | 0.3077 | 0.7143 | 0.8452 |
| 0.0919 | 3.2793 | 2912 | 0.7122 | 0.3077 | 0.7122 | 0.8439 |
| 0.0919 | 3.2815 | 2914 | 0.7041 | 0.3077 | 0.7041 | 0.8391 |
| 0.0919 | 3.2838 | 2916 | 0.6966 | 0.3836 | 0.6966 | 0.8346 |
| 0.0919 | 3.2860 | 2918 | 0.7047 | 0.3836 | 0.7047 | 0.8395 |
| 0.0919 | 3.2883 | 2920 | 0.7180 | 0.4324 | 0.7180 | 0.8474 |
| 0.0919 | 3.2905 | 2922 | 0.7461 | 0.3544 | 0.7461 | 0.8638 |
| 0.0919 | 3.2928 | 2924 | 0.7798 | 0.3077 | 0.7798 | 0.8831 |
| 0.0919 | 3.2950 | 2926 | 0.7965 | 0.3077 | 0.7965 | 0.8925 |
| 0.0919 | 3.2973 | 2928 | 0.7819 | 0.3077 | 0.7819 | 0.8843 |
| 0.0919 | 3.2995 | 2930 | 0.7503 | 0.3077 | 0.7503 | 0.8662 |
| 0.0919 | 3.3018 | 2932 | 0.7308 | 0.4324 | 0.7308 | 0.8549 |
| 0.0919 | 3.3041 | 2934 | 0.7118 | 0.3478 | 0.7118 | 0.8437 |
| 0.0919 | 3.3063 | 2936 | 0.7063 | 0.1818 | 0.7063 | 0.8404 |
| 0.0919 | 3.3086 | 2938 | 0.6994 | 0.3143 | 0.6994 | 0.8363 |
| 0.0919 | 3.3108 | 2940 | 0.6946 | 0.3478 | 0.6946 | 0.8334 |
| 0.0919 | 3.3131 | 2942 | 0.7049 | 0.4324 | 0.7049 | 0.8396 |
| 0.0919 | 3.3153 | 2944 | 0.7361 | 0.3077 | 0.7361 | 0.8580 |
| 0.0919 | 3.3176 | 2946 | 0.7816 | 0.1892 | 0.7816 | 0.8841 |
| 0.0919 | 3.3198 | 2948 | 0.7899 | 0.1370 | 0.7899 | 0.8887 |
| 0.0919 | 3.3221 | 2950 | 0.7744 | 0.1892 | 0.7744 | 0.8800 |
| 0.0919 | 3.3243 | 2952 | 0.7479 | 0.1892 | 0.7479 | 0.8648 |
| 0.0919 | 3.3266 | 2954 | 0.6972 | 0.3333 | 0.6972 | 0.8350 |
| 0.0919 | 3.3288 | 2956 | 0.6588 | 0.3478 | 0.6588 | 0.8117 |
| 0.0919 | 3.3311 | 2958 | 0.6515 | 0.3478 | 0.6515 | 0.8071 |
| 0.0919 | 3.3333 | 2960 | 0.6508 | 0.3478 | 0.6508 | 0.8067 |
| 0.0919 | 3.3356 | 2962 | 0.6579 | 0.3478 | 0.6579 | 0.8111 |
| 0.0919 | 3.3378 | 2964 | 0.6779 | 0.3836 | 0.6779 | 0.8233 |
| 0.0919 | 3.3401 | 2966 | 0.7216 | 0.2597 | 0.7216 | 0.8495 |
| 0.0919 | 3.3423 | 2968 | 0.7844 | 0.1370 | 0.7844 | 0.8857 |
| 0.0919 | 3.3446 | 2970 | 0.7995 | 0.1370 | 0.7995 | 0.8942 |
| 0.0919 | 3.3468 | 2972 | 0.7922 | 0.24 | 0.7922 | 0.8901 |
| 0.0919 | 3.3491 | 2974 | 0.7586 | 0.3077 | 0.7586 | 0.8710 |
| 0.0919 | 3.3514 | 2976 | 0.7274 | 0.4324 | 0.7274 | 0.8529 |
| 0.0919 | 3.3536 | 2978 | 0.7213 | 0.4324 | 0.7213 | 0.8493 |
| 0.0919 | 3.3559 | 2980 | 0.7174 | 0.3478 | 0.7174 | 0.8470 |
| 0.0919 | 3.3581 | 2982 | 0.7139 | 0.4324 | 0.7139 | 0.8449 |
| 0.0919 | 3.3604 | 2984 | 0.7228 | 0.4324 | 0.7228 | 0.8502 |
| 0.0919 | 3.3626 | 2986 | 0.7343 | 0.3836 | 0.7343 | 0.8569 |
| 0.0919 | 3.3649 | 2988 | 0.7250 | 0.3836 | 0.7250 | 0.8515 |
| 0.0919 | 3.3671 | 2990 | 0.7000 | 0.3836 | 0.7000 | 0.8366 |
| 0.0919 | 3.3694 | 2992 | 0.6795 | 0.3836 | 0.6795 | 0.8243 |
| 0.0919 | 3.3716 | 2994 | 0.6769 | 0.3836 | 0.6769 | 0.8227 |
| 0.0919 | 3.3739 | 2996 | 0.6673 | 0.4324 | 0.6673 | 0.8169 |
| 0.0919 | 3.3761 | 2998 | 0.6602 | 0.3478 | 0.6602 | 0.8125 |
| 0.0801 | 3.3784 | 3000 | 0.6748 | 0.4324 | 0.6748 | 0.8215 |
| 0.0801 | 3.3806 | 3002 | 0.7107 | 0.4167 | 0.7107 | 0.8430 |
| 0.0801 | 3.3829 | 3004 | 0.7604 | 0.3377 | 0.7604 | 0.8720 |
| 0.0801 | 3.3851 | 3006 | 0.7636 | 0.3077 | 0.7636 | 0.8738 |
| 0.0801 | 3.3874 | 3008 | 0.7597 | 0.3077 | 0.7597 | 0.8716 |
| 0.0801 | 3.3896 | 3010 | 0.7324 | 0.4324 | 0.7324 | 0.8558 |
| 0.0801 | 3.3919 | 3012 | 0.7095 | 0.4324 | 0.7095 | 0.8423 |
| 0.0801 | 3.3941 | 3014 | 0.6917 | 0.3478 | 0.6917 | 0.8317 |
| 0.0801 | 3.3964 | 3016 | 0.6853 | 0.2286 | 0.6853 | 0.8279 |
| 0.0801 | 3.3986 | 3018 | 0.6707 | 0.3478 | 0.6707 | 0.8189 |
| 0.0801 | 3.4009 | 3020 | 0.6565 | 0.3478 | 0.6565 | 0.8102 |
| 0.0801 | 3.4032 | 3022 | 0.6643 | 0.4324 | 0.6643 | 0.8150 |
| 0.0801 | 3.4054 | 3024 | 0.7036 | 0.3836 | 0.7036 | 0.8388 |
| 0.0801 | 3.4077 | 3026 | 0.7326 | 0.3478 | 0.7326 | 0.8559 |
| 0.0801 | 3.4099 | 3028 | 0.7376 | 0.2941 | 0.7376 | 0.8588 |
| 0.0801 | 3.4122 | 3030 | 0.7317 | 0.3478 | 0.7317 | 0.8554 |
| 0.0801 | 3.4144 | 3032 | 0.7481 | 0.2703 | 0.7481 | 0.8650 |
| 0.0801 | 3.4167 | 3034 | 0.7251 | 0.3662 | 0.7251 | 0.8515 |
| 0.0801 | 3.4189 | 3036 | 0.6748 | 0.4324 | 0.6748 | 0.8215 |
| 0.0801 | 3.4212 | 3038 | 0.6315 | 0.4324 | 0.6315 | 0.7947 |
| 0.0801 | 3.4234 | 3040 | 0.6125 | 0.3438 | 0.6125 | 0.7826 |
| 0.0801 | 3.4257 | 3042 | 0.6092 | 0.1639 | 0.6092 | 0.7805 |
| 0.0801 | 3.4279 | 3044 | 0.6031 | 0.2000 | 0.6031 | 0.7766 |
| 0.0801 | 3.4302 | 3046 | 0.6050 | 0.3438 | 0.6050 | 0.7778 |
| 0.0801 | 3.4324 | 3048 | 0.6289 | 0.4324 | 0.6289 | 0.7931 |
| 0.0801 | 3.4347 | 3050 | 0.6877 | 0.4167 | 0.6877 | 0.8293 |
| 0.0801 | 3.4369 | 3052 | 0.7380 | 0.24 | 0.7380 | 0.8591 |
| 0.0801 | 3.4392 | 3054 | 0.7504 | 0.24 | 0.7504 | 0.8663 |
| 0.0801 | 3.4414 | 3056 | 0.7199 | 0.3143 | 0.7199 | 0.8484 |
| 0.0801 | 3.4437 | 3058 | 0.6721 | 0.4658 | 0.6721 | 0.8198 |
| 0.0801 | 3.4459 | 3060 | 0.6528 | 0.4324 | 0.6528 | 0.8079 |
| 0.0801 | 3.4482 | 3062 | 0.6513 | 0.4324 | 0.6513 | 0.8071 |
| 0.0801 | 3.4505 | 3064 | 0.6647 | 0.4658 | 0.6647 | 0.8153 |
| 0.0801 | 3.4527 | 3066 | 0.6817 | 0.4658 | 0.6817 | 0.8256 |
| 0.0801 | 3.4550 | 3068 | 0.7154 | 0.3846 | 0.7154 | 0.8458 |
| 0.0801 | 3.4572 | 3070 | 0.7358 | 0.3846 | 0.7358 | 0.8578 |
| 0.0801 | 3.4595 | 3072 | 0.7248 | 0.3846 | 0.7248 | 0.8513 |
| 0.0801 | 3.4617 | 3074 | 0.7106 | 0.3846 | 0.7106 | 0.8430 |
| 0.0801 | 3.4640 | 3076 | 0.7114 | 0.4324 | 0.7114 | 0.8434 |
| 0.0801 | 3.4662 | 3078 | 0.7098 | 0.4324 | 0.7098 | 0.8425 |
| 0.0801 | 3.4685 | 3080 | 0.7089 | 0.4324 | 0.7089 | 0.8420 |
| 0.0801 | 3.4707 | 3082 | 0.7158 | 0.4324 | 0.7158 | 0.8460 |
| 0.0801 | 3.4730 | 3084 | 0.7231 | 0.4324 | 0.7231 | 0.8504 |
| 0.0801 | 3.4752 | 3086 | 0.7547 | 0.3544 | 0.7547 | 0.8687 |
| 0.0801 | 3.4775 | 3088 | 0.7659 | 0.3544 | 0.7659 | 0.8752 |
| 0.0801 | 3.4797 | 3090 | 0.7509 | 0.3544 | 0.7509 | 0.8666 |
| 0.0801 | 3.4820 | 3092 | 0.7535 | 0.3544 | 0.7535 | 0.8680 |
| 0.0801 | 3.4842 | 3094 | 0.7656 | 0.3544 | 0.7656 | 0.8750 |
| 0.0801 | 3.4865 | 3096 | 0.7634 | 0.3544 | 0.7634 | 0.8737 |
| 0.0801 | 3.4887 | 3098 | 0.7702 | 0.3544 | 0.7702 | 0.8776 |
| 0.0801 | 3.4910 | 3100 | 0.7869 | 0.3846 | 0.7869 | 0.8871 |
| 0.0801 | 3.4932 | 3102 | 0.7956 | 0.3846 | 0.7956 | 0.8920 |
| 0.0801 | 3.4955 | 3104 | 0.7737 | 0.3544 | 0.7737 | 0.8796 |
| 0.0801 | 3.4977 | 3106 | 0.7602 | 0.3544 | 0.7602 | 0.8719 |
| 0.0801 | 3.5 | 3108 | 0.7679 | 0.3544 | 0.7679 | 0.8763 |
| 0.0801 | 3.5023 | 3110 | 0.7695 | 0.3544 | 0.7695 | 0.8772 |
| 0.0801 | 3.5045 | 3112 | 0.7513 | 0.3544 | 0.7513 | 0.8668 |
| 0.0801 | 3.5068 | 3114 | 0.7341 | 0.3544 | 0.7341 | 0.8568 |
| 0.0801 | 3.5090 | 3116 | 0.7037 | 0.4324 | 0.7037 | 0.8389 |
| 0.0801 | 3.5113 | 3118 | 0.6999 | 0.4658 | 0.6999 | 0.8366 |
| 0.0801 | 3.5135 | 3120 | 0.7199 | 0.4658 | 0.7199 | 0.8484 |
| 0.0801 | 3.5158 | 3122 | 0.7197 | 0.4658 | 0.7197 | 0.8483 |
| 0.0801 | 3.5180 | 3124 | 0.7156 | 0.4324 | 0.7156 | 0.8459 |
| 0.0801 | 3.5203 | 3126 | 0.7375 | 0.3544 | 0.7375 | 0.8588 |
| 0.0801 | 3.5225 | 3128 | 0.7668 | 0.3846 | 0.7668 | 0.8757 |
| 0.0801 | 3.5248 | 3130 | 0.7636 | 0.3846 | 0.7636 | 0.8738 |
| 0.0801 | 3.5270 | 3132 | 0.7407 | 0.3544 | 0.7407 | 0.8606 |
| 0.0801 | 3.5293 | 3134 | 0.7207 | 0.4324 | 0.7207 | 0.8489 |
| 0.0801 | 3.5315 | 3136 | 0.7060 | 0.4324 | 0.7060 | 0.8403 |
| 0.0801 | 3.5338 | 3138 | 0.7098 | 0.4324 | 0.7098 | 0.8425 |
| 0.0801 | 3.5360 | 3140 | 0.7153 | 0.4324 | 0.7153 | 0.8458 |
| 0.0801 | 3.5383 | 3142 | 0.7205 | 0.3544 | 0.7205 | 0.8488 |
| 0.0801 | 3.5405 | 3144 | 0.7249 | 0.3846 | 0.7249 | 0.8514 |
| 0.0801 | 3.5428 | 3146 | 0.7331 | 0.3846 | 0.7331 | 0.8562 |
| 0.0801 | 3.5450 | 3148 | 0.7228 | 0.3544 | 0.7228 | 0.8502 |
| 0.0801 | 3.5473 | 3150 | 0.7111 | 0.4324 | 0.7111 | 0.8433 |
| 0.0801 | 3.5495 | 3152 | 0.7202 | 0.3438 | 0.7202 | 0.8487 |
| 0.0801 | 3.5518 | 3154 | 0.7221 | 0.3438 | 0.7221 | 0.8498 |
| 0.0801 | 3.5541 | 3156 | 0.7266 | 0.4324 | 0.7266 | 0.8524 |
| 0.0801 | 3.5563 | 3158 | 0.7594 | 0.3544 | 0.7594 | 0.8714 |
| 0.0801 | 3.5586 | 3160 | 0.7936 | 0.2895 | 0.7936 | 0.8908 |
| 0.0801 | 3.5608 | 3162 | 0.7913 | 0.24 | 0.7913 | 0.8895 |
| 0.0801 | 3.5631 | 3164 | 0.7588 | 0.3377 | 0.7588 | 0.8711 |
| 0.0801 | 3.5653 | 3166 | 0.7291 | 0.2895 | 0.7291 | 0.8539 |
| 0.0801 | 3.5676 | 3168 | 0.6811 | 0.4658 | 0.6811 | 0.8253 |
| 0.0801 | 3.5698 | 3170 | 0.6479 | 0.3438 | 0.6479 | 0.8049 |
| 0.0801 | 3.5721 | 3172 | 0.6460 | 0.3438 | 0.6460 | 0.8038 |
| 0.0801 | 3.5743 | 3174 | 0.6480 | 0.3438 | 0.6480 | 0.8050 |
| 0.0801 | 3.5766 | 3176 | 0.6506 | 0.3438 | 0.6506 | 0.8066 |
| 0.0801 | 3.5788 | 3178 | 0.6638 | 0.4348 | 0.6638 | 0.8147 |
| 0.0801 | 3.5811 | 3180 | 0.6966 | 0.4324 | 0.6966 | 0.8346 |
| 0.0801 | 3.5833 | 3182 | 0.7471 | 0.3077 | 0.7471 | 0.8643 |
| 0.0801 | 3.5856 | 3184 | 0.7878 | 0.3377 | 0.7878 | 0.8876 |
| 0.0801 | 3.5878 | 3186 | 0.7802 | 0.3077 | 0.7802 | 0.8833 |
| 0.0801 | 3.5901 | 3188 | 0.7542 | 0.3077 | 0.7542 | 0.8684 |
| 0.0801 | 3.5923 | 3190 | 0.7326 | 0.4324 | 0.7326 | 0.8559 |
| 0.0801 | 3.5946 | 3192 | 0.7192 | 0.4324 | 0.7192 | 0.8480 |
| 0.0801 | 3.5968 | 3194 | 0.7064 | 0.4324 | 0.7064 | 0.8405 |
| 0.0801 | 3.5991 | 3196 | 0.6967 | 0.4324 | 0.6967 | 0.8347 |
| 0.0801 | 3.6014 | 3198 | 0.7015 | 0.4324 | 0.7015 | 0.8375 |
| 0.0801 | 3.6036 | 3200 | 0.7204 | 0.3836 | 0.7204 | 0.8487 |
| 0.0801 | 3.6059 | 3202 | 0.7338 | 0.4167 | 0.7338 | 0.8566 |
| 0.0801 | 3.6081 | 3204 | 0.7404 | 0.4167 | 0.7404 | 0.8604 |
| 0.0801 | 3.6104 | 3206 | 0.7308 | 0.3836 | 0.7308 | 0.8549 |
| 0.0801 | 3.6126 | 3208 | 0.7223 | 0.4324 | 0.7223 | 0.8499 |
| 0.0801 | 3.6149 | 3210 | 0.7277 | 0.4324 | 0.7277 | 0.8530 |
| 0.0801 | 3.6171 | 3212 | 0.7412 | 0.4324 | 0.7412 | 0.8609 |
| 0.0801 | 3.6194 | 3214 | 0.7448 | 0.4324 | 0.7448 | 0.8630 |
| 0.0801 | 3.6216 | 3216 | 0.7458 | 0.4324 | 0.7458 | 0.8636 |
| 0.0801 | 3.6239 | 3218 | 0.7561 | 0.3836 | 0.7561 | 0.8695 |
| 0.0801 | 3.6261 | 3220 | 0.7558 | 0.3836 | 0.7558 | 0.8693 |
| 0.0801 | 3.6284 | 3222 | 0.7451 | 0.4324 | 0.7451 | 0.8632 |
| 0.0801 | 3.6306 | 3224 | 0.7407 | 0.4324 | 0.7407 | 0.8606 |
| 0.0801 | 3.6329 | 3226 | 0.7470 | 0.4324 | 0.7470 | 0.8643 |
| 0.0801 | 3.6351 | 3228 | 0.7566 | 0.4324 | 0.7566 | 0.8698 |
| 0.0801 | 3.6374 | 3230 | 0.7648 | 0.4324 | 0.7648 | 0.8745 |
| 0.0801 | 3.6396 | 3232 | 0.7622 | 0.4324 | 0.7622 | 0.8730 |
| 0.0801 | 3.6419 | 3234 | 0.7625 | 0.3684 | 0.7625 | 0.8732 |
| 0.0801 | 3.6441 | 3236 | 0.7575 | 0.3684 | 0.7575 | 0.8703 |
| 0.0801 | 3.6464 | 3238 | 0.7505 | 0.4324 | 0.7505 | 0.8663 |
| 0.0801 | 3.6486 | 3240 | 0.7395 | 0.4324 | 0.7395 | 0.8600 |
| 0.0801 | 3.6509 | 3242 | 0.7240 | 0.4324 | 0.7240 | 0.8509 |
| 0.0801 | 3.6532 | 3244 | 0.7144 | 0.4324 | 0.7144 | 0.8452 |
| 0.0801 | 3.6554 | 3246 | 0.7055 | 0.4324 | 0.7055 | 0.8400 |
| 0.0801 | 3.6577 | 3248 | 0.6981 | 0.4324 | 0.6981 | 0.8355 |
| 0.0801 | 3.6599 | 3250 | 0.6928 | 0.4324 | 0.6928 | 0.8324 |
| 0.0801 | 3.6622 | 3252 | 0.6998 | 0.4324 | 0.6998 | 0.8365 |
| 0.0801 | 3.6644 | 3254 | 0.7009 | 0.4324 | 0.7009 | 0.8372 |
| 0.0801 | 3.6667 | 3256 | 0.7110 | 0.4324 | 0.7110 | 0.8432 |
| 0.0801 | 3.6689 | 3258 | 0.7030 | 0.3684 | 0.7030 | 0.8385 |
| 0.0801 | 3.6712 | 3260 | 0.7046 | 0.3662 | 0.7046 | 0.8394 |
| 0.0801 | 3.6734 | 3262 | 0.7029 | 0.3684 | 0.7029 | 0.8384 |
| 0.0801 | 3.6757 | 3264 | 0.7155 | 0.4324 | 0.7155 | 0.8459 |
| 0.0801 | 3.6779 | 3266 | 0.7159 | 0.3836 | 0.7159 | 0.8461 |
| 0.0801 | 3.6802 | 3268 | 0.7059 | 0.4324 | 0.7059 | 0.8402 |
| 0.0801 | 3.6824 | 3270 | 0.7082 | 0.4 | 0.7082 | 0.8415 |
| 0.0801 | 3.6847 | 3272 | 0.7198 | 0.4324 | 0.7198 | 0.8484 |
| 0.0801 | 3.6869 | 3274 | 0.7459 | 0.3836 | 0.7459 | 0.8637 |
| 0.0801 | 3.6892 | 3276 | 0.7576 | 0.3836 | 0.7576 | 0.8704 |
| 0.0801 | 3.6914 | 3278 | 0.7604 | 0.3836 | 0.7604 | 0.8720 |
| 0.0801 | 3.6937 | 3280 | 0.7673 | 0.3836 | 0.7673 | 0.8760 |
| 0.0801 | 3.6959 | 3282 | 0.7701 | 0.3077 | 0.7701 | 0.8775 |
| 0.0801 | 3.6982 | 3284 | 0.7559 | 0.3077 | 0.7559 | 0.8694 |
| 0.0801 | 3.7005 | 3286 | 0.7292 | 0.3077 | 0.7292 | 0.8540 |
| 0.0801 | 3.7027 | 3288 | 0.7122 | 0.3333 | 0.7122 | 0.8439 |
| 0.0801 | 3.7050 | 3290 | 0.6946 | 0.2817 | 0.6946 | 0.8334 |
| 0.0801 | 3.7072 | 3292 | 0.6906 | 0.2609 | 0.6906 | 0.8310 |
| 0.0801 | 3.7095 | 3294 | 0.7139 | 0.2609 | 0.7139 | 0.8449 |
| 0.0801 | 3.7117 | 3296 | 0.7336 | 0.1370 | 0.7336 | 0.8565 |
| 0.0801 | 3.7140 | 3298 | 0.7118 | 0.2609 | 0.7118 | 0.8437 |
| 0.0801 | 3.7162 | 3300 | 0.7120 | 0.2817 | 0.7120 | 0.8438 |
| 0.0801 | 3.7185 | 3302 | 0.6963 | 0.3836 | 0.6963 | 0.8345 |
| 0.0801 | 3.7207 | 3304 | 0.6966 | 0.3836 | 0.6966 | 0.8346 |
| 0.0801 | 3.7230 | 3306 | 0.7116 | 0.3836 | 0.7116 | 0.8435 |
| 0.0801 | 3.7252 | 3308 | 0.7046 | 0.3836 | 0.7046 | 0.8394 |
| 0.0801 | 3.7275 | 3310 | 0.7002 | 0.3478 | 0.7002 | 0.8368 |
| 0.0801 | 3.7297 | 3312 | 0.6809 | 0.3478 | 0.6809 | 0.8251 |
| 0.0801 | 3.7320 | 3314 | 0.6756 | 0.3438 | 0.6756 | 0.8220 |
| 0.0801 | 3.7342 | 3316 | 0.6700 | 0.3478 | 0.6700 | 0.8185 |
| 0.0801 | 3.7365 | 3318 | 0.6881 | 0.3836 | 0.6881 | 0.8295 |
| 0.0801 | 3.7387 | 3320 | 0.7090 | 0.3377 | 0.7090 | 0.8420 |
| 0.0801 | 3.7410 | 3322 | 0.7026 | 0.3377 | 0.7026 | 0.8382 |
| 0.0801 | 3.7432 | 3324 | 0.6941 | 0.3377 | 0.6941 | 0.8331 |
| 0.0801 | 3.7455 | 3326 | 0.6849 | 0.3377 | 0.6849 | 0.8276 |
| 0.0801 | 3.7477 | 3328 | 0.6695 | 0.3836 | 0.6695 | 0.8182 |
| 0.0801 | 3.75 | 3330 | 0.6460 | 0.2941 | 0.6460 | 0.8037 |
| 0.0801 | 3.7523 | 3332 | 0.6431 | 0.2759 | 0.6431 | 0.8019 |
| 0.0801 | 3.7545 | 3334 | 0.6460 | 0.2857 | 0.6460 | 0.8038 |
| 0.0801 | 3.7568 | 3336 | 0.6553 | 0.3836 | 0.6553 | 0.8095 |
| 0.0801 | 3.7590 | 3338 | 0.6867 | 0.4167 | 0.6867 | 0.8287 |
| 0.0801 | 3.7613 | 3340 | 0.7477 | 0.1370 | 0.7477 | 0.8647 |
| 0.0801 | 3.7635 | 3342 | 0.7956 | 0.1370 | 0.7956 | 0.8919 |
| 0.0801 | 3.7658 | 3344 | 0.7849 | 0.1370 | 0.7849 | 0.8859 |
| 0.0801 | 3.7680 | 3346 | 0.7390 | 0.24 | 0.7390 | 0.8596 |
| 0.0801 | 3.7703 | 3348 | 0.6888 | 0.2941 | 0.6888 | 0.8299 |
| 0.0801 | 3.7725 | 3350 | 0.6695 | 0.3390 | 0.6695 | 0.8182 |
| 0.0801 | 3.7748 | 3352 | 0.6628 | 0.3390 | 0.6628 | 0.8141 |
| 0.0801 | 3.7770 | 3354 | 0.6674 | 0.3390 | 0.6674 | 0.8169 |
| 0.0801 | 3.7793 | 3356 | 0.6923 | 0.3284 | 0.6923 | 0.8321 |
| 0.0801 | 3.7815 | 3358 | 0.7318 | 0.3377 | 0.7318 | 0.8555 |
| 0.0801 | 3.7838 | 3360 | 0.7587 | 0.3377 | 0.7587 | 0.8710 |
| 0.0801 | 3.7860 | 3362 | 0.7517 | 0.3377 | 0.7517 | 0.8670 |
| 0.0801 | 3.7883 | 3364 | 0.7269 | 0.3377 | 0.7269 | 0.8526 |
| 0.0801 | 3.7905 | 3366 | 0.6901 | 0.4324 | 0.6901 | 0.8307 |
| 0.0801 | 3.7928 | 3368 | 0.6780 | 0.3478 | 0.6780 | 0.8234 |
| 0.0801 | 3.7950 | 3370 | 0.6775 | 0.3836 | 0.6775 | 0.8231 |
| 0.0801 | 3.7973 | 3372 | 0.6576 | 0.3438 | 0.6576 | 0.8109 |
| 0.0801 | 3.7995 | 3374 | 0.6576 | 0.3438 | 0.6576 | 0.8109 |
| 0.0801 | 3.8018 | 3376 | 0.6766 | 0.3836 | 0.6766 | 0.8226 |
| 0.0801 | 3.8041 | 3378 | 0.7005 | 0.3077 | 0.7005 | 0.8370 |
| 0.0801 | 3.8063 | 3380 | 0.7220 | 0.2597 | 0.7220 | 0.8497 |
| 0.0801 | 3.8086 | 3382 | 0.7222 | 0.3077 | 0.7222 | 0.8498 |
| 0.0801 | 3.8108 | 3384 | 0.6994 | 0.3077 | 0.6994 | 0.8363 |
| 0.0801 | 3.8131 | 3386 | 0.6820 | 0.3478 | 0.6820 | 0.8258 |
| 0.0801 | 3.8153 | 3388 | 0.6848 | 0.3478 | 0.6848 | 0.8275 |
| 0.0801 | 3.8176 | 3390 | 0.7072 | 0.3544 | 0.7072 | 0.8409 |
| 0.0801 | 3.8198 | 3392 | 0.7211 | 0.3077 | 0.7211 | 0.8492 |
| 0.0801 | 3.8221 | 3394 | 0.7176 | 0.3077 | 0.7176 | 0.8471 |
| 0.0801 | 3.8243 | 3396 | 0.7057 | 0.3077 | 0.7057 | 0.8401 |
| 0.0801 | 3.8266 | 3398 | 0.7008 | 0.3836 | 0.7008 | 0.8371 |
| 0.0801 | 3.8288 | 3400 | 0.7004 | 0.3836 | 0.7004 | 0.8369 |
| 0.0801 | 3.8311 | 3402 | 0.7100 | 0.3377 | 0.7100 | 0.8426 |
| 0.0801 | 3.8333 | 3404 | 0.7239 | 0.3377 | 0.7239 | 0.8508 |
| 0.0801 | 3.8356 | 3406 | 0.7188 | 0.3377 | 0.7188 | 0.8478 |
| 0.0801 | 3.8378 | 3408 | 0.7015 | 0.3836 | 0.7015 | 0.8376 |
| 0.0801 | 3.8401 | 3410 | 0.6991 | 0.3836 | 0.6991 | 0.8361 |
| 0.0801 | 3.8423 | 3412 | 0.6992 | 0.3077 | 0.6992 | 0.8362 |
| 0.0801 | 3.8446 | 3414 | 0.6897 | 0.4324 | 0.6897 | 0.8305 |
| 0.0801 | 3.8468 | 3416 | 0.6898 | 0.4324 | 0.6898 | 0.8305 |
| 0.0801 | 3.8491 | 3418 | 0.6970 | 0.3077 | 0.6970 | 0.8349 |
| 0.0801 | 3.8514 | 3420 | 0.7199 | 0.3377 | 0.7199 | 0.8484 |
| 0.0801 | 3.8536 | 3422 | 0.7261 | 0.2703 | 0.7261 | 0.8521 |
| 0.0801 | 3.8559 | 3424 | 0.7302 | 0.2192 | 0.7302 | 0.8545 |
| 0.0801 | 3.8581 | 3426 | 0.7084 | 0.2192 | 0.7084 | 0.8417 |
| 0.0801 | 3.8604 | 3428 | 0.6963 | 0.3478 | 0.6963 | 0.8345 |
| 0.0801 | 3.8626 | 3430 | 0.6753 | 0.3478 | 0.6753 | 0.8218 |
| 0.0801 | 3.8649 | 3432 | 0.6572 | 0.4 | 0.6572 | 0.8107 |
| 0.0801 | 3.8671 | 3434 | 0.6646 | 0.3662 | 0.6646 | 0.8152 |
| 0.0801 | 3.8694 | 3436 | 0.6962 | 0.4 | 0.6962 | 0.8344 |
| 0.0801 | 3.8716 | 3438 | 0.7398 | 0.1667 | 0.7398 | 0.8601 |
| 0.0801 | 3.8739 | 3440 | 0.7929 | 0.1667 | 0.7929 | 0.8904 |
| 0.0801 | 3.8761 | 3442 | 0.7991 | 0.1667 | 0.7991 | 0.8939 |
| 0.0801 | 3.8784 | 3444 | 0.7612 | 0.2192 | 0.7612 | 0.8725 |
| 0.0801 | 3.8806 | 3446 | 0.7361 | 0.3077 | 0.7361 | 0.8579 |
| 0.0801 | 3.8829 | 3448 | 0.7350 | 0.3077 | 0.7350 | 0.8573 |
| 0.0801 | 3.8851 | 3450 | 0.7374 | 0.3077 | 0.7374 | 0.8587 |
| 0.0801 | 3.8874 | 3452 | 0.7438 | 0.3077 | 0.7438 | 0.8624 |
| 0.0801 | 3.8896 | 3454 | 0.7626 | 0.3077 | 0.7626 | 0.8733 |
| 0.0801 | 3.8919 | 3456 | 0.7960 | 0.3077 | 0.7960 | 0.8922 |
| 0.0801 | 3.8941 | 3458 | 0.8017 | 0.3077 | 0.8017 | 0.8954 |
| 0.0801 | 3.8964 | 3460 | 0.7733 | 0.3077 | 0.7733 | 0.8794 |
| 0.0801 | 3.8986 | 3462 | 0.7341 | 0.3077 | 0.7341 | 0.8568 |
| 0.0801 | 3.9009 | 3464 | 0.7113 | 0.4348 | 0.7113 | 0.8434 |
| 0.0801 | 3.9032 | 3466 | 0.6998 | 0.4348 | 0.6998 | 0.8365 |
| 0.0801 | 3.9054 | 3468 | 0.7010 | 0.3824 | 0.7010 | 0.8372 |
| 0.0801 | 3.9077 | 3470 | 0.7236 | 0.3077 | 0.7236 | 0.8506 |
| 0.0801 | 3.9099 | 3472 | 0.7605 | 0.24 | 0.7605 | 0.8721 |
| 0.0801 | 3.9122 | 3474 | 0.8322 | 0.1667 | 0.8322 | 0.9123 |
| 0.0801 | 3.9144 | 3476 | 0.8726 | 0.1667 | 0.8726 | 0.9341 |
| 0.0801 | 3.9167 | 3478 | 0.8631 | 0.1667 | 0.8631 | 0.9290 |
| 0.0801 | 3.9189 | 3480 | 0.7982 | 0.1667 | 0.7982 | 0.8934 |
| 0.0801 | 3.9212 | 3482 | 0.7216 | 0.2703 | 0.7216 | 0.8495 |
| 0.0801 | 3.9234 | 3484 | 0.6687 | 0.4348 | 0.6687 | 0.8177 |
| 0.0801 | 3.9257 | 3486 | 0.6572 | 0.4348 | 0.6572 | 0.8107 |
| 0.0801 | 3.9279 | 3488 | 0.6583 | 0.4348 | 0.6583 | 0.8114 |
| 0.0801 | 3.9302 | 3490 | 0.6684 | 0.4324 | 0.6684 | 0.8176 |
| 0.0801 | 3.9324 | 3492 | 0.6935 | 0.2597 | 0.6935 | 0.8327 |
| 0.0801 | 3.9347 | 3494 | 0.7212 | 0.2703 | 0.7212 | 0.8492 |
| 0.0801 | 3.9369 | 3496 | 0.7369 | 0.1667 | 0.7369 | 0.8584 |
| 0.0801 | 3.9392 | 3498 | 0.7211 | 0.3200 | 0.7211 | 0.8492 |
| 0.0697 | 3.9414 | 3500 | 0.6796 | 0.4324 | 0.6796 | 0.8244 |
| 0.0697 | 3.9437 | 3502 | 0.6493 | 0.4348 | 0.6493 | 0.8058 |
| 0.0697 | 3.9459 | 3504 | 0.6486 | 0.4348 | 0.6486 | 0.8054 |
| 0.0697 | 3.9482 | 3506 | 0.6661 | 0.4324 | 0.6661 | 0.8161 |
| 0.0697 | 3.9505 | 3508 | 0.6786 | 0.4324 | 0.6786 | 0.8238 |
| 0.0697 | 3.9527 | 3510 | 0.7079 | 0.3377 | 0.7079 | 0.8413 |
| 0.0697 | 3.9550 | 3512 | 0.7230 | 0.2895 | 0.7230 | 0.8503 |
| 0.0697 | 3.9572 | 3514 | 0.7191 | 0.2895 | 0.7191 | 0.8480 |
| 0.0697 | 3.9595 | 3516 | 0.6978 | 0.3846 | 0.6978 | 0.8353 |
| 0.0697 | 3.9617 | 3518 | 0.6711 | 0.4324 | 0.6711 | 0.8192 |
| 0.0697 | 3.9640 | 3520 | 0.6475 | 0.4324 | 0.6475 | 0.8047 |
| 0.0697 | 3.9662 | 3522 | 0.6403 | 0.4324 | 0.6403 | 0.8002 |
| 0.0697 | 3.9685 | 3524 | 0.6461 | 0.4324 | 0.6461 | 0.8038 |
| 0.0697 | 3.9707 | 3526 | 0.6495 | 0.4658 | 0.6495 | 0.8059 |
| 0.0697 | 3.9730 | 3528 | 0.6656 | 0.4167 | 0.6656 | 0.8158 |
| 0.0697 | 3.9752 | 3530 | 0.6534 | 0.4167 | 0.6534 | 0.8083 |
| 0.0697 | 3.9775 | 3532 | 0.6571 | 0.4167 | 0.6571 | 0.8106 |
| 0.0697 | 3.9797 | 3534 | 0.6725 | 0.3662 | 0.6725 | 0.8201 |
| 0.0697 | 3.9820 | 3536 | 0.6955 | 0.2609 | 0.6955 | 0.8340 |
| 0.0697 | 3.9842 | 3538 | 0.6870 | 0.2609 | 0.6870 | 0.8289 |
| 0.0697 | 3.9865 | 3540 | 0.6555 | 0.3662 | 0.6555 | 0.8096 |
| 0.0697 | 3.9887 | 3542 | 0.6423 | 0.4167 | 0.6423 | 0.8014 |
| 0.0697 | 3.9910 | 3544 | 0.6387 | 0.4658 | 0.6387 | 0.7992 |
| 0.0697 | 3.9932 | 3546 | 0.6355 | 0.4658 | 0.6355 | 0.7972 |
| 0.0697 | 3.9955 | 3548 | 0.6453 | 0.4167 | 0.6453 | 0.8033 |
| 0.0697 | 3.9977 | 3550 | 0.6502 | 0.3662 | 0.6502 | 0.8064 |
| 0.0697 | 4.0 | 3552 | 0.6606 | 0.3662 | 0.6606 | 0.8127 |
| 0.0697 | 4.0023 | 3554 | 0.6598 | 0.4167 | 0.6598 | 0.8123 |
| 0.0697 | 4.0045 | 3556 | 0.6784 | 0.3143 | 0.6784 | 0.8236 |
| 0.0697 | 4.0068 | 3558 | 0.7039 | 0.24 | 0.7039 | 0.8390 |
| 0.0697 | 4.0090 | 3560 | 0.7138 | 0.2703 | 0.7138 | 0.8449 |
| 0.0697 | 4.0113 | 3562 | 0.7175 | 0.2703 | 0.7175 | 0.8470 |
| 0.0697 | 4.0135 | 3564 | 0.7011 | 0.24 | 0.7011 | 0.8373 |
| 0.0697 | 4.0158 | 3566 | 0.6975 | 0.2105 | 0.6975 | 0.8352 |
| 0.0697 | 4.0180 | 3568 | 0.6905 | 0.3333 | 0.6905 | 0.8310 |
| 0.0697 | 4.0203 | 3570 | 0.6961 | 0.3333 | 0.6961 | 0.8343 |
| 0.0697 | 4.0225 | 3572 | 0.7014 | 0.2597 | 0.7014 | 0.8375 |
| 0.0697 | 4.0248 | 3574 | 0.7261 | 0.2895 | 0.7261 | 0.8521 |
| 0.0697 | 4.0270 | 3576 | 0.7421 | 0.2703 | 0.7421 | 0.8614 |
| 0.0697 | 4.0293 | 3578 | 0.7577 | 0.2192 | 0.7577 | 0.8705 |
| 0.0697 | 4.0315 | 3580 | 0.7454 | 0.2192 | 0.7454 | 0.8633 |
| 0.0697 | 4.0338 | 3582 | 0.7164 | 0.2192 | 0.7164 | 0.8464 |
| 0.0697 | 4.0360 | 3584 | 0.6990 | 0.24 | 0.6990 | 0.8361 |
| 0.0697 | 4.0383 | 3586 | 0.6952 | 0.2105 | 0.6952 | 0.8338 |
| 0.0697 | 4.0405 | 3588 | 0.6848 | 0.3544 | 0.6848 | 0.8276 |
| 0.0697 | 4.0428 | 3590 | 0.6903 | 0.3544 | 0.6903 | 0.8308 |
| 0.0697 | 4.0450 | 3592 | 0.6958 | 0.3544 | 0.6958 | 0.8342 |
| 0.0697 | 4.0473 | 3594 | 0.7051 | 0.3544 | 0.7051 | 0.8397 |
| 0.0697 | 4.0495 | 3596 | 0.7257 | 0.2597 | 0.7257 | 0.8519 |
| 0.0697 | 4.0518 | 3598 | 0.7254 | 0.2105 | 0.7254 | 0.8517 |
| 0.0697 | 4.0541 | 3600 | 0.7225 | 0.1892 | 0.7225 | 0.8500 |
| 0.0697 | 4.0563 | 3602 | 0.7249 | 0.2192 | 0.7249 | 0.8514 |
| 0.0697 | 4.0586 | 3604 | 0.7105 | 0.2192 | 0.7105 | 0.8429 |
| 0.0697 | 4.0608 | 3606 | 0.6940 | 0.1600 | 0.6940 | 0.8331 |
| 0.0697 | 4.0631 | 3608 | 0.6880 | 0.2105 | 0.6880 | 0.8295 |
| 0.0697 | 4.0653 | 3610 | 0.6898 | 0.3077 | 0.6898 | 0.8306 |
| 0.0697 | 4.0676 | 3612 | 0.6909 | 0.4324 | 0.6909 | 0.8312 |
| 0.0697 | 4.0698 | 3614 | 0.7004 | 0.3544 | 0.7004 | 0.8369 |
| 0.0697 | 4.0721 | 3616 | 0.7065 | 0.3544 | 0.7065 | 0.8405 |
| 0.0697 | 4.0743 | 3618 | 0.7203 | 0.3544 | 0.7203 | 0.8487 |
| 0.0697 | 4.0766 | 3620 | 0.7581 | 0.2105 | 0.7581 | 0.8707 |
| 0.0697 | 4.0788 | 3622 | 0.8078 | 0.1667 | 0.8078 | 0.8987 |
| 0.0697 | 4.0811 | 3624 | 0.8045 | 0.1667 | 0.8045 | 0.8969 |
| 0.0697 | 4.0833 | 3626 | 0.7539 | 0.1667 | 0.7539 | 0.8683 |
| 0.0697 | 4.0856 | 3628 | 0.6857 | 0.2192 | 0.6857 | 0.8281 |
| 0.0697 | 4.0878 | 3630 | 0.6382 | 0.2000 | 0.6382 | 0.7989 |
| 0.0697 | 4.0901 | 3632 | 0.6159 | 0.3438 | 0.6159 | 0.7848 |
| 0.0697 | 4.0923 | 3634 | 0.6185 | 0.3438 | 0.6185 | 0.7864 |
| 0.0697 | 4.0946 | 3636 | 0.6405 | 0.2623 | 0.6405 | 0.8003 |
| 0.0697 | 4.0968 | 3638 | 0.6977 | 0.2192 | 0.6977 | 0.8353 |
| 0.0697 | 4.0991 | 3640 | 0.7719 | 0.1667 | 0.7719 | 0.8786 |
| 0.0697 | 4.1014 | 3642 | 0.7901 | 0.1667 | 0.7901 | 0.8889 |
| 0.0697 | 4.1036 | 3644 | 0.7605 | 0.2703 | 0.7605 | 0.8720 |
| 0.0697 | 4.1059 | 3646 | 0.7176 | 0.2895 | 0.7176 | 0.8471 |
| 0.0697 | 4.1081 | 3648 | 0.6744 | 0.4324 | 0.6744 | 0.8212 |
| 0.0697 | 4.1104 | 3650 | 0.6608 | 0.4324 | 0.6608 | 0.8129 |
| 0.0697 | 4.1126 | 3652 | 0.6635 | 0.4324 | 0.6635 | 0.8146 |
| 0.0697 | 4.1149 | 3654 | 0.6641 | 0.4324 | 0.6641 | 0.8149 |
| 0.0697 | 4.1171 | 3656 | 0.6881 | 0.3377 | 0.6881 | 0.8295 |
| 0.0697 | 4.1194 | 3658 | 0.7251 | 0.24 | 0.7251 | 0.8516 |
| 0.0697 | 4.1216 | 3660 | 0.7709 | 0.1892 | 0.7709 | 0.8780 |
| 0.0697 | 4.1239 | 3662 | 0.7789 | 0.1892 | 0.7789 | 0.8825 |
| 0.0697 | 4.1261 | 3664 | 0.7493 | 0.1892 | 0.7493 | 0.8656 |
| 0.0697 | 4.1284 | 3666 | 0.6985 | 0.2895 | 0.6985 | 0.8358 |
| 0.0697 | 4.1306 | 3668 | 0.6732 | 0.4658 | 0.6732 | 0.8205 |
| 0.0697 | 4.1329 | 3670 | 0.6724 | 0.4658 | 0.6724 | 0.8200 |
| 0.0697 | 4.1351 | 3672 | 0.6842 | 0.4658 | 0.6842 | 0.8272 |
| 0.0697 | 4.1374 | 3674 | 0.7016 | 0.3846 | 0.7016 | 0.8376 |
| 0.0697 | 4.1396 | 3676 | 0.7308 | 0.2895 | 0.7308 | 0.8549 |
| 0.0697 | 4.1419 | 3678 | 0.7417 | 0.2895 | 0.7417 | 0.8612 |
| 0.0697 | 4.1441 | 3680 | 0.7474 | 0.2895 | 0.7474 | 0.8645 |
| 0.0697 | 4.1464 | 3682 | 0.7541 | 0.2895 | 0.7541 | 0.8684 |
| 0.0697 | 4.1486 | 3684 | 0.7290 | 0.2895 | 0.7290 | 0.8538 |
| 0.0697 | 4.1509 | 3686 | 0.7102 | 0.4658 | 0.7102 | 0.8428 |
| 0.0697 | 4.1532 | 3688 | 0.6948 | 0.4324 | 0.6948 | 0.8336 |
| 0.0697 | 4.1554 | 3690 | 0.6762 | 0.4348 | 0.6762 | 0.8223 |
| 0.0697 | 4.1577 | 3692 | 0.6721 | 0.3438 | 0.6721 | 0.8198 |
| 0.0697 | 4.1599 | 3694 | 0.6860 | 0.4324 | 0.6860 | 0.8283 |
| 0.0697 | 4.1622 | 3696 | 0.7207 | 0.4324 | 0.7207 | 0.8490 |
| 0.0697 | 4.1644 | 3698 | 0.7757 | 0.2895 | 0.7757 | 0.8807 |
| 0.0697 | 4.1667 | 3700 | 0.8221 | 0.1667 | 0.8221 | 0.9067 |
| 0.0697 | 4.1689 | 3702 | 0.8245 | 0.1667 | 0.8245 | 0.9080 |
| 0.0697 | 4.1712 | 3704 | 0.7812 | 0.2192 | 0.7812 | 0.8838 |
| 0.0697 | 4.1734 | 3706 | 0.7218 | 0.3333 | 0.7218 | 0.8496 |
| 0.0697 | 4.1757 | 3708 | 0.7044 | 0.3333 | 0.7044 | 0.8393 |
| 0.0697 | 4.1779 | 3710 | 0.6925 | 0.4324 | 0.6925 | 0.8322 |
| 0.0697 | 4.1802 | 3712 | 0.7062 | 0.3333 | 0.7062 | 0.8404 |
| 0.0697 | 4.1824 | 3714 | 0.7505 | 0.2597 | 0.7505 | 0.8663 |
| 0.0697 | 4.1847 | 3716 | 0.8212 | 0.2192 | 0.8212 | 0.9062 |
| 0.0697 | 4.1869 | 3718 | 0.8634 | 0.2192 | 0.8634 | 0.9292 |
| 0.0697 | 4.1892 | 3720 | 0.8496 | 0.2192 | 0.8496 | 0.9217 |
| 0.0697 | 4.1914 | 3722 | 0.8080 | 0.24 | 0.8080 | 0.8989 |
| 0.0697 | 4.1937 | 3724 | 0.7452 | 0.2597 | 0.7452 | 0.8633 |
| 0.0697 | 4.1959 | 3726 | 0.7099 | 0.3077 | 0.7099 | 0.8426 |
| 0.0697 | 4.1982 | 3728 | 0.7001 | 0.3836 | 0.7001 | 0.8367 |
| 0.0697 | 4.2005 | 3730 | 0.7060 | 0.3836 | 0.7060 | 0.8403 |
| 0.0697 | 4.2027 | 3732 | 0.7041 | 0.4324 | 0.7041 | 0.8391 |
| 0.0697 | 4.2050 | 3734 | 0.7144 | 0.3836 | 0.7144 | 0.8452 |
| 0.0697 | 4.2072 | 3736 | 0.7330 | 0.3836 | 0.7330 | 0.8561 |
| 0.0697 | 4.2095 | 3738 | 0.7496 | 0.3836 | 0.7496 | 0.8658 |
| 0.0697 | 4.2117 | 3740 | 0.7748 | 0.3077 | 0.7748 | 0.8802 |
| 0.0697 | 4.2140 | 3742 | 0.7771 | 0.3077 | 0.7771 | 0.8815 |
| 0.0697 | 4.2162 | 3744 | 0.7655 | 0.3077 | 0.7655 | 0.8749 |
| 0.0697 | 4.2185 | 3746 | 0.7581 | 0.3077 | 0.7581 | 0.8707 |
| 0.0697 | 4.2207 | 3748 | 0.7305 | 0.3836 | 0.7305 | 0.8547 |
| 0.0697 | 4.2230 | 3750 | 0.6940 | 0.4324 | 0.6940 | 0.8331 |
| 0.0697 | 4.2252 | 3752 | 0.6674 | 0.3478 | 0.6674 | 0.8169 |
| 0.0697 | 4.2275 | 3754 | 0.6568 | 0.3478 | 0.6568 | 0.8105 |
| 0.0697 | 4.2297 | 3756 | 0.6682 | 0.3836 | 0.6682 | 0.8175 |
| 0.0697 | 4.2320 | 3758 | 0.7044 | 0.4507 | 0.7044 | 0.8393 |
| 0.0697 | 4.2342 | 3760 | 0.7262 | 0.2703 | 0.7262 | 0.8522 |
| 0.0697 | 4.2365 | 3762 | 0.7271 | 0.2703 | 0.7271 | 0.8527 |
| 0.0697 | 4.2387 | 3764 | 0.6984 | 0.4507 | 0.6984 | 0.8357 |
| 0.0697 | 4.2410 | 3766 | 0.6925 | 0.4167 | 0.6925 | 0.8322 |
| 0.0697 | 4.2432 | 3768 | 0.7116 | 0.3377 | 0.7116 | 0.8436 |
| 0.0697 | 4.2455 | 3770 | 0.7102 | 0.3377 | 0.7102 | 0.8428 |
| 0.0697 | 4.2477 | 3772 | 0.7093 | 0.3377 | 0.7093 | 0.8422 |
| 0.0697 | 4.25 | 3774 | 0.6931 | 0.4324 | 0.6931 | 0.8325 |
| 0.0697 | 4.2523 | 3776 | 0.6678 | 0.4324 | 0.6678 | 0.8172 |
| 0.0697 | 4.2545 | 3778 | 0.6479 | 0.3478 | 0.6479 | 0.8049 |
| 0.0697 | 4.2568 | 3780 | 0.6428 | 0.3478 | 0.6428 | 0.8017 |
| 0.0697 | 4.2590 | 3782 | 0.6518 | 0.4324 | 0.6518 | 0.8073 |
| 0.0697 | 4.2613 | 3784 | 0.6925 | 0.4 | 0.6925 | 0.8322 |
| 0.0697 | 4.2635 | 3786 | 0.7314 | 0.2192 | 0.7314 | 0.8552 |
| 0.0697 | 4.2658 | 3788 | 0.7702 | 0.2192 | 0.7702 | 0.8776 |
| 0.0697 | 4.2680 | 3790 | 0.7614 | 0.2192 | 0.7614 | 0.8726 |
| 0.0697 | 4.2703 | 3792 | 0.7663 | 0.2192 | 0.7663 | 0.8754 |
| 0.0697 | 4.2725 | 3794 | 0.7392 | 0.2703 | 0.7392 | 0.8598 |
| 0.0697 | 4.2748 | 3796 | 0.6818 | 0.4 | 0.6818 | 0.8257 |
| 0.0697 | 4.2770 | 3798 | 0.6323 | 0.3284 | 0.6323 | 0.7952 |
| 0.0697 | 4.2793 | 3800 | 0.6167 | 0.2941 | 0.6167 | 0.7853 |
| 0.0697 | 4.2815 | 3802 | 0.6251 | 0.3284 | 0.6251 | 0.7907 |
| 0.0697 | 4.2838 | 3804 | 0.6487 | 0.4507 | 0.6487 | 0.8054 |
| 0.0697 | 4.2860 | 3806 | 0.7038 | 0.3478 | 0.7038 | 0.8389 |
| 0.0697 | 4.2883 | 3808 | 0.7660 | 0.2192 | 0.7660 | 0.8752 |
| 0.0697 | 4.2905 | 3810 | 0.7761 | 0.2192 | 0.7761 | 0.8810 |
| 0.0697 | 4.2928 | 3812 | 0.7532 | 0.2703 | 0.7532 | 0.8679 |
| 0.0697 | 4.2950 | 3814 | 0.7166 | 0.4 | 0.7166 | 0.8466 |
| 0.0697 | 4.2973 | 3816 | 0.6875 | 0.4167 | 0.6875 | 0.8291 |
| 0.0697 | 4.2995 | 3818 | 0.6821 | 0.3836 | 0.6821 | 0.8259 |
| 0.0697 | 4.3018 | 3820 | 0.6997 | 0.4 | 0.6997 | 0.8365 |
| 0.0697 | 4.3041 | 3822 | 0.7166 | 0.4 | 0.7166 | 0.8465 |
| 0.0697 | 4.3063 | 3824 | 0.7490 | 0.4 | 0.7490 | 0.8654 |
| 0.0697 | 4.3086 | 3826 | 0.7995 | 0.2192 | 0.7995 | 0.8942 |
| 0.0697 | 4.3108 | 3828 | 0.8136 | 0.1667 | 0.8136 | 0.9020 |
| 0.0697 | 4.3131 | 3830 | 0.7929 | 0.2192 | 0.7929 | 0.8905 |
| 0.0697 | 4.3153 | 3832 | 0.7628 | 0.3200 | 0.7628 | 0.8734 |
| 0.0697 | 4.3176 | 3834 | 0.7291 | 0.4 | 0.7291 | 0.8538 |
| 0.0697 | 4.3198 | 3836 | 0.7096 | 0.3333 | 0.7096 | 0.8424 |
| 0.0697 | 4.3221 | 3838 | 0.7041 | 0.3836 | 0.7041 | 0.8391 |
| 0.0697 | 4.3243 | 3840 | 0.6862 | 0.3836 | 0.6862 | 0.8284 |
| 0.0697 | 4.3266 | 3842 | 0.6659 | 0.4324 | 0.6659 | 0.8160 |
| 0.0697 | 4.3288 | 3844 | 0.6691 | 0.3836 | 0.6691 | 0.8180 |
| 0.0697 | 4.3311 | 3846 | 0.6919 | 0.3836 | 0.6919 | 0.8318 |
| 0.0697 | 4.3333 | 3848 | 0.7243 | 0.2895 | 0.7243 | 0.8511 |
| 0.0697 | 4.3356 | 3850 | 0.7445 | 0.3200 | 0.7445 | 0.8628 |
| 0.0697 | 4.3378 | 3852 | 0.7559 | 0.3200 | 0.7559 | 0.8694 |
| 0.0697 | 4.3401 | 3854 | 0.7311 | 0.3200 | 0.7311 | 0.8550 |
| 0.0697 | 4.3423 | 3856 | 0.6956 | 0.3662 | 0.6956 | 0.8340 |
| 0.0697 | 4.3446 | 3858 | 0.6794 | 0.3662 | 0.6794 | 0.8243 |
| 0.0697 | 4.3468 | 3860 | 0.6704 | 0.4167 | 0.6704 | 0.8188 |
| 0.0697 | 4.3491 | 3862 | 0.6755 | 0.3836 | 0.6755 | 0.8219 |
| 0.0697 | 4.3514 | 3864 | 0.6928 | 0.3836 | 0.6928 | 0.8324 |
| 0.0697 | 4.3536 | 3866 | 0.7368 | 0.3836 | 0.7368 | 0.8584 |
| 0.0697 | 4.3559 | 3868 | 0.7804 | 0.2895 | 0.7804 | 0.8834 |
| 0.0697 | 4.3581 | 3870 | 0.8138 | 0.2895 | 0.8138 | 0.9021 |
| 0.0697 | 4.3604 | 3872 | 0.8187 | 0.3200 | 0.8187 | 0.9048 |
| 0.0697 | 4.3626 | 3874 | 0.7871 | 0.2895 | 0.7871 | 0.8872 |
| 0.0697 | 4.3649 | 3876 | 0.7393 | 0.3836 | 0.7393 | 0.8598 |
| 0.0697 | 4.3671 | 3878 | 0.6982 | 0.3836 | 0.6982 | 0.8356 |
| 0.0697 | 4.3694 | 3880 | 0.6706 | 0.2941 | 0.6706 | 0.8189 |
| 0.0697 | 4.3716 | 3882 | 0.6625 | 0.2941 | 0.6625 | 0.8140 |
| 0.0697 | 4.3739 | 3884 | 0.6627 | 0.2941 | 0.6627 | 0.8141 |
| 0.0697 | 4.3761 | 3886 | 0.6807 | 0.3836 | 0.6807 | 0.8251 |
| 0.0697 | 4.3784 | 3888 | 0.7137 | 0.3662 | 0.7137 | 0.8448 |
| 0.0697 | 4.3806 | 3890 | 0.7249 | 0.3662 | 0.7249 | 0.8514 |
| 0.0697 | 4.3829 | 3892 | 0.7078 | 0.4167 | 0.7078 | 0.8413 |
| 0.0697 | 4.3851 | 3894 | 0.6804 | 0.3836 | 0.6804 | 0.8249 |
| 0.0697 | 4.3874 | 3896 | 0.6735 | 0.2941 | 0.6735 | 0.8207 |
| 0.0697 | 4.3896 | 3898 | 0.6687 | 0.3478 | 0.6687 | 0.8177 |
| 0.0697 | 4.3919 | 3900 | 0.6789 | 0.4324 | 0.6789 | 0.8239 |
| 0.0697 | 4.3941 | 3902 | 0.7066 | 0.3836 | 0.7066 | 0.8406 |
| 0.0697 | 4.3964 | 3904 | 0.7620 | 0.2895 | 0.7620 | 0.8729 |
| 0.0697 | 4.3986 | 3906 | 0.8131 | 0.3200 | 0.8131 | 0.9017 |
| 0.0697 | 4.4009 | 3908 | 0.8148 | 0.3200 | 0.8148 | 0.9027 |
| 0.0697 | 4.4032 | 3910 | 0.7736 | 0.2895 | 0.7736 | 0.8796 |
| 0.0697 | 4.4054 | 3912 | 0.7296 | 0.3077 | 0.7296 | 0.8541 |
| 0.0697 | 4.4077 | 3914 | 0.7137 | 0.3836 | 0.7137 | 0.8448 |
| 0.0697 | 4.4099 | 3916 | 0.6988 | 0.3836 | 0.6988 | 0.8359 |
| 0.0697 | 4.4122 | 3918 | 0.6791 | 0.3478 | 0.6791 | 0.8241 |
| 0.0697 | 4.4144 | 3920 | 0.6675 | 0.3478 | 0.6675 | 0.8170 |
| 0.0697 | 4.4167 | 3922 | 0.6594 | 0.3478 | 0.6594 | 0.8120 |
| 0.0697 | 4.4189 | 3924 | 0.6589 | 0.3478 | 0.6589 | 0.8117 |
| 0.0697 | 4.4212 | 3926 | 0.6546 | 0.2941 | 0.6546 | 0.8091 |
| 0.0697 | 4.4234 | 3928 | 0.6628 | 0.3333 | 0.6628 | 0.8141 |
| 0.0697 | 4.4257 | 3930 | 0.6831 | 0.3662 | 0.6831 | 0.8265 |
| 0.0697 | 4.4279 | 3932 | 0.7005 | 0.3662 | 0.7005 | 0.8370 |
| 0.0697 | 4.4302 | 3934 | 0.7196 | 0.2895 | 0.7196 | 0.8483 |
| 0.0697 | 4.4324 | 3936 | 0.7273 | 0.2895 | 0.7273 | 0.8528 |
| 0.0697 | 4.4347 | 3938 | 0.7080 | 0.3662 | 0.7080 | 0.8414 |
| 0.0697 | 4.4369 | 3940 | 0.7117 | 0.3662 | 0.7117 | 0.8436 |
| 0.0697 | 4.4392 | 3942 | 0.7213 | 0.2895 | 0.7213 | 0.8493 |
| 0.0697 | 4.4414 | 3944 | 0.7183 | 0.3077 | 0.7183 | 0.8475 |
| 0.0697 | 4.4437 | 3946 | 0.7217 | 0.3077 | 0.7217 | 0.8495 |
| 0.0697 | 4.4459 | 3948 | 0.7337 | 0.3077 | 0.7337 | 0.8565 |
| 0.0697 | 4.4482 | 3950 | 0.7401 | 0.2895 | 0.7401 | 0.8603 |
| 0.0697 | 4.4505 | 3952 | 0.7232 | 0.3077 | 0.7232 | 0.8504 |
| 0.0697 | 4.4527 | 3954 | 0.7037 | 0.3836 | 0.7037 | 0.8389 |
| 0.0697 | 4.4550 | 3956 | 0.6938 | 0.3478 | 0.6938 | 0.8329 |
| 0.0697 | 4.4572 | 3958 | 0.6825 | 0.3478 | 0.6825 | 0.8261 |
| 0.0697 | 4.4595 | 3960 | 0.6754 | 0.3478 | 0.6754 | 0.8218 |
| 0.0697 | 4.4617 | 3962 | 0.6743 | 0.3478 | 0.6743 | 0.8211 |
| 0.0697 | 4.4640 | 3964 | 0.6712 | 0.3478 | 0.6712 | 0.8193 |
| 0.0697 | 4.4662 | 3966 | 0.6700 | 0.3478 | 0.6700 | 0.8186 |
| 0.0697 | 4.4685 | 3968 | 0.6703 | 0.3478 | 0.6703 | 0.8187 |
| 0.0697 | 4.4707 | 3970 | 0.6734 | 0.3478 | 0.6734 | 0.8206 |
| 0.0697 | 4.4730 | 3972 | 0.6830 | 0.3478 | 0.6830 | 0.8265 |
| 0.0697 | 4.4752 | 3974 | 0.6916 | 0.4324 | 0.6916 | 0.8316 |
| 0.0697 | 4.4775 | 3976 | 0.7040 | 0.4324 | 0.7040 | 0.8391 |
| 0.0697 | 4.4797 | 3978 | 0.7174 | 0.3077 | 0.7174 | 0.8470 |
| 0.0697 | 4.4820 | 3980 | 0.7210 | 0.3077 | 0.7210 | 0.8491 |
| 0.0697 | 4.4842 | 3982 | 0.7086 | 0.3836 | 0.7086 | 0.8418 |
| 0.0697 | 4.4865 | 3984 | 0.7006 | 0.3662 | 0.7006 | 0.8370 |
| 0.0697 | 4.4887 | 3986 | 0.6812 | 0.3836 | 0.6812 | 0.8254 |
| 0.0697 | 4.4910 | 3988 | 0.6629 | 0.3478 | 0.6629 | 0.8142 |
| 0.0697 | 4.4932 | 3990 | 0.6508 | 0.3438 | 0.6508 | 0.8067 |
| 0.0697 | 4.4955 | 3992 | 0.6499 | 0.3438 | 0.6499 | 0.8062 |
| 0.0697 | 4.4977 | 3994 | 0.6513 | 0.3438 | 0.6513 | 0.8070 |
| 0.0697 | 4.5 | 3996 | 0.6582 | 0.3438 | 0.6582 | 0.8113 |
| 0.0697 | 4.5023 | 3998 | 0.6741 | 0.3478 | 0.6741 | 0.8210 |
| 0.0636 | 4.5045 | 4000 | 0.6941 | 0.3662 | 0.6941 | 0.8331 |
| 0.0636 | 4.5068 | 4002 | 0.7027 | 0.2895 | 0.7027 | 0.8383 |
| 0.0636 | 4.5090 | 4004 | 0.7029 | 0.1892 | 0.7029 | 0.8384 |
| 0.0636 | 4.5113 | 4006 | 0.6830 | 0.3662 | 0.6830 | 0.8264 |
| 0.0636 | 4.5135 | 4008 | 0.6651 | 0.4324 | 0.6651 | 0.8155 |
| 0.0636 | 4.5158 | 4010 | 0.6540 | 0.3478 | 0.6540 | 0.8087 |
| 0.0636 | 4.5180 | 4012 | 0.6661 | 0.3478 | 0.6661 | 0.8162 |
| 0.0636 | 4.5203 | 4014 | 0.6788 | 0.3478 | 0.6788 | 0.8239 |
| 0.0636 | 4.5225 | 4016 | 0.6926 | 0.4324 | 0.6926 | 0.8322 |
| 0.0636 | 4.5248 | 4018 | 0.7036 | 0.4324 | 0.7036 | 0.8388 |
| 0.0636 | 4.5270 | 4020 | 0.7082 | 0.4658 | 0.7082 | 0.8416 |
| 0.0636 | 4.5293 | 4022 | 0.7098 | 0.2895 | 0.7098 | 0.8425 |
| 0.0636 | 4.5315 | 4024 | 0.6901 | 0.3662 | 0.6901 | 0.8307 |
| 0.0636 | 4.5338 | 4026 | 0.6663 | 0.4167 | 0.6663 | 0.8163 |
| 0.0636 | 4.5360 | 4028 | 0.6519 | 0.3478 | 0.6519 | 0.8074 |
| 0.0636 | 4.5383 | 4030 | 0.6592 | 0.3478 | 0.6592 | 0.8119 |
| 0.0636 | 4.5405 | 4032 | 0.6808 | 0.3478 | 0.6808 | 0.8251 |
| 0.0636 | 4.5428 | 4034 | 0.7066 | 0.4324 | 0.7066 | 0.8406 |
| 0.0636 | 4.5450 | 4036 | 0.7284 | 0.4324 | 0.7284 | 0.8535 |
| 0.0636 | 4.5473 | 4038 | 0.7444 | 0.3544 | 0.7444 | 0.8628 |
| 0.0636 | 4.5495 | 4040 | 0.7539 | 0.3077 | 0.7539 | 0.8683 |
| 0.0636 | 4.5518 | 4042 | 0.7802 | 0.24 | 0.7802 | 0.8833 |
| 0.0636 | 4.5541 | 4044 | 0.7887 | 0.1892 | 0.7887 | 0.8881 |
| 0.0636 | 4.5563 | 4046 | 0.7781 | 0.1892 | 0.7781 | 0.8821 |
| 0.0636 | 4.5586 | 4048 | 0.7389 | 0.24 | 0.7389 | 0.8596 |
| 0.0636 | 4.5608 | 4050 | 0.7016 | 0.3836 | 0.7016 | 0.8376 |
| 0.0636 | 4.5631 | 4052 | 0.6768 | 0.4348 | 0.6768 | 0.8227 |
| 0.0636 | 4.5653 | 4054 | 0.6783 | 0.3077 | 0.6783 | 0.8236 |
| 0.0636 | 4.5676 | 4056 | 0.6886 | 0.3077 | 0.6886 | 0.8298 |
| 0.0636 | 4.5698 | 4058 | 0.6919 | 0.4 | 0.6919 | 0.8318 |
| 0.0636 | 4.5721 | 4060 | 0.7033 | 0.4324 | 0.7033 | 0.8386 |
| 0.0636 | 4.5743 | 4062 | 0.7276 | 0.3836 | 0.7276 | 0.8530 |
| 0.0636 | 4.5766 | 4064 | 0.7623 | 0.24 | 0.7623 | 0.8731 |
| 0.0636 | 4.5788 | 4066 | 0.7800 | 0.1892 | 0.7800 | 0.8832 |
| 0.0636 | 4.5811 | 4068 | 0.7647 | 0.24 | 0.7647 | 0.8745 |
| 0.0636 | 4.5833 | 4070 | 0.7320 | 0.24 | 0.7320 | 0.8556 |
| 0.0636 | 4.5856 | 4072 | 0.6916 | 0.4324 | 0.6916 | 0.8316 |
| 0.0636 | 4.5878 | 4074 | 0.6719 | 0.4324 | 0.6719 | 0.8197 |
| 0.0636 | 4.5901 | 4076 | 0.6707 | 0.4 | 0.6707 | 0.8190 |
| 0.0636 | 4.5923 | 4078 | 0.6736 | 0.4324 | 0.6736 | 0.8208 |
| 0.0636 | 4.5946 | 4080 | 0.6874 | 0.4324 | 0.6874 | 0.8291 |
| 0.0636 | 4.5968 | 4082 | 0.6975 | 0.4324 | 0.6975 | 0.8352 |
| 0.0636 | 4.5991 | 4084 | 0.6986 | 0.4324 | 0.6986 | 0.8358 |
| 0.0636 | 4.6014 | 4086 | 0.6947 | 0.4324 | 0.6947 | 0.8335 |
| 0.0636 | 4.6036 | 4088 | 0.6922 | 0.4 | 0.6922 | 0.8320 |
| 0.0636 | 4.6059 | 4090 | 0.6929 | 0.4324 | 0.6929 | 0.8324 |
| 0.0636 | 4.6081 | 4092 | 0.7000 | 0.4324 | 0.7000 | 0.8367 |
| 0.0636 | 4.6104 | 4094 | 0.7179 | 0.4324 | 0.7179 | 0.8473 |
| 0.0636 | 4.6126 | 4096 | 0.7572 | 0.24 | 0.7572 | 0.8702 |
| 0.0636 | 4.6149 | 4098 | 0.7806 | 0.24 | 0.7806 | 0.8835 |
| 0.0636 | 4.6171 | 4100 | 0.7702 | 0.24 | 0.7702 | 0.8776 |
| 0.0636 | 4.6194 | 4102 | 0.7574 | 0.24 | 0.7574 | 0.8703 |
| 0.0636 | 4.6216 | 4104 | 0.7304 | 0.4167 | 0.7304 | 0.8546 |
| 0.0636 | 4.6239 | 4106 | 0.7082 | 0.4324 | 0.7082 | 0.8416 |
| 0.0636 | 4.6261 | 4108 | 0.6883 | 0.4324 | 0.6883 | 0.8296 |
| 0.0636 | 4.6284 | 4110 | 0.6761 | 0.4324 | 0.6761 | 0.8223 |
| 0.0636 | 4.6306 | 4112 | 0.6648 | 0.3478 | 0.6648 | 0.8153 |
| 0.0636 | 4.6329 | 4114 | 0.6632 | 0.3478 | 0.6632 | 0.8144 |
| 0.0636 | 4.6351 | 4116 | 0.6740 | 0.4324 | 0.6740 | 0.8210 |
| 0.0636 | 4.6374 | 4118 | 0.6922 | 0.4324 | 0.6922 | 0.8320 |
| 0.0636 | 4.6396 | 4120 | 0.7121 | 0.4324 | 0.7121 | 0.8438 |
| 0.0636 | 4.6419 | 4122 | 0.7110 | 0.4324 | 0.7110 | 0.8432 |
| 0.0636 | 4.6441 | 4124 | 0.7000 | 0.4324 | 0.7000 | 0.8367 |
| 0.0636 | 4.6464 | 4126 | 0.6940 | 0.4324 | 0.6940 | 0.8331 |
| 0.0636 | 4.6486 | 4128 | 0.6949 | 0.4324 | 0.6949 | 0.8336 |
| 0.0636 | 4.6509 | 4130 | 0.6870 | 0.4324 | 0.6870 | 0.8289 |
| 0.0636 | 4.6532 | 4132 | 0.6754 | 0.4324 | 0.6754 | 0.8219 |
| 0.0636 | 4.6554 | 4134 | 0.6617 | 0.3478 | 0.6617 | 0.8135 |
| 0.0636 | 4.6577 | 4136 | 0.6560 | 0.3478 | 0.6560 | 0.8099 |
| 0.0636 | 4.6599 | 4138 | 0.6533 | 0.3478 | 0.6533 | 0.8083 |
| 0.0636 | 4.6622 | 4140 | 0.6612 | 0.3478 | 0.6612 | 0.8131 |
| 0.0636 | 4.6644 | 4142 | 0.6890 | 0.4167 | 0.6890 | 0.8301 |
| 0.0636 | 4.6667 | 4144 | 0.7081 | 0.3662 | 0.7081 | 0.8415 |
| 0.0636 | 4.6689 | 4146 | 0.7328 | 0.3143 | 0.7328 | 0.8561 |
| 0.0636 | 4.6712 | 4148 | 0.7266 | 0.3143 | 0.7266 | 0.8524 |
| 0.0636 | 4.6734 | 4150 | 0.7086 | 0.3143 | 0.7086 | 0.8418 |
| 0.0636 | 4.6757 | 4152 | 0.6855 | 0.4167 | 0.6855 | 0.8279 |
| 0.0636 | 4.6779 | 4154 | 0.6657 | 0.3478 | 0.6657 | 0.8159 |
| 0.0636 | 4.6802 | 4156 | 0.6632 | 0.3478 | 0.6632 | 0.8143 |
| 0.0636 | 4.6824 | 4158 | 0.6760 | 0.3478 | 0.6760 | 0.8222 |
| 0.0636 | 4.6847 | 4160 | 0.6868 | 0.4324 | 0.6868 | 0.8287 |
| 0.0636 | 4.6869 | 4162 | 0.6988 | 0.3478 | 0.6988 | 0.8359 |
| 0.0636 | 4.6892 | 4164 | 0.7020 | 0.3478 | 0.7020 | 0.8379 |
| 0.0636 | 4.6914 | 4166 | 0.7045 | 0.3478 | 0.7045 | 0.8393 |
| 0.0636 | 4.6937 | 4168 | 0.7018 | 0.3478 | 0.7018 | 0.8378 |
| 0.0636 | 4.6959 | 4170 | 0.7020 | 0.3478 | 0.7020 | 0.8378 |
| 0.0636 | 4.6982 | 4172 | 0.7079 | 0.3478 | 0.7079 | 0.8414 |
| 0.0636 | 4.7005 | 4174 | 0.7289 | 0.2941 | 0.7289 | 0.8537 |
| 0.0636 | 4.7027 | 4176 | 0.7506 | 0.2895 | 0.7506 | 0.8664 |
| 0.0636 | 4.7050 | 4178 | 0.7660 | 0.24 | 0.7660 | 0.8752 |
| 0.0636 | 4.7072 | 4180 | 0.7488 | 0.24 | 0.7488 | 0.8654 |
| 0.0636 | 4.7095 | 4182 | 0.7249 | 0.2727 | 0.7249 | 0.8514 |
| 0.0636 | 4.7117 | 4184 | 0.7084 | 0.3478 | 0.7084 | 0.8417 |
| 0.0636 | 4.7140 | 4186 | 0.7148 | 0.3478 | 0.7148 | 0.8455 |
| 0.0636 | 4.7162 | 4188 | 0.7291 | 0.3478 | 0.7291 | 0.8539 |
| 0.0636 | 4.7185 | 4190 | 0.7432 | 0.2817 | 0.7432 | 0.8621 |
| 0.0636 | 4.7207 | 4192 | 0.7507 | 0.1667 | 0.7507 | 0.8664 |
| 0.0636 | 4.7230 | 4194 | 0.7471 | 0.3478 | 0.7471 | 0.8644 |
| 0.0636 | 4.7252 | 4196 | 0.7496 | 0.3478 | 0.7496 | 0.8658 |
| 0.0636 | 4.7275 | 4198 | 0.7686 | 0.3077 | 0.7686 | 0.8767 |
| 0.0636 | 4.7297 | 4200 | 0.7830 | 0.3377 | 0.7830 | 0.8849 |
| 0.0636 | 4.7320 | 4202 | 0.7739 | 0.3377 | 0.7739 | 0.8797 |
| 0.0636 | 4.7342 | 4204 | 0.7651 | 0.3377 | 0.7651 | 0.8747 |
| 0.0636 | 4.7365 | 4206 | 0.7605 | 0.3377 | 0.7605 | 0.8721 |
| 0.0636 | 4.7387 | 4208 | 0.7551 | 0.3077 | 0.7551 | 0.8690 |
| 0.0636 | 4.7410 | 4210 | 0.7538 | 0.3544 | 0.7538 | 0.8682 |
| 0.0636 | 4.7432 | 4212 | 0.7582 | 0.4324 | 0.7582 | 0.8708 |
| 0.0636 | 4.7455 | 4214 | 0.7599 | 0.4324 | 0.7599 | 0.8717 |
| 0.0636 | 4.7477 | 4216 | 0.7663 | 0.3544 | 0.7663 | 0.8754 |
| 0.0636 | 4.75 | 4218 | 0.7759 | 0.3544 | 0.7759 | 0.8809 |
| 0.0636 | 4.7523 | 4220 | 0.7680 | 0.3544 | 0.7680 | 0.8763 |
| 0.0636 | 4.7545 | 4222 | 0.7648 | 0.3478 | 0.7648 | 0.8745 |
| 0.0636 | 4.7568 | 4224 | 0.7717 | 0.1972 | 0.7717 | 0.8785 |
| 0.0636 | 4.7590 | 4226 | 0.7661 | 0.1972 | 0.7661 | 0.8753 |
| 0.0636 | 4.7613 | 4228 | 0.7465 | 0.1972 | 0.7465 | 0.8640 |
| 0.0636 | 4.7635 | 4230 | 0.7166 | 0.3478 | 0.7166 | 0.8465 |
| 0.0636 | 4.7658 | 4232 | 0.7013 | 0.3478 | 0.7013 | 0.8374 |
| 0.0636 | 4.7680 | 4234 | 0.7316 | 0.4167 | 0.7316 | 0.8553 |
| 0.0636 | 4.7703 | 4236 | 0.7782 | 0.2192 | 0.7782 | 0.8822 |
| 0.0636 | 4.7725 | 4238 | 0.8007 | 0.1538 | 0.8007 | 0.8948 |
| 0.0636 | 4.7748 | 4240 | 0.7816 | 0.2192 | 0.7816 | 0.8841 |
| 0.0636 | 4.7770 | 4242 | 0.7502 | 0.2703 | 0.7502 | 0.8662 |
| 0.0636 | 4.7793 | 4244 | 0.7163 | 0.3662 | 0.7163 | 0.8463 |
| 0.0636 | 4.7815 | 4246 | 0.6869 | 0.3478 | 0.6869 | 0.8288 |
| 0.0636 | 4.7838 | 4248 | 0.6820 | 0.3478 | 0.6820 | 0.8258 |
| 0.0636 | 4.7860 | 4250 | 0.6905 | 0.3478 | 0.6905 | 0.8310 |
| 0.0636 | 4.7883 | 4252 | 0.7016 | 0.3478 | 0.7016 | 0.8376 |
| 0.0636 | 4.7905 | 4254 | 0.7267 | 0.3836 | 0.7267 | 0.8525 |
| 0.0636 | 4.7928 | 4256 | 0.7688 | 0.3377 | 0.7688 | 0.8768 |
| 0.0636 | 4.7950 | 4258 | 0.7928 | 0.2222 | 0.7928 | 0.8904 |
| 0.0636 | 4.7973 | 4260 | 0.7918 | 0.2895 | 0.7918 | 0.8898 |
| 0.0636 | 4.7995 | 4262 | 0.7608 | 0.3377 | 0.7608 | 0.8723 |
| 0.0636 | 4.8018 | 4264 | 0.7279 | 0.4167 | 0.7279 | 0.8532 |
| 0.0636 | 4.8041 | 4266 | 0.7011 | 0.3478 | 0.7011 | 0.8373 |
| 0.0636 | 4.8063 | 4268 | 0.6888 | 0.3478 | 0.6888 | 0.8299 |
| 0.0636 | 4.8086 | 4270 | 0.6828 | 0.3478 | 0.6828 | 0.8263 |
| 0.0636 | 4.8108 | 4272 | 0.6830 | 0.3478 | 0.6830 | 0.8264 |
| 0.0636 | 4.8131 | 4274 | 0.6866 | 0.3478 | 0.6866 | 0.8286 |
| 0.0636 | 4.8153 | 4276 | 0.6991 | 0.4324 | 0.6991 | 0.8361 |
| 0.0636 | 4.8176 | 4278 | 0.7140 | 0.4324 | 0.7140 | 0.8450 |
| 0.0636 | 4.8198 | 4280 | 0.7245 | 0.4324 | 0.7245 | 0.8511 |
| 0.0636 | 4.8221 | 4282 | 0.7345 | 0.3836 | 0.7345 | 0.8570 |
| 0.0636 | 4.8243 | 4284 | 0.7275 | 0.4324 | 0.7275 | 0.8529 |
| 0.0636 | 4.8266 | 4286 | 0.7171 | 0.4324 | 0.7171 | 0.8468 |
| 0.0636 | 4.8288 | 4288 | 0.7138 | 0.4324 | 0.7138 | 0.8449 |
| 0.0636 | 4.8311 | 4290 | 0.7102 | 0.3200 | 0.7102 | 0.8428 |
| 0.0636 | 4.8333 | 4292 | 0.7213 | 0.2286 | 0.7213 | 0.8493 |
| 0.0636 | 4.8356 | 4294 | 0.7297 | 0.3200 | 0.7297 | 0.8542 |
| 0.0636 | 4.8378 | 4296 | 0.7338 | 0.4324 | 0.7338 | 0.8566 |
| 0.0636 | 4.8401 | 4298 | 0.7408 | 0.4324 | 0.7408 | 0.8607 |
| 0.0636 | 4.8423 | 4300 | 0.7457 | 0.4324 | 0.7457 | 0.8636 |
| 0.0636 | 4.8446 | 4302 | 0.7479 | 0.4324 | 0.7479 | 0.8648 |
| 0.0636 | 4.8468 | 4304 | 0.7466 | 0.4658 | 0.7466 | 0.8640 |
| 0.0636 | 4.8491 | 4306 | 0.7400 | 0.4324 | 0.7400 | 0.8602 |
| 0.0636 | 4.8514 | 4308 | 0.7255 | 0.4324 | 0.7255 | 0.8518 |
| 0.0636 | 4.8536 | 4310 | 0.7131 | 0.4167 | 0.7131 | 0.8445 |
| 0.0636 | 4.8559 | 4312 | 0.7081 | 0.4167 | 0.7081 | 0.8415 |
| 0.0636 | 4.8581 | 4314 | 0.6994 | 0.3836 | 0.6994 | 0.8363 |
| 0.0636 | 4.8604 | 4316 | 0.6928 | 0.4324 | 0.6928 | 0.8323 |
| 0.0636 | 4.8626 | 4318 | 0.6974 | 0.4324 | 0.6974 | 0.8351 |
| 0.0636 | 4.8649 | 4320 | 0.7031 | 0.4324 | 0.7031 | 0.8385 |
| 0.0636 | 4.8671 | 4322 | 0.7143 | 0.4324 | 0.7143 | 0.8452 |
| 0.0636 | 4.8694 | 4324 | 0.7303 | 0.3077 | 0.7303 | 0.8546 |
| 0.0636 | 4.8716 | 4326 | 0.7426 | 0.3377 | 0.7426 | 0.8617 |
| 0.0636 | 4.8739 | 4328 | 0.7359 | 0.3077 | 0.7359 | 0.8578 |
| 0.0636 | 4.8761 | 4330 | 0.7191 | 0.3836 | 0.7191 | 0.8480 |
| 0.0636 | 4.8784 | 4332 | 0.7083 | 0.4324 | 0.7083 | 0.8416 |
| 0.0636 | 4.8806 | 4334 | 0.7006 | 0.4324 | 0.7006 | 0.8370 |
| 0.0636 | 4.8829 | 4336 | 0.7000 | 0.4324 | 0.7000 | 0.8367 |
| 0.0636 | 4.8851 | 4338 | 0.7015 | 0.4324 | 0.7015 | 0.8376 |
| 0.0636 | 4.8874 | 4340 | 0.7047 | 0.4324 | 0.7047 | 0.8395 |
| 0.0636 | 4.8896 | 4342 | 0.7077 | 0.4324 | 0.7077 | 0.8413 |
| 0.0636 | 4.8919 | 4344 | 0.7104 | 0.4324 | 0.7104 | 0.8428 |
| 0.0636 | 4.8941 | 4346 | 0.7245 | 0.3377 | 0.7245 | 0.8512 |
| 0.0636 | 4.8964 | 4348 | 0.7358 | 0.2895 | 0.7358 | 0.8578 |
| 0.0636 | 4.8986 | 4350 | 0.7304 | 0.2895 | 0.7304 | 0.8546 |
| 0.0636 | 4.9009 | 4352 | 0.7339 | 0.2895 | 0.7339 | 0.8567 |
| 0.0636 | 4.9032 | 4354 | 0.7244 | 0.3377 | 0.7244 | 0.8511 |
| 0.0636 | 4.9054 | 4356 | 0.7098 | 0.3544 | 0.7099 | 0.8425 |
| 0.0636 | 4.9077 | 4358 | 0.7056 | 0.4324 | 0.7056 | 0.8400 |
| 0.0636 | 4.9099 | 4360 | 0.7033 | 0.4324 | 0.7033 | 0.8386 |
| 0.0636 | 4.9122 | 4362 | 0.7092 | 0.4324 | 0.7092 | 0.8421 |
| 0.0636 | 4.9144 | 4364 | 0.7206 | 0.3544 | 0.7206 | 0.8489 |
| 0.0636 | 4.9167 | 4366 | 0.7350 | 0.3377 | 0.7350 | 0.8573 |
| 0.0636 | 4.9189 | 4368 | 0.7376 | 0.2895 | 0.7376 | 0.8588 |
| 0.0636 | 4.9212 | 4370 | 0.7363 | 0.2895 | 0.7363 | 0.8581 |
| 0.0636 | 4.9234 | 4372 | 0.7143 | 0.2895 | 0.7143 | 0.8452 |
| 0.0636 | 4.9257 | 4374 | 0.6925 | 0.3377 | 0.6925 | 0.8322 |
| 0.0636 | 4.9279 | 4376 | 0.6933 | 0.3377 | 0.6933 | 0.8326 |
| 0.0636 | 4.9302 | 4378 | 0.6940 | 0.3377 | 0.6940 | 0.8330 |
| 0.0636 | 4.9324 | 4380 | 0.7034 | 0.3377 | 0.7034 | 0.8387 |
| 0.0636 | 4.9347 | 4382 | 0.7215 | 0.2895 | 0.7215 | 0.8494 |
| 0.0636 | 4.9369 | 4384 | 0.7253 | 0.2895 | 0.7253 | 0.8517 |
| 0.0636 | 4.9392 | 4386 | 0.7186 | 0.3377 | 0.7186 | 0.8477 |
| 0.0636 | 4.9414 | 4388 | 0.7145 | 0.3077 | 0.7145 | 0.8453 |
| 0.0636 | 4.9437 | 4390 | 0.7110 | 0.3836 | 0.7110 | 0.8432 |
| 0.0636 | 4.9459 | 4392 | 0.7170 | 0.4324 | 0.7170 | 0.8468 |
| 0.0636 | 4.9482 | 4394 | 0.7218 | 0.4324 | 0.7218 | 0.8496 |
| 0.0636 | 4.9505 | 4396 | 0.7324 | 0.4324 | 0.7324 | 0.8558 |
| 0.0636 | 4.9527 | 4398 | 0.7485 | 0.3077 | 0.7485 | 0.8651 |
| 0.0636 | 4.9550 | 4400 | 0.7734 | 0.2895 | 0.7734 | 0.8795 |
| 0.0636 | 4.9572 | 4402 | 0.7718 | 0.2597 | 0.7718 | 0.8785 |
| 0.0636 | 4.9595 | 4404 | 0.7657 | 0.3077 | 0.7657 | 0.8751 |
| 0.0636 | 4.9617 | 4406 | 0.7461 | 0.3077 | 0.7461 | 0.8638 |
| 0.0636 | 4.9640 | 4408 | 0.7299 | 0.3836 | 0.7299 | 0.8543 |
| 0.0636 | 4.9662 | 4410 | 0.7039 | 0.4324 | 0.7039 | 0.8390 |
| 0.0636 | 4.9685 | 4412 | 0.6847 | 0.3478 | 0.6847 | 0.8275 |
| 0.0636 | 4.9707 | 4414 | 0.6745 | 0.3478 | 0.6745 | 0.8213 |
| 0.0636 | 4.9730 | 4416 | 0.6748 | 0.3836 | 0.6748 | 0.8215 |
| 0.0636 | 4.9752 | 4418 | 0.6882 | 0.3333 | 0.6882 | 0.8296 |
| 0.0636 | 4.9775 | 4420 | 0.7107 | 0.3333 | 0.7107 | 0.8430 |
| 0.0636 | 4.9797 | 4422 | 0.7229 | 0.2597 | 0.7229 | 0.8502 |
| 0.0636 | 4.9820 | 4424 | 0.7212 | 0.2597 | 0.7212 | 0.8492 |
| 0.0636 | 4.9842 | 4426 | 0.7125 | 0.4324 | 0.7125 | 0.8441 |
| 0.0636 | 4.9865 | 4428 | 0.7115 | 0.3478 | 0.7115 | 0.8435 |
| 0.0636 | 4.9887 | 4430 | 0.7092 | 0.3438 | 0.7092 | 0.8421 |
| 0.0636 | 4.9910 | 4432 | 0.7045 | 0.3438 | 0.7045 | 0.8394 |
| 0.0636 | 4.9932 | 4434 | 0.6900 | 0.3478 | 0.6900 | 0.8307 |
| 0.0636 | 4.9955 | 4436 | 0.6761 | 0.3478 | 0.6761 | 0.8223 |
| 0.0636 | 4.9977 | 4438 | 0.6651 | 0.3478 | 0.6651 | 0.8155 |
| 0.0636 | 5.0 | 4440 | 0.6694 | 0.4324 | 0.6694 | 0.8182 |
| 0.0636 | 5.0023 | 4442 | 0.6735 | 0.3662 | 0.6735 | 0.8207 |
| 0.0636 | 5.0045 | 4444 | 0.6675 | 0.3662 | 0.6675 | 0.8170 |
| 0.0636 | 5.0068 | 4446 | 0.6544 | 0.3662 | 0.6544 | 0.8089 |
| 0.0636 | 5.0090 | 4448 | 0.6421 | 0.3662 | 0.6421 | 0.8013 |
| 0.0636 | 5.0113 | 4450 | 0.6406 | 0.3333 | 0.6406 | 0.8004 |
| 0.0636 | 5.0135 | 4452 | 0.6399 | 0.3438 | 0.6399 | 0.7999 |
| 0.0636 | 5.0158 | 4454 | 0.6485 | 0.4324 | 0.6485 | 0.8053 |
| 0.0636 | 5.0180 | 4456 | 0.6542 | 0.4324 | 0.6542 | 0.8088 |
| 0.0636 | 5.0203 | 4458 | 0.6686 | 0.4324 | 0.6686 | 0.8177 |
| 0.0636 | 5.0225 | 4460 | 0.6846 | 0.4167 | 0.6846 | 0.8274 |
| 0.0636 | 5.0248 | 4462 | 0.6922 | 0.4167 | 0.6922 | 0.8320 |
| 0.0636 | 5.0270 | 4464 | 0.6908 | 0.4324 | 0.6908 | 0.8312 |
| 0.0636 | 5.0293 | 4466 | 0.6798 | 0.3478 | 0.6798 | 0.8245 |
| 0.0636 | 5.0315 | 4468 | 0.6691 | 0.3478 | 0.6691 | 0.8180 |
| 0.0636 | 5.0338 | 4470 | 0.6665 | 0.3478 | 0.6665 | 0.8164 |
| 0.0636 | 5.0360 | 4472 | 0.6645 | 0.3478 | 0.6645 | 0.8151 |
| 0.0636 | 5.0383 | 4474 | 0.6682 | 0.3478 | 0.6682 | 0.8175 |
| 0.0636 | 5.0405 | 4476 | 0.6851 | 0.4658 | 0.6851 | 0.8277 |
| 0.0636 | 5.0428 | 4478 | 0.7074 | 0.4167 | 0.7074 | 0.8411 |
| 0.0636 | 5.0450 | 4480 | 0.7122 | 0.4658 | 0.7122 | 0.8439 |
| 0.0636 | 5.0473 | 4482 | 0.7133 | 0.4658 | 0.7133 | 0.8446 |
| 0.0636 | 5.0495 | 4484 | 0.7070 | 0.4324 | 0.7070 | 0.8409 |
| 0.0636 | 5.0518 | 4486 | 0.7033 | 0.4324 | 0.7033 | 0.8387 |
| 0.0636 | 5.0541 | 4488 | 0.6952 | 0.4324 | 0.6952 | 0.8338 |
| 0.0636 | 5.0563 | 4490 | 0.6839 | 0.4324 | 0.6839 | 0.8270 |
| 0.0636 | 5.0586 | 4492 | 0.6820 | 0.4324 | 0.6820 | 0.8258 |
| 0.0636 | 5.0608 | 4494 | 0.6884 | 0.4658 | 0.6884 | 0.8297 |
| 0.0636 | 5.0631 | 4496 | 0.6848 | 0.4167 | 0.6848 | 0.8275 |
| 0.0636 | 5.0653 | 4498 | 0.6831 | 0.3662 | 0.6831 | 0.8265 |
| 0.0591 | 5.0676 | 4500 | 0.6736 | 0.3662 | 0.6736 | 0.8207 |
| 0.0591 | 5.0698 | 4502 | 0.6683 | 0.3662 | 0.6683 | 0.8175 |
| 0.0591 | 5.0721 | 4504 | 0.6767 | 0.3662 | 0.6767 | 0.8226 |
| 0.0591 | 5.0743 | 4506 | 0.6799 | 0.3662 | 0.6799 | 0.8245 |
| 0.0591 | 5.0766 | 4508 | 0.6884 | 0.4167 | 0.6884 | 0.8297 |
| 0.0591 | 5.0788 | 4510 | 0.7086 | 0.4658 | 0.7086 | 0.8418 |
| 0.0591 | 5.0811 | 4512 | 0.7278 | 0.4324 | 0.7278 | 0.8531 |
| 0.0591 | 5.0833 | 4514 | 0.7399 | 0.4324 | 0.7399 | 0.8602 |
| 0.0591 | 5.0856 | 4516 | 0.7501 | 0.4324 | 0.7501 | 0.8661 |
| 0.0591 | 5.0878 | 4518 | 0.7491 | 0.4324 | 0.7491 | 0.8655 |
| 0.0591 | 5.0901 | 4520 | 0.7439 | 0.4658 | 0.7439 | 0.8625 |
| 0.0591 | 5.0923 | 4522 | 0.7406 | 0.4167 | 0.7406 | 0.8606 |
| 0.0591 | 5.0946 | 4524 | 0.7498 | 0.4167 | 0.7498 | 0.8659 |
| 0.0591 | 5.0968 | 4526 | 0.7392 | 0.4167 | 0.7392 | 0.8598 |
| 0.0591 | 5.0991 | 4528 | 0.7335 | 0.4167 | 0.7335 | 0.8564 |
| 0.0591 | 5.1014 | 4530 | 0.7233 | 0.4167 | 0.7233 | 0.8505 |
| 0.0591 | 5.1036 | 4532 | 0.7073 | 0.4658 | 0.7073 | 0.8410 |
| 0.0591 | 5.1059 | 4534 | 0.6956 | 0.4324 | 0.6956 | 0.8340 |
| 0.0591 | 5.1081 | 4536 | 0.6912 | 0.3478 | 0.6912 | 0.8314 |
| 0.0591 | 5.1104 | 4538 | 0.6913 | 0.3478 | 0.6913 | 0.8314 |
| 0.0591 | 5.1126 | 4540 | 0.6930 | 0.3478 | 0.6930 | 0.8325 |
| 0.0591 | 5.1149 | 4542 | 0.7065 | 0.4324 | 0.7065 | 0.8406 |
| 0.0591 | 5.1171 | 4544 | 0.7355 | 0.4658 | 0.7355 | 0.8576 |
| 0.0591 | 5.1194 | 4546 | 0.7547 | 0.3377 | 0.7547 | 0.8687 |
| 0.0591 | 5.1216 | 4548 | 0.7511 | 0.2895 | 0.7511 | 0.8667 |
| 0.0591 | 5.1239 | 4550 | 0.7276 | 0.4167 | 0.7276 | 0.8530 |
| 0.0591 | 5.1261 | 4552 | 0.7007 | 0.4658 | 0.7007 | 0.8371 |
| 0.0591 | 5.1284 | 4554 | 0.6910 | 0.4658 | 0.6910 | 0.8313 |
| 0.0591 | 5.1306 | 4556 | 0.6982 | 0.4658 | 0.6982 | 0.8356 |
| 0.0591 | 5.1329 | 4558 | 0.7109 | 0.4658 | 0.7109 | 0.8431 |
| 0.0591 | 5.1351 | 4560 | 0.7158 | 0.4658 | 0.7158 | 0.8460 |
| 0.0591 | 5.1374 | 4562 | 0.7316 | 0.4658 | 0.7316 | 0.8553 |
| 0.0591 | 5.1396 | 4564 | 0.7267 | 0.4658 | 0.7267 | 0.8525 |
| 0.0591 | 5.1419 | 4566 | 0.7113 | 0.4658 | 0.7113 | 0.8434 |
| 0.0591 | 5.1441 | 4568 | 0.6979 | 0.4324 | 0.6979 | 0.8354 |
| 0.0591 | 5.1464 | 4570 | 0.6964 | 0.1972 | 0.6964 | 0.8345 |
| 0.0591 | 5.1486 | 4572 | 0.7110 | 0.1972 | 0.7110 | 0.8432 |
| 0.0591 | 5.1509 | 4574 | 0.7169 | 0.1972 | 0.7169 | 0.8467 |
| 0.0591 | 5.1532 | 4576 | 0.7252 | 0.4324 | 0.7252 | 0.8516 |
| 0.0591 | 5.1554 | 4578 | 0.7354 | 0.4324 | 0.7354 | 0.8575 |
| 0.0591 | 5.1577 | 4580 | 0.7431 | 0.4324 | 0.7431 | 0.8620 |
| 0.0591 | 5.1599 | 4582 | 0.7358 | 0.4324 | 0.7358 | 0.8578 |
| 0.0591 | 5.1622 | 4584 | 0.7229 | 0.4324 | 0.7229 | 0.8502 |
| 0.0591 | 5.1644 | 4586 | 0.7083 | 0.4324 | 0.7083 | 0.8416 |
| 0.0591 | 5.1667 | 4588 | 0.6963 | 0.4324 | 0.6963 | 0.8344 |
| 0.0591 | 5.1689 | 4590 | 0.6829 | 0.4658 | 0.6829 | 0.8264 |
| 0.0591 | 5.1712 | 4592 | 0.6740 | 0.4658 | 0.6740 | 0.8210 |
| 0.0591 | 5.1734 | 4594 | 0.6811 | 0.4658 | 0.6811 | 0.8253 |
| 0.0591 | 5.1757 | 4596 | 0.6868 | 0.4167 | 0.6868 | 0.8287 |
| 0.0591 | 5.1779 | 4598 | 0.6907 | 0.3662 | 0.6907 | 0.8311 |
| 0.0591 | 5.1802 | 4600 | 0.6863 | 0.3662 | 0.6863 | 0.8284 |
| 0.0591 | 5.1824 | 4602 | 0.6897 | 0.3662 | 0.6897 | 0.8305 |
| 0.0591 | 5.1847 | 4604 | 0.7077 | 0.2895 | 0.7077 | 0.8413 |
| 0.0591 | 5.1869 | 4606 | 0.7228 | 0.2895 | 0.7228 | 0.8502 |
| 0.0591 | 5.1892 | 4608 | 0.7205 | 0.2895 | 0.7205 | 0.8488 |
| 0.0591 | 5.1914 | 4610 | 0.7072 | 0.3377 | 0.7072 | 0.8409 |
| 0.0591 | 5.1937 | 4612 | 0.6902 | 0.4167 | 0.6902 | 0.8308 |
| 0.0591 | 5.1959 | 4614 | 0.6842 | 0.4167 | 0.6842 | 0.8272 |
| 0.0591 | 5.1982 | 4616 | 0.6854 | 0.4167 | 0.6854 | 0.8279 |
| 0.0591 | 5.2005 | 4618 | 0.6884 | 0.4167 | 0.6884 | 0.8297 |
| 0.0591 | 5.2027 | 4620 | 0.7001 | 0.3377 | 0.7001 | 0.8367 |
| 0.0591 | 5.2050 | 4622 | 0.7015 | 0.3377 | 0.7015 | 0.8375 |
| 0.0591 | 5.2072 | 4624 | 0.6929 | 0.3824 | 0.6929 | 0.8324 |
| 0.0591 | 5.2095 | 4626 | 0.6841 | 0.3824 | 0.6841 | 0.8271 |
| 0.0591 | 5.2117 | 4628 | 0.6755 | 0.3824 | 0.6755 | 0.8219 |
| 0.0591 | 5.2140 | 4630 | 0.6750 | 0.3824 | 0.6750 | 0.8216 |
| 0.0591 | 5.2162 | 4632 | 0.6839 | 0.3284 | 0.6839 | 0.8270 |
| 0.0591 | 5.2185 | 4634 | 0.6988 | 0.3377 | 0.6988 | 0.8359 |
| 0.0591 | 5.2207 | 4636 | 0.7143 | 0.3377 | 0.7143 | 0.8451 |
| 0.0591 | 5.2230 | 4638 | 0.7131 | 0.3377 | 0.7131 | 0.8445 |
| 0.0591 | 5.2252 | 4640 | 0.6989 | 0.3284 | 0.6989 | 0.8360 |
| 0.0591 | 5.2275 | 4642 | 0.6855 | 0.3478 | 0.6855 | 0.8279 |
| 0.0591 | 5.2297 | 4644 | 0.6866 | 0.3438 | 0.6866 | 0.8286 |
| 0.0591 | 5.2320 | 4646 | 0.6890 | 0.3438 | 0.6890 | 0.8301 |
| 0.0591 | 5.2342 | 4648 | 0.6996 | 0.3438 | 0.6996 | 0.8364 |
| 0.0591 | 5.2365 | 4650 | 0.7134 | 0.3478 | 0.7134 | 0.8446 |
| 0.0591 | 5.2387 | 4652 | 0.7317 | 0.4324 | 0.7317 | 0.8554 |
| 0.0591 | 5.2410 | 4654 | 0.7418 | 0.4167 | 0.7418 | 0.8613 |
| 0.0591 | 5.2432 | 4656 | 0.7530 | 0.3377 | 0.7530 | 0.8678 |
| 0.0591 | 5.2455 | 4658 | 0.7473 | 0.3377 | 0.7473 | 0.8645 |
| 0.0591 | 5.2477 | 4660 | 0.7397 | 0.3377 | 0.7397 | 0.8600 |
| 0.0591 | 5.25 | 4662 | 0.7260 | 0.4167 | 0.7260 | 0.8521 |
| 0.0591 | 5.2523 | 4664 | 0.7082 | 0.4324 | 0.7082 | 0.8416 |
| 0.0591 | 5.2545 | 4666 | 0.7032 | 0.3478 | 0.7032 | 0.8386 |
| 0.0591 | 5.2568 | 4668 | 0.7041 | 0.4324 | 0.7041 | 0.8391 |
| 0.0591 | 5.2590 | 4670 | 0.7138 | 0.4658 | 0.7138 | 0.8448 |
| 0.0591 | 5.2613 | 4672 | 0.7316 | 0.3377 | 0.7316 | 0.8554 |
| 0.0591 | 5.2635 | 4674 | 0.7429 | 0.3377 | 0.7429 | 0.8619 |
| 0.0591 | 5.2658 | 4676 | 0.7441 | 0.3377 | 0.7441 | 0.8626 |
| 0.0591 | 5.2680 | 4678 | 0.7470 | 0.2895 | 0.7470 | 0.8643 |
| 0.0591 | 5.2703 | 4680 | 0.7329 | 0.2895 | 0.7329 | 0.8561 |
| 0.0591 | 5.2725 | 4682 | 0.7016 | 0.4167 | 0.7016 | 0.8376 |
| 0.0591 | 5.2748 | 4684 | 0.6814 | 0.4167 | 0.6814 | 0.8255 |
| 0.0591 | 5.2770 | 4686 | 0.6757 | 0.3824 | 0.6757 | 0.8220 |
| 0.0591 | 5.2793 | 4688 | 0.6771 | 0.3824 | 0.6771 | 0.8228 |
| 0.0591 | 5.2815 | 4690 | 0.6891 | 0.4167 | 0.6891 | 0.8301 |
| 0.0591 | 5.2838 | 4692 | 0.6990 | 0.3836 | 0.6990 | 0.8361 |
| 0.0591 | 5.2860 | 4694 | 0.7101 | 0.4324 | 0.7101 | 0.8427 |
| 0.0591 | 5.2883 | 4696 | 0.7093 | 0.4324 | 0.7093 | 0.8422 |
| 0.0591 | 5.2905 | 4698 | 0.7085 | 0.4324 | 0.7085 | 0.8417 |
| 0.0591 | 5.2928 | 4700 | 0.7021 | 0.3478 | 0.7021 | 0.8379 |
| 0.0591 | 5.2950 | 4702 | 0.7004 | 0.3478 | 0.7004 | 0.8369 |
| 0.0591 | 5.2973 | 4704 | 0.7024 | 0.3478 | 0.7024 | 0.8381 |
| 0.0591 | 5.2995 | 4706 | 0.7182 | 0.4167 | 0.7182 | 0.8475 |
| 0.0591 | 5.3018 | 4708 | 0.7314 | 0.4167 | 0.7314 | 0.8552 |
| 0.0591 | 5.3041 | 4710 | 0.7394 | 0.24 | 0.7394 | 0.8599 |
| 0.0591 | 5.3063 | 4712 | 0.7564 | 0.1892 | 0.7564 | 0.8697 |
| 0.0591 | 5.3086 | 4714 | 0.7668 | 0.1892 | 0.7668 | 0.8756 |
| 0.0591 | 5.3108 | 4716 | 0.7583 | 0.1892 | 0.7583 | 0.8708 |
| 0.0591 | 5.3131 | 4718 | 0.7304 | 0.4167 | 0.7304 | 0.8546 |
| 0.0591 | 5.3153 | 4720 | 0.7199 | 0.4167 | 0.7199 | 0.8485 |
| 0.0591 | 5.3176 | 4722 | 0.7096 | 0.2941 | 0.7096 | 0.8424 |
| 0.0591 | 5.3198 | 4724 | 0.6981 | 0.3478 | 0.6981 | 0.8355 |
| 0.0591 | 5.3221 | 4726 | 0.6980 | 0.3478 | 0.6980 | 0.8355 |
| 0.0591 | 5.3243 | 4728 | 0.7101 | 0.3478 | 0.7101 | 0.8427 |
| 0.0591 | 5.3266 | 4730 | 0.7391 | 0.3377 | 0.7391 | 0.8597 |
| 0.0591 | 5.3288 | 4732 | 0.7759 | 0.1892 | 0.7759 | 0.8809 |
| 0.0591 | 5.3311 | 4734 | 0.7933 | 0.1892 | 0.7933 | 0.8907 |
| 0.0591 | 5.3333 | 4736 | 0.7794 | 0.1892 | 0.7794 | 0.8828 |
| 0.0591 | 5.3356 | 4738 | 0.7416 | 0.1892 | 0.7416 | 0.8612 |
| 0.0591 | 5.3378 | 4740 | 0.7021 | 0.3284 | 0.7021 | 0.8379 |
| 0.0591 | 5.3401 | 4742 | 0.6844 | 0.2857 | 0.6844 | 0.8273 |
| 0.0591 | 5.3423 | 4744 | 0.6829 | 0.2857 | 0.6829 | 0.8264 |
| 0.0591 | 5.3446 | 4746 | 0.6943 | 0.2941 | 0.6943 | 0.8332 |
| 0.0591 | 5.3468 | 4748 | 0.7211 | 0.25 | 0.7211 | 0.8492 |
| 0.0591 | 5.3491 | 4750 | 0.7504 | 0.3377 | 0.7504 | 0.8662 |
| 0.0591 | 5.3514 | 4752 | 0.7646 | 0.2895 | 0.7646 | 0.8744 |
| 0.0591 | 5.3536 | 4754 | 0.7740 | 0.3377 | 0.7740 | 0.8798 |
| 0.0591 | 5.3559 | 4756 | 0.7820 | 0.3377 | 0.7820 | 0.8843 |
| 0.0591 | 5.3581 | 4758 | 0.7803 | 0.3544 | 0.7803 | 0.8834 |
| 0.0591 | 5.3604 | 4760 | 0.7793 | 0.3544 | 0.7793 | 0.8828 |
| 0.0591 | 5.3626 | 4762 | 0.7812 | 0.3544 | 0.7812 | 0.8838 |
| 0.0591 | 5.3649 | 4764 | 0.7784 | 0.3544 | 0.7784 | 0.8822 |
| 0.0591 | 5.3671 | 4766 | 0.7718 | 0.3544 | 0.7718 | 0.8785 |
| 0.0591 | 5.3694 | 4768 | 0.7699 | 0.3077 | 0.7699 | 0.8775 |
| 0.0591 | 5.3716 | 4770 | 0.7644 | 0.3377 | 0.7644 | 0.8743 |
| 0.0591 | 5.3739 | 4772 | 0.7492 | 0.3377 | 0.7492 | 0.8656 |
| 0.0591 | 5.3761 | 4774 | 0.7257 | 0.3377 | 0.7257 | 0.8519 |
| 0.0591 | 5.3784 | 4776 | 0.7196 | 0.3377 | 0.7196 | 0.8483 |
| 0.0591 | 5.3806 | 4778 | 0.7075 | 0.3284 | 0.7075 | 0.8411 |
| 0.0591 | 5.3829 | 4780 | 0.7080 | 0.3284 | 0.7080 | 0.8414 |
| 0.0591 | 5.3851 | 4782 | 0.7074 | 0.3284 | 0.7074 | 0.8411 |
| 0.0591 | 5.3874 | 4784 | 0.7266 | 0.3143 | 0.7266 | 0.8524 |
| 0.0591 | 5.3896 | 4786 | 0.7635 | 0.1892 | 0.7635 | 0.8738 |
| 0.0591 | 5.3919 | 4788 | 0.7836 | 0.1667 | 0.7836 | 0.8852 |
| 0.0591 | 5.3941 | 4790 | 0.7829 | 0.1667 | 0.7829 | 0.8848 |
| 0.0591 | 5.3964 | 4792 | 0.7556 | 0.1667 | 0.7556 | 0.8693 |
| 0.0591 | 5.3986 | 4794 | 0.7226 | 0.2609 | 0.7226 | 0.8501 |
| 0.0591 | 5.4009 | 4796 | 0.6999 | 0.2154 | 0.6999 | 0.8366 |
| 0.0591 | 5.4032 | 4798 | 0.7010 | 0.3143 | 0.7010 | 0.8373 |
| 0.0591 | 5.4054 | 4800 | 0.7043 | 0.3143 | 0.7043 | 0.8392 |
| 0.0591 | 5.4077 | 4802 | 0.7137 | 0.4167 | 0.7137 | 0.8448 |
| 0.0591 | 5.4099 | 4804 | 0.7059 | 0.3836 | 0.7059 | 0.8402 |
| 0.0591 | 5.4122 | 4806 | 0.7007 | 0.3478 | 0.7007 | 0.8371 |
| 0.0591 | 5.4144 | 4808 | 0.7016 | 0.3438 | 0.7016 | 0.8376 |
| 0.0591 | 5.4167 | 4810 | 0.7097 | 0.3438 | 0.7097 | 0.8424 |
| 0.0591 | 5.4189 | 4812 | 0.7198 | 0.3478 | 0.7198 | 0.8484 |
| 0.0591 | 5.4212 | 4814 | 0.7330 | 0.4324 | 0.7330 | 0.8562 |
| 0.0591 | 5.4234 | 4816 | 0.7649 | 0.3544 | 0.7649 | 0.8746 |
| 0.0591 | 5.4257 | 4818 | 0.7899 | 0.3377 | 0.7899 | 0.8888 |
| 0.0591 | 5.4279 | 4820 | 0.7884 | 0.3377 | 0.7884 | 0.8879 |
| 0.0591 | 5.4302 | 4822 | 0.7677 | 0.3544 | 0.7677 | 0.8762 |
| 0.0591 | 5.4324 | 4824 | 0.7503 | 0.3544 | 0.7503 | 0.8662 |
| 0.0591 | 5.4347 | 4826 | 0.7459 | 0.3544 | 0.7459 | 0.8637 |
| 0.0591 | 5.4369 | 4828 | 0.7489 | 0.3077 | 0.7489 | 0.8654 |
| 0.0591 | 5.4392 | 4830 | 0.7444 | 0.3077 | 0.7444 | 0.8628 |
| 0.0591 | 5.4414 | 4832 | 0.7500 | 0.3377 | 0.7500 | 0.8660 |
| 0.0591 | 5.4437 | 4834 | 0.7601 | 0.2895 | 0.7601 | 0.8718 |
| 0.0591 | 5.4459 | 4836 | 0.7465 | 0.2895 | 0.7465 | 0.8640 |
| 0.0591 | 5.4482 | 4838 | 0.7236 | 0.4167 | 0.7236 | 0.8506 |
| 0.0591 | 5.4505 | 4840 | 0.7121 | 0.3836 | 0.7121 | 0.8439 |
| 0.0591 | 5.4527 | 4842 | 0.7158 | 0.3836 | 0.7158 | 0.8461 |
| 0.0591 | 5.4550 | 4844 | 0.7272 | 0.3077 | 0.7272 | 0.8528 |
| 0.0591 | 5.4572 | 4846 | 0.7346 | 0.3077 | 0.7346 | 0.8571 |
| 0.0591 | 5.4595 | 4848 | 0.7357 | 0.3077 | 0.7357 | 0.8577 |
| 0.0591 | 5.4617 | 4850 | 0.7385 | 0.3077 | 0.7385 | 0.8594 |
| 0.0591 | 5.4640 | 4852 | 0.7417 | 0.3077 | 0.7417 | 0.8612 |
| 0.0591 | 5.4662 | 4854 | 0.7360 | 0.3077 | 0.7360 | 0.8579 |
| 0.0591 | 5.4685 | 4856 | 0.7208 | 0.3836 | 0.7208 | 0.8490 |
| 0.0591 | 5.4707 | 4858 | 0.7219 | 0.3836 | 0.7219 | 0.8496 |
| 0.0591 | 5.4730 | 4860 | 0.7285 | 0.3377 | 0.7285 | 0.8535 |
| 0.0591 | 5.4752 | 4862 | 0.7556 | 0.24 | 0.7556 | 0.8693 |
| 0.0591 | 5.4775 | 4864 | 0.7713 | 0.1892 | 0.7713 | 0.8783 |
| 0.0591 | 5.4797 | 4866 | 0.7602 | 0.1892 | 0.7602 | 0.8719 |
| 0.0591 | 5.4820 | 4868 | 0.7371 | 0.24 | 0.7371 | 0.8586 |
| 0.0591 | 5.4842 | 4870 | 0.7202 | 0.3143 | 0.7202 | 0.8487 |
| 0.0591 | 5.4865 | 4872 | 0.7129 | 0.2154 | 0.7129 | 0.8444 |
| 0.0591 | 5.4887 | 4874 | 0.7027 | 0.2941 | 0.7027 | 0.8383 |
| 0.0591 | 5.4910 | 4876 | 0.6935 | 0.3438 | 0.6935 | 0.8327 |
| 0.0591 | 5.4932 | 4878 | 0.6977 | 0.3438 | 0.6977 | 0.8353 |
| 0.0591 | 5.4955 | 4880 | 0.7104 | 0.3438 | 0.7104 | 0.8429 |
| 0.0591 | 5.4977 | 4882 | 0.7226 | 0.3438 | 0.7226 | 0.8500 |
| 0.0591 | 5.5 | 4884 | 0.7397 | 0.3478 | 0.7397 | 0.8601 |
| 0.0591 | 5.5023 | 4886 | 0.7511 | 0.3478 | 0.7511 | 0.8667 |
| 0.0591 | 5.5045 | 4888 | 0.7559 | 0.3478 | 0.7559 | 0.8694 |
| 0.0591 | 5.5068 | 4890 | 0.7578 | 0.3478 | 0.7578 | 0.8705 |
| 0.0591 | 5.5090 | 4892 | 0.7473 | 0.3478 | 0.7473 | 0.8645 |
| 0.0591 | 5.5113 | 4894 | 0.7376 | 0.3478 | 0.7376 | 0.8589 |
| 0.0591 | 5.5135 | 4896 | 0.7328 | 0.3478 | 0.7328 | 0.8560 |
| 0.0591 | 5.5158 | 4898 | 0.7333 | 0.3478 | 0.7333 | 0.8564 |
| 0.0591 | 5.5180 | 4900 | 0.7372 | 0.3478 | 0.7372 | 0.8586 |
| 0.0591 | 5.5203 | 4902 | 0.7370 | 0.3478 | 0.7370 | 0.8585 |
| 0.0591 | 5.5225 | 4904 | 0.7348 | 0.3478 | 0.7348 | 0.8572 |
| 0.0591 | 5.5248 | 4906 | 0.7312 | 0.3478 | 0.7312 | 0.8551 |
| 0.0591 | 5.5270 | 4908 | 0.7318 | 0.3478 | 0.7318 | 0.8554 |
| 0.0591 | 5.5293 | 4910 | 0.7280 | 0.3478 | 0.7280 | 0.8532 |
| 0.0591 | 5.5315 | 4912 | 0.7265 | 0.3478 | 0.7265 | 0.8523 |
| 0.0591 | 5.5338 | 4914 | 0.7285 | 0.3478 | 0.7285 | 0.8535 |
| 0.0591 | 5.5360 | 4916 | 0.7386 | 0.3478 | 0.7386 | 0.8594 |
| 0.0591 | 5.5383 | 4918 | 0.7450 | 0.3478 | 0.7450 | 0.8631 |
| 0.0591 | 5.5405 | 4920 | 0.7553 | 0.3478 | 0.7553 | 0.8691 |
| 0.0591 | 5.5428 | 4922 | 0.7618 | 0.3478 | 0.7618 | 0.8728 |
| 0.0591 | 5.5450 | 4924 | 0.7626 | 0.3478 | 0.7626 | 0.8733 |
| 0.0591 | 5.5473 | 4926 | 0.7635 | 0.3478 | 0.7635 | 0.8738 |
| 0.0591 | 5.5495 | 4928 | 0.7560 | 0.3478 | 0.7560 | 0.8695 |
| 0.0591 | 5.5518 | 4930 | 0.7504 | 0.3478 | 0.7504 | 0.8663 |
| 0.0591 | 5.5541 | 4932 | 0.7427 | 0.3478 | 0.7427 | 0.8618 |
| 0.0591 | 5.5563 | 4934 | 0.7456 | 0.3478 | 0.7456 | 0.8635 |
| 0.0591 | 5.5586 | 4936 | 0.7477 | 0.4324 | 0.7477 | 0.8647 |
| 0.0591 | 5.5608 | 4938 | 0.7564 | 0.4658 | 0.7564 | 0.8697 |
| 0.0591 | 5.5631 | 4940 | 0.7570 | 0.4324 | 0.7570 | 0.8701 |
| 0.0591 | 5.5653 | 4942 | 0.7562 | 0.4324 | 0.7562 | 0.8696 |
| 0.0591 | 5.5676 | 4944 | 0.7607 | 0.4324 | 0.7607 | 0.8722 |
| 0.0591 | 5.5698 | 4946 | 0.7630 | 0.3478 | 0.7630 | 0.8735 |
| 0.0591 | 5.5721 | 4948 | 0.7626 | 0.3478 | 0.7626 | 0.8733 |
| 0.0591 | 5.5743 | 4950 | 0.7641 | 0.3478 | 0.7641 | 0.8741 |
| 0.0591 | 5.5766 | 4952 | 0.7647 | 0.3478 | 0.7647 | 0.8745 |
| 0.0591 | 5.5788 | 4954 | 0.7686 | 0.3478 | 0.7686 | 0.8767 |
| 0.0591 | 5.5811 | 4956 | 0.7767 | 0.2703 | 0.7767 | 0.8813 |
| 0.0591 | 5.5833 | 4958 | 0.7882 | 0.3544 | 0.7882 | 0.8878 |
| 0.0591 | 5.5856 | 4960 | 0.7953 | 0.3846 | 0.7953 | 0.8918 |
| 0.0591 | 5.5878 | 4962 | 0.7932 | 0.3846 | 0.7932 | 0.8906 |
| 0.0591 | 5.5901 | 4964 | 0.7878 | 0.3846 | 0.7878 | 0.8876 |
| 0.0591 | 5.5923 | 4966 | 0.7860 | 0.3544 | 0.7860 | 0.8866 |
| 0.0591 | 5.5946 | 4968 | 0.7837 | 0.3544 | 0.7837 | 0.8853 |
| 0.0591 | 5.5968 | 4970 | 0.7754 | 0.2703 | 0.7754 | 0.8806 |
| 0.0591 | 5.5991 | 4972 | 0.7716 | 0.3478 | 0.7716 | 0.8784 |
| 0.0591 | 5.6014 | 4974 | 0.7725 | 0.3478 | 0.7725 | 0.8789 |
| 0.0591 | 5.6036 | 4976 | 0.7796 | 0.3544 | 0.7796 | 0.8829 |
| 0.0591 | 5.6059 | 4978 | 0.7772 | 0.3544 | 0.7772 | 0.8816 |
| 0.0591 | 5.6081 | 4980 | 0.7685 | 0.3478 | 0.7685 | 0.8766 |
| 0.0591 | 5.6104 | 4982 | 0.7562 | 0.3478 | 0.7562 | 0.8696 |
| 0.0591 | 5.6126 | 4984 | 0.7490 | 0.3478 | 0.7490 | 0.8655 |
| 0.0591 | 5.6149 | 4986 | 0.7430 | 0.3438 | 0.7430 | 0.8619 |
| 0.0591 | 5.6171 | 4988 | 0.7313 | 0.3438 | 0.7313 | 0.8551 |
| 0.0591 | 5.6194 | 4990 | 0.7216 | 0.3438 | 0.7216 | 0.8494 |
| 0.0591 | 5.6216 | 4992 | 0.7227 | 0.3478 | 0.7227 | 0.8501 |
| 0.0591 | 5.6239 | 4994 | 0.7247 | 0.3478 | 0.7247 | 0.8513 |
| 0.0591 | 5.6261 | 4996 | 0.7261 | 0.3478 | 0.7261 | 0.8521 |
| 0.0591 | 5.6284 | 4998 | 0.7160 | 0.3478 | 0.7160 | 0.8462 |
| 0.0491 | 5.6306 | 5000 | 0.7121 | 0.3478 | 0.7121 | 0.8439 |
| 0.0491 | 5.6329 | 5002 | 0.7090 | 0.3478 | 0.7090 | 0.8420 |
| 0.0491 | 5.6351 | 5004 | 0.7009 | 0.3438 | 0.7009 | 0.8372 |
| 0.0491 | 5.6374 | 5006 | 0.6987 | 0.3438 | 0.6987 | 0.8359 |
| 0.0491 | 5.6396 | 5008 | 0.7026 | 0.3478 | 0.7026 | 0.8382 |
| 0.0491 | 5.6419 | 5010 | 0.7126 | 0.4324 | 0.7126 | 0.8441 |
| 0.0491 | 5.6441 | 5012 | 0.7292 | 0.4658 | 0.7292 | 0.8539 |
| 0.0491 | 5.6464 | 5014 | 0.7343 | 0.4658 | 0.7343 | 0.8569 |
| 0.0491 | 5.6486 | 5016 | 0.7390 | 0.4658 | 0.7390 | 0.8596 |
| 0.0491 | 5.6509 | 5018 | 0.7255 | 0.4324 | 0.7255 | 0.8518 |
| 0.0491 | 5.6532 | 5020 | 0.7075 | 0.3478 | 0.7075 | 0.8411 |
| 0.0491 | 5.6554 | 5022 | 0.7033 | 0.3438 | 0.7033 | 0.8386 |
| 0.0491 | 5.6577 | 5024 | 0.7062 | 0.3438 | 0.7062 | 0.8404 |
| 0.0491 | 5.6599 | 5026 | 0.7109 | 0.3438 | 0.7109 | 0.8432 |
| 0.0491 | 5.6622 | 5028 | 0.7131 | 0.3478 | 0.7131 | 0.8445 |
| 0.0491 | 5.6644 | 5030 | 0.7216 | 0.4324 | 0.7216 | 0.8494 |
| 0.0491 | 5.6667 | 5032 | 0.7493 | 0.4167 | 0.7493 | 0.8656 |
| 0.0491 | 5.6689 | 5034 | 0.7739 | 0.1892 | 0.7739 | 0.8797 |
| 0.0491 | 5.6712 | 5036 | 0.7769 | 0.1892 | 0.7769 | 0.8814 |
| 0.0491 | 5.6734 | 5038 | 0.7533 | 0.2609 | 0.7533 | 0.8679 |
| 0.0491 | 5.6757 | 5040 | 0.7386 | 0.3143 | 0.7386 | 0.8594 |
| 0.0491 | 5.6779 | 5042 | 0.7187 | 0.3836 | 0.7187 | 0.8477 |
| 0.0491 | 5.6802 | 5044 | 0.7105 | 0.3478 | 0.7105 | 0.8429 |
| 0.0491 | 5.6824 | 5046 | 0.7136 | 0.3478 | 0.7136 | 0.8447 |
| 0.0491 | 5.6847 | 5048 | 0.7236 | 0.3478 | 0.7236 | 0.8507 |
| 0.0491 | 5.6869 | 5050 | 0.7309 | 0.3478 | 0.7309 | 0.8549 |
| 0.0491 | 5.6892 | 5052 | 0.7462 | 0.4324 | 0.7462 | 0.8638 |
| 0.0491 | 5.6914 | 5054 | 0.7601 | 0.4324 | 0.7601 | 0.8718 |
| 0.0491 | 5.6937 | 5056 | 0.7552 | 0.4324 | 0.7552 | 0.8690 |
| 0.0491 | 5.6959 | 5058 | 0.7507 | 0.4324 | 0.7507 | 0.8664 |
| 0.0491 | 5.6982 | 5060 | 0.7454 | 0.2941 | 0.7454 | 0.8634 |
| 0.0491 | 5.7005 | 5062 | 0.7397 | 0.2941 | 0.7397 | 0.8600 |
| 0.0491 | 5.7027 | 5064 | 0.7278 | 0.3478 | 0.7278 | 0.8531 |
| 0.0491 | 5.7050 | 5066 | 0.7143 | 0.3478 | 0.7143 | 0.8452 |
| 0.0491 | 5.7072 | 5068 | 0.6984 | 0.3438 | 0.6984 | 0.8357 |
| 0.0491 | 5.7095 | 5070 | 0.6931 | 0.3438 | 0.6931 | 0.8325 |
| 0.0491 | 5.7117 | 5072 | 0.6962 | 0.3438 | 0.6962 | 0.8344 |
| 0.0491 | 5.7140 | 5074 | 0.7074 | 0.3478 | 0.7074 | 0.8411 |
| 0.0491 | 5.7162 | 5076 | 0.7226 | 0.3478 | 0.7226 | 0.8501 |
| 0.0491 | 5.7185 | 5078 | 0.7320 | 0.3478 | 0.7320 | 0.8556 |
| 0.0491 | 5.7207 | 5080 | 0.7433 | 0.3478 | 0.7433 | 0.8622 |
| 0.0491 | 5.7230 | 5082 | 0.7436 | 0.3478 | 0.7436 | 0.8623 |
| 0.0491 | 5.7252 | 5084 | 0.7497 | 0.3478 | 0.7497 | 0.8659 |
| 0.0491 | 5.7275 | 5086 | 0.7585 | 0.3478 | 0.7585 | 0.8709 |
| 0.0491 | 5.7297 | 5088 | 0.7641 | 0.3478 | 0.7641 | 0.8741 |
| 0.0491 | 5.7320 | 5090 | 0.7671 | 0.3478 | 0.7671 | 0.8758 |
| 0.0491 | 5.7342 | 5092 | 0.7707 | 0.4324 | 0.7707 | 0.8779 |
| 0.0491 | 5.7365 | 5094 | 0.7743 | 0.4324 | 0.7743 | 0.8800 |
| 0.0491 | 5.7387 | 5096 | 0.7797 | 0.3544 | 0.7797 | 0.8830 |
| 0.0491 | 5.7410 | 5098 | 0.7967 | 0.3377 | 0.7967 | 0.8926 |
| 0.0491 | 5.7432 | 5100 | 0.8016 | 0.3377 | 0.8016 | 0.8953 |
| 0.0491 | 5.7455 | 5102 | 0.8041 | 0.3377 | 0.8041 | 0.8967 |
| 0.0491 | 5.7477 | 5104 | 0.7934 | 0.3544 | 0.7934 | 0.8907 |
| 0.0491 | 5.75 | 5106 | 0.7761 | 0.3478 | 0.7761 | 0.8809 |
| 0.0491 | 5.7523 | 5108 | 0.7716 | 0.3478 | 0.7716 | 0.8784 |
| 0.0491 | 5.7545 | 5110 | 0.7652 | 0.3478 | 0.7652 | 0.8748 |
| 0.0491 | 5.7568 | 5112 | 0.7583 | 0.3478 | 0.7583 | 0.8708 |
| 0.0491 | 5.7590 | 5114 | 0.7500 | 0.3478 | 0.7500 | 0.8660 |
| 0.0491 | 5.7613 | 5116 | 0.7413 | 0.3478 | 0.7413 | 0.8610 |
| 0.0491 | 5.7635 | 5118 | 0.7368 | 0.3478 | 0.7368 | 0.8584 |
| 0.0491 | 5.7658 | 5120 | 0.7408 | 0.3478 | 0.7408 | 0.8607 |
| 0.0491 | 5.7680 | 5122 | 0.7553 | 0.4324 | 0.7553 | 0.8691 |
| 0.0491 | 5.7703 | 5124 | 0.7821 | 0.3662 | 0.7821 | 0.8843 |
| 0.0491 | 5.7725 | 5126 | 0.7986 | 0.24 | 0.7986 | 0.8936 |
| 0.0491 | 5.7748 | 5128 | 0.8061 | 0.24 | 0.8061 | 0.8978 |
| 0.0491 | 5.7770 | 5130 | 0.8118 | 0.2105 | 0.8118 | 0.9010 |
| 0.0491 | 5.7793 | 5132 | 0.7988 | 0.4324 | 0.7988 | 0.8937 |
| 0.0491 | 5.7815 | 5134 | 0.7817 | 0.3478 | 0.7817 | 0.8841 |
| 0.0491 | 5.7838 | 5136 | 0.7741 | 0.3478 | 0.7741 | 0.8798 |
| 0.0491 | 5.7860 | 5138 | 0.7712 | 0.3478 | 0.7712 | 0.8782 |
| 0.0491 | 5.7883 | 5140 | 0.7714 | 0.3478 | 0.7714 | 0.8783 |
| 0.0491 | 5.7905 | 5142 | 0.7781 | 0.2817 | 0.7781 | 0.8821 |
| 0.0491 | 5.7928 | 5144 | 0.8009 | 0.24 | 0.8009 | 0.8949 |
| 0.0491 | 5.7950 | 5146 | 0.8087 | 0.1892 | 0.8087 | 0.8993 |
| 0.0491 | 5.7973 | 5148 | 0.7979 | 0.24 | 0.7979 | 0.8933 |
| 0.0491 | 5.7995 | 5150 | 0.7786 | 0.3143 | 0.7786 | 0.8824 |
| 0.0491 | 5.8018 | 5152 | 0.7618 | 0.3836 | 0.7618 | 0.8728 |
| 0.0491 | 5.8041 | 5154 | 0.7503 | 0.3478 | 0.7503 | 0.8662 |
| 0.0491 | 5.8063 | 5156 | 0.7523 | 0.3478 | 0.7523 | 0.8674 |
| 0.0491 | 5.8086 | 5158 | 0.7617 | 0.3478 | 0.7617 | 0.8728 |
| 0.0491 | 5.8108 | 5160 | 0.7787 | 0.3333 | 0.7787 | 0.8824 |
| 0.0491 | 5.8131 | 5162 | 0.7966 | 0.24 | 0.7966 | 0.8925 |
| 0.0491 | 5.8153 | 5164 | 0.8009 | 0.24 | 0.8009 | 0.8949 |
| 0.0491 | 5.8176 | 5166 | 0.7871 | 0.3143 | 0.7871 | 0.8872 |
| 0.0491 | 5.8198 | 5168 | 0.7723 | 0.2941 | 0.7723 | 0.8788 |
| 0.0491 | 5.8221 | 5170 | 0.7588 | 0.3478 | 0.7588 | 0.8711 |
| 0.0491 | 5.8243 | 5172 | 0.7535 | 0.3478 | 0.7535 | 0.8680 |
| 0.0491 | 5.8266 | 5174 | 0.7512 | 0.3478 | 0.7512 | 0.8667 |
| 0.0491 | 5.8288 | 5176 | 0.7533 | 0.3478 | 0.7533 | 0.8679 |
| 0.0491 | 5.8311 | 5178 | 0.7641 | 0.3478 | 0.7641 | 0.8742 |
| 0.0491 | 5.8333 | 5180 | 0.7733 | 0.3478 | 0.7733 | 0.8794 |
| 0.0491 | 5.8356 | 5182 | 0.7810 | 0.4324 | 0.7810 | 0.8837 |
| 0.0491 | 5.8378 | 5184 | 0.7799 | 0.4324 | 0.7799 | 0.8831 |
| 0.0491 | 5.8401 | 5186 | 0.7800 | 0.4167 | 0.7800 | 0.8832 |
| 0.0491 | 5.8423 | 5188 | 0.7682 | 0.4167 | 0.7682 | 0.8765 |
| 0.0491 | 5.8446 | 5190 | 0.7488 | 0.3836 | 0.7488 | 0.8653 |
| 0.0491 | 5.8468 | 5192 | 0.7376 | 0.3478 | 0.7376 | 0.8588 |
| 0.0491 | 5.8491 | 5194 | 0.7290 | 0.3478 | 0.7290 | 0.8538 |
| 0.0491 | 5.8514 | 5196 | 0.7226 | 0.3478 | 0.7226 | 0.8500 |
| 0.0491 | 5.8536 | 5198 | 0.7238 | 0.3478 | 0.7238 | 0.8508 |
| 0.0491 | 5.8559 | 5200 | 0.7372 | 0.4167 | 0.7372 | 0.8586 |
| 0.0491 | 5.8581 | 5202 | 0.7470 | 0.3662 | 0.7470 | 0.8643 |
| 0.0491 | 5.8604 | 5204 | 0.7478 | 0.4167 | 0.7478 | 0.8648 |
| 0.0491 | 5.8626 | 5206 | 0.7483 | 0.4167 | 0.7483 | 0.8650 |
| 0.0491 | 5.8649 | 5208 | 0.7362 | 0.2941 | 0.7362 | 0.8580 |
| 0.0491 | 5.8671 | 5210 | 0.7283 | 0.3478 | 0.7283 | 0.8534 |
| 0.0491 | 5.8694 | 5212 | 0.7279 | 0.2941 | 0.7279 | 0.8532 |
| 0.0491 | 5.8716 | 5214 | 0.7365 | 0.2388 | 0.7365 | 0.8582 |
| 0.0491 | 5.8739 | 5216 | 0.7543 | 0.3662 | 0.7543 | 0.8685 |
| 0.0491 | 5.8761 | 5218 | 0.7764 | 0.3143 | 0.7764 | 0.8811 |
| 0.0491 | 5.8784 | 5220 | 0.7874 | 0.3143 | 0.7874 | 0.8874 |
| 0.0491 | 5.8806 | 5222 | 0.7924 | 0.3143 | 0.7924 | 0.8902 |
| 0.0491 | 5.8829 | 5224 | 0.7823 | 0.3143 | 0.7823 | 0.8845 |
| 0.0491 | 5.8851 | 5226 | 0.7613 | 0.3662 | 0.7613 | 0.8725 |
| 0.0491 | 5.8874 | 5228 | 0.7380 | 0.2727 | 0.7380 | 0.8591 |
| 0.0491 | 5.8896 | 5230 | 0.7261 | 0.2727 | 0.7261 | 0.8521 |
| 0.0491 | 5.8919 | 5232 | 0.7243 | 0.2727 | 0.7243 | 0.8511 |
| 0.0491 | 5.8941 | 5234 | 0.7290 | 0.3662 | 0.7290 | 0.8538 |
| 0.0491 | 5.8964 | 5236 | 0.7264 | 0.3662 | 0.7264 | 0.8523 |
| 0.0491 | 5.8986 | 5238 | 0.7382 | 0.3143 | 0.7382 | 0.8592 |
| 0.0491 | 5.9009 | 5240 | 0.7578 | 0.3143 | 0.7578 | 0.8705 |
| 0.0491 | 5.9032 | 5242 | 0.7778 | 0.2941 | 0.7778 | 0.8819 |
| 0.0491 | 5.9054 | 5244 | 0.7868 | 0.2192 | 0.7868 | 0.8870 |
| 0.0491 | 5.9077 | 5246 | 0.7755 | 0.3143 | 0.7755 | 0.8806 |
| 0.0491 | 5.9099 | 5248 | 0.7572 | 0.3143 | 0.7572 | 0.8702 |
| 0.0491 | 5.9122 | 5250 | 0.7385 | 0.3662 | 0.7385 | 0.8594 |
| 0.0491 | 5.9144 | 5252 | 0.7224 | 0.3824 | 0.7224 | 0.8499 |
| 0.0491 | 5.9167 | 5254 | 0.7254 | 0.3478 | 0.7254 | 0.8517 |
| 0.0491 | 5.9189 | 5256 | 0.7387 | 0.3438 | 0.7387 | 0.8595 |
| 0.0491 | 5.9212 | 5258 | 0.7484 | 0.2286 | 0.7484 | 0.8651 |
| 0.0491 | 5.9234 | 5260 | 0.7551 | 0.3478 | 0.7551 | 0.8690 |
| 0.0491 | 5.9257 | 5262 | 0.7597 | 0.4324 | 0.7597 | 0.8716 |
| 0.0491 | 5.9279 | 5264 | 0.7758 | 0.4658 | 0.7758 | 0.8808 |
| 0.0491 | 5.9302 | 5266 | 0.7929 | 0.3662 | 0.7929 | 0.8904 |
| 0.0491 | 5.9324 | 5268 | 0.8105 | 0.2895 | 0.8105 | 0.9003 |
| 0.0491 | 5.9347 | 5270 | 0.8068 | 0.2895 | 0.8068 | 0.8982 |
| 0.0491 | 5.9369 | 5272 | 0.7905 | 0.3662 | 0.7905 | 0.8891 |
| 0.0491 | 5.9392 | 5274 | 0.7760 | 0.3662 | 0.7760 | 0.8809 |
| 0.0491 | 5.9414 | 5276 | 0.7573 | 0.4167 | 0.7573 | 0.8702 |
| 0.0491 | 5.9437 | 5278 | 0.7402 | 0.4167 | 0.7402 | 0.8603 |
| 0.0491 | 5.9459 | 5280 | 0.7180 | 0.4324 | 0.7180 | 0.8474 |
| 0.0491 | 5.9482 | 5282 | 0.7061 | 0.3478 | 0.7061 | 0.8403 |
| 0.0491 | 5.9505 | 5284 | 0.7025 | 0.3478 | 0.7025 | 0.8382 |
| 0.0491 | 5.9527 | 5286 | 0.7072 | 0.4324 | 0.7072 | 0.8409 |
| 0.0491 | 5.9550 | 5288 | 0.7254 | 0.4658 | 0.7254 | 0.8517 |
| 0.0491 | 5.9572 | 5290 | 0.7538 | 0.3662 | 0.7538 | 0.8682 |
| 0.0491 | 5.9595 | 5292 | 0.7655 | 0.3662 | 0.7655 | 0.8750 |
| 0.0491 | 5.9617 | 5294 | 0.7569 | 0.4167 | 0.7569 | 0.8700 |
| 0.0491 | 5.9640 | 5296 | 0.7390 | 0.4658 | 0.7390 | 0.8597 |
| 0.0491 | 5.9662 | 5298 | 0.7228 | 0.4324 | 0.7228 | 0.8502 |
| 0.0491 | 5.9685 | 5300 | 0.7238 | 0.2286 | 0.7238 | 0.8508 |
| 0.0491 | 5.9707 | 5302 | 0.7271 | 0.2286 | 0.7271 | 0.8527 |
| 0.0491 | 5.9730 | 5304 | 0.7344 | 0.4324 | 0.7344 | 0.8570 |
| 0.0491 | 5.9752 | 5306 | 0.7422 | 0.4324 | 0.7422 | 0.8615 |
| 0.0491 | 5.9775 | 5308 | 0.7461 | 0.4324 | 0.7461 | 0.8638 |
| 0.0491 | 5.9797 | 5310 | 0.7581 | 0.4658 | 0.7581 | 0.8707 |
| 0.0491 | 5.9820 | 5312 | 0.7552 | 0.4658 | 0.7552 | 0.8690 |
| 0.0491 | 5.9842 | 5314 | 0.7470 | 0.4324 | 0.7470 | 0.8643 |
| 0.0491 | 5.9865 | 5316 | 0.7406 | 0.4324 | 0.7406 | 0.8606 |
| 0.0491 | 5.9887 | 5318 | 0.7398 | 0.4324 | 0.7398 | 0.8601 |
| 0.0491 | 5.9910 | 5320 | 0.7486 | 0.4324 | 0.7486 | 0.8652 |
| 0.0491 | 5.9932 | 5322 | 0.7601 | 0.3836 | 0.7601 | 0.8719 |
| 0.0491 | 5.9955 | 5324 | 0.7574 | 0.3836 | 0.7574 | 0.8703 |
| 0.0491 | 5.9977 | 5326 | 0.7505 | 0.3836 | 0.7505 | 0.8663 |
| 0.0491 | 6.0 | 5328 | 0.7311 | 0.3836 | 0.7311 | 0.8550 |
| 0.0491 | 6.0023 | 5330 | 0.7138 | 0.3836 | 0.7138 | 0.8448 |
| 0.0491 | 6.0045 | 5332 | 0.7056 | 0.3478 | 0.7056 | 0.8400 |
| 0.0491 | 6.0068 | 5334 | 0.7098 | 0.3478 | 0.7098 | 0.8425 |
| 0.0491 | 6.0090 | 5336 | 0.7148 | 0.3478 | 0.7148 | 0.8454 |
| 0.0491 | 6.0113 | 5338 | 0.7142 | 0.3478 | 0.7142 | 0.8451 |
| 0.0491 | 6.0135 | 5340 | 0.7204 | 0.4324 | 0.7204 | 0.8487 |
| 0.0491 | 6.0158 | 5342 | 0.7426 | 0.3836 | 0.7426 | 0.8617 |
| 0.0491 | 6.0180 | 5344 | 0.7739 | 0.2895 | 0.7739 | 0.8797 |
| 0.0491 | 6.0203 | 5346 | 0.7956 | 0.2895 | 0.7956 | 0.8920 |
| 0.0491 | 6.0225 | 5348 | 0.7972 | 0.24 | 0.7972 | 0.8929 |
| 0.0491 | 6.0248 | 5350 | 0.7756 | 0.2895 | 0.7756 | 0.8807 |
| 0.0491 | 6.0270 | 5352 | 0.7418 | 0.3662 | 0.7418 | 0.8613 |
| 0.0491 | 6.0293 | 5354 | 0.7065 | 0.3836 | 0.7065 | 0.8405 |
| 0.0491 | 6.0315 | 5356 | 0.6906 | 0.2941 | 0.6906 | 0.8310 |
| 0.0491 | 6.0338 | 5358 | 0.6895 | 0.2941 | 0.6895 | 0.8304 |
| 0.0491 | 6.0360 | 5360 | 0.6948 | 0.3836 | 0.6948 | 0.8336 |
| 0.0491 | 6.0383 | 5362 | 0.7114 | 0.4167 | 0.7114 | 0.8435 |
| 0.0491 | 6.0405 | 5364 | 0.7357 | 0.3662 | 0.7357 | 0.8577 |
| 0.0491 | 6.0428 | 5366 | 0.7604 | 0.3662 | 0.7604 | 0.8720 |
| 0.0491 | 6.0450 | 5368 | 0.7791 | 0.3143 | 0.7791 | 0.8826 |
| 0.0491 | 6.0473 | 5370 | 0.7866 | 0.24 | 0.7866 | 0.8869 |
| 0.0491 | 6.0495 | 5372 | 0.7892 | 0.24 | 0.7892 | 0.8884 |
| 0.0491 | 6.0518 | 5374 | 0.7678 | 0.3662 | 0.7678 | 0.8762 |
| 0.0491 | 6.0541 | 5376 | 0.7394 | 0.3662 | 0.7394 | 0.8599 |
| 0.0491 | 6.0563 | 5378 | 0.7223 | 0.3836 | 0.7223 | 0.8499 |
| 0.0491 | 6.0586 | 5380 | 0.7143 | 0.2941 | 0.7143 | 0.8451 |
| 0.0491 | 6.0608 | 5382 | 0.7132 | 0.3478 | 0.7132 | 0.8445 |
| 0.0491 | 6.0631 | 5384 | 0.7210 | 0.3836 | 0.7210 | 0.8491 |
| 0.0491 | 6.0653 | 5386 | 0.7381 | 0.3662 | 0.7381 | 0.8591 |
| 0.0491 | 6.0676 | 5388 | 0.7527 | 0.3662 | 0.7527 | 0.8676 |
| 0.0491 | 6.0698 | 5390 | 0.7658 | 0.3662 | 0.7658 | 0.8751 |
| 0.0491 | 6.0721 | 5392 | 0.7825 | 0.3662 | 0.7825 | 0.8846 |
| 0.0491 | 6.0743 | 5394 | 0.7842 | 0.3662 | 0.7842 | 0.8855 |
| 0.0491 | 6.0766 | 5396 | 0.7744 | 0.3662 | 0.7744 | 0.8800 |
| 0.0491 | 6.0788 | 5398 | 0.7660 | 0.3662 | 0.7660 | 0.8752 |
| 0.0491 | 6.0811 | 5400 | 0.7569 | 0.3662 | 0.7569 | 0.8700 |
| 0.0491 | 6.0833 | 5402 | 0.7418 | 0.3662 | 0.7418 | 0.8613 |
| 0.0491 | 6.0856 | 5404 | 0.7390 | 0.3662 | 0.7390 | 0.8597 |
| 0.0491 | 6.0878 | 5406 | 0.7385 | 0.3662 | 0.7385 | 0.8594 |
| 0.0491 | 6.0901 | 5408 | 0.7468 | 0.3662 | 0.7468 | 0.8642 |
| 0.0491 | 6.0923 | 5410 | 0.7649 | 0.3143 | 0.7649 | 0.8746 |
| 0.0491 | 6.0946 | 5412 | 0.7698 | 0.3143 | 0.7698 | 0.8774 |
| 0.0491 | 6.0968 | 5414 | 0.7604 | 0.3662 | 0.7604 | 0.8720 |
| 0.0491 | 6.0991 | 5416 | 0.7587 | 0.3662 | 0.7587 | 0.8710 |
| 0.0491 | 6.1014 | 5418 | 0.7588 | 0.3662 | 0.7588 | 0.8711 |
| 0.0491 | 6.1036 | 5420 | 0.7624 | 0.3662 | 0.7624 | 0.8732 |
| 0.0491 | 6.1059 | 5422 | 0.7663 | 0.4167 | 0.7663 | 0.8754 |
| 0.0491 | 6.1081 | 5424 | 0.7567 | 0.4658 | 0.7567 | 0.8699 |
| 0.0491 | 6.1104 | 5426 | 0.7482 | 0.4324 | 0.7482 | 0.8650 |
| 0.0491 | 6.1126 | 5428 | 0.7470 | 0.4324 | 0.7470 | 0.8643 |
| 0.0491 | 6.1149 | 5430 | 0.7499 | 0.4658 | 0.7499 | 0.8659 |
| 0.0491 | 6.1171 | 5432 | 0.7573 | 0.4167 | 0.7573 | 0.8702 |
| 0.0491 | 6.1194 | 5434 | 0.7586 | 0.3662 | 0.7586 | 0.8710 |
| 0.0491 | 6.1216 | 5436 | 0.7524 | 0.4167 | 0.7524 | 0.8674 |
| 0.0491 | 6.1239 | 5438 | 0.7427 | 0.4167 | 0.7427 | 0.8618 |
| 0.0491 | 6.1261 | 5440 | 0.7400 | 0.3662 | 0.7400 | 0.8602 |
| 0.0491 | 6.1284 | 5442 | 0.7330 | 0.3662 | 0.7330 | 0.8562 |
| 0.0491 | 6.1306 | 5444 | 0.7209 | 0.3662 | 0.7209 | 0.8490 |
| 0.0491 | 6.1329 | 5446 | 0.7105 | 0.2727 | 0.7105 | 0.8429 |
| 0.0491 | 6.1351 | 5448 | 0.7117 | 0.2727 | 0.7117 | 0.8437 |
| 0.0491 | 6.1374 | 5450 | 0.7143 | 0.3662 | 0.7143 | 0.8452 |
| 0.0491 | 6.1396 | 5452 | 0.7324 | 0.3662 | 0.7324 | 0.8558 |
| 0.0491 | 6.1419 | 5454 | 0.7448 | 0.3143 | 0.7448 | 0.8630 |
| 0.0491 | 6.1441 | 5456 | 0.7516 | 0.24 | 0.7516 | 0.8669 |
| 0.0491 | 6.1464 | 5458 | 0.7416 | 0.3662 | 0.7416 | 0.8612 |
| 0.0491 | 6.1486 | 5460 | 0.7245 | 0.3662 | 0.7245 | 0.8512 |
| 0.0491 | 6.1509 | 5462 | 0.7157 | 0.2727 | 0.7157 | 0.8460 |
| 0.0491 | 6.1532 | 5464 | 0.7130 | 0.3824 | 0.7130 | 0.8444 |
| 0.0491 | 6.1554 | 5466 | 0.7104 | 0.3478 | 0.7104 | 0.8429 |
| 0.0491 | 6.1577 | 5468 | 0.7103 | 0.3438 | 0.7103 | 0.8428 |
| 0.0491 | 6.1599 | 5470 | 0.7151 | 0.3478 | 0.7151 | 0.8457 |
| 0.0491 | 6.1622 | 5472 | 0.7236 | 0.3824 | 0.7236 | 0.8506 |
| 0.0491 | 6.1644 | 5474 | 0.7371 | 0.3662 | 0.7371 | 0.8585 |
| 0.0491 | 6.1667 | 5476 | 0.7395 | 0.3662 | 0.7395 | 0.8600 |
| 0.0491 | 6.1689 | 5478 | 0.7420 | 0.3662 | 0.7420 | 0.8614 |
| 0.0491 | 6.1712 | 5480 | 0.7408 | 0.2895 | 0.7408 | 0.8607 |
| 0.0491 | 6.1734 | 5482 | 0.7361 | 0.3662 | 0.7361 | 0.8580 |
| 0.0491 | 6.1757 | 5484 | 0.7220 | 0.3662 | 0.7220 | 0.8497 |
| 0.0491 | 6.1779 | 5486 | 0.7138 | 0.4658 | 0.7138 | 0.8449 |
| 0.0491 | 6.1802 | 5488 | 0.7110 | 0.3824 | 0.7110 | 0.8432 |
| 0.0491 | 6.1824 | 5490 | 0.7140 | 0.3662 | 0.7140 | 0.8450 |
| 0.0491 | 6.1847 | 5492 | 0.7283 | 0.2895 | 0.7283 | 0.8534 |
| 0.0491 | 6.1869 | 5494 | 0.7433 | 0.2895 | 0.7433 | 0.8622 |
| 0.0491 | 6.1892 | 5496 | 0.7527 | 0.2895 | 0.7527 | 0.8676 |
| 0.0491 | 6.1914 | 5498 | 0.7451 | 0.2895 | 0.7451 | 0.8632 |
| 0.0472 | 6.1937 | 5500 | 0.7252 | 0.2895 | 0.7252 | 0.8516 |
| 0.0472 | 6.1959 | 5502 | 0.7098 | 0.2857 | 0.7098 | 0.8425 |
| 0.0472 | 6.1982 | 5504 | 0.7104 | 0.3438 | 0.7104 | 0.8428 |
| 0.0472 | 6.2005 | 5506 | 0.7089 | 0.3438 | 0.7089 | 0.8420 |
| 0.0472 | 6.2027 | 5508 | 0.7088 | 0.3438 | 0.7088 | 0.8419 |
| 0.0472 | 6.2050 | 5510 | 0.7057 | 0.3438 | 0.7057 | 0.8401 |
| 0.0472 | 6.2072 | 5512 | 0.7049 | 0.3438 | 0.7049 | 0.8396 |
| 0.0472 | 6.2095 | 5514 | 0.7010 | 0.3438 | 0.7010 | 0.8372 |
| 0.0472 | 6.2117 | 5516 | 0.7027 | 0.2388 | 0.7027 | 0.8382 |
| 0.0472 | 6.2140 | 5518 | 0.7152 | 0.3662 | 0.7152 | 0.8457 |
| 0.0472 | 6.2162 | 5520 | 0.7227 | 0.3662 | 0.7227 | 0.8501 |
| 0.0472 | 6.2185 | 5522 | 0.7164 | 0.3662 | 0.7164 | 0.8464 |
| 0.0472 | 6.2207 | 5524 | 0.7042 | 0.2727 | 0.7042 | 0.8391 |
| 0.0472 | 6.2230 | 5526 | 0.6947 | 0.2388 | 0.6947 | 0.8335 |
| 0.0472 | 6.2252 | 5528 | 0.6960 | 0.2388 | 0.6960 | 0.8343 |
| 0.0472 | 6.2275 | 5530 | 0.7039 | 0.2388 | 0.7039 | 0.8390 |
| 0.0472 | 6.2297 | 5532 | 0.7193 | 0.3662 | 0.7193 | 0.8481 |
| 0.0472 | 6.2320 | 5534 | 0.7337 | 0.3662 | 0.7337 | 0.8565 |
| 0.0472 | 6.2342 | 5536 | 0.7466 | 0.2895 | 0.7466 | 0.8640 |
| 0.0472 | 6.2365 | 5538 | 0.7580 | 0.24 | 0.7580 | 0.8706 |
| 0.0472 | 6.2387 | 5540 | 0.7563 | 0.24 | 0.7563 | 0.8697 |
| 0.0472 | 6.2410 | 5542 | 0.7489 | 0.3662 | 0.7489 | 0.8654 |
| 0.0472 | 6.2432 | 5544 | 0.7353 | 0.3333 | 0.7353 | 0.8575 |
| 0.0472 | 6.2455 | 5546 | 0.7174 | 0.3333 | 0.7174 | 0.8470 |
| 0.0472 | 6.2477 | 5548 | 0.7099 | 0.2941 | 0.7099 | 0.8425 |
| 0.0472 | 6.25 | 5550 | 0.7048 | 0.2857 | 0.7048 | 0.8395 |
| 0.0472 | 6.2523 | 5552 | 0.7109 | 0.2941 | 0.7109 | 0.8431 |
| 0.0472 | 6.2545 | 5554 | 0.7255 | 0.3333 | 0.7255 | 0.8518 |
| 0.0472 | 6.2568 | 5556 | 0.7453 | 0.3143 | 0.7453 | 0.8633 |
| 0.0472 | 6.2590 | 5558 | 0.7529 | 0.24 | 0.7529 | 0.8677 |
| 0.0472 | 6.2613 | 5560 | 0.7538 | 0.24 | 0.7538 | 0.8682 |
| 0.0472 | 6.2635 | 5562 | 0.7471 | 0.3662 | 0.7471 | 0.8643 |
| 0.0472 | 6.2658 | 5564 | 0.7430 | 0.3333 | 0.7430 | 0.8620 |
| 0.0472 | 6.2680 | 5566 | 0.7413 | 0.2941 | 0.7413 | 0.8610 |
| 0.0472 | 6.2703 | 5568 | 0.7461 | 0.3478 | 0.7461 | 0.8638 |
| 0.0472 | 6.2725 | 5570 | 0.7483 | 0.3438 | 0.7483 | 0.8651 |
| 0.0472 | 6.2748 | 5572 | 0.7496 | 0.3478 | 0.7496 | 0.8658 |
| 0.0472 | 6.2770 | 5574 | 0.7554 | 0.2597 | 0.7554 | 0.8692 |
| 0.0472 | 6.2793 | 5576 | 0.7707 | 0.2895 | 0.7707 | 0.8779 |
| 0.0472 | 6.2815 | 5578 | 0.7747 | 0.2895 | 0.7747 | 0.8802 |
| 0.0472 | 6.2838 | 5580 | 0.7617 | 0.2895 | 0.7617 | 0.8728 |
| 0.0472 | 6.2860 | 5582 | 0.7502 | 0.2895 | 0.7502 | 0.8661 |
| 0.0472 | 6.2883 | 5584 | 0.7391 | 0.2895 | 0.7391 | 0.8597 |
| 0.0472 | 6.2905 | 5586 | 0.7242 | 0.3662 | 0.7242 | 0.8510 |
| 0.0472 | 6.2928 | 5588 | 0.7162 | 0.3662 | 0.7162 | 0.8463 |
| 0.0472 | 6.2950 | 5590 | 0.7156 | 0.2623 | 0.7156 | 0.8459 |
| 0.0472 | 6.2973 | 5592 | 0.7176 | 0.2258 | 0.7176 | 0.8471 |
| 0.0472 | 6.2995 | 5594 | 0.7187 | 0.2258 | 0.7187 | 0.8478 |
| 0.0472 | 6.3018 | 5596 | 0.7246 | 0.3284 | 0.7246 | 0.8512 |
| 0.0472 | 6.3041 | 5598 | 0.7265 | 0.3284 | 0.7265 | 0.8523 |
| 0.0472 | 6.3063 | 5600 | 0.7305 | 0.3284 | 0.7305 | 0.8547 |
| 0.0472 | 6.3086 | 5602 | 0.7367 | 0.3333 | 0.7367 | 0.8583 |
| 0.0472 | 6.3108 | 5604 | 0.7569 | 0.2895 | 0.7569 | 0.8700 |
| 0.0472 | 6.3131 | 5606 | 0.7705 | 0.2895 | 0.7705 | 0.8778 |
| 0.0472 | 6.3153 | 5608 | 0.7714 | 0.2895 | 0.7714 | 0.8783 |
| 0.0472 | 6.3176 | 5610 | 0.7584 | 0.2895 | 0.7584 | 0.8709 |
| 0.0472 | 6.3198 | 5612 | 0.7399 | 0.3662 | 0.7399 | 0.8601 |
| 0.0472 | 6.3221 | 5614 | 0.7321 | 0.3284 | 0.7321 | 0.8556 |
| 0.0472 | 6.3243 | 5616 | 0.7380 | 0.3636 | 0.7380 | 0.8591 |
| 0.0472 | 6.3266 | 5618 | 0.7459 | 0.3662 | 0.7459 | 0.8637 |
| 0.0472 | 6.3288 | 5620 | 0.7548 | 0.3662 | 0.7548 | 0.8688 |
| 0.0472 | 6.3311 | 5622 | 0.7618 | 0.3662 | 0.7618 | 0.8728 |
| 0.0472 | 6.3333 | 5624 | 0.7526 | 0.3662 | 0.7526 | 0.8675 |
| 0.0472 | 6.3356 | 5626 | 0.7360 | 0.3636 | 0.7360 | 0.8579 |
| 0.0472 | 6.3378 | 5628 | 0.7235 | 0.3824 | 0.7235 | 0.8506 |
| 0.0472 | 6.3401 | 5630 | 0.7207 | 0.3824 | 0.7207 | 0.8489 |
| 0.0472 | 6.3423 | 5632 | 0.7222 | 0.4348 | 0.7222 | 0.8498 |
| 0.0472 | 6.3446 | 5634 | 0.7287 | 0.4348 | 0.7287 | 0.8536 |
| 0.0472 | 6.3468 | 5636 | 0.7419 | 0.4348 | 0.7419 | 0.8613 |
| 0.0472 | 6.3491 | 5638 | 0.7650 | 0.3333 | 0.7650 | 0.8746 |
| 0.0472 | 6.3514 | 5640 | 0.7805 | 0.2895 | 0.7805 | 0.8835 |
| 0.0472 | 6.3536 | 5642 | 0.7864 | 0.2895 | 0.7864 | 0.8868 |
| 0.0472 | 6.3559 | 5644 | 0.7858 | 0.2895 | 0.7858 | 0.8865 |
| 0.0472 | 6.3581 | 5646 | 0.7724 | 0.2895 | 0.7724 | 0.8788 |
| 0.0472 | 6.3604 | 5648 | 0.7474 | 0.3662 | 0.7474 | 0.8645 |
| 0.0472 | 6.3626 | 5650 | 0.7224 | 0.3636 | 0.7224 | 0.8499 |
| 0.0472 | 6.3649 | 5652 | 0.7058 | 0.2258 | 0.7058 | 0.8401 |
| 0.0472 | 6.3671 | 5654 | 0.7038 | 0.2258 | 0.7038 | 0.8390 |
| 0.0472 | 6.3694 | 5656 | 0.7145 | 0.3636 | 0.7145 | 0.8453 |
| 0.0472 | 6.3716 | 5658 | 0.7295 | 0.3662 | 0.7295 | 0.8541 |
| 0.0472 | 6.3739 | 5660 | 0.7340 | 0.3662 | 0.7340 | 0.8567 |
| 0.0472 | 6.3761 | 5662 | 0.7377 | 0.3662 | 0.7377 | 0.8589 |
| 0.0472 | 6.3784 | 5664 | 0.7409 | 0.3662 | 0.7409 | 0.8608 |
| 0.0472 | 6.3806 | 5666 | 0.7323 | 0.3662 | 0.7323 | 0.8557 |
| 0.0472 | 6.3829 | 5668 | 0.7320 | 0.3333 | 0.7320 | 0.8556 |
| 0.0472 | 6.3851 | 5670 | 0.7264 | 0.3836 | 0.7264 | 0.8523 |
| 0.0472 | 6.3874 | 5672 | 0.7200 | 0.2857 | 0.7200 | 0.8485 |
| 0.0472 | 6.3896 | 5674 | 0.7250 | 0.3836 | 0.7250 | 0.8514 |
| 0.0472 | 6.3919 | 5676 | 0.7215 | 0.3438 | 0.7215 | 0.8494 |
| 0.0472 | 6.3941 | 5678 | 0.7156 | 0.3438 | 0.7156 | 0.8460 |
| 0.0472 | 6.3964 | 5680 | 0.7161 | 0.3438 | 0.7161 | 0.8462 |
| 0.0472 | 6.3986 | 5682 | 0.7195 | 0.3438 | 0.7195 | 0.8482 |
| 0.0472 | 6.4009 | 5684 | 0.7265 | 0.3438 | 0.7265 | 0.8523 |
| 0.0472 | 6.4032 | 5686 | 0.7345 | 0.4324 | 0.7345 | 0.8570 |
| 0.0472 | 6.4054 | 5688 | 0.7392 | 0.3836 | 0.7392 | 0.8598 |
| 0.0472 | 6.4077 | 5690 | 0.7385 | 0.3333 | 0.7385 | 0.8593 |
| 0.0472 | 6.4099 | 5692 | 0.7478 | 0.3662 | 0.7478 | 0.8648 |
| 0.0472 | 6.4122 | 5694 | 0.7583 | 0.2895 | 0.7583 | 0.8708 |
| 0.0472 | 6.4144 | 5696 | 0.7649 | 0.2895 | 0.7649 | 0.8746 |
| 0.0472 | 6.4167 | 5698 | 0.7778 | 0.2895 | 0.7778 | 0.8820 |
| 0.0472 | 6.4189 | 5700 | 0.7760 | 0.2895 | 0.7760 | 0.8809 |
| 0.0472 | 6.4212 | 5702 | 0.7670 | 0.2895 | 0.7670 | 0.8758 |
| 0.0472 | 6.4234 | 5704 | 0.7462 | 0.3333 | 0.7462 | 0.8638 |
| 0.0472 | 6.4257 | 5706 | 0.7201 | 0.2941 | 0.7201 | 0.8486 |
| 0.0472 | 6.4279 | 5708 | 0.6997 | 0.3438 | 0.6997 | 0.8365 |
| 0.0472 | 6.4302 | 5710 | 0.6924 | 0.3077 | 0.6924 | 0.8321 |
| 0.0472 | 6.4324 | 5712 | 0.6951 | 0.2727 | 0.6951 | 0.8337 |
| 0.0472 | 6.4347 | 5714 | 0.6984 | 0.3077 | 0.6984 | 0.8357 |
| 0.0472 | 6.4369 | 5716 | 0.7071 | 0.3438 | 0.7071 | 0.8409 |
| 0.0472 | 6.4392 | 5718 | 0.7263 | 0.3478 | 0.7263 | 0.8522 |
| 0.0472 | 6.4414 | 5720 | 0.7413 | 0.3836 | 0.7413 | 0.8610 |
| 0.0472 | 6.4437 | 5722 | 0.7497 | 0.3333 | 0.7497 | 0.8658 |
| 0.0472 | 6.4459 | 5724 | 0.7591 | 0.2895 | 0.7591 | 0.8713 |
| 0.0472 | 6.4482 | 5726 | 0.7574 | 0.2895 | 0.7574 | 0.8703 |
| 0.0472 | 6.4505 | 5728 | 0.7568 | 0.2895 | 0.7568 | 0.8699 |
| 0.0472 | 6.4527 | 5730 | 0.7403 | 0.3836 | 0.7403 | 0.8604 |
| 0.0472 | 6.4550 | 5732 | 0.7249 | 0.2941 | 0.7249 | 0.8514 |
| 0.0472 | 6.4572 | 5734 | 0.7182 | 0.3438 | 0.7182 | 0.8474 |
| 0.0472 | 6.4595 | 5736 | 0.7217 | 0.2857 | 0.7217 | 0.8495 |
| 0.0472 | 6.4617 | 5738 | 0.7355 | 0.4167 | 0.7355 | 0.8576 |
| 0.0472 | 6.4640 | 5740 | 0.7545 | 0.24 | 0.7545 | 0.8686 |
| 0.0472 | 6.4662 | 5742 | 0.7683 | 0.24 | 0.7683 | 0.8766 |
| 0.0472 | 6.4685 | 5744 | 0.7831 | 0.24 | 0.7831 | 0.8849 |
| 0.0472 | 6.4707 | 5746 | 0.7814 | 0.24 | 0.7814 | 0.8840 |
| 0.0472 | 6.4730 | 5748 | 0.7776 | 0.3377 | 0.7776 | 0.8818 |
| 0.0472 | 6.4752 | 5750 | 0.7808 | 0.3377 | 0.7808 | 0.8836 |
| 0.0472 | 6.4775 | 5752 | 0.7773 | 0.3377 | 0.7773 | 0.8817 |
| 0.0472 | 6.4797 | 5754 | 0.7676 | 0.3077 | 0.7676 | 0.8761 |
| 0.0472 | 6.4820 | 5756 | 0.7629 | 0.3836 | 0.7629 | 0.8735 |
| 0.0472 | 6.4842 | 5758 | 0.7623 | 0.3377 | 0.7623 | 0.8731 |
| 0.0472 | 6.4865 | 5760 | 0.7569 | 0.4167 | 0.7569 | 0.8700 |
| 0.0472 | 6.4887 | 5762 | 0.7598 | 0.4167 | 0.7598 | 0.8717 |
| 0.0472 | 6.4910 | 5764 | 0.7708 | 0.3377 | 0.7708 | 0.8780 |
| 0.0472 | 6.4932 | 5766 | 0.7798 | 0.3377 | 0.7798 | 0.8831 |
| 0.0472 | 6.4955 | 5768 | 0.7843 | 0.2895 | 0.7843 | 0.8856 |
| 0.0472 | 6.4977 | 5770 | 0.7848 | 0.2895 | 0.7848 | 0.8859 |
| 0.0472 | 6.5 | 5772 | 0.7697 | 0.2895 | 0.7697 | 0.8773 |
| 0.0472 | 6.5023 | 5774 | 0.7467 | 0.3377 | 0.7467 | 0.8641 |
| 0.0472 | 6.5045 | 5776 | 0.7372 | 0.4167 | 0.7372 | 0.8586 |
| 0.0472 | 6.5068 | 5778 | 0.7393 | 0.4167 | 0.7393 | 0.8598 |
| 0.0472 | 6.5090 | 5780 | 0.7438 | 0.3377 | 0.7438 | 0.8625 |
| 0.0472 | 6.5113 | 5782 | 0.7443 | 0.3377 | 0.7443 | 0.8627 |
| 0.0472 | 6.5135 | 5784 | 0.7437 | 0.4167 | 0.7437 | 0.8624 |
| 0.0472 | 6.5158 | 5786 | 0.7402 | 0.4324 | 0.7402 | 0.8604 |
| 0.0472 | 6.5180 | 5788 | 0.7399 | 0.4324 | 0.7399 | 0.8602 |
| 0.0472 | 6.5203 | 5790 | 0.7404 | 0.4324 | 0.7404 | 0.8605 |
| 0.0472 | 6.5225 | 5792 | 0.7360 | 0.4 | 0.7360 | 0.8579 |
| 0.0472 | 6.5248 | 5794 | 0.7329 | 0.1818 | 0.7329 | 0.8561 |
| 0.0472 | 6.5270 | 5796 | 0.7302 | 0.3438 | 0.7302 | 0.8545 |
| 0.0472 | 6.5293 | 5798 | 0.7304 | 0.4324 | 0.7304 | 0.8546 |
| 0.0472 | 6.5315 | 5800 | 0.7324 | 0.4167 | 0.7324 | 0.8558 |
| 0.0472 | 6.5338 | 5802 | 0.7397 | 0.3377 | 0.7397 | 0.8601 |
| 0.0472 | 6.5360 | 5804 | 0.7360 | 0.2895 | 0.7360 | 0.8579 |
| 0.0472 | 6.5383 | 5806 | 0.7270 | 0.2895 | 0.7270 | 0.8526 |
| 0.0472 | 6.5405 | 5808 | 0.7074 | 0.3662 | 0.7074 | 0.8411 |
| 0.0472 | 6.5428 | 5810 | 0.6979 | 0.3662 | 0.6979 | 0.8354 |
| 0.0472 | 6.5450 | 5812 | 0.6886 | 0.3662 | 0.6886 | 0.8298 |
| 0.0472 | 6.5473 | 5814 | 0.6807 | 0.4167 | 0.6807 | 0.8251 |
| 0.0472 | 6.5495 | 5816 | 0.6818 | 0.4167 | 0.6818 | 0.8257 |
| 0.0472 | 6.5518 | 5818 | 0.6892 | 0.4167 | 0.6892 | 0.8302 |
| 0.0472 | 6.5541 | 5820 | 0.6975 | 0.3662 | 0.6975 | 0.8352 |
| 0.0472 | 6.5563 | 5822 | 0.7071 | 0.3662 | 0.7071 | 0.8409 |
| 0.0472 | 6.5586 | 5824 | 0.7025 | 0.4167 | 0.7025 | 0.8382 |
| 0.0472 | 6.5608 | 5826 | 0.6925 | 0.4167 | 0.6925 | 0.8322 |
| 0.0472 | 6.5631 | 5828 | 0.6888 | 0.4167 | 0.6888 | 0.8299 |
| 0.0472 | 6.5653 | 5830 | 0.6926 | 0.4167 | 0.6926 | 0.8322 |
| 0.0472 | 6.5676 | 5832 | 0.6945 | 0.3836 | 0.6945 | 0.8334 |
| 0.0472 | 6.5698 | 5834 | 0.6925 | 0.3824 | 0.6925 | 0.8322 |
| 0.0472 | 6.5721 | 5836 | 0.6943 | 0.3824 | 0.6943 | 0.8332 |
| 0.0472 | 6.5743 | 5838 | 0.7020 | 0.3836 | 0.7020 | 0.8378 |
| 0.0472 | 6.5766 | 5840 | 0.7110 | 0.4167 | 0.7110 | 0.8432 |
| 0.0472 | 6.5788 | 5842 | 0.7136 | 0.3662 | 0.7136 | 0.8448 |
| 0.0472 | 6.5811 | 5844 | 0.7098 | 0.3662 | 0.7098 | 0.8425 |
| 0.0472 | 6.5833 | 5846 | 0.7085 | 0.3662 | 0.7085 | 0.8417 |
| 0.0472 | 6.5856 | 5848 | 0.7062 | 0.3662 | 0.7062 | 0.8403 |
| 0.0472 | 6.5878 | 5850 | 0.6996 | 0.4167 | 0.6996 | 0.8364 |
| 0.0472 | 6.5901 | 5852 | 0.6953 | 0.3284 | 0.6953 | 0.8338 |
| 0.0472 | 6.5923 | 5854 | 0.6926 | 0.3226 | 0.6926 | 0.8322 |
| 0.0472 | 6.5946 | 5856 | 0.6923 | 0.3226 | 0.6923 | 0.8320 |
| 0.0472 | 6.5968 | 5858 | 0.6955 | 0.3226 | 0.6955 | 0.8340 |
| 0.0472 | 6.5991 | 5860 | 0.6987 | 0.2623 | 0.6987 | 0.8359 |
| 0.0472 | 6.6014 | 5862 | 0.7076 | 0.2727 | 0.7076 | 0.8412 |
| 0.0472 | 6.6036 | 5864 | 0.7119 | 0.3662 | 0.7119 | 0.8437 |
| 0.0472 | 6.6059 | 5866 | 0.7103 | 0.3662 | 0.7103 | 0.8428 |
| 0.0472 | 6.6081 | 5868 | 0.7052 | 0.3662 | 0.7052 | 0.8397 |
| 0.0472 | 6.6104 | 5870 | 0.7054 | 0.3662 | 0.7054 | 0.8399 |
| 0.0472 | 6.6126 | 5872 | 0.6959 | 0.2623 | 0.6959 | 0.8342 |
| 0.0472 | 6.6149 | 5874 | 0.6861 | 0.2623 | 0.6861 | 0.8283 |
| 0.0472 | 6.6171 | 5876 | 0.6736 | 0.2857 | 0.6736 | 0.8207 |
| 0.0472 | 6.6194 | 5878 | 0.6718 | 0.3438 | 0.6718 | 0.8197 |
| 0.0472 | 6.6216 | 5880 | 0.6776 | 0.3438 | 0.6776 | 0.8232 |
| 0.0472 | 6.6239 | 5882 | 0.6881 | 0.3438 | 0.6881 | 0.8295 |
| 0.0472 | 6.6261 | 5884 | 0.7017 | 0.3438 | 0.7017 | 0.8377 |
| 0.0472 | 6.6284 | 5886 | 0.7176 | 0.2857 | 0.7176 | 0.8471 |
| 0.0472 | 6.6306 | 5888 | 0.7229 | 0.2941 | 0.7229 | 0.8502 |
| 0.0472 | 6.6329 | 5890 | 0.7217 | 0.2857 | 0.7217 | 0.8495 |
| 0.0472 | 6.6351 | 5892 | 0.7208 | 0.2857 | 0.7208 | 0.8490 |
| 0.0472 | 6.6374 | 5894 | 0.7135 | 0.2857 | 0.7135 | 0.8447 |
| 0.0472 | 6.6396 | 5896 | 0.7082 | 0.2857 | 0.7082 | 0.8415 |
| 0.0472 | 6.6419 | 5898 | 0.7061 | 0.3438 | 0.7061 | 0.8403 |
| 0.0472 | 6.6441 | 5900 | 0.7085 | 0.2857 | 0.7085 | 0.8417 |
| 0.0472 | 6.6464 | 5902 | 0.7102 | 0.2857 | 0.7102 | 0.8427 |
| 0.0472 | 6.6486 | 5904 | 0.7199 | 0.2258 | 0.7199 | 0.8485 |
| 0.0472 | 6.6509 | 5906 | 0.7279 | 0.2388 | 0.7279 | 0.8532 |
| 0.0472 | 6.6532 | 5908 | 0.7269 | 0.2258 | 0.7269 | 0.8526 |
| 0.0472 | 6.6554 | 5910 | 0.7306 | 0.2258 | 0.7306 | 0.8547 |
| 0.0472 | 6.6577 | 5912 | 0.7371 | 0.2388 | 0.7371 | 0.8586 |
| 0.0472 | 6.6599 | 5914 | 0.7475 | 0.2895 | 0.7475 | 0.8646 |
| 0.0472 | 6.6622 | 5916 | 0.7471 | 0.2895 | 0.7471 | 0.8643 |
| 0.0472 | 6.6644 | 5918 | 0.7505 | 0.2597 | 0.7505 | 0.8663 |
| 0.0472 | 6.6667 | 5920 | 0.7633 | 0.2895 | 0.7633 | 0.8737 |
| 0.0472 | 6.6689 | 5922 | 0.7704 | 0.2895 | 0.7704 | 0.8777 |
| 0.0472 | 6.6712 | 5924 | 0.7782 | 0.2895 | 0.7782 | 0.8822 |
| 0.0472 | 6.6734 | 5926 | 0.7751 | 0.2895 | 0.7751 | 0.8804 |
| 0.0472 | 6.6757 | 5928 | 0.7633 | 0.2895 | 0.7633 | 0.8737 |
| 0.0472 | 6.6779 | 5930 | 0.7495 | 0.1972 | 0.7495 | 0.8658 |
| 0.0472 | 6.6802 | 5932 | 0.7430 | 0.1818 | 0.7430 | 0.8620 |
| 0.0472 | 6.6824 | 5934 | 0.7366 | 0.1818 | 0.7366 | 0.8583 |
| 0.0472 | 6.6847 | 5936 | 0.7308 | 0.1493 | 0.7308 | 0.8549 |
| 0.0472 | 6.6869 | 5938 | 0.7340 | 0.1818 | 0.7340 | 0.8568 |
| 0.0472 | 6.6892 | 5940 | 0.7471 | 0.1972 | 0.7471 | 0.8643 |
| 0.0472 | 6.6914 | 5942 | 0.7681 | 0.1429 | 0.7681 | 0.8764 |
| 0.0472 | 6.6937 | 5944 | 0.7799 | 0.24 | 0.7799 | 0.8831 |
| 0.0472 | 6.6959 | 5946 | 0.7745 | 0.24 | 0.7745 | 0.8801 |
| 0.0472 | 6.6982 | 5948 | 0.7563 | 0.1972 | 0.7563 | 0.8697 |
| 0.0472 | 6.7005 | 5950 | 0.7373 | 0.1667 | 0.7373 | 0.8587 |
| 0.0472 | 6.7027 | 5952 | 0.7304 | 0.2609 | 0.7304 | 0.8546 |
| 0.0472 | 6.7050 | 5954 | 0.7285 | 0.3438 | 0.7285 | 0.8535 |
| 0.0472 | 6.7072 | 5956 | 0.7319 | 0.3438 | 0.7319 | 0.8555 |
| 0.0472 | 6.7095 | 5958 | 0.7377 | 0.3438 | 0.7377 | 0.8589 |
| 0.0472 | 6.7117 | 5960 | 0.7404 | 0.3438 | 0.7404 | 0.8605 |
| 0.0472 | 6.7140 | 5962 | 0.7467 | 0.2609 | 0.7467 | 0.8641 |
| 0.0472 | 6.7162 | 5964 | 0.7584 | 0.2192 | 0.7584 | 0.8709 |
| 0.0472 | 6.7185 | 5966 | 0.7673 | 0.1667 | 0.7673 | 0.8759 |
| 0.0472 | 6.7207 | 5968 | 0.7668 | 0.1972 | 0.7668 | 0.8757 |
| 0.0472 | 6.7230 | 5970 | 0.7583 | 0.1972 | 0.7583 | 0.8708 |
| 0.0472 | 6.7252 | 5972 | 0.7395 | 0.1667 | 0.7395 | 0.8599 |
| 0.0472 | 6.7275 | 5974 | 0.7218 | 0.2857 | 0.7218 | 0.8496 |
| 0.0472 | 6.7297 | 5976 | 0.7119 | 0.2857 | 0.7119 | 0.8438 |
| 0.0472 | 6.7320 | 5978 | 0.7099 | 0.2857 | 0.7099 | 0.8426 |
| 0.0472 | 6.7342 | 5980 | 0.7091 | 0.2857 | 0.7091 | 0.8421 |
| 0.0472 | 6.7365 | 5982 | 0.7149 | 0.2857 | 0.7149 | 0.8455 |
| 0.0472 | 6.7387 | 5984 | 0.7220 | 0.2857 | 0.7220 | 0.8497 |
| 0.0472 | 6.7410 | 5986 | 0.7257 | 0.2857 | 0.7257 | 0.8519 |
| 0.0472 | 6.7432 | 5988 | 0.7373 | 0.2623 | 0.7373 | 0.8586 |
| 0.0472 | 6.7455 | 5990 | 0.7427 | 0.1231 | 0.7427 | 0.8618 |
| 0.0472 | 6.7477 | 5992 | 0.7418 | 0.1231 | 0.7418 | 0.8613 |
| 0.0472 | 6.75 | 5994 | 0.7341 | 0.2857 | 0.7341 | 0.8568 |
| 0.0472 | 6.7523 | 5996 | 0.7302 | 0.2857 | 0.7302 | 0.8545 |
| 0.0472 | 6.7545 | 5998 | 0.7271 | 0.2857 | 0.7271 | 0.8527 |
| 0.0456 | 6.7568 | 6000 | 0.7255 | 0.3438 | 0.7255 | 0.8518 |
| 0.0456 | 6.7590 | 6002 | 0.7271 | 0.3438 | 0.7271 | 0.8527 |
| 0.0456 | 6.7613 | 6004 | 0.7317 | 0.2857 | 0.7317 | 0.8554 |
| 0.0456 | 6.7635 | 6006 | 0.7298 | 0.3438 | 0.7298 | 0.8543 |
| 0.0456 | 6.7658 | 6008 | 0.7288 | 0.3438 | 0.7288 | 0.8537 |
| 0.0456 | 6.7680 | 6010 | 0.7277 | 0.3438 | 0.7277 | 0.8530 |
| 0.0456 | 6.7703 | 6012 | 0.7280 | 0.3438 | 0.7280 | 0.8532 |
| 0.0456 | 6.7725 | 6014 | 0.7290 | 0.3438 | 0.7290 | 0.8538 |
| 0.0456 | 6.7748 | 6016 | 0.7305 | 0.3438 | 0.7305 | 0.8547 |
| 0.0456 | 6.7770 | 6018 | 0.7314 | 0.3438 | 0.7314 | 0.8552 |
| 0.0456 | 6.7793 | 6020 | 0.7306 | 0.3438 | 0.7306 | 0.8548 |
| 0.0456 | 6.7815 | 6022 | 0.7308 | 0.3478 | 0.7308 | 0.8548 |
| 0.0456 | 6.7838 | 6024 | 0.7336 | 0.3478 | 0.7336 | 0.8565 |
| 0.0456 | 6.7860 | 6026 | 0.7361 | 0.3478 | 0.7361 | 0.8580 |
| 0.0456 | 6.7883 | 6028 | 0.7312 | 0.3478 | 0.7312 | 0.8551 |
| 0.0456 | 6.7905 | 6030 | 0.7226 | 0.3438 | 0.7226 | 0.8501 |
| 0.0456 | 6.7928 | 6032 | 0.7154 | 0.3438 | 0.7154 | 0.8458 |
| 0.0456 | 6.7950 | 6034 | 0.7112 | 0.3438 | 0.7112 | 0.8433 |
| 0.0456 | 6.7973 | 6036 | 0.7114 | 0.3438 | 0.7114 | 0.8434 |
| 0.0456 | 6.7995 | 6038 | 0.7144 | 0.3438 | 0.7144 | 0.8453 |
| 0.0456 | 6.8018 | 6040 | 0.7133 | 0.3438 | 0.7133 | 0.8446 |
| 0.0456 | 6.8041 | 6042 | 0.7107 | 0.3438 | 0.7107 | 0.8430 |
| 0.0456 | 6.8063 | 6044 | 0.7035 | 0.3438 | 0.7035 | 0.8388 |
| 0.0456 | 6.8086 | 6046 | 0.6996 | 0.3438 | 0.6996 | 0.8365 |
| 0.0456 | 6.8108 | 6048 | 0.6995 | 0.3438 | 0.6995 | 0.8363 |
| 0.0456 | 6.8131 | 6050 | 0.7078 | 0.3438 | 0.7078 | 0.8413 |
| 0.0456 | 6.8153 | 6052 | 0.7231 | 0.3284 | 0.7231 | 0.8504 |
| 0.0456 | 6.8176 | 6054 | 0.7392 | 0.3284 | 0.7392 | 0.8598 |
| 0.0456 | 6.8198 | 6056 | 0.7475 | 0.1429 | 0.7475 | 0.8646 |
| 0.0456 | 6.8221 | 6058 | 0.7414 | 0.2154 | 0.7414 | 0.8610 |
| 0.0456 | 6.8243 | 6060 | 0.7260 | 0.3284 | 0.7260 | 0.8521 |
| 0.0456 | 6.8266 | 6062 | 0.7114 | 0.2857 | 0.7114 | 0.8435 |
| 0.0456 | 6.8288 | 6064 | 0.7076 | 0.2857 | 0.7076 | 0.8412 |
| 0.0456 | 6.8311 | 6066 | 0.7059 | 0.2857 | 0.7059 | 0.8402 |
| 0.0456 | 6.8333 | 6068 | 0.7002 | 0.3438 | 0.7002 | 0.8368 |
| 0.0456 | 6.8356 | 6070 | 0.6969 | 0.3438 | 0.6969 | 0.8348 |
| 0.0456 | 6.8378 | 6072 | 0.6989 | 0.3438 | 0.6989 | 0.8360 |
| 0.0456 | 6.8401 | 6074 | 0.7044 | 0.3438 | 0.7044 | 0.8393 |
| 0.0456 | 6.8423 | 6076 | 0.7083 | 0.3226 | 0.7083 | 0.8416 |
| 0.0456 | 6.8446 | 6078 | 0.7133 | 0.3226 | 0.7133 | 0.8446 |
| 0.0456 | 6.8468 | 6080 | 0.7169 | 0.3284 | 0.7169 | 0.8467 |
| 0.0456 | 6.8491 | 6082 | 0.7145 | 0.3824 | 0.7145 | 0.8453 |
| 0.0456 | 6.8514 | 6084 | 0.7119 | 0.3810 | 0.7119 | 0.8437 |
| 0.0456 | 6.8536 | 6086 | 0.7089 | 0.3438 | 0.7089 | 0.8419 |
| 0.0456 | 6.8559 | 6088 | 0.7080 | 0.3438 | 0.7080 | 0.8414 |
| 0.0456 | 6.8581 | 6090 | 0.7093 | 0.3438 | 0.7093 | 0.8422 |
| 0.0456 | 6.8604 | 6092 | 0.7110 | 0.3438 | 0.7110 | 0.8432 |
| 0.0456 | 6.8626 | 6094 | 0.7135 | 0.3438 | 0.7135 | 0.8447 |
| 0.0456 | 6.8649 | 6096 | 0.7234 | 0.3824 | 0.7234 | 0.8505 |
| 0.0456 | 6.8671 | 6098 | 0.7330 | 0.2727 | 0.7330 | 0.8562 |
| 0.0456 | 6.8694 | 6100 | 0.7341 | 0.1972 | 0.7341 | 0.8568 |
| 0.0456 | 6.8716 | 6102 | 0.7255 | 0.2727 | 0.7255 | 0.8518 |
| 0.0456 | 6.8739 | 6104 | 0.7150 | 0.3284 | 0.7150 | 0.8456 |
| 0.0456 | 6.8761 | 6106 | 0.7039 | 0.3810 | 0.7039 | 0.8390 |
| 0.0456 | 6.8784 | 6108 | 0.6968 | 0.3438 | 0.6968 | 0.8348 |
| 0.0456 | 6.8806 | 6110 | 0.6972 | 0.3438 | 0.6972 | 0.8350 |
| 0.0456 | 6.8829 | 6112 | 0.6986 | 0.3438 | 0.6986 | 0.8358 |
| 0.0456 | 6.8851 | 6114 | 0.7055 | 0.2727 | 0.7055 | 0.8400 |
| 0.0456 | 6.8874 | 6116 | 0.7214 | 0.2727 | 0.7214 | 0.8494 |
| 0.0456 | 6.8896 | 6118 | 0.7274 | 0.1972 | 0.7274 | 0.8529 |
| 0.0456 | 6.8919 | 6120 | 0.7263 | 0.1972 | 0.7263 | 0.8522 |
| 0.0456 | 6.8941 | 6122 | 0.7247 | 0.2895 | 0.7247 | 0.8513 |
| 0.0456 | 6.8964 | 6124 | 0.7121 | 0.2727 | 0.7121 | 0.8439 |
| 0.0456 | 6.8986 | 6126 | 0.6944 | 0.2623 | 0.6944 | 0.8333 |
| 0.0456 | 6.9009 | 6128 | 0.6803 | 0.2857 | 0.6803 | 0.8248 |
| 0.0456 | 6.9032 | 6130 | 0.6734 | 0.3438 | 0.6734 | 0.8206 |
| 0.0456 | 6.9054 | 6132 | 0.6733 | 0.3438 | 0.6733 | 0.8206 |
| 0.0456 | 6.9077 | 6134 | 0.6769 | 0.2857 | 0.6769 | 0.8227 |
| 0.0456 | 6.9099 | 6136 | 0.6894 | 0.3226 | 0.6894 | 0.8303 |
| 0.0456 | 6.9122 | 6138 | 0.7029 | 0.2727 | 0.7029 | 0.8384 |
| 0.0456 | 6.9144 | 6140 | 0.7085 | 0.3284 | 0.7085 | 0.8417 |
| 0.0456 | 6.9167 | 6142 | 0.7135 | 0.3824 | 0.7135 | 0.8447 |
| 0.0456 | 6.9189 | 6144 | 0.7229 | 0.4324 | 0.7229 | 0.8502 |
| 0.0456 | 6.9212 | 6146 | 0.7351 | 0.4324 | 0.7351 | 0.8574 |
| 0.0456 | 6.9234 | 6148 | 0.7393 | 0.4324 | 0.7393 | 0.8598 |
| 0.0456 | 6.9257 | 6150 | 0.7428 | 0.3478 | 0.7428 | 0.8618 |
| 0.0456 | 6.9279 | 6152 | 0.7466 | 0.3438 | 0.7466 | 0.8640 |
| 0.0456 | 6.9302 | 6154 | 0.7533 | 0.3077 | 0.7533 | 0.8679 |
| 0.0456 | 6.9324 | 6156 | 0.7528 | 0.3077 | 0.7528 | 0.8677 |
| 0.0456 | 6.9347 | 6158 | 0.7469 | 0.3478 | 0.7469 | 0.8642 |
| 0.0456 | 6.9369 | 6160 | 0.7408 | 0.3478 | 0.7408 | 0.8607 |
| 0.0456 | 6.9392 | 6162 | 0.7327 | 0.3438 | 0.7327 | 0.8560 |
| 0.0456 | 6.9414 | 6164 | 0.7251 | 0.3438 | 0.7251 | 0.8515 |
| 0.0456 | 6.9437 | 6166 | 0.7184 | 0.3478 | 0.7184 | 0.8476 |
| 0.0456 | 6.9459 | 6168 | 0.7157 | 0.3478 | 0.7157 | 0.8460 |
| 0.0456 | 6.9482 | 6170 | 0.7196 | 0.4167 | 0.7196 | 0.8483 |
| 0.0456 | 6.9505 | 6172 | 0.7140 | 0.4167 | 0.7140 | 0.8450 |
| 0.0456 | 6.9527 | 6174 | 0.7053 | 0.4167 | 0.7053 | 0.8398 |
| 0.0456 | 6.9550 | 6176 | 0.7024 | 0.4167 | 0.7024 | 0.8381 |
| 0.0456 | 6.9572 | 6178 | 0.6875 | 0.3284 | 0.6875 | 0.8292 |
| 0.0456 | 6.9595 | 6180 | 0.6664 | 0.3226 | 0.6664 | 0.8163 |
| 0.0456 | 6.9617 | 6182 | 0.6444 | 0.3226 | 0.6444 | 0.8028 |
| 0.0456 | 6.9640 | 6184 | 0.6339 | 0.3810 | 0.6339 | 0.7962 |
| 0.0456 | 6.9662 | 6186 | 0.6320 | 0.3226 | 0.6320 | 0.7950 |
| 0.0456 | 6.9685 | 6188 | 0.6395 | 0.3226 | 0.6395 | 0.7997 |
| 0.0456 | 6.9707 | 6190 | 0.6483 | 0.3226 | 0.6483 | 0.8051 |
| 0.0456 | 6.9730 | 6192 | 0.6593 | 0.2623 | 0.6593 | 0.8120 |
| 0.0456 | 6.9752 | 6194 | 0.6805 | 0.2154 | 0.6805 | 0.8249 |
| 0.0456 | 6.9775 | 6196 | 0.6995 | 0.3143 | 0.6995 | 0.8363 |
| 0.0456 | 6.9797 | 6198 | 0.7047 | 0.3143 | 0.7047 | 0.8395 |
| 0.0456 | 6.9820 | 6200 | 0.6993 | 0.3662 | 0.6993 | 0.8362 |
| 0.0456 | 6.9842 | 6202 | 0.6884 | 0.3284 | 0.6884 | 0.8297 |
| 0.0456 | 6.9865 | 6204 | 0.6836 | 0.3438 | 0.6836 | 0.8268 |
| 0.0456 | 6.9887 | 6206 | 0.6842 | 0.3438 | 0.6842 | 0.8272 |
| 0.0456 | 6.9910 | 6208 | 0.6913 | 0.3438 | 0.6913 | 0.8314 |
| 0.0456 | 6.9932 | 6210 | 0.6949 | 0.3438 | 0.6949 | 0.8336 |
| 0.0456 | 6.9955 | 6212 | 0.6984 | 0.3478 | 0.6984 | 0.8357 |
| 0.0456 | 6.9977 | 6214 | 0.6965 | 0.3438 | 0.6965 | 0.8346 |
| 0.0456 | 7.0 | 6216 | 0.6950 | 0.3478 | 0.6950 | 0.8337 |
| 0.0456 | 7.0023 | 6218 | 0.6926 | 0.3824 | 0.6926 | 0.8322 |
| 0.0456 | 7.0045 | 6220 | 0.6945 | 0.3662 | 0.6945 | 0.8334 |
| 0.0456 | 7.0068 | 6222 | 0.6916 | 0.3143 | 0.6916 | 0.8316 |
| 0.0456 | 7.0090 | 6224 | 0.6839 | 0.2623 | 0.6839 | 0.8270 |
| 0.0456 | 7.0113 | 6226 | 0.6783 | 0.2623 | 0.6783 | 0.8236 |
| 0.0456 | 7.0135 | 6228 | 0.6791 | 0.2623 | 0.6791 | 0.8241 |
| 0.0456 | 7.0158 | 6230 | 0.6860 | 0.2623 | 0.6860 | 0.8283 |
| 0.0456 | 7.0180 | 6232 | 0.6919 | 0.2623 | 0.6919 | 0.8318 |
| 0.0456 | 7.0203 | 6234 | 0.6938 | 0.2727 | 0.6938 | 0.8330 |
| 0.0456 | 7.0225 | 6236 | 0.7012 | 0.2727 | 0.7012 | 0.8374 |
| 0.0456 | 7.0248 | 6238 | 0.7025 | 0.2727 | 0.7025 | 0.8381 |
| 0.0456 | 7.0270 | 6240 | 0.7037 | 0.3478 | 0.7037 | 0.8389 |
| 0.0456 | 7.0293 | 6242 | 0.7060 | 0.3478 | 0.7060 | 0.8402 |
| 0.0456 | 7.0315 | 6244 | 0.7099 | 0.3478 | 0.7099 | 0.8425 |
| 0.0456 | 7.0338 | 6246 | 0.7160 | 0.3478 | 0.7160 | 0.8462 |
| 0.0456 | 7.0360 | 6248 | 0.7232 | 0.3284 | 0.7232 | 0.8504 |
| 0.0456 | 7.0383 | 6250 | 0.7230 | 0.3284 | 0.7230 | 0.8503 |
| 0.0456 | 7.0405 | 6252 | 0.7224 | 0.2727 | 0.7224 | 0.8500 |
| 0.0456 | 7.0428 | 6254 | 0.7191 | 0.2727 | 0.7191 | 0.8480 |
| 0.0456 | 7.0450 | 6256 | 0.7089 | 0.2727 | 0.7089 | 0.8420 |
| 0.0456 | 7.0473 | 6258 | 0.6982 | 0.2727 | 0.6982 | 0.8356 |
| 0.0456 | 7.0495 | 6260 | 0.6869 | 0.2258 | 0.6869 | 0.8288 |
| 0.0456 | 7.0518 | 6262 | 0.6853 | 0.2857 | 0.6853 | 0.8278 |
| 0.0456 | 7.0541 | 6264 | 0.6898 | 0.2857 | 0.6898 | 0.8305 |
| 0.0456 | 7.0563 | 6266 | 0.6926 | 0.2857 | 0.6926 | 0.8322 |
| 0.0456 | 7.0586 | 6268 | 0.7013 | 0.2857 | 0.7013 | 0.8374 |
| 0.0456 | 7.0608 | 6270 | 0.7081 | 0.2941 | 0.7081 | 0.8415 |
| 0.0456 | 7.0631 | 6272 | 0.7134 | 0.2941 | 0.7134 | 0.8446 |
| 0.0456 | 7.0653 | 6274 | 0.7147 | 0.3438 | 0.7147 | 0.8454 |
| 0.0456 | 7.0676 | 6276 | 0.7131 | 0.3438 | 0.7131 | 0.8445 |
| 0.0456 | 7.0698 | 6278 | 0.7105 | 0.3438 | 0.7105 | 0.8429 |
| 0.0456 | 7.0721 | 6280 | 0.7046 | 0.3438 | 0.7046 | 0.8394 |
| 0.0456 | 7.0743 | 6282 | 0.7001 | 0.3438 | 0.7001 | 0.8367 |
| 0.0456 | 7.0766 | 6284 | 0.6998 | 0.3438 | 0.6998 | 0.8366 |
| 0.0456 | 7.0788 | 6286 | 0.6970 | 0.2857 | 0.6970 | 0.8349 |
| 0.0456 | 7.0811 | 6288 | 0.6974 | 0.2857 | 0.6974 | 0.8351 |
| 0.0456 | 7.0833 | 6290 | 0.7006 | 0.2941 | 0.7006 | 0.8370 |
| 0.0456 | 7.0856 | 6292 | 0.6953 | 0.2941 | 0.6953 | 0.8339 |
| 0.0456 | 7.0878 | 6294 | 0.6875 | 0.2857 | 0.6875 | 0.8292 |
| 0.0456 | 7.0901 | 6296 | 0.6806 | 0.2857 | 0.6806 | 0.8250 |
| 0.0456 | 7.0923 | 6298 | 0.6781 | 0.2857 | 0.6781 | 0.8235 |
| 0.0456 | 7.0946 | 6300 | 0.6755 | 0.2857 | 0.6755 | 0.8219 |
| 0.0456 | 7.0968 | 6302 | 0.6762 | 0.2857 | 0.6762 | 0.8223 |
| 0.0456 | 7.0991 | 6304 | 0.6825 | 0.2857 | 0.6825 | 0.8261 |
| 0.0456 | 7.1014 | 6306 | 0.6932 | 0.2857 | 0.6932 | 0.8326 |
| 0.0456 | 7.1036 | 6308 | 0.6984 | 0.3836 | 0.6984 | 0.8357 |
| 0.0456 | 7.1059 | 6310 | 0.7095 | 0.3333 | 0.7095 | 0.8423 |
| 0.0456 | 7.1081 | 6312 | 0.7172 | 0.3333 | 0.7172 | 0.8469 |
| 0.0456 | 7.1104 | 6314 | 0.7161 | 0.3836 | 0.7161 | 0.8462 |
| 0.0456 | 7.1126 | 6316 | 0.7115 | 0.3836 | 0.7115 | 0.8435 |
| 0.0456 | 7.1149 | 6318 | 0.7069 | 0.2857 | 0.7069 | 0.8408 |
| 0.0456 | 7.1171 | 6320 | 0.7039 | 0.3438 | 0.7039 | 0.8390 |
| 0.0456 | 7.1194 | 6322 | 0.7015 | 0.3438 | 0.7015 | 0.8376 |
| 0.0456 | 7.1216 | 6324 | 0.7034 | 0.3438 | 0.7034 | 0.8387 |
| 0.0456 | 7.1239 | 6326 | 0.7078 | 0.3438 | 0.7078 | 0.8413 |
| 0.0456 | 7.1261 | 6328 | 0.7100 | 0.3438 | 0.7100 | 0.8426 |
| 0.0456 | 7.1284 | 6330 | 0.7159 | 0.3836 | 0.7159 | 0.8461 |
| 0.0456 | 7.1306 | 6332 | 0.7216 | 0.3836 | 0.7216 | 0.8495 |
| 0.0456 | 7.1329 | 6334 | 0.7252 | 0.3836 | 0.7252 | 0.8516 |
| 0.0456 | 7.1351 | 6336 | 0.7332 | 0.3836 | 0.7332 | 0.8563 |
| 0.0456 | 7.1374 | 6338 | 0.7328 | 0.3836 | 0.7328 | 0.8560 |
| 0.0456 | 7.1396 | 6340 | 0.7250 | 0.3836 | 0.7250 | 0.8515 |
| 0.0456 | 7.1419 | 6342 | 0.7174 | 0.3836 | 0.7174 | 0.8470 |
| 0.0456 | 7.1441 | 6344 | 0.7180 | 0.3836 | 0.7180 | 0.8474 |
| 0.0456 | 7.1464 | 6346 | 0.7121 | 0.3836 | 0.7121 | 0.8439 |
| 0.0456 | 7.1486 | 6348 | 0.7013 | 0.3836 | 0.7013 | 0.8375 |
| 0.0456 | 7.1509 | 6350 | 0.6865 | 0.3836 | 0.6865 | 0.8286 |
| 0.0456 | 7.1532 | 6352 | 0.6789 | 0.2941 | 0.6789 | 0.8240 |
| 0.0456 | 7.1554 | 6354 | 0.6760 | 0.2941 | 0.6760 | 0.8222 |
| 0.0456 | 7.1577 | 6356 | 0.6760 | 0.2941 | 0.6760 | 0.8222 |
| 0.0456 | 7.1599 | 6358 | 0.6818 | 0.3836 | 0.6818 | 0.8257 |
| 0.0456 | 7.1622 | 6360 | 0.6842 | 0.3836 | 0.6842 | 0.8271 |
| 0.0456 | 7.1644 | 6362 | 0.6838 | 0.2941 | 0.6838 | 0.8269 |
| 0.0456 | 7.1667 | 6364 | 0.6825 | 0.2941 | 0.6825 | 0.8262 |
| 0.0456 | 7.1689 | 6366 | 0.6824 | 0.3478 | 0.6824 | 0.8261 |
| 0.0456 | 7.1712 | 6368 | 0.6838 | 0.3438 | 0.6838 | 0.8269 |
| 0.0456 | 7.1734 | 6370 | 0.6833 | 0.3438 | 0.6833 | 0.8266 |
| 0.0456 | 7.1757 | 6372 | 0.6856 | 0.3478 | 0.6856 | 0.8280 |
| 0.0456 | 7.1779 | 6374 | 0.6909 | 0.2941 | 0.6909 | 0.8312 |
| 0.0456 | 7.1802 | 6376 | 0.6941 | 0.2941 | 0.6941 | 0.8331 |
| 0.0456 | 7.1824 | 6378 | 0.6894 | 0.3284 | 0.6894 | 0.8303 |
| 0.0456 | 7.1847 | 6380 | 0.6867 | 0.3284 | 0.6867 | 0.8287 |
| 0.0456 | 7.1869 | 6382 | 0.6790 | 0.2941 | 0.6790 | 0.8240 |
| 0.0456 | 7.1892 | 6384 | 0.6745 | 0.2941 | 0.6745 | 0.8213 |
| 0.0456 | 7.1914 | 6386 | 0.6714 | 0.3438 | 0.6714 | 0.8194 |
| 0.0456 | 7.1937 | 6388 | 0.6718 | 0.3438 | 0.6718 | 0.8196 |
| 0.0456 | 7.1959 | 6390 | 0.6753 | 0.2857 | 0.6753 | 0.8218 |
| 0.0456 | 7.1982 | 6392 | 0.6782 | 0.3478 | 0.6782 | 0.8236 |
| 0.0456 | 7.2005 | 6394 | 0.6860 | 0.2941 | 0.6860 | 0.8283 |
| 0.0456 | 7.2027 | 6396 | 0.6935 | 0.3284 | 0.6935 | 0.8327 |
| 0.0456 | 7.2050 | 6398 | 0.7001 | 0.3284 | 0.7001 | 0.8367 |
| 0.0456 | 7.2072 | 6400 | 0.7070 | 0.3284 | 0.7070 | 0.8408 |
| 0.0456 | 7.2095 | 6402 | 0.7081 | 0.3284 | 0.7081 | 0.8415 |
| 0.0456 | 7.2117 | 6404 | 0.7138 | 0.3662 | 0.7138 | 0.8449 |
| 0.0456 | 7.2140 | 6406 | 0.7248 | 0.3662 | 0.7248 | 0.8513 |
| 0.0456 | 7.2162 | 6408 | 0.7290 | 0.3662 | 0.7290 | 0.8538 |
| 0.0456 | 7.2185 | 6410 | 0.7330 | 0.3662 | 0.7330 | 0.8561 |
| 0.0456 | 7.2207 | 6412 | 0.7380 | 0.3662 | 0.7380 | 0.8591 |
| 0.0456 | 7.2230 | 6414 | 0.7303 | 0.3662 | 0.7303 | 0.8546 |
| 0.0456 | 7.2252 | 6416 | 0.7257 | 0.4167 | 0.7257 | 0.8519 |
| 0.0456 | 7.2275 | 6418 | 0.7178 | 0.2941 | 0.7178 | 0.8472 |
| 0.0456 | 7.2297 | 6420 | 0.7122 | 0.3478 | 0.7122 | 0.8439 |
| 0.0456 | 7.2320 | 6422 | 0.7076 | 0.3478 | 0.7076 | 0.8412 |
| 0.0456 | 7.2342 | 6424 | 0.7089 | 0.3478 | 0.7089 | 0.8420 |
| 0.0456 | 7.2365 | 6426 | 0.7160 | 0.3478 | 0.7160 | 0.8462 |
| 0.0456 | 7.2387 | 6428 | 0.7199 | 0.3478 | 0.7199 | 0.8485 |
| 0.0456 | 7.2410 | 6430 | 0.7289 | 0.3478 | 0.7289 | 0.8537 |
| 0.0456 | 7.2432 | 6432 | 0.7323 | 0.3478 | 0.7323 | 0.8558 |
| 0.0456 | 7.2455 | 6434 | 0.7281 | 0.3478 | 0.7281 | 0.8533 |
| 0.0456 | 7.2477 | 6436 | 0.7241 | 0.3478 | 0.7241 | 0.8509 |
| 0.0456 | 7.25 | 6438 | 0.7189 | 0.3478 | 0.7189 | 0.8479 |
| 0.0456 | 7.2523 | 6440 | 0.7163 | 0.3478 | 0.7163 | 0.8463 |
| 0.0456 | 7.2545 | 6442 | 0.7161 | 0.3478 | 0.7161 | 0.8463 |
| 0.0456 | 7.2568 | 6444 | 0.7161 | 0.3478 | 0.7161 | 0.8462 |
| 0.0456 | 7.2590 | 6446 | 0.7175 | 0.3478 | 0.7175 | 0.8471 |
| 0.0456 | 7.2613 | 6448 | 0.7208 | 0.3478 | 0.7208 | 0.8490 |
| 0.0456 | 7.2635 | 6450 | 0.7236 | 0.3478 | 0.7236 | 0.8506 |
| 0.0456 | 7.2658 | 6452 | 0.7230 | 0.3478 | 0.7230 | 0.8503 |
| 0.0456 | 7.2680 | 6454 | 0.7269 | 0.2941 | 0.7269 | 0.8526 |
| 0.0456 | 7.2703 | 6456 | 0.7257 | 0.3836 | 0.7257 | 0.8519 |
| 0.0456 | 7.2725 | 6458 | 0.7174 | 0.3478 | 0.7174 | 0.8470 |
| 0.0456 | 7.2748 | 6460 | 0.7105 | 0.3478 | 0.7105 | 0.8429 |
| 0.0456 | 7.2770 | 6462 | 0.7087 | 0.2941 | 0.7087 | 0.8419 |
| 0.0456 | 7.2793 | 6464 | 0.7086 | 0.3284 | 0.7086 | 0.8418 |
| 0.0456 | 7.2815 | 6466 | 0.7097 | 0.3284 | 0.7097 | 0.8424 |
| 0.0456 | 7.2838 | 6468 | 0.7147 | 0.4167 | 0.7147 | 0.8454 |
| 0.0456 | 7.2860 | 6470 | 0.7237 | 0.4167 | 0.7237 | 0.8507 |
| 0.0456 | 7.2883 | 6472 | 0.7306 | 0.3836 | 0.7306 | 0.8548 |
| 0.0456 | 7.2905 | 6474 | 0.7378 | 0.3836 | 0.7378 | 0.8590 |
| 0.0456 | 7.2928 | 6476 | 0.7382 | 0.4324 | 0.7382 | 0.8592 |
| 0.0456 | 7.2950 | 6478 | 0.7407 | 0.3478 | 0.7407 | 0.8607 |
| 0.0456 | 7.2973 | 6480 | 0.7370 | 0.3478 | 0.7370 | 0.8585 |
| 0.0456 | 7.2995 | 6482 | 0.7327 | 0.3478 | 0.7327 | 0.8560 |
| 0.0456 | 7.3018 | 6484 | 0.7292 | 0.3478 | 0.7292 | 0.8539 |
| 0.0456 | 7.3041 | 6486 | 0.7232 | 0.3478 | 0.7232 | 0.8504 |
| 0.0456 | 7.3063 | 6488 | 0.7192 | 0.3478 | 0.7192 | 0.8480 |
| 0.0456 | 7.3086 | 6490 | 0.7153 | 0.3478 | 0.7153 | 0.8457 |
| 0.0456 | 7.3108 | 6492 | 0.7143 | 0.2941 | 0.7143 | 0.8452 |
| 0.0456 | 7.3131 | 6494 | 0.7210 | 0.2727 | 0.7210 | 0.8491 |
| 0.0456 | 7.3153 | 6496 | 0.7298 | 0.3662 | 0.7298 | 0.8543 |
| 0.0456 | 7.3176 | 6498 | 0.7307 | 0.3662 | 0.7307 | 0.8548 |
| 0.0415 | 7.3198 | 6500 | 0.7276 | 0.3662 | 0.7276 | 0.8530 |
| 0.0415 | 7.3221 | 6502 | 0.7196 | 0.2727 | 0.7196 | 0.8483 |
| 0.0415 | 7.3243 | 6504 | 0.7101 | 0.3284 | 0.7101 | 0.8427 |
| 0.0415 | 7.3266 | 6506 | 0.7078 | 0.3824 | 0.7078 | 0.8413 |
| 0.0415 | 7.3288 | 6508 | 0.7124 | 0.3824 | 0.7124 | 0.8440 |
| 0.0415 | 7.3311 | 6510 | 0.7115 | 0.3824 | 0.7115 | 0.8435 |
| 0.0415 | 7.3333 | 6512 | 0.7056 | 0.3478 | 0.7056 | 0.8400 |
| 0.0415 | 7.3356 | 6514 | 0.7009 | 0.3438 | 0.7009 | 0.8372 |
| 0.0415 | 7.3378 | 6516 | 0.7002 | 0.3438 | 0.7002 | 0.8368 |
| 0.0415 | 7.3401 | 6518 | 0.6995 | 0.3438 | 0.6995 | 0.8364 |
| 0.0415 | 7.3423 | 6520 | 0.6997 | 0.3438 | 0.6997 | 0.8365 |
| 0.0415 | 7.3446 | 6522 | 0.7031 | 0.3478 | 0.7031 | 0.8385 |
| 0.0415 | 7.3468 | 6524 | 0.7083 | 0.3824 | 0.7083 | 0.8416 |
| 0.0415 | 7.3491 | 6526 | 0.7158 | 0.4167 | 0.7158 | 0.8461 |
| 0.0415 | 7.3514 | 6528 | 0.7154 | 0.4167 | 0.7154 | 0.8458 |
| 0.0415 | 7.3536 | 6530 | 0.7100 | 0.4167 | 0.7100 | 0.8426 |
| 0.0415 | 7.3559 | 6532 | 0.7064 | 0.4167 | 0.7064 | 0.8405 |
| 0.0415 | 7.3581 | 6534 | 0.6979 | 0.3284 | 0.6979 | 0.8354 |
| 0.0415 | 7.3604 | 6536 | 0.6885 | 0.3284 | 0.6885 | 0.8298 |
| 0.0415 | 7.3626 | 6538 | 0.6787 | 0.3284 | 0.6787 | 0.8238 |
| 0.0415 | 7.3649 | 6540 | 0.6727 | 0.3810 | 0.6727 | 0.8202 |
| 0.0415 | 7.3671 | 6542 | 0.6677 | 0.3810 | 0.6677 | 0.8171 |
| 0.0415 | 7.3694 | 6544 | 0.6658 | 0.3438 | 0.6658 | 0.8159 |
| 0.0415 | 7.3716 | 6546 | 0.6688 | 0.3438 | 0.6688 | 0.8178 |
| 0.0415 | 7.3739 | 6548 | 0.6743 | 0.3438 | 0.6743 | 0.8212 |
| 0.0415 | 7.3761 | 6550 | 0.6796 | 0.3438 | 0.6796 | 0.8244 |
| 0.0415 | 7.3784 | 6552 | 0.6878 | 0.3438 | 0.6878 | 0.8293 |
| 0.0415 | 7.3806 | 6554 | 0.6937 | 0.3824 | 0.6937 | 0.8329 |
| 0.0415 | 7.3829 | 6556 | 0.6991 | 0.4658 | 0.6991 | 0.8361 |
| 0.0415 | 7.3851 | 6558 | 0.7014 | 0.4324 | 0.7014 | 0.8375 |
| 0.0415 | 7.3874 | 6560 | 0.7042 | 0.4324 | 0.7042 | 0.8391 |
| 0.0415 | 7.3896 | 6562 | 0.7099 | 0.4324 | 0.7099 | 0.8425 |
| 0.0415 | 7.3919 | 6564 | 0.7105 | 0.4324 | 0.7105 | 0.8429 |
| 0.0415 | 7.3941 | 6566 | 0.7137 | 0.4324 | 0.7137 | 0.8448 |
| 0.0415 | 7.3964 | 6568 | 0.7161 | 0.4324 | 0.7161 | 0.8462 |
| 0.0415 | 7.3986 | 6570 | 0.7218 | 0.4324 | 0.7218 | 0.8496 |
| 0.0415 | 7.4009 | 6572 | 0.7224 | 0.4324 | 0.7224 | 0.8499 |
| 0.0415 | 7.4032 | 6574 | 0.7226 | 0.4324 | 0.7226 | 0.8501 |
| 0.0415 | 7.4054 | 6576 | 0.7156 | 0.3438 | 0.7156 | 0.8459 |
| 0.0415 | 7.4077 | 6578 | 0.7102 | 0.3438 | 0.7102 | 0.8427 |
| 0.0415 | 7.4099 | 6580 | 0.7061 | 0.3438 | 0.7061 | 0.8403 |
| 0.0415 | 7.4122 | 6582 | 0.7015 | 0.3438 | 0.7015 | 0.8376 |
| 0.0415 | 7.4144 | 6584 | 0.6997 | 0.3438 | 0.6997 | 0.8365 |
| 0.0415 | 7.4167 | 6586 | 0.7011 | 0.3438 | 0.7011 | 0.8373 |
| 0.0415 | 7.4189 | 6588 | 0.7021 | 0.3438 | 0.7021 | 0.8379 |
| 0.0415 | 7.4212 | 6590 | 0.7032 | 0.3438 | 0.7032 | 0.8386 |
| 0.0415 | 7.4234 | 6592 | 0.7047 | 0.3438 | 0.7047 | 0.8394 |
| 0.0415 | 7.4257 | 6594 | 0.7103 | 0.3438 | 0.7103 | 0.8428 |
| 0.0415 | 7.4279 | 6596 | 0.7200 | 0.1972 | 0.7200 | 0.8485 |
| 0.0415 | 7.4302 | 6598 | 0.7205 | 0.2895 | 0.7205 | 0.8488 |
| 0.0415 | 7.4324 | 6600 | 0.7152 | 0.2727 | 0.7152 | 0.8457 |
| 0.0415 | 7.4347 | 6602 | 0.7096 | 0.2727 | 0.7096 | 0.8424 |
| 0.0415 | 7.4369 | 6604 | 0.7014 | 0.3438 | 0.7014 | 0.8375 |
| 0.0415 | 7.4392 | 6606 | 0.7015 | 0.3438 | 0.7015 | 0.8376 |
| 0.0415 | 7.4414 | 6608 | 0.7074 | 0.3438 | 0.7074 | 0.8410 |
| 0.0415 | 7.4437 | 6610 | 0.7140 | 0.3438 | 0.7140 | 0.8450 |
| 0.0415 | 7.4459 | 6612 | 0.7227 | 0.3438 | 0.7227 | 0.8501 |
| 0.0415 | 7.4482 | 6614 | 0.7344 | 0.2703 | 0.7344 | 0.8570 |
| 0.0415 | 7.4505 | 6616 | 0.7412 | 0.2597 | 0.7412 | 0.8609 |
| 0.0415 | 7.4527 | 6618 | 0.7442 | 0.2597 | 0.7442 | 0.8627 |
| 0.0415 | 7.4550 | 6620 | 0.7424 | 0.2597 | 0.7424 | 0.8616 |
| 0.0415 | 7.4572 | 6622 | 0.7331 | 0.3478 | 0.7331 | 0.8562 |
| 0.0415 | 7.4595 | 6624 | 0.7199 | 0.3438 | 0.7199 | 0.8485 |
| 0.0415 | 7.4617 | 6626 | 0.7108 | 0.3438 | 0.7108 | 0.8431 |
| 0.0415 | 7.4640 | 6628 | 0.7039 | 0.3438 | 0.7039 | 0.8390 |
| 0.0415 | 7.4662 | 6630 | 0.6988 | 0.3438 | 0.6988 | 0.8360 |
| 0.0415 | 7.4685 | 6632 | 0.6956 | 0.3438 | 0.6956 | 0.8341 |
| 0.0415 | 7.4707 | 6634 | 0.6937 | 0.3438 | 0.6937 | 0.8329 |
| 0.0415 | 7.4730 | 6636 | 0.6951 | 0.3438 | 0.6951 | 0.8337 |
| 0.0415 | 7.4752 | 6638 | 0.6999 | 0.3438 | 0.6999 | 0.8366 |
| 0.0415 | 7.4775 | 6640 | 0.7016 | 0.2258 | 0.7016 | 0.8376 |
| 0.0415 | 7.4797 | 6642 | 0.7025 | 0.2258 | 0.7025 | 0.8381 |
| 0.0415 | 7.4820 | 6644 | 0.7044 | 0.2258 | 0.7044 | 0.8393 |
| 0.0415 | 7.4842 | 6646 | 0.7092 | 0.2623 | 0.7092 | 0.8421 |
| 0.0415 | 7.4865 | 6648 | 0.7150 | 0.2727 | 0.7150 | 0.8456 |
| 0.0415 | 7.4887 | 6650 | 0.7190 | 0.2727 | 0.7190 | 0.8480 |
| 0.0415 | 7.4910 | 6652 | 0.7210 | 0.2154 | 0.7210 | 0.8491 |
| 0.0415 | 7.4932 | 6654 | 0.7210 | 0.2154 | 0.7210 | 0.8491 |
| 0.0415 | 7.4955 | 6656 | 0.7175 | 0.2154 | 0.7175 | 0.8471 |
| 0.0415 | 7.4977 | 6658 | 0.7128 | 0.2000 | 0.7128 | 0.8443 |
| 0.0415 | 7.5 | 6660 | 0.7081 | 0.2623 | 0.7081 | 0.8415 |
| 0.0415 | 7.5023 | 6662 | 0.7016 | 0.2857 | 0.7016 | 0.8376 |
| 0.0415 | 7.5045 | 6664 | 0.6949 | 0.3438 | 0.6949 | 0.8336 |
| 0.0415 | 7.5068 | 6666 | 0.6927 | 0.3438 | 0.6927 | 0.8323 |
| 0.0415 | 7.5090 | 6668 | 0.6953 | 0.3438 | 0.6953 | 0.8338 |
| 0.0415 | 7.5113 | 6670 | 0.7035 | 0.3438 | 0.7035 | 0.8388 |
| 0.0415 | 7.5135 | 6672 | 0.7133 | 0.3438 | 0.7133 | 0.8446 |
| 0.0415 | 7.5158 | 6674 | 0.7270 | 0.2727 | 0.7270 | 0.8527 |
| 0.0415 | 7.5180 | 6676 | 0.7391 | 0.24 | 0.7391 | 0.8597 |
| 0.0415 | 7.5203 | 6678 | 0.7484 | 0.24 | 0.7484 | 0.8651 |
| 0.0415 | 7.5225 | 6680 | 0.7469 | 0.24 | 0.7469 | 0.8643 |
| 0.0415 | 7.5248 | 6682 | 0.7379 | 0.24 | 0.7379 | 0.8590 |
| 0.0415 | 7.5270 | 6684 | 0.7220 | 0.3143 | 0.7220 | 0.8497 |
| 0.0415 | 7.5293 | 6686 | 0.7065 | 0.3438 | 0.7065 | 0.8405 |
| 0.0415 | 7.5315 | 6688 | 0.7005 | 0.3438 | 0.7005 | 0.8370 |
| 0.0415 | 7.5338 | 6690 | 0.6989 | 0.3438 | 0.6989 | 0.8360 |
| 0.0415 | 7.5360 | 6692 | 0.7011 | 0.3438 | 0.7011 | 0.8373 |
| 0.0415 | 7.5383 | 6694 | 0.7020 | 0.3438 | 0.7020 | 0.8378 |
| 0.0415 | 7.5405 | 6696 | 0.7099 | 0.3438 | 0.7099 | 0.8425 |
| 0.0415 | 7.5428 | 6698 | 0.7246 | 0.3662 | 0.7246 | 0.8513 |
| 0.0415 | 7.5450 | 6700 | 0.7328 | 0.3662 | 0.7328 | 0.8560 |
| 0.0415 | 7.5473 | 6702 | 0.7456 | 0.2895 | 0.7456 | 0.8635 |
| 0.0415 | 7.5495 | 6704 | 0.7610 | 0.24 | 0.7610 | 0.8724 |
| 0.0415 | 7.5518 | 6706 | 0.7636 | 0.24 | 0.7636 | 0.8738 |
| 0.0415 | 7.5541 | 6708 | 0.7618 | 0.24 | 0.7618 | 0.8728 |
| 0.0415 | 7.5563 | 6710 | 0.7575 | 0.24 | 0.7575 | 0.8704 |
| 0.0415 | 7.5586 | 6712 | 0.7467 | 0.2895 | 0.7467 | 0.8641 |
| 0.0415 | 7.5608 | 6714 | 0.7323 | 0.3662 | 0.7323 | 0.8558 |
| 0.0415 | 7.5631 | 6716 | 0.7215 | 0.4348 | 0.7215 | 0.8494 |
| 0.0415 | 7.5653 | 6718 | 0.7172 | 0.3438 | 0.7172 | 0.8469 |
| 0.0415 | 7.5676 | 6720 | 0.7181 | 0.3438 | 0.7181 | 0.8474 |
| 0.0415 | 7.5698 | 6722 | 0.7207 | 0.3438 | 0.7207 | 0.8489 |
| 0.0415 | 7.5721 | 6724 | 0.7237 | 0.2154 | 0.7237 | 0.8507 |
| 0.0415 | 7.5743 | 6726 | 0.7225 | 0.3438 | 0.7225 | 0.8500 |
| 0.0415 | 7.5766 | 6728 | 0.7221 | 0.3438 | 0.7221 | 0.8498 |
| 0.0415 | 7.5788 | 6730 | 0.7250 | 0.3438 | 0.7250 | 0.8515 |
| 0.0415 | 7.5811 | 6732 | 0.7334 | 0.4706 | 0.7334 | 0.8564 |
| 0.0415 | 7.5833 | 6734 | 0.7454 | 0.3662 | 0.7454 | 0.8633 |
| 0.0415 | 7.5856 | 6736 | 0.7511 | 0.2895 | 0.7511 | 0.8666 |
| 0.0415 | 7.5878 | 6738 | 0.7504 | 0.3662 | 0.7504 | 0.8662 |
| 0.0415 | 7.5901 | 6740 | 0.7430 | 0.4324 | 0.7430 | 0.8620 |
| 0.0415 | 7.5923 | 6742 | 0.7385 | 0.3438 | 0.7385 | 0.8594 |
| 0.0415 | 7.5946 | 6744 | 0.7400 | 0.3438 | 0.7400 | 0.8602 |
| 0.0415 | 7.5968 | 6746 | 0.7435 | 0.3438 | 0.7435 | 0.8622 |
| 0.0415 | 7.5991 | 6748 | 0.7443 | 0.3478 | 0.7443 | 0.8627 |
| 0.0415 | 7.6014 | 6750 | 0.7432 | 0.4324 | 0.7432 | 0.8621 |
| 0.0415 | 7.6036 | 6752 | 0.7368 | 0.4324 | 0.7368 | 0.8584 |
| 0.0415 | 7.6059 | 6754 | 0.7303 | 0.4348 | 0.7303 | 0.8546 |
| 0.0415 | 7.6081 | 6756 | 0.7228 | 0.3438 | 0.7228 | 0.8502 |
| 0.0415 | 7.6104 | 6758 | 0.7173 | 0.3438 | 0.7173 | 0.8469 |
| 0.0415 | 7.6126 | 6760 | 0.7133 | 0.3438 | 0.7133 | 0.8446 |
| 0.0415 | 7.6149 | 6762 | 0.7090 | 0.3438 | 0.7090 | 0.8420 |
| 0.0415 | 7.6171 | 6764 | 0.7075 | 0.3438 | 0.7075 | 0.8412 |
| 0.0415 | 7.6194 | 6766 | 0.7068 | 0.4179 | 0.7068 | 0.8407 |
| 0.0415 | 7.6216 | 6768 | 0.7109 | 0.3636 | 0.7109 | 0.8431 |
| 0.0415 | 7.6239 | 6770 | 0.7122 | 0.3143 | 0.7122 | 0.8439 |
| 0.0415 | 7.6261 | 6772 | 0.7168 | 0.3143 | 0.7168 | 0.8466 |
| 0.0415 | 7.6284 | 6774 | 0.7177 | 0.3143 | 0.7177 | 0.8472 |
| 0.0415 | 7.6306 | 6776 | 0.7221 | 0.3143 | 0.7221 | 0.8498 |
| 0.0415 | 7.6329 | 6778 | 0.7206 | 0.3662 | 0.7206 | 0.8489 |
| 0.0415 | 7.6351 | 6780 | 0.7166 | 0.3662 | 0.7166 | 0.8465 |
| 0.0415 | 7.6374 | 6782 | 0.7153 | 0.2941 | 0.7153 | 0.8458 |
| 0.0415 | 7.6396 | 6784 | 0.7184 | 0.3438 | 0.7184 | 0.8476 |
| 0.0415 | 7.6419 | 6786 | 0.7246 | 0.3438 | 0.7246 | 0.8513 |
| 0.0415 | 7.6441 | 6788 | 0.7349 | 0.3478 | 0.7349 | 0.8573 |
| 0.0415 | 7.6464 | 6790 | 0.7461 | 0.4324 | 0.7461 | 0.8637 |
| 0.0415 | 7.6486 | 6792 | 0.7573 | 0.4324 | 0.7573 | 0.8703 |
| 0.0415 | 7.6509 | 6794 | 0.7697 | 0.3836 | 0.7697 | 0.8773 |
| 0.0415 | 7.6532 | 6796 | 0.7746 | 0.3836 | 0.7746 | 0.8801 |
| 0.0415 | 7.6554 | 6798 | 0.7734 | 0.3836 | 0.7734 | 0.8794 |
| 0.0415 | 7.6577 | 6800 | 0.7675 | 0.3836 | 0.7675 | 0.8761 |
| 0.0415 | 7.6599 | 6802 | 0.7569 | 0.3836 | 0.7569 | 0.8700 |
| 0.0415 | 7.6622 | 6804 | 0.7446 | 0.4324 | 0.7446 | 0.8629 |
| 0.0415 | 7.6644 | 6806 | 0.7356 | 0.3478 | 0.7356 | 0.8577 |
| 0.0415 | 7.6667 | 6808 | 0.7240 | 0.3438 | 0.7240 | 0.8509 |
| 0.0415 | 7.6689 | 6810 | 0.7156 | 0.3438 | 0.7156 | 0.8459 |
| 0.0415 | 7.6712 | 6812 | 0.7127 | 0.3438 | 0.7127 | 0.8442 |
| 0.0415 | 7.6734 | 6814 | 0.7148 | 0.3478 | 0.7148 | 0.8455 |
| 0.0415 | 7.6757 | 6816 | 0.7196 | 0.2941 | 0.7196 | 0.8483 |
| 0.0415 | 7.6779 | 6818 | 0.7237 | 0.3836 | 0.7237 | 0.8507 |
| 0.0415 | 7.6802 | 6820 | 0.7300 | 0.4167 | 0.7300 | 0.8544 |
| 0.0415 | 7.6824 | 6822 | 0.7325 | 0.3662 | 0.7325 | 0.8558 |
| 0.0415 | 7.6847 | 6824 | 0.7311 | 0.3662 | 0.7311 | 0.8551 |
| 0.0415 | 7.6869 | 6826 | 0.7284 | 0.3662 | 0.7284 | 0.8535 |
| 0.0415 | 7.6892 | 6828 | 0.7194 | 0.3662 | 0.7194 | 0.8482 |
| 0.0415 | 7.6914 | 6830 | 0.7158 | 0.3662 | 0.7158 | 0.8460 |
| 0.0415 | 7.6937 | 6832 | 0.7108 | 0.3662 | 0.7108 | 0.8431 |
| 0.0415 | 7.6959 | 6834 | 0.6989 | 0.3284 | 0.6989 | 0.8360 |
| 0.0415 | 7.6982 | 6836 | 0.6854 | 0.2857 | 0.6854 | 0.8279 |
| 0.0415 | 7.7005 | 6838 | 0.6739 | 0.2857 | 0.6739 | 0.8209 |
| 0.0415 | 7.7027 | 6840 | 0.6684 | 0.3438 | 0.6684 | 0.8176 |
| 0.0415 | 7.7050 | 6842 | 0.6690 | 0.3438 | 0.6690 | 0.8179 |
| 0.0415 | 7.7072 | 6844 | 0.6726 | 0.3438 | 0.6726 | 0.8201 |
| 0.0415 | 7.7095 | 6846 | 0.6816 | 0.3438 | 0.6816 | 0.8256 |
| 0.0415 | 7.7117 | 6848 | 0.6925 | 0.2857 | 0.6925 | 0.8322 |
| 0.0415 | 7.7140 | 6850 | 0.7067 | 0.2941 | 0.7067 | 0.8406 |
| 0.0415 | 7.7162 | 6852 | 0.7179 | 0.4167 | 0.7179 | 0.8473 |
| 0.0415 | 7.7185 | 6854 | 0.7257 | 0.4167 | 0.7257 | 0.8519 |
| 0.0415 | 7.7207 | 6856 | 0.7284 | 0.4167 | 0.7284 | 0.8535 |
| 0.0415 | 7.7230 | 6858 | 0.7239 | 0.3836 | 0.7239 | 0.8508 |
| 0.0415 | 7.7252 | 6860 | 0.7183 | 0.3836 | 0.7183 | 0.8475 |
| 0.0415 | 7.7275 | 6862 | 0.7157 | 0.2941 | 0.7157 | 0.8460 |
| 0.0415 | 7.7297 | 6864 | 0.7136 | 0.2941 | 0.7136 | 0.8448 |
| 0.0415 | 7.7320 | 6866 | 0.7152 | 0.3836 | 0.7152 | 0.8457 |
| 0.0415 | 7.7342 | 6868 | 0.7161 | 0.4167 | 0.7161 | 0.8462 |
| 0.0415 | 7.7365 | 6870 | 0.7183 | 0.4167 | 0.7183 | 0.8475 |
| 0.0415 | 7.7387 | 6872 | 0.7175 | 0.3836 | 0.7175 | 0.8470 |
| 0.0415 | 7.7410 | 6874 | 0.7187 | 0.3836 | 0.7187 | 0.8478 |
| 0.0415 | 7.7432 | 6876 | 0.7174 | 0.3836 | 0.7174 | 0.8470 |
| 0.0415 | 7.7455 | 6878 | 0.7215 | 0.3836 | 0.7215 | 0.8494 |
| 0.0415 | 7.7477 | 6880 | 0.7263 | 0.4167 | 0.7263 | 0.8522 |
| 0.0415 | 7.75 | 6882 | 0.7248 | 0.4167 | 0.7248 | 0.8514 |
| 0.0415 | 7.7523 | 6884 | 0.7202 | 0.4167 | 0.7202 | 0.8486 |
| 0.0415 | 7.7545 | 6886 | 0.7149 | 0.3478 | 0.7149 | 0.8455 |
| 0.0415 | 7.7568 | 6888 | 0.7124 | 0.3478 | 0.7124 | 0.8441 |
| 0.0415 | 7.7590 | 6890 | 0.7062 | 0.3478 | 0.7062 | 0.8403 |
| 0.0415 | 7.7613 | 6892 | 0.6978 | 0.3438 | 0.6978 | 0.8353 |
| 0.0415 | 7.7635 | 6894 | 0.6926 | 0.3438 | 0.6926 | 0.8322 |
| 0.0415 | 7.7658 | 6896 | 0.6938 | 0.3478 | 0.6938 | 0.8329 |
| 0.0415 | 7.7680 | 6898 | 0.7019 | 0.4167 | 0.7019 | 0.8378 |
| 0.0415 | 7.7703 | 6900 | 0.7043 | 0.4167 | 0.7043 | 0.8392 |
| 0.0415 | 7.7725 | 6902 | 0.7001 | 0.4167 | 0.7001 | 0.8367 |
| 0.0415 | 7.7748 | 6904 | 0.6970 | 0.4167 | 0.6970 | 0.8349 |
| 0.0415 | 7.7770 | 6906 | 0.6996 | 0.4167 | 0.6996 | 0.8364 |
| 0.0415 | 7.7793 | 6908 | 0.6994 | 0.4167 | 0.6994 | 0.8363 |
| 0.0415 | 7.7815 | 6910 | 0.7019 | 0.4167 | 0.7019 | 0.8378 |
| 0.0415 | 7.7838 | 6912 | 0.6994 | 0.4167 | 0.6994 | 0.8363 |
| 0.0415 | 7.7860 | 6914 | 0.6937 | 0.4167 | 0.6937 | 0.8329 |
| 0.0415 | 7.7883 | 6916 | 0.6921 | 0.3438 | 0.6921 | 0.8320 |
| 0.0415 | 7.7905 | 6918 | 0.6913 | 0.3438 | 0.6913 | 0.8314 |
| 0.0415 | 7.7928 | 6920 | 0.6927 | 0.3438 | 0.6927 | 0.8323 |
| 0.0415 | 7.7950 | 6922 | 0.6926 | 0.3438 | 0.6926 | 0.8322 |
| 0.0415 | 7.7973 | 6924 | 0.6941 | 0.3438 | 0.6941 | 0.8331 |
| 0.0415 | 7.7995 | 6926 | 0.7010 | 0.4324 | 0.7010 | 0.8373 |
| 0.0415 | 7.8018 | 6928 | 0.7101 | 0.4167 | 0.7101 | 0.8427 |
| 0.0415 | 7.8041 | 6930 | 0.7209 | 0.4167 | 0.7209 | 0.8491 |
| 0.0415 | 7.8063 | 6932 | 0.7270 | 0.4167 | 0.7270 | 0.8526 |
| 0.0415 | 7.8086 | 6934 | 0.7306 | 0.4167 | 0.7306 | 0.8548 |
| 0.0415 | 7.8108 | 6936 | 0.7298 | 0.4167 | 0.7298 | 0.8543 |
| 0.0415 | 7.8131 | 6938 | 0.7224 | 0.4167 | 0.7224 | 0.8499 |
| 0.0415 | 7.8153 | 6940 | 0.7206 | 0.4167 | 0.7206 | 0.8489 |
| 0.0415 | 7.8176 | 6942 | 0.7195 | 0.4167 | 0.7195 | 0.8482 |
| 0.0415 | 7.8198 | 6944 | 0.7222 | 0.4167 | 0.7222 | 0.8498 |
| 0.0415 | 7.8221 | 6946 | 0.7210 | 0.3836 | 0.7210 | 0.8491 |
| 0.0415 | 7.8243 | 6948 | 0.7178 | 0.3836 | 0.7178 | 0.8472 |
| 0.0415 | 7.8266 | 6950 | 0.7149 | 0.3478 | 0.7149 | 0.8455 |
| 0.0415 | 7.8288 | 6952 | 0.7150 | 0.3438 | 0.7150 | 0.8456 |
| 0.0415 | 7.8311 | 6954 | 0.7142 | 0.3438 | 0.7142 | 0.8451 |
| 0.0415 | 7.8333 | 6956 | 0.7133 | 0.3438 | 0.7133 | 0.8445 |
| 0.0415 | 7.8356 | 6958 | 0.7117 | 0.3438 | 0.7117 | 0.8436 |
| 0.0415 | 7.8378 | 6960 | 0.7148 | 0.3836 | 0.7148 | 0.8454 |
| 0.0415 | 7.8401 | 6962 | 0.7161 | 0.4324 | 0.7161 | 0.8462 |
| 0.0415 | 7.8423 | 6964 | 0.7195 | 0.3836 | 0.7195 | 0.8482 |
| 0.0415 | 7.8446 | 6966 | 0.7229 | 0.3836 | 0.7229 | 0.8502 |
| 0.0415 | 7.8468 | 6968 | 0.7264 | 0.3836 | 0.7264 | 0.8523 |
| 0.0415 | 7.8491 | 6970 | 0.7313 | 0.3836 | 0.7313 | 0.8552 |
| 0.0415 | 7.8514 | 6972 | 0.7397 | 0.3836 | 0.7397 | 0.8600 |
| 0.0415 | 7.8536 | 6974 | 0.7433 | 0.4167 | 0.7433 | 0.8622 |
| 0.0415 | 7.8559 | 6976 | 0.7476 | 0.4167 | 0.7476 | 0.8646 |
| 0.0415 | 7.8581 | 6978 | 0.7424 | 0.4167 | 0.7424 | 0.8617 |
| 0.0415 | 7.8604 | 6980 | 0.7368 | 0.4167 | 0.7368 | 0.8584 |
| 0.0415 | 7.8626 | 6982 | 0.7329 | 0.4167 | 0.7329 | 0.8561 |
| 0.0415 | 7.8649 | 6984 | 0.7247 | 0.4167 | 0.7247 | 0.8513 |
| 0.0415 | 7.8671 | 6986 | 0.7247 | 0.4167 | 0.7247 | 0.8513 |
| 0.0415 | 7.8694 | 6988 | 0.7242 | 0.4167 | 0.7242 | 0.8510 |
| 0.0415 | 7.8716 | 6990 | 0.7181 | 0.4167 | 0.7181 | 0.8474 |
| 0.0415 | 7.8739 | 6992 | 0.7175 | 0.4324 | 0.7175 | 0.8471 |
| 0.0415 | 7.8761 | 6994 | 0.7148 | 0.4324 | 0.7148 | 0.8454 |
| 0.0415 | 7.8784 | 6996 | 0.7150 | 0.4324 | 0.7150 | 0.8456 |
| 0.0415 | 7.8806 | 6998 | 0.7179 | 0.4324 | 0.7179 | 0.8473 |
| 0.04 | 7.8829 | 7000 | 0.7207 | 0.4658 | 0.7207 | 0.8490 |
| 0.04 | 7.8851 | 7002 | 0.7210 | 0.4167 | 0.7210 | 0.8491 |
| 0.04 | 7.8874 | 7004 | 0.7251 | 0.4167 | 0.7251 | 0.8515 |
| 0.04 | 7.8896 | 7006 | 0.7344 | 0.4167 | 0.7344 | 0.8570 |
| 0.04 | 7.8919 | 7008 | 0.7381 | 0.4167 | 0.7381 | 0.8591 |
| 0.04 | 7.8941 | 7010 | 0.7407 | 0.4167 | 0.7407 | 0.8606 |
| 0.04 | 7.8964 | 7012 | 0.7353 | 0.4167 | 0.7353 | 0.8575 |
| 0.04 | 7.8986 | 7014 | 0.7229 | 0.4167 | 0.7229 | 0.8502 |
| 0.04 | 7.9009 | 7016 | 0.7093 | 0.4167 | 0.7093 | 0.8422 |
| 0.04 | 7.9032 | 7018 | 0.7031 | 0.4167 | 0.7031 | 0.8385 |
| 0.04 | 7.9054 | 7020 | 0.6978 | 0.3284 | 0.6978 | 0.8353 |
| 0.04 | 7.9077 | 7022 | 0.6972 | 0.3284 | 0.6972 | 0.8350 |
| 0.04 | 7.9099 | 7024 | 0.6989 | 0.4167 | 0.6989 | 0.8360 |
| 0.04 | 7.9122 | 7026 | 0.7079 | 0.4167 | 0.7079 | 0.8414 |
| 0.04 | 7.9144 | 7028 | 0.7158 | 0.4167 | 0.7158 | 0.8460 |
| 0.04 | 7.9167 | 7030 | 0.7286 | 0.3662 | 0.7286 | 0.8536 |
| 0.04 | 7.9189 | 7032 | 0.7383 | 0.24 | 0.7383 | 0.8592 |
| 0.04 | 7.9212 | 7034 | 0.7366 | 0.24 | 0.7366 | 0.8583 |
| 0.04 | 7.9234 | 7036 | 0.7247 | 0.3143 | 0.7247 | 0.8513 |
| 0.04 | 7.9257 | 7038 | 0.7191 | 0.3662 | 0.7191 | 0.8480 |
| 0.04 | 7.9279 | 7040 | 0.7119 | 0.4167 | 0.7119 | 0.8437 |
| 0.04 | 7.9302 | 7042 | 0.7053 | 0.4167 | 0.7053 | 0.8398 |
| 0.04 | 7.9324 | 7044 | 0.7061 | 0.4167 | 0.7061 | 0.8403 |
| 0.04 | 7.9347 | 7046 | 0.7087 | 0.4167 | 0.7087 | 0.8418 |
| 0.04 | 7.9369 | 7048 | 0.7109 | 0.4167 | 0.7109 | 0.8431 |
| 0.04 | 7.9392 | 7050 | 0.7147 | 0.4658 | 0.7147 | 0.8454 |
| 0.04 | 7.9414 | 7052 | 0.7215 | 0.4658 | 0.7215 | 0.8494 |
| 0.04 | 7.9437 | 7054 | 0.7228 | 0.4324 | 0.7228 | 0.8502 |
| 0.04 | 7.9459 | 7056 | 0.7261 | 0.3478 | 0.7261 | 0.8521 |
| 0.04 | 7.9482 | 7058 | 0.7256 | 0.3478 | 0.7256 | 0.8518 |
| 0.04 | 7.9505 | 7060 | 0.7259 | 0.3478 | 0.7259 | 0.8520 |
| 0.04 | 7.9527 | 7062 | 0.7257 | 0.3478 | 0.7257 | 0.8519 |
| 0.04 | 7.9550 | 7064 | 0.7243 | 0.3478 | 0.7243 | 0.8511 |
| 0.04 | 7.9572 | 7066 | 0.7232 | 0.3478 | 0.7232 | 0.8504 |
| 0.04 | 7.9595 | 7068 | 0.7233 | 0.3478 | 0.7233 | 0.8504 |
| 0.04 | 7.9617 | 7070 | 0.7253 | 0.4324 | 0.7253 | 0.8516 |
| 0.04 | 7.9640 | 7072 | 0.7302 | 0.4167 | 0.7302 | 0.8545 |
| 0.04 | 7.9662 | 7074 | 0.7327 | 0.4167 | 0.7327 | 0.8560 |
| 0.04 | 7.9685 | 7076 | 0.7315 | 0.4167 | 0.7315 | 0.8553 |
| 0.04 | 7.9707 | 7078 | 0.7233 | 0.4167 | 0.7233 | 0.8505 |
| 0.04 | 7.9730 | 7080 | 0.7149 | 0.2941 | 0.7149 | 0.8455 |
| 0.04 | 7.9752 | 7082 | 0.7083 | 0.3478 | 0.7083 | 0.8416 |
| 0.04 | 7.9775 | 7084 | 0.7044 | 0.3438 | 0.7044 | 0.8393 |
| 0.04 | 7.9797 | 7086 | 0.7027 | 0.3438 | 0.7027 | 0.8383 |
| 0.04 | 7.9820 | 7088 | 0.7044 | 0.3438 | 0.7044 | 0.8393 |
| 0.04 | 7.9842 | 7090 | 0.7076 | 0.3438 | 0.7076 | 0.8412 |
| 0.04 | 7.9865 | 7092 | 0.7113 | 0.3438 | 0.7113 | 0.8434 |
| 0.04 | 7.9887 | 7094 | 0.7173 | 0.3478 | 0.7173 | 0.8469 |
| 0.04 | 7.9910 | 7096 | 0.7273 | 0.4167 | 0.7273 | 0.8528 |
| 0.04 | 7.9932 | 7098 | 0.7428 | 0.4167 | 0.7428 | 0.8618 |
| 0.04 | 7.9955 | 7100 | 0.7530 | 0.3377 | 0.7530 | 0.8678 |
| 0.04 | 7.9977 | 7102 | 0.7560 | 0.3377 | 0.7560 | 0.8695 |
| 0.04 | 8.0 | 7104 | 0.7496 | 0.3377 | 0.7496 | 0.8658 |
| 0.04 | 8.0023 | 7106 | 0.7349 | 0.4167 | 0.7349 | 0.8572 |
| 0.04 | 8.0045 | 7108 | 0.7170 | 0.4167 | 0.7170 | 0.8467 |
| 0.04 | 8.0068 | 7110 | 0.6983 | 0.3226 | 0.6983 | 0.8356 |
| 0.04 | 8.0090 | 7112 | 0.6843 | 0.3158 | 0.6843 | 0.8272 |
| 0.04 | 8.0113 | 7114 | 0.6807 | 0.3390 | 0.6807 | 0.8251 |
| 0.04 | 8.0135 | 7116 | 0.6807 | 0.3390 | 0.6807 | 0.8250 |
| 0.04 | 8.0158 | 7118 | 0.6804 | 0.3390 | 0.6804 | 0.8249 |
| 0.04 | 8.0180 | 7120 | 0.6829 | 0.3390 | 0.6829 | 0.8264 |
| 0.04 | 8.0203 | 7122 | 0.6877 | 0.3390 | 0.6877 | 0.8293 |
| 0.04 | 8.0225 | 7124 | 0.6965 | 0.3226 | 0.6965 | 0.8345 |
| 0.04 | 8.0248 | 7126 | 0.7076 | 0.3226 | 0.7076 | 0.8412 |
| 0.04 | 8.0270 | 7128 | 0.7199 | 0.3284 | 0.7199 | 0.8485 |
| 0.04 | 8.0293 | 7130 | 0.7309 | 0.3284 | 0.7309 | 0.8549 |
| 0.04 | 8.0315 | 7132 | 0.7374 | 0.4167 | 0.7374 | 0.8587 |
| 0.04 | 8.0338 | 7134 | 0.7419 | 0.4167 | 0.7419 | 0.8613 |
| 0.04 | 8.0360 | 7136 | 0.7431 | 0.4167 | 0.7431 | 0.8621 |
| 0.04 | 8.0383 | 7138 | 0.7389 | 0.4167 | 0.7389 | 0.8596 |
| 0.04 | 8.0405 | 7140 | 0.7290 | 0.3284 | 0.7290 | 0.8538 |
| 0.04 | 8.0428 | 7142 | 0.7242 | 0.3284 | 0.7242 | 0.8510 |
| 0.04 | 8.0450 | 7144 | 0.7225 | 0.3284 | 0.7225 | 0.8500 |
| 0.04 | 8.0473 | 7146 | 0.7209 | 0.3284 | 0.7209 | 0.8491 |
| 0.04 | 8.0495 | 7148 | 0.7144 | 0.3284 | 0.7144 | 0.8452 |
| 0.04 | 8.0518 | 7150 | 0.7066 | 0.3226 | 0.7066 | 0.8406 |
| 0.04 | 8.0541 | 7152 | 0.7029 | 0.3226 | 0.7029 | 0.8384 |
| 0.04 | 8.0563 | 7154 | 0.7021 | 0.2857 | 0.7021 | 0.8379 |
| 0.04 | 8.0586 | 7156 | 0.7008 | 0.3226 | 0.7008 | 0.8371 |
| 0.04 | 8.0608 | 7158 | 0.7032 | 0.3226 | 0.7032 | 0.8386 |
| 0.04 | 8.0631 | 7160 | 0.7088 | 0.3226 | 0.7088 | 0.8419 |
| 0.04 | 8.0653 | 7162 | 0.7202 | 0.3284 | 0.7202 | 0.8487 |
| 0.04 | 8.0676 | 7164 | 0.7285 | 0.4167 | 0.7285 | 0.8535 |
| 0.04 | 8.0698 | 7166 | 0.7333 | 0.4167 | 0.7333 | 0.8563 |
| 0.04 | 8.0721 | 7168 | 0.7310 | 0.4167 | 0.7310 | 0.8550 |
| 0.04 | 8.0743 | 7170 | 0.7284 | 0.4167 | 0.7284 | 0.8535 |
| 0.04 | 8.0766 | 7172 | 0.7240 | 0.4167 | 0.7240 | 0.8509 |
| 0.04 | 8.0788 | 7174 | 0.7227 | 0.4167 | 0.7227 | 0.8501 |
| 0.04 | 8.0811 | 7176 | 0.7210 | 0.4167 | 0.7210 | 0.8491 |
| 0.04 | 8.0833 | 7178 | 0.7181 | 0.4167 | 0.7181 | 0.8474 |
| 0.04 | 8.0856 | 7180 | 0.7114 | 0.4167 | 0.7114 | 0.8434 |
| 0.04 | 8.0878 | 7182 | 0.7056 | 0.3284 | 0.7056 | 0.8400 |
| 0.04 | 8.0901 | 7184 | 0.7017 | 0.3284 | 0.7017 | 0.8377 |
| 0.04 | 8.0923 | 7186 | 0.7022 | 0.4167 | 0.7022 | 0.8380 |
| 0.04 | 8.0946 | 7188 | 0.7100 | 0.4167 | 0.7100 | 0.8426 |
| 0.04 | 8.0968 | 7190 | 0.7169 | 0.4167 | 0.7169 | 0.8467 |
| 0.04 | 8.0991 | 7192 | 0.7169 | 0.4167 | 0.7169 | 0.8467 |
| 0.04 | 8.1014 | 7194 | 0.7174 | 0.4167 | 0.7174 | 0.8470 |
| 0.04 | 8.1036 | 7196 | 0.7151 | 0.4167 | 0.7151 | 0.8456 |
| 0.04 | 8.1059 | 7198 | 0.7106 | 0.4167 | 0.7106 | 0.8430 |
| 0.04 | 8.1081 | 7200 | 0.7077 | 0.4167 | 0.7077 | 0.8412 |
| 0.04 | 8.1104 | 7202 | 0.7020 | 0.3284 | 0.7020 | 0.8378 |
| 0.04 | 8.1126 | 7204 | 0.7007 | 0.3226 | 0.7007 | 0.8371 |
| 0.04 | 8.1149 | 7206 | 0.6985 | 0.3438 | 0.6985 | 0.8358 |
| 0.04 | 8.1171 | 7208 | 0.6949 | 0.3438 | 0.6949 | 0.8336 |
| 0.04 | 8.1194 | 7210 | 0.6938 | 0.3438 | 0.6938 | 0.8329 |
| 0.04 | 8.1216 | 7212 | 0.6961 | 0.3438 | 0.6961 | 0.8343 |
| 0.04 | 8.1239 | 7214 | 0.7001 | 0.3438 | 0.7001 | 0.8367 |
| 0.04 | 8.1261 | 7216 | 0.7037 | 0.3438 | 0.7037 | 0.8389 |
| 0.04 | 8.1284 | 7218 | 0.7087 | 0.3438 | 0.7087 | 0.8418 |
| 0.04 | 8.1306 | 7220 | 0.7156 | 0.3478 | 0.7156 | 0.8459 |
| 0.04 | 8.1329 | 7222 | 0.7220 | 0.4658 | 0.7220 | 0.8497 |
| 0.04 | 8.1351 | 7224 | 0.7277 | 0.4167 | 0.7277 | 0.8531 |
| 0.04 | 8.1374 | 7226 | 0.7329 | 0.4167 | 0.7329 | 0.8561 |
| 0.04 | 8.1396 | 7228 | 0.7339 | 0.4167 | 0.7339 | 0.8567 |
| 0.04 | 8.1419 | 7230 | 0.7282 | 0.4167 | 0.7282 | 0.8533 |
| 0.04 | 8.1441 | 7232 | 0.7216 | 0.4167 | 0.7216 | 0.8495 |
| 0.04 | 8.1464 | 7234 | 0.7162 | 0.4167 | 0.7162 | 0.8463 |
| 0.04 | 8.1486 | 7236 | 0.7097 | 0.3824 | 0.7097 | 0.8424 |
| 0.04 | 8.1509 | 7238 | 0.7044 | 0.3478 | 0.7044 | 0.8393 |
| 0.04 | 8.1532 | 7240 | 0.7003 | 0.3824 | 0.7003 | 0.8368 |
| 0.04 | 8.1554 | 7242 | 0.7002 | 0.3824 | 0.7002 | 0.8368 |
| 0.04 | 8.1577 | 7244 | 0.7012 | 0.3284 | 0.7012 | 0.8374 |
| 0.04 | 8.1599 | 7246 | 0.7047 | 0.3284 | 0.7047 | 0.8395 |
| 0.04 | 8.1622 | 7248 | 0.7116 | 0.4167 | 0.7116 | 0.8435 |
| 0.04 | 8.1644 | 7250 | 0.7181 | 0.4167 | 0.7181 | 0.8474 |
| 0.04 | 8.1667 | 7252 | 0.7213 | 0.4167 | 0.7213 | 0.8493 |
| 0.04 | 8.1689 | 7254 | 0.7223 | 0.4167 | 0.7223 | 0.8499 |
| 0.04 | 8.1712 | 7256 | 0.7213 | 0.4167 | 0.7213 | 0.8493 |
| 0.04 | 8.1734 | 7258 | 0.7202 | 0.4167 | 0.7202 | 0.8486 |
| 0.04 | 8.1757 | 7260 | 0.7163 | 0.3284 | 0.7163 | 0.8464 |
| 0.04 | 8.1779 | 7262 | 0.7142 | 0.3284 | 0.7142 | 0.8451 |
| 0.04 | 8.1802 | 7264 | 0.7133 | 0.3284 | 0.7133 | 0.8446 |
| 0.04 | 8.1824 | 7266 | 0.7113 | 0.3284 | 0.7113 | 0.8434 |
| 0.04 | 8.1847 | 7268 | 0.7074 | 0.3284 | 0.7074 | 0.8411 |
| 0.04 | 8.1869 | 7270 | 0.7046 | 0.3824 | 0.7046 | 0.8394 |
| 0.04 | 8.1892 | 7272 | 0.7045 | 0.3438 | 0.7045 | 0.8393 |
| 0.04 | 8.1914 | 7274 | 0.7060 | 0.3438 | 0.7060 | 0.8402 |
| 0.04 | 8.1937 | 7276 | 0.7082 | 0.3478 | 0.7082 | 0.8416 |
| 0.04 | 8.1959 | 7278 | 0.7104 | 0.3478 | 0.7104 | 0.8428 |
| 0.04 | 8.1982 | 7280 | 0.7135 | 0.3478 | 0.7135 | 0.8447 |
| 0.04 | 8.2005 | 7282 | 0.7167 | 0.3478 | 0.7167 | 0.8466 |
| 0.04 | 8.2027 | 7284 | 0.7215 | 0.3478 | 0.7215 | 0.8494 |
| 0.04 | 8.2050 | 7286 | 0.7252 | 0.3284 | 0.7252 | 0.8516 |
| 0.04 | 8.2072 | 7288 | 0.7234 | 0.4167 | 0.7234 | 0.8505 |
| 0.04 | 8.2095 | 7290 | 0.7185 | 0.3284 | 0.7185 | 0.8476 |
| 0.04 | 8.2117 | 7292 | 0.7148 | 0.3284 | 0.7148 | 0.8455 |
| 0.04 | 8.2140 | 7294 | 0.7135 | 0.4167 | 0.7135 | 0.8447 |
| 0.04 | 8.2162 | 7296 | 0.7118 | 0.4167 | 0.7118 | 0.8437 |
| 0.04 | 8.2185 | 7298 | 0.7091 | 0.4167 | 0.7091 | 0.8421 |
| 0.04 | 8.2207 | 7300 | 0.7043 | 0.4167 | 0.7043 | 0.8392 |
| 0.04 | 8.2230 | 7302 | 0.6992 | 0.4167 | 0.6992 | 0.8362 |
| 0.04 | 8.2252 | 7304 | 0.6902 | 0.3284 | 0.6902 | 0.8308 |
| 0.04 | 8.2275 | 7306 | 0.6829 | 0.3284 | 0.6829 | 0.8264 |
| 0.04 | 8.2297 | 7308 | 0.6814 | 0.3284 | 0.6814 | 0.8255 |
| 0.04 | 8.2320 | 7310 | 0.6850 | 0.3284 | 0.6850 | 0.8277 |
| 0.04 | 8.2342 | 7312 | 0.6908 | 0.3284 | 0.6908 | 0.8311 |
| 0.04 | 8.2365 | 7314 | 0.6952 | 0.4167 | 0.6952 | 0.8338 |
| 0.04 | 8.2387 | 7316 | 0.6977 | 0.4167 | 0.6977 | 0.8353 |
| 0.04 | 8.2410 | 7318 | 0.7008 | 0.4167 | 0.7008 | 0.8371 |
| 0.04 | 8.2432 | 7320 | 0.7038 | 0.4167 | 0.7038 | 0.8389 |
| 0.04 | 8.2455 | 7322 | 0.7075 | 0.4167 | 0.7075 | 0.8411 |
| 0.04 | 8.2477 | 7324 | 0.7127 | 0.4167 | 0.7127 | 0.8442 |
| 0.04 | 8.25 | 7326 | 0.7120 | 0.4167 | 0.7120 | 0.8438 |
| 0.04 | 8.2523 | 7328 | 0.7075 | 0.4167 | 0.7075 | 0.8411 |
| 0.04 | 8.2545 | 7330 | 0.7049 | 0.3824 | 0.7049 | 0.8396 |
| 0.04 | 8.2568 | 7332 | 0.7037 | 0.4658 | 0.7037 | 0.8389 |
| 0.04 | 8.2590 | 7334 | 0.7056 | 0.4167 | 0.7056 | 0.8400 |
| 0.04 | 8.2613 | 7336 | 0.7058 | 0.4167 | 0.7058 | 0.8401 |
| 0.04 | 8.2635 | 7338 | 0.7066 | 0.4167 | 0.7066 | 0.8406 |
| 0.04 | 8.2658 | 7340 | 0.7097 | 0.4167 | 0.7097 | 0.8425 |
| 0.04 | 8.2680 | 7342 | 0.7168 | 0.4167 | 0.7168 | 0.8466 |
| 0.04 | 8.2703 | 7344 | 0.7248 | 0.4167 | 0.7248 | 0.8514 |
| 0.04 | 8.2725 | 7346 | 0.7329 | 0.3662 | 0.7329 | 0.8561 |
| 0.04 | 8.2748 | 7348 | 0.7338 | 0.3662 | 0.7338 | 0.8566 |
| 0.04 | 8.2770 | 7350 | 0.7282 | 0.4167 | 0.7282 | 0.8533 |
| 0.04 | 8.2793 | 7352 | 0.7226 | 0.4167 | 0.7226 | 0.8501 |
| 0.04 | 8.2815 | 7354 | 0.7113 | 0.4167 | 0.7113 | 0.8434 |
| 0.04 | 8.2838 | 7356 | 0.7035 | 0.4167 | 0.7035 | 0.8388 |
| 0.04 | 8.2860 | 7358 | 0.6985 | 0.4167 | 0.6985 | 0.8358 |
| 0.04 | 8.2883 | 7360 | 0.6970 | 0.3284 | 0.6970 | 0.8349 |
| 0.04 | 8.2905 | 7362 | 0.6961 | 0.3284 | 0.6961 | 0.8343 |
| 0.04 | 8.2928 | 7364 | 0.6924 | 0.3226 | 0.6924 | 0.8321 |
| 0.04 | 8.2950 | 7366 | 0.6911 | 0.3810 | 0.6911 | 0.8313 |
| 0.04 | 8.2973 | 7368 | 0.6943 | 0.3284 | 0.6943 | 0.8333 |
| 0.04 | 8.2995 | 7370 | 0.7017 | 0.4167 | 0.7017 | 0.8377 |
| 0.04 | 8.3018 | 7372 | 0.7063 | 0.4167 | 0.7063 | 0.8404 |
| 0.04 | 8.3041 | 7374 | 0.7094 | 0.4167 | 0.7094 | 0.8422 |
| 0.04 | 8.3063 | 7376 | 0.7135 | 0.4167 | 0.7135 | 0.8447 |
| 0.04 | 8.3086 | 7378 | 0.7148 | 0.4167 | 0.7148 | 0.8455 |
| 0.04 | 8.3108 | 7380 | 0.7159 | 0.4167 | 0.7159 | 0.8461 |
| 0.04 | 8.3131 | 7382 | 0.7126 | 0.4167 | 0.7126 | 0.8441 |
| 0.04 | 8.3153 | 7384 | 0.7098 | 0.4167 | 0.7098 | 0.8425 |
| 0.04 | 8.3176 | 7386 | 0.7062 | 0.4167 | 0.7062 | 0.8403 |
| 0.04 | 8.3198 | 7388 | 0.7056 | 0.4167 | 0.7056 | 0.8400 |
| 0.04 | 8.3221 | 7390 | 0.7042 | 0.4167 | 0.7042 | 0.8391 |
| 0.04 | 8.3243 | 7392 | 0.7040 | 0.4167 | 0.7040 | 0.8391 |
| 0.04 | 8.3266 | 7394 | 0.7064 | 0.4167 | 0.7064 | 0.8405 |
| 0.04 | 8.3288 | 7396 | 0.7130 | 0.4167 | 0.7130 | 0.8444 |
| 0.04 | 8.3311 | 7398 | 0.7180 | 0.4167 | 0.7180 | 0.8473 |
| 0.04 | 8.3333 | 7400 | 0.7160 | 0.4167 | 0.7160 | 0.8462 |
| 0.04 | 8.3356 | 7402 | 0.7137 | 0.4167 | 0.7137 | 0.8448 |
| 0.04 | 8.3378 | 7404 | 0.7061 | 0.4167 | 0.7061 | 0.8403 |
| 0.04 | 8.3401 | 7406 | 0.6987 | 0.4167 | 0.6987 | 0.8359 |
| 0.04 | 8.3423 | 7408 | 0.6930 | 0.4167 | 0.6930 | 0.8325 |
| 0.04 | 8.3446 | 7410 | 0.6883 | 0.4167 | 0.6883 | 0.8297 |
| 0.04 | 8.3468 | 7412 | 0.6901 | 0.4167 | 0.6901 | 0.8307 |
| 0.04 | 8.3491 | 7414 | 0.6948 | 0.4167 | 0.6948 | 0.8335 |
| 0.04 | 8.3514 | 7416 | 0.6959 | 0.4167 | 0.6959 | 0.8342 |
| 0.04 | 8.3536 | 7418 | 0.6968 | 0.4167 | 0.6968 | 0.8348 |
| 0.04 | 8.3559 | 7420 | 0.6952 | 0.4167 | 0.6952 | 0.8338 |
| 0.04 | 8.3581 | 7422 | 0.6940 | 0.3284 | 0.6940 | 0.8331 |
| 0.04 | 8.3604 | 7424 | 0.6946 | 0.3284 | 0.6946 | 0.8334 |
| 0.04 | 8.3626 | 7426 | 0.6962 | 0.3284 | 0.6962 | 0.8344 |
| 0.04 | 8.3649 | 7428 | 0.6954 | 0.3824 | 0.6954 | 0.8339 |
| 0.04 | 8.3671 | 7430 | 0.6960 | 0.3824 | 0.6960 | 0.8343 |
| 0.04 | 8.3694 | 7432 | 0.6967 | 0.3824 | 0.6967 | 0.8347 |
| 0.04 | 8.3716 | 7434 | 0.6951 | 0.3824 | 0.6951 | 0.8337 |
| 0.04 | 8.3739 | 7436 | 0.6920 | 0.3810 | 0.6920 | 0.8319 |
| 0.04 | 8.3761 | 7438 | 0.6914 | 0.3810 | 0.6914 | 0.8315 |
| 0.04 | 8.3784 | 7440 | 0.6911 | 0.3810 | 0.6911 | 0.8313 |
| 0.04 | 8.3806 | 7442 | 0.6900 | 0.3810 | 0.6900 | 0.8307 |
| 0.04 | 8.3829 | 7444 | 0.6925 | 0.3284 | 0.6925 | 0.8322 |
| 0.04 | 8.3851 | 7446 | 0.6963 | 0.3284 | 0.6963 | 0.8344 |
| 0.04 | 8.3874 | 7448 | 0.6990 | 0.3284 | 0.6990 | 0.8361 |
| 0.04 | 8.3896 | 7450 | 0.7004 | 0.3284 | 0.7004 | 0.8369 |
| 0.04 | 8.3919 | 7452 | 0.7045 | 0.3284 | 0.7045 | 0.8393 |
| 0.04 | 8.3941 | 7454 | 0.7105 | 0.4167 | 0.7105 | 0.8429 |
| 0.04 | 8.3964 | 7456 | 0.7115 | 0.4167 | 0.7115 | 0.8435 |
| 0.04 | 8.3986 | 7458 | 0.7150 | 0.4167 | 0.7150 | 0.8456 |
| 0.04 | 8.4009 | 7460 | 0.7157 | 0.4167 | 0.7157 | 0.8460 |
| 0.04 | 8.4032 | 7462 | 0.7130 | 0.4167 | 0.7130 | 0.8444 |
| 0.04 | 8.4054 | 7464 | 0.7059 | 0.4167 | 0.7059 | 0.8402 |
| 0.04 | 8.4077 | 7466 | 0.7018 | 0.4167 | 0.7018 | 0.8377 |
| 0.04 | 8.4099 | 7468 | 0.6998 | 0.4167 | 0.6998 | 0.8365 |
| 0.04 | 8.4122 | 7470 | 0.6947 | 0.3284 | 0.6947 | 0.8335 |
| 0.04 | 8.4144 | 7472 | 0.6923 | 0.3284 | 0.6923 | 0.8320 |
| 0.04 | 8.4167 | 7474 | 0.6870 | 0.3226 | 0.6870 | 0.8289 |
| 0.04 | 8.4189 | 7476 | 0.6852 | 0.3226 | 0.6852 | 0.8278 |
| 0.04 | 8.4212 | 7478 | 0.6876 | 0.3226 | 0.6876 | 0.8292 |
| 0.04 | 8.4234 | 7480 | 0.6942 | 0.3284 | 0.6942 | 0.8332 |
| 0.04 | 8.4257 | 7482 | 0.7050 | 0.4167 | 0.7050 | 0.8397 |
| 0.04 | 8.4279 | 7484 | 0.7171 | 0.4167 | 0.7171 | 0.8468 |
| 0.04 | 8.4302 | 7486 | 0.7214 | 0.4167 | 0.7214 | 0.8494 |
| 0.04 | 8.4324 | 7488 | 0.7197 | 0.4167 | 0.7197 | 0.8483 |
| 0.04 | 8.4347 | 7490 | 0.7135 | 0.4167 | 0.7135 | 0.8447 |
| 0.04 | 8.4369 | 7492 | 0.7029 | 0.4167 | 0.7029 | 0.8384 |
| 0.04 | 8.4392 | 7494 | 0.6958 | 0.3284 | 0.6958 | 0.8342 |
| 0.04 | 8.4414 | 7496 | 0.6891 | 0.3226 | 0.6891 | 0.8301 |
| 0.04 | 8.4437 | 7498 | 0.6842 | 0.3810 | 0.6842 | 0.8272 |
| 0.0358 | 8.4459 | 7500 | 0.6837 | 0.3226 | 0.6837 | 0.8268 |
| 0.0358 | 8.4482 | 7502 | 0.6863 | 0.3226 | 0.6863 | 0.8285 |
| 0.0358 | 8.4505 | 7504 | 0.6884 | 0.3284 | 0.6884 | 0.8297 |
| 0.0358 | 8.4527 | 7506 | 0.6881 | 0.3284 | 0.6881 | 0.8295 |
| 0.0358 | 8.4550 | 7508 | 0.6853 | 0.3284 | 0.6853 | 0.8278 |
| 0.0358 | 8.4572 | 7510 | 0.6876 | 0.3284 | 0.6876 | 0.8292 |
| 0.0358 | 8.4595 | 7512 | 0.6911 | 0.3284 | 0.6911 | 0.8313 |
| 0.0358 | 8.4617 | 7514 | 0.6979 | 0.4167 | 0.6979 | 0.8354 |
| 0.0358 | 8.4640 | 7516 | 0.7047 | 0.4167 | 0.7047 | 0.8395 |
| 0.0358 | 8.4662 | 7518 | 0.7133 | 0.4167 | 0.7133 | 0.8446 |
| 0.0358 | 8.4685 | 7520 | 0.7143 | 0.4167 | 0.7143 | 0.8451 |
| 0.0358 | 8.4707 | 7522 | 0.7134 | 0.4167 | 0.7134 | 0.8446 |
| 0.0358 | 8.4730 | 7524 | 0.7097 | 0.4167 | 0.7097 | 0.8425 |
| 0.0358 | 8.4752 | 7526 | 0.7053 | 0.4167 | 0.7053 | 0.8398 |
| 0.0358 | 8.4775 | 7528 | 0.6974 | 0.4167 | 0.6974 | 0.8351 |
| 0.0358 | 8.4797 | 7530 | 0.6890 | 0.3284 | 0.6890 | 0.8301 |
| 0.0358 | 8.4820 | 7532 | 0.6811 | 0.3226 | 0.6811 | 0.8253 |
| 0.0358 | 8.4842 | 7534 | 0.6783 | 0.3226 | 0.6783 | 0.8236 |
| 0.0358 | 8.4865 | 7536 | 0.6797 | 0.3226 | 0.6797 | 0.8244 |
| 0.0358 | 8.4887 | 7538 | 0.6805 | 0.3226 | 0.6805 | 0.8249 |
| 0.0358 | 8.4910 | 7540 | 0.6841 | 0.3284 | 0.6841 | 0.8271 |
| 0.0358 | 8.4932 | 7542 | 0.6907 | 0.3284 | 0.6907 | 0.8311 |
| 0.0358 | 8.4955 | 7544 | 0.6946 | 0.3284 | 0.6946 | 0.8334 |
| 0.0358 | 8.4977 | 7546 | 0.6977 | 0.3284 | 0.6977 | 0.8353 |
| 0.0358 | 8.5 | 7548 | 0.7002 | 0.3284 | 0.7002 | 0.8368 |
| 0.0358 | 8.5023 | 7550 | 0.7039 | 0.3662 | 0.7039 | 0.8390 |
| 0.0358 | 8.5045 | 7552 | 0.7078 | 0.3662 | 0.7078 | 0.8413 |
| 0.0358 | 8.5068 | 7554 | 0.7048 | 0.3662 | 0.7048 | 0.8395 |
| 0.0358 | 8.5090 | 7556 | 0.7018 | 0.3662 | 0.7018 | 0.8377 |
| 0.0358 | 8.5113 | 7558 | 0.7000 | 0.3662 | 0.7000 | 0.8367 |
| 0.0358 | 8.5135 | 7560 | 0.6923 | 0.2727 | 0.6923 | 0.8321 |
| 0.0358 | 8.5158 | 7562 | 0.6868 | 0.3284 | 0.6868 | 0.8287 |
| 0.0358 | 8.5180 | 7564 | 0.6834 | 0.3284 | 0.6834 | 0.8267 |
| 0.0358 | 8.5203 | 7566 | 0.6761 | 0.3284 | 0.6761 | 0.8222 |
| 0.0358 | 8.5225 | 7568 | 0.6699 | 0.3284 | 0.6699 | 0.8185 |
| 0.0358 | 8.5248 | 7570 | 0.6684 | 0.3284 | 0.6684 | 0.8175 |
| 0.0358 | 8.5270 | 7572 | 0.6674 | 0.3284 | 0.6674 | 0.8170 |
| 0.0358 | 8.5293 | 7574 | 0.6712 | 0.3284 | 0.6712 | 0.8193 |
| 0.0358 | 8.5315 | 7576 | 0.6737 | 0.3284 | 0.6737 | 0.8208 |
| 0.0358 | 8.5338 | 7578 | 0.6735 | 0.3284 | 0.6735 | 0.8207 |
| 0.0358 | 8.5360 | 7580 | 0.6738 | 0.3284 | 0.6738 | 0.8209 |
| 0.0358 | 8.5383 | 7582 | 0.6766 | 0.3284 | 0.6766 | 0.8226 |
| 0.0358 | 8.5405 | 7584 | 0.6754 | 0.3284 | 0.6754 | 0.8218 |
| 0.0358 | 8.5428 | 7586 | 0.6720 | 0.3284 | 0.6720 | 0.8198 |
| 0.0358 | 8.5450 | 7588 | 0.6709 | 0.3284 | 0.6709 | 0.8191 |
| 0.0358 | 8.5473 | 7590 | 0.6684 | 0.3226 | 0.6684 | 0.8176 |
| 0.0358 | 8.5495 | 7592 | 0.6688 | 0.3226 | 0.6688 | 0.8178 |
| 0.0358 | 8.5518 | 7594 | 0.6691 | 0.3284 | 0.6691 | 0.8180 |
| 0.0358 | 8.5541 | 7596 | 0.6722 | 0.3284 | 0.6722 | 0.8199 |
| 0.0358 | 8.5563 | 7598 | 0.6798 | 0.3284 | 0.6798 | 0.8245 |
| 0.0358 | 8.5586 | 7600 | 0.6918 | 0.4167 | 0.6918 | 0.8317 |
| 0.0358 | 8.5608 | 7602 | 0.7077 | 0.3662 | 0.7077 | 0.8413 |
| 0.0358 | 8.5631 | 7604 | 0.7207 | 0.24 | 0.7207 | 0.8490 |
| 0.0358 | 8.5653 | 7606 | 0.7293 | 0.24 | 0.7293 | 0.8540 |
| 0.0358 | 8.5676 | 7608 | 0.7319 | 0.24 | 0.7319 | 0.8555 |
| 0.0358 | 8.5698 | 7610 | 0.7258 | 0.24 | 0.7258 | 0.8520 |
| 0.0358 | 8.5721 | 7612 | 0.7130 | 0.3143 | 0.7130 | 0.8444 |
| 0.0358 | 8.5743 | 7614 | 0.7034 | 0.3143 | 0.7034 | 0.8387 |
| 0.0358 | 8.5766 | 7616 | 0.6920 | 0.3662 | 0.6920 | 0.8319 |
| 0.0358 | 8.5788 | 7618 | 0.6796 | 0.3284 | 0.6796 | 0.8244 |
| 0.0358 | 8.5811 | 7620 | 0.6716 | 0.3284 | 0.6716 | 0.8195 |
| 0.0358 | 8.5833 | 7622 | 0.6671 | 0.3284 | 0.6671 | 0.8168 |
| 0.0358 | 8.5856 | 7624 | 0.6659 | 0.3226 | 0.6659 | 0.8160 |
| 0.0358 | 8.5878 | 7626 | 0.6673 | 0.3284 | 0.6673 | 0.8169 |
| 0.0358 | 8.5901 | 7628 | 0.6692 | 0.3284 | 0.6692 | 0.8180 |
| 0.0358 | 8.5923 | 7630 | 0.6703 | 0.3284 | 0.6703 | 0.8187 |
| 0.0358 | 8.5946 | 7632 | 0.6683 | 0.3226 | 0.6683 | 0.8175 |
| 0.0358 | 8.5968 | 7634 | 0.6692 | 0.3226 | 0.6692 | 0.8181 |
| 0.0358 | 8.5991 | 7636 | 0.6739 | 0.3226 | 0.6739 | 0.8209 |
| 0.0358 | 8.6014 | 7638 | 0.6813 | 0.3284 | 0.6813 | 0.8254 |
| 0.0358 | 8.6036 | 7640 | 0.6907 | 0.4167 | 0.6907 | 0.8311 |
| 0.0358 | 8.6059 | 7642 | 0.6983 | 0.3662 | 0.6983 | 0.8357 |
| 0.0358 | 8.6081 | 7644 | 0.6998 | 0.3662 | 0.6998 | 0.8366 |
| 0.0358 | 8.6104 | 7646 | 0.6982 | 0.4167 | 0.6982 | 0.8356 |
| 0.0358 | 8.6126 | 7648 | 0.6969 | 0.4167 | 0.6969 | 0.8348 |
| 0.0358 | 8.6149 | 7650 | 0.6943 | 0.4167 | 0.6943 | 0.8333 |
| 0.0358 | 8.6171 | 7652 | 0.6891 | 0.3284 | 0.6891 | 0.8301 |
| 0.0358 | 8.6194 | 7654 | 0.6863 | 0.3226 | 0.6863 | 0.8285 |
| 0.0358 | 8.6216 | 7656 | 0.6827 | 0.3226 | 0.6827 | 0.8262 |
| 0.0358 | 8.6239 | 7658 | 0.6835 | 0.3226 | 0.6835 | 0.8267 |
| 0.0358 | 8.6261 | 7660 | 0.6849 | 0.2857 | 0.6849 | 0.8276 |
| 0.0358 | 8.6284 | 7662 | 0.6859 | 0.2857 | 0.6859 | 0.8282 |
| 0.0358 | 8.6306 | 7664 | 0.6870 | 0.2857 | 0.6870 | 0.8289 |
| 0.0358 | 8.6329 | 7666 | 0.6912 | 0.3226 | 0.6912 | 0.8314 |
| 0.0358 | 8.6351 | 7668 | 0.6991 | 0.3284 | 0.6991 | 0.8361 |
| 0.0358 | 8.6374 | 7670 | 0.7039 | 0.3284 | 0.7039 | 0.8390 |
| 0.0358 | 8.6396 | 7672 | 0.7031 | 0.3284 | 0.7031 | 0.8385 |
| 0.0358 | 8.6419 | 7674 | 0.7025 | 0.3284 | 0.7025 | 0.8382 |
| 0.0358 | 8.6441 | 7676 | 0.7005 | 0.3284 | 0.7005 | 0.8369 |
| 0.0358 | 8.6464 | 7678 | 0.6996 | 0.3284 | 0.6996 | 0.8364 |
| 0.0358 | 8.6486 | 7680 | 0.7003 | 0.2727 | 0.7003 | 0.8368 |
| 0.0358 | 8.6509 | 7682 | 0.7030 | 0.2727 | 0.7030 | 0.8385 |
| 0.0358 | 8.6532 | 7684 | 0.7035 | 0.2727 | 0.7035 | 0.8388 |
| 0.0358 | 8.6554 | 7686 | 0.7062 | 0.2727 | 0.7062 | 0.8403 |
| 0.0358 | 8.6577 | 7688 | 0.7050 | 0.2727 | 0.7050 | 0.8396 |
| 0.0358 | 8.6599 | 7690 | 0.7030 | 0.2727 | 0.7030 | 0.8384 |
| 0.0358 | 8.6622 | 7692 | 0.7026 | 0.2727 | 0.7026 | 0.8382 |
| 0.0358 | 8.6644 | 7694 | 0.7078 | 0.3143 | 0.7078 | 0.8413 |
| 0.0358 | 8.6667 | 7696 | 0.7167 | 0.3143 | 0.7167 | 0.8466 |
| 0.0358 | 8.6689 | 7698 | 0.7238 | 0.3143 | 0.7238 | 0.8508 |
| 0.0358 | 8.6712 | 7700 | 0.7274 | 0.3143 | 0.7274 | 0.8529 |
| 0.0358 | 8.6734 | 7702 | 0.7294 | 0.3143 | 0.7294 | 0.8541 |
| 0.0358 | 8.6757 | 7704 | 0.7341 | 0.2609 | 0.7341 | 0.8568 |
| 0.0358 | 8.6779 | 7706 | 0.7326 | 0.2609 | 0.7326 | 0.8559 |
| 0.0358 | 8.6802 | 7708 | 0.7258 | 0.3143 | 0.7258 | 0.8519 |
| 0.0358 | 8.6824 | 7710 | 0.7183 | 0.3143 | 0.7183 | 0.8475 |
| 0.0358 | 8.6847 | 7712 | 0.7110 | 0.3143 | 0.7110 | 0.8432 |
| 0.0358 | 8.6869 | 7714 | 0.7008 | 0.2727 | 0.7008 | 0.8371 |
| 0.0358 | 8.6892 | 7716 | 0.6911 | 0.2727 | 0.6911 | 0.8313 |
| 0.0358 | 8.6914 | 7718 | 0.6867 | 0.3284 | 0.6867 | 0.8287 |
| 0.0358 | 8.6937 | 7720 | 0.6846 | 0.3284 | 0.6846 | 0.8274 |
| 0.0358 | 8.6959 | 7722 | 0.6816 | 0.3284 | 0.6816 | 0.8256 |
| 0.0358 | 8.6982 | 7724 | 0.6813 | 0.3284 | 0.6813 | 0.8254 |
| 0.0358 | 8.7005 | 7726 | 0.6842 | 0.3284 | 0.6842 | 0.8271 |
| 0.0358 | 8.7027 | 7728 | 0.6855 | 0.3284 | 0.6855 | 0.8279 |
| 0.0358 | 8.7050 | 7730 | 0.6903 | 0.3284 | 0.6903 | 0.8308 |
| 0.0358 | 8.7072 | 7732 | 0.6960 | 0.2727 | 0.6960 | 0.8343 |
| 0.0358 | 8.7095 | 7734 | 0.7000 | 0.3662 | 0.7000 | 0.8366 |
| 0.0358 | 8.7117 | 7736 | 0.7006 | 0.3662 | 0.7006 | 0.8370 |
| 0.0358 | 8.7140 | 7738 | 0.6962 | 0.2727 | 0.6962 | 0.8344 |
| 0.0358 | 8.7162 | 7740 | 0.6925 | 0.3284 | 0.6925 | 0.8322 |
| 0.0358 | 8.7185 | 7742 | 0.6891 | 0.3284 | 0.6891 | 0.8301 |
| 0.0358 | 8.7207 | 7744 | 0.6869 | 0.3284 | 0.6869 | 0.8288 |
| 0.0358 | 8.7230 | 7746 | 0.6852 | 0.3284 | 0.6852 | 0.8278 |
| 0.0358 | 8.7252 | 7748 | 0.6882 | 0.3284 | 0.6882 | 0.8296 |
| 0.0358 | 8.7275 | 7750 | 0.6921 | 0.3284 | 0.6921 | 0.8320 |
| 0.0358 | 8.7297 | 7752 | 0.6937 | 0.4167 | 0.6937 | 0.8329 |
| 0.0358 | 8.7320 | 7754 | 0.6956 | 0.4167 | 0.6956 | 0.8341 |
| 0.0358 | 8.7342 | 7756 | 0.6952 | 0.4167 | 0.6952 | 0.8338 |
| 0.0358 | 8.7365 | 7758 | 0.6916 | 0.4167 | 0.6916 | 0.8316 |
| 0.0358 | 8.7387 | 7760 | 0.6884 | 0.3284 | 0.6884 | 0.8297 |
| 0.0358 | 8.7410 | 7762 | 0.6899 | 0.3284 | 0.6899 | 0.8306 |
| 0.0358 | 8.7432 | 7764 | 0.6930 | 0.3284 | 0.6930 | 0.8325 |
| 0.0358 | 8.7455 | 7766 | 0.6989 | 0.4167 | 0.6989 | 0.8360 |
| 0.0358 | 8.7477 | 7768 | 0.7046 | 0.4167 | 0.7046 | 0.8394 |
| 0.0358 | 8.75 | 7770 | 0.7070 | 0.4167 | 0.7070 | 0.8408 |
| 0.0358 | 8.7523 | 7772 | 0.7071 | 0.4167 | 0.7071 | 0.8409 |
| 0.0358 | 8.7545 | 7774 | 0.7092 | 0.4167 | 0.7092 | 0.8421 |
| 0.0358 | 8.7568 | 7776 | 0.7078 | 0.4167 | 0.7078 | 0.8413 |
| 0.0358 | 8.7590 | 7778 | 0.7057 | 0.4167 | 0.7057 | 0.8400 |
| 0.0358 | 8.7613 | 7780 | 0.7042 | 0.4167 | 0.7042 | 0.8392 |
| 0.0358 | 8.7635 | 7782 | 0.7069 | 0.4167 | 0.7069 | 0.8408 |
| 0.0358 | 8.7658 | 7784 | 0.7065 | 0.4167 | 0.7065 | 0.8406 |
| 0.0358 | 8.7680 | 7786 | 0.7019 | 0.4167 | 0.7019 | 0.8378 |
| 0.0358 | 8.7703 | 7788 | 0.6981 | 0.4167 | 0.6981 | 0.8355 |
| 0.0358 | 8.7725 | 7790 | 0.6958 | 0.4167 | 0.6958 | 0.8342 |
| 0.0358 | 8.7748 | 7792 | 0.6967 | 0.4167 | 0.6967 | 0.8347 |
| 0.0358 | 8.7770 | 7794 | 0.6968 | 0.4167 | 0.6968 | 0.8347 |
| 0.0358 | 8.7793 | 7796 | 0.6969 | 0.4167 | 0.6969 | 0.8348 |
| 0.0358 | 8.7815 | 7798 | 0.7005 | 0.4167 | 0.7005 | 0.8370 |
| 0.0358 | 8.7838 | 7800 | 0.7007 | 0.4167 | 0.7007 | 0.8371 |
| 0.0358 | 8.7860 | 7802 | 0.6998 | 0.4167 | 0.6998 | 0.8366 |
| 0.0358 | 8.7883 | 7804 | 0.7008 | 0.4167 | 0.7008 | 0.8371 |
| 0.0358 | 8.7905 | 7806 | 0.7034 | 0.4167 | 0.7034 | 0.8387 |
| 0.0358 | 8.7928 | 7808 | 0.7078 | 0.4167 | 0.7078 | 0.8413 |
| 0.0358 | 8.7950 | 7810 | 0.7101 | 0.4167 | 0.7101 | 0.8427 |
| 0.0358 | 8.7973 | 7812 | 0.7115 | 0.4167 | 0.7115 | 0.8435 |
| 0.0358 | 8.7995 | 7814 | 0.7135 | 0.4167 | 0.7135 | 0.8447 |
| 0.0358 | 8.8018 | 7816 | 0.7122 | 0.4167 | 0.7122 | 0.8439 |
| 0.0358 | 8.8041 | 7818 | 0.7122 | 0.4167 | 0.7122 | 0.8439 |
| 0.0358 | 8.8063 | 7820 | 0.7101 | 0.4167 | 0.7101 | 0.8427 |
| 0.0358 | 8.8086 | 7822 | 0.7066 | 0.4167 | 0.7066 | 0.8406 |
| 0.0358 | 8.8108 | 7824 | 0.7025 | 0.4167 | 0.7025 | 0.8382 |
| 0.0358 | 8.8131 | 7826 | 0.6980 | 0.4167 | 0.6980 | 0.8354 |
| 0.0358 | 8.8153 | 7828 | 0.6918 | 0.3824 | 0.6918 | 0.8318 |
| 0.0358 | 8.8176 | 7830 | 0.6865 | 0.3478 | 0.6865 | 0.8285 |
| 0.0358 | 8.8198 | 7832 | 0.6832 | 0.3438 | 0.6832 | 0.8266 |
| 0.0358 | 8.8221 | 7834 | 0.6812 | 0.3438 | 0.6812 | 0.8254 |
| 0.0358 | 8.8243 | 7836 | 0.6812 | 0.3438 | 0.6812 | 0.8254 |
| 0.0358 | 8.8266 | 7838 | 0.6818 | 0.3438 | 0.6818 | 0.8257 |
| 0.0358 | 8.8288 | 7840 | 0.6837 | 0.3438 | 0.6837 | 0.8269 |
| 0.0358 | 8.8311 | 7842 | 0.6863 | 0.3438 | 0.6863 | 0.8284 |
| 0.0358 | 8.8333 | 7844 | 0.6910 | 0.3824 | 0.6910 | 0.8313 |
| 0.0358 | 8.8356 | 7846 | 0.6957 | 0.4658 | 0.6957 | 0.8341 |
| 0.0358 | 8.8378 | 7848 | 0.7020 | 0.4167 | 0.7020 | 0.8379 |
| 0.0358 | 8.8401 | 7850 | 0.7062 | 0.4167 | 0.7062 | 0.8403 |
| 0.0358 | 8.8423 | 7852 | 0.7066 | 0.4167 | 0.7066 | 0.8406 |
| 0.0358 | 8.8446 | 7854 | 0.7067 | 0.4167 | 0.7067 | 0.8406 |
| 0.0358 | 8.8468 | 7856 | 0.7055 | 0.4167 | 0.7055 | 0.8399 |
| 0.0358 | 8.8491 | 7858 | 0.7063 | 0.4167 | 0.7063 | 0.8404 |
| 0.0358 | 8.8514 | 7860 | 0.7050 | 0.4167 | 0.7050 | 0.8397 |
| 0.0358 | 8.8536 | 7862 | 0.7009 | 0.4167 | 0.7009 | 0.8372 |
| 0.0358 | 8.8559 | 7864 | 0.6944 | 0.4658 | 0.6944 | 0.8333 |
| 0.0358 | 8.8581 | 7866 | 0.6876 | 0.3824 | 0.6876 | 0.8292 |
| 0.0358 | 8.8604 | 7868 | 0.6834 | 0.3824 | 0.6834 | 0.8267 |
| 0.0358 | 8.8626 | 7870 | 0.6815 | 0.3824 | 0.6815 | 0.8256 |
| 0.0358 | 8.8649 | 7872 | 0.6809 | 0.3824 | 0.6809 | 0.8252 |
| 0.0358 | 8.8671 | 7874 | 0.6831 | 0.3824 | 0.6831 | 0.8265 |
| 0.0358 | 8.8694 | 7876 | 0.6873 | 0.4167 | 0.6873 | 0.8290 |
| 0.0358 | 8.8716 | 7878 | 0.6925 | 0.4167 | 0.6925 | 0.8321 |
| 0.0358 | 8.8739 | 7880 | 0.6951 | 0.4167 | 0.6951 | 0.8337 |
| 0.0358 | 8.8761 | 7882 | 0.6985 | 0.4167 | 0.6985 | 0.8358 |
| 0.0358 | 8.8784 | 7884 | 0.6996 | 0.4167 | 0.6996 | 0.8364 |
| 0.0358 | 8.8806 | 7886 | 0.7027 | 0.4167 | 0.7027 | 0.8383 |
| 0.0358 | 8.8829 | 7888 | 0.7050 | 0.4167 | 0.7050 | 0.8396 |
| 0.0358 | 8.8851 | 7890 | 0.7076 | 0.4167 | 0.7076 | 0.8412 |
| 0.0358 | 8.8874 | 7892 | 0.7074 | 0.4167 | 0.7074 | 0.8411 |
| 0.0358 | 8.8896 | 7894 | 0.7064 | 0.4167 | 0.7064 | 0.8405 |
| 0.0358 | 8.8919 | 7896 | 0.7074 | 0.4167 | 0.7074 | 0.8411 |
| 0.0358 | 8.8941 | 7898 | 0.7057 | 0.4167 | 0.7057 | 0.8401 |
| 0.0358 | 8.8964 | 7900 | 0.7020 | 0.3478 | 0.7020 | 0.8379 |
| 0.0358 | 8.8986 | 7902 | 0.6972 | 0.3478 | 0.6972 | 0.8350 |
| 0.0358 | 8.9009 | 7904 | 0.6917 | 0.3438 | 0.6917 | 0.8317 |
| 0.0358 | 8.9032 | 7906 | 0.6879 | 0.3438 | 0.6879 | 0.8294 |
| 0.0358 | 8.9054 | 7908 | 0.6859 | 0.3438 | 0.6859 | 0.8282 |
| 0.0358 | 8.9077 | 7910 | 0.6869 | 0.3438 | 0.6869 | 0.8288 |
| 0.0358 | 8.9099 | 7912 | 0.6901 | 0.3438 | 0.6901 | 0.8307 |
| 0.0358 | 8.9122 | 7914 | 0.6955 | 0.2941 | 0.6955 | 0.8340 |
| 0.0358 | 8.9144 | 7916 | 0.7009 | 0.3284 | 0.7009 | 0.8372 |
| 0.0358 | 8.9167 | 7918 | 0.7076 | 0.4167 | 0.7076 | 0.8412 |
| 0.0358 | 8.9189 | 7920 | 0.7126 | 0.4167 | 0.7126 | 0.8442 |
| 0.0358 | 8.9212 | 7922 | 0.7157 | 0.4167 | 0.7157 | 0.8460 |
| 0.0358 | 8.9234 | 7924 | 0.7198 | 0.4167 | 0.7198 | 0.8484 |
| 0.0358 | 8.9257 | 7926 | 0.7213 | 0.4167 | 0.7213 | 0.8493 |
| 0.0358 | 8.9279 | 7928 | 0.7186 | 0.4167 | 0.7186 | 0.8477 |
| 0.0358 | 8.9302 | 7930 | 0.7151 | 0.4167 | 0.7151 | 0.8456 |
| 0.0358 | 8.9324 | 7932 | 0.7117 | 0.4167 | 0.7117 | 0.8436 |
| 0.0358 | 8.9347 | 7934 | 0.7072 | 0.4167 | 0.7072 | 0.8409 |
| 0.0358 | 8.9369 | 7936 | 0.7019 | 0.3284 | 0.7019 | 0.8378 |
| 0.0358 | 8.9392 | 7938 | 0.6980 | 0.3478 | 0.6980 | 0.8355 |
| 0.0358 | 8.9414 | 7940 | 0.6966 | 0.3478 | 0.6966 | 0.8346 |
| 0.0358 | 8.9437 | 7942 | 0.6953 | 0.3438 | 0.6953 | 0.8338 |
| 0.0358 | 8.9459 | 7944 | 0.6951 | 0.3438 | 0.6951 | 0.8337 |
| 0.0358 | 8.9482 | 7946 | 0.6957 | 0.3438 | 0.6957 | 0.8341 |
| 0.0358 | 8.9505 | 7948 | 0.6973 | 0.3438 | 0.6973 | 0.8351 |
| 0.0358 | 8.9527 | 7950 | 0.6977 | 0.3478 | 0.6977 | 0.8353 |
| 0.0358 | 8.9550 | 7952 | 0.6996 | 0.3478 | 0.6996 | 0.8364 |
| 0.0358 | 8.9572 | 7954 | 0.7024 | 0.3284 | 0.7024 | 0.8381 |
| 0.0358 | 8.9595 | 7956 | 0.7065 | 0.4167 | 0.7065 | 0.8405 |
| 0.0358 | 8.9617 | 7958 | 0.7123 | 0.4167 | 0.7123 | 0.8440 |
| 0.0358 | 8.9640 | 7960 | 0.7154 | 0.4167 | 0.7154 | 0.8458 |
| 0.0358 | 8.9662 | 7962 | 0.7167 | 0.4167 | 0.7167 | 0.8466 |
| 0.0358 | 8.9685 | 7964 | 0.7141 | 0.4167 | 0.7141 | 0.8450 |
| 0.0358 | 8.9707 | 7966 | 0.7082 | 0.4167 | 0.7082 | 0.8415 |
| 0.0358 | 8.9730 | 7968 | 0.7026 | 0.4167 | 0.7026 | 0.8382 |
| 0.0358 | 8.9752 | 7970 | 0.6973 | 0.4167 | 0.6973 | 0.8350 |
| 0.0358 | 8.9775 | 7972 | 0.6932 | 0.3284 | 0.6932 | 0.8326 |
| 0.0358 | 8.9797 | 7974 | 0.6901 | 0.3284 | 0.6901 | 0.8307 |
| 0.0358 | 8.9820 | 7976 | 0.6893 | 0.3226 | 0.6893 | 0.8303 |
| 0.0358 | 8.9842 | 7978 | 0.6908 | 0.3226 | 0.6908 | 0.8311 |
| 0.0358 | 8.9865 | 7980 | 0.6928 | 0.3226 | 0.6928 | 0.8323 |
| 0.0358 | 8.9887 | 7982 | 0.6971 | 0.3284 | 0.6971 | 0.8349 |
| 0.0358 | 8.9910 | 7984 | 0.7010 | 0.3284 | 0.7010 | 0.8373 |
| 0.0358 | 8.9932 | 7986 | 0.7051 | 0.4167 | 0.7051 | 0.8397 |
| 0.0358 | 8.9955 | 7988 | 0.7080 | 0.4167 | 0.7080 | 0.8414 |
| 0.0358 | 8.9977 | 7990 | 0.7118 | 0.4167 | 0.7118 | 0.8437 |
| 0.0358 | 9.0 | 7992 | 0.7167 | 0.4167 | 0.7167 | 0.8466 |
| 0.0358 | 9.0023 | 7994 | 0.7206 | 0.4167 | 0.7206 | 0.8489 |
| 0.0358 | 9.0045 | 7996 | 0.7219 | 0.4167 | 0.7219 | 0.8497 |
| 0.0358 | 9.0068 | 7998 | 0.7192 | 0.4167 | 0.7192 | 0.8481 |
| 0.0349 | 9.0090 | 8000 | 0.7183 | 0.4167 | 0.7183 | 0.8475 |
| 0.0349 | 9.0113 | 8002 | 0.7203 | 0.4167 | 0.7203 | 0.8487 |
| 0.0349 | 9.0135 | 8004 | 0.7185 | 0.4167 | 0.7185 | 0.8476 |
| 0.0349 | 9.0158 | 8006 | 0.7156 | 0.4167 | 0.7156 | 0.8459 |
| 0.0349 | 9.0180 | 8008 | 0.7129 | 0.4167 | 0.7129 | 0.8443 |
| 0.0349 | 9.0203 | 8010 | 0.7099 | 0.4167 | 0.7099 | 0.8425 |
| 0.0349 | 9.0225 | 8012 | 0.7053 | 0.4167 | 0.7053 | 0.8398 |
| 0.0349 | 9.0248 | 8014 | 0.7013 | 0.4167 | 0.7013 | 0.8375 |
| 0.0349 | 9.0270 | 8016 | 0.6986 | 0.4167 | 0.6986 | 0.8358 |
| 0.0349 | 9.0293 | 8018 | 0.6989 | 0.4167 | 0.6989 | 0.8360 |
| 0.0349 | 9.0315 | 8020 | 0.6994 | 0.4167 | 0.6994 | 0.8363 |
| 0.0349 | 9.0338 | 8022 | 0.7006 | 0.4167 | 0.7006 | 0.8370 |
| 0.0349 | 9.0360 | 8024 | 0.6988 | 0.4167 | 0.6988 | 0.8360 |
| 0.0349 | 9.0383 | 8026 | 0.6947 | 0.4167 | 0.6947 | 0.8335 |
| 0.0349 | 9.0405 | 8028 | 0.6929 | 0.3284 | 0.6929 | 0.8324 |
| 0.0349 | 9.0428 | 8030 | 0.6933 | 0.4167 | 0.6933 | 0.8326 |
| 0.0349 | 9.0450 | 8032 | 0.6924 | 0.4167 | 0.6924 | 0.8321 |
| 0.0349 | 9.0473 | 8034 | 0.6925 | 0.4167 | 0.6925 | 0.8322 |
| 0.0349 | 9.0495 | 8036 | 0.6926 | 0.4167 | 0.6926 | 0.8322 |
| 0.0349 | 9.0518 | 8038 | 0.6909 | 0.3284 | 0.6909 | 0.8312 |
| 0.0349 | 9.0541 | 8040 | 0.6887 | 0.3284 | 0.6887 | 0.8299 |
| 0.0349 | 9.0563 | 8042 | 0.6866 | 0.3824 | 0.6866 | 0.8286 |
| 0.0349 | 9.0586 | 8044 | 0.6845 | 0.3824 | 0.6845 | 0.8274 |
| 0.0349 | 9.0608 | 8046 | 0.6810 | 0.3824 | 0.6810 | 0.8252 |
| 0.0349 | 9.0631 | 8048 | 0.6787 | 0.3284 | 0.6787 | 0.8238 |
| 0.0349 | 9.0653 | 8050 | 0.6779 | 0.3284 | 0.6779 | 0.8233 |
| 0.0349 | 9.0676 | 8052 | 0.6792 | 0.3284 | 0.6792 | 0.8241 |
| 0.0349 | 9.0698 | 8054 | 0.6817 | 0.3284 | 0.6817 | 0.8256 |
| 0.0349 | 9.0721 | 8056 | 0.6823 | 0.3284 | 0.6823 | 0.8260 |
| 0.0349 | 9.0743 | 8058 | 0.6808 | 0.3284 | 0.6808 | 0.8251 |
| 0.0349 | 9.0766 | 8060 | 0.6793 | 0.3284 | 0.6793 | 0.8242 |
| 0.0349 | 9.0788 | 8062 | 0.6792 | 0.3284 | 0.6792 | 0.8242 |
| 0.0349 | 9.0811 | 8064 | 0.6789 | 0.3284 | 0.6789 | 0.8240 |
| 0.0349 | 9.0833 | 8066 | 0.6787 | 0.3284 | 0.6787 | 0.8238 |
| 0.0349 | 9.0856 | 8068 | 0.6792 | 0.3284 | 0.6792 | 0.8241 |
| 0.0349 | 9.0878 | 8070 | 0.6813 | 0.3284 | 0.6813 | 0.8254 |
| 0.0349 | 9.0901 | 8072 | 0.6832 | 0.3284 | 0.6832 | 0.8266 |
| 0.0349 | 9.0923 | 8074 | 0.6860 | 0.3284 | 0.6860 | 0.8282 |
| 0.0349 | 9.0946 | 8076 | 0.6886 | 0.4167 | 0.6886 | 0.8298 |
| 0.0349 | 9.0968 | 8078 | 0.6890 | 0.4167 | 0.6890 | 0.8301 |
| 0.0349 | 9.0991 | 8080 | 0.6897 | 0.4167 | 0.6897 | 0.8305 |
| 0.0349 | 9.1014 | 8082 | 0.6917 | 0.4167 | 0.6917 | 0.8317 |
| 0.0349 | 9.1036 | 8084 | 0.6946 | 0.4167 | 0.6946 | 0.8334 |
| 0.0349 | 9.1059 | 8086 | 0.6967 | 0.4167 | 0.6967 | 0.8347 |
| 0.0349 | 9.1081 | 8088 | 0.6996 | 0.4167 | 0.6996 | 0.8364 |
| 0.0349 | 9.1104 | 8090 | 0.7024 | 0.4167 | 0.7024 | 0.8381 |
| 0.0349 | 9.1126 | 8092 | 0.7063 | 0.4167 | 0.7063 | 0.8404 |
| 0.0349 | 9.1149 | 8094 | 0.7082 | 0.4167 | 0.7082 | 0.8415 |
| 0.0349 | 9.1171 | 8096 | 0.7083 | 0.4167 | 0.7083 | 0.8416 |
| 0.0349 | 9.1194 | 8098 | 0.7099 | 0.4167 | 0.7099 | 0.8426 |
| 0.0349 | 9.1216 | 8100 | 0.7112 | 0.4167 | 0.7112 | 0.8433 |
| 0.0349 | 9.1239 | 8102 | 0.7125 | 0.4167 | 0.7125 | 0.8441 |
| 0.0349 | 9.1261 | 8104 | 0.7123 | 0.4167 | 0.7123 | 0.8440 |
| 0.0349 | 9.1284 | 8106 | 0.7128 | 0.4167 | 0.7128 | 0.8443 |
| 0.0349 | 9.1306 | 8108 | 0.7131 | 0.4167 | 0.7131 | 0.8445 |
| 0.0349 | 9.1329 | 8110 | 0.7135 | 0.3284 | 0.7135 | 0.8447 |
| 0.0349 | 9.1351 | 8112 | 0.7132 | 0.3284 | 0.7132 | 0.8445 |
| 0.0349 | 9.1374 | 8114 | 0.7117 | 0.3284 | 0.7117 | 0.8436 |
| 0.0349 | 9.1396 | 8116 | 0.7118 | 0.3284 | 0.7118 | 0.8437 |
| 0.0349 | 9.1419 | 8118 | 0.7107 | 0.3284 | 0.7107 | 0.8430 |
| 0.0349 | 9.1441 | 8120 | 0.7111 | 0.3824 | 0.7111 | 0.8433 |
| 0.0349 | 9.1464 | 8122 | 0.7118 | 0.3824 | 0.7118 | 0.8437 |
| 0.0349 | 9.1486 | 8124 | 0.7105 | 0.3824 | 0.7105 | 0.8429 |
| 0.0349 | 9.1509 | 8126 | 0.7101 | 0.3478 | 0.7101 | 0.8427 |
| 0.0349 | 9.1532 | 8128 | 0.7102 | 0.3478 | 0.7102 | 0.8428 |
| 0.0349 | 9.1554 | 8130 | 0.7116 | 0.3478 | 0.7116 | 0.8436 |
| 0.0349 | 9.1577 | 8132 | 0.7149 | 0.3824 | 0.7149 | 0.8455 |
| 0.0349 | 9.1599 | 8134 | 0.7190 | 0.3284 | 0.7190 | 0.8480 |
| 0.0349 | 9.1622 | 8136 | 0.7207 | 0.3284 | 0.7207 | 0.8489 |
| 0.0349 | 9.1644 | 8138 | 0.7219 | 0.4167 | 0.7219 | 0.8496 |
| 0.0349 | 9.1667 | 8140 | 0.7236 | 0.4167 | 0.7236 | 0.8506 |
| 0.0349 | 9.1689 | 8142 | 0.7229 | 0.4167 | 0.7229 | 0.8503 |
| 0.0349 | 9.1712 | 8144 | 0.7213 | 0.4167 | 0.7213 | 0.8493 |
| 0.0349 | 9.1734 | 8146 | 0.7208 | 0.4167 | 0.7208 | 0.8490 |
| 0.0349 | 9.1757 | 8148 | 0.7215 | 0.4167 | 0.7215 | 0.8494 |
| 0.0349 | 9.1779 | 8150 | 0.7221 | 0.4167 | 0.7221 | 0.8498 |
| 0.0349 | 9.1802 | 8152 | 0.7209 | 0.4167 | 0.7209 | 0.8490 |
| 0.0349 | 9.1824 | 8154 | 0.7199 | 0.4167 | 0.7199 | 0.8484 |
| 0.0349 | 9.1847 | 8156 | 0.7190 | 0.4167 | 0.7190 | 0.8480 |
| 0.0349 | 9.1869 | 8158 | 0.7193 | 0.4167 | 0.7193 | 0.8481 |
| 0.0349 | 9.1892 | 8160 | 0.7176 | 0.4167 | 0.7176 | 0.8471 |
| 0.0349 | 9.1914 | 8162 | 0.7168 | 0.4167 | 0.7168 | 0.8466 |
| 0.0349 | 9.1937 | 8164 | 0.7163 | 0.4167 | 0.7163 | 0.8463 |
| 0.0349 | 9.1959 | 8166 | 0.7164 | 0.4167 | 0.7164 | 0.8464 |
| 0.0349 | 9.1982 | 8168 | 0.7160 | 0.4167 | 0.7160 | 0.8462 |
| 0.0349 | 9.2005 | 8170 | 0.7159 | 0.4167 | 0.7159 | 0.8461 |
| 0.0349 | 9.2027 | 8172 | 0.7162 | 0.4167 | 0.7162 | 0.8463 |
| 0.0349 | 9.2050 | 8174 | 0.7170 | 0.4167 | 0.7170 | 0.8468 |
| 0.0349 | 9.2072 | 8176 | 0.7159 | 0.4167 | 0.7159 | 0.8461 |
| 0.0349 | 9.2095 | 8178 | 0.7153 | 0.4167 | 0.7153 | 0.8457 |
| 0.0349 | 9.2117 | 8180 | 0.7150 | 0.4167 | 0.7150 | 0.8456 |
| 0.0349 | 9.2140 | 8182 | 0.7153 | 0.4167 | 0.7153 | 0.8457 |
| 0.0349 | 9.2162 | 8184 | 0.7128 | 0.4167 | 0.7128 | 0.8443 |
| 0.0349 | 9.2185 | 8186 | 0.7094 | 0.3284 | 0.7094 | 0.8423 |
| 0.0349 | 9.2207 | 8188 | 0.7062 | 0.3284 | 0.7062 | 0.8404 |
| 0.0349 | 9.2230 | 8190 | 0.7052 | 0.3284 | 0.7052 | 0.8397 |
| 0.0349 | 9.2252 | 8192 | 0.7037 | 0.3824 | 0.7037 | 0.8389 |
| 0.0349 | 9.2275 | 8194 | 0.7036 | 0.3824 | 0.7036 | 0.8388 |
| 0.0349 | 9.2297 | 8196 | 0.7041 | 0.3824 | 0.7041 | 0.8391 |
| 0.0349 | 9.2320 | 8198 | 0.7064 | 0.3824 | 0.7064 | 0.8405 |
| 0.0349 | 9.2342 | 8200 | 0.7085 | 0.3824 | 0.7085 | 0.8417 |
| 0.0349 | 9.2365 | 8202 | 0.7098 | 0.3824 | 0.7098 | 0.8425 |
| 0.0349 | 9.2387 | 8204 | 0.7091 | 0.3824 | 0.7091 | 0.8421 |
| 0.0349 | 9.2410 | 8206 | 0.7072 | 0.3824 | 0.7072 | 0.8410 |
| 0.0349 | 9.2432 | 8208 | 0.7041 | 0.3824 | 0.7041 | 0.8391 |
| 0.0349 | 9.2455 | 8210 | 0.7019 | 0.3824 | 0.7019 | 0.8378 |
| 0.0349 | 9.2477 | 8212 | 0.6996 | 0.3824 | 0.6996 | 0.8364 |
| 0.0349 | 9.25 | 8214 | 0.6977 | 0.3824 | 0.6977 | 0.8353 |
| 0.0349 | 9.2523 | 8216 | 0.6956 | 0.3810 | 0.6956 | 0.8340 |
| 0.0349 | 9.2545 | 8218 | 0.6936 | 0.3810 | 0.6936 | 0.8328 |
| 0.0349 | 9.2568 | 8220 | 0.6918 | 0.3810 | 0.6918 | 0.8317 |
| 0.0349 | 9.2590 | 8222 | 0.6898 | 0.3810 | 0.6898 | 0.8305 |
| 0.0349 | 9.2613 | 8224 | 0.6892 | 0.3810 | 0.6892 | 0.8302 |
| 0.0349 | 9.2635 | 8226 | 0.6894 | 0.3810 | 0.6894 | 0.8303 |
| 0.0349 | 9.2658 | 8228 | 0.6913 | 0.3810 | 0.6913 | 0.8314 |
| 0.0349 | 9.2680 | 8230 | 0.6931 | 0.3226 | 0.6931 | 0.8325 |
| 0.0349 | 9.2703 | 8232 | 0.6958 | 0.3284 | 0.6958 | 0.8341 |
| 0.0349 | 9.2725 | 8234 | 0.6982 | 0.3284 | 0.6982 | 0.8356 |
| 0.0349 | 9.2748 | 8236 | 0.7013 | 0.3284 | 0.7013 | 0.8374 |
| 0.0349 | 9.2770 | 8238 | 0.7051 | 0.3284 | 0.7051 | 0.8397 |
| 0.0349 | 9.2793 | 8240 | 0.7083 | 0.3284 | 0.7083 | 0.8416 |
| 0.0349 | 9.2815 | 8242 | 0.7099 | 0.3284 | 0.7099 | 0.8425 |
| 0.0349 | 9.2838 | 8244 | 0.7109 | 0.3284 | 0.7109 | 0.8431 |
| 0.0349 | 9.2860 | 8246 | 0.7109 | 0.3284 | 0.7109 | 0.8432 |
| 0.0349 | 9.2883 | 8248 | 0.7112 | 0.3284 | 0.7112 | 0.8433 |
| 0.0349 | 9.2905 | 8250 | 0.7129 | 0.3284 | 0.7129 | 0.8443 |
| 0.0349 | 9.2928 | 8252 | 0.7135 | 0.3284 | 0.7135 | 0.8447 |
| 0.0349 | 9.2950 | 8254 | 0.7152 | 0.3284 | 0.7152 | 0.8457 |
| 0.0349 | 9.2973 | 8256 | 0.7172 | 0.3284 | 0.7172 | 0.8469 |
| 0.0349 | 9.2995 | 8258 | 0.7192 | 0.4167 | 0.7192 | 0.8481 |
| 0.0349 | 9.3018 | 8260 | 0.7197 | 0.4167 | 0.7197 | 0.8483 |
| 0.0349 | 9.3041 | 8262 | 0.7185 | 0.3284 | 0.7185 | 0.8476 |
| 0.0349 | 9.3063 | 8264 | 0.7166 | 0.3284 | 0.7166 | 0.8465 |
| 0.0349 | 9.3086 | 8266 | 0.7139 | 0.3284 | 0.7139 | 0.8449 |
| 0.0349 | 9.3108 | 8268 | 0.7112 | 0.3284 | 0.7112 | 0.8433 |
| 0.0349 | 9.3131 | 8270 | 0.7081 | 0.3284 | 0.7081 | 0.8415 |
| 0.0349 | 9.3153 | 8272 | 0.7066 | 0.3284 | 0.7066 | 0.8406 |
| 0.0349 | 9.3176 | 8274 | 0.7068 | 0.3284 | 0.7068 | 0.8407 |
| 0.0349 | 9.3198 | 8276 | 0.7090 | 0.3284 | 0.7090 | 0.8420 |
| 0.0349 | 9.3221 | 8278 | 0.7107 | 0.3284 | 0.7107 | 0.8430 |
| 0.0349 | 9.3243 | 8280 | 0.7106 | 0.3284 | 0.7106 | 0.8430 |
| 0.0349 | 9.3266 | 8282 | 0.7110 | 0.3284 | 0.7110 | 0.8432 |
| 0.0349 | 9.3288 | 8284 | 0.7105 | 0.3284 | 0.7105 | 0.8429 |
| 0.0349 | 9.3311 | 8286 | 0.7116 | 0.3284 | 0.7116 | 0.8436 |
| 0.0349 | 9.3333 | 8288 | 0.7133 | 0.4167 | 0.7133 | 0.8446 |
| 0.0349 | 9.3356 | 8290 | 0.7144 | 0.4167 | 0.7144 | 0.8452 |
| 0.0349 | 9.3378 | 8292 | 0.7147 | 0.4167 | 0.7147 | 0.8454 |
| 0.0349 | 9.3401 | 8294 | 0.7157 | 0.4167 | 0.7157 | 0.8460 |
| 0.0349 | 9.3423 | 8296 | 0.7154 | 0.4167 | 0.7154 | 0.8458 |
| 0.0349 | 9.3446 | 8298 | 0.7153 | 0.4167 | 0.7153 | 0.8458 |
| 0.0349 | 9.3468 | 8300 | 0.7169 | 0.4167 | 0.7169 | 0.8467 |
| 0.0349 | 9.3491 | 8302 | 0.7159 | 0.4167 | 0.7159 | 0.8461 |
| 0.0349 | 9.3514 | 8304 | 0.7131 | 0.4167 | 0.7131 | 0.8445 |
| 0.0349 | 9.3536 | 8306 | 0.7099 | 0.4167 | 0.7099 | 0.8426 |
| 0.0349 | 9.3559 | 8308 | 0.7074 | 0.4167 | 0.7074 | 0.8411 |
| 0.0349 | 9.3581 | 8310 | 0.7042 | 0.3284 | 0.7042 | 0.8392 |
| 0.0349 | 9.3604 | 8312 | 0.7009 | 0.3284 | 0.7009 | 0.8372 |
| 0.0349 | 9.3626 | 8314 | 0.6999 | 0.3284 | 0.6999 | 0.8366 |
| 0.0349 | 9.3649 | 8316 | 0.6997 | 0.3284 | 0.6997 | 0.8365 |
| 0.0349 | 9.3671 | 8318 | 0.7014 | 0.3284 | 0.7014 | 0.8375 |
| 0.0349 | 9.3694 | 8320 | 0.7039 | 0.3284 | 0.7039 | 0.8390 |
| 0.0349 | 9.3716 | 8322 | 0.7069 | 0.3284 | 0.7069 | 0.8408 |
| 0.0349 | 9.3739 | 8324 | 0.7082 | 0.3284 | 0.7082 | 0.8416 |
| 0.0349 | 9.3761 | 8326 | 0.7077 | 0.3284 | 0.7077 | 0.8413 |
| 0.0349 | 9.3784 | 8328 | 0.7073 | 0.3284 | 0.7073 | 0.8410 |
| 0.0349 | 9.3806 | 8330 | 0.7059 | 0.3284 | 0.7059 | 0.8402 |
| 0.0349 | 9.3829 | 8332 | 0.7030 | 0.3284 | 0.7030 | 0.8384 |
| 0.0349 | 9.3851 | 8334 | 0.6992 | 0.3284 | 0.6992 | 0.8362 |
| 0.0349 | 9.3874 | 8336 | 0.6961 | 0.3284 | 0.6961 | 0.8343 |
| 0.0349 | 9.3896 | 8338 | 0.6933 | 0.3284 | 0.6933 | 0.8326 |
| 0.0349 | 9.3919 | 8340 | 0.6915 | 0.3284 | 0.6915 | 0.8316 |
| 0.0349 | 9.3941 | 8342 | 0.6897 | 0.3284 | 0.6897 | 0.8305 |
| 0.0349 | 9.3964 | 8344 | 0.6884 | 0.3284 | 0.6884 | 0.8297 |
| 0.0349 | 9.3986 | 8346 | 0.6877 | 0.3284 | 0.6878 | 0.8293 |
| 0.0349 | 9.4009 | 8348 | 0.6887 | 0.3284 | 0.6887 | 0.8299 |
| 0.0349 | 9.4032 | 8350 | 0.6899 | 0.3284 | 0.6899 | 0.8306 |
| 0.0349 | 9.4054 | 8352 | 0.6915 | 0.3284 | 0.6915 | 0.8316 |
| 0.0349 | 9.4077 | 8354 | 0.6910 | 0.3284 | 0.6910 | 0.8313 |
| 0.0349 | 9.4099 | 8356 | 0.6911 | 0.3284 | 0.6911 | 0.8313 |
| 0.0349 | 9.4122 | 8358 | 0.6907 | 0.3284 | 0.6907 | 0.8311 |
| 0.0349 | 9.4144 | 8360 | 0.6917 | 0.3284 | 0.6917 | 0.8317 |
| 0.0349 | 9.4167 | 8362 | 0.6935 | 0.3284 | 0.6935 | 0.8327 |
| 0.0349 | 9.4189 | 8364 | 0.6965 | 0.3284 | 0.6965 | 0.8346 |
| 0.0349 | 9.4212 | 8366 | 0.7003 | 0.3284 | 0.7003 | 0.8369 |
| 0.0349 | 9.4234 | 8368 | 0.7027 | 0.4167 | 0.7027 | 0.8383 |
| 0.0349 | 9.4257 | 8370 | 0.7043 | 0.4167 | 0.7043 | 0.8392 |
| 0.0349 | 9.4279 | 8372 | 0.7052 | 0.4167 | 0.7052 | 0.8397 |
| 0.0349 | 9.4302 | 8374 | 0.7038 | 0.4167 | 0.7038 | 0.8389 |
| 0.0349 | 9.4324 | 8376 | 0.7031 | 0.4167 | 0.7031 | 0.8385 |
| 0.0349 | 9.4347 | 8378 | 0.7026 | 0.4167 | 0.7026 | 0.8382 |
| 0.0349 | 9.4369 | 8380 | 0.7031 | 0.4167 | 0.7031 | 0.8385 |
| 0.0349 | 9.4392 | 8382 | 0.7038 | 0.4167 | 0.7038 | 0.8390 |
| 0.0349 | 9.4414 | 8384 | 0.7064 | 0.4167 | 0.7064 | 0.8405 |
| 0.0349 | 9.4437 | 8386 | 0.7087 | 0.4167 | 0.7087 | 0.8419 |
| 0.0349 | 9.4459 | 8388 | 0.7103 | 0.4167 | 0.7103 | 0.8428 |
| 0.0349 | 9.4482 | 8390 | 0.7105 | 0.4167 | 0.7105 | 0.8429 |
| 0.0349 | 9.4505 | 8392 | 0.7110 | 0.4167 | 0.7110 | 0.8432 |
| 0.0349 | 9.4527 | 8394 | 0.7117 | 0.4167 | 0.7117 | 0.8436 |
| 0.0349 | 9.4550 | 8396 | 0.7108 | 0.4167 | 0.7108 | 0.8431 |
| 0.0349 | 9.4572 | 8398 | 0.7085 | 0.4167 | 0.7085 | 0.8418 |
| 0.0349 | 9.4595 | 8400 | 0.7058 | 0.4167 | 0.7058 | 0.8401 |
| 0.0349 | 9.4617 | 8402 | 0.7028 | 0.4167 | 0.7028 | 0.8383 |
| 0.0349 | 9.4640 | 8404 | 0.6998 | 0.3284 | 0.6998 | 0.8366 |
| 0.0349 | 9.4662 | 8406 | 0.6982 | 0.3284 | 0.6982 | 0.8356 |
| 0.0349 | 9.4685 | 8408 | 0.6969 | 0.3284 | 0.6969 | 0.8348 |
| 0.0349 | 9.4707 | 8410 | 0.6969 | 0.3284 | 0.6969 | 0.8348 |
| 0.0349 | 9.4730 | 8412 | 0.6973 | 0.3284 | 0.6973 | 0.8350 |
| 0.0349 | 9.4752 | 8414 | 0.6981 | 0.3284 | 0.6981 | 0.8355 |
| 0.0349 | 9.4775 | 8416 | 0.6979 | 0.3284 | 0.6979 | 0.8354 |
| 0.0349 | 9.4797 | 8418 | 0.6978 | 0.3284 | 0.6978 | 0.8353 |
| 0.0349 | 9.4820 | 8420 | 0.6987 | 0.3284 | 0.6987 | 0.8359 |
| 0.0349 | 9.4842 | 8422 | 0.6996 | 0.3284 | 0.6996 | 0.8364 |
| 0.0349 | 9.4865 | 8424 | 0.7012 | 0.4167 | 0.7012 | 0.8374 |
| 0.0349 | 9.4887 | 8426 | 0.7024 | 0.4167 | 0.7024 | 0.8381 |
| 0.0349 | 9.4910 | 8428 | 0.7025 | 0.4167 | 0.7025 | 0.8382 |
| 0.0349 | 9.4932 | 8430 | 0.7028 | 0.4167 | 0.7028 | 0.8383 |
| 0.0349 | 9.4955 | 8432 | 0.7026 | 0.4167 | 0.7026 | 0.8382 |
| 0.0349 | 9.4977 | 8434 | 0.7024 | 0.4167 | 0.7024 | 0.8381 |
| 0.0349 | 9.5 | 8436 | 0.7032 | 0.4167 | 0.7032 | 0.8386 |
| 0.0349 | 9.5023 | 8438 | 0.7048 | 0.4167 | 0.7048 | 0.8395 |
| 0.0349 | 9.5045 | 8440 | 0.7051 | 0.4167 | 0.7051 | 0.8397 |
| 0.0349 | 9.5068 | 8442 | 0.7065 | 0.4167 | 0.7065 | 0.8405 |
| 0.0349 | 9.5090 | 8444 | 0.7071 | 0.4167 | 0.7071 | 0.8409 |
| 0.0349 | 9.5113 | 8446 | 0.7062 | 0.4167 | 0.7062 | 0.8404 |
| 0.0349 | 9.5135 | 8448 | 0.7042 | 0.4167 | 0.7042 | 0.8392 |
| 0.0349 | 9.5158 | 8450 | 0.7032 | 0.4167 | 0.7032 | 0.8385 |
| 0.0349 | 9.5180 | 8452 | 0.7020 | 0.4167 | 0.7020 | 0.8378 |
| 0.0349 | 9.5203 | 8454 | 0.7015 | 0.4167 | 0.7015 | 0.8375 |
| 0.0349 | 9.5225 | 8456 | 0.7008 | 0.4167 | 0.7008 | 0.8372 |
| 0.0349 | 9.5248 | 8458 | 0.7004 | 0.4167 | 0.7004 | 0.8369 |
| 0.0349 | 9.5270 | 8460 | 0.7005 | 0.4167 | 0.7005 | 0.8369 |
| 0.0349 | 9.5293 | 8462 | 0.7002 | 0.4167 | 0.7002 | 0.8368 |
| 0.0349 | 9.5315 | 8464 | 0.7012 | 0.4167 | 0.7012 | 0.8374 |
| 0.0349 | 9.5338 | 8466 | 0.7032 | 0.4167 | 0.7032 | 0.8385 |
| 0.0349 | 9.5360 | 8468 | 0.7037 | 0.4167 | 0.7037 | 0.8389 |
| 0.0349 | 9.5383 | 8470 | 0.7042 | 0.4167 | 0.7042 | 0.8391 |
| 0.0349 | 9.5405 | 8472 | 0.7052 | 0.4167 | 0.7052 | 0.8398 |
| 0.0349 | 9.5428 | 8474 | 0.7068 | 0.4167 | 0.7068 | 0.8407 |
| 0.0349 | 9.5450 | 8476 | 0.7072 | 0.4167 | 0.7072 | 0.8410 |
| 0.0349 | 9.5473 | 8478 | 0.7070 | 0.4167 | 0.7070 | 0.8409 |
| 0.0349 | 9.5495 | 8480 | 0.7062 | 0.4167 | 0.7062 | 0.8404 |
| 0.0349 | 9.5518 | 8482 | 0.7068 | 0.4167 | 0.7068 | 0.8407 |
| 0.0349 | 9.5541 | 8484 | 0.7068 | 0.4167 | 0.7068 | 0.8407 |
| 0.0349 | 9.5563 | 8486 | 0.7064 | 0.4167 | 0.7064 | 0.8404 |
| 0.0349 | 9.5586 | 8488 | 0.7067 | 0.4167 | 0.7067 | 0.8406 |
| 0.0349 | 9.5608 | 8490 | 0.7057 | 0.4167 | 0.7057 | 0.8401 |
| 0.0349 | 9.5631 | 8492 | 0.7040 | 0.4167 | 0.7040 | 0.8390 |
| 0.0349 | 9.5653 | 8494 | 0.7019 | 0.3284 | 0.7019 | 0.8378 |
| 0.0349 | 9.5676 | 8496 | 0.7011 | 0.3284 | 0.7011 | 0.8373 |
| 0.0349 | 9.5698 | 8498 | 0.7014 | 0.3284 | 0.7014 | 0.8375 |
| 0.0316 | 9.5721 | 8500 | 0.7022 | 0.3284 | 0.7022 | 0.8380 |
| 0.0316 | 9.5743 | 8502 | 0.7020 | 0.3284 | 0.7020 | 0.8379 |
| 0.0316 | 9.5766 | 8504 | 0.7025 | 0.3284 | 0.7025 | 0.8382 |
| 0.0316 | 9.5788 | 8506 | 0.7030 | 0.3284 | 0.7030 | 0.8384 |
| 0.0316 | 9.5811 | 8508 | 0.7044 | 0.3284 | 0.7044 | 0.8393 |
| 0.0316 | 9.5833 | 8510 | 0.7057 | 0.4167 | 0.7057 | 0.8401 |
| 0.0316 | 9.5856 | 8512 | 0.7072 | 0.4167 | 0.7072 | 0.8409 |
| 0.0316 | 9.5878 | 8514 | 0.7088 | 0.4167 | 0.7088 | 0.8419 |
| 0.0316 | 9.5901 | 8516 | 0.7095 | 0.4167 | 0.7095 | 0.8423 |
| 0.0316 | 9.5923 | 8518 | 0.7094 | 0.4167 | 0.7094 | 0.8423 |
| 0.0316 | 9.5946 | 8520 | 0.7095 | 0.4167 | 0.7095 | 0.8423 |
| 0.0316 | 9.5968 | 8522 | 0.7098 | 0.4167 | 0.7098 | 0.8425 |
| 0.0316 | 9.5991 | 8524 | 0.7097 | 0.4167 | 0.7097 | 0.8424 |
| 0.0316 | 9.6014 | 8526 | 0.7095 | 0.4167 | 0.7095 | 0.8423 |
| 0.0316 | 9.6036 | 8528 | 0.7081 | 0.4167 | 0.7081 | 0.8415 |
| 0.0316 | 9.6059 | 8530 | 0.7070 | 0.4167 | 0.7070 | 0.8409 |
| 0.0316 | 9.6081 | 8532 | 0.7057 | 0.4167 | 0.7057 | 0.8400 |
| 0.0316 | 9.6104 | 8534 | 0.7039 | 0.4167 | 0.7039 | 0.8390 |
| 0.0316 | 9.6126 | 8536 | 0.7024 | 0.4167 | 0.7024 | 0.8381 |
| 0.0316 | 9.6149 | 8538 | 0.7005 | 0.3284 | 0.7005 | 0.8370 |
| 0.0316 | 9.6171 | 8540 | 0.7001 | 0.3284 | 0.7001 | 0.8367 |
| 0.0316 | 9.6194 | 8542 | 0.7002 | 0.3284 | 0.7002 | 0.8368 |
| 0.0316 | 9.6216 | 8544 | 0.7005 | 0.3284 | 0.7005 | 0.8370 |
| 0.0316 | 9.6239 | 8546 | 0.7018 | 0.3284 | 0.7018 | 0.8377 |
| 0.0316 | 9.6261 | 8548 | 0.7034 | 0.4167 | 0.7034 | 0.8387 |
| 0.0316 | 9.6284 | 8550 | 0.7042 | 0.4167 | 0.7042 | 0.8392 |
| 0.0316 | 9.6306 | 8552 | 0.7043 | 0.4167 | 0.7043 | 0.8392 |
| 0.0316 | 9.6329 | 8554 | 0.7040 | 0.3284 | 0.7040 | 0.8391 |
| 0.0316 | 9.6351 | 8556 | 0.7033 | 0.3284 | 0.7033 | 0.8386 |
| 0.0316 | 9.6374 | 8558 | 0.7026 | 0.3284 | 0.7026 | 0.8382 |
| 0.0316 | 9.6396 | 8560 | 0.7024 | 0.3284 | 0.7024 | 0.8381 |
| 0.0316 | 9.6419 | 8562 | 0.7024 | 0.3284 | 0.7024 | 0.8381 |
| 0.0316 | 9.6441 | 8564 | 0.7036 | 0.3284 | 0.7036 | 0.8388 |
| 0.0316 | 9.6464 | 8566 | 0.7047 | 0.4167 | 0.7047 | 0.8395 |
| 0.0316 | 9.6486 | 8568 | 0.7052 | 0.4167 | 0.7052 | 0.8398 |
| 0.0316 | 9.6509 | 8570 | 0.7059 | 0.4167 | 0.7059 | 0.8402 |
| 0.0316 | 9.6532 | 8572 | 0.7062 | 0.4167 | 0.7062 | 0.8403 |
| 0.0316 | 9.6554 | 8574 | 0.7067 | 0.4167 | 0.7067 | 0.8407 |
| 0.0316 | 9.6577 | 8576 | 0.7070 | 0.4167 | 0.7070 | 0.8408 |
| 0.0316 | 9.6599 | 8578 | 0.7065 | 0.4167 | 0.7065 | 0.8405 |
| 0.0316 | 9.6622 | 8580 | 0.7053 | 0.4167 | 0.7053 | 0.8398 |
| 0.0316 | 9.6644 | 8582 | 0.7039 | 0.3284 | 0.7039 | 0.8390 |
| 0.0316 | 9.6667 | 8584 | 0.7028 | 0.3284 | 0.7028 | 0.8383 |
| 0.0316 | 9.6689 | 8586 | 0.7012 | 0.3284 | 0.7012 | 0.8374 |
| 0.0316 | 9.6712 | 8588 | 0.7002 | 0.3284 | 0.7002 | 0.8368 |
| 0.0316 | 9.6734 | 8590 | 0.7000 | 0.3284 | 0.7000 | 0.8367 |
| 0.0316 | 9.6757 | 8592 | 0.7002 | 0.3284 | 0.7002 | 0.8368 |
| 0.0316 | 9.6779 | 8594 | 0.7001 | 0.3284 | 0.7001 | 0.8367 |
| 0.0316 | 9.6802 | 8596 | 0.6996 | 0.3284 | 0.6996 | 0.8364 |
| 0.0316 | 9.6824 | 8598 | 0.6993 | 0.3284 | 0.6993 | 0.8362 |
| 0.0316 | 9.6847 | 8600 | 0.6993 | 0.3284 | 0.6993 | 0.8362 |
| 0.0316 | 9.6869 | 8602 | 0.6998 | 0.3284 | 0.6998 | 0.8365 |
| 0.0316 | 9.6892 | 8604 | 0.7011 | 0.3284 | 0.7011 | 0.8373 |
| 0.0316 | 9.6914 | 8606 | 0.7032 | 0.4167 | 0.7032 | 0.8386 |
| 0.0316 | 9.6937 | 8608 | 0.7056 | 0.4167 | 0.7056 | 0.8400 |
| 0.0316 | 9.6959 | 8610 | 0.7074 | 0.4167 | 0.7074 | 0.8411 |
| 0.0316 | 9.6982 | 8612 | 0.7087 | 0.4167 | 0.7087 | 0.8419 |
| 0.0316 | 9.7005 | 8614 | 0.7102 | 0.4167 | 0.7102 | 0.8427 |
| 0.0316 | 9.7027 | 8616 | 0.7108 | 0.4167 | 0.7108 | 0.8431 |
| 0.0316 | 9.7050 | 8618 | 0.7115 | 0.4167 | 0.7115 | 0.8435 |
| 0.0316 | 9.7072 | 8620 | 0.7117 | 0.4167 | 0.7117 | 0.8436 |
| 0.0316 | 9.7095 | 8622 | 0.7113 | 0.4167 | 0.7113 | 0.8434 |
| 0.0316 | 9.7117 | 8624 | 0.7105 | 0.4167 | 0.7105 | 0.8429 |
| 0.0316 | 9.7140 | 8626 | 0.7097 | 0.4167 | 0.7097 | 0.8424 |
| 0.0316 | 9.7162 | 8628 | 0.7090 | 0.4167 | 0.7090 | 0.8420 |
| 0.0316 | 9.7185 | 8630 | 0.7078 | 0.4167 | 0.7078 | 0.8413 |
| 0.0316 | 9.7207 | 8632 | 0.7066 | 0.4167 | 0.7066 | 0.8406 |
| 0.0316 | 9.7230 | 8634 | 0.7058 | 0.4167 | 0.7058 | 0.8401 |
| 0.0316 | 9.7252 | 8636 | 0.7063 | 0.4167 | 0.7063 | 0.8404 |
| 0.0316 | 9.7275 | 8638 | 0.7071 | 0.4167 | 0.7071 | 0.8409 |
| 0.0316 | 9.7297 | 8640 | 0.7070 | 0.4167 | 0.7070 | 0.8409 |
| 0.0316 | 9.7320 | 8642 | 0.7071 | 0.4167 | 0.7071 | 0.8409 |
| 0.0316 | 9.7342 | 8644 | 0.7073 | 0.4167 | 0.7073 | 0.8410 |
| 0.0316 | 9.7365 | 8646 | 0.7075 | 0.4167 | 0.7075 | 0.8412 |
| 0.0316 | 9.7387 | 8648 | 0.7075 | 0.4167 | 0.7075 | 0.8411 |
| 0.0316 | 9.7410 | 8650 | 0.7071 | 0.4167 | 0.7071 | 0.8409 |
| 0.0316 | 9.7432 | 8652 | 0.7065 | 0.4167 | 0.7065 | 0.8405 |
| 0.0316 | 9.7455 | 8654 | 0.7059 | 0.4167 | 0.7059 | 0.8402 |
| 0.0316 | 9.7477 | 8656 | 0.7052 | 0.4167 | 0.7052 | 0.8397 |
| 0.0316 | 9.75 | 8658 | 0.7045 | 0.4167 | 0.7045 | 0.8393 |
| 0.0316 | 9.7523 | 8660 | 0.7043 | 0.4167 | 0.7043 | 0.8392 |
| 0.0316 | 9.7545 | 8662 | 0.7040 | 0.4167 | 0.7040 | 0.8391 |
| 0.0316 | 9.7568 | 8664 | 0.7034 | 0.4167 | 0.7034 | 0.8387 |
| 0.0316 | 9.7590 | 8666 | 0.7029 | 0.4167 | 0.7029 | 0.8384 |
| 0.0316 | 9.7613 | 8668 | 0.7030 | 0.3284 | 0.7030 | 0.8385 |
| 0.0316 | 9.7635 | 8670 | 0.7032 | 0.3284 | 0.7032 | 0.8386 |
| 0.0316 | 9.7658 | 8672 | 0.7026 | 0.3284 | 0.7026 | 0.8382 |
| 0.0316 | 9.7680 | 8674 | 0.7022 | 0.3284 | 0.7022 | 0.8380 |
| 0.0316 | 9.7703 | 8676 | 0.7018 | 0.3284 | 0.7018 | 0.8377 |
| 0.0316 | 9.7725 | 8678 | 0.7014 | 0.3284 | 0.7014 | 0.8375 |
| 0.0316 | 9.7748 | 8680 | 0.7012 | 0.3284 | 0.7012 | 0.8374 |
| 0.0316 | 9.7770 | 8682 | 0.7007 | 0.3284 | 0.7007 | 0.8370 |
| 0.0316 | 9.7793 | 8684 | 0.7003 | 0.3284 | 0.7003 | 0.8368 |
| 0.0316 | 9.7815 | 8686 | 0.7005 | 0.3284 | 0.7005 | 0.8370 |
| 0.0316 | 9.7838 | 8688 | 0.7007 | 0.3284 | 0.7007 | 0.8371 |
| 0.0316 | 9.7860 | 8690 | 0.7014 | 0.3284 | 0.7014 | 0.8375 |
| 0.0316 | 9.7883 | 8692 | 0.7018 | 0.3284 | 0.7018 | 0.8377 |
| 0.0316 | 9.7905 | 8694 | 0.7017 | 0.3284 | 0.7017 | 0.8376 |
| 0.0316 | 9.7928 | 8696 | 0.7019 | 0.3284 | 0.7019 | 0.8378 |
| 0.0316 | 9.7950 | 8698 | 0.7026 | 0.3284 | 0.7026 | 0.8382 |
| 0.0316 | 9.7973 | 8700 | 0.7033 | 0.3284 | 0.7033 | 0.8386 |
| 0.0316 | 9.7995 | 8702 | 0.7037 | 0.3284 | 0.7037 | 0.8389 |
| 0.0316 | 9.8018 | 8704 | 0.7038 | 0.3284 | 0.7038 | 0.8389 |
| 0.0316 | 9.8041 | 8706 | 0.7037 | 0.3284 | 0.7037 | 0.8389 |
| 0.0316 | 9.8063 | 8708 | 0.7040 | 0.3284 | 0.7040 | 0.8390 |
| 0.0316 | 9.8086 | 8710 | 0.7045 | 0.4167 | 0.7045 | 0.8393 |
| 0.0316 | 9.8108 | 8712 | 0.7049 | 0.4167 | 0.7049 | 0.8396 |
| 0.0316 | 9.8131 | 8714 | 0.7050 | 0.4167 | 0.7050 | 0.8396 |
| 0.0316 | 9.8153 | 8716 | 0.7048 | 0.4167 | 0.7048 | 0.8395 |
| 0.0316 | 9.8176 | 8718 | 0.7045 | 0.4167 | 0.7045 | 0.8394 |
| 0.0316 | 9.8198 | 8720 | 0.7042 | 0.4167 | 0.7042 | 0.8392 |
| 0.0316 | 9.8221 | 8722 | 0.7044 | 0.4167 | 0.7044 | 0.8393 |
| 0.0316 | 9.8243 | 8724 | 0.7044 | 0.4167 | 0.7044 | 0.8393 |
| 0.0316 | 9.8266 | 8726 | 0.7040 | 0.4167 | 0.7040 | 0.8391 |
| 0.0316 | 9.8288 | 8728 | 0.7039 | 0.4167 | 0.7039 | 0.8390 |
| 0.0316 | 9.8311 | 8730 | 0.7037 | 0.4167 | 0.7037 | 0.8389 |
| 0.0316 | 9.8333 | 8732 | 0.7036 | 0.4167 | 0.7036 | 0.8388 |
| 0.0316 | 9.8356 | 8734 | 0.7036 | 0.4167 | 0.7036 | 0.8388 |
| 0.0316 | 9.8378 | 8736 | 0.7035 | 0.4167 | 0.7035 | 0.8387 |
| 0.0316 | 9.8401 | 8738 | 0.7029 | 0.3284 | 0.7029 | 0.8384 |
| 0.0316 | 9.8423 | 8740 | 0.7026 | 0.3284 | 0.7026 | 0.8382 |
| 0.0316 | 9.8446 | 8742 | 0.7020 | 0.3284 | 0.7020 | 0.8379 |
| 0.0316 | 9.8468 | 8744 | 0.7014 | 0.3284 | 0.7014 | 0.8375 |
| 0.0316 | 9.8491 | 8746 | 0.7009 | 0.3284 | 0.7009 | 0.8372 |
| 0.0316 | 9.8514 | 8748 | 0.7003 | 0.3284 | 0.7003 | 0.8369 |
| 0.0316 | 9.8536 | 8750 | 0.7002 | 0.3284 | 0.7002 | 0.8368 |
| 0.0316 | 9.8559 | 8752 | 0.7001 | 0.3284 | 0.7001 | 0.8367 |
| 0.0316 | 9.8581 | 8754 | 0.6998 | 0.3284 | 0.6998 | 0.8366 |
| 0.0316 | 9.8604 | 8756 | 0.7000 | 0.3284 | 0.7000 | 0.8366 |
| 0.0316 | 9.8626 | 8758 | 0.7001 | 0.3284 | 0.7001 | 0.8367 |
| 0.0316 | 9.8649 | 8760 | 0.7001 | 0.3284 | 0.7001 | 0.8367 |
| 0.0316 | 9.8671 | 8762 | 0.7003 | 0.3284 | 0.7003 | 0.8368 |
| 0.0316 | 9.8694 | 8764 | 0.7002 | 0.3284 | 0.7002 | 0.8368 |
| 0.0316 | 9.8716 | 8766 | 0.7001 | 0.3284 | 0.7001 | 0.8367 |
| 0.0316 | 9.8739 | 8768 | 0.7001 | 0.3284 | 0.7001 | 0.8367 |
| 0.0316 | 9.8761 | 8770 | 0.7000 | 0.3284 | 0.7000 | 0.8367 |
| 0.0316 | 9.8784 | 8772 | 0.7002 | 0.3284 | 0.7002 | 0.8368 |
| 0.0316 | 9.8806 | 8774 | 0.7006 | 0.3284 | 0.7006 | 0.8370 |
| 0.0316 | 9.8829 | 8776 | 0.7010 | 0.3284 | 0.7010 | 0.8372 |
| 0.0316 | 9.8851 | 8778 | 0.7015 | 0.3284 | 0.7015 | 0.8376 |
| 0.0316 | 9.8874 | 8780 | 0.7018 | 0.4167 | 0.7018 | 0.8377 |
| 0.0316 | 9.8896 | 8782 | 0.7022 | 0.4167 | 0.7022 | 0.8380 |
| 0.0316 | 9.8919 | 8784 | 0.7024 | 0.4167 | 0.7024 | 0.8381 |
| 0.0316 | 9.8941 | 8786 | 0.7029 | 0.4167 | 0.7029 | 0.8384 |
| 0.0316 | 9.8964 | 8788 | 0.7035 | 0.4167 | 0.7035 | 0.8387 |
| 0.0316 | 9.8986 | 8790 | 0.7039 | 0.4167 | 0.7039 | 0.8390 |
| 0.0316 | 9.9009 | 8792 | 0.7043 | 0.4167 | 0.7043 | 0.8392 |
| 0.0316 | 9.9032 | 8794 | 0.7044 | 0.4167 | 0.7044 | 0.8393 |
| 0.0316 | 9.9054 | 8796 | 0.7043 | 0.4167 | 0.7043 | 0.8392 |
| 0.0316 | 9.9077 | 8798 | 0.7043 | 0.4167 | 0.7043 | 0.8392 |
| 0.0316 | 9.9099 | 8800 | 0.7043 | 0.4167 | 0.7043 | 0.8392 |
| 0.0316 | 9.9122 | 8802 | 0.7043 | 0.4167 | 0.7043 | 0.8392 |
| 0.0316 | 9.9144 | 8804 | 0.7044 | 0.4167 | 0.7044 | 0.8393 |
| 0.0316 | 9.9167 | 8806 | 0.7046 | 0.4167 | 0.7046 | 0.8394 |
| 0.0316 | 9.9189 | 8808 | 0.7047 | 0.4167 | 0.7047 | 0.8395 |
| 0.0316 | 9.9212 | 8810 | 0.7049 | 0.4167 | 0.7049 | 0.8396 |
| 0.0316 | 9.9234 | 8812 | 0.7049 | 0.4167 | 0.7049 | 0.8396 |
| 0.0316 | 9.9257 | 8814 | 0.7049 | 0.4167 | 0.7049 | 0.8396 |
| 0.0316 | 9.9279 | 8816 | 0.7047 | 0.4167 | 0.7047 | 0.8394 |
| 0.0316 | 9.9302 | 8818 | 0.7046 | 0.4167 | 0.7046 | 0.8394 |
| 0.0316 | 9.9324 | 8820 | 0.7044 | 0.4167 | 0.7044 | 0.8393 |
| 0.0316 | 9.9347 | 8822 | 0.7043 | 0.4167 | 0.7043 | 0.8392 |
| 0.0316 | 9.9369 | 8824 | 0.7041 | 0.4167 | 0.7041 | 0.8391 |
| 0.0316 | 9.9392 | 8826 | 0.7037 | 0.4167 | 0.7037 | 0.8389 |
| 0.0316 | 9.9414 | 8828 | 0.7035 | 0.4167 | 0.7035 | 0.8388 |
| 0.0316 | 9.9437 | 8830 | 0.7035 | 0.4167 | 0.7035 | 0.8387 |
| 0.0316 | 9.9459 | 8832 | 0.7033 | 0.4167 | 0.7033 | 0.8386 |
| 0.0316 | 9.9482 | 8834 | 0.7031 | 0.4167 | 0.7031 | 0.8385 |
| 0.0316 | 9.9505 | 8836 | 0.7029 | 0.4167 | 0.7029 | 0.8384 |
| 0.0316 | 9.9527 | 8838 | 0.7028 | 0.4167 | 0.7028 | 0.8383 |
| 0.0316 | 9.9550 | 8840 | 0.7027 | 0.4167 | 0.7027 | 0.8383 |
| 0.0316 | 9.9572 | 8842 | 0.7025 | 0.4167 | 0.7025 | 0.8382 |
| 0.0316 | 9.9595 | 8844 | 0.7025 | 0.4167 | 0.7025 | 0.8381 |
| 0.0316 | 9.9617 | 8846 | 0.7024 | 0.4167 | 0.7024 | 0.8381 |
| 0.0316 | 9.9640 | 8848 | 0.7024 | 0.4167 | 0.7024 | 0.8381 |
| 0.0316 | 9.9662 | 8850 | 0.7024 | 0.4167 | 0.7024 | 0.8381 |
| 0.0316 | 9.9685 | 8852 | 0.7025 | 0.4167 | 0.7025 | 0.8382 |
| 0.0316 | 9.9707 | 8854 | 0.7027 | 0.4167 | 0.7027 | 0.8383 |
| 0.0316 | 9.9730 | 8856 | 0.7027 | 0.4167 | 0.7027 | 0.8383 |
| 0.0316 | 9.9752 | 8858 | 0.7027 | 0.4167 | 0.7027 | 0.8383 |
| 0.0316 | 9.9775 | 8860 | 0.7026 | 0.4167 | 0.7026 | 0.8382 |
| 0.0316 | 9.9797 | 8862 | 0.7026 | 0.4167 | 0.7026 | 0.8382 |
| 0.0316 | 9.9820 | 8864 | 0.7025 | 0.4167 | 0.7025 | 0.8381 |
| 0.0316 | 9.9842 | 8866 | 0.7024 | 0.4167 | 0.7024 | 0.8381 |
| 0.0316 | 9.9865 | 8868 | 0.7023 | 0.4167 | 0.7023 | 0.8380 |
| 0.0316 | 9.9887 | 8870 | 0.7023 | 0.4167 | 0.7023 | 0.8380 |
| 0.0316 | 9.9910 | 8872 | 0.7022 | 0.4167 | 0.7022 | 0.8380 |
| 0.0316 | 9.9932 | 8874 | 0.7022 | 0.4167 | 0.7022 | 0.8380 |
| 0.0316 | 9.9955 | 8876 | 0.7022 | 0.4167 | 0.7022 | 0.8380 |
| 0.0316 | 9.9977 | 8878 | 0.7022 | 0.4167 | 0.7022 | 0.8380 |
| 0.0316 | 10.0 | 8880 | 0.7022 | 0.4167 | 0.7022 | 0.8380 |
### Framework versions
- Transformers 4.44.2
- Pytorch 2.4.0+cu118
- Datasets 2.21.0
- Tokenizers 0.19.1
|
JuniperChinenye/d2
|
JuniperChinenye
| 2024-11-16T23:09:30Z
| 5
| 0
|
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2024-11-15T10:51:12Z
|
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
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|
rewicks/monolingual_de_8k-shared_ep15
|
rewicks
| 2024-11-16T23:05:45Z
| 168
| 0
|
transformers
|
[
"transformers",
"safetensors",
"marian",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2024-11-07T00:17:14Z
|
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
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<!-- Provide a longer summary of what this model is. -->
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- **Shared by [optional]:** [More Information Needed]
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<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
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[More Information Needed]
## Training Details
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## Evaluation
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#### Metrics
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[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
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|
Soponnnn/food_classifier
|
Soponnnn
| 2024-11-16T23:00:33Z
| 63
| 0
|
transformers
|
[
"transformers",
"tf",
"vit",
"image-classification",
"generated_from_keras_callback",
"base_model:google/vit-base-patch16-224-in21k",
"base_model:finetune:google/vit-base-patch16-224-in21k",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
image-classification
| 2024-11-16T22:32:04Z
|
---
library_name: transformers
license: apache-2.0
base_model: google/vit-base-patch16-224-in21k
tags:
- generated_from_keras_callback
model-index:
- name: Soponnnn/food_classifier
results: []
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# Soponnnn/food_classifier
This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.3916
- Validation Loss: 0.3630
- Train Accuracy: 0.916
- Epoch: 4
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'AdamWeightDecay', 'learning_rate': {'module': 'keras.optimizers.schedules', 'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 3e-05, 'decay_steps': 20000, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, 'registered_name': None}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.01}
- training_precision: float32
### Training results
| Train Loss | Validation Loss | Train Accuracy | Epoch |
|:----------:|:---------------:|:--------------:|:-----:|
| 2.8264 | 1.7259 | 0.779 | 0 |
| 1.2602 | 0.8512 | 0.871 | 1 |
| 0.7141 | 0.5674 | 0.885 | 2 |
| 0.5119 | 0.4395 | 0.908 | 3 |
| 0.3916 | 0.3630 | 0.916 | 4 |
### Framework versions
- Transformers 4.46.2
- TensorFlow 2.17.1
- Datasets 3.1.0
- Tokenizers 0.20.3
|
Triangle104/Chronos-Gold-12B-1.0-Q4_K_M-GGUF
|
Triangle104
| 2024-11-16T22:57:27Z
| 28
| 0
|
transformers
|
[
"transformers",
"gguf",
"general-purpose",
"roleplay",
"storywriting",
"merge",
"finetune",
"llama-cpp",
"gguf-my-repo",
"base_model:elinas/Chronos-Gold-12B-1.0",
"base_model:quantized:elinas/Chronos-Gold-12B-1.0",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2024-11-10T05:35:13Z
|
---
license: apache-2.0
library_name: transformers
tags:
- general-purpose
- roleplay
- storywriting
- merge
- finetune
- llama-cpp
- gguf-my-repo
base_model: elinas/Chronos-Gold-12B-1.0
model-index:
- name: Chronos-Gold-12B-1.0
results:
- task:
type: text-generation
name: Text Generation
dataset:
name: IFEval (0-Shot)
type: HuggingFaceH4/ifeval
args:
num_few_shot: 0
metrics:
- type: inst_level_strict_acc and prompt_level_strict_acc
value: 31.66
name: strict accuracy
source:
url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=elinas/Chronos-Gold-12B-1.0
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: BBH (3-Shot)
type: BBH
args:
num_few_shot: 3
metrics:
- type: acc_norm
value: 35.91
name: normalized accuracy
source:
url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=elinas/Chronos-Gold-12B-1.0
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: MATH Lvl 5 (4-Shot)
type: hendrycks/competition_math
args:
num_few_shot: 4
metrics:
- type: exact_match
value: 4.38
name: exact match
source:
url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=elinas/Chronos-Gold-12B-1.0
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: GPQA (0-shot)
type: Idavidrein/gpqa
args:
num_few_shot: 0
metrics:
- type: acc_norm
value: 9.06
name: acc_norm
source:
url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=elinas/Chronos-Gold-12B-1.0
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: MuSR (0-shot)
type: TAUR-Lab/MuSR
args:
num_few_shot: 0
metrics:
- type: acc_norm
value: 19.42
name: acc_norm
source:
url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=elinas/Chronos-Gold-12B-1.0
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: MMLU-PRO (5-shot)
type: TIGER-Lab/MMLU-Pro
config: main
split: test
args:
num_few_shot: 5
metrics:
- type: acc
value: 27.98
name: accuracy
source:
url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=elinas/Chronos-Gold-12B-1.0
name: Open LLM Leaderboard
---
# Triangle104/Chronos-Gold-12B-1.0-Q4_K_M-GGUF
This model was converted to GGUF format from [`elinas/Chronos-Gold-12B-1.0`](https://huggingface.co/elinas/Chronos-Gold-12B-1.0) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space.
Refer to the [original model card](https://huggingface.co/elinas/Chronos-Gold-12B-1.0) for more details on the model.
---
Model details:
-
Chronos Gold 12B 1.0 is a very unique model that applies to domain areas such as general chatbot functionatliy, roleplay, and storywriting. The model has been observed to write up to 2250 tokens in a single sequence. The model was trained at a sequence length of 16384 (16k) and will still retain the apparent 128k context length from Mistral-Nemo, though it deteriorates over time like regular Nemo does based on the RULER Test
As a result, is recommended to keep your sequence length max at 16384, or you will experience performance degredation.
The base model is mistralai/Mistral-Nemo-Base-2407 which was heavily modified to produce a more coherent model, comparable to much larger models.
Chronos Gold 12B-1.0 re-creates the uniqueness of the original Chronos with significiantly enhanced prompt adherence (following), coherence, a modern dataset, as well as supporting a majority of "character card" formats in applications like SillyTavern.
It went through an iterative and objective merge process as my previous models and was further finetuned on a dataset curated for it.
The specifics of the model will not be disclosed at the time due to dataset ownership.
Instruct Template
This model uses ChatML - below is an example. It is a preset in many frontends.
<|im_start|>system
A system prompt describing how you'd like your bot to act.<|im_end|>
<|im_start|>user
Hello there!<|im_end|>
<|im_start|>assistant
I can assist you or we can discuss other things?<|im_end|>
<|im_start|>user
I was wondering how transformers work?<|im_end|>
<|im_start|>assistant
---
## Use with llama.cpp
Install llama.cpp through brew (works on Mac and Linux)
```bash
brew install llama.cpp
```
Invoke the llama.cpp server or the CLI.
### CLI:
```bash
llama-cli --hf-repo Triangle104/Chronos-Gold-12B-1.0-Q4_K_M-GGUF --hf-file chronos-gold-12b-1.0-q4_k_m.gguf -p "The meaning to life and the universe is"
```
### Server:
```bash
llama-server --hf-repo Triangle104/Chronos-Gold-12B-1.0-Q4_K_M-GGUF --hf-file chronos-gold-12b-1.0-q4_k_m.gguf -c 2048
```
Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well.
Step 1: Clone llama.cpp from GitHub.
```
git clone https://github.com/ggerganov/llama.cpp
```
Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux).
```
cd llama.cpp && LLAMA_CURL=1 make
```
Step 3: Run inference through the main binary.
```
./llama-cli --hf-repo Triangle104/Chronos-Gold-12B-1.0-Q4_K_M-GGUF --hf-file chronos-gold-12b-1.0-q4_k_m.gguf -p "The meaning to life and the universe is"
```
or
```
./llama-server --hf-repo Triangle104/Chronos-Gold-12B-1.0-Q4_K_M-GGUF --hf-file chronos-gold-12b-1.0-q4_k_m.gguf -c 2048
```
|
e22vvb/mt5-base_EN_spider_no_decode
|
e22vvb
| 2024-11-16T22:55:42Z
| 105
| 0
|
transformers
|
[
"transformers",
"safetensors",
"mt5",
"text2text-generation",
"generated_from_trainer",
"base_model:google/mt5-base",
"base_model:finetune:google/mt5-base",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2024-11-16T11:34:31Z
|
---
library_name: transformers
license: apache-2.0
base_model: google/mt5-base
tags:
- generated_from_trainer
model-index:
- name: mt5-base_EN_spider_no_decode
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# mt5-base_EN_spider_no_decode
This model is a fine-tuned version of [google/mt5-base](https://huggingface.co/google/mt5-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: nan
- Rouge2 Precision: 0.0109
- Rouge2 Recall: 0.0036
- Rouge2 Fmeasure: 0.005
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 1
- eval_batch_size: 1
- seed: 42
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 15
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge2 Precision | Rouge2 Recall | Rouge2 Fmeasure |
|:-------------:|:-----:|:------:|:---------------:|:----------------:|:-------------:|:---------------:|
| 0.0 | 1.0 | 9693 | nan | 0.0109 | 0.0036 | 0.005 |
| 0.0 | 2.0 | 19386 | nan | 0.0109 | 0.0036 | 0.005 |
| 0.0 | 3.0 | 29079 | nan | 0.0109 | 0.0036 | 0.005 |
| 0.0 | 4.0 | 38772 | nan | 0.0109 | 0.0036 | 0.005 |
| 0.0 | 5.0 | 48465 | nan | 0.0109 | 0.0036 | 0.005 |
| 0.0 | 6.0 | 58158 | nan | 0.0109 | 0.0036 | 0.005 |
| 0.0 | 7.0 | 67851 | nan | 0.0109 | 0.0036 | 0.005 |
| 0.0 | 8.0 | 77544 | nan | 0.0109 | 0.0036 | 0.005 |
| 0.0 | 9.0 | 87237 | nan | 0.0109 | 0.0036 | 0.005 |
| 0.0 | 10.0 | 96930 | nan | 0.0109 | 0.0036 | 0.005 |
| 0.0 | 11.0 | 106623 | nan | 0.0109 | 0.0036 | 0.005 |
| 0.0 | 12.0 | 116316 | nan | 0.0109 | 0.0036 | 0.005 |
| 0.0 | 13.0 | 126009 | nan | 0.0109 | 0.0036 | 0.005 |
| 0.0 | 14.0 | 135702 | nan | 0.0109 | 0.0036 | 0.005 |
| 0.0 | 15.0 | 145395 | nan | 0.0109 | 0.0036 | 0.005 |
### Framework versions
- Transformers 4.46.2
- Pytorch 2.2.2
- Datasets 2.16.1
- Tokenizers 0.20.3
|
Triangle104/Chronos-Gold-12B-1.0-Q4_K_S-GGUF
|
Triangle104
| 2024-11-16T22:52:25Z
| 7
| 0
|
transformers
|
[
"transformers",
"gguf",
"general-purpose",
"roleplay",
"storywriting",
"merge",
"finetune",
"llama-cpp",
"gguf-my-repo",
"base_model:elinas/Chronos-Gold-12B-1.0",
"base_model:quantized:elinas/Chronos-Gold-12B-1.0",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2024-11-10T05:41:57Z
|
---
license: apache-2.0
library_name: transformers
tags:
- general-purpose
- roleplay
- storywriting
- merge
- finetune
- llama-cpp
- gguf-my-repo
base_model: elinas/Chronos-Gold-12B-1.0
model-index:
- name: Chronos-Gold-12B-1.0
results:
- task:
type: text-generation
name: Text Generation
dataset:
name: IFEval (0-Shot)
type: HuggingFaceH4/ifeval
args:
num_few_shot: 0
metrics:
- type: inst_level_strict_acc and prompt_level_strict_acc
value: 31.66
name: strict accuracy
source:
url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=elinas/Chronos-Gold-12B-1.0
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: BBH (3-Shot)
type: BBH
args:
num_few_shot: 3
metrics:
- type: acc_norm
value: 35.91
name: normalized accuracy
source:
url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=elinas/Chronos-Gold-12B-1.0
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: MATH Lvl 5 (4-Shot)
type: hendrycks/competition_math
args:
num_few_shot: 4
metrics:
- type: exact_match
value: 4.38
name: exact match
source:
url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=elinas/Chronos-Gold-12B-1.0
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: GPQA (0-shot)
type: Idavidrein/gpqa
args:
num_few_shot: 0
metrics:
- type: acc_norm
value: 9.06
name: acc_norm
source:
url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=elinas/Chronos-Gold-12B-1.0
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: MuSR (0-shot)
type: TAUR-Lab/MuSR
args:
num_few_shot: 0
metrics:
- type: acc_norm
value: 19.42
name: acc_norm
source:
url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=elinas/Chronos-Gold-12B-1.0
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: MMLU-PRO (5-shot)
type: TIGER-Lab/MMLU-Pro
config: main
split: test
args:
num_few_shot: 5
metrics:
- type: acc
value: 27.98
name: accuracy
source:
url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=elinas/Chronos-Gold-12B-1.0
name: Open LLM Leaderboard
---
# Triangle104/Chronos-Gold-12B-1.0-Q4_K_S-GGUF
This model was converted to GGUF format from [`elinas/Chronos-Gold-12B-1.0`](https://huggingface.co/elinas/Chronos-Gold-12B-1.0) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space.
Refer to the [original model card](https://huggingface.co/elinas/Chronos-Gold-12B-1.0) for more details on the model.
---
Model details:
-
Chronos Gold 12B 1.0 is a very unique model that applies to domain areas such as general chatbot functionatliy, roleplay, and storywriting. The model has been observed to write up to 2250 tokens in a single sequence. The model was trained at a sequence length of 16384 (16k) and will still retain the apparent 128k context length from Mistral-Nemo, though it deteriorates over time like regular Nemo does based on the RULER Test
As a result, is recommended to keep your sequence length max at 16384, or you will experience performance degredation.
The base model is mistralai/Mistral-Nemo-Base-2407 which was heavily modified to produce a more coherent model, comparable to much larger models.
Chronos Gold 12B-1.0 re-creates the uniqueness of the original Chronos with significiantly enhanced prompt adherence (following), coherence, a modern dataset, as well as supporting a majority of "character card" formats in applications like SillyTavern.
It went through an iterative and objective merge process as my previous models and was further finetuned on a dataset curated for it.
The specifics of the model will not be disclosed at the time due to dataset ownership.
Instruct Template
This model uses ChatML - below is an example. It is a preset in many frontends.
<|im_start|>system
A system prompt describing how you'd like your bot to act.<|im_end|>
<|im_start|>user
Hello there!<|im_end|>
<|im_start|>assistant
I can assist you or we can discuss other things?<|im_end|>
<|im_start|>user
I was wondering how transformers work?<|im_end|>
<|im_start|>assistant
---
## Use with llama.cpp
Install llama.cpp through brew (works on Mac and Linux)
```bash
brew install llama.cpp
```
Invoke the llama.cpp server or the CLI.
### CLI:
```bash
llama-cli --hf-repo Triangle104/Chronos-Gold-12B-1.0-Q4_K_S-GGUF --hf-file chronos-gold-12b-1.0-q4_k_s.gguf -p "The meaning to life and the universe is"
```
### Server:
```bash
llama-server --hf-repo Triangle104/Chronos-Gold-12B-1.0-Q4_K_S-GGUF --hf-file chronos-gold-12b-1.0-q4_k_s.gguf -c 2048
```
Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well.
Step 1: Clone llama.cpp from GitHub.
```
git clone https://github.com/ggerganov/llama.cpp
```
Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux).
```
cd llama.cpp && LLAMA_CURL=1 make
```
Step 3: Run inference through the main binary.
```
./llama-cli --hf-repo Triangle104/Chronos-Gold-12B-1.0-Q4_K_S-GGUF --hf-file chronos-gold-12b-1.0-q4_k_s.gguf -p "The meaning to life and the universe is"
```
or
```
./llama-server --hf-repo Triangle104/Chronos-Gold-12B-1.0-Q4_K_S-GGUF --hf-file chronos-gold-12b-1.0-q4_k_s.gguf -c 2048
```
|
futuremojo/test-3.1-8B
|
futuremojo
| 2024-11-16T22:51:52Z
| 5
| 0
|
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"text-generation-inference",
"unsloth",
"trl",
"sft",
"en",
"base_model:unsloth/Meta-Llama-3.1-8B",
"base_model:finetune:unsloth/Meta-Llama-3.1-8B",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2024-11-16T22:49:30Z
|
---
base_model: unsloth/Meta-Llama-3.1-8B
language:
- en
license: apache-2.0
tags:
- text-generation-inference
- transformers
- unsloth
- llama
- trl
- sft
---
# Uploaded model
- **Developed by:** futuremojo
- **License:** apache-2.0
- **Finetuned from model :** unsloth/Meta-Llama-3.1-8B
This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
ParisNeo/Llama-3.2-1B-Instruct-lollms-smart-router
|
ParisNeo
| 2024-11-16T22:46:31Z
| 8
| 0
| null |
[
"safetensors",
"llama",
"router",
"text2text-generation",
"en",
"dataset:ParisNeo/lollms_smart_router_dataset",
"base_model:unsloth/Llama-3.2-1B-Instruct",
"base_model:finetune:unsloth/Llama-3.2-1B-Instruct",
"license:llama3.2",
"region:us"
] |
text2text-generation
| 2024-11-16T22:37:08Z
|
---
license: llama3.2
datasets:
- ParisNeo/lollms_smart_router_dataset
language:
- en
metrics:
- accuracy
base_model:
- unsloth/Llama-3.2-1B-Instruct
pipeline_tag: text2text-generation
tags:
- router
---
|
Triangle104/Mistral-Nemo-Instruct-2407-abliterated-Q8_0-GGUF
|
Triangle104
| 2024-11-16T22:44:32Z
| 5
| 0
| null |
[
"gguf",
"llama-cpp",
"gguf-my-repo",
"en",
"fr",
"de",
"es",
"it",
"pt",
"ru",
"zh",
"ja",
"base_model:natong19/Mistral-Nemo-Instruct-2407-abliterated",
"base_model:quantized:natong19/Mistral-Nemo-Instruct-2407-abliterated",
"license:apache-2.0",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2024-09-18T18:00:52Z
|
---
language:
- en
- fr
- de
- es
- it
- pt
- ru
- zh
- ja
license: apache-2.0
base_model: natong19/Mistral-Nemo-Instruct-2407-abliterated
tags:
- llama-cpp
- gguf-my-repo
---
# Triangle104/Mistral-Nemo-Instruct-2407-abliterated-Q8_0-GGUF
This model was converted to GGUF format from [`natong19/Mistral-Nemo-Instruct-2407-abliterated`](https://huggingface.co/natong19/Mistral-Nemo-Instruct-2407-abliterated) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space.
Refer to the [original model card](https://huggingface.co/natong19/Mistral-Nemo-Instruct-2407-abliterated) for more details on the model.
---
Model details:
-
Abliterated version of Mistral-Nemo-Instruct-2407, a Large Language Model (LLM) trained jointly by Mistral AI and NVIDIA that significantly outperforms existing models smaller or similar in size. The model's strongest refusal directions have been ablated via weight orthogonalization, but the model may still refuse your request, misunderstand your intent, or provide unsolicited advice regarding ethics or safety.
Key features
Trained with a 128k context window
Trained on a large proportion of multilingual and code data
Drop-in replacement of Mistral 7B
Quickstart
from transformers import AutoModelForCausalLM, AutoTokenizer
import torch
model_id = "natong19/Mistral-Nemo-Instruct-2407-abliterated"
device = "cuda"
tokenizer = AutoTokenizer.from_pretrained(model_id)
conversation = [{"role": "user", "content": "Where's the capital of France?"}]
tool_use_prompt = tokenizer.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True,
)
inputs = tokenizer(tool_use_prompt, return_tensors="pt", return_token_type_ids=False).to(device)
model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype=torch.bfloat16, device_map="auto")
outputs = model.generate(**inputs, max_new_tokens=128)
print(tokenizer.decode(outputs[0][len(inputs["input_ids"][0]):], skip_special_tokens=True))
---
## Use with llama.cpp
Install llama.cpp through brew (works on Mac and Linux)
```bash
brew install llama.cpp
```
Invoke the llama.cpp server or the CLI.
### CLI:
```bash
llama-cli --hf-repo Triangle104/Mistral-Nemo-Instruct-2407-abliterated-Q8_0-GGUF --hf-file mistral-nemo-instruct-2407-abliterated-q8_0.gguf -p "The meaning to life and the universe is"
```
### Server:
```bash
llama-server --hf-repo Triangle104/Mistral-Nemo-Instruct-2407-abliterated-Q8_0-GGUF --hf-file mistral-nemo-instruct-2407-abliterated-q8_0.gguf -c 2048
```
Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well.
Step 1: Clone llama.cpp from GitHub.
```
git clone https://github.com/ggerganov/llama.cpp
```
Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux).
```
cd llama.cpp && LLAMA_CURL=1 make
```
Step 3: Run inference through the main binary.
```
./llama-cli --hf-repo Triangle104/Mistral-Nemo-Instruct-2407-abliterated-Q8_0-GGUF --hf-file mistral-nemo-instruct-2407-abliterated-q8_0.gguf -p "The meaning to life and the universe is"
```
or
```
./llama-server --hf-repo Triangle104/Mistral-Nemo-Instruct-2407-abliterated-Q8_0-GGUF --hf-file mistral-nemo-instruct-2407-abliterated-q8_0.gguf -c 2048
```
|
Triangle104/Mistral-Nemo-Instruct-2407-abliterated-Q6_K-GGUF
|
Triangle104
| 2024-11-16T22:41:19Z
| 6
| 1
| null |
[
"gguf",
"llama-cpp",
"gguf-my-repo",
"en",
"fr",
"de",
"es",
"it",
"pt",
"ru",
"zh",
"ja",
"base_model:natong19/Mistral-Nemo-Instruct-2407-abliterated",
"base_model:quantized:natong19/Mistral-Nemo-Instruct-2407-abliterated",
"license:apache-2.0",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2024-09-18T17:18:53Z
|
---
language:
- en
- fr
- de
- es
- it
- pt
- ru
- zh
- ja
license: apache-2.0
base_model: natong19/Mistral-Nemo-Instruct-2407-abliterated
tags:
- llama-cpp
- gguf-my-repo
---
# Triangle104/Mistral-Nemo-Instruct-2407-abliterated-Q6_K-GGUF
This model was converted to GGUF format from [`natong19/Mistral-Nemo-Instruct-2407-abliterated`](https://huggingface.co/natong19/Mistral-Nemo-Instruct-2407-abliterated) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space.
Refer to the [original model card](https://huggingface.co/natong19/Mistral-Nemo-Instruct-2407-abliterated) for more details on the model.
---
Model details:
-
Abliterated version of Mistral-Nemo-Instruct-2407, a Large Language Model (LLM) trained jointly by Mistral AI and NVIDIA that significantly outperforms existing models smaller or similar in size. The model's strongest refusal directions have been ablated via weight orthogonalization, but the model may still refuse your request, misunderstand your intent, or provide unsolicited advice regarding ethics or safety.
Key features
Trained with a 128k context window
Trained on a large proportion of multilingual and code data
Drop-in replacement of Mistral 7B
Quickstart
from transformers import AutoModelForCausalLM, AutoTokenizer
import torch
model_id = "natong19/Mistral-Nemo-Instruct-2407-abliterated"
device = "cuda"
tokenizer = AutoTokenizer.from_pretrained(model_id)
conversation = [{"role": "user", "content": "Where's the capital of France?"}]
tool_use_prompt = tokenizer.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True,
)
inputs = tokenizer(tool_use_prompt, return_tensors="pt", return_token_type_ids=False).to(device)
model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype=torch.bfloat16, device_map="auto")
outputs = model.generate(**inputs, max_new_tokens=128)
print(tokenizer.decode(outputs[0][len(inputs["input_ids"][0]):], skip_special_tokens=True))
---
## Use with llama.cpp
Install llama.cpp through brew (works on Mac and Linux)
```bash
brew install llama.cpp
```
Invoke the llama.cpp server or the CLI.
### CLI:
```bash
llama-cli --hf-repo Triangle104/Mistral-Nemo-Instruct-2407-abliterated-Q6_K-GGUF --hf-file mistral-nemo-instruct-2407-abliterated-q6_k.gguf -p "The meaning to life and the universe is"
```
### Server:
```bash
llama-server --hf-repo Triangle104/Mistral-Nemo-Instruct-2407-abliterated-Q6_K-GGUF --hf-file mistral-nemo-instruct-2407-abliterated-q6_k.gguf -c 2048
```
Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well.
Step 1: Clone llama.cpp from GitHub.
```
git clone https://github.com/ggerganov/llama.cpp
```
Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux).
```
cd llama.cpp && LLAMA_CURL=1 make
```
Step 3: Run inference through the main binary.
```
./llama-cli --hf-repo Triangle104/Mistral-Nemo-Instruct-2407-abliterated-Q6_K-GGUF --hf-file mistral-nemo-instruct-2407-abliterated-q6_k.gguf -p "The meaning to life and the universe is"
```
or
```
./llama-server --hf-repo Triangle104/Mistral-Nemo-Instruct-2407-abliterated-Q6_K-GGUF --hf-file mistral-nemo-instruct-2407-abliterated-q6_k.gguf -c 2048
```
|
silveroxides/NoobAI-XL-V-Pred-0.5
|
silveroxides
| 2024-11-16T22:40:16Z
| 17
| 0
|
diffusers
|
[
"diffusers",
"safetensors",
"license:other",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionXLPipeline",
"region:us"
] |
text-to-image
| 2024-11-16T21:36:41Z
|
---
license: other
license_name: faipl-1.0-sd
license_link: https://freedevproject.org/faipl-1.0-sd/
---
|
vietlethe/bkad-deformable-detr_best
|
vietlethe
| 2024-11-16T22:35:09Z
| 132
| 0
|
transformers
|
[
"transformers",
"safetensors",
"deformable_detr",
"object-detection",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] |
object-detection
| 2024-11-16T14:24:51Z
|
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
ND911/flux-mini-GGUF
|
ND911
| 2024-11-16T22:28:44Z
| 55
| 0
| null |
[
"gguf",
"base_model:TencentARC/flux-mini",
"base_model:quantized:TencentARC/flux-mini",
"license:apache-2.0",
"region:us"
] | null | 2024-11-16T22:21:31Z
|
---
license: apache-2.0
base_model:
- TencentARC/flux-mini
---
GGUFs for model [flux-mini](https://huggingface.co/TencentARC/flux-mini)
|
RichardErkhov/webbigdata_-_ALMA-7B-Ja-V2-gguf
|
RichardErkhov
| 2024-11-16T22:26:19Z
| 112
| 1
| null |
[
"gguf",
"arxiv:2309.11674",
"endpoints_compatible",
"region:us"
] | null | 2024-11-16T19:13:06Z
|
Quantization made by Richard Erkhov.
[Github](https://github.com/RichardErkhov)
[Discord](https://discord.gg/pvy7H8DZMG)
[Request more models](https://github.com/RichardErkhov/quant_request)
ALMA-7B-Ja-V2 - GGUF
- Model creator: https://huggingface.co/webbigdata/
- Original model: https://huggingface.co/webbigdata/ALMA-7B-Ja-V2/
| Name | Quant method | Size |
| ---- | ---- | ---- |
| [ALMA-7B-Ja-V2.Q2_K.gguf](https://huggingface.co/RichardErkhov/webbigdata_-_ALMA-7B-Ja-V2-gguf/blob/main/ALMA-7B-Ja-V2.Q2_K.gguf) | Q2_K | 2.36GB |
| [ALMA-7B-Ja-V2.Q3_K_S.gguf](https://huggingface.co/RichardErkhov/webbigdata_-_ALMA-7B-Ja-V2-gguf/blob/main/ALMA-7B-Ja-V2.Q3_K_S.gguf) | Q3_K_S | 2.75GB |
| [ALMA-7B-Ja-V2.Q3_K.gguf](https://huggingface.co/RichardErkhov/webbigdata_-_ALMA-7B-Ja-V2-gguf/blob/main/ALMA-7B-Ja-V2.Q3_K.gguf) | Q3_K | 3.07GB |
| [ALMA-7B-Ja-V2.Q3_K_M.gguf](https://huggingface.co/RichardErkhov/webbigdata_-_ALMA-7B-Ja-V2-gguf/blob/main/ALMA-7B-Ja-V2.Q3_K_M.gguf) | Q3_K_M | 3.07GB |
| [ALMA-7B-Ja-V2.Q3_K_L.gguf](https://huggingface.co/RichardErkhov/webbigdata_-_ALMA-7B-Ja-V2-gguf/blob/main/ALMA-7B-Ja-V2.Q3_K_L.gguf) | Q3_K_L | 3.35GB |
| [ALMA-7B-Ja-V2.IQ4_XS.gguf](https://huggingface.co/RichardErkhov/webbigdata_-_ALMA-7B-Ja-V2-gguf/blob/main/ALMA-7B-Ja-V2.IQ4_XS.gguf) | IQ4_XS | 3.4GB |
| [ALMA-7B-Ja-V2.Q4_0.gguf](https://huggingface.co/RichardErkhov/webbigdata_-_ALMA-7B-Ja-V2-gguf/blob/main/ALMA-7B-Ja-V2.Q4_0.gguf) | Q4_0 | 3.56GB |
| [ALMA-7B-Ja-V2.IQ4_NL.gguf](https://huggingface.co/RichardErkhov/webbigdata_-_ALMA-7B-Ja-V2-gguf/blob/main/ALMA-7B-Ja-V2.IQ4_NL.gguf) | IQ4_NL | 3.58GB |
| [ALMA-7B-Ja-V2.Q4_K_S.gguf](https://huggingface.co/RichardErkhov/webbigdata_-_ALMA-7B-Ja-V2-gguf/blob/main/ALMA-7B-Ja-V2.Q4_K_S.gguf) | Q4_K_S | 3.59GB |
| [ALMA-7B-Ja-V2.Q4_K.gguf](https://huggingface.co/RichardErkhov/webbigdata_-_ALMA-7B-Ja-V2-gguf/blob/main/ALMA-7B-Ja-V2.Q4_K.gguf) | Q4_K | 3.8GB |
| [ALMA-7B-Ja-V2.Q4_K_M.gguf](https://huggingface.co/RichardErkhov/webbigdata_-_ALMA-7B-Ja-V2-gguf/blob/main/ALMA-7B-Ja-V2.Q4_K_M.gguf) | Q4_K_M | 3.8GB |
| [ALMA-7B-Ja-V2.Q4_1.gguf](https://huggingface.co/RichardErkhov/webbigdata_-_ALMA-7B-Ja-V2-gguf/blob/main/ALMA-7B-Ja-V2.Q4_1.gguf) | Q4_1 | 3.95GB |
| [ALMA-7B-Ja-V2.Q5_0.gguf](https://huggingface.co/RichardErkhov/webbigdata_-_ALMA-7B-Ja-V2-gguf/blob/main/ALMA-7B-Ja-V2.Q5_0.gguf) | Q5_0 | 4.33GB |
| [ALMA-7B-Ja-V2.Q5_K_S.gguf](https://huggingface.co/RichardErkhov/webbigdata_-_ALMA-7B-Ja-V2-gguf/blob/main/ALMA-7B-Ja-V2.Q5_K_S.gguf) | Q5_K_S | 4.33GB |
| [ALMA-7B-Ja-V2.Q5_K.gguf](https://huggingface.co/RichardErkhov/webbigdata_-_ALMA-7B-Ja-V2-gguf/blob/main/ALMA-7B-Ja-V2.Q5_K.gguf) | Q5_K | 4.45GB |
| [ALMA-7B-Ja-V2.Q5_K_M.gguf](https://huggingface.co/RichardErkhov/webbigdata_-_ALMA-7B-Ja-V2-gguf/blob/main/ALMA-7B-Ja-V2.Q5_K_M.gguf) | Q5_K_M | 4.45GB |
| [ALMA-7B-Ja-V2.Q5_1.gguf](https://huggingface.co/RichardErkhov/webbigdata_-_ALMA-7B-Ja-V2-gguf/blob/main/ALMA-7B-Ja-V2.Q5_1.gguf) | Q5_1 | 4.72GB |
| [ALMA-7B-Ja-V2.Q6_K.gguf](https://huggingface.co/RichardErkhov/webbigdata_-_ALMA-7B-Ja-V2-gguf/blob/main/ALMA-7B-Ja-V2.Q6_K.gguf) | Q6_K | 5.15GB |
| [ALMA-7B-Ja-V2.Q8_0.gguf](https://huggingface.co/RichardErkhov/webbigdata_-_ALMA-7B-Ja-V2-gguf/blob/main/ALMA-7B-Ja-V2.Q8_0.gguf) | Q8_0 | 6.67GB |
Original model description:
---
inference: false
language:
- ja
- en
- de
- is
- zh
- cs
license: llama2
---
# New Translation model released.
[C3TR-Adapter](https://huggingface.co/webbigdata/C3TR-Adapter) is the QLoRA adapter for google/gemma-7b.
Despite the 4-bit quantization, the memory GPU requirement has increased to 8.1 GB.
However, it is possible to run it with the free version of Colab and the performance is much improved!
# webbigdata/ALMA-7B-Ja-V2
ALMA-7B-Ja-V2は日本語から英語、英語から日本語の翻訳が可能な機械翻訳モデルです。
The ALMA-7B-Ja-V2 is a machine translation model capable of translating from Japanese to English and English to Japanese.
ALMA-7B-Ja-V2は以前のモデル([ALMA-7B-Ja](https://huggingface.co/webbigdata/ALMA-7B-Ja))に更に学習を追加し、性能を向上しています。
The ALMA-7B-Ja-V2 adds further learning to the previous model ([ALMA-7B-Ja](https://huggingface.co/webbigdata/ALMA-7B-Ja)) and improves performance.
日本語と英語間に加えて、このモデルは以下の言語間の翻訳能力も持っていますが、日英、英日翻訳を主目的にしています。
In addition to translation between Japanese and English, this model also has the ability to translate between the following languages, but is primarily intended for Japanese-English and English-Japanese translation.
- ドイツ語 German(de) and 英語 English(en)
- 中国語 Chinese(zh) and 英語 English(en)
- アイスランド語 Icelandic(is) and 英語 English(en)
- チェコ語 Czech(cs) and 英語 English(en)
# ベンチマーク結果
以下の三種の指標を使って翻訳性能を確認しました。
The following three metrics were used to check translation performance.
数字が大きいほど性能が良い事を意味します。
The higher the number, the better the performance.
## BLEU
翻訳テキストが元のテキストにどれだけ似ているかを評価する指標です。しかし、単語の出現頻度だけを見ているため、語順の正確さや文の流暢さを十分に評価できないという弱点があります
A metric that evaluates how similar the translated text is to the original text. However, since it mainly looks at the frequency of word appearances, it may not effectively evaluate the accuracy of word order or the fluency of sentences.
### chrF++
文字の組み合わせの一致度と語順に基づいて、翻訳の正確さを評価する指標です。弱点としては、長い文章の評価には不向きであることが挙げられます。
A method to evaluate translation accuracy based on how well character combinations match and the order of words. A drawback is that it might not be suitable for evaluating longer sentences.
### comet
機械学習モデルを使って翻訳の品質を自動的に評価するためのツール、人間の主観的評価に近いと言われていますが、機械学習ベースであるため、元々のモデルが学習に使ったデータに大きく依存するという弱点があります。
A tool that uses machine learning models to automatically evaluate the quality of translations, although it is said to be similar to the evaluation ratings performed by humans. Because it is machine learning based, it has the weakness that the original model is highly dependent on the data used for training.
## vs. NLLB-200
Meta社の200言語以上の翻訳に対応した超多言語対応機械翻訳モデルNLLB-200シリーズと比較したベンチマーク結果は以下です。
Benchmark results compared to Meta's NLLB-200 series of super multilingual machine translation models, which support translations in over 200 languages, are shown below.
| Model Name | file size |E->J chrf++/F2|E->J comet|J->E chrf++/F2|J->E comet |
|------------------------------|-----------|--------------|----------|--------------|-----------|
| NLLB-200-Distilled | 2.46GB | 23.6/- | - | 50.2/- | - |
| NLLB-200-Distilled | 5.48GB | 25.4/- | - | 54.2/- | - |
| NLLB-200 | 5.48GB | 24.2/- | - | 53.6/- | - |
| NLLB-200 | 17.58GB | 25.2/- | - | 55.1/- | - |
| NLLB-200 | 220.18GB | 27.9/33.2 | 0.8908 | 55.8/59.8 | 0.8792 |
## previous our model(ALMA-7B-Ja)
| Model Name | file size |E->J chrf++/F2|E->J comet|J->E chrf++/F2|J->E comet |
|------------------------------|-----------|--------------|----------|--------------|-----------|
| webbigdata-ALMA-7B-Ja-q4_K_S | 3.6GB | -/24.2 | 0.8210 | -/54.2 | 0.8559 |
| ALMA-7B-Ja-GPTQ-Ja-En | 3.9GB | -/30.8 | 0.8743 | -/60.9 | 0.8743 |
| ALMA-Ja(Ours) | 13.48GB | -/31.8 | 0.8811 | -/61.6 | 0.8773 |
## ALMA-7B-Ja-V2
| Model Name | file size |E->J chrf++/F2|E->J comet|J->E chrf++/F2|J->E comet |
|------------------------------|-----------|--------------|----------|--------------|-----------|
| ALMA-7B-Ja-V2-GPTQ-Ja-En | 3.9GB | -/33.0 | 0.8818 | -/62.0 | 0.8774 |
| ALMA-Ja-V2(Ours) | 13.48GB | -/33.9 | 0.8820 | -/63.1 | 0.8873 |
| ALMA-Ja-V2-Lora(Ours) | 13.48GB | -/33.7 | 0.8843 | -/61.1 | 0.8775 |
ALMA-7B-Ja-V2を様々なジャンルの文章を現実世界のアプリケーションと比較した結果は以下です。
Here are the results of a comparison of various genres of writing with the actual application.
## 政府の公式文章 Government Official Announcements
| |e->j chrF2++|e->j BLEU|e->j comet|j->e chrF2++|j->e BLEU|j->e comet|
|--------------------------|------------|---------|----------|------------|---------|----------|
| ALMA-7B-Ja-V2-GPTQ-Ja-En | 25.3 | 15.00 | 0.8848 | 60.3 | 26.82 | 0.6189 |
| ALMA-Ja-V2 | 27.2 | 15.60 | 0.8868 | 58.5 | 29.27 | 0.6155 |
| ALMA-7B-Ja-V2-Lora | 24.5 | 13.58 | 0.8670 | 50.7 | 21.85 | 0.6196 |
| SeamlessM4T | 27.3 | 16.76 | 0.9070 | 54.2 | 25.76 | 0.5656 |
| gpt-3.5 | 34.6 | 28.33 | 0.8895 | 74.5 | 49.20 | 0.6382 |
| gpt-4.0 | 36.5 | 28.07 | 0.9255 | 62.5 | 33.63 | 0.6320 |
| google-translate | 43.5 | 35.37 | 0.9181 | 62.7 | 29.22 | 0.6446 |
| deepl | 43.5 | 35.74 | 0.9301 | 60.1 | 27.40 | 0.6389 |
## 古典文学 Classical Literature
| |e->j chrF2++|e->j BLEU|e->j comet|j->e chrF2++|j->e BLEU|j->e comet|
|--------------------------|------------|---------|----------|------------|---------|----------|
| ALMA-7B-Ja-V2-GPTQ-Ja-En | 11.8 | 7.24 | 0.6943 | 31.9 | 9.71 | 0.5617 |
| ALMA-Ja-V2 | 10.7 | 4.93 | 0.7202 | 32.9 | 10.52 | 0.5638 |
| ALMA-7B-Ja-V2-Lora | 12.3 | 7.25 | 0.7076 | 32.5 | 11.14 | 0.5441 |
| gpt-3.5 | - | - | 0.6367 | 69.3 | 46.34 | 0.4922 |
| gpt-4.0 | 13.3 | 8.33 | 0.7074 | 44.3 | 23.75 | 0.5518 |
| deepl | 14.4 | 9.18 | 0.7149 | 34.6 | 10.68 | 0.5787 |
| google-translate | 13.5 | 8.57 | 0.7432 | 31.7 | 7.94 | 0.5856 |
## 二次創作 Fanfiction
| |e->j chrF2++|e->j BLEU|e->j comet|j->e chrF2++|j->e BLEU|j->e comet|
|--------------------------|------------|---------|----------|------------|---------|----------|
| ALMA-7B-Ja-V2-GPTQ-Ja-En | 27.6 | 18.28 | 0.8643 | 52.1 | 24.58 | 0.6106 |
| ALMA-Ja-V2 | 20.4 | 8.45 | 0.7870 | 48.7 | 23.06 | 0.6050 |
| ALMA-7B-Ja-V2-Lora | 23.9 | 18.55 | 0.8634 | 55.6 | 29.91 | 0.6093 |
| SeamlessM4T | 25.5 | 19.97 | 0.8657 | 42.2 | 14.39 | 0.5554 |
| gpt-3.5 | 31.2 | 23.37 | 0.9001 | - | - | 0.5948 |
| gpt-4.0 | 30.7 | 24.31 | 0.8848 | 53.9 | 24.89 | 0.6163 |
| google-translate | 32.4 | 25.36 | 0.8968 | 58.5 | 29.88 | 0.6022 |
| deepl | 33.5 | 28.38 | 0.9094 | 60.0 | 31.14 | 0.6124 |
## サンプルコード sample code
Googleの無料WebツールであるColabを使うとALMA_7B_Ja_V2の性能を簡単に確かめる事ができます。
Using Colab, Google's free web tool, you can easily verify the performance of ALMA_7B_Ja_V2.
[Sample Code For Free Colab](https://github.com/webbigdata-jp/python_sample/blob/main/ALMA_7B_Ja_V2_Free_Colab_sample.ipynb)
## その他の版 Other Version
### llama.cpp
[llama.cpp](https://github.com/ggerganov/llama.cpp)の主な目的はMacBook上で4ビット整数量子化を使用して LLaMA モデルを実行する事です。
The main purpose of [llama.cpp](https://github.com/ggerganov/llama.cpp) is to run the LLaMA model using 4-bit integer quantization on a MacBook.
4ビット量子化に伴い、性能はやや低下しますが、mmngaさんが作成してくれた[webbigdata-ALMA-7B-Ja-V2-gguf](https://huggingface.co/mmnga/webbigdata-ALMA-7B-Ja-V2-gguf)を使うとMacやGPUを搭載していないWindows、Linuxで本モデルを動かす事ができます。
Although performance is somewhat reduced with 4-bit quantization, [webbigdata-ALMA-7B-Ja-V2-gguf](https://huggingface.co/mmnga/webbigdata-ALMA-7B-Ja-V2-gguf), created by mmnga, can be used to run this model on Mac, Windows and Linux without a GPU.
[GPU無版のColabで動かすサンプルはこちら](https://github.com/webbigdata-jp/python_sample/blob/main/ALMA_7B_Ja_V2_gguf_Free_Colab_sample.ipynb)です。
[Here is Colab(without GPU) sample code](https://github.com/webbigdata-jp/python_sample/blob/main/ALMA_7B_Ja_V2_gguf_Free_Colab_sample.ipynb).
### GPTQ
GPTQはモデルサイズを小さくする手法(量子化といいます)です。
GPTQ is a technique (called quantization) that reduces model size.
[ALMA-7B-Ja-V2-GPTQ-Ja-En](https://huggingface.co/webbigdata/ALMA-7B-Ja-V2-GPTQ-Ja-En)はGPTQ量子化版で、モデルサイズ(3.9GB)とメモリ使用量を削減し、速度を向上しています。
[ALMA-7B-Ja-V2-GPTQ-Ja-En](https://huggingface.co/webbigdata/ALMA-7B-Ja-V2-GPTQ-Ja-En) is a quantized GPTQ version, which reduces model size (3.9 GB) and memory usage and increases speed.
ただし、性能は少し落ちてしまいます。また、日本語と英語以外の言語への翻訳能力は著しく低下しているはずです。
However, performance is slightly reduced. Also, the ability to translate into languages other than Japanese and English should be significantly reduced.
[Sample Code For Free Colab webbigdata/ALMA-7B-Ja-V2-GPTQ-Ja-En](https://github.com/webbigdata-jp/python_sample/blob/master/ALMA_7B_Ja_V2_GPTQ_Ja_En_Free_Colab_sample.ipynb)
ファイル全体を一度に翻訳したい場合は、以下のColabをお試しください。
If you want to translate the entire txt file at once, try Colab below.
[ALMA_7B_Ja_GPTQ_Ja_En_batch_translation_sample](https://github.com/webbigdata-jp/python_sample/blob/master/ALMA_7B_Ja_V2_GPTQ_Ja_En_batch_translation_sample.ipynb)
**ALMA** (**A**dvanced **L**anguage **M**odel-based tr**A**nslator) is an LLM-based translation model, which adopts a new translation model paradigm: it begins with fine-tuning on monolingual data and is further optimized using high-quality parallel data. This two-step fine-tuning process ensures strong translation performance.
Please find more details in their [paper](https://arxiv.org/abs/2309.11674).
```
@misc{xu2023paradigm,
title={A Paradigm Shift in Machine Translation: Boosting Translation Performance of Large Language Models},
author={Haoran Xu and Young Jin Kim and Amr Sharaf and Hany Hassan Awadalla},
year={2023},
eprint={2309.11674},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
Original Model [ALMA-7B](https://huggingface.co/haoranxu/ALMA-7B). (26.95GB)
Prevous Model [ALMA-7B-Ja](https://huggingface.co/webbigdata/ALMA-7B-Ja). (13.3 GB)
## about this work
- **This work was done by :** [webbigdata](https://webbigdata.jp/post-21151/).
|
Triangle104/Mistral-Nemo-Instruct-2407-abliterated-Q4_K_S-GGUF
|
Triangle104
| 2024-11-16T22:20:33Z
| 5
| 0
| null |
[
"gguf",
"llama-cpp",
"gguf-my-repo",
"en",
"fr",
"de",
"es",
"it",
"pt",
"ru",
"zh",
"ja",
"base_model:natong19/Mistral-Nemo-Instruct-2407-abliterated",
"base_model:quantized:natong19/Mistral-Nemo-Instruct-2407-abliterated",
"license:apache-2.0",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2024-09-18T16:41:55Z
|
---
language:
- en
- fr
- de
- es
- it
- pt
- ru
- zh
- ja
license: apache-2.0
base_model: natong19/Mistral-Nemo-Instruct-2407-abliterated
tags:
- llama-cpp
- gguf-my-repo
---
# Triangle104/Mistral-Nemo-Instruct-2407-abliterated-Q4_K_S-GGUF
This model was converted to GGUF format from [`natong19/Mistral-Nemo-Instruct-2407-abliterated`](https://huggingface.co/natong19/Mistral-Nemo-Instruct-2407-abliterated) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space.
Refer to the [original model card](https://huggingface.co/natong19/Mistral-Nemo-Instruct-2407-abliterated) for more details on the model.
---
Model details:
-
Abliterated version of Mistral-Nemo-Instruct-2407, a Large Language Model (LLM) trained jointly by Mistral AI and NVIDIA that significantly outperforms existing models smaller or similar in size. The model's strongest refusal directions have been ablated via weight orthogonalization, but the model may still refuse your request, misunderstand your intent, or provide unsolicited advice regarding ethics or safety.
Key features
Trained with a 128k context window
Trained on a large proportion of multilingual and code data
Drop-in replacement of Mistral 7B
Quickstart
from transformers import AutoModelForCausalLM, AutoTokenizer
import torch
model_id = "natong19/Mistral-Nemo-Instruct-2407-abliterated"
device = "cuda"
tokenizer = AutoTokenizer.from_pretrained(model_id)
conversation = [{"role": "user", "content": "Where's the capital of France?"}]
tool_use_prompt = tokenizer.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True,
)
inputs = tokenizer(tool_use_prompt, return_tensors="pt", return_token_type_ids=False).to(device)
model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype=torch.bfloat16, device_map="auto")
outputs = model.generate(**inputs, max_new_tokens=128)
print(tokenizer.decode(outputs[0][len(inputs["input_ids"][0]):], skip_special_tokens=True))
---
## Use with llama.cpp
Install llama.cpp through brew (works on Mac and Linux)
```bash
brew install llama.cpp
```
Invoke the llama.cpp server or the CLI.
### CLI:
```bash
llama-cli --hf-repo Triangle104/Mistral-Nemo-Instruct-2407-abliterated-Q4_K_S-GGUF --hf-file mistral-nemo-instruct-2407-abliterated-q4_k_s.gguf -p "The meaning to life and the universe is"
```
### Server:
```bash
llama-server --hf-repo Triangle104/Mistral-Nemo-Instruct-2407-abliterated-Q4_K_S-GGUF --hf-file mistral-nemo-instruct-2407-abliterated-q4_k_s.gguf -c 2048
```
Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well.
Step 1: Clone llama.cpp from GitHub.
```
git clone https://github.com/ggerganov/llama.cpp
```
Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux).
```
cd llama.cpp && LLAMA_CURL=1 make
```
Step 3: Run inference through the main binary.
```
./llama-cli --hf-repo Triangle104/Mistral-Nemo-Instruct-2407-abliterated-Q4_K_S-GGUF --hf-file mistral-nemo-instruct-2407-abliterated-q4_k_s.gguf -p "The meaning to life and the universe is"
```
or
```
./llama-server --hf-repo Triangle104/Mistral-Nemo-Instruct-2407-abliterated-Q4_K_S-GGUF --hf-file mistral-nemo-instruct-2407-abliterated-q4_k_s.gguf -c 2048
```
|
rewicks/monolingual_de_8k-shared_ep9
|
rewicks
| 2024-11-16T22:20:01Z
| 165
| 0
|
transformers
|
[
"transformers",
"safetensors",
"marian",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2024-11-06T23:42:21Z
|
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
mradermacher/Samantha-Qwen-2-7B-i1-GGUF
|
mradermacher
| 2024-11-16T22:18:15Z
| 60
| 0
|
transformers
|
[
"transformers",
"gguf",
"en",
"zh",
"dataset:macadeliccc/opus_samantha",
"dataset:HuggingfaceH4/ultrachat_200k",
"dataset:teknium/OpenHermes-2.5",
"dataset:Sao10K/Claude-3-Opus-Instruct-15K",
"base_model:macadeliccc/Samantha-Qwen-2-7B",
"base_model:quantized:macadeliccc/Samantha-Qwen-2-7B",
"license:apache-2.0",
"endpoints_compatible",
"region:us",
"imatrix",
"conversational"
] | null | 2024-11-16T20:55:52Z
|
---
base_model: macadeliccc/Samantha-Qwen-2-7B
datasets:
- macadeliccc/opus_samantha
- HuggingfaceH4/ultrachat_200k
- teknium/OpenHermes-2.5
- Sao10K/Claude-3-Opus-Instruct-15K
language:
- en
- zh
library_name: transformers
license: apache-2.0
quantized_by: mradermacher
---
## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
<!-- ### tags: nicoboss -->
weighted/imatrix quants of https://huggingface.co/macadeliccc/Samantha-Qwen-2-7B
<!-- provided-files -->
static quants are available at https://huggingface.co/mradermacher/Samantha-Qwen-2-7B-GGUF
## Usage
If you are unsure how to use GGUF files, refer to one of [TheBloke's
READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for
more details, including on how to concatenate multi-part files.
## Provided Quants
(sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants)
| Link | Type | Size/GB | Notes |
|:-----|:-----|--------:|:------|
| [GGUF](https://huggingface.co/mradermacher/Samantha-Qwen-2-7B-i1-GGUF/resolve/main/Samantha-Qwen-2-7B.i1-IQ1_S.gguf) | i1-IQ1_S | 2.0 | for the desperate |
| [GGUF](https://huggingface.co/mradermacher/Samantha-Qwen-2-7B-i1-GGUF/resolve/main/Samantha-Qwen-2-7B.i1-IQ1_M.gguf) | i1-IQ1_M | 2.1 | mostly desperate |
| [GGUF](https://huggingface.co/mradermacher/Samantha-Qwen-2-7B-i1-GGUF/resolve/main/Samantha-Qwen-2-7B.i1-IQ2_XXS.gguf) | i1-IQ2_XXS | 2.4 | |
| [GGUF](https://huggingface.co/mradermacher/Samantha-Qwen-2-7B-i1-GGUF/resolve/main/Samantha-Qwen-2-7B.i1-IQ2_XS.gguf) | i1-IQ2_XS | 2.6 | |
| [GGUF](https://huggingface.co/mradermacher/Samantha-Qwen-2-7B-i1-GGUF/resolve/main/Samantha-Qwen-2-7B.i1-IQ2_S.gguf) | i1-IQ2_S | 2.7 | |
| [GGUF](https://huggingface.co/mradermacher/Samantha-Qwen-2-7B-i1-GGUF/resolve/main/Samantha-Qwen-2-7B.i1-IQ2_M.gguf) | i1-IQ2_M | 2.9 | |
| [GGUF](https://huggingface.co/mradermacher/Samantha-Qwen-2-7B-i1-GGUF/resolve/main/Samantha-Qwen-2-7B.i1-Q2_K.gguf) | i1-Q2_K | 3.1 | IQ3_XXS probably better |
| [GGUF](https://huggingface.co/mradermacher/Samantha-Qwen-2-7B-i1-GGUF/resolve/main/Samantha-Qwen-2-7B.i1-IQ3_XXS.gguf) | i1-IQ3_XXS | 3.2 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/Samantha-Qwen-2-7B-i1-GGUF/resolve/main/Samantha-Qwen-2-7B.i1-IQ3_XS.gguf) | i1-IQ3_XS | 3.4 | |
| [GGUF](https://huggingface.co/mradermacher/Samantha-Qwen-2-7B-i1-GGUF/resolve/main/Samantha-Qwen-2-7B.i1-Q3_K_S.gguf) | i1-Q3_K_S | 3.6 | IQ3_XS probably better |
| [GGUF](https://huggingface.co/mradermacher/Samantha-Qwen-2-7B-i1-GGUF/resolve/main/Samantha-Qwen-2-7B.i1-IQ3_S.gguf) | i1-IQ3_S | 3.6 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/Samantha-Qwen-2-7B-i1-GGUF/resolve/main/Samantha-Qwen-2-7B.i1-IQ3_M.gguf) | i1-IQ3_M | 3.7 | |
| [GGUF](https://huggingface.co/mradermacher/Samantha-Qwen-2-7B-i1-GGUF/resolve/main/Samantha-Qwen-2-7B.i1-Q3_K_M.gguf) | i1-Q3_K_M | 3.9 | IQ3_S probably better |
| [GGUF](https://huggingface.co/mradermacher/Samantha-Qwen-2-7B-i1-GGUF/resolve/main/Samantha-Qwen-2-7B.i1-Q3_K_L.gguf) | i1-Q3_K_L | 4.2 | IQ3_M probably better |
| [GGUF](https://huggingface.co/mradermacher/Samantha-Qwen-2-7B-i1-GGUF/resolve/main/Samantha-Qwen-2-7B.i1-IQ4_XS.gguf) | i1-IQ4_XS | 4.3 | |
| [GGUF](https://huggingface.co/mradermacher/Samantha-Qwen-2-7B-i1-GGUF/resolve/main/Samantha-Qwen-2-7B.i1-Q4_0_4_4.gguf) | i1-Q4_0_4_4 | 4.5 | fast on arm, low quality |
| [GGUF](https://huggingface.co/mradermacher/Samantha-Qwen-2-7B-i1-GGUF/resolve/main/Samantha-Qwen-2-7B.i1-Q4_0_4_8.gguf) | i1-Q4_0_4_8 | 4.5 | fast on arm+i8mm, low quality |
| [GGUF](https://huggingface.co/mradermacher/Samantha-Qwen-2-7B-i1-GGUF/resolve/main/Samantha-Qwen-2-7B.i1-Q4_0_8_8.gguf) | i1-Q4_0_8_8 | 4.5 | fast on arm+sve, low quality |
| [GGUF](https://huggingface.co/mradermacher/Samantha-Qwen-2-7B-i1-GGUF/resolve/main/Samantha-Qwen-2-7B.i1-Q4_0.gguf) | i1-Q4_0 | 4.5 | fast, low quality |
| [GGUF](https://huggingface.co/mradermacher/Samantha-Qwen-2-7B-i1-GGUF/resolve/main/Samantha-Qwen-2-7B.i1-Q4_K_S.gguf) | i1-Q4_K_S | 4.6 | optimal size/speed/quality |
| [GGUF](https://huggingface.co/mradermacher/Samantha-Qwen-2-7B-i1-GGUF/resolve/main/Samantha-Qwen-2-7B.i1-Q4_K_M.gguf) | i1-Q4_K_M | 4.8 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Samantha-Qwen-2-7B-i1-GGUF/resolve/main/Samantha-Qwen-2-7B.i1-Q5_K_S.gguf) | i1-Q5_K_S | 5.4 | |
| [GGUF](https://huggingface.co/mradermacher/Samantha-Qwen-2-7B-i1-GGUF/resolve/main/Samantha-Qwen-2-7B.i1-Q5_K_M.gguf) | i1-Q5_K_M | 5.5 | |
| [GGUF](https://huggingface.co/mradermacher/Samantha-Qwen-2-7B-i1-GGUF/resolve/main/Samantha-Qwen-2-7B.i1-Q6_K.gguf) | i1-Q6_K | 6.4 | practically like static Q6_K |
Here is a handy graph by ikawrakow comparing some lower-quality quant
types (lower is better):

And here are Artefact2's thoughts on the matter:
https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9
## FAQ / Model Request
See https://huggingface.co/mradermacher/model_requests for some answers to
questions you might have and/or if you want some other model quantized.
## Thanks
I thank my company, [nethype GmbH](https://www.nethype.de/), for letting
me use its servers and providing upgrades to my workstation to enable
this work in my free time. Additional thanks to [@nicoboss](https://huggingface.co/nicoboss) for giving me access to his private supercomputer, enabling me to provide many more imatrix quants, at much higher quality, than I would otherwise be able to.
<!-- end -->
|
featherless-ai-quants/VeriUs-VeriUS-LLM-8b-v0.2-GGUF
|
featherless-ai-quants
| 2024-11-16T22:10:50Z
| 12
| 0
| null |
[
"gguf",
"text-generation",
"base_model:VeriUs/VeriUS-LLM-8b-v0.2",
"base_model:quantized:VeriUs/VeriUS-LLM-8b-v0.2",
"endpoints_compatible",
"region:us",
"conversational"
] |
text-generation
| 2024-11-08T23:57:00Z
|
---
base_model: VeriUs/VeriUS-LLM-8b-v0.2
pipeline_tag: text-generation
quantized_by: featherless-ai-quants
---
# VeriUs/VeriUS-LLM-8b-v0.2 GGUF Quantizations 🚀

*Optimized GGUF quantization files for enhanced model performance*
> Powered by [Featherless AI](https://featherless.ai) - run any model you'd like for a simple small fee.
---
## Available Quantizations 📊
| Quantization Type | File | Size |
|-------------------|------|------|
| IQ4_XS | [VeriUs-VeriUS-LLM-8b-v0.2-IQ4_XS.gguf](https://huggingface.co/featherless-ai-quants/VeriUs-VeriUS-LLM-8b-v0.2-GGUF/blob/main/VeriUs-VeriUS-LLM-8b-v0.2-IQ4_XS.gguf) | 4276.62 MB |
| Q2_K | [VeriUs-VeriUS-LLM-8b-v0.2-Q2_K.gguf](https://huggingface.co/featherless-ai-quants/VeriUs-VeriUS-LLM-8b-v0.2-GGUF/blob/main/VeriUs-VeriUS-LLM-8b-v0.2-Q2_K.gguf) | 3031.86 MB |
| Q3_K_L | [VeriUs-VeriUS-LLM-8b-v0.2-Q3_K_L.gguf](https://huggingface.co/featherless-ai-quants/VeriUs-VeriUS-LLM-8b-v0.2-GGUF/blob/main/VeriUs-VeriUS-LLM-8b-v0.2-Q3_K_L.gguf) | 4121.74 MB |
| Q3_K_M | [VeriUs-VeriUS-LLM-8b-v0.2-Q3_K_M.gguf](https://huggingface.co/featherless-ai-quants/VeriUs-VeriUS-LLM-8b-v0.2-GGUF/blob/main/VeriUs-VeriUS-LLM-8b-v0.2-Q3_K_M.gguf) | 3832.74 MB |
| Q3_K_S | [VeriUs-VeriUS-LLM-8b-v0.2-Q3_K_S.gguf](https://huggingface.co/featherless-ai-quants/VeriUs-VeriUS-LLM-8b-v0.2-GGUF/blob/main/VeriUs-VeriUS-LLM-8b-v0.2-Q3_K_S.gguf) | 3494.74 MB |
| Q4_K_M | [VeriUs-VeriUS-LLM-8b-v0.2-Q4_K_M.gguf](https://huggingface.co/featherless-ai-quants/VeriUs-VeriUS-LLM-8b-v0.2-GGUF/blob/main/VeriUs-VeriUS-LLM-8b-v0.2-Q4_K_M.gguf) | 4692.78 MB |
| Q4_K_S | [VeriUs-VeriUS-LLM-8b-v0.2-Q4_K_S.gguf](https://huggingface.co/featherless-ai-quants/VeriUs-VeriUS-LLM-8b-v0.2-GGUF/blob/main/VeriUs-VeriUS-LLM-8b-v0.2-Q4_K_S.gguf) | 4475.28 MB |
| Q5_K_M | [VeriUs-VeriUS-LLM-8b-v0.2-Q5_K_M.gguf](https://huggingface.co/featherless-ai-quants/VeriUs-VeriUS-LLM-8b-v0.2-GGUF/blob/main/VeriUs-VeriUS-LLM-8b-v0.2-Q5_K_M.gguf) | 5467.40 MB |
| Q5_K_S | [VeriUs-VeriUS-LLM-8b-v0.2-Q5_K_S.gguf](https://huggingface.co/featherless-ai-quants/VeriUs-VeriUS-LLM-8b-v0.2-GGUF/blob/main/VeriUs-VeriUS-LLM-8b-v0.2-Q5_K_S.gguf) | 5339.90 MB |
| Q6_K | [VeriUs-VeriUS-LLM-8b-v0.2-Q6_K.gguf](https://huggingface.co/featherless-ai-quants/VeriUs-VeriUS-LLM-8b-v0.2-GGUF/blob/main/VeriUs-VeriUS-LLM-8b-v0.2-Q6_K.gguf) | 6290.44 MB |
| Q8_0 | [VeriUs-VeriUS-LLM-8b-v0.2-Q8_0.gguf](https://huggingface.co/featherless-ai-quants/VeriUs-VeriUS-LLM-8b-v0.2-GGUF/blob/main/VeriUs-VeriUS-LLM-8b-v0.2-Q8_0.gguf) | 8145.11 MB |
---
## ⚡ Powered by [Featherless AI](https://featherless.ai)
### Key Features
- 🔥 **Instant Hosting** - Deploy any Llama model on HuggingFace instantly
- 🛠️ **Zero Infrastructure** - No server setup or maintenance required
- 📚 **Vast Compatibility** - Support for 2400+ models and counting
- 💎 **Affordable Pricing** - Starting at just $10/month
---
**Links:**
[Get Started](https://featherless.ai) | [Documentation](https://featherless.ai/docs) | [Models](https://featherless.ai/models)
|
rewicks/monolingual_de_8k-shared_ep6
|
rewicks
| 2024-11-16T21:59:28Z
| 159
| 0
|
transformers
|
[
"transformers",
"safetensors",
"marian",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2024-11-01T18:21:43Z
|
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
ANAS12345Nouri/results
|
ANAS12345Nouri
| 2024-11-16T21:54:51Z
| 105
| 0
|
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"distilbert",
"text-classification",
"generated_from_trainer",
"base_model:distilbert/distilbert-base-uncased",
"base_model:finetune:distilbert/distilbert-base-uncased",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2024-11-16T21:01:39Z
|
---
library_name: transformers
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: results
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# results
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0129
- Accuracy: 0.9975
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 349 | 0.0194 | 0.9950 |
| 0.0587 | 2.0 | 698 | 0.0129 | 0.9975 |
### Framework versions
- Transformers 4.46.2
- Pytorch 2.5.1+cu121
- Tokenizers 0.20.3
|
glif-loradex-trainer/insectagon_slerf_prodigy
|
glif-loradex-trainer
| 2024-11-16T21:51:27Z
| 27
| 0
|
diffusers
|
[
"diffusers",
"text-to-image",
"template:sd-lora",
"base_model:black-forest-labs/FLUX.1-dev",
"base_model:finetune:black-forest-labs/FLUX.1-dev",
"license:other",
"region:us",
"flux",
"lora",
"base_model:adapter:black-forest-labs/FLUX.1-dev"
] |
text-to-image
| 2024-11-16T21:50:37Z
|
---
tags:
- diffusers
- text-to-image
- template:sd-lora
- base_model:black-forest-labs/FLUX.1-dev
- base_model:finetune:black-forest-labs/FLUX.1-dev
- license:other
- region:us
- flux
- lora
widget:
- output:
url: samples/1731793686809__000003000_0.jpg
text: slerf sitting alone in the jungle crying [sl3rf]
- output:
url: samples/1731793711840__000003000_1.jpg
text: Trump dancing with an angry face next to slerf [sl3rf]
- output:
url: samples/1731793736782__000003000_2.jpg
text: An 8-bit super street fighter game with slerf vs Pepe [sl3rf]
- output:
url: samples/1731793761738__000003000_3.jpg
text: An exciting action scene featuring slerf [sl3rf]
- output:
url: samples/1731793786784__000003000_4.jpg
text: a Japanese anime dramatic scene with slerf and a human woman [sl3rf]
- output:
url: samples/1731793811847__000003000_5.jpg
text: slerf sitting and explaining life to a sad child [sl3rf]
base_model: black-forest-labs/FLUX.1-dev
trigger: sl3rf
instance_prompt: sl3rf
license: other
license_name: flux-1-dev-non-commercial-license
license_link: https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md
---
# slerf_prodigy
Model trained with [AI Toolkit by Ostris](https://github.com/ostris/ai-toolkit) under the [Glif Loradex program](https://huggingface.co/glif-loradex-trainer) by [Glif](https://glif.app) user `insectagon`.
<Gallery />
## Trigger words
You should use `sl3rf` to trigger the image generation.
## Download model
Weights for this model are available in Safetensors format.
[Download](/glif-loradex-trainer/insectagon_slerf_prodigy/tree/main) them in the Files & versions tab.
## License
This model is licensed under the [flux-1-dev-non-commercial-license](https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md).
|
rewicks/monolingual_de_8k-shared_ep5
|
rewicks
| 2024-11-16T21:51:12Z
| 159
| 0
|
transformers
|
[
"transformers",
"safetensors",
"marian",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2024-11-01T18:14:29Z
|
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
MayBashendy/Arabic_FineTuningAraBERT_AugV4_k40_task2_organization_fold1
|
MayBashendy
| 2024-11-16T21:50:40Z
| 161
| 0
|
transformers
|
[
"transformers",
"safetensors",
"bert",
"text-classification",
"generated_from_trainer",
"base_model:aubmindlab/bert-base-arabertv02",
"base_model:finetune:aubmindlab/bert-base-arabertv02",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2024-11-16T21:20:16Z
|
---
library_name: transformers
base_model: aubmindlab/bert-base-arabertv02
tags:
- generated_from_trainer
model-index:
- name: Arabic_FineTuningAraBERT_AugV4_k40_task2_organization_fold1
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# Arabic_FineTuningAraBERT_AugV4_k40_task2_organization_fold1
This model is a fine-tuned version of [aubmindlab/bert-base-arabertv02](https://huggingface.co/aubmindlab/bert-base-arabertv02) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6031
- Qwk: 0.2727
- Mse: 0.6031
- Rmse: 0.7766
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Qwk | Mse | Rmse |
|:-------------:|:------:|:----:|:---------------:|:-------:|:------:|:------:|
| No log | 0.0034 | 2 | 3.3861 | 0.0094 | 3.3861 | 1.8401 |
| No log | 0.0067 | 4 | 1.9590 | -0.0465 | 1.9590 | 1.3997 |
| No log | 0.0101 | 6 | 1.5696 | 0.0 | 1.5696 | 1.2529 |
| No log | 0.0134 | 8 | 1.1142 | 0.0 | 1.1142 | 1.0555 |
| No log | 0.0168 | 10 | 0.6878 | 0.0308 | 0.6878 | 0.8293 |
| No log | 0.0201 | 12 | 0.5778 | 0.0 | 0.5778 | 0.7601 |
| No log | 0.0235 | 14 | 0.6342 | 0.0 | 0.6342 | 0.7964 |
| No log | 0.0268 | 16 | 0.8248 | 0.0 | 0.8248 | 0.9082 |
| No log | 0.0302 | 18 | 0.9463 | 0.1818 | 0.9463 | 0.9728 |
| No log | 0.0336 | 20 | 0.9863 | 0.2000 | 0.9863 | 0.9931 |
| No log | 0.0369 | 22 | 0.8741 | 0.0 | 0.8741 | 0.9350 |
| No log | 0.0403 | 24 | 0.8111 | 0.0 | 0.8111 | 0.9006 |
| No log | 0.0436 | 26 | 0.8040 | 0.0 | 0.8040 | 0.8966 |
| No log | 0.0470 | 28 | 0.7081 | 0.0 | 0.7081 | 0.8415 |
| No log | 0.0503 | 30 | 0.6966 | 0.0 | 0.6966 | 0.8346 |
| No log | 0.0537 | 32 | 0.7650 | 0.0 | 0.7650 | 0.8747 |
| No log | 0.0570 | 34 | 0.7871 | 0.0 | 0.7871 | 0.8872 |
| No log | 0.0604 | 36 | 0.7897 | 0.0 | 0.7897 | 0.8887 |
| No log | 0.0638 | 38 | 0.7714 | 0.0 | 0.7714 | 0.8783 |
| No log | 0.0671 | 40 | 0.7914 | 0.0 | 0.7914 | 0.8896 |
| No log | 0.0705 | 42 | 0.9470 | 0.0400 | 0.9470 | 0.9731 |
| No log | 0.0738 | 44 | 1.0301 | 0.1000 | 1.0301 | 1.0149 |
| No log | 0.0772 | 46 | 0.8891 | 0.0400 | 0.8891 | 0.9429 |
| No log | 0.0805 | 48 | 0.8629 | 0.0400 | 0.8629 | 0.9289 |
| No log | 0.0839 | 50 | 0.8866 | 0.0727 | 0.8866 | 0.9416 |
| No log | 0.0872 | 52 | 0.8915 | 0.0727 | 0.8915 | 0.9442 |
| No log | 0.0906 | 54 | 0.8144 | 0.0400 | 0.8144 | 0.9025 |
| No log | 0.0940 | 56 | 0.7265 | 0.0 | 0.7265 | 0.8523 |
| No log | 0.0973 | 58 | 0.7314 | 0.1176 | 0.7314 | 0.8552 |
| No log | 0.1007 | 60 | 0.7817 | 0.1429 | 0.7817 | 0.8841 |
| No log | 0.1040 | 62 | 0.9344 | 0.0571 | 0.9344 | 0.9666 |
| No log | 0.1074 | 64 | 1.0281 | 0.0 | 1.0281 | 1.0140 |
| No log | 0.1107 | 66 | 1.2230 | -0.0941 | 1.2230 | 1.1059 |
| No log | 0.1141 | 68 | 1.2240 | -0.0500 | 1.2240 | 1.1064 |
| No log | 0.1174 | 70 | 1.3387 | -0.1053 | 1.3387 | 1.1570 |
| No log | 0.1208 | 72 | 1.2949 | -0.0421 | 1.2949 | 1.1379 |
| No log | 0.1242 | 74 | 1.1283 | 0.0 | 1.1283 | 1.0622 |
| No log | 0.1275 | 76 | 1.0083 | 0.0 | 1.0083 | 1.0041 |
| No log | 0.1309 | 78 | 1.0035 | 0.0 | 1.0035 | 1.0017 |
| No log | 0.1342 | 80 | 1.0365 | 0.0 | 1.0365 | 1.0181 |
| No log | 0.1376 | 82 | 1.1310 | 0.0 | 1.1310 | 1.0635 |
| No log | 0.1409 | 84 | 1.2358 | -0.1053 | 1.2358 | 1.1117 |
| No log | 0.1443 | 86 | 1.2112 | -0.0667 | 1.2112 | 1.1005 |
| No log | 0.1477 | 88 | 0.9978 | 0.0526 | 0.9978 | 0.9989 |
| No log | 0.1510 | 90 | 0.9070 | 0.2727 | 0.9070 | 0.9524 |
| No log | 0.1544 | 92 | 0.8388 | 0.2154 | 0.8388 | 0.9159 |
| No log | 0.1577 | 94 | 0.9962 | 0.1429 | 0.9962 | 0.9981 |
| No log | 0.1611 | 96 | 1.1511 | -0.0235 | 1.1511 | 1.0729 |
| No log | 0.1644 | 98 | 1.0801 | 0.0526 | 1.0801 | 1.0393 |
| No log | 0.1678 | 100 | 1.2609 | -0.0396 | 1.2609 | 1.1229 |
| No log | 0.1711 | 102 | 1.5159 | 0.0182 | 1.5159 | 1.2312 |
| No log | 0.1745 | 104 | 1.6729 | 0.0571 | 1.6729 | 1.2934 |
| No log | 0.1779 | 106 | 1.6244 | 0.0444 | 1.6244 | 1.2745 |
| No log | 0.1812 | 108 | 1.3764 | 0.0541 | 1.3764 | 1.1732 |
| No log | 0.1846 | 110 | 1.0727 | 0.0 | 1.0727 | 1.0357 |
| No log | 0.1879 | 112 | 0.9391 | 0.2308 | 0.9391 | 0.9691 |
| No log | 0.1913 | 114 | 1.2003 | 0.0233 | 1.2003 | 1.0956 |
| No log | 0.1946 | 116 | 1.4759 | 0.0640 | 1.4759 | 1.2149 |
| No log | 0.1980 | 118 | 1.6762 | 0.0571 | 1.6762 | 1.2947 |
| No log | 0.2013 | 120 | 1.6807 | 0.0276 | 1.6807 | 1.2964 |
| No log | 0.2047 | 122 | 1.5912 | 0.0571 | 1.5912 | 1.2614 |
| No log | 0.2081 | 124 | 1.3248 | -0.0364 | 1.3248 | 1.1510 |
| No log | 0.2114 | 126 | 1.1365 | -0.0941 | 1.1365 | 1.0661 |
| No log | 0.2148 | 128 | 1.0055 | 0.0526 | 1.0055 | 1.0027 |
| No log | 0.2181 | 130 | 0.9968 | 0.1127 | 0.9968 | 0.9984 |
| No log | 0.2215 | 132 | 1.0455 | -0.0500 | 1.0455 | 1.0225 |
| No log | 0.2248 | 134 | 1.2071 | -0.0200 | 1.2071 | 1.0987 |
| No log | 0.2282 | 136 | 1.4034 | 0.0727 | 1.4034 | 1.1846 |
| No log | 0.2315 | 138 | 1.3800 | 0.0943 | 1.3800 | 1.1748 |
| No log | 0.2349 | 140 | 1.1560 | 0.1099 | 1.1560 | 1.0752 |
| No log | 0.2383 | 142 | 0.9478 | 0.2817 | 0.9478 | 0.9735 |
| No log | 0.2416 | 144 | 1.0940 | 0.1628 | 1.0940 | 1.0459 |
| No log | 0.2450 | 146 | 1.3740 | 0.0571 | 1.3740 | 1.1722 |
| No log | 0.2483 | 148 | 1.2304 | -0.0200 | 1.2304 | 1.1092 |
| No log | 0.2517 | 150 | 1.2788 | -0.0200 | 1.2788 | 1.1308 |
| No log | 0.2550 | 152 | 1.4742 | 0.0348 | 1.4742 | 1.2142 |
| No log | 0.2584 | 154 | 1.4280 | 0.0571 | 1.4280 | 1.1950 |
| No log | 0.2617 | 156 | 1.3344 | 0.0571 | 1.3344 | 1.1552 |
| No log | 0.2651 | 158 | 1.0915 | 0.1882 | 1.0915 | 1.0447 |
| No log | 0.2685 | 160 | 1.0018 | 0.1481 | 1.0018 | 1.0009 |
| No log | 0.2718 | 162 | 1.0918 | 0.1481 | 1.0918 | 1.0449 |
| No log | 0.2752 | 164 | 1.1944 | 0.0625 | 1.1944 | 1.0929 |
| No log | 0.2785 | 166 | 1.4864 | 0.0571 | 1.4864 | 1.2192 |
| No log | 0.2819 | 168 | 1.6699 | 0.1120 | 1.6699 | 1.2922 |
| No log | 0.2852 | 170 | 1.8801 | 0.0769 | 1.8801 | 1.3712 |
| No log | 0.2886 | 172 | 1.8079 | 0.0769 | 1.8079 | 1.3446 |
| No log | 0.2919 | 174 | 1.3006 | 0.1000 | 1.3006 | 1.1404 |
| No log | 0.2953 | 176 | 0.9367 | 0.2500 | 0.9367 | 0.9678 |
| No log | 0.2987 | 178 | 0.6548 | 0.2941 | 0.6548 | 0.8092 |
| No log | 0.3020 | 180 | 0.6681 | 0.2388 | 0.6681 | 0.8174 |
| No log | 0.3054 | 182 | 0.9518 | 0.3200 | 0.9518 | 0.9756 |
| No log | 0.3087 | 184 | 1.4219 | 0.0640 | 1.4219 | 1.1924 |
| No log | 0.3121 | 186 | 1.6955 | 0.0769 | 1.6955 | 1.3021 |
| No log | 0.3154 | 188 | 2.1345 | 0.0662 | 2.1345 | 1.4610 |
| No log | 0.3188 | 190 | 2.1810 | 0.1156 | 2.1810 | 1.4768 |
| No log | 0.3221 | 192 | 1.6387 | 0.0500 | 1.6387 | 1.2801 |
| No log | 0.3255 | 194 | 1.3502 | 0.1474 | 1.3502 | 1.1620 |
| No log | 0.3289 | 196 | 1.3111 | 0.1600 | 1.3111 | 1.1450 |
| No log | 0.3322 | 198 | 1.0492 | 0.2500 | 1.0492 | 1.0243 |
| No log | 0.3356 | 200 | 0.9980 | 0.1600 | 0.9980 | 0.9990 |
| No log | 0.3389 | 202 | 1.1870 | 0.2105 | 1.1870 | 1.0895 |
| No log | 0.3423 | 204 | 1.1934 | 0.1333 | 1.1934 | 1.0924 |
| No log | 0.3456 | 206 | 1.2929 | 0.1000 | 1.2929 | 1.1371 |
| No log | 0.3490 | 208 | 1.5316 | 0.1460 | 1.5316 | 1.2376 |
| No log | 0.3523 | 210 | 1.4451 | 0.1654 | 1.4451 | 1.2021 |
| No log | 0.3557 | 212 | 1.1605 | 0.1628 | 1.1605 | 1.0773 |
| No log | 0.3591 | 214 | 1.1988 | 0.1031 | 1.1988 | 1.0949 |
| No log | 0.3624 | 216 | 1.5263 | 0.1260 | 1.5263 | 1.2354 |
| No log | 0.3658 | 218 | 1.8499 | 0.0921 | 1.8499 | 1.3601 |
| No log | 0.3691 | 220 | 1.6816 | 0.0769 | 1.6816 | 1.2968 |
| No log | 0.3725 | 222 | 1.3247 | 0.1143 | 1.3247 | 1.1509 |
| No log | 0.3758 | 224 | 1.1407 | -0.0667 | 1.1407 | 1.0680 |
| No log | 0.3792 | 226 | 1.0215 | 0.1000 | 1.0215 | 1.0107 |
| No log | 0.3826 | 228 | 0.8919 | 0.3284 | 0.8919 | 0.9444 |
| No log | 0.3859 | 230 | 0.8927 | 0.3333 | 0.8927 | 0.9448 |
| No log | 0.3893 | 232 | 0.9908 | 0.1951 | 0.9908 | 0.9954 |
| No log | 0.3926 | 234 | 1.3472 | 0.0667 | 1.3472 | 1.1607 |
| No log | 0.3960 | 236 | 1.4740 | -0.0571 | 1.4740 | 1.2141 |
| No log | 0.3993 | 238 | 1.2259 | 0.1099 | 1.2259 | 1.1072 |
| No log | 0.4027 | 240 | 0.7819 | 0.3014 | 0.7819 | 0.8843 |
| No log | 0.4060 | 242 | 0.6611 | 0.24 | 0.6611 | 0.8131 |
| No log | 0.4094 | 244 | 0.6911 | 0.2817 | 0.6911 | 0.8313 |
| No log | 0.4128 | 246 | 0.9828 | 0.3846 | 0.9828 | 0.9914 |
| No log | 0.4161 | 248 | 1.6584 | 0.0294 | 1.6584 | 1.2878 |
| No log | 0.4195 | 250 | 1.9112 | 0.0444 | 1.9112 | 1.3825 |
| No log | 0.4228 | 252 | 1.6978 | 0.0444 | 1.6978 | 1.3030 |
| No log | 0.4262 | 254 | 1.2953 | 0.0211 | 1.2953 | 1.1381 |
| No log | 0.4295 | 256 | 0.9844 | 0.0250 | 0.9844 | 0.9922 |
| No log | 0.4329 | 258 | 0.9921 | 0.0250 | 0.9921 | 0.9960 |
| No log | 0.4362 | 260 | 0.9175 | 0.2817 | 0.9175 | 0.9578 |
| No log | 0.4396 | 262 | 0.8582 | 0.3014 | 0.8582 | 0.9264 |
| No log | 0.4430 | 264 | 0.8768 | 0.3014 | 0.8768 | 0.9364 |
| No log | 0.4463 | 266 | 1.0457 | 0.1379 | 1.0457 | 1.0226 |
| No log | 0.4497 | 268 | 1.2818 | 0.1099 | 1.2818 | 1.1322 |
| No log | 0.4530 | 270 | 1.3458 | 0.1099 | 1.3458 | 1.1601 |
| No log | 0.4564 | 272 | 1.0197 | 0.2963 | 1.0197 | 1.0098 |
| No log | 0.4597 | 274 | 0.8281 | 0.3333 | 0.8281 | 0.9100 |
| No log | 0.4631 | 276 | 0.7981 | 0.2703 | 0.7981 | 0.8934 |
| No log | 0.4664 | 278 | 0.9400 | 0.2963 | 0.9400 | 0.9695 |
| No log | 0.4698 | 280 | 1.2418 | 0.2500 | 1.2418 | 1.1143 |
| No log | 0.4732 | 282 | 1.2774 | 0.2588 | 1.2774 | 1.1302 |
| No log | 0.4765 | 284 | 1.1488 | 0.2500 | 1.1488 | 1.0718 |
| No log | 0.4799 | 286 | 0.8696 | 0.2500 | 0.8696 | 0.9325 |
| No log | 0.4832 | 288 | 0.7157 | 0.2941 | 0.7157 | 0.8460 |
| No log | 0.4866 | 290 | 0.7319 | 0.1972 | 0.7319 | 0.8555 |
| No log | 0.4899 | 292 | 0.8583 | 0.2500 | 0.8583 | 0.9264 |
| No log | 0.4933 | 294 | 0.8345 | 0.2500 | 0.8345 | 0.9135 |
| No log | 0.4966 | 296 | 0.7423 | 0.3684 | 0.7423 | 0.8616 |
| No log | 0.5 | 298 | 0.6336 | 0.3544 | 0.6336 | 0.7960 |
| No log | 0.5034 | 300 | 0.6369 | 0.3544 | 0.6369 | 0.7981 |
| No log | 0.5067 | 302 | 0.6687 | 0.3544 | 0.6687 | 0.8177 |
| No log | 0.5101 | 304 | 0.9305 | 0.2683 | 0.9305 | 0.9646 |
| No log | 0.5134 | 306 | 1.0859 | 0.2500 | 1.0859 | 1.0421 |
| No log | 0.5168 | 308 | 0.9297 | 0.2963 | 0.9297 | 0.9642 |
| No log | 0.5201 | 310 | 0.7603 | 0.3684 | 0.7603 | 0.8719 |
| No log | 0.5235 | 312 | 0.7323 | 0.4156 | 0.7323 | 0.8557 |
| No log | 0.5268 | 314 | 0.7704 | 0.4156 | 0.7704 | 0.8777 |
| No log | 0.5302 | 316 | 0.8623 | 0.3684 | 0.8623 | 0.9286 |
| No log | 0.5336 | 318 | 1.0097 | 0.3294 | 1.0097 | 1.0048 |
| No log | 0.5369 | 320 | 1.0675 | 0.3077 | 1.0675 | 1.0332 |
| No log | 0.5403 | 322 | 1.0015 | 0.2268 | 1.0015 | 1.0007 |
| No log | 0.5436 | 324 | 0.8466 | 0.3182 | 0.8466 | 0.9201 |
| No log | 0.5470 | 326 | 0.7236 | 0.3077 | 0.7236 | 0.8507 |
| No log | 0.5503 | 328 | 0.7482 | 0.2258 | 0.7482 | 0.8650 |
| No log | 0.5537 | 330 | 0.8240 | 0.3448 | 0.8240 | 0.9078 |
| No log | 0.5570 | 332 | 0.9032 | 0.2826 | 0.9032 | 0.9504 |
| No log | 0.5604 | 334 | 0.9711 | 0.2826 | 0.9711 | 0.9855 |
| No log | 0.5638 | 336 | 1.0665 | 0.2222 | 1.0665 | 1.0327 |
| No log | 0.5671 | 338 | 1.0094 | 0.2718 | 1.0094 | 1.0047 |
| No log | 0.5705 | 340 | 0.8528 | 0.3226 | 0.8528 | 0.9235 |
| No log | 0.5738 | 342 | 0.7592 | 0.3333 | 0.7592 | 0.8713 |
| No log | 0.5772 | 344 | 0.8918 | 0.2963 | 0.8918 | 0.9443 |
| No log | 0.5805 | 346 | 1.0396 | 0.2963 | 1.0396 | 1.0196 |
| No log | 0.5839 | 348 | 1.0481 | 0.2500 | 1.0481 | 1.0238 |
| No log | 0.5872 | 350 | 0.8642 | 0.2817 | 0.8642 | 0.9296 |
| No log | 0.5906 | 352 | 0.8107 | 0.3333 | 0.8107 | 0.9004 |
| No log | 0.5940 | 354 | 0.9382 | 0.4156 | 0.9382 | 0.9686 |
| No log | 0.5973 | 356 | 1.1263 | 0.1522 | 1.1263 | 1.0613 |
| No log | 0.6007 | 358 | 1.0273 | 0.3415 | 1.0273 | 1.0135 |
| No log | 0.6040 | 360 | 0.7902 | 0.3836 | 0.7902 | 0.8890 |
| No log | 0.6074 | 362 | 0.6841 | 0.3377 | 0.6841 | 0.8271 |
| No log | 0.6107 | 364 | 0.6532 | 0.3077 | 0.6532 | 0.8082 |
| No log | 0.6141 | 366 | 0.6926 | 0.3377 | 0.6926 | 0.8322 |
| No log | 0.6174 | 368 | 0.8854 | 0.4156 | 0.8854 | 0.9410 |
| No log | 0.6208 | 370 | 1.2281 | 0.1765 | 1.2281 | 1.1082 |
| No log | 0.6242 | 372 | 1.2686 | 0.0943 | 1.2686 | 1.1263 |
| No log | 0.6275 | 374 | 1.0774 | 0.1522 | 1.0774 | 1.0380 |
| No log | 0.6309 | 376 | 0.8467 | 0.4156 | 0.8467 | 0.9201 |
| No log | 0.6342 | 378 | 0.7712 | 0.3846 | 0.7712 | 0.8782 |
| No log | 0.6376 | 380 | 0.8058 | 0.4156 | 0.8058 | 0.8977 |
| No log | 0.6409 | 382 | 0.8961 | 0.4156 | 0.8961 | 0.9466 |
| No log | 0.6443 | 384 | 0.9404 | 0.3684 | 0.9404 | 0.9697 |
| No log | 0.6477 | 386 | 0.9793 | 0.3684 | 0.9793 | 0.9896 |
| No log | 0.6510 | 388 | 0.9646 | 0.3684 | 0.9646 | 0.9822 |
| No log | 0.6544 | 390 | 0.9589 | 0.3684 | 0.9589 | 0.9792 |
| No log | 0.6577 | 392 | 0.9504 | 0.3684 | 0.9504 | 0.9749 |
| No log | 0.6611 | 394 | 1.1467 | 0.2826 | 1.1467 | 1.0709 |
| No log | 0.6644 | 396 | 1.2812 | 0.0377 | 1.2812 | 1.1319 |
| No log | 0.6678 | 398 | 1.1390 | 0.3023 | 1.1390 | 1.0672 |
| No log | 0.6711 | 400 | 0.8600 | 0.3133 | 0.8600 | 0.9274 |
| No log | 0.6745 | 402 | 0.7391 | 0.2941 | 0.7391 | 0.8597 |
| No log | 0.6779 | 404 | 0.7492 | 0.2941 | 0.7492 | 0.8655 |
| No log | 0.6812 | 406 | 0.8961 | 0.3448 | 0.8961 | 0.9466 |
| No log | 0.6846 | 408 | 1.0121 | 0.3023 | 1.0121 | 1.0060 |
| No log | 0.6879 | 410 | 1.0281 | 0.3023 | 1.0281 | 1.0140 |
| No log | 0.6913 | 412 | 0.9498 | 0.3200 | 0.9498 | 0.9746 |
| No log | 0.6946 | 414 | 1.0121 | 0.3200 | 1.0121 | 1.0060 |
| No log | 0.6980 | 416 | 1.0686 | 0.2418 | 1.0686 | 1.0338 |
| No log | 0.7013 | 418 | 1.1040 | 0.2418 | 1.1040 | 1.0507 |
| No log | 0.7047 | 420 | 1.0428 | 0.3200 | 1.0428 | 1.0212 |
| No log | 0.7081 | 422 | 0.9763 | 0.3200 | 0.9763 | 0.9881 |
| No log | 0.7114 | 424 | 0.9888 | 0.3200 | 0.9888 | 0.9944 |
| No log | 0.7148 | 426 | 0.9063 | 0.3200 | 0.9063 | 0.9520 |
| No log | 0.7181 | 428 | 0.8820 | 0.3200 | 0.8820 | 0.9392 |
| No log | 0.7215 | 430 | 0.8996 | 0.3200 | 0.8996 | 0.9485 |
| No log | 0.7248 | 432 | 0.9398 | 0.3200 | 0.9398 | 0.9694 |
| No log | 0.7282 | 434 | 0.9347 | 0.3684 | 0.9347 | 0.9668 |
| No log | 0.7315 | 436 | 1.0028 | 0.3200 | 1.0028 | 1.0014 |
| No log | 0.7349 | 438 | 1.1339 | 0.2500 | 1.1339 | 1.0648 |
| No log | 0.7383 | 440 | 1.1366 | 0.2500 | 1.1366 | 1.0661 |
| No log | 0.7416 | 442 | 0.9376 | 0.2500 | 0.9376 | 0.9683 |
| No log | 0.7450 | 444 | 0.8388 | 0.3684 | 0.8388 | 0.9159 |
| No log | 0.7483 | 446 | 0.7579 | 0.3684 | 0.7579 | 0.8706 |
| No log | 0.7517 | 448 | 0.7191 | 0.3846 | 0.7191 | 0.8480 |
| No log | 0.7550 | 450 | 0.7177 | 0.3846 | 0.7177 | 0.8472 |
| No log | 0.7584 | 452 | 0.7905 | 0.3684 | 0.7905 | 0.8891 |
| No log | 0.7617 | 454 | 0.8139 | 0.3200 | 0.8139 | 0.9022 |
| No log | 0.7651 | 456 | 0.9996 | 0.2500 | 0.9996 | 0.9998 |
| No log | 0.7685 | 458 | 1.2190 | 0.25 | 1.2190 | 1.1041 |
| No log | 0.7718 | 460 | 1.1855 | 0.25 | 1.1855 | 1.0888 |
| No log | 0.7752 | 462 | 1.0065 | 0.2418 | 1.0065 | 1.0033 |
| No log | 0.7785 | 464 | 0.7870 | 0.1818 | 0.7870 | 0.8871 |
| No log | 0.7819 | 466 | 0.7133 | 0.3014 | 0.7133 | 0.8446 |
| No log | 0.7852 | 468 | 0.7422 | 0.25 | 0.7422 | 0.8615 |
| No log | 0.7886 | 470 | 0.8911 | 0.2105 | 0.8911 | 0.9440 |
| No log | 0.7919 | 472 | 1.0571 | 0.2500 | 1.0571 | 1.0282 |
| No log | 0.7953 | 474 | 1.0707 | 0.2500 | 1.0707 | 1.0347 |
| No log | 0.7987 | 476 | 0.9784 | 0.1600 | 0.9784 | 0.9892 |
| No log | 0.8020 | 478 | 0.9346 | 0.1818 | 0.9346 | 0.9668 |
| No log | 0.8054 | 480 | 0.9563 | 0.1818 | 0.9563 | 0.9779 |
| No log | 0.8087 | 482 | 1.0391 | 0.0250 | 1.0391 | 1.0193 |
| No log | 0.8121 | 484 | 1.0083 | 0.0488 | 1.0083 | 1.0042 |
| No log | 0.8154 | 486 | 0.8705 | 0.25 | 0.8705 | 0.9330 |
| No log | 0.8188 | 488 | 0.8097 | 0.3014 | 0.8097 | 0.8998 |
| No log | 0.8221 | 490 | 0.7891 | 0.3014 | 0.7891 | 0.8883 |
| No log | 0.8255 | 492 | 0.8257 | 0.25 | 0.8257 | 0.9087 |
| No log | 0.8289 | 494 | 0.8985 | 0.1972 | 0.8985 | 0.9479 |
| No log | 0.8322 | 496 | 1.0012 | 0.0930 | 1.0012 | 1.0006 |
| No log | 0.8356 | 498 | 0.9429 | 0.2683 | 0.9429 | 0.9710 |
| 0.4824 | 0.8389 | 500 | 0.8088 | 0.3014 | 0.8088 | 0.8993 |
| 0.4824 | 0.8423 | 502 | 0.8054 | 0.3014 | 0.8054 | 0.8974 |
| 0.4824 | 0.8456 | 504 | 0.8956 | 0.3014 | 0.8956 | 0.9464 |
| 0.4824 | 0.8490 | 506 | 1.0544 | 0.1031 | 1.0544 | 1.0269 |
| 0.4824 | 0.8523 | 508 | 1.1640 | 0.0792 | 1.1640 | 1.0789 |
| 0.4824 | 0.8557 | 510 | 1.2044 | 0.0792 | 1.2044 | 1.0975 |
| 0.4824 | 0.8591 | 512 | 1.1498 | 0.0667 | 1.1498 | 1.0723 |
| 0.4824 | 0.8624 | 514 | 0.9960 | 0.0930 | 0.9960 | 0.9980 |
| 0.4824 | 0.8658 | 516 | 0.9063 | 0.1972 | 0.9063 | 0.9520 |
| 0.4824 | 0.8691 | 518 | 0.9267 | 0.1972 | 0.9267 | 0.9626 |
| 0.4824 | 0.8725 | 520 | 0.9865 | 0.2895 | 0.9865 | 0.9932 |
| 0.4824 | 0.8758 | 522 | 0.9530 | 0.25 | 0.9530 | 0.9762 |
| 0.4824 | 0.8792 | 524 | 0.8951 | 0.25 | 0.8951 | 0.9461 |
| 0.4824 | 0.8826 | 526 | 0.9575 | 0.2895 | 0.9575 | 0.9785 |
| 0.4824 | 0.8859 | 528 | 1.1186 | 0.0667 | 1.1186 | 1.0576 |
| 0.4824 | 0.8893 | 530 | 1.1454 | 0.0667 | 1.1454 | 1.0702 |
| 0.4824 | 0.8926 | 532 | 1.0482 | 0.1176 | 1.0482 | 1.0238 |
| 0.4824 | 0.8960 | 534 | 0.9973 | 0.2222 | 0.9973 | 0.9987 |
| 0.4824 | 0.8993 | 536 | 1.0416 | 0.0930 | 1.0416 | 1.0206 |
| 0.4824 | 0.9027 | 538 | 1.0258 | 0.3133 | 1.0258 | 1.0128 |
| 0.4824 | 0.9060 | 540 | 1.0136 | 0.2247 | 1.0136 | 1.0068 |
| 0.4824 | 0.9094 | 542 | 1.0058 | 0.2247 | 1.0058 | 1.0029 |
| 0.4824 | 0.9128 | 544 | 1.1583 | 0.1429 | 1.1583 | 1.0762 |
| 0.4824 | 0.9161 | 546 | 1.2539 | 0.0792 | 1.2539 | 1.1198 |
| 0.4824 | 0.9195 | 548 | 1.1928 | 0.0792 | 1.1928 | 1.0921 |
| 0.4824 | 0.9228 | 550 | 1.1511 | 0.0816 | 1.1511 | 1.0729 |
| 0.4824 | 0.9262 | 552 | 1.1437 | 0.1176 | 1.1437 | 1.0694 |
| 0.4824 | 0.9295 | 554 | 1.3132 | 0.0377 | 1.3132 | 1.1459 |
| 0.4824 | 0.9329 | 556 | 1.4239 | 0.0690 | 1.4239 | 1.1933 |
| 0.4824 | 0.9362 | 558 | 1.3551 | 0.0 | 1.3551 | 1.1641 |
| 0.4824 | 0.9396 | 560 | 1.1653 | 0.0211 | 1.1653 | 1.0795 |
| 0.4824 | 0.9430 | 562 | 1.1100 | 0.0667 | 1.1100 | 1.0536 |
| 0.4824 | 0.9463 | 564 | 0.9941 | 0.0800 | 0.9941 | 0.9970 |
| 0.4824 | 0.9497 | 566 | 0.9963 | 0.0800 | 0.9963 | 0.9982 |
| 0.4824 | 0.9530 | 568 | 0.9739 | 0.1316 | 0.9739 | 0.9869 |
| 0.4824 | 0.9564 | 570 | 1.0306 | 0.2222 | 1.0306 | 1.0152 |
| 0.4824 | 0.9597 | 572 | 1.1120 | 0.1099 | 1.1120 | 1.0545 |
| 0.4824 | 0.9631 | 574 | 1.1067 | 0.1628 | 1.1067 | 1.0520 |
| 0.4824 | 0.9664 | 576 | 1.1791 | 0.1111 | 1.1791 | 1.0858 |
| 0.4824 | 0.9698 | 578 | 1.2335 | 0.1111 | 1.2335 | 1.1106 |
| 0.4824 | 0.9732 | 580 | 1.0905 | 0.1923 | 1.0905 | 1.0443 |
| 0.4824 | 0.9765 | 582 | 0.9068 | 0.4156 | 0.9068 | 0.9523 |
| 0.4824 | 0.9799 | 584 | 0.8314 | 0.2222 | 0.8314 | 0.9118 |
| 0.4824 | 0.9832 | 586 | 0.8065 | 0.2222 | 0.8065 | 0.8981 |
| 0.4824 | 0.9866 | 588 | 0.8161 | 0.3684 | 0.8161 | 0.9034 |
| 0.4824 | 0.9899 | 590 | 0.9787 | 0.2069 | 0.9787 | 0.9893 |
| 0.4824 | 0.9933 | 592 | 1.0964 | 0.2069 | 1.0964 | 1.0471 |
| 0.4824 | 0.9966 | 594 | 1.1436 | 0.1099 | 1.1436 | 1.0694 |
| 0.4824 | 1.0 | 596 | 1.0614 | 0.1628 | 1.0614 | 1.0302 |
| 0.4824 | 1.0034 | 598 | 0.9739 | 0.2963 | 0.9739 | 0.9869 |
| 0.4824 | 1.0067 | 600 | 0.7909 | 0.3333 | 0.7909 | 0.8894 |
| 0.4824 | 1.0101 | 602 | 0.7285 | 0.3333 | 0.7285 | 0.8535 |
| 0.4824 | 1.0134 | 604 | 0.7764 | 0.3333 | 0.7764 | 0.8811 |
| 0.4824 | 1.0168 | 606 | 0.9413 | 0.3200 | 0.9413 | 0.9702 |
| 0.4824 | 1.0201 | 608 | 1.0345 | 0.2500 | 1.0345 | 1.0171 |
| 0.4824 | 1.0235 | 610 | 1.0475 | 0.2500 | 1.0475 | 1.0235 |
| 0.4824 | 1.0268 | 612 | 1.0005 | 0.2826 | 1.0005 | 1.0002 |
| 0.4824 | 1.0302 | 614 | 0.9675 | 0.3448 | 0.9675 | 0.9836 |
| 0.4824 | 1.0336 | 616 | 0.9791 | 0.3448 | 0.9791 | 0.9895 |
| 0.4824 | 1.0369 | 618 | 0.9614 | 0.3133 | 0.9614 | 0.9805 |
| 0.4824 | 1.0403 | 620 | 0.8594 | 0.3571 | 0.8594 | 0.9271 |
| 0.4824 | 1.0436 | 622 | 0.7803 | 0.4146 | 0.7803 | 0.8833 |
| 0.4824 | 1.0470 | 624 | 0.8076 | 0.3721 | 0.8076 | 0.8987 |
| 0.4824 | 1.0503 | 626 | 0.9364 | 0.3571 | 0.9364 | 0.9677 |
| 0.4824 | 1.0537 | 628 | 1.0011 | 0.3226 | 1.0011 | 1.0006 |
| 0.4824 | 1.0570 | 630 | 0.9962 | 0.3226 | 0.9962 | 0.9981 |
| 0.4824 | 1.0604 | 632 | 1.0136 | 0.2826 | 1.0136 | 1.0068 |
| 0.4824 | 1.0638 | 634 | 0.9556 | 0.2817 | 0.9556 | 0.9776 |
| 0.4824 | 1.0671 | 636 | 0.8312 | 0.3333 | 0.8312 | 0.9117 |
| 0.4824 | 1.0705 | 638 | 0.7468 | 0.3824 | 0.7468 | 0.8642 |
| 0.4824 | 1.0738 | 640 | 0.7011 | 0.3478 | 0.7011 | 0.8373 |
| 0.4824 | 1.0772 | 642 | 0.7475 | 0.3824 | 0.7475 | 0.8646 |
| 0.4824 | 1.0805 | 644 | 0.8797 | 0.2817 | 0.8797 | 0.9379 |
| 0.4824 | 1.0839 | 646 | 1.0393 | 0.2222 | 1.0393 | 1.0195 |
| 0.4824 | 1.0872 | 648 | 1.0683 | 0.2222 | 1.0683 | 1.0336 |
| 0.4824 | 1.0906 | 650 | 0.9535 | 0.2105 | 0.9535 | 0.9765 |
| 0.4824 | 1.0940 | 652 | 0.8625 | 0.2105 | 0.8625 | 0.9287 |
| 0.4824 | 1.0973 | 654 | 0.9284 | 0.2105 | 0.9284 | 0.9635 |
| 0.4824 | 1.1007 | 656 | 1.1564 | 0.0792 | 1.1564 | 1.0753 |
| 0.4824 | 1.1040 | 658 | 1.2838 | 0.0377 | 1.2838 | 1.1330 |
| 0.4824 | 1.1074 | 660 | 1.2263 | 0.0792 | 1.2263 | 1.1074 |
| 0.4824 | 1.1107 | 662 | 1.0349 | 0.1628 | 1.0349 | 1.0173 |
| 0.4824 | 1.1141 | 664 | 0.8035 | 0.3544 | 0.8035 | 0.8964 |
| 0.4824 | 1.1174 | 666 | 0.7187 | 0.4000 | 0.7187 | 0.8478 |
| 0.4824 | 1.1208 | 668 | 0.6686 | 0.4 | 0.6686 | 0.8177 |
| 0.4824 | 1.1242 | 670 | 0.7254 | 0.4000 | 0.7254 | 0.8517 |
| 0.4824 | 1.1275 | 672 | 0.8483 | 0.4 | 0.8483 | 0.9211 |
| 0.4824 | 1.1309 | 674 | 1.0471 | 0.2041 | 1.0471 | 1.0233 |
| 0.4824 | 1.1342 | 676 | 1.0533 | 0.1553 | 1.0533 | 1.0263 |
| 0.4824 | 1.1376 | 678 | 0.9738 | 0.3864 | 0.9738 | 0.9868 |
| 0.4824 | 1.1409 | 680 | 1.0417 | 0.1553 | 1.0417 | 1.0207 |
| 0.4824 | 1.1443 | 682 | 1.0522 | 0.1099 | 1.0522 | 1.0258 |
| 0.4824 | 1.1477 | 684 | 0.9188 | 0.2817 | 0.9188 | 0.9586 |
| 0.4824 | 1.1510 | 686 | 0.7561 | 0.3836 | 0.7561 | 0.8695 |
| 0.4824 | 1.1544 | 688 | 0.7002 | 0.3824 | 0.7002 | 0.8368 |
| 0.4824 | 1.1577 | 690 | 0.7517 | 0.3836 | 0.7517 | 0.8670 |
| 0.4824 | 1.1611 | 692 | 0.9102 | 0.1600 | 0.9102 | 0.9541 |
| 0.4824 | 1.1644 | 694 | 1.1447 | 0.1333 | 1.1447 | 1.0699 |
| 0.4824 | 1.1678 | 696 | 1.1770 | 0.0211 | 1.1770 | 1.0849 |
| 0.4824 | 1.1711 | 698 | 1.0163 | 0.2963 | 1.0163 | 1.0081 |
| 0.4824 | 1.1745 | 700 | 0.8248 | 0.4 | 0.8248 | 0.9082 |
| 0.4824 | 1.1779 | 702 | 0.6909 | 0.3014 | 0.6909 | 0.8312 |
| 0.4824 | 1.1812 | 704 | 0.6736 | 0.4324 | 0.6736 | 0.8207 |
| 0.4824 | 1.1846 | 706 | 0.6990 | 0.2857 | 0.6990 | 0.8360 |
| 0.4824 | 1.1879 | 708 | 0.8143 | 0.4 | 0.8143 | 0.9024 |
| 0.4824 | 1.1913 | 710 | 1.0409 | 0.2653 | 1.0409 | 1.0203 |
| 0.4824 | 1.1946 | 712 | 1.1457 | 0.2268 | 1.1457 | 1.0704 |
| 0.4824 | 1.1980 | 714 | 1.0720 | 0.2268 | 1.0720 | 1.0354 |
| 0.4824 | 1.2013 | 716 | 0.8693 | 0.2921 | 0.8693 | 0.9323 |
| 0.4824 | 1.2047 | 718 | 0.7136 | 0.4 | 0.7136 | 0.8448 |
| 0.4824 | 1.2081 | 720 | 0.6765 | 0.3704 | 0.6765 | 0.8225 |
| 0.4824 | 1.2114 | 722 | 0.6884 | 0.3704 | 0.6884 | 0.8297 |
| 0.4824 | 1.2148 | 724 | 0.7613 | 0.4 | 0.7613 | 0.8725 |
| 0.4824 | 1.2181 | 726 | 0.9005 | 0.3226 | 0.9005 | 0.9489 |
| 0.4824 | 1.2215 | 728 | 0.9623 | 0.2826 | 0.9623 | 0.9810 |
| 0.4824 | 1.2248 | 730 | 0.8739 | 0.3617 | 0.8739 | 0.9349 |
| 0.4824 | 1.2282 | 732 | 0.7631 | 0.3855 | 0.7631 | 0.8736 |
| 0.4824 | 1.2315 | 734 | 0.7261 | 0.5063 | 0.7261 | 0.8521 |
| 0.4824 | 1.2349 | 736 | 0.7118 | 0.4324 | 0.7118 | 0.8437 |
| 0.4824 | 1.2383 | 738 | 0.7761 | 0.3855 | 0.7761 | 0.8810 |
| 0.4824 | 1.2416 | 740 | 0.9024 | 0.2963 | 0.9024 | 0.9499 |
| 0.4824 | 1.2450 | 742 | 0.9321 | 0.2826 | 0.9321 | 0.9654 |
| 0.4824 | 1.2483 | 744 | 0.8241 | 0.2826 | 0.8241 | 0.9078 |
| 0.4824 | 1.2517 | 746 | 0.7054 | 0.3836 | 0.7054 | 0.8399 |
| 0.4824 | 1.2550 | 748 | 0.6262 | 0.4324 | 0.6262 | 0.7913 |
| 0.4824 | 1.2584 | 750 | 0.5590 | 0.3636 | 0.5590 | 0.7477 |
| 0.4824 | 1.2617 | 752 | 0.5563 | 0.3636 | 0.5563 | 0.7459 |
| 0.4824 | 1.2651 | 754 | 0.5828 | 0.3284 | 0.5828 | 0.7634 |
| 0.4824 | 1.2685 | 756 | 0.6796 | 0.2817 | 0.6796 | 0.8244 |
| 0.4824 | 1.2718 | 758 | 0.7834 | 0.3684 | 0.7834 | 0.8851 |
| 0.4824 | 1.2752 | 760 | 0.8446 | 0.3684 | 0.8446 | 0.9190 |
| 0.4824 | 1.2785 | 762 | 0.7839 | 0.3684 | 0.7839 | 0.8854 |
| 0.4824 | 1.2819 | 764 | 0.6922 | 0.3684 | 0.6922 | 0.8320 |
| 0.4824 | 1.2852 | 766 | 0.6299 | 0.3284 | 0.6299 | 0.7937 |
| 0.4824 | 1.2886 | 768 | 0.6475 | 0.3284 | 0.6475 | 0.8047 |
| 0.4824 | 1.2919 | 770 | 0.6776 | 0.4156 | 0.6776 | 0.8232 |
| 0.4824 | 1.2953 | 772 | 0.6851 | 0.4156 | 0.6851 | 0.8277 |
| 0.4824 | 1.2987 | 774 | 0.7146 | 0.4156 | 0.7146 | 0.8453 |
| 0.4824 | 1.3020 | 776 | 0.7954 | 0.2963 | 0.7954 | 0.8918 |
| 0.4824 | 1.3054 | 778 | 0.9495 | 0.2963 | 0.9495 | 0.9744 |
| 0.4824 | 1.3087 | 780 | 0.9516 | 0.2963 | 0.9516 | 0.9755 |
| 0.4824 | 1.3121 | 782 | 0.8567 | 0.2963 | 0.8567 | 0.9256 |
| 0.4824 | 1.3154 | 784 | 0.6724 | 0.4156 | 0.6724 | 0.8200 |
| 0.4824 | 1.3188 | 786 | 0.5603 | 0.4146 | 0.5603 | 0.7486 |
| 0.4824 | 1.3221 | 788 | 0.5385 | 0.3846 | 0.5385 | 0.7338 |
| 0.4824 | 1.3255 | 790 | 0.5409 | 0.4156 | 0.5409 | 0.7354 |
| 0.4824 | 1.3289 | 792 | 0.5812 | 0.4304 | 0.5812 | 0.7624 |
| 0.4824 | 1.3322 | 794 | 0.6495 | 0.3684 | 0.6495 | 0.8059 |
| 0.4824 | 1.3356 | 796 | 0.7493 | 0.3684 | 0.7493 | 0.8656 |
| 0.4824 | 1.3389 | 798 | 0.8107 | 0.2963 | 0.8107 | 0.9004 |
| 0.4824 | 1.3423 | 800 | 0.8409 | 0.2826 | 0.8409 | 0.9170 |
| 0.4824 | 1.3456 | 802 | 0.7778 | 0.3448 | 0.7778 | 0.8819 |
| 0.4824 | 1.3490 | 804 | 0.6584 | 0.3333 | 0.6584 | 0.8114 |
| 0.4824 | 1.3523 | 806 | 0.5855 | 0.4324 | 0.5855 | 0.7652 |
| 0.4824 | 1.3557 | 808 | 0.5700 | 0.48 | 0.5700 | 0.7550 |
| 0.4824 | 1.3591 | 810 | 0.6326 | 0.3333 | 0.6326 | 0.7953 |
| 0.4824 | 1.3624 | 812 | 0.8064 | 0.3448 | 0.8064 | 0.8980 |
| 0.4824 | 1.3658 | 814 | 1.0870 | 0.2268 | 1.0870 | 1.0426 |
| 0.4824 | 1.3691 | 816 | 1.1920 | 0.1869 | 1.1920 | 1.0918 |
| 0.4824 | 1.3725 | 818 | 1.0490 | 0.2268 | 1.0490 | 1.0242 |
| 0.4824 | 1.3758 | 820 | 0.8061 | 0.3864 | 0.8061 | 0.8979 |
| 0.4824 | 1.3792 | 822 | 0.6178 | 0.4474 | 0.6178 | 0.7860 |
| 0.4824 | 1.3826 | 824 | 0.5627 | 0.4474 | 0.5627 | 0.7502 |
| 0.4824 | 1.3859 | 826 | 0.5619 | 0.4474 | 0.5619 | 0.7496 |
| 0.4824 | 1.3893 | 828 | 0.6252 | 0.48 | 0.6252 | 0.7907 |
| 0.4824 | 1.3926 | 830 | 0.7014 | 0.4156 | 0.7014 | 0.8375 |
| 0.4824 | 1.3960 | 832 | 0.7511 | 0.3684 | 0.7511 | 0.8666 |
| 0.4824 | 1.3993 | 834 | 0.7325 | 0.3684 | 0.7325 | 0.8559 |
| 0.4824 | 1.4027 | 836 | 0.6948 | 0.3684 | 0.6948 | 0.8336 |
| 0.4824 | 1.4060 | 838 | 0.7137 | 0.3684 | 0.7137 | 0.8448 |
| 0.4824 | 1.4094 | 840 | 0.8106 | 0.3200 | 0.8106 | 0.9003 |
| 0.4824 | 1.4128 | 842 | 0.9751 | 0.2500 | 0.9751 | 0.9875 |
| 0.4824 | 1.4161 | 844 | 0.9924 | 0.2500 | 0.9924 | 0.9962 |
| 0.4824 | 1.4195 | 846 | 0.8832 | 0.2963 | 0.8832 | 0.9398 |
| 0.4824 | 1.4228 | 848 | 0.7127 | 0.3684 | 0.7127 | 0.8442 |
| 0.4824 | 1.4262 | 850 | 0.6202 | 0.4615 | 0.6202 | 0.7875 |
| 0.4824 | 1.4295 | 852 | 0.5877 | 0.4474 | 0.5877 | 0.7666 |
| 0.4824 | 1.4329 | 854 | 0.5628 | 0.5063 | 0.5628 | 0.7502 |
| 0.4824 | 1.4362 | 856 | 0.5831 | 0.5063 | 0.5831 | 0.7636 |
| 0.4824 | 1.4396 | 858 | 0.6148 | 0.4474 | 0.6148 | 0.7841 |
| 0.4824 | 1.4430 | 860 | 0.6280 | 0.4474 | 0.6280 | 0.7925 |
| 0.4824 | 1.4463 | 862 | 0.6250 | 0.4000 | 0.6250 | 0.7905 |
| 0.4824 | 1.4497 | 864 | 0.6226 | 0.4000 | 0.6226 | 0.7890 |
| 0.4824 | 1.4530 | 866 | 0.6305 | 0.4000 | 0.6305 | 0.7940 |
| 0.4824 | 1.4564 | 868 | 0.6625 | 0.4474 | 0.6625 | 0.8139 |
| 0.4824 | 1.4597 | 870 | 0.6986 | 0.4324 | 0.6986 | 0.8358 |
| 0.4824 | 1.4631 | 872 | 0.6993 | 0.4324 | 0.6993 | 0.8363 |
| 0.4824 | 1.4664 | 874 | 0.7366 | 0.2817 | 0.7366 | 0.8582 |
| 0.4824 | 1.4698 | 876 | 0.7741 | 0.2817 | 0.7741 | 0.8798 |
| 0.4824 | 1.4732 | 878 | 0.7007 | 0.3333 | 0.7007 | 0.8371 |
| 0.4824 | 1.4765 | 880 | 0.6258 | 0.4324 | 0.6258 | 0.7911 |
| 0.4824 | 1.4799 | 882 | 0.5988 | 0.3684 | 0.5988 | 0.7739 |
| 0.4824 | 1.4832 | 884 | 0.6113 | 0.3377 | 0.6113 | 0.7819 |
| 0.4824 | 1.4866 | 886 | 0.7014 | 0.3333 | 0.7014 | 0.8375 |
| 0.4824 | 1.4899 | 888 | 0.9463 | 0.2963 | 0.9463 | 0.9728 |
| 0.4824 | 1.4933 | 890 | 1.2298 | 0.1474 | 1.2298 | 1.1090 |
| 0.4824 | 1.4966 | 892 | 1.2934 | 0.1714 | 1.2934 | 1.1373 |
| 0.4824 | 1.5 | 894 | 1.1764 | 0.0667 | 1.1764 | 1.0846 |
| 0.4824 | 1.5034 | 896 | 1.0283 | 0.2500 | 1.0283 | 1.0140 |
| 0.4824 | 1.5067 | 898 | 0.8890 | 0.2817 | 0.8890 | 0.9428 |
| 0.4824 | 1.5101 | 900 | 0.7720 | 0.2817 | 0.7720 | 0.8786 |
| 0.4824 | 1.5134 | 902 | 0.6531 | 0.2388 | 0.6531 | 0.8081 |
| 0.4824 | 1.5168 | 904 | 0.6124 | 0.1562 | 0.6124 | 0.7826 |
| 0.4824 | 1.5201 | 906 | 0.6061 | 0.2727 | 0.6061 | 0.7785 |
| 0.4824 | 1.5235 | 908 | 0.6586 | 0.3200 | 0.6586 | 0.8116 |
| 0.4824 | 1.5268 | 910 | 0.7850 | 0.3333 | 0.7850 | 0.8860 |
| 0.4824 | 1.5302 | 912 | 0.8682 | 0.4156 | 0.8682 | 0.9318 |
| 0.4824 | 1.5336 | 914 | 0.9922 | 0.2963 | 0.9922 | 0.9961 |
| 0.4824 | 1.5369 | 916 | 1.1869 | 0.1176 | 1.1869 | 1.0894 |
| 0.4824 | 1.5403 | 918 | 1.1829 | 0.1176 | 1.1829 | 1.0876 |
| 0.4824 | 1.5436 | 920 | 0.9951 | 0.2826 | 0.9951 | 0.9975 |
| 0.4824 | 1.5470 | 922 | 0.8197 | 0.4156 | 0.8197 | 0.9054 |
| 0.4824 | 1.5503 | 924 | 0.7441 | 0.3836 | 0.7441 | 0.8626 |
| 0.4824 | 1.5537 | 926 | 0.7570 | 0.3684 | 0.7570 | 0.8701 |
| 0.4824 | 1.5570 | 928 | 0.8014 | 0.3684 | 0.8014 | 0.8952 |
| 0.4824 | 1.5604 | 930 | 0.9321 | 0.2963 | 0.9321 | 0.9654 |
| 0.4824 | 1.5638 | 932 | 1.0350 | 0.2963 | 1.0350 | 1.0173 |
| 0.4824 | 1.5671 | 934 | 0.9712 | 0.2963 | 0.9712 | 0.9855 |
| 0.4824 | 1.5705 | 936 | 0.8054 | 0.3684 | 0.8054 | 0.8974 |
| 0.4824 | 1.5738 | 938 | 0.7160 | 0.2817 | 0.7160 | 0.8462 |
| 0.4824 | 1.5772 | 940 | 0.6768 | 0.2817 | 0.6768 | 0.8227 |
| 0.4824 | 1.5805 | 942 | 0.6385 | 0.3014 | 0.6385 | 0.7991 |
| 0.4824 | 1.5839 | 944 | 0.6630 | 0.3846 | 0.6630 | 0.8143 |
| 0.4824 | 1.5872 | 946 | 0.7488 | 0.3684 | 0.7488 | 0.8653 |
| 0.4824 | 1.5906 | 948 | 0.8685 | 0.3684 | 0.8685 | 0.9319 |
| 0.4824 | 1.5940 | 950 | 0.9408 | 0.2963 | 0.9408 | 0.9700 |
| 0.4824 | 1.5973 | 952 | 0.9224 | 0.2963 | 0.9224 | 0.9604 |
| 0.4824 | 1.6007 | 954 | 0.7957 | 0.3684 | 0.7957 | 0.8920 |
| 0.4824 | 1.6040 | 956 | 0.6375 | 0.4324 | 0.6375 | 0.7984 |
| 0.4824 | 1.6074 | 958 | 0.5481 | 0.2857 | 0.5481 | 0.7403 |
| 0.4824 | 1.6107 | 960 | 0.5307 | 0.2857 | 0.5307 | 0.7285 |
| 0.4824 | 1.6141 | 962 | 0.5343 | 0.2857 | 0.5343 | 0.7310 |
| 0.4824 | 1.6174 | 964 | 0.5931 | 0.4348 | 0.5931 | 0.7701 |
| 0.4824 | 1.6208 | 966 | 0.7275 | 0.3684 | 0.7275 | 0.8529 |
| 0.4824 | 1.6242 | 968 | 0.8949 | 0.3684 | 0.8949 | 0.9460 |
| 0.4824 | 1.6275 | 970 | 1.0380 | 0.1649 | 1.0380 | 1.0188 |
| 0.4824 | 1.6309 | 972 | 1.0165 | 0.1649 | 1.0165 | 1.0082 |
| 0.4824 | 1.6342 | 974 | 0.8670 | 0.3448 | 0.8670 | 0.9311 |
| 0.4824 | 1.6376 | 976 | 0.6817 | 0.4474 | 0.6817 | 0.8257 |
| 0.4824 | 1.6409 | 978 | 0.6117 | 0.3836 | 0.6117 | 0.7821 |
| 0.4824 | 1.6443 | 980 | 0.6425 | 0.3514 | 0.6425 | 0.8016 |
| 0.4824 | 1.6477 | 982 | 0.6312 | 0.3836 | 0.6312 | 0.7945 |
| 0.4824 | 1.6510 | 984 | 0.6259 | 0.3846 | 0.6259 | 0.7912 |
| 0.4824 | 1.6544 | 986 | 0.7064 | 0.4156 | 0.7064 | 0.8405 |
| 0.4824 | 1.6577 | 988 | 0.8702 | 0.2963 | 0.8702 | 0.9328 |
| 0.4824 | 1.6611 | 990 | 0.9005 | 0.2963 | 0.9005 | 0.9489 |
| 0.4824 | 1.6644 | 992 | 0.8163 | 0.3684 | 0.8163 | 0.9035 |
| 0.4824 | 1.6678 | 994 | 0.6984 | 0.3333 | 0.6984 | 0.8357 |
| 0.4824 | 1.6711 | 996 | 0.5802 | 0.3836 | 0.5802 | 0.7617 |
| 0.4824 | 1.6745 | 998 | 0.5328 | 0.4 | 0.5328 | 0.7299 |
| 0.1734 | 1.6779 | 1000 | 0.5214 | 0.4167 | 0.5214 | 0.7221 |
| 0.1734 | 1.6812 | 1002 | 0.5334 | 0.4167 | 0.5334 | 0.7304 |
| 0.1734 | 1.6846 | 1004 | 0.5654 | 0.3662 | 0.5654 | 0.7519 |
| 0.1734 | 1.6879 | 1006 | 0.6439 | 0.4324 | 0.6439 | 0.8024 |
| 0.1734 | 1.6913 | 1008 | 0.7479 | 0.2817 | 0.7479 | 0.8648 |
| 0.1734 | 1.6946 | 1010 | 0.7637 | 0.3333 | 0.7637 | 0.8739 |
| 0.1734 | 1.6980 | 1012 | 0.7170 | 0.4324 | 0.7170 | 0.8468 |
| 0.1734 | 1.7013 | 1014 | 0.6675 | 0.4156 | 0.6675 | 0.8170 |
| 0.1734 | 1.7047 | 1016 | 0.6528 | 0.4156 | 0.6528 | 0.8079 |
| 0.1734 | 1.7081 | 1018 | 0.6564 | 0.4156 | 0.6564 | 0.8102 |
| 0.1734 | 1.7114 | 1020 | 0.6973 | 0.4156 | 0.6973 | 0.8351 |
| 0.1734 | 1.7148 | 1022 | 0.7796 | 0.3836 | 0.7796 | 0.8830 |
| 0.1734 | 1.7181 | 1024 | 0.8979 | 0.2105 | 0.8979 | 0.9476 |
| 0.1734 | 1.7215 | 1026 | 0.9492 | 0.2105 | 0.9492 | 0.9742 |
| 0.1734 | 1.7248 | 1028 | 0.9281 | 0.1600 | 0.9281 | 0.9634 |
| 0.1734 | 1.7282 | 1030 | 0.8342 | 0.1600 | 0.8342 | 0.9134 |
| 0.1734 | 1.7315 | 1032 | 0.7352 | 0.2817 | 0.7352 | 0.8574 |
| 0.1734 | 1.7349 | 1034 | 0.6565 | 0.3333 | 0.6565 | 0.8103 |
| 0.1734 | 1.7383 | 1036 | 0.6327 | 0.3333 | 0.6327 | 0.7954 |
| 0.1734 | 1.7416 | 1038 | 0.6588 | 0.3333 | 0.6588 | 0.8117 |
| 0.1734 | 1.7450 | 1040 | 0.7165 | 0.3333 | 0.7165 | 0.8465 |
| 0.1734 | 1.7483 | 1042 | 0.7756 | 0.2817 | 0.7756 | 0.8807 |
| 0.1734 | 1.7517 | 1044 | 0.8257 | 0.2105 | 0.8257 | 0.9087 |
| 0.1734 | 1.7550 | 1046 | 0.7982 | 0.2105 | 0.7982 | 0.8934 |
| 0.1734 | 1.7584 | 1048 | 0.7071 | 0.3333 | 0.7071 | 0.8409 |
| 0.1734 | 1.7617 | 1050 | 0.6663 | 0.3333 | 0.6663 | 0.8163 |
| 0.1734 | 1.7651 | 1052 | 0.6885 | 0.2817 | 0.6885 | 0.8297 |
| 0.1734 | 1.7685 | 1054 | 0.7528 | 0.2817 | 0.7528 | 0.8677 |
| 0.1734 | 1.7718 | 1056 | 0.8230 | 0.2105 | 0.8230 | 0.9072 |
| 0.1734 | 1.7752 | 1058 | 0.8081 | 0.2817 | 0.8081 | 0.8989 |
| 0.1734 | 1.7785 | 1060 | 0.7396 | 0.2817 | 0.7396 | 0.8600 |
| 0.1734 | 1.7819 | 1062 | 0.7199 | 0.2817 | 0.7199 | 0.8484 |
| 0.1734 | 1.7852 | 1064 | 0.6713 | 0.3333 | 0.6713 | 0.8193 |
| 0.1734 | 1.7886 | 1066 | 0.6666 | 0.3333 | 0.6666 | 0.8165 |
| 0.1734 | 1.7919 | 1068 | 0.7129 | 0.2817 | 0.7129 | 0.8443 |
| 0.1734 | 1.7953 | 1070 | 0.7938 | 0.2817 | 0.7938 | 0.8909 |
| 0.1734 | 1.7987 | 1072 | 0.8077 | 0.2817 | 0.8077 | 0.8987 |
| 0.1734 | 1.8020 | 1074 | 0.7428 | 0.2817 | 0.7428 | 0.8619 |
| 0.1734 | 1.8054 | 1076 | 0.7396 | 0.2817 | 0.7396 | 0.8600 |
| 0.1734 | 1.8087 | 1078 | 0.7161 | 0.2817 | 0.7161 | 0.8462 |
| 0.1734 | 1.8121 | 1080 | 0.7074 | 0.3333 | 0.7074 | 0.8410 |
| 0.1734 | 1.8154 | 1082 | 0.7506 | 0.2817 | 0.7506 | 0.8664 |
| 0.1734 | 1.8188 | 1084 | 0.7758 | 0.2817 | 0.7758 | 0.8808 |
| 0.1734 | 1.8221 | 1086 | 0.7737 | 0.2817 | 0.7737 | 0.8796 |
| 0.1734 | 1.8255 | 1088 | 0.7973 | 0.2817 | 0.7973 | 0.8929 |
| 0.1734 | 1.8289 | 1090 | 0.8901 | 0.2817 | 0.8901 | 0.9434 |
| 0.1734 | 1.8322 | 1092 | 0.9063 | 0.2817 | 0.9063 | 0.9520 |
| 0.1734 | 1.8356 | 1094 | 0.8264 | 0.2817 | 0.8264 | 0.9091 |
| 0.1734 | 1.8389 | 1096 | 0.7722 | 0.2817 | 0.7722 | 0.8788 |
| 0.1734 | 1.8423 | 1098 | 0.7264 | 0.2817 | 0.7264 | 0.8523 |
| 0.1734 | 1.8456 | 1100 | 0.6797 | 0.2727 | 0.6797 | 0.8244 |
| 0.1734 | 1.8490 | 1102 | 0.6335 | 0.3824 | 0.6335 | 0.7959 |
| 0.1734 | 1.8523 | 1104 | 0.6495 | 0.3824 | 0.6495 | 0.8059 |
| 0.1734 | 1.8557 | 1106 | 0.7266 | 0.3333 | 0.7266 | 0.8524 |
| 0.1734 | 1.8591 | 1108 | 0.8498 | 0.2105 | 0.8498 | 0.9219 |
| 0.1734 | 1.8624 | 1110 | 0.9372 | 0.2963 | 0.9372 | 0.9681 |
| 0.1734 | 1.8658 | 1112 | 0.9130 | 0.3415 | 0.9130 | 0.9555 |
| 0.1734 | 1.8691 | 1114 | 0.8020 | 0.4324 | 0.8020 | 0.8955 |
| 0.1734 | 1.8725 | 1116 | 0.7243 | 0.4324 | 0.7243 | 0.8510 |
| 0.1734 | 1.8758 | 1118 | 0.7157 | 0.4324 | 0.7157 | 0.8460 |
| 0.1734 | 1.8792 | 1120 | 0.7909 | 0.4324 | 0.7909 | 0.8893 |
| 0.1734 | 1.8826 | 1122 | 0.8660 | 0.2597 | 0.8660 | 0.9306 |
| 0.1734 | 1.8859 | 1124 | 0.9218 | 0.2963 | 0.9218 | 0.9601 |
| 0.1734 | 1.8893 | 1126 | 0.9011 | 0.2963 | 0.9011 | 0.9493 |
| 0.1734 | 1.8926 | 1128 | 0.8024 | 0.2817 | 0.8024 | 0.8957 |
| 0.1734 | 1.8960 | 1130 | 0.7293 | 0.3333 | 0.7293 | 0.8540 |
| 0.1734 | 1.8993 | 1132 | 0.6578 | 0.3836 | 0.6578 | 0.8110 |
| 0.1734 | 1.9027 | 1134 | 0.6411 | 0.4324 | 0.6411 | 0.8007 |
| 0.1734 | 1.9060 | 1136 | 0.6668 | 0.4324 | 0.6668 | 0.8166 |
| 0.1734 | 1.9094 | 1138 | 0.7377 | 0.4324 | 0.7377 | 0.8589 |
| 0.1734 | 1.9128 | 1140 | 0.8187 | 0.3077 | 0.8187 | 0.9048 |
| 0.1734 | 1.9161 | 1142 | 0.9314 | 0.3226 | 0.9314 | 0.9651 |
| 0.1734 | 1.9195 | 1144 | 0.9316 | 0.2500 | 0.9316 | 0.9652 |
| 0.1734 | 1.9228 | 1146 | 0.8394 | 0.3333 | 0.8394 | 0.9162 |
| 0.1734 | 1.9262 | 1148 | 0.7512 | 0.3684 | 0.7512 | 0.8667 |
| 0.1734 | 1.9295 | 1150 | 0.7163 | 0.2703 | 0.7163 | 0.8463 |
| 0.1734 | 1.9329 | 1152 | 0.7044 | 0.2703 | 0.7044 | 0.8393 |
| 0.1734 | 1.9362 | 1154 | 0.7248 | 0.4156 | 0.7248 | 0.8514 |
| 0.1734 | 1.9396 | 1156 | 0.8007 | 0.3250 | 0.8007 | 0.8948 |
| 0.1734 | 1.9430 | 1158 | 0.8951 | 0.2105 | 0.8951 | 0.9461 |
| 0.1734 | 1.9463 | 1160 | 1.0302 | 0.2963 | 1.0302 | 1.0150 |
| 0.1734 | 1.9497 | 1162 | 1.0326 | 0.2963 | 1.0326 | 1.0162 |
| 0.1734 | 1.9530 | 1164 | 0.9628 | 0.2105 | 0.9628 | 0.9812 |
| 0.1734 | 1.9564 | 1166 | 0.9103 | 0.2105 | 0.9103 | 0.9541 |
| 0.1734 | 1.9597 | 1168 | 0.7821 | 0.2105 | 0.7821 | 0.8844 |
| 0.1734 | 1.9631 | 1170 | 0.6704 | 0.2388 | 0.6704 | 0.8188 |
| 0.1734 | 1.9664 | 1172 | 0.6554 | 0.3478 | 0.6554 | 0.8096 |
| 0.1734 | 1.9698 | 1174 | 0.6991 | 0.3014 | 0.6991 | 0.8361 |
| 0.1734 | 1.9732 | 1176 | 0.7113 | 0.3514 | 0.7113 | 0.8434 |
| 0.1734 | 1.9765 | 1178 | 0.7190 | 0.3514 | 0.7190 | 0.8479 |
| 0.1734 | 1.9799 | 1180 | 0.6859 | 0.3662 | 0.6859 | 0.8282 |
| 0.1734 | 1.9832 | 1182 | 0.6619 | 0.3836 | 0.6619 | 0.8136 |
| 0.1734 | 1.9866 | 1184 | 0.6711 | 0.3836 | 0.6711 | 0.8192 |
| 0.1734 | 1.9899 | 1186 | 0.6947 | 0.4156 | 0.6947 | 0.8335 |
| 0.1734 | 1.9933 | 1188 | 0.7996 | 0.3294 | 0.7996 | 0.8942 |
| 0.1734 | 1.9966 | 1190 | 0.8820 | 0.3617 | 0.8820 | 0.9392 |
| 0.1734 | 2.0 | 1192 | 0.8384 | 0.3617 | 0.8384 | 0.9156 |
| 0.1734 | 2.0034 | 1194 | 0.7266 | 0.3514 | 0.7266 | 0.8524 |
| 0.1734 | 2.0067 | 1196 | 0.6169 | 0.3684 | 0.6169 | 0.7854 |
| 0.1734 | 2.0101 | 1198 | 0.5502 | 0.4348 | 0.5502 | 0.7418 |
| 0.1734 | 2.0134 | 1200 | 0.5372 | 0.4348 | 0.5372 | 0.7329 |
| 0.1734 | 2.0168 | 1202 | 0.5365 | 0.4348 | 0.5365 | 0.7325 |
| 0.1734 | 2.0201 | 1204 | 0.5590 | 0.4179 | 0.5590 | 0.7477 |
| 0.1734 | 2.0235 | 1206 | 0.6490 | 0.3200 | 0.6490 | 0.8056 |
| 0.1734 | 2.0268 | 1208 | 0.7375 | 0.3514 | 0.7375 | 0.8588 |
| 0.1734 | 2.0302 | 1210 | 0.7435 | 0.2785 | 0.7435 | 0.8622 |
| 0.1734 | 2.0336 | 1212 | 0.6725 | 0.3200 | 0.6725 | 0.8201 |
| 0.1734 | 2.0369 | 1214 | 0.6125 | 0.4167 | 0.6125 | 0.7826 |
| 0.1734 | 2.0403 | 1216 | 0.6012 | 0.4324 | 0.6012 | 0.7754 |
| 0.1734 | 2.0436 | 1218 | 0.5987 | 0.4167 | 0.5987 | 0.7737 |
| 0.1734 | 2.0470 | 1220 | 0.6274 | 0.4 | 0.6274 | 0.7921 |
| 0.1734 | 2.0503 | 1222 | 0.6695 | 0.3514 | 0.6695 | 0.8182 |
| 0.1734 | 2.0537 | 1224 | 0.6812 | 0.3514 | 0.6812 | 0.8253 |
| 0.1734 | 2.0570 | 1226 | 0.6921 | 0.3514 | 0.6921 | 0.8319 |
| 0.1734 | 2.0604 | 1228 | 0.7242 | 0.3836 | 0.7242 | 0.8510 |
| 0.1734 | 2.0638 | 1230 | 0.7562 | 0.3836 | 0.7562 | 0.8696 |
| 0.1734 | 2.0671 | 1232 | 0.6958 | 0.3478 | 0.6958 | 0.8341 |
| 0.1734 | 2.0705 | 1234 | 0.6200 | 0.3478 | 0.6200 | 0.7874 |
| 0.1734 | 2.0738 | 1236 | 0.5865 | 0.3478 | 0.5865 | 0.7658 |
| 0.1734 | 2.0772 | 1238 | 0.5963 | 0.3478 | 0.5963 | 0.7722 |
| 0.1734 | 2.0805 | 1240 | 0.6506 | 0.3478 | 0.6506 | 0.8066 |
| 0.1734 | 2.0839 | 1242 | 0.7121 | 0.3478 | 0.7121 | 0.8439 |
| 0.1734 | 2.0872 | 1244 | 0.7477 | 0.3478 | 0.7477 | 0.8647 |
| 0.1734 | 2.0906 | 1246 | 0.7940 | 0.3836 | 0.7940 | 0.8910 |
| 0.1734 | 2.0940 | 1248 | 0.7751 | 0.3836 | 0.7751 | 0.8804 |
| 0.1734 | 2.0973 | 1250 | 0.7002 | 0.3514 | 0.7002 | 0.8368 |
| 0.1734 | 2.1007 | 1252 | 0.6172 | 0.3478 | 0.6172 | 0.7856 |
| 0.1734 | 2.1040 | 1254 | 0.5954 | 0.3478 | 0.5954 | 0.7716 |
| 0.1734 | 2.1074 | 1256 | 0.6114 | 0.3478 | 0.6114 | 0.7819 |
| 0.1734 | 2.1107 | 1258 | 0.6673 | 0.3478 | 0.6673 | 0.8169 |
| 0.1734 | 2.1141 | 1260 | 0.7571 | 0.3333 | 0.7571 | 0.8701 |
| 0.1734 | 2.1174 | 1262 | 0.8221 | 0.3684 | 0.8221 | 0.9067 |
| 0.1734 | 2.1208 | 1264 | 0.8251 | 0.3684 | 0.8251 | 0.9084 |
| 0.1734 | 2.1242 | 1266 | 0.7454 | 0.3333 | 0.7454 | 0.8634 |
| 0.1734 | 2.1275 | 1268 | 0.6667 | 0.3284 | 0.6667 | 0.8165 |
| 0.1734 | 2.1309 | 1270 | 0.6193 | 0.3284 | 0.6193 | 0.7870 |
| 0.1734 | 2.1342 | 1272 | 0.5641 | 0.2941 | 0.5641 | 0.7511 |
| 0.1734 | 2.1376 | 1274 | 0.5523 | 0.3662 | 0.5523 | 0.7431 |
| 0.1734 | 2.1409 | 1276 | 0.5757 | 0.4 | 0.5757 | 0.7588 |
| 0.1734 | 2.1443 | 1278 | 0.6376 | 0.2941 | 0.6376 | 0.7985 |
| 0.1734 | 2.1477 | 1280 | 0.8035 | 0.3684 | 0.8035 | 0.8964 |
| 0.1734 | 2.1510 | 1282 | 1.0372 | 0.2826 | 1.0372 | 1.0184 |
| 0.1734 | 2.1544 | 1284 | 1.1714 | 0.125 | 1.1714 | 1.0823 |
| 0.1734 | 2.1577 | 1286 | 1.1493 | 0.125 | 1.1493 | 1.0720 |
| 0.1734 | 2.1611 | 1288 | 1.0324 | 0.3200 | 1.0324 | 1.0161 |
| 0.1734 | 2.1644 | 1290 | 0.8619 | 0.2286 | 0.8619 | 0.9284 |
| 0.1734 | 2.1678 | 1292 | 0.6771 | 0.2727 | 0.6771 | 0.8228 |
| 0.1734 | 2.1711 | 1294 | 0.5711 | 0.3077 | 0.5711 | 0.7557 |
| 0.1734 | 2.1745 | 1296 | 0.5506 | 0.25 | 0.5506 | 0.7421 |
| 0.1734 | 2.1779 | 1298 | 0.5634 | 0.3636 | 0.5634 | 0.7506 |
| 0.1734 | 2.1812 | 1300 | 0.6044 | 0.3662 | 0.6044 | 0.7774 |
| 0.1734 | 2.1846 | 1302 | 0.6748 | 0.3478 | 0.6748 | 0.8214 |
| 0.1734 | 2.1879 | 1304 | 0.7477 | 0.25 | 0.7477 | 0.8647 |
| 0.1734 | 2.1913 | 1306 | 0.7739 | 0.25 | 0.7739 | 0.8797 |
| 0.1734 | 2.1946 | 1308 | 0.7421 | 0.25 | 0.7421 | 0.8615 |
| 0.1734 | 2.1980 | 1310 | 0.7280 | 0.25 | 0.7280 | 0.8532 |
| 0.1734 | 2.2013 | 1312 | 0.7090 | 0.2388 | 0.7090 | 0.8420 |
| 0.1734 | 2.2047 | 1314 | 0.7137 | 0.25 | 0.7137 | 0.8448 |
| 0.1734 | 2.2081 | 1316 | 0.6970 | 0.2388 | 0.6970 | 0.8349 |
| 0.1734 | 2.2114 | 1318 | 0.6406 | 0.3478 | 0.6406 | 0.8003 |
| 0.1734 | 2.2148 | 1320 | 0.6199 | 0.3143 | 0.6199 | 0.7873 |
| 0.1734 | 2.2181 | 1322 | 0.6266 | 0.3143 | 0.6266 | 0.7916 |
| 0.1734 | 2.2215 | 1324 | 0.6602 | 0.3478 | 0.6602 | 0.8125 |
| 0.1734 | 2.2248 | 1326 | 0.7194 | 0.2941 | 0.7194 | 0.8482 |
| 0.1734 | 2.2282 | 1328 | 0.7674 | 0.3836 | 0.7674 | 0.8760 |
| 0.1734 | 2.2315 | 1330 | 0.7819 | 0.3846 | 0.7819 | 0.8843 |
| 0.1734 | 2.2349 | 1332 | 0.7411 | 0.3478 | 0.7411 | 0.8608 |
| 0.1734 | 2.2383 | 1334 | 0.6976 | 0.3662 | 0.6976 | 0.8352 |
| 0.1734 | 2.2416 | 1336 | 0.6953 | 0.3662 | 0.6953 | 0.8338 |
| 0.1734 | 2.2450 | 1338 | 0.7309 | 0.3478 | 0.7309 | 0.8549 |
| 0.1734 | 2.2483 | 1340 | 0.7091 | 0.3478 | 0.7091 | 0.8421 |
| 0.1734 | 2.2517 | 1342 | 0.6484 | 0.3662 | 0.6484 | 0.8053 |
| 0.1734 | 2.2550 | 1344 | 0.6131 | 0.3662 | 0.6131 | 0.7830 |
| 0.1734 | 2.2584 | 1346 | 0.5923 | 0.3662 | 0.5923 | 0.7696 |
| 0.1734 | 2.2617 | 1348 | 0.5909 | 0.3636 | 0.5909 | 0.7687 |
| 0.1734 | 2.2651 | 1350 | 0.6043 | 0.3636 | 0.6043 | 0.7774 |
| 0.1734 | 2.2685 | 1352 | 0.6329 | 0.3662 | 0.6329 | 0.7956 |
| 0.1734 | 2.2718 | 1354 | 0.7011 | 0.4 | 0.7011 | 0.8373 |
| 0.1734 | 2.2752 | 1356 | 0.7954 | 0.4 | 0.7954 | 0.8918 |
| 0.1734 | 2.2785 | 1358 | 0.8540 | 0.3478 | 0.8540 | 0.9241 |
| 0.1734 | 2.2819 | 1360 | 0.8859 | 0.3478 | 0.8859 | 0.9412 |
| 0.1734 | 2.2852 | 1362 | 0.8256 | 0.3478 | 0.8256 | 0.9086 |
| 0.1734 | 2.2886 | 1364 | 0.7273 | 0.4 | 0.7273 | 0.8528 |
| 0.1734 | 2.2919 | 1366 | 0.6557 | 0.3333 | 0.6557 | 0.8098 |
| 0.1734 | 2.2953 | 1368 | 0.6309 | 0.2609 | 0.6309 | 0.7943 |
| 0.1734 | 2.2987 | 1370 | 0.6288 | 0.2609 | 0.6288 | 0.7930 |
| 0.1734 | 2.3020 | 1372 | 0.6395 | 0.2192 | 0.6395 | 0.7997 |
| 0.1734 | 2.3054 | 1374 | 0.6804 | 0.3662 | 0.6804 | 0.8248 |
| 0.1734 | 2.3087 | 1376 | 0.7554 | 0.4 | 0.7554 | 0.8692 |
| 0.1734 | 2.3121 | 1378 | 0.7968 | 0.4 | 0.7968 | 0.8926 |
| 0.1734 | 2.3154 | 1380 | 0.7725 | 0.3662 | 0.7725 | 0.8789 |
| 0.1734 | 2.3188 | 1382 | 0.7753 | 0.3662 | 0.7753 | 0.8805 |
| 0.1734 | 2.3221 | 1384 | 0.7806 | 0.3662 | 0.7806 | 0.8835 |
| 0.1734 | 2.3255 | 1386 | 0.7425 | 0.3014 | 0.7425 | 0.8617 |
| 0.1734 | 2.3289 | 1388 | 0.7293 | 0.24 | 0.7293 | 0.8540 |
| 0.1734 | 2.3322 | 1390 | 0.7239 | 0.24 | 0.7239 | 0.8508 |
| 0.1734 | 2.3356 | 1392 | 0.7342 | 0.2703 | 0.7342 | 0.8569 |
| 0.1734 | 2.3389 | 1394 | 0.7692 | 0.3662 | 0.7692 | 0.8770 |
| 0.1734 | 2.3423 | 1396 | 0.8449 | 0.3478 | 0.8449 | 0.9192 |
| 0.1734 | 2.3456 | 1398 | 0.8564 | 0.3478 | 0.8564 | 0.9254 |
| 0.1734 | 2.3490 | 1400 | 0.7983 | 0.3478 | 0.7983 | 0.8935 |
| 0.1734 | 2.3523 | 1402 | 0.7050 | 0.4 | 0.7050 | 0.8396 |
| 0.1734 | 2.3557 | 1404 | 0.6303 | 0.4167 | 0.6303 | 0.7939 |
| 0.1734 | 2.3591 | 1406 | 0.6076 | 0.3662 | 0.6076 | 0.7795 |
| 0.1734 | 2.3624 | 1408 | 0.5913 | 0.3636 | 0.5913 | 0.7690 |
| 0.1734 | 2.3658 | 1410 | 0.5936 | 0.3143 | 0.5936 | 0.7705 |
| 0.1734 | 2.3691 | 1412 | 0.6333 | 0.2941 | 0.6333 | 0.7958 |
| 0.1734 | 2.3725 | 1414 | 0.6735 | 0.3284 | 0.6735 | 0.8207 |
| 0.1734 | 2.3758 | 1416 | 0.6598 | 0.2941 | 0.6598 | 0.8123 |
| 0.1734 | 2.3792 | 1418 | 0.6099 | 0.3143 | 0.6099 | 0.7810 |
| 0.1734 | 2.3826 | 1420 | 0.5835 | 0.4167 | 0.5835 | 0.7639 |
| 0.1734 | 2.3859 | 1422 | 0.5946 | 0.4167 | 0.5946 | 0.7711 |
| 0.1734 | 2.3893 | 1424 | 0.6143 | 0.4167 | 0.6143 | 0.7838 |
| 0.1734 | 2.3926 | 1426 | 0.6297 | 0.4167 | 0.6297 | 0.7935 |
| 0.1734 | 2.3960 | 1428 | 0.6257 | 0.3836 | 0.6257 | 0.7910 |
| 0.1734 | 2.3993 | 1430 | 0.6337 | 0.2703 | 0.6337 | 0.7960 |
| 0.1734 | 2.4027 | 1432 | 0.6203 | 0.24 | 0.6203 | 0.7876 |
| 0.1734 | 2.4060 | 1434 | 0.6280 | 0.24 | 0.6280 | 0.7925 |
| 0.1734 | 2.4094 | 1436 | 0.6276 | 0.24 | 0.6276 | 0.7922 |
| 0.1734 | 2.4128 | 1438 | 0.6514 | 0.3836 | 0.6514 | 0.8071 |
| 0.1734 | 2.4161 | 1440 | 0.6718 | 0.3662 | 0.6718 | 0.8196 |
| 0.1734 | 2.4195 | 1442 | 0.6876 | 0.3143 | 0.6876 | 0.8292 |
| 0.1734 | 2.4228 | 1444 | 0.6791 | 0.4 | 0.6791 | 0.8241 |
| 0.1734 | 2.4262 | 1446 | 0.6399 | 0.3143 | 0.6399 | 0.7999 |
| 0.1734 | 2.4295 | 1448 | 0.5960 | 0.3143 | 0.5960 | 0.7720 |
| 0.1734 | 2.4329 | 1450 | 0.5814 | 0.3143 | 0.5814 | 0.7625 |
| 0.1734 | 2.4362 | 1452 | 0.5814 | 0.3143 | 0.5814 | 0.7625 |
| 0.1734 | 2.4396 | 1454 | 0.5698 | 0.3478 | 0.5698 | 0.7549 |
| 0.1734 | 2.4430 | 1456 | 0.5428 | 0.3143 | 0.5428 | 0.7368 |
| 0.1734 | 2.4463 | 1458 | 0.5415 | 0.3478 | 0.5415 | 0.7359 |
| 0.1734 | 2.4497 | 1460 | 0.5750 | 0.3824 | 0.5750 | 0.7583 |
| 0.1734 | 2.4530 | 1462 | 0.6477 | 0.4167 | 0.6477 | 0.8048 |
| 0.1734 | 2.4564 | 1464 | 0.7012 | 0.4167 | 0.7012 | 0.8374 |
| 0.1734 | 2.4597 | 1466 | 0.6734 | 0.4167 | 0.6734 | 0.8206 |
| 0.1734 | 2.4631 | 1468 | 0.6044 | 0.3824 | 0.6044 | 0.7775 |
| 0.1734 | 2.4664 | 1470 | 0.5447 | 0.4348 | 0.5447 | 0.7380 |
| 0.1734 | 2.4698 | 1472 | 0.5400 | 0.4857 | 0.5400 | 0.7349 |
| 0.1734 | 2.4732 | 1474 | 0.5577 | 0.4348 | 0.5577 | 0.7468 |
| 0.1734 | 2.4765 | 1476 | 0.5708 | 0.4348 | 0.5708 | 0.7555 |
| 0.1734 | 2.4799 | 1478 | 0.5767 | 0.4348 | 0.5767 | 0.7594 |
| 0.1734 | 2.4832 | 1480 | 0.5858 | 0.4348 | 0.5858 | 0.7654 |
| 0.1734 | 2.4866 | 1482 | 0.5798 | 0.4348 | 0.5798 | 0.7614 |
| 0.1734 | 2.4899 | 1484 | 0.5769 | 0.4348 | 0.5769 | 0.7595 |
| 0.1734 | 2.4933 | 1486 | 0.5925 | 0.3284 | 0.5925 | 0.7697 |
| 0.1734 | 2.4966 | 1488 | 0.5585 | 0.4348 | 0.5585 | 0.7473 |
| 0.1734 | 2.5 | 1490 | 0.5441 | 0.4857 | 0.5441 | 0.7377 |
| 0.1734 | 2.5034 | 1492 | 0.5502 | 0.4857 | 0.5502 | 0.7417 |
| 0.1734 | 2.5067 | 1494 | 0.5556 | 0.4507 | 0.5556 | 0.7454 |
| 0.1734 | 2.5101 | 1496 | 0.5526 | 0.4167 | 0.5526 | 0.7434 |
| 0.1734 | 2.5134 | 1498 | 0.5492 | 0.4156 | 0.5492 | 0.7411 |
| 0.1226 | 2.5168 | 1500 | 0.5589 | 0.4156 | 0.5589 | 0.7476 |
| 0.1226 | 2.5201 | 1502 | 0.5832 | 0.4167 | 0.5832 | 0.7637 |
| 0.1226 | 2.5235 | 1504 | 0.5984 | 0.3662 | 0.5984 | 0.7735 |
| 0.1226 | 2.5268 | 1506 | 0.6044 | 0.4474 | 0.6044 | 0.7774 |
| 0.1226 | 2.5302 | 1508 | 0.5977 | 0.3662 | 0.5977 | 0.7731 |
| 0.1226 | 2.5336 | 1510 | 0.5934 | 0.4167 | 0.5934 | 0.7703 |
| 0.1226 | 2.5369 | 1512 | 0.5971 | 0.4935 | 0.5971 | 0.7727 |
| 0.1226 | 2.5403 | 1514 | 0.6127 | 0.3846 | 0.6127 | 0.7827 |
| 0.1226 | 2.5436 | 1516 | 0.6212 | 0.48 | 0.6212 | 0.7882 |
| 0.1226 | 2.5470 | 1518 | 0.6578 | 0.4324 | 0.6578 | 0.8110 |
| 0.1226 | 2.5503 | 1520 | 0.7396 | 0.4156 | 0.7396 | 0.8600 |
| 0.1226 | 2.5537 | 1522 | 0.7324 | 0.4156 | 0.7324 | 0.8558 |
| 0.1226 | 2.5570 | 1524 | 0.6388 | 0.4167 | 0.6388 | 0.7992 |
| 0.1226 | 2.5604 | 1526 | 0.5543 | 0.4 | 0.5543 | 0.7445 |
| 0.1226 | 2.5638 | 1528 | 0.5268 | 0.3143 | 0.5268 | 0.7258 |
| 0.1226 | 2.5671 | 1530 | 0.5214 | 0.3143 | 0.5214 | 0.7221 |
| 0.1226 | 2.5705 | 1532 | 0.5252 | 0.2059 | 0.5252 | 0.7247 |
| 0.1226 | 2.5738 | 1534 | 0.5479 | 0.4 | 0.5479 | 0.7402 |
| 0.1226 | 2.5772 | 1536 | 0.6247 | 0.3284 | 0.6247 | 0.7904 |
| 0.1226 | 2.5805 | 1538 | 0.7177 | 0.4167 | 0.7177 | 0.8472 |
| 0.1226 | 2.5839 | 1540 | 0.7593 | 0.4167 | 0.7593 | 0.8714 |
| 0.1226 | 2.5872 | 1542 | 0.7436 | 0.4167 | 0.7436 | 0.8623 |
| 0.1226 | 2.5906 | 1544 | 0.6691 | 0.3284 | 0.6691 | 0.8180 |
| 0.1226 | 2.5940 | 1546 | 0.5902 | 0.4348 | 0.5902 | 0.7682 |
| 0.1226 | 2.5973 | 1548 | 0.5391 | 0.2609 | 0.5391 | 0.7342 |
| 0.1226 | 2.6007 | 1550 | 0.5301 | 0.2727 | 0.5301 | 0.7281 |
| 0.1226 | 2.6040 | 1552 | 0.5299 | 0.1231 | 0.5299 | 0.7279 |
| 0.1226 | 2.6074 | 1554 | 0.5398 | 0.2609 | 0.5398 | 0.7347 |
| 0.1226 | 2.6107 | 1556 | 0.5690 | 0.4857 | 0.5690 | 0.7543 |
| 0.1226 | 2.6141 | 1558 | 0.6288 | 0.3824 | 0.6288 | 0.7930 |
| 0.1226 | 2.6174 | 1560 | 0.6707 | 0.3824 | 0.6707 | 0.8190 |
| 0.1226 | 2.6208 | 1562 | 0.6732 | 0.3824 | 0.6732 | 0.8205 |
| 0.1226 | 2.6242 | 1564 | 0.6385 | 0.3824 | 0.6385 | 0.7991 |
| 0.1226 | 2.6275 | 1566 | 0.6281 | 0.4348 | 0.6281 | 0.7925 |
| 0.1226 | 2.6309 | 1568 | 0.6551 | 0.3824 | 0.6551 | 0.8094 |
| 0.1226 | 2.6342 | 1570 | 0.6489 | 0.3824 | 0.6489 | 0.8055 |
| 0.1226 | 2.6376 | 1572 | 0.6490 | 0.3284 | 0.6490 | 0.8056 |
| 0.1226 | 2.6409 | 1574 | 0.6054 | 0.4348 | 0.6054 | 0.7780 |
| 0.1226 | 2.6443 | 1576 | 0.5598 | 0.4 | 0.5598 | 0.7482 |
| 0.1226 | 2.6477 | 1578 | 0.5462 | 0.4 | 0.5462 | 0.7391 |
| 0.1226 | 2.6510 | 1580 | 0.5519 | 0.4 | 0.5519 | 0.7429 |
| 0.1226 | 2.6544 | 1582 | 0.5788 | 0.3478 | 0.5788 | 0.7608 |
| 0.1226 | 2.6577 | 1584 | 0.6095 | 0.3824 | 0.6095 | 0.7807 |
| 0.1226 | 2.6611 | 1586 | 0.5933 | 0.3478 | 0.5933 | 0.7702 |
| 0.1226 | 2.6644 | 1588 | 0.6086 | 0.3824 | 0.6086 | 0.7801 |
| 0.1226 | 2.6678 | 1590 | 0.6970 | 0.4156 | 0.6970 | 0.8349 |
| 0.1226 | 2.6711 | 1592 | 0.7672 | 0.4156 | 0.7672 | 0.8759 |
| 0.1226 | 2.6745 | 1594 | 0.7551 | 0.4156 | 0.7551 | 0.8690 |
| 0.1226 | 2.6779 | 1596 | 0.6757 | 0.4156 | 0.6757 | 0.8220 |
| 0.1226 | 2.6812 | 1598 | 0.5772 | 0.4 | 0.5772 | 0.7598 |
| 0.1226 | 2.6846 | 1600 | 0.5318 | 0.3836 | 0.5318 | 0.7293 |
| 0.1226 | 2.6879 | 1602 | 0.5206 | 0.5135 | 0.5206 | 0.7216 |
| 0.1226 | 2.6913 | 1604 | 0.5234 | 0.3836 | 0.5234 | 0.7235 |
| 0.1226 | 2.6946 | 1606 | 0.5624 | 0.3478 | 0.5624 | 0.7499 |
| 0.1226 | 2.6980 | 1608 | 0.6305 | 0.3824 | 0.6305 | 0.7941 |
| 0.1226 | 2.7013 | 1610 | 0.6608 | 0.3824 | 0.6608 | 0.8129 |
| 0.1226 | 2.7047 | 1612 | 0.6298 | 0.3824 | 0.6298 | 0.7936 |
| 0.1226 | 2.7081 | 1614 | 0.6047 | 0.3824 | 0.6047 | 0.7776 |
| 0.1226 | 2.7114 | 1616 | 0.5586 | 0.3662 | 0.5586 | 0.7474 |
| 0.1226 | 2.7148 | 1618 | 0.5284 | 0.5479 | 0.5284 | 0.7269 |
| 0.1226 | 2.7181 | 1620 | 0.5119 | 0.5135 | 0.5119 | 0.7154 |
| 0.1226 | 2.7215 | 1622 | 0.5089 | 0.5479 | 0.5089 | 0.7133 |
| 0.1226 | 2.7248 | 1624 | 0.5218 | 0.4348 | 0.5218 | 0.7224 |
| 0.1226 | 2.7282 | 1626 | 0.5778 | 0.3824 | 0.5778 | 0.7601 |
| 0.1226 | 2.7315 | 1628 | 0.6385 | 0.3284 | 0.6385 | 0.7991 |
| 0.1226 | 2.7349 | 1630 | 0.6546 | 0.3284 | 0.6546 | 0.8091 |
| 0.1226 | 2.7383 | 1632 | 0.6160 | 0.3284 | 0.6160 | 0.7849 |
| 0.1226 | 2.7416 | 1634 | 0.5682 | 0.3824 | 0.5682 | 0.7538 |
| 0.1226 | 2.7450 | 1636 | 0.5222 | 0.3824 | 0.5222 | 0.7227 |
| 0.1226 | 2.7483 | 1638 | 0.5188 | 0.3836 | 0.5188 | 0.7203 |
| 0.1226 | 2.7517 | 1640 | 0.5331 | 0.4 | 0.5331 | 0.7301 |
| 0.1226 | 2.7550 | 1642 | 0.5674 | 0.5352 | 0.5674 | 0.7533 |
| 0.1226 | 2.7584 | 1644 | 0.6119 | 0.3478 | 0.6119 | 0.7822 |
| 0.1226 | 2.7617 | 1646 | 0.6141 | 0.3478 | 0.6141 | 0.7836 |
| 0.1226 | 2.7651 | 1648 | 0.5861 | 0.3478 | 0.5861 | 0.7655 |
| 0.1226 | 2.7685 | 1650 | 0.5795 | 0.3824 | 0.5795 | 0.7613 |
| 0.1226 | 2.7718 | 1652 | 0.5517 | 0.3824 | 0.5517 | 0.7428 |
| 0.1226 | 2.7752 | 1654 | 0.5279 | 0.3478 | 0.5279 | 0.7266 |
| 0.1226 | 2.7785 | 1656 | 0.5288 | 0.3824 | 0.5288 | 0.7272 |
| 0.1226 | 2.7819 | 1658 | 0.5551 | 0.3824 | 0.5551 | 0.7450 |
| 0.1226 | 2.7852 | 1660 | 0.5784 | 0.3824 | 0.5784 | 0.7605 |
| 0.1226 | 2.7886 | 1662 | 0.6033 | 0.3284 | 0.6033 | 0.7767 |
| 0.1226 | 2.7919 | 1664 | 0.6134 | 0.3824 | 0.6134 | 0.7832 |
| 0.1226 | 2.7953 | 1666 | 0.6110 | 0.3824 | 0.6110 | 0.7817 |
| 0.1226 | 2.7987 | 1668 | 0.6013 | 0.3824 | 0.6013 | 0.7754 |
| 0.1226 | 2.8020 | 1670 | 0.5820 | 0.3478 | 0.5820 | 0.7629 |
| 0.1226 | 2.8054 | 1672 | 0.5836 | 0.4167 | 0.5836 | 0.7639 |
| 0.1226 | 2.8087 | 1674 | 0.5655 | 0.4167 | 0.5655 | 0.7520 |
| 0.1226 | 2.8121 | 1676 | 0.5513 | 0.5479 | 0.5513 | 0.7425 |
| 0.1226 | 2.8154 | 1678 | 0.5633 | 0.3662 | 0.5633 | 0.7505 |
| 0.1226 | 2.8188 | 1680 | 0.6012 | 0.3824 | 0.6012 | 0.7754 |
| 0.1226 | 2.8221 | 1682 | 0.5877 | 0.3824 | 0.5877 | 0.7666 |
| 0.1226 | 2.8255 | 1684 | 0.5574 | 0.3824 | 0.5574 | 0.7466 |
| 0.1226 | 2.8289 | 1686 | 0.5290 | 0.4658 | 0.5290 | 0.7273 |
| 0.1226 | 2.8322 | 1688 | 0.5298 | 0.5135 | 0.5298 | 0.7279 |
| 0.1226 | 2.8356 | 1690 | 0.5325 | 0.4167 | 0.5325 | 0.7297 |
| 0.1226 | 2.8389 | 1692 | 0.5460 | 0.3478 | 0.5460 | 0.7390 |
| 0.1226 | 2.8423 | 1694 | 0.5547 | 0.3824 | 0.5547 | 0.7448 |
| 0.1226 | 2.8456 | 1696 | 0.5449 | 0.3824 | 0.5449 | 0.7381 |
| 0.1226 | 2.8490 | 1698 | 0.5435 | 0.3284 | 0.5435 | 0.7372 |
| 0.1226 | 2.8523 | 1700 | 0.5373 | 0.3284 | 0.5373 | 0.7330 |
| 0.1226 | 2.8557 | 1702 | 0.5387 | 0.3284 | 0.5387 | 0.7340 |
| 0.1226 | 2.8591 | 1704 | 0.5271 | 0.3284 | 0.5271 | 0.7260 |
| 0.1226 | 2.8624 | 1706 | 0.5454 | 0.3284 | 0.5454 | 0.7385 |
| 0.1226 | 2.8658 | 1708 | 0.5680 | 0.3284 | 0.5680 | 0.7537 |
| 0.1226 | 2.8691 | 1710 | 0.6257 | 0.3333 | 0.6257 | 0.7910 |
| 0.1226 | 2.8725 | 1712 | 0.6456 | 0.3333 | 0.6456 | 0.8035 |
| 0.1226 | 2.8758 | 1714 | 0.6189 | 0.3333 | 0.6189 | 0.7867 |
| 0.1226 | 2.8792 | 1716 | 0.5956 | 0.3333 | 0.5956 | 0.7718 |
| 0.1226 | 2.8826 | 1718 | 0.5736 | 0.3284 | 0.5736 | 0.7573 |
| 0.1226 | 2.8859 | 1720 | 0.5529 | 0.2941 | 0.5529 | 0.7436 |
| 0.1226 | 2.8893 | 1722 | 0.5624 | 0.3662 | 0.5624 | 0.7499 |
| 0.1226 | 2.8926 | 1724 | 0.6000 | 0.2941 | 0.6000 | 0.7746 |
| 0.1226 | 2.8960 | 1726 | 0.6255 | 0.3333 | 0.6255 | 0.7909 |
| 0.1226 | 2.8993 | 1728 | 0.6076 | 0.3478 | 0.6076 | 0.7795 |
| 0.1226 | 2.9027 | 1730 | 0.5863 | 0.3662 | 0.5863 | 0.7657 |
| 0.1226 | 2.9060 | 1732 | 0.5913 | 0.3662 | 0.5913 | 0.7690 |
| 0.1226 | 2.9094 | 1734 | 0.5823 | 0.3662 | 0.5823 | 0.7631 |
| 0.1226 | 2.9128 | 1736 | 0.6075 | 0.3662 | 0.6075 | 0.7794 |
| 0.1226 | 2.9161 | 1738 | 0.6536 | 0.3333 | 0.6536 | 0.8085 |
| 0.1226 | 2.9195 | 1740 | 0.6804 | 0.3333 | 0.6804 | 0.8249 |
| 0.1226 | 2.9228 | 1742 | 0.7263 | 0.3333 | 0.7263 | 0.8522 |
| 0.1226 | 2.9262 | 1744 | 0.7326 | 0.3333 | 0.7326 | 0.8559 |
| 0.1226 | 2.9295 | 1746 | 0.7113 | 0.3333 | 0.7113 | 0.8434 |
| 0.1226 | 2.9329 | 1748 | 0.6511 | 0.3333 | 0.6511 | 0.8069 |
| 0.1226 | 2.9362 | 1750 | 0.6025 | 0.3478 | 0.6025 | 0.7762 |
| 0.1226 | 2.9396 | 1752 | 0.5973 | 0.4 | 0.5973 | 0.7729 |
| 0.1226 | 2.9430 | 1754 | 0.6195 | 0.3514 | 0.6195 | 0.7871 |
| 0.1226 | 2.9463 | 1756 | 0.6554 | 0.4156 | 0.6554 | 0.8096 |
| 0.1226 | 2.9497 | 1758 | 0.6396 | 0.4156 | 0.6396 | 0.7998 |
| 0.1226 | 2.9530 | 1760 | 0.5955 | 0.3478 | 0.5955 | 0.7717 |
| 0.1226 | 2.9564 | 1762 | 0.5573 | 0.4167 | 0.5573 | 0.7466 |
| 0.1226 | 2.9597 | 1764 | 0.5407 | 0.4167 | 0.5407 | 0.7353 |
| 0.1226 | 2.9631 | 1766 | 0.5427 | 0.4167 | 0.5427 | 0.7367 |
| 0.1226 | 2.9664 | 1768 | 0.5649 | 0.4 | 0.5649 | 0.7516 |
| 0.1226 | 2.9698 | 1770 | 0.5745 | 0.4 | 0.5745 | 0.7580 |
| 0.1226 | 2.9732 | 1772 | 0.5940 | 0.3284 | 0.5940 | 0.7707 |
| 0.1226 | 2.9765 | 1774 | 0.6017 | 0.4167 | 0.6017 | 0.7757 |
| 0.1226 | 2.9799 | 1776 | 0.5710 | 0.4 | 0.5710 | 0.7556 |
| 0.1226 | 2.9832 | 1778 | 0.5454 | 0.4167 | 0.5454 | 0.7385 |
| 0.1226 | 2.9866 | 1780 | 0.5522 | 0.4167 | 0.5522 | 0.7431 |
| 0.1226 | 2.9899 | 1782 | 0.5807 | 0.5263 | 0.5807 | 0.7621 |
| 0.1226 | 2.9933 | 1784 | 0.6263 | 0.48 | 0.6263 | 0.7914 |
| 0.1226 | 2.9966 | 1786 | 0.6275 | 0.48 | 0.6275 | 0.7922 |
| 0.1226 | 3.0 | 1788 | 0.5877 | 0.5263 | 0.5877 | 0.7666 |
| 0.1226 | 3.0034 | 1790 | 0.5557 | 0.3836 | 0.5557 | 0.7454 |
| 0.1226 | 3.0067 | 1792 | 0.5559 | 0.3143 | 0.5559 | 0.7456 |
| 0.1226 | 3.0101 | 1794 | 0.5637 | 0.3143 | 0.5637 | 0.7508 |
| 0.1226 | 3.0134 | 1796 | 0.5791 | 0.3836 | 0.5791 | 0.7610 |
| 0.1226 | 3.0168 | 1798 | 0.6329 | 0.4507 | 0.6329 | 0.7955 |
| 0.1226 | 3.0201 | 1800 | 0.6999 | 0.5063 | 0.6999 | 0.8366 |
| 0.1226 | 3.0235 | 1802 | 0.7491 | 0.4667 | 0.7491 | 0.8655 |
| 0.1226 | 3.0268 | 1804 | 0.7552 | 0.4156 | 0.7552 | 0.8690 |
| 0.1226 | 3.0302 | 1806 | 0.7437 | 0.4156 | 0.7437 | 0.8624 |
| 0.1226 | 3.0336 | 1808 | 0.7088 | 0.3284 | 0.7088 | 0.8419 |
| 0.1226 | 3.0369 | 1810 | 0.6341 | 0.3284 | 0.6341 | 0.7963 |
| 0.1226 | 3.0403 | 1812 | 0.5986 | 0.1923 | 0.5986 | 0.7737 |
| 0.1226 | 3.0436 | 1814 | 0.6002 | 0.1923 | 0.6002 | 0.7747 |
| 0.1226 | 3.0470 | 1816 | 0.6476 | 0.3284 | 0.6476 | 0.8048 |
| 0.1226 | 3.0503 | 1818 | 0.6817 | 0.3284 | 0.6817 | 0.8257 |
| 0.1226 | 3.0537 | 1820 | 0.6903 | 0.3284 | 0.6903 | 0.8308 |
| 0.1226 | 3.0570 | 1822 | 0.6872 | 0.3824 | 0.6872 | 0.8290 |
| 0.1226 | 3.0604 | 1824 | 0.6389 | 0.3824 | 0.6389 | 0.7993 |
| 0.1226 | 3.0638 | 1826 | 0.6180 | 0.3824 | 0.6180 | 0.7861 |
| 0.1226 | 3.0671 | 1828 | 0.6012 | 0.3824 | 0.6012 | 0.7753 |
| 0.1226 | 3.0705 | 1830 | 0.5947 | 0.3824 | 0.5947 | 0.7712 |
| 0.1226 | 3.0738 | 1832 | 0.5937 | 0.3824 | 0.5937 | 0.7705 |
| 0.1226 | 3.0772 | 1834 | 0.6034 | 0.3824 | 0.6034 | 0.7768 |
| 0.1226 | 3.0805 | 1836 | 0.5853 | 0.3824 | 0.5853 | 0.7650 |
| 0.1226 | 3.0839 | 1838 | 0.5855 | 0.3824 | 0.5855 | 0.7652 |
| 0.1226 | 3.0872 | 1840 | 0.6213 | 0.3284 | 0.6213 | 0.7882 |
| 0.1226 | 3.0906 | 1842 | 0.6255 | 0.3284 | 0.6255 | 0.7909 |
| 0.1226 | 3.0940 | 1844 | 0.6022 | 0.3824 | 0.6022 | 0.7760 |
| 0.1226 | 3.0973 | 1846 | 0.6315 | 0.3284 | 0.6315 | 0.7947 |
| 0.1226 | 3.1007 | 1848 | 0.6853 | 0.3333 | 0.6853 | 0.8278 |
| 0.1226 | 3.1040 | 1850 | 0.7018 | 0.3333 | 0.7018 | 0.8377 |
| 0.1226 | 3.1074 | 1852 | 0.6454 | 0.3284 | 0.6454 | 0.8034 |
| 0.1226 | 3.1107 | 1854 | 0.5700 | 0.3824 | 0.5700 | 0.7550 |
| 0.1226 | 3.1141 | 1856 | 0.5323 | 0.3284 | 0.5323 | 0.7296 |
| 0.1226 | 3.1174 | 1858 | 0.5359 | 0.4 | 0.5359 | 0.7321 |
| 0.1226 | 3.1208 | 1860 | 0.5695 | 0.3824 | 0.5695 | 0.7547 |
| 0.1226 | 3.1242 | 1862 | 0.6588 | 0.3284 | 0.6588 | 0.8117 |
| 0.1226 | 3.1275 | 1864 | 0.7660 | 0.2817 | 0.7660 | 0.8752 |
| 0.1226 | 3.1309 | 1866 | 0.8284 | 0.3684 | 0.8284 | 0.9101 |
| 0.1226 | 3.1342 | 1868 | 0.8083 | 0.2817 | 0.8083 | 0.8991 |
| 0.1226 | 3.1376 | 1870 | 0.7113 | 0.3284 | 0.7113 | 0.8434 |
| 0.1226 | 3.1409 | 1872 | 0.6080 | 0.3824 | 0.6080 | 0.7797 |
| 0.1226 | 3.1443 | 1874 | 0.5739 | 0.3438 | 0.5739 | 0.7575 |
| 0.1226 | 3.1477 | 1876 | 0.5578 | 0.3636 | 0.5578 | 0.7469 |
| 0.1226 | 3.1510 | 1878 | 0.5717 | 0.3636 | 0.5717 | 0.7561 |
| 0.1226 | 3.1544 | 1880 | 0.6183 | 0.4857 | 0.6183 | 0.7863 |
| 0.1226 | 3.1577 | 1882 | 0.6984 | 0.3824 | 0.6984 | 0.8357 |
| 0.1226 | 3.1611 | 1884 | 0.7269 | 0.3284 | 0.7269 | 0.8526 |
| 0.1226 | 3.1644 | 1886 | 0.6967 | 0.3284 | 0.6967 | 0.8347 |
| 0.1226 | 3.1678 | 1888 | 0.6308 | 0.3824 | 0.6308 | 0.7942 |
| 0.1226 | 3.1711 | 1890 | 0.5652 | 0.4 | 0.5652 | 0.7518 |
| 0.1226 | 3.1745 | 1892 | 0.5502 | 0.4 | 0.5502 | 0.7418 |
| 0.1226 | 3.1779 | 1894 | 0.5560 | 0.3438 | 0.5560 | 0.7456 |
| 0.1226 | 3.1812 | 1896 | 0.5866 | 0.3284 | 0.5866 | 0.7659 |
| 0.1226 | 3.1846 | 1898 | 0.6014 | 0.3284 | 0.6014 | 0.7755 |
| 0.1226 | 3.1879 | 1900 | 0.6370 | 0.3284 | 0.6370 | 0.7981 |
| 0.1226 | 3.1913 | 1902 | 0.6700 | 0.3284 | 0.6700 | 0.8185 |
| 0.1226 | 3.1946 | 1904 | 0.6484 | 0.3284 | 0.6484 | 0.8052 |
| 0.1226 | 3.1980 | 1906 | 0.5939 | 0.4348 | 0.5939 | 0.7707 |
| 0.1226 | 3.2013 | 1908 | 0.5469 | 0.4 | 0.5469 | 0.7395 |
| 0.1226 | 3.2047 | 1910 | 0.5384 | 0.4 | 0.5384 | 0.7337 |
| 0.1226 | 3.2081 | 1912 | 0.5420 | 0.4 | 0.5420 | 0.7362 |
| 0.1226 | 3.2114 | 1914 | 0.5721 | 0.4348 | 0.5721 | 0.7564 |
| 0.1226 | 3.2148 | 1916 | 0.6049 | 0.3284 | 0.6049 | 0.7778 |
| 0.1226 | 3.2181 | 1918 | 0.6178 | 0.3284 | 0.6178 | 0.7860 |
| 0.1226 | 3.2215 | 1920 | 0.5954 | 0.3284 | 0.5954 | 0.7716 |
| 0.1226 | 3.2248 | 1922 | 0.5859 | 0.3284 | 0.5859 | 0.7655 |
| 0.1226 | 3.2282 | 1924 | 0.5511 | 0.3438 | 0.5511 | 0.7423 |
| 0.1226 | 3.2315 | 1926 | 0.5429 | 0.3438 | 0.5429 | 0.7368 |
| 0.1226 | 3.2349 | 1928 | 0.5689 | 0.3824 | 0.5689 | 0.7542 |
| 0.1226 | 3.2383 | 1930 | 0.6035 | 0.3824 | 0.6035 | 0.7769 |
| 0.1226 | 3.2416 | 1932 | 0.6133 | 0.3824 | 0.6133 | 0.7831 |
| 0.1226 | 3.2450 | 1934 | 0.6578 | 0.3824 | 0.6578 | 0.8110 |
| 0.1226 | 3.2483 | 1936 | 0.7428 | 0.3333 | 0.7428 | 0.8618 |
| 0.1226 | 3.2517 | 1938 | 0.7682 | 0.3864 | 0.7682 | 0.8765 |
| 0.1226 | 3.2550 | 1940 | 0.7361 | 0.3333 | 0.7361 | 0.8580 |
| 0.1226 | 3.2584 | 1942 | 0.6764 | 0.3284 | 0.6764 | 0.8225 |
| 0.1226 | 3.2617 | 1944 | 0.5936 | 0.3824 | 0.5936 | 0.7705 |
| 0.1226 | 3.2651 | 1946 | 0.5146 | 0.3636 | 0.5146 | 0.7174 |
| 0.1226 | 3.2685 | 1948 | 0.4945 | 0.4348 | 0.4945 | 0.7032 |
| 0.1226 | 3.2718 | 1950 | 0.4920 | 0.4348 | 0.4920 | 0.7014 |
| 0.1226 | 3.2752 | 1952 | 0.4977 | 0.3284 | 0.4977 | 0.7054 |
| 0.1226 | 3.2785 | 1954 | 0.5356 | 0.4507 | 0.5356 | 0.7319 |
| 0.1226 | 3.2819 | 1956 | 0.6130 | 0.3284 | 0.6130 | 0.7829 |
| 0.1226 | 3.2852 | 1958 | 0.6493 | 0.4167 | 0.6493 | 0.8058 |
| 0.1226 | 3.2886 | 1960 | 0.6266 | 0.3284 | 0.6266 | 0.7916 |
| 0.1226 | 3.2919 | 1962 | 0.5839 | 0.3284 | 0.5839 | 0.7642 |
| 0.1226 | 3.2953 | 1964 | 0.5520 | 0.3284 | 0.5520 | 0.7430 |
| 0.1226 | 3.2987 | 1966 | 0.5225 | 0.3824 | 0.5225 | 0.7229 |
| 0.1226 | 3.3020 | 1968 | 0.4901 | 0.3636 | 0.4901 | 0.7001 |
| 0.1226 | 3.3054 | 1970 | 0.4831 | 0.3636 | 0.4831 | 0.6950 |
| 0.1226 | 3.3087 | 1972 | 0.5035 | 0.4507 | 0.5035 | 0.7095 |
| 0.1226 | 3.3121 | 1974 | 0.5280 | 0.3824 | 0.5280 | 0.7266 |
| 0.1226 | 3.3154 | 1976 | 0.5783 | 0.3284 | 0.5783 | 0.7605 |
| 0.1226 | 3.3188 | 1978 | 0.6630 | 0.4167 | 0.6630 | 0.8142 |
| 0.1226 | 3.3221 | 1980 | 0.7480 | 0.3684 | 0.7480 | 0.8649 |
| 0.1226 | 3.3255 | 1982 | 0.7629 | 0.3684 | 0.7629 | 0.8734 |
| 0.1226 | 3.3289 | 1984 | 0.7104 | 0.2727 | 0.7104 | 0.8428 |
| 0.1226 | 3.3322 | 1986 | 0.6754 | 0.2727 | 0.6754 | 0.8218 |
| 0.1226 | 3.3356 | 1988 | 0.6182 | 0.3284 | 0.6182 | 0.7862 |
| 0.1226 | 3.3389 | 1990 | 0.5670 | 0.3824 | 0.5670 | 0.7530 |
| 0.1226 | 3.3423 | 1992 | 0.5607 | 0.4348 | 0.5607 | 0.7488 |
| 0.1226 | 3.3456 | 1994 | 0.5815 | 0.3824 | 0.5815 | 0.7626 |
| 0.1226 | 3.3490 | 1996 | 0.6020 | 0.3824 | 0.6020 | 0.7759 |
| 0.1226 | 3.3523 | 1998 | 0.6339 | 0.3824 | 0.6339 | 0.7961 |
| 0.092 | 3.3557 | 2000 | 0.7027 | 0.3284 | 0.7027 | 0.8383 |
| 0.092 | 3.3591 | 2002 | 0.7294 | 0.3333 | 0.7294 | 0.8541 |
| 0.092 | 3.3624 | 2004 | 0.7182 | 0.3333 | 0.7182 | 0.8475 |
| 0.092 | 3.3658 | 2006 | 0.6874 | 0.3824 | 0.6874 | 0.8291 |
| 0.092 | 3.3691 | 2008 | 0.6426 | 0.4857 | 0.6426 | 0.8016 |
| 0.092 | 3.3725 | 2010 | 0.6294 | 0.4857 | 0.6294 | 0.7933 |
| 0.092 | 3.3758 | 2012 | 0.5975 | 0.4167 | 0.5975 | 0.7730 |
| 0.092 | 3.3792 | 2014 | 0.6029 | 0.4507 | 0.6029 | 0.7764 |
| 0.092 | 3.3826 | 2016 | 0.6245 | 0.4857 | 0.6245 | 0.7903 |
| 0.092 | 3.3859 | 2018 | 0.6607 | 0.3824 | 0.6607 | 0.8128 |
| 0.092 | 3.3893 | 2020 | 0.7047 | 0.2817 | 0.7047 | 0.8394 |
| 0.092 | 3.3926 | 2022 | 0.6856 | 0.3333 | 0.6856 | 0.8280 |
| 0.092 | 3.3960 | 2024 | 0.6260 | 0.4857 | 0.6260 | 0.7912 |
| 0.092 | 3.3993 | 2026 | 0.5806 | 0.3284 | 0.5806 | 0.7620 |
| 0.092 | 3.4027 | 2028 | 0.5680 | 0.2941 | 0.5680 | 0.7536 |
| 0.092 | 3.4060 | 2030 | 0.5698 | 0.2941 | 0.5698 | 0.7548 |
| 0.092 | 3.4094 | 2032 | 0.5745 | 0.2941 | 0.5745 | 0.7579 |
| 0.092 | 3.4128 | 2034 | 0.5914 | 0.2941 | 0.5914 | 0.7691 |
| 0.092 | 3.4161 | 2036 | 0.6062 | 0.4507 | 0.6062 | 0.7786 |
| 0.092 | 3.4195 | 2038 | 0.6268 | 0.4348 | 0.6268 | 0.7917 |
| 0.092 | 3.4228 | 2040 | 0.6366 | 0.4 | 0.6366 | 0.7979 |
| 0.092 | 3.4262 | 2042 | 0.6221 | 0.4507 | 0.6221 | 0.7887 |
| 0.092 | 3.4295 | 2044 | 0.5963 | 0.3284 | 0.5963 | 0.7722 |
| 0.092 | 3.4329 | 2046 | 0.5833 | 0.3284 | 0.5833 | 0.7637 |
| 0.092 | 3.4362 | 2048 | 0.6081 | 0.3077 | 0.6081 | 0.7798 |
| 0.092 | 3.4396 | 2050 | 0.6361 | 0.4348 | 0.6361 | 0.7976 |
| 0.092 | 3.4430 | 2052 | 0.6896 | 0.3284 | 0.6896 | 0.8304 |
| 0.092 | 3.4463 | 2054 | 0.7640 | 0.2727 | 0.7640 | 0.8740 |
| 0.092 | 3.4497 | 2056 | 0.7706 | 0.2727 | 0.7706 | 0.8778 |
| 0.092 | 3.4530 | 2058 | 0.7053 | 0.3284 | 0.7053 | 0.8398 |
| 0.092 | 3.4564 | 2060 | 0.6185 | 0.25 | 0.6185 | 0.7865 |
| 0.092 | 3.4597 | 2062 | 0.5853 | 0.2727 | 0.5853 | 0.7650 |
| 0.092 | 3.4631 | 2064 | 0.5803 | 0.2727 | 0.5803 | 0.7618 |
| 0.092 | 3.4664 | 2066 | 0.5945 | 0.2727 | 0.5945 | 0.7710 |
| 0.092 | 3.4698 | 2068 | 0.6387 | 0.3284 | 0.6387 | 0.7992 |
| 0.092 | 3.4732 | 2070 | 0.6630 | 0.3284 | 0.6630 | 0.8143 |
| 0.092 | 3.4765 | 2072 | 0.6375 | 0.3284 | 0.6375 | 0.7984 |
| 0.092 | 3.4799 | 2074 | 0.5854 | 0.3284 | 0.5854 | 0.7651 |
| 0.092 | 3.4832 | 2076 | 0.5662 | 0.3478 | 0.5662 | 0.7524 |
| 0.092 | 3.4866 | 2078 | 0.5544 | 0.25 | 0.5544 | 0.7446 |
| 0.092 | 3.4899 | 2080 | 0.5437 | 0.3284 | 0.5437 | 0.7374 |
| 0.092 | 3.4933 | 2082 | 0.5550 | 0.3284 | 0.5550 | 0.7450 |
| 0.092 | 3.4966 | 2084 | 0.5791 | 0.3478 | 0.5791 | 0.7610 |
| 0.092 | 3.5 | 2086 | 0.6408 | 0.2941 | 0.6408 | 0.8005 |
| 0.092 | 3.5034 | 2088 | 0.6867 | 0.3284 | 0.6867 | 0.8286 |
| 0.092 | 3.5067 | 2090 | 0.7078 | 0.3284 | 0.7078 | 0.8413 |
| 0.092 | 3.5101 | 2092 | 0.6917 | 0.3284 | 0.6917 | 0.8317 |
| 0.092 | 3.5134 | 2094 | 0.6460 | 0.2941 | 0.6460 | 0.8038 |
| 0.092 | 3.5168 | 2096 | 0.5947 | 0.2941 | 0.5947 | 0.7712 |
| 0.092 | 3.5201 | 2098 | 0.5559 | 0.3478 | 0.5559 | 0.7456 |
| 0.092 | 3.5235 | 2100 | 0.5353 | 0.3284 | 0.5353 | 0.7317 |
| 0.092 | 3.5268 | 2102 | 0.5318 | 0.2941 | 0.5318 | 0.7293 |
| 0.092 | 3.5302 | 2104 | 0.5373 | 0.2941 | 0.5373 | 0.7330 |
| 0.092 | 3.5336 | 2106 | 0.5583 | 0.4 | 0.5583 | 0.7472 |
| 0.092 | 3.5369 | 2108 | 0.6068 | 0.2941 | 0.6068 | 0.7790 |
| 0.092 | 3.5403 | 2110 | 0.6771 | 0.2941 | 0.6771 | 0.8228 |
| 0.092 | 3.5436 | 2112 | 0.7317 | 0.25 | 0.7317 | 0.8554 |
| 0.092 | 3.5470 | 2114 | 0.8156 | 0.25 | 0.8156 | 0.9031 |
| 0.092 | 3.5503 | 2116 | 0.8519 | 0.25 | 0.8519 | 0.9230 |
| 0.092 | 3.5537 | 2118 | 0.8113 | 0.25 | 0.8113 | 0.9007 |
| 0.092 | 3.5570 | 2120 | 0.7371 | 0.25 | 0.7371 | 0.8586 |
| 0.092 | 3.5604 | 2122 | 0.6608 | 0.2941 | 0.6608 | 0.8129 |
| 0.092 | 3.5638 | 2124 | 0.6238 | 0.3478 | 0.6238 | 0.7898 |
| 0.092 | 3.5671 | 2126 | 0.6315 | 0.3478 | 0.6315 | 0.7947 |
| 0.092 | 3.5705 | 2128 | 0.6580 | 0.2941 | 0.6580 | 0.8112 |
| 0.092 | 3.5738 | 2130 | 0.6824 | 0.2941 | 0.6824 | 0.8261 |
| 0.092 | 3.5772 | 2132 | 0.7281 | 0.2192 | 0.7281 | 0.8533 |
| 0.092 | 3.5805 | 2134 | 0.7522 | 0.2192 | 0.7522 | 0.8673 |
| 0.092 | 3.5839 | 2136 | 0.7832 | 0.25 | 0.7832 | 0.8850 |
| 0.092 | 3.5872 | 2138 | 0.7829 | 0.25 | 0.7829 | 0.8848 |
| 0.092 | 3.5906 | 2140 | 0.7233 | 0.2192 | 0.7233 | 0.8505 |
| 0.092 | 3.5940 | 2142 | 0.6647 | 0.3478 | 0.6647 | 0.8153 |
| 0.092 | 3.5973 | 2144 | 0.6292 | 0.4167 | 0.6292 | 0.7932 |
| 0.092 | 3.6007 | 2146 | 0.6318 | 0.4 | 0.6318 | 0.7949 |
| 0.092 | 3.6040 | 2148 | 0.6548 | 0.3478 | 0.6548 | 0.8092 |
| 0.092 | 3.6074 | 2150 | 0.7089 | 0.2941 | 0.7089 | 0.8419 |
| 0.092 | 3.6107 | 2152 | 0.7280 | 0.3284 | 0.7280 | 0.8532 |
| 0.092 | 3.6141 | 2154 | 0.7062 | 0.3284 | 0.7062 | 0.8404 |
| 0.092 | 3.6174 | 2156 | 0.6628 | 0.2941 | 0.6628 | 0.8141 |
| 0.092 | 3.6208 | 2158 | 0.6164 | 0.4 | 0.6164 | 0.7851 |
| 0.092 | 3.6242 | 2160 | 0.5864 | 0.4 | 0.5864 | 0.7658 |
| 0.092 | 3.6275 | 2162 | 0.5985 | 0.4 | 0.5985 | 0.7736 |
| 0.092 | 3.6309 | 2164 | 0.6373 | 0.3284 | 0.6373 | 0.7983 |
| 0.092 | 3.6342 | 2166 | 0.6763 | 0.3284 | 0.6763 | 0.8224 |
| 0.092 | 3.6376 | 2168 | 0.6693 | 0.3284 | 0.6693 | 0.8181 |
| 0.092 | 3.6409 | 2170 | 0.6483 | 0.4348 | 0.6483 | 0.8051 |
| 0.092 | 3.6443 | 2172 | 0.6250 | 0.4 | 0.6250 | 0.7905 |
| 0.092 | 3.6477 | 2174 | 0.6104 | 0.4 | 0.6104 | 0.7813 |
| 0.092 | 3.6510 | 2176 | 0.5827 | 0.4 | 0.5827 | 0.7634 |
| 0.092 | 3.6544 | 2178 | 0.5531 | 0.4167 | 0.5531 | 0.7437 |
| 0.092 | 3.6577 | 2180 | 0.5450 | 0.3836 | 0.5450 | 0.7383 |
| 0.092 | 3.6611 | 2182 | 0.5455 | 0.4167 | 0.5455 | 0.7386 |
| 0.092 | 3.6644 | 2184 | 0.5589 | 0.4507 | 0.5589 | 0.7476 |
| 0.092 | 3.6678 | 2186 | 0.5802 | 0.4 | 0.5802 | 0.7617 |
| 0.092 | 3.6711 | 2188 | 0.5798 | 0.4507 | 0.5798 | 0.7615 |
| 0.092 | 3.6745 | 2190 | 0.5896 | 0.4 | 0.5896 | 0.7678 |
| 0.092 | 3.6779 | 2192 | 0.6203 | 0.4348 | 0.6203 | 0.7876 |
| 0.092 | 3.6812 | 2194 | 0.6454 | 0.4348 | 0.6454 | 0.8034 |
| 0.092 | 3.6846 | 2196 | 0.6187 | 0.4348 | 0.6187 | 0.7866 |
| 0.092 | 3.6879 | 2198 | 0.5804 | 0.4348 | 0.5804 | 0.7618 |
| 0.092 | 3.6913 | 2200 | 0.5668 | 0.4 | 0.5668 | 0.7529 |
| 0.092 | 3.6946 | 2202 | 0.5517 | 0.4167 | 0.5517 | 0.7428 |
| 0.092 | 3.6980 | 2204 | 0.5402 | 0.3836 | 0.5402 | 0.7350 |
| 0.092 | 3.7013 | 2206 | 0.5422 | 0.2941 | 0.5422 | 0.7364 |
| 0.092 | 3.7047 | 2208 | 0.5453 | 0.2941 | 0.5453 | 0.7385 |
| 0.092 | 3.7081 | 2210 | 0.5588 | 0.3836 | 0.5588 | 0.7475 |
| 0.092 | 3.7114 | 2212 | 0.5855 | 0.4 | 0.5855 | 0.7652 |
| 0.092 | 3.7148 | 2214 | 0.6437 | 0.3824 | 0.6437 | 0.8023 |
| 0.092 | 3.7181 | 2216 | 0.6623 | 0.3824 | 0.6623 | 0.8138 |
| 0.092 | 3.7215 | 2218 | 0.6942 | 0.2727 | 0.6942 | 0.8332 |
| 0.092 | 3.7248 | 2220 | 0.7676 | 0.2727 | 0.7676 | 0.8761 |
| 0.092 | 3.7282 | 2222 | 0.7784 | 0.2727 | 0.7784 | 0.8823 |
| 0.092 | 3.7315 | 2224 | 0.7244 | 0.2727 | 0.7244 | 0.8511 |
| 0.092 | 3.7349 | 2226 | 0.6385 | 0.3284 | 0.6385 | 0.7991 |
| 0.092 | 3.7383 | 2228 | 0.5751 | 0.3824 | 0.5751 | 0.7583 |
| 0.092 | 3.7416 | 2230 | 0.5574 | 0.4 | 0.5574 | 0.7466 |
| 0.092 | 3.7450 | 2232 | 0.5700 | 0.3824 | 0.5700 | 0.7550 |
| 0.092 | 3.7483 | 2234 | 0.6255 | 0.3284 | 0.6255 | 0.7909 |
| 0.092 | 3.7517 | 2236 | 0.6856 | 0.3284 | 0.6856 | 0.8280 |
| 0.092 | 3.7550 | 2238 | 0.7352 | 0.3284 | 0.7352 | 0.8574 |
| 0.092 | 3.7584 | 2240 | 0.7213 | 0.3284 | 0.7213 | 0.8493 |
| 0.092 | 3.7617 | 2242 | 0.6579 | 0.3284 | 0.6579 | 0.8111 |
| 0.092 | 3.7651 | 2244 | 0.6166 | 0.4348 | 0.6166 | 0.7852 |
| 0.092 | 3.7685 | 2246 | 0.5997 | 0.4 | 0.5997 | 0.7744 |
| 0.092 | 3.7718 | 2248 | 0.5921 | 0.4348 | 0.5921 | 0.7695 |
| 0.092 | 3.7752 | 2250 | 0.5801 | 0.4348 | 0.5801 | 0.7617 |
| 0.092 | 3.7785 | 2252 | 0.5826 | 0.4348 | 0.5826 | 0.7633 |
| 0.092 | 3.7819 | 2254 | 0.5684 | 0.4348 | 0.5684 | 0.7539 |
| 0.092 | 3.7852 | 2256 | 0.5357 | 0.4 | 0.5357 | 0.7319 |
| 0.092 | 3.7886 | 2258 | 0.5263 | 0.3077 | 0.5263 | 0.7255 |
| 0.092 | 3.7919 | 2260 | 0.5298 | 0.4 | 0.5298 | 0.7279 |
| 0.092 | 3.7953 | 2262 | 0.5454 | 0.4 | 0.5454 | 0.7385 |
| 0.092 | 3.7987 | 2264 | 0.5648 | 0.4 | 0.5648 | 0.7515 |
| 0.092 | 3.8020 | 2266 | 0.5841 | 0.4 | 0.5841 | 0.7642 |
| 0.092 | 3.8054 | 2268 | 0.5970 | 0.4 | 0.5970 | 0.7726 |
| 0.092 | 3.8087 | 2270 | 0.5771 | 0.4 | 0.5771 | 0.7596 |
| 0.092 | 3.8121 | 2272 | 0.5685 | 0.4 | 0.5685 | 0.7540 |
| 0.092 | 3.8154 | 2274 | 0.5598 | 0.4167 | 0.5598 | 0.7482 |
| 0.092 | 3.8188 | 2276 | 0.5573 | 0.3836 | 0.5573 | 0.7465 |
| 0.092 | 3.8221 | 2278 | 0.5581 | 0.3836 | 0.5581 | 0.7470 |
| 0.092 | 3.8255 | 2280 | 0.5652 | 0.3836 | 0.5652 | 0.7518 |
| 0.092 | 3.8289 | 2282 | 0.5744 | 0.4 | 0.5744 | 0.7579 |
| 0.092 | 3.8322 | 2284 | 0.5837 | 0.4 | 0.5837 | 0.7640 |
| 0.092 | 3.8356 | 2286 | 0.5621 | 0.4 | 0.5621 | 0.7498 |
| 0.092 | 3.8389 | 2288 | 0.5246 | 0.4167 | 0.5246 | 0.7243 |
| 0.092 | 3.8423 | 2290 | 0.5000 | 0.3836 | 0.5000 | 0.7071 |
| 0.092 | 3.8456 | 2292 | 0.4906 | 0.3836 | 0.4906 | 0.7004 |
| 0.092 | 3.8490 | 2294 | 0.4829 | 0.3836 | 0.4829 | 0.6949 |
| 0.092 | 3.8523 | 2296 | 0.4878 | 0.4167 | 0.4878 | 0.6984 |
| 0.092 | 3.8557 | 2298 | 0.5210 | 0.3478 | 0.5210 | 0.7218 |
| 0.092 | 3.8591 | 2300 | 0.5586 | 0.4324 | 0.5586 | 0.7474 |
| 0.092 | 3.8624 | 2302 | 0.5805 | 0.4324 | 0.5805 | 0.7619 |
| 0.092 | 3.8658 | 2304 | 0.5689 | 0.4324 | 0.5689 | 0.7543 |
| 0.092 | 3.8691 | 2306 | 0.5362 | 0.4474 | 0.5362 | 0.7323 |
| 0.092 | 3.8725 | 2308 | 0.5128 | 0.4 | 0.5128 | 0.7161 |
| 0.092 | 3.8758 | 2310 | 0.5128 | 0.3684 | 0.5128 | 0.7161 |
| 0.092 | 3.8792 | 2312 | 0.5138 | 0.3684 | 0.5138 | 0.7168 |
| 0.092 | 3.8826 | 2314 | 0.5169 | 0.3684 | 0.5169 | 0.7190 |
| 0.092 | 3.8859 | 2316 | 0.5285 | 0.5 | 0.5285 | 0.7270 |
| 0.092 | 3.8893 | 2318 | 0.5340 | 0.5 | 0.5340 | 0.7307 |
| 0.092 | 3.8926 | 2320 | 0.5547 | 0.4 | 0.5547 | 0.7448 |
| 0.092 | 3.8960 | 2322 | 0.5733 | 0.4324 | 0.5733 | 0.7572 |
| 0.092 | 3.8993 | 2324 | 0.5985 | 0.4324 | 0.5985 | 0.7736 |
| 0.092 | 3.9027 | 2326 | 0.6124 | 0.3836 | 0.6124 | 0.7826 |
| 0.092 | 3.9060 | 2328 | 0.6310 | 0.3836 | 0.6310 | 0.7943 |
| 0.092 | 3.9094 | 2330 | 0.6231 | 0.2941 | 0.6231 | 0.7893 |
| 0.092 | 3.9128 | 2332 | 0.5753 | 0.2941 | 0.5753 | 0.7585 |
| 0.092 | 3.9161 | 2334 | 0.5452 | 0.2941 | 0.5452 | 0.7384 |
| 0.092 | 3.9195 | 2336 | 0.5124 | 0.2373 | 0.5124 | 0.7158 |
| 0.092 | 3.9228 | 2338 | 0.5016 | 0.2000 | 0.5016 | 0.7082 |
| 0.092 | 3.9262 | 2340 | 0.5000 | 0.2000 | 0.5000 | 0.7071 |
| 0.092 | 3.9295 | 2342 | 0.5082 | 0.3636 | 0.5082 | 0.7129 |
| 0.092 | 3.9329 | 2344 | 0.5247 | 0.3143 | 0.5247 | 0.7244 |
| 0.092 | 3.9362 | 2346 | 0.5552 | 0.3478 | 0.5552 | 0.7451 |
| 0.092 | 3.9396 | 2348 | 0.5732 | 0.3478 | 0.5732 | 0.7571 |
| 0.092 | 3.9430 | 2350 | 0.5683 | 0.3478 | 0.5683 | 0.7539 |
| 0.092 | 3.9463 | 2352 | 0.5436 | 0.4507 | 0.5436 | 0.7373 |
| 0.092 | 3.9497 | 2354 | 0.5337 | 0.4507 | 0.5337 | 0.7306 |
| 0.092 | 3.9530 | 2356 | 0.5233 | 0.3836 | 0.5233 | 0.7234 |
| 0.092 | 3.9564 | 2358 | 0.5198 | 0.4507 | 0.5198 | 0.7210 |
| 0.092 | 3.9597 | 2360 | 0.5245 | 0.3143 | 0.5245 | 0.7243 |
| 0.092 | 3.9631 | 2362 | 0.5280 | 0.3478 | 0.5280 | 0.7266 |
| 0.092 | 3.9664 | 2364 | 0.5297 | 0.3478 | 0.5297 | 0.7278 |
| 0.092 | 3.9698 | 2366 | 0.5434 | 0.3478 | 0.5434 | 0.7372 |
| 0.092 | 3.9732 | 2368 | 0.5620 | 0.3478 | 0.5620 | 0.7496 |
| 0.092 | 3.9765 | 2370 | 0.5703 | 0.3478 | 0.5703 | 0.7552 |
| 0.092 | 3.9799 | 2372 | 0.5487 | 0.3478 | 0.5487 | 0.7408 |
| 0.092 | 3.9832 | 2374 | 0.5279 | 0.3478 | 0.5279 | 0.7266 |
| 0.092 | 3.9866 | 2376 | 0.5172 | 0.3478 | 0.5172 | 0.7192 |
| 0.092 | 3.9899 | 2378 | 0.5079 | 0.3662 | 0.5079 | 0.7127 |
| 0.092 | 3.9933 | 2380 | 0.5069 | 0.3662 | 0.5069 | 0.7120 |
| 0.092 | 3.9966 | 2382 | 0.5123 | 0.3662 | 0.5123 | 0.7157 |
| 0.092 | 4.0 | 2384 | 0.5213 | 0.3662 | 0.5213 | 0.7220 |
| 0.092 | 4.0034 | 2386 | 0.5413 | 0.3143 | 0.5413 | 0.7357 |
| 0.092 | 4.0067 | 2388 | 0.5601 | 0.4324 | 0.5601 | 0.7484 |
| 0.092 | 4.0101 | 2390 | 0.5716 | 0.4324 | 0.5716 | 0.7560 |
| 0.092 | 4.0134 | 2392 | 0.5758 | 0.4 | 0.5758 | 0.7588 |
| 0.092 | 4.0168 | 2394 | 0.5888 | 0.4 | 0.5888 | 0.7674 |
| 0.092 | 4.0201 | 2396 | 0.5849 | 0.4 | 0.5849 | 0.7648 |
| 0.092 | 4.0235 | 2398 | 0.5650 | 0.3143 | 0.5650 | 0.7517 |
| 0.092 | 4.0268 | 2400 | 0.5514 | 0.3662 | 0.5514 | 0.7426 |
| 0.092 | 4.0302 | 2402 | 0.5516 | 0.3143 | 0.5516 | 0.7427 |
| 0.092 | 4.0336 | 2404 | 0.5495 | 0.3143 | 0.5495 | 0.7413 |
| 0.092 | 4.0369 | 2406 | 0.5710 | 0.3143 | 0.5710 | 0.7557 |
| 0.092 | 4.0403 | 2408 | 0.6157 | 0.4324 | 0.6157 | 0.7847 |
| 0.092 | 4.0436 | 2410 | 0.6373 | 0.4658 | 0.6373 | 0.7983 |
| 0.092 | 4.0470 | 2412 | 0.6489 | 0.4658 | 0.6489 | 0.8056 |
| 0.092 | 4.0503 | 2414 | 0.6342 | 0.3824 | 0.6342 | 0.7964 |
| 0.092 | 4.0537 | 2416 | 0.6222 | 0.3478 | 0.6222 | 0.7888 |
| 0.092 | 4.0570 | 2418 | 0.6157 | 0.3143 | 0.6157 | 0.7847 |
| 0.092 | 4.0604 | 2420 | 0.6093 | 0.3662 | 0.6093 | 0.7806 |
| 0.092 | 4.0638 | 2422 | 0.6020 | 0.3836 | 0.6020 | 0.7759 |
| 0.092 | 4.0671 | 2424 | 0.5928 | 0.3836 | 0.5928 | 0.7699 |
| 0.092 | 4.0705 | 2426 | 0.5939 | 0.3836 | 0.5939 | 0.7706 |
| 0.092 | 4.0738 | 2428 | 0.6068 | 0.3333 | 0.6068 | 0.7789 |
| 0.092 | 4.0772 | 2430 | 0.6469 | 0.3143 | 0.6469 | 0.8043 |
| 0.092 | 4.0805 | 2432 | 0.6923 | 0.3824 | 0.6923 | 0.8320 |
| 0.092 | 4.0839 | 2434 | 0.7356 | 0.4658 | 0.7356 | 0.8577 |
| 0.092 | 4.0872 | 2436 | 0.7457 | 0.4167 | 0.7457 | 0.8635 |
| 0.092 | 4.0906 | 2438 | 0.7767 | 0.3662 | 0.7767 | 0.8813 |
| 0.092 | 4.0940 | 2440 | 0.7830 | 0.3662 | 0.7830 | 0.8849 |
| 0.092 | 4.0973 | 2442 | 0.7750 | 0.3662 | 0.7750 | 0.8803 |
| 0.092 | 4.1007 | 2444 | 0.7121 | 0.2727 | 0.7121 | 0.8439 |
| 0.092 | 4.1040 | 2446 | 0.6388 | 0.3824 | 0.6388 | 0.7992 |
| 0.092 | 4.1074 | 2448 | 0.6026 | 0.3478 | 0.6026 | 0.7763 |
| 0.092 | 4.1107 | 2450 | 0.5983 | 0.3143 | 0.5983 | 0.7735 |
| 0.092 | 4.1141 | 2452 | 0.6312 | 0.3478 | 0.6312 | 0.7945 |
| 0.092 | 4.1174 | 2454 | 0.6583 | 0.3824 | 0.6583 | 0.8114 |
| 0.092 | 4.1208 | 2456 | 0.6799 | 0.4167 | 0.6799 | 0.8246 |
| 0.092 | 4.1242 | 2458 | 0.6854 | 0.4167 | 0.6854 | 0.8279 |
| 0.092 | 4.1275 | 2460 | 0.6643 | 0.3824 | 0.6643 | 0.8150 |
| 0.092 | 4.1309 | 2462 | 0.6148 | 0.3824 | 0.6148 | 0.7841 |
| 0.092 | 4.1342 | 2464 | 0.5969 | 0.3143 | 0.5969 | 0.7726 |
| 0.092 | 4.1376 | 2466 | 0.6102 | 0.3478 | 0.6102 | 0.7812 |
| 0.092 | 4.1409 | 2468 | 0.6505 | 0.4658 | 0.6505 | 0.8066 |
| 0.092 | 4.1443 | 2470 | 0.7117 | 0.4658 | 0.7117 | 0.8436 |
| 0.092 | 4.1477 | 2472 | 0.7757 | 0.3662 | 0.7757 | 0.8807 |
| 0.092 | 4.1510 | 2474 | 0.7849 | 0.3662 | 0.7849 | 0.8859 |
| 0.092 | 4.1544 | 2476 | 0.7490 | 0.3662 | 0.7490 | 0.8654 |
| 0.092 | 4.1577 | 2478 | 0.6963 | 0.3662 | 0.6963 | 0.8344 |
| 0.092 | 4.1611 | 2480 | 0.6535 | 0.3824 | 0.6535 | 0.8084 |
| 0.092 | 4.1644 | 2482 | 0.5933 | 0.3824 | 0.5933 | 0.7702 |
| 0.092 | 4.1678 | 2484 | 0.5469 | 0.3226 | 0.5469 | 0.7395 |
| 0.092 | 4.1711 | 2486 | 0.5345 | 0.3226 | 0.5345 | 0.7311 |
| 0.092 | 4.1745 | 2488 | 0.5417 | 0.3226 | 0.5417 | 0.7360 |
| 0.092 | 4.1779 | 2490 | 0.5434 | 0.3226 | 0.5434 | 0.7371 |
| 0.092 | 4.1812 | 2492 | 0.5535 | 0.3226 | 0.5535 | 0.7440 |
| 0.092 | 4.1846 | 2494 | 0.5700 | 0.25 | 0.5700 | 0.7550 |
| 0.092 | 4.1879 | 2496 | 0.5813 | 0.4167 | 0.5813 | 0.7625 |
| 0.092 | 4.1913 | 2498 | 0.5980 | 0.4167 | 0.5980 | 0.7733 |
| 0.0819 | 4.1946 | 2500 | 0.6133 | 0.4167 | 0.6133 | 0.7831 |
| 0.0819 | 4.1980 | 2502 | 0.6060 | 0.3662 | 0.6060 | 0.7785 |
| 0.0819 | 4.2013 | 2504 | 0.5788 | 0.4167 | 0.5788 | 0.7608 |
| 0.0819 | 4.2047 | 2506 | 0.5689 | 0.4179 | 0.5689 | 0.7542 |
| 0.0819 | 4.2081 | 2508 | 0.5750 | 0.4167 | 0.5750 | 0.7583 |
| 0.0819 | 4.2114 | 2510 | 0.5860 | 0.4 | 0.5860 | 0.7655 |
| 0.0819 | 4.2148 | 2512 | 0.5973 | 0.4 | 0.5973 | 0.7729 |
| 0.0819 | 4.2181 | 2514 | 0.6217 | 0.3478 | 0.6217 | 0.7885 |
| 0.0819 | 4.2215 | 2516 | 0.6820 | 0.3824 | 0.6820 | 0.8258 |
| 0.0819 | 4.2248 | 2518 | 0.7541 | 0.3662 | 0.7541 | 0.8684 |
| 0.0819 | 4.2282 | 2520 | 0.7672 | 0.3662 | 0.7672 | 0.8759 |
| 0.0819 | 4.2315 | 2522 | 0.7217 | 0.3662 | 0.7217 | 0.8495 |
| 0.0819 | 4.2349 | 2524 | 0.6429 | 0.3824 | 0.6429 | 0.8018 |
| 0.0819 | 4.2383 | 2526 | 0.5900 | 0.3824 | 0.5900 | 0.7681 |
| 0.0819 | 4.2416 | 2528 | 0.5627 | 0.3607 | 0.5627 | 0.7502 |
| 0.0819 | 4.2450 | 2530 | 0.5662 | 0.3226 | 0.5662 | 0.7524 |
| 0.0819 | 4.2483 | 2532 | 0.5874 | 0.4507 | 0.5874 | 0.7664 |
| 0.0819 | 4.2517 | 2534 | 0.6348 | 0.4 | 0.6349 | 0.7968 |
| 0.0819 | 4.2550 | 2536 | 0.6678 | 0.4348 | 0.6678 | 0.8172 |
| 0.0819 | 4.2584 | 2538 | 0.7038 | 0.4658 | 0.7038 | 0.8389 |
| 0.0819 | 4.2617 | 2540 | 0.6885 | 0.3824 | 0.6885 | 0.8298 |
| 0.0819 | 4.2651 | 2542 | 0.6363 | 0.3824 | 0.6363 | 0.7977 |
| 0.0819 | 4.2685 | 2544 | 0.5799 | 0.3077 | 0.5799 | 0.7615 |
| 0.0819 | 4.2718 | 2546 | 0.5521 | 0.3226 | 0.5521 | 0.7430 |
| 0.0819 | 4.2752 | 2548 | 0.5437 | 0.3226 | 0.5437 | 0.7374 |
| 0.0819 | 4.2785 | 2550 | 0.5481 | 0.3226 | 0.5481 | 0.7403 |
| 0.0819 | 4.2819 | 2552 | 0.5677 | 0.3636 | 0.5677 | 0.7535 |
| 0.0819 | 4.2852 | 2554 | 0.5937 | 0.4 | 0.5937 | 0.7705 |
| 0.0819 | 4.2886 | 2556 | 0.6343 | 0.3478 | 0.6343 | 0.7964 |
| 0.0819 | 4.2919 | 2558 | 0.6406 | 0.3478 | 0.6406 | 0.8004 |
| 0.0819 | 4.2953 | 2560 | 0.6467 | 0.3478 | 0.6467 | 0.8042 |
| 0.0819 | 4.2987 | 2562 | 0.6414 | 0.3478 | 0.6414 | 0.8009 |
| 0.0819 | 4.3020 | 2564 | 0.6244 | 0.4 | 0.6244 | 0.7902 |
| 0.0819 | 4.3054 | 2566 | 0.5995 | 0.4507 | 0.5995 | 0.7743 |
| 0.0819 | 4.3087 | 2568 | 0.5853 | 0.4167 | 0.5853 | 0.7650 |
| 0.0819 | 4.3121 | 2570 | 0.5862 | 0.4167 | 0.5862 | 0.7656 |
| 0.0819 | 4.3154 | 2572 | 0.5948 | 0.4167 | 0.5948 | 0.7712 |
| 0.0819 | 4.3188 | 2574 | 0.5948 | 0.4167 | 0.5948 | 0.7712 |
| 0.0819 | 4.3221 | 2576 | 0.5974 | 0.4167 | 0.5974 | 0.7729 |
| 0.0819 | 4.3255 | 2578 | 0.6068 | 0.4507 | 0.6068 | 0.7790 |
| 0.0819 | 4.3289 | 2580 | 0.5973 | 0.4507 | 0.5973 | 0.7728 |
| 0.0819 | 4.3322 | 2582 | 0.5957 | 0.4507 | 0.5957 | 0.7718 |
| 0.0819 | 4.3356 | 2584 | 0.5984 | 0.4 | 0.5984 | 0.7736 |
| 0.0819 | 4.3389 | 2586 | 0.5993 | 0.4 | 0.5993 | 0.7742 |
| 0.0819 | 4.3423 | 2588 | 0.6187 | 0.4 | 0.6187 | 0.7866 |
| 0.0819 | 4.3456 | 2590 | 0.6554 | 0.3478 | 0.6554 | 0.8095 |
| 0.0819 | 4.3490 | 2592 | 0.6720 | 0.3478 | 0.6720 | 0.8197 |
| 0.0819 | 4.3523 | 2594 | 0.6563 | 0.3478 | 0.6563 | 0.8102 |
| 0.0819 | 4.3557 | 2596 | 0.6356 | 0.4 | 0.6356 | 0.7973 |
| 0.0819 | 4.3591 | 2598 | 0.6368 | 0.4 | 0.6368 | 0.7980 |
| 0.0819 | 4.3624 | 2600 | 0.6153 | 0.3836 | 0.6153 | 0.7844 |
| 0.0819 | 4.3658 | 2602 | 0.6026 | 0.3836 | 0.6026 | 0.7763 |
| 0.0819 | 4.3691 | 2604 | 0.5958 | 0.3836 | 0.5958 | 0.7719 |
| 0.0819 | 4.3725 | 2606 | 0.6017 | 0.4167 | 0.6017 | 0.7757 |
| 0.0819 | 4.3758 | 2608 | 0.6281 | 0.3478 | 0.6281 | 0.7925 |
| 0.0819 | 4.3792 | 2610 | 0.6278 | 0.3478 | 0.6278 | 0.7923 |
| 0.0819 | 4.3826 | 2612 | 0.6343 | 0.2941 | 0.6343 | 0.7965 |
| 0.0819 | 4.3859 | 2614 | 0.6611 | 0.2941 | 0.6611 | 0.8131 |
| 0.0819 | 4.3893 | 2616 | 0.6837 | 0.3284 | 0.6837 | 0.8269 |
| 0.0819 | 4.3926 | 2618 | 0.7133 | 0.3284 | 0.7133 | 0.8446 |
| 0.0819 | 4.3960 | 2620 | 0.7080 | 0.3284 | 0.7080 | 0.8414 |
| 0.0819 | 4.3993 | 2622 | 0.6684 | 0.2941 | 0.6684 | 0.8175 |
| 0.0819 | 4.4027 | 2624 | 0.6221 | 0.3478 | 0.6221 | 0.7887 |
| 0.0819 | 4.4060 | 2626 | 0.5933 | 0.3284 | 0.5933 | 0.7703 |
| 0.0819 | 4.4094 | 2628 | 0.5978 | 0.2941 | 0.5978 | 0.7732 |
| 0.0819 | 4.4128 | 2630 | 0.6111 | 0.2941 | 0.6111 | 0.7818 |
| 0.0819 | 4.4161 | 2632 | 0.6223 | 0.4167 | 0.6223 | 0.7889 |
| 0.0819 | 4.4195 | 2634 | 0.6408 | 0.3662 | 0.6408 | 0.8005 |
| 0.0819 | 4.4228 | 2636 | 0.6523 | 0.3478 | 0.6523 | 0.8077 |
| 0.0819 | 4.4262 | 2638 | 0.6738 | 0.4324 | 0.6738 | 0.8209 |
| 0.0819 | 4.4295 | 2640 | 0.6911 | 0.3836 | 0.6911 | 0.8313 |
| 0.0819 | 4.4329 | 2642 | 0.6719 | 0.4658 | 0.6719 | 0.8197 |
| 0.0819 | 4.4362 | 2644 | 0.6359 | 0.3478 | 0.6359 | 0.7975 |
| 0.0819 | 4.4396 | 2646 | 0.6007 | 0.3077 | 0.6007 | 0.7751 |
| 0.0819 | 4.4430 | 2648 | 0.5755 | 0.3284 | 0.5755 | 0.7586 |
| 0.0819 | 4.4463 | 2650 | 0.5687 | 0.2857 | 0.5687 | 0.7541 |
| 0.0819 | 4.4497 | 2652 | 0.5705 | 0.2857 | 0.5705 | 0.7553 |
| 0.0819 | 4.4530 | 2654 | 0.5776 | 0.2857 | 0.5776 | 0.7600 |
| 0.0819 | 4.4564 | 2656 | 0.5995 | 0.2941 | 0.5995 | 0.7743 |
| 0.0819 | 4.4597 | 2658 | 0.6170 | 0.2941 | 0.6170 | 0.7855 |
| 0.0819 | 4.4631 | 2660 | 0.6210 | 0.2941 | 0.6210 | 0.7881 |
| 0.0819 | 4.4664 | 2662 | 0.6218 | 0.2941 | 0.6218 | 0.7886 |
| 0.0819 | 4.4698 | 2664 | 0.6205 | 0.2941 | 0.6205 | 0.7877 |
| 0.0819 | 4.4732 | 2666 | 0.6136 | 0.2941 | 0.6136 | 0.7833 |
| 0.0819 | 4.4765 | 2668 | 0.6068 | 0.2941 | 0.6068 | 0.7790 |
| 0.0819 | 4.4799 | 2670 | 0.6058 | 0.2727 | 0.6058 | 0.7784 |
| 0.0819 | 4.4832 | 2672 | 0.6058 | 0.2727 | 0.6058 | 0.7783 |
| 0.0819 | 4.4866 | 2674 | 0.5924 | 0.2258 | 0.5924 | 0.7697 |
| 0.0819 | 4.4899 | 2676 | 0.5841 | 0.2258 | 0.5841 | 0.7643 |
| 0.0819 | 4.4933 | 2678 | 0.5810 | 0.2258 | 0.5810 | 0.7622 |
| 0.0819 | 4.4966 | 2680 | 0.5822 | 0.2258 | 0.5822 | 0.7630 |
| 0.0819 | 4.5 | 2682 | 0.5848 | 0.2388 | 0.5848 | 0.7647 |
| 0.0819 | 4.5034 | 2684 | 0.5879 | 0.2388 | 0.5879 | 0.7668 |
| 0.0819 | 4.5067 | 2686 | 0.5810 | 0.2388 | 0.5810 | 0.7623 |
| 0.0819 | 4.5101 | 2688 | 0.5707 | 0.2388 | 0.5707 | 0.7555 |
| 0.0819 | 4.5134 | 2690 | 0.5653 | 0.1739 | 0.5653 | 0.7518 |
| 0.0819 | 4.5168 | 2692 | 0.5627 | 0.2388 | 0.5627 | 0.7502 |
| 0.0819 | 4.5201 | 2694 | 0.5735 | 0.2388 | 0.5735 | 0.7573 |
| 0.0819 | 4.5235 | 2696 | 0.5857 | 0.2388 | 0.5857 | 0.7653 |
| 0.0819 | 4.5268 | 2698 | 0.6050 | 0.1739 | 0.6050 | 0.7778 |
| 0.0819 | 4.5302 | 2700 | 0.6128 | 0.2703 | 0.6128 | 0.7828 |
| 0.0819 | 4.5336 | 2702 | 0.6194 | 0.3662 | 0.6194 | 0.7870 |
| 0.0819 | 4.5369 | 2704 | 0.6141 | 0.3662 | 0.6141 | 0.7836 |
| 0.0819 | 4.5403 | 2706 | 0.6005 | 0.2727 | 0.6005 | 0.7750 |
| 0.0819 | 4.5436 | 2708 | 0.5872 | 0.1493 | 0.5872 | 0.7663 |
| 0.0819 | 4.5470 | 2710 | 0.5774 | 0.1739 | 0.5774 | 0.7599 |
| 0.0819 | 4.5503 | 2712 | 0.5775 | 0.1739 | 0.5775 | 0.7600 |
| 0.0819 | 4.5537 | 2714 | 0.5942 | 0.3226 | 0.5942 | 0.7708 |
| 0.0819 | 4.5570 | 2716 | 0.5818 | 0.1231 | 0.5818 | 0.7627 |
| 0.0819 | 4.5604 | 2718 | 0.5639 | 0.1739 | 0.5639 | 0.7509 |
| 0.0819 | 4.5638 | 2720 | 0.5555 | 0.1176 | 0.5555 | 0.7453 |
| 0.0819 | 4.5671 | 2722 | 0.5565 | 0.2727 | 0.5565 | 0.7460 |
| 0.0819 | 4.5705 | 2724 | 0.5618 | 0.2727 | 0.5618 | 0.7495 |
| 0.0819 | 4.5738 | 2726 | 0.5680 | 0.2727 | 0.5680 | 0.7537 |
| 0.0819 | 4.5772 | 2728 | 0.5690 | 0.2727 | 0.5690 | 0.7543 |
| 0.0819 | 4.5805 | 2730 | 0.5732 | 0.2727 | 0.5732 | 0.7571 |
| 0.0819 | 4.5839 | 2732 | 0.5722 | 0.2727 | 0.5722 | 0.7564 |
| 0.0819 | 4.5872 | 2734 | 0.5695 | 0.2727 | 0.5695 | 0.7547 |
| 0.0819 | 4.5906 | 2736 | 0.5631 | 0.2727 | 0.5631 | 0.7504 |
| 0.0819 | 4.5940 | 2738 | 0.5594 | 0.2258 | 0.5594 | 0.7479 |
| 0.0819 | 4.5973 | 2740 | 0.5561 | 0.2105 | 0.5561 | 0.7457 |
| 0.0819 | 4.6007 | 2742 | 0.5570 | 0.2105 | 0.5570 | 0.7463 |
| 0.0819 | 4.6040 | 2744 | 0.5635 | 0.2105 | 0.5635 | 0.7507 |
| 0.0819 | 4.6074 | 2746 | 0.5749 | 0.2105 | 0.5749 | 0.7582 |
| 0.0819 | 4.6107 | 2748 | 0.5822 | 0.2388 | 0.5822 | 0.7630 |
| 0.0819 | 4.6141 | 2750 | 0.5924 | 0.2388 | 0.5924 | 0.7697 |
| 0.0819 | 4.6174 | 2752 | 0.5977 | 0.2388 | 0.5977 | 0.7731 |
| 0.0819 | 4.6208 | 2754 | 0.5965 | 0.2388 | 0.5965 | 0.7723 |
| 0.0819 | 4.6242 | 2756 | 0.5917 | 0.2388 | 0.5917 | 0.7692 |
| 0.0819 | 4.6275 | 2758 | 0.5832 | 0.2388 | 0.5832 | 0.7637 |
| 0.0819 | 4.6309 | 2760 | 0.5782 | 0.2258 | 0.5782 | 0.7604 |
| 0.0819 | 4.6342 | 2762 | 0.5779 | 0.2388 | 0.5779 | 0.7602 |
| 0.0819 | 4.6376 | 2764 | 0.5769 | 0.2727 | 0.5769 | 0.7595 |
| 0.0819 | 4.6409 | 2766 | 0.5654 | 0.2623 | 0.5654 | 0.7519 |
| 0.0819 | 4.6443 | 2768 | 0.5550 | 0.2623 | 0.5550 | 0.7450 |
| 0.0819 | 4.6477 | 2770 | 0.5495 | 0.2623 | 0.5495 | 0.7413 |
| 0.0819 | 4.6510 | 2772 | 0.5476 | 0.2623 | 0.5476 | 0.7400 |
| 0.0819 | 4.6544 | 2774 | 0.5500 | 0.2727 | 0.5500 | 0.7416 |
| 0.0819 | 4.6577 | 2776 | 0.5523 | 0.2727 | 0.5523 | 0.7432 |
| 0.0819 | 4.6611 | 2778 | 0.5641 | 0.2727 | 0.5641 | 0.7510 |
| 0.0819 | 4.6644 | 2780 | 0.5838 | 0.2727 | 0.5838 | 0.7641 |
| 0.0819 | 4.6678 | 2782 | 0.5980 | 0.2727 | 0.5980 | 0.7733 |
| 0.0819 | 4.6711 | 2784 | 0.6094 | 0.3662 | 0.6094 | 0.7807 |
| 0.0819 | 4.6745 | 2786 | 0.6063 | 0.3662 | 0.6063 | 0.7787 |
| 0.0819 | 4.6779 | 2788 | 0.5868 | 0.2727 | 0.5868 | 0.7661 |
| 0.0819 | 4.6812 | 2790 | 0.5589 | 0.2727 | 0.5589 | 0.7476 |
| 0.0819 | 4.6846 | 2792 | 0.5478 | 0.2727 | 0.5478 | 0.7401 |
| 0.0819 | 4.6879 | 2794 | 0.5469 | 0.2727 | 0.5469 | 0.7395 |
| 0.0819 | 4.6913 | 2796 | 0.5449 | 0.2727 | 0.5449 | 0.7382 |
| 0.0819 | 4.6946 | 2798 | 0.5472 | 0.2727 | 0.5472 | 0.7398 |
| 0.0819 | 4.6980 | 2800 | 0.5577 | 0.2727 | 0.5577 | 0.7468 |
| 0.0819 | 4.7013 | 2802 | 0.5650 | 0.2727 | 0.5650 | 0.7517 |
| 0.0819 | 4.7047 | 2804 | 0.5723 | 0.2727 | 0.5723 | 0.7565 |
| 0.0819 | 4.7081 | 2806 | 0.5743 | 0.2727 | 0.5743 | 0.7578 |
| 0.0819 | 4.7114 | 2808 | 0.5834 | 0.2727 | 0.5834 | 0.7638 |
| 0.0819 | 4.7148 | 2810 | 0.5880 | 0.3077 | 0.5880 | 0.7668 |
| 0.0819 | 4.7181 | 2812 | 0.5932 | 0.3077 | 0.5932 | 0.7702 |
| 0.0819 | 4.7215 | 2814 | 0.5800 | 0.2727 | 0.5800 | 0.7616 |
| 0.0819 | 4.7248 | 2816 | 0.5709 | 0.2388 | 0.5709 | 0.7556 |
| 0.0819 | 4.7282 | 2818 | 0.5694 | 0.2388 | 0.5694 | 0.7546 |
| 0.0819 | 4.7315 | 2820 | 0.5770 | 0.2941 | 0.5770 | 0.7596 |
| 0.0819 | 4.7349 | 2822 | 0.5843 | 0.2941 | 0.5843 | 0.7644 |
| 0.0819 | 4.7383 | 2824 | 0.5814 | 0.2941 | 0.5814 | 0.7625 |
| 0.0819 | 4.7416 | 2826 | 0.5796 | 0.2941 | 0.5796 | 0.7613 |
| 0.0819 | 4.7450 | 2828 | 0.5852 | 0.2388 | 0.5852 | 0.7650 |
| 0.0819 | 4.7483 | 2830 | 0.6056 | 0.3662 | 0.6056 | 0.7782 |
| 0.0819 | 4.7517 | 2832 | 0.6361 | 0.3478 | 0.6361 | 0.7975 |
| 0.0819 | 4.7550 | 2834 | 0.6305 | 0.3478 | 0.6305 | 0.7940 |
| 0.0819 | 4.7584 | 2836 | 0.5967 | 0.3077 | 0.5967 | 0.7725 |
| 0.0819 | 4.7617 | 2838 | 0.5821 | 0.3077 | 0.5821 | 0.7630 |
| 0.0819 | 4.7651 | 2840 | 0.5846 | 0.25 | 0.5846 | 0.7646 |
| 0.0819 | 4.7685 | 2842 | 0.5813 | 0.3077 | 0.5813 | 0.7624 |
| 0.0819 | 4.7718 | 2844 | 0.5887 | 0.2857 | 0.5887 | 0.7672 |
| 0.0819 | 4.7752 | 2846 | 0.5785 | 0.3438 | 0.5785 | 0.7606 |
| 0.0819 | 4.7785 | 2848 | 0.5670 | 0.3077 | 0.5670 | 0.7530 |
| 0.0819 | 4.7819 | 2850 | 0.5695 | 0.3000 | 0.5695 | 0.7547 |
| 0.0819 | 4.7852 | 2852 | 0.6013 | 0.3284 | 0.6013 | 0.7754 |
| 0.0819 | 4.7886 | 2854 | 0.6494 | 0.3284 | 0.6494 | 0.8058 |
| 0.0819 | 4.7919 | 2856 | 0.6938 | 0.2727 | 0.6938 | 0.8329 |
| 0.0819 | 4.7953 | 2858 | 0.7004 | 0.2727 | 0.7004 | 0.8369 |
| 0.0819 | 4.7987 | 2860 | 0.6851 | 0.3284 | 0.6851 | 0.8277 |
| 0.0819 | 4.8020 | 2862 | 0.6374 | 0.3284 | 0.6374 | 0.7984 |
| 0.0819 | 4.8054 | 2864 | 0.5794 | 0.4 | 0.5794 | 0.7612 |
| 0.0819 | 4.8087 | 2866 | 0.5444 | 0.25 | 0.5444 | 0.7378 |
| 0.0819 | 4.8121 | 2868 | 0.5265 | 0.2105 | 0.5265 | 0.7256 |
| 0.0819 | 4.8154 | 2870 | 0.5192 | 0.2105 | 0.5192 | 0.7206 |
| 0.0819 | 4.8188 | 2872 | 0.5200 | 0.2105 | 0.5200 | 0.7211 |
| 0.0819 | 4.8221 | 2874 | 0.5285 | 0.25 | 0.5285 | 0.7270 |
| 0.0819 | 4.8255 | 2876 | 0.5448 | 0.2727 | 0.5448 | 0.7381 |
| 0.0819 | 4.8289 | 2878 | 0.5776 | 0.3478 | 0.5776 | 0.7600 |
| 0.0819 | 4.8322 | 2880 | 0.5861 | 0.3284 | 0.5861 | 0.7655 |
| 0.0819 | 4.8356 | 2882 | 0.5795 | 0.3284 | 0.5795 | 0.7612 |
| 0.0819 | 4.8389 | 2884 | 0.5562 | 0.3143 | 0.5562 | 0.7458 |
| 0.0819 | 4.8423 | 2886 | 0.5275 | 0.2727 | 0.5275 | 0.7263 |
| 0.0819 | 4.8456 | 2888 | 0.5135 | 0.2105 | 0.5135 | 0.7166 |
| 0.0819 | 4.8490 | 2890 | 0.5106 | 0.2105 | 0.5106 | 0.7146 |
| 0.0819 | 4.8523 | 2892 | 0.5149 | 0.2105 | 0.5149 | 0.7176 |
| 0.0819 | 4.8557 | 2894 | 0.5167 | 0.2759 | 0.5167 | 0.7188 |
| 0.0819 | 4.8591 | 2896 | 0.5124 | 0.2857 | 0.5124 | 0.7158 |
| 0.0819 | 4.8624 | 2898 | 0.5161 | 0.2258 | 0.5161 | 0.7184 |
| 0.0819 | 4.8658 | 2900 | 0.5299 | 0.2388 | 0.5299 | 0.7279 |
| 0.0819 | 4.8691 | 2902 | 0.5706 | 0.3284 | 0.5706 | 0.7554 |
| 0.0819 | 4.8725 | 2904 | 0.6002 | 0.4167 | 0.6002 | 0.7747 |
| 0.0819 | 4.8758 | 2906 | 0.5953 | 0.3284 | 0.5953 | 0.7715 |
| 0.0819 | 4.8792 | 2908 | 0.5767 | 0.3284 | 0.5767 | 0.7594 |
| 0.0819 | 4.8826 | 2910 | 0.5512 | 0.2857 | 0.5512 | 0.7424 |
| 0.0819 | 4.8859 | 2912 | 0.5253 | 0.2727 | 0.5253 | 0.7248 |
| 0.0819 | 4.8893 | 2914 | 0.5110 | 0.2727 | 0.5110 | 0.7149 |
| 0.0819 | 4.8926 | 2916 | 0.5018 | 0.2388 | 0.5018 | 0.7084 |
| 0.0819 | 4.8960 | 2918 | 0.5000 | 0.2727 | 0.5000 | 0.7071 |
| 0.0819 | 4.8993 | 2920 | 0.5041 | 0.2727 | 0.5041 | 0.7100 |
| 0.0819 | 4.9027 | 2922 | 0.5225 | 0.2727 | 0.5225 | 0.7228 |
| 0.0819 | 4.9060 | 2924 | 0.5371 | 0.3143 | 0.5371 | 0.7329 |
| 0.0819 | 4.9094 | 2926 | 0.5499 | 0.3478 | 0.5499 | 0.7415 |
| 0.0819 | 4.9128 | 2928 | 0.5477 | 0.3478 | 0.5477 | 0.7401 |
| 0.0819 | 4.9161 | 2930 | 0.5252 | 0.2727 | 0.5252 | 0.7247 |
| 0.0819 | 4.9195 | 2932 | 0.5073 | 0.3284 | 0.5073 | 0.7123 |
| 0.0819 | 4.9228 | 2934 | 0.5025 | 0.2941 | 0.5025 | 0.7089 |
| 0.0819 | 4.9262 | 2936 | 0.5035 | 0.2941 | 0.5035 | 0.7096 |
| 0.0819 | 4.9295 | 2938 | 0.5060 | 0.2941 | 0.5060 | 0.7114 |
| 0.0819 | 4.9329 | 2940 | 0.5150 | 0.2727 | 0.5150 | 0.7176 |
| 0.0819 | 4.9362 | 2942 | 0.5289 | 0.2727 | 0.5289 | 0.7272 |
| 0.0819 | 4.9396 | 2944 | 0.5396 | 0.2727 | 0.5396 | 0.7346 |
| 0.0819 | 4.9430 | 2946 | 0.5369 | 0.2727 | 0.5369 | 0.7327 |
| 0.0819 | 4.9463 | 2948 | 0.5341 | 0.2727 | 0.5341 | 0.7308 |
| 0.0819 | 4.9497 | 2950 | 0.5261 | 0.2727 | 0.5261 | 0.7253 |
| 0.0819 | 4.9530 | 2952 | 0.5186 | 0.2727 | 0.5186 | 0.7201 |
| 0.0819 | 4.9564 | 2954 | 0.5052 | 0.2727 | 0.5052 | 0.7108 |
| 0.0819 | 4.9597 | 2956 | 0.4983 | 0.2727 | 0.4983 | 0.7059 |
| 0.0819 | 4.9631 | 2958 | 0.5017 | 0.2727 | 0.5017 | 0.7083 |
| 0.0819 | 4.9664 | 2960 | 0.5121 | 0.2727 | 0.5121 | 0.7156 |
| 0.0819 | 4.9698 | 2962 | 0.5263 | 0.3077 | 0.5263 | 0.7254 |
| 0.0819 | 4.9732 | 2964 | 0.5397 | 0.3077 | 0.5397 | 0.7346 |
| 0.0819 | 4.9765 | 2966 | 0.5445 | 0.3077 | 0.5445 | 0.7379 |
| 0.0819 | 4.9799 | 2968 | 0.5479 | 0.3077 | 0.5479 | 0.7402 |
| 0.0819 | 4.9832 | 2970 | 0.5427 | 0.3077 | 0.5427 | 0.7367 |
| 0.0819 | 4.9866 | 2972 | 0.5343 | 0.3077 | 0.5343 | 0.7309 |
| 0.0819 | 4.9899 | 2974 | 0.5278 | 0.2258 | 0.5278 | 0.7265 |
| 0.0819 | 4.9933 | 2976 | 0.5301 | 0.2857 | 0.5301 | 0.7281 |
| 0.0819 | 4.9966 | 2978 | 0.5337 | 0.2388 | 0.5337 | 0.7305 |
| 0.0819 | 5.0 | 2980 | 0.5455 | 0.3077 | 0.5455 | 0.7386 |
| 0.0819 | 5.0034 | 2982 | 0.5784 | 0.4 | 0.5784 | 0.7606 |
| 0.0819 | 5.0067 | 2984 | 0.6050 | 0.4 | 0.6050 | 0.7778 |
| 0.0819 | 5.0101 | 2986 | 0.6029 | 0.4 | 0.6029 | 0.7765 |
| 0.0819 | 5.0134 | 2988 | 0.5863 | 0.4 | 0.5863 | 0.7657 |
| 0.0819 | 5.0168 | 2990 | 0.5759 | 0.4 | 0.5759 | 0.7589 |
| 0.0819 | 5.0201 | 2992 | 0.5776 | 0.3077 | 0.5776 | 0.7600 |
| 0.0819 | 5.0235 | 2994 | 0.5651 | 0.3077 | 0.5651 | 0.7517 |
| 0.0819 | 5.0268 | 2996 | 0.5416 | 0.3077 | 0.5416 | 0.7359 |
| 0.0819 | 5.0302 | 2998 | 0.5347 | 0.2623 | 0.5347 | 0.7312 |
| 0.0728 | 5.0336 | 3000 | 0.5393 | 0.2258 | 0.5393 | 0.7343 |
| 0.0728 | 5.0369 | 3002 | 0.5483 | 0.2388 | 0.5483 | 0.7405 |
| 0.0728 | 5.0403 | 3004 | 0.5640 | 0.2388 | 0.5640 | 0.7510 |
| 0.0728 | 5.0436 | 3006 | 0.5882 | 0.3077 | 0.5882 | 0.7670 |
| 0.0728 | 5.0470 | 3008 | 0.6122 | 0.4 | 0.6122 | 0.7825 |
| 0.0728 | 5.0503 | 3010 | 0.6130 | 0.3662 | 0.6130 | 0.7830 |
| 0.0728 | 5.0537 | 3012 | 0.6014 | 0.2388 | 0.6014 | 0.7755 |
| 0.0728 | 5.0570 | 3014 | 0.5979 | 0.2388 | 0.5979 | 0.7733 |
| 0.0728 | 5.0604 | 3016 | 0.5970 | 0.2388 | 0.5970 | 0.7726 |
| 0.0728 | 5.0638 | 3018 | 0.6015 | 0.2388 | 0.6015 | 0.7756 |
| 0.0728 | 5.0671 | 3020 | 0.6016 | 0.2388 | 0.6016 | 0.7756 |
| 0.0728 | 5.0705 | 3022 | 0.5916 | 0.2388 | 0.5916 | 0.7692 |
| 0.0728 | 5.0738 | 3024 | 0.5804 | 0.2388 | 0.5804 | 0.7618 |
| 0.0728 | 5.0772 | 3026 | 0.5736 | 0.2941 | 0.5736 | 0.7573 |
| 0.0728 | 5.0805 | 3028 | 0.5777 | 0.2388 | 0.5777 | 0.7601 |
| 0.0728 | 5.0839 | 3030 | 0.5903 | 0.2727 | 0.5903 | 0.7683 |
| 0.0728 | 5.0872 | 3032 | 0.6106 | 0.2727 | 0.6106 | 0.7814 |
| 0.0728 | 5.0906 | 3034 | 0.6439 | 0.2941 | 0.6439 | 0.8025 |
| 0.0728 | 5.0940 | 3036 | 0.6578 | 0.2941 | 0.6578 | 0.8110 |
| 0.0728 | 5.0973 | 3038 | 0.6544 | 0.2941 | 0.6544 | 0.8090 |
| 0.0728 | 5.1007 | 3040 | 0.6297 | 0.3478 | 0.6297 | 0.7935 |
| 0.0728 | 5.1040 | 3042 | 0.6094 | 0.3662 | 0.6094 | 0.7807 |
| 0.0728 | 5.1074 | 3044 | 0.5989 | 0.2727 | 0.5989 | 0.7739 |
| 0.0728 | 5.1107 | 3046 | 0.5880 | 0.2727 | 0.5880 | 0.7668 |
| 0.0728 | 5.1141 | 3048 | 0.5862 | 0.2727 | 0.5862 | 0.7656 |
| 0.0728 | 5.1174 | 3050 | 0.5947 | 0.2727 | 0.5947 | 0.7712 |
| 0.0728 | 5.1208 | 3052 | 0.5994 | 0.3662 | 0.5994 | 0.7742 |
| 0.0728 | 5.1242 | 3054 | 0.6006 | 0.3662 | 0.6006 | 0.7750 |
| 0.0728 | 5.1275 | 3056 | 0.5917 | 0.2727 | 0.5917 | 0.7692 |
| 0.0728 | 5.1309 | 3058 | 0.5858 | 0.2727 | 0.5858 | 0.7654 |
| 0.0728 | 5.1342 | 3060 | 0.5872 | 0.2727 | 0.5872 | 0.7663 |
| 0.0728 | 5.1376 | 3062 | 0.5901 | 0.3284 | 0.5901 | 0.7682 |
| 0.0728 | 5.1409 | 3064 | 0.6035 | 0.3284 | 0.6035 | 0.7768 |
| 0.0728 | 5.1443 | 3066 | 0.6137 | 0.2941 | 0.6137 | 0.7834 |
| 0.0728 | 5.1477 | 3068 | 0.6246 | 0.3284 | 0.6246 | 0.7903 |
| 0.0728 | 5.1510 | 3070 | 0.6301 | 0.4167 | 0.6301 | 0.7938 |
| 0.0728 | 5.1544 | 3072 | 0.6328 | 0.3662 | 0.6328 | 0.7955 |
| 0.0728 | 5.1577 | 3074 | 0.6429 | 0.3662 | 0.6429 | 0.8018 |
| 0.0728 | 5.1611 | 3076 | 0.6550 | 0.3143 | 0.6550 | 0.8093 |
| 0.0728 | 5.1644 | 3078 | 0.6427 | 0.3662 | 0.6427 | 0.8017 |
| 0.0728 | 5.1678 | 3080 | 0.6280 | 0.3662 | 0.6280 | 0.7925 |
| 0.0728 | 5.1711 | 3082 | 0.6169 | 0.3284 | 0.6169 | 0.7854 |
| 0.0728 | 5.1745 | 3084 | 0.6159 | 0.3284 | 0.6159 | 0.7848 |
| 0.0728 | 5.1779 | 3086 | 0.6235 | 0.1739 | 0.6235 | 0.7896 |
| 0.0728 | 5.1812 | 3088 | 0.6333 | 0.1739 | 0.6333 | 0.7958 |
| 0.0728 | 5.1846 | 3090 | 0.6399 | 0.1739 | 0.6399 | 0.7999 |
| 0.0728 | 5.1879 | 3092 | 0.6433 | 0.3836 | 0.6433 | 0.8021 |
| 0.0728 | 5.1913 | 3094 | 0.6558 | 0.4167 | 0.6558 | 0.8098 |
| 0.0728 | 5.1946 | 3096 | 0.6632 | 0.4167 | 0.6632 | 0.8144 |
| 0.0728 | 5.1980 | 3098 | 0.6529 | 0.4167 | 0.6529 | 0.8080 |
| 0.0728 | 5.2013 | 3100 | 0.6444 | 0.3662 | 0.6444 | 0.8027 |
| 0.0728 | 5.2047 | 3102 | 0.6446 | 0.3662 | 0.6446 | 0.8029 |
| 0.0728 | 5.2081 | 3104 | 0.6452 | 0.3662 | 0.6452 | 0.8032 |
| 0.0728 | 5.2114 | 3106 | 0.6480 | 0.3143 | 0.6480 | 0.8050 |
| 0.0728 | 5.2148 | 3108 | 0.6331 | 0.3662 | 0.6331 | 0.7957 |
| 0.0728 | 5.2181 | 3110 | 0.6118 | 0.2727 | 0.6118 | 0.7822 |
| 0.0728 | 5.2215 | 3112 | 0.6035 | 0.2727 | 0.6035 | 0.7769 |
| 0.0728 | 5.2248 | 3114 | 0.6084 | 0.2727 | 0.6084 | 0.7800 |
| 0.0728 | 5.2282 | 3116 | 0.6229 | 0.2727 | 0.6229 | 0.7892 |
| 0.0728 | 5.2315 | 3118 | 0.6472 | 0.3143 | 0.6472 | 0.8045 |
| 0.0728 | 5.2349 | 3120 | 0.6691 | 0.3143 | 0.6691 | 0.8180 |
| 0.0728 | 5.2383 | 3122 | 0.6801 | 0.3143 | 0.6801 | 0.8247 |
| 0.0728 | 5.2416 | 3124 | 0.6726 | 0.3143 | 0.6726 | 0.8201 |
| 0.0728 | 5.2450 | 3126 | 0.6438 | 0.3662 | 0.6438 | 0.8024 |
| 0.0728 | 5.2483 | 3128 | 0.6276 | 0.3284 | 0.6276 | 0.7922 |
| 0.0728 | 5.2517 | 3130 | 0.6347 | 0.2059 | 0.6347 | 0.7967 |
| 0.0728 | 5.2550 | 3132 | 0.6341 | 0.2059 | 0.6341 | 0.7963 |
| 0.0728 | 5.2584 | 3134 | 0.6356 | 0.2727 | 0.6356 | 0.7972 |
| 0.0728 | 5.2617 | 3136 | 0.6506 | 0.3662 | 0.6506 | 0.8066 |
| 0.0728 | 5.2651 | 3138 | 0.6607 | 0.3662 | 0.6607 | 0.8129 |
| 0.0728 | 5.2685 | 3140 | 0.6588 | 0.3662 | 0.6588 | 0.8117 |
| 0.0728 | 5.2718 | 3142 | 0.6433 | 0.3662 | 0.6433 | 0.8021 |
| 0.0728 | 5.2752 | 3144 | 0.6332 | 0.3662 | 0.6332 | 0.7957 |
| 0.0728 | 5.2785 | 3146 | 0.6348 | 0.3662 | 0.6348 | 0.7968 |
| 0.0728 | 5.2819 | 3148 | 0.6352 | 0.3662 | 0.6352 | 0.7970 |
| 0.0728 | 5.2852 | 3150 | 0.6387 | 0.3662 | 0.6387 | 0.7992 |
| 0.0728 | 5.2886 | 3152 | 0.6328 | 0.3662 | 0.6328 | 0.7955 |
| 0.0728 | 5.2919 | 3154 | 0.6339 | 0.3662 | 0.6339 | 0.7962 |
| 0.0728 | 5.2953 | 3156 | 0.6411 | 0.3662 | 0.6411 | 0.8007 |
| 0.0728 | 5.2987 | 3158 | 0.6522 | 0.3662 | 0.6522 | 0.8076 |
| 0.0728 | 5.3020 | 3160 | 0.6702 | 0.3662 | 0.6702 | 0.8186 |
| 0.0728 | 5.3054 | 3162 | 0.6637 | 0.3662 | 0.6637 | 0.8147 |
| 0.0728 | 5.3087 | 3164 | 0.6508 | 0.2727 | 0.6508 | 0.8067 |
| 0.0728 | 5.3121 | 3166 | 0.6436 | 0.2727 | 0.6436 | 0.8023 |
| 0.0728 | 5.3154 | 3168 | 0.6388 | 0.2388 | 0.6388 | 0.7992 |
| 0.0728 | 5.3188 | 3170 | 0.6456 | 0.2727 | 0.6456 | 0.8035 |
| 0.0728 | 5.3221 | 3172 | 0.6712 | 0.3662 | 0.6712 | 0.8193 |
| 0.0728 | 5.3255 | 3174 | 0.6881 | 0.3662 | 0.6881 | 0.8295 |
| 0.0728 | 5.3289 | 3176 | 0.6785 | 0.3662 | 0.6785 | 0.8237 |
| 0.0728 | 5.3322 | 3178 | 0.6870 | 0.3143 | 0.6870 | 0.8289 |
| 0.0728 | 5.3356 | 3180 | 0.7074 | 0.3514 | 0.7074 | 0.8411 |
| 0.0728 | 5.3389 | 3182 | 0.7261 | 0.3836 | 0.7261 | 0.8521 |
| 0.0728 | 5.3423 | 3184 | 0.7132 | 0.3836 | 0.7132 | 0.8445 |
| 0.0728 | 5.3456 | 3186 | 0.6725 | 0.2609 | 0.6725 | 0.8201 |
| 0.0728 | 5.3490 | 3188 | 0.6497 | 0.2727 | 0.6497 | 0.8060 |
| 0.0728 | 5.3523 | 3190 | 0.6542 | 0.2727 | 0.6542 | 0.8088 |
| 0.0728 | 5.3557 | 3192 | 0.6619 | 0.2727 | 0.6619 | 0.8136 |
| 0.0728 | 5.3591 | 3194 | 0.6762 | 0.3662 | 0.6762 | 0.8223 |
| 0.0728 | 5.3624 | 3196 | 0.6942 | 0.4474 | 0.6942 | 0.8332 |
| 0.0728 | 5.3658 | 3198 | 0.7124 | 0.4 | 0.7124 | 0.8441 |
| 0.0728 | 5.3691 | 3200 | 0.7167 | 0.4 | 0.7167 | 0.8466 |
| 0.0728 | 5.3725 | 3202 | 0.7069 | 0.4474 | 0.7069 | 0.8408 |
| 0.0728 | 5.3758 | 3204 | 0.6949 | 0.4474 | 0.6949 | 0.8336 |
| 0.0728 | 5.3792 | 3206 | 0.6677 | 0.3662 | 0.6677 | 0.8171 |
| 0.0728 | 5.3826 | 3208 | 0.6450 | 0.2727 | 0.6450 | 0.8031 |
| 0.0728 | 5.3859 | 3210 | 0.6384 | 0.2727 | 0.6384 | 0.7990 |
| 0.0728 | 5.3893 | 3212 | 0.6323 | 0.2727 | 0.6323 | 0.7952 |
| 0.0728 | 5.3926 | 3214 | 0.6336 | 0.2727 | 0.6336 | 0.7960 |
| 0.0728 | 5.3960 | 3216 | 0.6296 | 0.2727 | 0.6296 | 0.7935 |
| 0.0728 | 5.3993 | 3218 | 0.6298 | 0.2727 | 0.6298 | 0.7936 |
| 0.0728 | 5.4027 | 3220 | 0.6278 | 0.2727 | 0.6278 | 0.7924 |
| 0.0728 | 5.4060 | 3222 | 0.6302 | 0.2727 | 0.6302 | 0.7939 |
| 0.0728 | 5.4094 | 3224 | 0.6249 | 0.2727 | 0.6249 | 0.7905 |
| 0.0728 | 5.4128 | 3226 | 0.6135 | 0.2727 | 0.6135 | 0.7833 |
| 0.0728 | 5.4161 | 3228 | 0.6082 | 0.3284 | 0.6082 | 0.7799 |
| 0.0728 | 5.4195 | 3230 | 0.6097 | 0.3284 | 0.6097 | 0.7808 |
| 0.0728 | 5.4228 | 3232 | 0.6182 | 0.2727 | 0.6182 | 0.7862 |
| 0.0728 | 5.4262 | 3234 | 0.6215 | 0.2727 | 0.6215 | 0.7883 |
| 0.0728 | 5.4295 | 3236 | 0.6152 | 0.3284 | 0.6152 | 0.7844 |
| 0.0728 | 5.4329 | 3238 | 0.6080 | 0.3284 | 0.6080 | 0.7798 |
| 0.0728 | 5.4362 | 3240 | 0.6029 | 0.3284 | 0.6029 | 0.7765 |
| 0.0728 | 5.4396 | 3242 | 0.6009 | 0.3284 | 0.6009 | 0.7752 |
| 0.0728 | 5.4430 | 3244 | 0.6057 | 0.3284 | 0.6057 | 0.7782 |
| 0.0728 | 5.4463 | 3246 | 0.6107 | 0.3284 | 0.6107 | 0.7815 |
| 0.0728 | 5.4497 | 3248 | 0.6072 | 0.3284 | 0.6072 | 0.7792 |
| 0.0728 | 5.4530 | 3250 | 0.6115 | 0.3284 | 0.6115 | 0.7820 |
| 0.0728 | 5.4564 | 3252 | 0.6228 | 0.2727 | 0.6228 | 0.7892 |
| 0.0728 | 5.4597 | 3254 | 0.6257 | 0.2727 | 0.6257 | 0.7910 |
| 0.0728 | 5.4631 | 3256 | 0.6333 | 0.2727 | 0.6333 | 0.7958 |
| 0.0728 | 5.4664 | 3258 | 0.6377 | 0.2727 | 0.6377 | 0.7986 |
| 0.0728 | 5.4698 | 3260 | 0.6324 | 0.3284 | 0.6324 | 0.7952 |
| 0.0728 | 5.4732 | 3262 | 0.6283 | 0.3284 | 0.6283 | 0.7926 |
| 0.0728 | 5.4765 | 3264 | 0.6183 | 0.3284 | 0.6183 | 0.7863 |
| 0.0728 | 5.4799 | 3266 | 0.6132 | 0.3284 | 0.6132 | 0.7831 |
| 0.0728 | 5.4832 | 3268 | 0.6115 | 0.3284 | 0.6115 | 0.7820 |
| 0.0728 | 5.4866 | 3270 | 0.6115 | 0.3284 | 0.6115 | 0.7820 |
| 0.0728 | 5.4899 | 3272 | 0.6095 | 0.3284 | 0.6095 | 0.7807 |
| 0.0728 | 5.4933 | 3274 | 0.6124 | 0.3284 | 0.6124 | 0.7826 |
| 0.0728 | 5.4966 | 3276 | 0.6179 | 0.2727 | 0.6179 | 0.7861 |
| 0.0728 | 5.5 | 3278 | 0.6279 | 0.2727 | 0.6279 | 0.7924 |
| 0.0728 | 5.5034 | 3280 | 0.6198 | 0.2727 | 0.6198 | 0.7873 |
| 0.0728 | 5.5067 | 3282 | 0.6057 | 0.2727 | 0.6057 | 0.7783 |
| 0.0728 | 5.5101 | 3284 | 0.6107 | 0.2727 | 0.6107 | 0.7815 |
| 0.0728 | 5.5134 | 3286 | 0.6333 | 0.2727 | 0.6333 | 0.7958 |
| 0.0728 | 5.5168 | 3288 | 0.6415 | 0.4 | 0.6415 | 0.8009 |
| 0.0728 | 5.5201 | 3290 | 0.6357 | 0.3077 | 0.6357 | 0.7973 |
| 0.0728 | 5.5235 | 3292 | 0.6272 | 0.2727 | 0.6272 | 0.7919 |
| 0.0728 | 5.5268 | 3294 | 0.6283 | 0.2727 | 0.6283 | 0.7927 |
| 0.0728 | 5.5302 | 3296 | 0.6443 | 0.3662 | 0.6443 | 0.8027 |
| 0.0728 | 5.5336 | 3298 | 0.6471 | 0.3662 | 0.6471 | 0.8044 |
| 0.0728 | 5.5369 | 3300 | 0.6308 | 0.2727 | 0.6308 | 0.7942 |
| 0.0728 | 5.5403 | 3302 | 0.6204 | 0.2727 | 0.6204 | 0.7876 |
| 0.0728 | 5.5436 | 3304 | 0.6099 | 0.2727 | 0.6099 | 0.7810 |
| 0.0728 | 5.5470 | 3306 | 0.6086 | 0.2727 | 0.6086 | 0.7801 |
| 0.0728 | 5.5503 | 3308 | 0.6137 | 0.2727 | 0.6137 | 0.7834 |
| 0.0728 | 5.5537 | 3310 | 0.6062 | 0.2727 | 0.6062 | 0.7786 |
| 0.0728 | 5.5570 | 3312 | 0.6108 | 0.2727 | 0.6108 | 0.7815 |
| 0.0728 | 5.5604 | 3314 | 0.6235 | 0.3077 | 0.6235 | 0.7896 |
| 0.0728 | 5.5638 | 3316 | 0.6351 | 0.3077 | 0.6351 | 0.7969 |
| 0.0728 | 5.5671 | 3318 | 0.6441 | 0.25 | 0.6441 | 0.8025 |
| 0.0728 | 5.5705 | 3320 | 0.6621 | 0.3478 | 0.6621 | 0.8137 |
| 0.0728 | 5.5738 | 3322 | 0.6511 | 0.4 | 0.6511 | 0.8069 |
| 0.0728 | 5.5772 | 3324 | 0.6315 | 0.2727 | 0.6315 | 0.7947 |
| 0.0728 | 5.5805 | 3326 | 0.6310 | 0.2727 | 0.6310 | 0.7944 |
| 0.0728 | 5.5839 | 3328 | 0.6145 | 0.2727 | 0.6145 | 0.7839 |
| 0.0728 | 5.5872 | 3330 | 0.5983 | 0.2727 | 0.5983 | 0.7735 |
| 0.0728 | 5.5906 | 3332 | 0.5918 | 0.2727 | 0.5918 | 0.7693 |
| 0.0728 | 5.5940 | 3334 | 0.5896 | 0.2727 | 0.5896 | 0.7679 |
| 0.0728 | 5.5973 | 3336 | 0.6002 | 0.2727 | 0.6002 | 0.7747 |
| 0.0728 | 5.6007 | 3338 | 0.6098 | 0.25 | 0.6098 | 0.7809 |
| 0.0728 | 5.6040 | 3340 | 0.6202 | 0.3478 | 0.6202 | 0.7875 |
| 0.0728 | 5.6074 | 3342 | 0.6056 | 0.25 | 0.6056 | 0.7782 |
| 0.0728 | 5.6107 | 3344 | 0.5898 | 0.25 | 0.5898 | 0.7680 |
| 0.0728 | 5.6141 | 3346 | 0.5928 | 0.25 | 0.5928 | 0.7699 |
| 0.0728 | 5.6174 | 3348 | 0.6094 | 0.25 | 0.6094 | 0.7806 |
| 0.0728 | 5.6208 | 3350 | 0.6191 | 0.3478 | 0.6191 | 0.7869 |
| 0.0728 | 5.6242 | 3352 | 0.6354 | 0.3478 | 0.6354 | 0.7971 |
| 0.0728 | 5.6275 | 3354 | 0.6337 | 0.3143 | 0.6337 | 0.7960 |
| 0.0728 | 5.6309 | 3356 | 0.6267 | 0.3143 | 0.6267 | 0.7916 |
| 0.0728 | 5.6342 | 3358 | 0.6209 | 0.3662 | 0.6209 | 0.7879 |
| 0.0728 | 5.6376 | 3360 | 0.6237 | 0.3662 | 0.6237 | 0.7898 |
| 0.0728 | 5.6409 | 3362 | 0.6233 | 0.3662 | 0.6233 | 0.7895 |
| 0.0728 | 5.6443 | 3364 | 0.6060 | 0.2727 | 0.6060 | 0.7784 |
| 0.0728 | 5.6477 | 3366 | 0.5862 | 0.2727 | 0.5862 | 0.7656 |
| 0.0728 | 5.6510 | 3368 | 0.5746 | 0.3284 | 0.5746 | 0.7580 |
| 0.0728 | 5.6544 | 3370 | 0.5633 | 0.2941 | 0.5633 | 0.7505 |
| 0.0728 | 5.6577 | 3372 | 0.5583 | 0.3284 | 0.5583 | 0.7472 |
| 0.0728 | 5.6611 | 3374 | 0.5578 | 0.2727 | 0.5578 | 0.7469 |
| 0.0728 | 5.6644 | 3376 | 0.5681 | 0.2727 | 0.5681 | 0.7537 |
| 0.0728 | 5.6678 | 3378 | 0.5913 | 0.2154 | 0.5913 | 0.7689 |
| 0.0728 | 5.6711 | 3380 | 0.6230 | 0.4 | 0.6230 | 0.7893 |
| 0.0728 | 5.6745 | 3382 | 0.6297 | 0.4658 | 0.6297 | 0.7935 |
| 0.0728 | 5.6779 | 3384 | 0.6116 | 0.3143 | 0.6116 | 0.7821 |
| 0.0728 | 5.6812 | 3386 | 0.5832 | 0.2727 | 0.5832 | 0.7637 |
| 0.0728 | 5.6846 | 3388 | 0.5641 | 0.2727 | 0.5641 | 0.7510 |
| 0.0728 | 5.6879 | 3390 | 0.5510 | 0.3284 | 0.5510 | 0.7423 |
| 0.0728 | 5.6913 | 3392 | 0.5503 | 0.3478 | 0.5503 | 0.7418 |
| 0.0728 | 5.6946 | 3394 | 0.5550 | 0.3478 | 0.5550 | 0.7450 |
| 0.0728 | 5.6980 | 3396 | 0.5620 | 0.3478 | 0.5620 | 0.7497 |
| 0.0728 | 5.7013 | 3398 | 0.5796 | 0.2727 | 0.5796 | 0.7613 |
| 0.0728 | 5.7047 | 3400 | 0.6097 | 0.2727 | 0.6097 | 0.7808 |
| 0.0728 | 5.7081 | 3402 | 0.6254 | 0.4 | 0.6254 | 0.7908 |
| 0.0728 | 5.7114 | 3404 | 0.6173 | 0.3143 | 0.6173 | 0.7857 |
| 0.0728 | 5.7148 | 3406 | 0.5959 | 0.2727 | 0.5959 | 0.7720 |
| 0.0728 | 5.7181 | 3408 | 0.5764 | 0.2727 | 0.5764 | 0.7592 |
| 0.0728 | 5.7215 | 3410 | 0.5601 | 0.2727 | 0.5601 | 0.7484 |
| 0.0728 | 5.7248 | 3412 | 0.5559 | 0.2727 | 0.5559 | 0.7456 |
| 0.0728 | 5.7282 | 3414 | 0.5597 | 0.2727 | 0.5597 | 0.7481 |
| 0.0728 | 5.7315 | 3416 | 0.5649 | 0.2727 | 0.5649 | 0.7516 |
| 0.0728 | 5.7349 | 3418 | 0.5749 | 0.2941 | 0.5749 | 0.7582 |
| 0.0728 | 5.7383 | 3420 | 0.5849 | 0.2727 | 0.5849 | 0.7648 |
| 0.0728 | 5.7416 | 3422 | 0.5960 | 0.2727 | 0.5960 | 0.7720 |
| 0.0728 | 5.7450 | 3424 | 0.5955 | 0.2727 | 0.5955 | 0.7717 |
| 0.0728 | 5.7483 | 3426 | 0.5905 | 0.2727 | 0.5905 | 0.7684 |
| 0.0728 | 5.7517 | 3428 | 0.5852 | 0.2727 | 0.5852 | 0.7650 |
| 0.0728 | 5.7550 | 3430 | 0.5807 | 0.2388 | 0.5807 | 0.7621 |
| 0.0728 | 5.7584 | 3432 | 0.5799 | 0.2941 | 0.5799 | 0.7615 |
| 0.0728 | 5.7617 | 3434 | 0.5823 | 0.3478 | 0.5823 | 0.7631 |
| 0.0728 | 5.7651 | 3436 | 0.5854 | 0.2941 | 0.5854 | 0.7651 |
| 0.0728 | 5.7685 | 3438 | 0.5849 | 0.2941 | 0.5849 | 0.7648 |
| 0.0728 | 5.7718 | 3440 | 0.5851 | 0.2388 | 0.5851 | 0.7649 |
| 0.0728 | 5.7752 | 3442 | 0.5862 | 0.3478 | 0.5862 | 0.7657 |
| 0.0728 | 5.7785 | 3444 | 0.5954 | 0.3478 | 0.5954 | 0.7716 |
| 0.0728 | 5.7819 | 3446 | 0.5983 | 0.3478 | 0.5983 | 0.7735 |
| 0.0728 | 5.7852 | 3448 | 0.5988 | 0.3478 | 0.5988 | 0.7738 |
| 0.0728 | 5.7886 | 3450 | 0.5956 | 0.3284 | 0.5956 | 0.7717 |
| 0.0728 | 5.7919 | 3452 | 0.5959 | 0.2727 | 0.5959 | 0.7720 |
| 0.0728 | 5.7953 | 3454 | 0.6020 | 0.2154 | 0.6020 | 0.7759 |
| 0.0728 | 5.7987 | 3456 | 0.6022 | 0.2154 | 0.6022 | 0.7760 |
| 0.0728 | 5.8020 | 3458 | 0.6099 | 0.2154 | 0.6099 | 0.7810 |
| 0.0728 | 5.8054 | 3460 | 0.6087 | 0.2154 | 0.6087 | 0.7802 |
| 0.0728 | 5.8087 | 3462 | 0.5940 | 0.2154 | 0.5940 | 0.7707 |
| 0.0728 | 5.8121 | 3464 | 0.5753 | 0.2727 | 0.5753 | 0.7585 |
| 0.0728 | 5.8154 | 3466 | 0.5622 | 0.2727 | 0.5622 | 0.7498 |
| 0.0728 | 5.8188 | 3468 | 0.5551 | 0.2727 | 0.5551 | 0.7451 |
| 0.0728 | 5.8221 | 3470 | 0.5562 | 0.2727 | 0.5562 | 0.7458 |
| 0.0728 | 5.8255 | 3472 | 0.5662 | 0.2154 | 0.5662 | 0.7525 |
| 0.0728 | 5.8289 | 3474 | 0.5777 | 0.2154 | 0.5777 | 0.7601 |
| 0.0728 | 5.8322 | 3476 | 0.5903 | 0.2154 | 0.5903 | 0.7683 |
| 0.0728 | 5.8356 | 3478 | 0.5977 | 0.2154 | 0.5977 | 0.7731 |
| 0.0728 | 5.8389 | 3480 | 0.6039 | 0.2154 | 0.6039 | 0.7771 |
| 0.0728 | 5.8423 | 3482 | 0.5981 | 0.2727 | 0.5981 | 0.7734 |
| 0.0728 | 5.8456 | 3484 | 0.5968 | 0.2727 | 0.5968 | 0.7725 |
| 0.0728 | 5.8490 | 3486 | 0.5941 | 0.2727 | 0.5941 | 0.7708 |
| 0.0728 | 5.8523 | 3488 | 0.6040 | 0.2154 | 0.6040 | 0.7772 |
| 0.0728 | 5.8557 | 3490 | 0.6075 | 0.2154 | 0.6075 | 0.7794 |
| 0.0728 | 5.8591 | 3492 | 0.6001 | 0.2727 | 0.6001 | 0.7747 |
| 0.0728 | 5.8624 | 3494 | 0.5932 | 0.2727 | 0.5932 | 0.7702 |
| 0.0728 | 5.8658 | 3496 | 0.5987 | 0.2727 | 0.5987 | 0.7738 |
| 0.0728 | 5.8691 | 3498 | 0.6094 | 0.2154 | 0.6094 | 0.7807 |
| 0.0601 | 5.8725 | 3500 | 0.6042 | 0.2727 | 0.6042 | 0.7773 |
| 0.0601 | 5.8758 | 3502 | 0.6002 | 0.2727 | 0.6002 | 0.7747 |
| 0.0601 | 5.8792 | 3504 | 0.5966 | 0.2727 | 0.5966 | 0.7724 |
| 0.0601 | 5.8826 | 3506 | 0.5918 | 0.2727 | 0.5918 | 0.7693 |
| 0.0601 | 5.8859 | 3508 | 0.5868 | 0.2727 | 0.5868 | 0.7660 |
| 0.0601 | 5.8893 | 3510 | 0.5830 | 0.2727 | 0.5830 | 0.7635 |
| 0.0601 | 5.8926 | 3512 | 0.5827 | 0.2727 | 0.5827 | 0.7633 |
| 0.0601 | 5.8960 | 3514 | 0.5945 | 0.2154 | 0.5945 | 0.7710 |
| 0.0601 | 5.8993 | 3516 | 0.6140 | 0.2154 | 0.6140 | 0.7836 |
| 0.0601 | 5.9027 | 3518 | 0.6152 | 0.2154 | 0.6152 | 0.7844 |
| 0.0601 | 5.9060 | 3520 | 0.6105 | 0.2154 | 0.6105 | 0.7813 |
| 0.0601 | 5.9094 | 3522 | 0.5962 | 0.2154 | 0.5962 | 0.7721 |
| 0.0601 | 5.9128 | 3524 | 0.5861 | 0.2154 | 0.5861 | 0.7656 |
| 0.0601 | 5.9161 | 3526 | 0.5735 | 0.2154 | 0.5735 | 0.7573 |
| 0.0601 | 5.9195 | 3528 | 0.5612 | 0.2000 | 0.5612 | 0.7491 |
| 0.0601 | 5.9228 | 3530 | 0.5577 | 0.1818 | 0.5577 | 0.7468 |
| 0.0601 | 5.9262 | 3532 | 0.5618 | 0.2000 | 0.5618 | 0.7495 |
| 0.0601 | 5.9295 | 3534 | 0.5757 | 0.2154 | 0.5757 | 0.7587 |
| 0.0601 | 5.9329 | 3536 | 0.6021 | 0.2154 | 0.6021 | 0.7759 |
| 0.0601 | 5.9362 | 3538 | 0.6258 | 0.2154 | 0.6258 | 0.7910 |
| 0.0601 | 5.9396 | 3540 | 0.6270 | 0.2154 | 0.6270 | 0.7919 |
| 0.0601 | 5.9430 | 3542 | 0.6081 | 0.2154 | 0.6081 | 0.7798 |
| 0.0601 | 5.9463 | 3544 | 0.5978 | 0.2154 | 0.5978 | 0.7732 |
| 0.0601 | 5.9497 | 3546 | 0.5961 | 0.2154 | 0.5961 | 0.7721 |
| 0.0601 | 5.9530 | 3548 | 0.5968 | 0.2727 | 0.5968 | 0.7725 |
| 0.0601 | 5.9564 | 3550 | 0.5963 | 0.2727 | 0.5963 | 0.7722 |
| 0.0601 | 5.9597 | 3552 | 0.5954 | 0.2154 | 0.5954 | 0.7716 |
| 0.0601 | 5.9631 | 3554 | 0.5992 | 0.2154 | 0.5992 | 0.7741 |
| 0.0601 | 5.9664 | 3556 | 0.6057 | 0.2154 | 0.6057 | 0.7783 |
| 0.0601 | 5.9698 | 3558 | 0.6139 | 0.2154 | 0.6139 | 0.7835 |
| 0.0601 | 5.9732 | 3560 | 0.6374 | 0.2154 | 0.6374 | 0.7984 |
| 0.0601 | 5.9765 | 3562 | 0.6525 | 0.2609 | 0.6525 | 0.8078 |
| 0.0601 | 5.9799 | 3564 | 0.6510 | 0.1905 | 0.6510 | 0.8069 |
| 0.0601 | 5.9832 | 3566 | 0.6455 | 0.1905 | 0.6455 | 0.8034 |
| 0.0601 | 5.9866 | 3568 | 0.6223 | 0.1905 | 0.6223 | 0.7889 |
| 0.0601 | 5.9899 | 3570 | 0.6015 | 0.1562 | 0.6015 | 0.7756 |
| 0.0601 | 5.9933 | 3572 | 0.5843 | 0.2000 | 0.5843 | 0.7644 |
| 0.0601 | 5.9966 | 3574 | 0.5765 | 0.2000 | 0.5765 | 0.7593 |
| 0.0601 | 6.0 | 3576 | 0.5764 | 0.2000 | 0.5764 | 0.7592 |
| 0.0601 | 6.0034 | 3578 | 0.5859 | 0.2154 | 0.5859 | 0.7655 |
| 0.0601 | 6.0067 | 3580 | 0.5949 | 0.2154 | 0.5949 | 0.7713 |
| 0.0601 | 6.0101 | 3582 | 0.6057 | 0.2154 | 0.6057 | 0.7783 |
| 0.0601 | 6.0134 | 3584 | 0.6092 | 0.2727 | 0.6092 | 0.7805 |
| 0.0601 | 6.0168 | 3586 | 0.6110 | 0.2727 | 0.6110 | 0.7817 |
| 0.0601 | 6.0201 | 3588 | 0.6177 | 0.2727 | 0.6177 | 0.7859 |
| 0.0601 | 6.0235 | 3590 | 0.6351 | 0.2154 | 0.6351 | 0.7969 |
| 0.0601 | 6.0268 | 3592 | 0.6623 | 0.1562 | 0.6623 | 0.8138 |
| 0.0601 | 6.0302 | 3594 | 0.6903 | 0.2609 | 0.6903 | 0.8308 |
| 0.0601 | 6.0336 | 3596 | 0.7021 | 0.3284 | 0.7021 | 0.8379 |
| 0.0601 | 6.0369 | 3598 | 0.7060 | 0.3284 | 0.7060 | 0.8402 |
| 0.0601 | 6.0403 | 3600 | 0.6887 | 0.2941 | 0.6887 | 0.8299 |
| 0.0601 | 6.0436 | 3602 | 0.6625 | 0.1562 | 0.6625 | 0.8140 |
| 0.0601 | 6.0470 | 3604 | 0.6375 | 0.1562 | 0.6375 | 0.7985 |
| 0.0601 | 6.0503 | 3606 | 0.6220 | 0.1562 | 0.6220 | 0.7887 |
| 0.0601 | 6.0537 | 3608 | 0.6064 | 0.1356 | 0.6064 | 0.7787 |
| 0.0601 | 6.0570 | 3610 | 0.5917 | 0.25 | 0.5917 | 0.7692 |
| 0.0601 | 6.0604 | 3612 | 0.5907 | 0.1818 | 0.5907 | 0.7686 |
| 0.0601 | 6.0638 | 3614 | 0.6009 | 0.1356 | 0.6009 | 0.7752 |
| 0.0601 | 6.0671 | 3616 | 0.6188 | 0.1562 | 0.6188 | 0.7866 |
| 0.0601 | 6.0705 | 3618 | 0.6459 | 0.1905 | 0.6459 | 0.8037 |
| 0.0601 | 6.0738 | 3620 | 0.6668 | 0.2258 | 0.6668 | 0.8166 |
| 0.0601 | 6.0772 | 3622 | 0.6657 | 0.1905 | 0.6657 | 0.8159 |
| 0.0601 | 6.0805 | 3624 | 0.6455 | 0.1562 | 0.6455 | 0.8034 |
| 0.0601 | 6.0839 | 3626 | 0.6173 | 0.2727 | 0.6173 | 0.7857 |
| 0.0601 | 6.0872 | 3628 | 0.6031 | 0.3284 | 0.6031 | 0.7766 |
| 0.0601 | 6.0906 | 3630 | 0.6003 | 0.2857 | 0.6003 | 0.7748 |
| 0.0601 | 6.0940 | 3632 | 0.5955 | 0.2857 | 0.5955 | 0.7717 |
| 0.0601 | 6.0973 | 3634 | 0.5927 | 0.2759 | 0.5927 | 0.7699 |
| 0.0601 | 6.1007 | 3636 | 0.5904 | 0.2759 | 0.5904 | 0.7684 |
| 0.0601 | 6.1040 | 3638 | 0.5925 | 0.3226 | 0.5925 | 0.7697 |
| 0.0601 | 6.1074 | 3640 | 0.6033 | 0.2727 | 0.6033 | 0.7767 |
| 0.0601 | 6.1107 | 3642 | 0.6198 | 0.2154 | 0.6198 | 0.7873 |
| 0.0601 | 6.1141 | 3644 | 0.6282 | 0.2154 | 0.6282 | 0.7926 |
| 0.0601 | 6.1174 | 3646 | 0.6290 | 0.2154 | 0.6290 | 0.7931 |
| 0.0601 | 6.1208 | 3648 | 0.6161 | 0.2154 | 0.6161 | 0.7849 |
| 0.0601 | 6.1242 | 3650 | 0.6007 | 0.2154 | 0.6007 | 0.7750 |
| 0.0601 | 6.1275 | 3652 | 0.5934 | 0.2154 | 0.5934 | 0.7704 |
| 0.0601 | 6.1309 | 3654 | 0.5963 | 0.2727 | 0.5963 | 0.7722 |
| 0.0601 | 6.1342 | 3656 | 0.6022 | 0.2727 | 0.6022 | 0.7760 |
| 0.0601 | 6.1376 | 3658 | 0.6067 | 0.3824 | 0.6067 | 0.7789 |
| 0.0601 | 6.1409 | 3660 | 0.6048 | 0.2609 | 0.6048 | 0.7777 |
| 0.0601 | 6.1443 | 3662 | 0.6028 | 0.3284 | 0.6028 | 0.7764 |
| 0.0601 | 6.1477 | 3664 | 0.6008 | 0.3284 | 0.6008 | 0.7751 |
| 0.0601 | 6.1510 | 3666 | 0.5954 | 0.3284 | 0.5954 | 0.7716 |
| 0.0601 | 6.1544 | 3668 | 0.5894 | 0.3284 | 0.5894 | 0.7677 |
| 0.0601 | 6.1577 | 3670 | 0.5853 | 0.2623 | 0.5853 | 0.7651 |
| 0.0601 | 6.1611 | 3672 | 0.5825 | 0.2623 | 0.5825 | 0.7632 |
| 0.0601 | 6.1644 | 3674 | 0.5841 | 0.2727 | 0.5841 | 0.7642 |
| 0.0601 | 6.1678 | 3676 | 0.5890 | 0.2154 | 0.5890 | 0.7674 |
| 0.0601 | 6.1711 | 3678 | 0.6023 | 0.2154 | 0.6023 | 0.7761 |
| 0.0601 | 6.1745 | 3680 | 0.6306 | 0.2154 | 0.6306 | 0.7941 |
| 0.0601 | 6.1779 | 3682 | 0.6531 | 0.3143 | 0.6531 | 0.8082 |
| 0.0601 | 6.1812 | 3684 | 0.6521 | 0.3143 | 0.6521 | 0.8075 |
| 0.0601 | 6.1846 | 3686 | 0.6360 | 0.2154 | 0.6360 | 0.7975 |
| 0.0601 | 6.1879 | 3688 | 0.6121 | 0.2154 | 0.6121 | 0.7823 |
| 0.0601 | 6.1913 | 3690 | 0.6016 | 0.2727 | 0.6016 | 0.7756 |
| 0.0601 | 6.1946 | 3692 | 0.6100 | 0.2373 | 0.6100 | 0.7810 |
| 0.0601 | 6.1980 | 3694 | 0.6252 | 0.1639 | 0.6252 | 0.7907 |
| 0.0601 | 6.2013 | 3696 | 0.6260 | 0.1639 | 0.6260 | 0.7912 |
| 0.0601 | 6.2047 | 3698 | 0.6098 | 0.1639 | 0.6098 | 0.7809 |
| 0.0601 | 6.2081 | 3700 | 0.5951 | 0.2623 | 0.5951 | 0.7714 |
| 0.0601 | 6.2114 | 3702 | 0.5898 | 0.2623 | 0.5898 | 0.7680 |
| 0.0601 | 6.2148 | 3704 | 0.5971 | 0.2154 | 0.5971 | 0.7727 |
| 0.0601 | 6.2181 | 3706 | 0.6017 | 0.2154 | 0.6017 | 0.7757 |
| 0.0601 | 6.2215 | 3708 | 0.6013 | 0.2154 | 0.6013 | 0.7754 |
| 0.0601 | 6.2248 | 3710 | 0.5973 | 0.2154 | 0.5973 | 0.7728 |
| 0.0601 | 6.2282 | 3712 | 0.5976 | 0.2727 | 0.5976 | 0.7731 |
| 0.0601 | 6.2315 | 3714 | 0.5943 | 0.2727 | 0.5943 | 0.7709 |
| 0.0601 | 6.2349 | 3716 | 0.5996 | 0.2727 | 0.5996 | 0.7743 |
| 0.0601 | 6.2383 | 3718 | 0.6074 | 0.3284 | 0.6074 | 0.7794 |
| 0.0601 | 6.2416 | 3720 | 0.6148 | 0.2727 | 0.6148 | 0.7841 |
| 0.0601 | 6.2450 | 3722 | 0.6189 | 0.2727 | 0.6189 | 0.7867 |
| 0.0601 | 6.2483 | 3724 | 0.6243 | 0.2727 | 0.6243 | 0.7901 |
| 0.0601 | 6.2517 | 3726 | 0.6329 | 0.3662 | 0.6329 | 0.7955 |
| 0.0601 | 6.2550 | 3728 | 0.6374 | 0.3662 | 0.6374 | 0.7984 |
| 0.0601 | 6.2584 | 3730 | 0.6426 | 0.3662 | 0.6426 | 0.8016 |
| 0.0601 | 6.2617 | 3732 | 0.6378 | 0.3662 | 0.6378 | 0.7986 |
| 0.0601 | 6.2651 | 3734 | 0.6234 | 0.3662 | 0.6234 | 0.7896 |
| 0.0601 | 6.2685 | 3736 | 0.6094 | 0.2727 | 0.6094 | 0.7806 |
| 0.0601 | 6.2718 | 3738 | 0.6049 | 0.2059 | 0.6049 | 0.7777 |
| 0.0601 | 6.2752 | 3740 | 0.6101 | 0.1818 | 0.6101 | 0.7811 |
| 0.0601 | 6.2785 | 3742 | 0.6140 | 0.1818 | 0.6140 | 0.7836 |
| 0.0601 | 6.2819 | 3744 | 0.6102 | 0.1972 | 0.6102 | 0.7812 |
| 0.0601 | 6.2852 | 3746 | 0.6066 | 0.1739 | 0.6066 | 0.7789 |
| 0.0601 | 6.2886 | 3748 | 0.6044 | 0.2059 | 0.6044 | 0.7774 |
| 0.0601 | 6.2919 | 3750 | 0.6052 | 0.2727 | 0.6052 | 0.7780 |
| 0.0601 | 6.2953 | 3752 | 0.6135 | 0.2727 | 0.6135 | 0.7832 |
| 0.0601 | 6.2987 | 3754 | 0.6325 | 0.3143 | 0.6325 | 0.7953 |
| 0.0601 | 6.3020 | 3756 | 0.6481 | 0.3143 | 0.6481 | 0.8050 |
| 0.0601 | 6.3054 | 3758 | 0.6678 | 0.3143 | 0.6678 | 0.8172 |
| 0.0601 | 6.3087 | 3760 | 0.6875 | 0.2609 | 0.6875 | 0.8291 |
| 0.0601 | 6.3121 | 3762 | 0.6842 | 0.2941 | 0.6842 | 0.8272 |
| 0.0601 | 6.3154 | 3764 | 0.6602 | 0.2609 | 0.6602 | 0.8125 |
| 0.0601 | 6.3188 | 3766 | 0.6262 | 0.3143 | 0.6262 | 0.7913 |
| 0.0601 | 6.3221 | 3768 | 0.5958 | 0.2154 | 0.5958 | 0.7719 |
| 0.0601 | 6.3255 | 3770 | 0.5825 | 0.3284 | 0.5825 | 0.7632 |
| 0.0601 | 6.3289 | 3772 | 0.5805 | 0.1356 | 0.5805 | 0.7619 |
| 0.0601 | 6.3322 | 3774 | 0.5793 | 0.1356 | 0.5793 | 0.7611 |
| 0.0601 | 6.3356 | 3776 | 0.5782 | 0.1356 | 0.5782 | 0.7604 |
| 0.0601 | 6.3389 | 3778 | 0.5744 | 0.1356 | 0.5744 | 0.7579 |
| 0.0601 | 6.3423 | 3780 | 0.5739 | 0.2727 | 0.5739 | 0.7576 |
| 0.0601 | 6.3456 | 3782 | 0.5832 | 0.2154 | 0.5832 | 0.7637 |
| 0.0601 | 6.3490 | 3784 | 0.6000 | 0.2154 | 0.6000 | 0.7746 |
| 0.0601 | 6.3523 | 3786 | 0.6172 | 0.3143 | 0.6172 | 0.7856 |
| 0.0601 | 6.3557 | 3788 | 0.6218 | 0.3143 | 0.6218 | 0.7886 |
| 0.0601 | 6.3591 | 3790 | 0.6119 | 0.3143 | 0.6119 | 0.7822 |
| 0.0601 | 6.3624 | 3792 | 0.5957 | 0.2154 | 0.5957 | 0.7718 |
| 0.0601 | 6.3658 | 3794 | 0.5873 | 0.1818 | 0.5873 | 0.7664 |
| 0.0601 | 6.3691 | 3796 | 0.5869 | 0.2388 | 0.5869 | 0.7661 |
| 0.0601 | 6.3725 | 3798 | 0.5892 | 0.1818 | 0.5892 | 0.7676 |
| 0.0601 | 6.3758 | 3800 | 0.5940 | 0.2154 | 0.5940 | 0.7707 |
| 0.0601 | 6.3792 | 3802 | 0.6003 | 0.2154 | 0.6003 | 0.7748 |
| 0.0601 | 6.3826 | 3804 | 0.6020 | 0.2154 | 0.6020 | 0.7759 |
| 0.0601 | 6.3859 | 3806 | 0.5994 | 0.2154 | 0.5994 | 0.7742 |
| 0.0601 | 6.3893 | 3808 | 0.6029 | 0.2154 | 0.6029 | 0.7765 |
| 0.0601 | 6.3926 | 3810 | 0.6046 | 0.2154 | 0.6046 | 0.7775 |
| 0.0601 | 6.3960 | 3812 | 0.6059 | 0.2154 | 0.6059 | 0.7784 |
| 0.0601 | 6.3993 | 3814 | 0.6029 | 0.2154 | 0.6029 | 0.7765 |
| 0.0601 | 6.4027 | 3816 | 0.6001 | 0.2154 | 0.6001 | 0.7747 |
| 0.0601 | 6.4060 | 3818 | 0.5913 | 0.2154 | 0.5913 | 0.7690 |
| 0.0601 | 6.4094 | 3820 | 0.5846 | 0.2154 | 0.5846 | 0.7646 |
| 0.0601 | 6.4128 | 3822 | 0.5826 | 0.2154 | 0.5826 | 0.7633 |
| 0.0601 | 6.4161 | 3824 | 0.5840 | 0.2154 | 0.5840 | 0.7642 |
| 0.0601 | 6.4195 | 3826 | 0.5866 | 0.2154 | 0.5866 | 0.7659 |
| 0.0601 | 6.4228 | 3828 | 0.5963 | 0.2154 | 0.5963 | 0.7722 |
| 0.0601 | 6.4262 | 3830 | 0.6098 | 0.2154 | 0.6098 | 0.7809 |
| 0.0601 | 6.4295 | 3832 | 0.6174 | 0.2154 | 0.6174 | 0.7857 |
| 0.0601 | 6.4329 | 3834 | 0.6260 | 0.3143 | 0.6260 | 0.7912 |
| 0.0601 | 6.4362 | 3836 | 0.6202 | 0.2154 | 0.6202 | 0.7875 |
| 0.0601 | 6.4396 | 3838 | 0.6091 | 0.2154 | 0.6091 | 0.7804 |
| 0.0601 | 6.4430 | 3840 | 0.5928 | 0.2154 | 0.5928 | 0.7699 |
| 0.0601 | 6.4463 | 3842 | 0.5813 | 0.2154 | 0.5813 | 0.7625 |
| 0.0601 | 6.4497 | 3844 | 0.5792 | 0.2727 | 0.5792 | 0.7611 |
| 0.0601 | 6.4530 | 3846 | 0.5816 | 0.2388 | 0.5816 | 0.7626 |
| 0.0601 | 6.4564 | 3848 | 0.5830 | 0.2388 | 0.5830 | 0.7635 |
| 0.0601 | 6.4597 | 3850 | 0.5837 | 0.1818 | 0.5837 | 0.7640 |
| 0.0601 | 6.4631 | 3852 | 0.5895 | 0.2154 | 0.5895 | 0.7678 |
| 0.0601 | 6.4664 | 3854 | 0.6026 | 0.2154 | 0.6026 | 0.7762 |
| 0.0601 | 6.4698 | 3856 | 0.6251 | 0.2154 | 0.6251 | 0.7907 |
| 0.0601 | 6.4732 | 3858 | 0.6356 | 0.3143 | 0.6356 | 0.7972 |
| 0.0601 | 6.4765 | 3860 | 0.6361 | 0.3143 | 0.6361 | 0.7975 |
| 0.0601 | 6.4799 | 3862 | 0.6388 | 0.3143 | 0.6388 | 0.7992 |
| 0.0601 | 6.4832 | 3864 | 0.6433 | 0.3143 | 0.6433 | 0.8020 |
| 0.0601 | 6.4866 | 3866 | 0.6453 | 0.3478 | 0.6453 | 0.8033 |
| 0.0601 | 6.4899 | 3868 | 0.6378 | 0.3478 | 0.6378 | 0.7986 |
| 0.0601 | 6.4933 | 3870 | 0.6173 | 0.3143 | 0.6173 | 0.7857 |
| 0.0601 | 6.4966 | 3872 | 0.6010 | 0.2154 | 0.6010 | 0.7752 |
| 0.0601 | 6.5 | 3874 | 0.5852 | 0.2154 | 0.5852 | 0.7650 |
| 0.0601 | 6.5034 | 3876 | 0.5753 | 0.2154 | 0.5753 | 0.7585 |
| 0.0601 | 6.5067 | 3878 | 0.5729 | 0.2154 | 0.5729 | 0.7569 |
| 0.0601 | 6.5101 | 3880 | 0.5764 | 0.2154 | 0.5764 | 0.7592 |
| 0.0601 | 6.5134 | 3882 | 0.5919 | 0.2154 | 0.5919 | 0.7693 |
| 0.0601 | 6.5168 | 3884 | 0.6069 | 0.2154 | 0.6069 | 0.7790 |
| 0.0601 | 6.5201 | 3886 | 0.6211 | 0.25 | 0.6211 | 0.7881 |
| 0.0601 | 6.5235 | 3888 | 0.6186 | 0.2154 | 0.6186 | 0.7865 |
| 0.0601 | 6.5268 | 3890 | 0.6084 | 0.2154 | 0.6084 | 0.7800 |
| 0.0601 | 6.5302 | 3892 | 0.6053 | 0.2154 | 0.6053 | 0.7780 |
| 0.0601 | 6.5336 | 3894 | 0.5984 | 0.2154 | 0.5984 | 0.7736 |
| 0.0601 | 6.5369 | 3896 | 0.5970 | 0.2154 | 0.5970 | 0.7727 |
| 0.0601 | 6.5403 | 3898 | 0.5977 | 0.2154 | 0.5977 | 0.7731 |
| 0.0601 | 6.5436 | 3900 | 0.5907 | 0.2154 | 0.5907 | 0.7686 |
| 0.0601 | 6.5470 | 3902 | 0.5774 | 0.2154 | 0.5774 | 0.7599 |
| 0.0601 | 6.5503 | 3904 | 0.5699 | 0.2154 | 0.5699 | 0.7549 |
| 0.0601 | 6.5537 | 3906 | 0.5714 | 0.2154 | 0.5714 | 0.7559 |
| 0.0601 | 6.5570 | 3908 | 0.5732 | 0.2154 | 0.5732 | 0.7571 |
| 0.0601 | 6.5604 | 3910 | 0.5791 | 0.2154 | 0.5791 | 0.7610 |
| 0.0601 | 6.5638 | 3912 | 0.5940 | 0.2154 | 0.5940 | 0.7707 |
| 0.0601 | 6.5671 | 3914 | 0.6079 | 0.2154 | 0.6079 | 0.7797 |
| 0.0601 | 6.5705 | 3916 | 0.6162 | 0.3143 | 0.6162 | 0.7850 |
| 0.0601 | 6.5738 | 3918 | 0.6152 | 0.3143 | 0.6152 | 0.7844 |
| 0.0601 | 6.5772 | 3920 | 0.5996 | 0.2154 | 0.5996 | 0.7743 |
| 0.0601 | 6.5805 | 3922 | 0.5872 | 0.2727 | 0.5872 | 0.7663 |
| 0.0601 | 6.5839 | 3924 | 0.5801 | 0.2727 | 0.5801 | 0.7616 |
| 0.0601 | 6.5872 | 3926 | 0.5724 | 0.2727 | 0.5724 | 0.7566 |
| 0.0601 | 6.5906 | 3928 | 0.5709 | 0.2388 | 0.5709 | 0.7556 |
| 0.0601 | 6.5940 | 3930 | 0.5743 | 0.2388 | 0.5743 | 0.7578 |
| 0.0601 | 6.5973 | 3932 | 0.5769 | 0.2388 | 0.5769 | 0.7596 |
| 0.0601 | 6.6007 | 3934 | 0.5827 | 0.2727 | 0.5827 | 0.7634 |
| 0.0601 | 6.6040 | 3936 | 0.5945 | 0.2727 | 0.5945 | 0.7710 |
| 0.0601 | 6.6074 | 3938 | 0.6056 | 0.3662 | 0.6056 | 0.7782 |
| 0.0601 | 6.6107 | 3940 | 0.6122 | 0.3143 | 0.6122 | 0.7824 |
| 0.0601 | 6.6141 | 3942 | 0.6073 | 0.3143 | 0.6073 | 0.7793 |
| 0.0601 | 6.6174 | 3944 | 0.5902 | 0.3662 | 0.5902 | 0.7682 |
| 0.0601 | 6.6208 | 3946 | 0.5798 | 0.2727 | 0.5798 | 0.7614 |
| 0.0601 | 6.6242 | 3948 | 0.5799 | 0.2727 | 0.5799 | 0.7615 |
| 0.0601 | 6.6275 | 3950 | 0.5850 | 0.2154 | 0.5850 | 0.7648 |
| 0.0601 | 6.6309 | 3952 | 0.5860 | 0.2154 | 0.5860 | 0.7655 |
| 0.0601 | 6.6342 | 3954 | 0.5814 | 0.2727 | 0.5814 | 0.7625 |
| 0.0601 | 6.6376 | 3956 | 0.5761 | 0.2727 | 0.5761 | 0.7590 |
| 0.0601 | 6.6409 | 3958 | 0.5767 | 0.2727 | 0.5767 | 0.7594 |
| 0.0601 | 6.6443 | 3960 | 0.5795 | 0.2727 | 0.5795 | 0.7613 |
| 0.0601 | 6.6477 | 3962 | 0.5749 | 0.2727 | 0.5749 | 0.7582 |
| 0.0601 | 6.6510 | 3964 | 0.5760 | 0.2727 | 0.5760 | 0.7589 |
| 0.0601 | 6.6544 | 3966 | 0.5840 | 0.2727 | 0.5840 | 0.7642 |
| 0.0601 | 6.6577 | 3968 | 0.5826 | 0.2727 | 0.5826 | 0.7633 |
| 0.0601 | 6.6611 | 3970 | 0.5782 | 0.2727 | 0.5782 | 0.7604 |
| 0.0601 | 6.6644 | 3972 | 0.5815 | 0.2727 | 0.5815 | 0.7626 |
| 0.0601 | 6.6678 | 3974 | 0.5860 | 0.2727 | 0.5860 | 0.7655 |
| 0.0601 | 6.6711 | 3976 | 0.5851 | 0.2727 | 0.5851 | 0.7649 |
| 0.0601 | 6.6745 | 3978 | 0.5840 | 0.2727 | 0.5840 | 0.7642 |
| 0.0601 | 6.6779 | 3980 | 0.5836 | 0.2727 | 0.5836 | 0.7640 |
| 0.0601 | 6.6812 | 3982 | 0.5843 | 0.2727 | 0.5843 | 0.7644 |
| 0.0601 | 6.6846 | 3984 | 0.5882 | 0.2727 | 0.5882 | 0.7669 |
| 0.0601 | 6.6879 | 3986 | 0.5968 | 0.3662 | 0.5968 | 0.7725 |
| 0.0601 | 6.6913 | 3988 | 0.5990 | 0.3662 | 0.5990 | 0.7739 |
| 0.0601 | 6.6946 | 3990 | 0.5987 | 0.3662 | 0.5987 | 0.7738 |
| 0.0601 | 6.6980 | 3992 | 0.5886 | 0.2727 | 0.5886 | 0.7672 |
| 0.0601 | 6.7013 | 3994 | 0.5747 | 0.2727 | 0.5747 | 0.7581 |
| 0.0601 | 6.7047 | 3996 | 0.5704 | 0.2727 | 0.5704 | 0.7553 |
| 0.0601 | 6.7081 | 3998 | 0.5702 | 0.2727 | 0.5702 | 0.7551 |
| 0.0589 | 6.7114 | 4000 | 0.5753 | 0.2727 | 0.5753 | 0.7585 |
| 0.0589 | 6.7148 | 4002 | 0.5840 | 0.2727 | 0.5840 | 0.7642 |
| 0.0589 | 6.7181 | 4004 | 0.6000 | 0.3662 | 0.6000 | 0.7746 |
| 0.0589 | 6.7215 | 4006 | 0.6182 | 0.3143 | 0.6182 | 0.7862 |
| 0.0589 | 6.7248 | 4008 | 0.6219 | 0.3143 | 0.6219 | 0.7886 |
| 0.0589 | 6.7282 | 4010 | 0.6170 | 0.3662 | 0.6170 | 0.7855 |
| 0.0589 | 6.7315 | 4012 | 0.6122 | 0.3662 | 0.6122 | 0.7824 |
| 0.0589 | 6.7349 | 4014 | 0.6008 | 0.3662 | 0.6008 | 0.7751 |
| 0.0589 | 6.7383 | 4016 | 0.5942 | 0.2727 | 0.5942 | 0.7708 |
| 0.0589 | 6.7416 | 4018 | 0.5906 | 0.2388 | 0.5906 | 0.7685 |
| 0.0589 | 6.7450 | 4020 | 0.5937 | 0.2388 | 0.5937 | 0.7705 |
| 0.0589 | 6.7483 | 4022 | 0.6006 | 0.2388 | 0.6006 | 0.7750 |
| 0.0589 | 6.7517 | 4024 | 0.6109 | 0.3662 | 0.6109 | 0.7816 |
| 0.0589 | 6.7550 | 4026 | 0.6207 | 0.3662 | 0.6207 | 0.7879 |
| 0.0589 | 6.7584 | 4028 | 0.6347 | 0.3662 | 0.6347 | 0.7967 |
| 0.0589 | 6.7617 | 4030 | 0.6382 | 0.3143 | 0.6382 | 0.7989 |
| 0.0589 | 6.7651 | 4032 | 0.6279 | 0.3143 | 0.6279 | 0.7924 |
| 0.0589 | 6.7685 | 4034 | 0.6102 | 0.3662 | 0.6102 | 0.7812 |
| 0.0589 | 6.7718 | 4036 | 0.5954 | 0.2727 | 0.5954 | 0.7716 |
| 0.0589 | 6.7752 | 4038 | 0.5880 | 0.2388 | 0.5880 | 0.7668 |
| 0.0589 | 6.7785 | 4040 | 0.5871 | 0.2857 | 0.5871 | 0.7662 |
| 0.0589 | 6.7819 | 4042 | 0.5870 | 0.2857 | 0.5870 | 0.7661 |
| 0.0589 | 6.7852 | 4044 | 0.5886 | 0.2857 | 0.5886 | 0.7672 |
| 0.0589 | 6.7886 | 4046 | 0.5911 | 0.2258 | 0.5911 | 0.7689 |
| 0.0589 | 6.7919 | 4048 | 0.5955 | 0.2388 | 0.5955 | 0.7717 |
| 0.0589 | 6.7953 | 4050 | 0.6072 | 0.2727 | 0.6072 | 0.7792 |
| 0.0589 | 6.7987 | 4052 | 0.6199 | 0.3662 | 0.6199 | 0.7873 |
| 0.0589 | 6.8020 | 4054 | 0.6282 | 0.3143 | 0.6282 | 0.7926 |
| 0.0589 | 6.8054 | 4056 | 0.6308 | 0.3143 | 0.6308 | 0.7942 |
| 0.0589 | 6.8087 | 4058 | 0.6325 | 0.3662 | 0.6325 | 0.7953 |
| 0.0589 | 6.8121 | 4060 | 0.6310 | 0.3662 | 0.6310 | 0.7944 |
| 0.0589 | 6.8154 | 4062 | 0.6218 | 0.3662 | 0.6218 | 0.7886 |
| 0.0589 | 6.8188 | 4064 | 0.6056 | 0.2727 | 0.6056 | 0.7782 |
| 0.0589 | 6.8221 | 4066 | 0.5928 | 0.2727 | 0.5928 | 0.7700 |
| 0.0589 | 6.8255 | 4068 | 0.5876 | 0.2623 | 0.5876 | 0.7665 |
| 0.0589 | 6.8289 | 4070 | 0.5883 | 0.2623 | 0.5883 | 0.7670 |
| 0.0589 | 6.8322 | 4072 | 0.5907 | 0.2727 | 0.5907 | 0.7686 |
| 0.0589 | 6.8356 | 4074 | 0.5948 | 0.2727 | 0.5948 | 0.7712 |
| 0.0589 | 6.8389 | 4076 | 0.5976 | 0.2727 | 0.5976 | 0.7730 |
| 0.0589 | 6.8423 | 4078 | 0.5998 | 0.3662 | 0.5998 | 0.7744 |
| 0.0589 | 6.8456 | 4080 | 0.6036 | 0.3662 | 0.6036 | 0.7769 |
| 0.0589 | 6.8490 | 4082 | 0.6095 | 0.3143 | 0.6095 | 0.7807 |
| 0.0589 | 6.8523 | 4084 | 0.6101 | 0.3143 | 0.6101 | 0.7811 |
| 0.0589 | 6.8557 | 4086 | 0.6050 | 0.3143 | 0.6050 | 0.7778 |
| 0.0589 | 6.8591 | 4088 | 0.6003 | 0.3143 | 0.6003 | 0.7748 |
| 0.0589 | 6.8624 | 4090 | 0.5922 | 0.2727 | 0.5922 | 0.7696 |
| 0.0589 | 6.8658 | 4092 | 0.5867 | 0.2727 | 0.5867 | 0.7659 |
| 0.0589 | 6.8691 | 4094 | 0.5865 | 0.2727 | 0.5865 | 0.7658 |
| 0.0589 | 6.8725 | 4096 | 0.5889 | 0.2727 | 0.5889 | 0.7674 |
| 0.0589 | 6.8758 | 4098 | 0.5890 | 0.2727 | 0.5890 | 0.7675 |
| 0.0589 | 6.8792 | 4100 | 0.5915 | 0.2727 | 0.5915 | 0.7691 |
| 0.0589 | 6.8826 | 4102 | 0.6014 | 0.3662 | 0.6014 | 0.7755 |
| 0.0589 | 6.8859 | 4104 | 0.6187 | 0.3662 | 0.6187 | 0.7866 |
| 0.0589 | 6.8893 | 4106 | 0.6251 | 0.3662 | 0.6251 | 0.7906 |
| 0.0589 | 6.8926 | 4108 | 0.6342 | 0.3143 | 0.6342 | 0.7964 |
| 0.0589 | 6.8960 | 4110 | 0.6402 | 0.3143 | 0.6402 | 0.8001 |
| 0.0589 | 6.8993 | 4112 | 0.6452 | 0.3143 | 0.6452 | 0.8032 |
| 0.0589 | 6.9027 | 4114 | 0.6338 | 0.3143 | 0.6338 | 0.7961 |
| 0.0589 | 6.9060 | 4116 | 0.6138 | 0.3662 | 0.6138 | 0.7834 |
| 0.0589 | 6.9094 | 4118 | 0.5995 | 0.3662 | 0.5995 | 0.7743 |
| 0.0589 | 6.9128 | 4120 | 0.5927 | 0.2623 | 0.5927 | 0.7699 |
| 0.0589 | 6.9161 | 4122 | 0.5911 | 0.2623 | 0.5911 | 0.7688 |
| 0.0589 | 6.9195 | 4124 | 0.5888 | 0.2623 | 0.5888 | 0.7674 |
| 0.0589 | 6.9228 | 4126 | 0.5913 | 0.2623 | 0.5913 | 0.7690 |
| 0.0589 | 6.9262 | 4128 | 0.5973 | 0.3636 | 0.5973 | 0.7729 |
| 0.0589 | 6.9295 | 4130 | 0.6094 | 0.3662 | 0.6094 | 0.7806 |
| 0.0589 | 6.9329 | 4132 | 0.6212 | 0.3143 | 0.6212 | 0.7882 |
| 0.0589 | 6.9362 | 4134 | 0.6359 | 0.3143 | 0.6359 | 0.7974 |
| 0.0589 | 6.9396 | 4136 | 0.6523 | 0.3143 | 0.6523 | 0.8076 |
| 0.0589 | 6.9430 | 4138 | 0.6573 | 0.3143 | 0.6573 | 0.8107 |
| 0.0589 | 6.9463 | 4140 | 0.6469 | 0.3143 | 0.6469 | 0.8043 |
| 0.0589 | 6.9497 | 4142 | 0.6316 | 0.3143 | 0.6316 | 0.7948 |
| 0.0589 | 6.9530 | 4144 | 0.6097 | 0.3077 | 0.6097 | 0.7809 |
| 0.0589 | 6.9564 | 4146 | 0.5987 | 0.2623 | 0.5987 | 0.7737 |
| 0.0589 | 6.9597 | 4148 | 0.5937 | 0.2623 | 0.5937 | 0.7705 |
| 0.0589 | 6.9631 | 4150 | 0.5934 | 0.2623 | 0.5934 | 0.7703 |
| 0.0589 | 6.9664 | 4152 | 0.5948 | 0.2258 | 0.5948 | 0.7713 |
| 0.0589 | 6.9698 | 4154 | 0.6018 | 0.2258 | 0.6018 | 0.7758 |
| 0.0589 | 6.9732 | 4156 | 0.6142 | 0.3636 | 0.6142 | 0.7837 |
| 0.0589 | 6.9765 | 4158 | 0.6367 | 0.3143 | 0.6367 | 0.7979 |
| 0.0589 | 6.9799 | 4160 | 0.6531 | 0.3143 | 0.6531 | 0.8081 |
| 0.0589 | 6.9832 | 4162 | 0.6571 | 0.3143 | 0.6571 | 0.8106 |
| 0.0589 | 6.9866 | 4164 | 0.6485 | 0.3143 | 0.6485 | 0.8053 |
| 0.0589 | 6.9899 | 4166 | 0.6332 | 0.3143 | 0.6332 | 0.7957 |
| 0.0589 | 6.9933 | 4168 | 0.6243 | 0.3143 | 0.6243 | 0.7901 |
| 0.0589 | 6.9966 | 4170 | 0.6077 | 0.2154 | 0.6077 | 0.7796 |
| 0.0589 | 7.0 | 4172 | 0.5901 | 0.2623 | 0.5901 | 0.7682 |
| 0.0589 | 7.0034 | 4174 | 0.5785 | 0.2258 | 0.5785 | 0.7606 |
| 0.0589 | 7.0067 | 4176 | 0.5740 | 0.2258 | 0.5740 | 0.7576 |
| 0.0589 | 7.0101 | 4178 | 0.5752 | 0.2258 | 0.5752 | 0.7584 |
| 0.0589 | 7.0134 | 4180 | 0.5812 | 0.2258 | 0.5812 | 0.7624 |
| 0.0589 | 7.0168 | 4182 | 0.5883 | 0.2727 | 0.5883 | 0.7670 |
| 0.0589 | 7.0201 | 4184 | 0.6044 | 0.3143 | 0.6044 | 0.7774 |
| 0.0589 | 7.0235 | 4186 | 0.6265 | 0.3143 | 0.6265 | 0.7915 |
| 0.0589 | 7.0268 | 4188 | 0.6380 | 0.3143 | 0.6380 | 0.7988 |
| 0.0589 | 7.0302 | 4190 | 0.6479 | 0.3143 | 0.6479 | 0.8049 |
| 0.0589 | 7.0336 | 4192 | 0.6447 | 0.3143 | 0.6447 | 0.8029 |
| 0.0589 | 7.0369 | 4194 | 0.6285 | 0.3143 | 0.6285 | 0.7928 |
| 0.0589 | 7.0403 | 4196 | 0.6141 | 0.3662 | 0.6141 | 0.7836 |
| 0.0589 | 7.0436 | 4198 | 0.6076 | 0.3662 | 0.6076 | 0.7795 |
| 0.0589 | 7.0470 | 4200 | 0.6060 | 0.3662 | 0.6060 | 0.7785 |
| 0.0589 | 7.0503 | 4202 | 0.6017 | 0.2727 | 0.6017 | 0.7757 |
| 0.0589 | 7.0537 | 4204 | 0.6016 | 0.2727 | 0.6016 | 0.7756 |
| 0.0589 | 7.0570 | 4206 | 0.6094 | 0.3143 | 0.6094 | 0.7806 |
| 0.0589 | 7.0604 | 4208 | 0.6244 | 0.3143 | 0.6244 | 0.7902 |
| 0.0589 | 7.0638 | 4210 | 0.6443 | 0.3478 | 0.6443 | 0.8027 |
| 0.0589 | 7.0671 | 4212 | 0.6695 | 0.2941 | 0.6695 | 0.8182 |
| 0.0589 | 7.0705 | 4214 | 0.6818 | 0.2941 | 0.6818 | 0.8257 |
| 0.0589 | 7.0738 | 4216 | 0.6730 | 0.3284 | 0.6730 | 0.8204 |
| 0.0589 | 7.0772 | 4218 | 0.6586 | 0.3284 | 0.6586 | 0.8115 |
| 0.0589 | 7.0805 | 4220 | 0.6323 | 0.2941 | 0.6323 | 0.7952 |
| 0.0589 | 7.0839 | 4222 | 0.6065 | 0.25 | 0.6065 | 0.7788 |
| 0.0589 | 7.0872 | 4224 | 0.5974 | 0.3077 | 0.5974 | 0.7729 |
| 0.0589 | 7.0906 | 4226 | 0.5926 | 0.3077 | 0.5926 | 0.7698 |
| 0.0589 | 7.0940 | 4228 | 0.5927 | 0.3077 | 0.5927 | 0.7698 |
| 0.0589 | 7.0973 | 4230 | 0.5924 | 0.3077 | 0.5924 | 0.7697 |
| 0.0589 | 7.1007 | 4232 | 0.5973 | 0.3143 | 0.5973 | 0.7729 |
| 0.0589 | 7.1040 | 4234 | 0.6079 | 0.3143 | 0.6079 | 0.7797 |
| 0.0589 | 7.1074 | 4236 | 0.6169 | 0.3143 | 0.6169 | 0.7854 |
| 0.0589 | 7.1107 | 4238 | 0.6185 | 0.3143 | 0.6185 | 0.7864 |
| 0.0589 | 7.1141 | 4240 | 0.6204 | 0.3143 | 0.6204 | 0.7876 |
| 0.0589 | 7.1174 | 4242 | 0.6179 | 0.3143 | 0.6179 | 0.7860 |
| 0.0589 | 7.1208 | 4244 | 0.6184 | 0.3143 | 0.6184 | 0.7864 |
| 0.0589 | 7.1242 | 4246 | 0.6230 | 0.3143 | 0.6230 | 0.7893 |
| 0.0589 | 7.1275 | 4248 | 0.6246 | 0.3143 | 0.6246 | 0.7903 |
| 0.0589 | 7.1309 | 4250 | 0.6326 | 0.3143 | 0.6326 | 0.7954 |
| 0.0589 | 7.1342 | 4252 | 0.6288 | 0.3143 | 0.6288 | 0.7930 |
| 0.0589 | 7.1376 | 4254 | 0.6196 | 0.3143 | 0.6196 | 0.7872 |
| 0.0589 | 7.1409 | 4256 | 0.6095 | 0.3143 | 0.6095 | 0.7807 |
| 0.0589 | 7.1443 | 4258 | 0.6020 | 0.2154 | 0.6020 | 0.7759 |
| 0.0589 | 7.1477 | 4260 | 0.6022 | 0.2154 | 0.6022 | 0.7760 |
| 0.0589 | 7.1510 | 4262 | 0.6037 | 0.2154 | 0.6037 | 0.7770 |
| 0.0589 | 7.1544 | 4264 | 0.6117 | 0.3143 | 0.6117 | 0.7821 |
| 0.0589 | 7.1577 | 4266 | 0.6242 | 0.3143 | 0.6242 | 0.7901 |
| 0.0589 | 7.1611 | 4268 | 0.6475 | 0.3143 | 0.6475 | 0.8047 |
| 0.0589 | 7.1644 | 4270 | 0.6646 | 0.2609 | 0.6646 | 0.8152 |
| 0.0589 | 7.1678 | 4272 | 0.6716 | 0.2609 | 0.6716 | 0.8195 |
| 0.0589 | 7.1711 | 4274 | 0.6732 | 0.2609 | 0.6732 | 0.8205 |
| 0.0589 | 7.1745 | 4276 | 0.6618 | 0.2609 | 0.6618 | 0.8135 |
| 0.0589 | 7.1779 | 4278 | 0.6571 | 0.2941 | 0.6571 | 0.8106 |
| 0.0589 | 7.1812 | 4280 | 0.6483 | 0.2941 | 0.6483 | 0.8052 |
| 0.0589 | 7.1846 | 4282 | 0.6425 | 0.2941 | 0.6425 | 0.8015 |
| 0.0589 | 7.1879 | 4284 | 0.6495 | 0.2941 | 0.6495 | 0.8059 |
| 0.0589 | 7.1913 | 4286 | 0.6439 | 0.2609 | 0.6439 | 0.8024 |
| 0.0589 | 7.1946 | 4288 | 0.6307 | 0.1562 | 0.6307 | 0.7942 |
| 0.0589 | 7.1980 | 4290 | 0.6124 | 0.2154 | 0.6124 | 0.7826 |
| 0.0589 | 7.2013 | 4292 | 0.6049 | 0.2154 | 0.6049 | 0.7778 |
| 0.0589 | 7.2047 | 4294 | 0.6025 | 0.2727 | 0.6025 | 0.7762 |
| 0.0589 | 7.2081 | 4296 | 0.6095 | 0.2727 | 0.6095 | 0.7807 |
| 0.0589 | 7.2114 | 4298 | 0.6225 | 0.3143 | 0.6225 | 0.7890 |
| 0.0589 | 7.2148 | 4300 | 0.6392 | 0.3143 | 0.6392 | 0.7995 |
| 0.0589 | 7.2181 | 4302 | 0.6510 | 0.3143 | 0.6510 | 0.8068 |
| 0.0589 | 7.2215 | 4304 | 0.6643 | 0.3143 | 0.6643 | 0.8151 |
| 0.0589 | 7.2248 | 4306 | 0.6647 | 0.2609 | 0.6647 | 0.8153 |
| 0.0589 | 7.2282 | 4308 | 0.6508 | 0.3143 | 0.6508 | 0.8067 |
| 0.0589 | 7.2315 | 4310 | 0.6384 | 0.3143 | 0.6384 | 0.7990 |
| 0.0589 | 7.2349 | 4312 | 0.6259 | 0.3143 | 0.6259 | 0.7911 |
| 0.0589 | 7.2383 | 4314 | 0.6194 | 0.2154 | 0.6194 | 0.7870 |
| 0.0589 | 7.2416 | 4316 | 0.6196 | 0.2154 | 0.6196 | 0.7871 |
| 0.0589 | 7.2450 | 4318 | 0.6215 | 0.2154 | 0.6215 | 0.7884 |
| 0.0589 | 7.2483 | 4320 | 0.6265 | 0.3143 | 0.6265 | 0.7915 |
| 0.0589 | 7.2517 | 4322 | 0.6390 | 0.2609 | 0.6390 | 0.7994 |
| 0.0589 | 7.2550 | 4324 | 0.6574 | 0.2609 | 0.6574 | 0.8108 |
| 0.0589 | 7.2584 | 4326 | 0.6670 | 0.2941 | 0.6670 | 0.8167 |
| 0.0589 | 7.2617 | 4328 | 0.6633 | 0.2941 | 0.6633 | 0.8144 |
| 0.0589 | 7.2651 | 4330 | 0.6553 | 0.2941 | 0.6553 | 0.8095 |
| 0.0589 | 7.2685 | 4332 | 0.6514 | 0.2941 | 0.6514 | 0.8071 |
| 0.0589 | 7.2718 | 4334 | 0.6395 | 0.2609 | 0.6395 | 0.7997 |
| 0.0589 | 7.2752 | 4336 | 0.6407 | 0.2609 | 0.6407 | 0.8004 |
| 0.0589 | 7.2785 | 4338 | 0.6480 | 0.2941 | 0.6480 | 0.8050 |
| 0.0589 | 7.2819 | 4340 | 0.6601 | 0.2941 | 0.6601 | 0.8125 |
| 0.0589 | 7.2852 | 4342 | 0.6746 | 0.2941 | 0.6746 | 0.8213 |
| 0.0589 | 7.2886 | 4344 | 0.6757 | 0.2941 | 0.6757 | 0.8220 |
| 0.0589 | 7.2919 | 4346 | 0.6737 | 0.2609 | 0.6737 | 0.8208 |
| 0.0589 | 7.2953 | 4348 | 0.6546 | 0.3143 | 0.6546 | 0.8091 |
| 0.0589 | 7.2987 | 4350 | 0.6336 | 0.3143 | 0.6336 | 0.7960 |
| 0.0589 | 7.3020 | 4352 | 0.6148 | 0.3143 | 0.6148 | 0.7841 |
| 0.0589 | 7.3054 | 4354 | 0.6025 | 0.2154 | 0.6025 | 0.7762 |
| 0.0589 | 7.3087 | 4356 | 0.5955 | 0.2154 | 0.5955 | 0.7717 |
| 0.0589 | 7.3121 | 4358 | 0.5921 | 0.2154 | 0.5921 | 0.7695 |
| 0.0589 | 7.3154 | 4360 | 0.5887 | 0.2727 | 0.5887 | 0.7673 |
| 0.0589 | 7.3188 | 4362 | 0.5900 | 0.2727 | 0.5900 | 0.7681 |
| 0.0589 | 7.3221 | 4364 | 0.5929 | 0.2154 | 0.5929 | 0.7700 |
| 0.0589 | 7.3255 | 4366 | 0.5986 | 0.2154 | 0.5986 | 0.7737 |
| 0.0589 | 7.3289 | 4368 | 0.6080 | 0.2154 | 0.6080 | 0.7797 |
| 0.0589 | 7.3322 | 4370 | 0.6211 | 0.2154 | 0.6211 | 0.7881 |
| 0.0589 | 7.3356 | 4372 | 0.6262 | 0.2154 | 0.6262 | 0.7913 |
| 0.0589 | 7.3389 | 4374 | 0.6313 | 0.2154 | 0.6313 | 0.7946 |
| 0.0589 | 7.3423 | 4376 | 0.6328 | 0.2154 | 0.6328 | 0.7955 |
| 0.0589 | 7.3456 | 4378 | 0.6292 | 0.2154 | 0.6292 | 0.7932 |
| 0.0589 | 7.3490 | 4380 | 0.6179 | 0.2154 | 0.6179 | 0.7861 |
| 0.0589 | 7.3523 | 4382 | 0.6056 | 0.2727 | 0.6056 | 0.7782 |
| 0.0589 | 7.3557 | 4384 | 0.6058 | 0.3284 | 0.6058 | 0.7783 |
| 0.0589 | 7.3591 | 4386 | 0.6088 | 0.3284 | 0.6088 | 0.7803 |
| 0.0589 | 7.3624 | 4388 | 0.6164 | 0.2727 | 0.6164 | 0.7851 |
| 0.0589 | 7.3658 | 4390 | 0.6308 | 0.2154 | 0.6308 | 0.7942 |
| 0.0589 | 7.3691 | 4392 | 0.6381 | 0.3143 | 0.6381 | 0.7988 |
| 0.0589 | 7.3725 | 4394 | 0.6414 | 0.3143 | 0.6414 | 0.8009 |
| 0.0589 | 7.3758 | 4396 | 0.6408 | 0.3143 | 0.6408 | 0.8005 |
| 0.0589 | 7.3792 | 4398 | 0.6371 | 0.3143 | 0.6371 | 0.7982 |
| 0.0589 | 7.3826 | 4400 | 0.6315 | 0.2154 | 0.6315 | 0.7947 |
| 0.0589 | 7.3859 | 4402 | 0.6162 | 0.2154 | 0.6162 | 0.7850 |
| 0.0589 | 7.3893 | 4404 | 0.6068 | 0.3284 | 0.6068 | 0.7790 |
| 0.0589 | 7.3926 | 4406 | 0.6033 | 0.3284 | 0.6033 | 0.7767 |
| 0.0589 | 7.3960 | 4408 | 0.5972 | 0.3284 | 0.5972 | 0.7728 |
| 0.0589 | 7.3993 | 4410 | 0.5965 | 0.3284 | 0.5965 | 0.7723 |
| 0.0589 | 7.4027 | 4412 | 0.6037 | 0.2154 | 0.6037 | 0.7770 |
| 0.0589 | 7.4060 | 4414 | 0.6161 | 0.2154 | 0.6161 | 0.7849 |
| 0.0589 | 7.4094 | 4416 | 0.6387 | 0.2154 | 0.6387 | 0.7992 |
| 0.0589 | 7.4128 | 4418 | 0.6603 | 0.3143 | 0.6603 | 0.8126 |
| 0.0589 | 7.4161 | 4420 | 0.6709 | 0.3143 | 0.6709 | 0.8191 |
| 0.0589 | 7.4195 | 4422 | 0.6787 | 0.3143 | 0.6787 | 0.8238 |
| 0.0589 | 7.4228 | 4424 | 0.6805 | 0.3143 | 0.6805 | 0.8249 |
| 0.0589 | 7.4262 | 4426 | 0.6711 | 0.3143 | 0.6711 | 0.8192 |
| 0.0589 | 7.4295 | 4428 | 0.6583 | 0.3143 | 0.6583 | 0.8114 |
| 0.0589 | 7.4329 | 4430 | 0.6493 | 0.3143 | 0.6493 | 0.8058 |
| 0.0589 | 7.4362 | 4432 | 0.6473 | 0.3143 | 0.6473 | 0.8045 |
| 0.0589 | 7.4396 | 4434 | 0.6491 | 0.3143 | 0.6491 | 0.8057 |
| 0.0589 | 7.4430 | 4436 | 0.6516 | 0.3143 | 0.6516 | 0.8072 |
| 0.0589 | 7.4463 | 4438 | 0.6439 | 0.2154 | 0.6439 | 0.8024 |
| 0.0589 | 7.4497 | 4440 | 0.6309 | 0.3284 | 0.6309 | 0.7943 |
| 0.0589 | 7.4530 | 4442 | 0.6271 | 0.3284 | 0.6271 | 0.7919 |
| 0.0589 | 7.4564 | 4444 | 0.6282 | 0.3284 | 0.6282 | 0.7926 |
| 0.0589 | 7.4597 | 4446 | 0.6298 | 0.3284 | 0.6298 | 0.7936 |
| 0.0589 | 7.4631 | 4448 | 0.6276 | 0.3284 | 0.6276 | 0.7922 |
| 0.0589 | 7.4664 | 4450 | 0.6256 | 0.3284 | 0.6256 | 0.7909 |
| 0.0589 | 7.4698 | 4452 | 0.6303 | 0.3284 | 0.6303 | 0.7939 |
| 0.0589 | 7.4732 | 4454 | 0.6404 | 0.2154 | 0.6404 | 0.8003 |
| 0.0589 | 7.4765 | 4456 | 0.6541 | 0.3143 | 0.6541 | 0.8088 |
| 0.0589 | 7.4799 | 4458 | 0.6616 | 0.3143 | 0.6616 | 0.8134 |
| 0.0589 | 7.4832 | 4460 | 0.6683 | 0.3143 | 0.6683 | 0.8175 |
| 0.0589 | 7.4866 | 4462 | 0.6699 | 0.3143 | 0.6699 | 0.8185 |
| 0.0589 | 7.4899 | 4464 | 0.6691 | 0.3143 | 0.6691 | 0.8180 |
| 0.0589 | 7.4933 | 4466 | 0.6596 | 0.3143 | 0.6596 | 0.8122 |
| 0.0589 | 7.4966 | 4468 | 0.6459 | 0.2154 | 0.6459 | 0.8037 |
| 0.0589 | 7.5 | 4470 | 0.6428 | 0.2154 | 0.6428 | 0.8017 |
| 0.0589 | 7.5034 | 4472 | 0.6328 | 0.2154 | 0.6328 | 0.7955 |
| 0.0589 | 7.5067 | 4474 | 0.6281 | 0.2154 | 0.6281 | 0.7925 |
| 0.0589 | 7.5101 | 4476 | 0.6208 | 0.2154 | 0.6208 | 0.7879 |
| 0.0589 | 7.5134 | 4478 | 0.6202 | 0.2154 | 0.6202 | 0.7875 |
| 0.0589 | 7.5168 | 4480 | 0.6169 | 0.2154 | 0.6169 | 0.7854 |
| 0.0589 | 7.5201 | 4482 | 0.6187 | 0.2154 | 0.6187 | 0.7866 |
| 0.0589 | 7.5235 | 4484 | 0.6235 | 0.2154 | 0.6235 | 0.7896 |
| 0.0589 | 7.5268 | 4486 | 0.6217 | 0.2154 | 0.6217 | 0.7885 |
| 0.0589 | 7.5302 | 4488 | 0.6176 | 0.2154 | 0.6176 | 0.7859 |
| 0.0589 | 7.5336 | 4490 | 0.6221 | 0.2154 | 0.6221 | 0.7887 |
| 0.0589 | 7.5369 | 4492 | 0.6306 | 0.2154 | 0.6306 | 0.7941 |
| 0.0589 | 7.5403 | 4494 | 0.6450 | 0.2154 | 0.6450 | 0.8031 |
| 0.0589 | 7.5436 | 4496 | 0.6555 | 0.2154 | 0.6555 | 0.8096 |
| 0.0589 | 7.5470 | 4498 | 0.6615 | 0.3143 | 0.6615 | 0.8133 |
| 0.0516 | 7.5503 | 4500 | 0.6658 | 0.3143 | 0.6658 | 0.8160 |
| 0.0516 | 7.5537 | 4502 | 0.6777 | 0.3143 | 0.6777 | 0.8232 |
| 0.0516 | 7.5570 | 4504 | 0.6817 | 0.3143 | 0.6817 | 0.8256 |
| 0.0516 | 7.5604 | 4506 | 0.6783 | 0.3143 | 0.6783 | 0.8236 |
| 0.0516 | 7.5638 | 4508 | 0.6876 | 0.3143 | 0.6876 | 0.8292 |
| 0.0516 | 7.5671 | 4510 | 0.6946 | 0.2609 | 0.6946 | 0.8334 |
| 0.0516 | 7.5705 | 4512 | 0.7060 | 0.2941 | 0.7060 | 0.8403 |
| 0.0516 | 7.5738 | 4514 | 0.7096 | 0.2941 | 0.7096 | 0.8424 |
| 0.0516 | 7.5772 | 4516 | 0.7019 | 0.2941 | 0.7019 | 0.8378 |
| 0.0516 | 7.5805 | 4518 | 0.6901 | 0.2609 | 0.6901 | 0.8307 |
| 0.0516 | 7.5839 | 4520 | 0.6695 | 0.2154 | 0.6695 | 0.8182 |
| 0.0516 | 7.5872 | 4522 | 0.6598 | 0.2154 | 0.6598 | 0.8123 |
| 0.0516 | 7.5906 | 4524 | 0.6504 | 0.2154 | 0.6504 | 0.8065 |
| 0.0516 | 7.5940 | 4526 | 0.6440 | 0.2154 | 0.6440 | 0.8025 |
| 0.0516 | 7.5973 | 4528 | 0.6353 | 0.2154 | 0.6353 | 0.7970 |
| 0.0516 | 7.6007 | 4530 | 0.6297 | 0.2154 | 0.6297 | 0.7935 |
| 0.0516 | 7.6040 | 4532 | 0.6254 | 0.2154 | 0.6254 | 0.7908 |
| 0.0516 | 7.6074 | 4534 | 0.6298 | 0.2154 | 0.6298 | 0.7936 |
| 0.0516 | 7.6107 | 4536 | 0.6310 | 0.2154 | 0.6310 | 0.7944 |
| 0.0516 | 7.6141 | 4538 | 0.6249 | 0.2727 | 0.6249 | 0.7905 |
| 0.0516 | 7.6174 | 4540 | 0.6257 | 0.2727 | 0.6257 | 0.7910 |
| 0.0516 | 7.6208 | 4542 | 0.6310 | 0.2727 | 0.6310 | 0.7943 |
| 0.0516 | 7.6242 | 4544 | 0.6355 | 0.2727 | 0.6355 | 0.7972 |
| 0.0516 | 7.6275 | 4546 | 0.6426 | 0.2727 | 0.6426 | 0.8016 |
| 0.0516 | 7.6309 | 4548 | 0.6553 | 0.2727 | 0.6553 | 0.8095 |
| 0.0516 | 7.6342 | 4550 | 0.6685 | 0.3143 | 0.6685 | 0.8176 |
| 0.0516 | 7.6376 | 4552 | 0.6716 | 0.3143 | 0.6716 | 0.8195 |
| 0.0516 | 7.6409 | 4554 | 0.6638 | 0.3143 | 0.6638 | 0.8148 |
| 0.0516 | 7.6443 | 4556 | 0.6504 | 0.3143 | 0.6504 | 0.8065 |
| 0.0516 | 7.6477 | 4558 | 0.6438 | 0.2154 | 0.6438 | 0.8024 |
| 0.0516 | 7.6510 | 4560 | 0.6339 | 0.2154 | 0.6339 | 0.7962 |
| 0.0516 | 7.6544 | 4562 | 0.6192 | 0.2727 | 0.6192 | 0.7869 |
| 0.0516 | 7.6577 | 4564 | 0.6120 | 0.2727 | 0.6120 | 0.7823 |
| 0.0516 | 7.6611 | 4566 | 0.6148 | 0.2154 | 0.6148 | 0.7841 |
| 0.0516 | 7.6644 | 4568 | 0.6250 | 0.2154 | 0.6250 | 0.7906 |
| 0.0516 | 7.6678 | 4570 | 0.6328 | 0.2154 | 0.6328 | 0.7955 |
| 0.0516 | 7.6711 | 4572 | 0.6461 | 0.2154 | 0.6461 | 0.8038 |
| 0.0516 | 7.6745 | 4574 | 0.6648 | 0.2941 | 0.6648 | 0.8154 |
| 0.0516 | 7.6779 | 4576 | 0.6707 | 0.2941 | 0.6707 | 0.8189 |
| 0.0516 | 7.6812 | 4578 | 0.6622 | 0.2609 | 0.6622 | 0.8138 |
| 0.0516 | 7.6846 | 4580 | 0.6544 | 0.3143 | 0.6544 | 0.8089 |
| 0.0516 | 7.6879 | 4582 | 0.6537 | 0.3143 | 0.6537 | 0.8085 |
| 0.0516 | 7.6913 | 4584 | 0.6625 | 0.2609 | 0.6625 | 0.8140 |
| 0.0516 | 7.6946 | 4586 | 0.6630 | 0.2609 | 0.6630 | 0.8142 |
| 0.0516 | 7.6980 | 4588 | 0.6514 | 0.3143 | 0.6514 | 0.8071 |
| 0.0516 | 7.7013 | 4590 | 0.6368 | 0.2154 | 0.6368 | 0.7980 |
| 0.0516 | 7.7047 | 4592 | 0.6356 | 0.2154 | 0.6356 | 0.7973 |
| 0.0516 | 7.7081 | 4594 | 0.6330 | 0.2154 | 0.6330 | 0.7956 |
| 0.0516 | 7.7114 | 4596 | 0.6310 | 0.2154 | 0.6310 | 0.7944 |
| 0.0516 | 7.7148 | 4598 | 0.6316 | 0.2154 | 0.6316 | 0.7947 |
| 0.0516 | 7.7181 | 4600 | 0.6235 | 0.2154 | 0.6235 | 0.7896 |
| 0.0516 | 7.7215 | 4602 | 0.6133 | 0.2154 | 0.6133 | 0.7832 |
| 0.0516 | 7.7248 | 4604 | 0.6090 | 0.2154 | 0.6090 | 0.7804 |
| 0.0516 | 7.7282 | 4606 | 0.6106 | 0.2154 | 0.6106 | 0.7814 |
| 0.0516 | 7.7315 | 4608 | 0.6104 | 0.2154 | 0.6104 | 0.7813 |
| 0.0516 | 7.7349 | 4610 | 0.6104 | 0.2154 | 0.6104 | 0.7813 |
| 0.0516 | 7.7383 | 4612 | 0.6083 | 0.2154 | 0.6083 | 0.7800 |
| 0.0516 | 7.7416 | 4614 | 0.6144 | 0.2154 | 0.6144 | 0.7838 |
| 0.0516 | 7.7450 | 4616 | 0.6217 | 0.2154 | 0.6217 | 0.7885 |
| 0.0516 | 7.7483 | 4618 | 0.6313 | 0.2154 | 0.6313 | 0.7945 |
| 0.0516 | 7.7517 | 4620 | 0.6409 | 0.3143 | 0.6409 | 0.8006 |
| 0.0516 | 7.7550 | 4622 | 0.6466 | 0.3143 | 0.6466 | 0.8041 |
| 0.0516 | 7.7584 | 4624 | 0.6425 | 0.2154 | 0.6425 | 0.8015 |
| 0.0516 | 7.7617 | 4626 | 0.6346 | 0.2727 | 0.6346 | 0.7966 |
| 0.0516 | 7.7651 | 4628 | 0.6293 | 0.2727 | 0.6293 | 0.7933 |
| 0.0516 | 7.7685 | 4630 | 0.6204 | 0.2727 | 0.6204 | 0.7876 |
| 0.0516 | 7.7718 | 4632 | 0.6143 | 0.2727 | 0.6143 | 0.7838 |
| 0.0516 | 7.7752 | 4634 | 0.6127 | 0.2727 | 0.6127 | 0.7828 |
| 0.0516 | 7.7785 | 4636 | 0.6176 | 0.2154 | 0.6176 | 0.7859 |
| 0.0516 | 7.7819 | 4638 | 0.6214 | 0.2154 | 0.6214 | 0.7883 |
| 0.0516 | 7.7852 | 4640 | 0.6223 | 0.2154 | 0.6223 | 0.7889 |
| 0.0516 | 7.7886 | 4642 | 0.6245 | 0.2154 | 0.6245 | 0.7903 |
| 0.0516 | 7.7919 | 4644 | 0.6293 | 0.2154 | 0.6293 | 0.7933 |
| 0.0516 | 7.7953 | 4646 | 0.6313 | 0.2154 | 0.6313 | 0.7945 |
| 0.0516 | 7.7987 | 4648 | 0.6271 | 0.3284 | 0.6271 | 0.7919 |
| 0.0516 | 7.8020 | 4650 | 0.6199 | 0.3284 | 0.6199 | 0.7874 |
| 0.0516 | 7.8054 | 4652 | 0.6204 | 0.3284 | 0.6204 | 0.7876 |
| 0.0516 | 7.8087 | 4654 | 0.6257 | 0.3284 | 0.6257 | 0.7910 |
| 0.0516 | 7.8121 | 4656 | 0.6310 | 0.2154 | 0.6310 | 0.7943 |
| 0.0516 | 7.8154 | 4658 | 0.6393 | 0.2154 | 0.6393 | 0.7996 |
| 0.0516 | 7.8188 | 4660 | 0.6407 | 0.2154 | 0.6407 | 0.8004 |
| 0.0516 | 7.8221 | 4662 | 0.6373 | 0.2154 | 0.6373 | 0.7983 |
| 0.0516 | 7.8255 | 4664 | 0.6406 | 0.2154 | 0.6406 | 0.8003 |
| 0.0516 | 7.8289 | 4666 | 0.6461 | 0.2154 | 0.6461 | 0.8038 |
| 0.0516 | 7.8322 | 4668 | 0.6484 | 0.2154 | 0.6484 | 0.8053 |
| 0.0516 | 7.8356 | 4670 | 0.6556 | 0.25 | 0.6556 | 0.8097 |
| 0.0516 | 7.8389 | 4672 | 0.6586 | 0.25 | 0.6586 | 0.8115 |
| 0.0516 | 7.8423 | 4674 | 0.6544 | 0.2154 | 0.6544 | 0.8089 |
| 0.0516 | 7.8456 | 4676 | 0.6625 | 0.2154 | 0.6625 | 0.8140 |
| 0.0516 | 7.8490 | 4678 | 0.6658 | 0.2154 | 0.6658 | 0.8160 |
| 0.0516 | 7.8523 | 4680 | 0.6574 | 0.2154 | 0.6574 | 0.8108 |
| 0.0516 | 7.8557 | 4682 | 0.6402 | 0.2154 | 0.6402 | 0.8001 |
| 0.0516 | 7.8591 | 4684 | 0.6263 | 0.2154 | 0.6263 | 0.7914 |
| 0.0516 | 7.8624 | 4686 | 0.6230 | 0.2154 | 0.6230 | 0.7893 |
| 0.0516 | 7.8658 | 4688 | 0.6289 | 0.2154 | 0.6289 | 0.7930 |
| 0.0516 | 7.8691 | 4690 | 0.6337 | 0.2154 | 0.6337 | 0.7961 |
| 0.0516 | 7.8725 | 4692 | 0.6351 | 0.2154 | 0.6351 | 0.7969 |
| 0.0516 | 7.8758 | 4694 | 0.6361 | 0.2154 | 0.6361 | 0.7975 |
| 0.0516 | 7.8792 | 4696 | 0.6332 | 0.2154 | 0.6332 | 0.7958 |
| 0.0516 | 7.8826 | 4698 | 0.6262 | 0.2154 | 0.6262 | 0.7913 |
| 0.0516 | 7.8859 | 4700 | 0.6150 | 0.2154 | 0.6150 | 0.7842 |
| 0.0516 | 7.8893 | 4702 | 0.6113 | 0.2154 | 0.6113 | 0.7819 |
| 0.0516 | 7.8926 | 4704 | 0.6143 | 0.2154 | 0.6143 | 0.7837 |
| 0.0516 | 7.8960 | 4706 | 0.6190 | 0.2154 | 0.6190 | 0.7868 |
| 0.0516 | 7.8993 | 4708 | 0.6238 | 0.2154 | 0.6238 | 0.7898 |
| 0.0516 | 7.9027 | 4710 | 0.6372 | 0.1562 | 0.6372 | 0.7982 |
| 0.0516 | 7.9060 | 4712 | 0.6524 | 0.1905 | 0.6524 | 0.8077 |
| 0.0516 | 7.9094 | 4714 | 0.6598 | 0.1905 | 0.6598 | 0.8123 |
| 0.0516 | 7.9128 | 4716 | 0.6662 | 0.1905 | 0.6662 | 0.8162 |
| 0.0516 | 7.9161 | 4718 | 0.6711 | 0.1905 | 0.6711 | 0.8192 |
| 0.0516 | 7.9195 | 4720 | 0.6824 | 0.2941 | 0.6824 | 0.8261 |
| 0.0516 | 7.9228 | 4722 | 0.6845 | 0.2941 | 0.6845 | 0.8274 |
| 0.0516 | 7.9262 | 4724 | 0.6821 | 0.2941 | 0.6821 | 0.8259 |
| 0.0516 | 7.9295 | 4726 | 0.6810 | 0.2941 | 0.6810 | 0.8252 |
| 0.0516 | 7.9329 | 4728 | 0.6650 | 0.2609 | 0.6650 | 0.8155 |
| 0.0516 | 7.9362 | 4730 | 0.6500 | 0.2154 | 0.6500 | 0.8062 |
| 0.0516 | 7.9396 | 4732 | 0.6348 | 0.2154 | 0.6348 | 0.7967 |
| 0.0516 | 7.9430 | 4734 | 0.6192 | 0.2154 | 0.6192 | 0.7869 |
| 0.0516 | 7.9463 | 4736 | 0.6059 | 0.2154 | 0.6059 | 0.7784 |
| 0.0516 | 7.9497 | 4738 | 0.6011 | 0.3284 | 0.6011 | 0.7753 |
| 0.0516 | 7.9530 | 4740 | 0.6037 | 0.3284 | 0.6037 | 0.7770 |
| 0.0516 | 7.9564 | 4742 | 0.6091 | 0.2727 | 0.6091 | 0.7804 |
| 0.0516 | 7.9597 | 4744 | 0.6138 | 0.2727 | 0.6138 | 0.7835 |
| 0.0516 | 7.9631 | 4746 | 0.6267 | 0.2154 | 0.6267 | 0.7916 |
| 0.0516 | 7.9664 | 4748 | 0.6350 | 0.2154 | 0.6350 | 0.7969 |
| 0.0516 | 7.9698 | 4750 | 0.6325 | 0.2154 | 0.6325 | 0.7953 |
| 0.0516 | 7.9732 | 4752 | 0.6221 | 0.2154 | 0.6221 | 0.7887 |
| 0.0516 | 7.9765 | 4754 | 0.6168 | 0.2154 | 0.6168 | 0.7854 |
| 0.0516 | 7.9799 | 4756 | 0.6095 | 0.2154 | 0.6095 | 0.7807 |
| 0.0516 | 7.9832 | 4758 | 0.6081 | 0.2154 | 0.6081 | 0.7798 |
| 0.0516 | 7.9866 | 4760 | 0.6109 | 0.2154 | 0.6109 | 0.7816 |
| 0.0516 | 7.9899 | 4762 | 0.6124 | 0.2154 | 0.6124 | 0.7826 |
| 0.0516 | 7.9933 | 4764 | 0.6154 | 0.2154 | 0.6154 | 0.7845 |
| 0.0516 | 7.9966 | 4766 | 0.6238 | 0.2154 | 0.6238 | 0.7898 |
| 0.0516 | 8.0 | 4768 | 0.6351 | 0.3143 | 0.6351 | 0.7970 |
| 0.0516 | 8.0034 | 4770 | 0.6524 | 0.3143 | 0.6524 | 0.8077 |
| 0.0516 | 8.0067 | 4772 | 0.6551 | 0.3143 | 0.6551 | 0.8094 |
| 0.0516 | 8.0101 | 4774 | 0.6457 | 0.3143 | 0.6457 | 0.8035 |
| 0.0516 | 8.0134 | 4776 | 0.6282 | 0.3143 | 0.6282 | 0.7926 |
| 0.0516 | 8.0168 | 4778 | 0.6052 | 0.2154 | 0.6052 | 0.7779 |
| 0.0516 | 8.0201 | 4780 | 0.5886 | 0.2154 | 0.5886 | 0.7672 |
| 0.0516 | 8.0235 | 4782 | 0.5835 | 0.2154 | 0.5835 | 0.7639 |
| 0.0516 | 8.0268 | 4784 | 0.5790 | 0.2154 | 0.5790 | 0.7609 |
| 0.0516 | 8.0302 | 4786 | 0.5777 | 0.2727 | 0.5777 | 0.7600 |
| 0.0516 | 8.0336 | 4788 | 0.5812 | 0.3284 | 0.5812 | 0.7623 |
| 0.0516 | 8.0369 | 4790 | 0.5839 | 0.3284 | 0.5839 | 0.7642 |
| 0.0516 | 8.0403 | 4792 | 0.5918 | 0.3284 | 0.5918 | 0.7693 |
| 0.0516 | 8.0436 | 4794 | 0.6017 | 0.2154 | 0.6017 | 0.7757 |
| 0.0516 | 8.0470 | 4796 | 0.6061 | 0.2727 | 0.6061 | 0.7785 |
| 0.0516 | 8.0503 | 4798 | 0.6115 | 0.2727 | 0.6115 | 0.7820 |
| 0.0516 | 8.0537 | 4800 | 0.6111 | 0.2727 | 0.6111 | 0.7817 |
| 0.0516 | 8.0570 | 4802 | 0.6047 | 0.3284 | 0.6047 | 0.7776 |
| 0.0516 | 8.0604 | 4804 | 0.5964 | 0.3284 | 0.5964 | 0.7723 |
| 0.0516 | 8.0638 | 4806 | 0.5909 | 0.3284 | 0.5909 | 0.7687 |
| 0.0516 | 8.0671 | 4808 | 0.5890 | 0.3284 | 0.5890 | 0.7675 |
| 0.0516 | 8.0705 | 4810 | 0.5902 | 0.3284 | 0.5902 | 0.7682 |
| 0.0516 | 8.0738 | 4812 | 0.5942 | 0.3284 | 0.5942 | 0.7708 |
| 0.0516 | 8.0772 | 4814 | 0.5958 | 0.3284 | 0.5958 | 0.7719 |
| 0.0516 | 8.0805 | 4816 | 0.5945 | 0.2727 | 0.5945 | 0.7710 |
| 0.0516 | 8.0839 | 4818 | 0.5944 | 0.2727 | 0.5944 | 0.7710 |
| 0.0516 | 8.0872 | 4820 | 0.5926 | 0.2727 | 0.5926 | 0.7698 |
| 0.0516 | 8.0906 | 4822 | 0.5913 | 0.2727 | 0.5913 | 0.7689 |
| 0.0516 | 8.0940 | 4824 | 0.5890 | 0.2727 | 0.5890 | 0.7675 |
| 0.0516 | 8.0973 | 4826 | 0.5877 | 0.2727 | 0.5877 | 0.7666 |
| 0.0516 | 8.1007 | 4828 | 0.5892 | 0.3284 | 0.5892 | 0.7676 |
| 0.0516 | 8.1040 | 4830 | 0.5887 | 0.3824 | 0.5887 | 0.7673 |
| 0.0516 | 8.1074 | 4832 | 0.5872 | 0.3824 | 0.5872 | 0.7663 |
| 0.0516 | 8.1107 | 4834 | 0.5890 | 0.3824 | 0.5890 | 0.7675 |
| 0.0516 | 8.1141 | 4836 | 0.5905 | 0.3824 | 0.5905 | 0.7685 |
| 0.0516 | 8.1174 | 4838 | 0.5935 | 0.3284 | 0.5935 | 0.7704 |
| 0.0516 | 8.1208 | 4840 | 0.5936 | 0.3284 | 0.5936 | 0.7704 |
| 0.0516 | 8.1242 | 4842 | 0.5956 | 0.3284 | 0.5956 | 0.7718 |
| 0.0516 | 8.1275 | 4844 | 0.5973 | 0.3284 | 0.5973 | 0.7728 |
| 0.0516 | 8.1309 | 4846 | 0.6016 | 0.3284 | 0.6016 | 0.7756 |
| 0.0516 | 8.1342 | 4848 | 0.6093 | 0.2727 | 0.6093 | 0.7806 |
| 0.0516 | 8.1376 | 4850 | 0.6156 | 0.2154 | 0.6156 | 0.7846 |
| 0.0516 | 8.1409 | 4852 | 0.6247 | 0.2154 | 0.6247 | 0.7904 |
| 0.0516 | 8.1443 | 4854 | 0.6336 | 0.3143 | 0.6336 | 0.7960 |
| 0.0516 | 8.1477 | 4856 | 0.6466 | 0.3143 | 0.6466 | 0.8041 |
| 0.0516 | 8.1510 | 4858 | 0.6521 | 0.3143 | 0.6521 | 0.8075 |
| 0.0516 | 8.1544 | 4860 | 0.6510 | 0.3143 | 0.6510 | 0.8068 |
| 0.0516 | 8.1577 | 4862 | 0.6450 | 0.3143 | 0.6450 | 0.8031 |
| 0.0516 | 8.1611 | 4864 | 0.6402 | 0.3143 | 0.6402 | 0.8001 |
| 0.0516 | 8.1644 | 4866 | 0.6403 | 0.3143 | 0.6403 | 0.8002 |
| 0.0516 | 8.1678 | 4868 | 0.6313 | 0.2154 | 0.6313 | 0.7945 |
| 0.0516 | 8.1711 | 4870 | 0.6237 | 0.2154 | 0.6237 | 0.7897 |
| 0.0516 | 8.1745 | 4872 | 0.6148 | 0.2727 | 0.6148 | 0.7841 |
| 0.0516 | 8.1779 | 4874 | 0.6088 | 0.3284 | 0.6088 | 0.7802 |
| 0.0516 | 8.1812 | 4876 | 0.6082 | 0.3284 | 0.6082 | 0.7799 |
| 0.0516 | 8.1846 | 4878 | 0.6120 | 0.3284 | 0.6120 | 0.7823 |
| 0.0516 | 8.1879 | 4880 | 0.6210 | 0.2727 | 0.6210 | 0.7881 |
| 0.0516 | 8.1913 | 4882 | 0.6333 | 0.2154 | 0.6333 | 0.7958 |
| 0.0516 | 8.1946 | 4884 | 0.6449 | 0.3143 | 0.6449 | 0.8031 |
| 0.0516 | 8.1980 | 4886 | 0.6529 | 0.3143 | 0.6529 | 0.8080 |
| 0.0516 | 8.2013 | 4888 | 0.6553 | 0.3143 | 0.6553 | 0.8095 |
| 0.0516 | 8.2047 | 4890 | 0.6517 | 0.3143 | 0.6517 | 0.8073 |
| 0.0516 | 8.2081 | 4892 | 0.6408 | 0.2154 | 0.6408 | 0.8005 |
| 0.0516 | 8.2114 | 4894 | 0.6294 | 0.2154 | 0.6294 | 0.7934 |
| 0.0516 | 8.2148 | 4896 | 0.6218 | 0.2154 | 0.6218 | 0.7885 |
| 0.0516 | 8.2181 | 4898 | 0.6101 | 0.2154 | 0.6101 | 0.7811 |
| 0.0516 | 8.2215 | 4900 | 0.6007 | 0.2154 | 0.6007 | 0.7751 |
| 0.0516 | 8.2248 | 4902 | 0.5919 | 0.2727 | 0.5919 | 0.7694 |
| 0.0516 | 8.2282 | 4904 | 0.5856 | 0.3226 | 0.5856 | 0.7652 |
| 0.0516 | 8.2315 | 4906 | 0.5834 | 0.3793 | 0.5834 | 0.7638 |
| 0.0516 | 8.2349 | 4908 | 0.5828 | 0.3390 | 0.5828 | 0.7634 |
| 0.0516 | 8.2383 | 4910 | 0.5846 | 0.3793 | 0.5846 | 0.7646 |
| 0.0516 | 8.2416 | 4912 | 0.5863 | 0.3810 | 0.5863 | 0.7657 |
| 0.0516 | 8.2450 | 4914 | 0.5896 | 0.3824 | 0.5896 | 0.7678 |
| 0.0516 | 8.2483 | 4916 | 0.5980 | 0.2727 | 0.5980 | 0.7733 |
| 0.0516 | 8.2517 | 4918 | 0.6145 | 0.2154 | 0.6145 | 0.7839 |
| 0.0516 | 8.2550 | 4920 | 0.6310 | 0.2154 | 0.6310 | 0.7944 |
| 0.0516 | 8.2584 | 4922 | 0.6365 | 0.2154 | 0.6365 | 0.7978 |
| 0.0516 | 8.2617 | 4924 | 0.6334 | 0.2154 | 0.6334 | 0.7959 |
| 0.0516 | 8.2651 | 4926 | 0.6266 | 0.2154 | 0.6266 | 0.7916 |
| 0.0516 | 8.2685 | 4928 | 0.6237 | 0.2154 | 0.6237 | 0.7897 |
| 0.0516 | 8.2718 | 4930 | 0.6137 | 0.2154 | 0.6137 | 0.7834 |
| 0.0516 | 8.2752 | 4932 | 0.6105 | 0.2154 | 0.6105 | 0.7813 |
| 0.0516 | 8.2785 | 4934 | 0.6090 | 0.2154 | 0.6090 | 0.7804 |
| 0.0516 | 8.2819 | 4936 | 0.6037 | 0.2154 | 0.6037 | 0.7770 |
| 0.0516 | 8.2852 | 4938 | 0.5987 | 0.2154 | 0.5987 | 0.7737 |
| 0.0516 | 8.2886 | 4940 | 0.5987 | 0.2727 | 0.5987 | 0.7738 |
| 0.0516 | 8.2919 | 4942 | 0.5994 | 0.2727 | 0.5994 | 0.7742 |
| 0.0516 | 8.2953 | 4944 | 0.6032 | 0.2727 | 0.6032 | 0.7767 |
| 0.0516 | 8.2987 | 4946 | 0.6107 | 0.2727 | 0.6107 | 0.7815 |
| 0.0516 | 8.3020 | 4948 | 0.6150 | 0.2727 | 0.6150 | 0.7842 |
| 0.0516 | 8.3054 | 4950 | 0.6139 | 0.2727 | 0.6139 | 0.7835 |
| 0.0516 | 8.3087 | 4952 | 0.6157 | 0.2727 | 0.6157 | 0.7847 |
| 0.0516 | 8.3121 | 4954 | 0.6190 | 0.2727 | 0.6190 | 0.7868 |
| 0.0516 | 8.3154 | 4956 | 0.6252 | 0.2727 | 0.6252 | 0.7907 |
| 0.0516 | 8.3188 | 4958 | 0.6305 | 0.2154 | 0.6305 | 0.7940 |
| 0.0516 | 8.3221 | 4960 | 0.6304 | 0.2154 | 0.6304 | 0.7940 |
| 0.0516 | 8.3255 | 4962 | 0.6255 | 0.2154 | 0.6255 | 0.7909 |
| 0.0516 | 8.3289 | 4964 | 0.6251 | 0.2154 | 0.6251 | 0.7907 |
| 0.0516 | 8.3322 | 4966 | 0.6247 | 0.2154 | 0.6247 | 0.7904 |
| 0.0516 | 8.3356 | 4968 | 0.6298 | 0.2154 | 0.6298 | 0.7936 |
| 0.0516 | 8.3389 | 4970 | 0.6363 | 0.2154 | 0.6363 | 0.7977 |
| 0.0516 | 8.3423 | 4972 | 0.6374 | 0.2154 | 0.6374 | 0.7983 |
| 0.0516 | 8.3456 | 4974 | 0.6345 | 0.2154 | 0.6345 | 0.7966 |
| 0.0516 | 8.3490 | 4976 | 0.6306 | 0.2154 | 0.6306 | 0.7941 |
| 0.0516 | 8.3523 | 4978 | 0.6265 | 0.2154 | 0.6265 | 0.7915 |
| 0.0516 | 8.3557 | 4980 | 0.6218 | 0.2154 | 0.6218 | 0.7886 |
| 0.0516 | 8.3591 | 4982 | 0.6142 | 0.2154 | 0.6142 | 0.7837 |
| 0.0516 | 8.3624 | 4984 | 0.6109 | 0.2154 | 0.6109 | 0.7816 |
| 0.0516 | 8.3658 | 4986 | 0.6105 | 0.2154 | 0.6105 | 0.7814 |
| 0.0516 | 8.3691 | 4988 | 0.6154 | 0.2154 | 0.6154 | 0.7845 |
| 0.0516 | 8.3725 | 4990 | 0.6215 | 0.2154 | 0.6215 | 0.7883 |
| 0.0516 | 8.3758 | 4992 | 0.6253 | 0.2154 | 0.6253 | 0.7908 |
| 0.0516 | 8.3792 | 4994 | 0.6320 | 0.2154 | 0.6320 | 0.7950 |
| 0.0516 | 8.3826 | 4996 | 0.6420 | 0.2154 | 0.6420 | 0.8012 |
| 0.0516 | 8.3859 | 4998 | 0.6479 | 0.3143 | 0.6479 | 0.8049 |
| 0.0465 | 8.3893 | 5000 | 0.6527 | 0.3143 | 0.6527 | 0.8079 |
| 0.0465 | 8.3926 | 5002 | 0.6522 | 0.3143 | 0.6522 | 0.8076 |
| 0.0465 | 8.3960 | 5004 | 0.6450 | 0.3143 | 0.6450 | 0.8031 |
| 0.0465 | 8.3993 | 5006 | 0.6350 | 0.2154 | 0.6350 | 0.7969 |
| 0.0465 | 8.4027 | 5008 | 0.6238 | 0.2727 | 0.6238 | 0.7898 |
| 0.0465 | 8.4060 | 5010 | 0.6176 | 0.2727 | 0.6176 | 0.7859 |
| 0.0465 | 8.4094 | 5012 | 0.6179 | 0.2727 | 0.6179 | 0.7860 |
| 0.0465 | 8.4128 | 5014 | 0.6205 | 0.2727 | 0.6205 | 0.7877 |
| 0.0465 | 8.4161 | 5016 | 0.6168 | 0.2727 | 0.6168 | 0.7854 |
| 0.0465 | 8.4195 | 5018 | 0.6164 | 0.2727 | 0.6164 | 0.7851 |
| 0.0465 | 8.4228 | 5020 | 0.6173 | 0.2727 | 0.6173 | 0.7857 |
| 0.0465 | 8.4262 | 5022 | 0.6208 | 0.2727 | 0.6208 | 0.7879 |
| 0.0465 | 8.4295 | 5024 | 0.6238 | 0.2727 | 0.6238 | 0.7898 |
| 0.0465 | 8.4329 | 5026 | 0.6315 | 0.2154 | 0.6315 | 0.7946 |
| 0.0465 | 8.4362 | 5028 | 0.6373 | 0.2154 | 0.6373 | 0.7983 |
| 0.0465 | 8.4396 | 5030 | 0.6384 | 0.2154 | 0.6384 | 0.7990 |
| 0.0465 | 8.4430 | 5032 | 0.6322 | 0.2154 | 0.6322 | 0.7951 |
| 0.0465 | 8.4463 | 5034 | 0.6204 | 0.2727 | 0.6204 | 0.7876 |
| 0.0465 | 8.4497 | 5036 | 0.6102 | 0.2727 | 0.6102 | 0.7811 |
| 0.0465 | 8.4530 | 5038 | 0.6032 | 0.3226 | 0.6032 | 0.7767 |
| 0.0465 | 8.4564 | 5040 | 0.5993 | 0.3226 | 0.5993 | 0.7741 |
| 0.0465 | 8.4597 | 5042 | 0.5980 | 0.3226 | 0.5980 | 0.7733 |
| 0.0465 | 8.4631 | 5044 | 0.6004 | 0.2623 | 0.6004 | 0.7748 |
| 0.0465 | 8.4664 | 5046 | 0.6066 | 0.2727 | 0.6066 | 0.7788 |
| 0.0465 | 8.4698 | 5048 | 0.6165 | 0.2154 | 0.6165 | 0.7851 |
| 0.0465 | 8.4732 | 5050 | 0.6217 | 0.2154 | 0.6217 | 0.7885 |
| 0.0465 | 8.4765 | 5052 | 0.6229 | 0.2154 | 0.6229 | 0.7892 |
| 0.0465 | 8.4799 | 5054 | 0.6217 | 0.2154 | 0.6217 | 0.7885 |
| 0.0465 | 8.4832 | 5056 | 0.6129 | 0.2727 | 0.6129 | 0.7829 |
| 0.0465 | 8.4866 | 5058 | 0.6055 | 0.2727 | 0.6055 | 0.7781 |
| 0.0465 | 8.4899 | 5060 | 0.5963 | 0.3226 | 0.5963 | 0.7722 |
| 0.0465 | 8.4933 | 5062 | 0.5910 | 0.3226 | 0.5910 | 0.7688 |
| 0.0465 | 8.4966 | 5064 | 0.5878 | 0.3226 | 0.5878 | 0.7667 |
| 0.0465 | 8.5 | 5066 | 0.5870 | 0.3226 | 0.5870 | 0.7662 |
| 0.0465 | 8.5034 | 5068 | 0.5911 | 0.3226 | 0.5911 | 0.7688 |
| 0.0465 | 8.5067 | 5070 | 0.5956 | 0.3226 | 0.5956 | 0.7718 |
| 0.0465 | 8.5101 | 5072 | 0.5979 | 0.2623 | 0.5979 | 0.7732 |
| 0.0465 | 8.5134 | 5074 | 0.6044 | 0.2727 | 0.6044 | 0.7774 |
| 0.0465 | 8.5168 | 5076 | 0.6091 | 0.2154 | 0.6091 | 0.7804 |
| 0.0465 | 8.5201 | 5078 | 0.6168 | 0.2154 | 0.6168 | 0.7854 |
| 0.0465 | 8.5235 | 5080 | 0.6179 | 0.2154 | 0.6179 | 0.7860 |
| 0.0465 | 8.5268 | 5082 | 0.6212 | 0.2154 | 0.6212 | 0.7882 |
| 0.0465 | 8.5302 | 5084 | 0.6309 | 0.2154 | 0.6309 | 0.7943 |
| 0.0465 | 8.5336 | 5086 | 0.6382 | 0.3143 | 0.6382 | 0.7989 |
| 0.0465 | 8.5369 | 5088 | 0.6424 | 0.3143 | 0.6424 | 0.8015 |
| 0.0465 | 8.5403 | 5090 | 0.6388 | 0.3143 | 0.6388 | 0.7993 |
| 0.0465 | 8.5436 | 5092 | 0.6295 | 0.2154 | 0.6295 | 0.7934 |
| 0.0465 | 8.5470 | 5094 | 0.6200 | 0.2727 | 0.6200 | 0.7874 |
| 0.0465 | 8.5503 | 5096 | 0.6096 | 0.3284 | 0.6096 | 0.7808 |
| 0.0465 | 8.5537 | 5098 | 0.6055 | 0.3284 | 0.6055 | 0.7781 |
| 0.0465 | 8.5570 | 5100 | 0.6043 | 0.3284 | 0.6043 | 0.7773 |
| 0.0465 | 8.5604 | 5102 | 0.6070 | 0.3284 | 0.6070 | 0.7791 |
| 0.0465 | 8.5638 | 5104 | 0.6089 | 0.3284 | 0.6089 | 0.7803 |
| 0.0465 | 8.5671 | 5106 | 0.6102 | 0.3284 | 0.6102 | 0.7811 |
| 0.0465 | 8.5705 | 5108 | 0.6139 | 0.3284 | 0.6139 | 0.7835 |
| 0.0465 | 8.5738 | 5110 | 0.6168 | 0.3284 | 0.6168 | 0.7853 |
| 0.0465 | 8.5772 | 5112 | 0.6185 | 0.3284 | 0.6185 | 0.7864 |
| 0.0465 | 8.5805 | 5114 | 0.6225 | 0.3284 | 0.6225 | 0.7890 |
| 0.0465 | 8.5839 | 5116 | 0.6242 | 0.2727 | 0.6242 | 0.7901 |
| 0.0465 | 8.5872 | 5118 | 0.6229 | 0.2727 | 0.6229 | 0.7893 |
| 0.0465 | 8.5906 | 5120 | 0.6215 | 0.2727 | 0.6215 | 0.7883 |
| 0.0465 | 8.5940 | 5122 | 0.6145 | 0.2727 | 0.6145 | 0.7839 |
| 0.0465 | 8.5973 | 5124 | 0.6083 | 0.2727 | 0.6083 | 0.7799 |
| 0.0465 | 8.6007 | 5126 | 0.6009 | 0.2727 | 0.6009 | 0.7752 |
| 0.0465 | 8.6040 | 5128 | 0.5938 | 0.2727 | 0.5938 | 0.7706 |
| 0.0465 | 8.6074 | 5130 | 0.5906 | 0.2727 | 0.5906 | 0.7685 |
| 0.0465 | 8.6107 | 5132 | 0.5899 | 0.2727 | 0.5899 | 0.7680 |
| 0.0465 | 8.6141 | 5134 | 0.5865 | 0.2727 | 0.5865 | 0.7658 |
| 0.0465 | 8.6174 | 5136 | 0.5822 | 0.2727 | 0.5822 | 0.7630 |
| 0.0465 | 8.6208 | 5138 | 0.5824 | 0.2727 | 0.5824 | 0.7632 |
| 0.0465 | 8.6242 | 5140 | 0.5871 | 0.2727 | 0.5871 | 0.7662 |
| 0.0465 | 8.6275 | 5142 | 0.5933 | 0.2154 | 0.5933 | 0.7702 |
| 0.0465 | 8.6309 | 5144 | 0.5987 | 0.2154 | 0.5987 | 0.7737 |
| 0.0465 | 8.6342 | 5146 | 0.6020 | 0.2154 | 0.6020 | 0.7759 |
| 0.0465 | 8.6376 | 5148 | 0.6032 | 0.2154 | 0.6032 | 0.7767 |
| 0.0465 | 8.6409 | 5150 | 0.6007 | 0.2154 | 0.6007 | 0.7750 |
| 0.0465 | 8.6443 | 5152 | 0.5994 | 0.2727 | 0.5994 | 0.7742 |
| 0.0465 | 8.6477 | 5154 | 0.5978 | 0.2727 | 0.5978 | 0.7732 |
| 0.0465 | 8.6510 | 5156 | 0.5949 | 0.2727 | 0.5949 | 0.7713 |
| 0.0465 | 8.6544 | 5158 | 0.5949 | 0.2727 | 0.5949 | 0.7713 |
| 0.0465 | 8.6577 | 5160 | 0.5996 | 0.2727 | 0.5996 | 0.7743 |
| 0.0465 | 8.6611 | 5162 | 0.6084 | 0.2727 | 0.6084 | 0.7800 |
| 0.0465 | 8.6644 | 5164 | 0.6177 | 0.2727 | 0.6177 | 0.7860 |
| 0.0465 | 8.6678 | 5166 | 0.6219 | 0.3662 | 0.6219 | 0.7886 |
| 0.0465 | 8.6711 | 5168 | 0.6240 | 0.3662 | 0.6240 | 0.7899 |
| 0.0465 | 8.6745 | 5170 | 0.6245 | 0.3143 | 0.6245 | 0.7902 |
| 0.0465 | 8.6779 | 5172 | 0.6232 | 0.3143 | 0.6232 | 0.7894 |
| 0.0465 | 8.6812 | 5174 | 0.6194 | 0.2154 | 0.6194 | 0.7870 |
| 0.0465 | 8.6846 | 5176 | 0.6146 | 0.2154 | 0.6146 | 0.7840 |
| 0.0465 | 8.6879 | 5178 | 0.6066 | 0.2154 | 0.6066 | 0.7789 |
| 0.0465 | 8.6913 | 5180 | 0.6008 | 0.2727 | 0.6008 | 0.7751 |
| 0.0465 | 8.6946 | 5182 | 0.6016 | 0.2727 | 0.6016 | 0.7757 |
| 0.0465 | 8.6980 | 5184 | 0.6018 | 0.2727 | 0.6018 | 0.7758 |
| 0.0465 | 8.7013 | 5186 | 0.6035 | 0.2727 | 0.6035 | 0.7768 |
| 0.0465 | 8.7047 | 5188 | 0.6088 | 0.2727 | 0.6088 | 0.7803 |
| 0.0465 | 8.7081 | 5190 | 0.6133 | 0.2154 | 0.6133 | 0.7832 |
| 0.0465 | 8.7114 | 5192 | 0.6174 | 0.2154 | 0.6174 | 0.7858 |
| 0.0465 | 8.7148 | 5194 | 0.6213 | 0.2154 | 0.6213 | 0.7883 |
| 0.0465 | 8.7181 | 5196 | 0.6194 | 0.2154 | 0.6194 | 0.7870 |
| 0.0465 | 8.7215 | 5198 | 0.6110 | 0.2154 | 0.6110 | 0.7816 |
| 0.0465 | 8.7248 | 5200 | 0.6048 | 0.2727 | 0.6048 | 0.7777 |
| 0.0465 | 8.7282 | 5202 | 0.6026 | 0.2727 | 0.6026 | 0.7763 |
| 0.0465 | 8.7315 | 5204 | 0.6037 | 0.2727 | 0.6037 | 0.7770 |
| 0.0465 | 8.7349 | 5206 | 0.6031 | 0.2727 | 0.6031 | 0.7766 |
| 0.0465 | 8.7383 | 5208 | 0.6013 | 0.2727 | 0.6013 | 0.7754 |
| 0.0465 | 8.7416 | 5210 | 0.6009 | 0.2727 | 0.6009 | 0.7752 |
| 0.0465 | 8.7450 | 5212 | 0.5999 | 0.2727 | 0.5999 | 0.7745 |
| 0.0465 | 8.7483 | 5214 | 0.5966 | 0.2727 | 0.5966 | 0.7724 |
| 0.0465 | 8.7517 | 5216 | 0.5944 | 0.2727 | 0.5944 | 0.7710 |
| 0.0465 | 8.7550 | 5218 | 0.5955 | 0.2727 | 0.5955 | 0.7717 |
| 0.0465 | 8.7584 | 5220 | 0.5960 | 0.3284 | 0.5960 | 0.7720 |
| 0.0465 | 8.7617 | 5222 | 0.5952 | 0.3284 | 0.5952 | 0.7715 |
| 0.0465 | 8.7651 | 5224 | 0.5953 | 0.3284 | 0.5953 | 0.7715 |
| 0.0465 | 8.7685 | 5226 | 0.5979 | 0.3284 | 0.5979 | 0.7733 |
| 0.0465 | 8.7718 | 5228 | 0.6013 | 0.2727 | 0.6013 | 0.7754 |
| 0.0465 | 8.7752 | 5230 | 0.6061 | 0.2727 | 0.6061 | 0.7785 |
| 0.0465 | 8.7785 | 5232 | 0.6136 | 0.2727 | 0.6136 | 0.7833 |
| 0.0465 | 8.7819 | 5234 | 0.6183 | 0.2727 | 0.6183 | 0.7863 |
| 0.0465 | 8.7852 | 5236 | 0.6225 | 0.2727 | 0.6225 | 0.7890 |
| 0.0465 | 8.7886 | 5238 | 0.6223 | 0.2727 | 0.6223 | 0.7889 |
| 0.0465 | 8.7919 | 5240 | 0.6218 | 0.2727 | 0.6218 | 0.7886 |
| 0.0465 | 8.7953 | 5242 | 0.6224 | 0.2727 | 0.6224 | 0.7889 |
| 0.0465 | 8.7987 | 5244 | 0.6179 | 0.2727 | 0.6179 | 0.7861 |
| 0.0465 | 8.8020 | 5246 | 0.6152 | 0.2727 | 0.6152 | 0.7844 |
| 0.0465 | 8.8054 | 5248 | 0.6181 | 0.2727 | 0.6181 | 0.7862 |
| 0.0465 | 8.8087 | 5250 | 0.6175 | 0.2727 | 0.6175 | 0.7858 |
| 0.0465 | 8.8121 | 5252 | 0.6195 | 0.2727 | 0.6195 | 0.7871 |
| 0.0465 | 8.8154 | 5254 | 0.6206 | 0.2154 | 0.6206 | 0.7878 |
| 0.0465 | 8.8188 | 5256 | 0.6223 | 0.2154 | 0.6223 | 0.7889 |
| 0.0465 | 8.8221 | 5258 | 0.6224 | 0.2154 | 0.6224 | 0.7889 |
| 0.0465 | 8.8255 | 5260 | 0.6196 | 0.2154 | 0.6196 | 0.7872 |
| 0.0465 | 8.8289 | 5262 | 0.6148 | 0.2727 | 0.6148 | 0.7841 |
| 0.0465 | 8.8322 | 5264 | 0.6139 | 0.2727 | 0.6139 | 0.7835 |
| 0.0465 | 8.8356 | 5266 | 0.6153 | 0.2727 | 0.6153 | 0.7844 |
| 0.0465 | 8.8389 | 5268 | 0.6129 | 0.2727 | 0.6129 | 0.7829 |
| 0.0465 | 8.8423 | 5270 | 0.6134 | 0.2727 | 0.6134 | 0.7832 |
| 0.0465 | 8.8456 | 5272 | 0.6151 | 0.2727 | 0.6151 | 0.7843 |
| 0.0465 | 8.8490 | 5274 | 0.6167 | 0.2727 | 0.6167 | 0.7853 |
| 0.0465 | 8.8523 | 5276 | 0.6209 | 0.2727 | 0.6209 | 0.7879 |
| 0.0465 | 8.8557 | 5278 | 0.6229 | 0.2727 | 0.6229 | 0.7892 |
| 0.0465 | 8.8591 | 5280 | 0.6212 | 0.2727 | 0.6212 | 0.7882 |
| 0.0465 | 8.8624 | 5282 | 0.6172 | 0.2727 | 0.6172 | 0.7856 |
| 0.0465 | 8.8658 | 5284 | 0.6164 | 0.2727 | 0.6164 | 0.7851 |
| 0.0465 | 8.8691 | 5286 | 0.6132 | 0.2727 | 0.6132 | 0.7831 |
| 0.0465 | 8.8725 | 5288 | 0.6131 | 0.2727 | 0.6131 | 0.7830 |
| 0.0465 | 8.8758 | 5290 | 0.6164 | 0.2727 | 0.6164 | 0.7851 |
| 0.0465 | 8.8792 | 5292 | 0.6205 | 0.2727 | 0.6205 | 0.7877 |
| 0.0465 | 8.8826 | 5294 | 0.6250 | 0.2727 | 0.6250 | 0.7906 |
| 0.0465 | 8.8859 | 5296 | 0.6268 | 0.2727 | 0.6268 | 0.7917 |
| 0.0465 | 8.8893 | 5298 | 0.6278 | 0.2727 | 0.6278 | 0.7923 |
| 0.0465 | 8.8926 | 5300 | 0.6287 | 0.2727 | 0.6287 | 0.7929 |
| 0.0465 | 8.8960 | 5302 | 0.6311 | 0.2727 | 0.6311 | 0.7944 |
| 0.0465 | 8.8993 | 5304 | 0.6301 | 0.2727 | 0.6301 | 0.7938 |
| 0.0465 | 8.9027 | 5306 | 0.6295 | 0.2727 | 0.6295 | 0.7934 |
| 0.0465 | 8.9060 | 5308 | 0.6278 | 0.2727 | 0.6278 | 0.7924 |
| 0.0465 | 8.9094 | 5310 | 0.6244 | 0.2727 | 0.6244 | 0.7902 |
| 0.0465 | 8.9128 | 5312 | 0.6237 | 0.2727 | 0.6237 | 0.7897 |
| 0.0465 | 8.9161 | 5314 | 0.6239 | 0.2727 | 0.6239 | 0.7899 |
| 0.0465 | 8.9195 | 5316 | 0.6260 | 0.2727 | 0.6260 | 0.7912 |
| 0.0465 | 8.9228 | 5318 | 0.6259 | 0.2727 | 0.6259 | 0.7911 |
| 0.0465 | 8.9262 | 5320 | 0.6224 | 0.2727 | 0.6224 | 0.7889 |
| 0.0465 | 8.9295 | 5322 | 0.6207 | 0.2727 | 0.6207 | 0.7878 |
| 0.0465 | 8.9329 | 5324 | 0.6174 | 0.2727 | 0.6174 | 0.7858 |
| 0.0465 | 8.9362 | 5326 | 0.6151 | 0.2727 | 0.6151 | 0.7843 |
| 0.0465 | 8.9396 | 5328 | 0.6151 | 0.2727 | 0.6151 | 0.7843 |
| 0.0465 | 8.9430 | 5330 | 0.6151 | 0.2727 | 0.6151 | 0.7843 |
| 0.0465 | 8.9463 | 5332 | 0.6148 | 0.2727 | 0.6148 | 0.7841 |
| 0.0465 | 8.9497 | 5334 | 0.6175 | 0.2727 | 0.6175 | 0.7858 |
| 0.0465 | 8.9530 | 5336 | 0.6200 | 0.2727 | 0.6200 | 0.7874 |
| 0.0465 | 8.9564 | 5338 | 0.6207 | 0.2727 | 0.6207 | 0.7879 |
| 0.0465 | 8.9597 | 5340 | 0.6222 | 0.2727 | 0.6222 | 0.7888 |
| 0.0465 | 8.9631 | 5342 | 0.6227 | 0.2727 | 0.6227 | 0.7891 |
| 0.0465 | 8.9664 | 5344 | 0.6187 | 0.2727 | 0.6187 | 0.7866 |
| 0.0465 | 8.9698 | 5346 | 0.6141 | 0.2727 | 0.6141 | 0.7836 |
| 0.0465 | 8.9732 | 5348 | 0.6090 | 0.2727 | 0.6090 | 0.7804 |
| 0.0465 | 8.9765 | 5350 | 0.6053 | 0.3284 | 0.6053 | 0.7780 |
| 0.0465 | 8.9799 | 5352 | 0.6032 | 0.3284 | 0.6032 | 0.7767 |
| 0.0465 | 8.9832 | 5354 | 0.6031 | 0.3824 | 0.6031 | 0.7766 |
| 0.0465 | 8.9866 | 5356 | 0.6038 | 0.3824 | 0.6038 | 0.7771 |
| 0.0465 | 8.9899 | 5358 | 0.6048 | 0.3478 | 0.6048 | 0.7777 |
| 0.0465 | 8.9933 | 5360 | 0.6069 | 0.3824 | 0.6069 | 0.7790 |
| 0.0465 | 8.9966 | 5362 | 0.6082 | 0.3824 | 0.6082 | 0.7799 |
| 0.0465 | 9.0 | 5364 | 0.6107 | 0.3824 | 0.6107 | 0.7815 |
| 0.0465 | 9.0034 | 5366 | 0.6124 | 0.3824 | 0.6124 | 0.7826 |
| 0.0465 | 9.0067 | 5368 | 0.6143 | 0.3284 | 0.6143 | 0.7837 |
| 0.0465 | 9.0101 | 5370 | 0.6179 | 0.2727 | 0.6179 | 0.7860 |
| 0.0465 | 9.0134 | 5372 | 0.6234 | 0.2727 | 0.6234 | 0.7896 |
| 0.0465 | 9.0168 | 5374 | 0.6289 | 0.2727 | 0.6289 | 0.7930 |
| 0.0465 | 9.0201 | 5376 | 0.6320 | 0.2727 | 0.6320 | 0.7950 |
| 0.0465 | 9.0235 | 5378 | 0.6367 | 0.3662 | 0.6367 | 0.7979 |
| 0.0465 | 9.0268 | 5380 | 0.6392 | 0.3662 | 0.6392 | 0.7995 |
| 0.0465 | 9.0302 | 5382 | 0.6389 | 0.3143 | 0.6389 | 0.7993 |
| 0.0465 | 9.0336 | 5384 | 0.6374 | 0.3143 | 0.6374 | 0.7983 |
| 0.0465 | 9.0369 | 5386 | 0.6337 | 0.2154 | 0.6337 | 0.7960 |
| 0.0465 | 9.0403 | 5388 | 0.6276 | 0.2727 | 0.6276 | 0.7922 |
| 0.0465 | 9.0436 | 5390 | 0.6217 | 0.2727 | 0.6217 | 0.7885 |
| 0.0465 | 9.0470 | 5392 | 0.6166 | 0.2727 | 0.6166 | 0.7852 |
| 0.0465 | 9.0503 | 5394 | 0.6141 | 0.2727 | 0.6141 | 0.7836 |
| 0.0465 | 9.0537 | 5396 | 0.6143 | 0.2727 | 0.6143 | 0.7837 |
| 0.0465 | 9.0570 | 5398 | 0.6157 | 0.2727 | 0.6157 | 0.7847 |
| 0.0465 | 9.0604 | 5400 | 0.6190 | 0.2727 | 0.6190 | 0.7868 |
| 0.0465 | 9.0638 | 5402 | 0.6228 | 0.2727 | 0.6228 | 0.7892 |
| 0.0465 | 9.0671 | 5404 | 0.6215 | 0.2727 | 0.6215 | 0.7883 |
| 0.0465 | 9.0705 | 5406 | 0.6196 | 0.2727 | 0.6196 | 0.7871 |
| 0.0465 | 9.0738 | 5408 | 0.6196 | 0.2727 | 0.6196 | 0.7872 |
| 0.0465 | 9.0772 | 5410 | 0.6189 | 0.2727 | 0.6189 | 0.7867 |
| 0.0465 | 9.0805 | 5412 | 0.6189 | 0.2727 | 0.6189 | 0.7867 |
| 0.0465 | 9.0839 | 5414 | 0.6196 | 0.2727 | 0.6196 | 0.7871 |
| 0.0465 | 9.0872 | 5416 | 0.6195 | 0.2727 | 0.6194 | 0.7871 |
| 0.0465 | 9.0906 | 5418 | 0.6184 | 0.2727 | 0.6184 | 0.7864 |
| 0.0465 | 9.0940 | 5420 | 0.6152 | 0.2727 | 0.6152 | 0.7844 |
| 0.0465 | 9.0973 | 5422 | 0.6116 | 0.2727 | 0.6116 | 0.7820 |
| 0.0465 | 9.1007 | 5424 | 0.6095 | 0.2727 | 0.6095 | 0.7807 |
| 0.0465 | 9.1040 | 5426 | 0.6082 | 0.2727 | 0.6082 | 0.7799 |
| 0.0465 | 9.1074 | 5428 | 0.6086 | 0.2727 | 0.6086 | 0.7801 |
| 0.0465 | 9.1107 | 5430 | 0.6081 | 0.2727 | 0.6081 | 0.7798 |
| 0.0465 | 9.1141 | 5432 | 0.6084 | 0.2727 | 0.6084 | 0.7800 |
| 0.0465 | 9.1174 | 5434 | 0.6070 | 0.2727 | 0.6070 | 0.7791 |
| 0.0465 | 9.1208 | 5436 | 0.6069 | 0.2727 | 0.6069 | 0.7790 |
| 0.0465 | 9.1242 | 5438 | 0.6080 | 0.2727 | 0.6080 | 0.7797 |
| 0.0465 | 9.1275 | 5440 | 0.6111 | 0.2727 | 0.6111 | 0.7818 |
| 0.0465 | 9.1309 | 5442 | 0.6148 | 0.2727 | 0.6148 | 0.7841 |
| 0.0465 | 9.1342 | 5444 | 0.6195 | 0.2727 | 0.6195 | 0.7871 |
| 0.0465 | 9.1376 | 5446 | 0.6275 | 0.3143 | 0.6275 | 0.7921 |
| 0.0465 | 9.1409 | 5448 | 0.6321 | 0.3143 | 0.6321 | 0.7950 |
| 0.0465 | 9.1443 | 5450 | 0.6368 | 0.3143 | 0.6368 | 0.7980 |
| 0.0465 | 9.1477 | 5452 | 0.6414 | 0.3143 | 0.6414 | 0.8009 |
| 0.0465 | 9.1510 | 5454 | 0.6405 | 0.3143 | 0.6405 | 0.8003 |
| 0.0465 | 9.1544 | 5456 | 0.6352 | 0.3143 | 0.6352 | 0.7970 |
| 0.0465 | 9.1577 | 5458 | 0.6310 | 0.3143 | 0.6310 | 0.7944 |
| 0.0465 | 9.1611 | 5460 | 0.6247 | 0.3662 | 0.6247 | 0.7904 |
| 0.0465 | 9.1644 | 5462 | 0.6166 | 0.2727 | 0.6166 | 0.7852 |
| 0.0465 | 9.1678 | 5464 | 0.6102 | 0.2727 | 0.6102 | 0.7812 |
| 0.0465 | 9.1711 | 5466 | 0.6055 | 0.2727 | 0.6055 | 0.7781 |
| 0.0465 | 9.1745 | 5468 | 0.6023 | 0.2727 | 0.6023 | 0.7761 |
| 0.0465 | 9.1779 | 5470 | 0.5998 | 0.2727 | 0.5998 | 0.7745 |
| 0.0465 | 9.1812 | 5472 | 0.5995 | 0.2727 | 0.5995 | 0.7743 |
| 0.0465 | 9.1846 | 5474 | 0.6018 | 0.2727 | 0.6018 | 0.7758 |
| 0.0465 | 9.1879 | 5476 | 0.6058 | 0.2727 | 0.6058 | 0.7784 |
| 0.0465 | 9.1913 | 5478 | 0.6102 | 0.2727 | 0.6102 | 0.7812 |
| 0.0465 | 9.1946 | 5480 | 0.6116 | 0.2727 | 0.6116 | 0.7820 |
| 0.0465 | 9.1980 | 5482 | 0.6102 | 0.2727 | 0.6102 | 0.7811 |
| 0.0465 | 9.2013 | 5484 | 0.6094 | 0.2727 | 0.6094 | 0.7806 |
| 0.0465 | 9.2047 | 5486 | 0.6071 | 0.2727 | 0.6071 | 0.7792 |
| 0.0465 | 9.2081 | 5488 | 0.6036 | 0.2727 | 0.6036 | 0.7769 |
| 0.0465 | 9.2114 | 5490 | 0.6010 | 0.2727 | 0.6010 | 0.7753 |
| 0.0465 | 9.2148 | 5492 | 0.5982 | 0.2727 | 0.5982 | 0.7734 |
| 0.0465 | 9.2181 | 5494 | 0.5967 | 0.2727 | 0.5967 | 0.7725 |
| 0.0465 | 9.2215 | 5496 | 0.5971 | 0.2727 | 0.5971 | 0.7727 |
| 0.0465 | 9.2248 | 5498 | 0.5983 | 0.2727 | 0.5983 | 0.7735 |
| 0.0421 | 9.2282 | 5500 | 0.5996 | 0.2727 | 0.5996 | 0.7744 |
| 0.0421 | 9.2315 | 5502 | 0.5999 | 0.2727 | 0.5999 | 0.7745 |
| 0.0421 | 9.2349 | 5504 | 0.6003 | 0.2727 | 0.6003 | 0.7748 |
| 0.0421 | 9.2383 | 5506 | 0.6019 | 0.2727 | 0.6019 | 0.7758 |
| 0.0421 | 9.2416 | 5508 | 0.6035 | 0.2727 | 0.6035 | 0.7768 |
| 0.0421 | 9.2450 | 5510 | 0.6048 | 0.2727 | 0.6048 | 0.7777 |
| 0.0421 | 9.2483 | 5512 | 0.6038 | 0.2727 | 0.6038 | 0.7770 |
| 0.0421 | 9.2517 | 5514 | 0.6023 | 0.2727 | 0.6023 | 0.7761 |
| 0.0421 | 9.2550 | 5516 | 0.6023 | 0.2727 | 0.6023 | 0.7761 |
| 0.0421 | 9.2584 | 5518 | 0.6041 | 0.2727 | 0.6041 | 0.7772 |
| 0.0421 | 9.2617 | 5520 | 0.6049 | 0.2727 | 0.6049 | 0.7778 |
| 0.0421 | 9.2651 | 5522 | 0.6084 | 0.2727 | 0.6084 | 0.7800 |
| 0.0421 | 9.2685 | 5524 | 0.6117 | 0.2727 | 0.6117 | 0.7821 |
| 0.0421 | 9.2718 | 5526 | 0.6146 | 0.2727 | 0.6146 | 0.7840 |
| 0.0421 | 9.2752 | 5528 | 0.6194 | 0.2154 | 0.6194 | 0.7870 |
| 0.0421 | 9.2785 | 5530 | 0.6213 | 0.2154 | 0.6213 | 0.7882 |
| 0.0421 | 9.2819 | 5532 | 0.6241 | 0.2154 | 0.6241 | 0.7900 |
| 0.0421 | 9.2852 | 5534 | 0.6284 | 0.3143 | 0.6284 | 0.7927 |
| 0.0421 | 9.2886 | 5536 | 0.6296 | 0.3143 | 0.6296 | 0.7935 |
| 0.0421 | 9.2919 | 5538 | 0.6279 | 0.2154 | 0.6279 | 0.7924 |
| 0.0421 | 9.2953 | 5540 | 0.6235 | 0.2154 | 0.6235 | 0.7896 |
| 0.0421 | 9.2987 | 5542 | 0.6177 | 0.2727 | 0.6177 | 0.7860 |
| 0.0421 | 9.3020 | 5544 | 0.6110 | 0.2727 | 0.6110 | 0.7817 |
| 0.0421 | 9.3054 | 5546 | 0.6048 | 0.2727 | 0.6048 | 0.7777 |
| 0.0421 | 9.3087 | 5548 | 0.6001 | 0.2727 | 0.6001 | 0.7746 |
| 0.0421 | 9.3121 | 5550 | 0.5980 | 0.3284 | 0.5980 | 0.7733 |
| 0.0421 | 9.3154 | 5552 | 0.5982 | 0.3284 | 0.5982 | 0.7734 |
| 0.0421 | 9.3188 | 5554 | 0.5997 | 0.3284 | 0.5997 | 0.7744 |
| 0.0421 | 9.3221 | 5556 | 0.6030 | 0.2727 | 0.6030 | 0.7765 |
| 0.0421 | 9.3255 | 5558 | 0.6067 | 0.2727 | 0.6067 | 0.7789 |
| 0.0421 | 9.3289 | 5560 | 0.6092 | 0.2727 | 0.6092 | 0.7805 |
| 0.0421 | 9.3322 | 5562 | 0.6111 | 0.2727 | 0.6111 | 0.7818 |
| 0.0421 | 9.3356 | 5564 | 0.6143 | 0.2727 | 0.6143 | 0.7838 |
| 0.0421 | 9.3389 | 5566 | 0.6188 | 0.2727 | 0.6188 | 0.7867 |
| 0.0421 | 9.3423 | 5568 | 0.6240 | 0.2154 | 0.6240 | 0.7899 |
| 0.0421 | 9.3456 | 5570 | 0.6290 | 0.2154 | 0.6290 | 0.7931 |
| 0.0421 | 9.3490 | 5572 | 0.6315 | 0.2154 | 0.6315 | 0.7946 |
| 0.0421 | 9.3523 | 5574 | 0.6321 | 0.2154 | 0.6321 | 0.7951 |
| 0.0421 | 9.3557 | 5576 | 0.6311 | 0.2154 | 0.6311 | 0.7944 |
| 0.0421 | 9.3591 | 5578 | 0.6287 | 0.2154 | 0.6287 | 0.7929 |
| 0.0421 | 9.3624 | 5580 | 0.6258 | 0.2154 | 0.6258 | 0.7911 |
| 0.0421 | 9.3658 | 5582 | 0.6233 | 0.2727 | 0.6233 | 0.7895 |
| 0.0421 | 9.3691 | 5584 | 0.6225 | 0.2727 | 0.6225 | 0.7890 |
| 0.0421 | 9.3725 | 5586 | 0.6201 | 0.2727 | 0.6201 | 0.7875 |
| 0.0421 | 9.3758 | 5588 | 0.6189 | 0.2727 | 0.6189 | 0.7867 |
| 0.0421 | 9.3792 | 5590 | 0.6177 | 0.2727 | 0.6177 | 0.7859 |
| 0.0421 | 9.3826 | 5592 | 0.6184 | 0.2727 | 0.6184 | 0.7864 |
| 0.0421 | 9.3859 | 5594 | 0.6192 | 0.2727 | 0.6192 | 0.7869 |
| 0.0421 | 9.3893 | 5596 | 0.6208 | 0.2727 | 0.6208 | 0.7879 |
| 0.0421 | 9.3926 | 5598 | 0.6193 | 0.2727 | 0.6193 | 0.7869 |
| 0.0421 | 9.3960 | 5600 | 0.6158 | 0.2727 | 0.6158 | 0.7847 |
| 0.0421 | 9.3993 | 5602 | 0.6134 | 0.2727 | 0.6134 | 0.7832 |
| 0.0421 | 9.4027 | 5604 | 0.6121 | 0.2727 | 0.6121 | 0.7824 |
| 0.0421 | 9.4060 | 5606 | 0.6132 | 0.2727 | 0.6132 | 0.7830 |
| 0.0421 | 9.4094 | 5608 | 0.6140 | 0.2727 | 0.6140 | 0.7836 |
| 0.0421 | 9.4128 | 5610 | 0.6166 | 0.2727 | 0.6166 | 0.7852 |
| 0.0421 | 9.4161 | 5612 | 0.6191 | 0.2727 | 0.6191 | 0.7869 |
| 0.0421 | 9.4195 | 5614 | 0.6236 | 0.2154 | 0.6236 | 0.7897 |
| 0.0421 | 9.4228 | 5616 | 0.6261 | 0.2154 | 0.6261 | 0.7913 |
| 0.0421 | 9.4262 | 5618 | 0.6288 | 0.2154 | 0.6288 | 0.7930 |
| 0.0421 | 9.4295 | 5620 | 0.6292 | 0.2154 | 0.6292 | 0.7932 |
| 0.0421 | 9.4329 | 5622 | 0.6266 | 0.2154 | 0.6266 | 0.7916 |
| 0.0421 | 9.4362 | 5624 | 0.6227 | 0.2154 | 0.6227 | 0.7891 |
| 0.0421 | 9.4396 | 5626 | 0.6180 | 0.2154 | 0.6180 | 0.7861 |
| 0.0421 | 9.4430 | 5628 | 0.6138 | 0.2727 | 0.6138 | 0.7834 |
| 0.0421 | 9.4463 | 5630 | 0.6102 | 0.2727 | 0.6102 | 0.7811 |
| 0.0421 | 9.4497 | 5632 | 0.6073 | 0.2727 | 0.6073 | 0.7793 |
| 0.0421 | 9.4530 | 5634 | 0.6046 | 0.2727 | 0.6046 | 0.7776 |
| 0.0421 | 9.4564 | 5636 | 0.6032 | 0.2727 | 0.6032 | 0.7767 |
| 0.0421 | 9.4597 | 5638 | 0.6015 | 0.2727 | 0.6015 | 0.7755 |
| 0.0421 | 9.4631 | 5640 | 0.6017 | 0.2727 | 0.6017 | 0.7757 |
| 0.0421 | 9.4664 | 5642 | 0.6039 | 0.2727 | 0.6039 | 0.7771 |
| 0.0421 | 9.4698 | 5644 | 0.6076 | 0.2727 | 0.6076 | 0.7795 |
| 0.0421 | 9.4732 | 5646 | 0.6118 | 0.2727 | 0.6118 | 0.7822 |
| 0.0421 | 9.4765 | 5648 | 0.6156 | 0.2154 | 0.6156 | 0.7846 |
| 0.0421 | 9.4799 | 5650 | 0.6198 | 0.2154 | 0.6198 | 0.7873 |
| 0.0421 | 9.4832 | 5652 | 0.6219 | 0.2154 | 0.6219 | 0.7886 |
| 0.0421 | 9.4866 | 5654 | 0.6242 | 0.2154 | 0.6242 | 0.7900 |
| 0.0421 | 9.4899 | 5656 | 0.6256 | 0.2154 | 0.6256 | 0.7910 |
| 0.0421 | 9.4933 | 5658 | 0.6293 | 0.2154 | 0.6293 | 0.7933 |
| 0.0421 | 9.4966 | 5660 | 0.6308 | 0.2154 | 0.6308 | 0.7942 |
| 0.0421 | 9.5 | 5662 | 0.6311 | 0.2154 | 0.6311 | 0.7944 |
| 0.0421 | 9.5034 | 5664 | 0.6292 | 0.2154 | 0.6292 | 0.7932 |
| 0.0421 | 9.5067 | 5666 | 0.6248 | 0.2154 | 0.6248 | 0.7904 |
| 0.0421 | 9.5101 | 5668 | 0.6193 | 0.2154 | 0.6193 | 0.7870 |
| 0.0421 | 9.5134 | 5670 | 0.6132 | 0.2154 | 0.6132 | 0.7831 |
| 0.0421 | 9.5168 | 5672 | 0.6092 | 0.2154 | 0.6092 | 0.7805 |
| 0.0421 | 9.5201 | 5674 | 0.6071 | 0.2154 | 0.6071 | 0.7792 |
| 0.0421 | 9.5235 | 5676 | 0.6052 | 0.2154 | 0.6052 | 0.7779 |
| 0.0421 | 9.5268 | 5678 | 0.6031 | 0.2727 | 0.6031 | 0.7766 |
| 0.0421 | 9.5302 | 5680 | 0.6022 | 0.2727 | 0.6022 | 0.7760 |
| 0.0421 | 9.5336 | 5682 | 0.6021 | 0.2727 | 0.6021 | 0.7760 |
| 0.0421 | 9.5369 | 5684 | 0.6015 | 0.2727 | 0.6015 | 0.7756 |
| 0.0421 | 9.5403 | 5686 | 0.6027 | 0.2727 | 0.6027 | 0.7763 |
| 0.0421 | 9.5436 | 5688 | 0.6033 | 0.2727 | 0.6033 | 0.7767 |
| 0.0421 | 9.5470 | 5690 | 0.6041 | 0.2154 | 0.6041 | 0.7773 |
| 0.0421 | 9.5503 | 5692 | 0.6063 | 0.2154 | 0.6063 | 0.7786 |
| 0.0421 | 9.5537 | 5694 | 0.6090 | 0.2154 | 0.6090 | 0.7804 |
| 0.0421 | 9.5570 | 5696 | 0.6105 | 0.2154 | 0.6105 | 0.7813 |
| 0.0421 | 9.5604 | 5698 | 0.6124 | 0.2154 | 0.6124 | 0.7826 |
| 0.0421 | 9.5638 | 5700 | 0.6134 | 0.2154 | 0.6134 | 0.7832 |
| 0.0421 | 9.5671 | 5702 | 0.6130 | 0.2154 | 0.6130 | 0.7830 |
| 0.0421 | 9.5705 | 5704 | 0.6110 | 0.2154 | 0.6110 | 0.7816 |
| 0.0421 | 9.5738 | 5706 | 0.6097 | 0.2154 | 0.6097 | 0.7808 |
| 0.0421 | 9.5772 | 5708 | 0.6099 | 0.2154 | 0.6099 | 0.7810 |
| 0.0421 | 9.5805 | 5710 | 0.6101 | 0.2154 | 0.6101 | 0.7811 |
| 0.0421 | 9.5839 | 5712 | 0.6095 | 0.2154 | 0.6095 | 0.7807 |
| 0.0421 | 9.5872 | 5714 | 0.6097 | 0.2154 | 0.6097 | 0.7808 |
| 0.0421 | 9.5906 | 5716 | 0.6111 | 0.2154 | 0.6111 | 0.7817 |
| 0.0421 | 9.5940 | 5718 | 0.6135 | 0.2154 | 0.6135 | 0.7832 |
| 0.0421 | 9.5973 | 5720 | 0.6146 | 0.2154 | 0.6146 | 0.7840 |
| 0.0421 | 9.6007 | 5722 | 0.6160 | 0.2154 | 0.6160 | 0.7849 |
| 0.0421 | 9.6040 | 5724 | 0.6163 | 0.2154 | 0.6163 | 0.7850 |
| 0.0421 | 9.6074 | 5726 | 0.6175 | 0.2154 | 0.6175 | 0.7858 |
| 0.0421 | 9.6107 | 5728 | 0.6172 | 0.2154 | 0.6172 | 0.7856 |
| 0.0421 | 9.6141 | 5730 | 0.6159 | 0.2154 | 0.6159 | 0.7848 |
| 0.0421 | 9.6174 | 5732 | 0.6148 | 0.2154 | 0.6148 | 0.7841 |
| 0.0421 | 9.6208 | 5734 | 0.6154 | 0.2154 | 0.6154 | 0.7845 |
| 0.0421 | 9.6242 | 5736 | 0.6165 | 0.2154 | 0.6165 | 0.7852 |
| 0.0421 | 9.6275 | 5738 | 0.6182 | 0.2154 | 0.6182 | 0.7863 |
| 0.0421 | 9.6309 | 5740 | 0.6204 | 0.2154 | 0.6204 | 0.7877 |
| 0.0421 | 9.6342 | 5742 | 0.6209 | 0.2154 | 0.6209 | 0.7880 |
| 0.0421 | 9.6376 | 5744 | 0.6198 | 0.2154 | 0.6198 | 0.7873 |
| 0.0421 | 9.6409 | 5746 | 0.6199 | 0.2154 | 0.6199 | 0.7873 |
| 0.0421 | 9.6443 | 5748 | 0.6204 | 0.2154 | 0.6204 | 0.7877 |
| 0.0421 | 9.6477 | 5750 | 0.6219 | 0.2154 | 0.6219 | 0.7886 |
| 0.0421 | 9.6510 | 5752 | 0.6226 | 0.2154 | 0.6226 | 0.7891 |
| 0.0421 | 9.6544 | 5754 | 0.6243 | 0.2154 | 0.6243 | 0.7902 |
| 0.0421 | 9.6577 | 5756 | 0.6254 | 0.3143 | 0.6254 | 0.7908 |
| 0.0421 | 9.6611 | 5758 | 0.6265 | 0.3143 | 0.6265 | 0.7915 |
| 0.0421 | 9.6644 | 5760 | 0.6263 | 0.3143 | 0.6263 | 0.7914 |
| 0.0421 | 9.6678 | 5762 | 0.6256 | 0.3143 | 0.6256 | 0.7909 |
| 0.0421 | 9.6711 | 5764 | 0.6239 | 0.2154 | 0.6239 | 0.7899 |
| 0.0421 | 9.6745 | 5766 | 0.6232 | 0.2154 | 0.6232 | 0.7894 |
| 0.0421 | 9.6779 | 5768 | 0.6238 | 0.2154 | 0.6238 | 0.7898 |
| 0.0421 | 9.6812 | 5770 | 0.6232 | 0.2154 | 0.6232 | 0.7894 |
| 0.0421 | 9.6846 | 5772 | 0.6210 | 0.2154 | 0.6210 | 0.7880 |
| 0.0421 | 9.6879 | 5774 | 0.6200 | 0.2154 | 0.6200 | 0.7874 |
| 0.0421 | 9.6913 | 5776 | 0.6195 | 0.2154 | 0.6195 | 0.7871 |
| 0.0421 | 9.6946 | 5778 | 0.6205 | 0.2154 | 0.6205 | 0.7877 |
| 0.0421 | 9.6980 | 5780 | 0.6223 | 0.2154 | 0.6223 | 0.7888 |
| 0.0421 | 9.7013 | 5782 | 0.6239 | 0.2154 | 0.6239 | 0.7899 |
| 0.0421 | 9.7047 | 5784 | 0.6254 | 0.2154 | 0.6254 | 0.7908 |
| 0.0421 | 9.7081 | 5786 | 0.6255 | 0.2154 | 0.6255 | 0.7909 |
| 0.0421 | 9.7114 | 5788 | 0.6246 | 0.2154 | 0.6246 | 0.7903 |
| 0.0421 | 9.7148 | 5790 | 0.6236 | 0.2154 | 0.6236 | 0.7897 |
| 0.0421 | 9.7181 | 5792 | 0.6218 | 0.2154 | 0.6218 | 0.7885 |
| 0.0421 | 9.7215 | 5794 | 0.6188 | 0.2154 | 0.6188 | 0.7866 |
| 0.0421 | 9.7248 | 5796 | 0.6162 | 0.2154 | 0.6162 | 0.7850 |
| 0.0421 | 9.7282 | 5798 | 0.6131 | 0.2154 | 0.6131 | 0.7830 |
| 0.0421 | 9.7315 | 5800 | 0.6105 | 0.2154 | 0.6105 | 0.7813 |
| 0.0421 | 9.7349 | 5802 | 0.6091 | 0.2154 | 0.6091 | 0.7805 |
| 0.0421 | 9.7383 | 5804 | 0.6089 | 0.2154 | 0.6089 | 0.7803 |
| 0.0421 | 9.7416 | 5806 | 0.6083 | 0.2154 | 0.6083 | 0.7799 |
| 0.0421 | 9.7450 | 5808 | 0.6082 | 0.2154 | 0.6082 | 0.7799 |
| 0.0421 | 9.7483 | 5810 | 0.6083 | 0.2154 | 0.6083 | 0.7799 |
| 0.0421 | 9.7517 | 5812 | 0.6085 | 0.2154 | 0.6085 | 0.7801 |
| 0.0421 | 9.7550 | 5814 | 0.6083 | 0.2154 | 0.6083 | 0.7799 |
| 0.0421 | 9.7584 | 5816 | 0.6077 | 0.2154 | 0.6077 | 0.7796 |
| 0.0421 | 9.7617 | 5818 | 0.6075 | 0.2154 | 0.6075 | 0.7794 |
| 0.0421 | 9.7651 | 5820 | 0.6072 | 0.2154 | 0.6072 | 0.7792 |
| 0.0421 | 9.7685 | 5822 | 0.6065 | 0.2154 | 0.6065 | 0.7788 |
| 0.0421 | 9.7718 | 5824 | 0.6065 | 0.2154 | 0.6065 | 0.7788 |
| 0.0421 | 9.7752 | 5826 | 0.6063 | 0.2154 | 0.6063 | 0.7786 |
| 0.0421 | 9.7785 | 5828 | 0.6066 | 0.2154 | 0.6066 | 0.7789 |
| 0.0421 | 9.7819 | 5830 | 0.6078 | 0.2154 | 0.6078 | 0.7796 |
| 0.0421 | 9.7852 | 5832 | 0.6098 | 0.2154 | 0.6098 | 0.7809 |
| 0.0421 | 9.7886 | 5834 | 0.6115 | 0.2154 | 0.6115 | 0.7820 |
| 0.0421 | 9.7919 | 5836 | 0.6124 | 0.2154 | 0.6124 | 0.7826 |
| 0.0421 | 9.7953 | 5838 | 0.6134 | 0.2154 | 0.6134 | 0.7832 |
| 0.0421 | 9.7987 | 5840 | 0.6136 | 0.2154 | 0.6136 | 0.7833 |
| 0.0421 | 9.8020 | 5842 | 0.6133 | 0.2154 | 0.6133 | 0.7831 |
| 0.0421 | 9.8054 | 5844 | 0.6130 | 0.2154 | 0.6130 | 0.7829 |
| 0.0421 | 9.8087 | 5846 | 0.6131 | 0.2154 | 0.6131 | 0.7830 |
| 0.0421 | 9.8121 | 5848 | 0.6133 | 0.2154 | 0.6133 | 0.7832 |
| 0.0421 | 9.8154 | 5850 | 0.6138 | 0.2154 | 0.6138 | 0.7834 |
| 0.0421 | 9.8188 | 5852 | 0.6139 | 0.2154 | 0.6139 | 0.7835 |
| 0.0421 | 9.8221 | 5854 | 0.6139 | 0.2154 | 0.6139 | 0.7835 |
| 0.0421 | 9.8255 | 5856 | 0.6146 | 0.2154 | 0.6146 | 0.7840 |
| 0.0421 | 9.8289 | 5858 | 0.6154 | 0.2154 | 0.6154 | 0.7845 |
| 0.0421 | 9.8322 | 5860 | 0.6154 | 0.2154 | 0.6154 | 0.7845 |
| 0.0421 | 9.8356 | 5862 | 0.6148 | 0.2154 | 0.6148 | 0.7841 |
| 0.0421 | 9.8389 | 5864 | 0.6135 | 0.2154 | 0.6135 | 0.7833 |
| 0.0421 | 9.8423 | 5866 | 0.6121 | 0.2154 | 0.6121 | 0.7824 |
| 0.0421 | 9.8456 | 5868 | 0.6111 | 0.2154 | 0.6111 | 0.7817 |
| 0.0421 | 9.8490 | 5870 | 0.6106 | 0.2154 | 0.6106 | 0.7814 |
| 0.0421 | 9.8523 | 5872 | 0.6103 | 0.2154 | 0.6103 | 0.7812 |
| 0.0421 | 9.8557 | 5874 | 0.6106 | 0.2154 | 0.6106 | 0.7814 |
| 0.0421 | 9.8591 | 5876 | 0.6105 | 0.2154 | 0.6105 | 0.7813 |
| 0.0421 | 9.8624 | 5878 | 0.6107 | 0.2154 | 0.6107 | 0.7815 |
| 0.0421 | 9.8658 | 5880 | 0.6109 | 0.2154 | 0.6109 | 0.7816 |
| 0.0421 | 9.8691 | 5882 | 0.6110 | 0.2154 | 0.6110 | 0.7817 |
| 0.0421 | 9.8725 | 5884 | 0.6108 | 0.2154 | 0.6108 | 0.7815 |
| 0.0421 | 9.8758 | 5886 | 0.6107 | 0.2154 | 0.6107 | 0.7815 |
| 0.0421 | 9.8792 | 5888 | 0.6106 | 0.2154 | 0.6106 | 0.7814 |
| 0.0421 | 9.8826 | 5890 | 0.6101 | 0.2154 | 0.6101 | 0.7811 |
| 0.0421 | 9.8859 | 5892 | 0.6093 | 0.2154 | 0.6093 | 0.7806 |
| 0.0421 | 9.8893 | 5894 | 0.6082 | 0.2154 | 0.6082 | 0.7799 |
| 0.0421 | 9.8926 | 5896 | 0.6071 | 0.2727 | 0.6071 | 0.7792 |
| 0.0421 | 9.8960 | 5898 | 0.6065 | 0.2727 | 0.6065 | 0.7788 |
| 0.0421 | 9.8993 | 5900 | 0.6058 | 0.2727 | 0.6058 | 0.7783 |
| 0.0421 | 9.9027 | 5902 | 0.6051 | 0.2727 | 0.6051 | 0.7779 |
| 0.0421 | 9.9060 | 5904 | 0.6045 | 0.2727 | 0.6045 | 0.7775 |
| 0.0421 | 9.9094 | 5906 | 0.6040 | 0.2727 | 0.6040 | 0.7771 |
| 0.0421 | 9.9128 | 5908 | 0.6039 | 0.2727 | 0.6039 | 0.7771 |
| 0.0421 | 9.9161 | 5910 | 0.6040 | 0.2727 | 0.6040 | 0.7772 |
| 0.0421 | 9.9195 | 5912 | 0.6038 | 0.2727 | 0.6038 | 0.7770 |
| 0.0421 | 9.9228 | 5914 | 0.6037 | 0.2727 | 0.6037 | 0.7770 |
| 0.0421 | 9.9262 | 5916 | 0.6036 | 0.2727 | 0.6036 | 0.7769 |
| 0.0421 | 9.9295 | 5918 | 0.6034 | 0.2727 | 0.6034 | 0.7768 |
| 0.0421 | 9.9329 | 5920 | 0.6034 | 0.2727 | 0.6034 | 0.7768 |
| 0.0421 | 9.9362 | 5922 | 0.6033 | 0.2727 | 0.6033 | 0.7767 |
| 0.0421 | 9.9396 | 5924 | 0.6031 | 0.2727 | 0.6031 | 0.7766 |
| 0.0421 | 9.9430 | 5926 | 0.6030 | 0.2727 | 0.6030 | 0.7765 |
| 0.0421 | 9.9463 | 5928 | 0.6028 | 0.2727 | 0.6028 | 0.7764 |
| 0.0421 | 9.9497 | 5930 | 0.6025 | 0.2727 | 0.6025 | 0.7762 |
| 0.0421 | 9.9530 | 5932 | 0.6024 | 0.2727 | 0.6024 | 0.7761 |
| 0.0421 | 9.9564 | 5934 | 0.6023 | 0.2727 | 0.6023 | 0.7760 |
| 0.0421 | 9.9597 | 5936 | 0.6022 | 0.2727 | 0.6022 | 0.7760 |
| 0.0421 | 9.9631 | 5938 | 0.6021 | 0.2727 | 0.6021 | 0.7759 |
| 0.0421 | 9.9664 | 5940 | 0.6020 | 0.2727 | 0.6020 | 0.7759 |
| 0.0421 | 9.9698 | 5942 | 0.6021 | 0.2727 | 0.6021 | 0.7760 |
| 0.0421 | 9.9732 | 5944 | 0.6023 | 0.2727 | 0.6023 | 0.7761 |
| 0.0421 | 9.9765 | 5946 | 0.6025 | 0.2727 | 0.6025 | 0.7762 |
| 0.0421 | 9.9799 | 5948 | 0.6026 | 0.2727 | 0.6026 | 0.7763 |
| 0.0421 | 9.9832 | 5950 | 0.6027 | 0.2727 | 0.6027 | 0.7763 |
| 0.0421 | 9.9866 | 5952 | 0.6028 | 0.2727 | 0.6028 | 0.7764 |
| 0.0421 | 9.9899 | 5954 | 0.6029 | 0.2727 | 0.6029 | 0.7765 |
| 0.0421 | 9.9933 | 5956 | 0.6030 | 0.2727 | 0.6030 | 0.7765 |
| 0.0421 | 9.9966 | 5958 | 0.6031 | 0.2727 | 0.6031 | 0.7766 |
| 0.0421 | 10.0 | 5960 | 0.6031 | 0.2727 | 0.6031 | 0.7766 |
### Framework versions
- Transformers 4.44.2
- Pytorch 2.4.0+cu118
- Datasets 2.21.0
- Tokenizers 0.19.1
|
Celanova/laura_test_01
|
Celanova
| 2024-11-16T21:45:50Z
| 26
| 0
|
diffusers
|
[
"diffusers",
"flux",
"lora",
"replicate",
"text-to-image",
"en",
"base_model:black-forest-labs/FLUX.1-dev",
"base_model:adapter:black-forest-labs/FLUX.1-dev",
"license:other",
"region:us"
] |
text-to-image
| 2024-11-16T12:58:39Z
|
---
license: other
license_name: flux-1-dev-non-commercial-license
license_link: https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md
language:
- en
tags:
- flux
- diffusers
- lora
- replicate
base_model: "black-forest-labs/FLUX.1-dev"
pipeline_tag: text-to-image
# widget:
# - text: >-
# prompt
# output:
# url: https://...
instance_prompt: TOK
---
# Laura_Test_01
<Gallery />
Trained on Replicate using:
https://replicate.com/ostris/flux-dev-lora-trainer/train
## Trigger words
You should use `TOK` to trigger the image generation.
## Use it with the [🧨 diffusers library](https://github.com/huggingface/diffusers)
```py
from diffusers import AutoPipelineForText2Image
import torch
pipeline = AutoPipelineForText2Image.from_pretrained('black-forest-labs/FLUX.1-dev', torch_dtype=torch.float16).to('cuda')
pipeline.load_lora_weights('Celanova/laura_test_01', weight_name='lora.safetensors')
image = pipeline('your prompt').images[0]
```
For more details, including weighting, merging and fusing LoRAs, check the [documentation on loading LoRAs in diffusers](https://huggingface.co/docs/diffusers/main/en/using-diffusers/loading_adapters)
|
rewicks/monolingual_de_8k-shared_ep4
|
rewicks
| 2024-11-16T21:43:40Z
| 163
| 0
|
transformers
|
[
"transformers",
"safetensors",
"marian",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2024-11-01T18:07:09Z
|
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
mradermacher/granite-3.0-1b-a400m-instruct-GGUF
|
mradermacher
| 2024-11-16T21:33:13Z
| 16
| 0
|
transformers
|
[
"transformers",
"gguf",
"language",
"granite-3.0",
"en",
"base_model:ibm-granite/granite-3.0-1b-a400m-instruct",
"base_model:quantized:ibm-granite/granite-3.0-1b-a400m-instruct",
"license:apache-2.0",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2024-11-16T21:22:35Z
|
---
base_model: ibm-granite/granite-3.0-1b-a400m-instruct
language:
- en
library_name: transformers
license: apache-2.0
quantized_by: mradermacher
tags:
- language
- granite-3.0
---
## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
<!-- ### tags: -->
static quants of https://huggingface.co/ibm-granite/granite-3.0-1b-a400m-instruct
<!-- provided-files -->
weighted/imatrix quants are available at https://huggingface.co/mradermacher/granite-3.0-1b-a400m-instruct-i1-GGUF
## Usage
If you are unsure how to use GGUF files, refer to one of [TheBloke's
READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for
more details, including on how to concatenate multi-part files.
## Provided Quants
(sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants)
| Link | Type | Size/GB | Notes |
|:-----|:-----|--------:|:------|
| [GGUF](https://huggingface.co/mradermacher/granite-3.0-1b-a400m-instruct-GGUF/resolve/main/granite-3.0-1b-a400m-instruct.Q2_K.gguf) | Q2_K | 0.6 | |
| [GGUF](https://huggingface.co/mradermacher/granite-3.0-1b-a400m-instruct-GGUF/resolve/main/granite-3.0-1b-a400m-instruct.Q3_K_S.gguf) | Q3_K_S | 0.7 | |
| [GGUF](https://huggingface.co/mradermacher/granite-3.0-1b-a400m-instruct-GGUF/resolve/main/granite-3.0-1b-a400m-instruct.Q3_K_M.gguf) | Q3_K_M | 0.8 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/granite-3.0-1b-a400m-instruct-GGUF/resolve/main/granite-3.0-1b-a400m-instruct.Q3_K_L.gguf) | Q3_K_L | 0.8 | |
| [GGUF](https://huggingface.co/mradermacher/granite-3.0-1b-a400m-instruct-GGUF/resolve/main/granite-3.0-1b-a400m-instruct.IQ4_XS.gguf) | IQ4_XS | 0.8 | |
| [GGUF](https://huggingface.co/mradermacher/granite-3.0-1b-a400m-instruct-GGUF/resolve/main/granite-3.0-1b-a400m-instruct.Q4_0_4_4.gguf) | Q4_0_4_4 | 0.9 | fast on arm, low quality |
| [GGUF](https://huggingface.co/mradermacher/granite-3.0-1b-a400m-instruct-GGUF/resolve/main/granite-3.0-1b-a400m-instruct.Q4_K_S.gguf) | Q4_K_S | 0.9 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/granite-3.0-1b-a400m-instruct-GGUF/resolve/main/granite-3.0-1b-a400m-instruct.Q4_K_M.gguf) | Q4_K_M | 0.9 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/granite-3.0-1b-a400m-instruct-GGUF/resolve/main/granite-3.0-1b-a400m-instruct.Q5_K_S.gguf) | Q5_K_S | 1.0 | |
| [GGUF](https://huggingface.co/mradermacher/granite-3.0-1b-a400m-instruct-GGUF/resolve/main/granite-3.0-1b-a400m-instruct.Q5_K_M.gguf) | Q5_K_M | 1.1 | |
| [GGUF](https://huggingface.co/mradermacher/granite-3.0-1b-a400m-instruct-GGUF/resolve/main/granite-3.0-1b-a400m-instruct.Q6_K.gguf) | Q6_K | 1.2 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/granite-3.0-1b-a400m-instruct-GGUF/resolve/main/granite-3.0-1b-a400m-instruct.Q8_0.gguf) | Q8_0 | 1.5 | fast, best quality |
| [GGUF](https://huggingface.co/mradermacher/granite-3.0-1b-a400m-instruct-GGUF/resolve/main/granite-3.0-1b-a400m-instruct.f16.gguf) | f16 | 2.8 | 16 bpw, overkill |
Here is a handy graph by ikawrakow comparing some lower-quality quant
types (lower is better):

And here are Artefact2's thoughts on the matter:
https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9
## FAQ / Model Request
See https://huggingface.co/mradermacher/model_requests for some answers to
questions you might have and/or if you want some other model quantized.
## Thanks
I thank my company, [nethype GmbH](https://www.nethype.de/), for letting
me use its servers and providing upgrades to my workstation to enable
this work in my free time.
<!-- end -->
|
mradermacher/CodeLlama-7B-KStack-GGUF
|
mradermacher
| 2024-11-16T21:30:27Z
| 44
| 0
|
transformers
|
[
"transformers",
"gguf",
"code",
"en",
"dataset:JetBrains/KStack",
"base_model:JetBrains/CodeLlama-7B-KStack",
"base_model:quantized:JetBrains/CodeLlama-7B-KStack",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2024-11-16T21:15:11Z
|
---
base_model: JetBrains/CodeLlama-7B-KStack
datasets:
- JetBrains/KStack
language:
- en
library_name: transformers
license: apache-2.0
quantized_by: mradermacher
tags:
- code
---
## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
<!-- ### tags: -->
static quants of https://huggingface.co/JetBrains/CodeLlama-7B-KStack
<!-- provided-files -->
weighted/imatrix quants are available at https://huggingface.co/mradermacher/CodeLlama-7B-KStack-i1-GGUF
## Usage
If you are unsure how to use GGUF files, refer to one of [TheBloke's
READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for
more details, including on how to concatenate multi-part files.
## Provided Quants
(sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants)
| Link | Type | Size/GB | Notes |
|:-----|:-----|--------:|:------|
| [GGUF](https://huggingface.co/mradermacher/CodeLlama-7B-KStack-GGUF/resolve/main/CodeLlama-7B-KStack.Q2_K.gguf) | Q2_K | 2.6 | |
| [GGUF](https://huggingface.co/mradermacher/CodeLlama-7B-KStack-GGUF/resolve/main/CodeLlama-7B-KStack.Q3_K_S.gguf) | Q3_K_S | 3.0 | |
| [GGUF](https://huggingface.co/mradermacher/CodeLlama-7B-KStack-GGUF/resolve/main/CodeLlama-7B-KStack.Q3_K_M.gguf) | Q3_K_M | 3.4 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/CodeLlama-7B-KStack-GGUF/resolve/main/CodeLlama-7B-KStack.Q3_K_L.gguf) | Q3_K_L | 3.7 | |
| [GGUF](https://huggingface.co/mradermacher/CodeLlama-7B-KStack-GGUF/resolve/main/CodeLlama-7B-KStack.IQ4_XS.gguf) | IQ4_XS | 3.7 | |
| [GGUF](https://huggingface.co/mradermacher/CodeLlama-7B-KStack-GGUF/resolve/main/CodeLlama-7B-KStack.Q4_0_4_4.gguf) | Q4_0_4_4 | 3.9 | fast on arm, low quality |
| [GGUF](https://huggingface.co/mradermacher/CodeLlama-7B-KStack-GGUF/resolve/main/CodeLlama-7B-KStack.Q4_K_S.gguf) | Q4_K_S | 4.0 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/CodeLlama-7B-KStack-GGUF/resolve/main/CodeLlama-7B-KStack.Q4_K_M.gguf) | Q4_K_M | 4.2 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/CodeLlama-7B-KStack-GGUF/resolve/main/CodeLlama-7B-KStack.Q5_K_S.gguf) | Q5_K_S | 4.8 | |
| [GGUF](https://huggingface.co/mradermacher/CodeLlama-7B-KStack-GGUF/resolve/main/CodeLlama-7B-KStack.Q5_K_M.gguf) | Q5_K_M | 4.9 | |
| [GGUF](https://huggingface.co/mradermacher/CodeLlama-7B-KStack-GGUF/resolve/main/CodeLlama-7B-KStack.Q6_K.gguf) | Q6_K | 5.6 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/CodeLlama-7B-KStack-GGUF/resolve/main/CodeLlama-7B-KStack.Q8_0.gguf) | Q8_0 | 7.3 | fast, best quality |
| [GGUF](https://huggingface.co/mradermacher/CodeLlama-7B-KStack-GGUF/resolve/main/CodeLlama-7B-KStack.f16.gguf) | f16 | 13.6 | 16 bpw, overkill |
Here is a handy graph by ikawrakow comparing some lower-quality quant
types (lower is better):

And here are Artefact2's thoughts on the matter:
https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9
## FAQ / Model Request
See https://huggingface.co/mradermacher/model_requests for some answers to
questions you might have and/or if you want some other model quantized.
## Thanks
I thank my company, [nethype GmbH](https://www.nethype.de/), for letting
me use its servers and providing upgrades to my workstation to enable
this work in my free time.
<!-- end -->
|
SERHANI/skin_disease
|
SERHANI
| 2024-11-16T21:18:55Z
| 5
| 0
|
ultralytics
|
[
"ultralytics",
"yolov11",
"object-detection",
"pytorch",
"computer-vision",
"en",
"dataset:custom",
"license:mit",
"endpoints_compatible",
"region:us"
] |
object-detection
| 2024-11-15T23:39:07Z
|
---
language: en
tags:
- yolov11
- object-detection
- pytorch
- computer-vision
library_name: ultralytics
pipeline_tag: object-detection
datasets: custom
license: mit
---
# YOLO Custom Model
# YOLO Custom Model
This is a YOLO model trained on custom data.
## Model Description
- Model Type: YOLO
- Training Data: Custom Dataset
- Input: Images
- Output: Bounding boxes with class predictions
## Usage
```python
from ultralytics import YOLO
# Load the model
model = YOLO('model.pt')
# Make predictions
results = model('image.jpg')
```
## Training Details
- Framework: Ultralytics YOLOv8
- Training Device: cuda
|
htigenai/finetune_test
|
htigenai
| 2024-11-16T21:15:09Z
| 5
| 0
| null |
[
"safetensors",
"llama",
"unsloth",
"trl",
"sft",
"text-classification",
"en",
"arxiv:1910.09700",
"base_model:unsloth/Meta-Llama-3.1-8B-Instruct",
"base_model:finetune:unsloth/Meta-Llama-3.1-8B-Instruct",
"license:bigscience-bloom-rail-1.0",
"region:us"
] |
text-classification
| 2024-11-14T15:11:08Z
|
---
license: bigscience-bloom-rail-1.0
tags:
- unsloth
- trl
- sft
language:
- en
metrics:
- accuracy
base_model:
- unsloth/Meta-Llama-3.1-8B-Instruct
pipeline_tag: text-classification
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
This modelcard aims to be a base template for new models. It has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/modelcard_template.md?plain=1).
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
mradermacher/Mistral-7B-v0.2-OpenHermes-GGUF
|
mradermacher
| 2024-11-16T21:14:20Z
| 9
| 0
|
transformers
|
[
"transformers",
"gguf",
"text-generation-inference",
"unsloth",
"mistral",
"trl",
"sft",
"en",
"base_model:macadeliccc/Mistral-7B-v0.2-OpenHermes",
"base_model:quantized:macadeliccc/Mistral-7B-v0.2-OpenHermes",
"license:apache-2.0",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2024-11-16T20:58:01Z
|
---
base_model: macadeliccc/Mistral-7B-v0.2-OpenHermes
language:
- en
library_name: transformers
license: apache-2.0
quantized_by: mradermacher
tags:
- text-generation-inference
- transformers
- unsloth
- mistral
- trl
- sft
---
## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
<!-- ### tags: -->
static quants of https://huggingface.co/macadeliccc/Mistral-7B-v0.2-OpenHermes
<!-- provided-files -->
weighted/imatrix quants are available at https://huggingface.co/mradermacher/Mistral-7B-v0.2-OpenHermes-i1-GGUF
## Usage
If you are unsure how to use GGUF files, refer to one of [TheBloke's
READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for
more details, including on how to concatenate multi-part files.
## Provided Quants
(sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants)
| Link | Type | Size/GB | Notes |
|:-----|:-----|--------:|:------|
| [GGUF](https://huggingface.co/mradermacher/Mistral-7B-v0.2-OpenHermes-GGUF/resolve/main/Mistral-7B-v0.2-OpenHermes.Q2_K.gguf) | Q2_K | 2.8 | |
| [GGUF](https://huggingface.co/mradermacher/Mistral-7B-v0.2-OpenHermes-GGUF/resolve/main/Mistral-7B-v0.2-OpenHermes.Q3_K_S.gguf) | Q3_K_S | 3.3 | |
| [GGUF](https://huggingface.co/mradermacher/Mistral-7B-v0.2-OpenHermes-GGUF/resolve/main/Mistral-7B-v0.2-OpenHermes.Q3_K_M.gguf) | Q3_K_M | 3.6 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/Mistral-7B-v0.2-OpenHermes-GGUF/resolve/main/Mistral-7B-v0.2-OpenHermes.Q3_K_L.gguf) | Q3_K_L | 3.9 | |
| [GGUF](https://huggingface.co/mradermacher/Mistral-7B-v0.2-OpenHermes-GGUF/resolve/main/Mistral-7B-v0.2-OpenHermes.IQ4_XS.gguf) | IQ4_XS | 4.0 | |
| [GGUF](https://huggingface.co/mradermacher/Mistral-7B-v0.2-OpenHermes-GGUF/resolve/main/Mistral-7B-v0.2-OpenHermes.Q4_0_4_4.gguf) | Q4_0_4_4 | 4.2 | fast on arm, low quality |
| [GGUF](https://huggingface.co/mradermacher/Mistral-7B-v0.2-OpenHermes-GGUF/resolve/main/Mistral-7B-v0.2-OpenHermes.Q4_K_S.gguf) | Q4_K_S | 4.2 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Mistral-7B-v0.2-OpenHermes-GGUF/resolve/main/Mistral-7B-v0.2-OpenHermes.Q4_K_M.gguf) | Q4_K_M | 4.5 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Mistral-7B-v0.2-OpenHermes-GGUF/resolve/main/Mistral-7B-v0.2-OpenHermes.Q5_K_S.gguf) | Q5_K_S | 5.1 | |
| [GGUF](https://huggingface.co/mradermacher/Mistral-7B-v0.2-OpenHermes-GGUF/resolve/main/Mistral-7B-v0.2-OpenHermes.Q5_K_M.gguf) | Q5_K_M | 5.2 | |
| [GGUF](https://huggingface.co/mradermacher/Mistral-7B-v0.2-OpenHermes-GGUF/resolve/main/Mistral-7B-v0.2-OpenHermes.Q6_K.gguf) | Q6_K | 6.0 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/Mistral-7B-v0.2-OpenHermes-GGUF/resolve/main/Mistral-7B-v0.2-OpenHermes.Q8_0.gguf) | Q8_0 | 7.8 | fast, best quality |
| [GGUF](https://huggingface.co/mradermacher/Mistral-7B-v0.2-OpenHermes-GGUF/resolve/main/Mistral-7B-v0.2-OpenHermes.f16.gguf) | f16 | 14.6 | 16 bpw, overkill |
Here is a handy graph by ikawrakow comparing some lower-quality quant
types (lower is better):

And here are Artefact2's thoughts on the matter:
https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9
## FAQ / Model Request
See https://huggingface.co/mradermacher/model_requests for some answers to
questions you might have and/or if you want some other model quantized.
## Thanks
I thank my company, [nethype GmbH](https://www.nethype.de/), for letting
me use its servers and providing upgrades to my workstation to enable
this work in my free time.
<!-- end -->
|
Farhang87/qwen-3b-soap-peft-merged
|
Farhang87
| 2024-11-16T21:03:47Z
| 130
| 0
|
transformers
|
[
"transformers",
"safetensors",
"qwen2",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2024-11-16T21:01:07Z
|
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
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## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
hangingardens/hihihi
|
hangingardens
| 2024-11-16T20:53:01Z
| 129
| 0
|
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"qwen2",
"text-generation",
"generated_from_trainer",
"trl",
"sft",
"conversational",
"base_model:Qwen/Qwen2.5-0.5B",
"base_model:finetune:Qwen/Qwen2.5-0.5B",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2024-11-16T20:51:59Z
|
---
base_model: Qwen/Qwen2.5-0.5B
library_name: transformers
model_name: llama-ioi
tags:
- generated_from_trainer
- trl
- sft
licence: license
---
# Model Card for llama-ioi
This model is a fine-tuned version of [Qwen/Qwen2.5-0.5B](https://huggingface.co/Qwen/Qwen2.5-0.5B).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="hangingardens/llama-ioi", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
[<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="150" height="24"/>](https://wandb.ai/ath3great-sda/huggingface/runs/kwb16nas)
This model was trained with SFT.
### Framework versions
- TRL: 0.12.1
- Transformers: 4.46.2
- Pytorch: 2.5.1+cu121
- Datasets: 3.1.0
- Tokenizers: 0.20.3
## Citations
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
```
|
ccgomezn/sentiment-analysis-model
|
ccgomezn
| 2024-11-16T20:49:16Z
| 161
| 0
|
transformers
|
[
"transformers",
"safetensors",
"bert",
"text-classification",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2024-11-16T20:48:53Z
|
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
MayBashendy/Arabic_FineTuningAraBERT_AugV4_k35_task2_organization_fold0
|
MayBashendy
| 2024-11-16T20:21:56Z
| 165
| 0
|
transformers
|
[
"transformers",
"safetensors",
"bert",
"text-classification",
"generated_from_trainer",
"base_model:aubmindlab/bert-base-arabertv02",
"base_model:finetune:aubmindlab/bert-base-arabertv02",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2024-11-16T19:45:19Z
|
---
library_name: transformers
base_model: aubmindlab/bert-base-arabertv02
tags:
- generated_from_trainer
model-index:
- name: Arabic_FineTuningAraBERT_AugV4_k35_task2_organization_fold0
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# Arabic_FineTuningAraBERT_AugV4_k35_task2_organization_fold0
This model is a fine-tuned version of [aubmindlab/bert-base-arabertv02](https://huggingface.co/aubmindlab/bert-base-arabertv02) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5076
- Qwk: 0.5191
- Mse: 0.5076
- Rmse: 0.7125
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Qwk | Mse | Rmse |
|:-------------:|:------:|:----:|:---------------:|:-------:|:------:|:------:|
| No log | 0.0052 | 2 | 3.5255 | 0.0 | 3.5255 | 1.8776 |
| No log | 0.0104 | 4 | 2.7185 | 0.0499 | 2.7185 | 1.6488 |
| No log | 0.0157 | 6 | 1.0625 | 0.0 | 1.0625 | 1.0308 |
| No log | 0.0209 | 8 | 0.7473 | 0.0567 | 0.7473 | 0.8645 |
| No log | 0.0261 | 10 | 0.8056 | 0.0679 | 0.8056 | 0.8976 |
| No log | 0.0313 | 12 | 0.7860 | 0.0679 | 0.7860 | 0.8866 |
| No log | 0.0366 | 14 | 0.6875 | 0.2184 | 0.6875 | 0.8292 |
| No log | 0.0418 | 16 | 0.7856 | 0.0679 | 0.7856 | 0.8864 |
| No log | 0.0470 | 18 | 0.7640 | 0.0679 | 0.7640 | 0.8741 |
| No log | 0.0522 | 20 | 0.6260 | 0.2423 | 0.6260 | 0.7912 |
| No log | 0.0574 | 22 | 0.6861 | 0.0679 | 0.6861 | 0.8283 |
| No log | 0.0627 | 24 | 0.6304 | 0.0679 | 0.6304 | 0.7940 |
| No log | 0.0679 | 26 | 0.7731 | 0.0679 | 0.7731 | 0.8792 |
| No log | 0.0731 | 28 | 1.1833 | 0.2300 | 1.1833 | 1.0878 |
| No log | 0.0783 | 30 | 1.5777 | 0.0772 | 1.5777 | 1.2561 |
| No log | 0.0836 | 32 | 1.9853 | -0.0519 | 1.9853 | 1.4090 |
| No log | 0.0888 | 34 | 1.9607 | -0.0519 | 1.9607 | 1.4003 |
| No log | 0.0940 | 36 | 1.6056 | -0.0129 | 1.6056 | 1.2671 |
| No log | 0.0992 | 38 | 1.1513 | 0.0982 | 1.1513 | 1.0730 |
| No log | 0.1044 | 40 | 0.8199 | 0.0 | 0.8199 | 0.9055 |
| No log | 0.1097 | 42 | 0.6758 | 0.0 | 0.6758 | 0.8221 |
| No log | 0.1149 | 44 | 0.8131 | 0.0708 | 0.8131 | 0.9017 |
| No log | 0.1201 | 46 | 0.9115 | 0.1840 | 0.9115 | 0.9547 |
| No log | 0.1253 | 48 | 0.9850 | 0.2913 | 0.9850 | 0.9925 |
| No log | 0.1305 | 50 | 1.1270 | 0.2701 | 1.1270 | 1.0616 |
| No log | 0.1358 | 52 | 1.2096 | 0.2701 | 1.2096 | 1.0998 |
| No log | 0.1410 | 54 | 1.2903 | 0.2242 | 1.2903 | 1.1359 |
| No log | 0.1462 | 56 | 0.9316 | 0.3769 | 0.9316 | 0.9652 |
| No log | 0.1514 | 58 | 0.9260 | 0.3736 | 0.9260 | 0.9623 |
| No log | 0.1567 | 60 | 0.9495 | 0.3769 | 0.9495 | 0.9744 |
| No log | 0.1619 | 62 | 0.9120 | 0.3736 | 0.9120 | 0.9550 |
| No log | 0.1671 | 64 | 1.2076 | 0.1314 | 1.2076 | 1.0989 |
| No log | 0.1723 | 66 | 1.1130 | 0.2242 | 1.1130 | 1.0550 |
| No log | 0.1775 | 68 | 0.9866 | 0.2139 | 0.9866 | 0.9933 |
| No log | 0.1828 | 70 | 0.7412 | 0.3833 | 0.7412 | 0.8609 |
| No log | 0.1880 | 72 | 0.8579 | 0.4112 | 0.8579 | 0.9262 |
| No log | 0.1932 | 74 | 1.1225 | 0.2242 | 1.1225 | 1.0595 |
| No log | 0.1984 | 76 | 0.9132 | 0.3736 | 0.9132 | 0.9556 |
| No log | 0.2037 | 78 | 0.7651 | 0.37 | 0.7651 | 0.8747 |
| No log | 0.2089 | 80 | 0.7380 | 0.37 | 0.7380 | 0.8591 |
| No log | 0.2141 | 82 | 0.7372 | 0.3659 | 0.7372 | 0.8586 |
| No log | 0.2193 | 84 | 0.8668 | 0.37 | 0.8668 | 0.9310 |
| No log | 0.2245 | 86 | 1.0947 | 0.3145 | 1.0947 | 1.0463 |
| No log | 0.2298 | 88 | 0.8336 | 0.4099 | 0.8336 | 0.9130 |
| No log | 0.2350 | 90 | 0.5513 | 0.4688 | 0.5513 | 0.7425 |
| No log | 0.2402 | 92 | 0.6256 | 0.4288 | 0.6256 | 0.7909 |
| No log | 0.2454 | 94 | 0.6721 | 0.4288 | 0.6721 | 0.8198 |
| No log | 0.2507 | 96 | 1.0538 | 0.3505 | 1.0538 | 1.0265 |
| No log | 0.2559 | 98 | 1.3659 | 0.2242 | 1.3659 | 1.1687 |
| No log | 0.2611 | 100 | 1.1305 | 0.2242 | 1.1305 | 1.0632 |
| No log | 0.2663 | 102 | 0.7601 | 0.4099 | 0.7601 | 0.8718 |
| No log | 0.2715 | 104 | 0.5410 | 0.2158 | 0.5410 | 0.7356 |
| No log | 0.2768 | 106 | 0.5617 | 0.2516 | 0.5617 | 0.7495 |
| No log | 0.2820 | 108 | 0.6281 | 0.2188 | 0.6281 | 0.7925 |
| No log | 0.2872 | 110 | 0.8814 | 0.3769 | 0.8814 | 0.9388 |
| No log | 0.2924 | 112 | 1.1731 | 0.2242 | 1.1731 | 1.0831 |
| No log | 0.2977 | 114 | 1.1280 | 0.2242 | 1.1280 | 1.0621 |
| No log | 0.3029 | 116 | 0.9518 | 0.3831 | 0.9518 | 0.9756 |
| No log | 0.3081 | 118 | 1.1439 | 0.3259 | 1.1439 | 1.0695 |
| No log | 0.3133 | 120 | 1.5645 | 0.2242 | 1.5645 | 1.2508 |
| No log | 0.3185 | 122 | 1.6882 | 0.1563 | 1.6882 | 1.2993 |
| No log | 0.3238 | 124 | 1.4742 | 0.2336 | 1.4742 | 1.2142 |
| No log | 0.3290 | 126 | 0.9614 | 0.2797 | 0.9614 | 0.9805 |
| No log | 0.3342 | 128 | 0.5783 | 0.6419 | 0.5783 | 0.7605 |
| No log | 0.3394 | 130 | 0.5236 | 0.6546 | 0.5236 | 0.7236 |
| No log | 0.3446 | 132 | 0.6322 | 0.5333 | 0.6322 | 0.7951 |
| No log | 0.3499 | 134 | 0.8290 | 0.3547 | 0.8290 | 0.9105 |
| No log | 0.3551 | 136 | 0.9470 | 0.2242 | 0.9470 | 0.9731 |
| No log | 0.3603 | 138 | 0.9605 | 0.3207 | 0.9605 | 0.9801 |
| No log | 0.3655 | 140 | 0.9015 | 0.3207 | 0.9015 | 0.9495 |
| No log | 0.3708 | 142 | 0.6268 | 0.4482 | 0.6268 | 0.7917 |
| No log | 0.3760 | 144 | 0.4845 | 0.6546 | 0.4845 | 0.6960 |
| No log | 0.3812 | 146 | 0.5771 | 0.4878 | 0.5771 | 0.7597 |
| No log | 0.3864 | 148 | 0.6350 | 0.4482 | 0.6350 | 0.7969 |
| No log | 0.3916 | 150 | 0.6239 | 0.4566 | 0.6239 | 0.7899 |
| No log | 0.3969 | 152 | 0.6909 | 0.4482 | 0.6909 | 0.8312 |
| No log | 0.4021 | 154 | 0.7445 | 0.3860 | 0.7445 | 0.8629 |
| No log | 0.4073 | 156 | 0.7086 | 0.4482 | 0.7086 | 0.8418 |
| No log | 0.4125 | 158 | 0.6552 | 0.4503 | 0.6552 | 0.8094 |
| No log | 0.4178 | 160 | 0.7211 | 0.3833 | 0.7211 | 0.8492 |
| No log | 0.4230 | 162 | 0.7332 | 0.3405 | 0.7332 | 0.8563 |
| No log | 0.4282 | 164 | 0.8323 | 0.3458 | 0.8323 | 0.9123 |
| No log | 0.4334 | 166 | 0.7411 | 0.4085 | 0.7411 | 0.8609 |
| No log | 0.4386 | 168 | 0.8028 | 0.3000 | 0.8028 | 0.8960 |
| No log | 0.4439 | 170 | 0.7374 | 0.4085 | 0.7374 | 0.8587 |
| No log | 0.4491 | 172 | 0.5968 | 0.4187 | 0.5968 | 0.7725 |
| No log | 0.4543 | 174 | 0.5416 | 0.5 | 0.5416 | 0.7360 |
| No log | 0.4595 | 176 | 0.5802 | 0.6105 | 0.5802 | 0.7617 |
| No log | 0.4648 | 178 | 0.5358 | 0.6866 | 0.5358 | 0.7320 |
| No log | 0.4700 | 180 | 0.4468 | 0.6232 | 0.4468 | 0.6684 |
| No log | 0.4752 | 182 | 0.4908 | 0.7191 | 0.4908 | 0.7006 |
| No log | 0.4804 | 184 | 0.6619 | 0.3903 | 0.6619 | 0.8136 |
| No log | 0.4856 | 186 | 0.7301 | 0.3860 | 0.7301 | 0.8545 |
| No log | 0.4909 | 188 | 0.7122 | 0.4492 | 0.7122 | 0.8439 |
| No log | 0.4961 | 190 | 0.6481 | 0.5317 | 0.6481 | 0.8050 |
| No log | 0.5013 | 192 | 0.4985 | 0.6105 | 0.4985 | 0.7060 |
| No log | 0.5065 | 194 | 0.4590 | 0.6546 | 0.4590 | 0.6775 |
| No log | 0.5117 | 196 | 0.5603 | 0.5625 | 0.5603 | 0.7485 |
| No log | 0.5170 | 198 | 0.8932 | 0.3351 | 0.8932 | 0.9451 |
| No log | 0.5222 | 200 | 1.1404 | 0.3194 | 1.1404 | 1.0679 |
| No log | 0.5274 | 202 | 1.0385 | 0.2701 | 1.0385 | 1.0191 |
| No log | 0.5326 | 204 | 0.7123 | 0.4209 | 0.7123 | 0.8440 |
| No log | 0.5379 | 206 | 0.5398 | 0.5701 | 0.5398 | 0.7347 |
| No log | 0.5431 | 208 | 0.5037 | 0.6419 | 0.5037 | 0.7097 |
| No log | 0.5483 | 210 | 0.5681 | 0.6419 | 0.5681 | 0.7537 |
| No log | 0.5535 | 212 | 0.7708 | 0.3670 | 0.7708 | 0.8779 |
| No log | 0.5587 | 214 | 0.9646 | 0.3351 | 0.9646 | 0.9821 |
| No log | 0.5640 | 216 | 1.0703 | 0.3351 | 1.0703 | 1.0346 |
| No log | 0.5692 | 218 | 0.9430 | 0.3670 | 0.9430 | 0.9711 |
| No log | 0.5744 | 220 | 0.6865 | 0.3723 | 0.6865 | 0.8286 |
| No log | 0.5796 | 222 | 0.5915 | 0.5073 | 0.5915 | 0.7691 |
| No log | 0.5849 | 224 | 0.6224 | 0.4581 | 0.6224 | 0.7889 |
| No log | 0.5901 | 226 | 0.6392 | 0.4913 | 0.6392 | 0.7995 |
| No log | 0.5953 | 228 | 0.6275 | 0.4202 | 0.6275 | 0.7922 |
| No log | 0.6005 | 230 | 0.6201 | 0.4018 | 0.6201 | 0.7874 |
| No log | 0.6057 | 232 | 0.6943 | 0.4292 | 0.6943 | 0.8332 |
| No log | 0.6110 | 234 | 0.8222 | 0.3967 | 0.8222 | 0.9068 |
| No log | 0.6162 | 236 | 0.9091 | 0.3636 | 0.9091 | 0.9534 |
| No log | 0.6214 | 238 | 0.8078 | 0.3723 | 0.8078 | 0.8988 |
| No log | 0.6266 | 240 | 0.5949 | 0.6232 | 0.5949 | 0.7713 |
| No log | 0.6319 | 242 | 0.5172 | 0.6602 | 0.5172 | 0.7192 |
| No log | 0.6371 | 244 | 0.5032 | 0.6602 | 0.5032 | 0.7093 |
| No log | 0.6423 | 246 | 0.5416 | 0.6602 | 0.5416 | 0.7360 |
| No log | 0.6475 | 248 | 0.6998 | 0.3780 | 0.6998 | 0.8366 |
| No log | 0.6527 | 250 | 0.8316 | 0.4209 | 0.8316 | 0.9119 |
| No log | 0.6580 | 252 | 0.8461 | 0.4209 | 0.8461 | 0.9198 |
| No log | 0.6632 | 254 | 0.7429 | 0.4018 | 0.7429 | 0.8619 |
| No log | 0.6684 | 256 | 0.6445 | 0.3438 | 0.6445 | 0.8028 |
| No log | 0.6736 | 258 | 0.5911 | 0.5625 | 0.5911 | 0.7688 |
| No log | 0.6789 | 260 | 0.6421 | 0.3486 | 0.6421 | 0.8013 |
| No log | 0.6841 | 262 | 0.6815 | 0.375 | 0.6815 | 0.8255 |
| No log | 0.6893 | 264 | 0.7988 | 0.4553 | 0.7988 | 0.8938 |
| No log | 0.6945 | 266 | 0.7620 | 0.4553 | 0.7620 | 0.8730 |
| No log | 0.6997 | 268 | 0.6638 | 0.375 | 0.6638 | 0.8147 |
| No log | 0.7050 | 270 | 0.6803 | 0.375 | 0.6803 | 0.8248 |
| No log | 0.7102 | 272 | 0.6928 | 0.375 | 0.6928 | 0.8323 |
| No log | 0.7154 | 274 | 0.7303 | 0.375 | 0.7303 | 0.8546 |
| No log | 0.7206 | 276 | 0.6934 | 0.4018 | 0.6934 | 0.8327 |
| No log | 0.7258 | 278 | 0.7118 | 0.4018 | 0.7118 | 0.8437 |
| No log | 0.7311 | 280 | 0.6599 | 0.4667 | 0.6599 | 0.8123 |
| No log | 0.7363 | 282 | 0.6512 | 0.3980 | 0.6512 | 0.8070 |
| No log | 0.7415 | 284 | 0.6429 | 0.3980 | 0.6429 | 0.8018 |
| No log | 0.7467 | 286 | 0.6683 | 0.3980 | 0.6683 | 0.8175 |
| No log | 0.7520 | 288 | 0.6584 | 0.3980 | 0.6584 | 0.8114 |
| No log | 0.7572 | 290 | 0.6735 | 0.3679 | 0.6735 | 0.8207 |
| No log | 0.7624 | 292 | 0.7486 | 0.375 | 0.7486 | 0.8652 |
| No log | 0.7676 | 294 | 0.8101 | 0.4018 | 0.8101 | 0.9000 |
| No log | 0.7728 | 296 | 0.8416 | 0.3656 | 0.8416 | 0.9174 |
| No log | 0.7781 | 298 | 0.7581 | 0.3877 | 0.7581 | 0.8707 |
| No log | 0.7833 | 300 | 0.6005 | 0.3957 | 0.6005 | 0.7749 |
| No log | 0.7885 | 302 | 0.5677 | 0.3029 | 0.5677 | 0.7535 |
| No log | 0.7937 | 304 | 0.5984 | 0.2881 | 0.5984 | 0.7735 |
| No log | 0.7990 | 306 | 0.7199 | 0.2948 | 0.7199 | 0.8485 |
| No log | 0.8042 | 308 | 0.8809 | 0.3115 | 0.8809 | 0.9386 |
| No log | 0.8094 | 310 | 1.0590 | 0.3505 | 1.0590 | 1.0291 |
| No log | 0.8146 | 312 | 1.1970 | 0.3505 | 1.1970 | 1.0941 |
| No log | 0.8198 | 314 | 1.0756 | 0.3505 | 1.0756 | 1.0371 |
| No log | 0.8251 | 316 | 0.8289 | 0.4085 | 0.8289 | 0.9104 |
| No log | 0.8303 | 318 | 0.7377 | 0.2625 | 0.7377 | 0.8589 |
| No log | 0.8355 | 320 | 0.7538 | 0.3460 | 0.7538 | 0.8682 |
| No log | 0.8407 | 322 | 0.8080 | 0.2829 | 0.8080 | 0.8989 |
| No log | 0.8460 | 324 | 0.8835 | 0.2914 | 0.8835 | 0.9399 |
| No log | 0.8512 | 326 | 1.0729 | 0.2961 | 1.0729 | 1.0358 |
| No log | 0.8564 | 328 | 1.0941 | 0.2718 | 1.0941 | 1.0460 |
| No log | 0.8616 | 330 | 0.8624 | 0.3288 | 0.8624 | 0.9287 |
| No log | 0.8668 | 332 | 0.7975 | 0.3616 | 0.7975 | 0.8930 |
| No log | 0.8721 | 334 | 0.8608 | 0.3288 | 0.8608 | 0.9278 |
| No log | 0.8773 | 336 | 0.8626 | 0.3184 | 0.8626 | 0.9288 |
| No log | 0.8825 | 338 | 0.8671 | 0.3405 | 0.8671 | 0.9312 |
| No log | 0.8877 | 340 | 0.7609 | 0.2606 | 0.7609 | 0.8723 |
| No log | 0.8930 | 342 | 0.7479 | 0.2606 | 0.7479 | 0.8648 |
| No log | 0.8982 | 344 | 0.7215 | 0.3277 | 0.7215 | 0.8494 |
| No log | 0.9034 | 346 | 0.7301 | 0.4085 | 0.7301 | 0.8544 |
| No log | 0.9086 | 348 | 0.8639 | 0.3458 | 0.8639 | 0.9294 |
| No log | 0.9138 | 350 | 0.9781 | 0.3505 | 0.9781 | 0.9890 |
| No log | 0.9191 | 352 | 0.9196 | 0.4209 | 0.9196 | 0.9589 |
| No log | 0.9243 | 354 | 0.7356 | 0.4034 | 0.7356 | 0.8577 |
| No log | 0.9295 | 356 | 0.5491 | 0.4381 | 0.5491 | 0.7410 |
| No log | 0.9347 | 358 | 0.4784 | 0.6854 | 0.4784 | 0.6917 |
| No log | 0.9399 | 360 | 0.4719 | 0.6690 | 0.4719 | 0.6869 |
| No log | 0.9452 | 362 | 0.5579 | 0.4688 | 0.5579 | 0.7469 |
| No log | 0.9504 | 364 | 0.6499 | 0.4290 | 0.6499 | 0.8062 |
| No log | 0.9556 | 366 | 0.5906 | 0.4018 | 0.5906 | 0.7685 |
| No log | 0.9608 | 368 | 0.6507 | 0.375 | 0.6507 | 0.8066 |
| No log | 0.9661 | 370 | 0.7879 | 0.3948 | 0.7879 | 0.8876 |
| No log | 0.9713 | 372 | 0.7745 | 0.4209 | 0.7745 | 0.8801 |
| No log | 0.9765 | 374 | 0.6066 | 0.4581 | 0.6066 | 0.7789 |
| No log | 0.9817 | 376 | 0.4675 | 0.5855 | 0.4675 | 0.6838 |
| No log | 0.9869 | 378 | 0.4496 | 0.6917 | 0.4496 | 0.6705 |
| No log | 0.9922 | 380 | 0.5040 | 0.4776 | 0.5040 | 0.7099 |
| No log | 0.9974 | 382 | 0.5917 | 0.4597 | 0.5917 | 0.7692 |
| No log | 1.0026 | 384 | 0.6555 | 0.4597 | 0.6555 | 0.8096 |
| No log | 1.0078 | 386 | 0.7443 | 0.4209 | 0.7443 | 0.8627 |
| No log | 1.0131 | 388 | 0.7244 | 0.4209 | 0.7244 | 0.8511 |
| No log | 1.0183 | 390 | 0.6396 | 0.4581 | 0.6396 | 0.7997 |
| No log | 1.0235 | 392 | 0.6512 | 0.4581 | 0.6512 | 0.8070 |
| No log | 1.0287 | 394 | 0.7234 | 0.3558 | 0.7234 | 0.8505 |
| No log | 1.0339 | 396 | 0.8631 | 0.3458 | 0.8631 | 0.9291 |
| No log | 1.0392 | 398 | 0.9027 | 0.3458 | 0.9027 | 0.9501 |
| No log | 1.0444 | 400 | 0.8108 | 0.3458 | 0.8108 | 0.9004 |
| No log | 1.0496 | 402 | 0.7884 | 0.4099 | 0.7884 | 0.8879 |
| No log | 1.0548 | 404 | 0.8182 | 0.3458 | 0.8182 | 0.9046 |
| No log | 1.0601 | 406 | 0.9435 | 0.3458 | 0.9435 | 0.9713 |
| No log | 1.0653 | 408 | 0.9057 | 0.3458 | 0.9057 | 0.9517 |
| No log | 1.0705 | 410 | 0.7102 | 0.3558 | 0.7102 | 0.8427 |
| No log | 1.0757 | 412 | 0.6031 | 0.4688 | 0.6031 | 0.7766 |
| No log | 1.0809 | 414 | 0.6099 | 0.5 | 0.6099 | 0.7809 |
| No log | 1.0862 | 416 | 0.7293 | 0.3927 | 0.7293 | 0.8540 |
| No log | 1.0914 | 418 | 0.8640 | 0.3505 | 0.8640 | 0.9295 |
| No log | 1.0966 | 420 | 0.8205 | 0.3505 | 0.8205 | 0.9058 |
| No log | 1.1018 | 422 | 0.7130 | 0.3458 | 0.7130 | 0.8444 |
| No log | 1.1070 | 424 | 0.5745 | 0.5 | 0.5745 | 0.7580 |
| No log | 1.1123 | 426 | 0.5252 | 0.5 | 0.5252 | 0.7247 |
| No log | 1.1175 | 428 | 0.5066 | 0.4688 | 0.5066 | 0.7117 |
| No log | 1.1227 | 430 | 0.5331 | 0.5 | 0.5331 | 0.7301 |
| No log | 1.1279 | 432 | 0.5446 | 0.5 | 0.5446 | 0.7380 |
| No log | 1.1332 | 434 | 0.5241 | 0.4688 | 0.5241 | 0.7239 |
| No log | 1.1384 | 436 | 0.4773 | 0.6123 | 0.4773 | 0.6908 |
| No log | 1.1436 | 438 | 0.4587 | 0.6169 | 0.4587 | 0.6773 |
| No log | 1.1488 | 440 | 0.5195 | 0.5 | 0.5195 | 0.7207 |
| No log | 1.1540 | 442 | 0.6942 | 0.3505 | 0.6942 | 0.8332 |
| No log | 1.1593 | 444 | 0.8627 | 0.3505 | 0.8627 | 0.9288 |
| No log | 1.1645 | 446 | 0.8610 | 0.3505 | 0.8610 | 0.9279 |
| No log | 1.1697 | 448 | 0.7443 | 0.3505 | 0.7443 | 0.8628 |
| No log | 1.1749 | 450 | 0.5767 | 0.5 | 0.5767 | 0.7594 |
| No log | 1.1802 | 452 | 0.4588 | 0.5855 | 0.4588 | 0.6773 |
| No log | 1.1854 | 454 | 0.4379 | 0.5954 | 0.4379 | 0.6617 |
| No log | 1.1906 | 456 | 0.4482 | 0.5855 | 0.4482 | 0.6695 |
| No log | 1.1958 | 458 | 0.5164 | 0.5 | 0.5164 | 0.7186 |
| No log | 1.2010 | 460 | 0.5730 | 0.496 | 0.5730 | 0.7570 |
| No log | 1.2063 | 462 | 0.5714 | 0.496 | 0.5714 | 0.7559 |
| No log | 1.2115 | 464 | 0.5480 | 0.5767 | 0.5480 | 0.7403 |
| No log | 1.2167 | 466 | 0.5581 | 0.5767 | 0.5581 | 0.7471 |
| No log | 1.2219 | 468 | 0.6295 | 0.496 | 0.6295 | 0.7934 |
| No log | 1.2272 | 470 | 0.7004 | 0.3927 | 0.7004 | 0.8369 |
| No log | 1.2324 | 472 | 0.8263 | 0.3505 | 0.8263 | 0.9090 |
| No log | 1.2376 | 474 | 0.7866 | 0.3458 | 0.7866 | 0.8869 |
| No log | 1.2428 | 476 | 0.6526 | 0.4878 | 0.6526 | 0.8079 |
| No log | 1.2480 | 478 | 0.5044 | 0.5786 | 0.5044 | 0.7102 |
| No log | 1.2533 | 480 | 0.4526 | 0.5855 | 0.4526 | 0.6728 |
| No log | 1.2585 | 482 | 0.4792 | 0.5855 | 0.4792 | 0.6923 |
| No log | 1.2637 | 484 | 0.5314 | 0.4688 | 0.5314 | 0.7289 |
| No log | 1.2689 | 486 | 0.6419 | 0.496 | 0.6419 | 0.8012 |
| No log | 1.2742 | 488 | 0.7498 | 0.3948 | 0.7498 | 0.8659 |
| No log | 1.2794 | 490 | 0.8130 | 0.3948 | 0.8130 | 0.9017 |
| No log | 1.2846 | 492 | 0.7191 | 0.4292 | 0.7191 | 0.8480 |
| No log | 1.2898 | 494 | 0.6576 | 0.4375 | 0.6576 | 0.8109 |
| No log | 1.2950 | 496 | 0.6488 | 0.4378 | 0.6488 | 0.8055 |
| No log | 1.3003 | 498 | 0.5975 | 0.4776 | 0.5975 | 0.7730 |
| 0.437 | 1.3055 | 500 | 0.5946 | 0.4381 | 0.5946 | 0.7711 |
| 0.437 | 1.3107 | 502 | 0.6070 | 0.4688 | 0.6070 | 0.7791 |
| 0.437 | 1.3159 | 504 | 0.6507 | 0.4597 | 0.6507 | 0.8066 |
| 0.437 | 1.3211 | 506 | 0.7606 | 0.4085 | 0.7606 | 0.8721 |
| 0.437 | 1.3264 | 508 | 0.7871 | 0.4085 | 0.7871 | 0.8872 |
| 0.437 | 1.3316 | 510 | 0.7459 | 0.4085 | 0.7459 | 0.8636 |
| 0.437 | 1.3368 | 512 | 0.6699 | 0.4085 | 0.6699 | 0.8185 |
| 0.437 | 1.3420 | 514 | 0.5932 | 0.3401 | 0.5932 | 0.7702 |
| 0.437 | 1.3473 | 516 | 0.5836 | 0.3201 | 0.5836 | 0.7640 |
| 0.437 | 1.3525 | 518 | 0.6154 | 0.4465 | 0.6154 | 0.7845 |
| 0.437 | 1.3577 | 520 | 0.6627 | 0.4465 | 0.6627 | 0.8141 |
| 0.437 | 1.3629 | 522 | 0.6726 | 0.3980 | 0.6726 | 0.8201 |
| 0.437 | 1.3681 | 524 | 0.6378 | 0.4597 | 0.6378 | 0.7986 |
| 0.437 | 1.3734 | 526 | 0.5831 | 0.4187 | 0.5831 | 0.7636 |
| 0.437 | 1.3786 | 528 | 0.5679 | 0.3731 | 0.5679 | 0.7536 |
| 0.437 | 1.3838 | 530 | 0.5850 | 0.3770 | 0.5850 | 0.7649 |
| 0.437 | 1.3890 | 532 | 0.6834 | 0.4085 | 0.6834 | 0.8267 |
| 0.437 | 1.3943 | 534 | 0.8176 | 0.3505 | 0.8176 | 0.9042 |
| 0.437 | 1.3995 | 536 | 0.8869 | 0.3505 | 0.8869 | 0.9418 |
| 0.437 | 1.4047 | 538 | 0.9103 | 0.3505 | 0.9103 | 0.9541 |
| 0.437 | 1.4099 | 540 | 0.8294 | 0.3246 | 0.8294 | 0.9107 |
| 0.437 | 1.4151 | 542 | 0.7444 | 0.3558 | 0.7444 | 0.8628 |
| 0.437 | 1.4204 | 544 | 0.7648 | 0.3184 | 0.7648 | 0.8745 |
| 0.437 | 1.4256 | 546 | 0.8218 | 0.3184 | 0.8218 | 0.9065 |
| 0.437 | 1.4308 | 548 | 0.8330 | 0.3246 | 0.8330 | 0.9127 |
| 0.437 | 1.4360 | 550 | 0.7844 | 0.3184 | 0.7844 | 0.8857 |
| 0.437 | 1.4413 | 552 | 0.7182 | 0.3226 | 0.7182 | 0.8475 |
| 0.437 | 1.4465 | 554 | 0.7424 | 0.3288 | 0.7424 | 0.8617 |
| 0.437 | 1.4517 | 556 | 0.7886 | 0.3288 | 0.7886 | 0.8880 |
| 0.437 | 1.4569 | 558 | 0.8505 | 0.3246 | 0.8505 | 0.9222 |
| 0.437 | 1.4621 | 560 | 0.9041 | 0.3246 | 0.9041 | 0.9509 |
| 0.437 | 1.4674 | 562 | 0.9191 | 0.3246 | 0.9191 | 0.9587 |
| 0.437 | 1.4726 | 564 | 0.9443 | 0.3246 | 0.9443 | 0.9718 |
| 0.437 | 1.4778 | 566 | 0.8936 | 0.3246 | 0.8936 | 0.9453 |
| 0.437 | 1.4830 | 568 | 0.7941 | 0.3184 | 0.7941 | 0.8911 |
| 0.437 | 1.4883 | 570 | 0.7370 | 0.3770 | 0.7370 | 0.8585 |
| 0.437 | 1.4935 | 572 | 0.6652 | 0.3770 | 0.6652 | 0.8156 |
| 0.437 | 1.4987 | 574 | 0.6346 | 0.3770 | 0.6346 | 0.7966 |
| 0.437 | 1.5039 | 576 | 0.6910 | 0.3770 | 0.6910 | 0.8312 |
| 0.437 | 1.5091 | 578 | 0.7670 | 0.3458 | 0.7670 | 0.8758 |
| 0.437 | 1.5144 | 580 | 0.7094 | 0.3770 | 0.7094 | 0.8423 |
| 0.437 | 1.5196 | 582 | 0.6235 | 0.4 | 0.6235 | 0.7896 |
| 0.437 | 1.5248 | 584 | 0.6079 | 0.4 | 0.6079 | 0.7797 |
| 0.437 | 1.5300 | 586 | 0.5711 | 0.4465 | 0.5711 | 0.7557 |
| 0.437 | 1.5352 | 588 | 0.5904 | 0.4465 | 0.5904 | 0.7684 |
| 0.437 | 1.5405 | 590 | 0.6197 | 0.4378 | 0.6197 | 0.7872 |
| 0.437 | 1.5457 | 592 | 0.6939 | 0.4 | 0.6939 | 0.8330 |
| 0.437 | 1.5509 | 594 | 0.7700 | 0.3512 | 0.7700 | 0.8775 |
| 0.437 | 1.5561 | 596 | 0.7374 | 0.4018 | 0.7374 | 0.8587 |
| 0.437 | 1.5614 | 598 | 0.6736 | 0.4378 | 0.6736 | 0.8207 |
| 0.437 | 1.5666 | 600 | 0.6156 | 0.4465 | 0.6156 | 0.7846 |
| 0.437 | 1.5718 | 602 | 0.6499 | 0.4465 | 0.6499 | 0.8062 |
| 0.437 | 1.5770 | 604 | 0.6736 | 0.4465 | 0.6736 | 0.8207 |
| 0.437 | 1.5822 | 606 | 0.6825 | 0.3842 | 0.6825 | 0.8261 |
| 0.437 | 1.5875 | 608 | 0.7095 | 0.375 | 0.7095 | 0.8423 |
| 0.437 | 1.5927 | 610 | 0.7024 | 0.3486 | 0.7024 | 0.8381 |
| 0.437 | 1.5979 | 612 | 0.6512 | 0.4831 | 0.6512 | 0.8070 |
| 0.437 | 1.6031 | 614 | 0.6602 | 0.4465 | 0.6602 | 0.8125 |
| 0.437 | 1.6084 | 616 | 0.6705 | 0.475 | 0.6705 | 0.8189 |
| 0.437 | 1.6136 | 618 | 0.7090 | 0.4378 | 0.7090 | 0.8420 |
| 0.437 | 1.6188 | 620 | 0.7182 | 0.4106 | 0.7182 | 0.8475 |
| 0.437 | 1.6240 | 622 | 0.7463 | 0.4106 | 0.7463 | 0.8639 |
| 0.437 | 1.6292 | 624 | 0.7445 | 0.375 | 0.7445 | 0.8629 |
| 0.437 | 1.6345 | 626 | 0.7097 | 0.4106 | 0.7097 | 0.8424 |
| 0.437 | 1.6397 | 628 | 0.7537 | 0.3780 | 0.7537 | 0.8682 |
| 0.437 | 1.6449 | 630 | 0.6916 | 0.4118 | 0.6916 | 0.8316 |
| 0.437 | 1.6501 | 632 | 0.6013 | 0.5832 | 0.6013 | 0.7754 |
| 0.437 | 1.6554 | 634 | 0.5740 | 0.5832 | 0.5740 | 0.7577 |
| 0.437 | 1.6606 | 636 | 0.5692 | 0.5832 | 0.5692 | 0.7545 |
| 0.437 | 1.6658 | 638 | 0.6004 | 0.5477 | 0.6004 | 0.7749 |
| 0.437 | 1.6710 | 640 | 0.6415 | 0.4118 | 0.6415 | 0.8009 |
| 0.437 | 1.6762 | 642 | 0.6931 | 0.3780 | 0.6931 | 0.8325 |
| 0.437 | 1.6815 | 644 | 0.7287 | 0.3529 | 0.7287 | 0.8536 |
| 0.437 | 1.6867 | 646 | 0.7077 | 0.5103 | 0.7077 | 0.8412 |
| 0.437 | 1.6919 | 648 | 0.6495 | 0.6083 | 0.6495 | 0.8059 |
| 0.437 | 1.6971 | 650 | 0.6029 | 0.6578 | 0.6029 | 0.7765 |
| 0.437 | 1.7023 | 652 | 0.5664 | 0.6578 | 0.5664 | 0.7526 |
| 0.437 | 1.7076 | 654 | 0.4991 | 0.6578 | 0.4991 | 0.7065 |
| 0.437 | 1.7128 | 656 | 0.4743 | 0.6818 | 0.4743 | 0.6887 |
| 0.437 | 1.7180 | 658 | 0.5854 | 0.3980 | 0.5854 | 0.7651 |
| 0.437 | 1.7232 | 660 | 0.7977 | 0.3833 | 0.7977 | 0.8931 |
| 0.437 | 1.7285 | 662 | 0.8647 | 0.3458 | 0.8647 | 0.9299 |
| 0.437 | 1.7337 | 664 | 0.7759 | 0.3833 | 0.7759 | 0.8808 |
| 0.437 | 1.7389 | 666 | 0.6738 | 0.4202 | 0.6738 | 0.8208 |
| 0.437 | 1.7441 | 668 | 0.5490 | 0.3980 | 0.5490 | 0.7409 |
| 0.437 | 1.7493 | 670 | 0.4558 | 0.6602 | 0.4558 | 0.6751 |
| 0.437 | 1.7546 | 672 | 0.4331 | 0.6416 | 0.4331 | 0.6581 |
| 0.437 | 1.7598 | 674 | 0.4553 | 0.6866 | 0.4553 | 0.6748 |
| 0.437 | 1.7650 | 676 | 0.5012 | 0.6866 | 0.5012 | 0.7080 |
| 0.437 | 1.7702 | 678 | 0.5602 | 0.6866 | 0.5602 | 0.7485 |
| 0.437 | 1.7755 | 680 | 0.6027 | 0.5737 | 0.6027 | 0.7763 |
| 0.437 | 1.7807 | 682 | 0.6091 | 0.5737 | 0.6091 | 0.7805 |
| 0.437 | 1.7859 | 684 | 0.5704 | 0.5812 | 0.5704 | 0.7552 |
| 0.437 | 1.7911 | 686 | 0.5408 | 0.5812 | 0.5408 | 0.7354 |
| 0.437 | 1.7963 | 688 | 0.5732 | 0.6123 | 0.5732 | 0.7571 |
| 0.437 | 1.8016 | 690 | 0.6437 | 0.3388 | 0.6437 | 0.8023 |
| 0.437 | 1.8068 | 692 | 0.6664 | 0.3656 | 0.6664 | 0.8163 |
| 0.437 | 1.8120 | 694 | 0.6156 | 0.3529 | 0.6156 | 0.7846 |
| 0.437 | 1.8172 | 696 | 0.5703 | 0.5477 | 0.5703 | 0.7552 |
| 0.437 | 1.8225 | 698 | 0.5319 | 0.6818 | 0.5319 | 0.7293 |
| 0.437 | 1.8277 | 700 | 0.4903 | 0.6866 | 0.4903 | 0.7002 |
| 0.437 | 1.8329 | 702 | 0.4800 | 0.6866 | 0.4800 | 0.6928 |
| 0.437 | 1.8381 | 704 | 0.4783 | 0.6866 | 0.4783 | 0.6916 |
| 0.437 | 1.8433 | 706 | 0.5037 | 0.6818 | 0.5037 | 0.7097 |
| 0.437 | 1.8486 | 708 | 0.5029 | 0.6473 | 0.5029 | 0.7091 |
| 0.437 | 1.8538 | 710 | 0.4965 | 0.5477 | 0.4965 | 0.7046 |
| 0.437 | 1.8590 | 712 | 0.4864 | 0.5473 | 0.4864 | 0.6974 |
| 0.437 | 1.8642 | 714 | 0.4971 | 0.5477 | 0.4971 | 0.7050 |
| 0.437 | 1.8695 | 716 | 0.5399 | 0.4378 | 0.5399 | 0.7348 |
| 0.437 | 1.8747 | 718 | 0.6016 | 0.3780 | 0.6016 | 0.7756 |
| 0.437 | 1.8799 | 720 | 0.6196 | 0.3780 | 0.6196 | 0.7872 |
| 0.437 | 1.8851 | 722 | 0.6467 | 0.3780 | 0.6467 | 0.8042 |
| 0.437 | 1.8903 | 724 | 0.5952 | 0.3529 | 0.5952 | 0.7715 |
| 0.437 | 1.8956 | 726 | 0.5371 | 0.6818 | 0.5371 | 0.7329 |
| 0.437 | 1.9008 | 728 | 0.5094 | 0.6818 | 0.5094 | 0.7137 |
| 0.437 | 1.9060 | 730 | 0.5180 | 0.6866 | 0.5180 | 0.7197 |
| 0.437 | 1.9112 | 732 | 0.5686 | 0.6818 | 0.5686 | 0.7541 |
| 0.437 | 1.9164 | 734 | 0.6186 | 0.5679 | 0.6186 | 0.7865 |
| 0.437 | 1.9217 | 736 | 0.6399 | 0.5679 | 0.6399 | 0.7999 |
| 0.437 | 1.9269 | 738 | 0.6292 | 0.3866 | 0.6292 | 0.7932 |
| 0.437 | 1.9321 | 740 | 0.5688 | 0.6473 | 0.5688 | 0.7542 |
| 0.437 | 1.9373 | 742 | 0.5574 | 0.4381 | 0.5574 | 0.7466 |
| 0.437 | 1.9426 | 744 | 0.5934 | 0.4286 | 0.5934 | 0.7703 |
| 0.437 | 1.9478 | 746 | 0.5489 | 0.4597 | 0.5489 | 0.7409 |
| 0.437 | 1.9530 | 748 | 0.4784 | 0.3980 | 0.4784 | 0.6916 |
| 0.437 | 1.9582 | 750 | 0.4098 | 0.6917 | 0.4098 | 0.6402 |
| 0.437 | 1.9634 | 752 | 0.3930 | 0.6747 | 0.3930 | 0.6269 |
| 0.437 | 1.9687 | 754 | 0.4008 | 0.7158 | 0.4008 | 0.6331 |
| 0.437 | 1.9739 | 756 | 0.4682 | 0.6917 | 0.4682 | 0.6843 |
| 0.437 | 1.9791 | 758 | 0.5585 | 0.6769 | 0.5585 | 0.7473 |
| 0.437 | 1.9843 | 760 | 0.5792 | 0.5103 | 0.5792 | 0.7611 |
| 0.437 | 1.9896 | 762 | 0.5194 | 0.6686 | 0.5194 | 0.7207 |
| 0.437 | 1.9948 | 764 | 0.4627 | 0.7158 | 0.4627 | 0.6802 |
| 0.437 | 2.0 | 766 | 0.4493 | 0.7158 | 0.4493 | 0.6703 |
| 0.437 | 2.0052 | 768 | 0.4505 | 0.7158 | 0.4505 | 0.6712 |
| 0.437 | 2.0104 | 770 | 0.5185 | 0.5767 | 0.5185 | 0.7200 |
| 0.437 | 2.0157 | 772 | 0.5953 | 0.3927 | 0.5953 | 0.7715 |
| 0.437 | 2.0209 | 774 | 0.5848 | 0.4375 | 0.5848 | 0.7647 |
| 0.437 | 2.0261 | 776 | 0.5298 | 0.5767 | 0.5298 | 0.7279 |
| 0.437 | 2.0313 | 778 | 0.4751 | 0.7158 | 0.4751 | 0.6892 |
| 0.437 | 2.0366 | 780 | 0.4588 | 0.7158 | 0.4588 | 0.6774 |
| 0.437 | 2.0418 | 782 | 0.4696 | 0.7158 | 0.4696 | 0.6853 |
| 0.437 | 2.0470 | 784 | 0.4956 | 0.7158 | 0.4956 | 0.7040 |
| 0.437 | 2.0522 | 786 | 0.4798 | 0.7158 | 0.4798 | 0.6927 |
| 0.437 | 2.0574 | 788 | 0.4823 | 0.7158 | 0.4823 | 0.6945 |
| 0.437 | 2.0627 | 790 | 0.4930 | 0.6232 | 0.4930 | 0.7021 |
| 0.437 | 2.0679 | 792 | 0.5343 | 0.4776 | 0.5343 | 0.7310 |
| 0.437 | 2.0731 | 794 | 0.5077 | 0.4776 | 0.5077 | 0.7125 |
| 0.437 | 2.0783 | 796 | 0.4464 | 0.7158 | 0.4464 | 0.6681 |
| 0.437 | 2.0836 | 798 | 0.4455 | 0.6477 | 0.4455 | 0.6674 |
| 0.437 | 2.0888 | 800 | 0.4565 | 0.6477 | 0.4565 | 0.6757 |
| 0.437 | 2.0940 | 802 | 0.4576 | 0.6866 | 0.4576 | 0.6764 |
| 0.437 | 2.0992 | 804 | 0.5118 | 0.7158 | 0.5118 | 0.7154 |
| 0.437 | 2.1044 | 806 | 0.6649 | 0.375 | 0.6649 | 0.8154 |
| 0.437 | 2.1097 | 808 | 0.8024 | 0.3927 | 0.8024 | 0.8957 |
| 0.437 | 2.1149 | 810 | 0.7870 | 0.3927 | 0.7870 | 0.8871 |
| 0.437 | 2.1201 | 812 | 0.7199 | 0.3927 | 0.7199 | 0.8485 |
| 0.437 | 2.1253 | 814 | 0.6352 | 0.4597 | 0.6352 | 0.7970 |
| 0.437 | 2.1305 | 816 | 0.5372 | 0.4776 | 0.5372 | 0.7329 |
| 0.437 | 2.1358 | 818 | 0.5208 | 0.4474 | 0.5208 | 0.7217 |
| 0.437 | 2.1410 | 820 | 0.5199 | 0.4381 | 0.5199 | 0.7210 |
| 0.437 | 2.1462 | 822 | 0.5327 | 0.4381 | 0.5327 | 0.7298 |
| 0.437 | 2.1514 | 824 | 0.6131 | 0.4688 | 0.6131 | 0.7830 |
| 0.437 | 2.1567 | 826 | 0.7255 | 0.4290 | 0.7255 | 0.8517 |
| 0.437 | 2.1619 | 828 | 0.7696 | 0.4118 | 0.7696 | 0.8773 |
| 0.437 | 2.1671 | 830 | 0.6905 | 0.4118 | 0.6905 | 0.8310 |
| 0.437 | 2.1723 | 832 | 0.6413 | 0.4118 | 0.6413 | 0.8008 |
| 0.437 | 2.1775 | 834 | 0.6382 | 0.4118 | 0.6382 | 0.7989 |
| 0.437 | 2.1828 | 836 | 0.6403 | 0.4118 | 0.6403 | 0.8002 |
| 0.437 | 2.1880 | 838 | 0.6242 | 0.4727 | 0.6242 | 0.7901 |
| 0.437 | 2.1932 | 840 | 0.6127 | 0.4727 | 0.6127 | 0.7828 |
| 0.437 | 2.1984 | 842 | 0.5418 | 0.475 | 0.5418 | 0.7361 |
| 0.437 | 2.2037 | 844 | 0.5106 | 0.475 | 0.5106 | 0.7146 |
| 0.437 | 2.2089 | 846 | 0.4750 | 0.475 | 0.4750 | 0.6892 |
| 0.437 | 2.2141 | 848 | 0.4545 | 0.6966 | 0.4545 | 0.6742 |
| 0.437 | 2.2193 | 850 | 0.4761 | 0.5767 | 0.4761 | 0.6900 |
| 0.437 | 2.2245 | 852 | 0.5362 | 0.475 | 0.5362 | 0.7323 |
| 0.437 | 2.2298 | 854 | 0.6210 | 0.4667 | 0.6210 | 0.7880 |
| 0.437 | 2.2350 | 856 | 0.7089 | 0.3927 | 0.7089 | 0.8420 |
| 0.437 | 2.2402 | 858 | 0.7413 | 0.3927 | 0.7413 | 0.8610 |
| 0.437 | 2.2454 | 860 | 0.6737 | 0.4372 | 0.6737 | 0.8208 |
| 0.437 | 2.2507 | 862 | 0.5480 | 0.475 | 0.5480 | 0.7403 |
| 0.437 | 2.2559 | 864 | 0.4877 | 0.5832 | 0.4877 | 0.6984 |
| 0.437 | 2.2611 | 866 | 0.4790 | 0.5922 | 0.4790 | 0.6921 |
| 0.437 | 2.2663 | 868 | 0.4986 | 0.4831 | 0.4986 | 0.7061 |
| 0.437 | 2.2715 | 870 | 0.5156 | 0.4831 | 0.5156 | 0.7181 |
| 0.437 | 2.2768 | 872 | 0.5743 | 0.4465 | 0.5743 | 0.7578 |
| 0.437 | 2.2820 | 874 | 0.6708 | 0.4727 | 0.6708 | 0.8190 |
| 0.437 | 2.2872 | 876 | 0.7489 | 0.4648 | 0.7489 | 0.8654 |
| 0.437 | 2.2924 | 878 | 0.7229 | 0.4648 | 0.7229 | 0.8502 |
| 0.437 | 2.2977 | 880 | 0.6289 | 0.4465 | 0.6289 | 0.7931 |
| 0.437 | 2.3029 | 882 | 0.5869 | 0.4465 | 0.5869 | 0.7661 |
| 0.437 | 2.3081 | 884 | 0.5501 | 0.4831 | 0.5501 | 0.7417 |
| 0.437 | 2.3133 | 886 | 0.5468 | 0.4831 | 0.5468 | 0.7394 |
| 0.437 | 2.3185 | 888 | 0.5445 | 0.4831 | 0.5445 | 0.7379 |
| 0.437 | 2.3238 | 890 | 0.5345 | 0.4831 | 0.5345 | 0.7311 |
| 0.437 | 2.3290 | 892 | 0.5662 | 0.5039 | 0.5662 | 0.7524 |
| 0.437 | 2.3342 | 894 | 0.5773 | 0.4667 | 0.5773 | 0.7598 |
| 0.437 | 2.3394 | 896 | 0.5754 | 0.5039 | 0.5754 | 0.7586 |
| 0.437 | 2.3446 | 898 | 0.5370 | 0.4831 | 0.5370 | 0.7328 |
| 0.437 | 2.3499 | 900 | 0.5030 | 0.7158 | 0.5030 | 0.7093 |
| 0.437 | 2.3551 | 902 | 0.5516 | 0.6294 | 0.5516 | 0.7427 |
| 0.437 | 2.3603 | 904 | 0.6126 | 0.5610 | 0.6126 | 0.7827 |
| 0.437 | 2.3655 | 906 | 0.5711 | 0.6219 | 0.5711 | 0.7557 |
| 0.437 | 2.3708 | 908 | 0.4972 | 0.6351 | 0.4972 | 0.7051 |
| 0.437 | 2.3760 | 910 | 0.5093 | 0.5832 | 0.5093 | 0.7137 |
| 0.437 | 2.3812 | 912 | 0.6038 | 0.4375 | 0.6038 | 0.7771 |
| 0.437 | 2.3864 | 914 | 0.6190 | 0.4648 | 0.6190 | 0.7868 |
| 0.437 | 2.3916 | 916 | 0.5808 | 0.4378 | 0.5808 | 0.7621 |
| 0.437 | 2.3969 | 918 | 0.5271 | 0.4378 | 0.5271 | 0.7260 |
| 0.437 | 2.4021 | 920 | 0.4973 | 0.4831 | 0.4973 | 0.7052 |
| 0.437 | 2.4073 | 922 | 0.5087 | 0.6866 | 0.5087 | 0.7132 |
| 0.437 | 2.4125 | 924 | 0.5487 | 0.6866 | 0.5487 | 0.7407 |
| 0.437 | 2.4178 | 926 | 0.5874 | 0.6866 | 0.5874 | 0.7664 |
| 0.437 | 2.4230 | 928 | 0.6187 | 0.5832 | 0.6187 | 0.7866 |
| 0.437 | 2.4282 | 930 | 0.6525 | 0.4803 | 0.6525 | 0.8078 |
| 0.437 | 2.4334 | 932 | 0.6471 | 0.4457 | 0.6471 | 0.8045 |
| 0.437 | 2.4386 | 934 | 0.6292 | 0.4106 | 0.6292 | 0.7932 |
| 0.437 | 2.4439 | 936 | 0.5865 | 0.4094 | 0.5865 | 0.7658 |
| 0.437 | 2.4491 | 938 | 0.5374 | 0.4465 | 0.5374 | 0.7331 |
| 0.437 | 2.4543 | 940 | 0.5152 | 0.4831 | 0.5152 | 0.7178 |
| 0.437 | 2.4595 | 942 | 0.5203 | 0.4831 | 0.5203 | 0.7213 |
| 0.437 | 2.4648 | 944 | 0.5112 | 0.4831 | 0.5112 | 0.7150 |
| 0.437 | 2.4700 | 946 | 0.5123 | 0.5832 | 0.5123 | 0.7158 |
| 0.437 | 2.4752 | 948 | 0.5058 | 0.6182 | 0.5058 | 0.7112 |
| 0.437 | 2.4804 | 950 | 0.5203 | 0.6866 | 0.5203 | 0.7213 |
| 0.437 | 2.4856 | 952 | 0.5358 | 0.5895 | 0.5358 | 0.7320 |
| 0.437 | 2.4909 | 954 | 0.5468 | 0.4550 | 0.5468 | 0.7395 |
| 0.437 | 2.4961 | 956 | 0.5607 | 0.4831 | 0.5607 | 0.7488 |
| 0.437 | 2.5013 | 958 | 0.5671 | 0.4831 | 0.5671 | 0.7531 |
| 0.437 | 2.5065 | 960 | 0.5800 | 0.4465 | 0.5800 | 0.7615 |
| 0.437 | 2.5117 | 962 | 0.6227 | 0.4094 | 0.6227 | 0.7891 |
| 0.437 | 2.5170 | 964 | 0.6500 | 0.4185 | 0.6500 | 0.8062 |
| 0.437 | 2.5222 | 966 | 0.6629 | 0.4550 | 0.6629 | 0.8142 |
| 0.437 | 2.5274 | 968 | 0.6737 | 0.4550 | 0.6737 | 0.8208 |
| 0.437 | 2.5326 | 970 | 0.6826 | 0.4185 | 0.6826 | 0.8262 |
| 0.437 | 2.5379 | 972 | 0.6935 | 0.4094 | 0.6935 | 0.8327 |
| 0.437 | 2.5431 | 974 | 0.6617 | 0.4094 | 0.6617 | 0.8134 |
| 0.437 | 2.5483 | 976 | 0.6342 | 0.4094 | 0.6342 | 0.7964 |
| 0.437 | 2.5535 | 978 | 0.5964 | 0.4094 | 0.5964 | 0.7723 |
| 0.437 | 2.5587 | 980 | 0.5692 | 0.4465 | 0.5692 | 0.7544 |
| 0.437 | 2.5640 | 982 | 0.5350 | 0.4831 | 0.5350 | 0.7314 |
| 0.437 | 2.5692 | 984 | 0.5243 | 0.4550 | 0.5243 | 0.7241 |
| 0.437 | 2.5744 | 986 | 0.5309 | 0.4550 | 0.5309 | 0.7286 |
| 0.437 | 2.5796 | 988 | 0.5235 | 0.5895 | 0.5235 | 0.7235 |
| 0.437 | 2.5849 | 990 | 0.5475 | 0.5895 | 0.5475 | 0.7399 |
| 0.437 | 2.5901 | 992 | 0.5591 | 0.6182 | 0.5591 | 0.7477 |
| 0.437 | 2.5953 | 994 | 0.5880 | 0.4831 | 0.5880 | 0.7668 |
| 0.437 | 2.6005 | 996 | 0.6256 | 0.4727 | 0.6256 | 0.7909 |
| 0.437 | 2.6057 | 998 | 0.6018 | 0.475 | 0.6018 | 0.7758 |
| 0.151 | 2.6110 | 1000 | 0.5198 | 0.475 | 0.5198 | 0.7210 |
| 0.151 | 2.6162 | 1002 | 0.4707 | 0.6818 | 0.4707 | 0.6861 |
| 0.151 | 2.6214 | 1004 | 0.4370 | 0.7158 | 0.4370 | 0.6610 |
| 0.151 | 2.6266 | 1006 | 0.4371 | 0.6866 | 0.4371 | 0.6612 |
| 0.151 | 2.6319 | 1008 | 0.4507 | 0.6866 | 0.4507 | 0.6714 |
| 0.151 | 2.6371 | 1010 | 0.5019 | 0.6818 | 0.5019 | 0.7085 |
| 0.151 | 2.6423 | 1012 | 0.6023 | 0.5065 | 0.6023 | 0.7761 |
| 0.151 | 2.6475 | 1014 | 0.6489 | 0.4757 | 0.6489 | 0.8056 |
| 0.151 | 2.6527 | 1016 | 0.5787 | 0.4831 | 0.5787 | 0.7607 |
| 0.151 | 2.6580 | 1018 | 0.5116 | 0.5832 | 0.5116 | 0.7153 |
| 0.151 | 2.6632 | 1020 | 0.4961 | 0.4831 | 0.4961 | 0.7044 |
| 0.151 | 2.6684 | 1022 | 0.5111 | 0.4831 | 0.5111 | 0.7149 |
| 0.151 | 2.6736 | 1024 | 0.4898 | 0.4831 | 0.4898 | 0.6999 |
| 0.151 | 2.6789 | 1026 | 0.4734 | 0.5832 | 0.4734 | 0.6881 |
| 0.151 | 2.6841 | 1028 | 0.4835 | 0.5832 | 0.4835 | 0.6953 |
| 0.151 | 2.6893 | 1030 | 0.4724 | 0.6818 | 0.4724 | 0.6873 |
| 0.151 | 2.6945 | 1032 | 0.4825 | 0.7158 | 0.4825 | 0.6946 |
| 0.151 | 2.6997 | 1034 | 0.5011 | 0.7158 | 0.5011 | 0.7079 |
| 0.151 | 2.7050 | 1036 | 0.5490 | 0.4831 | 0.5490 | 0.7409 |
| 0.151 | 2.7102 | 1038 | 0.6411 | 0.4457 | 0.6411 | 0.8007 |
| 0.151 | 2.7154 | 1040 | 0.7299 | 0.4034 | 0.7299 | 0.8543 |
| 0.151 | 2.7206 | 1042 | 0.7455 | 0.4034 | 0.7455 | 0.8634 |
| 0.151 | 2.7258 | 1044 | 0.6911 | 0.4648 | 0.6911 | 0.8313 |
| 0.151 | 2.7311 | 1046 | 0.5825 | 0.475 | 0.5825 | 0.7632 |
| 0.151 | 2.7363 | 1048 | 0.4789 | 0.6182 | 0.4789 | 0.6921 |
| 0.151 | 2.7415 | 1050 | 0.4459 | 0.7158 | 0.4459 | 0.6677 |
| 0.151 | 2.7467 | 1052 | 0.4395 | 0.7158 | 0.4395 | 0.6630 |
| 0.151 | 2.7520 | 1054 | 0.4507 | 0.6182 | 0.4507 | 0.6713 |
| 0.151 | 2.7572 | 1056 | 0.5047 | 0.475 | 0.5047 | 0.7104 |
| 0.151 | 2.7624 | 1058 | 0.6379 | 0.4913 | 0.6379 | 0.7987 |
| 0.151 | 2.7676 | 1060 | 0.7391 | 0.3833 | 0.7391 | 0.8597 |
| 0.151 | 2.7728 | 1062 | 0.7528 | 0.3458 | 0.7528 | 0.8677 |
| 0.151 | 2.7781 | 1064 | 0.7077 | 0.3833 | 0.7077 | 0.8412 |
| 0.151 | 2.7833 | 1066 | 0.6524 | 0.4913 | 0.6524 | 0.8077 |
| 0.151 | 2.7885 | 1068 | 0.6574 | 0.4597 | 0.6574 | 0.8108 |
| 0.151 | 2.7937 | 1070 | 0.6489 | 0.4286 | 0.6489 | 0.8056 |
| 0.151 | 2.7990 | 1072 | 0.6630 | 0.375 | 0.6630 | 0.8143 |
| 0.151 | 2.8042 | 1074 | 0.7000 | 0.3780 | 0.7000 | 0.8367 |
| 0.151 | 2.8094 | 1076 | 0.7601 | 0.4049 | 0.7601 | 0.8718 |
| 0.151 | 2.8146 | 1078 | 0.8346 | 0.3723 | 0.8346 | 0.9135 |
| 0.151 | 2.8198 | 1080 | 0.9184 | 0.3636 | 0.9184 | 0.9584 |
| 0.151 | 2.8251 | 1082 | 0.9886 | 0.3636 | 0.9886 | 0.9943 |
| 0.151 | 2.8303 | 1084 | 0.9618 | 0.3636 | 0.9618 | 0.9807 |
| 0.151 | 2.8355 | 1086 | 0.8782 | 0.3723 | 0.8782 | 0.9371 |
| 0.151 | 2.8407 | 1088 | 0.7497 | 0.3807 | 0.7497 | 0.8659 |
| 0.151 | 2.8460 | 1090 | 0.6611 | 0.3866 | 0.6611 | 0.8131 |
| 0.151 | 2.8512 | 1092 | 0.6332 | 0.4803 | 0.6332 | 0.7958 |
| 0.151 | 2.8564 | 1094 | 0.6058 | 0.5477 | 0.6058 | 0.7783 |
| 0.151 | 2.8616 | 1096 | 0.6181 | 0.4457 | 0.6181 | 0.7862 |
| 0.151 | 2.8668 | 1098 | 0.7219 | 0.4049 | 0.7219 | 0.8497 |
| 0.151 | 2.8721 | 1100 | 0.8382 | 0.3723 | 0.8382 | 0.9155 |
| 0.151 | 2.8773 | 1102 | 0.8518 | 0.3967 | 0.8518 | 0.9229 |
| 0.151 | 2.8825 | 1104 | 0.7702 | 0.3723 | 0.7702 | 0.8776 |
| 0.151 | 2.8877 | 1106 | 0.6630 | 0.4667 | 0.6630 | 0.8142 |
| 0.151 | 2.8930 | 1108 | 0.5750 | 0.475 | 0.5750 | 0.7583 |
| 0.151 | 2.8982 | 1110 | 0.5465 | 0.475 | 0.5465 | 0.7393 |
| 0.151 | 2.9034 | 1112 | 0.5480 | 0.475 | 0.5480 | 0.7403 |
| 0.151 | 2.9086 | 1114 | 0.5618 | 0.475 | 0.5618 | 0.7496 |
| 0.151 | 2.9138 | 1116 | 0.5830 | 0.475 | 0.5830 | 0.7635 |
| 0.151 | 2.9191 | 1118 | 0.6020 | 0.475 | 0.6020 | 0.7759 |
| 0.151 | 2.9243 | 1120 | 0.5842 | 0.475 | 0.5842 | 0.7643 |
| 0.151 | 2.9295 | 1122 | 0.5659 | 0.4465 | 0.5659 | 0.7522 |
| 0.151 | 2.9347 | 1124 | 0.5615 | 0.4465 | 0.5615 | 0.7494 |
| 0.151 | 2.9399 | 1126 | 0.5574 | 0.4465 | 0.5574 | 0.7466 |
| 0.151 | 2.9452 | 1128 | 0.5353 | 0.4831 | 0.5353 | 0.7316 |
| 0.151 | 2.9504 | 1130 | 0.5392 | 0.4831 | 0.5392 | 0.7343 |
| 0.151 | 2.9556 | 1132 | 0.5683 | 0.475 | 0.5683 | 0.7539 |
| 0.151 | 2.9608 | 1134 | 0.5875 | 0.475 | 0.5875 | 0.7665 |
| 0.151 | 2.9661 | 1136 | 0.5952 | 0.475 | 0.5952 | 0.7715 |
| 0.151 | 2.9713 | 1138 | 0.6082 | 0.4727 | 0.6082 | 0.7799 |
| 0.151 | 2.9765 | 1140 | 0.5760 | 0.475 | 0.5760 | 0.7589 |
| 0.151 | 2.9817 | 1142 | 0.5178 | 0.475 | 0.5178 | 0.7196 |
| 0.151 | 2.9869 | 1144 | 0.4877 | 0.5767 | 0.4877 | 0.6983 |
| 0.151 | 2.9922 | 1146 | 0.4830 | 0.5767 | 0.4830 | 0.6950 |
| 0.151 | 2.9974 | 1148 | 0.5085 | 0.5767 | 0.5085 | 0.7131 |
| 0.151 | 3.0026 | 1150 | 0.5425 | 0.5767 | 0.5425 | 0.7365 |
| 0.151 | 3.0078 | 1152 | 0.6197 | 0.4128 | 0.6197 | 0.7872 |
| 0.151 | 3.0131 | 1154 | 0.6817 | 0.4034 | 0.6817 | 0.8257 |
| 0.151 | 3.0183 | 1156 | 0.6846 | 0.4034 | 0.6846 | 0.8274 |
| 0.151 | 3.0235 | 1158 | 0.6143 | 0.4034 | 0.6143 | 0.7838 |
| 0.151 | 3.0287 | 1160 | 0.5548 | 0.4688 | 0.5548 | 0.7449 |
| 0.151 | 3.0339 | 1162 | 0.5013 | 0.6769 | 0.5013 | 0.7081 |
| 0.151 | 3.0392 | 1164 | 0.4673 | 0.6526 | 0.4673 | 0.6836 |
| 0.151 | 3.0444 | 1166 | 0.4669 | 0.6866 | 0.4669 | 0.6833 |
| 0.151 | 3.0496 | 1168 | 0.4824 | 0.6866 | 0.4824 | 0.6945 |
| 0.151 | 3.0548 | 1170 | 0.5091 | 0.6866 | 0.5091 | 0.7135 |
| 0.151 | 3.0601 | 1172 | 0.5379 | 0.6818 | 0.5379 | 0.7334 |
| 0.151 | 3.0653 | 1174 | 0.5818 | 0.4118 | 0.5818 | 0.7627 |
| 0.151 | 3.0705 | 1176 | 0.6224 | 0.4118 | 0.6224 | 0.7889 |
| 0.151 | 3.0757 | 1178 | 0.6417 | 0.4118 | 0.6417 | 0.8010 |
| 0.151 | 3.0809 | 1180 | 0.6060 | 0.4727 | 0.6060 | 0.7784 |
| 0.151 | 3.0862 | 1182 | 0.5662 | 0.475 | 0.5662 | 0.7525 |
| 0.151 | 3.0914 | 1184 | 0.5276 | 0.475 | 0.5276 | 0.7263 |
| 0.151 | 3.0966 | 1186 | 0.5306 | 0.475 | 0.5306 | 0.7284 |
| 0.151 | 3.1018 | 1188 | 0.5499 | 0.4688 | 0.5499 | 0.7416 |
| 0.151 | 3.1070 | 1190 | 0.5185 | 0.5767 | 0.5185 | 0.7201 |
| 0.151 | 3.1123 | 1192 | 0.4687 | 0.5832 | 0.4687 | 0.6846 |
| 0.151 | 3.1175 | 1194 | 0.4468 | 0.7158 | 0.4468 | 0.6684 |
| 0.151 | 3.1227 | 1196 | 0.4453 | 0.7158 | 0.4453 | 0.6673 |
| 0.151 | 3.1279 | 1198 | 0.4526 | 0.7158 | 0.4526 | 0.6727 |
| 0.151 | 3.1332 | 1200 | 0.4773 | 0.5832 | 0.4773 | 0.6908 |
| 0.151 | 3.1384 | 1202 | 0.5416 | 0.5767 | 0.5416 | 0.7359 |
| 0.151 | 3.1436 | 1204 | 0.6117 | 0.5619 | 0.6117 | 0.7821 |
| 0.151 | 3.1488 | 1206 | 0.6469 | 0.4372 | 0.6469 | 0.8043 |
| 0.151 | 3.1540 | 1208 | 0.6281 | 0.4375 | 0.6281 | 0.7926 |
| 0.151 | 3.1593 | 1210 | 0.5621 | 0.475 | 0.5621 | 0.7497 |
| 0.151 | 3.1645 | 1212 | 0.5211 | 0.5767 | 0.5211 | 0.7219 |
| 0.151 | 3.1697 | 1214 | 0.4974 | 0.5832 | 0.4974 | 0.7053 |
| 0.151 | 3.1749 | 1216 | 0.5090 | 0.7158 | 0.5090 | 0.7135 |
| 0.151 | 3.1802 | 1218 | 0.5479 | 0.7158 | 0.5479 | 0.7402 |
| 0.151 | 3.1854 | 1220 | 0.6050 | 0.5359 | 0.6050 | 0.7778 |
| 0.151 | 3.1906 | 1222 | 0.7075 | 0.4128 | 0.7075 | 0.8411 |
| 0.151 | 3.1958 | 1224 | 0.7748 | 0.4128 | 0.7748 | 0.8802 |
| 0.151 | 3.2010 | 1226 | 0.7484 | 0.4128 | 0.7484 | 0.8651 |
| 0.151 | 3.2063 | 1228 | 0.6703 | 0.3889 | 0.6703 | 0.8187 |
| 0.151 | 3.2115 | 1230 | 0.6226 | 0.4451 | 0.6226 | 0.7891 |
| 0.151 | 3.2167 | 1232 | 0.5862 | 0.4465 | 0.5862 | 0.7657 |
| 0.151 | 3.2219 | 1234 | 0.5833 | 0.5477 | 0.5833 | 0.7638 |
| 0.151 | 3.2272 | 1236 | 0.5836 | 0.4465 | 0.5836 | 0.7639 |
| 0.151 | 3.2324 | 1238 | 0.5773 | 0.4465 | 0.5773 | 0.7598 |
| 0.151 | 3.2376 | 1240 | 0.5636 | 0.4465 | 0.5636 | 0.7508 |
| 0.151 | 3.2428 | 1242 | 0.5652 | 0.4465 | 0.5652 | 0.7518 |
| 0.151 | 3.2480 | 1244 | 0.5740 | 0.4465 | 0.5740 | 0.7576 |
| 0.151 | 3.2533 | 1246 | 0.5845 | 0.4465 | 0.5845 | 0.7645 |
| 0.151 | 3.2585 | 1248 | 0.5984 | 0.4381 | 0.5984 | 0.7735 |
| 0.151 | 3.2637 | 1250 | 0.5817 | 0.4381 | 0.5817 | 0.7627 |
| 0.151 | 3.2689 | 1252 | 0.5755 | 0.4381 | 0.5755 | 0.7586 |
| 0.151 | 3.2742 | 1254 | 0.5991 | 0.4381 | 0.5991 | 0.7740 |
| 0.151 | 3.2794 | 1256 | 0.6046 | 0.4381 | 0.6046 | 0.7775 |
| 0.151 | 3.2846 | 1258 | 0.5637 | 0.4381 | 0.5637 | 0.7508 |
| 0.151 | 3.2898 | 1260 | 0.5390 | 0.4465 | 0.5390 | 0.7342 |
| 0.151 | 3.2950 | 1262 | 0.5552 | 0.4465 | 0.5552 | 0.7451 |
| 0.151 | 3.3003 | 1264 | 0.5904 | 0.4457 | 0.5904 | 0.7683 |
| 0.151 | 3.3055 | 1266 | 0.5805 | 0.4465 | 0.5805 | 0.7619 |
| 0.151 | 3.3107 | 1268 | 0.5595 | 0.4465 | 0.5595 | 0.7480 |
| 0.151 | 3.3159 | 1270 | 0.5556 | 0.4465 | 0.5556 | 0.7454 |
| 0.151 | 3.3211 | 1272 | 0.5769 | 0.4465 | 0.5769 | 0.7595 |
| 0.151 | 3.3264 | 1274 | 0.6040 | 0.4465 | 0.6040 | 0.7772 |
| 0.151 | 3.3316 | 1276 | 0.6535 | 0.4375 | 0.6535 | 0.8084 |
| 0.151 | 3.3368 | 1278 | 0.6413 | 0.4378 | 0.6413 | 0.8008 |
| 0.151 | 3.3420 | 1280 | 0.6306 | 0.4465 | 0.6306 | 0.7941 |
| 0.151 | 3.3473 | 1282 | 0.6628 | 0.4457 | 0.6628 | 0.8141 |
| 0.151 | 3.3525 | 1284 | 0.7146 | 0.4727 | 0.7146 | 0.8453 |
| 0.151 | 3.3577 | 1286 | 0.7705 | 0.4128 | 0.7705 | 0.8778 |
| 0.151 | 3.3629 | 1288 | 0.7418 | 0.4457 | 0.7418 | 0.8613 |
| 0.151 | 3.3681 | 1290 | 0.6688 | 0.4465 | 0.6688 | 0.8178 |
| 0.151 | 3.3734 | 1292 | 0.5836 | 0.6473 | 0.5837 | 0.7640 |
| 0.151 | 3.3786 | 1294 | 0.5503 | 0.6818 | 0.5503 | 0.7418 |
| 0.151 | 3.3838 | 1296 | 0.5517 | 0.6473 | 0.5517 | 0.7428 |
| 0.151 | 3.3890 | 1298 | 0.5884 | 0.4465 | 0.5884 | 0.7671 |
| 0.151 | 3.3943 | 1300 | 0.6264 | 0.4465 | 0.6264 | 0.7914 |
| 0.151 | 3.3995 | 1302 | 0.6737 | 0.4 | 0.6737 | 0.8208 |
| 0.151 | 3.4047 | 1304 | 0.7026 | 0.4 | 0.7026 | 0.8382 |
| 0.151 | 3.4099 | 1306 | 0.6896 | 0.4288 | 0.6896 | 0.8304 |
| 0.151 | 3.4151 | 1308 | 0.6484 | 0.4 | 0.6484 | 0.8052 |
| 0.151 | 3.4204 | 1310 | 0.6349 | 0.4 | 0.6349 | 0.7968 |
| 0.151 | 3.4256 | 1312 | 0.6459 | 0.4 | 0.6459 | 0.8037 |
| 0.151 | 3.4308 | 1314 | 0.6440 | 0.4 | 0.6440 | 0.8025 |
| 0.151 | 3.4360 | 1316 | 0.6608 | 0.4 | 0.6608 | 0.8129 |
| 0.151 | 3.4413 | 1318 | 0.6564 | 0.4 | 0.6564 | 0.8102 |
| 0.151 | 3.4465 | 1320 | 0.6288 | 0.4094 | 0.6288 | 0.7930 |
| 0.151 | 3.4517 | 1322 | 0.6099 | 0.4094 | 0.6099 | 0.7809 |
| 0.151 | 3.4569 | 1324 | 0.6293 | 0.4378 | 0.6293 | 0.7933 |
| 0.151 | 3.4621 | 1326 | 0.6584 | 0.4 | 0.6584 | 0.8114 |
| 0.151 | 3.4674 | 1328 | 0.6615 | 0.4 | 0.6615 | 0.8133 |
| 0.151 | 3.4726 | 1330 | 0.6403 | 0.4094 | 0.6403 | 0.8002 |
| 0.151 | 3.4778 | 1332 | 0.5875 | 0.4465 | 0.5875 | 0.7665 |
| 0.151 | 3.4830 | 1334 | 0.5488 | 0.5477 | 0.5488 | 0.7408 |
| 0.151 | 3.4883 | 1336 | 0.5123 | 0.6818 | 0.5123 | 0.7158 |
| 0.151 | 3.4935 | 1338 | 0.5041 | 0.6818 | 0.5041 | 0.7100 |
| 0.151 | 3.4987 | 1340 | 0.5059 | 0.6818 | 0.5059 | 0.7113 |
| 0.151 | 3.5039 | 1342 | 0.5308 | 0.6473 | 0.5308 | 0.7285 |
| 0.151 | 3.5091 | 1344 | 0.5592 | 0.4465 | 0.5592 | 0.7478 |
| 0.151 | 3.5144 | 1346 | 0.6156 | 0.4465 | 0.6156 | 0.7846 |
| 0.151 | 3.5196 | 1348 | 0.6223 | 0.4378 | 0.6223 | 0.7889 |
| 0.151 | 3.5248 | 1350 | 0.5827 | 0.4378 | 0.5827 | 0.7634 |
| 0.151 | 3.5300 | 1352 | 0.5362 | 0.4465 | 0.5362 | 0.7323 |
| 0.151 | 3.5352 | 1354 | 0.5117 | 0.4465 | 0.5117 | 0.7153 |
| 0.151 | 3.5405 | 1356 | 0.4821 | 0.5832 | 0.4821 | 0.6943 |
| 0.151 | 3.5457 | 1358 | 0.4794 | 0.5832 | 0.4794 | 0.6924 |
| 0.151 | 3.5509 | 1360 | 0.4845 | 0.5832 | 0.4845 | 0.6960 |
| 0.151 | 3.5561 | 1362 | 0.4956 | 0.5832 | 0.4956 | 0.7040 |
| 0.151 | 3.5614 | 1364 | 0.5262 | 0.5832 | 0.5262 | 0.7254 |
| 0.151 | 3.5666 | 1366 | 0.5416 | 0.5832 | 0.5416 | 0.7360 |
| 0.151 | 3.5718 | 1368 | 0.5296 | 0.5832 | 0.5296 | 0.7277 |
| 0.151 | 3.5770 | 1370 | 0.4924 | 0.5832 | 0.4924 | 0.7017 |
| 0.151 | 3.5822 | 1372 | 0.4609 | 0.6182 | 0.4609 | 0.6789 |
| 0.151 | 3.5875 | 1374 | 0.4576 | 0.6182 | 0.4576 | 0.6765 |
| 0.151 | 3.5927 | 1376 | 0.4674 | 0.6182 | 0.4674 | 0.6836 |
| 0.151 | 3.5979 | 1378 | 0.4857 | 0.5832 | 0.4857 | 0.6970 |
| 0.151 | 3.6031 | 1380 | 0.5060 | 0.5477 | 0.5060 | 0.7113 |
| 0.151 | 3.6084 | 1382 | 0.5504 | 0.475 | 0.5504 | 0.7419 |
| 0.151 | 3.6136 | 1384 | 0.5826 | 0.4378 | 0.5826 | 0.7633 |
| 0.151 | 3.6188 | 1386 | 0.5993 | 0.4667 | 0.5993 | 0.7741 |
| 0.151 | 3.6240 | 1388 | 0.6161 | 0.4667 | 0.6161 | 0.7849 |
| 0.151 | 3.6292 | 1390 | 0.5950 | 0.4667 | 0.5950 | 0.7714 |
| 0.151 | 3.6345 | 1392 | 0.5589 | 0.4378 | 0.5589 | 0.7476 |
| 0.151 | 3.6397 | 1394 | 0.5377 | 0.4378 | 0.5377 | 0.7333 |
| 0.151 | 3.6449 | 1396 | 0.5396 | 0.4378 | 0.5396 | 0.7346 |
| 0.151 | 3.6501 | 1398 | 0.5417 | 0.4378 | 0.5417 | 0.7360 |
| 0.151 | 3.6554 | 1400 | 0.5357 | 0.4465 | 0.5357 | 0.7319 |
| 0.151 | 3.6606 | 1402 | 0.5199 | 0.5477 | 0.5199 | 0.7211 |
| 0.151 | 3.6658 | 1404 | 0.5116 | 0.6182 | 0.5116 | 0.7153 |
| 0.151 | 3.6710 | 1406 | 0.5011 | 0.6182 | 0.5011 | 0.7079 |
| 0.151 | 3.6762 | 1408 | 0.5059 | 0.5618 | 0.5059 | 0.7113 |
| 0.151 | 3.6815 | 1410 | 0.5262 | 0.6182 | 0.5262 | 0.7254 |
| 0.151 | 3.6867 | 1412 | 0.5608 | 0.6182 | 0.5608 | 0.7489 |
| 0.151 | 3.6919 | 1414 | 0.6206 | 0.4465 | 0.6206 | 0.7878 |
| 0.151 | 3.6971 | 1416 | 0.6791 | 0.4378 | 0.6791 | 0.8241 |
| 0.151 | 3.7023 | 1418 | 0.7452 | 0.4378 | 0.7452 | 0.8633 |
| 0.151 | 3.7076 | 1420 | 0.7964 | 0.4 | 0.7964 | 0.8924 |
| 0.151 | 3.7128 | 1422 | 0.8129 | 0.4581 | 0.8129 | 0.9016 |
| 0.151 | 3.7180 | 1424 | 0.7755 | 0.4581 | 0.7755 | 0.8806 |
| 0.151 | 3.7232 | 1426 | 0.7052 | 0.4 | 0.7052 | 0.8398 |
| 0.151 | 3.7285 | 1428 | 0.6269 | 0.4080 | 0.6269 | 0.7918 |
| 0.151 | 3.7337 | 1430 | 0.5794 | 0.4474 | 0.5794 | 0.7612 |
| 0.151 | 3.7389 | 1432 | 0.5673 | 0.5546 | 0.5673 | 0.7532 |
| 0.151 | 3.7441 | 1434 | 0.5705 | 0.5922 | 0.5705 | 0.7553 |
| 0.151 | 3.7493 | 1436 | 0.5907 | 0.5832 | 0.5907 | 0.7686 |
| 0.151 | 3.7546 | 1438 | 0.6313 | 0.4465 | 0.6313 | 0.7945 |
| 0.151 | 3.7598 | 1440 | 0.6890 | 0.4465 | 0.6890 | 0.8301 |
| 0.151 | 3.7650 | 1442 | 0.7534 | 0.4094 | 0.7534 | 0.8680 |
| 0.151 | 3.7702 | 1444 | 0.8371 | 0.3569 | 0.8371 | 0.9149 |
| 0.151 | 3.7755 | 1446 | 0.8713 | 0.3807 | 0.8713 | 0.9334 |
| 0.151 | 3.7807 | 1448 | 0.8378 | 0.3807 | 0.8378 | 0.9153 |
| 0.151 | 3.7859 | 1450 | 0.7490 | 0.4094 | 0.7490 | 0.8654 |
| 0.151 | 3.7911 | 1452 | 0.6585 | 0.4094 | 0.6585 | 0.8115 |
| 0.151 | 3.7963 | 1454 | 0.6052 | 0.4465 | 0.6052 | 0.7780 |
| 0.151 | 3.8016 | 1456 | 0.5924 | 0.4831 | 0.5924 | 0.7697 |
| 0.151 | 3.8068 | 1458 | 0.5991 | 0.4831 | 0.5991 | 0.7740 |
| 0.151 | 3.8120 | 1460 | 0.6205 | 0.4831 | 0.6205 | 0.7877 |
| 0.151 | 3.8172 | 1462 | 0.6535 | 0.4465 | 0.6535 | 0.8084 |
| 0.151 | 3.8225 | 1464 | 0.6423 | 0.4465 | 0.6423 | 0.8014 |
| 0.151 | 3.8277 | 1466 | 0.6062 | 0.4831 | 0.6062 | 0.7786 |
| 0.151 | 3.8329 | 1468 | 0.5489 | 0.6182 | 0.5489 | 0.7409 |
| 0.151 | 3.8381 | 1470 | 0.5133 | 0.6866 | 0.5133 | 0.7164 |
| 0.151 | 3.8433 | 1472 | 0.5077 | 0.6866 | 0.5077 | 0.7125 |
| 0.151 | 3.8486 | 1474 | 0.4969 | 0.6578 | 0.4969 | 0.7049 |
| 0.151 | 3.8538 | 1476 | 0.4924 | 0.6866 | 0.4924 | 0.7017 |
| 0.151 | 3.8590 | 1478 | 0.5148 | 0.6182 | 0.5148 | 0.7175 |
| 0.151 | 3.8642 | 1480 | 0.5705 | 0.4831 | 0.5705 | 0.7553 |
| 0.151 | 3.8695 | 1482 | 0.5820 | 0.4381 | 0.5820 | 0.7629 |
| 0.151 | 3.8747 | 1484 | 0.5670 | 0.4381 | 0.5670 | 0.7530 |
| 0.151 | 3.8799 | 1486 | 0.5695 | 0.4381 | 0.5695 | 0.7546 |
| 0.151 | 3.8851 | 1488 | 0.5404 | 0.4381 | 0.5404 | 0.7351 |
| 0.151 | 3.8903 | 1490 | 0.5012 | 0.4831 | 0.5012 | 0.7080 |
| 0.151 | 3.8956 | 1492 | 0.4797 | 0.6182 | 0.4797 | 0.6926 |
| 0.151 | 3.9008 | 1494 | 0.4754 | 0.6182 | 0.4754 | 0.6895 |
| 0.151 | 3.9060 | 1496 | 0.4817 | 0.6182 | 0.4817 | 0.6940 |
| 0.151 | 3.9112 | 1498 | 0.5006 | 0.6182 | 0.5006 | 0.7075 |
| 0.1087 | 3.9164 | 1500 | 0.5178 | 0.5832 | 0.5178 | 0.7196 |
| 0.1087 | 3.9217 | 1502 | 0.5303 | 0.5832 | 0.5303 | 0.7282 |
| 0.1087 | 3.9269 | 1504 | 0.5586 | 0.4465 | 0.5586 | 0.7474 |
| 0.1087 | 3.9321 | 1506 | 0.5736 | 0.4465 | 0.5736 | 0.7574 |
| 0.1087 | 3.9373 | 1508 | 0.5594 | 0.5832 | 0.5594 | 0.7480 |
| 0.1087 | 3.9426 | 1510 | 0.5496 | 0.5832 | 0.5496 | 0.7413 |
| 0.1087 | 3.9478 | 1512 | 0.5508 | 0.5832 | 0.5508 | 0.7421 |
| 0.1087 | 3.9530 | 1514 | 0.5442 | 0.5832 | 0.5442 | 0.7377 |
| 0.1087 | 3.9582 | 1516 | 0.5444 | 0.5832 | 0.5444 | 0.7379 |
| 0.1087 | 3.9634 | 1518 | 0.5291 | 0.5832 | 0.5291 | 0.7274 |
| 0.1087 | 3.9687 | 1520 | 0.5214 | 0.5832 | 0.5214 | 0.7220 |
| 0.1087 | 3.9739 | 1522 | 0.5146 | 0.5832 | 0.5146 | 0.7173 |
| 0.1087 | 3.9791 | 1524 | 0.5073 | 0.7158 | 0.5073 | 0.7122 |
| 0.1087 | 3.9843 | 1526 | 0.5217 | 0.6182 | 0.5217 | 0.7223 |
| 0.1087 | 3.9896 | 1528 | 0.5394 | 0.6182 | 0.5394 | 0.7344 |
| 0.1087 | 3.9948 | 1530 | 0.5578 | 0.6182 | 0.5578 | 0.7469 |
| 0.1087 | 4.0 | 1532 | 0.5681 | 0.5477 | 0.5681 | 0.7538 |
| 0.1087 | 4.0052 | 1534 | 0.5860 | 0.5477 | 0.5860 | 0.7655 |
| 0.1087 | 4.0104 | 1536 | 0.6060 | 0.4457 | 0.6060 | 0.7785 |
| 0.1087 | 4.0157 | 1538 | 0.6319 | 0.4727 | 0.6319 | 0.7949 |
| 0.1087 | 4.0209 | 1540 | 0.6327 | 0.4375 | 0.6327 | 0.7954 |
| 0.1087 | 4.0261 | 1542 | 0.6155 | 0.4378 | 0.6155 | 0.7845 |
| 0.1087 | 4.0313 | 1544 | 0.5774 | 0.4378 | 0.5774 | 0.7599 |
| 0.1087 | 4.0366 | 1546 | 0.5354 | 0.4465 | 0.5354 | 0.7317 |
| 0.1087 | 4.0418 | 1548 | 0.5064 | 0.4465 | 0.5064 | 0.7116 |
| 0.1087 | 4.0470 | 1550 | 0.4966 | 0.5477 | 0.4966 | 0.7047 |
| 0.1087 | 4.0522 | 1552 | 0.5088 | 0.5477 | 0.5088 | 0.7133 |
| 0.1087 | 4.0574 | 1554 | 0.5360 | 0.4465 | 0.5360 | 0.7321 |
| 0.1087 | 4.0627 | 1556 | 0.5805 | 0.3980 | 0.5805 | 0.7619 |
| 0.1087 | 4.0679 | 1558 | 0.6126 | 0.4 | 0.6126 | 0.7827 |
| 0.1087 | 4.0731 | 1560 | 0.6186 | 0.4288 | 0.6186 | 0.7865 |
| 0.1087 | 4.0783 | 1562 | 0.6329 | 0.3903 | 0.6329 | 0.7956 |
| 0.1087 | 4.0836 | 1564 | 0.6147 | 0.3877 | 0.6147 | 0.7841 |
| 0.1087 | 4.0888 | 1566 | 0.5724 | 0.4286 | 0.5724 | 0.7566 |
| 0.1087 | 4.0940 | 1568 | 0.5254 | 0.3980 | 0.5254 | 0.7248 |
| 0.1087 | 4.0992 | 1570 | 0.4918 | 0.4831 | 0.4918 | 0.7013 |
| 0.1087 | 4.1044 | 1572 | 0.4874 | 0.6182 | 0.4874 | 0.6982 |
| 0.1087 | 4.1097 | 1574 | 0.4935 | 0.5895 | 0.4935 | 0.7025 |
| 0.1087 | 4.1149 | 1576 | 0.5073 | 0.6866 | 0.5073 | 0.7122 |
| 0.1087 | 4.1201 | 1578 | 0.5368 | 0.5895 | 0.5368 | 0.7327 |
| 0.1087 | 4.1253 | 1580 | 0.5569 | 0.5895 | 0.5569 | 0.7463 |
| 0.1087 | 4.1305 | 1582 | 0.5651 | 0.5895 | 0.5651 | 0.7517 |
| 0.1087 | 4.1358 | 1584 | 0.5660 | 0.5895 | 0.5660 | 0.7523 |
| 0.1087 | 4.1410 | 1586 | 0.5634 | 0.6083 | 0.5634 | 0.7506 |
| 0.1087 | 4.1462 | 1588 | 0.5563 | 0.6083 | 0.5563 | 0.7459 |
| 0.1087 | 4.1514 | 1590 | 0.5516 | 0.6083 | 0.5516 | 0.7427 |
| 0.1087 | 4.1567 | 1592 | 0.5228 | 0.6182 | 0.5228 | 0.7230 |
| 0.1087 | 4.1619 | 1594 | 0.4943 | 0.6578 | 0.4943 | 0.7030 |
| 0.1087 | 4.1671 | 1596 | 0.4801 | 0.6578 | 0.4801 | 0.6929 |
| 0.1087 | 4.1723 | 1598 | 0.4789 | 0.6708 | 0.4789 | 0.6920 |
| 0.1087 | 4.1775 | 1600 | 0.4819 | 0.6708 | 0.4819 | 0.6942 |
| 0.1087 | 4.1828 | 1602 | 0.4821 | 0.6578 | 0.4821 | 0.6943 |
| 0.1087 | 4.1880 | 1604 | 0.4879 | 0.6866 | 0.4879 | 0.6985 |
| 0.1087 | 4.1932 | 1606 | 0.5216 | 0.6083 | 0.5216 | 0.7222 |
| 0.1087 | 4.1984 | 1608 | 0.5823 | 0.4803 | 0.5823 | 0.7631 |
| 0.1087 | 4.2037 | 1610 | 0.6175 | 0.4288 | 0.6175 | 0.7858 |
| 0.1087 | 4.2089 | 1612 | 0.6134 | 0.4 | 0.6134 | 0.7832 |
| 0.1087 | 4.2141 | 1614 | 0.5872 | 0.4457 | 0.5872 | 0.7663 |
| 0.1087 | 4.2193 | 1616 | 0.5621 | 0.4803 | 0.5621 | 0.7498 |
| 0.1087 | 4.2245 | 1618 | 0.5344 | 0.4831 | 0.5344 | 0.7310 |
| 0.1087 | 4.2298 | 1620 | 0.5025 | 0.7158 | 0.5025 | 0.7089 |
| 0.1087 | 4.2350 | 1622 | 0.4876 | 0.6578 | 0.4876 | 0.6983 |
| 0.1087 | 4.2402 | 1624 | 0.4885 | 0.6708 | 0.4885 | 0.6989 |
| 0.1087 | 4.2454 | 1626 | 0.4866 | 0.6578 | 0.4866 | 0.6976 |
| 0.1087 | 4.2507 | 1628 | 0.4863 | 0.6578 | 0.4863 | 0.6973 |
| 0.1087 | 4.2559 | 1630 | 0.5045 | 0.7158 | 0.5045 | 0.7103 |
| 0.1087 | 4.2611 | 1632 | 0.5242 | 0.7158 | 0.5242 | 0.7240 |
| 0.1087 | 4.2663 | 1634 | 0.5335 | 0.7158 | 0.5335 | 0.7304 |
| 0.1087 | 4.2715 | 1636 | 0.5303 | 0.6182 | 0.5303 | 0.7282 |
| 0.1087 | 4.2768 | 1638 | 0.5112 | 0.7158 | 0.5112 | 0.7150 |
| 0.1087 | 4.2820 | 1640 | 0.4982 | 0.7158 | 0.4982 | 0.7058 |
| 0.1087 | 4.2872 | 1642 | 0.4951 | 0.7158 | 0.4951 | 0.7037 |
| 0.1087 | 4.2924 | 1644 | 0.4967 | 0.7158 | 0.4967 | 0.7048 |
| 0.1087 | 4.2977 | 1646 | 0.4886 | 0.7158 | 0.4886 | 0.6990 |
| 0.1087 | 4.3029 | 1648 | 0.4890 | 0.7158 | 0.4890 | 0.6993 |
| 0.1087 | 4.3081 | 1650 | 0.5029 | 0.5191 | 0.5029 | 0.7092 |
| 0.1087 | 4.3133 | 1652 | 0.5324 | 0.5191 | 0.5324 | 0.7297 |
| 0.1087 | 4.3185 | 1654 | 0.5551 | 0.4831 | 0.5551 | 0.7450 |
| 0.1087 | 4.3238 | 1656 | 0.5567 | 0.5191 | 0.5567 | 0.7461 |
| 0.1087 | 4.3290 | 1658 | 0.5322 | 0.5191 | 0.5322 | 0.7295 |
| 0.1087 | 4.3342 | 1660 | 0.4907 | 0.7158 | 0.4907 | 0.7005 |
| 0.1087 | 4.3394 | 1662 | 0.4782 | 0.7158 | 0.4782 | 0.6915 |
| 0.1087 | 4.3446 | 1664 | 0.4843 | 0.7158 | 0.4844 | 0.6960 |
| 0.1087 | 4.3499 | 1666 | 0.5063 | 0.7158 | 0.5063 | 0.7116 |
| 0.1087 | 4.3551 | 1668 | 0.5318 | 0.7158 | 0.5318 | 0.7292 |
| 0.1087 | 4.3603 | 1670 | 0.5565 | 0.7009 | 0.5565 | 0.7460 |
| 0.1087 | 4.3655 | 1672 | 0.5866 | 0.5145 | 0.5866 | 0.7659 |
| 0.1087 | 4.3708 | 1674 | 0.5829 | 0.5145 | 0.5829 | 0.7635 |
| 0.1087 | 4.3760 | 1676 | 0.5511 | 0.5191 | 0.5511 | 0.7424 |
| 0.1087 | 4.3812 | 1678 | 0.5204 | 0.5191 | 0.5204 | 0.7214 |
| 0.1087 | 4.3864 | 1680 | 0.4847 | 0.7158 | 0.4847 | 0.6962 |
| 0.1087 | 4.3916 | 1682 | 0.4682 | 0.7158 | 0.4682 | 0.6843 |
| 0.1087 | 4.3969 | 1684 | 0.4729 | 0.5191 | 0.4729 | 0.6877 |
| 0.1087 | 4.4021 | 1686 | 0.4877 | 0.4831 | 0.4877 | 0.6983 |
| 0.1087 | 4.4073 | 1688 | 0.4970 | 0.4831 | 0.4970 | 0.7050 |
| 0.1087 | 4.4125 | 1690 | 0.5031 | 0.4831 | 0.5031 | 0.7093 |
| 0.1087 | 4.4178 | 1692 | 0.5260 | 0.4831 | 0.5260 | 0.7253 |
| 0.1087 | 4.4230 | 1694 | 0.5362 | 0.4803 | 0.5362 | 0.7322 |
| 0.1087 | 4.4282 | 1696 | 0.5341 | 0.5145 | 0.5341 | 0.7308 |
| 0.1087 | 4.4334 | 1698 | 0.5439 | 0.5145 | 0.5439 | 0.7375 |
| 0.1087 | 4.4386 | 1700 | 0.5485 | 0.5145 | 0.5485 | 0.7406 |
| 0.1087 | 4.4439 | 1702 | 0.5357 | 0.6083 | 0.5357 | 0.7319 |
| 0.1087 | 4.4491 | 1704 | 0.5353 | 0.5145 | 0.5353 | 0.7316 |
| 0.1087 | 4.4543 | 1706 | 0.5235 | 0.5895 | 0.5235 | 0.7235 |
| 0.1087 | 4.4595 | 1708 | 0.5020 | 0.6866 | 0.5020 | 0.7085 |
| 0.1087 | 4.4648 | 1710 | 0.5000 | 0.6866 | 0.5000 | 0.7071 |
| 0.1087 | 4.4700 | 1712 | 0.5091 | 0.5895 | 0.5091 | 0.7135 |
| 0.1087 | 4.4752 | 1714 | 0.5279 | 0.4909 | 0.5279 | 0.7265 |
| 0.1087 | 4.4804 | 1716 | 0.5395 | 0.4909 | 0.5395 | 0.7345 |
| 0.1087 | 4.4856 | 1718 | 0.5746 | 0.5477 | 0.5746 | 0.7580 |
| 0.1087 | 4.4909 | 1720 | 0.6330 | 0.4727 | 0.6330 | 0.7956 |
| 0.1087 | 4.4961 | 1722 | 0.7159 | 0.4375 | 0.7159 | 0.8461 |
| 0.1087 | 4.5013 | 1724 | 0.7623 | 0.3807 | 0.7623 | 0.8731 |
| 0.1087 | 4.5065 | 1726 | 0.7566 | 0.3807 | 0.7566 | 0.8699 |
| 0.1087 | 4.5117 | 1728 | 0.7119 | 0.3780 | 0.7119 | 0.8438 |
| 0.1087 | 4.5170 | 1730 | 0.6611 | 0.4375 | 0.6611 | 0.8131 |
| 0.1087 | 4.5222 | 1732 | 0.6187 | 0.5073 | 0.6187 | 0.7866 |
| 0.1087 | 4.5274 | 1734 | 0.5915 | 0.4831 | 0.5915 | 0.7691 |
| 0.1087 | 4.5326 | 1736 | 0.5861 | 0.4831 | 0.5861 | 0.7656 |
| 0.1087 | 4.5379 | 1738 | 0.5912 | 0.5116 | 0.5912 | 0.7689 |
| 0.1087 | 4.5431 | 1740 | 0.5993 | 0.4378 | 0.5993 | 0.7742 |
| 0.1087 | 4.5483 | 1742 | 0.5792 | 0.475 | 0.5792 | 0.7611 |
| 0.1087 | 4.5535 | 1744 | 0.5689 | 0.4465 | 0.5689 | 0.7542 |
| 0.1087 | 4.5587 | 1746 | 0.5607 | 0.4465 | 0.5607 | 0.7488 |
| 0.1087 | 4.5640 | 1748 | 0.5504 | 0.4465 | 0.5504 | 0.7419 |
| 0.1087 | 4.5692 | 1750 | 0.5419 | 0.4831 | 0.5419 | 0.7361 |
| 0.1087 | 4.5744 | 1752 | 0.5481 | 0.4803 | 0.5481 | 0.7403 |
| 0.1087 | 4.5796 | 1754 | 0.5365 | 0.4831 | 0.5365 | 0.7325 |
| 0.1087 | 4.5849 | 1756 | 0.5043 | 0.5191 | 0.5043 | 0.7101 |
| 0.1087 | 4.5901 | 1758 | 0.4690 | 0.7158 | 0.4690 | 0.6849 |
| 0.1087 | 4.5953 | 1760 | 0.4568 | 0.7158 | 0.4568 | 0.6758 |
| 0.1087 | 4.6005 | 1762 | 0.4598 | 0.7158 | 0.4598 | 0.6781 |
| 0.1087 | 4.6057 | 1764 | 0.4767 | 0.7158 | 0.4767 | 0.6904 |
| 0.1087 | 4.6110 | 1766 | 0.5166 | 0.5191 | 0.5166 | 0.7187 |
| 0.1087 | 4.6162 | 1768 | 0.5557 | 0.5191 | 0.5557 | 0.7454 |
| 0.1087 | 4.6214 | 1770 | 0.5898 | 0.4803 | 0.5898 | 0.7680 |
| 0.1087 | 4.6266 | 1772 | 0.5923 | 0.4831 | 0.5923 | 0.7696 |
| 0.1087 | 4.6319 | 1774 | 0.5669 | 0.4831 | 0.5669 | 0.7529 |
| 0.1087 | 4.6371 | 1776 | 0.5397 | 0.4831 | 0.5397 | 0.7346 |
| 0.1087 | 4.6423 | 1778 | 0.5160 | 0.5191 | 0.5160 | 0.7184 |
| 0.1087 | 4.6475 | 1780 | 0.5069 | 0.5243 | 0.5069 | 0.7119 |
| 0.1087 | 4.6527 | 1782 | 0.5035 | 0.5243 | 0.5035 | 0.7096 |
| 0.1087 | 4.6580 | 1784 | 0.4999 | 0.5243 | 0.4999 | 0.7070 |
| 0.1087 | 4.6632 | 1786 | 0.5015 | 0.5243 | 0.5015 | 0.7081 |
| 0.1087 | 4.6684 | 1788 | 0.5162 | 0.4831 | 0.5162 | 0.7185 |
| 0.1087 | 4.6736 | 1790 | 0.5469 | 0.4465 | 0.5469 | 0.7395 |
| 0.1087 | 4.6789 | 1792 | 0.5603 | 0.4465 | 0.5603 | 0.7485 |
| 0.1087 | 4.6841 | 1794 | 0.5574 | 0.4465 | 0.5574 | 0.7466 |
| 0.1087 | 4.6893 | 1796 | 0.5450 | 0.4465 | 0.5450 | 0.7383 |
| 0.1087 | 4.6945 | 1798 | 0.5291 | 0.5191 | 0.5291 | 0.7274 |
| 0.1087 | 4.6997 | 1800 | 0.5247 | 0.5191 | 0.5247 | 0.7243 |
| 0.1087 | 4.7050 | 1802 | 0.5117 | 0.5191 | 0.5117 | 0.7153 |
| 0.1087 | 4.7102 | 1804 | 0.4930 | 0.5191 | 0.4930 | 0.7021 |
| 0.1087 | 4.7154 | 1806 | 0.4890 | 0.7158 | 0.4890 | 0.6993 |
| 0.1087 | 4.7206 | 1808 | 0.4921 | 0.6182 | 0.4921 | 0.7015 |
| 0.1087 | 4.7258 | 1810 | 0.4971 | 0.5191 | 0.4971 | 0.7050 |
| 0.1087 | 4.7311 | 1812 | 0.5189 | 0.5191 | 0.5189 | 0.7203 |
| 0.1087 | 4.7363 | 1814 | 0.5265 | 0.5145 | 0.5265 | 0.7256 |
| 0.1087 | 4.7415 | 1816 | 0.5444 | 0.4803 | 0.5444 | 0.7379 |
| 0.1087 | 4.7467 | 1818 | 0.5675 | 0.4106 | 0.5675 | 0.7533 |
| 0.1087 | 4.7520 | 1820 | 0.5634 | 0.4106 | 0.5634 | 0.7506 |
| 0.1087 | 4.7572 | 1822 | 0.5461 | 0.4094 | 0.5461 | 0.7390 |
| 0.1087 | 4.7624 | 1824 | 0.5307 | 0.4831 | 0.5307 | 0.7285 |
| 0.1087 | 4.7676 | 1826 | 0.5378 | 0.4465 | 0.5378 | 0.7333 |
| 0.1087 | 4.7728 | 1828 | 0.5605 | 0.4106 | 0.5605 | 0.7486 |
| 0.1087 | 4.7781 | 1830 | 0.5682 | 0.4106 | 0.5682 | 0.7538 |
| 0.1087 | 4.7833 | 1832 | 0.5458 | 0.4831 | 0.5458 | 0.7388 |
| 0.1087 | 4.7885 | 1834 | 0.5137 | 0.5191 | 0.5137 | 0.7167 |
| 0.1087 | 4.7937 | 1836 | 0.4944 | 0.5191 | 0.4944 | 0.7031 |
| 0.1087 | 4.7990 | 1838 | 0.4916 | 0.5191 | 0.4916 | 0.7012 |
| 0.1087 | 4.8042 | 1840 | 0.5017 | 0.5191 | 0.5017 | 0.7083 |
| 0.1087 | 4.8094 | 1842 | 0.5280 | 0.5191 | 0.5280 | 0.7266 |
| 0.1087 | 4.8146 | 1844 | 0.5444 | 0.5145 | 0.5444 | 0.7378 |
| 0.1087 | 4.8198 | 1846 | 0.5507 | 0.5145 | 0.5507 | 0.7421 |
| 0.1087 | 4.8251 | 1848 | 0.5529 | 0.5145 | 0.5529 | 0.7436 |
| 0.1087 | 4.8303 | 1850 | 0.5443 | 0.5145 | 0.5443 | 0.7378 |
| 0.1087 | 4.8355 | 1852 | 0.5294 | 0.5191 | 0.5294 | 0.7276 |
| 0.1087 | 4.8407 | 1854 | 0.5214 | 0.5191 | 0.5214 | 0.7221 |
| 0.1087 | 4.8460 | 1856 | 0.5238 | 0.5191 | 0.5238 | 0.7238 |
| 0.1087 | 4.8512 | 1858 | 0.5388 | 0.5191 | 0.5388 | 0.7340 |
| 0.1087 | 4.8564 | 1860 | 0.5607 | 0.5191 | 0.5607 | 0.7488 |
| 0.1087 | 4.8616 | 1862 | 0.5811 | 0.5145 | 0.5811 | 0.7623 |
| 0.1087 | 4.8668 | 1864 | 0.6052 | 0.5145 | 0.6052 | 0.7779 |
| 0.1087 | 4.8721 | 1866 | 0.6126 | 0.5145 | 0.6126 | 0.7827 |
| 0.1087 | 4.8773 | 1868 | 0.6222 | 0.5145 | 0.6222 | 0.7888 |
| 0.1087 | 4.8825 | 1870 | 0.6303 | 0.5145 | 0.6303 | 0.7939 |
| 0.1087 | 4.8877 | 1872 | 0.6211 | 0.5191 | 0.6211 | 0.7881 |
| 0.1087 | 4.8930 | 1874 | 0.6022 | 0.5191 | 0.6022 | 0.7760 |
| 0.1087 | 4.8982 | 1876 | 0.5943 | 0.5191 | 0.5943 | 0.7709 |
| 0.1087 | 4.9034 | 1878 | 0.5771 | 0.5191 | 0.5771 | 0.7597 |
| 0.1087 | 4.9086 | 1880 | 0.5469 | 0.5191 | 0.5469 | 0.7395 |
| 0.1087 | 4.9138 | 1882 | 0.5263 | 0.5243 | 0.5263 | 0.7255 |
| 0.1087 | 4.9191 | 1884 | 0.5108 | 0.4944 | 0.5108 | 0.7147 |
| 0.1087 | 4.9243 | 1886 | 0.5064 | 0.6091 | 0.5064 | 0.7116 |
| 0.1087 | 4.9295 | 1888 | 0.5149 | 0.4944 | 0.5149 | 0.7176 |
| 0.1087 | 4.9347 | 1890 | 0.5379 | 0.5191 | 0.5379 | 0.7334 |
| 0.1087 | 4.9399 | 1892 | 0.5704 | 0.4831 | 0.5704 | 0.7552 |
| 0.1087 | 4.9452 | 1894 | 0.6045 | 0.4831 | 0.6045 | 0.7775 |
| 0.1087 | 4.9504 | 1896 | 0.6192 | 0.4831 | 0.6192 | 0.7869 |
| 0.1087 | 4.9556 | 1898 | 0.6220 | 0.4831 | 0.6220 | 0.7887 |
| 0.1087 | 4.9608 | 1900 | 0.6358 | 0.4831 | 0.6358 | 0.7974 |
| 0.1087 | 4.9661 | 1902 | 0.6594 | 0.4451 | 0.6594 | 0.8120 |
| 0.1087 | 4.9713 | 1904 | 0.6713 | 0.4118 | 0.6713 | 0.8193 |
| 0.1087 | 4.9765 | 1906 | 0.6778 | 0.4118 | 0.6778 | 0.8233 |
| 0.1087 | 4.9817 | 1908 | 0.6622 | 0.4779 | 0.6622 | 0.8138 |
| 0.1087 | 4.9869 | 1910 | 0.6253 | 0.4831 | 0.6253 | 0.7907 |
| 0.1087 | 4.9922 | 1912 | 0.5825 | 0.5191 | 0.5825 | 0.7632 |
| 0.1087 | 4.9974 | 1914 | 0.5583 | 0.5191 | 0.5583 | 0.7472 |
| 0.1087 | 5.0026 | 1916 | 0.5426 | 0.4909 | 0.5426 | 0.7366 |
| 0.1087 | 5.0078 | 1918 | 0.5403 | 0.4944 | 0.5403 | 0.7351 |
| 0.1087 | 5.0131 | 1920 | 0.5475 | 0.4909 | 0.5475 | 0.7399 |
| 0.1087 | 5.0183 | 1922 | 0.5467 | 0.4909 | 0.5467 | 0.7394 |
| 0.1087 | 5.0235 | 1924 | 0.5447 | 0.4909 | 0.5447 | 0.7380 |
| 0.1087 | 5.0287 | 1926 | 0.5543 | 0.5191 | 0.5543 | 0.7445 |
| 0.1087 | 5.0339 | 1928 | 0.5615 | 0.5191 | 0.5615 | 0.7493 |
| 0.1087 | 5.0392 | 1930 | 0.5838 | 0.4831 | 0.5838 | 0.7641 |
| 0.1087 | 5.0444 | 1932 | 0.6130 | 0.4831 | 0.6130 | 0.7830 |
| 0.1087 | 5.0496 | 1934 | 0.6176 | 0.4803 | 0.6176 | 0.7859 |
| 0.1087 | 5.0548 | 1936 | 0.5958 | 0.4831 | 0.5958 | 0.7719 |
| 0.1087 | 5.0601 | 1938 | 0.5639 | 0.5191 | 0.5639 | 0.7509 |
| 0.1087 | 5.0653 | 1940 | 0.5279 | 0.5191 | 0.5279 | 0.7265 |
| 0.1087 | 5.0705 | 1942 | 0.5108 | 0.5191 | 0.5108 | 0.7147 |
| 0.1087 | 5.0757 | 1944 | 0.5136 | 0.5191 | 0.5136 | 0.7167 |
| 0.1087 | 5.0809 | 1946 | 0.5314 | 0.5191 | 0.5314 | 0.7290 |
| 0.1087 | 5.0862 | 1948 | 0.5428 | 0.4831 | 0.5428 | 0.7367 |
| 0.1087 | 5.0914 | 1950 | 0.5441 | 0.4831 | 0.5441 | 0.7376 |
| 0.1087 | 5.0966 | 1952 | 0.5376 | 0.4831 | 0.5376 | 0.7332 |
| 0.1087 | 5.1018 | 1954 | 0.5177 | 0.5191 | 0.5177 | 0.7195 |
| 0.1087 | 5.1070 | 1956 | 0.4944 | 0.5191 | 0.4944 | 0.7032 |
| 0.1087 | 5.1123 | 1958 | 0.4912 | 0.5191 | 0.4912 | 0.7009 |
| 0.1087 | 5.1175 | 1960 | 0.4941 | 0.5191 | 0.4941 | 0.7029 |
| 0.1087 | 5.1227 | 1962 | 0.5137 | 0.4831 | 0.5137 | 0.7167 |
| 0.1087 | 5.1279 | 1964 | 0.5453 | 0.4831 | 0.5453 | 0.7384 |
| 0.1087 | 5.1332 | 1966 | 0.5796 | 0.4831 | 0.5796 | 0.7613 |
| 0.1087 | 5.1384 | 1968 | 0.5860 | 0.4831 | 0.5860 | 0.7655 |
| 0.1087 | 5.1436 | 1970 | 0.5851 | 0.4831 | 0.5851 | 0.7649 |
| 0.1087 | 5.1488 | 1972 | 0.5782 | 0.4831 | 0.5782 | 0.7604 |
| 0.1087 | 5.1540 | 1974 | 0.5610 | 0.4831 | 0.5610 | 0.7490 |
| 0.1087 | 5.1593 | 1976 | 0.5371 | 0.5191 | 0.5371 | 0.7329 |
| 0.1087 | 5.1645 | 1978 | 0.5198 | 0.5191 | 0.5198 | 0.7210 |
| 0.1087 | 5.1697 | 1980 | 0.5109 | 0.5191 | 0.5109 | 0.7147 |
| 0.1087 | 5.1749 | 1982 | 0.5076 | 0.5191 | 0.5076 | 0.7125 |
| 0.1087 | 5.1802 | 1984 | 0.5132 | 0.5191 | 0.5132 | 0.7164 |
| 0.1087 | 5.1854 | 1986 | 0.5293 | 0.5191 | 0.5293 | 0.7276 |
| 0.1087 | 5.1906 | 1988 | 0.5460 | 0.4831 | 0.5460 | 0.7389 |
| 0.1087 | 5.1958 | 1990 | 0.5591 | 0.4465 | 0.5591 | 0.7477 |
| 0.1087 | 5.2010 | 1992 | 0.5564 | 0.4465 | 0.5564 | 0.7459 |
| 0.1087 | 5.2063 | 1994 | 0.5370 | 0.4465 | 0.5370 | 0.7328 |
| 0.1087 | 5.2115 | 1996 | 0.5160 | 0.4831 | 0.5160 | 0.7183 |
| 0.1087 | 5.2167 | 1998 | 0.4970 | 0.4831 | 0.4970 | 0.7050 |
| 0.0837 | 5.2219 | 2000 | 0.5025 | 0.4831 | 0.5025 | 0.7089 |
| 0.0837 | 5.2272 | 2002 | 0.5232 | 0.4831 | 0.5232 | 0.7233 |
| 0.0837 | 5.2324 | 2004 | 0.5484 | 0.4831 | 0.5484 | 0.7406 |
| 0.0837 | 5.2376 | 2006 | 0.5527 | 0.4831 | 0.5527 | 0.7434 |
| 0.0837 | 5.2428 | 2008 | 0.5542 | 0.4831 | 0.5542 | 0.7444 |
| 0.0837 | 5.2480 | 2010 | 0.5465 | 0.4831 | 0.5465 | 0.7393 |
| 0.0837 | 5.2533 | 2012 | 0.5490 | 0.4831 | 0.5490 | 0.7410 |
| 0.0837 | 5.2585 | 2014 | 0.5592 | 0.4831 | 0.5592 | 0.7478 |
| 0.0837 | 5.2637 | 2016 | 0.5784 | 0.4465 | 0.5784 | 0.7605 |
| 0.0837 | 5.2689 | 2018 | 0.5889 | 0.4094 | 0.5889 | 0.7674 |
| 0.0837 | 5.2742 | 2020 | 0.5833 | 0.4094 | 0.5833 | 0.7638 |
| 0.0837 | 5.2794 | 2022 | 0.5686 | 0.4094 | 0.5686 | 0.7540 |
| 0.0837 | 5.2846 | 2024 | 0.5638 | 0.4094 | 0.5638 | 0.7508 |
| 0.0837 | 5.2898 | 2026 | 0.5495 | 0.4094 | 0.5495 | 0.7413 |
| 0.0837 | 5.2950 | 2028 | 0.5332 | 0.4465 | 0.5332 | 0.7302 |
| 0.0837 | 5.3003 | 2030 | 0.5157 | 0.5191 | 0.5157 | 0.7181 |
| 0.0837 | 5.3055 | 2032 | 0.5142 | 0.5191 | 0.5142 | 0.7171 |
| 0.0837 | 5.3107 | 2034 | 0.5081 | 0.5191 | 0.5081 | 0.7128 |
| 0.0837 | 5.3159 | 2036 | 0.5031 | 0.5191 | 0.5031 | 0.7093 |
| 0.0837 | 5.3211 | 2038 | 0.5005 | 0.5191 | 0.5005 | 0.7075 |
| 0.0837 | 5.3264 | 2040 | 0.5119 | 0.4831 | 0.5119 | 0.7155 |
| 0.0837 | 5.3316 | 2042 | 0.5317 | 0.4094 | 0.5317 | 0.7292 |
| 0.0837 | 5.3368 | 2044 | 0.5693 | 0.4094 | 0.5693 | 0.7545 |
| 0.0837 | 5.3420 | 2046 | 0.5952 | 0.4286 | 0.5952 | 0.7715 |
| 0.0837 | 5.3473 | 2048 | 0.5914 | 0.4286 | 0.5914 | 0.7690 |
| 0.0837 | 5.3525 | 2050 | 0.5615 | 0.3980 | 0.5615 | 0.7493 |
| 0.0837 | 5.3577 | 2052 | 0.5302 | 0.4094 | 0.5302 | 0.7282 |
| 0.0837 | 5.3629 | 2054 | 0.5035 | 0.4094 | 0.5035 | 0.7096 |
| 0.0837 | 5.3681 | 2056 | 0.4882 | 0.5191 | 0.4882 | 0.6987 |
| 0.0837 | 5.3734 | 2058 | 0.4804 | 0.5191 | 0.4804 | 0.6931 |
| 0.0837 | 5.3786 | 2060 | 0.4823 | 0.5191 | 0.4823 | 0.6945 |
| 0.0837 | 5.3838 | 2062 | 0.4900 | 0.7158 | 0.4900 | 0.7000 |
| 0.0837 | 5.3890 | 2064 | 0.5119 | 0.5191 | 0.5119 | 0.7155 |
| 0.0837 | 5.3943 | 2066 | 0.5274 | 0.5191 | 0.5274 | 0.7262 |
| 0.0837 | 5.3995 | 2068 | 0.5378 | 0.5191 | 0.5378 | 0.7334 |
| 0.0837 | 5.4047 | 2070 | 0.5263 | 0.7158 | 0.5263 | 0.7255 |
| 0.0837 | 5.4099 | 2072 | 0.5202 | 0.7158 | 0.5202 | 0.7212 |
| 0.0837 | 5.4151 | 2074 | 0.5199 | 0.7158 | 0.5199 | 0.7210 |
| 0.0837 | 5.4204 | 2076 | 0.5006 | 0.7158 | 0.5006 | 0.7075 |
| 0.0837 | 5.4256 | 2078 | 0.4814 | 0.6866 | 0.4814 | 0.6939 |
| 0.0837 | 5.4308 | 2080 | 0.4762 | 0.6578 | 0.4762 | 0.6900 |
| 0.0837 | 5.4360 | 2082 | 0.4764 | 0.6578 | 0.4764 | 0.6902 |
| 0.0837 | 5.4413 | 2084 | 0.4754 | 0.6866 | 0.4754 | 0.6895 |
| 0.0837 | 5.4465 | 2086 | 0.4728 | 0.6866 | 0.4728 | 0.6876 |
| 0.0837 | 5.4517 | 2088 | 0.4780 | 0.7158 | 0.4780 | 0.6913 |
| 0.0837 | 5.4569 | 2090 | 0.4827 | 0.7158 | 0.4827 | 0.6947 |
| 0.0837 | 5.4621 | 2092 | 0.4809 | 0.7158 | 0.4809 | 0.6935 |
| 0.0837 | 5.4674 | 2094 | 0.4773 | 0.7158 | 0.4773 | 0.6909 |
| 0.0837 | 5.4726 | 2096 | 0.4798 | 0.7158 | 0.4798 | 0.6927 |
| 0.0837 | 5.4778 | 2098 | 0.4886 | 0.7158 | 0.4886 | 0.6990 |
| 0.0837 | 5.4830 | 2100 | 0.4996 | 0.6182 | 0.4996 | 0.7068 |
| 0.0837 | 5.4883 | 2102 | 0.5179 | 0.4831 | 0.5179 | 0.7196 |
| 0.0837 | 5.4935 | 2104 | 0.5443 | 0.4465 | 0.5443 | 0.7378 |
| 0.0837 | 5.4987 | 2106 | 0.5436 | 0.4465 | 0.5436 | 0.7373 |
| 0.0837 | 5.5039 | 2108 | 0.5418 | 0.4465 | 0.5418 | 0.7360 |
| 0.0837 | 5.5091 | 2110 | 0.5191 | 0.4831 | 0.5191 | 0.7205 |
| 0.0837 | 5.5144 | 2112 | 0.5036 | 0.4831 | 0.5036 | 0.7096 |
| 0.0837 | 5.5196 | 2114 | 0.4996 | 0.4831 | 0.4996 | 0.7068 |
| 0.0837 | 5.5248 | 2116 | 0.4949 | 0.4831 | 0.4949 | 0.7035 |
| 0.0837 | 5.5300 | 2118 | 0.4996 | 0.4831 | 0.4996 | 0.7068 |
| 0.0837 | 5.5352 | 2120 | 0.5115 | 0.4831 | 0.5115 | 0.7152 |
| 0.0837 | 5.5405 | 2122 | 0.5371 | 0.4465 | 0.5371 | 0.7329 |
| 0.0837 | 5.5457 | 2124 | 0.5528 | 0.4465 | 0.5528 | 0.7435 |
| 0.0837 | 5.5509 | 2126 | 0.5588 | 0.4094 | 0.5588 | 0.7475 |
| 0.0837 | 5.5561 | 2128 | 0.5406 | 0.4094 | 0.5406 | 0.7353 |
| 0.0837 | 5.5614 | 2130 | 0.5172 | 0.4465 | 0.5172 | 0.7192 |
| 0.0837 | 5.5666 | 2132 | 0.4944 | 0.4831 | 0.4944 | 0.7031 |
| 0.0837 | 5.5718 | 2134 | 0.4844 | 0.4831 | 0.4844 | 0.6960 |
| 0.0837 | 5.5770 | 2136 | 0.4854 | 0.6182 | 0.4854 | 0.6967 |
| 0.0837 | 5.5822 | 2138 | 0.5008 | 0.6182 | 0.5008 | 0.7076 |
| 0.0837 | 5.5875 | 2140 | 0.5193 | 0.6182 | 0.5193 | 0.7206 |
| 0.0837 | 5.5927 | 2142 | 0.5292 | 0.6182 | 0.5292 | 0.7275 |
| 0.0837 | 5.5979 | 2144 | 0.5261 | 0.6182 | 0.5261 | 0.7253 |
| 0.0837 | 5.6031 | 2146 | 0.5317 | 0.6182 | 0.5317 | 0.7291 |
| 0.0837 | 5.6084 | 2148 | 0.5327 | 0.6182 | 0.5327 | 0.7299 |
| 0.0837 | 5.6136 | 2150 | 0.5393 | 0.4831 | 0.5393 | 0.7344 |
| 0.0837 | 5.6188 | 2152 | 0.5469 | 0.4831 | 0.5469 | 0.7395 |
| 0.0837 | 5.6240 | 2154 | 0.5439 | 0.4831 | 0.5439 | 0.7375 |
| 0.0837 | 5.6292 | 2156 | 0.5505 | 0.4831 | 0.5505 | 0.7420 |
| 0.0837 | 5.6345 | 2158 | 0.5525 | 0.4831 | 0.5525 | 0.7433 |
| 0.0837 | 5.6397 | 2160 | 0.5570 | 0.4831 | 0.5570 | 0.7463 |
| 0.0837 | 5.6449 | 2162 | 0.5543 | 0.4831 | 0.5543 | 0.7445 |
| 0.0837 | 5.6501 | 2164 | 0.5351 | 0.5191 | 0.5351 | 0.7315 |
| 0.0837 | 5.6554 | 2166 | 0.5182 | 0.7158 | 0.5182 | 0.7199 |
| 0.0837 | 5.6606 | 2168 | 0.5136 | 0.7158 | 0.5136 | 0.7166 |
| 0.0837 | 5.6658 | 2170 | 0.5105 | 0.7158 | 0.5105 | 0.7145 |
| 0.0837 | 5.6710 | 2172 | 0.5083 | 0.7158 | 0.5083 | 0.7129 |
| 0.0837 | 5.6762 | 2174 | 0.5041 | 0.7158 | 0.5041 | 0.7100 |
| 0.0837 | 5.6815 | 2176 | 0.5013 | 0.7158 | 0.5013 | 0.7080 |
| 0.0837 | 5.6867 | 2178 | 0.5074 | 0.6818 | 0.5074 | 0.7123 |
| 0.0837 | 5.6919 | 2180 | 0.5210 | 0.4831 | 0.5210 | 0.7218 |
| 0.0837 | 5.6971 | 2182 | 0.5428 | 0.4465 | 0.5428 | 0.7367 |
| 0.0837 | 5.7023 | 2184 | 0.5496 | 0.4457 | 0.5496 | 0.7413 |
| 0.0837 | 5.7076 | 2186 | 0.5506 | 0.4465 | 0.5506 | 0.7420 |
| 0.0837 | 5.7128 | 2188 | 0.5239 | 0.4465 | 0.5239 | 0.7238 |
| 0.0837 | 5.7180 | 2190 | 0.4891 | 0.4831 | 0.4891 | 0.6994 |
| 0.0837 | 5.7232 | 2192 | 0.4602 | 0.4831 | 0.4602 | 0.6784 |
| 0.0837 | 5.7285 | 2194 | 0.4447 | 0.7325 | 0.4447 | 0.6668 |
| 0.0837 | 5.7337 | 2196 | 0.4412 | 0.7513 | 0.4412 | 0.6642 |
| 0.0837 | 5.7389 | 2198 | 0.4451 | 0.7325 | 0.4451 | 0.6671 |
| 0.0837 | 5.7441 | 2200 | 0.4519 | 0.7325 | 0.4519 | 0.6722 |
| 0.0837 | 5.7493 | 2202 | 0.4658 | 0.7158 | 0.4658 | 0.6825 |
| 0.0837 | 5.7546 | 2204 | 0.4824 | 0.6182 | 0.4824 | 0.6946 |
| 0.0837 | 5.7598 | 2206 | 0.5075 | 0.5191 | 0.5075 | 0.7124 |
| 0.0837 | 5.7650 | 2208 | 0.5444 | 0.4831 | 0.5444 | 0.7378 |
| 0.0837 | 5.7702 | 2210 | 0.5601 | 0.4779 | 0.5601 | 0.7484 |
| 0.0837 | 5.7755 | 2212 | 0.5523 | 0.4803 | 0.5523 | 0.7432 |
| 0.0837 | 5.7807 | 2214 | 0.5399 | 0.5191 | 0.5399 | 0.7348 |
| 0.0837 | 5.7859 | 2216 | 0.5338 | 0.6182 | 0.5338 | 0.7306 |
| 0.0837 | 5.7911 | 2218 | 0.5331 | 0.5191 | 0.5331 | 0.7301 |
| 0.0837 | 5.7963 | 2220 | 0.5211 | 0.7158 | 0.5211 | 0.7219 |
| 0.0837 | 5.8016 | 2222 | 0.5135 | 0.6866 | 0.5135 | 0.7166 |
| 0.0837 | 5.8068 | 2224 | 0.5085 | 0.6866 | 0.5085 | 0.7131 |
| 0.0837 | 5.8120 | 2226 | 0.5031 | 0.6866 | 0.5031 | 0.7093 |
| 0.0837 | 5.8172 | 2228 | 0.5013 | 0.6866 | 0.5013 | 0.7080 |
| 0.0837 | 5.8225 | 2230 | 0.5088 | 0.7158 | 0.5088 | 0.7133 |
| 0.0837 | 5.8277 | 2232 | 0.5207 | 0.5191 | 0.5207 | 0.7216 |
| 0.0837 | 5.8329 | 2234 | 0.5211 | 0.5191 | 0.5211 | 0.7219 |
| 0.0837 | 5.8381 | 2236 | 0.5144 | 0.5191 | 0.5144 | 0.7172 |
| 0.0837 | 5.8433 | 2238 | 0.5094 | 0.5191 | 0.5094 | 0.7137 |
| 0.0837 | 5.8486 | 2240 | 0.4995 | 0.5191 | 0.4995 | 0.7067 |
| 0.0837 | 5.8538 | 2242 | 0.4928 | 0.5191 | 0.4928 | 0.7020 |
| 0.0837 | 5.8590 | 2244 | 0.5053 | 0.5191 | 0.5053 | 0.7109 |
| 0.0837 | 5.8642 | 2246 | 0.5234 | 0.5191 | 0.5234 | 0.7234 |
| 0.0837 | 5.8695 | 2248 | 0.5479 | 0.4779 | 0.5479 | 0.7402 |
| 0.0837 | 5.8747 | 2250 | 0.5471 | 0.4779 | 0.5471 | 0.7397 |
| 0.0837 | 5.8799 | 2252 | 0.5262 | 0.4831 | 0.5262 | 0.7254 |
| 0.0837 | 5.8851 | 2254 | 0.4915 | 0.5191 | 0.4915 | 0.7011 |
| 0.0837 | 5.8903 | 2256 | 0.4664 | 0.6182 | 0.4664 | 0.6829 |
| 0.0837 | 5.8956 | 2258 | 0.4484 | 0.7158 | 0.4484 | 0.6696 |
| 0.0837 | 5.9008 | 2260 | 0.4444 | 0.7158 | 0.4444 | 0.6666 |
| 0.0837 | 5.9060 | 2262 | 0.4458 | 0.7158 | 0.4458 | 0.6677 |
| 0.0837 | 5.9112 | 2264 | 0.4505 | 0.7158 | 0.4505 | 0.6712 |
| 0.0837 | 5.9164 | 2266 | 0.4669 | 0.5191 | 0.4669 | 0.6833 |
| 0.0837 | 5.9217 | 2268 | 0.4830 | 0.5191 | 0.4830 | 0.6950 |
| 0.0837 | 5.9269 | 2270 | 0.5084 | 0.4831 | 0.5084 | 0.7130 |
| 0.0837 | 5.9321 | 2272 | 0.5337 | 0.4457 | 0.5337 | 0.7306 |
| 0.0837 | 5.9373 | 2274 | 0.5491 | 0.4727 | 0.5491 | 0.7410 |
| 0.0837 | 5.9426 | 2276 | 0.5498 | 0.4727 | 0.5498 | 0.7415 |
| 0.0837 | 5.9478 | 2278 | 0.5262 | 0.4465 | 0.5262 | 0.7254 |
| 0.0837 | 5.9530 | 2280 | 0.5003 | 0.4465 | 0.5003 | 0.7073 |
| 0.0837 | 5.9582 | 2282 | 0.4771 | 0.4831 | 0.4771 | 0.6907 |
| 0.0837 | 5.9634 | 2284 | 0.4660 | 0.4831 | 0.4660 | 0.6827 |
| 0.0837 | 5.9687 | 2286 | 0.4545 | 0.6182 | 0.4545 | 0.6742 |
| 0.0837 | 5.9739 | 2288 | 0.4571 | 0.6182 | 0.4571 | 0.6761 |
| 0.0837 | 5.9791 | 2290 | 0.4659 | 0.6182 | 0.4659 | 0.6825 |
| 0.0837 | 5.9843 | 2292 | 0.4752 | 0.6182 | 0.4752 | 0.6893 |
| 0.0837 | 5.9896 | 2294 | 0.4805 | 0.5191 | 0.4805 | 0.6932 |
| 0.0837 | 5.9948 | 2296 | 0.4917 | 0.4831 | 0.4917 | 0.7012 |
| 0.0837 | 6.0 | 2298 | 0.5167 | 0.4803 | 0.5167 | 0.7188 |
| 0.0837 | 6.0052 | 2300 | 0.5317 | 0.4803 | 0.5317 | 0.7292 |
| 0.0837 | 6.0104 | 2302 | 0.5529 | 0.4803 | 0.5529 | 0.7436 |
| 0.0837 | 6.0157 | 2304 | 0.5694 | 0.4779 | 0.5694 | 0.7546 |
| 0.0837 | 6.0209 | 2306 | 0.5630 | 0.4779 | 0.5630 | 0.7504 |
| 0.0837 | 6.0261 | 2308 | 0.5448 | 0.4803 | 0.5448 | 0.7381 |
| 0.0837 | 6.0313 | 2310 | 0.5157 | 0.5191 | 0.5157 | 0.7181 |
| 0.0837 | 6.0366 | 2312 | 0.4854 | 0.6182 | 0.4854 | 0.6967 |
| 0.0837 | 6.0418 | 2314 | 0.4735 | 0.6182 | 0.4735 | 0.6881 |
| 0.0837 | 6.0470 | 2316 | 0.4668 | 0.6182 | 0.4668 | 0.6833 |
| 0.0837 | 6.0522 | 2318 | 0.4548 | 0.7158 | 0.4548 | 0.6744 |
| 0.0837 | 6.0574 | 2320 | 0.4511 | 0.7158 | 0.4511 | 0.6717 |
| 0.0837 | 6.0627 | 2322 | 0.4551 | 0.7158 | 0.4551 | 0.6746 |
| 0.0837 | 6.0679 | 2324 | 0.4717 | 0.6182 | 0.4717 | 0.6868 |
| 0.0837 | 6.0731 | 2326 | 0.4901 | 0.5191 | 0.4901 | 0.7001 |
| 0.0837 | 6.0783 | 2328 | 0.4998 | 0.4831 | 0.4998 | 0.7070 |
| 0.0837 | 6.0836 | 2330 | 0.5342 | 0.475 | 0.5342 | 0.7309 |
| 0.0837 | 6.0888 | 2332 | 0.5605 | 0.4727 | 0.5605 | 0.7487 |
| 0.0837 | 6.0940 | 2334 | 0.5793 | 0.4706 | 0.5793 | 0.7611 |
| 0.0837 | 6.0992 | 2336 | 0.5675 | 0.4727 | 0.5675 | 0.7533 |
| 0.0837 | 6.1044 | 2338 | 0.5461 | 0.475 | 0.5461 | 0.7390 |
| 0.0837 | 6.1097 | 2340 | 0.5277 | 0.475 | 0.5277 | 0.7264 |
| 0.0837 | 6.1149 | 2342 | 0.5049 | 0.475 | 0.5049 | 0.7106 |
| 0.0837 | 6.1201 | 2344 | 0.4710 | 0.4831 | 0.4710 | 0.6863 |
| 0.0837 | 6.1253 | 2346 | 0.4438 | 0.6182 | 0.4438 | 0.6662 |
| 0.0837 | 6.1305 | 2348 | 0.4263 | 0.7325 | 0.4263 | 0.6529 |
| 0.0837 | 6.1358 | 2350 | 0.4218 | 0.7014 | 0.4218 | 0.6494 |
| 0.0837 | 6.1410 | 2352 | 0.4207 | 0.7014 | 0.4207 | 0.6486 |
| 0.0837 | 6.1462 | 2354 | 0.4206 | 0.7014 | 0.4206 | 0.6485 |
| 0.0837 | 6.1514 | 2356 | 0.4243 | 0.7014 | 0.4243 | 0.6514 |
| 0.0837 | 6.1567 | 2358 | 0.4386 | 0.7158 | 0.4386 | 0.6623 |
| 0.0837 | 6.1619 | 2360 | 0.4631 | 0.5191 | 0.4631 | 0.6805 |
| 0.0837 | 6.1671 | 2362 | 0.5078 | 0.475 | 0.5078 | 0.7126 |
| 0.0837 | 6.1723 | 2364 | 0.5400 | 0.4727 | 0.5400 | 0.7348 |
| 0.0837 | 6.1775 | 2366 | 0.5438 | 0.4727 | 0.5438 | 0.7374 |
| 0.0837 | 6.1828 | 2368 | 0.5294 | 0.4727 | 0.5294 | 0.7276 |
| 0.0837 | 6.1880 | 2370 | 0.5002 | 0.475 | 0.5002 | 0.7073 |
| 0.0837 | 6.1932 | 2372 | 0.4669 | 0.5191 | 0.4669 | 0.6833 |
| 0.0837 | 6.1984 | 2374 | 0.4415 | 0.7158 | 0.4415 | 0.6644 |
| 0.0837 | 6.2037 | 2376 | 0.4335 | 0.7158 | 0.4335 | 0.6584 |
| 0.0837 | 6.2089 | 2378 | 0.4336 | 0.7325 | 0.4336 | 0.6585 |
| 0.0837 | 6.2141 | 2380 | 0.4379 | 0.7325 | 0.4379 | 0.6617 |
| 0.0837 | 6.2193 | 2382 | 0.4482 | 0.6182 | 0.4482 | 0.6695 |
| 0.0837 | 6.2245 | 2384 | 0.4624 | 0.6182 | 0.4624 | 0.6800 |
| 0.0837 | 6.2298 | 2386 | 0.4663 | 0.6182 | 0.4663 | 0.6829 |
| 0.0837 | 6.2350 | 2388 | 0.4791 | 0.5191 | 0.4791 | 0.6922 |
| 0.0837 | 6.2402 | 2390 | 0.4895 | 0.4465 | 0.4895 | 0.6997 |
| 0.0837 | 6.2454 | 2392 | 0.5087 | 0.4465 | 0.5087 | 0.7132 |
| 0.0837 | 6.2507 | 2394 | 0.5251 | 0.4465 | 0.5251 | 0.7246 |
| 0.0837 | 6.2559 | 2396 | 0.5319 | 0.4465 | 0.5319 | 0.7293 |
| 0.0837 | 6.2611 | 2398 | 0.5349 | 0.4465 | 0.5349 | 0.7313 |
| 0.0837 | 6.2663 | 2400 | 0.5198 | 0.4465 | 0.5198 | 0.7209 |
| 0.0837 | 6.2715 | 2402 | 0.4962 | 0.6182 | 0.4962 | 0.7044 |
| 0.0837 | 6.2768 | 2404 | 0.4793 | 0.6182 | 0.4793 | 0.6923 |
| 0.0837 | 6.2820 | 2406 | 0.4668 | 0.6866 | 0.4668 | 0.6832 |
| 0.0837 | 6.2872 | 2408 | 0.4635 | 0.6866 | 0.4635 | 0.6808 |
| 0.0837 | 6.2924 | 2410 | 0.4625 | 0.6866 | 0.4625 | 0.6801 |
| 0.0837 | 6.2977 | 2412 | 0.4679 | 0.6866 | 0.4679 | 0.6840 |
| 0.0837 | 6.3029 | 2414 | 0.4796 | 0.6182 | 0.4796 | 0.6925 |
| 0.0837 | 6.3081 | 2416 | 0.4983 | 0.5191 | 0.4983 | 0.7059 |
| 0.0837 | 6.3133 | 2418 | 0.5122 | 0.5191 | 0.5122 | 0.7157 |
| 0.0837 | 6.3185 | 2420 | 0.5243 | 0.4831 | 0.5243 | 0.7241 |
| 0.0837 | 6.3238 | 2422 | 0.5272 | 0.4831 | 0.5272 | 0.7261 |
| 0.0837 | 6.3290 | 2424 | 0.5334 | 0.4831 | 0.5334 | 0.7304 |
| 0.0837 | 6.3342 | 2426 | 0.5172 | 0.5191 | 0.5172 | 0.7192 |
| 0.0837 | 6.3394 | 2428 | 0.4885 | 0.5191 | 0.4885 | 0.6989 |
| 0.0837 | 6.3446 | 2430 | 0.4661 | 0.7158 | 0.4661 | 0.6827 |
| 0.0837 | 6.3499 | 2432 | 0.4526 | 0.7158 | 0.4526 | 0.6727 |
| 0.0837 | 6.3551 | 2434 | 0.4470 | 0.7158 | 0.4470 | 0.6686 |
| 0.0837 | 6.3603 | 2436 | 0.4469 | 0.7158 | 0.4469 | 0.6685 |
| 0.0837 | 6.3655 | 2438 | 0.4516 | 0.6182 | 0.4516 | 0.6720 |
| 0.0837 | 6.3708 | 2440 | 0.4597 | 0.5191 | 0.4597 | 0.6780 |
| 0.0837 | 6.3760 | 2442 | 0.4666 | 0.5191 | 0.4666 | 0.6831 |
| 0.0837 | 6.3812 | 2444 | 0.4649 | 0.5191 | 0.4649 | 0.6818 |
| 0.0837 | 6.3864 | 2446 | 0.4600 | 0.5191 | 0.4600 | 0.6782 |
| 0.0837 | 6.3916 | 2448 | 0.4558 | 0.5191 | 0.4558 | 0.6752 |
| 0.0837 | 6.3969 | 2450 | 0.4583 | 0.5191 | 0.4583 | 0.6770 |
| 0.0837 | 6.4021 | 2452 | 0.4660 | 0.5191 | 0.4660 | 0.6826 |
| 0.0837 | 6.4073 | 2454 | 0.4671 | 0.5191 | 0.4671 | 0.6834 |
| 0.0837 | 6.4125 | 2456 | 0.4702 | 0.6182 | 0.4702 | 0.6857 |
| 0.0837 | 6.4178 | 2458 | 0.4803 | 0.6182 | 0.4803 | 0.6930 |
| 0.0837 | 6.4230 | 2460 | 0.4809 | 0.6182 | 0.4809 | 0.6934 |
| 0.0837 | 6.4282 | 2462 | 0.4793 | 0.7158 | 0.4793 | 0.6923 |
| 0.0837 | 6.4334 | 2464 | 0.4737 | 0.7158 | 0.4737 | 0.6882 |
| 0.0837 | 6.4386 | 2466 | 0.4676 | 0.7158 | 0.4676 | 0.6838 |
| 0.0837 | 6.4439 | 2468 | 0.4646 | 0.7158 | 0.4646 | 0.6816 |
| 0.0837 | 6.4491 | 2470 | 0.4710 | 0.7158 | 0.4710 | 0.6863 |
| 0.0837 | 6.4543 | 2472 | 0.4882 | 0.5191 | 0.4882 | 0.6987 |
| 0.0837 | 6.4595 | 2474 | 0.5033 | 0.4831 | 0.5033 | 0.7094 |
| 0.0837 | 6.4648 | 2476 | 0.5241 | 0.4831 | 0.5241 | 0.7239 |
| 0.0837 | 6.4700 | 2478 | 0.5437 | 0.4465 | 0.5437 | 0.7374 |
| 0.0837 | 6.4752 | 2480 | 0.5557 | 0.4457 | 0.5557 | 0.7455 |
| 0.0837 | 6.4804 | 2482 | 0.5580 | 0.4779 | 0.5580 | 0.7470 |
| 0.0837 | 6.4856 | 2484 | 0.5459 | 0.4831 | 0.5459 | 0.7389 |
| 0.0837 | 6.4909 | 2486 | 0.5265 | 0.5191 | 0.5265 | 0.7256 |
| 0.0837 | 6.4961 | 2488 | 0.5050 | 0.5191 | 0.5050 | 0.7107 |
| 0.0837 | 6.5013 | 2490 | 0.4843 | 0.6866 | 0.4843 | 0.6959 |
| 0.0837 | 6.5065 | 2492 | 0.4742 | 0.6866 | 0.4742 | 0.6886 |
| 0.0837 | 6.5117 | 2494 | 0.4712 | 0.6866 | 0.4712 | 0.6865 |
| 0.0837 | 6.5170 | 2496 | 0.4756 | 0.6866 | 0.4756 | 0.6896 |
| 0.0837 | 6.5222 | 2498 | 0.4855 | 0.5191 | 0.4855 | 0.6968 |
| 0.0653 | 6.5274 | 2500 | 0.4954 | 0.5191 | 0.4954 | 0.7038 |
| 0.0653 | 6.5326 | 2502 | 0.5042 | 0.4831 | 0.5042 | 0.7101 |
| 0.0653 | 6.5379 | 2504 | 0.5165 | 0.4776 | 0.5165 | 0.7187 |
| 0.0653 | 6.5431 | 2506 | 0.5333 | 0.4381 | 0.5333 | 0.7303 |
| 0.0653 | 6.5483 | 2508 | 0.5581 | 0.4381 | 0.5581 | 0.7470 |
| 0.0653 | 6.5535 | 2510 | 0.5738 | 0.4381 | 0.5739 | 0.7575 |
| 0.0653 | 6.5587 | 2512 | 0.5734 | 0.4381 | 0.5734 | 0.7572 |
| 0.0653 | 6.5640 | 2514 | 0.5535 | 0.4381 | 0.5535 | 0.7440 |
| 0.0653 | 6.5692 | 2516 | 0.5364 | 0.4381 | 0.5364 | 0.7324 |
| 0.0653 | 6.5744 | 2518 | 0.5286 | 0.4831 | 0.5286 | 0.7270 |
| 0.0653 | 6.5796 | 2520 | 0.5243 | 0.4831 | 0.5243 | 0.7241 |
| 0.0653 | 6.5849 | 2522 | 0.5203 | 0.5191 | 0.5203 | 0.7213 |
| 0.0653 | 6.5901 | 2524 | 0.5148 | 0.5191 | 0.5148 | 0.7175 |
| 0.0653 | 6.5953 | 2526 | 0.5138 | 0.5191 | 0.5138 | 0.7168 |
| 0.0653 | 6.6005 | 2528 | 0.5105 | 0.5191 | 0.5105 | 0.7145 |
| 0.0653 | 6.6057 | 2530 | 0.4967 | 0.5191 | 0.4967 | 0.7048 |
| 0.0653 | 6.6110 | 2532 | 0.4914 | 0.5191 | 0.4914 | 0.7010 |
| 0.0653 | 6.6162 | 2534 | 0.4931 | 0.5191 | 0.4931 | 0.7022 |
| 0.0653 | 6.6214 | 2536 | 0.5017 | 0.5191 | 0.5017 | 0.7083 |
| 0.0653 | 6.6266 | 2538 | 0.5191 | 0.4465 | 0.5191 | 0.7205 |
| 0.0653 | 6.6319 | 2540 | 0.5355 | 0.4465 | 0.5355 | 0.7318 |
| 0.0653 | 6.6371 | 2542 | 0.5490 | 0.4465 | 0.5490 | 0.7410 |
| 0.0653 | 6.6423 | 2544 | 0.5604 | 0.4381 | 0.5604 | 0.7486 |
| 0.0653 | 6.6475 | 2546 | 0.5590 | 0.4381 | 0.5590 | 0.7477 |
| 0.0653 | 6.6527 | 2548 | 0.5394 | 0.4381 | 0.5394 | 0.7344 |
| 0.0653 | 6.6580 | 2550 | 0.5238 | 0.4465 | 0.5238 | 0.7237 |
| 0.0653 | 6.6632 | 2552 | 0.5091 | 0.4465 | 0.5091 | 0.7135 |
| 0.0653 | 6.6684 | 2554 | 0.4956 | 0.4465 | 0.4956 | 0.7040 |
| 0.0653 | 6.6736 | 2556 | 0.4948 | 0.4465 | 0.4948 | 0.7034 |
| 0.0653 | 6.6789 | 2558 | 0.4979 | 0.4465 | 0.4979 | 0.7056 |
| 0.0653 | 6.6841 | 2560 | 0.4937 | 0.4831 | 0.4937 | 0.7026 |
| 0.0653 | 6.6893 | 2562 | 0.4909 | 0.4831 | 0.4909 | 0.7006 |
| 0.0653 | 6.6945 | 2564 | 0.4897 | 0.5191 | 0.4897 | 0.6998 |
| 0.0653 | 6.6997 | 2566 | 0.4950 | 0.4831 | 0.4950 | 0.7036 |
| 0.0653 | 6.7050 | 2568 | 0.5061 | 0.4465 | 0.5061 | 0.7114 |
| 0.0653 | 6.7102 | 2570 | 0.5178 | 0.4465 | 0.5178 | 0.7196 |
| 0.0653 | 6.7154 | 2572 | 0.5331 | 0.4465 | 0.5331 | 0.7302 |
| 0.0653 | 6.7206 | 2574 | 0.5572 | 0.4465 | 0.5572 | 0.7465 |
| 0.0653 | 6.7258 | 2576 | 0.5851 | 0.4465 | 0.5851 | 0.7649 |
| 0.0653 | 6.7311 | 2578 | 0.6054 | 0.4465 | 0.6054 | 0.7781 |
| 0.0653 | 6.7363 | 2580 | 0.6017 | 0.4465 | 0.6017 | 0.7757 |
| 0.0653 | 6.7415 | 2582 | 0.5853 | 0.4465 | 0.5853 | 0.7650 |
| 0.0653 | 6.7467 | 2584 | 0.5647 | 0.4465 | 0.5647 | 0.7515 |
| 0.0653 | 6.7520 | 2586 | 0.5457 | 0.4465 | 0.5457 | 0.7387 |
| 0.0653 | 6.7572 | 2588 | 0.5360 | 0.4465 | 0.5360 | 0.7321 |
| 0.0653 | 6.7624 | 2590 | 0.5399 | 0.4465 | 0.5399 | 0.7348 |
| 0.0653 | 6.7676 | 2592 | 0.5501 | 0.4465 | 0.5501 | 0.7417 |
| 0.0653 | 6.7728 | 2594 | 0.5639 | 0.4465 | 0.5639 | 0.7509 |
| 0.0653 | 6.7781 | 2596 | 0.5625 | 0.4465 | 0.5625 | 0.7500 |
| 0.0653 | 6.7833 | 2598 | 0.5504 | 0.4465 | 0.5504 | 0.7419 |
| 0.0653 | 6.7885 | 2600 | 0.5333 | 0.4465 | 0.5333 | 0.7303 |
| 0.0653 | 6.7937 | 2602 | 0.5112 | 0.5191 | 0.5112 | 0.7150 |
| 0.0653 | 6.7990 | 2604 | 0.4900 | 0.6182 | 0.4900 | 0.7000 |
| 0.0653 | 6.8042 | 2606 | 0.4834 | 0.6182 | 0.4834 | 0.6952 |
| 0.0653 | 6.8094 | 2608 | 0.4918 | 0.6182 | 0.4918 | 0.7013 |
| 0.0653 | 6.8146 | 2610 | 0.5153 | 0.4465 | 0.5153 | 0.7178 |
| 0.0653 | 6.8198 | 2612 | 0.5546 | 0.4465 | 0.5546 | 0.7447 |
| 0.0653 | 6.8251 | 2614 | 0.5906 | 0.4465 | 0.5906 | 0.7685 |
| 0.0653 | 6.8303 | 2616 | 0.6312 | 0.4465 | 0.6312 | 0.7945 |
| 0.0653 | 6.8355 | 2618 | 0.6567 | 0.4375 | 0.6567 | 0.8104 |
| 0.0653 | 6.8407 | 2620 | 0.6556 | 0.4375 | 0.6556 | 0.8097 |
| 0.0653 | 6.8460 | 2622 | 0.6328 | 0.4465 | 0.6328 | 0.7955 |
| 0.0653 | 6.8512 | 2624 | 0.5921 | 0.4465 | 0.5921 | 0.7695 |
| 0.0653 | 6.8564 | 2626 | 0.5456 | 0.4465 | 0.5456 | 0.7386 |
| 0.0653 | 6.8616 | 2628 | 0.5242 | 0.4465 | 0.5242 | 0.7240 |
| 0.0653 | 6.8668 | 2630 | 0.5128 | 0.4831 | 0.5128 | 0.7161 |
| 0.0653 | 6.8721 | 2632 | 0.5078 | 0.5191 | 0.5078 | 0.7126 |
| 0.0653 | 6.8773 | 2634 | 0.5020 | 0.6182 | 0.5020 | 0.7085 |
| 0.0653 | 6.8825 | 2636 | 0.5100 | 0.5191 | 0.5100 | 0.7141 |
| 0.0653 | 6.8877 | 2638 | 0.5130 | 0.5191 | 0.5130 | 0.7162 |
| 0.0653 | 6.8930 | 2640 | 0.5207 | 0.5191 | 0.5207 | 0.7216 |
| 0.0653 | 6.8982 | 2642 | 0.5234 | 0.5191 | 0.5234 | 0.7234 |
| 0.0653 | 6.9034 | 2644 | 0.5320 | 0.5191 | 0.5320 | 0.7294 |
| 0.0653 | 6.9086 | 2646 | 0.5469 | 0.5191 | 0.5469 | 0.7395 |
| 0.0653 | 6.9138 | 2648 | 0.5545 | 0.5191 | 0.5545 | 0.7446 |
| 0.0653 | 6.9191 | 2650 | 0.5677 | 0.5191 | 0.5677 | 0.7535 |
| 0.0653 | 6.9243 | 2652 | 0.5826 | 0.4831 | 0.5826 | 0.7633 |
| 0.0653 | 6.9295 | 2654 | 0.6045 | 0.4831 | 0.6045 | 0.7775 |
| 0.0653 | 6.9347 | 2656 | 0.6189 | 0.4803 | 0.6189 | 0.7867 |
| 0.0653 | 6.9399 | 2658 | 0.6108 | 0.4831 | 0.6108 | 0.7816 |
| 0.0653 | 6.9452 | 2660 | 0.5861 | 0.4831 | 0.5861 | 0.7656 |
| 0.0653 | 6.9504 | 2662 | 0.5513 | 0.4831 | 0.5513 | 0.7425 |
| 0.0653 | 6.9556 | 2664 | 0.5178 | 0.5191 | 0.5178 | 0.7196 |
| 0.0653 | 6.9608 | 2666 | 0.4922 | 0.4944 | 0.4922 | 0.7016 |
| 0.0653 | 6.9661 | 2668 | 0.4770 | 0.5987 | 0.4770 | 0.6906 |
| 0.0653 | 6.9713 | 2670 | 0.4665 | 0.5987 | 0.4665 | 0.6830 |
| 0.0653 | 6.9765 | 2672 | 0.4604 | 0.5987 | 0.4604 | 0.6785 |
| 0.0653 | 6.9817 | 2674 | 0.4585 | 0.7014 | 0.4585 | 0.6771 |
| 0.0653 | 6.9869 | 2676 | 0.4593 | 0.7014 | 0.4593 | 0.6777 |
| 0.0653 | 6.9922 | 2678 | 0.4637 | 0.7014 | 0.4637 | 0.6810 |
| 0.0653 | 6.9974 | 2680 | 0.4702 | 0.5987 | 0.4702 | 0.6857 |
| 0.0653 | 7.0026 | 2682 | 0.4767 | 0.5895 | 0.4767 | 0.6905 |
| 0.0653 | 7.0078 | 2684 | 0.4922 | 0.5895 | 0.4922 | 0.7015 |
| 0.0653 | 7.0131 | 2686 | 0.5064 | 0.5191 | 0.5064 | 0.7116 |
| 0.0653 | 7.0183 | 2688 | 0.5092 | 0.5191 | 0.5092 | 0.7136 |
| 0.0653 | 7.0235 | 2690 | 0.5124 | 0.5191 | 0.5124 | 0.7158 |
| 0.0653 | 7.0287 | 2692 | 0.5191 | 0.5191 | 0.5191 | 0.7205 |
| 0.0653 | 7.0339 | 2694 | 0.5132 | 0.5191 | 0.5132 | 0.7164 |
| 0.0653 | 7.0392 | 2696 | 0.5028 | 0.5895 | 0.5028 | 0.7091 |
| 0.0653 | 7.0444 | 2698 | 0.5027 | 0.5895 | 0.5027 | 0.7090 |
| 0.0653 | 7.0496 | 2700 | 0.5036 | 0.5895 | 0.5036 | 0.7097 |
| 0.0653 | 7.0548 | 2702 | 0.5060 | 0.5895 | 0.5060 | 0.7113 |
| 0.0653 | 7.0601 | 2704 | 0.5072 | 0.5895 | 0.5072 | 0.7122 |
| 0.0653 | 7.0653 | 2706 | 0.5104 | 0.5895 | 0.5104 | 0.7144 |
| 0.0653 | 7.0705 | 2708 | 0.5088 | 0.5895 | 0.5088 | 0.7133 |
| 0.0653 | 7.0757 | 2710 | 0.5057 | 0.5895 | 0.5057 | 0.7112 |
| 0.0653 | 7.0809 | 2712 | 0.5047 | 0.5191 | 0.5047 | 0.7105 |
| 0.0653 | 7.0862 | 2714 | 0.5077 | 0.5191 | 0.5077 | 0.7126 |
| 0.0653 | 7.0914 | 2716 | 0.5208 | 0.4831 | 0.5208 | 0.7217 |
| 0.0653 | 7.0966 | 2718 | 0.5454 | 0.4465 | 0.5454 | 0.7385 |
| 0.0653 | 7.1018 | 2720 | 0.5637 | 0.4465 | 0.5637 | 0.7508 |
| 0.0653 | 7.1070 | 2722 | 0.5675 | 0.4465 | 0.5675 | 0.7533 |
| 0.0653 | 7.1123 | 2724 | 0.5557 | 0.4465 | 0.5557 | 0.7455 |
| 0.0653 | 7.1175 | 2726 | 0.5456 | 0.4831 | 0.5456 | 0.7386 |
| 0.0653 | 7.1227 | 2728 | 0.5342 | 0.4831 | 0.5342 | 0.7309 |
| 0.0653 | 7.1279 | 2730 | 0.5259 | 0.4831 | 0.5259 | 0.7252 |
| 0.0653 | 7.1332 | 2732 | 0.5143 | 0.5191 | 0.5143 | 0.7171 |
| 0.0653 | 7.1384 | 2734 | 0.5147 | 0.5191 | 0.5147 | 0.7174 |
| 0.0653 | 7.1436 | 2736 | 0.5137 | 0.5191 | 0.5137 | 0.7167 |
| 0.0653 | 7.1488 | 2738 | 0.5109 | 0.5191 | 0.5109 | 0.7147 |
| 0.0653 | 7.1540 | 2740 | 0.5034 | 0.5191 | 0.5034 | 0.7095 |
| 0.0653 | 7.1593 | 2742 | 0.5012 | 0.5191 | 0.5012 | 0.7079 |
| 0.0653 | 7.1645 | 2744 | 0.4951 | 0.5191 | 0.4951 | 0.7036 |
| 0.0653 | 7.1697 | 2746 | 0.4987 | 0.5191 | 0.4987 | 0.7062 |
| 0.0653 | 7.1749 | 2748 | 0.5004 | 0.5191 | 0.5004 | 0.7074 |
| 0.0653 | 7.1802 | 2750 | 0.5028 | 0.4831 | 0.5028 | 0.7091 |
| 0.0653 | 7.1854 | 2752 | 0.5016 | 0.4831 | 0.5016 | 0.7082 |
| 0.0653 | 7.1906 | 2754 | 0.5030 | 0.5191 | 0.5030 | 0.7092 |
| 0.0653 | 7.1958 | 2756 | 0.5031 | 0.5191 | 0.5031 | 0.7093 |
| 0.0653 | 7.2010 | 2758 | 0.5100 | 0.4831 | 0.5100 | 0.7142 |
| 0.0653 | 7.2063 | 2760 | 0.5150 | 0.4831 | 0.5150 | 0.7177 |
| 0.0653 | 7.2115 | 2762 | 0.5209 | 0.4831 | 0.5209 | 0.7218 |
| 0.0653 | 7.2167 | 2764 | 0.5215 | 0.4831 | 0.5215 | 0.7222 |
| 0.0653 | 7.2219 | 2766 | 0.5312 | 0.4465 | 0.5312 | 0.7289 |
| 0.0653 | 7.2272 | 2768 | 0.5343 | 0.4465 | 0.5343 | 0.7309 |
| 0.0653 | 7.2324 | 2770 | 0.5294 | 0.4831 | 0.5294 | 0.7276 |
| 0.0653 | 7.2376 | 2772 | 0.5193 | 0.4831 | 0.5193 | 0.7206 |
| 0.0653 | 7.2428 | 2774 | 0.5056 | 0.5191 | 0.5056 | 0.7110 |
| 0.0653 | 7.2480 | 2776 | 0.4970 | 0.5191 | 0.4970 | 0.7050 |
| 0.0653 | 7.2533 | 2778 | 0.4914 | 0.5191 | 0.4914 | 0.7010 |
| 0.0653 | 7.2585 | 2780 | 0.4883 | 0.5191 | 0.4883 | 0.6988 |
| 0.0653 | 7.2637 | 2782 | 0.4873 | 0.5191 | 0.4873 | 0.6981 |
| 0.0653 | 7.2689 | 2784 | 0.4832 | 0.5191 | 0.4832 | 0.6951 |
| 0.0653 | 7.2742 | 2786 | 0.4852 | 0.5191 | 0.4852 | 0.6965 |
| 0.0653 | 7.2794 | 2788 | 0.4941 | 0.4831 | 0.4941 | 0.7029 |
| 0.0653 | 7.2846 | 2790 | 0.5021 | 0.4831 | 0.5021 | 0.7086 |
| 0.0653 | 7.2898 | 2792 | 0.5061 | 0.4831 | 0.5061 | 0.7114 |
| 0.0653 | 7.2950 | 2794 | 0.5072 | 0.4831 | 0.5072 | 0.7122 |
| 0.0653 | 7.3003 | 2796 | 0.5036 | 0.4831 | 0.5036 | 0.7097 |
| 0.0653 | 7.3055 | 2798 | 0.5022 | 0.4831 | 0.5022 | 0.7087 |
| 0.0653 | 7.3107 | 2800 | 0.5034 | 0.4831 | 0.5034 | 0.7095 |
| 0.0653 | 7.3159 | 2802 | 0.5134 | 0.4831 | 0.5134 | 0.7165 |
| 0.0653 | 7.3211 | 2804 | 0.5134 | 0.4831 | 0.5134 | 0.7165 |
| 0.0653 | 7.3264 | 2806 | 0.5040 | 0.4831 | 0.5040 | 0.7100 |
| 0.0653 | 7.3316 | 2808 | 0.4929 | 0.4831 | 0.4929 | 0.7021 |
| 0.0653 | 7.3368 | 2810 | 0.4871 | 0.5191 | 0.4871 | 0.6979 |
| 0.0653 | 7.3420 | 2812 | 0.4845 | 0.5191 | 0.4845 | 0.6961 |
| 0.0653 | 7.3473 | 2814 | 0.4844 | 0.5191 | 0.4844 | 0.6960 |
| 0.0653 | 7.3525 | 2816 | 0.4913 | 0.5191 | 0.4913 | 0.7009 |
| 0.0653 | 7.3577 | 2818 | 0.5055 | 0.5191 | 0.5055 | 0.7110 |
| 0.0653 | 7.3629 | 2820 | 0.5124 | 0.5191 | 0.5124 | 0.7158 |
| 0.0653 | 7.3681 | 2822 | 0.5130 | 0.5191 | 0.5130 | 0.7163 |
| 0.0653 | 7.3734 | 2824 | 0.5154 | 0.5191 | 0.5154 | 0.7179 |
| 0.0653 | 7.3786 | 2826 | 0.5229 | 0.4831 | 0.5229 | 0.7231 |
| 0.0653 | 7.3838 | 2828 | 0.5294 | 0.4831 | 0.5294 | 0.7276 |
| 0.0653 | 7.3890 | 2830 | 0.5357 | 0.4831 | 0.5357 | 0.7319 |
| 0.0653 | 7.3943 | 2832 | 0.5301 | 0.5191 | 0.5301 | 0.7281 |
| 0.0653 | 7.3995 | 2834 | 0.5318 | 0.5191 | 0.5318 | 0.7292 |
| 0.0653 | 7.4047 | 2836 | 0.5284 | 0.5191 | 0.5284 | 0.7269 |
| 0.0653 | 7.4099 | 2838 | 0.5276 | 0.5191 | 0.5276 | 0.7264 |
| 0.0653 | 7.4151 | 2840 | 0.5355 | 0.5191 | 0.5355 | 0.7318 |
| 0.0653 | 7.4204 | 2842 | 0.5462 | 0.4831 | 0.5462 | 0.7391 |
| 0.0653 | 7.4256 | 2844 | 0.5452 | 0.4831 | 0.5452 | 0.7384 |
| 0.0653 | 7.4308 | 2846 | 0.5302 | 0.5191 | 0.5302 | 0.7282 |
| 0.0653 | 7.4360 | 2848 | 0.5120 | 0.6182 | 0.5120 | 0.7156 |
| 0.0653 | 7.4413 | 2850 | 0.4992 | 0.6182 | 0.4992 | 0.7065 |
| 0.0653 | 7.4465 | 2852 | 0.4986 | 0.7158 | 0.4986 | 0.7061 |
| 0.0653 | 7.4517 | 2854 | 0.5039 | 0.6182 | 0.5039 | 0.7099 |
| 0.0653 | 7.4569 | 2856 | 0.5103 | 0.6182 | 0.5103 | 0.7143 |
| 0.0653 | 7.4621 | 2858 | 0.5132 | 0.6182 | 0.5132 | 0.7164 |
| 0.0653 | 7.4674 | 2860 | 0.5224 | 0.6182 | 0.5224 | 0.7227 |
| 0.0653 | 7.4726 | 2862 | 0.5249 | 0.6182 | 0.5249 | 0.7245 |
| 0.0653 | 7.4778 | 2864 | 0.5207 | 0.6182 | 0.5207 | 0.7216 |
| 0.0653 | 7.4830 | 2866 | 0.5095 | 0.6182 | 0.5095 | 0.7138 |
| 0.0653 | 7.4883 | 2868 | 0.4996 | 0.6182 | 0.4996 | 0.7069 |
| 0.0653 | 7.4935 | 2870 | 0.4906 | 0.6182 | 0.4906 | 0.7004 |
| 0.0653 | 7.4987 | 2872 | 0.4850 | 0.7158 | 0.4850 | 0.6964 |
| 0.0653 | 7.5039 | 2874 | 0.4860 | 0.6182 | 0.4860 | 0.6971 |
| 0.0653 | 7.5091 | 2876 | 0.4877 | 0.6182 | 0.4877 | 0.6984 |
| 0.0653 | 7.5144 | 2878 | 0.4896 | 0.6182 | 0.4896 | 0.6997 |
| 0.0653 | 7.5196 | 2880 | 0.4990 | 0.6182 | 0.4990 | 0.7064 |
| 0.0653 | 7.5248 | 2882 | 0.5043 | 0.6182 | 0.5043 | 0.7102 |
| 0.0653 | 7.5300 | 2884 | 0.5080 | 0.6182 | 0.5080 | 0.7127 |
| 0.0653 | 7.5352 | 2886 | 0.5200 | 0.6182 | 0.5200 | 0.7211 |
| 0.0653 | 7.5405 | 2888 | 0.5299 | 0.5191 | 0.5299 | 0.7279 |
| 0.0653 | 7.5457 | 2890 | 0.5421 | 0.5191 | 0.5421 | 0.7363 |
| 0.0653 | 7.5509 | 2892 | 0.5559 | 0.5191 | 0.5559 | 0.7456 |
| 0.0653 | 7.5561 | 2894 | 0.5616 | 0.5191 | 0.5616 | 0.7494 |
| 0.0653 | 7.5614 | 2896 | 0.5671 | 0.5191 | 0.5671 | 0.7531 |
| 0.0653 | 7.5666 | 2898 | 0.5619 | 0.5191 | 0.5619 | 0.7496 |
| 0.0653 | 7.5718 | 2900 | 0.5451 | 0.5191 | 0.5451 | 0.7383 |
| 0.0653 | 7.5770 | 2902 | 0.5237 | 0.5191 | 0.5237 | 0.7236 |
| 0.0653 | 7.5822 | 2904 | 0.5076 | 0.5191 | 0.5076 | 0.7124 |
| 0.0653 | 7.5875 | 2906 | 0.4995 | 0.5191 | 0.4995 | 0.7068 |
| 0.0653 | 7.5927 | 2908 | 0.4963 | 0.5191 | 0.4963 | 0.7045 |
| 0.0653 | 7.5979 | 2910 | 0.4975 | 0.5191 | 0.4975 | 0.7053 |
| 0.0653 | 7.6031 | 2912 | 0.5063 | 0.5191 | 0.5063 | 0.7116 |
| 0.0653 | 7.6084 | 2914 | 0.5241 | 0.5191 | 0.5241 | 0.7239 |
| 0.0653 | 7.6136 | 2916 | 0.5357 | 0.5191 | 0.5357 | 0.7319 |
| 0.0653 | 7.6188 | 2918 | 0.5455 | 0.5191 | 0.5455 | 0.7386 |
| 0.0653 | 7.6240 | 2920 | 0.5577 | 0.5191 | 0.5577 | 0.7468 |
| 0.0653 | 7.6292 | 2922 | 0.5557 | 0.5191 | 0.5557 | 0.7454 |
| 0.0653 | 7.6345 | 2924 | 0.5433 | 0.5191 | 0.5433 | 0.7371 |
| 0.0653 | 7.6397 | 2926 | 0.5315 | 0.5191 | 0.5315 | 0.7291 |
| 0.0653 | 7.6449 | 2928 | 0.5151 | 0.5191 | 0.5151 | 0.7177 |
| 0.0653 | 7.6501 | 2930 | 0.4990 | 0.5191 | 0.4990 | 0.7064 |
| 0.0653 | 7.6554 | 2932 | 0.4912 | 0.6182 | 0.4912 | 0.7008 |
| 0.0653 | 7.6606 | 2934 | 0.4881 | 0.6182 | 0.4881 | 0.6987 |
| 0.0653 | 7.6658 | 2936 | 0.4860 | 0.7158 | 0.4860 | 0.6971 |
| 0.0653 | 7.6710 | 2938 | 0.4849 | 0.7158 | 0.4849 | 0.6963 |
| 0.0653 | 7.6762 | 2940 | 0.4846 | 0.7158 | 0.4846 | 0.6961 |
| 0.0653 | 7.6815 | 2942 | 0.4900 | 0.6182 | 0.4900 | 0.7000 |
| 0.0653 | 7.6867 | 2944 | 0.4958 | 0.5191 | 0.4958 | 0.7041 |
| 0.0653 | 7.6919 | 2946 | 0.5054 | 0.5191 | 0.5054 | 0.7109 |
| 0.0653 | 7.6971 | 2948 | 0.5190 | 0.5191 | 0.5190 | 0.7204 |
| 0.0653 | 7.7023 | 2950 | 0.5315 | 0.5191 | 0.5315 | 0.7291 |
| 0.0653 | 7.7076 | 2952 | 0.5356 | 0.5191 | 0.5356 | 0.7318 |
| 0.0653 | 7.7128 | 2954 | 0.5360 | 0.5191 | 0.5360 | 0.7321 |
| 0.0653 | 7.7180 | 2956 | 0.5441 | 0.5191 | 0.5441 | 0.7376 |
| 0.0653 | 7.7232 | 2958 | 0.5520 | 0.4831 | 0.5520 | 0.7430 |
| 0.0653 | 7.7285 | 2960 | 0.5574 | 0.4831 | 0.5574 | 0.7466 |
| 0.0653 | 7.7337 | 2962 | 0.5592 | 0.4831 | 0.5592 | 0.7478 |
| 0.0653 | 7.7389 | 2964 | 0.5624 | 0.4831 | 0.5624 | 0.7499 |
| 0.0653 | 7.7441 | 2966 | 0.5623 | 0.4831 | 0.5623 | 0.7499 |
| 0.0653 | 7.7493 | 2968 | 0.5560 | 0.5191 | 0.5560 | 0.7457 |
| 0.0653 | 7.7546 | 2970 | 0.5494 | 0.5191 | 0.5494 | 0.7412 |
| 0.0653 | 7.7598 | 2972 | 0.5440 | 0.5191 | 0.5440 | 0.7376 |
| 0.0653 | 7.7650 | 2974 | 0.5422 | 0.5191 | 0.5422 | 0.7364 |
| 0.0653 | 7.7702 | 2976 | 0.5397 | 0.5191 | 0.5397 | 0.7347 |
| 0.0653 | 7.7755 | 2978 | 0.5404 | 0.5191 | 0.5404 | 0.7351 |
| 0.0653 | 7.7807 | 2980 | 0.5367 | 0.5191 | 0.5367 | 0.7326 |
| 0.0653 | 7.7859 | 2982 | 0.5406 | 0.5191 | 0.5406 | 0.7353 |
| 0.0653 | 7.7911 | 2984 | 0.5370 | 0.5191 | 0.5370 | 0.7328 |
| 0.0653 | 7.7963 | 2986 | 0.5285 | 0.5191 | 0.5285 | 0.7270 |
| 0.0653 | 7.8016 | 2988 | 0.5190 | 0.5191 | 0.5190 | 0.7204 |
| 0.0653 | 7.8068 | 2990 | 0.5040 | 0.5191 | 0.5040 | 0.7099 |
| 0.0653 | 7.8120 | 2992 | 0.4904 | 0.5243 | 0.4904 | 0.7003 |
| 0.0653 | 7.8172 | 2994 | 0.4855 | 0.5243 | 0.4855 | 0.6968 |
| 0.0653 | 7.8225 | 2996 | 0.4880 | 0.5243 | 0.4880 | 0.6986 |
| 0.0653 | 7.8277 | 2998 | 0.4943 | 0.5243 | 0.4943 | 0.7030 |
| 0.0569 | 7.8329 | 3000 | 0.4977 | 0.5243 | 0.4977 | 0.7055 |
| 0.0569 | 7.8381 | 3002 | 0.5068 | 0.5191 | 0.5068 | 0.7119 |
| 0.0569 | 7.8433 | 3004 | 0.5190 | 0.5191 | 0.5190 | 0.7204 |
| 0.0569 | 7.8486 | 3006 | 0.5291 | 0.5191 | 0.5291 | 0.7274 |
| 0.0569 | 7.8538 | 3008 | 0.5416 | 0.4831 | 0.5416 | 0.7359 |
| 0.0569 | 7.8590 | 3010 | 0.5552 | 0.4465 | 0.5552 | 0.7451 |
| 0.0569 | 7.8642 | 3012 | 0.5613 | 0.4465 | 0.5613 | 0.7492 |
| 0.0569 | 7.8695 | 3014 | 0.5572 | 0.4465 | 0.5572 | 0.7464 |
| 0.0569 | 7.8747 | 3016 | 0.5537 | 0.4831 | 0.5537 | 0.7441 |
| 0.0569 | 7.8799 | 3018 | 0.5434 | 0.5191 | 0.5434 | 0.7372 |
| 0.0569 | 7.8851 | 3020 | 0.5345 | 0.5191 | 0.5345 | 0.7311 |
| 0.0569 | 7.8903 | 3022 | 0.5317 | 0.5191 | 0.5317 | 0.7292 |
| 0.0569 | 7.8956 | 3024 | 0.5355 | 0.5191 | 0.5355 | 0.7318 |
| 0.0569 | 7.9008 | 3026 | 0.5374 | 0.5191 | 0.5374 | 0.7331 |
| 0.0569 | 7.9060 | 3028 | 0.5448 | 0.5191 | 0.5448 | 0.7381 |
| 0.0569 | 7.9112 | 3030 | 0.5536 | 0.4831 | 0.5536 | 0.7440 |
| 0.0569 | 7.9164 | 3032 | 0.5626 | 0.4465 | 0.5626 | 0.7501 |
| 0.0569 | 7.9217 | 3034 | 0.5646 | 0.4465 | 0.5646 | 0.7514 |
| 0.0569 | 7.9269 | 3036 | 0.5622 | 0.4465 | 0.5622 | 0.7498 |
| 0.0569 | 7.9321 | 3038 | 0.5576 | 0.4465 | 0.5576 | 0.7467 |
| 0.0569 | 7.9373 | 3040 | 0.5529 | 0.4465 | 0.5529 | 0.7436 |
| 0.0569 | 7.9426 | 3042 | 0.5506 | 0.4474 | 0.5506 | 0.7420 |
| 0.0569 | 7.9478 | 3044 | 0.5512 | 0.4474 | 0.5512 | 0.7424 |
| 0.0569 | 7.9530 | 3046 | 0.5564 | 0.4465 | 0.5564 | 0.7459 |
| 0.0569 | 7.9582 | 3048 | 0.5620 | 0.4465 | 0.5620 | 0.7497 |
| 0.0569 | 7.9634 | 3050 | 0.5633 | 0.4465 | 0.5633 | 0.7505 |
| 0.0569 | 7.9687 | 3052 | 0.5566 | 0.4465 | 0.5566 | 0.7461 |
| 0.0569 | 7.9739 | 3054 | 0.5504 | 0.5191 | 0.5504 | 0.7419 |
| 0.0569 | 7.9791 | 3056 | 0.5458 | 0.5191 | 0.5458 | 0.7388 |
| 0.0569 | 7.9843 | 3058 | 0.5450 | 0.5191 | 0.5450 | 0.7382 |
| 0.0569 | 7.9896 | 3060 | 0.5470 | 0.5191 | 0.5470 | 0.7396 |
| 0.0569 | 7.9948 | 3062 | 0.5505 | 0.5191 | 0.5505 | 0.7420 |
| 0.0569 | 8.0 | 3064 | 0.5606 | 0.4465 | 0.5606 | 0.7488 |
| 0.0569 | 8.0052 | 3066 | 0.5695 | 0.4465 | 0.5695 | 0.7546 |
| 0.0569 | 8.0104 | 3068 | 0.5789 | 0.4465 | 0.5789 | 0.7609 |
| 0.0569 | 8.0157 | 3070 | 0.5752 | 0.4465 | 0.5752 | 0.7584 |
| 0.0569 | 8.0209 | 3072 | 0.5760 | 0.4465 | 0.5760 | 0.7589 |
| 0.0569 | 8.0261 | 3074 | 0.5787 | 0.4465 | 0.5787 | 0.7607 |
| 0.0569 | 8.0313 | 3076 | 0.5755 | 0.4465 | 0.5755 | 0.7586 |
| 0.0569 | 8.0366 | 3078 | 0.5716 | 0.4465 | 0.5716 | 0.7560 |
| 0.0569 | 8.0418 | 3080 | 0.5666 | 0.4465 | 0.5666 | 0.7527 |
| 0.0569 | 8.0470 | 3082 | 0.5596 | 0.4465 | 0.5596 | 0.7481 |
| 0.0569 | 8.0522 | 3084 | 0.5580 | 0.4465 | 0.5580 | 0.7470 |
| 0.0569 | 8.0574 | 3086 | 0.5617 | 0.4465 | 0.5617 | 0.7495 |
| 0.0569 | 8.0627 | 3088 | 0.5572 | 0.4465 | 0.5572 | 0.7464 |
| 0.0569 | 8.0679 | 3090 | 0.5492 | 0.5191 | 0.5492 | 0.7411 |
| 0.0569 | 8.0731 | 3092 | 0.5388 | 0.5191 | 0.5388 | 0.7340 |
| 0.0569 | 8.0783 | 3094 | 0.5304 | 0.5191 | 0.5304 | 0.7283 |
| 0.0569 | 8.0836 | 3096 | 0.5288 | 0.5191 | 0.5288 | 0.7272 |
| 0.0569 | 8.0888 | 3098 | 0.5344 | 0.5191 | 0.5344 | 0.7310 |
| 0.0569 | 8.0940 | 3100 | 0.5388 | 0.5191 | 0.5388 | 0.7340 |
| 0.0569 | 8.0992 | 3102 | 0.5497 | 0.5191 | 0.5497 | 0.7414 |
| 0.0569 | 8.1044 | 3104 | 0.5650 | 0.5191 | 0.5650 | 0.7516 |
| 0.0569 | 8.1097 | 3106 | 0.5778 | 0.4831 | 0.5778 | 0.7601 |
| 0.0569 | 8.1149 | 3108 | 0.5818 | 0.4831 | 0.5818 | 0.7627 |
| 0.0569 | 8.1201 | 3110 | 0.5767 | 0.4831 | 0.5767 | 0.7594 |
| 0.0569 | 8.1253 | 3112 | 0.5695 | 0.4831 | 0.5695 | 0.7547 |
| 0.0569 | 8.1305 | 3114 | 0.5591 | 0.4831 | 0.5591 | 0.7478 |
| 0.0569 | 8.1358 | 3116 | 0.5490 | 0.5191 | 0.5490 | 0.7409 |
| 0.0569 | 8.1410 | 3118 | 0.5381 | 0.5191 | 0.5381 | 0.7336 |
| 0.0569 | 8.1462 | 3120 | 0.5307 | 0.5191 | 0.5307 | 0.7285 |
| 0.0569 | 8.1514 | 3122 | 0.5291 | 0.5191 | 0.5291 | 0.7274 |
| 0.0569 | 8.1567 | 3124 | 0.5321 | 0.5191 | 0.5321 | 0.7295 |
| 0.0569 | 8.1619 | 3126 | 0.5365 | 0.5191 | 0.5365 | 0.7325 |
| 0.0569 | 8.1671 | 3128 | 0.5421 | 0.5191 | 0.5421 | 0.7363 |
| 0.0569 | 8.1723 | 3130 | 0.5477 | 0.5191 | 0.5477 | 0.7401 |
| 0.0569 | 8.1775 | 3132 | 0.5535 | 0.5191 | 0.5535 | 0.7439 |
| 0.0569 | 8.1828 | 3134 | 0.5546 | 0.5191 | 0.5546 | 0.7447 |
| 0.0569 | 8.1880 | 3136 | 0.5550 | 0.5191 | 0.5550 | 0.7450 |
| 0.0569 | 8.1932 | 3138 | 0.5493 | 0.5191 | 0.5493 | 0.7412 |
| 0.0569 | 8.1984 | 3140 | 0.5395 | 0.5191 | 0.5395 | 0.7345 |
| 0.0569 | 8.2037 | 3142 | 0.5279 | 0.5191 | 0.5279 | 0.7266 |
| 0.0569 | 8.2089 | 3144 | 0.5185 | 0.5191 | 0.5185 | 0.7201 |
| 0.0569 | 8.2141 | 3146 | 0.5141 | 0.5191 | 0.5141 | 0.7170 |
| 0.0569 | 8.2193 | 3148 | 0.5078 | 0.5191 | 0.5078 | 0.7126 |
| 0.0569 | 8.2245 | 3150 | 0.5035 | 0.5191 | 0.5035 | 0.7096 |
| 0.0569 | 8.2298 | 3152 | 0.4978 | 0.5191 | 0.4978 | 0.7055 |
| 0.0569 | 8.2350 | 3154 | 0.4965 | 0.5191 | 0.4965 | 0.7046 |
| 0.0569 | 8.2402 | 3156 | 0.4977 | 0.5191 | 0.4977 | 0.7055 |
| 0.0569 | 8.2454 | 3158 | 0.5001 | 0.5191 | 0.5001 | 0.7072 |
| 0.0569 | 8.2507 | 3160 | 0.5048 | 0.5191 | 0.5048 | 0.7105 |
| 0.0569 | 8.2559 | 3162 | 0.5132 | 0.5191 | 0.5132 | 0.7164 |
| 0.0569 | 8.2611 | 3164 | 0.5201 | 0.5191 | 0.5201 | 0.7212 |
| 0.0569 | 8.2663 | 3166 | 0.5259 | 0.5191 | 0.5259 | 0.7252 |
| 0.0569 | 8.2715 | 3168 | 0.5254 | 0.5191 | 0.5254 | 0.7248 |
| 0.0569 | 8.2768 | 3170 | 0.5217 | 0.5191 | 0.5217 | 0.7223 |
| 0.0569 | 8.2820 | 3172 | 0.5173 | 0.5191 | 0.5173 | 0.7192 |
| 0.0569 | 8.2872 | 3174 | 0.5066 | 0.5191 | 0.5066 | 0.7118 |
| 0.0569 | 8.2924 | 3176 | 0.4937 | 0.5191 | 0.4937 | 0.7026 |
| 0.0569 | 8.2977 | 3178 | 0.4863 | 0.6182 | 0.4863 | 0.6974 |
| 0.0569 | 8.3029 | 3180 | 0.4812 | 0.6182 | 0.4812 | 0.6937 |
| 0.0569 | 8.3081 | 3182 | 0.4800 | 0.6182 | 0.4800 | 0.6928 |
| 0.0569 | 8.3133 | 3184 | 0.4791 | 0.5191 | 0.4791 | 0.6922 |
| 0.0569 | 8.3185 | 3186 | 0.4818 | 0.5191 | 0.4818 | 0.6941 |
| 0.0569 | 8.3238 | 3188 | 0.4869 | 0.5191 | 0.4869 | 0.6978 |
| 0.0569 | 8.3290 | 3190 | 0.4934 | 0.5191 | 0.4934 | 0.7024 |
| 0.0569 | 8.3342 | 3192 | 0.4979 | 0.5191 | 0.4979 | 0.7056 |
| 0.0569 | 8.3394 | 3194 | 0.5046 | 0.5191 | 0.5046 | 0.7103 |
| 0.0569 | 8.3446 | 3196 | 0.5078 | 0.5191 | 0.5078 | 0.7126 |
| 0.0569 | 8.3499 | 3198 | 0.5132 | 0.5191 | 0.5132 | 0.7164 |
| 0.0569 | 8.3551 | 3200 | 0.5215 | 0.5191 | 0.5215 | 0.7222 |
| 0.0569 | 8.3603 | 3202 | 0.5270 | 0.5191 | 0.5270 | 0.7259 |
| 0.0569 | 8.3655 | 3204 | 0.5370 | 0.4465 | 0.5370 | 0.7328 |
| 0.0569 | 8.3708 | 3206 | 0.5415 | 0.4465 | 0.5415 | 0.7359 |
| 0.0569 | 8.3760 | 3208 | 0.5404 | 0.4465 | 0.5404 | 0.7351 |
| 0.0569 | 8.3812 | 3210 | 0.5367 | 0.5191 | 0.5367 | 0.7326 |
| 0.0569 | 8.3864 | 3212 | 0.5329 | 0.5191 | 0.5329 | 0.7300 |
| 0.0569 | 8.3916 | 3214 | 0.5245 | 0.5191 | 0.5245 | 0.7242 |
| 0.0569 | 8.3969 | 3216 | 0.5132 | 0.5191 | 0.5132 | 0.7164 |
| 0.0569 | 8.4021 | 3218 | 0.5039 | 0.6182 | 0.5039 | 0.7098 |
| 0.0569 | 8.4073 | 3220 | 0.5003 | 0.6182 | 0.5003 | 0.7073 |
| 0.0569 | 8.4125 | 3222 | 0.4983 | 0.6182 | 0.4983 | 0.7059 |
| 0.0569 | 8.4178 | 3224 | 0.4982 | 0.6182 | 0.4982 | 0.7058 |
| 0.0569 | 8.4230 | 3226 | 0.5002 | 0.6182 | 0.5002 | 0.7073 |
| 0.0569 | 8.4282 | 3228 | 0.5018 | 0.5191 | 0.5018 | 0.7084 |
| 0.0569 | 8.4334 | 3230 | 0.5083 | 0.5191 | 0.5083 | 0.7129 |
| 0.0569 | 8.4386 | 3232 | 0.5196 | 0.5191 | 0.5196 | 0.7208 |
| 0.0569 | 8.4439 | 3234 | 0.5301 | 0.4831 | 0.5301 | 0.7281 |
| 0.0569 | 8.4491 | 3236 | 0.5389 | 0.4465 | 0.5389 | 0.7341 |
| 0.0569 | 8.4543 | 3238 | 0.5427 | 0.4465 | 0.5427 | 0.7367 |
| 0.0569 | 8.4595 | 3240 | 0.5469 | 0.4465 | 0.5469 | 0.7396 |
| 0.0569 | 8.4648 | 3242 | 0.5433 | 0.4465 | 0.5433 | 0.7371 |
| 0.0569 | 8.4700 | 3244 | 0.5352 | 0.4465 | 0.5352 | 0.7316 |
| 0.0569 | 8.4752 | 3246 | 0.5228 | 0.4465 | 0.5228 | 0.7230 |
| 0.0569 | 8.4804 | 3248 | 0.5115 | 0.4831 | 0.5115 | 0.7152 |
| 0.0569 | 8.4856 | 3250 | 0.5067 | 0.4831 | 0.5067 | 0.7118 |
| 0.0569 | 8.4909 | 3252 | 0.5052 | 0.4831 | 0.5052 | 0.7108 |
| 0.0569 | 8.4961 | 3254 | 0.5027 | 0.5191 | 0.5027 | 0.7090 |
| 0.0569 | 8.5013 | 3256 | 0.5045 | 0.5191 | 0.5045 | 0.7103 |
| 0.0569 | 8.5065 | 3258 | 0.5027 | 0.5191 | 0.5027 | 0.7090 |
| 0.0569 | 8.5117 | 3260 | 0.4983 | 0.5191 | 0.4983 | 0.7059 |
| 0.0569 | 8.5170 | 3262 | 0.4967 | 0.5243 | 0.4967 | 0.7047 |
| 0.0569 | 8.5222 | 3264 | 0.4977 | 0.5243 | 0.4977 | 0.7055 |
| 0.0569 | 8.5274 | 3266 | 0.5015 | 0.5191 | 0.5015 | 0.7082 |
| 0.0569 | 8.5326 | 3268 | 0.5069 | 0.5191 | 0.5069 | 0.7120 |
| 0.0569 | 8.5379 | 3270 | 0.5102 | 0.5191 | 0.5102 | 0.7143 |
| 0.0569 | 8.5431 | 3272 | 0.5159 | 0.5191 | 0.5159 | 0.7183 |
| 0.0569 | 8.5483 | 3274 | 0.5221 | 0.5191 | 0.5221 | 0.7225 |
| 0.0569 | 8.5535 | 3276 | 0.5298 | 0.4831 | 0.5298 | 0.7279 |
| 0.0569 | 8.5587 | 3278 | 0.5434 | 0.4465 | 0.5434 | 0.7372 |
| 0.0569 | 8.5640 | 3280 | 0.5557 | 0.4465 | 0.5557 | 0.7455 |
| 0.0569 | 8.5692 | 3282 | 0.5649 | 0.4465 | 0.5649 | 0.7516 |
| 0.0569 | 8.5744 | 3284 | 0.5680 | 0.4465 | 0.5680 | 0.7536 |
| 0.0569 | 8.5796 | 3286 | 0.5636 | 0.4465 | 0.5636 | 0.7507 |
| 0.0569 | 8.5849 | 3288 | 0.5548 | 0.4465 | 0.5548 | 0.7449 |
| 0.0569 | 8.5901 | 3290 | 0.5470 | 0.4465 | 0.5470 | 0.7396 |
| 0.0569 | 8.5953 | 3292 | 0.5420 | 0.4465 | 0.5420 | 0.7362 |
| 0.0569 | 8.6005 | 3294 | 0.5361 | 0.4831 | 0.5361 | 0.7322 |
| 0.0569 | 8.6057 | 3296 | 0.5301 | 0.5191 | 0.5301 | 0.7281 |
| 0.0569 | 8.6110 | 3298 | 0.5261 | 0.5191 | 0.5261 | 0.7254 |
| 0.0569 | 8.6162 | 3300 | 0.5268 | 0.5191 | 0.5268 | 0.7258 |
| 0.0569 | 8.6214 | 3302 | 0.5290 | 0.5191 | 0.5290 | 0.7273 |
| 0.0569 | 8.6266 | 3304 | 0.5291 | 0.5191 | 0.5291 | 0.7274 |
| 0.0569 | 8.6319 | 3306 | 0.5306 | 0.4831 | 0.5306 | 0.7284 |
| 0.0569 | 8.6371 | 3308 | 0.5266 | 0.4831 | 0.5266 | 0.7257 |
| 0.0569 | 8.6423 | 3310 | 0.5214 | 0.5191 | 0.5214 | 0.7221 |
| 0.0569 | 8.6475 | 3312 | 0.5206 | 0.5191 | 0.5206 | 0.7215 |
| 0.0569 | 8.6527 | 3314 | 0.5226 | 0.4831 | 0.5226 | 0.7229 |
| 0.0569 | 8.6580 | 3316 | 0.5281 | 0.4831 | 0.5281 | 0.7267 |
| 0.0569 | 8.6632 | 3318 | 0.5286 | 0.4831 | 0.5286 | 0.7271 |
| 0.0569 | 8.6684 | 3320 | 0.5278 | 0.4831 | 0.5278 | 0.7265 |
| 0.0569 | 8.6736 | 3322 | 0.5284 | 0.4831 | 0.5284 | 0.7269 |
| 0.0569 | 8.6789 | 3324 | 0.5248 | 0.4831 | 0.5248 | 0.7244 |
| 0.0569 | 8.6841 | 3326 | 0.5217 | 0.4831 | 0.5217 | 0.7223 |
| 0.0569 | 8.6893 | 3328 | 0.5194 | 0.4831 | 0.5194 | 0.7207 |
| 0.0569 | 8.6945 | 3330 | 0.5195 | 0.4831 | 0.5195 | 0.7208 |
| 0.0569 | 8.6997 | 3332 | 0.5238 | 0.4465 | 0.5238 | 0.7237 |
| 0.0569 | 8.7050 | 3334 | 0.5282 | 0.4465 | 0.5282 | 0.7268 |
| 0.0569 | 8.7102 | 3336 | 0.5344 | 0.4465 | 0.5344 | 0.7310 |
| 0.0569 | 8.7154 | 3338 | 0.5384 | 0.4465 | 0.5384 | 0.7338 |
| 0.0569 | 8.7206 | 3340 | 0.5437 | 0.4465 | 0.5437 | 0.7373 |
| 0.0569 | 8.7258 | 3342 | 0.5422 | 0.4465 | 0.5422 | 0.7364 |
| 0.0569 | 8.7311 | 3344 | 0.5395 | 0.4465 | 0.5395 | 0.7345 |
| 0.0569 | 8.7363 | 3346 | 0.5373 | 0.4465 | 0.5373 | 0.7330 |
| 0.0569 | 8.7415 | 3348 | 0.5388 | 0.4465 | 0.5388 | 0.7341 |
| 0.0569 | 8.7467 | 3350 | 0.5394 | 0.4465 | 0.5394 | 0.7344 |
| 0.0569 | 8.7520 | 3352 | 0.5418 | 0.4465 | 0.5418 | 0.7361 |
| 0.0569 | 8.7572 | 3354 | 0.5440 | 0.4465 | 0.5440 | 0.7376 |
| 0.0569 | 8.7624 | 3356 | 0.5454 | 0.4465 | 0.5454 | 0.7385 |
| 0.0569 | 8.7676 | 3358 | 0.5459 | 0.4465 | 0.5459 | 0.7389 |
| 0.0569 | 8.7728 | 3360 | 0.5431 | 0.4465 | 0.5431 | 0.7369 |
| 0.0569 | 8.7781 | 3362 | 0.5379 | 0.4465 | 0.5379 | 0.7334 |
| 0.0569 | 8.7833 | 3364 | 0.5351 | 0.4465 | 0.5351 | 0.7315 |
| 0.0569 | 8.7885 | 3366 | 0.5341 | 0.4465 | 0.5341 | 0.7308 |
| 0.0569 | 8.7937 | 3368 | 0.5304 | 0.4465 | 0.5304 | 0.7283 |
| 0.0569 | 8.7990 | 3370 | 0.5279 | 0.4465 | 0.5279 | 0.7266 |
| 0.0569 | 8.8042 | 3372 | 0.5282 | 0.4465 | 0.5282 | 0.7268 |
| 0.0569 | 8.8094 | 3374 | 0.5286 | 0.4465 | 0.5286 | 0.7271 |
| 0.0569 | 8.8146 | 3376 | 0.5279 | 0.4465 | 0.5279 | 0.7266 |
| 0.0569 | 8.8198 | 3378 | 0.5293 | 0.4465 | 0.5293 | 0.7275 |
| 0.0569 | 8.8251 | 3380 | 0.5276 | 0.4465 | 0.5276 | 0.7264 |
| 0.0569 | 8.8303 | 3382 | 0.5246 | 0.4465 | 0.5246 | 0.7243 |
| 0.0569 | 8.8355 | 3384 | 0.5215 | 0.4831 | 0.5215 | 0.7222 |
| 0.0569 | 8.8407 | 3386 | 0.5218 | 0.4831 | 0.5218 | 0.7223 |
| 0.0569 | 8.8460 | 3388 | 0.5244 | 0.4831 | 0.5244 | 0.7241 |
| 0.0569 | 8.8512 | 3390 | 0.5279 | 0.4831 | 0.5279 | 0.7266 |
| 0.0569 | 8.8564 | 3392 | 0.5294 | 0.4831 | 0.5294 | 0.7276 |
| 0.0569 | 8.8616 | 3394 | 0.5317 | 0.5191 | 0.5317 | 0.7292 |
| 0.0569 | 8.8668 | 3396 | 0.5334 | 0.5191 | 0.5334 | 0.7303 |
| 0.0569 | 8.8721 | 3398 | 0.5364 | 0.5191 | 0.5364 | 0.7324 |
| 0.0569 | 8.8773 | 3400 | 0.5373 | 0.5191 | 0.5373 | 0.7330 |
| 0.0569 | 8.8825 | 3402 | 0.5396 | 0.5191 | 0.5396 | 0.7346 |
| 0.0569 | 8.8877 | 3404 | 0.5370 | 0.5191 | 0.5370 | 0.7328 |
| 0.0569 | 8.8930 | 3406 | 0.5340 | 0.5191 | 0.5340 | 0.7307 |
| 0.0569 | 8.8982 | 3408 | 0.5327 | 0.5191 | 0.5327 | 0.7298 |
| 0.0569 | 8.9034 | 3410 | 0.5332 | 0.5191 | 0.5332 | 0.7302 |
| 0.0569 | 8.9086 | 3412 | 0.5336 | 0.5191 | 0.5336 | 0.7305 |
| 0.0569 | 8.9138 | 3414 | 0.5331 | 0.5191 | 0.5331 | 0.7301 |
| 0.0569 | 8.9191 | 3416 | 0.5304 | 0.5191 | 0.5304 | 0.7283 |
| 0.0569 | 8.9243 | 3418 | 0.5283 | 0.5191 | 0.5283 | 0.7268 |
| 0.0569 | 8.9295 | 3420 | 0.5285 | 0.4831 | 0.5285 | 0.7270 |
| 0.0569 | 8.9347 | 3422 | 0.5279 | 0.4831 | 0.5279 | 0.7265 |
| 0.0569 | 8.9399 | 3424 | 0.5246 | 0.4831 | 0.5246 | 0.7243 |
| 0.0569 | 8.9452 | 3426 | 0.5187 | 0.4831 | 0.5187 | 0.7202 |
| 0.0569 | 8.9504 | 3428 | 0.5121 | 0.5191 | 0.5121 | 0.7156 |
| 0.0569 | 8.9556 | 3430 | 0.5092 | 0.5191 | 0.5092 | 0.7136 |
| 0.0569 | 8.9608 | 3432 | 0.5054 | 0.5191 | 0.5054 | 0.7109 |
| 0.0569 | 8.9661 | 3434 | 0.5019 | 0.5191 | 0.5019 | 0.7085 |
| 0.0569 | 8.9713 | 3436 | 0.4971 | 0.5191 | 0.4971 | 0.7050 |
| 0.0569 | 8.9765 | 3438 | 0.4941 | 0.6182 | 0.4941 | 0.7029 |
| 0.0569 | 8.9817 | 3440 | 0.4925 | 0.6182 | 0.4925 | 0.7018 |
| 0.0569 | 8.9869 | 3442 | 0.4917 | 0.6182 | 0.4917 | 0.7012 |
| 0.0569 | 8.9922 | 3444 | 0.4941 | 0.6182 | 0.4941 | 0.7029 |
| 0.0569 | 8.9974 | 3446 | 0.4940 | 0.6182 | 0.4940 | 0.7029 |
| 0.0569 | 9.0026 | 3448 | 0.4910 | 0.6182 | 0.4910 | 0.7007 |
| 0.0569 | 9.0078 | 3450 | 0.4907 | 0.6182 | 0.4907 | 0.7005 |
| 0.0569 | 9.0131 | 3452 | 0.4905 | 0.6182 | 0.4905 | 0.7004 |
| 0.0569 | 9.0183 | 3454 | 0.4927 | 0.6182 | 0.4927 | 0.7019 |
| 0.0569 | 9.0235 | 3456 | 0.4958 | 0.6182 | 0.4958 | 0.7041 |
| 0.0569 | 9.0287 | 3458 | 0.4985 | 0.5191 | 0.4985 | 0.7060 |
| 0.0569 | 9.0339 | 3460 | 0.5014 | 0.5191 | 0.5014 | 0.7081 |
| 0.0569 | 9.0392 | 3462 | 0.5058 | 0.5191 | 0.5058 | 0.7112 |
| 0.0569 | 9.0444 | 3464 | 0.5093 | 0.5191 | 0.5093 | 0.7136 |
| 0.0569 | 9.0496 | 3466 | 0.5132 | 0.5191 | 0.5132 | 0.7164 |
| 0.0569 | 9.0548 | 3468 | 0.5183 | 0.5191 | 0.5183 | 0.7200 |
| 0.0569 | 9.0601 | 3470 | 0.5238 | 0.5191 | 0.5238 | 0.7237 |
| 0.0569 | 9.0653 | 3472 | 0.5265 | 0.5191 | 0.5265 | 0.7256 |
| 0.0569 | 9.0705 | 3474 | 0.5281 | 0.4831 | 0.5281 | 0.7267 |
| 0.0569 | 9.0757 | 3476 | 0.5268 | 0.4831 | 0.5268 | 0.7258 |
| 0.0569 | 9.0809 | 3478 | 0.5244 | 0.4831 | 0.5244 | 0.7242 |
| 0.0569 | 9.0862 | 3480 | 0.5212 | 0.5191 | 0.5212 | 0.7220 |
| 0.0569 | 9.0914 | 3482 | 0.5169 | 0.5191 | 0.5169 | 0.7190 |
| 0.0569 | 9.0966 | 3484 | 0.5157 | 0.5191 | 0.5157 | 0.7181 |
| 0.0569 | 9.1018 | 3486 | 0.5141 | 0.5191 | 0.5141 | 0.7170 |
| 0.0569 | 9.1070 | 3488 | 0.5116 | 0.5191 | 0.5116 | 0.7153 |
| 0.0569 | 9.1123 | 3490 | 0.5106 | 0.5191 | 0.5106 | 0.7146 |
| 0.0569 | 9.1175 | 3492 | 0.5085 | 0.5191 | 0.5085 | 0.7131 |
| 0.0569 | 9.1227 | 3494 | 0.5080 | 0.5191 | 0.5080 | 0.7127 |
| 0.0569 | 9.1279 | 3496 | 0.5061 | 0.5191 | 0.5061 | 0.7114 |
| 0.0569 | 9.1332 | 3498 | 0.5034 | 0.5191 | 0.5034 | 0.7095 |
| 0.0493 | 9.1384 | 3500 | 0.5005 | 0.6182 | 0.5005 | 0.7075 |
| 0.0493 | 9.1436 | 3502 | 0.4990 | 0.6182 | 0.4990 | 0.7064 |
| 0.0493 | 9.1488 | 3504 | 0.4994 | 0.6182 | 0.4994 | 0.7067 |
| 0.0493 | 9.1540 | 3506 | 0.4999 | 0.6182 | 0.4999 | 0.7070 |
| 0.0493 | 9.1593 | 3508 | 0.5015 | 0.6182 | 0.5015 | 0.7082 |
| 0.0493 | 9.1645 | 3510 | 0.5039 | 0.6182 | 0.5039 | 0.7099 |
| 0.0493 | 9.1697 | 3512 | 0.5041 | 0.6182 | 0.5041 | 0.7100 |
| 0.0493 | 9.1749 | 3514 | 0.5047 | 0.6182 | 0.5047 | 0.7104 |
| 0.0493 | 9.1802 | 3516 | 0.5046 | 0.6182 | 0.5046 | 0.7103 |
| 0.0493 | 9.1854 | 3518 | 0.5050 | 0.6182 | 0.5050 | 0.7107 |
| 0.0493 | 9.1906 | 3520 | 0.5055 | 0.6182 | 0.5055 | 0.7110 |
| 0.0493 | 9.1958 | 3522 | 0.5048 | 0.6182 | 0.5048 | 0.7105 |
| 0.0493 | 9.2010 | 3524 | 0.5038 | 0.6182 | 0.5038 | 0.7098 |
| 0.0493 | 9.2063 | 3526 | 0.5022 | 0.6182 | 0.5022 | 0.7087 |
| 0.0493 | 9.2115 | 3528 | 0.5015 | 0.6182 | 0.5015 | 0.7081 |
| 0.0493 | 9.2167 | 3530 | 0.5021 | 0.6182 | 0.5021 | 0.7086 |
| 0.0493 | 9.2219 | 3532 | 0.5020 | 0.5191 | 0.5020 | 0.7085 |
| 0.0493 | 9.2272 | 3534 | 0.5006 | 0.5191 | 0.5006 | 0.7075 |
| 0.0493 | 9.2324 | 3536 | 0.4991 | 0.6182 | 0.4991 | 0.7065 |
| 0.0493 | 9.2376 | 3538 | 0.4996 | 0.5191 | 0.4996 | 0.7068 |
| 0.0493 | 9.2428 | 3540 | 0.4979 | 0.6182 | 0.4979 | 0.7056 |
| 0.0493 | 9.2480 | 3542 | 0.4956 | 0.6182 | 0.4956 | 0.7040 |
| 0.0493 | 9.2533 | 3544 | 0.4930 | 0.6182 | 0.4930 | 0.7021 |
| 0.0493 | 9.2585 | 3546 | 0.4903 | 0.6182 | 0.4903 | 0.7002 |
| 0.0493 | 9.2637 | 3548 | 0.4868 | 0.6182 | 0.4868 | 0.6977 |
| 0.0493 | 9.2689 | 3550 | 0.4848 | 0.6182 | 0.4848 | 0.6963 |
| 0.0493 | 9.2742 | 3552 | 0.4830 | 0.6182 | 0.4830 | 0.6949 |
| 0.0493 | 9.2794 | 3554 | 0.4827 | 0.6182 | 0.4827 | 0.6948 |
| 0.0493 | 9.2846 | 3556 | 0.4840 | 0.6182 | 0.4840 | 0.6957 |
| 0.0493 | 9.2898 | 3558 | 0.4865 | 0.6182 | 0.4865 | 0.6975 |
| 0.0493 | 9.2950 | 3560 | 0.4892 | 0.6182 | 0.4892 | 0.6994 |
| 0.0493 | 9.3003 | 3562 | 0.4922 | 0.6182 | 0.4922 | 0.7015 |
| 0.0493 | 9.3055 | 3564 | 0.4958 | 0.6182 | 0.4958 | 0.7041 |
| 0.0493 | 9.3107 | 3566 | 0.5010 | 0.6182 | 0.5010 | 0.7078 |
| 0.0493 | 9.3159 | 3568 | 0.5063 | 0.5191 | 0.5063 | 0.7116 |
| 0.0493 | 9.3211 | 3570 | 0.5138 | 0.5191 | 0.5138 | 0.7168 |
| 0.0493 | 9.3264 | 3572 | 0.5192 | 0.5191 | 0.5192 | 0.7205 |
| 0.0493 | 9.3316 | 3574 | 0.5223 | 0.5191 | 0.5223 | 0.7227 |
| 0.0493 | 9.3368 | 3576 | 0.5248 | 0.5191 | 0.5248 | 0.7244 |
| 0.0493 | 9.3420 | 3578 | 0.5243 | 0.5191 | 0.5243 | 0.7241 |
| 0.0493 | 9.3473 | 3580 | 0.5216 | 0.5191 | 0.5216 | 0.7222 |
| 0.0493 | 9.3525 | 3582 | 0.5195 | 0.5191 | 0.5195 | 0.7208 |
| 0.0493 | 9.3577 | 3584 | 0.5168 | 0.5191 | 0.5168 | 0.7189 |
| 0.0493 | 9.3629 | 3586 | 0.5161 | 0.5191 | 0.5161 | 0.7184 |
| 0.0493 | 9.3681 | 3588 | 0.5153 | 0.5191 | 0.5153 | 0.7178 |
| 0.0493 | 9.3734 | 3590 | 0.5147 | 0.5191 | 0.5147 | 0.7174 |
| 0.0493 | 9.3786 | 3592 | 0.5121 | 0.5191 | 0.5121 | 0.7156 |
| 0.0493 | 9.3838 | 3594 | 0.5106 | 0.5191 | 0.5106 | 0.7146 |
| 0.0493 | 9.3890 | 3596 | 0.5080 | 0.5191 | 0.5080 | 0.7127 |
| 0.0493 | 9.3943 | 3598 | 0.5052 | 0.5191 | 0.5052 | 0.7108 |
| 0.0493 | 9.3995 | 3600 | 0.5013 | 0.5191 | 0.5013 | 0.7080 |
| 0.0493 | 9.4047 | 3602 | 0.4973 | 0.5191 | 0.4973 | 0.7052 |
| 0.0493 | 9.4099 | 3604 | 0.4932 | 0.6182 | 0.4932 | 0.7023 |
| 0.0493 | 9.4151 | 3606 | 0.4895 | 0.6182 | 0.4895 | 0.6996 |
| 0.0493 | 9.4204 | 3608 | 0.4860 | 0.6182 | 0.4860 | 0.6971 |
| 0.0493 | 9.4256 | 3610 | 0.4841 | 0.6182 | 0.4841 | 0.6958 |
| 0.0493 | 9.4308 | 3612 | 0.4845 | 0.6182 | 0.4845 | 0.6961 |
| 0.0493 | 9.4360 | 3614 | 0.4864 | 0.6182 | 0.4864 | 0.6975 |
| 0.0493 | 9.4413 | 3616 | 0.4884 | 0.6182 | 0.4884 | 0.6988 |
| 0.0493 | 9.4465 | 3618 | 0.4905 | 0.6182 | 0.4905 | 0.7003 |
| 0.0493 | 9.4517 | 3620 | 0.4931 | 0.6182 | 0.4931 | 0.7022 |
| 0.0493 | 9.4569 | 3622 | 0.4953 | 0.6182 | 0.4953 | 0.7038 |
| 0.0493 | 9.4621 | 3624 | 0.4962 | 0.6182 | 0.4962 | 0.7044 |
| 0.0493 | 9.4674 | 3626 | 0.4983 | 0.6182 | 0.4983 | 0.7059 |
| 0.0493 | 9.4726 | 3628 | 0.4998 | 0.6182 | 0.4998 | 0.7069 |
| 0.0493 | 9.4778 | 3630 | 0.5006 | 0.6182 | 0.5006 | 0.7075 |
| 0.0493 | 9.4830 | 3632 | 0.5017 | 0.5191 | 0.5017 | 0.7083 |
| 0.0493 | 9.4883 | 3634 | 0.5024 | 0.5191 | 0.5024 | 0.7088 |
| 0.0493 | 9.4935 | 3636 | 0.5032 | 0.5191 | 0.5032 | 0.7093 |
| 0.0493 | 9.4987 | 3638 | 0.5022 | 0.6182 | 0.5022 | 0.7086 |
| 0.0493 | 9.5039 | 3640 | 0.5010 | 0.6182 | 0.5010 | 0.7078 |
| 0.0493 | 9.5091 | 3642 | 0.4998 | 0.6182 | 0.4998 | 0.7069 |
| 0.0493 | 9.5144 | 3644 | 0.5004 | 0.6182 | 0.5004 | 0.7074 |
| 0.0493 | 9.5196 | 3646 | 0.5013 | 0.6182 | 0.5013 | 0.7080 |
| 0.0493 | 9.5248 | 3648 | 0.5009 | 0.6182 | 0.5009 | 0.7078 |
| 0.0493 | 9.5300 | 3650 | 0.4998 | 0.6182 | 0.4998 | 0.7070 |
| 0.0493 | 9.5352 | 3652 | 0.4991 | 0.5191 | 0.4991 | 0.7065 |
| 0.0493 | 9.5405 | 3654 | 0.4991 | 0.5191 | 0.4991 | 0.7065 |
| 0.0493 | 9.5457 | 3656 | 0.4993 | 0.5191 | 0.4993 | 0.7066 |
| 0.0493 | 9.5509 | 3658 | 0.4996 | 0.5191 | 0.4996 | 0.7068 |
| 0.0493 | 9.5561 | 3660 | 0.5002 | 0.5191 | 0.5002 | 0.7072 |
| 0.0493 | 9.5614 | 3662 | 0.5009 | 0.5191 | 0.5009 | 0.7078 |
| 0.0493 | 9.5666 | 3664 | 0.5017 | 0.5191 | 0.5017 | 0.7083 |
| 0.0493 | 9.5718 | 3666 | 0.5022 | 0.5191 | 0.5022 | 0.7087 |
| 0.0493 | 9.5770 | 3668 | 0.5040 | 0.5191 | 0.5040 | 0.7099 |
| 0.0493 | 9.5822 | 3670 | 0.5051 | 0.5191 | 0.5051 | 0.7107 |
| 0.0493 | 9.5875 | 3672 | 0.5061 | 0.5191 | 0.5061 | 0.7114 |
| 0.0493 | 9.5927 | 3674 | 0.5067 | 0.5191 | 0.5067 | 0.7118 |
| 0.0493 | 9.5979 | 3676 | 0.5081 | 0.5191 | 0.5081 | 0.7128 |
| 0.0493 | 9.6031 | 3678 | 0.5088 | 0.5191 | 0.5088 | 0.7133 |
| 0.0493 | 9.6084 | 3680 | 0.5085 | 0.5191 | 0.5085 | 0.7131 |
| 0.0493 | 9.6136 | 3682 | 0.5086 | 0.5191 | 0.5086 | 0.7132 |
| 0.0493 | 9.6188 | 3684 | 0.5086 | 0.5191 | 0.5086 | 0.7131 |
| 0.0493 | 9.6240 | 3686 | 0.5086 | 0.5191 | 0.5086 | 0.7132 |
| 0.0493 | 9.6292 | 3688 | 0.5087 | 0.5191 | 0.5087 | 0.7132 |
| 0.0493 | 9.6345 | 3690 | 0.5076 | 0.5191 | 0.5076 | 0.7125 |
| 0.0493 | 9.6397 | 3692 | 0.5061 | 0.5191 | 0.5061 | 0.7114 |
| 0.0493 | 9.6449 | 3694 | 0.5045 | 0.5191 | 0.5045 | 0.7103 |
| 0.0493 | 9.6501 | 3696 | 0.5033 | 0.5191 | 0.5033 | 0.7095 |
| 0.0493 | 9.6554 | 3698 | 0.5019 | 0.5191 | 0.5019 | 0.7084 |
| 0.0493 | 9.6606 | 3700 | 0.5000 | 0.5191 | 0.5000 | 0.7071 |
| 0.0493 | 9.6658 | 3702 | 0.4989 | 0.5191 | 0.4989 | 0.7063 |
| 0.0493 | 9.6710 | 3704 | 0.4975 | 0.5191 | 0.4975 | 0.7053 |
| 0.0493 | 9.6762 | 3706 | 0.4971 | 0.5191 | 0.4971 | 0.7051 |
| 0.0493 | 9.6815 | 3708 | 0.4972 | 0.5191 | 0.4972 | 0.7051 |
| 0.0493 | 9.6867 | 3710 | 0.4972 | 0.6182 | 0.4972 | 0.7051 |
| 0.0493 | 9.6919 | 3712 | 0.4971 | 0.6182 | 0.4971 | 0.7051 |
| 0.0493 | 9.6971 | 3714 | 0.4972 | 0.6182 | 0.4972 | 0.7051 |
| 0.0493 | 9.7023 | 3716 | 0.4979 | 0.6182 | 0.4979 | 0.7056 |
| 0.0493 | 9.7076 | 3718 | 0.4989 | 0.6182 | 0.4989 | 0.7063 |
| 0.0493 | 9.7128 | 3720 | 0.5001 | 0.6182 | 0.5001 | 0.7072 |
| 0.0493 | 9.7180 | 3722 | 0.5010 | 0.6182 | 0.5010 | 0.7078 |
| 0.0493 | 9.7232 | 3724 | 0.5021 | 0.5191 | 0.5021 | 0.7086 |
| 0.0493 | 9.7285 | 3726 | 0.5028 | 0.5191 | 0.5028 | 0.7091 |
| 0.0493 | 9.7337 | 3728 | 0.5040 | 0.5191 | 0.5040 | 0.7099 |
| 0.0493 | 9.7389 | 3730 | 0.5056 | 0.5191 | 0.5056 | 0.7110 |
| 0.0493 | 9.7441 | 3732 | 0.5076 | 0.5191 | 0.5076 | 0.7124 |
| 0.0493 | 9.7493 | 3734 | 0.5089 | 0.5191 | 0.5089 | 0.7134 |
| 0.0493 | 9.7546 | 3736 | 0.5095 | 0.5191 | 0.5095 | 0.7138 |
| 0.0493 | 9.7598 | 3738 | 0.5099 | 0.5191 | 0.5099 | 0.7141 |
| 0.0493 | 9.7650 | 3740 | 0.5103 | 0.5191 | 0.5103 | 0.7143 |
| 0.0493 | 9.7702 | 3742 | 0.5105 | 0.5191 | 0.5105 | 0.7145 |
| 0.0493 | 9.7755 | 3744 | 0.5114 | 0.5191 | 0.5114 | 0.7151 |
| 0.0493 | 9.7807 | 3746 | 0.5124 | 0.5191 | 0.5124 | 0.7158 |
| 0.0493 | 9.7859 | 3748 | 0.5128 | 0.5191 | 0.5128 | 0.7161 |
| 0.0493 | 9.7911 | 3750 | 0.5139 | 0.5191 | 0.5139 | 0.7169 |
| 0.0493 | 9.7963 | 3752 | 0.5152 | 0.5191 | 0.5152 | 0.7178 |
| 0.0493 | 9.8016 | 3754 | 0.5158 | 0.5191 | 0.5158 | 0.7182 |
| 0.0493 | 9.8068 | 3756 | 0.5164 | 0.5191 | 0.5164 | 0.7186 |
| 0.0493 | 9.8120 | 3758 | 0.5163 | 0.5191 | 0.5163 | 0.7185 |
| 0.0493 | 9.8172 | 3760 | 0.5164 | 0.5191 | 0.5164 | 0.7186 |
| 0.0493 | 9.8225 | 3762 | 0.5162 | 0.5191 | 0.5162 | 0.7185 |
| 0.0493 | 9.8277 | 3764 | 0.5153 | 0.5191 | 0.5153 | 0.7179 |
| 0.0493 | 9.8329 | 3766 | 0.5144 | 0.5191 | 0.5144 | 0.7172 |
| 0.0493 | 9.8381 | 3768 | 0.5135 | 0.5191 | 0.5135 | 0.7166 |
| 0.0493 | 9.8433 | 3770 | 0.5124 | 0.5191 | 0.5124 | 0.7158 |
| 0.0493 | 9.8486 | 3772 | 0.5118 | 0.5191 | 0.5118 | 0.7154 |
| 0.0493 | 9.8538 | 3774 | 0.5116 | 0.5191 | 0.5116 | 0.7152 |
| 0.0493 | 9.8590 | 3776 | 0.5109 | 0.5191 | 0.5109 | 0.7148 |
| 0.0493 | 9.8642 | 3778 | 0.5099 | 0.5191 | 0.5099 | 0.7141 |
| 0.0493 | 9.8695 | 3780 | 0.5088 | 0.5191 | 0.5088 | 0.7133 |
| 0.0493 | 9.8747 | 3782 | 0.5077 | 0.5191 | 0.5077 | 0.7125 |
| 0.0493 | 9.8799 | 3784 | 0.5067 | 0.5191 | 0.5067 | 0.7119 |
| 0.0493 | 9.8851 | 3786 | 0.5062 | 0.5191 | 0.5062 | 0.7114 |
| 0.0493 | 9.8903 | 3788 | 0.5054 | 0.5191 | 0.5054 | 0.7109 |
| 0.0493 | 9.8956 | 3790 | 0.5046 | 0.5191 | 0.5046 | 0.7104 |
| 0.0493 | 9.9008 | 3792 | 0.5040 | 0.5191 | 0.5040 | 0.7099 |
| 0.0493 | 9.9060 | 3794 | 0.5037 | 0.5191 | 0.5037 | 0.7097 |
| 0.0493 | 9.9112 | 3796 | 0.5033 | 0.5191 | 0.5033 | 0.7094 |
| 0.0493 | 9.9164 | 3798 | 0.5033 | 0.5191 | 0.5033 | 0.7094 |
| 0.0493 | 9.9217 | 3800 | 0.5035 | 0.5191 | 0.5035 | 0.7095 |
| 0.0493 | 9.9269 | 3802 | 0.5038 | 0.5191 | 0.5038 | 0.7098 |
| 0.0493 | 9.9321 | 3804 | 0.5042 | 0.5191 | 0.5042 | 0.7101 |
| 0.0493 | 9.9373 | 3806 | 0.5048 | 0.5191 | 0.5048 | 0.7105 |
| 0.0493 | 9.9426 | 3808 | 0.5053 | 0.5191 | 0.5053 | 0.7108 |
| 0.0493 | 9.9478 | 3810 | 0.5057 | 0.5191 | 0.5057 | 0.7111 |
| 0.0493 | 9.9530 | 3812 | 0.5059 | 0.5191 | 0.5059 | 0.7112 |
| 0.0493 | 9.9582 | 3814 | 0.5061 | 0.5191 | 0.5061 | 0.7114 |
| 0.0493 | 9.9634 | 3816 | 0.5065 | 0.5191 | 0.5065 | 0.7117 |
| 0.0493 | 9.9687 | 3818 | 0.5068 | 0.5191 | 0.5068 | 0.7119 |
| 0.0493 | 9.9739 | 3820 | 0.5071 | 0.5191 | 0.5071 | 0.7121 |
| 0.0493 | 9.9791 | 3822 | 0.5073 | 0.5191 | 0.5073 | 0.7122 |
| 0.0493 | 9.9843 | 3824 | 0.5074 | 0.5191 | 0.5074 | 0.7123 |
| 0.0493 | 9.9896 | 3826 | 0.5075 | 0.5191 | 0.5075 | 0.7124 |
| 0.0493 | 9.9948 | 3828 | 0.5076 | 0.5191 | 0.5076 | 0.7125 |
| 0.0493 | 10.0 | 3830 | 0.5076 | 0.5191 | 0.5076 | 0.7125 |
### Framework versions
- Transformers 4.44.2
- Pytorch 2.4.0+cu118
- Datasets 2.21.0
- Tokenizers 0.19.1
|
mradermacher/gemma-1.1-2b-it-GGUF
|
mradermacher
| 2024-11-16T20:17:20Z
| 5
| 0
|
transformers
|
[
"transformers",
"gguf",
"en",
"base_model:google/gemma-1.1-2b-it",
"base_model:quantized:google/gemma-1.1-2b-it",
"license:gemma",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2024-11-16T19:56:25Z
|
---
base_model: google/gemma-1.1-2b-it
extra_gated_button_content: Acknowledge license
extra_gated_heading: Access Gemma on Hugging Face
extra_gated_prompt: To access Gemma on Hugging Face, you’re required to review and
agree to Google’s usage license. To do this, please ensure you’re logged-in to Hugging
Face and click below. Requests are processed immediately.
language:
- en
library_name: transformers
license: gemma
quantized_by: mradermacher
---
## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
<!-- ### tags: -->
static quants of https://huggingface.co/google/gemma-1.1-2b-it
<!-- provided-files -->
weighted/imatrix quants are available at https://huggingface.co/mradermacher/gemma-1.1-2b-it-i1-GGUF
## Usage
If you are unsure how to use GGUF files, refer to one of [TheBloke's
READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for
more details, including on how to concatenate multi-part files.
## Provided Quants
(sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants)
| Link | Type | Size/GB | Notes |
|:-----|:-----|--------:|:------|
| [GGUF](https://huggingface.co/mradermacher/gemma-1.1-2b-it-GGUF/resolve/main/gemma-1.1-2b-it.Q2_K.gguf) | Q2_K | 1.3 | |
| [GGUF](https://huggingface.co/mradermacher/gemma-1.1-2b-it-GGUF/resolve/main/gemma-1.1-2b-it.Q3_K_S.gguf) | Q3_K_S | 1.4 | |
| [GGUF](https://huggingface.co/mradermacher/gemma-1.1-2b-it-GGUF/resolve/main/gemma-1.1-2b-it.Q3_K_M.gguf) | Q3_K_M | 1.5 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/gemma-1.1-2b-it-GGUF/resolve/main/gemma-1.1-2b-it.Q3_K_L.gguf) | Q3_K_L | 1.6 | |
| [GGUF](https://huggingface.co/mradermacher/gemma-1.1-2b-it-GGUF/resolve/main/gemma-1.1-2b-it.IQ4_XS.gguf) | IQ4_XS | 1.6 | |
| [GGUF](https://huggingface.co/mradermacher/gemma-1.1-2b-it-GGUF/resolve/main/gemma-1.1-2b-it.Q4_0_4_4.gguf) | Q4_0_4_4 | 1.7 | fast on arm, low quality |
| [GGUF](https://huggingface.co/mradermacher/gemma-1.1-2b-it-GGUF/resolve/main/gemma-1.1-2b-it.Q4_K_S.gguf) | Q4_K_S | 1.7 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/gemma-1.1-2b-it-GGUF/resolve/main/gemma-1.1-2b-it.Q4_K_M.gguf) | Q4_K_M | 1.7 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/gemma-1.1-2b-it-GGUF/resolve/main/gemma-1.1-2b-it.Q5_K_S.gguf) | Q5_K_S | 1.9 | |
| [GGUF](https://huggingface.co/mradermacher/gemma-1.1-2b-it-GGUF/resolve/main/gemma-1.1-2b-it.Q5_K_M.gguf) | Q5_K_M | 1.9 | |
| [GGUF](https://huggingface.co/mradermacher/gemma-1.1-2b-it-GGUF/resolve/main/gemma-1.1-2b-it.Q6_K.gguf) | Q6_K | 2.2 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/gemma-1.1-2b-it-GGUF/resolve/main/gemma-1.1-2b-it.Q8_0.gguf) | Q8_0 | 2.8 | fast, best quality |
| [GGUF](https://huggingface.co/mradermacher/gemma-1.1-2b-it-GGUF/resolve/main/gemma-1.1-2b-it.f16.gguf) | f16 | 5.1 | 16 bpw, overkill |
Here is a handy graph by ikawrakow comparing some lower-quality quant
types (lower is better):

And here are Artefact2's thoughts on the matter:
https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9
## FAQ / Model Request
See https://huggingface.co/mradermacher/model_requests for some answers to
questions you might have and/or if you want some other model quantized.
## Thanks
I thank my company, [nethype GmbH](https://www.nethype.de/), for letting
me use its servers and providing upgrades to my workstation to enable
this work in my free time.
<!-- end -->
|
deepdml/whisper-small-eu-cv17
|
deepdml
| 2024-11-16T20:07:43Z
| 5
| 0
| null |
[
"tensorboard",
"safetensors",
"whisper",
"generated_from_trainer",
"automatic-speech-recognition",
"eu",
"dataset:mozilla-foundation/common_voice_17_0",
"base_model:openai/whisper-small",
"base_model:finetune:openai/whisper-small",
"license:apache-2.0",
"model-index",
"region:us"
] |
automatic-speech-recognition
| 2024-09-15T17:36:45Z
|
---
language:
- eu
license: apache-2.0
base_model: openai/whisper-small
tags:
- generated_from_trainer
datasets:
- mozilla-foundation/common_voice_17_0
metrics:
- wer
model-index:
- name: Whisper Small eu
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Common Voice 17.0
type: mozilla-foundation/common_voice_17_0
config: eu
split: test
args: eu
metrics:
- name: Wer
type: wer
value: 9.17402616438858
pipeline_tag: automatic-speech-recognition
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# Whisper Small eu
This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the Common Voice 17.0 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1443
- Wer: 9.1740
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 64
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- training_steps: 5000
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:------:|:----:|:---------------:|:-------:|
| 0.2128 | 0.7189 | 1000 | 0.2103 | 15.1316 |
| 0.1164 | 1.4378 | 2000 | 0.1626 | 11.0218 |
| 0.0722 | 2.1567 | 3000 | 0.1474 | 9.6403 |
| 0.0696 | 2.8756 | 4000 | 0.1420 | 9.1044 |
| 0.0445 | 3.5945 | 5000 | 0.1443 | 9.1740 |
### Framework versions
- Transformers 4.42.0.dev0
- Pytorch 2.3.0+cu121
- Datasets 2.19.1
- Tokenizers 0.19.1
|
RP3-AI/RP3-1b-1.1
|
RP3-AI
| 2024-11-16T19:59:42Z
| 120
| 1
|
transformers
|
[
"transformers",
"pytorch",
"llama",
"text-generation",
"text-generation-inference",
"unsloth",
"trl",
"sft",
"conversational",
"en",
"dataset:yahma/alpaca-cleaned",
"dataset:HuggingFaceH4/ultrachat_200k",
"base_model:RP3-AI/RP3-1b-1.0",
"base_model:finetune:RP3-AI/RP3-1b-1.0",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2024-11-16T19:32:34Z
|
---
base_model: RP3-AI/RP3-1b-1.0
tags:
- text-generation-inference
- transformers
- unsloth
- llama
- trl
- sft
license: apache-2.0
language:
- en
datasets:
- yahma/alpaca-cleaned
- HuggingFaceH4/ultrachat_200k
---
# Uploaded model
- **Developed by:** RP3-AI
- **License:** apache-2.0
- **Finetuned from model :** RP3-AI/RP3-1b-1.0
This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
eltorio/IDEFICS3_medical_instruct
|
eltorio
| 2024-11-16T19:52:26Z
| 11
| 2
|
peft
|
[
"peft",
"safetensors",
"image-text-to-text",
"en",
"dataset:ruslanmv/ai-medical-dataset",
"base_model:HuggingFaceM4/Idefics3-8B-Llama3",
"base_model:adapter:HuggingFaceM4/Idefics3-8B-Llama3",
"license:apache-2.0",
"region:us"
] |
image-text-to-text
| 2024-11-16T13:50:25Z
|
---
license: apache-2.0
datasets:
- ruslanmv/ai-medical-dataset
language:
- en
base_model:
- HuggingFaceM4/Idefics3-8B-Llama3
pipeline_tag: image-text-to-text
library_name: peft
---
# PLACEHOLDER FOR IDEFICS3_medical_instruct

|
AstroMLab/astrollama-3-8b-chat_summary
|
AstroMLab
| 2024-11-16T19:50:40Z
| 7
| 1
|
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"llama-3",
"astronomy",
"astrophysics",
"arxiv",
"en",
"arxiv:2409.19750",
"arxiv:2407.11194",
"license:mit",
"autotrain_compatible",
"text-generation-inference",
"region:us"
] |
text-generation
| 2024-07-04T16:57:34Z
|
---
license: mit
language:
- en
pipeline_tag: text-generation
tags:
- llama-3
- astronomy
- astrophysics
- arxiv
inference: false
base_model:
- meta-llama/Llama-3-8b-hf
---
# AstroLLaMA-3-8B-Chat_Summary
AstroLLaMA-3-8B-Chat_Summary is a specialized chat model for astronomy, developed by fine-tuning the AstroLLaMA-3-8B-Base_Summary model. This model was developed by the AstroMLab team. It is designed for instruction-following and chat-based interactions in the astronomy domain.
## Model Details
- **Base Architecture**: LLaMA-3-8b
- **Base Model**: AstroLLaMA-3-8B-Base_Summary (trained on summarized content from arXiv's astro-ph category papers)
- **Data Processing**:
1. Optical character recognition (OCR) on PDF files using the Nougat tool
2. Summarization of OCR'd text using Qwen-2-8B and LLaMA-3.1-8B, reducing content to about 1,000-4,000 tokens per paper
- **Fine-tuning Method**: Supervised Fine-Tuning (SFT)
- **SFT Dataset**:
- 10,356 astronomy-centered conversations generated from arXiv abstracts by GPT-4
- Full content of LIMA dataset
- 10,000 samples from Open Orca dataset
- 10,000 samples from UltraChat dataset
- **Training Details**:
- Learning rate: 3 × 10⁻⁷
- Training epochs: 1
- Total batch size: 48
- Maximum token length: 2048
- Warmup ratio: 0.03
- Cosine decay schedule for learning rate reduction
- **Primary Use**: Instruction-following and chat-based interactions for astronomy-related queries
- **Reference**: [Pan et al. 2024](https://arxiv.org/abs/2409.19750)
## Using the model for chat
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
import torch
# Load the model and tokenizer
tokenizer = AutoTokenizer.from_pretrained("AstroMLab/astrollama-3-8b-chat_summary")
model = AutoModelForCausalLM.from_pretrained("AstroMLab/astrollama-3-8b-chat_summary", device_map="auto")
# Function to generate a response
def generate_response(prompt, max_length=512):
full_prompt = f"###Human: {prompt}\n\n###Assistant:"
inputs = tokenizer(full_prompt, return_tensors="pt", truncation=True, max_length=max_length)
inputs = inputs.to(model.device)
# Generate a response
with torch.no_grad():
outputs = model.generate(
**inputs,
max_length=max_length,
num_return_sequences=1,
do_sample=True,
pad_token_id=tokenizer.eos_token_id,
eos_token_id=tokenizer.encode("###Human:", add_special_tokens=False)[0]
)
# Decode and return the response
response = tokenizer.decode(outputs[0], skip_special_tokens=False)
# Extract only the Assistant's response
assistant_response = response.split("###Assistant:")[-1].strip()
return assistant_response
# Example usage
user_input = "What are the main components of a galaxy?"
response = generate_response(user_input)
print(f"Human: {user_input}")
print(f"Assistant: {response}")
```
## Model Improvements and Performance
This model used the summarized content for training, which has led to improved performance compared to the AIC (Abstract, Introduction, Conclusion) version. The summarization process allows for the inclusion of more comprehensive information from each paper while maintaining a manageable token count.
Here's a performance comparison chart based upon the astronomical benchmarking Q&A as described in [Ting et al. 2024](https://arxiv.org/abs/2407.11194):
| Model | Score (%) |
|-------|-----------|
| LLaMA-3.1-8B | 73.7 |
| LLaMA-3-8B | 72.9 |
| **<span style="color:green">AstroLLaMA-3-8B-Base_Summary (AstroMLab)</span>** | **<span style="color:green">72.3</span>** |
| **<span style="color:green">AstroLLaMA-3-8B-Chat_Summary (AstroMLab)</span>** | **<span style="color:green">69.0</span>** |
| Gemma-2-9B | 71.5 |
| Qwen-2.5-7B | 70.4 |
| Yi-1.5-9B | 68.4 |
| InternLM-2.5-7B | 64.5 |
| Mistral-7B-v0.3 | 63.9 |
| ChatGLM3-6B | 50.4 |
As shown, AstroLLaMA-3-8B-Chat_Summary performs competitively, maintaining most of the performance of the base summary model. This demonstrates the effectiveness of the summarization approach in capturing and retaining key astronomical concepts, even after fine-tuning for chat interactions.
We also found that the model trained with summaries leads to better scores in general, especially with the instruct version, demonstrating that information density matters significantly in specialized domain training.
While AstroLLaMA-3-8B-Chat_Summary performs well among models in its class, it does not surpass the performance of the base LLaMA-3.1-8B model. This underscores the ongoing challenges in developing specialized models and the need for continued research in this area.
This model is released primarily for reproducibility purposes, allowing researchers to track the development process and compare different iterations of AstroLLaMA models.
For optimal performance and the most up-to-date capabilities in astronomy-related tasks, we recommend using AstroSage-8B, where further improvements have been made. The newer model incorporates expanded training data beyond astro-ph and features a greatly expanded fine-tuning process, resulting in significantly improved performance.
## Ethical Considerations
While this model is designed for scientific use, users should be mindful of potential misuse, such as generating misleading scientific content. Always verify model outputs against peer-reviewed sources for critical applications.
## Citation
If you use this model in your research, please cite:
```
@ARTICLE{2024arXiv240919750P,
author = {{Pan}, Rui and {Dung Nguyen}, Tuan and {Arora}, Hardik and {Accomazzi}, Alberto and {Ghosal}, Tirthankar and {Ting}, Yuan-Sen},
title = "{AstroMLab 2: AstroLLaMA-2-70B Model and Benchmarking Specialised LLMs for Astronomy}",
journal = {arXiv e-prints},
keywords = {Astrophysics - Instrumentation and Methods for Astrophysics, Computer Science - Computation and Language},
year = 2024,
month = sep,
eid = {arXiv:2409.19750},
pages = {arXiv:2409.19750},
doi = {10.48550/arXiv.2409.19750},
archivePrefix = {arXiv},
eprint = {2409.19750},
primaryClass = {astro-ph.IM},
adsurl = {https://ui.adsabs.harvard.edu/abs/2024arXiv240919750P},
adsnote = {Provided by the SAO/NASA Astrophysics Data System}
}
```
|
mradermacher/internlm2-7b-GGUF
|
mradermacher
| 2024-11-16T19:50:20Z
| 6
| 0
|
transformers
|
[
"transformers",
"gguf",
"en",
"base_model:internlm/internlm2-7b",
"base_model:quantized:internlm/internlm2-7b",
"license:other",
"endpoints_compatible",
"region:us"
] | null | 2024-11-16T19:32:34Z
|
---
base_model: internlm/internlm2-7b
language:
- en
library_name: transformers
license: other
quantized_by: mradermacher
---
## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
<!-- ### tags: -->
static quants of https://huggingface.co/internlm/internlm2-7b
<!-- provided-files -->
weighted/imatrix quants are available at https://huggingface.co/mradermacher/internlm2-7b-i1-GGUF
## Usage
If you are unsure how to use GGUF files, refer to one of [TheBloke's
READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for
more details, including on how to concatenate multi-part files.
## Provided Quants
(sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants)
| Link | Type | Size/GB | Notes |
|:-----|:-----|--------:|:------|
| [GGUF](https://huggingface.co/mradermacher/internlm2-7b-GGUF/resolve/main/internlm2-7b.Q2_K.gguf) | Q2_K | 3.1 | |
| [GGUF](https://huggingface.co/mradermacher/internlm2-7b-GGUF/resolve/main/internlm2-7b.Q3_K_S.gguf) | Q3_K_S | 3.6 | |
| [GGUF](https://huggingface.co/mradermacher/internlm2-7b-GGUF/resolve/main/internlm2-7b.Q3_K_M.gguf) | Q3_K_M | 3.9 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/internlm2-7b-GGUF/resolve/main/internlm2-7b.Q3_K_L.gguf) | Q3_K_L | 4.2 | |
| [GGUF](https://huggingface.co/mradermacher/internlm2-7b-GGUF/resolve/main/internlm2-7b.IQ4_XS.gguf) | IQ4_XS | 4.4 | |
| [GGUF](https://huggingface.co/mradermacher/internlm2-7b-GGUF/resolve/main/internlm2-7b.Q4_0_4_4.gguf) | Q4_0_4_4 | 4.6 | fast on arm, low quality |
| [GGUF](https://huggingface.co/mradermacher/internlm2-7b-GGUF/resolve/main/internlm2-7b.Q4_K_S.gguf) | Q4_K_S | 4.6 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/internlm2-7b-GGUF/resolve/main/internlm2-7b.Q4_K_M.gguf) | Q4_K_M | 4.8 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/internlm2-7b-GGUF/resolve/main/internlm2-7b.Q5_K_S.gguf) | Q5_K_S | 5.5 | |
| [GGUF](https://huggingface.co/mradermacher/internlm2-7b-GGUF/resolve/main/internlm2-7b.Q5_K_M.gguf) | Q5_K_M | 5.6 | |
| [GGUF](https://huggingface.co/mradermacher/internlm2-7b-GGUF/resolve/main/internlm2-7b.Q6_K.gguf) | Q6_K | 6.5 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/internlm2-7b-GGUF/resolve/main/internlm2-7b.Q8_0.gguf) | Q8_0 | 8.3 | fast, best quality |
| [GGUF](https://huggingface.co/mradermacher/internlm2-7b-GGUF/resolve/main/internlm2-7b.f16.gguf) | f16 | 15.6 | 16 bpw, overkill |
Here is a handy graph by ikawrakow comparing some lower-quality quant
types (lower is better):

And here are Artefact2's thoughts on the matter:
https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9
## FAQ / Model Request
See https://huggingface.co/mradermacher/model_requests for some answers to
questions you might have and/or if you want some other model quantized.
## Thanks
I thank my company, [nethype GmbH](https://www.nethype.de/), for letting
me use its servers and providing upgrades to my workstation to enable
this work in my free time.
<!-- end -->
|
aleixlahoz10/Llama-3.2-8b-banking77-intents
|
aleixlahoz10
| 2024-11-16T19:47:55Z
| 75
| 0
|
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"trl",
"sft",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"4-bit",
"bitsandbytes",
"region:us"
] |
text-generation
| 2024-11-16T19:45:14Z
|
---
library_name: transformers
tags:
- trl
- sft
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
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### Model Sources [optional]
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## Uses
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### Direct Use
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## Bias, Risks, and Limitations
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[More Information Needed]
### Recommendations
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Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
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## Training Details
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Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
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|
JohnLapTev/JohnLapTev
|
JohnLapTev
| 2024-11-16T19:46:38Z
| 6
| 0
|
diffusers
|
[
"diffusers",
"text-to-image",
"lora",
"template:diffusion-lora",
"base_model:black-forest-labs/FLUX.1-dev",
"base_model:adapter:black-forest-labs/FLUX.1-dev",
"region:us"
] |
text-to-image
| 2024-11-16T19:46:34Z
|
---
tags:
- text-to-image
- lora
- diffusers
- template:diffusion-lora
widget:
- text: >-
short dark hair, light brown hair and a beard., likely a bus, JohnLapTev.
The image is a high-resolution photograph featuring a man with a serious,
JohnLapTev. The image is a high-resolution photograph of a man with a
neutral expression. He has a medium complexion and a short beard that is
neatly trimmed and slightly messy, and a small, light brown hair styled in a
messy
output:
url: images/JohnLapTev_e000005_01_20241115020146.png
base_model: black-forest-labs/FLUX.1-dev
instance_prompt: JohnLapTev
---
# JohnLapTev
<Gallery />
## Model description
Face JohnLapTev
## Trigger words
You should use `JohnLapTev` to trigger the image generation.
## Download model
Weights for this model are available in Safetensors format.
[Download](/JohnLapTev/JohnLapTev/tree/main) them in the Files & versions tab.
|
AstroMLab/astrollama-2-7b-chat_aic
|
AstroMLab
| 2024-11-16T19:46:16Z
| 4
| 0
|
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"llama-2",
"astronomy",
"astrophysics",
"arxiv",
"en",
"arxiv:2401.01916",
"arxiv:2407.11194",
"base_model:meta-llama/Llama-2-7b-hf",
"base_model:finetune:meta-llama/Llama-2-7b-hf",
"license:mit",
"autotrain_compatible",
"text-generation-inference",
"region:us"
] |
text-generation
| 2024-03-15T23:15:38Z
|
---
license: mit
language:
- en
pipeline_tag: text-generation
tags:
- llama-2
- astronomy
- astrophysics
- arxiv
inference: false
base_model:
- meta-llama/Llama-2-7b-hf
---
# AstroLLaMA-2-7B-Chat_AIC
AstroLLaMA-2-7B-Chat_AIC is a specialized chat model for astronomy, developed by fine-tuning the AstroLLaMA-2-7B-Base_AIC model. This model was originally developed by the AstroLLaMA team as part of the UniverseTBD initiative. It is designed for instruction-following and chat-based interactions in the astronomy domain.
**Note**: This model is provided for completeness in the series of AstroLLaMA models. The core AstroLLaMA team has since moved on to develop more advanced models under AstroMLab. For the original UniverseTBD version, please visit [their repository](https://huggingface.co/universeTBD/astrollama-7b-chat-alpha).
## Model Details
- **Base Architecture**: LLaMA-2-7b
- **Base Model**: AstroLLaMA-2-7B-Base_AIC (trained on Abstract, Introduction, and Conclusion sections from arXiv's astro-ph category papers)
- **Fine-tuning Method**: Supervised Fine-Tuning (SFT)
- **SFT Dataset**:
- 10,356 astronomy-centered conversations generated from arXiv abstracts by GPT-4
- Full content of LIMA dataset
- 10,000 samples from Open Orca dataset
- 10,000 samples from UltraChat dataset
- **Primary Use**: Instruction-following and chat-based interactions for astronomy-related queries
- **Reference**: [Perkowski et al. 2024](https://arxiv.org/abs/2401.01916)
## Using the model for chat
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
import torch
# Load the model and tokenizer
tokenizer = AutoTokenizer.from_pretrained("AstroMLab/astrollama-2-7b-chat_aic")
model = AutoModelForCausalLM.from_pretrained("AstroMLab/astrollama-2-7b-chat_aic", device_map="auto")
# Function to generate a response
def generate_response(prompt, max_length=512):
full_prompt = f"###Human: {prompt}\n\n###Assistant:"
inputs = tokenizer(full_prompt, return_tensors="pt", truncation=True, max_length=max_length)
inputs = inputs.to(model.device)
# Generate a response
with torch.no_grad():
outputs = model.generate(
**inputs,
max_length=max_length,
num_return_sequences=1,
do_sample=True,
pad_token_id=tokenizer.eos_token_id,
eos_token_id=tokenizer.encode("###Human:", add_special_tokens=False)[0]
)
# Decode and return the response
response = tokenizer.decode(outputs[0], skip_special_tokens=False)
# Extract only the Assistant's response
assistant_response = response.split("###Assistant:")[-1].strip()
return assistant_response
# Example usage
user_input = "What are the main components of a galaxy?"
response = generate_response(user_input)
print(f"Human: {user_input}")
print(f"Assistant: {response}")
```
## Model Limitations and Biases
This model is specifically trained on astronomy literature and may not generalize well to other domains. Users should be aware of potential biases in the training data, which may reflect historical trends and biases in astronomical research publications. Additionally, the regex-based extraction method used for processing the LaTeX source files may introduce some biases or inconsistencies in the training data.
Importantly, this model has been superseded by more advanced versions. Here's a performance comparison chart based upon the astronomical benchmarking Q&A as described in [Ting et al. 2024](https://arxiv.org/abs/2407.11194).
| Model | Score (%) |
|-------|-----------|
| **AstroSage-LLaMA-3.1-8B (AstroMLab)** | **80.9** |
| **AstroLLaMA-2-70B (AstroMLab)** | **76.0** |
| LLaMA-3.1-8B | 73.7 |
| Gemma-2-9B | 71.5 |
| Qwen-2.5-7B | 70.4 |
| Yi-1.5-9B | 68.4 |
| InternLM-2.5-7B | 64.5 |
| Mistral-7B-v0.3 | 63.9 |
| ChatGLM3-6B | 50.4 |
| <span style="color:red">AstroLLaMA-2-7B-AIC</span> | <span style="color:red">44.3</span> |
| AstroLLaMA-2-7B-Abstract | 43.5 |
As shown, AstroLLaMA-2-7B series are outperformed by newer models. For state-of-the-art performance, we recommend using the latest models.
## Ethical Considerations
While this model is designed for scientific use, users should be mindful of potential misuse, such as generating misleading scientific content. Always verify model outputs against peer-reviewed sources for critical applications.
## Citation
If you use this model in your research, please cite:
```
@ARTICLE{2024RNAAS...8....7P,
author = {{Perkowski}, Ernest and {Pan}, Rui and {Nguyen}, Tuan Dung and {Ting}, Yuan-Sen and {Kruk}, Sandor and {Zhang}, Tong and {O'Neill}, Charlie and {Jablonska}, Maja and {Sun}, Zechang and {Smith}, Michael J. and {Liu}, Huiling and {Schawinski}, Kevin and {Iyer}, Kartheik and {Ciuc{\u{a}}}, Ioana and {UniverseTBD}},
title = "{AstroLLaMA-Chat: Scaling AstroLLaMA with Conversational and Diverse Datasets}",
journal = {Research Notes of the American Astronomical Society},
keywords = {Astronomy software, Publicly available software, Astronomical instrumentation, 1855, 1864, 799, Astrophysics - Instrumentation and Methods for Astrophysics, Astrophysics - Cosmology and Nongalactic Astrophysics, Astrophysics - Astrophysics of Galaxies, Astrophysics - Solar and Stellar Astrophysics, Computer Science - Computation and Language, Computer Science - Machine Learning},
year = 2024,
month = jan,
volume = {8},
number = {1},
eid = {7},
pages = {7},
doi = {10.3847/2515-5172/ad1abe},
archivePrefix = {arXiv},
eprint = {2401.01916},
primaryClass = {astro-ph.IM},
adsurl = {https://ui.adsabs.harvard.edu/abs/2024RNAAS...8....7P},
adsnote = {Provided by the SAO/NASA Astrophysics Data System}
}
```
|
AstroMLab/astrollama-2-70b-chat_aic
|
AstroMLab
| 2024-11-16T19:45:05Z
| 5
| 0
|
transformers
|
[
"transformers",
"pytorch",
"llama",
"text-generation",
"llama-2",
"astronomy",
"astrophysics",
"arxiv",
"en",
"arxiv:2409.19750",
"base_model:meta-llama/Llama-2-70b-hf",
"base_model:finetune:meta-llama/Llama-2-70b-hf",
"license:mit",
"autotrain_compatible",
"text-generation-inference",
"region:us"
] |
text-generation
| 2024-03-14T09:59:21Z
|
---
license: mit
language:
- en
pipeline_tag: text-generation
tags:
- llama-2
- astronomy
- astrophysics
- arxiv
inference: false
base_model:
- meta-llama/Llama-2-70b-hf
---
# AstroLLaMA-2-70B-Chat_AIC
AstroLLaMA-2-70B-Chat_AIC is a specialized chat model for astronomy, developed by fine-tuning the AstroLLaMA-2-70B-Base_AIC model. This model was developed by the AstroMLab team and is, to our best knowledge, one of the first specialized 70B parameter-level LLMs in astronomy designed for instruction-following and chat-based interactions.
## Model Details
- **Base Architecture**: LLaMA-2-70b
- **Base Model**: AstroLLaMA-2-70B-Base_AIC (trained on Abstract, Introduction, and Conclusion sections from arXiv's astro-ph category papers)
- **Fine-tuning Method**: Supervised Fine-Tuning (SFT)
- **SFT Dataset**:
- 10,356 astronomy-centered conversations generated from arXiv abstracts by GPT-4
- Full content of LIMA dataset
- 10,000 samples from Open Orca dataset
- 10,000 samples from UltraChat dataset
- **Training Details**:
- Learning rate: 3 × 10⁻⁷
- Training epochs: 1
- Total batch size: 48
- Maximum token length: 2048
- Warmup ratio: 0.03
- Cosine decay schedule for learning rate reduction
- **Primary Use**: Instruction-following and chat-based interactions for astronomy-related queries
- **Reference**: [Pan et al. 2024](https://arxiv.org/abs/2409.19750)
## Using the model for chat
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
import torch
# Load the model and tokenizer
tokenizer = AutoTokenizer.from_pretrained("AstroMLab/astrollama-2-70b-chat_aic")
model = AutoModelForCausalLM.from_pretrained("AstroMLab/astrollama-2-70b-chat_aic", device_map="auto")
# Function to generate a response
def generate_response(prompt, max_length=512):
full_prompt = f"###Human: {prompt}\n\n###Assistant:"
inputs = tokenizer(full_prompt, return_tensors="pt", truncation=True, max_length=max_length)
inputs = inputs.to(model.device)
# Generate a response
with torch.no_grad():
outputs = model.generate(
**inputs,
max_length=max_length,
num_return_sequences=1,
do_sample=True,
pad_token_id=tokenizer.eos_token_id,
eos_token_id=tokenizer.encode("###Human:", add_special_tokens=False)[0]
)
# Decode and return the response
response = tokenizer.decode(outputs[0], skip_special_tokens=False)
# Extract only the Assistant's response
assistant_response = response.split("###Assistant:")[-1].strip()
return assistant_response
# Example usage
user_input = "What are the main components of a galaxy?"
response = generate_response(user_input)
print(f"Human: {user_input}")
print(f"Assistant: {response}")
```
## Model Performance and Limitations
While the AstroLLaMA-2-70B-Base_AIC model demonstrated significant improvements over its baseline LLaMA-2-70B model, the chat version (AstroLLaMA-2-70B-Chat_AIC) experiences performance degradation due to limitations in the SFT process. Here's a performance comparison:
| Model | Score (%) |
|-------|-----------|
| **AstroSage-LLaMA-3.1-8B (AstroMLab)** | **80.9** |
| **<span style="color:green">AstroLLaMA-2-70B-Base (AstroMLab)</span>** | **<span style="color:green">76.0</span>** |
| LLaMA-3.1-8B | 73.7 |
| LLaMA-2-70B | 70.7 |
| Gemma-2-9B | 71.5 |
| Qwen-2.5-7B | 70.4 |
| Yi-1.5-9B | 68.4 |
| InternLM-2.5-7B | 64.5 |
| **<span style="color:green">AstroLLaMA-2-70B-Chat (AstroMLab)</span>** | **<span style="color:green">64.7</span>** |
| Mistral-7B-v0.3 | 63.9 |
| ChatGLM3-6B | 50.4 |
Key limitations:
1. **SFT Dataset Limitations**: The current SFT dataset, with only 30,000 Q&As (many not astronomy-focused), has proven inadequate for maintaining the base model's performance.
2. **Performance Degradation**: The chat model's performance (64.7%) is significantly lower than the base model (76.0%), indicating an 11.3-point decrement due to the SFT process.
3. **General Knowledge vs. Specialized Knowledge**: The current SFT process appears to deviate the model towards general answers, potentially at the cost of specialized astronomical knowledge.
These limitations underscore the challenges in developing specialized chat models and the critical importance of both the quantity and quality of training data, especially for the SFT process.
This model is released primarily for reproducibility purposes, allowing researchers to track the development process and compare different iterations of AstroLLaMA models.
For optimal performance and the most up-to-date capabilities in astronomy-related tasks, we recommend using AstroSage-LLaMA-3.1-8B, where these limitations have been addressed through expanded training data and refined fine-tuning processes.
## Ethical Considerations
While this model is designed for scientific use, users should be mindful of potential misuse, such as generating misleading scientific content. Always verify model outputs against peer-reviewed sources for critical applications.
## Citation
If you use this model in your research, please cite:
```
@ARTICLE{2024arXiv240919750P,
author = {{Pan}, Rui and {Dung Nguyen}, Tuan and {Arora}, Hardik and {Accomazzi}, Alberto and {Ghosal}, Tirthankar and {Ting}, Yuan-Sen},
title = "{AstroMLab 2: AstroLLaMA-2-70B Model and Benchmarking Specialised LLMs for Astronomy}",
journal = {arXiv e-prints},
keywords = {Astrophysics - Instrumentation and Methods for Astrophysics, Computer Science - Computation and Language},
year = 2024,
month = sep,
eid = {arXiv:2409.19750},
pages = {arXiv:2409.19750},
doi = {10.48550/arXiv.2409.19750},
archivePrefix = {arXiv},
eprint = {2409.19750},
primaryClass = {astro-ph.IM},
adsurl = {https://ui.adsabs.harvard.edu/abs/2024arXiv240919750P},
adsnote = {Provided by the SAO/NASA Astrophysics Data System}
}
```
|
AstroMLab/astrollama-2-70b-base_aic
|
AstroMLab
| 2024-11-16T19:44:20Z
| 4
| 0
|
transformers
|
[
"transformers",
"pytorch",
"llama",
"text-generation",
"llama-2",
"astronomy",
"astrophysics",
"arxiv",
"en",
"arxiv:2409.19750",
"arxiv:2407.11194",
"base_model:meta-llama/Llama-2-70b-hf",
"base_model:finetune:meta-llama/Llama-2-70b-hf",
"license:mit",
"autotrain_compatible",
"text-generation-inference",
"region:us"
] |
text-generation
| 2024-03-14T09:52:41Z
|
---
license: mit
language:
- en
pipeline_tag: text-generation
tags:
- llama-2
- astronomy
- astrophysics
- arxiv
inference: false
base_model:
- meta-llama/Llama-2-70b-hf
---
# AstroLLaMA-2-70B-Base_AIC
AstroLLaMA-2-70B-Base_AIC is a specialized base language model for astronomy, developed by fine-tuning Meta's LLaMA-2-70b architecture on astronomical literature. This model was developed by the AstroMLab team and is, to our best knowledge, the first specialized 70B parameter-level LLM in astronomy. It is designed for next token prediction tasks and is not an instruct/chat model.
## Model Details
- **Base Architecture**: LLaMA-2-70b
- **Training Data**: Abstract, Introduction, and Conclusion (AIC) sections from arXiv's astro-ph category papers (from arXiv's inception up to July 2023)
- **Data Processing**: The training data was derived from LaTeX source files using regex-based extraction methods to identify and extract the relevant sections (Abstract, Introduction, and Conclusion).
- **Fine-tuning Method**: Continual Pre-Training (CPT) using the LMFlow framework
- **Training Details**:
- Learning rate: 2 × 10⁻⁵
- Total batch size: 160
- Maximum token length: 2048
- Warmup ratio: 0.03
- Cosine decay schedule for learning rate reduction
- Training duration: 1 epoch (approximately 2,000 A100 GPU hours)
- **Primary Use**: Next token prediction for astronomy-related text generation and analysis
- **Reference**: [Pan et al. 2024](https://arxiv.org/abs/2409.19750)
## Generating text from a prompt
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
import torch
# Load the model and tokenizer
tokenizer = AutoTokenizer.from_pretrained("AstroMLab/astrollama-2-70b-base_aic")
model = AutoModelForCausalLM.from_pretrained("AstroMLab/astrollama-2-70b-base_aic", device_map="auto")
# Create the pipeline with explicit truncation
from transformers import pipeline
generator = pipeline(
"text-generation",
model=model,
tokenizer=tokenizer,
device_map="auto",
truncation=True,
max_length=512
)
# Example prompt from an astronomy paper
prompt = "In this letter, we report the discovery of the highest redshift, " \
"heavily obscured, radio-loud QSO candidate selected using JWST NIRCam/MIRI, " \
"mid-IR, sub-mm, and radio imaging in the COSMOS-Web field. "
# Set seed for reproducibility
torch.manual_seed(42)
# Generate text
generated_text = generator(prompt, do_sample=True)
print(generated_text[0]['generated_text'])
```
## Model Performance and Significance
AstroLLaMA-2-70B-Base_AIC demonstrates notable improvements over its baseline LLaMA-2-70B model, marking a crucial step in specialized astronomical LLMs. Here's a performance comparison chart based upon the astronomical benchmarking Q&A as described in [Ting et al. 2024](https://arxiv.org/abs/2407.11194):
| Model | Score (%) |
|-------|-----------|
| **AstroSage-LLaMA-3.1-8B (AstroMLab)** | **80.9** |
| **<span style="color:green">AstroLLaMA-2-70B-Base (AstroMLab)</span>** | **<span style="color:green">76.0</span>** |
| LLaMA-3.1-8B | 73.7 |
| LLaMA-2-70B | 70.7 |
| Gemma-2-9B | 71.5 |
| Qwen-2.5-7B | 70.4 |
| Yi-1.5-9B | 68.4 |
| InternLM-2.5-7B | 64.5 |
| Mistral-7B-v0.3 | 63.9 |
| ChatGLM3-6B | 50.4 |
It demonstrates that training specialized LLMs can be effective, especially at larger model scales.
## Ethical Considerations
While this model is designed for scientific use, users should be mindful of potential misuse, such as generating misleading scientific content. Always verify model outputs against peer-reviewed sources for critical applications.
## Citation
If you use this model in your research, please cite:
```
@ARTICLE{2024arXiv240919750P,
author = {{Pan}, Rui and {Dung Nguyen}, Tuan and {Arora}, Hardik and {Accomazzi}, Alberto and {Ghosal}, Tirthankar and {Ting}, Yuan-Sen},
title = "{AstroMLab 2: AstroLLaMA-2-70B Model and Benchmarking Specialised LLMs for Astronomy}",
journal = {arXiv e-prints},
keywords = {Astrophysics - Instrumentation and Methods for Astrophysics, Computer Science - Computation and Language},
year = 2024,
month = sep,
eid = {arXiv:2409.19750},
pages = {arXiv:2409.19750},
doi = {10.48550/arXiv.2409.19750},
archivePrefix = {arXiv},
eprint = {2409.19750},
primaryClass = {astro-ph.IM},
adsurl = {https://ui.adsabs.harvard.edu/abs/2024arXiv240919750P},
adsnote = {Provided by the SAO/NASA Astrophysics Data System}
}
```
|
Nutanix/llama-30b_checkpoint-1100_20241116-190945-merged
|
Nutanix
| 2024-11-16T19:33:32Z
| 6
| 0
|
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2024-11-16T19:10:10Z
|
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
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#### Hardware
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|
mradermacher/Fireball-Meta-Llama-3.1-8B-Instruct-0.001-128K-auto-i1-GGUF
|
mradermacher
| 2024-11-16T19:32:41Z
| 20
| 0
|
transformers
|
[
"transformers",
"gguf",
"text-generation-inference",
"unsloth",
"llama",
"trl",
"en",
"base_model:EpistemeAI/Fireball-Meta-Llama-3.1-8B-Instruct-0.001-128K-auto",
"base_model:quantized:EpistemeAI/Fireball-Meta-Llama-3.1-8B-Instruct-0.001-128K-auto",
"license:apache-2.0",
"endpoints_compatible",
"region:us",
"imatrix",
"conversational"
] | null | 2024-11-16T00:26:00Z
|
---
base_model: EpistemeAI/Fireball-Meta-Llama-3.1-8B-Instruct-0.001-128K-auto
language:
- en
library_name: transformers
license: apache-2.0
quantized_by: mradermacher
tags:
- text-generation-inference
- transformers
- unsloth
- llama
- trl
---
## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
<!-- ### tags: nicoboss -->
weighted/imatrix quants of https://huggingface.co/EpistemeAI/Fireball-Meta-Llama-3.1-8B-Instruct-0.001-128K-auto
<!-- provided-files -->
static quants are available at https://huggingface.co/mradermacher/Fireball-Meta-Llama-3.1-8B-Instruct-0.001-128K-auto-GGUF
## Usage
If you are unsure how to use GGUF files, refer to one of [TheBloke's
READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for
more details, including on how to concatenate multi-part files.
## Provided Quants
(sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants)
| Link | Type | Size/GB | Notes |
|:-----|:-----|--------:|:------|
| [GGUF](https://huggingface.co/mradermacher/Fireball-Meta-Llama-3.1-8B-Instruct-0.001-128K-auto-i1-GGUF/resolve/main/Fireball-Meta-Llama-3.1-8B-Instruct-0.001-128K-auto.i1-IQ1_S.gguf) | i1-IQ1_S | 2.1 | for the desperate |
| [GGUF](https://huggingface.co/mradermacher/Fireball-Meta-Llama-3.1-8B-Instruct-0.001-128K-auto-i1-GGUF/resolve/main/Fireball-Meta-Llama-3.1-8B-Instruct-0.001-128K-auto.i1-IQ1_M.gguf) | i1-IQ1_M | 2.3 | mostly desperate |
| [GGUF](https://huggingface.co/mradermacher/Fireball-Meta-Llama-3.1-8B-Instruct-0.001-128K-auto-i1-GGUF/resolve/main/Fireball-Meta-Llama-3.1-8B-Instruct-0.001-128K-auto.i1-IQ2_XXS.gguf) | i1-IQ2_XXS | 2.5 | |
| [GGUF](https://huggingface.co/mradermacher/Fireball-Meta-Llama-3.1-8B-Instruct-0.001-128K-auto-i1-GGUF/resolve/main/Fireball-Meta-Llama-3.1-8B-Instruct-0.001-128K-auto.i1-IQ2_XS.gguf) | i1-IQ2_XS | 2.7 | |
| [GGUF](https://huggingface.co/mradermacher/Fireball-Meta-Llama-3.1-8B-Instruct-0.001-128K-auto-i1-GGUF/resolve/main/Fireball-Meta-Llama-3.1-8B-Instruct-0.001-128K-auto.i1-IQ2_S.gguf) | i1-IQ2_S | 2.9 | |
| [GGUF](https://huggingface.co/mradermacher/Fireball-Meta-Llama-3.1-8B-Instruct-0.001-128K-auto-i1-GGUF/resolve/main/Fireball-Meta-Llama-3.1-8B-Instruct-0.001-128K-auto.i1-IQ2_M.gguf) | i1-IQ2_M | 3.0 | |
| [GGUF](https://huggingface.co/mradermacher/Fireball-Meta-Llama-3.1-8B-Instruct-0.001-128K-auto-i1-GGUF/resolve/main/Fireball-Meta-Llama-3.1-8B-Instruct-0.001-128K-auto.i1-Q2_K.gguf) | i1-Q2_K | 3.3 | IQ3_XXS probably better |
| [GGUF](https://huggingface.co/mradermacher/Fireball-Meta-Llama-3.1-8B-Instruct-0.001-128K-auto-i1-GGUF/resolve/main/Fireball-Meta-Llama-3.1-8B-Instruct-0.001-128K-auto.i1-IQ3_XXS.gguf) | i1-IQ3_XXS | 3.4 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/Fireball-Meta-Llama-3.1-8B-Instruct-0.001-128K-auto-i1-GGUF/resolve/main/Fireball-Meta-Llama-3.1-8B-Instruct-0.001-128K-auto.i1-IQ3_XS.gguf) | i1-IQ3_XS | 3.6 | |
| [GGUF](https://huggingface.co/mradermacher/Fireball-Meta-Llama-3.1-8B-Instruct-0.001-128K-auto-i1-GGUF/resolve/main/Fireball-Meta-Llama-3.1-8B-Instruct-0.001-128K-auto.i1-Q3_K_S.gguf) | i1-Q3_K_S | 3.8 | IQ3_XS probably better |
| [GGUF](https://huggingface.co/mradermacher/Fireball-Meta-Llama-3.1-8B-Instruct-0.001-128K-auto-i1-GGUF/resolve/main/Fireball-Meta-Llama-3.1-8B-Instruct-0.001-128K-auto.i1-IQ3_S.gguf) | i1-IQ3_S | 3.8 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/Fireball-Meta-Llama-3.1-8B-Instruct-0.001-128K-auto-i1-GGUF/resolve/main/Fireball-Meta-Llama-3.1-8B-Instruct-0.001-128K-auto.i1-IQ3_M.gguf) | i1-IQ3_M | 3.9 | |
| [GGUF](https://huggingface.co/mradermacher/Fireball-Meta-Llama-3.1-8B-Instruct-0.001-128K-auto-i1-GGUF/resolve/main/Fireball-Meta-Llama-3.1-8B-Instruct-0.001-128K-auto.i1-Q3_K_M.gguf) | i1-Q3_K_M | 4.1 | IQ3_S probably better |
| [GGUF](https://huggingface.co/mradermacher/Fireball-Meta-Llama-3.1-8B-Instruct-0.001-128K-auto-i1-GGUF/resolve/main/Fireball-Meta-Llama-3.1-8B-Instruct-0.001-128K-auto.i1-Q3_K_L.gguf) | i1-Q3_K_L | 4.4 | IQ3_M probably better |
| [GGUF](https://huggingface.co/mradermacher/Fireball-Meta-Llama-3.1-8B-Instruct-0.001-128K-auto-i1-GGUF/resolve/main/Fireball-Meta-Llama-3.1-8B-Instruct-0.001-128K-auto.i1-IQ4_XS.gguf) | i1-IQ4_XS | 4.5 | |
| [GGUF](https://huggingface.co/mradermacher/Fireball-Meta-Llama-3.1-8B-Instruct-0.001-128K-auto-i1-GGUF/resolve/main/Fireball-Meta-Llama-3.1-8B-Instruct-0.001-128K-auto.i1-Q4_0_4_4.gguf) | i1-Q4_0_4_4 | 4.8 | fast on arm, low quality |
| [GGUF](https://huggingface.co/mradermacher/Fireball-Meta-Llama-3.1-8B-Instruct-0.001-128K-auto-i1-GGUF/resolve/main/Fireball-Meta-Llama-3.1-8B-Instruct-0.001-128K-auto.i1-Q4_0_4_8.gguf) | i1-Q4_0_4_8 | 4.8 | fast on arm+i8mm, low quality |
| [GGUF](https://huggingface.co/mradermacher/Fireball-Meta-Llama-3.1-8B-Instruct-0.001-128K-auto-i1-GGUF/resolve/main/Fireball-Meta-Llama-3.1-8B-Instruct-0.001-128K-auto.i1-Q4_0_8_8.gguf) | i1-Q4_0_8_8 | 4.8 | fast on arm+sve, low quality |
| [GGUF](https://huggingface.co/mradermacher/Fireball-Meta-Llama-3.1-8B-Instruct-0.001-128K-auto-i1-GGUF/resolve/main/Fireball-Meta-Llama-3.1-8B-Instruct-0.001-128K-auto.i1-Q4_0.gguf) | i1-Q4_0 | 4.8 | fast, low quality |
| [GGUF](https://huggingface.co/mradermacher/Fireball-Meta-Llama-3.1-8B-Instruct-0.001-128K-auto-i1-GGUF/resolve/main/Fireball-Meta-Llama-3.1-8B-Instruct-0.001-128K-auto.i1-Q4_K_S.gguf) | i1-Q4_K_S | 4.8 | optimal size/speed/quality |
| [GGUF](https://huggingface.co/mradermacher/Fireball-Meta-Llama-3.1-8B-Instruct-0.001-128K-auto-i1-GGUF/resolve/main/Fireball-Meta-Llama-3.1-8B-Instruct-0.001-128K-auto.i1-Q4_K_M.gguf) | i1-Q4_K_M | 5.0 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Fireball-Meta-Llama-3.1-8B-Instruct-0.001-128K-auto-i1-GGUF/resolve/main/Fireball-Meta-Llama-3.1-8B-Instruct-0.001-128K-auto.i1-Q5_K_S.gguf) | i1-Q5_K_S | 5.7 | |
| [GGUF](https://huggingface.co/mradermacher/Fireball-Meta-Llama-3.1-8B-Instruct-0.001-128K-auto-i1-GGUF/resolve/main/Fireball-Meta-Llama-3.1-8B-Instruct-0.001-128K-auto.i1-Q5_K_M.gguf) | i1-Q5_K_M | 5.8 | |
| [GGUF](https://huggingface.co/mradermacher/Fireball-Meta-Llama-3.1-8B-Instruct-0.001-128K-auto-i1-GGUF/resolve/main/Fireball-Meta-Llama-3.1-8B-Instruct-0.001-128K-auto.i1-Q6_K.gguf) | i1-Q6_K | 6.7 | practically like static Q6_K |
Here is a handy graph by ikawrakow comparing some lower-quality quant
types (lower is better):

And here are Artefact2's thoughts on the matter:
https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9
## FAQ / Model Request
See https://huggingface.co/mradermacher/model_requests for some answers to
questions you might have and/or if you want some other model quantized.
## Thanks
I thank my company, [nethype GmbH](https://www.nethype.de/), for letting
me use its servers and providing upgrades to my workstation to enable
this work in my free time. Additional thanks to [@nicoboss](https://huggingface.co/nicoboss) for giving me access to his private supercomputer, enabling me to provide many more imatrix quants, at much higher quality, than I would otherwise be able to.
<!-- end -->
|
gokceuludogan/turna_tr_hateprint_5e6_w0.1_
|
gokceuludogan
| 2024-11-16T19:24:51Z
| 34
| 0
|
transformers
|
[
"transformers",
"safetensors",
"t5",
"arxiv:1910.09700",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | null | 2024-11-16T19:23:35Z
|
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
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## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
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## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
research-dump/twitter-roberta-base_wikidata_ent_outcome_prediction_v1
|
research-dump
| 2024-11-16T19:20:58Z
| 104
| 0
|
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"roberta",
"text-classification",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2024-11-14T06:33:01Z
|
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
Kukedlc/NeuralGemma-2B-Spanish
|
Kukedlc
| 2024-11-16T19:12:23Z
| 9
| 0
| null |
[
"safetensors",
"gemma2",
"merge",
"mergekit",
"lazymergekit",
"gemma-2-2b-it/2",
"Kukedlc/NeuralGemma2-2b-Spanish",
"Kukedlc/fusion_model_2",
"base_model:Kukedlc/NeuralGemma2-2b-Spanish",
"base_model:merge:Kukedlc/NeuralGemma2-2b-Spanish",
"base_model:Kukedlc/fusion_model_2",
"base_model:merge:Kukedlc/fusion_model_2",
"region:us"
] | null | 2024-11-16T19:04:01Z
|
---
base_model:
- gemma-2-2b-it/2
- Kukedlc/NeuralGemma2-2b-Spanish
- Kukedlc/fusion_model_2
tags:
- merge
- mergekit
- lazymergekit
- gemma-2-2b-it/2
- Kukedlc/NeuralGemma2-2b-Spanish
- Kukedlc/fusion_model_2
---
# NeuralGemma-2B-Spanish
NeuralGemma-2B-Spanish is a merge of the following models using [LazyMergekit](https://colab.research.google.com/drive/1obulZ1ROXHjYLn6PPZJwRR6GzgQogxxb?usp=sharing):
* [gemma-2-2b-it/2](https://huggingface.co/gemma-2-2b-it/2)
* [Kukedlc/NeuralGemma2-2b-Spanish](https://huggingface.co/Kukedlc/NeuralGemma2-2b-Spanish)
* [Kukedlc/fusion_model_2](https://huggingface.co/Kukedlc/fusion_model_2)
## 🧩 Configuration
```yaml
models:
- model: gemma-2-2b/2
# No parameters necessary for base model
- model: gemma-2-2b-it/2
parameters:
density: 0.53
weight: 0.4
- model: Kukedlc/NeuralGemma2-2b-Spanish
parameters:
density: 0.44
weight: 0.2
- model: Kukedlc/fusion_model_2
parameters:
density: 0.66
weight: 0.4
merge_method: dare_ties
base_model: gemma-2-2b/2
parameters:
int8_mask: true
dtype: bfloat16
```
## 💻 Usage
```python
!pip install -qU transformers accelerate
from transformers import AutoTokenizer
import transformers
import torch
model = "Kukedlc/NeuralGemma-2B-Spanish"
messages = [{"role": "user", "content": "What is a large language model?"}]
tokenizer = AutoTokenizer.from_pretrained(model)
prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
pipeline = transformers.pipeline(
"text-generation",
model=model,
torch_dtype=torch.float16,
device_map="auto",
)
outputs = pipeline(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95)
print(outputs[0]["generated_text"])
```
|
mradermacher/internlm2-chat-7b-sft-i1-GGUF
|
mradermacher
| 2024-11-16T19:07:30Z
| 7
| 0
|
transformers
|
[
"transformers",
"gguf",
"en",
"base_model:internlm/internlm2-chat-7b-sft",
"base_model:quantized:internlm/internlm2-chat-7b-sft",
"license:other",
"endpoints_compatible",
"region:us",
"imatrix",
"conversational"
] | null | 2024-11-16T17:38:23Z
|
---
base_model: internlm/internlm2-chat-7b-sft
language:
- en
library_name: transformers
license: other
quantized_by: mradermacher
---
## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
<!-- ### tags: nicoboss -->
weighted/imatrix quants of https://huggingface.co/internlm/internlm2-chat-7b-sft
<!-- provided-files -->
static quants are available at https://huggingface.co/mradermacher/internlm2-chat-7b-sft-GGUF
## Usage
If you are unsure how to use GGUF files, refer to one of [TheBloke's
READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for
more details, including on how to concatenate multi-part files.
## Provided Quants
(sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants)
| Link | Type | Size/GB | Notes |
|:-----|:-----|--------:|:------|
| [GGUF](https://huggingface.co/mradermacher/internlm2-chat-7b-sft-i1-GGUF/resolve/main/internlm2-chat-7b-sft.i1-IQ1_S.gguf) | i1-IQ1_S | 2.0 | for the desperate |
| [GGUF](https://huggingface.co/mradermacher/internlm2-chat-7b-sft-i1-GGUF/resolve/main/internlm2-chat-7b-sft.i1-IQ1_M.gguf) | i1-IQ1_M | 2.1 | mostly desperate |
| [GGUF](https://huggingface.co/mradermacher/internlm2-chat-7b-sft-i1-GGUF/resolve/main/internlm2-chat-7b-sft.i1-IQ2_XXS.gguf) | i1-IQ2_XXS | 2.3 | |
| [GGUF](https://huggingface.co/mradermacher/internlm2-chat-7b-sft-i1-GGUF/resolve/main/internlm2-chat-7b-sft.i1-IQ2_XS.gguf) | i1-IQ2_XS | 2.6 | |
| [GGUF](https://huggingface.co/mradermacher/internlm2-chat-7b-sft-i1-GGUF/resolve/main/internlm2-chat-7b-sft.i1-IQ2_S.gguf) | i1-IQ2_S | 2.7 | |
| [GGUF](https://huggingface.co/mradermacher/internlm2-chat-7b-sft-i1-GGUF/resolve/main/internlm2-chat-7b-sft.i1-IQ2_M.gguf) | i1-IQ2_M | 2.9 | |
| [GGUF](https://huggingface.co/mradermacher/internlm2-chat-7b-sft-i1-GGUF/resolve/main/internlm2-chat-7b-sft.i1-Q2_K.gguf) | i1-Q2_K | 3.1 | IQ3_XXS probably better |
| [GGUF](https://huggingface.co/mradermacher/internlm2-chat-7b-sft-i1-GGUF/resolve/main/internlm2-chat-7b-sft.i1-IQ3_XXS.gguf) | i1-IQ3_XXS | 3.2 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/internlm2-chat-7b-sft-i1-GGUF/resolve/main/internlm2-chat-7b-sft.i1-IQ3_XS.gguf) | i1-IQ3_XS | 3.4 | |
| [GGUF](https://huggingface.co/mradermacher/internlm2-chat-7b-sft-i1-GGUF/resolve/main/internlm2-chat-7b-sft.i1-Q3_K_S.gguf) | i1-Q3_K_S | 3.6 | IQ3_XS probably better |
| [GGUF](https://huggingface.co/mradermacher/internlm2-chat-7b-sft-i1-GGUF/resolve/main/internlm2-chat-7b-sft.i1-IQ3_S.gguf) | i1-IQ3_S | 3.6 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/internlm2-chat-7b-sft-i1-GGUF/resolve/main/internlm2-chat-7b-sft.i1-IQ3_M.gguf) | i1-IQ3_M | 3.7 | |
| [GGUF](https://huggingface.co/mradermacher/internlm2-chat-7b-sft-i1-GGUF/resolve/main/internlm2-chat-7b-sft.i1-Q3_K_M.gguf) | i1-Q3_K_M | 3.9 | IQ3_S probably better |
| [GGUF](https://huggingface.co/mradermacher/internlm2-chat-7b-sft-i1-GGUF/resolve/main/internlm2-chat-7b-sft.i1-Q3_K_L.gguf) | i1-Q3_K_L | 4.2 | IQ3_M probably better |
| [GGUF](https://huggingface.co/mradermacher/internlm2-chat-7b-sft-i1-GGUF/resolve/main/internlm2-chat-7b-sft.i1-IQ4_XS.gguf) | i1-IQ4_XS | 4.3 | |
| [GGUF](https://huggingface.co/mradermacher/internlm2-chat-7b-sft-i1-GGUF/resolve/main/internlm2-chat-7b-sft.i1-Q4_0_4_4.gguf) | i1-Q4_0_4_4 | 4.6 | fast on arm, low quality |
| [GGUF](https://huggingface.co/mradermacher/internlm2-chat-7b-sft-i1-GGUF/resolve/main/internlm2-chat-7b-sft.i1-Q4_0_4_8.gguf) | i1-Q4_0_4_8 | 4.6 | fast on arm+i8mm, low quality |
| [GGUF](https://huggingface.co/mradermacher/internlm2-chat-7b-sft-i1-GGUF/resolve/main/internlm2-chat-7b-sft.i1-Q4_0_8_8.gguf) | i1-Q4_0_8_8 | 4.6 | fast on arm+sve, low quality |
| [GGUF](https://huggingface.co/mradermacher/internlm2-chat-7b-sft-i1-GGUF/resolve/main/internlm2-chat-7b-sft.i1-Q4_0.gguf) | i1-Q4_0 | 4.6 | fast, low quality |
| [GGUF](https://huggingface.co/mradermacher/internlm2-chat-7b-sft-i1-GGUF/resolve/main/internlm2-chat-7b-sft.i1-Q4_K_S.gguf) | i1-Q4_K_S | 4.6 | optimal size/speed/quality |
| [GGUF](https://huggingface.co/mradermacher/internlm2-chat-7b-sft-i1-GGUF/resolve/main/internlm2-chat-7b-sft.i1-Q4_K_M.gguf) | i1-Q4_K_M | 4.8 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/internlm2-chat-7b-sft-i1-GGUF/resolve/main/internlm2-chat-7b-sft.i1-Q5_K_S.gguf) | i1-Q5_K_S | 5.5 | |
| [GGUF](https://huggingface.co/mradermacher/internlm2-chat-7b-sft-i1-GGUF/resolve/main/internlm2-chat-7b-sft.i1-Q5_K_M.gguf) | i1-Q5_K_M | 5.6 | |
| [GGUF](https://huggingface.co/mradermacher/internlm2-chat-7b-sft-i1-GGUF/resolve/main/internlm2-chat-7b-sft.i1-Q6_K.gguf) | i1-Q6_K | 6.5 | practically like static Q6_K |
Here is a handy graph by ikawrakow comparing some lower-quality quant
types (lower is better):

And here are Artefact2's thoughts on the matter:
https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9
## FAQ / Model Request
See https://huggingface.co/mradermacher/model_requests for some answers to
questions you might have and/or if you want some other model quantized.
## Thanks
I thank my company, [nethype GmbH](https://www.nethype.de/), for letting
me use its servers and providing upgrades to my workstation to enable
this work in my free time. Additional thanks to [@nicoboss](https://huggingface.co/nicoboss) for giving me access to his private supercomputer, enabling me to provide many more imatrix quants, at much higher quality, than I would otherwise be able to.
<!-- end -->
|
iwantmorebugs/xlm-roberta-base-finetuned-panx-de
|
iwantmorebugs
| 2024-11-16T19:07:26Z
| 134
| 0
|
transformers
|
[
"transformers",
"safetensors",
"xlm-roberta",
"token-classification",
"generated_from_trainer",
"base_model:FacebookAI/xlm-roberta-base",
"base_model:finetune:FacebookAI/xlm-roberta-base",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2024-11-03T22:27:58Z
|
---
library_name: transformers
license: mit
base_model: xlm-roberta-base
tags:
- generated_from_trainer
metrics:
- f1
model-index:
- name: xlm-roberta-base-finetuned-panx-de
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# xlm-roberta-base-finetuned-panx-de
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1376
- F1: 0.8601
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 24
- eval_batch_size: 24
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| No log | 1.0 | 525 | 0.1507 | 0.8244 |
| No log | 2.0 | 1050 | 0.1386 | 0.8463 |
| No log | 3.0 | 1575 | 0.1376 | 0.8601 |
### Framework versions
- Transformers 4.44.2
- Pytorch 2.4.1+cu124
- Datasets 3.0.1
- Tokenizers 0.19.1
|
Vincent-Fan/Llama-3.2-1B-Q4_0-GGUF
|
Vincent-Fan
| 2024-11-16T19:06:26Z
| 38
| 0
|
transformers
|
[
"transformers",
"gguf",
"facebook",
"meta",
"pytorch",
"llama",
"llama-3",
"llama-cpp",
"gguf-my-repo",
"text-generation",
"en",
"de",
"fr",
"it",
"pt",
"hi",
"es",
"th",
"base_model:meta-llama/Llama-3.2-1B",
"base_model:quantized:meta-llama/Llama-3.2-1B",
"license:llama3.2",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2024-11-16T19:06:19Z
|
---
language:
- en
- de
- fr
- it
- pt
- hi
- es
- th
library_name: transformers
pipeline_tag: text-generation
tags:
- facebook
- meta
- pytorch
- llama
- llama-3
- llama-cpp
- gguf-my-repo
license: llama3.2
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\ substances\n 11. Operation of critical infrastructure, transportation technologies,\
\ or heavy machinery\n 12. Self-harm or harm to others, including suicide, cutting,\
\ and eating disorders\n 13. Any content intended to incite or promote violence,\
\ abuse, or any infliction of bodily harm to an individual\n3. Intentionally deceive\
\ or mislead others, including use of Llama 3.2 related to the following:\n 14.\
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\ promoting, or further distributing spam\n 17. Impersonating another individual\
\ without consent, authorization, or legal right\n 18. Representing that the\
\ use of Llama 3.2 or outputs are human-generated\n 19. Generating or facilitating\
\ false online engagement, including fake reviews and other means of fake online\
\ engagement \n4. Fail to appropriately disclose to end users any known dangers\
\ of your AI system 5. Interact with third party tools, models, or software designed\
\ to generate unlawful content or engage in unlawful or harmful conduct and/or represent\
\ that the outputs of such tools, models, or software are associated with Meta or\
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\ rights granted under Section 1(a) of the Llama 3.2 Community License Agreement\
\ are not being granted to you if you are an individual domiciled in, or a company\
\ with a principal place of business in, the European Union. This restriction does\
\ not apply to end users of a product or service that incorporates any such multimodal\
\ models.\n\nPlease report any violation of this Policy, software “bug,” or other\
\ problems that could lead to a violation of this Policy through one of the following\
\ means:\n\n* Reporting issues with the model: [https://github.com/meta-llama/llama-models/issues](https://l.workplace.com/l.php?u=https%3A%2F%2Fgithub.com%2Fmeta-llama%2Fllama-models%2Fissues&h=AT0qV8W9BFT6NwihiOHRuKYQM_UnkzN_NmHMy91OT55gkLpgi4kQupHUl0ssR4dQsIQ8n3tfd0vtkobvsEvt1l4Ic6GXI2EeuHV8N08OG2WnbAmm0FL4ObkazC6G_256vN0lN9DsykCvCqGZ)\n\
* Reporting risky content generated by the model: [developers.facebook.com/llama_output_feedback](http://developers.facebook.com/llama_output_feedback)\n\
* Reporting bugs and security concerns: [facebook.com/whitehat/info](http://facebook.com/whitehat/info)\n\
* Reporting violations of the Acceptable Use Policy or unlicensed uses of Llama\
\ 3.2: [email protected]"
extra_gated_fields:
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type: select
options:
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? By clicking Submit below I accept the terms of the license and acknowledge that
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extra_gated_description: The information you provide will be collected, stored, processed
and shared in accordance with the [Meta Privacy Policy](https://www.facebook.com/privacy/policy/).
extra_gated_button_content: Submit
base_model: meta-llama/Llama-3.2-1B
---
# Vincent-Fan/Llama-3.2-1B-Q4_0-GGUF
This model was converted to GGUF format from [`meta-llama/Llama-3.2-1B`](https://huggingface.co/meta-llama/Llama-3.2-1B) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space.
Refer to the [original model card](https://huggingface.co/meta-llama/Llama-3.2-1B) for more details on the model.
## Use with llama.cpp
Install llama.cpp through brew (works on Mac and Linux)
```bash
brew install llama.cpp
```
Invoke the llama.cpp server or the CLI.
### CLI:
```bash
llama-cli --hf-repo Vincent-Fan/Llama-3.2-1B-Q4_0-GGUF --hf-file llama-3.2-1b-q4_0.gguf -p "The meaning to life and the universe is"
```
### Server:
```bash
llama-server --hf-repo Vincent-Fan/Llama-3.2-1B-Q4_0-GGUF --hf-file llama-3.2-1b-q4_0.gguf -c 2048
```
Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well.
Step 1: Clone llama.cpp from GitHub.
```
git clone https://github.com/ggerganov/llama.cpp
```
Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux).
```
cd llama.cpp && LLAMA_CURL=1 make
```
Step 3: Run inference through the main binary.
```
./llama-cli --hf-repo Vincent-Fan/Llama-3.2-1B-Q4_0-GGUF --hf-file llama-3.2-1b-q4_0.gguf -p "The meaning to life and the universe is"
```
or
```
./llama-server --hf-repo Vincent-Fan/Llama-3.2-1B-Q4_0-GGUF --hf-file llama-3.2-1b-q4_0.gguf -c 2048
```
|
MayBashendy/Arabic_FineTuningAraBERT_AugV4_k30_task2_organization_fold0
|
MayBashendy
| 2024-11-16T19:04:06Z
| 181
| 0
|
transformers
|
[
"transformers",
"safetensors",
"bert",
"text-classification",
"generated_from_trainer",
"base_model:aubmindlab/bert-base-arabertv02",
"base_model:finetune:aubmindlab/bert-base-arabertv02",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2024-11-16T18:35:55Z
|
---
library_name: transformers
base_model: aubmindlab/bert-base-arabertv02
tags:
- generated_from_trainer
model-index:
- name: Arabic_FineTuningAraBERT_AugV4_k30_task2_organization_fold0
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# Arabic_FineTuningAraBERT_AugV4_k30_task2_organization_fold0
This model is a fine-tuned version of [aubmindlab/bert-base-arabertv02](https://huggingface.co/aubmindlab/bert-base-arabertv02) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5840
- Qwk: 0.4538
- Mse: 0.5840
- Rmse: 0.7642
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Qwk | Mse | Rmse |
|:-------------:|:------:|:----:|:---------------:|:-------:|:------:|:------:|
| No log | 0.0061 | 2 | 3.6742 | 0.0 | 3.6742 | 1.9168 |
| No log | 0.0121 | 4 | 2.0757 | 0.0084 | 2.0757 | 1.4407 |
| No log | 0.0182 | 6 | 0.9827 | 0.0 | 0.9827 | 0.9913 |
| No log | 0.0242 | 8 | 0.7301 | 0.0099 | 0.7301 | 0.8545 |
| No log | 0.0303 | 10 | 0.7718 | 0.0258 | 0.7718 | 0.8785 |
| No log | 0.0364 | 12 | 1.0753 | 0.1637 | 1.0753 | 1.0370 |
| No log | 0.0424 | 14 | 1.3053 | 0.1758 | 1.3053 | 1.1425 |
| No log | 0.0485 | 16 | 1.2066 | 0.1840 | 1.2066 | 1.0985 |
| No log | 0.0545 | 18 | 1.0121 | 0.0 | 1.0121 | 1.0060 |
| No log | 0.0606 | 20 | 0.9236 | 0.0 | 0.9236 | 0.9611 |
| No log | 0.0667 | 22 | 0.8210 | -0.0418 | 0.8210 | 0.9061 |
| No log | 0.0727 | 24 | 0.8093 | 0.0 | 0.8093 | 0.8996 |
| No log | 0.0788 | 26 | 0.8539 | 0.0 | 0.8539 | 0.9240 |
| No log | 0.0848 | 28 | 0.8745 | 0.0 | 0.8745 | 0.9352 |
| No log | 0.0909 | 30 | 0.9780 | 0.1637 | 0.9780 | 0.9889 |
| No log | 0.0970 | 32 | 1.0970 | 0.0982 | 1.0970 | 1.0474 |
| No log | 0.1030 | 34 | 1.1453 | 0.2300 | 1.1453 | 1.0702 |
| No log | 0.1091 | 36 | 1.1345 | 0.2167 | 1.1345 | 1.0651 |
| No log | 0.1152 | 38 | 1.0353 | 0.3198 | 1.0353 | 1.0175 |
| No log | 0.1212 | 40 | 1.0428 | 0.2699 | 1.0428 | 1.0212 |
| No log | 0.1273 | 42 | 0.9384 | 0.3198 | 0.9384 | 0.9687 |
| No log | 0.1333 | 44 | 0.7851 | 0.1600 | 0.7851 | 0.8860 |
| No log | 0.1394 | 46 | 0.6937 | 0.0916 | 0.6937 | 0.8329 |
| No log | 0.1455 | 48 | 0.6811 | 0.0916 | 0.6811 | 0.8253 |
| No log | 0.1515 | 50 | 0.7105 | 0.0258 | 0.7105 | 0.8429 |
| No log | 0.1576 | 52 | 0.8085 | 0.1398 | 0.8085 | 0.8992 |
| No log | 0.1636 | 54 | 1.0291 | 0.2015 | 1.0291 | 1.0144 |
| No log | 0.1697 | 56 | 1.2739 | 0.1276 | 1.2739 | 1.1287 |
| No log | 0.1758 | 58 | 1.1995 | 0.1901 | 1.1995 | 1.0952 |
| No log | 0.1818 | 60 | 0.9597 | 0.2597 | 0.9597 | 0.9796 |
| No log | 0.1879 | 62 | 0.9573 | 0.1996 | 0.9573 | 0.9784 |
| No log | 0.1939 | 64 | 1.0038 | 0.1752 | 1.0038 | 1.0019 |
| No log | 0.2 | 66 | 1.1319 | 0.1901 | 1.1319 | 1.0639 |
| No log | 0.2061 | 68 | 1.1279 | 0.1322 | 1.1279 | 1.0620 |
| No log | 0.2121 | 70 | 0.9128 | 0.3277 | 0.9128 | 0.9554 |
| No log | 0.2182 | 72 | 0.6980 | 0.0916 | 0.6980 | 0.8355 |
| No log | 0.2242 | 74 | 0.6572 | 0.1141 | 0.6572 | 0.8107 |
| No log | 0.2303 | 76 | 0.7547 | 0.2986 | 0.7547 | 0.8688 |
| No log | 0.2364 | 78 | 1.0943 | 0.1886 | 1.0943 | 1.0461 |
| No log | 0.2424 | 80 | 1.4537 | 0.1172 | 1.4537 | 1.2057 |
| No log | 0.2485 | 82 | 1.7795 | -0.0312 | 1.7795 | 1.3340 |
| No log | 0.2545 | 84 | 1.6472 | 0.1192 | 1.6472 | 1.2834 |
| No log | 0.2606 | 86 | 1.1798 | 0.0731 | 1.1798 | 1.0862 |
| No log | 0.2667 | 88 | 1.1433 | 0.1527 | 1.1433 | 1.0692 |
| No log | 0.2727 | 90 | 1.1020 | 0.1901 | 1.1020 | 1.0497 |
| No log | 0.2788 | 92 | 1.2217 | 0.1418 | 1.2217 | 1.1053 |
| No log | 0.2848 | 94 | 1.2387 | 0.1649 | 1.2387 | 1.1130 |
| No log | 0.2909 | 96 | 0.9585 | 0.2814 | 0.9585 | 0.9790 |
| No log | 0.2970 | 98 | 0.7951 | 0.2588 | 0.7951 | 0.8917 |
| No log | 0.3030 | 100 | 0.7174 | 0.2727 | 0.7174 | 0.8470 |
| No log | 0.3091 | 102 | 0.7607 | 0.2727 | 0.7607 | 0.8722 |
| No log | 0.3152 | 104 | 0.9026 | 0.3687 | 0.9026 | 0.9501 |
| No log | 0.3212 | 106 | 1.0294 | 0.2304 | 1.0294 | 1.0146 |
| No log | 0.3273 | 108 | 1.1747 | 0.2150 | 1.1747 | 1.0839 |
| No log | 0.3333 | 110 | 1.1647 | 0.2632 | 1.1647 | 1.0792 |
| No log | 0.3394 | 112 | 0.9397 | 0.3037 | 0.9397 | 0.9694 |
| No log | 0.3455 | 114 | 0.7570 | 0.4615 | 0.7570 | 0.8701 |
| No log | 0.3515 | 116 | 0.6915 | 0.4615 | 0.6915 | 0.8316 |
| No log | 0.3576 | 118 | 0.6990 | 0.4615 | 0.6990 | 0.8360 |
| No log | 0.3636 | 120 | 0.8493 | 0.3731 | 0.8493 | 0.9216 |
| No log | 0.3697 | 122 | 1.0305 | 0.2535 | 1.0305 | 1.0151 |
| No log | 0.3758 | 124 | 0.9611 | 0.3770 | 0.9611 | 0.9803 |
| No log | 0.3818 | 126 | 0.7678 | 0.3731 | 0.7678 | 0.8763 |
| No log | 0.3879 | 128 | 0.7553 | 0.3731 | 0.7553 | 0.8691 |
| No log | 0.3939 | 130 | 0.6339 | 0.3687 | 0.6339 | 0.7962 |
| No log | 0.4 | 132 | 0.5653 | 0.3811 | 0.5653 | 0.7518 |
| No log | 0.4061 | 134 | 0.6429 | 0.4913 | 0.6429 | 0.8018 |
| No log | 0.4121 | 136 | 1.0369 | 0.3883 | 1.0369 | 1.0183 |
| No log | 0.4182 | 138 | 1.3083 | 0.2311 | 1.3083 | 1.1438 |
| No log | 0.4242 | 140 | 1.1665 | 0.37 | 1.1665 | 1.0800 |
| No log | 0.4303 | 142 | 1.0013 | 0.4000 | 1.0013 | 1.0006 |
| No log | 0.4364 | 144 | 1.0498 | 0.4000 | 1.0498 | 1.0246 |
| No log | 0.4424 | 146 | 1.0356 | 0.3984 | 1.0356 | 1.0176 |
| No log | 0.4485 | 148 | 0.9755 | 0.4209 | 0.9755 | 0.9877 |
| No log | 0.4545 | 150 | 0.8961 | 0.4209 | 0.8961 | 0.9466 |
| No log | 0.4606 | 152 | 0.9157 | 0.4209 | 0.9157 | 0.9569 |
| No log | 0.4667 | 154 | 1.2999 | 0.3457 | 1.2999 | 1.1401 |
| No log | 0.4727 | 156 | 1.4701 | 0.2364 | 1.4701 | 1.2125 |
| No log | 0.4788 | 158 | 1.2205 | 0.3226 | 1.2205 | 1.1048 |
| No log | 0.4848 | 160 | 0.7869 | 0.4581 | 0.7869 | 0.8871 |
| No log | 0.4909 | 162 | 0.6927 | 0.3980 | 0.6927 | 0.8323 |
| No log | 0.4970 | 164 | 0.8315 | 0.4566 | 0.8314 | 0.9118 |
| No log | 0.5030 | 166 | 1.0514 | 0.4774 | 1.0514 | 1.0254 |
| No log | 0.5091 | 168 | 1.1544 | 0.4774 | 1.1544 | 1.0744 |
| No log | 0.5152 | 170 | 1.4514 | 0.2732 | 1.4514 | 1.2048 |
| No log | 0.5212 | 172 | 1.2712 | 0.3581 | 1.2712 | 1.1275 |
| No log | 0.5273 | 174 | 1.0499 | 0.4522 | 1.0499 | 1.0247 |
| No log | 0.5333 | 176 | 0.8135 | 0.4847 | 0.8135 | 0.9019 |
| No log | 0.5394 | 178 | 0.6624 | 0.3616 | 0.6624 | 0.8139 |
| No log | 0.5455 | 180 | 0.6189 | 0.3571 | 0.6189 | 0.7867 |
| No log | 0.5515 | 182 | 0.7301 | 0.4566 | 0.7301 | 0.8545 |
| No log | 0.5576 | 184 | 1.0441 | 0.4482 | 1.0441 | 1.0218 |
| No log | 0.5636 | 186 | 1.2655 | 0.2718 | 1.2655 | 1.1250 |
| No log | 0.5697 | 188 | 1.1069 | 0.4209 | 1.1069 | 1.0521 |
| No log | 0.5758 | 190 | 0.8414 | 0.4581 | 0.8414 | 0.9173 |
| No log | 0.5818 | 192 | 0.7207 | 0.4288 | 0.7207 | 0.8489 |
| No log | 0.5879 | 194 | 0.7280 | 0.4288 | 0.7280 | 0.8532 |
| No log | 0.5939 | 196 | 0.9072 | 0.4581 | 0.9072 | 0.9525 |
| No log | 0.6 | 198 | 0.9368 | 0.3948 | 0.9368 | 0.9679 |
| No log | 0.6061 | 200 | 0.9791 | 0.4202 | 0.9791 | 0.9895 |
| No log | 0.6121 | 202 | 1.0521 | 0.4492 | 1.0521 | 1.0257 |
| No log | 0.6182 | 204 | 0.8897 | 0.4492 | 0.8897 | 0.9433 |
| No log | 0.6242 | 206 | 0.7397 | 0.4187 | 0.7397 | 0.8600 |
| No log | 0.6303 | 208 | 0.5892 | 0.3846 | 0.5892 | 0.7676 |
| No log | 0.6364 | 210 | 0.5636 | 0.3846 | 0.5636 | 0.7508 |
| No log | 0.6424 | 212 | 0.5842 | 0.3846 | 0.5842 | 0.7643 |
| No log | 0.6485 | 214 | 0.7316 | 0.4878 | 0.7316 | 0.8553 |
| No log | 0.6545 | 216 | 0.8008 | 0.3927 | 0.8008 | 0.8949 |
| No log | 0.6606 | 218 | 0.7269 | 0.3927 | 0.7269 | 0.8526 |
| No log | 0.6667 | 220 | 0.6511 | 0.4106 | 0.6511 | 0.8069 |
| No log | 0.6727 | 222 | 0.5628 | 0.6526 | 0.5628 | 0.7502 |
| No log | 0.6788 | 224 | 0.5868 | 0.5832 | 0.5868 | 0.7660 |
| No log | 0.6848 | 226 | 0.7054 | 0.3656 | 0.7054 | 0.8399 |
| No log | 0.6909 | 228 | 0.8483 | 0.4202 | 0.8483 | 0.9210 |
| No log | 0.6970 | 230 | 0.7352 | 0.4202 | 0.7352 | 0.8574 |
| No log | 0.7030 | 232 | 0.7281 | 0.4202 | 0.7281 | 0.8533 |
| No log | 0.7091 | 234 | 0.8369 | 0.4202 | 0.8369 | 0.9148 |
| No log | 0.7152 | 236 | 0.7672 | 0.4209 | 0.7672 | 0.8759 |
| No log | 0.7212 | 238 | 0.5993 | 0.4667 | 0.5993 | 0.7741 |
| No log | 0.7273 | 240 | 0.5568 | 0.4667 | 0.5568 | 0.7462 |
| No log | 0.7333 | 242 | 0.6972 | 0.3948 | 0.6972 | 0.8350 |
| No log | 0.7394 | 244 | 0.7346 | 0.3948 | 0.7346 | 0.8571 |
| No log | 0.7455 | 246 | 0.8186 | 0.3948 | 0.8186 | 0.9048 |
| No log | 0.7515 | 248 | 0.8058 | 0.3948 | 0.8058 | 0.8977 |
| No log | 0.7576 | 250 | 0.6028 | 0.4375 | 0.6028 | 0.7764 |
| No log | 0.7636 | 252 | 0.5302 | 0.6917 | 0.5302 | 0.7281 |
| No log | 0.7697 | 254 | 0.5628 | 0.5473 | 0.5628 | 0.7502 |
| No log | 0.7758 | 256 | 0.6446 | 0.3927 | 0.6446 | 0.8029 |
| No log | 0.7818 | 258 | 0.7503 | 0.3927 | 0.7503 | 0.8662 |
| No log | 0.7879 | 260 | 0.6511 | 0.4581 | 0.6511 | 0.8069 |
| No log | 0.7939 | 262 | 0.5849 | 0.496 | 0.5849 | 0.7648 |
| No log | 0.8 | 264 | 0.5542 | 0.5689 | 0.5542 | 0.7445 |
| No log | 0.8061 | 266 | 0.5910 | 0.6686 | 0.5910 | 0.7688 |
| No log | 0.8121 | 268 | 0.6380 | 0.5737 | 0.6380 | 0.7988 |
| No log | 0.8182 | 270 | 0.7901 | 0.4940 | 0.7901 | 0.8889 |
| No log | 0.8242 | 272 | 0.9745 | 0.4364 | 0.9745 | 0.9872 |
| No log | 0.8303 | 274 | 0.9724 | 0.4602 | 0.9724 | 0.9861 |
| No log | 0.8364 | 276 | 0.7301 | 0.4290 | 0.7301 | 0.8545 |
| No log | 0.8424 | 278 | 0.6096 | 0.6 | 0.6096 | 0.7808 |
| No log | 0.8485 | 280 | 0.5449 | 0.6419 | 0.5449 | 0.7382 |
| No log | 0.8545 | 282 | 0.5996 | 0.496 | 0.5996 | 0.7744 |
| No log | 0.8606 | 284 | 0.8137 | 0.3948 | 0.8137 | 0.9020 |
| No log | 0.8667 | 286 | 0.9029 | 0.3948 | 0.9029 | 0.9502 |
| No log | 0.8727 | 288 | 0.7780 | 0.3691 | 0.7780 | 0.8820 |
| No log | 0.8788 | 290 | 0.7019 | 0.4648 | 0.7019 | 0.8378 |
| No log | 0.8848 | 292 | 0.7927 | 0.3723 | 0.7927 | 0.8903 |
| No log | 0.8909 | 294 | 0.8575 | 0.3208 | 0.8575 | 0.9260 |
| No log | 0.8970 | 296 | 0.9576 | 0.3208 | 0.9576 | 0.9786 |
| No log | 0.9030 | 298 | 1.0616 | 0.3438 | 1.0616 | 1.0303 |
| No log | 0.9091 | 300 | 0.9414 | 0.3208 | 0.9414 | 0.9702 |
| No log | 0.9152 | 302 | 0.7463 | 0.3691 | 0.7463 | 0.8639 |
| No log | 0.9212 | 304 | 0.6396 | 0.3780 | 0.6396 | 0.7997 |
| No log | 0.9273 | 306 | 0.7055 | 0.3691 | 0.7055 | 0.8399 |
| No log | 0.9333 | 308 | 0.8337 | 0.3691 | 0.8337 | 0.9131 |
| No log | 0.9394 | 310 | 0.9785 | 0.4215 | 0.9785 | 0.9892 |
| No log | 0.9455 | 312 | 0.8804 | 0.4209 | 0.8804 | 0.9383 |
| No log | 0.9515 | 314 | 0.6899 | 0.4290 | 0.6899 | 0.8306 |
| No log | 0.9576 | 316 | 0.6312 | 0.3980 | 0.6312 | 0.7945 |
| No log | 0.9636 | 318 | 0.5434 | 0.4861 | 0.5434 | 0.7371 |
| No log | 0.9697 | 320 | 0.5626 | 0.4861 | 0.5626 | 0.7501 |
| No log | 0.9758 | 322 | 0.6981 | 0.3866 | 0.6981 | 0.8355 |
| No log | 0.9818 | 324 | 0.8956 | 0.3927 | 0.8956 | 0.9464 |
| No log | 0.9879 | 326 | 1.1517 | 0.4209 | 1.1517 | 1.0732 |
| No log | 0.9939 | 328 | 1.2466 | 0.3175 | 1.2466 | 1.1165 |
| No log | 1.0 | 330 | 1.0872 | 0.4215 | 1.0872 | 1.0427 |
| No log | 1.0061 | 332 | 0.8053 | 0.4049 | 0.8053 | 0.8974 |
| No log | 1.0121 | 334 | 0.6183 | 0.4474 | 0.6183 | 0.7863 |
| No log | 1.0182 | 336 | 0.5620 | 0.4474 | 0.5620 | 0.7496 |
| No log | 1.0242 | 338 | 0.5938 | 0.4688 | 0.5938 | 0.7706 |
| No log | 1.0303 | 340 | 0.6696 | 0.3616 | 0.6696 | 0.8183 |
| No log | 1.0364 | 342 | 0.7622 | 0.3948 | 0.7622 | 0.8730 |
| No log | 1.0424 | 344 | 0.7815 | 0.3948 | 0.7815 | 0.8840 |
| No log | 1.0485 | 346 | 0.7101 | 0.3948 | 0.7101 | 0.8427 |
| No log | 1.0545 | 348 | 0.6728 | 0.4290 | 0.6728 | 0.8203 |
| No log | 1.0606 | 350 | 0.5831 | 0.5786 | 0.5831 | 0.7636 |
| No log | 1.0667 | 352 | 0.6116 | 0.5786 | 0.6116 | 0.7821 |
| No log | 1.0727 | 354 | 0.6034 | 0.5786 | 0.6034 | 0.7768 |
| No log | 1.0788 | 356 | 0.5592 | 0.5786 | 0.5592 | 0.7478 |
| No log | 1.0848 | 358 | 0.5611 | 0.5473 | 0.5611 | 0.7491 |
| No log | 1.0909 | 360 | 0.5979 | 0.5473 | 0.5979 | 0.7732 |
| No log | 1.0970 | 362 | 0.6532 | 0.5347 | 0.6532 | 0.8082 |
| No log | 1.1030 | 364 | 0.6459 | 0.5625 | 0.6459 | 0.8037 |
| No log | 1.1091 | 366 | 0.7525 | 0.3967 | 0.7525 | 0.8675 |
| No log | 1.1152 | 368 | 0.9026 | 0.4215 | 0.9026 | 0.9500 |
| No log | 1.1212 | 370 | 1.0128 | 0.4215 | 1.0128 | 1.0064 |
| No log | 1.1273 | 372 | 0.9159 | 0.4209 | 0.9159 | 0.9570 |
| No log | 1.1333 | 374 | 0.7016 | 0.4878 | 0.7016 | 0.8376 |
| No log | 1.1394 | 376 | 0.6546 | 0.4597 | 0.6546 | 0.8091 |
| No log | 1.1455 | 378 | 0.7381 | 0.4581 | 0.7381 | 0.8591 |
| No log | 1.1515 | 380 | 0.7472 | 0.4581 | 0.7472 | 0.8644 |
| No log | 1.1576 | 382 | 0.6827 | 0.4106 | 0.6827 | 0.8263 |
| No log | 1.1636 | 384 | 0.7863 | 0.4106 | 0.7863 | 0.8867 |
| No log | 1.1697 | 386 | 0.9850 | 0.3807 | 0.9850 | 0.9925 |
| No log | 1.1758 | 388 | 1.0770 | 0.4062 | 1.0770 | 1.0378 |
| No log | 1.1818 | 390 | 0.9949 | 0.3831 | 0.9949 | 0.9974 |
| No log | 1.1879 | 392 | 0.9391 | 0.4062 | 0.9391 | 0.9691 |
| No log | 1.1939 | 394 | 0.8914 | 0.3967 | 0.8914 | 0.9441 |
| No log | 1.2 | 396 | 0.7151 | 0.3948 | 0.7151 | 0.8456 |
| No log | 1.2061 | 398 | 0.6711 | 0.4566 | 0.6711 | 0.8192 |
| No log | 1.2121 | 400 | 0.6976 | 0.4566 | 0.6976 | 0.8352 |
| No log | 1.2182 | 402 | 0.6054 | 0.4597 | 0.6054 | 0.7781 |
| No log | 1.2242 | 404 | 0.5774 | 0.4597 | 0.5774 | 0.7598 |
| No log | 1.2303 | 406 | 0.6074 | 0.4597 | 0.6074 | 0.7793 |
| No log | 1.2364 | 408 | 0.6358 | 0.5 | 0.6358 | 0.7974 |
| No log | 1.2424 | 410 | 0.6058 | 0.4381 | 0.6058 | 0.7783 |
| No log | 1.2485 | 412 | 0.6280 | 0.4381 | 0.6280 | 0.7924 |
| No log | 1.2545 | 414 | 0.7144 | 0.4292 | 0.7144 | 0.8452 |
| No log | 1.2606 | 416 | 0.7141 | 0.4215 | 0.7141 | 0.8450 |
| No log | 1.2667 | 418 | 0.6832 | 0.4292 | 0.6832 | 0.8266 |
| No log | 1.2727 | 420 | 0.6149 | 0.4292 | 0.6149 | 0.7842 |
| No log | 1.2788 | 422 | 0.6004 | 0.4924 | 0.6004 | 0.7749 |
| No log | 1.2848 | 424 | 0.6354 | 0.4292 | 0.6354 | 0.7971 |
| No log | 1.2909 | 426 | 0.7216 | 0.4293 | 0.7216 | 0.8494 |
| No log | 1.2970 | 428 | 0.6383 | 0.4034 | 0.6383 | 0.7990 |
| No log | 1.3030 | 430 | 0.5472 | 0.5767 | 0.5472 | 0.7397 |
| No log | 1.3091 | 432 | 0.5554 | 0.4779 | 0.5554 | 0.7452 |
| No log | 1.3152 | 434 | 0.5851 | 0.4779 | 0.5851 | 0.7649 |
| No log | 1.3212 | 436 | 0.6931 | 0.4034 | 0.6931 | 0.8325 |
| No log | 1.3273 | 438 | 0.8879 | 0.3967 | 0.8879 | 0.9423 |
| No log | 1.3333 | 440 | 0.8625 | 0.3967 | 0.8625 | 0.9287 |
| No log | 1.3394 | 442 | 0.7161 | 0.4137 | 0.7161 | 0.8462 |
| No log | 1.3455 | 444 | 0.5863 | 0.5035 | 0.5863 | 0.7657 |
| No log | 1.3515 | 446 | 0.5359 | 0.6526 | 0.5359 | 0.7320 |
| No log | 1.3576 | 448 | 0.5498 | 0.6526 | 0.5498 | 0.7415 |
| No log | 1.3636 | 450 | 0.6028 | 0.5035 | 0.6028 | 0.7764 |
| No log | 1.3697 | 452 | 0.6548 | 0.5035 | 0.6548 | 0.8092 |
| No log | 1.3758 | 454 | 0.6872 | 0.5035 | 0.6872 | 0.8290 |
| No log | 1.3818 | 456 | 0.7671 | 0.4615 | 0.7671 | 0.8758 |
| No log | 1.3879 | 458 | 0.7432 | 0.4631 | 0.7432 | 0.8621 |
| No log | 1.3939 | 460 | 0.6406 | 0.4706 | 0.6406 | 0.8004 |
| No log | 1.4 | 462 | 0.5560 | 0.4831 | 0.5560 | 0.7456 |
| No log | 1.4061 | 464 | 0.5423 | 0.6912 | 0.5423 | 0.7364 |
| No log | 1.4121 | 466 | 0.5525 | 0.4909 | 0.5525 | 0.7433 |
| No log | 1.4182 | 468 | 0.6172 | 0.4727 | 0.6172 | 0.7856 |
| No log | 1.4242 | 470 | 0.7508 | 0.4631 | 0.7508 | 0.8665 |
| No log | 1.4303 | 472 | 0.8400 | 0.4062 | 0.8400 | 0.9165 |
| No log | 1.4364 | 474 | 0.7630 | 0.3807 | 0.7630 | 0.8735 |
| No log | 1.4424 | 476 | 0.6000 | 0.5073 | 0.6000 | 0.7746 |
| No log | 1.4485 | 478 | 0.5294 | 0.6866 | 0.5294 | 0.7276 |
| No log | 1.4545 | 480 | 0.5342 | 0.6182 | 0.5342 | 0.7309 |
| No log | 1.4606 | 482 | 0.5829 | 0.5939 | 0.5829 | 0.7635 |
| No log | 1.4667 | 484 | 0.5525 | 0.6062 | 0.5525 | 0.7433 |
| No log | 1.4727 | 486 | 0.4575 | 0.6966 | 0.4575 | 0.6764 |
| No log | 1.4788 | 488 | 0.4333 | 0.6966 | 0.4333 | 0.6583 |
| No log | 1.4848 | 490 | 0.4354 | 0.6966 | 0.4354 | 0.6598 |
| No log | 1.4909 | 492 | 0.4966 | 0.6769 | 0.4966 | 0.7047 |
| No log | 1.4970 | 494 | 0.5364 | 0.6769 | 0.5364 | 0.7324 |
| No log | 1.5030 | 496 | 0.5173 | 0.6769 | 0.5173 | 0.7192 |
| No log | 1.5091 | 498 | 0.5006 | 0.6769 | 0.5006 | 0.7075 |
| 0.4638 | 1.5152 | 500 | 0.4956 | 0.6818 | 0.4956 | 0.7040 |
| 0.4638 | 1.5212 | 502 | 0.4878 | 0.6966 | 0.4878 | 0.6984 |
| 0.4638 | 1.5273 | 504 | 0.4679 | 0.6966 | 0.4679 | 0.6840 |
| 0.4638 | 1.5333 | 506 | 0.4561 | 0.6966 | 0.4561 | 0.6754 |
| 0.4638 | 1.5394 | 508 | 0.4997 | 0.6602 | 0.4997 | 0.7069 |
| 0.4638 | 1.5455 | 510 | 0.6914 | 0.4292 | 0.6914 | 0.8315 |
| 0.4638 | 1.5515 | 512 | 0.8475 | 0.3967 | 0.8475 | 0.9206 |
| 0.4638 | 1.5576 | 514 | 0.7916 | 0.3967 | 0.7916 | 0.8897 |
| 0.4638 | 1.5636 | 516 | 0.6227 | 0.3616 | 0.6227 | 0.7891 |
| 0.4638 | 1.5697 | 518 | 0.5374 | 0.4286 | 0.5374 | 0.7331 |
| 0.4638 | 1.5758 | 520 | 0.4772 | 0.5767 | 0.4772 | 0.6908 |
| 0.4638 | 1.5818 | 522 | 0.4889 | 0.5477 | 0.4889 | 0.6992 |
| 0.4638 | 1.5879 | 524 | 0.5942 | 0.4706 | 0.5942 | 0.7709 |
| 0.4638 | 1.5939 | 526 | 0.7750 | 0.4034 | 0.7750 | 0.8803 |
| 0.4638 | 1.6 | 528 | 0.8770 | 0.4074 | 0.8770 | 0.9365 |
| 0.4638 | 1.6061 | 530 | 0.8359 | 0.3860 | 0.8359 | 0.9143 |
| 0.4638 | 1.6121 | 532 | 0.6545 | 0.4286 | 0.6545 | 0.8090 |
| 0.4638 | 1.6182 | 534 | 0.5332 | 0.4286 | 0.5332 | 0.7302 |
| 0.4638 | 1.6242 | 536 | 0.5342 | 0.475 | 0.5342 | 0.7309 |
| 0.4638 | 1.6303 | 538 | 0.5881 | 0.5767 | 0.5881 | 0.7669 |
| 0.4638 | 1.6364 | 540 | 0.6684 | 0.5035 | 0.6684 | 0.8175 |
| 0.4638 | 1.6424 | 542 | 0.7392 | 0.3807 | 0.7392 | 0.8598 |
| 0.4638 | 1.6485 | 544 | 0.6915 | 0.4727 | 0.6915 | 0.8315 |
| 0.4638 | 1.6545 | 546 | 0.6519 | 0.5073 | 0.6519 | 0.8074 |
| 0.4638 | 1.6606 | 548 | 0.5905 | 0.4831 | 0.5905 | 0.7684 |
| 0.4638 | 1.6667 | 550 | 0.5476 | 0.6182 | 0.5476 | 0.7400 |
| 0.4638 | 1.6727 | 552 | 0.5458 | 0.5895 | 0.5458 | 0.7388 |
| 0.4638 | 1.6788 | 554 | 0.5819 | 0.5751 | 0.5819 | 0.7628 |
| 0.4638 | 1.6848 | 556 | 0.7189 | 0.4457 | 0.7189 | 0.8479 |
| 0.4638 | 1.6909 | 558 | 0.9419 | 0.3723 | 0.9419 | 0.9705 |
| 0.4638 | 1.6970 | 560 | 0.9620 | 0.3723 | 0.9620 | 0.9808 |
| 0.4638 | 1.7030 | 562 | 0.8407 | 0.3723 | 0.8407 | 0.9169 |
| 0.4638 | 1.7091 | 564 | 0.6513 | 0.4803 | 0.6513 | 0.8071 |
| 0.4638 | 1.7152 | 566 | 0.5994 | 0.6083 | 0.5994 | 0.7742 |
| 0.4638 | 1.7212 | 568 | 0.5751 | 0.6778 | 0.5751 | 0.7584 |
| 0.4638 | 1.7273 | 570 | 0.6158 | 0.4538 | 0.6158 | 0.7848 |
| 0.4638 | 1.7333 | 572 | 0.7281 | 0.4118 | 0.7281 | 0.8533 |
| 0.4638 | 1.7394 | 574 | 0.8133 | 0.3691 | 0.8133 | 0.9018 |
| 0.4638 | 1.7455 | 576 | 0.8059 | 0.3691 | 0.8059 | 0.8977 |
| 0.4638 | 1.7515 | 578 | 0.6766 | 0.4018 | 0.6766 | 0.8226 |
| 0.4638 | 1.7576 | 580 | 0.5539 | 0.4474 | 0.5539 | 0.7442 |
| 0.4638 | 1.7636 | 582 | 0.5547 | 0.4080 | 0.5547 | 0.7448 |
| 0.4638 | 1.7697 | 584 | 0.6184 | 0.3679 | 0.6184 | 0.7864 |
| 0.4638 | 1.7758 | 586 | 0.7512 | 0.3691 | 0.7512 | 0.8667 |
| 0.4638 | 1.7818 | 588 | 0.9403 | 0.3723 | 0.9403 | 0.9697 |
| 0.4638 | 1.7879 | 590 | 0.9486 | 0.3723 | 0.9486 | 0.9739 |
| 0.4638 | 1.7939 | 592 | 0.8373 | 0.3723 | 0.8373 | 0.9150 |
| 0.4638 | 1.8 | 594 | 0.7240 | 0.4106 | 0.7240 | 0.8509 |
| 0.4638 | 1.8061 | 596 | 0.6423 | 0.4192 | 0.6423 | 0.8014 |
| 0.4638 | 1.8121 | 598 | 0.6669 | 0.4094 | 0.6669 | 0.8167 |
| 0.4638 | 1.8182 | 600 | 0.6702 | 0.4192 | 0.6702 | 0.8186 |
| 0.4638 | 1.8242 | 602 | 0.7276 | 0.4288 | 0.7276 | 0.8530 |
| 0.4638 | 1.8303 | 604 | 0.8193 | 0.4581 | 0.8193 | 0.9051 |
| 0.4638 | 1.8364 | 606 | 0.7877 | 0.4581 | 0.7877 | 0.8875 |
| 0.4638 | 1.8424 | 608 | 0.7017 | 0.4106 | 0.7017 | 0.8377 |
| 0.4638 | 1.8485 | 610 | 0.6338 | 0.4185 | 0.6338 | 0.7961 |
| 0.4638 | 1.8545 | 612 | 0.6152 | 0.4185 | 0.6152 | 0.7843 |
| 0.4638 | 1.8606 | 614 | 0.6823 | 0.4192 | 0.6823 | 0.8260 |
| 0.4638 | 1.8667 | 616 | 0.7003 | 0.4192 | 0.7003 | 0.8368 |
| 0.4638 | 1.8727 | 618 | 0.6718 | 0.4185 | 0.6718 | 0.8196 |
| 0.4638 | 1.8788 | 620 | 0.6395 | 0.4550 | 0.6395 | 0.7997 |
| 0.4638 | 1.8848 | 622 | 0.6202 | 0.5545 | 0.6202 | 0.7875 |
| 0.4638 | 1.8909 | 624 | 0.6231 | 0.4550 | 0.6231 | 0.7893 |
| 0.4638 | 1.8970 | 626 | 0.6656 | 0.4118 | 0.6656 | 0.8158 |
| 0.4638 | 1.9030 | 628 | 0.7873 | 0.4049 | 0.7873 | 0.8873 |
| 0.4638 | 1.9091 | 630 | 0.8081 | 0.4062 | 0.8081 | 0.8989 |
| 0.4638 | 1.9152 | 632 | 0.7342 | 0.4049 | 0.7342 | 0.8569 |
| 0.4638 | 1.9212 | 634 | 0.5847 | 0.4550 | 0.5847 | 0.7647 |
| 0.4638 | 1.9273 | 636 | 0.5461 | 0.5545 | 0.5461 | 0.7390 |
| 0.4638 | 1.9333 | 638 | 0.5656 | 0.5481 | 0.5656 | 0.7521 |
| 0.4638 | 1.9394 | 640 | 0.6035 | 0.4850 | 0.6035 | 0.7769 |
| 0.4638 | 1.9455 | 642 | 0.6789 | 0.4128 | 0.6789 | 0.8239 |
| 0.4638 | 1.9515 | 644 | 0.7369 | 0.4293 | 0.7369 | 0.8584 |
| 0.4638 | 1.9576 | 646 | 0.6695 | 0.4034 | 0.6695 | 0.8182 |
| 0.4638 | 1.9636 | 648 | 0.5931 | 0.4199 | 0.5931 | 0.7701 |
| 0.4638 | 1.9697 | 650 | 0.6292 | 0.375 | 0.6292 | 0.7932 |
| 0.4638 | 1.9758 | 652 | 0.6258 | 0.375 | 0.6258 | 0.7911 |
| 0.4638 | 1.9818 | 654 | 0.6203 | 0.375 | 0.6203 | 0.7876 |
| 0.4638 | 1.9879 | 656 | 0.6307 | 0.375 | 0.6307 | 0.7942 |
| 0.4638 | 1.9939 | 658 | 0.6730 | 0.3599 | 0.6730 | 0.8204 |
| 0.4638 | 2.0 | 660 | 0.6320 | 0.4202 | 0.6320 | 0.7950 |
| 0.4638 | 2.0061 | 662 | 0.5657 | 0.4597 | 0.5657 | 0.7521 |
| 0.4638 | 2.0121 | 664 | 0.5863 | 0.4597 | 0.5863 | 0.7657 |
| 0.4638 | 2.0182 | 666 | 0.5885 | 0.4597 | 0.5885 | 0.7671 |
| 0.4638 | 2.0242 | 668 | 0.5263 | 0.5 | 0.5263 | 0.7255 |
| 0.4638 | 2.0303 | 670 | 0.5176 | 0.4465 | 0.5176 | 0.7195 |
| 0.4638 | 2.0364 | 672 | 0.5484 | 0.4727 | 0.5484 | 0.7405 |
| 0.4638 | 2.0424 | 674 | 0.5891 | 0.4457 | 0.5891 | 0.7676 |
| 0.4638 | 2.0485 | 676 | 0.7152 | 0.4128 | 0.7152 | 0.8457 |
| 0.4638 | 2.0545 | 678 | 0.7900 | 0.4137 | 0.7900 | 0.8888 |
| 0.4638 | 2.0606 | 680 | 0.7340 | 0.4128 | 0.7340 | 0.8568 |
| 0.4638 | 2.0667 | 682 | 0.6204 | 0.4527 | 0.6204 | 0.7877 |
| 0.4638 | 2.0727 | 684 | 0.5881 | 0.5481 | 0.5881 | 0.7669 |
| 0.4638 | 2.0788 | 686 | 0.6283 | 0.3653 | 0.6283 | 0.7927 |
| 0.4638 | 2.0848 | 688 | 0.7573 | 0.4128 | 0.7573 | 0.8702 |
| 0.4638 | 2.0909 | 690 | 0.8503 | 0.3967 | 0.8503 | 0.9221 |
| 0.4638 | 2.0970 | 692 | 0.7646 | 0.4292 | 0.7646 | 0.8744 |
| 0.4638 | 2.1030 | 694 | 0.6193 | 0.3889 | 0.6193 | 0.7869 |
| 0.4638 | 2.1091 | 696 | 0.5338 | 0.5895 | 0.5338 | 0.7306 |
| 0.4638 | 2.1152 | 698 | 0.5510 | 0.5545 | 0.5510 | 0.7423 |
| 0.4638 | 2.1212 | 700 | 0.6511 | 0.4128 | 0.6511 | 0.8069 |
| 0.4638 | 2.1273 | 702 | 0.9184 | 0.3752 | 0.9184 | 0.9583 |
| 0.4638 | 2.1333 | 704 | 1.0774 | 0.3883 | 1.0774 | 1.0380 |
| 0.4638 | 2.1394 | 706 | 1.0331 | 0.3883 | 1.0331 | 1.0164 |
| 0.4638 | 2.1455 | 708 | 0.8832 | 0.3883 | 0.8832 | 0.9398 |
| 0.4638 | 2.1515 | 710 | 0.7038 | 0.4553 | 0.7038 | 0.8389 |
| 0.4638 | 2.1576 | 712 | 0.6073 | 0.4286 | 0.6073 | 0.7793 |
| 0.4638 | 2.1636 | 714 | 0.6231 | 0.475 | 0.6231 | 0.7894 |
| 0.4638 | 2.1697 | 716 | 0.7106 | 0.4118 | 0.7106 | 0.8430 |
| 0.4638 | 2.1758 | 718 | 0.7882 | 0.4118 | 0.7882 | 0.8878 |
| 0.4638 | 2.1818 | 720 | 0.8497 | 0.3807 | 0.8497 | 0.9218 |
| 0.4638 | 2.1879 | 722 | 0.9240 | 0.3752 | 0.9240 | 0.9612 |
| 0.4638 | 2.1939 | 724 | 0.9146 | 0.3752 | 0.9146 | 0.9563 |
| 0.4638 | 2.2 | 726 | 0.8525 | 0.3636 | 0.8525 | 0.9233 |
| 0.4638 | 2.2061 | 728 | 0.6936 | 0.3558 | 0.6936 | 0.8328 |
| 0.4638 | 2.2121 | 730 | 0.6159 | 0.4597 | 0.6159 | 0.7848 |
| 0.4638 | 2.2182 | 732 | 0.5830 | 0.4597 | 0.5830 | 0.7635 |
| 0.4638 | 2.2242 | 734 | 0.6042 | 0.4597 | 0.6042 | 0.7773 |
| 0.4638 | 2.2303 | 736 | 0.6996 | 0.4581 | 0.6996 | 0.8364 |
| 0.4638 | 2.2364 | 738 | 0.7946 | 0.3967 | 0.7946 | 0.8914 |
| 0.4638 | 2.2424 | 740 | 0.9346 | 0.3636 | 0.9346 | 0.9668 |
| 0.4638 | 2.2485 | 742 | 0.9369 | 0.3636 | 0.9369 | 0.9679 |
| 0.4638 | 2.2545 | 744 | 0.8932 | 0.3967 | 0.8932 | 0.9451 |
| 0.4638 | 2.2606 | 746 | 0.8460 | 0.3967 | 0.8460 | 0.9198 |
| 0.4638 | 2.2667 | 748 | 0.7859 | 0.3967 | 0.7859 | 0.8865 |
| 0.4638 | 2.2727 | 750 | 0.6723 | 0.3948 | 0.6723 | 0.8200 |
| 0.4638 | 2.2788 | 752 | 0.5899 | 0.4465 | 0.5899 | 0.7680 |
| 0.4638 | 2.2848 | 754 | 0.5645 | 0.5191 | 0.5645 | 0.7514 |
| 0.4638 | 2.2909 | 756 | 0.5863 | 0.5477 | 0.5863 | 0.7657 |
| 0.4638 | 2.2970 | 758 | 0.6351 | 0.4706 | 0.6351 | 0.7970 |
| 0.4638 | 2.3030 | 760 | 0.6879 | 0.3807 | 0.6879 | 0.8294 |
| 0.4638 | 2.3091 | 762 | 0.7590 | 0.4049 | 0.7590 | 0.8712 |
| 0.4638 | 2.3152 | 764 | 0.7478 | 0.3807 | 0.7478 | 0.8648 |
| 0.4638 | 2.3212 | 766 | 0.7196 | 0.4128 | 0.7196 | 0.8483 |
| 0.4638 | 2.3273 | 768 | 0.6916 | 0.4205 | 0.6916 | 0.8316 |
| 0.4638 | 2.3333 | 770 | 0.6507 | 0.5679 | 0.6507 | 0.8067 |
| 0.4638 | 2.3394 | 772 | 0.6178 | 0.5812 | 0.6178 | 0.7860 |
| 0.4638 | 2.3455 | 774 | 0.6025 | 0.4803 | 0.6025 | 0.7762 |
| 0.4638 | 2.3515 | 776 | 0.6201 | 0.4706 | 0.6201 | 0.7874 |
| 0.4638 | 2.3576 | 778 | 0.7003 | 0.4631 | 0.7003 | 0.8369 |
| 0.4638 | 2.3636 | 780 | 0.7689 | 0.4049 | 0.7689 | 0.8769 |
| 0.4638 | 2.3697 | 782 | 0.7809 | 0.3948 | 0.7809 | 0.8837 |
| 0.4638 | 2.3758 | 784 | 0.6921 | 0.4049 | 0.6921 | 0.8319 |
| 0.4638 | 2.3818 | 786 | 0.5667 | 0.5073 | 0.5667 | 0.7528 |
| 0.4638 | 2.3879 | 788 | 0.4733 | 0.6866 | 0.4733 | 0.6880 |
| 0.4638 | 2.3939 | 790 | 0.4795 | 0.6407 | 0.4795 | 0.6924 |
| 0.4638 | 2.4 | 792 | 0.4783 | 0.6407 | 0.4783 | 0.6916 |
| 0.4638 | 2.4061 | 794 | 0.4500 | 0.7014 | 0.4500 | 0.6708 |
| 0.4638 | 2.4121 | 796 | 0.4747 | 0.5895 | 0.4747 | 0.6890 |
| 0.4638 | 2.4182 | 798 | 0.6412 | 0.496 | 0.6412 | 0.8007 |
| 0.4638 | 2.4242 | 800 | 0.8033 | 0.3883 | 0.8033 | 0.8963 |
| 0.4638 | 2.4303 | 802 | 0.8229 | 0.3883 | 0.8229 | 0.9071 |
| 0.4638 | 2.4364 | 804 | 0.7394 | 0.3636 | 0.7394 | 0.8599 |
| 0.4638 | 2.4424 | 806 | 0.5968 | 0.5347 | 0.5968 | 0.7725 |
| 0.4638 | 2.4485 | 808 | 0.4871 | 0.4550 | 0.4871 | 0.6979 |
| 0.4638 | 2.4545 | 810 | 0.4602 | 0.4550 | 0.4602 | 0.6784 |
| 0.4638 | 2.4606 | 812 | 0.4494 | 0.4776 | 0.4494 | 0.6704 |
| 0.4638 | 2.4667 | 814 | 0.4791 | 0.5084 | 0.4791 | 0.6922 |
| 0.4638 | 2.4727 | 816 | 0.5523 | 0.5 | 0.5523 | 0.7432 |
| 0.4638 | 2.4788 | 818 | 0.6652 | 0.4202 | 0.6652 | 0.8156 |
| 0.4638 | 2.4848 | 820 | 0.7665 | 0.4474 | 0.7665 | 0.8755 |
| 0.4638 | 2.4909 | 822 | 0.8153 | 0.3883 | 0.8153 | 0.9029 |
| 0.4638 | 2.4970 | 824 | 0.7533 | 0.4293 | 0.7533 | 0.8680 |
| 0.4638 | 2.5030 | 826 | 0.6531 | 0.4727 | 0.6531 | 0.8081 |
| 0.4638 | 2.5091 | 828 | 0.5626 | 0.4909 | 0.5626 | 0.7501 |
| 0.4638 | 2.5152 | 830 | 0.5309 | 0.4909 | 0.5309 | 0.7286 |
| 0.4638 | 2.5212 | 832 | 0.5604 | 0.4094 | 0.5604 | 0.7486 |
| 0.4638 | 2.5273 | 834 | 0.6433 | 0.4202 | 0.6433 | 0.8021 |
| 0.4638 | 2.5333 | 836 | 0.6842 | 0.4202 | 0.6842 | 0.8272 |
| 0.4638 | 2.5394 | 838 | 0.6313 | 0.4202 | 0.6313 | 0.7945 |
| 0.4638 | 2.5455 | 840 | 0.5165 | 0.4597 | 0.5165 | 0.7187 |
| 0.4638 | 2.5515 | 842 | 0.4747 | 0.5 | 0.4747 | 0.6890 |
| 0.4638 | 2.5576 | 844 | 0.4769 | 0.5 | 0.4769 | 0.6906 |
| 0.4638 | 2.5636 | 846 | 0.5004 | 0.4597 | 0.5004 | 0.7074 |
| 0.4638 | 2.5697 | 848 | 0.5611 | 0.4195 | 0.5611 | 0.7491 |
| 0.4638 | 2.5758 | 850 | 0.5951 | 0.4195 | 0.5951 | 0.7714 |
| 0.4638 | 2.5818 | 852 | 0.6216 | 0.4202 | 0.6216 | 0.7884 |
| 0.4638 | 2.5879 | 854 | 0.5610 | 0.4581 | 0.5610 | 0.7490 |
| 0.4638 | 2.5939 | 856 | 0.4716 | 0.5084 | 0.4716 | 0.6867 |
| 0.4638 | 2.6 | 858 | 0.4496 | 0.7282 | 0.4496 | 0.6705 |
| 0.4638 | 2.6061 | 860 | 0.4524 | 0.6966 | 0.4524 | 0.6726 |
| 0.4638 | 2.6121 | 862 | 0.5153 | 0.6123 | 0.5153 | 0.7178 |
| 0.4638 | 2.6182 | 864 | 0.6433 | 0.4553 | 0.6433 | 0.8020 |
| 0.4638 | 2.6242 | 866 | 0.7269 | 0.3967 | 0.7269 | 0.8526 |
| 0.4638 | 2.6303 | 868 | 0.6957 | 0.3967 | 0.6957 | 0.8341 |
| 0.4638 | 2.6364 | 870 | 0.6199 | 0.4034 | 0.6199 | 0.7873 |
| 0.4638 | 2.6424 | 872 | 0.5774 | 0.4779 | 0.5774 | 0.7599 |
| 0.4638 | 2.6485 | 874 | 0.5914 | 0.4779 | 0.5914 | 0.7690 |
| 0.4638 | 2.6545 | 876 | 0.6111 | 0.4128 | 0.6111 | 0.7818 |
| 0.4638 | 2.6606 | 878 | 0.6148 | 0.4757 | 0.6148 | 0.7841 |
| 0.4638 | 2.6667 | 880 | 0.6262 | 0.4757 | 0.6262 | 0.7913 |
| 0.4638 | 2.6727 | 882 | 0.5900 | 0.5914 | 0.5900 | 0.7681 |
| 0.4638 | 2.6788 | 884 | 0.5320 | 0.6874 | 0.5320 | 0.7294 |
| 0.4638 | 2.6848 | 886 | 0.4607 | 0.6866 | 0.4607 | 0.6787 |
| 0.4638 | 2.6909 | 888 | 0.4478 | 0.6966 | 0.4478 | 0.6692 |
| 0.4638 | 2.6970 | 890 | 0.4832 | 0.7115 | 0.4832 | 0.6951 |
| 0.4638 | 2.7030 | 892 | 0.5724 | 0.4292 | 0.5724 | 0.7566 |
| 0.4638 | 2.7091 | 894 | 0.7301 | 0.3883 | 0.7301 | 0.8544 |
| 0.4638 | 2.7152 | 896 | 0.7788 | 0.3883 | 0.7788 | 0.8825 |
| 0.4638 | 2.7212 | 898 | 0.7176 | 0.3883 | 0.7176 | 0.8471 |
| 0.4638 | 2.7273 | 900 | 0.5945 | 0.4566 | 0.5945 | 0.7710 |
| 0.4638 | 2.7333 | 902 | 0.4972 | 0.4474 | 0.4972 | 0.7051 |
| 0.4638 | 2.7394 | 904 | 0.4722 | 0.6866 | 0.4722 | 0.6872 |
| 0.4638 | 2.7455 | 906 | 0.5070 | 0.4550 | 0.5070 | 0.7121 |
| 0.4638 | 2.7515 | 908 | 0.6110 | 0.4615 | 0.6110 | 0.7816 |
| 0.4638 | 2.7576 | 910 | 0.7314 | 0.3636 | 0.7314 | 0.8552 |
| 0.4638 | 2.7636 | 912 | 0.8015 | 0.3636 | 0.8015 | 0.8953 |
| 0.4638 | 2.7697 | 914 | 0.7520 | 0.3636 | 0.7520 | 0.8672 |
| 0.4638 | 2.7758 | 916 | 0.6322 | 0.4566 | 0.6322 | 0.7951 |
| 0.4638 | 2.7818 | 918 | 0.5317 | 0.4465 | 0.5317 | 0.7292 |
| 0.4638 | 2.7879 | 920 | 0.4799 | 0.6526 | 0.4799 | 0.6928 |
| 0.4638 | 2.7939 | 922 | 0.4858 | 0.6526 | 0.4858 | 0.6970 |
| 0.4638 | 2.8 | 924 | 0.5357 | 0.4550 | 0.5357 | 0.7319 |
| 0.4638 | 2.8061 | 926 | 0.6425 | 0.4118 | 0.6425 | 0.8016 |
| 0.4638 | 2.8121 | 928 | 0.7630 | 0.3831 | 0.7630 | 0.8735 |
| 0.4638 | 2.8182 | 930 | 0.8972 | 0.4062 | 0.8972 | 0.9472 |
| 0.4638 | 2.8242 | 932 | 0.9796 | 0.3636 | 0.9796 | 0.9898 |
| 0.4638 | 2.8303 | 934 | 0.9561 | 0.3636 | 0.9561 | 0.9778 |
| 0.4638 | 2.8364 | 936 | 0.8211 | 0.3636 | 0.8211 | 0.9062 |
| 0.4638 | 2.8424 | 938 | 0.6526 | 0.4288 | 0.6526 | 0.8078 |
| 0.4638 | 2.8485 | 940 | 0.5346 | 0.475 | 0.5346 | 0.7312 |
| 0.4638 | 2.8545 | 942 | 0.4834 | 0.4550 | 0.4834 | 0.6953 |
| 0.4638 | 2.8606 | 944 | 0.4828 | 0.6866 | 0.4828 | 0.6949 |
| 0.4638 | 2.8667 | 946 | 0.5183 | 0.4550 | 0.5183 | 0.7199 |
| 0.4638 | 2.8727 | 948 | 0.5776 | 0.5073 | 0.5776 | 0.7600 |
| 0.4638 | 2.8788 | 950 | 0.6899 | 0.4631 | 0.6899 | 0.8306 |
| 0.4638 | 2.8848 | 952 | 0.7671 | 0.4293 | 0.7671 | 0.8759 |
| 0.4638 | 2.8909 | 954 | 0.7480 | 0.4293 | 0.7480 | 0.8649 |
| 0.4638 | 2.8970 | 956 | 0.6511 | 0.4372 | 0.6511 | 0.8069 |
| 0.4638 | 2.9030 | 958 | 0.5263 | 0.4550 | 0.5263 | 0.7254 |
| 0.4638 | 2.9091 | 960 | 0.4764 | 0.6866 | 0.4764 | 0.6902 |
| 0.4638 | 2.9152 | 962 | 0.4793 | 0.6866 | 0.4793 | 0.6923 |
| 0.4638 | 2.9212 | 964 | 0.5100 | 0.4909 | 0.5100 | 0.7142 |
| 0.4638 | 2.9273 | 966 | 0.5595 | 0.4550 | 0.5595 | 0.7480 |
| 0.4638 | 2.9333 | 968 | 0.5590 | 0.4909 | 0.5590 | 0.7476 |
| 0.4638 | 2.9394 | 970 | 0.5796 | 0.4550 | 0.5796 | 0.7613 |
| 0.4638 | 2.9455 | 972 | 0.5815 | 0.4550 | 0.5815 | 0.7626 |
| 0.4638 | 2.9515 | 974 | 0.5601 | 0.4550 | 0.5601 | 0.7484 |
| 0.4638 | 2.9576 | 976 | 0.5314 | 0.4831 | 0.5314 | 0.7289 |
| 0.4638 | 2.9636 | 978 | 0.5151 | 0.475 | 0.5151 | 0.7177 |
| 0.4638 | 2.9697 | 980 | 0.5102 | 0.4094 | 0.5102 | 0.7143 |
| 0.4638 | 2.9758 | 982 | 0.5223 | 0.4094 | 0.5223 | 0.7227 |
| 0.4638 | 2.9818 | 984 | 0.5378 | 0.4550 | 0.5378 | 0.7333 |
| 0.4638 | 2.9879 | 986 | 0.5719 | 0.4909 | 0.5719 | 0.7563 |
| 0.4638 | 2.9939 | 988 | 0.6394 | 0.4878 | 0.6394 | 0.7996 |
| 0.4638 | 3.0 | 990 | 0.6949 | 0.4588 | 0.6949 | 0.8336 |
| 0.4638 | 3.0061 | 992 | 0.7543 | 0.4128 | 0.7543 | 0.8685 |
| 0.4638 | 3.0121 | 994 | 0.7565 | 0.4128 | 0.7565 | 0.8698 |
| 0.4638 | 3.0182 | 996 | 0.6923 | 0.4118 | 0.6923 | 0.8320 |
| 0.4638 | 3.0242 | 998 | 0.6035 | 0.4550 | 0.6035 | 0.7768 |
| 0.16 | 3.0303 | 1000 | 0.5588 | 0.4909 | 0.5588 | 0.7475 |
| 0.16 | 3.0364 | 1002 | 0.5251 | 0.5895 | 0.5251 | 0.7246 |
| 0.16 | 3.0424 | 1004 | 0.5204 | 0.4909 | 0.5204 | 0.7214 |
| 0.16 | 3.0485 | 1006 | 0.5508 | 0.3814 | 0.5508 | 0.7422 |
| 0.16 | 3.0545 | 1008 | 0.6145 | 0.4667 | 0.6145 | 0.7839 |
| 0.16 | 3.0606 | 1010 | 0.7203 | 0.4553 | 0.7203 | 0.8487 |
| 0.16 | 3.0667 | 1012 | 0.7689 | 0.4215 | 0.7689 | 0.8769 |
| 0.16 | 3.0727 | 1014 | 0.7207 | 0.4553 | 0.7207 | 0.8490 |
| 0.16 | 3.0788 | 1016 | 0.6147 | 0.4378 | 0.6147 | 0.7840 |
| 0.16 | 3.0848 | 1018 | 0.5704 | 0.4185 | 0.5704 | 0.7553 |
| 0.16 | 3.0909 | 1020 | 0.5624 | 0.4550 | 0.5624 | 0.7499 |
| 0.16 | 3.0970 | 1022 | 0.6027 | 0.4465 | 0.6027 | 0.7763 |
| 0.16 | 3.1030 | 1024 | 0.7051 | 0.4648 | 0.7051 | 0.8397 |
| 0.16 | 3.1091 | 1026 | 0.7826 | 0.4215 | 0.7826 | 0.8847 |
| 0.16 | 3.1152 | 1028 | 0.7548 | 0.4215 | 0.7548 | 0.8688 |
| 0.16 | 3.1212 | 1030 | 0.6898 | 0.4648 | 0.6898 | 0.8305 |
| 0.16 | 3.1273 | 1032 | 0.6530 | 0.4667 | 0.6530 | 0.8081 |
| 0.16 | 3.1333 | 1034 | 0.6072 | 0.4378 | 0.6072 | 0.7792 |
| 0.16 | 3.1394 | 1036 | 0.6130 | 0.475 | 0.6130 | 0.7830 |
| 0.16 | 3.1455 | 1038 | 0.6449 | 0.5073 | 0.6449 | 0.8030 |
| 0.16 | 3.1515 | 1040 | 0.6836 | 0.5065 | 0.6836 | 0.8268 |
| 0.16 | 3.1576 | 1042 | 0.7029 | 0.5065 | 0.7029 | 0.8384 |
| 0.16 | 3.1636 | 1044 | 0.6889 | 0.5065 | 0.6889 | 0.8300 |
| 0.16 | 3.1697 | 1046 | 0.6322 | 0.4850 | 0.6322 | 0.7951 |
| 0.16 | 3.1758 | 1048 | 0.5821 | 0.5545 | 0.5821 | 0.7630 |
| 0.16 | 3.1818 | 1050 | 0.5513 | 0.5545 | 0.5513 | 0.7425 |
| 0.16 | 3.1879 | 1052 | 0.5172 | 0.5545 | 0.5172 | 0.7192 |
| 0.16 | 3.1939 | 1054 | 0.4910 | 0.5832 | 0.4910 | 0.7007 |
| 0.16 | 3.2 | 1056 | 0.4690 | 0.5922 | 0.4690 | 0.6848 |
| 0.16 | 3.2061 | 1058 | 0.4562 | 0.5922 | 0.4562 | 0.6754 |
| 0.16 | 3.2121 | 1060 | 0.4660 | 0.5832 | 0.4660 | 0.6826 |
| 0.16 | 3.2182 | 1062 | 0.4782 | 0.5832 | 0.4782 | 0.6915 |
| 0.16 | 3.2242 | 1064 | 0.4917 | 0.5895 | 0.4917 | 0.7012 |
| 0.16 | 3.2303 | 1066 | 0.5317 | 0.5832 | 0.5317 | 0.7292 |
| 0.16 | 3.2364 | 1068 | 0.5787 | 0.6123 | 0.5787 | 0.7607 |
| 0.16 | 3.2424 | 1070 | 0.6015 | 0.6083 | 0.6015 | 0.7755 |
| 0.16 | 3.2485 | 1072 | 0.6142 | 0.5995 | 0.6142 | 0.7837 |
| 0.16 | 3.2545 | 1074 | 0.6116 | 0.5145 | 0.6116 | 0.7820 |
| 0.16 | 3.2606 | 1076 | 0.6106 | 0.5073 | 0.6106 | 0.7814 |
| 0.16 | 3.2667 | 1078 | 0.6033 | 0.5116 | 0.6033 | 0.7767 |
| 0.16 | 3.2727 | 1080 | 0.6053 | 0.4378 | 0.6053 | 0.7780 |
| 0.16 | 3.2788 | 1082 | 0.5867 | 0.4185 | 0.5867 | 0.7660 |
| 0.16 | 3.2848 | 1084 | 0.6011 | 0.4185 | 0.6011 | 0.7753 |
| 0.16 | 3.2909 | 1086 | 0.6110 | 0.4192 | 0.6110 | 0.7816 |
| 0.16 | 3.2970 | 1088 | 0.6598 | 0.4192 | 0.6598 | 0.8123 |
| 0.16 | 3.3030 | 1090 | 0.6883 | 0.4199 | 0.6883 | 0.8296 |
| 0.16 | 3.3091 | 1092 | 0.6781 | 0.4199 | 0.6781 | 0.8234 |
| 0.16 | 3.3152 | 1094 | 0.6443 | 0.4878 | 0.6443 | 0.8027 |
| 0.16 | 3.3212 | 1096 | 0.6495 | 0.4199 | 0.6495 | 0.8059 |
| 0.16 | 3.3273 | 1098 | 0.7409 | 0.4372 | 0.7409 | 0.8607 |
| 0.16 | 3.3333 | 1100 | 0.8566 | 0.3636 | 0.8566 | 0.9255 |
| 0.16 | 3.3394 | 1102 | 0.8545 | 0.3883 | 0.8545 | 0.9244 |
| 0.16 | 3.3455 | 1104 | 0.7615 | 0.4202 | 0.7615 | 0.8727 |
| 0.16 | 3.3515 | 1106 | 0.6521 | 0.4018 | 0.6521 | 0.8075 |
| 0.16 | 3.3576 | 1108 | 0.6368 | 0.375 | 0.6368 | 0.7980 |
| 0.16 | 3.3636 | 1110 | 0.6495 | 0.4106 | 0.6495 | 0.8059 |
| 0.16 | 3.3697 | 1112 | 0.6349 | 0.4199 | 0.6349 | 0.7968 |
| 0.16 | 3.3758 | 1114 | 0.6892 | 0.4199 | 0.6892 | 0.8302 |
| 0.16 | 3.3818 | 1116 | 0.7066 | 0.4199 | 0.7066 | 0.8406 |
| 0.16 | 3.3879 | 1118 | 0.6867 | 0.4527 | 0.6867 | 0.8287 |
| 0.16 | 3.3939 | 1120 | 0.6831 | 0.4106 | 0.6831 | 0.8265 |
| 0.16 | 3.4 | 1122 | 0.7200 | 0.4018 | 0.7200 | 0.8486 |
| 0.16 | 3.4061 | 1124 | 0.7823 | 0.3599 | 0.7823 | 0.8845 |
| 0.16 | 3.4121 | 1126 | 0.7774 | 0.3599 | 0.7774 | 0.8817 |
| 0.16 | 3.4182 | 1128 | 0.7160 | 0.4290 | 0.7160 | 0.8461 |
| 0.16 | 3.4242 | 1130 | 0.6237 | 0.3717 | 0.6237 | 0.7898 |
| 0.16 | 3.4303 | 1132 | 0.5843 | 0.4550 | 0.5843 | 0.7644 |
| 0.16 | 3.4364 | 1134 | 0.5878 | 0.4550 | 0.5878 | 0.7667 |
| 0.16 | 3.4424 | 1136 | 0.6195 | 0.4185 | 0.6195 | 0.7871 |
| 0.16 | 3.4485 | 1138 | 0.6587 | 0.3814 | 0.6587 | 0.8116 |
| 0.16 | 3.4545 | 1140 | 0.6901 | 0.3842 | 0.6901 | 0.8307 |
| 0.16 | 3.4606 | 1142 | 0.6994 | 0.3842 | 0.6994 | 0.8363 |
| 0.16 | 3.4667 | 1144 | 0.6789 | 0.4538 | 0.6789 | 0.8240 |
| 0.16 | 3.4727 | 1146 | 0.7003 | 0.4538 | 0.7003 | 0.8368 |
| 0.16 | 3.4788 | 1148 | 0.7456 | 0.3866 | 0.7456 | 0.8635 |
| 0.16 | 3.4848 | 1150 | 0.7442 | 0.3653 | 0.7442 | 0.8627 |
| 0.16 | 3.4909 | 1152 | 0.7096 | 0.4527 | 0.7096 | 0.8424 |
| 0.16 | 3.4970 | 1154 | 0.6465 | 0.4878 | 0.6465 | 0.8041 |
| 0.16 | 3.5030 | 1156 | 0.6140 | 0.5895 | 0.6140 | 0.7836 |
| 0.16 | 3.5091 | 1158 | 0.6317 | 0.4878 | 0.6317 | 0.7948 |
| 0.16 | 3.5152 | 1160 | 0.7308 | 0.3188 | 0.7308 | 0.8549 |
| 0.16 | 3.5212 | 1162 | 0.9069 | 0.3883 | 0.9069 | 0.9523 |
| 0.16 | 3.5273 | 1164 | 1.0119 | 0.3883 | 1.0119 | 1.0059 |
| 0.16 | 3.5333 | 1166 | 0.9926 | 0.3883 | 0.9926 | 0.9963 |
| 0.16 | 3.5394 | 1168 | 0.8809 | 0.3883 | 0.8809 | 0.9386 |
| 0.16 | 3.5455 | 1170 | 0.7310 | 0.3656 | 0.7310 | 0.8550 |
| 0.16 | 3.5515 | 1172 | 0.5893 | 0.4080 | 0.5893 | 0.7677 |
| 0.16 | 3.5576 | 1174 | 0.5465 | 0.4909 | 0.5465 | 0.7392 |
| 0.16 | 3.5636 | 1176 | 0.5516 | 0.5895 | 0.5516 | 0.7427 |
| 0.16 | 3.5697 | 1178 | 0.6119 | 0.4550 | 0.6119 | 0.7822 |
| 0.16 | 3.5758 | 1180 | 0.7193 | 0.3437 | 0.7193 | 0.8481 |
| 0.16 | 3.5818 | 1182 | 0.8369 | 0.3393 | 0.8369 | 0.9148 |
| 0.16 | 3.5879 | 1184 | 0.8867 | 0.3883 | 0.8867 | 0.9416 |
| 0.16 | 3.5939 | 1186 | 0.8514 | 0.3883 | 0.8514 | 0.9227 |
| 0.16 | 3.6 | 1188 | 0.7591 | 0.3636 | 0.7591 | 0.8713 |
| 0.16 | 3.6061 | 1190 | 0.6465 | 0.3717 | 0.6465 | 0.8040 |
| 0.16 | 3.6121 | 1192 | 0.5800 | 0.4776 | 0.5800 | 0.7616 |
| 0.16 | 3.6182 | 1194 | 0.5561 | 0.5832 | 0.5561 | 0.7457 |
| 0.16 | 3.6242 | 1196 | 0.5718 | 0.4381 | 0.5718 | 0.7562 |
| 0.16 | 3.6303 | 1198 | 0.6226 | 0.4381 | 0.6226 | 0.7891 |
| 0.16 | 3.6364 | 1200 | 0.6464 | 0.4286 | 0.6464 | 0.8040 |
| 0.16 | 3.6424 | 1202 | 0.6550 | 0.4288 | 0.6550 | 0.8093 |
| 0.16 | 3.6485 | 1204 | 0.6922 | 0.3558 | 0.6922 | 0.8320 |
| 0.16 | 3.6545 | 1206 | 0.6956 | 0.3558 | 0.6956 | 0.8340 |
| 0.16 | 3.6606 | 1208 | 0.6560 | 0.4378 | 0.6560 | 0.8099 |
| 0.16 | 3.6667 | 1210 | 0.6035 | 0.4381 | 0.6035 | 0.7769 |
| 0.16 | 3.6727 | 1212 | 0.5934 | 0.4465 | 0.5934 | 0.7704 |
| 0.16 | 3.6788 | 1214 | 0.5974 | 0.4465 | 0.5974 | 0.7729 |
| 0.16 | 3.6848 | 1216 | 0.6032 | 0.4465 | 0.6032 | 0.7767 |
| 0.16 | 3.6909 | 1218 | 0.6024 | 0.475 | 0.6024 | 0.7761 |
| 0.16 | 3.6970 | 1220 | 0.6365 | 0.4581 | 0.6365 | 0.7978 |
| 0.16 | 3.7030 | 1222 | 0.6377 | 0.4566 | 0.6377 | 0.7986 |
| 0.16 | 3.7091 | 1224 | 0.6427 | 0.4566 | 0.6427 | 0.8017 |
| 0.16 | 3.7152 | 1226 | 0.5906 | 0.4288 | 0.5906 | 0.7685 |
| 0.16 | 3.7212 | 1228 | 0.5146 | 0.4381 | 0.5146 | 0.7174 |
| 0.16 | 3.7273 | 1230 | 0.4699 | 0.5545 | 0.4699 | 0.6855 |
| 0.16 | 3.7333 | 1232 | 0.4687 | 0.5832 | 0.4687 | 0.6846 |
| 0.16 | 3.7394 | 1234 | 0.5001 | 0.4094 | 0.5001 | 0.7071 |
| 0.16 | 3.7455 | 1236 | 0.5290 | 0.4378 | 0.5290 | 0.7273 |
| 0.16 | 3.7515 | 1238 | 0.5628 | 0.4378 | 0.5628 | 0.7502 |
| 0.16 | 3.7576 | 1240 | 0.5600 | 0.475 | 0.5600 | 0.7483 |
| 0.16 | 3.7636 | 1242 | 0.5668 | 0.5073 | 0.5668 | 0.7529 |
| 0.16 | 3.7697 | 1244 | 0.5561 | 0.5751 | 0.5561 | 0.7457 |
| 0.16 | 3.7758 | 1246 | 0.5675 | 0.6026 | 0.5675 | 0.7533 |
| 0.16 | 3.7818 | 1248 | 0.5720 | 0.5939 | 0.5720 | 0.7563 |
| 0.16 | 3.7879 | 1250 | 0.5296 | 0.6026 | 0.5296 | 0.7277 |
| 0.16 | 3.7939 | 1252 | 0.4957 | 0.6123 | 0.4957 | 0.7041 |
| 0.16 | 3.8 | 1254 | 0.4770 | 0.7115 | 0.4770 | 0.6906 |
| 0.16 | 3.8061 | 1256 | 0.4776 | 0.6123 | 0.4776 | 0.6911 |
| 0.16 | 3.8121 | 1258 | 0.4397 | 0.6818 | 0.4397 | 0.6631 |
| 0.16 | 3.8182 | 1260 | 0.4086 | 0.6866 | 0.4086 | 0.6392 |
| 0.16 | 3.8242 | 1262 | 0.4123 | 0.6866 | 0.4123 | 0.6421 |
| 0.16 | 3.8303 | 1264 | 0.4478 | 0.6526 | 0.4478 | 0.6692 |
| 0.16 | 3.8364 | 1266 | 0.4853 | 0.6686 | 0.4853 | 0.6967 |
| 0.16 | 3.8424 | 1268 | 0.4825 | 0.6866 | 0.4825 | 0.6946 |
| 0.16 | 3.8485 | 1270 | 0.5014 | 0.6733 | 0.5014 | 0.7081 |
| 0.16 | 3.8545 | 1272 | 0.5046 | 0.6733 | 0.5046 | 0.7104 |
| 0.16 | 3.8606 | 1274 | 0.5174 | 0.6733 | 0.5174 | 0.7193 |
| 0.16 | 3.8667 | 1276 | 0.5046 | 0.6733 | 0.5046 | 0.7104 |
| 0.16 | 3.8727 | 1278 | 0.4954 | 0.6966 | 0.4954 | 0.7039 |
| 0.16 | 3.8788 | 1280 | 0.4942 | 0.5922 | 0.4942 | 0.7030 |
| 0.16 | 3.8848 | 1282 | 0.5410 | 0.4667 | 0.5410 | 0.7355 |
| 0.16 | 3.8909 | 1284 | 0.5788 | 0.4648 | 0.5788 | 0.7608 |
| 0.16 | 3.8970 | 1286 | 0.5950 | 0.4288 | 0.5950 | 0.7713 |
| 0.16 | 3.9030 | 1288 | 0.5719 | 0.4667 | 0.5719 | 0.7563 |
| 0.16 | 3.9091 | 1290 | 0.5267 | 0.4776 | 0.5267 | 0.7257 |
| 0.16 | 3.9152 | 1292 | 0.4828 | 0.5922 | 0.4828 | 0.6949 |
| 0.16 | 3.9212 | 1294 | 0.4779 | 0.5922 | 0.4779 | 0.6913 |
| 0.16 | 3.9273 | 1296 | 0.5051 | 0.5922 | 0.5051 | 0.7107 |
| 0.16 | 3.9333 | 1298 | 0.5563 | 0.5922 | 0.5563 | 0.7459 |
| 0.16 | 3.9394 | 1300 | 0.5904 | 0.5922 | 0.5904 | 0.7684 |
| 0.16 | 3.9455 | 1302 | 0.6083 | 0.5922 | 0.6083 | 0.7799 |
| 0.16 | 3.9515 | 1304 | 0.5731 | 0.5922 | 0.5731 | 0.7571 |
| 0.16 | 3.9576 | 1306 | 0.5212 | 0.6966 | 0.5212 | 0.7219 |
| 0.16 | 3.9636 | 1308 | 0.5168 | 0.6966 | 0.5168 | 0.7189 |
| 0.16 | 3.9697 | 1310 | 0.5459 | 0.6966 | 0.5459 | 0.7389 |
| 0.16 | 3.9758 | 1312 | 0.5891 | 0.5145 | 0.5891 | 0.7675 |
| 0.16 | 3.9818 | 1314 | 0.5840 | 0.4803 | 0.5840 | 0.7642 |
| 0.16 | 3.9879 | 1316 | 0.5786 | 0.475 | 0.5786 | 0.7606 |
| 0.16 | 3.9939 | 1318 | 0.5230 | 0.6966 | 0.5230 | 0.7232 |
| 0.16 | 4.0 | 1320 | 0.4889 | 0.6966 | 0.4889 | 0.6992 |
| 0.16 | 4.0061 | 1322 | 0.4746 | 0.6966 | 0.4746 | 0.6889 |
| 0.16 | 4.0121 | 1324 | 0.4873 | 0.6966 | 0.4873 | 0.6981 |
| 0.16 | 4.0182 | 1326 | 0.5291 | 0.6966 | 0.5291 | 0.7274 |
| 0.16 | 4.0242 | 1328 | 0.5924 | 0.5832 | 0.5924 | 0.7697 |
| 0.16 | 4.0303 | 1330 | 0.6138 | 0.4706 | 0.6138 | 0.7834 |
| 0.16 | 4.0364 | 1332 | 0.6125 | 0.4757 | 0.6125 | 0.7826 |
| 0.16 | 4.0424 | 1334 | 0.5845 | 0.6211 | 0.5845 | 0.7645 |
| 0.16 | 4.0485 | 1336 | 0.5607 | 0.6613 | 0.5607 | 0.7488 |
| 0.16 | 4.0545 | 1338 | 0.5308 | 0.6866 | 0.5308 | 0.7286 |
| 0.16 | 4.0606 | 1340 | 0.5141 | 0.6866 | 0.5141 | 0.7170 |
| 0.16 | 4.0667 | 1342 | 0.5066 | 0.6866 | 0.5066 | 0.7118 |
| 0.16 | 4.0727 | 1344 | 0.5077 | 0.6526 | 0.5077 | 0.7125 |
| 0.16 | 4.0788 | 1346 | 0.5130 | 0.5767 | 0.5130 | 0.7162 |
| 0.16 | 4.0848 | 1348 | 0.4865 | 0.5855 | 0.4865 | 0.6975 |
| 0.16 | 4.0909 | 1350 | 0.4883 | 0.5855 | 0.4883 | 0.6988 |
| 0.16 | 4.0970 | 1352 | 0.5087 | 0.5767 | 0.5087 | 0.7132 |
| 0.16 | 4.1030 | 1354 | 0.5595 | 0.4727 | 0.5595 | 0.7480 |
| 0.16 | 4.1091 | 1356 | 0.5928 | 0.4727 | 0.5928 | 0.7699 |
| 0.16 | 4.1152 | 1358 | 0.5807 | 0.4727 | 0.5807 | 0.7620 |
| 0.16 | 4.1212 | 1360 | 0.5528 | 0.5812 | 0.5528 | 0.7435 |
| 0.16 | 4.1273 | 1362 | 0.5372 | 0.5812 | 0.5372 | 0.7329 |
| 0.16 | 4.1333 | 1364 | 0.5353 | 0.5812 | 0.5353 | 0.7316 |
| 0.16 | 4.1394 | 1366 | 0.5561 | 0.5812 | 0.5561 | 0.7457 |
| 0.16 | 4.1455 | 1368 | 0.6019 | 0.4779 | 0.6019 | 0.7758 |
| 0.16 | 4.1515 | 1370 | 0.7081 | 0.4049 | 0.7081 | 0.8415 |
| 0.16 | 4.1576 | 1372 | 0.7870 | 0.3636 | 0.7870 | 0.8871 |
| 0.16 | 4.1636 | 1374 | 0.7742 | 0.3636 | 0.7742 | 0.8799 |
| 0.16 | 4.1697 | 1376 | 0.6820 | 0.4290 | 0.6820 | 0.8258 |
| 0.16 | 4.1758 | 1378 | 0.5868 | 0.4803 | 0.5868 | 0.7660 |
| 0.16 | 4.1818 | 1380 | 0.5075 | 0.5812 | 0.5075 | 0.7124 |
| 0.16 | 4.1879 | 1382 | 0.4719 | 0.7014 | 0.4719 | 0.6869 |
| 0.16 | 4.1939 | 1384 | 0.4602 | 0.7014 | 0.4602 | 0.6784 |
| 0.16 | 4.2 | 1386 | 0.4663 | 0.5987 | 0.4663 | 0.6829 |
| 0.16 | 4.2061 | 1388 | 0.5052 | 0.4465 | 0.5052 | 0.7108 |
| 0.16 | 4.2121 | 1390 | 0.5664 | 0.475 | 0.5664 | 0.7526 |
| 0.16 | 4.2182 | 1392 | 0.6344 | 0.4290 | 0.6344 | 0.7965 |
| 0.16 | 4.2242 | 1394 | 0.7417 | 0.4202 | 0.7417 | 0.8612 |
| 0.16 | 4.2303 | 1396 | 0.7831 | 0.3636 | 0.7831 | 0.8849 |
| 0.16 | 4.2364 | 1398 | 0.7319 | 0.3927 | 0.7319 | 0.8555 |
| 0.16 | 4.2424 | 1400 | 0.6283 | 0.4727 | 0.6283 | 0.7927 |
| 0.16 | 4.2485 | 1402 | 0.5266 | 0.5832 | 0.5266 | 0.7257 |
| 0.16 | 4.2545 | 1404 | 0.5049 | 0.6733 | 0.5049 | 0.7106 |
| 0.16 | 4.2606 | 1406 | 0.5142 | 0.6733 | 0.5142 | 0.7171 |
| 0.16 | 4.2667 | 1408 | 0.5345 | 0.5481 | 0.5345 | 0.7311 |
| 0.16 | 4.2727 | 1410 | 0.5752 | 0.475 | 0.5752 | 0.7584 |
| 0.16 | 4.2788 | 1412 | 0.6312 | 0.4667 | 0.6312 | 0.7945 |
| 0.16 | 4.2848 | 1414 | 0.6788 | 0.3927 | 0.6788 | 0.8239 |
| 0.16 | 4.2909 | 1416 | 0.6775 | 0.4195 | 0.6775 | 0.8231 |
| 0.16 | 4.2970 | 1418 | 0.6248 | 0.4288 | 0.6248 | 0.7905 |
| 0.16 | 4.3030 | 1420 | 0.5406 | 0.4288 | 0.5406 | 0.7353 |
| 0.16 | 4.3091 | 1422 | 0.4842 | 0.5084 | 0.4842 | 0.6958 |
| 0.16 | 4.3152 | 1424 | 0.4653 | 0.5625 | 0.4653 | 0.6822 |
| 0.16 | 4.3212 | 1426 | 0.4478 | 0.5625 | 0.4478 | 0.6692 |
| 0.16 | 4.3273 | 1428 | 0.4453 | 0.7133 | 0.4453 | 0.6673 |
| 0.16 | 4.3333 | 1430 | 0.4614 | 0.5922 | 0.4614 | 0.6793 |
| 0.16 | 4.3394 | 1432 | 0.4822 | 0.5922 | 0.4822 | 0.6944 |
| 0.16 | 4.3455 | 1434 | 0.5391 | 0.4465 | 0.5391 | 0.7342 |
| 0.16 | 4.3515 | 1436 | 0.6047 | 0.4648 | 0.6047 | 0.7776 |
| 0.16 | 4.3576 | 1438 | 0.6219 | 0.4648 | 0.6219 | 0.7886 |
| 0.16 | 4.3636 | 1440 | 0.5870 | 0.4727 | 0.5870 | 0.7662 |
| 0.16 | 4.3697 | 1442 | 0.5309 | 0.4803 | 0.5309 | 0.7286 |
| 0.16 | 4.3758 | 1444 | 0.4760 | 0.6866 | 0.4760 | 0.6900 |
| 0.16 | 4.3818 | 1446 | 0.4664 | 0.6866 | 0.4664 | 0.6829 |
| 0.16 | 4.3879 | 1448 | 0.4764 | 0.6866 | 0.4764 | 0.6902 |
| 0.16 | 4.3939 | 1450 | 0.4956 | 0.6733 | 0.4956 | 0.7040 |
| 0.16 | 4.4 | 1452 | 0.5145 | 0.6733 | 0.5145 | 0.7173 |
| 0.16 | 4.4061 | 1454 | 0.5370 | 0.4850 | 0.5370 | 0.7328 |
| 0.16 | 4.4121 | 1456 | 0.5754 | 0.4779 | 0.5754 | 0.7586 |
| 0.16 | 4.4182 | 1458 | 0.5710 | 0.4727 | 0.5710 | 0.7556 |
| 0.16 | 4.4242 | 1460 | 0.5363 | 0.4779 | 0.5363 | 0.7323 |
| 0.16 | 4.4303 | 1462 | 0.4953 | 0.5481 | 0.4953 | 0.7038 |
| 0.16 | 4.4364 | 1464 | 0.4793 | 0.6866 | 0.4793 | 0.6923 |
| 0.16 | 4.4424 | 1466 | 0.4794 | 0.6866 | 0.4794 | 0.6924 |
| 0.16 | 4.4485 | 1468 | 0.4763 | 0.6866 | 0.4763 | 0.6901 |
| 0.16 | 4.4545 | 1470 | 0.5045 | 0.5481 | 0.5045 | 0.7103 |
| 0.16 | 4.4606 | 1472 | 0.5719 | 0.5 | 0.5719 | 0.7563 |
| 0.16 | 4.4667 | 1474 | 0.6112 | 0.4290 | 0.6112 | 0.7818 |
| 0.16 | 4.4727 | 1476 | 0.6379 | 0.3948 | 0.6379 | 0.7987 |
| 0.16 | 4.4788 | 1478 | 0.6062 | 0.4290 | 0.6062 | 0.7786 |
| 0.16 | 4.4848 | 1480 | 0.5413 | 0.4465 | 0.5413 | 0.7358 |
| 0.16 | 4.4909 | 1482 | 0.5059 | 0.4831 | 0.5059 | 0.7113 |
| 0.16 | 4.4970 | 1484 | 0.4952 | 0.5832 | 0.4952 | 0.7037 |
| 0.16 | 4.5030 | 1486 | 0.5067 | 0.5812 | 0.5067 | 0.7118 |
| 0.16 | 4.5091 | 1488 | 0.5400 | 0.4465 | 0.5400 | 0.7348 |
| 0.16 | 4.5152 | 1490 | 0.6104 | 0.4094 | 0.6104 | 0.7813 |
| 0.16 | 4.5212 | 1492 | 0.6815 | 0.4034 | 0.6815 | 0.8255 |
| 0.16 | 4.5273 | 1494 | 0.7107 | 0.3948 | 0.7107 | 0.8430 |
| 0.16 | 4.5333 | 1496 | 0.7632 | 0.3948 | 0.7632 | 0.8736 |
| 0.16 | 4.5394 | 1498 | 0.7455 | 0.3948 | 0.7455 | 0.8634 |
| 0.1037 | 4.5455 | 1500 | 0.6767 | 0.496 | 0.6767 | 0.8226 |
| 0.1037 | 4.5515 | 1502 | 0.5902 | 0.4094 | 0.5902 | 0.7682 |
| 0.1037 | 4.5576 | 1504 | 0.5560 | 0.4831 | 0.5560 | 0.7456 |
| 0.1037 | 4.5636 | 1506 | 0.5481 | 0.4831 | 0.5481 | 0.7403 |
| 0.1037 | 4.5697 | 1508 | 0.5713 | 0.4465 | 0.5713 | 0.7559 |
| 0.1037 | 4.5758 | 1510 | 0.6006 | 0.4094 | 0.6006 | 0.7750 |
| 0.1037 | 4.5818 | 1512 | 0.6253 | 0.4378 | 0.6253 | 0.7907 |
| 0.1037 | 4.5879 | 1514 | 0.6844 | 0.4034 | 0.6844 | 0.8273 |
| 0.1037 | 4.5939 | 1516 | 0.7096 | 0.4034 | 0.7096 | 0.8423 |
| 0.1037 | 4.6 | 1518 | 0.6775 | 0.4128 | 0.6775 | 0.8231 |
| 0.1037 | 4.6061 | 1520 | 0.6102 | 0.4803 | 0.6102 | 0.7812 |
| 0.1037 | 4.6121 | 1522 | 0.5732 | 0.4831 | 0.5732 | 0.7571 |
| 0.1037 | 4.6182 | 1524 | 0.5613 | 0.4831 | 0.5613 | 0.7492 |
| 0.1037 | 4.6242 | 1526 | 0.5477 | 0.4831 | 0.5477 | 0.7401 |
| 0.1037 | 4.6303 | 1528 | 0.5668 | 0.4831 | 0.5668 | 0.7529 |
| 0.1037 | 4.6364 | 1530 | 0.5838 | 0.4803 | 0.5838 | 0.7641 |
| 0.1037 | 4.6424 | 1532 | 0.6106 | 0.4451 | 0.6106 | 0.7814 |
| 0.1037 | 4.6485 | 1534 | 0.6328 | 0.3889 | 0.6328 | 0.7955 |
| 0.1037 | 4.6545 | 1536 | 0.6366 | 0.3889 | 0.6366 | 0.7979 |
| 0.1037 | 4.6606 | 1538 | 0.6038 | 0.4779 | 0.6038 | 0.7770 |
| 0.1037 | 4.6667 | 1540 | 0.5648 | 0.4803 | 0.5648 | 0.7515 |
| 0.1037 | 4.6727 | 1542 | 0.5332 | 0.4550 | 0.5332 | 0.7302 |
| 0.1037 | 4.6788 | 1544 | 0.5328 | 0.4550 | 0.5328 | 0.7299 |
| 0.1037 | 4.6848 | 1546 | 0.5615 | 0.4538 | 0.5615 | 0.7493 |
| 0.1037 | 4.6909 | 1548 | 0.5961 | 0.4803 | 0.5961 | 0.7721 |
| 0.1037 | 4.6970 | 1550 | 0.6241 | 0.4457 | 0.6241 | 0.7900 |
| 0.1037 | 4.7030 | 1552 | 0.6230 | 0.4457 | 0.6230 | 0.7893 |
| 0.1037 | 4.7091 | 1554 | 0.5882 | 0.4538 | 0.5882 | 0.7669 |
| 0.1037 | 4.7152 | 1556 | 0.5356 | 0.4909 | 0.5356 | 0.7318 |
| 0.1037 | 4.7212 | 1558 | 0.5118 | 0.6866 | 0.5118 | 0.7154 |
| 0.1037 | 4.7273 | 1560 | 0.4934 | 0.6866 | 0.4934 | 0.7024 |
| 0.1037 | 4.7333 | 1562 | 0.4915 | 0.6866 | 0.4915 | 0.7011 |
| 0.1037 | 4.7394 | 1564 | 0.5114 | 0.6866 | 0.5114 | 0.7151 |
| 0.1037 | 4.7455 | 1566 | 0.5610 | 0.4803 | 0.5610 | 0.7490 |
| 0.1037 | 4.7515 | 1568 | 0.6384 | 0.4451 | 0.6384 | 0.7990 |
| 0.1037 | 4.7576 | 1570 | 0.6700 | 0.4034 | 0.6700 | 0.8185 |
| 0.1037 | 4.7636 | 1572 | 0.6525 | 0.4128 | 0.6525 | 0.8078 |
| 0.1037 | 4.7697 | 1574 | 0.6186 | 0.4457 | 0.6186 | 0.7865 |
| 0.1037 | 4.7758 | 1576 | 0.5837 | 0.4878 | 0.5837 | 0.7640 |
| 0.1037 | 4.7818 | 1578 | 0.5434 | 0.6733 | 0.5434 | 0.7371 |
| 0.1037 | 4.7879 | 1580 | 0.5241 | 0.6733 | 0.5241 | 0.7239 |
| 0.1037 | 4.7939 | 1582 | 0.5154 | 0.6866 | 0.5154 | 0.7179 |
| 0.1037 | 4.8 | 1584 | 0.5216 | 0.5812 | 0.5216 | 0.7222 |
| 0.1037 | 4.8061 | 1586 | 0.5197 | 0.5481 | 0.5197 | 0.7209 |
| 0.1037 | 4.8121 | 1588 | 0.5416 | 0.4803 | 0.5416 | 0.7359 |
| 0.1037 | 4.8182 | 1590 | 0.5767 | 0.4457 | 0.5767 | 0.7594 |
| 0.1037 | 4.8242 | 1592 | 0.5952 | 0.4018 | 0.5952 | 0.7715 |
| 0.1037 | 4.8303 | 1594 | 0.5877 | 0.4018 | 0.5877 | 0.7666 |
| 0.1037 | 4.8364 | 1596 | 0.5400 | 0.475 | 0.5400 | 0.7348 |
| 0.1037 | 4.8424 | 1598 | 0.5032 | 0.4550 | 0.5032 | 0.7094 |
| 0.1037 | 4.8485 | 1600 | 0.4741 | 0.6866 | 0.4741 | 0.6886 |
| 0.1037 | 4.8545 | 1602 | 0.4713 | 0.6866 | 0.4713 | 0.6865 |
| 0.1037 | 4.8606 | 1604 | 0.4919 | 0.6866 | 0.4919 | 0.7013 |
| 0.1037 | 4.8667 | 1606 | 0.5379 | 0.4538 | 0.5379 | 0.7334 |
| 0.1037 | 4.8727 | 1608 | 0.5782 | 0.4538 | 0.5782 | 0.7604 |
| 0.1037 | 4.8788 | 1610 | 0.6159 | 0.4205 | 0.6159 | 0.7848 |
| 0.1037 | 4.8848 | 1612 | 0.5988 | 0.3969 | 0.5988 | 0.7738 |
| 0.1037 | 4.8909 | 1614 | 0.5885 | 0.5422 | 0.5885 | 0.7671 |
| 0.1037 | 4.8970 | 1616 | 0.5972 | 0.6306 | 0.5972 | 0.7728 |
| 0.1037 | 4.9030 | 1618 | 0.5987 | 0.6306 | 0.5987 | 0.7738 |
| 0.1037 | 4.9091 | 1620 | 0.5750 | 0.6733 | 0.5750 | 0.7583 |
| 0.1037 | 4.9152 | 1622 | 0.5728 | 0.6733 | 0.5728 | 0.7568 |
| 0.1037 | 4.9212 | 1624 | 0.5685 | 0.6733 | 0.5685 | 0.7540 |
| 0.1037 | 4.9273 | 1626 | 0.5718 | 0.6733 | 0.5718 | 0.7562 |
| 0.1037 | 4.9333 | 1628 | 0.5723 | 0.6733 | 0.5723 | 0.7565 |
| 0.1037 | 4.9394 | 1630 | 0.5779 | 0.5481 | 0.5779 | 0.7602 |
| 0.1037 | 4.9455 | 1632 | 0.6116 | 0.4527 | 0.6116 | 0.7820 |
| 0.1037 | 4.9515 | 1634 | 0.6294 | 0.4451 | 0.6294 | 0.7934 |
| 0.1037 | 4.9576 | 1636 | 0.6063 | 0.4192 | 0.6063 | 0.7786 |
| 0.1037 | 4.9636 | 1638 | 0.5739 | 0.4538 | 0.5739 | 0.7576 |
| 0.1037 | 4.9697 | 1640 | 0.5712 | 0.4538 | 0.5712 | 0.7558 |
| 0.1037 | 4.9758 | 1642 | 0.5664 | 0.4538 | 0.5664 | 0.7526 |
| 0.1037 | 4.9818 | 1644 | 0.5488 | 0.4878 | 0.5488 | 0.7408 |
| 0.1037 | 4.9879 | 1646 | 0.5451 | 0.4878 | 0.5451 | 0.7383 |
| 0.1037 | 4.9939 | 1648 | 0.5561 | 0.4878 | 0.5561 | 0.7457 |
| 0.1037 | 5.0 | 1650 | 0.6019 | 0.4192 | 0.6019 | 0.7758 |
| 0.1037 | 5.0061 | 1652 | 0.6757 | 0.4378 | 0.6757 | 0.8220 |
| 0.1037 | 5.0121 | 1654 | 0.6994 | 0.4290 | 0.6994 | 0.8363 |
| 0.1037 | 5.0182 | 1656 | 0.6716 | 0.4378 | 0.6716 | 0.8195 |
| 0.1037 | 5.0242 | 1658 | 0.6092 | 0.4094 | 0.6092 | 0.7805 |
| 0.1037 | 5.0303 | 1660 | 0.5598 | 0.4185 | 0.5598 | 0.7482 |
| 0.1037 | 5.0364 | 1662 | 0.5356 | 0.4185 | 0.5356 | 0.7318 |
| 0.1037 | 5.0424 | 1664 | 0.5416 | 0.4550 | 0.5416 | 0.7359 |
| 0.1037 | 5.0485 | 1666 | 0.5494 | 0.4550 | 0.5494 | 0.7412 |
| 0.1037 | 5.0545 | 1668 | 0.5550 | 0.4550 | 0.5550 | 0.7450 |
| 0.1037 | 5.0606 | 1670 | 0.5713 | 0.4185 | 0.5713 | 0.7559 |
| 0.1037 | 5.0667 | 1672 | 0.5995 | 0.4381 | 0.5995 | 0.7743 |
| 0.1037 | 5.0727 | 1674 | 0.5934 | 0.4381 | 0.5934 | 0.7703 |
| 0.1037 | 5.0788 | 1676 | 0.5691 | 0.4776 | 0.5691 | 0.7544 |
| 0.1037 | 5.0848 | 1678 | 0.5470 | 0.4776 | 0.5470 | 0.7396 |
| 0.1037 | 5.0909 | 1680 | 0.5156 | 0.4474 | 0.5156 | 0.7180 |
| 0.1037 | 5.0970 | 1682 | 0.5176 | 0.5545 | 0.5176 | 0.7194 |
| 0.1037 | 5.1030 | 1684 | 0.5393 | 0.5545 | 0.5393 | 0.7344 |
| 0.1037 | 5.1091 | 1686 | 0.5627 | 0.4550 | 0.5627 | 0.7501 |
| 0.1037 | 5.1152 | 1688 | 0.5688 | 0.4831 | 0.5688 | 0.7542 |
| 0.1037 | 5.1212 | 1690 | 0.5657 | 0.4550 | 0.5657 | 0.7521 |
| 0.1037 | 5.1273 | 1692 | 0.5654 | 0.4550 | 0.5654 | 0.7519 |
| 0.1037 | 5.1333 | 1694 | 0.5642 | 0.4550 | 0.5642 | 0.7511 |
| 0.1037 | 5.1394 | 1696 | 0.5613 | 0.4550 | 0.5613 | 0.7492 |
| 0.1037 | 5.1455 | 1698 | 0.5557 | 0.4550 | 0.5557 | 0.7454 |
| 0.1037 | 5.1515 | 1700 | 0.5374 | 0.4550 | 0.5374 | 0.7331 |
| 0.1037 | 5.1576 | 1702 | 0.5431 | 0.4474 | 0.5431 | 0.7370 |
| 0.1037 | 5.1636 | 1704 | 0.5486 | 0.4474 | 0.5486 | 0.7407 |
| 0.1037 | 5.1697 | 1706 | 0.5520 | 0.4909 | 0.5520 | 0.7430 |
| 0.1037 | 5.1758 | 1708 | 0.5703 | 0.4850 | 0.5703 | 0.7552 |
| 0.1037 | 5.1818 | 1710 | 0.5810 | 0.4850 | 0.5810 | 0.7622 |
| 0.1037 | 5.1879 | 1712 | 0.5799 | 0.5737 | 0.5799 | 0.7615 |
| 0.1037 | 5.1939 | 1714 | 0.5705 | 0.6733 | 0.5705 | 0.7553 |
| 0.1037 | 5.2 | 1716 | 0.5674 | 0.6733 | 0.5674 | 0.7533 |
| 0.1037 | 5.2061 | 1718 | 0.5622 | 0.6733 | 0.5622 | 0.7498 |
| 0.1037 | 5.2121 | 1720 | 0.5575 | 0.6733 | 0.5575 | 0.7467 |
| 0.1037 | 5.2182 | 1722 | 0.5447 | 0.5812 | 0.5447 | 0.7380 |
| 0.1037 | 5.2242 | 1724 | 0.5174 | 0.5812 | 0.5174 | 0.7193 |
| 0.1037 | 5.2303 | 1726 | 0.4987 | 0.5812 | 0.4987 | 0.7062 |
| 0.1037 | 5.2364 | 1728 | 0.4970 | 0.5812 | 0.4970 | 0.7050 |
| 0.1037 | 5.2424 | 1730 | 0.5139 | 0.4465 | 0.5139 | 0.7169 |
| 0.1037 | 5.2485 | 1732 | 0.5219 | 0.4465 | 0.5219 | 0.7224 |
| 0.1037 | 5.2545 | 1734 | 0.5222 | 0.4465 | 0.5222 | 0.7226 |
| 0.1037 | 5.2606 | 1736 | 0.5158 | 0.4465 | 0.5158 | 0.7182 |
| 0.1037 | 5.2667 | 1738 | 0.4979 | 0.4465 | 0.4979 | 0.7056 |
| 0.1037 | 5.2727 | 1740 | 0.4975 | 0.4474 | 0.4975 | 0.7054 |
| 0.1037 | 5.2788 | 1742 | 0.5050 | 0.4861 | 0.5050 | 0.7107 |
| 0.1037 | 5.2848 | 1744 | 0.5193 | 0.4878 | 0.5193 | 0.7206 |
| 0.1037 | 5.2909 | 1746 | 0.5349 | 0.4538 | 0.5349 | 0.7314 |
| 0.1037 | 5.2970 | 1748 | 0.5644 | 0.4538 | 0.5644 | 0.7513 |
| 0.1037 | 5.3030 | 1750 | 0.5925 | 0.4538 | 0.5925 | 0.7698 |
| 0.1037 | 5.3091 | 1752 | 0.6001 | 0.4192 | 0.6001 | 0.7747 |
| 0.1037 | 5.3152 | 1754 | 0.6365 | 0.4457 | 0.6365 | 0.7978 |
| 0.1037 | 5.3212 | 1756 | 0.6375 | 0.4457 | 0.6375 | 0.7984 |
| 0.1037 | 5.3273 | 1758 | 0.6040 | 0.4192 | 0.6040 | 0.7772 |
| 0.1037 | 5.3333 | 1760 | 0.5958 | 0.4538 | 0.5958 | 0.7718 |
| 0.1037 | 5.3394 | 1762 | 0.5969 | 0.4538 | 0.5969 | 0.7726 |
| 0.1037 | 5.3455 | 1764 | 0.6273 | 0.4538 | 0.6273 | 0.7920 |
| 0.1037 | 5.3515 | 1766 | 0.6739 | 0.4192 | 0.6739 | 0.8209 |
| 0.1037 | 5.3576 | 1768 | 0.7069 | 0.4457 | 0.7069 | 0.8408 |
| 0.1037 | 5.3636 | 1770 | 0.6864 | 0.4457 | 0.6864 | 0.8285 |
| 0.1037 | 5.3697 | 1772 | 0.6463 | 0.4192 | 0.6463 | 0.8039 |
| 0.1037 | 5.3758 | 1774 | 0.5974 | 0.4192 | 0.5974 | 0.7729 |
| 0.1037 | 5.3818 | 1776 | 0.5769 | 0.4192 | 0.5769 | 0.7595 |
| 0.1037 | 5.3879 | 1778 | 0.5947 | 0.4192 | 0.5947 | 0.7712 |
| 0.1037 | 5.3939 | 1780 | 0.6503 | 0.4457 | 0.6503 | 0.8064 |
| 0.1037 | 5.4 | 1782 | 0.7210 | 0.4924 | 0.7210 | 0.8491 |
| 0.1037 | 5.4061 | 1784 | 0.7456 | 0.4864 | 0.7456 | 0.8635 |
| 0.1037 | 5.4121 | 1786 | 0.7426 | 0.4864 | 0.7426 | 0.8617 |
| 0.1037 | 5.4182 | 1788 | 0.7031 | 0.4631 | 0.7031 | 0.8385 |
| 0.1037 | 5.4242 | 1790 | 0.6357 | 0.4192 | 0.6357 | 0.7973 |
| 0.1037 | 5.4303 | 1792 | 0.6018 | 0.4878 | 0.6018 | 0.7757 |
| 0.1037 | 5.4364 | 1794 | 0.5877 | 0.4919 | 0.5877 | 0.7666 |
| 0.1037 | 5.4424 | 1796 | 0.5893 | 0.5794 | 0.5893 | 0.7677 |
| 0.1037 | 5.4485 | 1798 | 0.6063 | 0.5794 | 0.6063 | 0.7786 |
| 0.1037 | 5.4545 | 1800 | 0.6250 | 0.4891 | 0.6250 | 0.7906 |
| 0.1037 | 5.4606 | 1802 | 0.6565 | 0.4199 | 0.6565 | 0.8102 |
| 0.1037 | 5.4667 | 1804 | 0.6684 | 0.4205 | 0.6684 | 0.8176 |
| 0.1037 | 5.4727 | 1806 | 0.6454 | 0.4199 | 0.6454 | 0.8034 |
| 0.1037 | 5.4788 | 1808 | 0.6389 | 0.4527 | 0.6389 | 0.7993 |
| 0.1037 | 5.4848 | 1810 | 0.6329 | 0.4527 | 0.6329 | 0.7956 |
| 0.1037 | 5.4909 | 1812 | 0.6272 | 0.4527 | 0.6272 | 0.7920 |
| 0.1037 | 5.4970 | 1814 | 0.6234 | 0.4527 | 0.6234 | 0.7896 |
| 0.1037 | 5.5030 | 1816 | 0.6304 | 0.4527 | 0.6304 | 0.7940 |
| 0.1037 | 5.5091 | 1818 | 0.6247 | 0.4527 | 0.6247 | 0.7904 |
| 0.1037 | 5.5152 | 1820 | 0.6294 | 0.4527 | 0.6294 | 0.7933 |
| 0.1037 | 5.5212 | 1822 | 0.6458 | 0.4527 | 0.6458 | 0.8036 |
| 0.1037 | 5.5273 | 1824 | 0.6178 | 0.4527 | 0.6178 | 0.7860 |
| 0.1037 | 5.5333 | 1826 | 0.6036 | 0.4527 | 0.6036 | 0.7769 |
| 0.1037 | 5.5394 | 1828 | 0.5890 | 0.4527 | 0.5890 | 0.7675 |
| 0.1037 | 5.5455 | 1830 | 0.5863 | 0.4527 | 0.5863 | 0.7657 |
| 0.1037 | 5.5515 | 1832 | 0.5822 | 0.4727 | 0.5822 | 0.7630 |
| 0.1037 | 5.5576 | 1834 | 0.6136 | 0.4706 | 0.6136 | 0.7833 |
| 0.1037 | 5.5636 | 1836 | 0.6611 | 0.4864 | 0.6611 | 0.8131 |
| 0.1037 | 5.5697 | 1838 | 0.7280 | 0.4819 | 0.7280 | 0.8532 |
| 0.1037 | 5.5758 | 1840 | 0.7544 | 0.4215 | 0.7544 | 0.8686 |
| 0.1037 | 5.5818 | 1842 | 0.7227 | 0.4819 | 0.7227 | 0.8501 |
| 0.1037 | 5.5879 | 1844 | 0.6873 | 0.4819 | 0.6873 | 0.8290 |
| 0.1037 | 5.5939 | 1846 | 0.6410 | 0.5205 | 0.6410 | 0.8007 |
| 0.1037 | 5.6 | 1848 | 0.5812 | 0.4727 | 0.5812 | 0.7624 |
| 0.1037 | 5.6061 | 1850 | 0.5193 | 0.4831 | 0.5193 | 0.7206 |
| 0.1037 | 5.6121 | 1852 | 0.4782 | 0.5545 | 0.4782 | 0.6915 |
| 0.1037 | 5.6182 | 1854 | 0.4749 | 0.5545 | 0.4749 | 0.6891 |
| 0.1037 | 5.6242 | 1856 | 0.4928 | 0.5545 | 0.4928 | 0.7020 |
| 0.1037 | 5.6303 | 1858 | 0.5246 | 0.5481 | 0.5246 | 0.7243 |
| 0.1037 | 5.6364 | 1860 | 0.5543 | 0.4803 | 0.5543 | 0.7445 |
| 0.1037 | 5.6424 | 1862 | 0.5536 | 0.4803 | 0.5536 | 0.7441 |
| 0.1037 | 5.6485 | 1864 | 0.5327 | 0.5751 | 0.5327 | 0.7299 |
| 0.1037 | 5.6545 | 1866 | 0.5001 | 0.5481 | 0.5001 | 0.7072 |
| 0.1037 | 5.6606 | 1868 | 0.4666 | 0.5545 | 0.4666 | 0.6831 |
| 0.1037 | 5.6667 | 1870 | 0.4468 | 0.5895 | 0.4468 | 0.6685 |
| 0.1037 | 5.6727 | 1872 | 0.4528 | 0.5895 | 0.4528 | 0.6729 |
| 0.1037 | 5.6788 | 1874 | 0.4712 | 0.5545 | 0.4712 | 0.6865 |
| 0.1037 | 5.6848 | 1876 | 0.5160 | 0.5545 | 0.5160 | 0.7183 |
| 0.1037 | 5.6909 | 1878 | 0.5558 | 0.4779 | 0.5558 | 0.7455 |
| 0.1037 | 5.6970 | 1880 | 0.5657 | 0.4779 | 0.5657 | 0.7521 |
| 0.1037 | 5.7030 | 1882 | 0.5593 | 0.4779 | 0.5593 | 0.7479 |
| 0.1037 | 5.7091 | 1884 | 0.5774 | 0.4779 | 0.5774 | 0.7599 |
| 0.1037 | 5.7152 | 1886 | 0.5973 | 0.4779 | 0.5973 | 0.7729 |
| 0.1037 | 5.7212 | 1888 | 0.6140 | 0.4779 | 0.6140 | 0.7836 |
| 0.1037 | 5.7273 | 1890 | 0.6136 | 0.4779 | 0.6136 | 0.7833 |
| 0.1037 | 5.7333 | 1892 | 0.6306 | 0.4706 | 0.6306 | 0.7941 |
| 0.1037 | 5.7394 | 1894 | 0.6186 | 0.4706 | 0.6186 | 0.7865 |
| 0.1037 | 5.7455 | 1896 | 0.5878 | 0.5039 | 0.5878 | 0.7667 |
| 0.1037 | 5.7515 | 1898 | 0.5585 | 0.475 | 0.5585 | 0.7474 |
| 0.1037 | 5.7576 | 1900 | 0.5192 | 0.4550 | 0.5192 | 0.7205 |
| 0.1037 | 5.7636 | 1902 | 0.4801 | 0.5545 | 0.4801 | 0.6929 |
| 0.1037 | 5.7697 | 1904 | 0.4677 | 0.5895 | 0.4677 | 0.6839 |
| 0.1037 | 5.7758 | 1906 | 0.4761 | 0.5895 | 0.4761 | 0.6900 |
| 0.1037 | 5.7818 | 1908 | 0.5085 | 0.5545 | 0.5085 | 0.7131 |
| 0.1037 | 5.7879 | 1910 | 0.5653 | 0.5422 | 0.5653 | 0.7518 |
| 0.1037 | 5.7939 | 1912 | 0.6004 | 0.4527 | 0.6004 | 0.7749 |
| 0.1037 | 5.8 | 1914 | 0.6098 | 0.4527 | 0.6098 | 0.7809 |
| 0.1037 | 5.8061 | 1916 | 0.5994 | 0.4527 | 0.5994 | 0.7742 |
| 0.1037 | 5.8121 | 1918 | 0.5900 | 0.4527 | 0.5900 | 0.7681 |
| 0.1037 | 5.8182 | 1920 | 0.5907 | 0.4527 | 0.5907 | 0.7686 |
| 0.1037 | 5.8242 | 1922 | 0.5977 | 0.4527 | 0.5977 | 0.7731 |
| 0.1037 | 5.8303 | 1924 | 0.5841 | 0.4527 | 0.5841 | 0.7643 |
| 0.1037 | 5.8364 | 1926 | 0.5912 | 0.4527 | 0.5912 | 0.7689 |
| 0.1037 | 5.8424 | 1928 | 0.5880 | 0.4527 | 0.5880 | 0.7668 |
| 0.1037 | 5.8485 | 1930 | 0.5681 | 0.5481 | 0.5681 | 0.7537 |
| 0.1037 | 5.8545 | 1932 | 0.5839 | 0.4527 | 0.5839 | 0.7641 |
| 0.1037 | 5.8606 | 1934 | 0.6290 | 0.4527 | 0.6290 | 0.7931 |
| 0.1037 | 5.8667 | 1936 | 0.6647 | 0.4527 | 0.6647 | 0.8153 |
| 0.1037 | 5.8727 | 1938 | 0.6611 | 0.4779 | 0.6611 | 0.8131 |
| 0.1037 | 5.8788 | 1940 | 0.6166 | 0.4527 | 0.6166 | 0.7852 |
| 0.1037 | 5.8848 | 1942 | 0.5724 | 0.4527 | 0.5724 | 0.7566 |
| 0.1037 | 5.8909 | 1944 | 0.5273 | 0.5481 | 0.5273 | 0.7262 |
| 0.1037 | 5.8970 | 1946 | 0.4956 | 0.6866 | 0.4956 | 0.7040 |
| 0.1037 | 5.9030 | 1948 | 0.4753 | 0.6866 | 0.4753 | 0.6894 |
| 0.1037 | 5.9091 | 1950 | 0.4813 | 0.6866 | 0.4813 | 0.6938 |
| 0.1037 | 5.9152 | 1952 | 0.5178 | 0.4550 | 0.5178 | 0.7196 |
| 0.1037 | 5.9212 | 1954 | 0.5678 | 0.4727 | 0.5678 | 0.7535 |
| 0.1037 | 5.9273 | 1956 | 0.5838 | 0.4375 | 0.5838 | 0.7641 |
| 0.1037 | 5.9333 | 1958 | 0.5704 | 0.4727 | 0.5704 | 0.7552 |
| 0.1037 | 5.9394 | 1960 | 0.5670 | 0.4457 | 0.5670 | 0.7530 |
| 0.1037 | 5.9455 | 1962 | 0.5770 | 0.4457 | 0.5770 | 0.7596 |
| 0.1037 | 5.9515 | 1964 | 0.5934 | 0.4527 | 0.5934 | 0.7704 |
| 0.1037 | 5.9576 | 1966 | 0.5832 | 0.4527 | 0.5832 | 0.7637 |
| 0.1037 | 5.9636 | 1968 | 0.5554 | 0.5422 | 0.5554 | 0.7453 |
| 0.1037 | 5.9697 | 1970 | 0.5470 | 0.5737 | 0.5470 | 0.7396 |
| 0.1037 | 5.9758 | 1972 | 0.5485 | 0.5422 | 0.5485 | 0.7406 |
| 0.1037 | 5.9818 | 1974 | 0.5501 | 0.4527 | 0.5501 | 0.7417 |
| 0.1037 | 5.9879 | 1976 | 0.5543 | 0.4527 | 0.5543 | 0.7445 |
| 0.1037 | 5.9939 | 1978 | 0.5679 | 0.4457 | 0.5679 | 0.7536 |
| 0.1037 | 6.0 | 1980 | 0.5546 | 0.4457 | 0.5546 | 0.7447 |
| 0.1037 | 6.0061 | 1982 | 0.5388 | 0.4474 | 0.5388 | 0.7340 |
| 0.1037 | 6.0121 | 1984 | 0.5395 | 0.4465 | 0.5395 | 0.7345 |
| 0.1037 | 6.0182 | 1986 | 0.5515 | 0.5 | 0.5515 | 0.7426 |
| 0.1037 | 6.0242 | 1988 | 0.5564 | 0.4648 | 0.5564 | 0.7459 |
| 0.1037 | 6.0303 | 1990 | 0.5644 | 0.4648 | 0.5644 | 0.7513 |
| 0.1037 | 6.0364 | 1992 | 0.5874 | 0.4648 | 0.5874 | 0.7664 |
| 0.1037 | 6.0424 | 1994 | 0.6194 | 0.4648 | 0.6194 | 0.7870 |
| 0.1037 | 6.0485 | 1996 | 0.6243 | 0.4648 | 0.6243 | 0.7901 |
| 0.1037 | 6.0545 | 1998 | 0.6034 | 0.4648 | 0.6034 | 0.7768 |
| 0.0815 | 6.0606 | 2000 | 0.5760 | 0.4378 | 0.5760 | 0.7590 |
| 0.0815 | 6.0667 | 2002 | 0.5595 | 0.4094 | 0.5595 | 0.7480 |
| 0.0815 | 6.0727 | 2004 | 0.5522 | 0.4192 | 0.5522 | 0.7431 |
| 0.0815 | 6.0788 | 2006 | 0.5708 | 0.4192 | 0.5708 | 0.7555 |
| 0.0815 | 6.0848 | 2008 | 0.5954 | 0.4451 | 0.5954 | 0.7717 |
| 0.0815 | 6.0909 | 2010 | 0.5822 | 0.4199 | 0.5822 | 0.7630 |
| 0.0815 | 6.0970 | 2012 | 0.5607 | 0.4527 | 0.5607 | 0.7488 |
| 0.0815 | 6.1030 | 2014 | 0.5389 | 0.5481 | 0.5389 | 0.7341 |
| 0.0815 | 6.1091 | 2016 | 0.5365 | 0.5481 | 0.5365 | 0.7325 |
| 0.0815 | 6.1152 | 2018 | 0.5367 | 0.5481 | 0.5367 | 0.7326 |
| 0.0815 | 6.1212 | 2020 | 0.5282 | 0.5481 | 0.5282 | 0.7268 |
| 0.0815 | 6.1273 | 2022 | 0.5175 | 0.5545 | 0.5175 | 0.7194 |
| 0.0815 | 6.1333 | 2024 | 0.5113 | 0.4550 | 0.5113 | 0.7150 |
| 0.0815 | 6.1394 | 2026 | 0.5165 | 0.4550 | 0.5165 | 0.7186 |
| 0.0815 | 6.1455 | 2028 | 0.5178 | 0.4474 | 0.5178 | 0.7196 |
| 0.0815 | 6.1515 | 2030 | 0.5456 | 0.4080 | 0.5456 | 0.7386 |
| 0.0815 | 6.1576 | 2032 | 0.5440 | 0.4080 | 0.5440 | 0.7375 |
| 0.0815 | 6.1636 | 2034 | 0.5213 | 0.4550 | 0.5213 | 0.7220 |
| 0.0815 | 6.1697 | 2036 | 0.5083 | 0.5545 | 0.5083 | 0.7130 |
| 0.0815 | 6.1758 | 2038 | 0.5080 | 0.5545 | 0.5080 | 0.7127 |
| 0.0815 | 6.1818 | 2040 | 0.5090 | 0.5545 | 0.5090 | 0.7134 |
| 0.0815 | 6.1879 | 2042 | 0.5302 | 0.5545 | 0.5302 | 0.7281 |
| 0.0815 | 6.1939 | 2044 | 0.5692 | 0.4185 | 0.5692 | 0.7544 |
| 0.0815 | 6.2 | 2046 | 0.5965 | 0.4199 | 0.5965 | 0.7724 |
| 0.0815 | 6.2061 | 2048 | 0.6013 | 0.4451 | 0.6013 | 0.7754 |
| 0.0815 | 6.2121 | 2050 | 0.5802 | 0.4192 | 0.5802 | 0.7617 |
| 0.0815 | 6.2182 | 2052 | 0.5564 | 0.4550 | 0.5564 | 0.7460 |
| 0.0815 | 6.2242 | 2054 | 0.5428 | 0.4550 | 0.5428 | 0.7367 |
| 0.0815 | 6.2303 | 2056 | 0.5480 | 0.4550 | 0.5480 | 0.7403 |
| 0.0815 | 6.2364 | 2058 | 0.5666 | 0.4538 | 0.5666 | 0.7527 |
| 0.0815 | 6.2424 | 2060 | 0.5792 | 0.4192 | 0.5792 | 0.7611 |
| 0.0815 | 6.2485 | 2062 | 0.5967 | 0.4457 | 0.5967 | 0.7725 |
| 0.0815 | 6.2545 | 2064 | 0.6221 | 0.4706 | 0.6221 | 0.7887 |
| 0.0815 | 6.2606 | 2066 | 0.6310 | 0.4706 | 0.6310 | 0.7943 |
| 0.0815 | 6.2667 | 2068 | 0.6231 | 0.4648 | 0.6231 | 0.7894 |
| 0.0815 | 6.2727 | 2070 | 0.5989 | 0.4199 | 0.5989 | 0.7739 |
| 0.0815 | 6.2788 | 2072 | 0.5690 | 0.4192 | 0.5690 | 0.7543 |
| 0.0815 | 6.2848 | 2074 | 0.5424 | 0.5812 | 0.5424 | 0.7365 |
| 0.0815 | 6.2909 | 2076 | 0.5429 | 0.5812 | 0.5429 | 0.7368 |
| 0.0815 | 6.2970 | 2078 | 0.5568 | 0.5812 | 0.5568 | 0.7462 |
| 0.0815 | 6.3030 | 2080 | 0.5854 | 0.4850 | 0.5854 | 0.7651 |
| 0.0815 | 6.3091 | 2082 | 0.6086 | 0.4527 | 0.6086 | 0.7801 |
| 0.0815 | 6.3152 | 2084 | 0.6188 | 0.4451 | 0.6188 | 0.7866 |
| 0.0815 | 6.3212 | 2086 | 0.6316 | 0.4706 | 0.6316 | 0.7947 |
| 0.0815 | 6.3273 | 2088 | 0.6253 | 0.4648 | 0.6253 | 0.7908 |
| 0.0815 | 6.3333 | 2090 | 0.6010 | 0.4527 | 0.6010 | 0.7752 |
| 0.0815 | 6.3394 | 2092 | 0.5827 | 0.4850 | 0.5827 | 0.7634 |
| 0.0815 | 6.3455 | 2094 | 0.5734 | 0.5737 | 0.5734 | 0.7572 |
| 0.0815 | 6.3515 | 2096 | 0.5693 | 0.5737 | 0.5693 | 0.7545 |
| 0.0815 | 6.3576 | 2098 | 0.5749 | 0.5737 | 0.5749 | 0.7582 |
| 0.0815 | 6.3636 | 2100 | 0.5798 | 0.5422 | 0.5798 | 0.7614 |
| 0.0815 | 6.3697 | 2102 | 0.5784 | 0.5422 | 0.5784 | 0.7605 |
| 0.0815 | 6.3758 | 2104 | 0.5799 | 0.5422 | 0.5799 | 0.7615 |
| 0.0815 | 6.3818 | 2106 | 0.5916 | 0.4779 | 0.5916 | 0.7691 |
| 0.0815 | 6.3879 | 2108 | 0.5811 | 0.5679 | 0.5811 | 0.7623 |
| 0.0815 | 6.3939 | 2110 | 0.5566 | 0.5812 | 0.5566 | 0.7460 |
| 0.0815 | 6.4 | 2112 | 0.5509 | 0.5812 | 0.5509 | 0.7422 |
| 0.0815 | 6.4061 | 2114 | 0.5546 | 0.5812 | 0.5546 | 0.7447 |
| 0.0815 | 6.4121 | 2116 | 0.5676 | 0.5812 | 0.5676 | 0.7534 |
| 0.0815 | 6.4182 | 2118 | 0.6008 | 0.4779 | 0.6008 | 0.7751 |
| 0.0815 | 6.4242 | 2120 | 0.6298 | 0.4706 | 0.6298 | 0.7936 |
| 0.0815 | 6.4303 | 2122 | 0.6471 | 0.4706 | 0.6471 | 0.8044 |
| 0.0815 | 6.4364 | 2124 | 0.6352 | 0.4706 | 0.6352 | 0.7970 |
| 0.0815 | 6.4424 | 2126 | 0.6190 | 0.4706 | 0.6190 | 0.7867 |
| 0.0815 | 6.4485 | 2128 | 0.5909 | 0.4538 | 0.5908 | 0.7687 |
| 0.0815 | 6.4545 | 2130 | 0.5719 | 0.4550 | 0.5719 | 0.7562 |
| 0.0815 | 6.4606 | 2132 | 0.5674 | 0.4909 | 0.5674 | 0.7533 |
| 0.0815 | 6.4667 | 2134 | 0.5703 | 0.4909 | 0.5703 | 0.7552 |
| 0.0815 | 6.4727 | 2136 | 0.5765 | 0.4550 | 0.5765 | 0.7593 |
| 0.0815 | 6.4788 | 2138 | 0.5950 | 0.4550 | 0.5950 | 0.7713 |
| 0.0815 | 6.4848 | 2140 | 0.6323 | 0.4451 | 0.6323 | 0.7952 |
| 0.0815 | 6.4909 | 2142 | 0.6492 | 0.4451 | 0.6492 | 0.8057 |
| 0.0815 | 6.4970 | 2144 | 0.6351 | 0.4451 | 0.6351 | 0.7969 |
| 0.0815 | 6.5030 | 2146 | 0.6048 | 0.4527 | 0.6048 | 0.7777 |
| 0.0815 | 6.5091 | 2148 | 0.5810 | 0.5871 | 0.5810 | 0.7622 |
| 0.0815 | 6.5152 | 2150 | 0.5756 | 0.5871 | 0.5756 | 0.7587 |
| 0.0815 | 6.5212 | 2152 | 0.5807 | 0.5871 | 0.5807 | 0.7620 |
| 0.0815 | 6.5273 | 2154 | 0.5925 | 0.5871 | 0.5925 | 0.7697 |
| 0.0815 | 6.5333 | 2156 | 0.6157 | 0.4550 | 0.6157 | 0.7847 |
| 0.0815 | 6.5394 | 2158 | 0.6486 | 0.4779 | 0.6486 | 0.8053 |
| 0.0815 | 6.5455 | 2160 | 0.6661 | 0.4451 | 0.6661 | 0.8161 |
| 0.0815 | 6.5515 | 2162 | 0.6948 | 0.4933 | 0.6948 | 0.8335 |
| 0.0815 | 6.5576 | 2164 | 0.6999 | 0.4933 | 0.6999 | 0.8366 |
| 0.0815 | 6.5636 | 2166 | 0.6705 | 0.4933 | 0.6705 | 0.8188 |
| 0.0815 | 6.5697 | 2168 | 0.6205 | 0.4465 | 0.6205 | 0.7877 |
| 0.0815 | 6.5758 | 2170 | 0.5687 | 0.4550 | 0.5687 | 0.7541 |
| 0.0815 | 6.5818 | 2172 | 0.5411 | 0.5895 | 0.5411 | 0.7356 |
| 0.0815 | 6.5879 | 2174 | 0.5255 | 0.5895 | 0.5255 | 0.7249 |
| 0.0815 | 6.5939 | 2176 | 0.5271 | 0.5895 | 0.5271 | 0.7260 |
| 0.0815 | 6.6 | 2178 | 0.5462 | 0.4550 | 0.5462 | 0.7390 |
| 0.0815 | 6.6061 | 2180 | 0.5923 | 0.4465 | 0.5923 | 0.7696 |
| 0.0815 | 6.6121 | 2182 | 0.6570 | 0.4965 | 0.6570 | 0.8106 |
| 0.0815 | 6.6182 | 2184 | 0.7350 | 0.4933 | 0.7350 | 0.8573 |
| 0.0815 | 6.6242 | 2186 | 0.7613 | 0.3967 | 0.7613 | 0.8725 |
| 0.0815 | 6.6303 | 2188 | 0.7417 | 0.4553 | 0.7417 | 0.8612 |
| 0.0815 | 6.6364 | 2190 | 0.6856 | 0.4687 | 0.6856 | 0.8280 |
| 0.0815 | 6.6424 | 2192 | 0.6077 | 0.4465 | 0.6077 | 0.7795 |
| 0.0815 | 6.6485 | 2194 | 0.5371 | 0.5545 | 0.5371 | 0.7329 |
| 0.0815 | 6.6545 | 2196 | 0.5090 | 0.5895 | 0.5090 | 0.7135 |
| 0.0815 | 6.6606 | 2198 | 0.5051 | 0.5895 | 0.5051 | 0.7107 |
| 0.0815 | 6.6667 | 2200 | 0.5202 | 0.5895 | 0.5202 | 0.7212 |
| 0.0815 | 6.6727 | 2202 | 0.5375 | 0.5545 | 0.5375 | 0.7331 |
| 0.0815 | 6.6788 | 2204 | 0.5669 | 0.4550 | 0.5669 | 0.7530 |
| 0.0815 | 6.6848 | 2206 | 0.6075 | 0.4199 | 0.6075 | 0.7794 |
| 0.0815 | 6.6909 | 2208 | 0.6545 | 0.4706 | 0.6545 | 0.8090 |
| 0.0815 | 6.6970 | 2210 | 0.6815 | 0.4687 | 0.6815 | 0.8255 |
| 0.0815 | 6.7030 | 2212 | 0.7026 | 0.4687 | 0.7026 | 0.8382 |
| 0.0815 | 6.7091 | 2214 | 0.6953 | 0.4687 | 0.6953 | 0.8338 |
| 0.0815 | 6.7152 | 2216 | 0.6680 | 0.4706 | 0.6680 | 0.8173 |
| 0.0815 | 6.7212 | 2218 | 0.6461 | 0.4451 | 0.6461 | 0.8038 |
| 0.0815 | 6.7273 | 2220 | 0.6413 | 0.4451 | 0.6413 | 0.8008 |
| 0.0815 | 6.7333 | 2222 | 0.6542 | 0.4451 | 0.6542 | 0.8088 |
| 0.0815 | 6.7394 | 2224 | 0.6803 | 0.4706 | 0.6803 | 0.8248 |
| 0.0815 | 6.7455 | 2226 | 0.7075 | 0.4933 | 0.7075 | 0.8411 |
| 0.0815 | 6.7515 | 2228 | 0.7116 | 0.4933 | 0.7116 | 0.8435 |
| 0.0815 | 6.7576 | 2230 | 0.6930 | 0.4687 | 0.6930 | 0.8325 |
| 0.0815 | 6.7636 | 2232 | 0.6623 | 0.4199 | 0.6623 | 0.8138 |
| 0.0815 | 6.7697 | 2234 | 0.6473 | 0.4199 | 0.6473 | 0.8046 |
| 0.0815 | 6.7758 | 2236 | 0.6357 | 0.4199 | 0.6357 | 0.7973 |
| 0.0815 | 6.7818 | 2238 | 0.6221 | 0.4527 | 0.6221 | 0.7887 |
| 0.0815 | 6.7879 | 2240 | 0.6087 | 0.4878 | 0.6087 | 0.7802 |
| 0.0815 | 6.7939 | 2242 | 0.5938 | 0.4878 | 0.5938 | 0.7706 |
| 0.0815 | 6.8 | 2244 | 0.5854 | 0.4878 | 0.5854 | 0.7651 |
| 0.0815 | 6.8061 | 2246 | 0.5839 | 0.4878 | 0.5839 | 0.7641 |
| 0.0815 | 6.8121 | 2248 | 0.6044 | 0.4192 | 0.6044 | 0.7774 |
| 0.0815 | 6.8182 | 2250 | 0.6407 | 0.4106 | 0.6407 | 0.8004 |
| 0.0815 | 6.8242 | 2252 | 0.6635 | 0.4372 | 0.6635 | 0.8145 |
| 0.0815 | 6.8303 | 2254 | 0.6568 | 0.4372 | 0.6568 | 0.8104 |
| 0.0815 | 6.8364 | 2256 | 0.6288 | 0.4106 | 0.6288 | 0.7930 |
| 0.0815 | 6.8424 | 2258 | 0.5868 | 0.4094 | 0.5868 | 0.7660 |
| 0.0815 | 6.8485 | 2260 | 0.5433 | 0.4909 | 0.5433 | 0.7371 |
| 0.0815 | 6.8545 | 2262 | 0.5198 | 0.5895 | 0.5198 | 0.7209 |
| 0.0815 | 6.8606 | 2264 | 0.5153 | 0.5895 | 0.5153 | 0.7178 |
| 0.0815 | 6.8667 | 2266 | 0.5165 | 0.5895 | 0.5165 | 0.7187 |
| 0.0815 | 6.8727 | 2268 | 0.5255 | 0.5895 | 0.5255 | 0.7249 |
| 0.0815 | 6.8788 | 2270 | 0.5492 | 0.5545 | 0.5492 | 0.7411 |
| 0.0815 | 6.8848 | 2272 | 0.5774 | 0.4192 | 0.5774 | 0.7599 |
| 0.0815 | 6.8909 | 2274 | 0.6220 | 0.4199 | 0.6220 | 0.7886 |
| 0.0815 | 6.8970 | 2276 | 0.6671 | 0.4451 | 0.6671 | 0.8168 |
| 0.0815 | 6.9030 | 2278 | 0.6800 | 0.4451 | 0.6800 | 0.8246 |
| 0.0815 | 6.9091 | 2280 | 0.6911 | 0.4631 | 0.6911 | 0.8313 |
| 0.0815 | 6.9152 | 2282 | 0.6890 | 0.4924 | 0.6890 | 0.8301 |
| 0.0815 | 6.9212 | 2284 | 0.6542 | 0.4375 | 0.6542 | 0.8088 |
| 0.0815 | 6.9273 | 2286 | 0.6085 | 0.4451 | 0.6085 | 0.7801 |
| 0.0815 | 6.9333 | 2288 | 0.5900 | 0.4457 | 0.5900 | 0.7681 |
| 0.0815 | 6.9394 | 2290 | 0.5679 | 0.4192 | 0.5679 | 0.7536 |
| 0.0815 | 6.9455 | 2292 | 0.5504 | 0.4192 | 0.5504 | 0.7419 |
| 0.0815 | 6.9515 | 2294 | 0.5339 | 0.4185 | 0.5339 | 0.7307 |
| 0.0815 | 6.9576 | 2296 | 0.5303 | 0.4550 | 0.5303 | 0.7282 |
| 0.0815 | 6.9636 | 2298 | 0.5392 | 0.4550 | 0.5392 | 0.7343 |
| 0.0815 | 6.9697 | 2300 | 0.5551 | 0.4538 | 0.5551 | 0.7451 |
| 0.0815 | 6.9758 | 2302 | 0.5636 | 0.4538 | 0.5636 | 0.7508 |
| 0.0815 | 6.9818 | 2304 | 0.5867 | 0.4538 | 0.5867 | 0.7660 |
| 0.0815 | 6.9879 | 2306 | 0.6060 | 0.4527 | 0.6060 | 0.7785 |
| 0.0815 | 6.9939 | 2308 | 0.6337 | 0.4451 | 0.6337 | 0.7961 |
| 0.0815 | 7.0 | 2310 | 0.6350 | 0.4451 | 0.6350 | 0.7968 |
| 0.0815 | 7.0061 | 2312 | 0.6355 | 0.4451 | 0.6355 | 0.7972 |
| 0.0815 | 7.0121 | 2314 | 0.6243 | 0.4451 | 0.6243 | 0.7901 |
| 0.0815 | 7.0182 | 2316 | 0.5967 | 0.4779 | 0.5967 | 0.7724 |
| 0.0815 | 7.0242 | 2318 | 0.5857 | 0.4803 | 0.5857 | 0.7653 |
| 0.0815 | 7.0303 | 2320 | 0.5889 | 0.5073 | 0.5889 | 0.7674 |
| 0.0815 | 7.0364 | 2322 | 0.5814 | 0.4727 | 0.5814 | 0.7625 |
| 0.0815 | 7.0424 | 2324 | 0.5571 | 0.4831 | 0.5571 | 0.7464 |
| 0.0815 | 7.0485 | 2326 | 0.5320 | 0.4550 | 0.5320 | 0.7294 |
| 0.0815 | 7.0545 | 2328 | 0.5089 | 0.5545 | 0.5089 | 0.7134 |
| 0.0815 | 7.0606 | 2330 | 0.4925 | 0.5545 | 0.4925 | 0.7018 |
| 0.0815 | 7.0667 | 2332 | 0.4892 | 0.5545 | 0.4892 | 0.6994 |
| 0.0815 | 7.0727 | 2334 | 0.5023 | 0.5545 | 0.5023 | 0.7087 |
| 0.0815 | 7.0788 | 2336 | 0.5107 | 0.5546 | 0.5107 | 0.7146 |
| 0.0815 | 7.0848 | 2338 | 0.5137 | 0.5546 | 0.5137 | 0.7167 |
| 0.0815 | 7.0909 | 2340 | 0.5216 | 0.5545 | 0.5216 | 0.7222 |
| 0.0815 | 7.0970 | 2342 | 0.5256 | 0.5545 | 0.5256 | 0.7250 |
| 0.0815 | 7.1030 | 2344 | 0.5314 | 0.5545 | 0.5314 | 0.7290 |
| 0.0815 | 7.1091 | 2346 | 0.5461 | 0.5481 | 0.5461 | 0.7390 |
| 0.0815 | 7.1152 | 2348 | 0.5540 | 0.5481 | 0.5540 | 0.7443 |
| 0.0815 | 7.1212 | 2350 | 0.5537 | 0.5481 | 0.5537 | 0.7441 |
| 0.0815 | 7.1273 | 2352 | 0.5471 | 0.5481 | 0.5471 | 0.7397 |
| 0.0815 | 7.1333 | 2354 | 0.5486 | 0.5481 | 0.5486 | 0.7407 |
| 0.0815 | 7.1394 | 2356 | 0.5535 | 0.5481 | 0.5535 | 0.7440 |
| 0.0815 | 7.1455 | 2358 | 0.5481 | 0.5481 | 0.5481 | 0.7403 |
| 0.0815 | 7.1515 | 2360 | 0.5534 | 0.5481 | 0.5534 | 0.7439 |
| 0.0815 | 7.1576 | 2362 | 0.5573 | 0.5481 | 0.5573 | 0.7466 |
| 0.0815 | 7.1636 | 2364 | 0.5539 | 0.5481 | 0.5539 | 0.7443 |
| 0.0815 | 7.1697 | 2366 | 0.5392 | 0.5481 | 0.5392 | 0.7343 |
| 0.0815 | 7.1758 | 2368 | 0.5321 | 0.5545 | 0.5321 | 0.7294 |
| 0.0815 | 7.1818 | 2370 | 0.5240 | 0.5895 | 0.5240 | 0.7239 |
| 0.0815 | 7.1879 | 2372 | 0.5208 | 0.5545 | 0.5208 | 0.7217 |
| 0.0815 | 7.1939 | 2374 | 0.5245 | 0.5545 | 0.5245 | 0.7242 |
| 0.0815 | 7.2 | 2376 | 0.5299 | 0.5545 | 0.5299 | 0.7280 |
| 0.0815 | 7.2061 | 2378 | 0.5441 | 0.4094 | 0.5441 | 0.7376 |
| 0.0815 | 7.2121 | 2380 | 0.5632 | 0.4094 | 0.5632 | 0.7505 |
| 0.0815 | 7.2182 | 2382 | 0.5587 | 0.4094 | 0.5587 | 0.7475 |
| 0.0815 | 7.2242 | 2384 | 0.5459 | 0.4080 | 0.5459 | 0.7388 |
| 0.0815 | 7.2303 | 2386 | 0.5449 | 0.4080 | 0.5449 | 0.7382 |
| 0.0815 | 7.2364 | 2388 | 0.5460 | 0.4080 | 0.5460 | 0.7389 |
| 0.0815 | 7.2424 | 2390 | 0.5611 | 0.4080 | 0.5611 | 0.7491 |
| 0.0815 | 7.2485 | 2392 | 0.5728 | 0.4094 | 0.5728 | 0.7569 |
| 0.0815 | 7.2545 | 2394 | 0.5695 | 0.4094 | 0.5695 | 0.7547 |
| 0.0815 | 7.2606 | 2396 | 0.5477 | 0.4185 | 0.5477 | 0.7401 |
| 0.0815 | 7.2667 | 2398 | 0.5236 | 0.5545 | 0.5236 | 0.7236 |
| 0.0815 | 7.2727 | 2400 | 0.5180 | 0.5895 | 0.5180 | 0.7197 |
| 0.0815 | 7.2788 | 2402 | 0.5222 | 0.5895 | 0.5222 | 0.7227 |
| 0.0815 | 7.2848 | 2404 | 0.5393 | 0.5545 | 0.5393 | 0.7344 |
| 0.0815 | 7.2909 | 2406 | 0.5644 | 0.4538 | 0.5644 | 0.7513 |
| 0.0815 | 7.2970 | 2408 | 0.6004 | 0.4199 | 0.6004 | 0.7748 |
| 0.0815 | 7.3030 | 2410 | 0.6380 | 0.4372 | 0.6380 | 0.7987 |
| 0.0815 | 7.3091 | 2412 | 0.6509 | 0.4893 | 0.6509 | 0.8068 |
| 0.0815 | 7.3152 | 2414 | 0.6405 | 0.4893 | 0.6405 | 0.8003 |
| 0.0815 | 7.3212 | 2416 | 0.6204 | 0.4375 | 0.6204 | 0.7877 |
| 0.0815 | 7.3273 | 2418 | 0.6035 | 0.4378 | 0.6035 | 0.7768 |
| 0.0815 | 7.3333 | 2420 | 0.5993 | 0.4378 | 0.5993 | 0.7742 |
| 0.0815 | 7.3394 | 2422 | 0.5882 | 0.4094 | 0.5882 | 0.7669 |
| 0.0815 | 7.3455 | 2424 | 0.5807 | 0.4080 | 0.5807 | 0.7620 |
| 0.0815 | 7.3515 | 2426 | 0.5653 | 0.4080 | 0.5653 | 0.7518 |
| 0.0815 | 7.3576 | 2428 | 0.5461 | 0.4080 | 0.5461 | 0.7390 |
| 0.0815 | 7.3636 | 2430 | 0.5270 | 0.4080 | 0.5270 | 0.7260 |
| 0.0815 | 7.3697 | 2432 | 0.5248 | 0.4080 | 0.5248 | 0.7244 |
| 0.0815 | 7.3758 | 2434 | 0.5363 | 0.4080 | 0.5363 | 0.7323 |
| 0.0815 | 7.3818 | 2436 | 0.5565 | 0.4080 | 0.5565 | 0.7460 |
| 0.0815 | 7.3879 | 2438 | 0.5690 | 0.4080 | 0.5690 | 0.7544 |
| 0.0815 | 7.3939 | 2440 | 0.5749 | 0.4080 | 0.5749 | 0.7583 |
| 0.0815 | 7.4 | 2442 | 0.5771 | 0.4080 | 0.5771 | 0.7596 |
| 0.0815 | 7.4061 | 2444 | 0.5600 | 0.4080 | 0.5600 | 0.7483 |
| 0.0815 | 7.4121 | 2446 | 0.5348 | 0.4080 | 0.5348 | 0.7313 |
| 0.0815 | 7.4182 | 2448 | 0.5182 | 0.5545 | 0.5182 | 0.7198 |
| 0.0815 | 7.4242 | 2450 | 0.5138 | 0.5895 | 0.5138 | 0.7168 |
| 0.0815 | 7.4303 | 2452 | 0.5190 | 0.5545 | 0.5190 | 0.7204 |
| 0.0815 | 7.4364 | 2454 | 0.5237 | 0.5545 | 0.5237 | 0.7237 |
| 0.0815 | 7.4424 | 2456 | 0.5350 | 0.5545 | 0.5350 | 0.7315 |
| 0.0815 | 7.4485 | 2458 | 0.5526 | 0.4185 | 0.5526 | 0.7434 |
| 0.0815 | 7.4545 | 2460 | 0.5723 | 0.4378 | 0.5723 | 0.7565 |
| 0.0815 | 7.4606 | 2462 | 0.5792 | 0.4378 | 0.5792 | 0.7610 |
| 0.0815 | 7.4667 | 2464 | 0.5715 | 0.4381 | 0.5715 | 0.7560 |
| 0.0815 | 7.4727 | 2466 | 0.5603 | 0.4381 | 0.5603 | 0.7486 |
| 0.0815 | 7.4788 | 2468 | 0.5585 | 0.4381 | 0.5585 | 0.7473 |
| 0.0815 | 7.4848 | 2470 | 0.5546 | 0.4465 | 0.5546 | 0.7447 |
| 0.0815 | 7.4909 | 2472 | 0.5402 | 0.4185 | 0.5402 | 0.7350 |
| 0.0815 | 7.4970 | 2474 | 0.5312 | 0.4550 | 0.5312 | 0.7288 |
| 0.0815 | 7.5030 | 2476 | 0.5281 | 0.5545 | 0.5281 | 0.7267 |
| 0.0815 | 7.5091 | 2478 | 0.5290 | 0.5545 | 0.5290 | 0.7273 |
| 0.0815 | 7.5152 | 2480 | 0.5421 | 0.4185 | 0.5421 | 0.7363 |
| 0.0815 | 7.5212 | 2482 | 0.5684 | 0.4185 | 0.5684 | 0.7539 |
| 0.0815 | 7.5273 | 2484 | 0.5978 | 0.4465 | 0.5978 | 0.7732 |
| 0.0815 | 7.5333 | 2486 | 0.6144 | 0.4375 | 0.6144 | 0.7838 |
| 0.0815 | 7.5394 | 2488 | 0.6106 | 0.4375 | 0.6106 | 0.7814 |
| 0.0815 | 7.5455 | 2490 | 0.6018 | 0.4457 | 0.6018 | 0.7758 |
| 0.0815 | 7.5515 | 2492 | 0.5946 | 0.4457 | 0.5946 | 0.7711 |
| 0.0815 | 7.5576 | 2494 | 0.5918 | 0.4192 | 0.5918 | 0.7693 |
| 0.0815 | 7.5636 | 2496 | 0.5973 | 0.4457 | 0.5973 | 0.7729 |
| 0.0815 | 7.5697 | 2498 | 0.6074 | 0.4451 | 0.6074 | 0.7793 |
| 0.067 | 7.5758 | 2500 | 0.6016 | 0.4451 | 0.6016 | 0.7756 |
| 0.067 | 7.5818 | 2502 | 0.6055 | 0.4451 | 0.6055 | 0.7781 |
| 0.067 | 7.5879 | 2504 | 0.6007 | 0.4457 | 0.6007 | 0.7751 |
| 0.067 | 7.5939 | 2506 | 0.5925 | 0.4457 | 0.5925 | 0.7697 |
| 0.067 | 7.6 | 2508 | 0.5841 | 0.4457 | 0.5841 | 0.7643 |
| 0.067 | 7.6061 | 2510 | 0.5696 | 0.4538 | 0.5696 | 0.7547 |
| 0.067 | 7.6121 | 2512 | 0.5559 | 0.5545 | 0.5559 | 0.7456 |
| 0.067 | 7.6182 | 2514 | 0.5591 | 0.5545 | 0.5591 | 0.7477 |
| 0.067 | 7.6242 | 2516 | 0.5764 | 0.5481 | 0.5764 | 0.7592 |
| 0.067 | 7.6303 | 2518 | 0.5907 | 0.4527 | 0.5907 | 0.7686 |
| 0.067 | 7.6364 | 2520 | 0.6046 | 0.4527 | 0.6046 | 0.7775 |
| 0.067 | 7.6424 | 2522 | 0.6093 | 0.4451 | 0.6093 | 0.7806 |
| 0.067 | 7.6485 | 2524 | 0.6025 | 0.4779 | 0.6025 | 0.7762 |
| 0.067 | 7.6545 | 2526 | 0.5986 | 0.4451 | 0.5986 | 0.7737 |
| 0.067 | 7.6606 | 2528 | 0.5854 | 0.4451 | 0.5854 | 0.7651 |
| 0.067 | 7.6667 | 2530 | 0.5612 | 0.4538 | 0.5612 | 0.7491 |
| 0.067 | 7.6727 | 2532 | 0.5348 | 0.5545 | 0.5348 | 0.7313 |
| 0.067 | 7.6788 | 2534 | 0.5210 | 0.5545 | 0.5210 | 0.7218 |
| 0.067 | 7.6848 | 2536 | 0.5239 | 0.4550 | 0.5239 | 0.7238 |
| 0.067 | 7.6909 | 2538 | 0.5389 | 0.4185 | 0.5389 | 0.7341 |
| 0.067 | 7.6970 | 2540 | 0.5459 | 0.4457 | 0.5459 | 0.7389 |
| 0.067 | 7.7030 | 2542 | 0.5451 | 0.4192 | 0.5451 | 0.7383 |
| 0.067 | 7.7091 | 2544 | 0.5450 | 0.5145 | 0.5450 | 0.7383 |
| 0.067 | 7.7152 | 2546 | 0.5443 | 0.5481 | 0.5443 | 0.7378 |
| 0.067 | 7.7212 | 2548 | 0.5529 | 0.5481 | 0.5529 | 0.7436 |
| 0.067 | 7.7273 | 2550 | 0.5549 | 0.5481 | 0.5549 | 0.7449 |
| 0.067 | 7.7333 | 2552 | 0.5465 | 0.5481 | 0.5465 | 0.7392 |
| 0.067 | 7.7394 | 2554 | 0.5371 | 0.5545 | 0.5371 | 0.7329 |
| 0.067 | 7.7455 | 2556 | 0.5400 | 0.5545 | 0.5400 | 0.7349 |
| 0.067 | 7.7515 | 2558 | 0.5384 | 0.5545 | 0.5384 | 0.7338 |
| 0.067 | 7.7576 | 2560 | 0.5392 | 0.5545 | 0.5392 | 0.7343 |
| 0.067 | 7.7636 | 2562 | 0.5546 | 0.5422 | 0.5546 | 0.7447 |
| 0.067 | 7.7697 | 2564 | 0.5650 | 0.5679 | 0.5650 | 0.7517 |
| 0.067 | 7.7758 | 2566 | 0.5740 | 0.5679 | 0.5740 | 0.7576 |
| 0.067 | 7.7818 | 2568 | 0.5774 | 0.5679 | 0.5774 | 0.7599 |
| 0.067 | 7.7879 | 2570 | 0.5735 | 0.5679 | 0.5735 | 0.7573 |
| 0.067 | 7.7939 | 2572 | 0.5709 | 0.5679 | 0.5709 | 0.7556 |
| 0.067 | 7.8 | 2574 | 0.5759 | 0.5679 | 0.5759 | 0.7589 |
| 0.067 | 7.8061 | 2576 | 0.5761 | 0.5679 | 0.5761 | 0.7590 |
| 0.067 | 7.8121 | 2578 | 0.5662 | 0.5751 | 0.5662 | 0.7525 |
| 0.067 | 7.8182 | 2580 | 0.5548 | 0.5751 | 0.5548 | 0.7448 |
| 0.067 | 7.8242 | 2582 | 0.5433 | 0.5832 | 0.5433 | 0.7371 |
| 0.067 | 7.8303 | 2584 | 0.5363 | 0.5545 | 0.5363 | 0.7323 |
| 0.067 | 7.8364 | 2586 | 0.5402 | 0.5832 | 0.5402 | 0.7350 |
| 0.067 | 7.8424 | 2588 | 0.5507 | 0.5832 | 0.5507 | 0.7421 |
| 0.067 | 7.8485 | 2590 | 0.5572 | 0.5832 | 0.5572 | 0.7465 |
| 0.067 | 7.8545 | 2592 | 0.5597 | 0.5832 | 0.5597 | 0.7481 |
| 0.067 | 7.8606 | 2594 | 0.5509 | 0.5832 | 0.5509 | 0.7422 |
| 0.067 | 7.8667 | 2596 | 0.5492 | 0.5545 | 0.5492 | 0.7411 |
| 0.067 | 7.8727 | 2598 | 0.5533 | 0.5545 | 0.5533 | 0.7439 |
| 0.067 | 7.8788 | 2600 | 0.5614 | 0.5481 | 0.5614 | 0.7492 |
| 0.067 | 7.8848 | 2602 | 0.5825 | 0.4451 | 0.5825 | 0.7632 |
| 0.067 | 7.8909 | 2604 | 0.6037 | 0.4451 | 0.6037 | 0.7770 |
| 0.067 | 7.8970 | 2606 | 0.6021 | 0.4451 | 0.6021 | 0.7759 |
| 0.067 | 7.9030 | 2608 | 0.5863 | 0.4199 | 0.5863 | 0.7657 |
| 0.067 | 7.9091 | 2610 | 0.5738 | 0.4192 | 0.5738 | 0.7575 |
| 0.067 | 7.9152 | 2612 | 0.5727 | 0.4192 | 0.5727 | 0.7568 |
| 0.067 | 7.9212 | 2614 | 0.5783 | 0.4192 | 0.5783 | 0.7605 |
| 0.067 | 7.9273 | 2616 | 0.5846 | 0.4192 | 0.5846 | 0.7646 |
| 0.067 | 7.9333 | 2618 | 0.5917 | 0.4199 | 0.5917 | 0.7692 |
| 0.067 | 7.9394 | 2620 | 0.5879 | 0.4192 | 0.5879 | 0.7668 |
| 0.067 | 7.9455 | 2622 | 0.5816 | 0.4192 | 0.5816 | 0.7626 |
| 0.067 | 7.9515 | 2624 | 0.5701 | 0.4192 | 0.5701 | 0.7550 |
| 0.067 | 7.9576 | 2626 | 0.5707 | 0.4192 | 0.5707 | 0.7554 |
| 0.067 | 7.9636 | 2628 | 0.5861 | 0.4192 | 0.5861 | 0.7655 |
| 0.067 | 7.9697 | 2630 | 0.5992 | 0.4192 | 0.5992 | 0.7741 |
| 0.067 | 7.9758 | 2632 | 0.5959 | 0.4192 | 0.5959 | 0.7719 |
| 0.067 | 7.9818 | 2634 | 0.5845 | 0.4192 | 0.5845 | 0.7645 |
| 0.067 | 7.9879 | 2636 | 0.5783 | 0.4192 | 0.5783 | 0.7605 |
| 0.067 | 7.9939 | 2638 | 0.5812 | 0.4192 | 0.5812 | 0.7624 |
| 0.067 | 8.0 | 2640 | 0.5823 | 0.4192 | 0.5823 | 0.7631 |
| 0.067 | 8.0061 | 2642 | 0.5847 | 0.4192 | 0.5847 | 0.7646 |
| 0.067 | 8.0121 | 2644 | 0.5834 | 0.4192 | 0.5834 | 0.7638 |
| 0.067 | 8.0182 | 2646 | 0.5867 | 0.4192 | 0.5867 | 0.7659 |
| 0.067 | 8.0242 | 2648 | 0.5989 | 0.4192 | 0.5989 | 0.7739 |
| 0.067 | 8.0303 | 2650 | 0.6059 | 0.4192 | 0.6059 | 0.7784 |
| 0.067 | 8.0364 | 2652 | 0.6079 | 0.4192 | 0.6079 | 0.7797 |
| 0.067 | 8.0424 | 2654 | 0.6213 | 0.4451 | 0.6213 | 0.7882 |
| 0.067 | 8.0485 | 2656 | 0.6228 | 0.4451 | 0.6228 | 0.7892 |
| 0.067 | 8.0545 | 2658 | 0.6126 | 0.4457 | 0.6126 | 0.7827 |
| 0.067 | 8.0606 | 2660 | 0.6045 | 0.4192 | 0.6045 | 0.7775 |
| 0.067 | 8.0667 | 2662 | 0.5863 | 0.4192 | 0.5863 | 0.7657 |
| 0.067 | 8.0727 | 2664 | 0.5752 | 0.4192 | 0.5752 | 0.7584 |
| 0.067 | 8.0788 | 2666 | 0.5795 | 0.4192 | 0.5795 | 0.7612 |
| 0.067 | 8.0848 | 2668 | 0.5868 | 0.4192 | 0.5868 | 0.7660 |
| 0.067 | 8.0909 | 2670 | 0.6056 | 0.4192 | 0.6056 | 0.7782 |
| 0.067 | 8.0970 | 2672 | 0.6152 | 0.4199 | 0.6152 | 0.7844 |
| 0.067 | 8.1030 | 2674 | 0.6119 | 0.4192 | 0.6119 | 0.7822 |
| 0.067 | 8.1091 | 2676 | 0.5952 | 0.4192 | 0.5952 | 0.7715 |
| 0.067 | 8.1152 | 2678 | 0.5856 | 0.4192 | 0.5856 | 0.7653 |
| 0.067 | 8.1212 | 2680 | 0.5748 | 0.4192 | 0.5748 | 0.7582 |
| 0.067 | 8.1273 | 2682 | 0.5640 | 0.4538 | 0.5640 | 0.7510 |
| 0.067 | 8.1333 | 2684 | 0.5627 | 0.4538 | 0.5627 | 0.7502 |
| 0.067 | 8.1394 | 2686 | 0.5685 | 0.4538 | 0.5685 | 0.7540 |
| 0.067 | 8.1455 | 2688 | 0.5779 | 0.4192 | 0.5779 | 0.7602 |
| 0.067 | 8.1515 | 2690 | 0.5835 | 0.4192 | 0.5835 | 0.7639 |
| 0.067 | 8.1576 | 2692 | 0.5992 | 0.4199 | 0.5992 | 0.7741 |
| 0.067 | 8.1636 | 2694 | 0.6182 | 0.4451 | 0.6182 | 0.7863 |
| 0.067 | 8.1697 | 2696 | 0.6280 | 0.4451 | 0.6280 | 0.7925 |
| 0.067 | 8.1758 | 2698 | 0.6390 | 0.4451 | 0.6390 | 0.7994 |
| 0.067 | 8.1818 | 2700 | 0.6343 | 0.4451 | 0.6343 | 0.7965 |
| 0.067 | 8.1879 | 2702 | 0.6265 | 0.4451 | 0.6265 | 0.7915 |
| 0.067 | 8.1939 | 2704 | 0.6240 | 0.4451 | 0.6240 | 0.7899 |
| 0.067 | 8.2 | 2706 | 0.6140 | 0.4457 | 0.6140 | 0.7836 |
| 0.067 | 8.2061 | 2708 | 0.6085 | 0.4457 | 0.6085 | 0.7801 |
| 0.067 | 8.2121 | 2710 | 0.5952 | 0.4192 | 0.5952 | 0.7715 |
| 0.067 | 8.2182 | 2712 | 0.5791 | 0.4192 | 0.5791 | 0.7610 |
| 0.067 | 8.2242 | 2714 | 0.5592 | 0.4550 | 0.5592 | 0.7478 |
| 0.067 | 8.2303 | 2716 | 0.5534 | 0.5545 | 0.5534 | 0.7439 |
| 0.067 | 8.2364 | 2718 | 0.5596 | 0.4550 | 0.5596 | 0.7480 |
| 0.067 | 8.2424 | 2720 | 0.5600 | 0.4550 | 0.5600 | 0.7483 |
| 0.067 | 8.2485 | 2722 | 0.5592 | 0.5545 | 0.5592 | 0.7478 |
| 0.067 | 8.2545 | 2724 | 0.5550 | 0.5545 | 0.5550 | 0.7450 |
| 0.067 | 8.2606 | 2726 | 0.5575 | 0.5545 | 0.5575 | 0.7467 |
| 0.067 | 8.2667 | 2728 | 0.5574 | 0.5545 | 0.5574 | 0.7466 |
| 0.067 | 8.2727 | 2730 | 0.5617 | 0.4550 | 0.5617 | 0.7494 |
| 0.067 | 8.2788 | 2732 | 0.5698 | 0.4457 | 0.5698 | 0.7548 |
| 0.067 | 8.2848 | 2734 | 0.5688 | 0.4465 | 0.5688 | 0.7542 |
| 0.067 | 8.2909 | 2736 | 0.5704 | 0.4465 | 0.5704 | 0.7553 |
| 0.067 | 8.2970 | 2738 | 0.5594 | 0.4465 | 0.5594 | 0.7479 |
| 0.067 | 8.3030 | 2740 | 0.5490 | 0.4550 | 0.5490 | 0.7410 |
| 0.067 | 8.3091 | 2742 | 0.5323 | 0.5545 | 0.5323 | 0.7296 |
| 0.067 | 8.3152 | 2744 | 0.5155 | 0.5545 | 0.5155 | 0.7180 |
| 0.067 | 8.3212 | 2746 | 0.5066 | 0.5545 | 0.5066 | 0.7118 |
| 0.067 | 8.3273 | 2748 | 0.5026 | 0.5545 | 0.5026 | 0.7090 |
| 0.067 | 8.3333 | 2750 | 0.5047 | 0.5545 | 0.5047 | 0.7104 |
| 0.067 | 8.3394 | 2752 | 0.5115 | 0.5545 | 0.5115 | 0.7152 |
| 0.067 | 8.3455 | 2754 | 0.5246 | 0.5545 | 0.5246 | 0.7243 |
| 0.067 | 8.3515 | 2756 | 0.5453 | 0.5545 | 0.5453 | 0.7385 |
| 0.067 | 8.3576 | 2758 | 0.5637 | 0.5545 | 0.5637 | 0.7508 |
| 0.067 | 8.3636 | 2760 | 0.5702 | 0.5545 | 0.5702 | 0.7551 |
| 0.067 | 8.3697 | 2762 | 0.5677 | 0.5545 | 0.5677 | 0.7534 |
| 0.067 | 8.3758 | 2764 | 0.5590 | 0.5545 | 0.5590 | 0.7476 |
| 0.067 | 8.3818 | 2766 | 0.5440 | 0.5545 | 0.5440 | 0.7375 |
| 0.067 | 8.3879 | 2768 | 0.5359 | 0.5545 | 0.5359 | 0.7321 |
| 0.067 | 8.3939 | 2770 | 0.5273 | 0.5545 | 0.5273 | 0.7262 |
| 0.067 | 8.4 | 2772 | 0.5166 | 0.5545 | 0.5166 | 0.7187 |
| 0.067 | 8.4061 | 2774 | 0.5138 | 0.5545 | 0.5138 | 0.7168 |
| 0.067 | 8.4121 | 2776 | 0.5130 | 0.5545 | 0.5130 | 0.7163 |
| 0.067 | 8.4182 | 2778 | 0.5190 | 0.5545 | 0.5190 | 0.7204 |
| 0.067 | 8.4242 | 2780 | 0.5307 | 0.5191 | 0.5307 | 0.7285 |
| 0.067 | 8.4303 | 2782 | 0.5414 | 0.5191 | 0.5414 | 0.7358 |
| 0.067 | 8.4364 | 2784 | 0.5442 | 0.5191 | 0.5442 | 0.7377 |
| 0.067 | 8.4424 | 2786 | 0.5421 | 0.5545 | 0.5421 | 0.7363 |
| 0.067 | 8.4485 | 2788 | 0.5439 | 0.5545 | 0.5439 | 0.7375 |
| 0.067 | 8.4545 | 2790 | 0.5409 | 0.5545 | 0.5409 | 0.7355 |
| 0.067 | 8.4606 | 2792 | 0.5387 | 0.5545 | 0.5387 | 0.7340 |
| 0.067 | 8.4667 | 2794 | 0.5340 | 0.5545 | 0.5340 | 0.7308 |
| 0.067 | 8.4727 | 2796 | 0.5278 | 0.5545 | 0.5278 | 0.7265 |
| 0.067 | 8.4788 | 2798 | 0.5178 | 0.5545 | 0.5178 | 0.7196 |
| 0.067 | 8.4848 | 2800 | 0.5137 | 0.5545 | 0.5137 | 0.7168 |
| 0.067 | 8.4909 | 2802 | 0.5145 | 0.5895 | 0.5145 | 0.7173 |
| 0.067 | 8.4970 | 2804 | 0.5189 | 0.5895 | 0.5189 | 0.7204 |
| 0.067 | 8.5030 | 2806 | 0.5245 | 0.5545 | 0.5245 | 0.7242 |
| 0.067 | 8.5091 | 2808 | 0.5255 | 0.5545 | 0.5255 | 0.7249 |
| 0.067 | 8.5152 | 2810 | 0.5310 | 0.5545 | 0.5310 | 0.7287 |
| 0.067 | 8.5212 | 2812 | 0.5379 | 0.5545 | 0.5379 | 0.7334 |
| 0.067 | 8.5273 | 2814 | 0.5422 | 0.5545 | 0.5422 | 0.7364 |
| 0.067 | 8.5333 | 2816 | 0.5523 | 0.5545 | 0.5523 | 0.7432 |
| 0.067 | 8.5394 | 2818 | 0.5678 | 0.4465 | 0.5678 | 0.7536 |
| 0.067 | 8.5455 | 2820 | 0.5871 | 0.4457 | 0.5871 | 0.7662 |
| 0.067 | 8.5515 | 2822 | 0.5953 | 0.4706 | 0.5953 | 0.7715 |
| 0.067 | 8.5576 | 2824 | 0.5913 | 0.4727 | 0.5913 | 0.7690 |
| 0.067 | 8.5636 | 2826 | 0.5766 | 0.4465 | 0.5766 | 0.7594 |
| 0.067 | 8.5697 | 2828 | 0.5606 | 0.4465 | 0.5606 | 0.7487 |
| 0.067 | 8.5758 | 2830 | 0.5434 | 0.5545 | 0.5434 | 0.7371 |
| 0.067 | 8.5818 | 2832 | 0.5331 | 0.5545 | 0.5331 | 0.7302 |
| 0.067 | 8.5879 | 2834 | 0.5266 | 0.5545 | 0.5266 | 0.7257 |
| 0.067 | 8.5939 | 2836 | 0.5172 | 0.5545 | 0.5172 | 0.7192 |
| 0.067 | 8.6 | 2838 | 0.5084 | 0.5545 | 0.5084 | 0.7130 |
| 0.067 | 8.6061 | 2840 | 0.5053 | 0.5545 | 0.5053 | 0.7109 |
| 0.067 | 8.6121 | 2842 | 0.5090 | 0.5545 | 0.5090 | 0.7135 |
| 0.067 | 8.6182 | 2844 | 0.5181 | 0.5545 | 0.5181 | 0.7198 |
| 0.067 | 8.6242 | 2846 | 0.5347 | 0.5545 | 0.5347 | 0.7312 |
| 0.067 | 8.6303 | 2848 | 0.5557 | 0.5545 | 0.5557 | 0.7455 |
| 0.067 | 8.6364 | 2850 | 0.5803 | 0.4192 | 0.5803 | 0.7618 |
| 0.067 | 8.6424 | 2852 | 0.5936 | 0.4192 | 0.5936 | 0.7705 |
| 0.067 | 8.6485 | 2854 | 0.5941 | 0.4199 | 0.5941 | 0.7708 |
| 0.067 | 8.6545 | 2856 | 0.5869 | 0.4192 | 0.5869 | 0.7661 |
| 0.067 | 8.6606 | 2858 | 0.5750 | 0.5481 | 0.5750 | 0.7583 |
| 0.067 | 8.6667 | 2860 | 0.5670 | 0.5481 | 0.5670 | 0.7530 |
| 0.067 | 8.6727 | 2862 | 0.5585 | 0.5545 | 0.5585 | 0.7473 |
| 0.067 | 8.6788 | 2864 | 0.5594 | 0.5545 | 0.5594 | 0.7479 |
| 0.067 | 8.6848 | 2866 | 0.5621 | 0.5545 | 0.5621 | 0.7498 |
| 0.067 | 8.6909 | 2868 | 0.5607 | 0.5545 | 0.5607 | 0.7488 |
| 0.067 | 8.6970 | 2870 | 0.5624 | 0.5545 | 0.5624 | 0.7499 |
| 0.067 | 8.7030 | 2872 | 0.5610 | 0.5545 | 0.5610 | 0.7490 |
| 0.067 | 8.7091 | 2874 | 0.5639 | 0.5545 | 0.5639 | 0.7509 |
| 0.067 | 8.7152 | 2876 | 0.5644 | 0.5545 | 0.5644 | 0.7513 |
| 0.067 | 8.7212 | 2878 | 0.5624 | 0.5545 | 0.5624 | 0.7500 |
| 0.067 | 8.7273 | 2880 | 0.5623 | 0.5545 | 0.5623 | 0.7499 |
| 0.067 | 8.7333 | 2882 | 0.5583 | 0.5545 | 0.5583 | 0.7472 |
| 0.067 | 8.7394 | 2884 | 0.5558 | 0.5545 | 0.5558 | 0.7455 |
| 0.067 | 8.7455 | 2886 | 0.5567 | 0.5545 | 0.5567 | 0.7461 |
| 0.067 | 8.7515 | 2888 | 0.5595 | 0.4185 | 0.5595 | 0.7480 |
| 0.067 | 8.7576 | 2890 | 0.5671 | 0.4185 | 0.5671 | 0.7530 |
| 0.067 | 8.7636 | 2892 | 0.5713 | 0.4185 | 0.5713 | 0.7559 |
| 0.067 | 8.7697 | 2894 | 0.5766 | 0.4192 | 0.5766 | 0.7594 |
| 0.067 | 8.7758 | 2896 | 0.5769 | 0.4192 | 0.5769 | 0.7595 |
| 0.067 | 8.7818 | 2898 | 0.5704 | 0.4185 | 0.5704 | 0.7553 |
| 0.067 | 8.7879 | 2900 | 0.5608 | 0.4185 | 0.5608 | 0.7489 |
| 0.067 | 8.7939 | 2902 | 0.5522 | 0.4550 | 0.5522 | 0.7431 |
| 0.067 | 8.8 | 2904 | 0.5437 | 0.5545 | 0.5437 | 0.7374 |
| 0.067 | 8.8061 | 2906 | 0.5409 | 0.5545 | 0.5409 | 0.7354 |
| 0.067 | 8.8121 | 2908 | 0.5454 | 0.5545 | 0.5454 | 0.7385 |
| 0.067 | 8.8182 | 2910 | 0.5516 | 0.5545 | 0.5516 | 0.7427 |
| 0.067 | 8.8242 | 2912 | 0.5575 | 0.5545 | 0.5575 | 0.7466 |
| 0.067 | 8.8303 | 2914 | 0.5581 | 0.5545 | 0.5581 | 0.7471 |
| 0.067 | 8.8364 | 2916 | 0.5560 | 0.5545 | 0.5560 | 0.7457 |
| 0.067 | 8.8424 | 2918 | 0.5592 | 0.5545 | 0.5592 | 0.7478 |
| 0.067 | 8.8485 | 2920 | 0.5626 | 0.5545 | 0.5626 | 0.7501 |
| 0.067 | 8.8545 | 2922 | 0.5639 | 0.5481 | 0.5639 | 0.7509 |
| 0.067 | 8.8606 | 2924 | 0.5637 | 0.5481 | 0.5637 | 0.7508 |
| 0.067 | 8.8667 | 2926 | 0.5631 | 0.5481 | 0.5631 | 0.7504 |
| 0.067 | 8.8727 | 2928 | 0.5658 | 0.4538 | 0.5658 | 0.7522 |
| 0.067 | 8.8788 | 2930 | 0.5688 | 0.4538 | 0.5688 | 0.7542 |
| 0.067 | 8.8848 | 2932 | 0.5719 | 0.4538 | 0.5719 | 0.7562 |
| 0.067 | 8.8909 | 2934 | 0.5756 | 0.4538 | 0.5756 | 0.7587 |
| 0.067 | 8.8970 | 2936 | 0.5754 | 0.4538 | 0.5754 | 0.7585 |
| 0.067 | 8.9030 | 2938 | 0.5786 | 0.4538 | 0.5786 | 0.7606 |
| 0.067 | 8.9091 | 2940 | 0.5811 | 0.4538 | 0.5811 | 0.7623 |
| 0.067 | 8.9152 | 2942 | 0.5810 | 0.4538 | 0.5810 | 0.7622 |
| 0.067 | 8.9212 | 2944 | 0.5761 | 0.4538 | 0.5761 | 0.7590 |
| 0.067 | 8.9273 | 2946 | 0.5747 | 0.4538 | 0.5747 | 0.7581 |
| 0.067 | 8.9333 | 2948 | 0.5745 | 0.4538 | 0.5745 | 0.7579 |
| 0.067 | 8.9394 | 2950 | 0.5739 | 0.5481 | 0.5739 | 0.7575 |
| 0.067 | 8.9455 | 2952 | 0.5705 | 0.5481 | 0.5705 | 0.7553 |
| 0.067 | 8.9515 | 2954 | 0.5646 | 0.5481 | 0.5646 | 0.7514 |
| 0.067 | 8.9576 | 2956 | 0.5634 | 0.5481 | 0.5634 | 0.7506 |
| 0.067 | 8.9636 | 2958 | 0.5650 | 0.5481 | 0.5650 | 0.7517 |
| 0.067 | 8.9697 | 2960 | 0.5689 | 0.5481 | 0.5689 | 0.7542 |
| 0.067 | 8.9758 | 2962 | 0.5713 | 0.5481 | 0.5713 | 0.7558 |
| 0.067 | 8.9818 | 2964 | 0.5730 | 0.5481 | 0.5730 | 0.7569 |
| 0.067 | 8.9879 | 2966 | 0.5721 | 0.5481 | 0.5721 | 0.7564 |
| 0.067 | 8.9939 | 2968 | 0.5713 | 0.5481 | 0.5713 | 0.7558 |
| 0.067 | 9.0 | 2970 | 0.5736 | 0.5481 | 0.5736 | 0.7574 |
| 0.067 | 9.0061 | 2972 | 0.5743 | 0.5481 | 0.5743 | 0.7578 |
| 0.067 | 9.0121 | 2974 | 0.5747 | 0.4538 | 0.5747 | 0.7581 |
| 0.067 | 9.0182 | 2976 | 0.5718 | 0.4538 | 0.5718 | 0.7562 |
| 0.067 | 9.0242 | 2978 | 0.5770 | 0.4538 | 0.5770 | 0.7596 |
| 0.067 | 9.0303 | 2980 | 0.5831 | 0.4538 | 0.5831 | 0.7636 |
| 0.067 | 9.0364 | 2982 | 0.5855 | 0.4538 | 0.5855 | 0.7652 |
| 0.067 | 9.0424 | 2984 | 0.5826 | 0.4538 | 0.5826 | 0.7633 |
| 0.067 | 9.0485 | 2986 | 0.5814 | 0.4538 | 0.5814 | 0.7625 |
| 0.067 | 9.0545 | 2988 | 0.5756 | 0.4538 | 0.5756 | 0.7587 |
| 0.067 | 9.0606 | 2990 | 0.5663 | 0.4550 | 0.5663 | 0.7525 |
| 0.067 | 9.0667 | 2992 | 0.5575 | 0.4550 | 0.5575 | 0.7467 |
| 0.067 | 9.0727 | 2994 | 0.5512 | 0.5545 | 0.5512 | 0.7424 |
| 0.067 | 9.0788 | 2996 | 0.5478 | 0.5545 | 0.5478 | 0.7401 |
| 0.067 | 9.0848 | 2998 | 0.5423 | 0.5545 | 0.5423 | 0.7364 |
| 0.0523 | 9.0909 | 3000 | 0.5365 | 0.5545 | 0.5365 | 0.7325 |
| 0.0523 | 9.0970 | 3002 | 0.5309 | 0.5545 | 0.5309 | 0.7286 |
| 0.0523 | 9.1030 | 3004 | 0.5270 | 0.5545 | 0.5270 | 0.7260 |
| 0.0523 | 9.1091 | 3006 | 0.5238 | 0.5545 | 0.5238 | 0.7237 |
| 0.0523 | 9.1152 | 3008 | 0.5220 | 0.5545 | 0.5220 | 0.7225 |
| 0.0523 | 9.1212 | 3010 | 0.5208 | 0.5545 | 0.5208 | 0.7217 |
| 0.0523 | 9.1273 | 3012 | 0.5223 | 0.5545 | 0.5223 | 0.7227 |
| 0.0523 | 9.1333 | 3014 | 0.5261 | 0.5545 | 0.5261 | 0.7253 |
| 0.0523 | 9.1394 | 3016 | 0.5316 | 0.5545 | 0.5316 | 0.7291 |
| 0.0523 | 9.1455 | 3018 | 0.5396 | 0.5545 | 0.5396 | 0.7346 |
| 0.0523 | 9.1515 | 3020 | 0.5477 | 0.5545 | 0.5477 | 0.7401 |
| 0.0523 | 9.1576 | 3022 | 0.5540 | 0.5545 | 0.5540 | 0.7443 |
| 0.0523 | 9.1636 | 3024 | 0.5635 | 0.5545 | 0.5635 | 0.7507 |
| 0.0523 | 9.1697 | 3026 | 0.5709 | 0.5545 | 0.5709 | 0.7556 |
| 0.0523 | 9.1758 | 3028 | 0.5821 | 0.4527 | 0.5821 | 0.7630 |
| 0.0523 | 9.1818 | 3030 | 0.5884 | 0.4527 | 0.5884 | 0.7670 |
| 0.0523 | 9.1879 | 3032 | 0.5949 | 0.4527 | 0.5949 | 0.7713 |
| 0.0523 | 9.1939 | 3034 | 0.5979 | 0.4527 | 0.5979 | 0.7732 |
| 0.0523 | 9.2 | 3036 | 0.5955 | 0.4527 | 0.5955 | 0.7717 |
| 0.0523 | 9.2061 | 3038 | 0.5887 | 0.4527 | 0.5887 | 0.7672 |
| 0.0523 | 9.2121 | 3040 | 0.5802 | 0.4527 | 0.5802 | 0.7617 |
| 0.0523 | 9.2182 | 3042 | 0.5711 | 0.5545 | 0.5711 | 0.7557 |
| 0.0523 | 9.2242 | 3044 | 0.5603 | 0.5545 | 0.5603 | 0.7485 |
| 0.0523 | 9.2303 | 3046 | 0.5543 | 0.5545 | 0.5543 | 0.7445 |
| 0.0523 | 9.2364 | 3048 | 0.5494 | 0.5545 | 0.5494 | 0.7412 |
| 0.0523 | 9.2424 | 3050 | 0.5439 | 0.5545 | 0.5439 | 0.7375 |
| 0.0523 | 9.2485 | 3052 | 0.5433 | 0.5545 | 0.5433 | 0.7371 |
| 0.0523 | 9.2545 | 3054 | 0.5476 | 0.5545 | 0.5476 | 0.7400 |
| 0.0523 | 9.2606 | 3056 | 0.5525 | 0.5545 | 0.5525 | 0.7433 |
| 0.0523 | 9.2667 | 3058 | 0.5607 | 0.5545 | 0.5607 | 0.7488 |
| 0.0523 | 9.2727 | 3060 | 0.5674 | 0.5545 | 0.5674 | 0.7532 |
| 0.0523 | 9.2788 | 3062 | 0.5740 | 0.5481 | 0.5740 | 0.7576 |
| 0.0523 | 9.2848 | 3064 | 0.5753 | 0.5481 | 0.5753 | 0.7585 |
| 0.0523 | 9.2909 | 3066 | 0.5744 | 0.5481 | 0.5744 | 0.7579 |
| 0.0523 | 9.2970 | 3068 | 0.5728 | 0.5481 | 0.5728 | 0.7569 |
| 0.0523 | 9.3030 | 3070 | 0.5703 | 0.5481 | 0.5703 | 0.7552 |
| 0.0523 | 9.3091 | 3072 | 0.5706 | 0.5481 | 0.5706 | 0.7554 |
| 0.0523 | 9.3152 | 3074 | 0.5708 | 0.5481 | 0.5708 | 0.7555 |
| 0.0523 | 9.3212 | 3076 | 0.5682 | 0.5545 | 0.5682 | 0.7538 |
| 0.0523 | 9.3273 | 3078 | 0.5620 | 0.5545 | 0.5620 | 0.7497 |
| 0.0523 | 9.3333 | 3080 | 0.5557 | 0.5545 | 0.5557 | 0.7455 |
| 0.0523 | 9.3394 | 3082 | 0.5512 | 0.5545 | 0.5512 | 0.7424 |
| 0.0523 | 9.3455 | 3084 | 0.5459 | 0.5545 | 0.5459 | 0.7388 |
| 0.0523 | 9.3515 | 3086 | 0.5415 | 0.5545 | 0.5415 | 0.7359 |
| 0.0523 | 9.3576 | 3088 | 0.5402 | 0.5545 | 0.5402 | 0.7350 |
| 0.0523 | 9.3636 | 3090 | 0.5412 | 0.5545 | 0.5412 | 0.7357 |
| 0.0523 | 9.3697 | 3092 | 0.5441 | 0.5545 | 0.5441 | 0.7376 |
| 0.0523 | 9.3758 | 3094 | 0.5491 | 0.5545 | 0.5491 | 0.7410 |
| 0.0523 | 9.3818 | 3096 | 0.5562 | 0.5545 | 0.5562 | 0.7458 |
| 0.0523 | 9.3879 | 3098 | 0.5619 | 0.5545 | 0.5619 | 0.7496 |
| 0.0523 | 9.3939 | 3100 | 0.5690 | 0.5481 | 0.5690 | 0.7543 |
| 0.0523 | 9.4 | 3102 | 0.5748 | 0.5481 | 0.5748 | 0.7582 |
| 0.0523 | 9.4061 | 3104 | 0.5804 | 0.4538 | 0.5804 | 0.7618 |
| 0.0523 | 9.4121 | 3106 | 0.5830 | 0.4538 | 0.5830 | 0.7636 |
| 0.0523 | 9.4182 | 3108 | 0.5821 | 0.4538 | 0.5821 | 0.7630 |
| 0.0523 | 9.4242 | 3110 | 0.5784 | 0.4538 | 0.5784 | 0.7605 |
| 0.0523 | 9.4303 | 3112 | 0.5724 | 0.5481 | 0.5724 | 0.7565 |
| 0.0523 | 9.4364 | 3114 | 0.5684 | 0.5481 | 0.5684 | 0.7539 |
| 0.0523 | 9.4424 | 3116 | 0.5635 | 0.5545 | 0.5635 | 0.7507 |
| 0.0523 | 9.4485 | 3118 | 0.5585 | 0.5545 | 0.5585 | 0.7473 |
| 0.0523 | 9.4545 | 3120 | 0.5563 | 0.5545 | 0.5563 | 0.7458 |
| 0.0523 | 9.4606 | 3122 | 0.5562 | 0.5545 | 0.5562 | 0.7458 |
| 0.0523 | 9.4667 | 3124 | 0.5581 | 0.5545 | 0.5581 | 0.7471 |
| 0.0523 | 9.4727 | 3126 | 0.5623 | 0.5545 | 0.5623 | 0.7499 |
| 0.0523 | 9.4788 | 3128 | 0.5674 | 0.5481 | 0.5674 | 0.7532 |
| 0.0523 | 9.4848 | 3130 | 0.5708 | 0.5481 | 0.5708 | 0.7555 |
| 0.0523 | 9.4909 | 3132 | 0.5727 | 0.5481 | 0.5727 | 0.7568 |
| 0.0523 | 9.4970 | 3134 | 0.5745 | 0.5481 | 0.5745 | 0.7579 |
| 0.0523 | 9.5030 | 3136 | 0.5793 | 0.5481 | 0.5793 | 0.7611 |
| 0.0523 | 9.5091 | 3138 | 0.5817 | 0.4538 | 0.5817 | 0.7627 |
| 0.0523 | 9.5152 | 3140 | 0.5847 | 0.4538 | 0.5847 | 0.7646 |
| 0.0523 | 9.5212 | 3142 | 0.5872 | 0.4538 | 0.5872 | 0.7663 |
| 0.0523 | 9.5273 | 3144 | 0.5888 | 0.4538 | 0.5888 | 0.7673 |
| 0.0523 | 9.5333 | 3146 | 0.5897 | 0.4538 | 0.5897 | 0.7679 |
| 0.0523 | 9.5394 | 3148 | 0.5892 | 0.4538 | 0.5892 | 0.7676 |
| 0.0523 | 9.5455 | 3150 | 0.5885 | 0.4538 | 0.5885 | 0.7671 |
| 0.0523 | 9.5515 | 3152 | 0.5863 | 0.4538 | 0.5863 | 0.7657 |
| 0.0523 | 9.5576 | 3154 | 0.5847 | 0.4538 | 0.5847 | 0.7646 |
| 0.0523 | 9.5636 | 3156 | 0.5838 | 0.4538 | 0.5838 | 0.7640 |
| 0.0523 | 9.5697 | 3158 | 0.5826 | 0.4538 | 0.5826 | 0.7633 |
| 0.0523 | 9.5758 | 3160 | 0.5818 | 0.4538 | 0.5818 | 0.7628 |
| 0.0523 | 9.5818 | 3162 | 0.5828 | 0.4538 | 0.5828 | 0.7634 |
| 0.0523 | 9.5879 | 3164 | 0.5817 | 0.4538 | 0.5817 | 0.7627 |
| 0.0523 | 9.5939 | 3166 | 0.5811 | 0.4538 | 0.5811 | 0.7623 |
| 0.0523 | 9.6 | 3168 | 0.5810 | 0.4538 | 0.5810 | 0.7622 |
| 0.0523 | 9.6061 | 3170 | 0.5817 | 0.4538 | 0.5817 | 0.7627 |
| 0.0523 | 9.6121 | 3172 | 0.5811 | 0.4538 | 0.5811 | 0.7623 |
| 0.0523 | 9.6182 | 3174 | 0.5812 | 0.4538 | 0.5812 | 0.7624 |
| 0.0523 | 9.6242 | 3176 | 0.5820 | 0.4192 | 0.5820 | 0.7629 |
| 0.0523 | 9.6303 | 3178 | 0.5840 | 0.4192 | 0.5840 | 0.7642 |
| 0.0523 | 9.6364 | 3180 | 0.5866 | 0.4192 | 0.5866 | 0.7659 |
| 0.0523 | 9.6424 | 3182 | 0.5867 | 0.4192 | 0.5867 | 0.7659 |
| 0.0523 | 9.6485 | 3184 | 0.5859 | 0.4192 | 0.5859 | 0.7654 |
| 0.0523 | 9.6545 | 3186 | 0.5829 | 0.4192 | 0.5829 | 0.7634 |
| 0.0523 | 9.6606 | 3188 | 0.5792 | 0.4192 | 0.5792 | 0.7611 |
| 0.0523 | 9.6667 | 3190 | 0.5764 | 0.4538 | 0.5764 | 0.7592 |
| 0.0523 | 9.6727 | 3192 | 0.5768 | 0.4538 | 0.5768 | 0.7595 |
| 0.0523 | 9.6788 | 3194 | 0.5784 | 0.4538 | 0.5784 | 0.7605 |
| 0.0523 | 9.6848 | 3196 | 0.5812 | 0.4538 | 0.5812 | 0.7624 |
| 0.0523 | 9.6909 | 3198 | 0.5818 | 0.4192 | 0.5818 | 0.7628 |
| 0.0523 | 9.6970 | 3200 | 0.5818 | 0.4538 | 0.5818 | 0.7628 |
| 0.0523 | 9.7030 | 3202 | 0.5829 | 0.4538 | 0.5829 | 0.7635 |
| 0.0523 | 9.7091 | 3204 | 0.5829 | 0.4538 | 0.5829 | 0.7635 |
| 0.0523 | 9.7152 | 3206 | 0.5829 | 0.4538 | 0.5829 | 0.7634 |
| 0.0523 | 9.7212 | 3208 | 0.5827 | 0.4538 | 0.5827 | 0.7634 |
| 0.0523 | 9.7273 | 3210 | 0.5835 | 0.4538 | 0.5835 | 0.7639 |
| 0.0523 | 9.7333 | 3212 | 0.5852 | 0.4538 | 0.5852 | 0.7650 |
| 0.0523 | 9.7394 | 3214 | 0.5876 | 0.4538 | 0.5876 | 0.7665 |
| 0.0523 | 9.7455 | 3216 | 0.5896 | 0.4538 | 0.5896 | 0.7679 |
| 0.0523 | 9.7515 | 3218 | 0.5907 | 0.4538 | 0.5907 | 0.7686 |
| 0.0523 | 9.7576 | 3220 | 0.5924 | 0.4538 | 0.5924 | 0.7697 |
| 0.0523 | 9.7636 | 3222 | 0.5923 | 0.4538 | 0.5923 | 0.7696 |
| 0.0523 | 9.7697 | 3224 | 0.5923 | 0.4538 | 0.5923 | 0.7696 |
| 0.0523 | 9.7758 | 3226 | 0.5925 | 0.4538 | 0.5925 | 0.7697 |
| 0.0523 | 9.7818 | 3228 | 0.5921 | 0.4538 | 0.5921 | 0.7695 |
| 0.0523 | 9.7879 | 3230 | 0.5920 | 0.4538 | 0.5920 | 0.7694 |
| 0.0523 | 9.7939 | 3232 | 0.5918 | 0.4538 | 0.5918 | 0.7693 |
| 0.0523 | 9.8 | 3234 | 0.5921 | 0.4538 | 0.5921 | 0.7695 |
| 0.0523 | 9.8061 | 3236 | 0.5927 | 0.4538 | 0.5927 | 0.7699 |
| 0.0523 | 9.8121 | 3238 | 0.5924 | 0.4538 | 0.5924 | 0.7697 |
| 0.0523 | 9.8182 | 3240 | 0.5928 | 0.4538 | 0.5928 | 0.7700 |
| 0.0523 | 9.8242 | 3242 | 0.5932 | 0.4192 | 0.5932 | 0.7702 |
| 0.0523 | 9.8303 | 3244 | 0.5923 | 0.4192 | 0.5923 | 0.7696 |
| 0.0523 | 9.8364 | 3246 | 0.5909 | 0.4538 | 0.5909 | 0.7687 |
| 0.0523 | 9.8424 | 3248 | 0.5894 | 0.4538 | 0.5894 | 0.7677 |
| 0.0523 | 9.8485 | 3250 | 0.5873 | 0.4538 | 0.5873 | 0.7664 |
| 0.0523 | 9.8545 | 3252 | 0.5857 | 0.4538 | 0.5857 | 0.7653 |
| 0.0523 | 9.8606 | 3254 | 0.5849 | 0.4538 | 0.5849 | 0.7648 |
| 0.0523 | 9.8667 | 3256 | 0.5841 | 0.4538 | 0.5841 | 0.7643 |
| 0.0523 | 9.8727 | 3258 | 0.5835 | 0.4538 | 0.5835 | 0.7639 |
| 0.0523 | 9.8788 | 3260 | 0.5831 | 0.4538 | 0.5831 | 0.7636 |
| 0.0523 | 9.8848 | 3262 | 0.5830 | 0.4538 | 0.5830 | 0.7635 |
| 0.0523 | 9.8909 | 3264 | 0.5827 | 0.4538 | 0.5827 | 0.7634 |
| 0.0523 | 9.8970 | 3266 | 0.5831 | 0.4538 | 0.5831 | 0.7636 |
| 0.0523 | 9.9030 | 3268 | 0.5835 | 0.4538 | 0.5835 | 0.7639 |
| 0.0523 | 9.9091 | 3270 | 0.5834 | 0.4538 | 0.5834 | 0.7638 |
| 0.0523 | 9.9152 | 3272 | 0.5827 | 0.4538 | 0.5827 | 0.7634 |
| 0.0523 | 9.9212 | 3274 | 0.5820 | 0.4538 | 0.5820 | 0.7629 |
| 0.0523 | 9.9273 | 3276 | 0.5820 | 0.4538 | 0.5820 | 0.7629 |
| 0.0523 | 9.9333 | 3278 | 0.5822 | 0.4538 | 0.5822 | 0.7631 |
| 0.0523 | 9.9394 | 3280 | 0.5824 | 0.4538 | 0.5824 | 0.7632 |
| 0.0523 | 9.9455 | 3282 | 0.5824 | 0.4538 | 0.5824 | 0.7631 |
| 0.0523 | 9.9515 | 3284 | 0.5826 | 0.4538 | 0.5826 | 0.7633 |
| 0.0523 | 9.9576 | 3286 | 0.5829 | 0.4538 | 0.5829 | 0.7635 |
| 0.0523 | 9.9636 | 3288 | 0.5833 | 0.4538 | 0.5833 | 0.7638 |
| 0.0523 | 9.9697 | 3290 | 0.5838 | 0.4538 | 0.5838 | 0.7640 |
| 0.0523 | 9.9758 | 3292 | 0.5840 | 0.4538 | 0.5840 | 0.7642 |
| 0.0523 | 9.9818 | 3294 | 0.5840 | 0.4538 | 0.5840 | 0.7642 |
| 0.0523 | 9.9879 | 3296 | 0.5840 | 0.4538 | 0.5840 | 0.7642 |
| 0.0523 | 9.9939 | 3298 | 0.5840 | 0.4538 | 0.5840 | 0.7642 |
| 0.0523 | 10.0 | 3300 | 0.5840 | 0.4538 | 0.5840 | 0.7642 |
### Framework versions
- Transformers 4.44.2
- Pytorch 2.4.0+cu118
- Datasets 2.21.0
- Tokenizers 0.19.1
|
nhbien/distilbert-base-uncased-finetuned-emotion
|
nhbien
| 2024-11-16T18:59:17Z
| 105
| 0
|
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"distilbert",
"text-classification",
"generated_from_trainer",
"base_model:distilbert/distilbert-base-uncased",
"base_model:finetune:distilbert/distilbert-base-uncased",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2024-11-16T17:57:22Z
|
---
library_name: transformers
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
model-index:
- name: distilbert-base-uncased-finetuned-emotion
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-emotion
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2195
- Accuracy: 0.929
- F1: 0.9289
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 0.8327 | 1.0 | 250 | 0.3231 | 0.9055 | 0.9043 |
| 0.2511 | 2.0 | 500 | 0.2195 | 0.929 | 0.9289 |
### Framework versions
- Transformers 4.46.2
- Pytorch 2.5.1+cu121
- Datasets 3.1.0
- Tokenizers 0.20.3
|
fbolanos/LRO_Arcee-Lite
|
fbolanos
| 2024-11-16T18:50:11Z
| 161
| 0
|
transformers
|
[
"transformers",
"safetensors",
"qwen2",
"text-generation",
"trl",
"sft",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2024-11-16T18:48:06Z
|
---
library_name: transformers
tags:
- trl
- sft
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
vonPipe/cs230MilestoneSelfies_prompt
|
vonPipe
| 2024-11-16T18:48:37Z
| 8
| 0
|
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"text-generation-inference",
"unsloth",
"trl",
"sft",
"conversational",
"en",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2024-11-16T18:28:51Z
|
---
base_model: unsloth/llama-3-8b-instruct-bnb-4bit
tags:
- text-generation-inference
- transformers
- unsloth
- llama
- trl
- sft
license: apache-2.0
language:
- en
---
# Uploaded model
- **Developed by:** vonPipe
- **License:** apache-2.0
- **Finetuned from model :** unsloth/llama-3-8b-instruct-bnb-4bit
This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
MayBashendy/Arabic_FineTuningAraBERT_AugV4_k25_task2_organization_fold1
|
MayBashendy
| 2024-11-16T18:35:00Z
| 185
| 0
|
transformers
|
[
"transformers",
"safetensors",
"bert",
"text-classification",
"generated_from_trainer",
"base_model:aubmindlab/bert-base-arabertv02",
"base_model:finetune:aubmindlab/bert-base-arabertv02",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2024-11-16T18:05:57Z
|
---
library_name: transformers
base_model: aubmindlab/bert-base-arabertv02
tags:
- generated_from_trainer
model-index:
- name: Arabic_FineTuningAraBERT_AugV4_k25_task2_organization_fold1
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# Arabic_FineTuningAraBERT_AugV4_k25_task2_organization_fold1
This model is a fine-tuned version of [aubmindlab/bert-base-arabertv02](https://huggingface.co/aubmindlab/bert-base-arabertv02) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8625
- Qwk: 0.3333
- Mse: 0.8625
- Rmse: 0.9287
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Qwk | Mse | Rmse |
|:-------------:|:------:|:----:|:---------------:|:-------:|:------:|:------:|
| No log | 0.0053 | 2 | 4.9698 | -0.0461 | 4.9698 | 2.2293 |
| No log | 0.0106 | 4 | 2.6967 | -0.0402 | 2.6967 | 1.6422 |
| No log | 0.0159 | 6 | 1.2728 | 0.1386 | 1.2728 | 1.1282 |
| No log | 0.0212 | 8 | 0.5525 | 0.1818 | 0.5525 | 0.7433 |
| No log | 0.0265 | 10 | 0.7113 | 0.0426 | 0.7113 | 0.8434 |
| No log | 0.0318 | 12 | 1.2104 | 0.1818 | 1.2104 | 1.1002 |
| No log | 0.0371 | 14 | 1.4721 | 0.0160 | 1.4721 | 1.2133 |
| No log | 0.0424 | 16 | 1.3509 | 0.1000 | 1.3509 | 1.1623 |
| No log | 0.0477 | 18 | 0.9160 | 0.1818 | 0.9160 | 0.9571 |
| No log | 0.0531 | 20 | 0.9121 | 0.2817 | 0.9121 | 0.9550 |
| No log | 0.0584 | 22 | 0.7283 | 0.0357 | 0.7283 | 0.8534 |
| No log | 0.0637 | 24 | 0.6336 | 0.125 | 0.6336 | 0.7960 |
| No log | 0.0690 | 26 | 0.6466 | 0.125 | 0.6466 | 0.8041 |
| No log | 0.0743 | 28 | 0.7008 | 0.0769 | 0.7008 | 0.8371 |
| No log | 0.0796 | 30 | 0.7061 | 0.1176 | 0.7061 | 0.8403 |
| No log | 0.0849 | 32 | 0.7593 | 0.0952 | 0.7593 | 0.8714 |
| No log | 0.0902 | 34 | 0.8541 | 0.1892 | 0.8541 | 0.9242 |
| No log | 0.0955 | 36 | 1.1845 | 0.0233 | 1.1845 | 1.0883 |
| No log | 0.1008 | 38 | 1.5034 | -0.0755 | 1.5034 | 1.2261 |
| No log | 0.1061 | 40 | 1.3656 | 0.1522 | 1.3656 | 1.1686 |
| No log | 0.1114 | 42 | 1.0018 | 0.1481 | 1.0018 | 1.0009 |
| No log | 0.1167 | 44 | 0.9637 | 0.2597 | 0.9637 | 0.9817 |
| No log | 0.1220 | 46 | 1.1043 | 0.2963 | 1.1043 | 1.0509 |
| No log | 0.1273 | 48 | 1.5411 | -0.0364 | 1.5411 | 1.2414 |
| No log | 0.1326 | 50 | 1.6206 | -0.0385 | 1.6206 | 1.2730 |
| No log | 0.1379 | 52 | 1.1918 | 0.0930 | 1.1918 | 1.0917 |
| No log | 0.1432 | 54 | 1.0962 | 0.1628 | 1.0962 | 1.0470 |
| No log | 0.1485 | 56 | 1.2718 | 0.0225 | 1.2718 | 1.1277 |
| No log | 0.1538 | 58 | 1.3871 | 0.0 | 1.3871 | 1.1778 |
| No log | 0.1592 | 60 | 1.4348 | -0.2273 | 1.4348 | 1.1978 |
| No log | 0.1645 | 62 | 1.2185 | -0.0870 | 1.2185 | 1.1038 |
| No log | 0.1698 | 64 | 1.1953 | -0.0870 | 1.1953 | 1.0933 |
| No log | 0.1751 | 66 | 1.0247 | -0.0588 | 1.0247 | 1.0123 |
| No log | 0.1804 | 68 | 0.8513 | 0.0625 | 0.8513 | 0.9227 |
| No log | 0.1857 | 70 | 0.8470 | 0.2154 | 0.8470 | 0.9203 |
| No log | 0.1910 | 72 | 0.8206 | 0.1818 | 0.8206 | 0.9059 |
| No log | 0.1963 | 74 | 0.8508 | 0.1818 | 0.8508 | 0.9224 |
| No log | 0.2016 | 76 | 1.1313 | 0.1522 | 1.1313 | 1.0636 |
| No log | 0.2069 | 78 | 1.2893 | 0.0792 | 1.2893 | 1.1355 |
| No log | 0.2122 | 80 | 1.1033 | 0.0741 | 1.1033 | 1.0504 |
| No log | 0.2175 | 82 | 0.9274 | 0.25 | 0.9274 | 0.9630 |
| No log | 0.2228 | 84 | 0.7237 | 0.1639 | 0.7237 | 0.8507 |
| No log | 0.2281 | 86 | 0.7279 | 0.1562 | 0.7279 | 0.8532 |
| No log | 0.2334 | 88 | 0.8269 | 0.2388 | 0.8269 | 0.9093 |
| No log | 0.2387 | 90 | 1.0978 | 0.1750 | 1.0978 | 1.0477 |
| No log | 0.2440 | 92 | 1.3326 | 0.1522 | 1.3326 | 1.1544 |
| No log | 0.2493 | 94 | 1.2010 | 0.1064 | 1.2010 | 1.0959 |
| No log | 0.2546 | 96 | 1.1412 | 0.2247 | 1.1412 | 1.0683 |
| No log | 0.2599 | 98 | 1.1415 | 0.2247 | 1.1415 | 1.0684 |
| No log | 0.2653 | 100 | 1.2730 | 0.1064 | 1.2730 | 1.1283 |
| No log | 0.2706 | 102 | 1.1909 | 0.2000 | 1.1909 | 1.0913 |
| No log | 0.2759 | 104 | 1.1575 | 0.2000 | 1.1575 | 1.0759 |
| No log | 0.2812 | 106 | 1.3283 | 0.1064 | 1.3283 | 1.1525 |
| No log | 0.2865 | 108 | 1.6396 | -0.0364 | 1.6396 | 1.2805 |
| No log | 0.2918 | 110 | 1.6008 | -0.0364 | 1.6008 | 1.2652 |
| No log | 0.2971 | 112 | 1.5563 | -0.0364 | 1.5563 | 1.2475 |
| No log | 0.3024 | 114 | 1.2096 | 0.2326 | 1.2096 | 1.0998 |
| No log | 0.3077 | 116 | 0.8964 | 0.1687 | 0.8964 | 0.9468 |
| No log | 0.3130 | 118 | 0.8413 | 0.1892 | 0.8413 | 0.9172 |
| No log | 0.3183 | 120 | 1.0101 | 0.25 | 1.0101 | 1.0051 |
| No log | 0.3236 | 122 | 1.2685 | 0.1099 | 1.2685 | 1.1263 |
| No log | 0.3289 | 124 | 1.3650 | 0.1099 | 1.3650 | 1.1683 |
| No log | 0.3342 | 126 | 1.1465 | 0.25 | 1.1465 | 1.0707 |
| No log | 0.3395 | 128 | 0.9268 | 0.1176 | 0.9268 | 0.9627 |
| No log | 0.3448 | 130 | 0.9472 | 0.1176 | 0.9472 | 0.9732 |
| No log | 0.3501 | 132 | 1.1642 | 0.2247 | 1.1642 | 1.0790 |
| No log | 0.3554 | 134 | 1.5580 | 0.1290 | 1.5580 | 1.2482 |
| No log | 0.3607 | 136 | 1.5579 | 0.1290 | 1.5579 | 1.2481 |
| No log | 0.3660 | 138 | 1.2024 | 0.2000 | 1.2024 | 1.0965 |
| No log | 0.3714 | 140 | 0.9862 | 0.1429 | 0.9862 | 0.9931 |
| No log | 0.3767 | 142 | 0.9727 | 0.1429 | 0.9727 | 0.9863 |
| No log | 0.3820 | 144 | 1.1246 | 0.2041 | 1.1246 | 1.0605 |
| No log | 0.3873 | 146 | 1.4551 | 0.1913 | 1.4551 | 1.2063 |
| No log | 0.3926 | 148 | 1.5263 | 0.1176 | 1.5263 | 1.2354 |
| No log | 0.3979 | 150 | 1.3644 | 0.1522 | 1.3644 | 1.1681 |
| No log | 0.4032 | 152 | 1.2418 | 0.1099 | 1.2418 | 1.1144 |
| No log | 0.4085 | 154 | 1.0237 | 0.1951 | 1.0237 | 1.0118 |
| No log | 0.4138 | 156 | 0.7803 | 0.24 | 0.7803 | 0.8834 |
| No log | 0.4191 | 158 | 0.6766 | 0.1972 | 0.6766 | 0.8226 |
| No log | 0.4244 | 160 | 0.6841 | 0.1972 | 0.6841 | 0.8271 |
| No log | 0.4297 | 162 | 0.8065 | 0.1750 | 0.8065 | 0.8980 |
| No log | 0.4350 | 164 | 1.1561 | 0.1481 | 1.1561 | 1.0752 |
| No log | 0.4403 | 166 | 1.6381 | 0.1148 | 1.6381 | 1.2799 |
| No log | 0.4456 | 168 | 1.6622 | 0.1148 | 1.6622 | 1.2892 |
| No log | 0.4509 | 170 | 1.3963 | 0.1031 | 1.3963 | 1.1817 |
| No log | 0.4562 | 172 | 1.0404 | 0.0930 | 1.0404 | 1.0200 |
| No log | 0.4615 | 174 | 0.8259 | 0.1818 | 0.8259 | 0.9088 |
| No log | 0.4668 | 176 | 0.8091 | 0.1972 | 0.8091 | 0.8995 |
| No log | 0.4721 | 178 | 0.9498 | 0.2025 | 0.9498 | 0.9746 |
| No log | 0.4775 | 180 | 1.2447 | -0.0235 | 1.2447 | 1.1156 |
| No log | 0.4828 | 182 | 1.4320 | 0.0400 | 1.4320 | 1.1967 |
| No log | 0.4881 | 184 | 1.3099 | -0.0667 | 1.3099 | 1.1445 |
| No log | 0.4934 | 186 | 1.0453 | 0.1000 | 1.0453 | 1.0224 |
| No log | 0.4987 | 188 | 0.8326 | 0.2727 | 0.8326 | 0.9125 |
| No log | 0.5040 | 190 | 0.7357 | 0.25 | 0.7357 | 0.8577 |
| No log | 0.5093 | 192 | 0.7305 | 0.2154 | 0.7305 | 0.8547 |
| No log | 0.5146 | 194 | 0.8711 | 0.2025 | 0.8711 | 0.9333 |
| No log | 0.5199 | 196 | 1.1841 | 0.0964 | 1.1841 | 1.0882 |
| No log | 0.5252 | 198 | 1.2862 | 0.1031 | 1.2862 | 1.1341 |
| No log | 0.5305 | 200 | 1.0818 | 0.2025 | 1.0818 | 1.0401 |
| No log | 0.5358 | 202 | 0.8123 | 0.24 | 0.8123 | 0.9013 |
| No log | 0.5411 | 204 | 0.7402 | 0.2857 | 0.7402 | 0.8603 |
| No log | 0.5464 | 206 | 0.7536 | 0.2857 | 0.7536 | 0.8681 |
| No log | 0.5517 | 208 | 0.8827 | 0.2025 | 0.8827 | 0.9395 |
| No log | 0.5570 | 210 | 1.2089 | 0.0816 | 1.2089 | 1.0995 |
| No log | 0.5623 | 212 | 1.6344 | 0.1148 | 1.6344 | 1.2785 |
| No log | 0.5676 | 214 | 1.8629 | 0.0455 | 1.8629 | 1.3649 |
| No log | 0.5729 | 216 | 1.6617 | -0.0364 | 1.6617 | 1.2891 |
| No log | 0.5782 | 218 | 1.2977 | 0.1099 | 1.2977 | 1.1392 |
| No log | 0.5836 | 220 | 0.9402 | 0.1818 | 0.9402 | 0.9696 |
| No log | 0.5889 | 222 | 0.7537 | 0.2286 | 0.7537 | 0.8682 |
| No log | 0.5942 | 224 | 0.7418 | 0.1818 | 0.7418 | 0.8613 |
| No log | 0.5995 | 226 | 0.8424 | 0.1266 | 0.8424 | 0.9178 |
| No log | 0.6048 | 228 | 1.0403 | 0.1220 | 1.0403 | 1.0199 |
| No log | 0.6101 | 230 | 1.3028 | 0.1099 | 1.3028 | 1.1414 |
| No log | 0.6154 | 232 | 1.4080 | 0.125 | 1.4080 | 1.1866 |
| No log | 0.6207 | 234 | 1.2624 | 0.0870 | 1.2624 | 1.1236 |
| No log | 0.6260 | 236 | 1.0804 | 0.1538 | 1.0804 | 1.0394 |
| No log | 0.6313 | 238 | 1.0171 | 0.1750 | 1.0171 | 1.0085 |
| No log | 0.6366 | 240 | 1.1414 | 0.2424 | 1.1414 | 1.0684 |
| No log | 0.6419 | 242 | 1.2628 | 0.2569 | 1.2628 | 1.1237 |
| No log | 0.6472 | 244 | 1.5775 | 0.1770 | 1.5775 | 1.2560 |
| No log | 0.6525 | 246 | 1.7381 | 0.1732 | 1.7381 | 1.3184 |
| No log | 0.6578 | 248 | 1.5838 | 0.1429 | 1.5838 | 1.2585 |
| No log | 0.6631 | 250 | 1.2566 | -0.0235 | 1.2566 | 1.1210 |
| No log | 0.6684 | 252 | 1.0685 | 0.1600 | 1.0685 | 1.0337 |
| No log | 0.6737 | 254 | 1.0180 | 0.2597 | 1.0180 | 1.0090 |
| No log | 0.6790 | 256 | 0.9626 | 0.2597 | 0.9626 | 0.9811 |
| No log | 0.6844 | 258 | 1.1390 | 0.2581 | 1.1390 | 1.0673 |
| No log | 0.6897 | 260 | 1.3847 | 0.1770 | 1.3847 | 1.1767 |
| No log | 0.6950 | 262 | 1.3892 | 0.1770 | 1.3892 | 1.1787 |
| No log | 0.7003 | 264 | 1.1365 | 0.0233 | 1.1365 | 1.0661 |
| No log | 0.7056 | 266 | 0.8138 | 0.2105 | 0.8138 | 0.9021 |
| No log | 0.7109 | 268 | 0.7530 | 0.1972 | 0.7530 | 0.8678 |
| No log | 0.7162 | 270 | 0.8534 | 0.2105 | 0.8534 | 0.9238 |
| No log | 0.7215 | 272 | 1.1308 | 0.0690 | 1.1308 | 1.0634 |
| No log | 0.7268 | 274 | 1.4518 | 0.1308 | 1.4518 | 1.2049 |
| No log | 0.7321 | 276 | 1.5259 | 0.1639 | 1.5259 | 1.2353 |
| No log | 0.7374 | 278 | 1.3991 | 0.0211 | 1.3991 | 1.1828 |
| No log | 0.7427 | 280 | 1.1479 | -0.0235 | 1.1479 | 1.0714 |
| No log | 0.7480 | 282 | 0.9526 | 0.2105 | 0.9526 | 0.9760 |
| No log | 0.7533 | 284 | 0.7727 | 0.2703 | 0.7727 | 0.8791 |
| No log | 0.7586 | 286 | 0.7044 | 0.2105 | 0.7044 | 0.8393 |
| No log | 0.7639 | 288 | 0.7375 | 0.24 | 0.7375 | 0.8588 |
| No log | 0.7692 | 290 | 0.9003 | 0.2105 | 0.9003 | 0.9488 |
| No log | 0.7745 | 292 | 1.0646 | 0.2105 | 1.0646 | 1.0318 |
| No log | 0.7798 | 294 | 1.2935 | 0.0667 | 1.2935 | 1.1373 |
| No log | 0.7851 | 296 | 1.2831 | 0.0667 | 1.2831 | 1.1328 |
| No log | 0.7905 | 298 | 1.0421 | 0.0488 | 1.0421 | 1.0208 |
| No log | 0.7958 | 300 | 0.8351 | 0.2192 | 0.8351 | 0.9138 |
| No log | 0.8011 | 302 | 0.8298 | 0.1892 | 0.8298 | 0.9109 |
| No log | 0.8064 | 304 | 0.8572 | 0.1892 | 0.8572 | 0.9259 |
| No log | 0.8117 | 306 | 0.9495 | 0.1538 | 0.9495 | 0.9744 |
| No log | 0.8170 | 308 | 1.0015 | 0.1538 | 1.0015 | 1.0007 |
| No log | 0.8223 | 310 | 1.0852 | 0.0241 | 1.0852 | 1.0417 |
| No log | 0.8276 | 312 | 1.1594 | 0.0241 | 1.1594 | 1.0768 |
| No log | 0.8329 | 314 | 1.1461 | 0.0241 | 1.1461 | 1.0706 |
| No log | 0.8382 | 316 | 0.9992 | 0.1538 | 0.9992 | 0.9996 |
| No log | 0.8435 | 318 | 0.9374 | 0.1892 | 0.9374 | 0.9682 |
| No log | 0.8488 | 320 | 0.9254 | 0.2192 | 0.9254 | 0.9620 |
| No log | 0.8541 | 322 | 0.8656 | 0.2192 | 0.8656 | 0.9304 |
| No log | 0.8594 | 324 | 0.8852 | 0.2192 | 0.8852 | 0.9408 |
| No log | 0.8647 | 326 | 0.9643 | 0.25 | 0.9643 | 0.9820 |
| No log | 0.8700 | 328 | 0.9876 | 0.2105 | 0.9876 | 0.9938 |
| No log | 0.8753 | 330 | 0.9135 | 0.2817 | 0.9135 | 0.9558 |
| No log | 0.8806 | 332 | 0.8051 | 0.2817 | 0.8051 | 0.8973 |
| No log | 0.8859 | 334 | 0.6167 | 0.2727 | 0.6167 | 0.7853 |
| No log | 0.8912 | 336 | 0.5383 | 0.25 | 0.5383 | 0.7337 |
| No log | 0.8966 | 338 | 0.5502 | 0.25 | 0.5502 | 0.7417 |
| No log | 0.9019 | 340 | 0.6601 | 0.2817 | 0.6601 | 0.8124 |
| No log | 0.9072 | 342 | 0.8253 | 0.2817 | 0.8253 | 0.9085 |
| No log | 0.9125 | 344 | 0.9159 | 0.2817 | 0.9159 | 0.9570 |
| No log | 0.9178 | 346 | 0.8537 | 0.2703 | 0.8537 | 0.9239 |
| No log | 0.9231 | 348 | 0.7879 | 0.3077 | 0.7879 | 0.8876 |
| No log | 0.9284 | 350 | 0.8533 | 0.3333 | 0.8533 | 0.9237 |
| No log | 0.9337 | 352 | 0.9999 | 0.4 | 0.9999 | 1.0000 |
| No log | 0.9390 | 354 | 1.1881 | 0.1356 | 1.1881 | 1.0900 |
| No log | 0.9443 | 356 | 1.2949 | 0.1538 | 1.2949 | 1.1379 |
| No log | 0.9496 | 358 | 1.2172 | 0.1724 | 1.2172 | 1.1033 |
| No log | 0.9549 | 360 | 0.9521 | 0.2895 | 0.9521 | 0.9758 |
| No log | 0.9602 | 362 | 0.8523 | 0.24 | 0.8523 | 0.9232 |
| No log | 0.9655 | 364 | 0.8608 | 0.24 | 0.8608 | 0.9278 |
| No log | 0.9708 | 366 | 0.8633 | 0.24 | 0.8633 | 0.9292 |
| No log | 0.9761 | 368 | 0.9343 | 0.2703 | 0.9343 | 0.9666 |
| No log | 0.9814 | 370 | 0.9660 | 0.2143 | 0.9660 | 0.9828 |
| No log | 0.9867 | 372 | 0.9917 | 0.1333 | 0.9917 | 0.9958 |
| No log | 0.9920 | 374 | 0.8610 | 0.3377 | 0.8610 | 0.9279 |
| No log | 0.9973 | 376 | 0.8128 | 0.3377 | 0.8128 | 0.9015 |
| No log | 1.0027 | 378 | 0.8662 | 0.4146 | 0.8662 | 0.9307 |
| No log | 1.0080 | 380 | 1.0132 | 0.2588 | 1.0132 | 1.0066 |
| No log | 1.0133 | 382 | 1.1690 | 0.2069 | 1.1690 | 1.0812 |
| No log | 1.0186 | 384 | 1.0859 | 0.2069 | 1.0859 | 1.0421 |
| No log | 1.0239 | 386 | 0.9234 | 0.3415 | 0.9234 | 0.9609 |
| No log | 1.0292 | 388 | 0.7759 | 0.1750 | 0.7759 | 0.8808 |
| No log | 1.0345 | 390 | 0.7707 | 0.1750 | 0.7707 | 0.8779 |
| No log | 1.0398 | 392 | 0.8047 | 0.2025 | 0.8047 | 0.8971 |
| No log | 1.0451 | 394 | 0.8778 | 0.2025 | 0.8778 | 0.9369 |
| No log | 1.0504 | 396 | 0.9889 | 0.3415 | 0.9889 | 0.9944 |
| No log | 1.0557 | 398 | 1.0419 | 0.3415 | 1.0419 | 1.0207 |
| No log | 1.0610 | 400 | 1.1056 | 0.3415 | 1.1056 | 1.0515 |
| No log | 1.0663 | 402 | 0.9872 | 0.2597 | 0.9872 | 0.9936 |
| No log | 1.0716 | 404 | 0.9286 | 0.2597 | 0.9286 | 0.9637 |
| No log | 1.0769 | 406 | 0.9269 | 0.2597 | 0.9269 | 0.9627 |
| No log | 1.0822 | 408 | 0.8771 | 0.3014 | 0.8771 | 0.9365 |
| No log | 1.0875 | 410 | 0.9359 | 0.2703 | 0.9359 | 0.9674 |
| No log | 1.0928 | 412 | 0.9616 | 0.3200 | 0.9616 | 0.9806 |
| No log | 1.0981 | 414 | 1.0417 | 0.2718 | 1.0417 | 1.0206 |
| No log | 1.1034 | 416 | 1.1084 | 0.2569 | 1.1084 | 1.0528 |
| No log | 1.1088 | 418 | 1.2938 | 0.2703 | 1.2938 | 1.1374 |
| No log | 1.1141 | 420 | 1.2923 | 0.1724 | 1.2923 | 1.1368 |
| No log | 1.1194 | 422 | 1.1045 | 0.1429 | 1.1045 | 1.0510 |
| No log | 1.1247 | 424 | 0.8539 | 0.2895 | 0.8539 | 0.9241 |
| No log | 1.1300 | 426 | 0.7961 | 0.2895 | 0.7961 | 0.8923 |
| No log | 1.1353 | 428 | 0.8174 | 0.2895 | 0.8174 | 0.9041 |
| No log | 1.1406 | 430 | 0.9699 | 0.1951 | 0.9699 | 0.9848 |
| No log | 1.1459 | 432 | 1.0581 | 0.1951 | 1.0581 | 1.0287 |
| No log | 1.1512 | 434 | 1.0454 | 0.1951 | 1.0454 | 1.0224 |
| No log | 1.1565 | 436 | 1.1451 | 0.1481 | 1.1451 | 1.0701 |
| No log | 1.1618 | 438 | 1.2900 | 0.0625 | 1.2900 | 1.1358 |
| No log | 1.1671 | 440 | 1.2308 | 0.1099 | 1.2308 | 1.1094 |
| No log | 1.1724 | 442 | 1.2352 | 0.1099 | 1.2352 | 1.1114 |
| No log | 1.1777 | 444 | 1.1408 | 0.0233 | 1.1408 | 1.0681 |
| No log | 1.1830 | 446 | 0.9732 | 0.2597 | 0.9732 | 0.9865 |
| No log | 1.1883 | 448 | 0.9282 | 0.2308 | 0.9282 | 0.9635 |
| No log | 1.1936 | 450 | 0.9222 | 0.2500 | 0.9222 | 0.9603 |
| No log | 1.1989 | 452 | 0.9645 | 0.2308 | 0.9645 | 0.9821 |
| No log | 1.2042 | 454 | 1.0100 | 0.2597 | 1.0100 | 1.0050 |
| No log | 1.2095 | 456 | 1.0409 | 0.1220 | 1.0409 | 1.0202 |
| No log | 1.2149 | 458 | 1.0781 | 0.1220 | 1.0781 | 1.0383 |
| No log | 1.2202 | 460 | 1.1187 | 0.0233 | 1.1187 | 1.0577 |
| No log | 1.2255 | 462 | 1.0044 | 0.2597 | 1.0044 | 1.0022 |
| No log | 1.2308 | 464 | 0.9153 | 0.2025 | 0.9153 | 0.9567 |
| No log | 1.2361 | 466 | 0.8487 | 0.3077 | 0.8487 | 0.9212 |
| No log | 1.2414 | 468 | 0.8608 | 0.2410 | 0.8608 | 0.9278 |
| No log | 1.2467 | 470 | 0.8862 | 0.2410 | 0.8862 | 0.9414 |
| No log | 1.2520 | 472 | 0.9716 | 0.2500 | 0.9716 | 0.9857 |
| No log | 1.2573 | 474 | 1.0358 | 0.1951 | 1.0358 | 1.0177 |
| No log | 1.2626 | 476 | 1.0660 | 0.0233 | 1.0660 | 1.0325 |
| No log | 1.2679 | 478 | 0.9939 | 0.1951 | 0.9939 | 0.9969 |
| No log | 1.2732 | 480 | 0.9260 | 0.2597 | 0.9260 | 0.9623 |
| No log | 1.2785 | 482 | 0.8267 | 0.2597 | 0.8267 | 0.9092 |
| No log | 1.2838 | 484 | 0.7920 | 0.2785 | 0.7920 | 0.8899 |
| No log | 1.2891 | 486 | 0.8866 | 0.2597 | 0.8866 | 0.9416 |
| No log | 1.2944 | 488 | 0.9134 | 0.2785 | 0.9134 | 0.9557 |
| No log | 1.2997 | 490 | 0.8668 | 0.1951 | 0.8668 | 0.9310 |
| No log | 1.3050 | 492 | 0.9663 | 0.2222 | 0.9663 | 0.9830 |
| No log | 1.3103 | 494 | 1.0207 | 0.1176 | 1.0207 | 1.0103 |
| No log | 1.3156 | 496 | 1.1817 | 0.1553 | 1.1817 | 1.0870 |
| No log | 1.3210 | 498 | 1.1771 | 0.1522 | 1.1771 | 1.0849 |
| 0.4359 | 1.3263 | 500 | 1.2184 | 0.1099 | 1.2184 | 1.1038 |
| 0.4359 | 1.3316 | 502 | 1.1213 | 0.1522 | 1.1213 | 1.0589 |
| 0.4359 | 1.3369 | 504 | 0.9535 | 0.1220 | 0.9535 | 0.9765 |
| 0.4359 | 1.3422 | 506 | 0.9332 | 0.1220 | 0.9332 | 0.9660 |
| 0.4359 | 1.3475 | 508 | 0.9453 | 0.1220 | 0.9453 | 0.9723 |
| 0.4359 | 1.3528 | 510 | 0.8510 | 0.2703 | 0.8510 | 0.9225 |
| 0.4359 | 1.3581 | 512 | 0.8625 | 0.2703 | 0.8625 | 0.9287 |
| 0.4359 | 1.3634 | 514 | 0.8953 | 0.2308 | 0.8953 | 0.9462 |
| 0.4359 | 1.3687 | 516 | 0.9750 | 0.1220 | 0.9750 | 0.9874 |
| 0.4359 | 1.3740 | 518 | 1.0586 | 0.0690 | 1.0586 | 1.0289 |
| 0.4359 | 1.3793 | 520 | 1.0486 | 0.0690 | 1.0486 | 1.0240 |
| 0.4359 | 1.3846 | 522 | 1.0554 | 0.0690 | 1.0554 | 1.0273 |
| 0.4359 | 1.3899 | 524 | 1.0058 | 0.0690 | 1.0058 | 1.0029 |
| 0.4359 | 1.3952 | 526 | 1.0437 | 0.0690 | 1.0437 | 1.0216 |
| 0.4359 | 1.4005 | 528 | 1.0544 | 0.0690 | 1.0544 | 1.0269 |
| 0.4359 | 1.4058 | 530 | 1.0188 | 0.0690 | 1.0188 | 1.0094 |
| 0.4359 | 1.4111 | 532 | 0.8804 | 0.3333 | 0.8804 | 0.9383 |
| 0.4359 | 1.4164 | 534 | 0.7778 | 0.3284 | 0.7778 | 0.8819 |
| 0.4359 | 1.4218 | 536 | 0.7953 | 0.3284 | 0.7953 | 0.8918 |
| 0.4359 | 1.4271 | 538 | 0.8198 | 0.3284 | 0.8198 | 0.9054 |
| 0.4359 | 1.4324 | 540 | 1.0001 | 0.0690 | 1.0001 | 1.0001 |
| 0.4359 | 1.4377 | 542 | 1.2029 | 0.0690 | 1.2029 | 1.0968 |
| 0.4359 | 1.4430 | 544 | 1.1242 | 0.1429 | 1.1242 | 1.0603 |
| 0.4359 | 1.4483 | 546 | 1.0654 | 0.1212 | 1.0654 | 1.0322 |
| 0.4359 | 1.4536 | 548 | 0.9893 | 0.2069 | 0.9893 | 0.9946 |
| 0.4359 | 1.4589 | 550 | 0.8246 | 0.3200 | 0.8246 | 0.9081 |
| 0.4359 | 1.4642 | 552 | 0.7839 | 0.3200 | 0.7839 | 0.8854 |
| 0.4359 | 1.4695 | 554 | 0.7552 | 0.3514 | 0.7552 | 0.8690 |
| 0.4359 | 1.4748 | 556 | 0.8395 | 0.2941 | 0.8395 | 0.9162 |
| 0.4359 | 1.4801 | 558 | 0.9967 | 0.1220 | 0.9967 | 0.9983 |
| 0.4359 | 1.4854 | 560 | 1.0228 | 0.1220 | 1.0228 | 1.0114 |
| 0.4359 | 1.4907 | 562 | 0.9615 | 0.1220 | 0.9615 | 0.9806 |
| 0.4359 | 1.4960 | 564 | 0.9112 | 0.2025 | 0.9112 | 0.9546 |
| 0.4359 | 1.5013 | 566 | 0.8319 | 0.2895 | 0.8319 | 0.9121 |
| 0.4359 | 1.5066 | 568 | 0.8489 | 0.3377 | 0.8489 | 0.9214 |
| 0.4359 | 1.5119 | 570 | 0.9081 | 0.3571 | 0.9081 | 0.9530 |
| 0.4359 | 1.5172 | 572 | 0.9932 | 0.2921 | 0.9932 | 0.9966 |
| 0.4359 | 1.5225 | 574 | 1.0648 | 0.2683 | 1.0648 | 1.0319 |
| 0.4359 | 1.5279 | 576 | 1.0240 | 0.3133 | 1.0240 | 1.0119 |
| 0.4359 | 1.5332 | 578 | 0.9069 | 0.2500 | 0.9069 | 0.9523 |
| 0.4359 | 1.5385 | 580 | 0.8532 | 0.2222 | 0.8532 | 0.9237 |
| 0.4359 | 1.5438 | 582 | 0.8475 | 0.3544 | 0.8475 | 0.9206 |
| 0.4359 | 1.5491 | 584 | 1.0096 | 0.1750 | 1.0096 | 1.0048 |
| 0.4359 | 1.5544 | 586 | 1.1261 | 0.0690 | 1.1261 | 1.0612 |
| 0.4359 | 1.5597 | 588 | 1.0552 | 0.0690 | 1.0552 | 1.0272 |
| 0.4359 | 1.5650 | 590 | 0.8566 | 0.3333 | 0.8566 | 0.9255 |
| 0.4359 | 1.5703 | 592 | 0.6833 | 0.2727 | 0.6833 | 0.8266 |
| 0.4359 | 1.5756 | 594 | 0.6422 | 0.3077 | 0.6422 | 0.8014 |
| 0.4359 | 1.5809 | 596 | 0.6501 | 0.1972 | 0.6501 | 0.8063 |
| 0.4359 | 1.5862 | 598 | 0.7305 | 0.2192 | 0.7305 | 0.8547 |
| 0.4359 | 1.5915 | 600 | 0.9051 | 0.3514 | 0.9051 | 0.9513 |
| 0.4359 | 1.5968 | 602 | 1.0072 | 0.1429 | 1.0072 | 1.0036 |
| 0.4359 | 1.6021 | 604 | 0.9388 | 0.2597 | 0.9388 | 0.9689 |
| 0.4359 | 1.6074 | 606 | 0.9744 | 0.3182 | 0.9744 | 0.9871 |
| 0.4359 | 1.6127 | 608 | 1.1893 | 0.1923 | 1.1893 | 1.0905 |
| 0.4359 | 1.6180 | 610 | 1.3149 | 0.1681 | 1.3149 | 1.1467 |
| 0.4359 | 1.6233 | 612 | 1.2315 | 0.2136 | 1.2315 | 1.1098 |
| 0.4359 | 1.6286 | 614 | 1.0071 | 0.0964 | 1.0071 | 1.0035 |
| 0.4359 | 1.6340 | 616 | 0.8333 | 0.2588 | 0.8333 | 0.9129 |
| 0.4359 | 1.6393 | 618 | 0.7632 | 0.3200 | 0.7632 | 0.8736 |
| 0.4359 | 1.6446 | 620 | 0.8076 | 0.2410 | 0.8076 | 0.8987 |
| 0.4359 | 1.6499 | 622 | 0.8529 | 0.2500 | 0.8529 | 0.9235 |
| 0.4359 | 1.6552 | 624 | 0.9931 | 0.1481 | 0.9931 | 0.9965 |
| 0.4359 | 1.6605 | 626 | 1.0387 | 0.1481 | 1.0387 | 1.0192 |
| 0.4359 | 1.6658 | 628 | 1.1422 | 0.0233 | 1.1422 | 1.0687 |
| 0.4359 | 1.6711 | 630 | 1.0285 | 0.1481 | 1.0285 | 1.0142 |
| 0.4359 | 1.6764 | 632 | 0.8640 | 0.1429 | 0.8640 | 0.9295 |
| 0.4359 | 1.6817 | 634 | 0.8065 | 0.2500 | 0.8065 | 0.8981 |
| 0.4359 | 1.6870 | 636 | 0.7584 | 0.3143 | 0.7584 | 0.8709 |
| 0.4359 | 1.6923 | 638 | 0.7610 | 0.3478 | 0.7610 | 0.8724 |
| 0.4359 | 1.6976 | 640 | 0.8304 | 0.2817 | 0.8304 | 0.9113 |
| 0.4359 | 1.7029 | 642 | 0.9042 | 0.1481 | 0.9042 | 0.9509 |
| 0.4359 | 1.7082 | 644 | 0.9225 | 0.1481 | 0.9225 | 0.9605 |
| 0.4359 | 1.7135 | 646 | 1.0454 | 0.1481 | 1.0454 | 1.0224 |
| 0.4359 | 1.7188 | 648 | 1.0685 | 0.0233 | 1.0685 | 1.0337 |
| 0.4359 | 1.7241 | 650 | 0.9079 | 0.1687 | 0.9079 | 0.9528 |
| 0.4359 | 1.7294 | 652 | 0.7549 | 0.3514 | 0.7549 | 0.8689 |
| 0.4359 | 1.7347 | 654 | 0.7073 | 0.25 | 0.7073 | 0.8410 |
| 0.4359 | 1.7401 | 656 | 0.7161 | 0.25 | 0.7161 | 0.8462 |
| 0.4359 | 1.7454 | 658 | 0.7257 | 0.3143 | 0.7257 | 0.8519 |
| 0.4359 | 1.7507 | 660 | 0.8024 | 0.2941 | 0.8024 | 0.8957 |
| 0.4359 | 1.7560 | 662 | 0.8613 | 0.2817 | 0.8613 | 0.9281 |
| 0.4359 | 1.7613 | 664 | 0.8332 | 0.25 | 0.8332 | 0.9128 |
| 0.4359 | 1.7666 | 666 | 0.8293 | 0.25 | 0.8293 | 0.9107 |
| 0.4359 | 1.7719 | 668 | 0.9079 | 0.2817 | 0.9079 | 0.9528 |
| 0.4359 | 1.7772 | 670 | 0.9441 | 0.0741 | 0.9441 | 0.9716 |
| 0.4359 | 1.7825 | 672 | 1.0296 | 0.0233 | 1.0296 | 1.0147 |
| 0.4359 | 1.7878 | 674 | 1.0632 | 0.0233 | 1.0632 | 1.0311 |
| 0.4359 | 1.7931 | 676 | 1.0279 | 0.0233 | 1.0279 | 1.0138 |
| 0.4359 | 1.7984 | 678 | 0.8685 | 0.3333 | 0.8685 | 0.9320 |
| 0.4359 | 1.8037 | 680 | 0.7659 | 0.3014 | 0.7659 | 0.8752 |
| 0.4359 | 1.8090 | 682 | 0.7016 | 0.3514 | 0.7016 | 0.8376 |
| 0.4359 | 1.8143 | 684 | 0.7220 | 0.3514 | 0.7220 | 0.8497 |
| 0.4359 | 1.8196 | 686 | 0.7963 | 0.3014 | 0.7963 | 0.8923 |
| 0.4359 | 1.8249 | 688 | 0.8421 | 0.3014 | 0.8421 | 0.9177 |
| 0.4359 | 1.8302 | 690 | 0.9278 | 0.1818 | 0.9278 | 0.9632 |
| 0.4359 | 1.8355 | 692 | 1.0118 | 0.1220 | 1.0118 | 1.0059 |
| 0.4359 | 1.8408 | 694 | 0.9891 | 0.1220 | 0.9891 | 0.9945 |
| 0.4359 | 1.8462 | 696 | 0.8541 | 0.2703 | 0.8541 | 0.9242 |
| 0.4359 | 1.8515 | 698 | 0.7768 | 0.3684 | 0.7768 | 0.8813 |
| 0.4359 | 1.8568 | 700 | 0.7940 | 0.2703 | 0.7940 | 0.8911 |
| 0.4359 | 1.8621 | 702 | 0.7789 | 0.3014 | 0.7789 | 0.8826 |
| 0.4359 | 1.8674 | 704 | 0.8688 | 0.3333 | 0.8688 | 0.9321 |
| 0.4359 | 1.8727 | 706 | 0.9245 | 0.1316 | 0.9245 | 0.9615 |
| 0.4359 | 1.8780 | 708 | 0.9612 | 0.0741 | 0.9612 | 0.9804 |
| 0.4359 | 1.8833 | 710 | 0.8589 | 0.3014 | 0.8589 | 0.9268 |
| 0.4359 | 1.8886 | 712 | 0.8433 | 0.3014 | 0.8433 | 0.9183 |
| 0.4359 | 1.8939 | 714 | 0.8701 | 0.3014 | 0.8701 | 0.9328 |
| 0.4359 | 1.8992 | 716 | 0.8923 | 0.3014 | 0.8923 | 0.9446 |
| 0.4359 | 1.9045 | 718 | 0.8621 | 0.2703 | 0.8621 | 0.9285 |
| 0.4359 | 1.9098 | 720 | 0.7903 | 0.3143 | 0.7903 | 0.8890 |
| 0.4359 | 1.9151 | 722 | 0.7552 | 0.3333 | 0.7552 | 0.8690 |
| 0.4359 | 1.9204 | 724 | 0.7738 | 0.3662 | 0.7738 | 0.8797 |
| 0.4359 | 1.9257 | 726 | 0.8990 | 0.2105 | 0.8990 | 0.9482 |
| 0.4359 | 1.9310 | 728 | 0.9626 | 0.0233 | 0.9626 | 0.9811 |
| 0.4359 | 1.9363 | 730 | 1.0389 | 0.0233 | 1.0389 | 1.0192 |
| 0.4359 | 1.9416 | 732 | 1.0276 | 0.0233 | 1.0276 | 1.0137 |
| 0.4359 | 1.9469 | 734 | 0.8949 | 0.2105 | 0.8949 | 0.9460 |
| 0.4359 | 1.9523 | 736 | 0.7746 | 0.2192 | 0.7746 | 0.8801 |
| 0.4359 | 1.9576 | 738 | 0.7035 | 0.3014 | 0.7035 | 0.8387 |
| 0.4359 | 1.9629 | 740 | 0.7011 | 0.4 | 0.7011 | 0.8373 |
| 0.4359 | 1.9682 | 742 | 0.7239 | 0.3514 | 0.7239 | 0.8508 |
| 0.4359 | 1.9735 | 744 | 0.8093 | 0.2597 | 0.8093 | 0.8996 |
| 0.4359 | 1.9788 | 746 | 0.9705 | 0.1951 | 0.9705 | 0.9852 |
| 0.4359 | 1.9841 | 748 | 1.0495 | 0.0233 | 1.0495 | 1.0244 |
| 0.4359 | 1.9894 | 750 | 1.0017 | 0.0690 | 1.0017 | 1.0008 |
| 0.4359 | 1.9947 | 752 | 0.8739 | 0.3250 | 0.8739 | 0.9348 |
| 0.4359 | 2.0 | 754 | 0.7744 | 0.3514 | 0.7744 | 0.8800 |
| 0.4359 | 2.0053 | 756 | 0.7441 | 0.2597 | 0.7441 | 0.8626 |
| 0.4359 | 2.0106 | 758 | 0.7601 | 0.2105 | 0.7601 | 0.8719 |
| 0.4359 | 2.0159 | 760 | 0.8467 | 0.3077 | 0.8467 | 0.9202 |
| 0.4359 | 2.0212 | 762 | 0.9883 | 0.2308 | 0.9883 | 0.9941 |
| 0.4359 | 2.0265 | 764 | 1.0875 | 0.0233 | 1.0875 | 1.0428 |
| 0.4359 | 2.0318 | 766 | 1.0704 | 0.1099 | 1.0704 | 1.0346 |
| 0.4359 | 2.0371 | 768 | 0.9278 | 0.1750 | 0.9278 | 0.9632 |
| 0.4359 | 2.0424 | 770 | 0.8119 | 0.3377 | 0.8119 | 0.9010 |
| 0.4359 | 2.0477 | 772 | 0.7495 | 0.2703 | 0.7495 | 0.8658 |
| 0.4359 | 2.0531 | 774 | 0.7502 | 0.3014 | 0.7502 | 0.8662 |
| 0.4359 | 2.0584 | 776 | 0.7877 | 0.3200 | 0.7877 | 0.8875 |
| 0.4359 | 2.0637 | 778 | 0.8918 | 0.25 | 0.8918 | 0.9444 |
| 0.4359 | 2.0690 | 780 | 0.9351 | 0.25 | 0.9351 | 0.9670 |
| 0.4359 | 2.0743 | 782 | 0.9479 | 0.25 | 0.9479 | 0.9736 |
| 0.4359 | 2.0796 | 784 | 0.8873 | 0.25 | 0.8873 | 0.9420 |
| 0.4359 | 2.0849 | 786 | 0.7768 | 0.3014 | 0.7768 | 0.8814 |
| 0.4359 | 2.0902 | 788 | 0.7189 | 0.2105 | 0.7189 | 0.8479 |
| 0.4359 | 2.0955 | 790 | 0.7191 | 0.25 | 0.7191 | 0.8480 |
| 0.4359 | 2.1008 | 792 | 0.7320 | 0.2105 | 0.7320 | 0.8556 |
| 0.4359 | 2.1061 | 794 | 0.8263 | 0.3377 | 0.8263 | 0.9090 |
| 0.4359 | 2.1114 | 796 | 1.0317 | 0.0488 | 1.0317 | 1.0157 |
| 0.4359 | 2.1167 | 798 | 1.1735 | 0.0233 | 1.1735 | 1.0833 |
| 0.4359 | 2.1220 | 800 | 1.1711 | 0.0233 | 1.1711 | 1.0822 |
| 0.4359 | 2.1273 | 802 | 1.0216 | 0.0741 | 1.0216 | 1.0107 |
| 0.4359 | 2.1326 | 804 | 0.8231 | 0.2727 | 0.8231 | 0.9072 |
| 0.4359 | 2.1379 | 806 | 0.6879 | 0.2609 | 0.6879 | 0.8294 |
| 0.4359 | 2.1432 | 808 | 0.6318 | 0.3478 | 0.6318 | 0.7949 |
| 0.4359 | 2.1485 | 810 | 0.6360 | 0.25 | 0.6360 | 0.7975 |
| 0.4359 | 2.1538 | 812 | 0.6814 | 0.3014 | 0.6814 | 0.8255 |
| 0.4359 | 2.1592 | 814 | 0.7831 | 0.3662 | 0.7831 | 0.8849 |
| 0.4359 | 2.1645 | 816 | 0.8966 | 0.2025 | 0.8966 | 0.9469 |
| 0.4359 | 2.1698 | 818 | 0.9405 | 0.2025 | 0.9405 | 0.9698 |
| 0.4359 | 2.1751 | 820 | 0.9085 | 0.2025 | 0.9085 | 0.9532 |
| 0.4359 | 2.1804 | 822 | 0.8113 | 0.3662 | 0.8113 | 0.9007 |
| 0.4359 | 2.1857 | 824 | 0.7191 | 0.3514 | 0.7191 | 0.8480 |
| 0.4359 | 2.1910 | 826 | 0.7133 | 0.3514 | 0.7133 | 0.8445 |
| 0.4359 | 2.1963 | 828 | 0.7376 | 0.3014 | 0.7376 | 0.8588 |
| 0.4359 | 2.2016 | 830 | 0.8193 | 0.3684 | 0.8193 | 0.9052 |
| 0.4359 | 2.2069 | 832 | 0.8368 | 0.3200 | 0.8368 | 0.9148 |
| 0.4359 | 2.2122 | 834 | 0.8330 | 0.3200 | 0.8330 | 0.9127 |
| 0.4359 | 2.2175 | 836 | 0.9181 | 0.3200 | 0.9181 | 0.9582 |
| 0.4359 | 2.2228 | 838 | 0.9287 | 0.3721 | 0.9287 | 0.9637 |
| 0.4359 | 2.2281 | 840 | 0.8828 | 0.3200 | 0.8828 | 0.9396 |
| 0.4359 | 2.2334 | 842 | 0.8533 | 0.3200 | 0.8533 | 0.9238 |
| 0.4359 | 2.2387 | 844 | 0.7590 | 0.3200 | 0.7590 | 0.8712 |
| 0.4359 | 2.2440 | 846 | 0.7018 | 0.3662 | 0.7018 | 0.8377 |
| 0.4359 | 2.2493 | 848 | 0.6901 | 0.3143 | 0.6901 | 0.8307 |
| 0.4359 | 2.2546 | 850 | 0.7133 | 0.3143 | 0.7133 | 0.8445 |
| 0.4359 | 2.2599 | 852 | 0.7129 | 0.3143 | 0.7129 | 0.8443 |
| 0.4359 | 2.2653 | 854 | 0.7229 | 0.3143 | 0.7229 | 0.8503 |
| 0.4359 | 2.2706 | 856 | 0.7083 | 0.3333 | 0.7083 | 0.8416 |
| 0.4359 | 2.2759 | 858 | 0.7132 | 0.3333 | 0.7132 | 0.8445 |
| 0.4359 | 2.2812 | 860 | 0.7706 | 0.3377 | 0.7706 | 0.8779 |
| 0.4359 | 2.2865 | 862 | 0.8187 | 0.3377 | 0.8187 | 0.9048 |
| 0.4359 | 2.2918 | 864 | 0.8681 | 0.3377 | 0.8681 | 0.9317 |
| 0.4359 | 2.2971 | 866 | 0.8983 | 0.2895 | 0.8983 | 0.9478 |
| 0.4359 | 2.3024 | 868 | 0.8906 | 0.2895 | 0.8906 | 0.9437 |
| 0.4359 | 2.3077 | 870 | 0.8506 | 0.3077 | 0.8506 | 0.9223 |
| 0.4359 | 2.3130 | 872 | 0.8426 | 0.3544 | 0.8426 | 0.9179 |
| 0.4359 | 2.3183 | 874 | 0.8113 | 0.3684 | 0.8113 | 0.9007 |
| 0.4359 | 2.3236 | 876 | 0.8148 | 0.3684 | 0.8148 | 0.9027 |
| 0.4359 | 2.3289 | 878 | 0.8498 | 0.3250 | 0.8498 | 0.9218 |
| 0.4359 | 2.3342 | 880 | 0.9294 | 0.2222 | 0.9294 | 0.9640 |
| 0.4359 | 2.3395 | 882 | 0.9534 | 0.1429 | 0.9534 | 0.9764 |
| 0.4359 | 2.3448 | 884 | 0.9786 | 0.1687 | 0.9786 | 0.9892 |
| 0.4359 | 2.3501 | 886 | 0.9146 | 0.2025 | 0.9146 | 0.9563 |
| 0.4359 | 2.3554 | 888 | 0.8381 | 0.2105 | 0.8381 | 0.9155 |
| 0.4359 | 2.3607 | 890 | 0.8144 | 0.2817 | 0.8144 | 0.9025 |
| 0.4359 | 2.3660 | 892 | 0.8444 | 0.2105 | 0.8444 | 0.9189 |
| 0.4359 | 2.3714 | 894 | 0.9553 | 0.2025 | 0.9553 | 0.9774 |
| 0.4359 | 2.3767 | 896 | 1.0238 | 0.1429 | 1.0238 | 1.0118 |
| 0.4359 | 2.3820 | 898 | 1.0778 | 0.0455 | 1.0778 | 1.0382 |
| 0.4359 | 2.3873 | 900 | 1.0131 | 0.1429 | 1.0131 | 1.0065 |
| 0.4359 | 2.3926 | 902 | 0.9928 | 0.0690 | 0.9928 | 0.9964 |
| 0.4359 | 2.3979 | 904 | 0.9428 | 0.1972 | 0.9428 | 0.9710 |
| 0.4359 | 2.4032 | 906 | 0.8714 | 0.25 | 0.8714 | 0.9335 |
| 0.4359 | 2.4085 | 908 | 0.8697 | 0.1972 | 0.8697 | 0.9326 |
| 0.4359 | 2.4138 | 910 | 0.8560 | 0.25 | 0.8560 | 0.9252 |
| 0.4359 | 2.4191 | 912 | 0.8420 | 0.25 | 0.8420 | 0.9176 |
| 0.4359 | 2.4244 | 914 | 0.8585 | 0.1892 | 0.8585 | 0.9265 |
| 0.4359 | 2.4297 | 916 | 0.9196 | 0.1892 | 0.9196 | 0.9590 |
| 0.4359 | 2.4350 | 918 | 1.0214 | 0.0690 | 1.0214 | 1.0106 |
| 0.4359 | 2.4403 | 920 | 1.0820 | 0.0690 | 1.0820 | 1.0402 |
| 0.4359 | 2.4456 | 922 | 1.0497 | 0.0690 | 1.0497 | 1.0245 |
| 0.4359 | 2.4509 | 924 | 1.0229 | 0.0233 | 1.0229 | 1.0114 |
| 0.4359 | 2.4562 | 926 | 1.0035 | 0.0233 | 1.0035 | 1.0018 |
| 0.4359 | 2.4615 | 928 | 0.9592 | 0.1951 | 0.9592 | 0.9794 |
| 0.4359 | 2.4668 | 930 | 0.9332 | 0.2597 | 0.9332 | 0.9660 |
| 0.4359 | 2.4721 | 932 | 0.9164 | 0.2025 | 0.9164 | 0.9573 |
| 0.4359 | 2.4775 | 934 | 0.9435 | 0.0714 | 0.9435 | 0.9713 |
| 0.4359 | 2.4828 | 936 | 1.0331 | 0.0 | 1.0331 | 1.0164 |
| 0.4359 | 2.4881 | 938 | 1.0846 | 0.0233 | 1.0846 | 1.0414 |
| 0.4359 | 2.4934 | 940 | 1.0434 | 0.0 | 1.0434 | 1.0215 |
| 0.4359 | 2.4987 | 942 | 1.0346 | 0.0 | 1.0346 | 1.0171 |
| 0.4359 | 2.5040 | 944 | 1.0922 | 0.0233 | 1.0922 | 1.0451 |
| 0.4359 | 2.5093 | 946 | 1.1104 | 0.0233 | 1.1104 | 1.0537 |
| 0.4359 | 2.5146 | 948 | 1.0557 | 0.1220 | 1.0557 | 1.0275 |
| 0.4359 | 2.5199 | 950 | 0.9977 | 0.1429 | 0.9977 | 0.9989 |
| 0.4359 | 2.5252 | 952 | 1.0400 | 0.1687 | 1.0400 | 1.0198 |
| 0.4359 | 2.5305 | 954 | 1.0307 | 0.1429 | 1.0307 | 1.0152 |
| 0.4359 | 2.5358 | 956 | 1.0059 | 0.1266 | 1.0059 | 1.0029 |
| 0.4359 | 2.5411 | 958 | 0.9303 | 0.2105 | 0.9303 | 0.9645 |
| 0.4359 | 2.5464 | 960 | 0.8735 | 0.2817 | 0.8735 | 0.9346 |
| 0.4359 | 2.5517 | 962 | 0.8521 | 0.2817 | 0.8521 | 0.9231 |
| 0.4359 | 2.5570 | 964 | 0.8548 | 0.2817 | 0.8548 | 0.9245 |
| 0.4359 | 2.5623 | 966 | 0.9196 | 0.2703 | 0.9196 | 0.9589 |
| 0.4359 | 2.5676 | 968 | 0.9605 | 0.2192 | 0.9605 | 0.9801 |
| 0.4359 | 2.5729 | 970 | 0.9271 | 0.2192 | 0.9271 | 0.9629 |
| 0.4359 | 2.5782 | 972 | 0.9264 | 0.2192 | 0.9264 | 0.9625 |
| 0.4359 | 2.5836 | 974 | 0.8948 | 0.2192 | 0.8948 | 0.9460 |
| 0.4359 | 2.5889 | 976 | 0.8680 | 0.2192 | 0.8680 | 0.9317 |
| 0.4359 | 2.5942 | 978 | 0.8926 | 0.2192 | 0.8926 | 0.9448 |
| 0.4359 | 2.5995 | 980 | 0.9619 | 0.2308 | 0.9619 | 0.9808 |
| 0.4359 | 2.6048 | 982 | 1.0959 | 0.0690 | 1.0959 | 1.0468 |
| 0.4359 | 2.6101 | 984 | 1.1276 | 0.0690 | 1.1276 | 1.0619 |
| 0.4359 | 2.6154 | 986 | 1.0475 | 0.0455 | 1.0475 | 1.0235 |
| 0.4359 | 2.6207 | 988 | 0.8787 | 0.2192 | 0.8787 | 0.9374 |
| 0.4359 | 2.6260 | 990 | 0.8275 | 0.1892 | 0.8275 | 0.9097 |
| 0.4359 | 2.6313 | 992 | 0.8721 | 0.2192 | 0.8721 | 0.9339 |
| 0.4359 | 2.6366 | 994 | 0.9605 | 0.1220 | 0.9605 | 0.9800 |
| 0.4359 | 2.6419 | 996 | 1.0673 | 0.0233 | 1.0673 | 1.0331 |
| 0.4359 | 2.6472 | 998 | 1.0669 | 0.0233 | 1.0669 | 1.0329 |
| 0.1499 | 2.6525 | 1000 | 0.9817 | 0.1220 | 0.9817 | 0.9908 |
| 0.1499 | 2.6578 | 1002 | 0.8530 | 0.1892 | 0.8530 | 0.9236 |
| 0.1499 | 2.6631 | 1004 | 0.7614 | 0.3836 | 0.7614 | 0.8726 |
| 0.1499 | 2.6684 | 1006 | 0.7612 | 0.3200 | 0.7612 | 0.8725 |
| 0.1499 | 2.6737 | 1008 | 0.7673 | 0.3200 | 0.7673 | 0.8759 |
| 0.1499 | 2.6790 | 1010 | 0.7846 | 0.3200 | 0.7846 | 0.8858 |
| 0.1499 | 2.6844 | 1012 | 0.8303 | 0.3077 | 0.8303 | 0.9112 |
| 0.1499 | 2.6897 | 1014 | 0.8988 | 0.1266 | 0.8988 | 0.9481 |
| 0.1499 | 2.6950 | 1016 | 0.9712 | 0.1039 | 0.9712 | 0.9855 |
| 0.1499 | 2.7003 | 1018 | 0.9637 | 0.1316 | 0.9637 | 0.9817 |
| 0.1499 | 2.7056 | 1020 | 0.9885 | 0.1316 | 0.9885 | 0.9942 |
| 0.1499 | 2.7109 | 1022 | 0.9514 | 0.1316 | 0.9514 | 0.9754 |
| 0.1499 | 2.7162 | 1024 | 0.8447 | 0.1316 | 0.8447 | 0.9191 |
| 0.1499 | 2.7215 | 1026 | 0.7675 | 0.1667 | 0.7675 | 0.8760 |
| 0.1499 | 2.7268 | 1028 | 0.7055 | 0.3143 | 0.7055 | 0.8399 |
| 0.1499 | 2.7321 | 1030 | 0.7067 | 0.24 | 0.7067 | 0.8407 |
| 0.1499 | 2.7374 | 1032 | 0.7698 | 0.1892 | 0.7698 | 0.8774 |
| 0.1499 | 2.7427 | 1034 | 0.8817 | 0.0769 | 0.8817 | 0.9390 |
| 0.1499 | 2.7480 | 1036 | 1.0923 | 0.0233 | 1.0923 | 1.0451 |
| 0.1499 | 2.7533 | 1038 | 1.2294 | 0.1099 | 1.2294 | 1.1088 |
| 0.1499 | 2.7586 | 1040 | 1.1940 | 0.1099 | 1.1940 | 1.0927 |
| 0.1499 | 2.7639 | 1042 | 1.0244 | 0.1220 | 1.0244 | 1.0121 |
| 0.1499 | 2.7692 | 1044 | 0.8279 | 0.24 | 0.8279 | 0.9099 |
| 0.1499 | 2.7745 | 1046 | 0.7556 | 0.3143 | 0.7556 | 0.8692 |
| 0.1499 | 2.7798 | 1048 | 0.7079 | 0.3143 | 0.7079 | 0.8413 |
| 0.1499 | 2.7851 | 1050 | 0.7045 | 0.3143 | 0.7045 | 0.8393 |
| 0.1499 | 2.7905 | 1052 | 0.7229 | 0.3143 | 0.7229 | 0.8502 |
| 0.1499 | 2.7958 | 1054 | 0.7794 | 0.2609 | 0.7794 | 0.8829 |
| 0.1499 | 2.8011 | 1056 | 0.8827 | 0.1892 | 0.8827 | 0.9395 |
| 0.1499 | 2.8064 | 1058 | 1.0246 | 0.1481 | 1.0246 | 1.0122 |
| 0.1499 | 2.8117 | 1060 | 1.0329 | 0.1481 | 1.0329 | 1.0163 |
| 0.1499 | 2.8170 | 1062 | 1.0139 | 0.1481 | 1.0139 | 1.0069 |
| 0.1499 | 2.8223 | 1064 | 0.8813 | 0.1316 | 0.8813 | 0.9388 |
| 0.1499 | 2.8276 | 1066 | 0.7348 | 0.2609 | 0.7348 | 0.8572 |
| 0.1499 | 2.8329 | 1068 | 0.6373 | 0.3836 | 0.6373 | 0.7983 |
| 0.1499 | 2.8382 | 1070 | 0.6316 | 0.3200 | 0.6316 | 0.7947 |
| 0.1499 | 2.8435 | 1072 | 0.6463 | 0.3200 | 0.6463 | 0.8039 |
| 0.1499 | 2.8488 | 1074 | 0.6744 | 0.3200 | 0.6744 | 0.8212 |
| 0.1499 | 2.8541 | 1076 | 0.7363 | 0.2817 | 0.7363 | 0.8581 |
| 0.1499 | 2.8594 | 1078 | 0.8752 | 0.1687 | 0.8752 | 0.9355 |
| 0.1499 | 2.8647 | 1080 | 0.9440 | 0.1481 | 0.9440 | 0.9716 |
| 0.1499 | 2.8700 | 1082 | 0.8983 | 0.2410 | 0.8983 | 0.9478 |
| 0.1499 | 2.8753 | 1084 | 0.7994 | 0.3143 | 0.7994 | 0.8941 |
| 0.1499 | 2.8806 | 1086 | 0.7696 | 0.3143 | 0.7696 | 0.8773 |
| 0.1499 | 2.8859 | 1088 | 0.7799 | 0.3143 | 0.7799 | 0.8831 |
| 0.1499 | 2.8912 | 1090 | 0.7665 | 0.3143 | 0.7665 | 0.8755 |
| 0.1499 | 2.8966 | 1092 | 0.7650 | 0.25 | 0.7650 | 0.8746 |
| 0.1499 | 2.9019 | 1094 | 0.7749 | 0.2192 | 0.7749 | 0.8803 |
| 0.1499 | 2.9072 | 1096 | 0.7820 | 0.2963 | 0.7820 | 0.8843 |
| 0.1499 | 2.9125 | 1098 | 0.7967 | 0.2963 | 0.7967 | 0.8926 |
| 0.1499 | 2.9178 | 1100 | 0.8105 | 0.25 | 0.8105 | 0.9003 |
| 0.1499 | 2.9231 | 1102 | 0.8733 | 0.3014 | 0.8733 | 0.9345 |
| 0.1499 | 2.9284 | 1104 | 0.8866 | 0.25 | 0.8866 | 0.9416 |
| 0.1499 | 2.9337 | 1106 | 0.8547 | 0.3284 | 0.8547 | 0.9245 |
| 0.1499 | 2.9390 | 1108 | 0.8067 | 0.3824 | 0.8067 | 0.8982 |
| 0.1499 | 2.9443 | 1110 | 0.7698 | 0.3143 | 0.7698 | 0.8774 |
| 0.1499 | 2.9496 | 1112 | 0.7328 | 0.3014 | 0.7328 | 0.8560 |
| 0.1499 | 2.9549 | 1114 | 0.7247 | 0.2963 | 0.7247 | 0.8513 |
| 0.1499 | 2.9602 | 1116 | 0.7089 | 0.3415 | 0.7089 | 0.8419 |
| 0.1499 | 2.9655 | 1118 | 0.6967 | 0.2703 | 0.6967 | 0.8347 |
| 0.1499 | 2.9708 | 1120 | 0.7019 | 0.3014 | 0.7019 | 0.8378 |
| 0.1499 | 2.9761 | 1122 | 0.7776 | 0.2388 | 0.7776 | 0.8818 |
| 0.1499 | 2.9814 | 1124 | 0.8814 | 0.2727 | 0.8814 | 0.9389 |
| 0.1499 | 2.9867 | 1126 | 0.9456 | 0.2105 | 0.9456 | 0.9724 |
| 0.1499 | 2.9920 | 1128 | 0.9515 | 0.1818 | 0.9515 | 0.9754 |
| 0.1499 | 2.9973 | 1130 | 0.9137 | 0.1818 | 0.9137 | 0.9559 |
| 0.1499 | 3.0027 | 1132 | 0.8754 | 0.2388 | 0.8754 | 0.9356 |
| 0.1499 | 3.0080 | 1134 | 0.7983 | 0.2388 | 0.7983 | 0.8935 |
| 0.1499 | 3.0133 | 1136 | 0.7229 | 0.3143 | 0.7229 | 0.8502 |
| 0.1499 | 3.0186 | 1138 | 0.6870 | 0.2059 | 0.6870 | 0.8289 |
| 0.1499 | 3.0239 | 1140 | 0.6741 | 0.2609 | 0.6741 | 0.8211 |
| 0.1499 | 3.0292 | 1142 | 0.6805 | 0.2727 | 0.6805 | 0.8249 |
| 0.1499 | 3.0345 | 1144 | 0.7287 | 0.3478 | 0.7287 | 0.8536 |
| 0.1499 | 3.0398 | 1146 | 0.8405 | 0.2388 | 0.8405 | 0.9168 |
| 0.1499 | 3.0451 | 1148 | 0.9819 | 0.0488 | 0.9819 | 0.9909 |
| 0.1499 | 3.0504 | 1150 | 1.0186 | 0.0488 | 1.0186 | 1.0092 |
| 0.1499 | 3.0557 | 1152 | 0.9496 | 0.1039 | 0.9496 | 0.9745 |
| 0.1499 | 3.0610 | 1154 | 0.8380 | 0.2941 | 0.8380 | 0.9154 |
| 0.1499 | 3.0663 | 1156 | 0.7819 | 0.3014 | 0.7819 | 0.8843 |
| 0.1499 | 3.0716 | 1158 | 0.7711 | 0.3200 | 0.7711 | 0.8781 |
| 0.1499 | 3.0769 | 1160 | 0.7747 | 0.3200 | 0.7747 | 0.8802 |
| 0.1499 | 3.0822 | 1162 | 0.8338 | 0.2817 | 0.8338 | 0.9131 |
| 0.1499 | 3.0875 | 1164 | 0.8946 | 0.3014 | 0.8946 | 0.9458 |
| 0.1499 | 3.0928 | 1166 | 0.8946 | 0.3014 | 0.8946 | 0.9458 |
| 0.1499 | 3.0981 | 1168 | 0.8542 | 0.2941 | 0.8542 | 0.9242 |
| 0.1499 | 3.1034 | 1170 | 0.7916 | 0.2941 | 0.7916 | 0.8897 |
| 0.1499 | 3.1088 | 1172 | 0.7626 | 0.3333 | 0.7626 | 0.8733 |
| 0.1499 | 3.1141 | 1174 | 0.7707 | 0.3014 | 0.7707 | 0.8779 |
| 0.1499 | 3.1194 | 1176 | 0.7866 | 0.3014 | 0.7866 | 0.8869 |
| 0.1499 | 3.1247 | 1178 | 0.7938 | 0.3478 | 0.7938 | 0.8910 |
| 0.1499 | 3.1300 | 1180 | 0.8186 | 0.3514 | 0.8186 | 0.9048 |
| 0.1499 | 3.1353 | 1182 | 0.8448 | 0.3514 | 0.8448 | 0.9192 |
| 0.1499 | 3.1406 | 1184 | 0.8635 | 0.3200 | 0.8635 | 0.9292 |
| 0.1499 | 3.1459 | 1186 | 0.8831 | 0.3200 | 0.8831 | 0.9397 |
| 0.1499 | 3.1512 | 1188 | 0.9104 | 0.3514 | 0.9104 | 0.9542 |
| 0.1499 | 3.1565 | 1190 | 0.9536 | 0.0800 | 0.9536 | 0.9765 |
| 0.1499 | 3.1618 | 1192 | 0.9471 | 0.0800 | 0.9471 | 0.9732 |
| 0.1499 | 3.1671 | 1194 | 0.8838 | 0.2286 | 0.8838 | 0.9401 |
| 0.1499 | 3.1724 | 1196 | 0.8013 | 0.2817 | 0.8013 | 0.8951 |
| 0.1499 | 3.1777 | 1198 | 0.7547 | 0.3333 | 0.7547 | 0.8687 |
| 0.1499 | 3.1830 | 1200 | 0.7264 | 0.3143 | 0.7264 | 0.8523 |
| 0.1499 | 3.1883 | 1202 | 0.7284 | 0.3478 | 0.7284 | 0.8535 |
| 0.1499 | 3.1936 | 1204 | 0.7456 | 0.3284 | 0.7456 | 0.8635 |
| 0.1499 | 3.1989 | 1206 | 0.7855 | 0.2609 | 0.7855 | 0.8863 |
| 0.1499 | 3.2042 | 1208 | 0.8180 | 0.2609 | 0.8180 | 0.9044 |
| 0.1499 | 3.2095 | 1210 | 0.8168 | 0.2609 | 0.8168 | 0.9038 |
| 0.1499 | 3.2149 | 1212 | 0.7632 | 0.2609 | 0.7632 | 0.8736 |
| 0.1499 | 3.2202 | 1214 | 0.7146 | 0.3143 | 0.7146 | 0.8454 |
| 0.1499 | 3.2255 | 1216 | 0.7077 | 0.1972 | 0.7077 | 0.8413 |
| 0.1499 | 3.2308 | 1218 | 0.7138 | 0.1972 | 0.7138 | 0.8449 |
| 0.1499 | 3.2361 | 1220 | 0.7272 | 0.3143 | 0.7272 | 0.8527 |
| 0.1499 | 3.2414 | 1222 | 0.7600 | 0.3200 | 0.7600 | 0.8718 |
| 0.1499 | 3.2467 | 1224 | 0.8432 | 0.3662 | 0.8432 | 0.9182 |
| 0.1499 | 3.2520 | 1226 | 0.9710 | 0.1538 | 0.9710 | 0.9854 |
| 0.1499 | 3.2573 | 1228 | 1.0720 | 0.0 | 1.0720 | 1.0354 |
| 0.1499 | 3.2626 | 1230 | 1.0463 | 0.0 | 1.0463 | 1.0229 |
| 0.1499 | 3.2679 | 1232 | 0.9653 | 0.1039 | 0.9653 | 0.9825 |
| 0.1499 | 3.2732 | 1234 | 0.8485 | 0.3143 | 0.8485 | 0.9212 |
| 0.1499 | 3.2785 | 1236 | 0.7896 | 0.3514 | 0.7896 | 0.8886 |
| 0.1499 | 3.2838 | 1238 | 0.7901 | 0.3200 | 0.7901 | 0.8889 |
| 0.1499 | 3.2891 | 1240 | 0.8021 | 0.3514 | 0.8021 | 0.8956 |
| 0.1499 | 3.2944 | 1242 | 0.8579 | 0.3333 | 0.8579 | 0.9262 |
| 0.1499 | 3.2997 | 1244 | 1.0027 | 0.0 | 1.0027 | 1.0014 |
| 0.1499 | 3.3050 | 1246 | 1.0826 | 0.0233 | 1.0826 | 1.0405 |
| 0.1499 | 3.3103 | 1248 | 1.0378 | 0.0233 | 1.0378 | 1.0187 |
| 0.1499 | 3.3156 | 1250 | 0.9177 | 0.2941 | 0.9177 | 0.9580 |
| 0.1499 | 3.3210 | 1252 | 0.7846 | 0.2817 | 0.7846 | 0.8858 |
| 0.1499 | 3.3263 | 1254 | 0.7394 | 0.3836 | 0.7394 | 0.8599 |
| 0.1499 | 3.3316 | 1256 | 0.7258 | 0.3836 | 0.7258 | 0.8520 |
| 0.1499 | 3.3369 | 1258 | 0.7440 | 0.3333 | 0.7440 | 0.8626 |
| 0.1499 | 3.3422 | 1260 | 0.7564 | 0.3333 | 0.7564 | 0.8697 |
| 0.1499 | 3.3475 | 1262 | 0.7387 | 0.3514 | 0.7387 | 0.8595 |
| 0.1499 | 3.3528 | 1264 | 0.7438 | 0.24 | 0.7438 | 0.8624 |
| 0.1499 | 3.3581 | 1266 | 0.7647 | 0.3514 | 0.7647 | 0.8745 |
| 0.1499 | 3.3634 | 1268 | 0.8116 | 0.3333 | 0.8116 | 0.9009 |
| 0.1499 | 3.3687 | 1270 | 0.8282 | 0.3333 | 0.8282 | 0.9101 |
| 0.1499 | 3.3740 | 1272 | 0.8476 | 0.2817 | 0.8476 | 0.9206 |
| 0.1499 | 3.3793 | 1274 | 0.8886 | 0.2609 | 0.8886 | 0.9426 |
| 0.1499 | 3.3846 | 1276 | 0.9050 | 0.2609 | 0.9050 | 0.9513 |
| 0.1499 | 3.3899 | 1278 | 0.8746 | 0.2817 | 0.8746 | 0.9352 |
| 0.1499 | 3.3952 | 1280 | 0.8411 | 0.24 | 0.8411 | 0.9171 |
| 0.1499 | 3.4005 | 1282 | 0.8353 | 0.2105 | 0.8353 | 0.9139 |
| 0.1499 | 3.4058 | 1284 | 0.8407 | 0.2105 | 0.8407 | 0.9169 |
| 0.1499 | 3.4111 | 1286 | 0.8515 | 0.24 | 0.8515 | 0.9228 |
| 0.1499 | 3.4164 | 1288 | 0.8656 | 0.2817 | 0.8656 | 0.9304 |
| 0.1499 | 3.4218 | 1290 | 0.8659 | 0.25 | 0.8659 | 0.9305 |
| 0.1499 | 3.4271 | 1292 | 0.8343 | 0.24 | 0.8343 | 0.9134 |
| 0.1499 | 3.4324 | 1294 | 0.8140 | 0.24 | 0.8140 | 0.9022 |
| 0.1499 | 3.4377 | 1296 | 0.8110 | 0.3333 | 0.8110 | 0.9006 |
| 0.1499 | 3.4430 | 1298 | 0.8284 | 0.2817 | 0.8284 | 0.9102 |
| 0.1499 | 3.4483 | 1300 | 0.8672 | 0.2286 | 0.8672 | 0.9312 |
| 0.1499 | 3.4536 | 1302 | 0.9084 | 0.1739 | 0.9084 | 0.9531 |
| 0.1499 | 3.4589 | 1304 | 0.9199 | 0.2388 | 0.9199 | 0.9591 |
| 0.1499 | 3.4642 | 1306 | 0.9048 | 0.2388 | 0.9048 | 0.9512 |
| 0.1499 | 3.4695 | 1308 | 0.9291 | 0.1127 | 0.9291 | 0.9639 |
| 0.1499 | 3.4748 | 1310 | 0.8870 | 0.2727 | 0.8870 | 0.9418 |
| 0.1499 | 3.4801 | 1312 | 0.8121 | 0.2388 | 0.8121 | 0.9012 |
| 0.1499 | 3.4854 | 1314 | 0.8019 | 0.2059 | 0.8019 | 0.8955 |
| 0.1499 | 3.4907 | 1316 | 0.8170 | 0.2059 | 0.8170 | 0.9039 |
| 0.1499 | 3.4960 | 1318 | 0.8082 | 0.2286 | 0.8082 | 0.8990 |
| 0.1499 | 3.5013 | 1320 | 0.8294 | 0.2286 | 0.8294 | 0.9107 |
| 0.1499 | 3.5066 | 1322 | 0.8676 | 0.2286 | 0.8676 | 0.9314 |
| 0.1499 | 3.5119 | 1324 | 0.9170 | 0.2286 | 0.9170 | 0.9576 |
| 0.1499 | 3.5172 | 1326 | 0.9584 | 0.1739 | 0.9584 | 0.9790 |
| 0.1499 | 3.5225 | 1328 | 0.9600 | 0.2286 | 0.9600 | 0.9798 |
| 0.1499 | 3.5279 | 1330 | 0.9193 | 0.2286 | 0.9193 | 0.9588 |
| 0.1499 | 3.5332 | 1332 | 0.8689 | 0.24 | 0.8689 | 0.9322 |
| 0.1499 | 3.5385 | 1334 | 0.8610 | 0.24 | 0.8610 | 0.9279 |
| 0.1499 | 3.5438 | 1336 | 0.8743 | 0.24 | 0.8743 | 0.9351 |
| 0.1499 | 3.5491 | 1338 | 0.8933 | 0.24 | 0.8933 | 0.9451 |
| 0.1499 | 3.5544 | 1340 | 0.9157 | 0.24 | 0.9157 | 0.9569 |
| 0.1499 | 3.5597 | 1342 | 0.9422 | 0.2192 | 0.9422 | 0.9707 |
| 0.1499 | 3.5650 | 1344 | 0.9673 | 0.2817 | 0.9673 | 0.9835 |
| 0.1499 | 3.5703 | 1346 | 0.9500 | 0.2286 | 0.9500 | 0.9747 |
| 0.1499 | 3.5756 | 1348 | 0.8893 | 0.2817 | 0.8893 | 0.9431 |
| 0.1499 | 3.5809 | 1350 | 0.8174 | 0.24 | 0.8174 | 0.9041 |
| 0.1499 | 3.5862 | 1352 | 0.7981 | 0.2105 | 0.7981 | 0.8934 |
| 0.1499 | 3.5915 | 1354 | 0.8005 | 0.2105 | 0.8005 | 0.8947 |
| 0.1499 | 3.5968 | 1356 | 0.8259 | 0.24 | 0.8259 | 0.9088 |
| 0.1499 | 3.6021 | 1358 | 0.8873 | 0.2286 | 0.8873 | 0.9420 |
| 0.1499 | 3.6074 | 1360 | 0.9738 | 0.1266 | 0.9738 | 0.9868 |
| 0.1499 | 3.6127 | 1362 | 0.9794 | 0.1266 | 0.9794 | 0.9897 |
| 0.1499 | 3.6180 | 1364 | 0.9207 | 0.2286 | 0.9207 | 0.9595 |
| 0.1499 | 3.6233 | 1366 | 0.8576 | 0.3333 | 0.8576 | 0.9261 |
| 0.1499 | 3.6286 | 1368 | 0.8462 | 0.2703 | 0.8462 | 0.9199 |
| 0.1499 | 3.6340 | 1370 | 0.8574 | 0.24 | 0.8574 | 0.9260 |
| 0.1499 | 3.6393 | 1372 | 0.8654 | 0.2105 | 0.8654 | 0.9303 |
| 0.1499 | 3.6446 | 1374 | 0.8721 | 0.2105 | 0.8721 | 0.9339 |
| 0.1499 | 3.6499 | 1376 | 0.8797 | 0.24 | 0.8797 | 0.9379 |
| 0.1499 | 3.6552 | 1378 | 0.8841 | 0.2703 | 0.8841 | 0.9403 |
| 0.1499 | 3.6605 | 1380 | 0.9051 | 0.2192 | 0.9051 | 0.9514 |
| 0.1499 | 3.6658 | 1382 | 0.9069 | 0.2817 | 0.9069 | 0.9523 |
| 0.1499 | 3.6711 | 1384 | 0.8662 | 0.2817 | 0.8662 | 0.9307 |
| 0.1499 | 3.6764 | 1386 | 0.7983 | 0.3333 | 0.7983 | 0.8935 |
| 0.1499 | 3.6817 | 1388 | 0.7688 | 0.3333 | 0.7688 | 0.8768 |
| 0.1499 | 3.6870 | 1390 | 0.7786 | 0.3662 | 0.7786 | 0.8824 |
| 0.1499 | 3.6923 | 1392 | 0.8152 | 0.2609 | 0.8152 | 0.9029 |
| 0.1499 | 3.6976 | 1394 | 0.8821 | 0.2192 | 0.8821 | 0.9392 |
| 0.1499 | 3.7029 | 1396 | 0.9067 | 0.1039 | 0.9067 | 0.9522 |
| 0.1499 | 3.7082 | 1398 | 0.9051 | 0.1316 | 0.9051 | 0.9514 |
| 0.1499 | 3.7135 | 1400 | 0.8520 | 0.25 | 0.8520 | 0.9230 |
| 0.1499 | 3.7188 | 1402 | 0.7832 | 0.2609 | 0.7832 | 0.8850 |
| 0.1499 | 3.7241 | 1404 | 0.7549 | 0.2609 | 0.7549 | 0.8689 |
| 0.1499 | 3.7294 | 1406 | 0.7535 | 0.3662 | 0.7535 | 0.8680 |
| 0.1499 | 3.7347 | 1408 | 0.7962 | 0.3143 | 0.7962 | 0.8923 |
| 0.1499 | 3.7401 | 1410 | 0.8781 | 0.1266 | 0.8781 | 0.9371 |
| 0.1499 | 3.7454 | 1412 | 0.9396 | 0.1750 | 0.9396 | 0.9693 |
| 0.1499 | 3.7507 | 1414 | 0.9573 | 0.1951 | 0.9573 | 0.9784 |
| 0.1499 | 3.7560 | 1416 | 0.9218 | 0.2703 | 0.9218 | 0.9601 |
| 0.1499 | 3.7613 | 1418 | 0.9057 | 0.3836 | 0.9057 | 0.9517 |
| 0.1499 | 3.7666 | 1420 | 0.8792 | 0.3836 | 0.8792 | 0.9376 |
| 0.1499 | 3.7719 | 1422 | 0.8765 | 0.24 | 0.8765 | 0.9362 |
| 0.1499 | 3.7772 | 1424 | 0.9029 | 0.24 | 0.9029 | 0.9502 |
| 0.1499 | 3.7825 | 1426 | 0.9373 | 0.24 | 0.9373 | 0.9681 |
| 0.1499 | 3.7878 | 1428 | 0.9603 | 0.24 | 0.9603 | 0.9799 |
| 0.1499 | 3.7931 | 1430 | 0.9971 | 0.1667 | 0.9971 | 0.9985 |
| 0.1499 | 3.7984 | 1432 | 1.0113 | 0.0800 | 1.0113 | 1.0056 |
| 0.1499 | 3.8037 | 1434 | 0.9945 | -0.0519 | 0.9945 | 0.9972 |
| 0.1499 | 3.8090 | 1436 | 0.9696 | 0.1081 | 0.9696 | 0.9847 |
| 0.1499 | 3.8143 | 1438 | 0.9086 | 0.1127 | 0.9086 | 0.9532 |
| 0.1499 | 3.8196 | 1440 | 0.8729 | 0.2609 | 0.8729 | 0.9343 |
| 0.1499 | 3.8249 | 1442 | 0.8741 | 0.1370 | 0.8741 | 0.9349 |
| 0.1499 | 3.8302 | 1444 | 0.8781 | 0.1370 | 0.8781 | 0.9370 |
| 0.1499 | 3.8355 | 1446 | 0.8658 | 0.2941 | 0.8658 | 0.9305 |
| 0.1499 | 3.8408 | 1448 | 0.8534 | 0.2941 | 0.8534 | 0.9238 |
| 0.1499 | 3.8462 | 1450 | 0.8701 | 0.2941 | 0.8701 | 0.9328 |
| 0.1499 | 3.8515 | 1452 | 0.8821 | 0.1127 | 0.8821 | 0.9392 |
| 0.1499 | 3.8568 | 1454 | 0.9219 | 0.24 | 0.9219 | 0.9602 |
| 0.1499 | 3.8621 | 1456 | 0.9856 | 0.2418 | 0.9856 | 0.9928 |
| 0.1499 | 3.8674 | 1458 | 1.0488 | 0.2418 | 1.0487 | 1.0241 |
| 0.1499 | 3.8727 | 1460 | 1.0900 | 0.2418 | 1.0900 | 1.0440 |
| 0.1499 | 3.8780 | 1462 | 1.1171 | 0.2418 | 1.1171 | 1.0569 |
| 0.1499 | 3.8833 | 1464 | 1.1345 | 0.2247 | 1.1345 | 1.0651 |
| 0.1499 | 3.8886 | 1466 | 1.1309 | 0.2683 | 1.1309 | 1.0634 |
| 0.1499 | 3.8939 | 1468 | 1.0816 | 0.2683 | 1.0816 | 1.0400 |
| 0.1499 | 3.8992 | 1470 | 1.0315 | 0.2410 | 1.0315 | 1.0156 |
| 0.1499 | 3.9045 | 1472 | 0.9873 | 0.2410 | 0.9873 | 0.9937 |
| 0.1499 | 3.9098 | 1474 | 0.9502 | 0.2410 | 0.9502 | 0.9748 |
| 0.1499 | 3.9151 | 1476 | 0.9309 | 0.2683 | 0.9309 | 0.9648 |
| 0.1499 | 3.9204 | 1478 | 0.8962 | 0.2683 | 0.8962 | 0.9467 |
| 0.1499 | 3.9257 | 1480 | 0.8721 | 0.24 | 0.8721 | 0.9338 |
| 0.1499 | 3.9310 | 1482 | 0.8827 | 0.1972 | 0.8827 | 0.9395 |
| 0.1499 | 3.9363 | 1484 | 0.8724 | 0.1972 | 0.8724 | 0.9340 |
| 0.1499 | 3.9416 | 1486 | 0.8533 | 0.24 | 0.8533 | 0.9238 |
| 0.1499 | 3.9469 | 1488 | 0.8424 | 0.24 | 0.8424 | 0.9178 |
| 0.1499 | 3.9523 | 1490 | 0.8538 | 0.24 | 0.8538 | 0.9240 |
| 0.1499 | 3.9576 | 1492 | 0.8445 | 0.2105 | 0.8445 | 0.9190 |
| 0.1499 | 3.9629 | 1494 | 0.8248 | 0.2410 | 0.8248 | 0.9082 |
| 0.1499 | 3.9682 | 1496 | 0.8312 | 0.2410 | 0.8312 | 0.9117 |
| 0.1499 | 3.9735 | 1498 | 0.8443 | 0.2105 | 0.8443 | 0.9188 |
| 0.1133 | 3.9788 | 1500 | 0.8499 | 0.2105 | 0.8499 | 0.9219 |
| 0.1133 | 3.9841 | 1502 | 0.8753 | 0.1892 | 0.8753 | 0.9356 |
| 0.1133 | 3.9894 | 1504 | 0.9400 | 0.3143 | 0.9400 | 0.9695 |
| 0.1133 | 3.9947 | 1506 | 0.9830 | 0.1818 | 0.9830 | 0.9915 |
| 0.1133 | 4.0 | 1508 | 0.9484 | 0.1818 | 0.9484 | 0.9738 |
| 0.1133 | 4.0053 | 1510 | 0.8904 | 0.2388 | 0.8904 | 0.9436 |
| 0.1133 | 4.0106 | 1512 | 0.8263 | 0.3143 | 0.8263 | 0.9090 |
| 0.1133 | 4.0159 | 1514 | 0.7768 | 0.1667 | 0.7768 | 0.8813 |
| 0.1133 | 4.0212 | 1516 | 0.7521 | 0.24 | 0.7521 | 0.8672 |
| 0.1133 | 4.0265 | 1518 | 0.7595 | 0.2105 | 0.7595 | 0.8715 |
| 0.1133 | 4.0318 | 1520 | 0.7809 | 0.2105 | 0.7809 | 0.8837 |
| 0.1133 | 4.0371 | 1522 | 0.7926 | 0.2105 | 0.7926 | 0.8903 |
| 0.1133 | 4.0424 | 1524 | 0.8022 | 0.24 | 0.8022 | 0.8956 |
| 0.1133 | 4.0477 | 1526 | 0.8312 | 0.1892 | 0.8312 | 0.9117 |
| 0.1133 | 4.0531 | 1528 | 0.8543 | 0.1972 | 0.8543 | 0.9243 |
| 0.1133 | 4.0584 | 1530 | 0.8617 | 0.1429 | 0.8617 | 0.9283 |
| 0.1133 | 4.0637 | 1532 | 0.8529 | 0.1429 | 0.8529 | 0.9235 |
| 0.1133 | 4.0690 | 1534 | 0.8327 | 0.1429 | 0.8327 | 0.9125 |
| 0.1133 | 4.0743 | 1536 | 0.8129 | 0.1429 | 0.8129 | 0.9016 |
| 0.1133 | 4.0796 | 1538 | 0.7710 | 0.1667 | 0.7710 | 0.8780 |
| 0.1133 | 4.0849 | 1540 | 0.7472 | 0.1892 | 0.7472 | 0.8644 |
| 0.1133 | 4.0902 | 1542 | 0.7526 | 0.1892 | 0.7526 | 0.8675 |
| 0.1133 | 4.0955 | 1544 | 0.7854 | 0.2192 | 0.7854 | 0.8862 |
| 0.1133 | 4.1008 | 1546 | 0.8411 | 0.3143 | 0.8411 | 0.9171 |
| 0.1133 | 4.1061 | 1548 | 0.8956 | 0.3143 | 0.8956 | 0.9464 |
| 0.1133 | 4.1114 | 1550 | 0.9194 | 0.2192 | 0.9194 | 0.9589 |
| 0.1133 | 4.1167 | 1552 | 0.9048 | 0.24 | 0.9048 | 0.9512 |
| 0.1133 | 4.1220 | 1554 | 0.8792 | 0.2597 | 0.8792 | 0.9377 |
| 0.1133 | 4.1273 | 1556 | 0.8651 | 0.2597 | 0.8651 | 0.9301 |
| 0.1133 | 4.1326 | 1558 | 0.8674 | 0.2105 | 0.8674 | 0.9313 |
| 0.1133 | 4.1379 | 1560 | 0.8878 | 0.1892 | 0.8878 | 0.9422 |
| 0.1133 | 4.1432 | 1562 | 0.9297 | 0.3662 | 0.9297 | 0.9642 |
| 0.1133 | 4.1485 | 1564 | 0.9665 | 0.0714 | 0.9665 | 0.9831 |
| 0.1133 | 4.1538 | 1566 | 0.9613 | 0.0506 | 0.9613 | 0.9805 |
| 0.1133 | 4.1592 | 1568 | 0.9063 | 0.3662 | 0.9063 | 0.9520 |
| 0.1133 | 4.1645 | 1570 | 0.8347 | 0.24 | 0.8347 | 0.9136 |
| 0.1133 | 4.1698 | 1572 | 0.8262 | 0.2105 | 0.8262 | 0.9090 |
| 0.1133 | 4.1751 | 1574 | 0.8403 | 0.2105 | 0.8403 | 0.9167 |
| 0.1133 | 4.1804 | 1576 | 0.8685 | 0.24 | 0.8685 | 0.9319 |
| 0.1133 | 4.1857 | 1578 | 0.9479 | 0.24 | 0.9479 | 0.9736 |
| 0.1133 | 4.1910 | 1580 | 1.0658 | 0.0455 | 1.0658 | 1.0324 |
| 0.1133 | 4.1963 | 1582 | 1.1254 | 0.0 | 1.1254 | 1.0608 |
| 0.1133 | 4.2016 | 1584 | 1.0884 | 0.0 | 1.0884 | 1.0433 |
| 0.1133 | 4.2069 | 1586 | 1.0025 | 0.0455 | 1.0025 | 1.0012 |
| 0.1133 | 4.2122 | 1588 | 0.9571 | 0.1538 | 0.9571 | 0.9783 |
| 0.1133 | 4.2175 | 1590 | 0.8836 | 0.1892 | 0.8836 | 0.9400 |
| 0.1133 | 4.2228 | 1592 | 0.8410 | 0.3662 | 0.8410 | 0.9171 |
| 0.1133 | 4.2281 | 1594 | 0.8162 | 0.4167 | 0.8162 | 0.9035 |
| 0.1133 | 4.2334 | 1596 | 0.8303 | 0.4167 | 0.8303 | 0.9112 |
| 0.1133 | 4.2387 | 1598 | 0.8855 | 0.2895 | 0.8855 | 0.9410 |
| 0.1133 | 4.2440 | 1600 | 0.9921 | 0.1266 | 0.9921 | 0.9960 |
| 0.1133 | 4.2493 | 1602 | 1.0352 | 0.0225 | 1.0352 | 1.0174 |
| 0.1133 | 4.2546 | 1604 | 0.9915 | 0.1266 | 0.9915 | 0.9957 |
| 0.1133 | 4.2599 | 1606 | 0.9109 | 0.1750 | 0.9109 | 0.9544 |
| 0.1133 | 4.2653 | 1608 | 0.8581 | 0.3662 | 0.8581 | 0.9263 |
| 0.1133 | 4.2706 | 1610 | 0.8300 | 0.3662 | 0.8300 | 0.9110 |
| 0.1133 | 4.2759 | 1612 | 0.8149 | 0.4167 | 0.8149 | 0.9027 |
| 0.1133 | 4.2812 | 1614 | 0.8326 | 0.3143 | 0.8326 | 0.9125 |
| 0.1133 | 4.2865 | 1616 | 0.8696 | 0.2609 | 0.8696 | 0.9325 |
| 0.1133 | 4.2918 | 1618 | 0.8966 | 0.2609 | 0.8966 | 0.9469 |
| 0.1133 | 4.2971 | 1620 | 0.9017 | 0.2609 | 0.9017 | 0.9496 |
| 0.1133 | 4.3024 | 1622 | 0.8668 | 0.2609 | 0.8668 | 0.9310 |
| 0.1133 | 4.3077 | 1624 | 0.8281 | 0.2609 | 0.8281 | 0.9100 |
| 0.1133 | 4.3130 | 1626 | 0.7878 | 0.3143 | 0.7878 | 0.8876 |
| 0.1133 | 4.3183 | 1628 | 0.7781 | 0.3143 | 0.7781 | 0.8821 |
| 0.1133 | 4.3236 | 1630 | 0.7940 | 0.3143 | 0.7940 | 0.8911 |
| 0.1133 | 4.3289 | 1632 | 0.8098 | 0.3143 | 0.8098 | 0.8999 |
| 0.1133 | 4.3342 | 1634 | 0.8311 | 0.3143 | 0.8311 | 0.9116 |
| 0.1133 | 4.3395 | 1636 | 0.8209 | 0.3333 | 0.8209 | 0.9061 |
| 0.1133 | 4.3448 | 1638 | 0.8041 | 0.3200 | 0.8041 | 0.8967 |
| 0.1133 | 4.3501 | 1640 | 0.8228 | 0.3200 | 0.8228 | 0.9071 |
| 0.1133 | 4.3554 | 1642 | 0.8690 | 0.3014 | 0.8690 | 0.9322 |
| 0.1133 | 4.3607 | 1644 | 0.9374 | 0.2597 | 0.9374 | 0.9682 |
| 0.1133 | 4.3660 | 1646 | 0.9513 | 0.1266 | 0.9513 | 0.9753 |
| 0.1133 | 4.3714 | 1648 | 0.9152 | 0.1266 | 0.9152 | 0.9567 |
| 0.1133 | 4.3767 | 1650 | 0.9063 | 0.1538 | 0.9063 | 0.9520 |
| 0.1133 | 4.3820 | 1652 | 0.9316 | 0.1039 | 0.9316 | 0.9652 |
| 0.1133 | 4.3873 | 1654 | 0.8939 | 0.1039 | 0.8939 | 0.9454 |
| 0.1133 | 4.3926 | 1656 | 0.8218 | 0.1538 | 0.8218 | 0.9065 |
| 0.1133 | 4.3979 | 1658 | 0.7747 | 0.1892 | 0.7747 | 0.8801 |
| 0.1133 | 4.4032 | 1660 | 0.7612 | 0.2609 | 0.7612 | 0.8725 |
| 0.1133 | 4.4085 | 1662 | 0.7822 | 0.1892 | 0.7822 | 0.8844 |
| 0.1133 | 4.4138 | 1664 | 0.7908 | 0.1892 | 0.7908 | 0.8893 |
| 0.1133 | 4.4191 | 1666 | 0.7935 | 0.1600 | 0.7935 | 0.8908 |
| 0.1133 | 4.4244 | 1668 | 0.7868 | 0.3836 | 0.7868 | 0.8870 |
| 0.1133 | 4.4297 | 1670 | 0.7896 | 0.3200 | 0.7896 | 0.8886 |
| 0.1133 | 4.4350 | 1672 | 0.7971 | 0.2597 | 0.7971 | 0.8928 |
| 0.1133 | 4.4403 | 1674 | 0.7960 | 0.3684 | 0.7960 | 0.8922 |
| 0.1133 | 4.4456 | 1676 | 0.8116 | 0.3836 | 0.8116 | 0.9009 |
| 0.1133 | 4.4509 | 1678 | 0.8337 | 0.1000 | 0.8337 | 0.9131 |
| 0.1133 | 4.4562 | 1680 | 0.8372 | 0.1266 | 0.8372 | 0.9150 |
| 0.1133 | 4.4615 | 1682 | 0.8488 | 0.1266 | 0.8488 | 0.9213 |
| 0.1133 | 4.4668 | 1684 | 0.8536 | 0.1266 | 0.8536 | 0.9239 |
| 0.1133 | 4.4721 | 1686 | 0.8410 | 0.1000 | 0.8410 | 0.9171 |
| 0.1133 | 4.4775 | 1688 | 0.8312 | 0.1600 | 0.8312 | 0.9117 |
| 0.1133 | 4.4828 | 1690 | 0.8341 | 0.1600 | 0.8341 | 0.9133 |
| 0.1133 | 4.4881 | 1692 | 0.8320 | 0.3333 | 0.8320 | 0.9121 |
| 0.1133 | 4.4934 | 1694 | 0.8135 | 0.3333 | 0.8135 | 0.9019 |
| 0.1133 | 4.4987 | 1696 | 0.8103 | 0.2817 | 0.8103 | 0.9002 |
| 0.1133 | 4.5040 | 1698 | 0.7998 | 0.3143 | 0.7998 | 0.8943 |
| 0.1133 | 4.5093 | 1700 | 0.7782 | 0.3143 | 0.7782 | 0.8822 |
| 0.1133 | 4.5146 | 1702 | 0.7613 | 0.3836 | 0.7613 | 0.8725 |
| 0.1133 | 4.5199 | 1704 | 0.7522 | 0.3836 | 0.7522 | 0.8673 |
| 0.1133 | 4.5252 | 1706 | 0.7586 | 0.3836 | 0.7586 | 0.8710 |
| 0.1133 | 4.5305 | 1708 | 0.7620 | 0.3836 | 0.7620 | 0.8729 |
| 0.1133 | 4.5358 | 1710 | 0.7652 | 0.3836 | 0.7652 | 0.8747 |
| 0.1133 | 4.5411 | 1712 | 0.7477 | 0.2105 | 0.7477 | 0.8647 |
| 0.1133 | 4.5464 | 1714 | 0.7304 | 0.2597 | 0.7304 | 0.8546 |
| 0.1133 | 4.5517 | 1716 | 0.7523 | 0.2105 | 0.7523 | 0.8674 |
| 0.1133 | 4.5570 | 1718 | 0.7903 | 0.3836 | 0.7903 | 0.8890 |
| 0.1133 | 4.5623 | 1720 | 0.7875 | 0.3836 | 0.7875 | 0.8874 |
| 0.1133 | 4.5676 | 1722 | 0.7593 | 0.2105 | 0.7593 | 0.8714 |
| 0.1133 | 4.5729 | 1724 | 0.7507 | 0.2105 | 0.7507 | 0.8665 |
| 0.1133 | 4.5782 | 1726 | 0.7444 | 0.2105 | 0.7444 | 0.8628 |
| 0.1133 | 4.5836 | 1728 | 0.7680 | 0.3836 | 0.7680 | 0.8764 |
| 0.1133 | 4.5889 | 1730 | 0.7768 | 0.3143 | 0.7768 | 0.8814 |
| 0.1133 | 4.5942 | 1732 | 0.7587 | 0.3143 | 0.7587 | 0.8710 |
| 0.1133 | 4.5995 | 1734 | 0.7419 | 0.3333 | 0.7419 | 0.8613 |
| 0.1133 | 4.6048 | 1736 | 0.7569 | 0.3143 | 0.7569 | 0.8700 |
| 0.1133 | 4.6101 | 1738 | 0.8179 | 0.2941 | 0.8179 | 0.9044 |
| 0.1133 | 4.6154 | 1740 | 0.8497 | 0.1667 | 0.8497 | 0.9218 |
| 0.1133 | 4.6207 | 1742 | 0.8302 | 0.2941 | 0.8302 | 0.9111 |
| 0.1133 | 4.6260 | 1744 | 0.7936 | 0.2817 | 0.7936 | 0.8908 |
| 0.1133 | 4.6313 | 1746 | 0.7441 | 0.3514 | 0.7441 | 0.8626 |
| 0.1133 | 4.6366 | 1748 | 0.7340 | 0.3200 | 0.7340 | 0.8567 |
| 0.1133 | 4.6419 | 1750 | 0.7453 | 0.3200 | 0.7453 | 0.8633 |
| 0.1133 | 4.6472 | 1752 | 0.7563 | 0.3200 | 0.7563 | 0.8697 |
| 0.1133 | 4.6525 | 1754 | 0.7804 | 0.3014 | 0.7804 | 0.8834 |
| 0.1133 | 4.6578 | 1756 | 0.7972 | 0.3143 | 0.7972 | 0.8929 |
| 0.1133 | 4.6631 | 1758 | 0.7889 | 0.3143 | 0.7889 | 0.8882 |
| 0.1133 | 4.6684 | 1760 | 0.7475 | 0.3143 | 0.7475 | 0.8646 |
| 0.1133 | 4.6737 | 1762 | 0.7281 | 0.3143 | 0.7281 | 0.8533 |
| 0.1133 | 4.6790 | 1764 | 0.7398 | 0.3143 | 0.7398 | 0.8601 |
| 0.1133 | 4.6844 | 1766 | 0.7619 | 0.3478 | 0.7619 | 0.8729 |
| 0.1133 | 4.6897 | 1768 | 0.7696 | 0.3478 | 0.7696 | 0.8773 |
| 0.1133 | 4.6950 | 1770 | 0.7474 | 0.3478 | 0.7474 | 0.8645 |
| 0.1133 | 4.7003 | 1772 | 0.7360 | 0.3143 | 0.7360 | 0.8579 |
| 0.1133 | 4.7056 | 1774 | 0.7492 | 0.3143 | 0.7492 | 0.8656 |
| 0.1133 | 4.7109 | 1776 | 0.7399 | 0.3514 | 0.7399 | 0.8602 |
| 0.1133 | 4.7162 | 1778 | 0.7560 | 0.3200 | 0.7560 | 0.8695 |
| 0.1133 | 4.7215 | 1780 | 0.7861 | 0.3200 | 0.7861 | 0.8866 |
| 0.1133 | 4.7268 | 1782 | 0.8186 | 0.3200 | 0.8186 | 0.9048 |
| 0.1133 | 4.7321 | 1784 | 0.8212 | 0.3200 | 0.8212 | 0.9062 |
| 0.1133 | 4.7374 | 1786 | 0.8043 | 0.2105 | 0.8043 | 0.8969 |
| 0.1133 | 4.7427 | 1788 | 0.8016 | 0.2105 | 0.8016 | 0.8953 |
| 0.1133 | 4.7480 | 1790 | 0.8291 | 0.3014 | 0.8291 | 0.9105 |
| 0.1133 | 4.7533 | 1792 | 0.8268 | 0.25 | 0.8268 | 0.9093 |
| 0.1133 | 4.7586 | 1794 | 0.7840 | 0.3514 | 0.7840 | 0.8854 |
| 0.1133 | 4.7639 | 1796 | 0.7628 | 0.2105 | 0.7628 | 0.8734 |
| 0.1133 | 4.7692 | 1798 | 0.7564 | 0.2105 | 0.7564 | 0.8697 |
| 0.1133 | 4.7745 | 1800 | 0.7549 | 0.2105 | 0.7549 | 0.8689 |
| 0.1133 | 4.7798 | 1802 | 0.7722 | 0.2105 | 0.7722 | 0.8788 |
| 0.1133 | 4.7851 | 1804 | 0.8240 | 0.3514 | 0.8240 | 0.9077 |
| 0.1133 | 4.7905 | 1806 | 0.8464 | 0.3514 | 0.8464 | 0.9200 |
| 0.1133 | 4.7958 | 1808 | 0.8393 | 0.3836 | 0.8393 | 0.9162 |
| 0.1133 | 4.8011 | 1810 | 0.8088 | 0.3836 | 0.8088 | 0.8993 |
| 0.1133 | 4.8064 | 1812 | 0.7842 | 0.3514 | 0.7842 | 0.8855 |
| 0.1133 | 4.8117 | 1814 | 0.7558 | 0.2105 | 0.7558 | 0.8694 |
| 0.1133 | 4.8170 | 1816 | 0.7483 | 0.2105 | 0.7483 | 0.8650 |
| 0.1133 | 4.8223 | 1818 | 0.7554 | 0.2105 | 0.7554 | 0.8692 |
| 0.1133 | 4.8276 | 1820 | 0.7457 | 0.2105 | 0.7457 | 0.8635 |
| 0.1133 | 4.8329 | 1822 | 0.7546 | 0.2105 | 0.7546 | 0.8687 |
| 0.1133 | 4.8382 | 1824 | 0.7849 | 0.2105 | 0.7849 | 0.8859 |
| 0.1133 | 4.8435 | 1826 | 0.7988 | 0.3200 | 0.7988 | 0.8938 |
| 0.1133 | 4.8488 | 1828 | 0.8153 | 0.3200 | 0.8153 | 0.9029 |
| 0.1133 | 4.8541 | 1830 | 0.8057 | 0.2105 | 0.8057 | 0.8976 |
| 0.1133 | 4.8594 | 1832 | 0.7951 | 0.2105 | 0.7951 | 0.8917 |
| 0.1133 | 4.8647 | 1834 | 0.7896 | 0.2105 | 0.7896 | 0.8886 |
| 0.1133 | 4.8700 | 1836 | 0.7958 | 0.2105 | 0.7958 | 0.8921 |
| 0.1133 | 4.8753 | 1838 | 0.7977 | 0.2105 | 0.7977 | 0.8931 |
| 0.1133 | 4.8806 | 1840 | 0.8081 | 0.3514 | 0.8081 | 0.8989 |
| 0.1133 | 4.8859 | 1842 | 0.8223 | 0.3478 | 0.8223 | 0.9068 |
| 0.1133 | 4.8912 | 1844 | 0.8032 | 0.2941 | 0.8032 | 0.8962 |
| 0.1133 | 4.8966 | 1846 | 0.7552 | 0.3478 | 0.7552 | 0.8690 |
| 0.1133 | 4.9019 | 1848 | 0.7235 | 0.3478 | 0.7235 | 0.8506 |
| 0.1133 | 4.9072 | 1850 | 0.7131 | 0.2105 | 0.7131 | 0.8444 |
| 0.1133 | 4.9125 | 1852 | 0.7341 | 0.1972 | 0.7341 | 0.8568 |
| 0.1133 | 4.9178 | 1854 | 0.7626 | 0.2105 | 0.7626 | 0.8732 |
| 0.1133 | 4.9231 | 1856 | 0.7878 | 0.2105 | 0.7878 | 0.8876 |
| 0.1133 | 4.9284 | 1858 | 0.8099 | 0.2105 | 0.8099 | 0.8999 |
| 0.1133 | 4.9337 | 1860 | 0.8314 | 0.2105 | 0.8314 | 0.9118 |
| 0.1133 | 4.9390 | 1862 | 0.8524 | 0.24 | 0.8524 | 0.9233 |
| 0.1133 | 4.9443 | 1864 | 0.8607 | 0.24 | 0.8607 | 0.9277 |
| 0.1133 | 4.9496 | 1866 | 0.8490 | 0.2105 | 0.8490 | 0.9214 |
| 0.1133 | 4.9549 | 1868 | 0.8250 | 0.2105 | 0.8250 | 0.9083 |
| 0.1133 | 4.9602 | 1870 | 0.8014 | 0.2105 | 0.8014 | 0.8952 |
| 0.1133 | 4.9655 | 1872 | 0.7899 | 0.2105 | 0.7899 | 0.8887 |
| 0.1133 | 4.9708 | 1874 | 0.7797 | 0.2105 | 0.7797 | 0.8830 |
| 0.1133 | 4.9761 | 1876 | 0.7752 | 0.2105 | 0.7752 | 0.8804 |
| 0.1133 | 4.9814 | 1878 | 0.7848 | 0.2105 | 0.7848 | 0.8859 |
| 0.1133 | 4.9867 | 1880 | 0.7909 | 0.2703 | 0.7909 | 0.8893 |
| 0.1133 | 4.9920 | 1882 | 0.8012 | 0.3014 | 0.8012 | 0.8951 |
| 0.1133 | 4.9973 | 1884 | 0.7964 | 0.3014 | 0.7964 | 0.8924 |
| 0.1133 | 5.0027 | 1886 | 0.7714 | 0.2105 | 0.7714 | 0.8783 |
| 0.1133 | 5.0080 | 1888 | 0.7516 | 0.2105 | 0.7516 | 0.8670 |
| 0.1133 | 5.0133 | 1890 | 0.7520 | 0.2105 | 0.7520 | 0.8672 |
| 0.1133 | 5.0186 | 1892 | 0.7692 | 0.2785 | 0.7692 | 0.8770 |
| 0.1133 | 5.0239 | 1894 | 0.7810 | 0.2857 | 0.7810 | 0.8838 |
| 0.1133 | 5.0292 | 1896 | 0.7853 | 0.2597 | 0.7853 | 0.8861 |
| 0.1133 | 5.0345 | 1898 | 0.7935 | 0.2105 | 0.7935 | 0.8908 |
| 0.1133 | 5.0398 | 1900 | 0.8207 | 0.3514 | 0.8207 | 0.9059 |
| 0.1133 | 5.0451 | 1902 | 0.8374 | 0.3333 | 0.8374 | 0.9151 |
| 0.1133 | 5.0504 | 1904 | 0.8365 | 0.3333 | 0.8365 | 0.9146 |
| 0.1133 | 5.0557 | 1906 | 0.8211 | 0.2192 | 0.8211 | 0.9061 |
| 0.1133 | 5.0610 | 1908 | 0.8236 | 0.3333 | 0.8236 | 0.9075 |
| 0.1133 | 5.0663 | 1910 | 0.8297 | 0.3662 | 0.8297 | 0.9109 |
| 0.1133 | 5.0716 | 1912 | 0.8270 | 0.3662 | 0.8270 | 0.9094 |
| 0.1133 | 5.0769 | 1914 | 0.8312 | 0.3662 | 0.8312 | 0.9117 |
| 0.1133 | 5.0822 | 1916 | 0.8596 | 0.1892 | 0.8596 | 0.9271 |
| 0.1133 | 5.0875 | 1918 | 0.8704 | 0.1892 | 0.8704 | 0.9330 |
| 0.1133 | 5.0928 | 1920 | 0.8556 | 0.1892 | 0.8556 | 0.9250 |
| 0.1133 | 5.0981 | 1922 | 0.8294 | 0.1892 | 0.8294 | 0.9107 |
| 0.1133 | 5.1034 | 1924 | 0.8363 | 0.1892 | 0.8363 | 0.9145 |
| 0.1133 | 5.1088 | 1926 | 0.8455 | 0.1892 | 0.8455 | 0.9195 |
| 0.1133 | 5.1141 | 1928 | 0.8426 | 0.3143 | 0.8426 | 0.9179 |
| 0.1133 | 5.1194 | 1930 | 0.8428 | 0.3333 | 0.8428 | 0.9180 |
| 0.1133 | 5.1247 | 1932 | 0.8391 | 0.24 | 0.8391 | 0.9160 |
| 0.1133 | 5.1300 | 1934 | 0.8396 | 0.2817 | 0.8396 | 0.9163 |
| 0.1133 | 5.1353 | 1936 | 0.8331 | 0.2817 | 0.8331 | 0.9127 |
| 0.1133 | 5.1406 | 1938 | 0.8283 | 0.3143 | 0.8283 | 0.9101 |
| 0.1133 | 5.1459 | 1940 | 0.8104 | 0.3143 | 0.8104 | 0.9002 |
| 0.1133 | 5.1512 | 1942 | 0.7780 | 0.3143 | 0.7780 | 0.8821 |
| 0.1133 | 5.1565 | 1944 | 0.7394 | 0.3143 | 0.7394 | 0.8599 |
| 0.1133 | 5.1618 | 1946 | 0.7295 | 0.3333 | 0.7295 | 0.8541 |
| 0.1133 | 5.1671 | 1948 | 0.7368 | 0.3333 | 0.7368 | 0.8584 |
| 0.1133 | 5.1724 | 1950 | 0.7614 | 0.3478 | 0.7614 | 0.8726 |
| 0.1133 | 5.1777 | 1952 | 0.8072 | 0.2941 | 0.8072 | 0.8985 |
| 0.1133 | 5.1830 | 1954 | 0.8700 | 0.1667 | 0.8700 | 0.9327 |
| 0.1133 | 5.1883 | 1956 | 0.8859 | 0.1667 | 0.8859 | 0.9412 |
| 0.1133 | 5.1936 | 1958 | 0.8670 | 0.2192 | 0.8670 | 0.9311 |
| 0.1133 | 5.1989 | 1960 | 0.8414 | 0.2703 | 0.8414 | 0.9173 |
| 0.1133 | 5.2042 | 1962 | 0.8296 | 0.3478 | 0.8296 | 0.9108 |
| 0.1133 | 5.2095 | 1964 | 0.8206 | 0.4 | 0.8206 | 0.9059 |
| 0.1133 | 5.2149 | 1966 | 0.8098 | 0.2941 | 0.8098 | 0.8999 |
| 0.1133 | 5.2202 | 1968 | 0.7820 | 0.2941 | 0.7820 | 0.8843 |
| 0.1133 | 5.2255 | 1970 | 0.7505 | 0.2941 | 0.7505 | 0.8663 |
| 0.1133 | 5.2308 | 1972 | 0.7437 | 0.3478 | 0.7437 | 0.8624 |
| 0.1133 | 5.2361 | 1974 | 0.7403 | 0.3662 | 0.7403 | 0.8604 |
| 0.1133 | 5.2414 | 1976 | 0.7376 | 0.3333 | 0.7376 | 0.8588 |
| 0.1133 | 5.2467 | 1978 | 0.7406 | 0.3333 | 0.7406 | 0.8606 |
| 0.1133 | 5.2520 | 1980 | 0.7567 | 0.3333 | 0.7567 | 0.8699 |
| 0.1133 | 5.2573 | 1982 | 0.7731 | 0.3333 | 0.7731 | 0.8793 |
| 0.1133 | 5.2626 | 1984 | 0.7955 | 0.3333 | 0.7955 | 0.8919 |
| 0.1133 | 5.2679 | 1986 | 0.8096 | 0.3333 | 0.8096 | 0.8998 |
| 0.1133 | 5.2732 | 1988 | 0.8241 | 0.2895 | 0.8241 | 0.9078 |
| 0.1133 | 5.2785 | 1990 | 0.8212 | 0.3077 | 0.8212 | 0.9062 |
| 0.1133 | 5.2838 | 1992 | 0.8263 | 0.3077 | 0.8263 | 0.9090 |
| 0.1133 | 5.2891 | 1994 | 0.8418 | 0.24 | 0.8418 | 0.9175 |
| 0.1133 | 5.2944 | 1996 | 0.8238 | 0.3377 | 0.8238 | 0.9076 |
| 0.1133 | 5.2997 | 1998 | 0.8017 | 0.3377 | 0.8017 | 0.8954 |
| 0.0835 | 5.3050 | 2000 | 0.7968 | 0.4167 | 0.7968 | 0.8926 |
| 0.0835 | 5.3103 | 2002 | 0.8123 | 0.2895 | 0.8123 | 0.9013 |
| 0.0835 | 5.3156 | 2004 | 0.8284 | 0.24 | 0.8284 | 0.9102 |
| 0.0835 | 5.3210 | 2006 | 0.8331 | 0.3377 | 0.8331 | 0.9127 |
| 0.0835 | 5.3263 | 2008 | 0.8230 | 0.3514 | 0.8230 | 0.9072 |
| 0.0835 | 5.3316 | 2010 | 0.8292 | 0.3514 | 0.8292 | 0.9106 |
| 0.0835 | 5.3369 | 2012 | 0.8244 | 0.3836 | 0.8244 | 0.9080 |
| 0.0835 | 5.3422 | 2014 | 0.8078 | 0.24 | 0.8078 | 0.8988 |
| 0.0835 | 5.3475 | 2016 | 0.8032 | 0.2703 | 0.8032 | 0.8962 |
| 0.0835 | 5.3528 | 2018 | 0.7938 | 0.2703 | 0.7938 | 0.8910 |
| 0.0835 | 5.3581 | 2020 | 0.7992 | 0.2703 | 0.7992 | 0.8940 |
| 0.0835 | 5.3634 | 2022 | 0.8287 | 0.3662 | 0.8287 | 0.9103 |
| 0.0835 | 5.3687 | 2024 | 0.8379 | 0.24 | 0.8379 | 0.9154 |
| 0.0835 | 5.3740 | 2026 | 0.8363 | 0.24 | 0.8363 | 0.9145 |
| 0.0835 | 5.3793 | 2028 | 0.8323 | 0.2703 | 0.8323 | 0.9123 |
| 0.0835 | 5.3846 | 2030 | 0.8409 | 0.2192 | 0.8409 | 0.9170 |
| 0.0835 | 5.3899 | 2032 | 0.8385 | 0.2192 | 0.8385 | 0.9157 |
| 0.0835 | 5.3952 | 2034 | 0.8372 | 0.2192 | 0.8372 | 0.9150 |
| 0.0835 | 5.4005 | 2036 | 0.8220 | 0.2703 | 0.8220 | 0.9067 |
| 0.0835 | 5.4058 | 2038 | 0.8092 | 0.3662 | 0.8092 | 0.8996 |
| 0.0835 | 5.4111 | 2040 | 0.7776 | 0.3662 | 0.7776 | 0.8818 |
| 0.0835 | 5.4164 | 2042 | 0.7533 | 0.3836 | 0.7533 | 0.8679 |
| 0.0835 | 5.4218 | 2044 | 0.7488 | 0.3836 | 0.7488 | 0.8654 |
| 0.0835 | 5.4271 | 2046 | 0.7613 | 0.3836 | 0.7613 | 0.8725 |
| 0.0835 | 5.4324 | 2048 | 0.7735 | 0.4167 | 0.7735 | 0.8795 |
| 0.0835 | 5.4377 | 2050 | 0.7735 | 0.3662 | 0.7735 | 0.8795 |
| 0.0835 | 5.4430 | 2052 | 0.7933 | 0.3662 | 0.7933 | 0.8907 |
| 0.0835 | 5.4483 | 2054 | 0.8086 | 0.2895 | 0.8086 | 0.8992 |
| 0.0835 | 5.4536 | 2056 | 0.8306 | 0.2895 | 0.8306 | 0.9114 |
| 0.0835 | 5.4589 | 2058 | 0.8315 | 0.2895 | 0.8315 | 0.9119 |
| 0.0835 | 5.4642 | 2060 | 0.8114 | 0.3377 | 0.8114 | 0.9008 |
| 0.0835 | 5.4695 | 2062 | 0.8004 | 0.3377 | 0.8004 | 0.8947 |
| 0.0835 | 5.4748 | 2064 | 0.7948 | 0.3377 | 0.7948 | 0.8915 |
| 0.0835 | 5.4801 | 2066 | 0.7912 | 0.3077 | 0.7912 | 0.8895 |
| 0.0835 | 5.4854 | 2068 | 0.7887 | 0.3250 | 0.7887 | 0.8881 |
| 0.0835 | 5.4907 | 2070 | 0.8022 | 0.3077 | 0.8022 | 0.8956 |
| 0.0835 | 5.4960 | 2072 | 0.8078 | 0.3377 | 0.8078 | 0.8988 |
| 0.0835 | 5.5013 | 2074 | 0.8115 | 0.2895 | 0.8115 | 0.9009 |
| 0.0835 | 5.5066 | 2076 | 0.8169 | 0.2895 | 0.8169 | 0.9038 |
| 0.0835 | 5.5119 | 2078 | 0.8104 | 0.2895 | 0.8104 | 0.9002 |
| 0.0835 | 5.5172 | 2080 | 0.8062 | 0.2895 | 0.8062 | 0.8979 |
| 0.0835 | 5.5225 | 2082 | 0.8120 | 0.3200 | 0.8120 | 0.9011 |
| 0.0835 | 5.5279 | 2084 | 0.8050 | 0.3200 | 0.8050 | 0.8972 |
| 0.0835 | 5.5332 | 2086 | 0.7945 | 0.4 | 0.7945 | 0.8914 |
| 0.0835 | 5.5385 | 2088 | 0.7983 | 0.3200 | 0.7983 | 0.8935 |
| 0.0835 | 5.5438 | 2090 | 0.8245 | 0.2703 | 0.8245 | 0.9080 |
| 0.0835 | 5.5491 | 2092 | 0.8457 | 0.2192 | 0.8457 | 0.9196 |
| 0.0835 | 5.5544 | 2094 | 0.8587 | 0.1667 | 0.8587 | 0.9266 |
| 0.0835 | 5.5597 | 2096 | 0.8229 | 0.1667 | 0.8229 | 0.9071 |
| 0.0835 | 5.5650 | 2098 | 0.7810 | 0.3478 | 0.7810 | 0.8838 |
| 0.0835 | 5.5703 | 2100 | 0.7584 | 0.3478 | 0.7584 | 0.8709 |
| 0.0835 | 5.5756 | 2102 | 0.7661 | 0.4 | 0.7661 | 0.8752 |
| 0.0835 | 5.5809 | 2104 | 0.7687 | 0.4 | 0.7687 | 0.8768 |
| 0.0835 | 5.5862 | 2106 | 0.7648 | 0.3333 | 0.7648 | 0.8745 |
| 0.0835 | 5.5915 | 2108 | 0.7668 | 0.3014 | 0.7667 | 0.8756 |
| 0.0835 | 5.5968 | 2110 | 0.7814 | 0.1892 | 0.7814 | 0.8840 |
| 0.0835 | 5.6021 | 2112 | 0.8095 | 0.1600 | 0.8095 | 0.8997 |
| 0.0835 | 5.6074 | 2114 | 0.8281 | 0.2105 | 0.8281 | 0.9100 |
| 0.0835 | 5.6127 | 2116 | 0.8513 | 0.2105 | 0.8513 | 0.9227 |
| 0.0835 | 5.6180 | 2118 | 0.8605 | 0.2105 | 0.8605 | 0.9276 |
| 0.0835 | 5.6233 | 2120 | 0.8526 | 0.2105 | 0.8526 | 0.9234 |
| 0.0835 | 5.6286 | 2122 | 0.8351 | 0.1600 | 0.8351 | 0.9138 |
| 0.0835 | 5.6340 | 2124 | 0.8234 | 0.3333 | 0.8234 | 0.9074 |
| 0.0835 | 5.6393 | 2126 | 0.8280 | 0.3200 | 0.8280 | 0.9100 |
| 0.0835 | 5.6446 | 2128 | 0.8149 | 0.3200 | 0.8149 | 0.9027 |
| 0.0835 | 5.6499 | 2130 | 0.7803 | 0.4 | 0.7803 | 0.8834 |
| 0.0835 | 5.6552 | 2132 | 0.7472 | 0.2192 | 0.7472 | 0.8644 |
| 0.0835 | 5.6605 | 2134 | 0.7365 | 0.1892 | 0.7365 | 0.8582 |
| 0.0835 | 5.6658 | 2136 | 0.7369 | 0.1600 | 0.7369 | 0.8584 |
| 0.0835 | 5.6711 | 2138 | 0.7454 | 0.2105 | 0.7454 | 0.8633 |
| 0.0835 | 5.6764 | 2140 | 0.7594 | 0.2105 | 0.7594 | 0.8714 |
| 0.0835 | 5.6817 | 2142 | 0.7721 | 0.1892 | 0.7721 | 0.8787 |
| 0.0835 | 5.6870 | 2144 | 0.8019 | 0.2192 | 0.8019 | 0.8955 |
| 0.0835 | 5.6923 | 2146 | 0.8356 | 0.2192 | 0.8356 | 0.9141 |
| 0.0835 | 5.6976 | 2148 | 0.8428 | 0.1892 | 0.8428 | 0.9180 |
| 0.0835 | 5.7029 | 2150 | 0.8343 | 0.2105 | 0.8343 | 0.9134 |
| 0.0835 | 5.7082 | 2152 | 0.8269 | 0.2597 | 0.8269 | 0.9093 |
| 0.0835 | 5.7135 | 2154 | 0.8330 | 0.2597 | 0.8330 | 0.9127 |
| 0.0835 | 5.7188 | 2156 | 0.8253 | 0.2597 | 0.8253 | 0.9085 |
| 0.0835 | 5.7241 | 2158 | 0.8070 | 0.2597 | 0.8070 | 0.8983 |
| 0.0835 | 5.7294 | 2160 | 0.8033 | 0.2105 | 0.8033 | 0.8963 |
| 0.0835 | 5.7347 | 2162 | 0.8306 | 0.3662 | 0.8306 | 0.9114 |
| 0.0835 | 5.7401 | 2164 | 0.8764 | 0.2388 | 0.8764 | 0.9362 |
| 0.0835 | 5.7454 | 2166 | 0.8909 | 0.2388 | 0.8909 | 0.9439 |
| 0.0835 | 5.7507 | 2168 | 0.8680 | 0.2388 | 0.8680 | 0.9317 |
| 0.0835 | 5.7560 | 2170 | 0.8376 | 0.2388 | 0.8376 | 0.9152 |
| 0.0835 | 5.7613 | 2172 | 0.8060 | 0.2941 | 0.8060 | 0.8978 |
| 0.0835 | 5.7666 | 2174 | 0.7845 | 0.2609 | 0.7845 | 0.8857 |
| 0.0835 | 5.7719 | 2176 | 0.7739 | 0.3662 | 0.7739 | 0.8797 |
| 0.0835 | 5.7772 | 2178 | 0.7822 | 0.25 | 0.7822 | 0.8844 |
| 0.0835 | 5.7825 | 2180 | 0.8017 | 0.3662 | 0.8017 | 0.8954 |
| 0.0835 | 5.7878 | 2182 | 0.8314 | 0.3662 | 0.8314 | 0.9118 |
| 0.0835 | 5.7931 | 2184 | 0.8734 | 0.3143 | 0.8734 | 0.9346 |
| 0.0835 | 5.7984 | 2186 | 0.8943 | 0.2895 | 0.8943 | 0.9457 |
| 0.0835 | 5.8037 | 2188 | 0.9171 | 0.3377 | 0.9171 | 0.9576 |
| 0.0835 | 5.8090 | 2190 | 0.9348 | 0.2895 | 0.9348 | 0.9669 |
| 0.0835 | 5.8143 | 2192 | 0.9584 | 0.1266 | 0.9584 | 0.9790 |
| 0.0835 | 5.8196 | 2194 | 0.9528 | 0.1266 | 0.9528 | 0.9761 |
| 0.0835 | 5.8249 | 2196 | 0.9554 | 0.0 | 0.9554 | 0.9774 |
| 0.0835 | 5.8302 | 2198 | 0.9535 | 0.0 | 0.9535 | 0.9765 |
| 0.0835 | 5.8355 | 2200 | 0.9341 | 0.1266 | 0.9341 | 0.9665 |
| 0.0835 | 5.8408 | 2202 | 0.8936 | 0.1266 | 0.8936 | 0.9453 |
| 0.0835 | 5.8462 | 2204 | 0.8626 | 0.2609 | 0.8626 | 0.9288 |
| 0.0835 | 5.8515 | 2206 | 0.8441 | 0.2609 | 0.8441 | 0.9188 |
| 0.0835 | 5.8568 | 2208 | 0.8505 | 0.2609 | 0.8505 | 0.9222 |
| 0.0835 | 5.8621 | 2210 | 0.8721 | 0.2609 | 0.8721 | 0.9339 |
| 0.0835 | 5.8674 | 2212 | 0.8868 | 0.2609 | 0.8868 | 0.9417 |
| 0.0835 | 5.8727 | 2214 | 0.8980 | 0.2609 | 0.8980 | 0.9476 |
| 0.0835 | 5.8780 | 2216 | 0.9060 | 0.2609 | 0.9060 | 0.9519 |
| 0.0835 | 5.8833 | 2218 | 0.9133 | 0.2609 | 0.9133 | 0.9557 |
| 0.0835 | 5.8886 | 2220 | 0.9123 | 0.2609 | 0.9123 | 0.9551 |
| 0.0835 | 5.8939 | 2222 | 0.9033 | 0.2609 | 0.9033 | 0.9504 |
| 0.0835 | 5.8992 | 2224 | 0.9033 | 0.2609 | 0.9033 | 0.9504 |
| 0.0835 | 5.9045 | 2226 | 0.8862 | 0.2609 | 0.8862 | 0.9414 |
| 0.0835 | 5.9098 | 2228 | 0.8586 | 0.2609 | 0.8586 | 0.9266 |
| 0.0835 | 5.9151 | 2230 | 0.8524 | 0.2609 | 0.8524 | 0.9233 |
| 0.0835 | 5.9204 | 2232 | 0.8543 | 0.2609 | 0.8543 | 0.9243 |
| 0.0835 | 5.9257 | 2234 | 0.8766 | 0.2609 | 0.8766 | 0.9363 |
| 0.0835 | 5.9310 | 2236 | 0.9081 | 0.2941 | 0.9081 | 0.9529 |
| 0.0835 | 5.9363 | 2238 | 0.9144 | 0.2941 | 0.9144 | 0.9562 |
| 0.0835 | 5.9416 | 2240 | 0.9162 | 0.2941 | 0.9162 | 0.9572 |
| 0.0835 | 5.9469 | 2242 | 0.8896 | 0.2609 | 0.8896 | 0.9432 |
| 0.0835 | 5.9523 | 2244 | 0.8851 | 0.2609 | 0.8851 | 0.9408 |
| 0.0835 | 5.9576 | 2246 | 0.8968 | 0.2609 | 0.8968 | 0.9470 |
| 0.0835 | 5.9629 | 2248 | 0.8961 | 0.3143 | 0.8961 | 0.9466 |
| 0.0835 | 5.9682 | 2250 | 0.9023 | 0.3143 | 0.9023 | 0.9499 |
| 0.0835 | 5.9735 | 2252 | 0.9003 | 0.2895 | 0.9003 | 0.9488 |
| 0.0835 | 5.9788 | 2254 | 0.8827 | 0.25 | 0.8827 | 0.9395 |
| 0.0835 | 5.9841 | 2256 | 0.8667 | 0.2703 | 0.8667 | 0.9310 |
| 0.0835 | 5.9894 | 2258 | 0.8644 | 0.2703 | 0.8644 | 0.9297 |
| 0.0835 | 5.9947 | 2260 | 0.8799 | 0.2703 | 0.8799 | 0.9380 |
| 0.0835 | 6.0 | 2262 | 0.9255 | 0.2895 | 0.9255 | 0.9620 |
| 0.0835 | 6.0053 | 2264 | 0.9573 | 0.1750 | 0.9573 | 0.9784 |
| 0.0835 | 6.0106 | 2266 | 0.9483 | 0.24 | 0.9483 | 0.9738 |
| 0.0835 | 6.0159 | 2268 | 0.9173 | 0.1538 | 0.9173 | 0.9578 |
| 0.0835 | 6.0212 | 2270 | 0.8766 | 0.2703 | 0.8766 | 0.9362 |
| 0.0835 | 6.0265 | 2272 | 0.8604 | 0.2192 | 0.8604 | 0.9276 |
| 0.0835 | 6.0318 | 2274 | 0.8592 | 0.3143 | 0.8592 | 0.9269 |
| 0.0835 | 6.0371 | 2276 | 0.8586 | 0.3143 | 0.8586 | 0.9266 |
| 0.0835 | 6.0424 | 2278 | 0.8573 | 0.3143 | 0.8573 | 0.9259 |
| 0.0835 | 6.0477 | 2280 | 0.8670 | 0.3143 | 0.8670 | 0.9311 |
| 0.0835 | 6.0531 | 2282 | 0.8611 | 0.3143 | 0.8611 | 0.9280 |
| 0.0835 | 6.0584 | 2284 | 0.8428 | 0.2192 | 0.8428 | 0.9180 |
| 0.0835 | 6.0637 | 2286 | 0.8335 | 0.2703 | 0.8335 | 0.9130 |
| 0.0835 | 6.0690 | 2288 | 0.8456 | 0.2703 | 0.8456 | 0.9196 |
| 0.0835 | 6.0743 | 2290 | 0.8801 | 0.2192 | 0.8801 | 0.9382 |
| 0.0835 | 6.0796 | 2292 | 0.9240 | 0.3662 | 0.9240 | 0.9613 |
| 0.0835 | 6.0849 | 2294 | 0.9416 | 0.24 | 0.9416 | 0.9703 |
| 0.0835 | 6.0902 | 2296 | 0.9346 | 0.3143 | 0.9346 | 0.9668 |
| 0.0835 | 6.0955 | 2298 | 0.9056 | 0.2192 | 0.9056 | 0.9516 |
| 0.0835 | 6.1008 | 2300 | 0.8870 | 0.24 | 0.8870 | 0.9418 |
| 0.0835 | 6.1061 | 2302 | 0.8802 | 0.24 | 0.8802 | 0.9382 |
| 0.0835 | 6.1114 | 2304 | 0.8749 | 0.2192 | 0.8749 | 0.9354 |
| 0.0835 | 6.1167 | 2306 | 0.8775 | 0.3662 | 0.8775 | 0.9368 |
| 0.0835 | 6.1220 | 2308 | 0.9078 | 0.24 | 0.9078 | 0.9528 |
| 0.0835 | 6.1273 | 2310 | 0.9581 | 0.0 | 0.9581 | 0.9788 |
| 0.0835 | 6.1326 | 2312 | 1.0047 | 0.0 | 1.0047 | 1.0024 |
| 0.0835 | 6.1379 | 2314 | 1.0029 | 0.0 | 1.0029 | 1.0015 |
| 0.0835 | 6.1432 | 2316 | 0.9555 | 0.1266 | 0.9555 | 0.9775 |
| 0.0835 | 6.1485 | 2318 | 0.8936 | 0.3143 | 0.8936 | 0.9453 |
| 0.0835 | 6.1538 | 2320 | 0.8577 | 0.25 | 0.8577 | 0.9261 |
| 0.0835 | 6.1592 | 2322 | 0.8514 | 0.24 | 0.8514 | 0.9227 |
| 0.0835 | 6.1645 | 2324 | 0.8641 | 0.1892 | 0.8641 | 0.9296 |
| 0.0835 | 6.1698 | 2326 | 0.8912 | 0.1892 | 0.8912 | 0.9440 |
| 0.0835 | 6.1751 | 2328 | 0.9235 | 0.2192 | 0.9235 | 0.9610 |
| 0.0835 | 6.1804 | 2330 | 0.9335 | 0.25 | 0.9335 | 0.9662 |
| 0.0835 | 6.1857 | 2332 | 0.9476 | 0.3662 | 0.9476 | 0.9734 |
| 0.0835 | 6.1910 | 2334 | 0.9440 | 0.2192 | 0.9440 | 0.9716 |
| 0.0835 | 6.1963 | 2336 | 0.9325 | 0.2192 | 0.9325 | 0.9656 |
| 0.0835 | 6.2016 | 2338 | 0.9140 | 0.2192 | 0.9140 | 0.9560 |
| 0.0835 | 6.2069 | 2340 | 0.8965 | 0.3662 | 0.8965 | 0.9468 |
| 0.0835 | 6.2122 | 2342 | 0.8726 | 0.1892 | 0.8726 | 0.9341 |
| 0.0835 | 6.2175 | 2344 | 0.8666 | 0.24 | 0.8666 | 0.9309 |
| 0.0835 | 6.2228 | 2346 | 0.8772 | 0.1892 | 0.8772 | 0.9366 |
| 0.0835 | 6.2281 | 2348 | 0.8928 | 0.3662 | 0.8928 | 0.9449 |
| 0.0835 | 6.2334 | 2350 | 0.9137 | 0.3143 | 0.9137 | 0.9559 |
| 0.0835 | 6.2387 | 2352 | 0.9258 | 0.2609 | 0.9258 | 0.9622 |
| 0.0835 | 6.2440 | 2354 | 0.9129 | 0.2609 | 0.9129 | 0.9555 |
| 0.0835 | 6.2493 | 2356 | 0.8752 | 0.3143 | 0.8752 | 0.9355 |
| 0.0835 | 6.2546 | 2358 | 0.8425 | 0.3662 | 0.8425 | 0.9179 |
| 0.0835 | 6.2599 | 2360 | 0.8269 | 0.2192 | 0.8269 | 0.9093 |
| 0.0835 | 6.2653 | 2362 | 0.8307 | 0.3662 | 0.8307 | 0.9114 |
| 0.0835 | 6.2706 | 2364 | 0.8489 | 0.3662 | 0.8489 | 0.9213 |
| 0.0835 | 6.2759 | 2366 | 0.8846 | 0.3143 | 0.8846 | 0.9405 |
| 0.0835 | 6.2812 | 2368 | 0.9015 | 0.2895 | 0.9015 | 0.9495 |
| 0.0835 | 6.2865 | 2370 | 0.8924 | 0.2895 | 0.8924 | 0.9447 |
| 0.0835 | 6.2918 | 2372 | 0.8617 | 0.3662 | 0.8617 | 0.9283 |
| 0.0835 | 6.2971 | 2374 | 0.8224 | 0.1892 | 0.8224 | 0.9069 |
| 0.0835 | 6.3024 | 2376 | 0.8069 | 0.2105 | 0.8069 | 0.8983 |
| 0.0835 | 6.3077 | 2378 | 0.8124 | 0.2105 | 0.8124 | 0.9013 |
| 0.0835 | 6.3130 | 2380 | 0.8327 | 0.2105 | 0.8327 | 0.9125 |
| 0.0835 | 6.3183 | 2382 | 0.8602 | 0.24 | 0.8602 | 0.9275 |
| 0.0835 | 6.3236 | 2384 | 0.9129 | 0.2895 | 0.9129 | 0.9555 |
| 0.0835 | 6.3289 | 2386 | 0.9539 | 0.1750 | 0.9539 | 0.9767 |
| 0.0835 | 6.3342 | 2388 | 0.9646 | 0.1750 | 0.9646 | 0.9822 |
| 0.0835 | 6.3395 | 2390 | 0.9395 | 0.1750 | 0.9395 | 0.9693 |
| 0.0835 | 6.3448 | 2392 | 0.8868 | 0.2895 | 0.8868 | 0.9417 |
| 0.0835 | 6.3501 | 2394 | 0.8450 | 0.2192 | 0.8450 | 0.9192 |
| 0.0835 | 6.3554 | 2396 | 0.8310 | 0.2703 | 0.8310 | 0.9116 |
| 0.0835 | 6.3607 | 2398 | 0.8291 | 0.2192 | 0.8291 | 0.9105 |
| 0.0835 | 6.3660 | 2400 | 0.8403 | 0.2895 | 0.8403 | 0.9167 |
| 0.0835 | 6.3714 | 2402 | 0.8628 | 0.2895 | 0.8628 | 0.9289 |
| 0.0835 | 6.3767 | 2404 | 0.8834 | 0.24 | 0.8834 | 0.9399 |
| 0.0835 | 6.3820 | 2406 | 0.9061 | 0.1892 | 0.9061 | 0.9519 |
| 0.0835 | 6.3873 | 2408 | 0.9243 | 0.1892 | 0.9243 | 0.9614 |
| 0.0835 | 6.3926 | 2410 | 0.9061 | 0.1892 | 0.9061 | 0.9519 |
| 0.0835 | 6.3979 | 2412 | 0.8929 | 0.1892 | 0.8929 | 0.9449 |
| 0.0835 | 6.4032 | 2414 | 0.8553 | 0.2895 | 0.8553 | 0.9248 |
| 0.0835 | 6.4085 | 2416 | 0.8231 | 0.3662 | 0.8231 | 0.9072 |
| 0.0835 | 6.4138 | 2418 | 0.8075 | 0.3662 | 0.8075 | 0.8986 |
| 0.0835 | 6.4191 | 2420 | 0.8115 | 0.3662 | 0.8115 | 0.9008 |
| 0.0835 | 6.4244 | 2422 | 0.8112 | 0.3662 | 0.8112 | 0.9007 |
| 0.0835 | 6.4297 | 2424 | 0.8234 | 0.3662 | 0.8234 | 0.9074 |
| 0.0835 | 6.4350 | 2426 | 0.8527 | 0.1892 | 0.8527 | 0.9234 |
| 0.0835 | 6.4403 | 2428 | 0.8927 | 0.1892 | 0.8927 | 0.9448 |
| 0.0835 | 6.4456 | 2430 | 0.9089 | 0.1892 | 0.9089 | 0.9534 |
| 0.0835 | 6.4509 | 2432 | 0.9137 | 0.2895 | 0.9137 | 0.9559 |
| 0.0835 | 6.4562 | 2434 | 0.9181 | 0.24 | 0.9181 | 0.9582 |
| 0.0835 | 6.4615 | 2436 | 0.9178 | 0.24 | 0.9178 | 0.9580 |
| 0.0835 | 6.4668 | 2438 | 0.9185 | 0.24 | 0.9185 | 0.9584 |
| 0.0835 | 6.4721 | 2440 | 0.9147 | 0.24 | 0.9147 | 0.9564 |
| 0.0835 | 6.4775 | 2442 | 0.8958 | 0.3662 | 0.8958 | 0.9464 |
| 0.0835 | 6.4828 | 2444 | 0.8703 | 0.3662 | 0.8703 | 0.9329 |
| 0.0835 | 6.4881 | 2446 | 0.8567 | 0.4167 | 0.8567 | 0.9256 |
| 0.0835 | 6.4934 | 2448 | 0.8536 | 0.3143 | 0.8536 | 0.9239 |
| 0.0835 | 6.4987 | 2450 | 0.8750 | 0.3143 | 0.8750 | 0.9354 |
| 0.0835 | 6.5040 | 2452 | 0.9024 | 0.1892 | 0.9024 | 0.9500 |
| 0.0835 | 6.5093 | 2454 | 0.9253 | 0.0506 | 0.9253 | 0.9619 |
| 0.0835 | 6.5146 | 2456 | 0.9386 | 0.0769 | 0.9386 | 0.9688 |
| 0.0835 | 6.5199 | 2458 | 0.9283 | 0.0769 | 0.9283 | 0.9635 |
| 0.0835 | 6.5252 | 2460 | 0.9093 | 0.0506 | 0.9093 | 0.9536 |
| 0.0835 | 6.5305 | 2462 | 0.8749 | 0.2609 | 0.8749 | 0.9354 |
| 0.0835 | 6.5358 | 2464 | 0.8517 | 0.2609 | 0.8517 | 0.9229 |
| 0.0835 | 6.5411 | 2466 | 0.8591 | 0.2609 | 0.8591 | 0.9269 |
| 0.0835 | 6.5464 | 2468 | 0.8601 | 0.2609 | 0.8601 | 0.9274 |
| 0.0835 | 6.5517 | 2470 | 0.8602 | 0.3143 | 0.8602 | 0.9275 |
| 0.0835 | 6.5570 | 2472 | 0.8767 | 0.3143 | 0.8767 | 0.9363 |
| 0.0835 | 6.5623 | 2474 | 0.9101 | 0.1892 | 0.9101 | 0.9540 |
| 0.0835 | 6.5676 | 2476 | 0.9261 | 0.1892 | 0.9261 | 0.9623 |
| 0.0835 | 6.5729 | 2478 | 0.9182 | 0.1892 | 0.9182 | 0.9582 |
| 0.0835 | 6.5782 | 2480 | 0.8982 | 0.1892 | 0.8982 | 0.9477 |
| 0.0835 | 6.5836 | 2482 | 0.8792 | 0.24 | 0.8792 | 0.9377 |
| 0.0835 | 6.5889 | 2484 | 0.8605 | 0.2895 | 0.8605 | 0.9276 |
| 0.0835 | 6.5942 | 2486 | 0.8559 | 0.2895 | 0.8559 | 0.9251 |
| 0.0835 | 6.5995 | 2488 | 0.8661 | 0.1892 | 0.8661 | 0.9306 |
| 0.0835 | 6.6048 | 2490 | 0.8870 | 0.1892 | 0.8870 | 0.9418 |
| 0.0835 | 6.6101 | 2492 | 0.8824 | 0.1892 | 0.8824 | 0.9394 |
| 0.0835 | 6.6154 | 2494 | 0.8800 | 0.1892 | 0.8800 | 0.9381 |
| 0.0835 | 6.6207 | 2496 | 0.8649 | 0.1892 | 0.8649 | 0.9300 |
| 0.0835 | 6.6260 | 2498 | 0.8618 | 0.1892 | 0.8618 | 0.9283 |
| 0.0607 | 6.6313 | 2500 | 0.8512 | 0.1892 | 0.8512 | 0.9226 |
| 0.0607 | 6.6366 | 2502 | 0.8326 | 0.1892 | 0.8326 | 0.9125 |
| 0.0607 | 6.6419 | 2504 | 0.8264 | 0.1892 | 0.8264 | 0.9091 |
| 0.0607 | 6.6472 | 2506 | 0.8198 | 0.3143 | 0.8198 | 0.9054 |
| 0.0607 | 6.6525 | 2508 | 0.8310 | 0.1892 | 0.8310 | 0.9116 |
| 0.0607 | 6.6578 | 2510 | 0.8537 | 0.1892 | 0.8537 | 0.9240 |
| 0.0607 | 6.6631 | 2512 | 0.8689 | 0.1892 | 0.8689 | 0.9322 |
| 0.0607 | 6.6684 | 2514 | 0.8744 | 0.1892 | 0.8744 | 0.9351 |
| 0.0607 | 6.6737 | 2516 | 0.8532 | 0.1892 | 0.8532 | 0.9237 |
| 0.0607 | 6.6790 | 2518 | 0.8280 | 0.24 | 0.8280 | 0.9099 |
| 0.0607 | 6.6844 | 2520 | 0.8057 | 0.4167 | 0.8057 | 0.8976 |
| 0.0607 | 6.6897 | 2522 | 0.7921 | 0.4167 | 0.7921 | 0.8900 |
| 0.0607 | 6.6950 | 2524 | 0.7870 | 0.4167 | 0.7870 | 0.8871 |
| 0.0607 | 6.7003 | 2526 | 0.7929 | 0.4167 | 0.7929 | 0.8905 |
| 0.0607 | 6.7056 | 2528 | 0.8143 | 0.3662 | 0.8143 | 0.9024 |
| 0.0607 | 6.7109 | 2530 | 0.8300 | 0.24 | 0.8300 | 0.9111 |
| 0.0607 | 6.7162 | 2532 | 0.8327 | 0.24 | 0.8327 | 0.9125 |
| 0.0607 | 6.7215 | 2534 | 0.8295 | 0.3662 | 0.8295 | 0.9107 |
| 0.0607 | 6.7268 | 2536 | 0.8349 | 0.4167 | 0.8349 | 0.9137 |
| 0.0607 | 6.7321 | 2538 | 0.8322 | 0.4167 | 0.8322 | 0.9123 |
| 0.0607 | 6.7374 | 2540 | 0.8276 | 0.4167 | 0.8276 | 0.9097 |
| 0.0607 | 6.7427 | 2542 | 0.8287 | 0.4167 | 0.8287 | 0.9103 |
| 0.0607 | 6.7480 | 2544 | 0.8355 | 0.3662 | 0.8355 | 0.9141 |
| 0.0607 | 6.7533 | 2546 | 0.8486 | 0.3662 | 0.8486 | 0.9212 |
| 0.0607 | 6.7586 | 2548 | 0.8523 | 0.3662 | 0.8523 | 0.9232 |
| 0.0607 | 6.7639 | 2550 | 0.8520 | 0.3662 | 0.8520 | 0.9231 |
| 0.0607 | 6.7692 | 2552 | 0.8429 | 0.3662 | 0.8429 | 0.9181 |
| 0.0607 | 6.7745 | 2554 | 0.8205 | 0.4167 | 0.8205 | 0.9058 |
| 0.0607 | 6.7798 | 2556 | 0.8016 | 0.4167 | 0.8016 | 0.8953 |
| 0.0607 | 6.7851 | 2558 | 0.7941 | 0.3514 | 0.7941 | 0.8912 |
| 0.0607 | 6.7905 | 2560 | 0.7894 | 0.4 | 0.7894 | 0.8885 |
| 0.0607 | 6.7958 | 2562 | 0.7881 | 0.2895 | 0.7881 | 0.8878 |
| 0.0607 | 6.8011 | 2564 | 0.7911 | 0.2895 | 0.7911 | 0.8895 |
| 0.0607 | 6.8064 | 2566 | 0.8008 | 0.4 | 0.8008 | 0.8949 |
| 0.0607 | 6.8117 | 2568 | 0.8208 | 0.4167 | 0.8208 | 0.9060 |
| 0.0607 | 6.8170 | 2570 | 0.8533 | 0.3662 | 0.8533 | 0.9238 |
| 0.0607 | 6.8223 | 2572 | 0.8749 | 0.3662 | 0.8749 | 0.9354 |
| 0.0607 | 6.8276 | 2574 | 0.9064 | 0.1892 | 0.9064 | 0.9520 |
| 0.0607 | 6.8329 | 2576 | 0.9163 | 0.0506 | 0.9163 | 0.9572 |
| 0.0607 | 6.8382 | 2578 | 0.9045 | 0.3662 | 0.9045 | 0.9510 |
| 0.0607 | 6.8435 | 2580 | 0.8837 | 0.3662 | 0.8837 | 0.9400 |
| 0.0607 | 6.8488 | 2582 | 0.8668 | 0.3514 | 0.8668 | 0.9310 |
| 0.0607 | 6.8541 | 2584 | 0.8637 | 0.4 | 0.8637 | 0.9294 |
| 0.0607 | 6.8594 | 2586 | 0.8598 | 0.4 | 0.8598 | 0.9272 |
| 0.0607 | 6.8647 | 2588 | 0.8574 | 0.3514 | 0.8574 | 0.9260 |
| 0.0607 | 6.8700 | 2590 | 0.8566 | 0.3514 | 0.8566 | 0.9255 |
| 0.0607 | 6.8753 | 2592 | 0.8525 | 0.4167 | 0.8525 | 0.9233 |
| 0.0607 | 6.8806 | 2594 | 0.8536 | 0.4167 | 0.8536 | 0.9239 |
| 0.0607 | 6.8859 | 2596 | 0.8527 | 0.3514 | 0.8527 | 0.9234 |
| 0.0607 | 6.8912 | 2598 | 0.8571 | 0.3514 | 0.8571 | 0.9258 |
| 0.0607 | 6.8966 | 2600 | 0.8575 | 0.3836 | 0.8575 | 0.9260 |
| 0.0607 | 6.9019 | 2602 | 0.8448 | 0.3836 | 0.8448 | 0.9191 |
| 0.0607 | 6.9072 | 2604 | 0.8324 | 0.4 | 0.8324 | 0.9124 |
| 0.0607 | 6.9125 | 2606 | 0.8204 | 0.4167 | 0.8204 | 0.9058 |
| 0.0607 | 6.9178 | 2608 | 0.8140 | 0.3836 | 0.8140 | 0.9022 |
| 0.0607 | 6.9231 | 2610 | 0.8184 | 0.4 | 0.8184 | 0.9047 |
| 0.0607 | 6.9284 | 2612 | 0.8256 | 0.4 | 0.8256 | 0.9086 |
| 0.0607 | 6.9337 | 2614 | 0.8444 | 0.4167 | 0.8444 | 0.9189 |
| 0.0607 | 6.9390 | 2616 | 0.8630 | 0.3662 | 0.8630 | 0.9290 |
| 0.0607 | 6.9443 | 2618 | 0.8693 | 0.3662 | 0.8693 | 0.9323 |
| 0.0607 | 6.9496 | 2620 | 0.8706 | 0.3333 | 0.8706 | 0.9331 |
| 0.0607 | 6.9549 | 2622 | 0.8760 | 0.3514 | 0.8760 | 0.9359 |
| 0.0607 | 6.9602 | 2624 | 0.8811 | 0.3514 | 0.8811 | 0.9387 |
| 0.0607 | 6.9655 | 2626 | 0.8903 | 0.3333 | 0.8903 | 0.9436 |
| 0.0607 | 6.9708 | 2628 | 0.8988 | 0.2597 | 0.8988 | 0.9480 |
| 0.0607 | 6.9761 | 2630 | 0.8947 | 0.2597 | 0.8947 | 0.9459 |
| 0.0607 | 6.9814 | 2632 | 0.8921 | 0.2895 | 0.8921 | 0.9445 |
| 0.0607 | 6.9867 | 2634 | 0.8882 | 0.2895 | 0.8882 | 0.9425 |
| 0.0607 | 6.9920 | 2636 | 0.8673 | 0.2895 | 0.8673 | 0.9313 |
| 0.0607 | 6.9973 | 2638 | 0.8439 | 0.2895 | 0.8439 | 0.9186 |
| 0.0607 | 7.0027 | 2640 | 0.8450 | 0.2895 | 0.8450 | 0.9192 |
| 0.0607 | 7.0080 | 2642 | 0.8440 | 0.3333 | 0.8440 | 0.9187 |
| 0.0607 | 7.0133 | 2644 | 0.8518 | 0.3333 | 0.8518 | 0.9229 |
| 0.0607 | 7.0186 | 2646 | 0.8542 | 0.3014 | 0.8542 | 0.9242 |
| 0.0607 | 7.0239 | 2648 | 0.8510 | 0.24 | 0.8510 | 0.9225 |
| 0.0607 | 7.0292 | 2650 | 0.8453 | 0.2597 | 0.8453 | 0.9194 |
| 0.0607 | 7.0345 | 2652 | 0.8465 | 0.2597 | 0.8465 | 0.9201 |
| 0.0607 | 7.0398 | 2654 | 0.8517 | 0.2597 | 0.8517 | 0.9229 |
| 0.0607 | 7.0451 | 2656 | 0.8533 | 0.2597 | 0.8533 | 0.9238 |
| 0.0607 | 7.0504 | 2658 | 0.8576 | 0.2597 | 0.8576 | 0.9261 |
| 0.0607 | 7.0557 | 2660 | 0.8565 | 0.2597 | 0.8565 | 0.9255 |
| 0.0607 | 7.0610 | 2662 | 0.8581 | 0.2597 | 0.8581 | 0.9264 |
| 0.0607 | 7.0663 | 2664 | 0.8649 | 0.2597 | 0.8649 | 0.9300 |
| 0.0607 | 7.0716 | 2666 | 0.8719 | 0.2597 | 0.8719 | 0.9337 |
| 0.0607 | 7.0769 | 2668 | 0.8817 | 0.2597 | 0.8817 | 0.9390 |
| 0.0607 | 7.0822 | 2670 | 0.8781 | 0.2597 | 0.8781 | 0.9371 |
| 0.0607 | 7.0875 | 2672 | 0.8625 | 0.2597 | 0.8625 | 0.9287 |
| 0.0607 | 7.0928 | 2674 | 0.8436 | 0.2597 | 0.8436 | 0.9185 |
| 0.0607 | 7.0981 | 2676 | 0.8265 | 0.2597 | 0.8265 | 0.9091 |
| 0.0607 | 7.1034 | 2678 | 0.8136 | 0.2597 | 0.8136 | 0.9020 |
| 0.0607 | 7.1088 | 2680 | 0.7963 | 0.2597 | 0.7963 | 0.8923 |
| 0.0607 | 7.1141 | 2682 | 0.7853 | 0.2597 | 0.7853 | 0.8862 |
| 0.0607 | 7.1194 | 2684 | 0.7775 | 0.2597 | 0.7775 | 0.8818 |
| 0.0607 | 7.1247 | 2686 | 0.7776 | 0.2597 | 0.7776 | 0.8818 |
| 0.0607 | 7.1300 | 2688 | 0.7899 | 0.2597 | 0.7899 | 0.8888 |
| 0.0607 | 7.1353 | 2690 | 0.8057 | 0.2597 | 0.8057 | 0.8976 |
| 0.0607 | 7.1406 | 2692 | 0.8240 | 0.2597 | 0.8240 | 0.9077 |
| 0.0607 | 7.1459 | 2694 | 0.8368 | 0.2597 | 0.8368 | 0.9148 |
| 0.0607 | 7.1512 | 2696 | 0.8449 | 0.2597 | 0.8449 | 0.9192 |
| 0.0607 | 7.1565 | 2698 | 0.8532 | 0.2597 | 0.8532 | 0.9237 |
| 0.0607 | 7.1618 | 2700 | 0.8573 | 0.2597 | 0.8573 | 0.9259 |
| 0.0607 | 7.1671 | 2702 | 0.8573 | 0.2597 | 0.8573 | 0.9259 |
| 0.0607 | 7.1724 | 2704 | 0.8614 | 0.2597 | 0.8614 | 0.9281 |
| 0.0607 | 7.1777 | 2706 | 0.8775 | 0.2597 | 0.8775 | 0.9368 |
| 0.0607 | 7.1830 | 2708 | 0.9005 | 0.2597 | 0.9005 | 0.9489 |
| 0.0607 | 7.1883 | 2710 | 0.9389 | 0.1892 | 0.9389 | 0.9690 |
| 0.0607 | 7.1936 | 2712 | 0.9588 | 0.3014 | 0.9588 | 0.9792 |
| 0.0607 | 7.1989 | 2714 | 0.9556 | 0.3014 | 0.9556 | 0.9776 |
| 0.0607 | 7.2042 | 2716 | 0.9363 | 0.1892 | 0.9363 | 0.9676 |
| 0.0607 | 7.2095 | 2718 | 0.9116 | 0.24 | 0.9116 | 0.9548 |
| 0.0607 | 7.2149 | 2720 | 0.8833 | 0.2597 | 0.8833 | 0.9399 |
| 0.0607 | 7.2202 | 2722 | 0.8665 | 0.2597 | 0.8665 | 0.9309 |
| 0.0607 | 7.2255 | 2724 | 0.8553 | 0.2597 | 0.8553 | 0.9248 |
| 0.0607 | 7.2308 | 2726 | 0.8554 | 0.2895 | 0.8554 | 0.9249 |
| 0.0607 | 7.2361 | 2728 | 0.8631 | 0.1892 | 0.8631 | 0.9291 |
| 0.0607 | 7.2414 | 2730 | 0.8695 | 0.3333 | 0.8695 | 0.9325 |
| 0.0607 | 7.2467 | 2732 | 0.8723 | 0.3662 | 0.8723 | 0.9340 |
| 0.0607 | 7.2520 | 2734 | 0.8596 | 0.3662 | 0.8596 | 0.9272 |
| 0.0607 | 7.2573 | 2736 | 0.8480 | 0.3333 | 0.8480 | 0.9209 |
| 0.0607 | 7.2626 | 2738 | 0.8269 | 0.1892 | 0.8269 | 0.9094 |
| 0.0607 | 7.2679 | 2740 | 0.8098 | 0.2895 | 0.8098 | 0.8999 |
| 0.0607 | 7.2732 | 2742 | 0.8051 | 0.2895 | 0.8051 | 0.8973 |
| 0.0607 | 7.2785 | 2744 | 0.8021 | 0.2895 | 0.8021 | 0.8956 |
| 0.0607 | 7.2838 | 2746 | 0.8079 | 0.2895 | 0.8079 | 0.8989 |
| 0.0607 | 7.2891 | 2748 | 0.8272 | 0.1892 | 0.8272 | 0.9095 |
| 0.0607 | 7.2944 | 2750 | 0.8491 | 0.3333 | 0.8491 | 0.9215 |
| 0.0607 | 7.2997 | 2752 | 0.8590 | 0.3333 | 0.8590 | 0.9268 |
| 0.0607 | 7.3050 | 2754 | 0.8597 | 0.3333 | 0.8597 | 0.9272 |
| 0.0607 | 7.3103 | 2756 | 0.8676 | 0.3333 | 0.8676 | 0.9314 |
| 0.0607 | 7.3156 | 2758 | 0.8629 | 0.3014 | 0.8629 | 0.9289 |
| 0.0607 | 7.3210 | 2760 | 0.8532 | 0.24 | 0.8532 | 0.9237 |
| 0.0607 | 7.3263 | 2762 | 0.8554 | 0.24 | 0.8554 | 0.9249 |
| 0.0607 | 7.3316 | 2764 | 0.8595 | 0.3333 | 0.8595 | 0.9271 |
| 0.0607 | 7.3369 | 2766 | 0.8609 | 0.3333 | 0.8609 | 0.9278 |
| 0.0607 | 7.3422 | 2768 | 0.8701 | 0.3333 | 0.8701 | 0.9328 |
| 0.0607 | 7.3475 | 2770 | 0.8840 | 0.3333 | 0.8840 | 0.9402 |
| 0.0607 | 7.3528 | 2772 | 0.8883 | 0.3333 | 0.8883 | 0.9425 |
| 0.0607 | 7.3581 | 2774 | 0.8905 | 0.3333 | 0.8905 | 0.9437 |
| 0.0607 | 7.3634 | 2776 | 0.8811 | 0.3514 | 0.8811 | 0.9387 |
| 0.0607 | 7.3687 | 2778 | 0.8692 | 0.24 | 0.8692 | 0.9323 |
| 0.0607 | 7.3740 | 2780 | 0.8570 | 0.2895 | 0.8570 | 0.9258 |
| 0.0607 | 7.3793 | 2782 | 0.8462 | 0.2597 | 0.8462 | 0.9199 |
| 0.0607 | 7.3846 | 2784 | 0.8381 | 0.2597 | 0.8381 | 0.9155 |
| 0.0607 | 7.3899 | 2786 | 0.8377 | 0.2597 | 0.8377 | 0.9152 |
| 0.0607 | 7.3952 | 2788 | 0.8425 | 0.2895 | 0.8425 | 0.9179 |
| 0.0607 | 7.4005 | 2790 | 0.8410 | 0.24 | 0.8410 | 0.9171 |
| 0.0607 | 7.4058 | 2792 | 0.8489 | 0.24 | 0.8489 | 0.9214 |
| 0.0607 | 7.4111 | 2794 | 0.8618 | 0.3836 | 0.8618 | 0.9283 |
| 0.0607 | 7.4164 | 2796 | 0.8891 | 0.3333 | 0.8891 | 0.9429 |
| 0.0607 | 7.4218 | 2798 | 0.9152 | 0.2609 | 0.9152 | 0.9567 |
| 0.0607 | 7.4271 | 2800 | 0.9259 | 0.2609 | 0.9259 | 0.9622 |
| 0.0607 | 7.4324 | 2802 | 0.9221 | 0.2609 | 0.9221 | 0.9603 |
| 0.0607 | 7.4377 | 2804 | 0.9008 | 0.2609 | 0.9008 | 0.9491 |
| 0.0607 | 7.4430 | 2806 | 0.8763 | 0.2609 | 0.8763 | 0.9361 |
| 0.0607 | 7.4483 | 2808 | 0.8563 | 0.2609 | 0.8563 | 0.9254 |
| 0.0607 | 7.4536 | 2810 | 0.8475 | 0.2609 | 0.8475 | 0.9206 |
| 0.0607 | 7.4589 | 2812 | 0.8278 | 0.2817 | 0.8278 | 0.9098 |
| 0.0607 | 7.4642 | 2814 | 0.8077 | 0.3333 | 0.8077 | 0.8987 |
| 0.0607 | 7.4695 | 2816 | 0.8063 | 0.3836 | 0.8063 | 0.8979 |
| 0.0607 | 7.4748 | 2818 | 0.8103 | 0.3836 | 0.8103 | 0.9002 |
| 0.0607 | 7.4801 | 2820 | 0.8143 | 0.3836 | 0.8143 | 0.9024 |
| 0.0607 | 7.4854 | 2822 | 0.8204 | 0.3836 | 0.8204 | 0.9058 |
| 0.0607 | 7.4907 | 2824 | 0.8315 | 0.3836 | 0.8315 | 0.9119 |
| 0.0607 | 7.4960 | 2826 | 0.8425 | 0.3836 | 0.8425 | 0.9179 |
| 0.0607 | 7.5013 | 2828 | 0.8539 | 0.3333 | 0.8539 | 0.9241 |
| 0.0607 | 7.5066 | 2830 | 0.8685 | 0.3333 | 0.8685 | 0.9319 |
| 0.0607 | 7.5119 | 2832 | 0.8890 | 0.2817 | 0.8890 | 0.9429 |
| 0.0607 | 7.5172 | 2834 | 0.9027 | 0.2817 | 0.9027 | 0.9501 |
| 0.0607 | 7.5225 | 2836 | 0.9044 | 0.2817 | 0.9044 | 0.9510 |
| 0.0607 | 7.5279 | 2838 | 0.8966 | 0.2817 | 0.8966 | 0.9469 |
| 0.0607 | 7.5332 | 2840 | 0.8814 | 0.3836 | 0.8814 | 0.9388 |
| 0.0607 | 7.5385 | 2842 | 0.8641 | 0.3836 | 0.8641 | 0.9296 |
| 0.0607 | 7.5438 | 2844 | 0.8543 | 0.2703 | 0.8543 | 0.9243 |
| 0.0607 | 7.5491 | 2846 | 0.8523 | 0.3836 | 0.8523 | 0.9232 |
| 0.0607 | 7.5544 | 2848 | 0.8519 | 0.3836 | 0.8519 | 0.9230 |
| 0.0607 | 7.5597 | 2850 | 0.8556 | 0.3333 | 0.8556 | 0.9250 |
| 0.0607 | 7.5650 | 2852 | 0.8625 | 0.2286 | 0.8625 | 0.9287 |
| 0.0607 | 7.5703 | 2854 | 0.8656 | 0.2609 | 0.8656 | 0.9304 |
| 0.0607 | 7.5756 | 2856 | 0.8579 | 0.2609 | 0.8579 | 0.9262 |
| 0.0607 | 7.5809 | 2858 | 0.8448 | 0.2609 | 0.8448 | 0.9191 |
| 0.0607 | 7.5862 | 2860 | 0.8428 | 0.2609 | 0.8428 | 0.9181 |
| 0.0607 | 7.5915 | 2862 | 0.8427 | 0.2609 | 0.8427 | 0.9180 |
| 0.0607 | 7.5968 | 2864 | 0.8411 | 0.2609 | 0.8411 | 0.9171 |
| 0.0607 | 7.6021 | 2866 | 0.8540 | 0.2609 | 0.8540 | 0.9241 |
| 0.0607 | 7.6074 | 2868 | 0.8746 | 0.2609 | 0.8746 | 0.9352 |
| 0.0607 | 7.6127 | 2870 | 0.8839 | 0.2609 | 0.8839 | 0.9402 |
| 0.0607 | 7.6180 | 2872 | 0.8963 | 0.2609 | 0.8963 | 0.9467 |
| 0.0607 | 7.6233 | 2874 | 0.8997 | 0.2609 | 0.8997 | 0.9485 |
| 0.0607 | 7.6286 | 2876 | 0.8954 | 0.2609 | 0.8954 | 0.9463 |
| 0.0607 | 7.6340 | 2878 | 0.8851 | 0.2609 | 0.8851 | 0.9408 |
| 0.0607 | 7.6393 | 2880 | 0.8791 | 0.3143 | 0.8791 | 0.9376 |
| 0.0607 | 7.6446 | 2882 | 0.8770 | 0.3662 | 0.8770 | 0.9365 |
| 0.0607 | 7.6499 | 2884 | 0.8827 | 0.3836 | 0.8827 | 0.9395 |
| 0.0607 | 7.6552 | 2886 | 0.8895 | 0.3836 | 0.8895 | 0.9432 |
| 0.0607 | 7.6605 | 2888 | 0.8927 | 0.2703 | 0.8927 | 0.9448 |
| 0.0607 | 7.6658 | 2890 | 0.8912 | 0.2703 | 0.8912 | 0.9440 |
| 0.0607 | 7.6711 | 2892 | 0.8945 | 0.2703 | 0.8945 | 0.9458 |
| 0.0607 | 7.6764 | 2894 | 0.9049 | 0.2703 | 0.9049 | 0.9513 |
| 0.0607 | 7.6817 | 2896 | 0.9123 | 0.3333 | 0.9123 | 0.9552 |
| 0.0607 | 7.6870 | 2898 | 0.9348 | 0.2817 | 0.9348 | 0.9668 |
| 0.0607 | 7.6923 | 2900 | 0.9590 | 0.1892 | 0.9590 | 0.9793 |
| 0.0607 | 7.6976 | 2902 | 0.9639 | 0.0506 | 0.9639 | 0.9818 |
| 0.0607 | 7.7029 | 2904 | 0.9614 | 0.0506 | 0.9614 | 0.9805 |
| 0.0607 | 7.7082 | 2906 | 0.9487 | 0.1892 | 0.9487 | 0.9740 |
| 0.0607 | 7.7135 | 2908 | 0.9225 | 0.2817 | 0.9225 | 0.9605 |
| 0.0607 | 7.7188 | 2910 | 0.8887 | 0.3333 | 0.8887 | 0.9427 |
| 0.0607 | 7.7241 | 2912 | 0.8638 | 0.2192 | 0.8638 | 0.9294 |
| 0.0607 | 7.7294 | 2914 | 0.8480 | 0.2703 | 0.8480 | 0.9209 |
| 0.0607 | 7.7347 | 2916 | 0.8415 | 0.2895 | 0.8415 | 0.9174 |
| 0.0607 | 7.7401 | 2918 | 0.8449 | 0.2597 | 0.8449 | 0.9192 |
| 0.0607 | 7.7454 | 2920 | 0.8525 | 0.2597 | 0.8525 | 0.9233 |
| 0.0607 | 7.7507 | 2922 | 0.8614 | 0.2597 | 0.8614 | 0.9281 |
| 0.0607 | 7.7560 | 2924 | 0.8730 | 0.2597 | 0.8730 | 0.9343 |
| 0.0607 | 7.7613 | 2926 | 0.8912 | 0.24 | 0.8912 | 0.9440 |
| 0.0607 | 7.7666 | 2928 | 0.9169 | 0.2192 | 0.9169 | 0.9576 |
| 0.0607 | 7.7719 | 2930 | 0.9555 | 0.1818 | 0.9555 | 0.9775 |
| 0.0607 | 7.7772 | 2932 | 0.9870 | 0.1316 | 0.9870 | 0.9935 |
| 0.0607 | 7.7825 | 2934 | 0.9990 | 0.0506 | 0.9990 | 0.9995 |
| 0.0607 | 7.7878 | 2936 | 0.9916 | 0.0506 | 0.9916 | 0.9958 |
| 0.0607 | 7.7931 | 2938 | 0.9687 | 0.1600 | 0.9687 | 0.9842 |
| 0.0607 | 7.7984 | 2940 | 0.9343 | 0.3333 | 0.9343 | 0.9666 |
| 0.0607 | 7.8037 | 2942 | 0.8961 | 0.3333 | 0.8961 | 0.9466 |
| 0.0607 | 7.8090 | 2944 | 0.8678 | 0.24 | 0.8678 | 0.9316 |
| 0.0607 | 7.8143 | 2946 | 0.8441 | 0.2105 | 0.8441 | 0.9188 |
| 0.0607 | 7.8196 | 2948 | 0.8350 | 0.2597 | 0.8350 | 0.9138 |
| 0.0607 | 7.8249 | 2950 | 0.8325 | 0.2597 | 0.8325 | 0.9124 |
| 0.0607 | 7.8302 | 2952 | 0.8341 | 0.2597 | 0.8341 | 0.9133 |
| 0.0607 | 7.8355 | 2954 | 0.8374 | 0.24 | 0.8374 | 0.9151 |
| 0.0607 | 7.8408 | 2956 | 0.8499 | 0.2192 | 0.8499 | 0.9219 |
| 0.0607 | 7.8462 | 2958 | 0.8788 | 0.3333 | 0.8788 | 0.9375 |
| 0.0607 | 7.8515 | 2960 | 0.9098 | 0.3143 | 0.9098 | 0.9538 |
| 0.0607 | 7.8568 | 2962 | 0.9362 | 0.2941 | 0.9362 | 0.9676 |
| 0.0607 | 7.8621 | 2964 | 0.9469 | 0.1370 | 0.9469 | 0.9731 |
| 0.0607 | 7.8674 | 2966 | 0.9380 | 0.3143 | 0.9380 | 0.9685 |
| 0.0607 | 7.8727 | 2968 | 0.9171 | 0.2817 | 0.9171 | 0.9576 |
| 0.0607 | 7.8780 | 2970 | 0.8915 | 0.3333 | 0.8915 | 0.9442 |
| 0.0607 | 7.8833 | 2972 | 0.8761 | 0.2192 | 0.8761 | 0.9360 |
| 0.0607 | 7.8886 | 2974 | 0.8602 | 0.2703 | 0.8602 | 0.9275 |
| 0.0607 | 7.8939 | 2976 | 0.8499 | 0.24 | 0.8499 | 0.9219 |
| 0.0607 | 7.8992 | 2978 | 0.8395 | 0.2597 | 0.8395 | 0.9162 |
| 0.0607 | 7.9045 | 2980 | 0.8360 | 0.2597 | 0.8360 | 0.9143 |
| 0.0607 | 7.9098 | 2982 | 0.8374 | 0.2895 | 0.8374 | 0.9151 |
| 0.0607 | 7.9151 | 2984 | 0.8456 | 0.2703 | 0.8456 | 0.9196 |
| 0.0607 | 7.9204 | 2986 | 0.8597 | 0.3333 | 0.8597 | 0.9272 |
| 0.0607 | 7.9257 | 2988 | 0.8787 | 0.2817 | 0.8787 | 0.9374 |
| 0.0607 | 7.9310 | 2990 | 0.8887 | 0.3143 | 0.8887 | 0.9427 |
| 0.0607 | 7.9363 | 2992 | 0.8880 | 0.2609 | 0.8880 | 0.9423 |
| 0.0607 | 7.9416 | 2994 | 0.8739 | 0.3143 | 0.8739 | 0.9348 |
| 0.0607 | 7.9469 | 2996 | 0.8618 | 0.3333 | 0.8618 | 0.9283 |
| 0.0607 | 7.9523 | 2998 | 0.8520 | 0.3333 | 0.8520 | 0.9230 |
| 0.0568 | 7.9576 | 3000 | 0.8455 | 0.3333 | 0.8455 | 0.9195 |
| 0.0568 | 7.9629 | 3002 | 0.8403 | 0.3662 | 0.8403 | 0.9167 |
| 0.0568 | 7.9682 | 3004 | 0.8305 | 0.3662 | 0.8305 | 0.9113 |
| 0.0568 | 7.9735 | 3006 | 0.8152 | 0.3333 | 0.8152 | 0.9029 |
| 0.0568 | 7.9788 | 3008 | 0.8082 | 0.3333 | 0.8082 | 0.8990 |
| 0.0568 | 7.9841 | 3010 | 0.8048 | 0.3333 | 0.8048 | 0.8971 |
| 0.0568 | 7.9894 | 3012 | 0.8022 | 0.3333 | 0.8022 | 0.8956 |
| 0.0568 | 7.9947 | 3014 | 0.7931 | 0.3333 | 0.7931 | 0.8906 |
| 0.0568 | 8.0 | 3016 | 0.7817 | 0.3333 | 0.7817 | 0.8841 |
| 0.0568 | 8.0053 | 3018 | 0.7743 | 0.24 | 0.7743 | 0.8799 |
| 0.0568 | 8.0106 | 3020 | 0.7765 | 0.2895 | 0.7765 | 0.8812 |
| 0.0568 | 8.0159 | 3022 | 0.7852 | 0.2895 | 0.7852 | 0.8861 |
| 0.0568 | 8.0212 | 3024 | 0.8009 | 0.2192 | 0.8009 | 0.8949 |
| 0.0568 | 8.0265 | 3026 | 0.8239 | 0.3333 | 0.8239 | 0.9077 |
| 0.0568 | 8.0318 | 3028 | 0.8372 | 0.3333 | 0.8372 | 0.9150 |
| 0.0568 | 8.0371 | 3030 | 0.8405 | 0.3333 | 0.8405 | 0.9168 |
| 0.0568 | 8.0424 | 3032 | 0.8506 | 0.3333 | 0.8506 | 0.9223 |
| 0.0568 | 8.0477 | 3034 | 0.8631 | 0.3333 | 0.8631 | 0.9290 |
| 0.0568 | 8.0531 | 3036 | 0.8787 | 0.3333 | 0.8787 | 0.9374 |
| 0.0568 | 8.0584 | 3038 | 0.8912 | 0.3333 | 0.8912 | 0.9440 |
| 0.0568 | 8.0637 | 3040 | 0.9067 | 0.3333 | 0.9067 | 0.9522 |
| 0.0568 | 8.0690 | 3042 | 0.9185 | 0.3333 | 0.9185 | 0.9584 |
| 0.0568 | 8.0743 | 3044 | 0.9268 | 0.3333 | 0.9268 | 0.9627 |
| 0.0568 | 8.0796 | 3046 | 0.9257 | 0.3333 | 0.9257 | 0.9621 |
| 0.0568 | 8.0849 | 3048 | 0.9267 | 0.2597 | 0.9267 | 0.9626 |
| 0.0568 | 8.0902 | 3050 | 0.9154 | 0.3333 | 0.9154 | 0.9568 |
| 0.0568 | 8.0955 | 3052 | 0.9066 | 0.3333 | 0.9066 | 0.9521 |
| 0.0568 | 8.1008 | 3054 | 0.8906 | 0.3333 | 0.8906 | 0.9437 |
| 0.0568 | 8.1061 | 3056 | 0.8723 | 0.3333 | 0.8723 | 0.9340 |
| 0.0568 | 8.1114 | 3058 | 0.8583 | 0.2817 | 0.8583 | 0.9264 |
| 0.0568 | 8.1167 | 3060 | 0.8486 | 0.2817 | 0.8486 | 0.9212 |
| 0.0568 | 8.1220 | 3062 | 0.8515 | 0.2817 | 0.8515 | 0.9228 |
| 0.0568 | 8.1273 | 3064 | 0.8615 | 0.2609 | 0.8615 | 0.9282 |
| 0.0568 | 8.1326 | 3066 | 0.8637 | 0.2609 | 0.8637 | 0.9293 |
| 0.0568 | 8.1379 | 3068 | 0.8566 | 0.2817 | 0.8566 | 0.9255 |
| 0.0568 | 8.1432 | 3070 | 0.8574 | 0.2817 | 0.8574 | 0.9260 |
| 0.0568 | 8.1485 | 3072 | 0.8531 | 0.3333 | 0.8531 | 0.9236 |
| 0.0568 | 8.1538 | 3074 | 0.8471 | 0.3333 | 0.8471 | 0.9204 |
| 0.0568 | 8.1592 | 3076 | 0.8469 | 0.3333 | 0.8469 | 0.9203 |
| 0.0568 | 8.1645 | 3078 | 0.8432 | 0.3333 | 0.8432 | 0.9182 |
| 0.0568 | 8.1698 | 3080 | 0.8439 | 0.3333 | 0.8439 | 0.9186 |
| 0.0568 | 8.1751 | 3082 | 0.8473 | 0.3333 | 0.8473 | 0.9205 |
| 0.0568 | 8.1804 | 3084 | 0.8520 | 0.3333 | 0.8520 | 0.9230 |
| 0.0568 | 8.1857 | 3086 | 0.8559 | 0.3333 | 0.8559 | 0.9252 |
| 0.0568 | 8.1910 | 3088 | 0.8519 | 0.3333 | 0.8519 | 0.9230 |
| 0.0568 | 8.1963 | 3090 | 0.8505 | 0.3333 | 0.8506 | 0.9223 |
| 0.0568 | 8.2016 | 3092 | 0.8540 | 0.3333 | 0.8540 | 0.9241 |
| 0.0568 | 8.2069 | 3094 | 0.8597 | 0.2286 | 0.8597 | 0.9272 |
| 0.0568 | 8.2122 | 3096 | 0.8668 | 0.1600 | 0.8668 | 0.9310 |
| 0.0568 | 8.2175 | 3098 | 0.8659 | 0.2597 | 0.8659 | 0.9305 |
| 0.0568 | 8.2228 | 3100 | 0.8567 | 0.3333 | 0.8567 | 0.9256 |
| 0.0568 | 8.2281 | 3102 | 0.8483 | 0.3333 | 0.8483 | 0.9210 |
| 0.0568 | 8.2334 | 3104 | 0.8462 | 0.3333 | 0.8462 | 0.9199 |
| 0.0568 | 8.2387 | 3106 | 0.8503 | 0.3333 | 0.8503 | 0.9221 |
| 0.0568 | 8.2440 | 3108 | 0.8567 | 0.3333 | 0.8567 | 0.9256 |
| 0.0568 | 8.2493 | 3110 | 0.8549 | 0.2192 | 0.8549 | 0.9246 |
| 0.0568 | 8.2546 | 3112 | 0.8526 | 0.2192 | 0.8526 | 0.9234 |
| 0.0568 | 8.2599 | 3114 | 0.8561 | 0.2192 | 0.8561 | 0.9253 |
| 0.0568 | 8.2653 | 3116 | 0.8623 | 0.3333 | 0.8623 | 0.9286 |
| 0.0568 | 8.2706 | 3118 | 0.8693 | 0.2192 | 0.8693 | 0.9323 |
| 0.0568 | 8.2759 | 3120 | 0.8792 | 0.2192 | 0.8792 | 0.9376 |
| 0.0568 | 8.2812 | 3122 | 0.8872 | 0.2192 | 0.8872 | 0.9419 |
| 0.0568 | 8.2865 | 3124 | 0.8911 | 0.2192 | 0.8911 | 0.9440 |
| 0.0568 | 8.2918 | 3126 | 0.8904 | 0.2192 | 0.8904 | 0.9436 |
| 0.0568 | 8.2971 | 3128 | 0.8874 | 0.1892 | 0.8874 | 0.9420 |
| 0.0568 | 8.3024 | 3130 | 0.8885 | 0.2192 | 0.8885 | 0.9426 |
| 0.0568 | 8.3077 | 3132 | 0.8851 | 0.1892 | 0.8851 | 0.9408 |
| 0.0568 | 8.3130 | 3134 | 0.8805 | 0.2192 | 0.8805 | 0.9383 |
| 0.0568 | 8.3183 | 3136 | 0.8734 | 0.2192 | 0.8734 | 0.9346 |
| 0.0568 | 8.3236 | 3138 | 0.8667 | 0.2192 | 0.8667 | 0.9310 |
| 0.0568 | 8.3289 | 3140 | 0.8668 | 0.2192 | 0.8668 | 0.9310 |
| 0.0568 | 8.3342 | 3142 | 0.8696 | 0.3333 | 0.8696 | 0.9325 |
| 0.0568 | 8.3395 | 3144 | 0.8677 | 0.3333 | 0.8677 | 0.9315 |
| 0.0568 | 8.3448 | 3146 | 0.8630 | 0.3333 | 0.8630 | 0.9290 |
| 0.0568 | 8.3501 | 3148 | 0.8652 | 0.3333 | 0.8652 | 0.9302 |
| 0.0568 | 8.3554 | 3150 | 0.8629 | 0.3333 | 0.8629 | 0.9289 |
| 0.0568 | 8.3607 | 3152 | 0.8650 | 0.3333 | 0.8650 | 0.9301 |
| 0.0568 | 8.3660 | 3154 | 0.8658 | 0.3333 | 0.8658 | 0.9305 |
| 0.0568 | 8.3714 | 3156 | 0.8683 | 0.3333 | 0.8683 | 0.9318 |
| 0.0568 | 8.3767 | 3158 | 0.8660 | 0.3333 | 0.8660 | 0.9306 |
| 0.0568 | 8.3820 | 3160 | 0.8602 | 0.3333 | 0.8602 | 0.9275 |
| 0.0568 | 8.3873 | 3162 | 0.8539 | 0.3333 | 0.8539 | 0.9241 |
| 0.0568 | 8.3926 | 3164 | 0.8528 | 0.3333 | 0.8528 | 0.9235 |
| 0.0568 | 8.3979 | 3166 | 0.8590 | 0.3333 | 0.8590 | 0.9268 |
| 0.0568 | 8.4032 | 3168 | 0.8649 | 0.3333 | 0.8649 | 0.9300 |
| 0.0568 | 8.4085 | 3170 | 0.8652 | 0.3333 | 0.8652 | 0.9301 |
| 0.0568 | 8.4138 | 3172 | 0.8612 | 0.3333 | 0.8612 | 0.9280 |
| 0.0568 | 8.4191 | 3174 | 0.8536 | 0.3333 | 0.8536 | 0.9239 |
| 0.0568 | 8.4244 | 3176 | 0.8431 | 0.3333 | 0.8431 | 0.9182 |
| 0.0568 | 8.4297 | 3178 | 0.8325 | 0.3333 | 0.8325 | 0.9124 |
| 0.0568 | 8.4350 | 3180 | 0.8321 | 0.3333 | 0.8321 | 0.9122 |
| 0.0568 | 8.4403 | 3182 | 0.8325 | 0.3333 | 0.8325 | 0.9124 |
| 0.0568 | 8.4456 | 3184 | 0.8333 | 0.2192 | 0.8333 | 0.9129 |
| 0.0568 | 8.4509 | 3186 | 0.8357 | 0.2192 | 0.8357 | 0.9142 |
| 0.0568 | 8.4562 | 3188 | 0.8383 | 0.2192 | 0.8383 | 0.9156 |
| 0.0568 | 8.4615 | 3190 | 0.8454 | 0.3333 | 0.8454 | 0.9195 |
| 0.0568 | 8.4668 | 3192 | 0.8561 | 0.3333 | 0.8561 | 0.9252 |
| 0.0568 | 8.4721 | 3194 | 0.8705 | 0.3333 | 0.8705 | 0.9330 |
| 0.0568 | 8.4775 | 3196 | 0.8819 | 0.3333 | 0.8819 | 0.9391 |
| 0.0568 | 8.4828 | 3198 | 0.8826 | 0.3333 | 0.8826 | 0.9395 |
| 0.0568 | 8.4881 | 3200 | 0.8745 | 0.3333 | 0.8745 | 0.9351 |
| 0.0568 | 8.4934 | 3202 | 0.8654 | 0.3333 | 0.8654 | 0.9303 |
| 0.0568 | 8.4987 | 3204 | 0.8585 | 0.3333 | 0.8585 | 0.9265 |
| 0.0568 | 8.5040 | 3206 | 0.8512 | 0.3333 | 0.8512 | 0.9226 |
| 0.0568 | 8.5093 | 3208 | 0.8466 | 0.2192 | 0.8466 | 0.9201 |
| 0.0568 | 8.5146 | 3210 | 0.8475 | 0.2192 | 0.8475 | 0.9206 |
| 0.0568 | 8.5199 | 3212 | 0.8521 | 0.2703 | 0.8521 | 0.9231 |
| 0.0568 | 8.5252 | 3214 | 0.8583 | 0.2192 | 0.8583 | 0.9265 |
| 0.0568 | 8.5305 | 3216 | 0.8660 | 0.3333 | 0.8660 | 0.9306 |
| 0.0568 | 8.5358 | 3218 | 0.8693 | 0.3333 | 0.8693 | 0.9324 |
| 0.0568 | 8.5411 | 3220 | 0.8734 | 0.3333 | 0.8734 | 0.9346 |
| 0.0568 | 8.5464 | 3222 | 0.8738 | 0.3333 | 0.8738 | 0.9348 |
| 0.0568 | 8.5517 | 3224 | 0.8822 | 0.3333 | 0.8822 | 0.9393 |
| 0.0568 | 8.5570 | 3226 | 0.8912 | 0.3333 | 0.8912 | 0.9440 |
| 0.0568 | 8.5623 | 3228 | 0.8905 | 0.3333 | 0.8905 | 0.9437 |
| 0.0568 | 8.5676 | 3230 | 0.8815 | 0.3333 | 0.8815 | 0.9389 |
| 0.0568 | 8.5729 | 3232 | 0.8703 | 0.3333 | 0.8703 | 0.9329 |
| 0.0568 | 8.5782 | 3234 | 0.8533 | 0.3333 | 0.8533 | 0.9237 |
| 0.0568 | 8.5836 | 3236 | 0.8418 | 0.3333 | 0.8418 | 0.9175 |
| 0.0568 | 8.5889 | 3238 | 0.8329 | 0.3333 | 0.8329 | 0.9126 |
| 0.0568 | 8.5942 | 3240 | 0.8268 | 0.3836 | 0.8268 | 0.9093 |
| 0.0568 | 8.5995 | 3242 | 0.8235 | 0.3333 | 0.8235 | 0.9075 |
| 0.0568 | 8.6048 | 3244 | 0.8260 | 0.3333 | 0.8260 | 0.9088 |
| 0.0568 | 8.6101 | 3246 | 0.8311 | 0.3333 | 0.8311 | 0.9116 |
| 0.0568 | 8.6154 | 3248 | 0.8315 | 0.3333 | 0.8315 | 0.9119 |
| 0.0568 | 8.6207 | 3250 | 0.8360 | 0.3333 | 0.8360 | 0.9143 |
| 0.0568 | 8.6260 | 3252 | 0.8371 | 0.3333 | 0.8371 | 0.9149 |
| 0.0568 | 8.6313 | 3254 | 0.8403 | 0.3333 | 0.8403 | 0.9167 |
| 0.0568 | 8.6366 | 3256 | 0.8425 | 0.3333 | 0.8425 | 0.9179 |
| 0.0568 | 8.6419 | 3258 | 0.8432 | 0.3333 | 0.8432 | 0.9183 |
| 0.0568 | 8.6472 | 3260 | 0.8419 | 0.3333 | 0.8419 | 0.9176 |
| 0.0568 | 8.6525 | 3262 | 0.8415 | 0.3333 | 0.8415 | 0.9173 |
| 0.0568 | 8.6578 | 3264 | 0.8433 | 0.2703 | 0.8433 | 0.9183 |
| 0.0568 | 8.6631 | 3266 | 0.8463 | 0.2703 | 0.8463 | 0.9199 |
| 0.0568 | 8.6684 | 3268 | 0.8486 | 0.24 | 0.8486 | 0.9212 |
| 0.0568 | 8.6737 | 3270 | 0.8484 | 0.24 | 0.8484 | 0.9211 |
| 0.0568 | 8.6790 | 3272 | 0.8490 | 0.24 | 0.8490 | 0.9214 |
| 0.0568 | 8.6844 | 3274 | 0.8481 | 0.24 | 0.8481 | 0.9209 |
| 0.0568 | 8.6897 | 3276 | 0.8495 | 0.24 | 0.8495 | 0.9217 |
| 0.0568 | 8.6950 | 3278 | 0.8534 | 0.24 | 0.8534 | 0.9238 |
| 0.0568 | 8.7003 | 3280 | 0.8613 | 0.2703 | 0.8613 | 0.9280 |
| 0.0568 | 8.7056 | 3282 | 0.8643 | 0.2192 | 0.8643 | 0.9297 |
| 0.0568 | 8.7109 | 3284 | 0.8627 | 0.3333 | 0.8627 | 0.9288 |
| 0.0568 | 8.7162 | 3286 | 0.8608 | 0.3333 | 0.8608 | 0.9278 |
| 0.0568 | 8.7215 | 3288 | 0.8592 | 0.3333 | 0.8592 | 0.9269 |
| 0.0568 | 8.7268 | 3290 | 0.8564 | 0.3333 | 0.8564 | 0.9254 |
| 0.0568 | 8.7321 | 3292 | 0.8571 | 0.3333 | 0.8571 | 0.9258 |
| 0.0568 | 8.7374 | 3294 | 0.8601 | 0.3333 | 0.8601 | 0.9274 |
| 0.0568 | 8.7427 | 3296 | 0.8615 | 0.3333 | 0.8615 | 0.9282 |
| 0.0568 | 8.7480 | 3298 | 0.8597 | 0.3333 | 0.8597 | 0.9272 |
| 0.0568 | 8.7533 | 3300 | 0.8638 | 0.3333 | 0.8638 | 0.9294 |
| 0.0568 | 8.7586 | 3302 | 0.8655 | 0.3333 | 0.8655 | 0.9303 |
| 0.0568 | 8.7639 | 3304 | 0.8677 | 0.3333 | 0.8677 | 0.9315 |
| 0.0568 | 8.7692 | 3306 | 0.8695 | 0.3333 | 0.8695 | 0.9325 |
| 0.0568 | 8.7745 | 3308 | 0.8656 | 0.3333 | 0.8656 | 0.9304 |
| 0.0568 | 8.7798 | 3310 | 0.8587 | 0.3333 | 0.8587 | 0.9266 |
| 0.0568 | 8.7851 | 3312 | 0.8481 | 0.3333 | 0.8481 | 0.9209 |
| 0.0568 | 8.7905 | 3314 | 0.8360 | 0.3333 | 0.8360 | 0.9143 |
| 0.0568 | 8.7958 | 3316 | 0.8304 | 0.3333 | 0.8304 | 0.9112 |
| 0.0568 | 8.8011 | 3318 | 0.8309 | 0.3333 | 0.8309 | 0.9116 |
| 0.0568 | 8.8064 | 3320 | 0.8327 | 0.3333 | 0.8327 | 0.9125 |
| 0.0568 | 8.8117 | 3322 | 0.8362 | 0.3333 | 0.8362 | 0.9144 |
| 0.0568 | 8.8170 | 3324 | 0.8423 | 0.3333 | 0.8423 | 0.9178 |
| 0.0568 | 8.8223 | 3326 | 0.8456 | 0.3333 | 0.8456 | 0.9196 |
| 0.0568 | 8.8276 | 3328 | 0.8434 | 0.3836 | 0.8434 | 0.9184 |
| 0.0568 | 8.8329 | 3330 | 0.8399 | 0.2703 | 0.8399 | 0.9165 |
| 0.0568 | 8.8382 | 3332 | 0.8396 | 0.2703 | 0.8396 | 0.9163 |
| 0.0568 | 8.8435 | 3334 | 0.8425 | 0.2703 | 0.8425 | 0.9179 |
| 0.0568 | 8.8488 | 3336 | 0.8471 | 0.2703 | 0.8471 | 0.9204 |
| 0.0568 | 8.8541 | 3338 | 0.8529 | 0.2703 | 0.8529 | 0.9235 |
| 0.0568 | 8.8594 | 3340 | 0.8551 | 0.24 | 0.8551 | 0.9247 |
| 0.0568 | 8.8647 | 3342 | 0.8560 | 0.24 | 0.8560 | 0.9252 |
| 0.0568 | 8.8700 | 3344 | 0.8554 | 0.24 | 0.8554 | 0.9249 |
| 0.0568 | 8.8753 | 3346 | 0.8531 | 0.24 | 0.8531 | 0.9237 |
| 0.0568 | 8.8806 | 3348 | 0.8551 | 0.24 | 0.8551 | 0.9247 |
| 0.0568 | 8.8859 | 3350 | 0.8582 | 0.24 | 0.8582 | 0.9264 |
| 0.0568 | 8.8912 | 3352 | 0.8610 | 0.2703 | 0.8610 | 0.9279 |
| 0.0568 | 8.8966 | 3354 | 0.8667 | 0.2703 | 0.8667 | 0.9310 |
| 0.0568 | 8.9019 | 3356 | 0.8734 | 0.2703 | 0.8734 | 0.9346 |
| 0.0568 | 8.9072 | 3358 | 0.8758 | 0.2703 | 0.8758 | 0.9359 |
| 0.0568 | 8.9125 | 3360 | 0.8797 | 0.2192 | 0.8797 | 0.9379 |
| 0.0568 | 8.9178 | 3362 | 0.8784 | 0.2192 | 0.8784 | 0.9372 |
| 0.0568 | 8.9231 | 3364 | 0.8748 | 0.2192 | 0.8748 | 0.9353 |
| 0.0568 | 8.9284 | 3366 | 0.8755 | 0.2192 | 0.8755 | 0.9357 |
| 0.0568 | 8.9337 | 3368 | 0.8772 | 0.2192 | 0.8772 | 0.9366 |
| 0.0568 | 8.9390 | 3370 | 0.8764 | 0.2703 | 0.8764 | 0.9362 |
| 0.0568 | 8.9443 | 3372 | 0.8730 | 0.2703 | 0.8730 | 0.9344 |
| 0.0568 | 8.9496 | 3374 | 0.8666 | 0.24 | 0.8666 | 0.9309 |
| 0.0568 | 8.9549 | 3376 | 0.8614 | 0.24 | 0.8614 | 0.9281 |
| 0.0568 | 8.9602 | 3378 | 0.8620 | 0.24 | 0.8620 | 0.9284 |
| 0.0568 | 8.9655 | 3380 | 0.8634 | 0.24 | 0.8634 | 0.9292 |
| 0.0568 | 8.9708 | 3382 | 0.8646 | 0.24 | 0.8646 | 0.9298 |
| 0.0568 | 8.9761 | 3384 | 0.8671 | 0.24 | 0.8671 | 0.9312 |
| 0.0568 | 8.9814 | 3386 | 0.8706 | 0.2703 | 0.8706 | 0.9331 |
| 0.0568 | 8.9867 | 3388 | 0.8781 | 0.2703 | 0.8781 | 0.9371 |
| 0.0568 | 8.9920 | 3390 | 0.8872 | 0.2703 | 0.8872 | 0.9419 |
| 0.0568 | 8.9973 | 3392 | 0.8926 | 0.3333 | 0.8926 | 0.9448 |
| 0.0568 | 9.0027 | 3394 | 0.9021 | 0.3333 | 0.9021 | 0.9498 |
| 0.0568 | 9.0080 | 3396 | 0.9127 | 0.3333 | 0.9127 | 0.9553 |
| 0.0568 | 9.0133 | 3398 | 0.9160 | 0.3333 | 0.9160 | 0.9571 |
| 0.0568 | 9.0186 | 3400 | 0.9205 | 0.3333 | 0.9205 | 0.9594 |
| 0.0568 | 9.0239 | 3402 | 0.9207 | 0.3333 | 0.9207 | 0.9595 |
| 0.0568 | 9.0292 | 3404 | 0.9143 | 0.3333 | 0.9143 | 0.9562 |
| 0.0568 | 9.0345 | 3406 | 0.9027 | 0.3333 | 0.9027 | 0.9501 |
| 0.0568 | 9.0398 | 3408 | 0.8907 | 0.3333 | 0.8907 | 0.9438 |
| 0.0568 | 9.0451 | 3410 | 0.8819 | 0.2192 | 0.8819 | 0.9391 |
| 0.0568 | 9.0504 | 3412 | 0.8757 | 0.2703 | 0.8757 | 0.9358 |
| 0.0568 | 9.0557 | 3414 | 0.8692 | 0.2703 | 0.8692 | 0.9323 |
| 0.0568 | 9.0610 | 3416 | 0.8658 | 0.24 | 0.8658 | 0.9305 |
| 0.0568 | 9.0663 | 3418 | 0.8632 | 0.24 | 0.8632 | 0.9291 |
| 0.0568 | 9.0716 | 3420 | 0.8580 | 0.24 | 0.8580 | 0.9263 |
| 0.0568 | 9.0769 | 3422 | 0.8545 | 0.2703 | 0.8545 | 0.9244 |
| 0.0568 | 9.0822 | 3424 | 0.8509 | 0.2703 | 0.8509 | 0.9224 |
| 0.0568 | 9.0875 | 3426 | 0.8478 | 0.2703 | 0.8478 | 0.9208 |
| 0.0568 | 9.0928 | 3428 | 0.8460 | 0.2703 | 0.8460 | 0.9198 |
| 0.0568 | 9.0981 | 3430 | 0.8439 | 0.2703 | 0.8439 | 0.9186 |
| 0.0568 | 9.1034 | 3432 | 0.8436 | 0.2703 | 0.8436 | 0.9185 |
| 0.0568 | 9.1088 | 3434 | 0.8452 | 0.3836 | 0.8452 | 0.9194 |
| 0.0568 | 9.1141 | 3436 | 0.8482 | 0.3333 | 0.8482 | 0.9210 |
| 0.0568 | 9.1194 | 3438 | 0.8510 | 0.3333 | 0.8510 | 0.9225 |
| 0.0568 | 9.1247 | 3440 | 0.8566 | 0.3333 | 0.8566 | 0.9255 |
| 0.0568 | 9.1300 | 3442 | 0.8670 | 0.2817 | 0.8670 | 0.9311 |
| 0.0568 | 9.1353 | 3444 | 0.8741 | 0.2817 | 0.8741 | 0.9349 |
| 0.0568 | 9.1406 | 3446 | 0.8757 | 0.2817 | 0.8757 | 0.9358 |
| 0.0568 | 9.1459 | 3448 | 0.8741 | 0.2817 | 0.8741 | 0.9349 |
| 0.0568 | 9.1512 | 3450 | 0.8723 | 0.3333 | 0.8723 | 0.9340 |
| 0.0568 | 9.1565 | 3452 | 0.8700 | 0.3333 | 0.8700 | 0.9327 |
| 0.0568 | 9.1618 | 3454 | 0.8695 | 0.3333 | 0.8695 | 0.9325 |
| 0.0568 | 9.1671 | 3456 | 0.8674 | 0.3333 | 0.8674 | 0.9313 |
| 0.0568 | 9.1724 | 3458 | 0.8644 | 0.3333 | 0.8644 | 0.9297 |
| 0.0568 | 9.1777 | 3460 | 0.8641 | 0.3333 | 0.8641 | 0.9296 |
| 0.0568 | 9.1830 | 3462 | 0.8628 | 0.3333 | 0.8628 | 0.9289 |
| 0.0568 | 9.1883 | 3464 | 0.8628 | 0.3333 | 0.8628 | 0.9289 |
| 0.0568 | 9.1936 | 3466 | 0.8646 | 0.3333 | 0.8646 | 0.9298 |
| 0.0568 | 9.1989 | 3468 | 0.8647 | 0.3333 | 0.8647 | 0.9299 |
| 0.0568 | 9.2042 | 3470 | 0.8604 | 0.3333 | 0.8604 | 0.9276 |
| 0.0568 | 9.2095 | 3472 | 0.8561 | 0.3333 | 0.8561 | 0.9252 |
| 0.0568 | 9.2149 | 3474 | 0.8480 | 0.3333 | 0.8480 | 0.9209 |
| 0.0568 | 9.2202 | 3476 | 0.8415 | 0.3333 | 0.8415 | 0.9173 |
| 0.0568 | 9.2255 | 3478 | 0.8351 | 0.3333 | 0.8351 | 0.9138 |
| 0.0568 | 9.2308 | 3480 | 0.8307 | 0.3333 | 0.8307 | 0.9114 |
| 0.0568 | 9.2361 | 3482 | 0.8306 | 0.3333 | 0.8306 | 0.9114 |
| 0.0568 | 9.2414 | 3484 | 0.8312 | 0.3333 | 0.8312 | 0.9117 |
| 0.0568 | 9.2467 | 3486 | 0.8343 | 0.3333 | 0.8343 | 0.9134 |
| 0.0568 | 9.2520 | 3488 | 0.8401 | 0.3333 | 0.8401 | 0.9166 |
| 0.0568 | 9.2573 | 3490 | 0.8445 | 0.3333 | 0.8445 | 0.9190 |
| 0.0568 | 9.2626 | 3492 | 0.8487 | 0.3333 | 0.8487 | 0.9212 |
| 0.0568 | 9.2679 | 3494 | 0.8538 | 0.3333 | 0.8538 | 0.9240 |
| 0.0568 | 9.2732 | 3496 | 0.8596 | 0.3333 | 0.8596 | 0.9271 |
| 0.0568 | 9.2785 | 3498 | 0.8685 | 0.3333 | 0.8685 | 0.9319 |
| 0.044 | 9.2838 | 3500 | 0.8743 | 0.3333 | 0.8743 | 0.9350 |
| 0.044 | 9.2891 | 3502 | 0.8759 | 0.3333 | 0.8759 | 0.9359 |
| 0.044 | 9.2944 | 3504 | 0.8750 | 0.3333 | 0.8750 | 0.9354 |
| 0.044 | 9.2997 | 3506 | 0.8751 | 0.3333 | 0.8751 | 0.9354 |
| 0.044 | 9.3050 | 3508 | 0.8725 | 0.3333 | 0.8725 | 0.9341 |
| 0.044 | 9.3103 | 3510 | 0.8678 | 0.3333 | 0.8678 | 0.9316 |
| 0.044 | 9.3156 | 3512 | 0.8647 | 0.3333 | 0.8647 | 0.9299 |
| 0.044 | 9.3210 | 3514 | 0.8629 | 0.3333 | 0.8629 | 0.9289 |
| 0.044 | 9.3263 | 3516 | 0.8619 | 0.3333 | 0.8619 | 0.9284 |
| 0.044 | 9.3316 | 3518 | 0.8606 | 0.3333 | 0.8606 | 0.9277 |
| 0.044 | 9.3369 | 3520 | 0.8604 | 0.3333 | 0.8604 | 0.9276 |
| 0.044 | 9.3422 | 3522 | 0.8602 | 0.3333 | 0.8602 | 0.9275 |
| 0.044 | 9.3475 | 3524 | 0.8617 | 0.3333 | 0.8617 | 0.9283 |
| 0.044 | 9.3528 | 3526 | 0.8644 | 0.3333 | 0.8644 | 0.9297 |
| 0.044 | 9.3581 | 3528 | 0.8679 | 0.3333 | 0.8679 | 0.9316 |
| 0.044 | 9.3634 | 3530 | 0.8722 | 0.3333 | 0.8722 | 0.9339 |
| 0.044 | 9.3687 | 3532 | 0.8766 | 0.3333 | 0.8766 | 0.9362 |
| 0.044 | 9.3740 | 3534 | 0.8819 | 0.3333 | 0.8819 | 0.9391 |
| 0.044 | 9.3793 | 3536 | 0.8851 | 0.3333 | 0.8851 | 0.9408 |
| 0.044 | 9.3846 | 3538 | 0.8886 | 0.3333 | 0.8886 | 0.9427 |
| 0.044 | 9.3899 | 3540 | 0.8876 | 0.3333 | 0.8876 | 0.9421 |
| 0.044 | 9.3952 | 3542 | 0.8830 | 0.3333 | 0.8830 | 0.9397 |
| 0.044 | 9.4005 | 3544 | 0.8768 | 0.3333 | 0.8768 | 0.9364 |
| 0.044 | 9.4058 | 3546 | 0.8707 | 0.3333 | 0.8707 | 0.9331 |
| 0.044 | 9.4111 | 3548 | 0.8663 | 0.3333 | 0.8663 | 0.9307 |
| 0.044 | 9.4164 | 3550 | 0.8620 | 0.3333 | 0.8620 | 0.9284 |
| 0.044 | 9.4218 | 3552 | 0.8608 | 0.3333 | 0.8608 | 0.9278 |
| 0.044 | 9.4271 | 3554 | 0.8578 | 0.3333 | 0.8578 | 0.9262 |
| 0.044 | 9.4324 | 3556 | 0.8542 | 0.3333 | 0.8542 | 0.9242 |
| 0.044 | 9.4377 | 3558 | 0.8505 | 0.3333 | 0.8505 | 0.9222 |
| 0.044 | 9.4430 | 3560 | 0.8497 | 0.3333 | 0.8497 | 0.9218 |
| 0.044 | 9.4483 | 3562 | 0.8514 | 0.3333 | 0.8514 | 0.9227 |
| 0.044 | 9.4536 | 3564 | 0.8513 | 0.3333 | 0.8513 | 0.9226 |
| 0.044 | 9.4589 | 3566 | 0.8482 | 0.3333 | 0.8482 | 0.9210 |
| 0.044 | 9.4642 | 3568 | 0.8447 | 0.3333 | 0.8447 | 0.9191 |
| 0.044 | 9.4695 | 3570 | 0.8425 | 0.3333 | 0.8425 | 0.9179 |
| 0.044 | 9.4748 | 3572 | 0.8418 | 0.3333 | 0.8418 | 0.9175 |
| 0.044 | 9.4801 | 3574 | 0.8416 | 0.3333 | 0.8416 | 0.9174 |
| 0.044 | 9.4854 | 3576 | 0.8426 | 0.3333 | 0.8426 | 0.9179 |
| 0.044 | 9.4907 | 3578 | 0.8438 | 0.3333 | 0.8438 | 0.9186 |
| 0.044 | 9.4960 | 3580 | 0.8444 | 0.3333 | 0.8444 | 0.9189 |
| 0.044 | 9.5013 | 3582 | 0.8436 | 0.3333 | 0.8436 | 0.9185 |
| 0.044 | 9.5066 | 3584 | 0.8418 | 0.3333 | 0.8418 | 0.9175 |
| 0.044 | 9.5119 | 3586 | 0.8391 | 0.3333 | 0.8391 | 0.9160 |
| 0.044 | 9.5172 | 3588 | 0.8389 | 0.3333 | 0.8389 | 0.9159 |
| 0.044 | 9.5225 | 3590 | 0.8380 | 0.3333 | 0.8380 | 0.9154 |
| 0.044 | 9.5279 | 3592 | 0.8369 | 0.3333 | 0.8369 | 0.9148 |
| 0.044 | 9.5332 | 3594 | 0.8370 | 0.3333 | 0.8370 | 0.9149 |
| 0.044 | 9.5385 | 3596 | 0.8384 | 0.3333 | 0.8384 | 0.9156 |
| 0.044 | 9.5438 | 3598 | 0.8414 | 0.3333 | 0.8414 | 0.9173 |
| 0.044 | 9.5491 | 3600 | 0.8433 | 0.3333 | 0.8433 | 0.9183 |
| 0.044 | 9.5544 | 3602 | 0.8458 | 0.3333 | 0.8458 | 0.9197 |
| 0.044 | 9.5597 | 3604 | 0.8458 | 0.3333 | 0.8458 | 0.9196 |
| 0.044 | 9.5650 | 3606 | 0.8456 | 0.3333 | 0.8456 | 0.9196 |
| 0.044 | 9.5703 | 3608 | 0.8450 | 0.3333 | 0.8450 | 0.9192 |
| 0.044 | 9.5756 | 3610 | 0.8468 | 0.3333 | 0.8468 | 0.9202 |
| 0.044 | 9.5809 | 3612 | 0.8504 | 0.3333 | 0.8504 | 0.9222 |
| 0.044 | 9.5862 | 3614 | 0.8525 | 0.3333 | 0.8525 | 0.9233 |
| 0.044 | 9.5915 | 3616 | 0.8537 | 0.3333 | 0.8537 | 0.9240 |
| 0.044 | 9.5968 | 3618 | 0.8543 | 0.3333 | 0.8543 | 0.9243 |
| 0.044 | 9.6021 | 3620 | 0.8540 | 0.3333 | 0.8540 | 0.9241 |
| 0.044 | 9.6074 | 3622 | 0.8549 | 0.3333 | 0.8549 | 0.9246 |
| 0.044 | 9.6127 | 3624 | 0.8543 | 0.3333 | 0.8543 | 0.9243 |
| 0.044 | 9.6180 | 3626 | 0.8541 | 0.3333 | 0.8541 | 0.9242 |
| 0.044 | 9.6233 | 3628 | 0.8554 | 0.3333 | 0.8554 | 0.9249 |
| 0.044 | 9.6286 | 3630 | 0.8562 | 0.3333 | 0.8562 | 0.9253 |
| 0.044 | 9.6340 | 3632 | 0.8553 | 0.3333 | 0.8553 | 0.9248 |
| 0.044 | 9.6393 | 3634 | 0.8539 | 0.3333 | 0.8539 | 0.9241 |
| 0.044 | 9.6446 | 3636 | 0.8525 | 0.3333 | 0.8525 | 0.9233 |
| 0.044 | 9.6499 | 3638 | 0.8523 | 0.3333 | 0.8523 | 0.9232 |
| 0.044 | 9.6552 | 3640 | 0.8533 | 0.3333 | 0.8533 | 0.9238 |
| 0.044 | 9.6605 | 3642 | 0.8549 | 0.3333 | 0.8549 | 0.9246 |
| 0.044 | 9.6658 | 3644 | 0.8549 | 0.3333 | 0.8549 | 0.9246 |
| 0.044 | 9.6711 | 3646 | 0.8539 | 0.3333 | 0.8539 | 0.9241 |
| 0.044 | 9.6764 | 3648 | 0.8531 | 0.3333 | 0.8531 | 0.9236 |
| 0.044 | 9.6817 | 3650 | 0.8520 | 0.3333 | 0.8520 | 0.9231 |
| 0.044 | 9.6870 | 3652 | 0.8520 | 0.3333 | 0.8520 | 0.9230 |
| 0.044 | 9.6923 | 3654 | 0.8523 | 0.3333 | 0.8523 | 0.9232 |
| 0.044 | 9.6976 | 3656 | 0.8541 | 0.3333 | 0.8541 | 0.9242 |
| 0.044 | 9.7029 | 3658 | 0.8557 | 0.3333 | 0.8557 | 0.9250 |
| 0.044 | 9.7082 | 3660 | 0.8563 | 0.3333 | 0.8563 | 0.9254 |
| 0.044 | 9.7135 | 3662 | 0.8574 | 0.3333 | 0.8574 | 0.9260 |
| 0.044 | 9.7188 | 3664 | 0.8582 | 0.3333 | 0.8582 | 0.9264 |
| 0.044 | 9.7241 | 3666 | 0.8594 | 0.3333 | 0.8594 | 0.9270 |
| 0.044 | 9.7294 | 3668 | 0.8590 | 0.3333 | 0.8590 | 0.9268 |
| 0.044 | 9.7347 | 3670 | 0.8588 | 0.3333 | 0.8588 | 0.9267 |
| 0.044 | 9.7401 | 3672 | 0.8577 | 0.3333 | 0.8577 | 0.9261 |
| 0.044 | 9.7454 | 3674 | 0.8581 | 0.3333 | 0.8581 | 0.9263 |
| 0.044 | 9.7507 | 3676 | 0.8590 | 0.3333 | 0.8590 | 0.9268 |
| 0.044 | 9.7560 | 3678 | 0.8595 | 0.3333 | 0.8595 | 0.9271 |
| 0.044 | 9.7613 | 3680 | 0.8591 | 0.3333 | 0.8591 | 0.9269 |
| 0.044 | 9.7666 | 3682 | 0.8596 | 0.3333 | 0.8596 | 0.9271 |
| 0.044 | 9.7719 | 3684 | 0.8597 | 0.3333 | 0.8597 | 0.9272 |
| 0.044 | 9.7772 | 3686 | 0.8599 | 0.3333 | 0.8599 | 0.9273 |
| 0.044 | 9.7825 | 3688 | 0.8596 | 0.3333 | 0.8596 | 0.9271 |
| 0.044 | 9.7878 | 3690 | 0.8591 | 0.3333 | 0.8591 | 0.9269 |
| 0.044 | 9.7931 | 3692 | 0.8585 | 0.3333 | 0.8585 | 0.9265 |
| 0.044 | 9.7984 | 3694 | 0.8583 | 0.3333 | 0.8583 | 0.9264 |
| 0.044 | 9.8037 | 3696 | 0.8582 | 0.3333 | 0.8582 | 0.9264 |
| 0.044 | 9.8090 | 3698 | 0.8585 | 0.3333 | 0.8585 | 0.9265 |
| 0.044 | 9.8143 | 3700 | 0.8591 | 0.3333 | 0.8591 | 0.9269 |
| 0.044 | 9.8196 | 3702 | 0.8603 | 0.3333 | 0.8603 | 0.9275 |
| 0.044 | 9.8249 | 3704 | 0.8615 | 0.3333 | 0.8615 | 0.9282 |
| 0.044 | 9.8302 | 3706 | 0.8622 | 0.3333 | 0.8622 | 0.9286 |
| 0.044 | 9.8355 | 3708 | 0.8626 | 0.3333 | 0.8626 | 0.9288 |
| 0.044 | 9.8408 | 3710 | 0.8625 | 0.3333 | 0.8625 | 0.9287 |
| 0.044 | 9.8462 | 3712 | 0.8624 | 0.3333 | 0.8624 | 0.9287 |
| 0.044 | 9.8515 | 3714 | 0.8617 | 0.3333 | 0.8617 | 0.9283 |
| 0.044 | 9.8568 | 3716 | 0.8612 | 0.3333 | 0.8612 | 0.9280 |
| 0.044 | 9.8621 | 3718 | 0.8605 | 0.3333 | 0.8605 | 0.9276 |
| 0.044 | 9.8674 | 3720 | 0.8605 | 0.3333 | 0.8605 | 0.9276 |
| 0.044 | 9.8727 | 3722 | 0.8607 | 0.3333 | 0.8607 | 0.9277 |
| 0.044 | 9.8780 | 3724 | 0.8612 | 0.3333 | 0.8612 | 0.9280 |
| 0.044 | 9.8833 | 3726 | 0.8617 | 0.3333 | 0.8617 | 0.9283 |
| 0.044 | 9.8886 | 3728 | 0.8622 | 0.3333 | 0.8622 | 0.9286 |
| 0.044 | 9.8939 | 3730 | 0.8627 | 0.3333 | 0.8627 | 0.9288 |
| 0.044 | 9.8992 | 3732 | 0.8626 | 0.3333 | 0.8626 | 0.9288 |
| 0.044 | 9.9045 | 3734 | 0.8625 | 0.3333 | 0.8625 | 0.9287 |
| 0.044 | 9.9098 | 3736 | 0.8625 | 0.3333 | 0.8625 | 0.9287 |
| 0.044 | 9.9151 | 3738 | 0.8623 | 0.3333 | 0.8623 | 0.9286 |
| 0.044 | 9.9204 | 3740 | 0.8621 | 0.3333 | 0.8621 | 0.9285 |
| 0.044 | 9.9257 | 3742 | 0.8620 | 0.3333 | 0.8620 | 0.9284 |
| 0.044 | 9.9310 | 3744 | 0.8618 | 0.3333 | 0.8618 | 0.9283 |
| 0.044 | 9.9363 | 3746 | 0.8617 | 0.3333 | 0.8617 | 0.9283 |
| 0.044 | 9.9416 | 3748 | 0.8617 | 0.3333 | 0.8617 | 0.9283 |
| 0.044 | 9.9469 | 3750 | 0.8618 | 0.3333 | 0.8618 | 0.9283 |
| 0.044 | 9.9523 | 3752 | 0.8619 | 0.3333 | 0.8619 | 0.9284 |
| 0.044 | 9.9576 | 3754 | 0.8621 | 0.3333 | 0.8621 | 0.9285 |
| 0.044 | 9.9629 | 3756 | 0.8623 | 0.3333 | 0.8623 | 0.9286 |
| 0.044 | 9.9682 | 3758 | 0.8625 | 0.3333 | 0.8625 | 0.9287 |
| 0.044 | 9.9735 | 3760 | 0.8625 | 0.3333 | 0.8625 | 0.9287 |
| 0.044 | 9.9788 | 3762 | 0.8625 | 0.3333 | 0.8625 | 0.9287 |
| 0.044 | 9.9841 | 3764 | 0.8625 | 0.3333 | 0.8625 | 0.9287 |
| 0.044 | 9.9894 | 3766 | 0.8625 | 0.3333 | 0.8625 | 0.9287 |
| 0.044 | 9.9947 | 3768 | 0.8625 | 0.3333 | 0.8625 | 0.9287 |
| 0.044 | 10.0 | 3770 | 0.8625 | 0.3333 | 0.8625 | 0.9287 |
### Framework versions
- Transformers 4.44.2
- Pytorch 2.4.0+cu118
- Datasets 2.21.0
- Tokenizers 0.19.1
|
cindyloo337/sbne-chicken-sd21-object-lora
|
cindyloo337
| 2024-11-16T18:31:08Z
| 6
| 0
|
diffusers
|
[
"diffusers",
"text-to-image",
"diffusers-training",
"lora",
"template:sd-lorastable-diffusion",
"stable-diffusion-diffusers",
"base_model:stabilityai/stable-diffusion-2-1",
"base_model:adapter:stabilityai/stable-diffusion-2-1",
"license:openrail++",
"region:us"
] |
text-to-image
| 2024-11-16T13:37:36Z
|
---
base_model: stabilityai/stable-diffusion-2-1
library_name: diffusers
license: openrail++
inference: true
instance_prompt: a <s0><s1> chicken
widget:
- text: a <s0><s1> chicken on a beach, in the style of <s0><s1>
output:
url: image_0.png
- text: a <s0><s1> chicken on a beach, in the style of <s0><s1>
output:
url: image_1.png
- text: a <s0><s1> chicken on a beach, in the style of <s0><s1>
output:
url: image_2.png
- text: a <s0><s1> chicken on a beach, in the style of <s0><s1>
output:
url: image_3.png
tags:
- text-to-image
- diffusers
- diffusers-training
- lora
- template:sd-lorastable-diffusion
- stable-diffusion-diffusers
- text-to-image
- diffusers
- diffusers-training
- lora
- template:sd-lorastable-diffusion
- stable-diffusion-diffusers
---
<!-- This model card has been generated automatically according to the information the training script had access to. You
should probably proofread and complete it, then remove this comment. -->
# SD1.5 LoRA DreamBooth - cindyloo337/sbne-chicken-sd21-object-lora
<Gallery />
## Model description
### These are cindyloo337/sbne-chicken-sd21-object-lora LoRA adaption weights for stabilityai/stable-diffusion-2-1.
## Download model
### Use it with UIs such as AUTOMATIC1111, Comfy UI, SD.Next, Invoke
- **LoRA**: download **[`sbne-chicken-sd21-object-lora.safetensors` here 💾](/cindyloo337/sbne-chicken-sd21-object-lora/blob/main/sbne-chicken-sd21-object-lora.safetensors)**.
- Place it on your `models/Lora` folder.
- On AUTOMATIC1111, load the LoRA by adding `<lora:sbne-chicken-sd21-object-lora:1>` to your prompt. On ComfyUI just [load it as a regular LoRA](https://comfyanonymous.github.io/ComfyUI_examples/lora/).
- *Embeddings*: download **[`sbne-chicken-sd21-object-lora_emb.safetensors` here 💾](/cindyloo337/sbne-chicken-sd21-object-lora/blob/main/sbne-chicken-sd21-object-lora_emb.safetensors)**.
- Place it on it on your `embeddings` folder
- Use it by adding `sbne-chicken-sd21-object-lora_emb` to your prompt. For example, `a sbne-chicken-sd21-object-lora_emb chicken`
(you need both the LoRA and the embeddings as they were trained together for this LoRA)
## Use it with the [🧨 diffusers library](https://github.com/huggingface/diffusers)
```py
from diffusers import AutoPipelineForText2Image
import torch
from huggingface_hub import hf_hub_download
from safetensors.torch import load_file
pipeline = AutoPipelineForText2Image.from_pretrained('runwayml/stable-diffusion-v1-5', torch_dtype=torch.float16).to('cuda')
pipeline.load_lora_weights('cindyloo337/sbne-chicken-sd21-object-lora', weight_name='pytorch_lora_weights.safetensors')
embedding_path = hf_hub_download(repo_id='cindyloo337/sbne-chicken-sd21-object-lora', filename='sbne-chicken-sd21-object-lora_emb.safetensors', repo_type="model")
state_dict = load_file(embedding_path)
pipeline.load_textual_inversion(state_dict["clip_l"], token=["<s0>", "<s1>"], text_encoder=pipeline.text_encoder, tokenizer=pipeline.tokenizer)
image = pipeline('a <s0><s1> chicken on a beach, in the style of <s0><s1>').images[0]
```
For more details, including weighting, merging and fusing LoRAs, check the [documentation on loading LoRAs in diffusers](https://huggingface.co/docs/diffusers/main/en/using-diffusers/loading_adapters)
## Trigger words
To trigger image generation of trained concept(or concepts) replace each concept identifier in you prompt with the new inserted tokens:
to trigger concept `TOK` → use `<s0><s1>` in your prompt
## Details
All [Files & versions](/cindyloo337/sbne-chicken-sd21-object-lora/tree/main).
The weights were trained using [🧨 diffusers Advanced Dreambooth Training Script](https://github.com/huggingface/diffusers/blob/main/examples/advanced_diffusion_training/train_dreambooth_lora_sd15_advanced.py).
LoRA for the text encoder was enabled. False.
Pivotal tuning was enabled: True.
Special VAE used for training: None.
## Intended uses & limitations
#### How to use
```python
# TODO: add an example code snippet for running this diffusion pipeline
```
#### Limitations and bias
[TODO: provide examples of latent issues and potential remediations]
## Training details
[TODO: describe the data used to train the model]
|
gokceuludogan/turna_generation_tr_hateprint_target
|
gokceuludogan
| 2024-11-16T18:23:20Z
| 118
| 0
|
transformers
|
[
"transformers",
"safetensors",
"t5",
"text2text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2024-11-16T18:20:39Z
|
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
griffio/vit-large-patch16-224-dungeon-geo-morphs-009
|
griffio
| 2024-11-16T18:20:38Z
| 219
| 1
|
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"vit",
"image-classification",
"generated_from_trainer",
"dataset:imagefolder",
"base_model:google/vit-large-patch16-224",
"base_model:finetune:google/vit-large-patch16-224",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
image-classification
| 2024-11-13T17:43:37Z
|
---
library_name: transformers
license: apache-2.0
base_model: google/vit-large-patch16-224
tags:
- image-classification
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: vit-large-patch16-224-dungeon-geo-morphs-009
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: dungeon-geo-morphs
type: imagefolder
config: default
split: validation
args: default
metrics:
- name: Accuracy
type: accuracy
value: 1.0
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# vit-large-patch16-224-dungeon-geo-morphs-009
This model is a fine-tuned version of [google/vit-large-patch16-224](https://huggingface.co/google/vit-large-patch16-224) on the dungeon-geo-morphs dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0429
- Accuracy: 1.0
## Model description
Dungeon Maps - Geo Morphs - with 0 to 3 entrances
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 32
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 35
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-------:|:----:|:---------------:|:--------:|
| 0.7303 | 6.5714 | 10 | 0.2536 | 0.9444 |
| 0.0464 | 13.2857 | 20 | 0.0737 | 0.9444 |
| 0.0017 | 19.8571 | 30 | 0.0429 | 1.0 |
### Framework versions
- Transformers 4.46.2
- Pytorch 2.5.0+cu121
- Datasets 3.1.0
- Tokenizers 0.20.3
|
flaneur-ml/whisper-tiny-us_en_bs128
|
flaneur-ml
| 2024-11-16T18:06:37Z
| 92
| 0
|
transformers
|
[
"transformers",
"safetensors",
"whisper",
"automatic-speech-recognition",
"generated_from_trainer",
"dataset:PolyAI/minds14",
"base_model:openai/whisper-tiny",
"base_model:finetune:openai/whisper-tiny",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2024-11-16T15:54:02Z
|
---
library_name: transformers
license: apache-2.0
base_model: openai/whisper-tiny
tags:
- generated_from_trainer
datasets:
- PolyAI/minds14
metrics:
- wer
model-index:
- name: whisper-tiny-us_en_bs128
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: PolyAI/minds14
type: PolyAI/minds14
config: en-US
split: train[450:]
args: en-US
metrics:
- name: Wer
type: wer
value: 0.3417945690672963
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# whisper-tiny-us_en_bs128
This model is a fine-tuned version of [openai/whisper-tiny](https://huggingface.co/openai/whisper-tiny) on the PolyAI/minds14 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8372
- Wer Ortho: 0.3399
- Wer: 0.3418
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 128
- eval_batch_size: 128
- seed: 42
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 50
- training_steps: 500
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer Ortho | Wer |
|:-------------:|:------:|:----:|:---------------:|:---------:|:------:|
| 0.1633 | 6.25 | 25 | 0.5503 | 0.3177 | 0.3164 |
| 0.0027 | 12.5 | 50 | 0.6676 | 0.3288 | 0.3294 |
| 0.0011 | 18.75 | 75 | 0.7095 | 0.3134 | 0.3182 |
| 0.0012 | 25.0 | 100 | 0.7296 | 0.3196 | 0.3176 |
| 0.0014 | 31.25 | 125 | 0.7460 | 0.3541 | 0.3583 |
| 0.005 | 37.5 | 150 | 0.7059 | 0.4405 | 0.4610 |
| 0.0009 | 43.75 | 175 | 0.7803 | 0.3924 | 0.3961 |
| 0.0004 | 50.0 | 200 | 0.7996 | 0.3455 | 0.3512 |
| 0.0001 | 56.25 | 225 | 0.8074 | 0.3411 | 0.3442 |
| 0.0001 | 62.5 | 250 | 0.8146 | 0.3424 | 0.3459 |
| 0.0001 | 68.75 | 275 | 0.8197 | 0.3430 | 0.3459 |
| 0.0001 | 75.0 | 300 | 0.8239 | 0.3399 | 0.3424 |
| 0.0001 | 81.25 | 325 | 0.8274 | 0.3374 | 0.3400 |
| 0.0001 | 87.5 | 350 | 0.8303 | 0.3356 | 0.3383 |
| 0.0001 | 93.75 | 375 | 0.8324 | 0.3368 | 0.3400 |
| 0.0001 | 100.0 | 400 | 0.8341 | 0.3368 | 0.3388 |
| 0.0001 | 106.25 | 425 | 0.8354 | 0.3405 | 0.3424 |
| 0.0001 | 112.5 | 450 | 0.8364 | 0.3399 | 0.3418 |
| 0.0001 | 118.75 | 475 | 0.8371 | 0.3399 | 0.3418 |
| 0.0001 | 125.0 | 500 | 0.8372 | 0.3399 | 0.3418 |
### Framework versions
- Transformers 4.46.2
- Pytorch 2.5.1+cu124
- Datasets 3.1.0
- Tokenizers 0.20.3
|
MayBashendy/Arabic_FineTuningAraBERT_AugV4_k25_task2_organization_fold0
|
MayBashendy
| 2024-11-16T18:05:55Z
| 161
| 0
|
transformers
|
[
"transformers",
"safetensors",
"bert",
"text-classification",
"generated_from_trainer",
"base_model:aubmindlab/bert-base-arabertv02",
"base_model:finetune:aubmindlab/bert-base-arabertv02",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2024-11-16T17:40:34Z
|
---
library_name: transformers
base_model: aubmindlab/bert-base-arabertv02
tags:
- generated_from_trainer
model-index:
- name: Arabic_FineTuningAraBERT_AugV4_k25_task2_organization_fold0
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# Arabic_FineTuningAraBERT_AugV4_k25_task2_organization_fold0
This model is a fine-tuned version of [aubmindlab/bert-base-arabertv02](https://huggingface.co/aubmindlab/bert-base-arabertv02) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4409
- Qwk: 0.7158
- Mse: 0.4409
- Rmse: 0.6640
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Qwk | Mse | Rmse |
|:-------------:|:------:|:----:|:---------------:|:-------:|:------:|:------:|
| No log | 0.0072 | 2 | 3.3370 | 0.0 | 3.3370 | 1.8267 |
| No log | 0.0144 | 4 | 1.7065 | -0.1442 | 1.7065 | 1.3063 |
| No log | 0.0217 | 6 | 0.8800 | 0.0 | 0.8800 | 0.9381 |
| No log | 0.0289 | 8 | 0.7250 | 0.0503 | 0.7250 | 0.8515 |
| No log | 0.0361 | 10 | 0.8052 | 0.0679 | 0.8052 | 0.8973 |
| No log | 0.0433 | 12 | 0.7051 | 0.1356 | 0.7051 | 0.8397 |
| No log | 0.0505 | 14 | 0.7181 | 0.1985 | 0.7181 | 0.8474 |
| No log | 0.0578 | 16 | 1.1234 | 0.2913 | 1.1234 | 1.0599 |
| No log | 0.0650 | 18 | 1.2398 | 0.2300 | 1.2398 | 1.1135 |
| No log | 0.0722 | 20 | 1.0551 | 0.0708 | 1.0551 | 1.0272 |
| No log | 0.0794 | 22 | 0.7424 | 0.0 | 0.7424 | 0.8616 |
| No log | 0.0866 | 24 | 0.5887 | 0.2423 | 0.5887 | 0.7673 |
| No log | 0.0939 | 26 | 0.6332 | 0.0 | 0.6332 | 0.7957 |
| No log | 0.1011 | 28 | 0.6933 | 0.0 | 0.6933 | 0.8326 |
| No log | 0.1083 | 30 | 0.6785 | 0.1398 | 0.6785 | 0.8237 |
| No log | 0.1155 | 32 | 0.5268 | 0.2759 | 0.5268 | 0.7258 |
| No log | 0.1227 | 34 | 0.4802 | 0.2921 | 0.4802 | 0.6929 |
| No log | 0.1300 | 36 | 0.5375 | 0.3846 | 0.5375 | 0.7331 |
| No log | 0.1372 | 38 | 0.8541 | 0.4099 | 0.8541 | 0.9242 |
| No log | 0.1444 | 40 | 1.6769 | 0.0474 | 1.6769 | 1.2949 |
| No log | 0.1516 | 42 | 1.8707 | -0.0636 | 1.8707 | 1.3677 |
| No log | 0.1588 | 44 | 1.6237 | -0.0579 | 1.6237 | 1.2742 |
| No log | 0.1661 | 46 | 1.0905 | 0.3659 | 1.0905 | 1.0443 |
| No log | 0.1733 | 48 | 0.5482 | 0.1558 | 0.5482 | 0.7404 |
| No log | 0.1805 | 50 | 0.4990 | 0.4665 | 0.4990 | 0.7064 |
| No log | 0.1877 | 52 | 0.5059 | 0.5205 | 0.5059 | 0.7113 |
| No log | 0.1949 | 54 | 0.5455 | 0.1985 | 0.5455 | 0.7386 |
| No log | 0.2022 | 56 | 0.8491 | 0.0386 | 0.8491 | 0.9215 |
| No log | 0.2094 | 58 | 1.2086 | 0.2814 | 1.2086 | 1.0994 |
| No log | 0.2166 | 60 | 1.4710 | 0.1825 | 1.4710 | 1.2128 |
| No log | 0.2238 | 62 | 1.4384 | 0.1707 | 1.4384 | 1.1994 |
| No log | 0.2310 | 64 | 1.1032 | 0.0386 | 1.1032 | 1.0503 |
| No log | 0.2383 | 66 | 0.9493 | 0.0386 | 0.9493 | 0.9743 |
| No log | 0.2455 | 68 | 0.9302 | 0.2814 | 0.9302 | 0.9645 |
| No log | 0.2527 | 70 | 1.2616 | 0.2701 | 1.2616 | 1.1232 |
| No log | 0.2599 | 72 | 1.3820 | 0.2847 | 1.3820 | 1.1756 |
| No log | 0.2671 | 74 | 1.1854 | 0.3262 | 1.1854 | 1.0888 |
| No log | 0.2744 | 76 | 0.9349 | 0.2814 | 0.9349 | 0.9669 |
| No log | 0.2816 | 78 | 0.5717 | 0.2348 | 0.5717 | 0.7561 |
| No log | 0.2888 | 80 | 0.4781 | 0.2613 | 0.4781 | 0.6915 |
| No log | 0.2960 | 82 | 0.5456 | 0.3277 | 0.5456 | 0.7386 |
| No log | 0.3032 | 84 | 0.7550 | 0.3277 | 0.7550 | 0.8689 |
| No log | 0.3105 | 86 | 0.7831 | 0.3277 | 0.7831 | 0.8849 |
| No log | 0.3177 | 88 | 0.7186 | 0.4085 | 0.7186 | 0.8477 |
| No log | 0.3249 | 90 | 0.7763 | 0.4085 | 0.7763 | 0.8811 |
| No log | 0.3321 | 92 | 0.6977 | 0.4085 | 0.6977 | 0.8353 |
| No log | 0.3394 | 94 | 0.6413 | 0.3277 | 0.6413 | 0.8008 |
| No log | 0.3466 | 96 | 0.8131 | 0.4085 | 0.8131 | 0.9017 |
| No log | 0.3538 | 98 | 1.2481 | 0.3077 | 1.2481 | 1.1172 |
| No log | 0.3610 | 100 | 1.4877 | 0.2708 | 1.4877 | 1.2197 |
| No log | 0.3682 | 102 | 1.3075 | 0.3145 | 1.3075 | 1.1435 |
| No log | 0.3755 | 104 | 0.8690 | 0.4085 | 0.8690 | 0.9322 |
| No log | 0.3827 | 106 | 0.5394 | 0.3201 | 0.5394 | 0.7344 |
| No log | 0.3899 | 108 | 0.5008 | 0.3980 | 0.5008 | 0.7077 |
| No log | 0.3971 | 110 | 0.6942 | 0.4503 | 0.6942 | 0.8332 |
| No log | 0.4043 | 112 | 1.1218 | 0.4112 | 1.1218 | 1.0591 |
| No log | 0.4116 | 114 | 1.0700 | 0.3736 | 1.0700 | 1.0344 |
| No log | 0.4188 | 116 | 0.7555 | 0.4085 | 0.7555 | 0.8692 |
| No log | 0.4260 | 118 | 0.5110 | 0.25 | 0.5110 | 0.7149 |
| No log | 0.4332 | 120 | 0.4507 | 0.2373 | 0.4507 | 0.6713 |
| No log | 0.4404 | 122 | 0.4655 | 0.3900 | 0.4655 | 0.6823 |
| No log | 0.4477 | 124 | 0.6058 | 0.4503 | 0.6058 | 0.7783 |
| No log | 0.4549 | 126 | 0.9602 | 0.4112 | 0.9602 | 0.9799 |
| No log | 0.4621 | 128 | 1.1441 | 0.2701 | 1.1441 | 1.0696 |
| No log | 0.4693 | 130 | 1.1725 | 0.2777 | 1.1725 | 1.0828 |
| No log | 0.4765 | 132 | 1.0399 | 0.3547 | 1.0399 | 1.0197 |
| No log | 0.4838 | 134 | 0.6757 | 0.4503 | 0.6757 | 0.8220 |
| No log | 0.4910 | 136 | 0.4514 | 0.3824 | 0.4514 | 0.6719 |
| No log | 0.4982 | 138 | 0.4481 | 0.3824 | 0.4481 | 0.6694 |
| No log | 0.5054 | 140 | 0.5083 | 0.1340 | 0.5083 | 0.7130 |
| No log | 0.5126 | 142 | 0.6746 | 0.4085 | 0.6746 | 0.8213 |
| No log | 0.5199 | 144 | 0.9387 | 0.3659 | 0.9387 | 0.9689 |
| No log | 0.5271 | 146 | 1.0689 | 0.3145 | 1.0689 | 1.0339 |
| No log | 0.5343 | 148 | 0.9266 | 0.4085 | 0.9266 | 0.9626 |
| No log | 0.5415 | 150 | 0.5831 | 0.4503 | 0.5831 | 0.7636 |
| No log | 0.5487 | 152 | 0.4306 | 0.5786 | 0.4306 | 0.6562 |
| No log | 0.5560 | 154 | 0.3904 | 0.6546 | 0.3904 | 0.6248 |
| No log | 0.5632 | 156 | 0.4107 | 0.6546 | 0.4107 | 0.6408 |
| No log | 0.5704 | 158 | 0.5344 | 0.5632 | 0.5344 | 0.7310 |
| No log | 0.5776 | 160 | 0.6099 | 0.5935 | 0.6099 | 0.7809 |
| No log | 0.5848 | 162 | 0.6165 | 0.5935 | 0.6165 | 0.7852 |
| No log | 0.5921 | 164 | 0.5206 | 0.5406 | 0.5206 | 0.7215 |
| No log | 0.5993 | 166 | 0.4986 | 0.6419 | 0.4986 | 0.7061 |
| No log | 0.6065 | 168 | 0.5202 | 0.6123 | 0.5202 | 0.7212 |
| No log | 0.6137 | 170 | 0.6122 | 0.5406 | 0.6122 | 0.7824 |
| No log | 0.6209 | 172 | 0.6628 | 0.5406 | 0.6628 | 0.8141 |
| No log | 0.6282 | 174 | 0.6920 | 0.5701 | 0.6920 | 0.8319 |
| No log | 0.6354 | 176 | 0.7284 | 0.5632 | 0.7284 | 0.8534 |
| No log | 0.6426 | 178 | 0.6926 | 0.5632 | 0.6926 | 0.8322 |
| No log | 0.6498 | 180 | 0.5794 | 0.5406 | 0.5794 | 0.7612 |
| No log | 0.6570 | 182 | 0.5187 | 0.6123 | 0.5187 | 0.7202 |
| No log | 0.6643 | 184 | 0.5937 | 0.6123 | 0.5937 | 0.7705 |
| No log | 0.6715 | 186 | 0.6348 | 0.6123 | 0.6348 | 0.7967 |
| No log | 0.6787 | 188 | 0.7821 | 0.4706 | 0.7821 | 0.8843 |
| No log | 0.6859 | 190 | 0.7878 | 0.5227 | 0.7878 | 0.8876 |
| No log | 0.6931 | 192 | 0.5766 | 0.5406 | 0.5766 | 0.7593 |
| No log | 0.7004 | 194 | 0.5025 | 0.5786 | 0.5025 | 0.7089 |
| No log | 0.7076 | 196 | 0.6735 | 0.4202 | 0.6735 | 0.8207 |
| No log | 0.7148 | 198 | 0.8307 | 0.3833 | 0.8307 | 0.9114 |
| No log | 0.7220 | 200 | 0.6776 | 0.4492 | 0.6776 | 0.8232 |
| No log | 0.7292 | 202 | 0.4791 | 0.4637 | 0.4791 | 0.6922 |
| No log | 0.7365 | 204 | 0.4001 | 0.6839 | 0.4001 | 0.6325 |
| No log | 0.7437 | 206 | 0.3907 | 0.6426 | 0.3907 | 0.6250 |
| No log | 0.7509 | 208 | 0.4220 | 0.5134 | 0.4220 | 0.6496 |
| No log | 0.7581 | 210 | 0.6331 | 0.4878 | 0.6331 | 0.7957 |
| No log | 0.7653 | 212 | 0.7664 | 0.4202 | 0.7664 | 0.8754 |
| No log | 0.7726 | 214 | 0.6932 | 0.6 | 0.6932 | 0.8326 |
| No log | 0.7798 | 216 | 0.5265 | 0.5406 | 0.5265 | 0.7256 |
| No log | 0.7870 | 218 | 0.4453 | 0.6546 | 0.4453 | 0.6673 |
| No log | 0.7942 | 220 | 0.4048 | 0.6917 | 0.4048 | 0.6363 |
| No log | 0.8014 | 222 | 0.4353 | 0.6546 | 0.4353 | 0.6598 |
| No log | 0.8087 | 224 | 0.5404 | 0.4878 | 0.5404 | 0.7351 |
| No log | 0.8159 | 226 | 0.6359 | 0.4878 | 0.6359 | 0.7974 |
| No log | 0.8231 | 228 | 0.5514 | 0.5258 | 0.5514 | 0.7426 |
| No log | 0.8303 | 230 | 0.4415 | 0.5786 | 0.4415 | 0.6645 |
| No log | 0.8375 | 232 | 0.4029 | 0.6602 | 0.4029 | 0.6348 |
| No log | 0.8448 | 234 | 0.4102 | 0.6091 | 0.4102 | 0.6405 |
| No log | 0.8520 | 236 | 0.4134 | 0.6602 | 0.4134 | 0.6430 |
| No log | 0.8592 | 238 | 0.5056 | 0.6866 | 0.5056 | 0.7111 |
| No log | 0.8664 | 240 | 0.6324 | 0.496 | 0.6324 | 0.7952 |
| No log | 0.8736 | 242 | 0.6236 | 0.5968 | 0.6236 | 0.7897 |
| No log | 0.8809 | 244 | 0.5313 | 0.6123 | 0.5313 | 0.7289 |
| No log | 0.8881 | 246 | 0.4973 | 0.6602 | 0.4973 | 0.7052 |
| No log | 0.8953 | 248 | 0.4619 | 0.6602 | 0.4619 | 0.6796 |
| No log | 0.9025 | 250 | 0.4713 | 0.6232 | 0.4713 | 0.6865 |
| No log | 0.9097 | 252 | 0.4896 | 0.6232 | 0.4896 | 0.6997 |
| No log | 0.9170 | 254 | 0.5583 | 0.6866 | 0.5583 | 0.7472 |
| No log | 0.9242 | 256 | 0.5697 | 0.6232 | 0.5697 | 0.7548 |
| No log | 0.9314 | 258 | 0.5570 | 0.6239 | 0.5570 | 0.7463 |
| No log | 0.9386 | 260 | 0.5794 | 0.6526 | 0.5794 | 0.7612 |
| No log | 0.9458 | 262 | 0.5873 | 0.6182 | 0.5873 | 0.7664 |
| No log | 0.9531 | 264 | 0.5303 | 0.6182 | 0.5303 | 0.7282 |
| No log | 0.9603 | 266 | 0.4873 | 0.6769 | 0.4873 | 0.6981 |
| No log | 0.9675 | 268 | 0.4762 | 0.5 | 0.4762 | 0.6901 |
| No log | 0.9747 | 270 | 0.5206 | 0.4913 | 0.5206 | 0.7215 |
| No log | 0.9819 | 272 | 0.5364 | 0.4177 | 0.5364 | 0.7324 |
| No log | 0.9892 | 274 | 0.5381 | 0.4177 | 0.5381 | 0.7336 |
| No log | 0.9964 | 276 | 0.5124 | 0.3846 | 0.5124 | 0.7159 |
| No log | 1.0036 | 278 | 0.4882 | 0.2986 | 0.4882 | 0.6987 |
| No log | 1.0108 | 280 | 0.5498 | 0.4597 | 0.5498 | 0.7415 |
| No log | 1.0181 | 282 | 0.6715 | 0.4503 | 0.6715 | 0.8195 |
| No log | 1.0253 | 284 | 0.5703 | 0.4597 | 0.5703 | 0.7552 |
| No log | 1.0325 | 286 | 0.4890 | 0.5786 | 0.4890 | 0.6993 |
| No log | 1.0397 | 288 | 0.4295 | 0.5786 | 0.4295 | 0.6554 |
| No log | 1.0469 | 290 | 0.4403 | 0.5786 | 0.4403 | 0.6636 |
| No log | 1.0542 | 292 | 0.4961 | 0.5786 | 0.4961 | 0.7043 |
| No log | 1.0614 | 294 | 0.5846 | 0.5786 | 0.5846 | 0.7646 |
| No log | 1.0686 | 296 | 0.6168 | 0.5786 | 0.6168 | 0.7853 |
| No log | 1.0758 | 298 | 0.5281 | 0.5786 | 0.5281 | 0.7267 |
| No log | 1.0830 | 300 | 0.4804 | 0.5922 | 0.4804 | 0.6931 |
| No log | 1.0903 | 302 | 0.4802 | 0.6232 | 0.4802 | 0.6929 |
| No log | 1.0975 | 304 | 0.5024 | 0.5786 | 0.5024 | 0.7088 |
| No log | 1.1047 | 306 | 0.5951 | 0.6105 | 0.5951 | 0.7714 |
| No log | 1.1119 | 308 | 0.7908 | 0.3458 | 0.7908 | 0.8893 |
| No log | 1.1191 | 310 | 0.8272 | 0.3458 | 0.8272 | 0.9095 |
| No log | 1.1264 | 312 | 0.6720 | 0.4503 | 0.6720 | 0.8197 |
| No log | 1.1336 | 314 | 0.5071 | 0.5786 | 0.5071 | 0.7121 |
| No log | 1.1408 | 316 | 0.4670 | 0.6232 | 0.4670 | 0.6834 |
| No log | 1.1480 | 318 | 0.4648 | 0.6232 | 0.4648 | 0.6818 |
| No log | 1.1552 | 320 | 0.4855 | 0.6232 | 0.4855 | 0.6968 |
| No log | 1.1625 | 322 | 0.5408 | 0.5473 | 0.5408 | 0.7354 |
| No log | 1.1697 | 324 | 0.5421 | 0.5473 | 0.5421 | 0.7363 |
| No log | 1.1769 | 326 | 0.5057 | 0.6232 | 0.5057 | 0.7111 |
| No log | 1.1841 | 328 | 0.5304 | 0.5473 | 0.5304 | 0.7283 |
| No log | 1.1913 | 330 | 0.5509 | 0.5786 | 0.5509 | 0.7422 |
| No log | 1.1986 | 332 | 0.4814 | 0.6546 | 0.4814 | 0.6939 |
| No log | 1.2058 | 334 | 0.4710 | 0.6866 | 0.4710 | 0.6863 |
| No log | 1.2130 | 336 | 0.4801 | 0.4688 | 0.4801 | 0.6929 |
| No log | 1.2202 | 338 | 0.5929 | 0.4187 | 0.5929 | 0.7700 |
| No log | 1.2274 | 340 | 0.7543 | 0.3833 | 0.7543 | 0.8685 |
| No log | 1.2347 | 342 | 0.7065 | 0.3803 | 0.7065 | 0.8406 |
| No log | 1.2419 | 344 | 0.5430 | 0.5 | 0.5430 | 0.7369 |
| No log | 1.2491 | 346 | 0.4356 | 0.6526 | 0.4356 | 0.6600 |
| No log | 1.2563 | 348 | 0.4450 | 0.5772 | 0.4450 | 0.6671 |
| No log | 1.2635 | 350 | 0.4364 | 0.7455 | 0.4364 | 0.6606 |
| No log | 1.2708 | 352 | 0.5341 | 0.5039 | 0.5341 | 0.7308 |
| No log | 1.2780 | 354 | 0.6419 | 0.4503 | 0.6419 | 0.8012 |
| No log | 1.2852 | 356 | 0.6591 | 0.4492 | 0.6591 | 0.8118 |
| No log | 1.2924 | 358 | 0.5415 | 0.5039 | 0.5415 | 0.7358 |
| No log | 1.2996 | 360 | 0.4750 | 0.6769 | 0.4750 | 0.6892 |
| No log | 1.3069 | 362 | 0.4612 | 0.6769 | 0.4612 | 0.6791 |
| No log | 1.3141 | 364 | 0.4561 | 0.6769 | 0.4561 | 0.6753 |
| No log | 1.3213 | 366 | 0.5131 | 0.475 | 0.5131 | 0.7163 |
| No log | 1.3285 | 368 | 0.5976 | 0.4597 | 0.5976 | 0.7730 |
| No log | 1.3357 | 370 | 0.6277 | 0.4581 | 0.6277 | 0.7923 |
| No log | 1.3430 | 372 | 0.6125 | 0.4581 | 0.6125 | 0.7826 |
| No log | 1.3502 | 374 | 0.5415 | 0.5786 | 0.5415 | 0.7358 |
| No log | 1.3574 | 376 | 0.4825 | 0.6818 | 0.4825 | 0.6946 |
| No log | 1.3646 | 378 | 0.4896 | 0.6818 | 0.4896 | 0.6997 |
| No log | 1.3718 | 380 | 0.5148 | 0.6686 | 0.5148 | 0.7175 |
| No log | 1.3791 | 382 | 0.5181 | 0.6686 | 0.5181 | 0.7198 |
| No log | 1.3863 | 384 | 0.5212 | 0.6719 | 0.5212 | 0.7219 |
| No log | 1.3935 | 386 | 0.4834 | 0.6719 | 0.4834 | 0.6953 |
| No log | 1.4007 | 388 | 0.4912 | 0.6719 | 0.4912 | 0.7008 |
| No log | 1.4079 | 390 | 0.5189 | 0.5701 | 0.5189 | 0.7203 |
| No log | 1.4152 | 392 | 0.5133 | 0.6719 | 0.5133 | 0.7165 |
| No log | 1.4224 | 394 | 0.4644 | 0.6419 | 0.4644 | 0.6815 |
| No log | 1.4296 | 396 | 0.4177 | 0.6546 | 0.4177 | 0.6463 |
| No log | 1.4368 | 398 | 0.4094 | 0.6866 | 0.4094 | 0.6398 |
| No log | 1.4440 | 400 | 0.4066 | 0.6866 | 0.4066 | 0.6377 |
| No log | 1.4513 | 402 | 0.4068 | 0.6473 | 0.4068 | 0.6378 |
| No log | 1.4585 | 404 | 0.4227 | 0.6358 | 0.4227 | 0.6502 |
| No log | 1.4657 | 406 | 0.4187 | 0.6358 | 0.4187 | 0.6470 |
| No log | 1.4729 | 408 | 0.4281 | 0.6358 | 0.4281 | 0.6543 |
| No log | 1.4801 | 410 | 0.4317 | 0.6638 | 0.4317 | 0.6571 |
| No log | 1.4874 | 412 | 0.5202 | 0.7208 | 0.5202 | 0.7213 |
| No log | 1.4946 | 414 | 0.5608 | 0.4878 | 0.5608 | 0.7489 |
| No log | 1.5018 | 416 | 0.5135 | 0.4177 | 0.5135 | 0.7166 |
| No log | 1.5090 | 418 | 0.4426 | 0.4268 | 0.4426 | 0.6653 |
| No log | 1.5162 | 420 | 0.3953 | 0.5882 | 0.3953 | 0.6288 |
| No log | 1.5235 | 422 | 0.3807 | 0.7032 | 0.3807 | 0.6170 |
| No log | 1.5307 | 424 | 0.4376 | 0.7208 | 0.4376 | 0.6615 |
| No log | 1.5379 | 426 | 0.6026 | 0.6471 | 0.6026 | 0.7763 |
| No log | 1.5451 | 428 | 0.6157 | 0.6471 | 0.6157 | 0.7846 |
| No log | 1.5523 | 430 | 0.5093 | 0.6818 | 0.5093 | 0.7137 |
| No log | 1.5596 | 432 | 0.5089 | 0.6450 | 0.5089 | 0.7134 |
| No log | 1.5668 | 434 | 0.5387 | 0.6450 | 0.5387 | 0.7339 |
| No log | 1.5740 | 436 | 0.6248 | 0.6306 | 0.6248 | 0.7905 |
| No log | 1.5812 | 438 | 0.8070 | 0.5342 | 0.8070 | 0.8983 |
| No log | 1.5884 | 440 | 0.7908 | 0.5658 | 0.7908 | 0.8893 |
| No log | 1.5957 | 442 | 0.6552 | 0.5939 | 0.6552 | 0.8094 |
| No log | 1.6029 | 444 | 0.4918 | 0.6358 | 0.4918 | 0.7013 |
| No log | 1.6101 | 446 | 0.4078 | 0.6473 | 0.4078 | 0.6386 |
| No log | 1.6173 | 448 | 0.4017 | 0.6966 | 0.4017 | 0.6338 |
| No log | 1.6245 | 450 | 0.3923 | 0.6473 | 0.3923 | 0.6263 |
| No log | 1.6318 | 452 | 0.4249 | 0.6769 | 0.4249 | 0.6518 |
| No log | 1.6390 | 454 | 0.5655 | 0.5625 | 0.5655 | 0.7520 |
| No log | 1.6462 | 456 | 0.7040 | 0.4878 | 0.7040 | 0.8391 |
| No log | 1.6534 | 458 | 0.6976 | 0.4878 | 0.6976 | 0.8352 |
| No log | 1.6606 | 460 | 0.5792 | 0.5632 | 0.5792 | 0.7610 |
| No log | 1.6679 | 462 | 0.5149 | 0.5689 | 0.5149 | 0.7176 |
| No log | 1.6751 | 464 | 0.4342 | 0.6358 | 0.4342 | 0.6589 |
| No log | 1.6823 | 466 | 0.4255 | 0.6866 | 0.4255 | 0.6523 |
| No log | 1.6895 | 468 | 0.4397 | 0.6866 | 0.4397 | 0.6631 |
| No log | 1.6968 | 470 | 0.4947 | 0.6358 | 0.4947 | 0.7033 |
| No log | 1.7040 | 472 | 0.5264 | 0.5415 | 0.5264 | 0.7256 |
| No log | 1.7112 | 474 | 0.5177 | 0.5701 | 0.5177 | 0.7195 |
| No log | 1.7184 | 476 | 0.5637 | 0.5632 | 0.5637 | 0.7508 |
| No log | 1.7256 | 478 | 0.5788 | 0.4878 | 0.5788 | 0.7608 |
| No log | 1.7329 | 480 | 0.6635 | 0.4878 | 0.6635 | 0.8146 |
| No log | 1.7401 | 482 | 0.6588 | 0.4878 | 0.6588 | 0.8117 |
| No log | 1.7473 | 484 | 0.5988 | 0.4913 | 0.5988 | 0.7738 |
| No log | 1.7545 | 486 | 0.5713 | 0.4913 | 0.5713 | 0.7559 |
| No log | 1.7617 | 488 | 0.5389 | 0.496 | 0.5389 | 0.7341 |
| No log | 1.7690 | 490 | 0.4633 | 0.5767 | 0.4633 | 0.6806 |
| No log | 1.7762 | 492 | 0.4275 | 0.6526 | 0.4275 | 0.6538 |
| No log | 1.7834 | 494 | 0.4882 | 0.6702 | 0.4882 | 0.6987 |
| No log | 1.7906 | 496 | 0.5150 | 0.6702 | 0.5150 | 0.7177 |
| No log | 1.7978 | 498 | 0.5078 | 0.6778 | 0.5078 | 0.7126 |
| 0.4635 | 1.8051 | 500 | 0.5264 | 0.6686 | 0.5264 | 0.7255 |
| 0.4635 | 1.8123 | 502 | 0.5071 | 0.6686 | 0.5071 | 0.7121 |
| 0.4635 | 1.8195 | 504 | 0.4890 | 0.5751 | 0.4890 | 0.6993 |
| 0.4635 | 1.8267 | 506 | 0.4451 | 0.6818 | 0.4451 | 0.6671 |
| 0.4635 | 1.8339 | 508 | 0.4112 | 0.6656 | 0.4112 | 0.6412 |
| 0.4635 | 1.8412 | 510 | 0.4044 | 0.6966 | 0.4044 | 0.6359 |
| 0.4635 | 1.8484 | 512 | 0.4282 | 0.5832 | 0.4282 | 0.6544 |
| 0.4635 | 1.8556 | 514 | 0.4579 | 0.5396 | 0.4579 | 0.6767 |
| 0.4635 | 1.8628 | 516 | 0.5049 | 0.4913 | 0.5049 | 0.7106 |
| 0.4635 | 1.8700 | 518 | 0.5883 | 0.4878 | 0.5883 | 0.7670 |
| 0.4635 | 1.8773 | 520 | 0.5972 | 0.4878 | 0.5972 | 0.7728 |
| 0.4635 | 1.8845 | 522 | 0.5210 | 0.5333 | 0.5210 | 0.7218 |
| 0.4635 | 1.8917 | 524 | 0.4319 | 0.5832 | 0.4319 | 0.6572 |
| 0.4635 | 1.8989 | 526 | 0.4014 | 0.7014 | 0.4014 | 0.6336 |
| 0.4635 | 1.9061 | 528 | 0.4170 | 0.7014 | 0.4170 | 0.6458 |
| 0.4635 | 1.9134 | 530 | 0.4293 | 0.6866 | 0.4293 | 0.6552 |
| 0.4635 | 1.9206 | 532 | 0.4745 | 0.5832 | 0.4745 | 0.6889 |
| 0.4635 | 1.9278 | 534 | 0.5848 | 0.5333 | 0.5848 | 0.7647 |
| 0.4635 | 1.9350 | 536 | 0.6664 | 0.3927 | 0.6664 | 0.8163 |
| 0.4635 | 1.9422 | 538 | 0.6276 | 0.3927 | 0.6276 | 0.7922 |
| 0.4635 | 1.9495 | 540 | 0.5441 | 0.5714 | 0.5441 | 0.7377 |
| 0.4635 | 1.9567 | 542 | 0.4678 | 0.4389 | 0.4678 | 0.6840 |
| 0.4635 | 1.9639 | 544 | 0.4343 | 0.5898 | 0.4343 | 0.6590 |
| 0.4635 | 1.9711 | 546 | 0.4307 | 0.5977 | 0.4307 | 0.6563 |
| 0.4635 | 1.9783 | 548 | 0.4364 | 0.5868 | 0.4364 | 0.6606 |
| 0.4635 | 1.9856 | 550 | 0.4868 | 0.5767 | 0.4868 | 0.6977 |
| 0.4635 | 1.9928 | 552 | 0.5853 | 0.5035 | 0.5853 | 0.7651 |
| 0.4635 | 2.0 | 554 | 0.5851 | 0.5035 | 0.5851 | 0.7649 |
| 0.4635 | 2.0072 | 556 | 0.5642 | 0.5035 | 0.5642 | 0.7511 |
| 0.4635 | 2.0144 | 558 | 0.5119 | 0.6818 | 0.5119 | 0.7155 |
| 0.4635 | 2.0217 | 560 | 0.4924 | 0.6818 | 0.4924 | 0.7017 |
| 0.4635 | 2.0289 | 562 | 0.5140 | 0.6473 | 0.5140 | 0.7170 |
| 0.4635 | 2.0361 | 564 | 0.5939 | 0.5035 | 0.5939 | 0.7707 |
| 0.4635 | 2.0433 | 566 | 0.6868 | 0.4687 | 0.6868 | 0.8288 |
| 0.4635 | 2.0505 | 568 | 0.7112 | 0.4687 | 0.7112 | 0.8433 |
| 0.4635 | 2.0578 | 570 | 0.6729 | 0.4687 | 0.6729 | 0.8203 |
| 0.4635 | 2.0650 | 572 | 0.5999 | 0.5035 | 0.5999 | 0.7745 |
| 0.4635 | 2.0722 | 574 | 0.5535 | 0.5767 | 0.5535 | 0.7440 |
| 0.4635 | 2.0794 | 576 | 0.5632 | 0.5767 | 0.5632 | 0.7505 |
| 0.4635 | 2.0866 | 578 | 0.5419 | 0.5767 | 0.5419 | 0.7361 |
| 0.4635 | 2.0939 | 580 | 0.5182 | 0.5767 | 0.5182 | 0.7198 |
| 0.4635 | 2.1011 | 582 | 0.5289 | 0.4776 | 0.5289 | 0.7272 |
| 0.4635 | 2.1083 | 584 | 0.5615 | 0.4286 | 0.5615 | 0.7494 |
| 0.4635 | 2.1155 | 586 | 0.5853 | 0.4187 | 0.5853 | 0.7650 |
| 0.4635 | 2.1227 | 588 | 0.5532 | 0.5396 | 0.5532 | 0.7438 |
| 0.4635 | 2.1300 | 590 | 0.5308 | 0.5396 | 0.5308 | 0.7285 |
| 0.4635 | 2.1372 | 592 | 0.5369 | 0.5477 | 0.5369 | 0.7327 |
| 0.4635 | 2.1444 | 594 | 0.4945 | 0.5832 | 0.4945 | 0.7032 |
| 0.4635 | 2.1516 | 596 | 0.4733 | 0.6818 | 0.4733 | 0.6880 |
| 0.4635 | 2.1588 | 598 | 0.4547 | 0.6416 | 0.4547 | 0.6743 |
| 0.4635 | 2.1661 | 600 | 0.4631 | 0.4861 | 0.4631 | 0.6805 |
| 0.4635 | 2.1733 | 602 | 0.5163 | 0.3877 | 0.5163 | 0.7185 |
| 0.4635 | 2.1805 | 604 | 0.6259 | 0.4503 | 0.6259 | 0.7911 |
| 0.4635 | 2.1877 | 606 | 0.7307 | 0.3458 | 0.7307 | 0.8548 |
| 0.4635 | 2.1949 | 608 | 0.7051 | 0.3458 | 0.7051 | 0.8397 |
| 0.4635 | 2.2022 | 610 | 0.6298 | 0.4492 | 0.6298 | 0.7936 |
| 0.4635 | 2.2094 | 612 | 0.5706 | 0.4 | 0.5706 | 0.7554 |
| 0.4635 | 2.2166 | 614 | 0.5357 | 0.4378 | 0.5357 | 0.7319 |
| 0.4635 | 2.2238 | 616 | 0.5211 | 0.5406 | 0.5211 | 0.7219 |
| 0.4635 | 2.2310 | 618 | 0.5302 | 0.5406 | 0.5302 | 0.7282 |
| 0.4635 | 2.2383 | 620 | 0.5310 | 0.5832 | 0.5310 | 0.7287 |
| 0.4635 | 2.2455 | 622 | 0.5376 | 0.6818 | 0.5376 | 0.7332 |
| 0.4635 | 2.2527 | 624 | 0.5512 | 0.7158 | 0.5512 | 0.7424 |
| 0.4635 | 2.2599 | 626 | 0.5578 | 0.6686 | 0.5578 | 0.7469 |
| 0.4635 | 2.2671 | 628 | 0.5325 | 0.5751 | 0.5325 | 0.7297 |
| 0.4635 | 2.2744 | 630 | 0.4806 | 0.7009 | 0.4806 | 0.6932 |
| 0.4635 | 2.2816 | 632 | 0.4301 | 0.7158 | 0.4301 | 0.6558 |
| 0.4635 | 2.2888 | 634 | 0.4178 | 0.6866 | 0.4178 | 0.6464 |
| 0.4635 | 2.2960 | 636 | 0.4097 | 0.7158 | 0.4097 | 0.6401 |
| 0.4635 | 2.3032 | 638 | 0.4177 | 0.7158 | 0.4177 | 0.6463 |
| 0.4635 | 2.3105 | 640 | 0.4570 | 0.7009 | 0.4570 | 0.6760 |
| 0.4635 | 2.3177 | 642 | 0.4769 | 0.7009 | 0.4769 | 0.6906 |
| 0.4635 | 2.3249 | 644 | 0.4742 | 0.7009 | 0.4742 | 0.6886 |
| 0.4635 | 2.3321 | 646 | 0.4734 | 0.7158 | 0.4734 | 0.6880 |
| 0.4635 | 2.3394 | 648 | 0.4763 | 0.6578 | 0.4763 | 0.6902 |
| 0.4635 | 2.3466 | 650 | 0.4862 | 0.6294 | 0.4862 | 0.6973 |
| 0.4635 | 2.3538 | 652 | 0.4637 | 0.6866 | 0.4637 | 0.6809 |
| 0.4635 | 2.3610 | 654 | 0.4687 | 0.7158 | 0.4687 | 0.6846 |
| 0.4635 | 2.3682 | 656 | 0.5341 | 0.6686 | 0.5341 | 0.7308 |
| 0.4635 | 2.3755 | 658 | 0.6368 | 0.4375 | 0.6368 | 0.7980 |
| 0.4635 | 2.3827 | 660 | 0.6687 | 0.4034 | 0.6687 | 0.8177 |
| 0.4635 | 2.3899 | 662 | 0.5927 | 0.4375 | 0.5927 | 0.7699 |
| 0.4635 | 2.3971 | 664 | 0.5109 | 0.5073 | 0.5109 | 0.7148 |
| 0.4635 | 2.4043 | 666 | 0.4591 | 0.7158 | 0.4591 | 0.6775 |
| 0.4635 | 2.4116 | 668 | 0.4391 | 0.7158 | 0.4391 | 0.6626 |
| 0.4635 | 2.4188 | 670 | 0.4430 | 0.7158 | 0.4430 | 0.6656 |
| 0.4635 | 2.4260 | 672 | 0.4470 | 0.7158 | 0.4470 | 0.6686 |
| 0.4635 | 2.4332 | 674 | 0.4678 | 0.7158 | 0.4678 | 0.6840 |
| 0.4635 | 2.4404 | 676 | 0.5049 | 0.5415 | 0.5049 | 0.7106 |
| 0.4635 | 2.4477 | 678 | 0.6016 | 0.4648 | 0.6016 | 0.7756 |
| 0.4635 | 2.4549 | 680 | 0.6627 | 0.4492 | 0.6627 | 0.8141 |
| 0.4635 | 2.4621 | 682 | 0.6275 | 0.4492 | 0.6275 | 0.7921 |
| 0.4635 | 2.4693 | 684 | 0.5863 | 0.4492 | 0.5863 | 0.7657 |
| 0.4635 | 2.4765 | 686 | 0.5532 | 0.4195 | 0.5532 | 0.7438 |
| 0.4635 | 2.4838 | 688 | 0.5492 | 0.4581 | 0.5492 | 0.7411 |
| 0.4635 | 2.4910 | 690 | 0.5222 | 0.4106 | 0.5222 | 0.7226 |
| 0.4635 | 2.4982 | 692 | 0.5038 | 0.5116 | 0.5038 | 0.7098 |
| 0.4635 | 2.5054 | 694 | 0.5320 | 0.5116 | 0.5320 | 0.7294 |
| 0.4635 | 2.5126 | 696 | 0.5916 | 0.5073 | 0.5916 | 0.7691 |
| 0.4635 | 2.5199 | 698 | 0.6102 | 0.5073 | 0.6102 | 0.7812 |
| 0.4635 | 2.5271 | 700 | 0.5982 | 0.6358 | 0.5982 | 0.7734 |
| 0.4635 | 2.5343 | 702 | 0.5630 | 0.6818 | 0.5630 | 0.7503 |
| 0.4635 | 2.5415 | 704 | 0.5493 | 0.6473 | 0.5493 | 0.7412 |
| 0.4635 | 2.5487 | 706 | 0.5281 | 0.6473 | 0.5281 | 0.7267 |
| 0.4635 | 2.5560 | 708 | 0.4929 | 0.6473 | 0.4929 | 0.7021 |
| 0.4635 | 2.5632 | 710 | 0.4821 | 0.7158 | 0.4821 | 0.6944 |
| 0.4635 | 2.5704 | 712 | 0.4824 | 0.7158 | 0.4824 | 0.6945 |
| 0.4635 | 2.5776 | 714 | 0.4853 | 0.6866 | 0.4853 | 0.6967 |
| 0.4635 | 2.5848 | 716 | 0.4949 | 0.6818 | 0.4949 | 0.7035 |
| 0.4635 | 2.5921 | 718 | 0.5199 | 0.5415 | 0.5199 | 0.7211 |
| 0.4635 | 2.5993 | 720 | 0.5313 | 0.5415 | 0.5313 | 0.7289 |
| 0.4635 | 2.6065 | 722 | 0.5270 | 0.5415 | 0.5270 | 0.7259 |
| 0.4635 | 2.6137 | 724 | 0.5023 | 0.5751 | 0.5023 | 0.7087 |
| 0.4635 | 2.6209 | 726 | 0.4826 | 0.6818 | 0.4826 | 0.6947 |
| 0.4635 | 2.6282 | 728 | 0.4640 | 0.6866 | 0.4640 | 0.6812 |
| 0.4635 | 2.6354 | 730 | 0.4689 | 0.6866 | 0.4689 | 0.6848 |
| 0.4635 | 2.6426 | 732 | 0.4791 | 0.6866 | 0.4791 | 0.6922 |
| 0.4635 | 2.6498 | 734 | 0.4695 | 0.6866 | 0.4695 | 0.6852 |
| 0.4635 | 2.6570 | 736 | 0.4856 | 0.5812 | 0.4856 | 0.6969 |
| 0.4635 | 2.6643 | 738 | 0.5668 | 0.5415 | 0.5668 | 0.7529 |
| 0.4635 | 2.6715 | 740 | 0.5798 | 0.5415 | 0.5798 | 0.7615 |
| 0.4635 | 2.6787 | 742 | 0.5964 | 0.5689 | 0.5964 | 0.7723 |
| 0.4635 | 2.6859 | 744 | 0.5615 | 0.5415 | 0.5615 | 0.7493 |
| 0.4635 | 2.6931 | 746 | 0.5611 | 0.5145 | 0.5611 | 0.7491 |
| 0.4635 | 2.7004 | 748 | 0.5201 | 0.5812 | 0.5201 | 0.7212 |
| 0.4635 | 2.7076 | 750 | 0.5191 | 0.6866 | 0.5191 | 0.7205 |
| 0.4635 | 2.7148 | 752 | 0.5594 | 0.5812 | 0.5594 | 0.7479 |
| 0.4635 | 2.7220 | 754 | 0.6015 | 0.5481 | 0.6015 | 0.7755 |
| 0.4635 | 2.7292 | 756 | 0.6774 | 0.5415 | 0.6774 | 0.8230 |
| 0.4635 | 2.7365 | 758 | 0.7529 | 0.4687 | 0.7529 | 0.8677 |
| 0.4635 | 2.7437 | 760 | 0.7656 | 0.4687 | 0.7656 | 0.8750 |
| 0.4635 | 2.7509 | 762 | 0.7155 | 0.5294 | 0.7155 | 0.8459 |
| 0.4635 | 2.7581 | 764 | 0.6567 | 0.5347 | 0.6567 | 0.8104 |
| 0.4635 | 2.7653 | 766 | 0.5740 | 0.5406 | 0.5740 | 0.7576 |
| 0.4635 | 2.7726 | 768 | 0.5309 | 0.4861 | 0.5309 | 0.7286 |
| 0.4635 | 2.7798 | 770 | 0.4978 | 0.4896 | 0.4978 | 0.7056 |
| 0.4635 | 2.7870 | 772 | 0.5243 | 0.5617 | 0.5243 | 0.7241 |
| 0.4635 | 2.7942 | 774 | 0.5617 | 0.4928 | 0.5617 | 0.7495 |
| 0.4635 | 2.8014 | 776 | 0.5321 | 0.5610 | 0.5321 | 0.7294 |
| 0.4635 | 2.8087 | 778 | 0.4908 | 0.6656 | 0.4908 | 0.7006 |
| 0.4635 | 2.8159 | 780 | 0.5261 | 0.5477 | 0.5261 | 0.7254 |
| 0.4635 | 2.8231 | 782 | 0.5961 | 0.5689 | 0.5961 | 0.7721 |
| 0.4635 | 2.8303 | 784 | 0.5918 | 0.5689 | 0.5918 | 0.7693 |
| 0.4635 | 2.8375 | 786 | 0.5380 | 0.5477 | 0.5380 | 0.7335 |
| 0.4635 | 2.8448 | 788 | 0.4909 | 0.4861 | 0.4909 | 0.7006 |
| 0.4635 | 2.8520 | 790 | 0.4901 | 0.6619 | 0.4901 | 0.7000 |
| 0.4635 | 2.8592 | 792 | 0.5170 | 0.6535 | 0.5170 | 0.7190 |
| 0.4635 | 2.8664 | 794 | 0.5217 | 0.6535 | 0.5217 | 0.7223 |
| 0.4635 | 2.8736 | 796 | 0.5179 | 0.6407 | 0.5179 | 0.7197 |
| 0.4635 | 2.8809 | 798 | 0.5346 | 0.6473 | 0.5346 | 0.7312 |
| 0.4635 | 2.8881 | 800 | 0.5935 | 0.5415 | 0.5935 | 0.7704 |
| 0.4635 | 2.8953 | 802 | 0.6264 | 0.5415 | 0.6264 | 0.7915 |
| 0.4635 | 2.9025 | 804 | 0.6224 | 0.5415 | 0.6224 | 0.7889 |
| 0.4635 | 2.9097 | 806 | 0.6667 | 0.5415 | 0.6667 | 0.8165 |
| 0.4635 | 2.9170 | 808 | 0.6952 | 0.4631 | 0.6952 | 0.8338 |
| 0.4635 | 2.9242 | 810 | 0.6354 | 0.4648 | 0.6354 | 0.7971 |
| 0.4635 | 2.9314 | 812 | 0.5677 | 0.5689 | 0.5677 | 0.7534 |
| 0.4635 | 2.9386 | 814 | 0.5320 | 0.5415 | 0.5320 | 0.7294 |
| 0.4635 | 2.9458 | 816 | 0.4970 | 0.5477 | 0.4970 | 0.7050 |
| 0.4635 | 2.9531 | 818 | 0.4807 | 0.5477 | 0.4807 | 0.6933 |
| 0.4635 | 2.9603 | 820 | 0.4673 | 0.5546 | 0.4673 | 0.6836 |
| 0.4635 | 2.9675 | 822 | 0.4681 | 0.5477 | 0.4681 | 0.6841 |
| 0.4635 | 2.9747 | 824 | 0.4686 | 0.5477 | 0.4686 | 0.6845 |
| 0.4635 | 2.9819 | 826 | 0.4717 | 0.5477 | 0.4717 | 0.6868 |
| 0.4635 | 2.9892 | 828 | 0.4955 | 0.5477 | 0.4955 | 0.7039 |
| 0.4635 | 2.9964 | 830 | 0.5314 | 0.5415 | 0.5314 | 0.7290 |
| 0.4635 | 3.0036 | 832 | 0.5802 | 0.5689 | 0.5802 | 0.7617 |
| 0.4635 | 3.0108 | 834 | 0.5664 | 0.4727 | 0.5664 | 0.7526 |
| 0.4635 | 3.0181 | 836 | 0.5473 | 0.5689 | 0.5473 | 0.7398 |
| 0.4635 | 3.0253 | 838 | 0.4947 | 0.5477 | 0.4947 | 0.7033 |
| 0.4635 | 3.0325 | 840 | 0.4646 | 0.6802 | 0.4646 | 0.6816 |
| 0.4635 | 3.0397 | 842 | 0.4852 | 0.6708 | 0.4852 | 0.6965 |
| 0.4635 | 3.0469 | 844 | 0.5211 | 0.6015 | 0.5211 | 0.7219 |
| 0.4635 | 3.0542 | 846 | 0.5423 | 0.6352 | 0.5423 | 0.7364 |
| 0.4635 | 3.0614 | 848 | 0.5269 | 0.7051 | 0.5269 | 0.7259 |
| 0.4635 | 3.0686 | 850 | 0.5255 | 0.6916 | 0.5255 | 0.7249 |
| 0.4635 | 3.0758 | 852 | 0.5120 | 0.7009 | 0.5120 | 0.7156 |
| 0.4635 | 3.0830 | 854 | 0.4864 | 0.7009 | 0.4864 | 0.6974 |
| 0.4635 | 3.0903 | 856 | 0.4535 | 0.7158 | 0.4535 | 0.6734 |
| 0.4635 | 3.0975 | 858 | 0.4246 | 0.7158 | 0.4246 | 0.6516 |
| 0.4635 | 3.1047 | 860 | 0.4171 | 0.7158 | 0.4171 | 0.6458 |
| 0.4635 | 3.1119 | 862 | 0.4172 | 0.7158 | 0.4172 | 0.6459 |
| 0.4635 | 3.1191 | 864 | 0.4267 | 0.7158 | 0.4267 | 0.6532 |
| 0.4635 | 3.1264 | 866 | 0.4494 | 0.7158 | 0.4494 | 0.6704 |
| 0.4635 | 3.1336 | 868 | 0.4603 | 0.7158 | 0.4603 | 0.6784 |
| 0.4635 | 3.1408 | 870 | 0.4694 | 0.7158 | 0.4694 | 0.6851 |
| 0.4635 | 3.1480 | 872 | 0.4715 | 0.7158 | 0.4715 | 0.6867 |
| 0.4635 | 3.1552 | 874 | 0.4715 | 0.7158 | 0.4715 | 0.6866 |
| 0.4635 | 3.1625 | 876 | 0.4795 | 0.7009 | 0.4795 | 0.6924 |
| 0.4635 | 3.1697 | 878 | 0.4950 | 0.6083 | 0.4950 | 0.7036 |
| 0.4635 | 3.1769 | 880 | 0.4948 | 0.6026 | 0.4948 | 0.7034 |
| 0.4635 | 3.1841 | 882 | 0.4726 | 0.6123 | 0.4726 | 0.6874 |
| 0.4635 | 3.1913 | 884 | 0.4696 | 0.5767 | 0.4696 | 0.6852 |
| 0.4635 | 3.1986 | 886 | 0.5091 | 0.4378 | 0.5091 | 0.7135 |
| 0.4635 | 3.2058 | 888 | 0.5173 | 0.4288 | 0.5173 | 0.7192 |
| 0.4635 | 3.2130 | 890 | 0.5137 | 0.4727 | 0.5137 | 0.7167 |
| 0.4635 | 3.2202 | 892 | 0.5029 | 0.5689 | 0.5029 | 0.7092 |
| 0.4635 | 3.2274 | 894 | 0.4641 | 0.6083 | 0.4641 | 0.6812 |
| 0.4635 | 3.2347 | 896 | 0.4172 | 0.7158 | 0.4172 | 0.6459 |
| 0.4635 | 3.2419 | 898 | 0.3949 | 0.7158 | 0.3949 | 0.6284 |
| 0.4635 | 3.2491 | 900 | 0.3988 | 0.7158 | 0.3988 | 0.6315 |
| 0.4635 | 3.2563 | 902 | 0.4184 | 0.6866 | 0.4184 | 0.6468 |
| 0.4635 | 3.2635 | 904 | 0.4428 | 0.6578 | 0.4428 | 0.6654 |
| 0.4635 | 3.2708 | 906 | 0.4520 | 0.6866 | 0.4520 | 0.6723 |
| 0.4635 | 3.2780 | 908 | 0.4399 | 0.6866 | 0.4399 | 0.6633 |
| 0.4635 | 3.2852 | 910 | 0.4196 | 0.6866 | 0.4196 | 0.6478 |
| 0.4635 | 3.2924 | 912 | 0.4044 | 0.7158 | 0.4044 | 0.6359 |
| 0.4635 | 3.2996 | 914 | 0.4025 | 0.7158 | 0.4025 | 0.6345 |
| 0.4635 | 3.3069 | 916 | 0.4145 | 0.7009 | 0.4145 | 0.6438 |
| 0.4635 | 3.3141 | 918 | 0.4229 | 0.6686 | 0.4229 | 0.6503 |
| 0.4635 | 3.3213 | 920 | 0.4322 | 0.6473 | 0.4322 | 0.6574 |
| 0.4635 | 3.3285 | 922 | 0.4200 | 0.6818 | 0.4200 | 0.6480 |
| 0.4635 | 3.3357 | 924 | 0.4195 | 0.6818 | 0.4195 | 0.6477 |
| 0.4635 | 3.3430 | 926 | 0.4187 | 0.7158 | 0.4187 | 0.6470 |
| 0.4635 | 3.3502 | 928 | 0.4289 | 0.7158 | 0.4289 | 0.6549 |
| 0.4635 | 3.3574 | 930 | 0.4395 | 0.7158 | 0.4395 | 0.6630 |
| 0.4635 | 3.3646 | 932 | 0.4403 | 0.7158 | 0.4403 | 0.6636 |
| 0.4635 | 3.3718 | 934 | 0.4524 | 0.7009 | 0.4524 | 0.6726 |
| 0.4635 | 3.3791 | 936 | 0.4605 | 0.7288 | 0.4605 | 0.6786 |
| 0.4635 | 3.3863 | 938 | 0.4477 | 0.7009 | 0.4477 | 0.6691 |
| 0.4635 | 3.3935 | 940 | 0.4332 | 0.7158 | 0.4332 | 0.6582 |
| 0.4635 | 3.4007 | 942 | 0.4308 | 0.6866 | 0.4308 | 0.6563 |
| 0.4635 | 3.4079 | 944 | 0.4258 | 0.6866 | 0.4258 | 0.6526 |
| 0.4635 | 3.4152 | 946 | 0.4307 | 0.6866 | 0.4307 | 0.6563 |
| 0.4635 | 3.4224 | 948 | 0.4424 | 0.6866 | 0.4424 | 0.6651 |
| 0.4635 | 3.4296 | 950 | 0.4482 | 0.6866 | 0.4482 | 0.6695 |
| 0.4635 | 3.4368 | 952 | 0.4706 | 0.6866 | 0.4706 | 0.6860 |
| 0.4635 | 3.4440 | 954 | 0.4645 | 0.6733 | 0.4645 | 0.6815 |
| 0.4635 | 3.4513 | 956 | 0.4423 | 0.6866 | 0.4423 | 0.6651 |
| 0.4635 | 3.4585 | 958 | 0.4394 | 0.6866 | 0.4394 | 0.6629 |
| 0.4635 | 3.4657 | 960 | 0.4522 | 0.6733 | 0.4522 | 0.6725 |
| 0.4635 | 3.4729 | 962 | 0.4525 | 0.6733 | 0.4525 | 0.6727 |
| 0.4635 | 3.4801 | 964 | 0.4304 | 0.6733 | 0.4304 | 0.6560 |
| 0.4635 | 3.4874 | 966 | 0.4031 | 0.6866 | 0.4031 | 0.6349 |
| 0.4635 | 3.4946 | 968 | 0.4004 | 0.6866 | 0.4004 | 0.6328 |
| 0.4635 | 3.5018 | 970 | 0.4015 | 0.6866 | 0.4015 | 0.6336 |
| 0.4635 | 3.5090 | 972 | 0.4254 | 0.6866 | 0.4254 | 0.6522 |
| 0.4635 | 3.5162 | 974 | 0.4877 | 0.7009 | 0.4877 | 0.6984 |
| 0.4635 | 3.5235 | 976 | 0.5322 | 0.6026 | 0.5322 | 0.7295 |
| 0.4635 | 3.5307 | 978 | 0.5298 | 0.6026 | 0.5298 | 0.7279 |
| 0.4635 | 3.5379 | 980 | 0.4946 | 0.7009 | 0.4946 | 0.7033 |
| 0.4635 | 3.5451 | 982 | 0.4453 | 0.7009 | 0.4453 | 0.6673 |
| 0.4635 | 3.5523 | 984 | 0.4207 | 0.7158 | 0.4207 | 0.6486 |
| 0.4635 | 3.5596 | 986 | 0.4199 | 0.7158 | 0.4199 | 0.6480 |
| 0.4635 | 3.5668 | 988 | 0.4297 | 0.7158 | 0.4297 | 0.6555 |
| 0.4635 | 3.5740 | 990 | 0.4466 | 0.6686 | 0.4466 | 0.6683 |
| 0.4635 | 3.5812 | 992 | 0.4656 | 0.6686 | 0.4656 | 0.6824 |
| 0.4635 | 3.5884 | 994 | 0.4771 | 0.6686 | 0.4771 | 0.6907 |
| 0.4635 | 3.5957 | 996 | 0.5038 | 0.6358 | 0.5038 | 0.7098 |
| 0.4635 | 3.6029 | 998 | 0.4975 | 0.6358 | 0.4975 | 0.7053 |
| 0.1472 | 3.6101 | 1000 | 0.4647 | 0.6818 | 0.4647 | 0.6817 |
| 0.1472 | 3.6173 | 1002 | 0.4463 | 0.6818 | 0.4463 | 0.6680 |
| 0.1472 | 3.6245 | 1004 | 0.4335 | 0.6818 | 0.4335 | 0.6584 |
| 0.1472 | 3.6318 | 1006 | 0.4279 | 0.6232 | 0.4279 | 0.6541 |
| 0.1472 | 3.6390 | 1008 | 0.4437 | 0.6419 | 0.4437 | 0.6661 |
| 0.1472 | 3.6462 | 1010 | 0.4733 | 0.4667 | 0.4733 | 0.6880 |
| 0.1472 | 3.6534 | 1012 | 0.4988 | 0.4667 | 0.4988 | 0.7062 |
| 0.1472 | 3.6606 | 1014 | 0.5041 | 0.4667 | 0.5041 | 0.7100 |
| 0.1472 | 3.6679 | 1016 | 0.4771 | 0.6419 | 0.4771 | 0.6907 |
| 0.1472 | 3.6751 | 1018 | 0.4668 | 0.6419 | 0.4668 | 0.6832 |
| 0.1472 | 3.6823 | 1020 | 0.4442 | 0.6473 | 0.4442 | 0.6665 |
| 0.1472 | 3.6895 | 1022 | 0.4384 | 0.7158 | 0.4384 | 0.6621 |
| 0.1472 | 3.6968 | 1024 | 0.4272 | 0.7158 | 0.4272 | 0.6536 |
| 0.1472 | 3.7040 | 1026 | 0.4209 | 0.7158 | 0.4209 | 0.6488 |
| 0.1472 | 3.7112 | 1028 | 0.4187 | 0.6866 | 0.4187 | 0.6470 |
| 0.1472 | 3.7184 | 1030 | 0.4266 | 0.7158 | 0.4266 | 0.6532 |
| 0.1472 | 3.7256 | 1032 | 0.4283 | 0.6818 | 0.4283 | 0.6545 |
| 0.1472 | 3.7329 | 1034 | 0.4476 | 0.6769 | 0.4476 | 0.6690 |
| 0.1472 | 3.7401 | 1036 | 0.4734 | 0.5347 | 0.4734 | 0.6880 |
| 0.1472 | 3.7473 | 1038 | 0.4674 | 0.5689 | 0.4674 | 0.6836 |
| 0.1472 | 3.7545 | 1040 | 0.4506 | 0.6818 | 0.4506 | 0.6712 |
| 0.1472 | 3.7617 | 1042 | 0.4285 | 0.6818 | 0.4285 | 0.6546 |
| 0.1472 | 3.7690 | 1044 | 0.4346 | 0.7158 | 0.4346 | 0.6593 |
| 0.1472 | 3.7762 | 1046 | 0.4476 | 0.7158 | 0.4476 | 0.6690 |
| 0.1472 | 3.7834 | 1048 | 0.4581 | 0.7009 | 0.4581 | 0.6768 |
| 0.1472 | 3.7906 | 1050 | 0.4483 | 0.6866 | 0.4483 | 0.6696 |
| 0.1472 | 3.7978 | 1052 | 0.4432 | 0.6866 | 0.4432 | 0.6658 |
| 0.1472 | 3.8051 | 1054 | 0.4371 | 0.6866 | 0.4371 | 0.6611 |
| 0.1472 | 3.8123 | 1056 | 0.4378 | 0.6866 | 0.4378 | 0.6617 |
| 0.1472 | 3.8195 | 1058 | 0.4436 | 0.7158 | 0.4436 | 0.6660 |
| 0.1472 | 3.8267 | 1060 | 0.4459 | 0.7158 | 0.4459 | 0.6678 |
| 0.1472 | 3.8339 | 1062 | 0.4454 | 0.7158 | 0.4454 | 0.6674 |
| 0.1472 | 3.8412 | 1064 | 0.4434 | 0.7158 | 0.4434 | 0.6658 |
| 0.1472 | 3.8484 | 1066 | 0.4385 | 0.7158 | 0.4385 | 0.6622 |
| 0.1472 | 3.8556 | 1068 | 0.4464 | 0.7158 | 0.4464 | 0.6681 |
| 0.1472 | 3.8628 | 1070 | 0.4550 | 0.7158 | 0.4550 | 0.6745 |
| 0.1472 | 3.8700 | 1072 | 0.4472 | 0.7158 | 0.4472 | 0.6687 |
| 0.1472 | 3.8773 | 1074 | 0.4669 | 0.5415 | 0.4669 | 0.6833 |
| 0.1472 | 3.8845 | 1076 | 0.4775 | 0.5415 | 0.4775 | 0.6910 |
| 0.1472 | 3.8917 | 1078 | 0.4692 | 0.6083 | 0.4692 | 0.6850 |
| 0.1472 | 3.8989 | 1080 | 0.4855 | 0.5415 | 0.4855 | 0.6968 |
| 0.1472 | 3.9061 | 1082 | 0.4778 | 0.6083 | 0.4778 | 0.6912 |
| 0.1472 | 3.9134 | 1084 | 0.4525 | 0.7158 | 0.4525 | 0.6727 |
| 0.1472 | 3.9206 | 1086 | 0.4383 | 0.7158 | 0.4383 | 0.6620 |
| 0.1472 | 3.9278 | 1088 | 0.4252 | 0.6477 | 0.4252 | 0.6521 |
| 0.1472 | 3.9350 | 1090 | 0.4221 | 0.6477 | 0.4221 | 0.6497 |
| 0.1472 | 3.9422 | 1092 | 0.4113 | 0.6477 | 0.4113 | 0.6414 |
| 0.1472 | 3.9495 | 1094 | 0.4049 | 0.6802 | 0.4049 | 0.6363 |
| 0.1472 | 3.9567 | 1096 | 0.4086 | 0.6802 | 0.4086 | 0.6392 |
| 0.1472 | 3.9639 | 1098 | 0.4160 | 0.7325 | 0.4160 | 0.6450 |
| 0.1472 | 3.9711 | 1100 | 0.4375 | 0.6182 | 0.4375 | 0.6614 |
| 0.1472 | 3.9783 | 1102 | 0.5113 | 0.5968 | 0.5113 | 0.7150 |
| 0.1472 | 3.9856 | 1104 | 0.5760 | 0.4648 | 0.5760 | 0.7589 |
| 0.1472 | 3.9928 | 1106 | 0.5625 | 0.5 | 0.5625 | 0.7500 |
| 0.1472 | 4.0 | 1108 | 0.4967 | 0.5968 | 0.4967 | 0.7048 |
| 0.1472 | 4.0072 | 1110 | 0.4548 | 0.5832 | 0.4548 | 0.6744 |
| 0.1472 | 4.0144 | 1112 | 0.4356 | 0.7158 | 0.4356 | 0.6600 |
| 0.1472 | 4.0217 | 1114 | 0.4486 | 0.7158 | 0.4486 | 0.6698 |
| 0.1472 | 4.0289 | 1116 | 0.4702 | 0.7158 | 0.4702 | 0.6857 |
| 0.1472 | 4.0361 | 1118 | 0.4930 | 0.6083 | 0.4930 | 0.7021 |
| 0.1472 | 4.0433 | 1120 | 0.4794 | 0.7158 | 0.4794 | 0.6924 |
| 0.1472 | 4.0505 | 1122 | 0.4700 | 0.7158 | 0.4700 | 0.6855 |
| 0.1472 | 4.0578 | 1124 | 0.4738 | 0.7158 | 0.4738 | 0.6884 |
| 0.1472 | 4.0650 | 1126 | 0.4931 | 0.5477 | 0.4931 | 0.7022 |
| 0.1472 | 4.0722 | 1128 | 0.5094 | 0.5689 | 0.5094 | 0.7137 |
| 0.1472 | 4.0794 | 1130 | 0.5002 | 0.5689 | 0.5002 | 0.7073 |
| 0.1472 | 4.0866 | 1132 | 0.4683 | 0.5477 | 0.4683 | 0.6843 |
| 0.1472 | 4.0939 | 1134 | 0.4545 | 0.5477 | 0.4545 | 0.6741 |
| 0.1472 | 4.1011 | 1136 | 0.4419 | 0.7158 | 0.4419 | 0.6647 |
| 0.1472 | 4.1083 | 1138 | 0.4376 | 0.7158 | 0.4376 | 0.6615 |
| 0.1472 | 4.1155 | 1140 | 0.4448 | 0.7158 | 0.4448 | 0.6669 |
| 0.1472 | 4.1227 | 1142 | 0.4631 | 0.7158 | 0.4631 | 0.6805 |
| 0.1472 | 4.1300 | 1144 | 0.4864 | 0.5832 | 0.4864 | 0.6974 |
| 0.1472 | 4.1372 | 1146 | 0.4995 | 0.5477 | 0.4995 | 0.7067 |
| 0.1472 | 4.1444 | 1148 | 0.4975 | 0.5477 | 0.4975 | 0.7053 |
| 0.1472 | 4.1516 | 1150 | 0.4837 | 0.5477 | 0.4837 | 0.6955 |
| 0.1472 | 4.1588 | 1152 | 0.4959 | 0.5477 | 0.4959 | 0.7042 |
| 0.1472 | 4.1661 | 1154 | 0.5003 | 0.5477 | 0.5003 | 0.7073 |
| 0.1472 | 4.1733 | 1156 | 0.4969 | 0.5832 | 0.4969 | 0.7049 |
| 0.1472 | 4.1805 | 1158 | 0.5202 | 0.5477 | 0.5202 | 0.7213 |
| 0.1472 | 4.1877 | 1160 | 0.5795 | 0.5477 | 0.5795 | 0.7613 |
| 0.1472 | 4.1949 | 1162 | 0.6301 | 0.4106 | 0.6301 | 0.7938 |
| 0.1472 | 4.2022 | 1164 | 0.6557 | 0.3529 | 0.6557 | 0.8097 |
| 0.1472 | 4.2094 | 1166 | 0.6875 | 0.4034 | 0.6875 | 0.8291 |
| 0.1472 | 4.2166 | 1168 | 0.6487 | 0.4106 | 0.6487 | 0.8054 |
| 0.1472 | 4.2238 | 1170 | 0.5847 | 0.4094 | 0.5847 | 0.7647 |
| 0.1472 | 4.2310 | 1172 | 0.5260 | 0.5477 | 0.5260 | 0.7253 |
| 0.1472 | 4.2383 | 1174 | 0.5112 | 0.5832 | 0.5112 | 0.7150 |
| 0.1472 | 4.2455 | 1176 | 0.5184 | 0.5832 | 0.5184 | 0.7200 |
| 0.1472 | 4.2527 | 1178 | 0.5549 | 0.5477 | 0.5549 | 0.7449 |
| 0.1472 | 4.2599 | 1180 | 0.6233 | 0.4094 | 0.6233 | 0.7895 |
| 0.1472 | 4.2671 | 1182 | 0.6463 | 0.4106 | 0.6463 | 0.8039 |
| 0.1472 | 4.2744 | 1184 | 0.6323 | 0.4094 | 0.6323 | 0.7952 |
| 0.1472 | 4.2816 | 1186 | 0.6036 | 0.4094 | 0.6036 | 0.7769 |
| 0.1472 | 4.2888 | 1188 | 0.5870 | 0.5477 | 0.5870 | 0.7662 |
| 0.1472 | 4.2960 | 1190 | 0.5701 | 0.5477 | 0.5701 | 0.7550 |
| 0.1472 | 4.3032 | 1192 | 0.5682 | 0.5477 | 0.5682 | 0.7538 |
| 0.1472 | 4.3105 | 1194 | 0.5706 | 0.5477 | 0.5706 | 0.7554 |
| 0.1472 | 4.3177 | 1196 | 0.5422 | 0.5895 | 0.5422 | 0.7363 |
| 0.1472 | 4.3249 | 1198 | 0.5364 | 0.5895 | 0.5364 | 0.7324 |
| 0.1472 | 4.3321 | 1200 | 0.5479 | 0.5895 | 0.5479 | 0.7402 |
| 0.1472 | 4.3394 | 1202 | 0.5505 | 0.5545 | 0.5505 | 0.7420 |
| 0.1472 | 4.3466 | 1204 | 0.5865 | 0.5477 | 0.5865 | 0.7658 |
| 0.1472 | 4.3538 | 1206 | 0.5969 | 0.5415 | 0.5969 | 0.7726 |
| 0.1472 | 4.3610 | 1208 | 0.5827 | 0.5832 | 0.5827 | 0.7633 |
| 0.1472 | 4.3682 | 1210 | 0.5510 | 0.5895 | 0.5510 | 0.7423 |
| 0.1472 | 4.3755 | 1212 | 0.5264 | 0.5895 | 0.5264 | 0.7255 |
| 0.1472 | 4.3827 | 1214 | 0.5131 | 0.6866 | 0.5131 | 0.7163 |
| 0.1472 | 4.3899 | 1216 | 0.5122 | 0.6866 | 0.5122 | 0.7157 |
| 0.1472 | 4.3971 | 1218 | 0.5180 | 0.5895 | 0.5180 | 0.7197 |
| 0.1472 | 4.4043 | 1220 | 0.5302 | 0.5895 | 0.5302 | 0.7281 |
| 0.1472 | 4.4116 | 1222 | 0.5368 | 0.6182 | 0.5368 | 0.7326 |
| 0.1472 | 4.4188 | 1224 | 0.5352 | 0.6182 | 0.5352 | 0.7316 |
| 0.1472 | 4.4260 | 1226 | 0.5188 | 0.5895 | 0.5188 | 0.7203 |
| 0.1472 | 4.4332 | 1228 | 0.4985 | 0.5895 | 0.4985 | 0.7060 |
| 0.1472 | 4.4404 | 1230 | 0.4864 | 0.5895 | 0.4864 | 0.6974 |
| 0.1472 | 4.4477 | 1232 | 0.4756 | 0.5895 | 0.4756 | 0.6897 |
| 0.1472 | 4.4549 | 1234 | 0.4712 | 0.5987 | 0.4712 | 0.6864 |
| 0.1472 | 4.4621 | 1236 | 0.4733 | 0.5895 | 0.4733 | 0.6880 |
| 0.1472 | 4.4693 | 1238 | 0.4952 | 0.5895 | 0.4952 | 0.7037 |
| 0.1472 | 4.4765 | 1240 | 0.5400 | 0.6182 | 0.5400 | 0.7349 |
| 0.1472 | 4.4838 | 1242 | 0.5687 | 0.5832 | 0.5687 | 0.7541 |
| 0.1472 | 4.4910 | 1244 | 0.5775 | 0.4831 | 0.5775 | 0.7599 |
| 0.1472 | 4.4982 | 1246 | 0.5638 | 0.4831 | 0.5638 | 0.7509 |
| 0.1472 | 4.5054 | 1248 | 0.5237 | 0.6182 | 0.5237 | 0.7237 |
| 0.1472 | 4.5126 | 1250 | 0.4948 | 0.6182 | 0.4948 | 0.7034 |
| 0.1472 | 4.5199 | 1252 | 0.4828 | 0.6182 | 0.4828 | 0.6948 |
| 0.1472 | 4.5271 | 1254 | 0.4743 | 0.6182 | 0.4743 | 0.6887 |
| 0.1472 | 4.5343 | 1256 | 0.4782 | 0.6182 | 0.4782 | 0.6915 |
| 0.1472 | 4.5415 | 1258 | 0.4955 | 0.5832 | 0.4955 | 0.7039 |
| 0.1472 | 4.5487 | 1260 | 0.4924 | 0.6182 | 0.4924 | 0.7017 |
| 0.1472 | 4.5560 | 1262 | 0.4766 | 0.7158 | 0.4766 | 0.6904 |
| 0.1472 | 4.5632 | 1264 | 0.4803 | 0.6866 | 0.4803 | 0.6931 |
| 0.1472 | 4.5704 | 1266 | 0.5005 | 0.6866 | 0.5005 | 0.7075 |
| 0.1472 | 4.5776 | 1268 | 0.5068 | 0.6866 | 0.5068 | 0.7119 |
| 0.1472 | 4.5848 | 1270 | 0.5212 | 0.6866 | 0.5212 | 0.7220 |
| 0.1472 | 4.5921 | 1272 | 0.5444 | 0.6182 | 0.5444 | 0.7378 |
| 0.1472 | 4.5993 | 1274 | 0.5565 | 0.6182 | 0.5565 | 0.7460 |
| 0.1472 | 4.6065 | 1276 | 0.5548 | 0.5895 | 0.5548 | 0.7449 |
| 0.1472 | 4.6137 | 1278 | 0.5496 | 0.6866 | 0.5496 | 0.7413 |
| 0.1472 | 4.6209 | 1280 | 0.5429 | 0.6866 | 0.5429 | 0.7368 |
| 0.1472 | 4.6282 | 1282 | 0.5302 | 0.5895 | 0.5302 | 0.7282 |
| 0.1472 | 4.6354 | 1284 | 0.5180 | 0.5895 | 0.5180 | 0.7197 |
| 0.1472 | 4.6426 | 1286 | 0.5262 | 0.5832 | 0.5262 | 0.7254 |
| 0.1472 | 4.6498 | 1288 | 0.5519 | 0.5116 | 0.5519 | 0.7429 |
| 0.1472 | 4.6570 | 1290 | 0.5843 | 0.4094 | 0.5843 | 0.7644 |
| 0.1472 | 4.6643 | 1292 | 0.5869 | 0.4094 | 0.5869 | 0.7661 |
| 0.1472 | 4.6715 | 1294 | 0.5609 | 0.5116 | 0.5609 | 0.7489 |
| 0.1472 | 4.6787 | 1296 | 0.5184 | 0.5832 | 0.5184 | 0.7200 |
| 0.1472 | 4.6859 | 1298 | 0.4959 | 0.6182 | 0.4959 | 0.7042 |
| 0.1472 | 4.6931 | 1300 | 0.4923 | 0.7014 | 0.4923 | 0.7016 |
| 0.1472 | 4.7004 | 1302 | 0.5239 | 0.7014 | 0.5239 | 0.7238 |
| 0.1472 | 4.7076 | 1304 | 0.5430 | 0.6192 | 0.5430 | 0.7369 |
| 0.1472 | 4.7148 | 1306 | 0.5452 | 0.6866 | 0.5452 | 0.7384 |
| 0.1472 | 4.7220 | 1308 | 0.5480 | 0.5895 | 0.5480 | 0.7403 |
| 0.1472 | 4.7292 | 1310 | 0.5643 | 0.6182 | 0.5643 | 0.7512 |
| 0.1472 | 4.7365 | 1312 | 0.5550 | 0.6182 | 0.5550 | 0.7450 |
| 0.1472 | 4.7437 | 1314 | 0.5307 | 0.5895 | 0.5307 | 0.7285 |
| 0.1472 | 4.7509 | 1316 | 0.5170 | 0.5895 | 0.5170 | 0.7190 |
| 0.1472 | 4.7581 | 1318 | 0.5148 | 0.5895 | 0.5148 | 0.7175 |
| 0.1472 | 4.7653 | 1320 | 0.5217 | 0.6182 | 0.5217 | 0.7223 |
| 0.1472 | 4.7726 | 1322 | 0.5544 | 0.5477 | 0.5544 | 0.7446 |
| 0.1472 | 4.7798 | 1324 | 0.5721 | 0.5477 | 0.5721 | 0.7564 |
| 0.1472 | 4.7870 | 1326 | 0.5626 | 0.5477 | 0.5626 | 0.7501 |
| 0.1472 | 4.7942 | 1328 | 0.5270 | 0.5477 | 0.5270 | 0.7259 |
| 0.1472 | 4.8014 | 1330 | 0.5008 | 0.6182 | 0.5008 | 0.7077 |
| 0.1472 | 4.8087 | 1332 | 0.4961 | 0.6182 | 0.4961 | 0.7044 |
| 0.1472 | 4.8159 | 1334 | 0.4893 | 0.5895 | 0.4893 | 0.6995 |
| 0.1472 | 4.8231 | 1336 | 0.4853 | 0.5895 | 0.4853 | 0.6966 |
| 0.1472 | 4.8303 | 1338 | 0.4930 | 0.5895 | 0.4930 | 0.7021 |
| 0.1472 | 4.8375 | 1340 | 0.5161 | 0.5895 | 0.5161 | 0.7184 |
| 0.1472 | 4.8448 | 1342 | 0.5379 | 0.5832 | 0.5379 | 0.7334 |
| 0.1472 | 4.8520 | 1344 | 0.5349 | 0.5832 | 0.5349 | 0.7314 |
| 0.1472 | 4.8592 | 1346 | 0.5310 | 0.6182 | 0.5310 | 0.7287 |
| 0.1472 | 4.8664 | 1348 | 0.5276 | 0.5895 | 0.5276 | 0.7263 |
| 0.1472 | 4.8736 | 1350 | 0.5426 | 0.5832 | 0.5426 | 0.7366 |
| 0.1472 | 4.8809 | 1352 | 0.5723 | 0.5832 | 0.5723 | 0.7565 |
| 0.1472 | 4.8881 | 1354 | 0.5666 | 0.5832 | 0.5666 | 0.7527 |
| 0.1472 | 4.8953 | 1356 | 0.5391 | 0.6182 | 0.5391 | 0.7342 |
| 0.1472 | 4.9025 | 1358 | 0.5253 | 0.6182 | 0.5253 | 0.7248 |
| 0.1472 | 4.9097 | 1360 | 0.5047 | 0.6182 | 0.5047 | 0.7104 |
| 0.1472 | 4.9170 | 1362 | 0.4991 | 0.6182 | 0.4991 | 0.7065 |
| 0.1472 | 4.9242 | 1364 | 0.4972 | 0.6182 | 0.4972 | 0.7051 |
| 0.1472 | 4.9314 | 1366 | 0.5029 | 0.6182 | 0.5029 | 0.7091 |
| 0.1472 | 4.9386 | 1368 | 0.5069 | 0.6182 | 0.5069 | 0.7120 |
| 0.1472 | 4.9458 | 1370 | 0.5334 | 0.5832 | 0.5334 | 0.7303 |
| 0.1472 | 4.9531 | 1372 | 0.5481 | 0.5832 | 0.5481 | 0.7403 |
| 0.1472 | 4.9603 | 1374 | 0.5703 | 0.5751 | 0.5703 | 0.7552 |
| 0.1472 | 4.9675 | 1376 | 0.5618 | 0.5751 | 0.5618 | 0.7496 |
| 0.1472 | 4.9747 | 1378 | 0.5263 | 0.5832 | 0.5263 | 0.7254 |
| 0.1472 | 4.9819 | 1380 | 0.5066 | 0.6182 | 0.5066 | 0.7117 |
| 0.1472 | 4.9892 | 1382 | 0.5004 | 0.6182 | 0.5004 | 0.7074 |
| 0.1472 | 4.9964 | 1384 | 0.4880 | 0.6182 | 0.4880 | 0.6986 |
| 0.1472 | 5.0036 | 1386 | 0.4933 | 0.6182 | 0.4933 | 0.7024 |
| 0.1472 | 5.0108 | 1388 | 0.4969 | 0.6182 | 0.4969 | 0.7049 |
| 0.1472 | 5.0181 | 1390 | 0.5139 | 0.5832 | 0.5139 | 0.7169 |
| 0.1472 | 5.0253 | 1392 | 0.5093 | 0.5832 | 0.5093 | 0.7136 |
| 0.1472 | 5.0325 | 1394 | 0.5131 | 0.5832 | 0.5131 | 0.7163 |
| 0.1472 | 5.0397 | 1396 | 0.4957 | 0.5832 | 0.4957 | 0.7041 |
| 0.1472 | 5.0469 | 1398 | 0.4750 | 0.6182 | 0.4750 | 0.6892 |
| 0.1472 | 5.0542 | 1400 | 0.4660 | 0.6182 | 0.4660 | 0.6826 |
| 0.1472 | 5.0614 | 1402 | 0.4728 | 0.6182 | 0.4728 | 0.6876 |
| 0.1472 | 5.0686 | 1404 | 0.4728 | 0.6182 | 0.4728 | 0.6876 |
| 0.1472 | 5.0758 | 1406 | 0.4693 | 0.6182 | 0.4693 | 0.6850 |
| 0.1472 | 5.0830 | 1408 | 0.4784 | 0.6182 | 0.4784 | 0.6917 |
| 0.1472 | 5.0903 | 1410 | 0.4924 | 0.6182 | 0.4924 | 0.7017 |
| 0.1472 | 5.0975 | 1412 | 0.5299 | 0.6182 | 0.5299 | 0.7279 |
| 0.1472 | 5.1047 | 1414 | 0.5525 | 0.5751 | 0.5525 | 0.7433 |
| 0.1472 | 5.1119 | 1416 | 0.5769 | 0.5751 | 0.5769 | 0.7595 |
| 0.1472 | 5.1191 | 1418 | 0.5707 | 0.5415 | 0.5707 | 0.7555 |
| 0.1472 | 5.1264 | 1420 | 0.5334 | 0.5832 | 0.5334 | 0.7304 |
| 0.1472 | 5.1336 | 1422 | 0.4883 | 0.6182 | 0.4883 | 0.6988 |
| 0.1472 | 5.1408 | 1424 | 0.4633 | 0.6182 | 0.4633 | 0.6807 |
| 0.1472 | 5.1480 | 1426 | 0.4581 | 0.6182 | 0.4581 | 0.6769 |
| 0.1472 | 5.1552 | 1428 | 0.4535 | 0.6182 | 0.4535 | 0.6734 |
| 0.1472 | 5.1625 | 1430 | 0.4562 | 0.6182 | 0.4562 | 0.6754 |
| 0.1472 | 5.1697 | 1432 | 0.4798 | 0.6182 | 0.4798 | 0.6927 |
| 0.1472 | 5.1769 | 1434 | 0.4946 | 0.6182 | 0.4946 | 0.7033 |
| 0.1472 | 5.1841 | 1436 | 0.4817 | 0.6182 | 0.4817 | 0.6941 |
| 0.1472 | 5.1913 | 1438 | 0.4665 | 0.6182 | 0.4665 | 0.6830 |
| 0.1472 | 5.1986 | 1440 | 0.4575 | 0.7158 | 0.4575 | 0.6764 |
| 0.1472 | 5.2058 | 1442 | 0.4427 | 0.6866 | 0.4427 | 0.6653 |
| 0.1472 | 5.2130 | 1444 | 0.4320 | 0.7014 | 0.4320 | 0.6573 |
| 0.1472 | 5.2202 | 1446 | 0.4343 | 0.7014 | 0.4343 | 0.6590 |
| 0.1472 | 5.2274 | 1448 | 0.4392 | 0.7014 | 0.4392 | 0.6627 |
| 0.1472 | 5.2347 | 1450 | 0.4475 | 0.6866 | 0.4475 | 0.6690 |
| 0.1472 | 5.2419 | 1452 | 0.4564 | 0.7158 | 0.4564 | 0.6756 |
| 0.1472 | 5.2491 | 1454 | 0.4612 | 0.6182 | 0.4612 | 0.6791 |
| 0.1472 | 5.2563 | 1456 | 0.4706 | 0.6182 | 0.4706 | 0.6860 |
| 0.1472 | 5.2635 | 1458 | 0.4877 | 0.6182 | 0.4877 | 0.6984 |
| 0.1472 | 5.2708 | 1460 | 0.4759 | 0.6182 | 0.4759 | 0.6899 |
| 0.1472 | 5.2780 | 1462 | 0.4665 | 0.6182 | 0.4665 | 0.6830 |
| 0.1472 | 5.2852 | 1464 | 0.4432 | 0.6182 | 0.4432 | 0.6657 |
| 0.1472 | 5.2924 | 1466 | 0.4244 | 0.6866 | 0.4244 | 0.6515 |
| 0.1472 | 5.2996 | 1468 | 0.4184 | 0.7014 | 0.4184 | 0.6468 |
| 0.1472 | 5.3069 | 1470 | 0.4210 | 0.7014 | 0.4210 | 0.6489 |
| 0.1472 | 5.3141 | 1472 | 0.4284 | 0.7014 | 0.4284 | 0.6545 |
| 0.1472 | 5.3213 | 1474 | 0.4481 | 0.7158 | 0.4481 | 0.6694 |
| 0.1472 | 5.3285 | 1476 | 0.4926 | 0.6182 | 0.4926 | 0.7019 |
| 0.1472 | 5.3357 | 1478 | 0.5447 | 0.6083 | 0.5447 | 0.7380 |
| 0.1472 | 5.3430 | 1480 | 0.5568 | 0.5145 | 0.5568 | 0.7462 |
| 0.1472 | 5.3502 | 1482 | 0.5566 | 0.5415 | 0.5566 | 0.7460 |
| 0.1472 | 5.3574 | 1484 | 0.5330 | 0.6182 | 0.5330 | 0.7301 |
| 0.1472 | 5.3646 | 1486 | 0.4987 | 0.6182 | 0.4987 | 0.7062 |
| 0.1472 | 5.3718 | 1488 | 0.4897 | 0.6182 | 0.4897 | 0.6998 |
| 0.1472 | 5.3791 | 1490 | 0.4810 | 0.6182 | 0.4810 | 0.6935 |
| 0.1472 | 5.3863 | 1492 | 0.4842 | 0.6182 | 0.4842 | 0.6958 |
| 0.1472 | 5.3935 | 1494 | 0.4842 | 0.6866 | 0.4842 | 0.6959 |
| 0.1472 | 5.4007 | 1496 | 0.4959 | 0.7158 | 0.4959 | 0.7042 |
| 0.1472 | 5.4079 | 1498 | 0.5056 | 0.7158 | 0.5056 | 0.7110 |
| 0.0929 | 5.4152 | 1500 | 0.5140 | 0.7158 | 0.5140 | 0.7169 |
| 0.0929 | 5.4224 | 1502 | 0.5080 | 0.6866 | 0.5080 | 0.7127 |
| 0.0929 | 5.4296 | 1504 | 0.5161 | 0.7158 | 0.5161 | 0.7184 |
| 0.0929 | 5.4368 | 1506 | 0.5427 | 0.6182 | 0.5427 | 0.7367 |
| 0.0929 | 5.4440 | 1508 | 0.5697 | 0.5191 | 0.5697 | 0.7548 |
| 0.0929 | 5.4513 | 1510 | 0.5620 | 0.5191 | 0.5620 | 0.7496 |
| 0.0929 | 5.4585 | 1512 | 0.5384 | 0.5191 | 0.5384 | 0.7337 |
| 0.0929 | 5.4657 | 1514 | 0.5109 | 0.6182 | 0.5109 | 0.7148 |
| 0.0929 | 5.4729 | 1516 | 0.4970 | 0.6866 | 0.4970 | 0.7050 |
| 0.0929 | 5.4801 | 1518 | 0.5011 | 0.6866 | 0.5011 | 0.7079 |
| 0.0929 | 5.4874 | 1520 | 0.5137 | 0.6866 | 0.5137 | 0.7167 |
| 0.0929 | 5.4946 | 1522 | 0.5244 | 0.7158 | 0.5244 | 0.7242 |
| 0.0929 | 5.5018 | 1524 | 0.5397 | 0.6182 | 0.5397 | 0.7347 |
| 0.0929 | 5.5090 | 1526 | 0.5621 | 0.6182 | 0.5621 | 0.7497 |
| 0.0929 | 5.5162 | 1528 | 0.5615 | 0.6182 | 0.5615 | 0.7493 |
| 0.0929 | 5.5235 | 1530 | 0.5376 | 0.6182 | 0.5376 | 0.7332 |
| 0.0929 | 5.5307 | 1532 | 0.5157 | 0.7158 | 0.5157 | 0.7181 |
| 0.0929 | 5.5379 | 1534 | 0.5023 | 0.7158 | 0.5024 | 0.7088 |
| 0.0929 | 5.5451 | 1536 | 0.5044 | 0.7158 | 0.5044 | 0.7102 |
| 0.0929 | 5.5523 | 1538 | 0.5220 | 0.5832 | 0.5220 | 0.7225 |
| 0.0929 | 5.5596 | 1540 | 0.5354 | 0.5832 | 0.5354 | 0.7317 |
| 0.0929 | 5.5668 | 1542 | 0.5348 | 0.4831 | 0.5348 | 0.7313 |
| 0.0929 | 5.5740 | 1544 | 0.5246 | 0.5832 | 0.5246 | 0.7243 |
| 0.0929 | 5.5812 | 1546 | 0.5094 | 0.5832 | 0.5094 | 0.7137 |
| 0.0929 | 5.5884 | 1548 | 0.4849 | 0.7158 | 0.4849 | 0.6963 |
| 0.0929 | 5.5957 | 1550 | 0.4766 | 0.7158 | 0.4766 | 0.6903 |
| 0.0929 | 5.6029 | 1552 | 0.4831 | 0.6866 | 0.4831 | 0.6951 |
| 0.0929 | 5.6101 | 1554 | 0.4875 | 0.6866 | 0.4875 | 0.6982 |
| 0.0929 | 5.6173 | 1556 | 0.4951 | 0.7158 | 0.4951 | 0.7037 |
| 0.0929 | 5.6245 | 1558 | 0.5123 | 0.7158 | 0.5123 | 0.7157 |
| 0.0929 | 5.6318 | 1560 | 0.5287 | 0.6182 | 0.5287 | 0.7271 |
| 0.0929 | 5.6390 | 1562 | 0.5410 | 0.5751 | 0.5410 | 0.7355 |
| 0.0929 | 5.6462 | 1564 | 0.5630 | 0.5751 | 0.5630 | 0.7503 |
| 0.0929 | 5.6534 | 1566 | 0.5586 | 0.4803 | 0.5586 | 0.7474 |
| 0.0929 | 5.6606 | 1568 | 0.5218 | 0.5832 | 0.5218 | 0.7223 |
| 0.0929 | 5.6679 | 1570 | 0.4766 | 0.5832 | 0.4766 | 0.6903 |
| 0.0929 | 5.6751 | 1572 | 0.4542 | 0.6182 | 0.4542 | 0.6739 |
| 0.0929 | 5.6823 | 1574 | 0.4411 | 0.7158 | 0.4411 | 0.6641 |
| 0.0929 | 5.6895 | 1576 | 0.4377 | 0.7158 | 0.4377 | 0.6616 |
| 0.0929 | 5.6968 | 1578 | 0.4341 | 0.7158 | 0.4341 | 0.6588 |
| 0.0929 | 5.7040 | 1580 | 0.4313 | 0.7158 | 0.4313 | 0.6567 |
| 0.0929 | 5.7112 | 1582 | 0.4344 | 0.7158 | 0.4344 | 0.6591 |
| 0.0929 | 5.7184 | 1584 | 0.4532 | 0.7158 | 0.4532 | 0.6732 |
| 0.0929 | 5.7256 | 1586 | 0.4752 | 0.5832 | 0.4752 | 0.6893 |
| 0.0929 | 5.7329 | 1588 | 0.5183 | 0.5751 | 0.5183 | 0.7199 |
| 0.0929 | 5.7401 | 1590 | 0.5351 | 0.6026 | 0.5351 | 0.7315 |
| 0.0929 | 5.7473 | 1592 | 0.5215 | 0.6026 | 0.5215 | 0.7221 |
| 0.0929 | 5.7545 | 1594 | 0.4931 | 0.5832 | 0.4931 | 0.7022 |
| 0.0929 | 5.7617 | 1596 | 0.4601 | 0.6182 | 0.4601 | 0.6783 |
| 0.0929 | 5.7690 | 1598 | 0.4368 | 0.7158 | 0.4368 | 0.6609 |
| 0.0929 | 5.7762 | 1600 | 0.4332 | 0.7158 | 0.4332 | 0.6582 |
| 0.0929 | 5.7834 | 1602 | 0.4354 | 0.7158 | 0.4354 | 0.6599 |
| 0.0929 | 5.7906 | 1604 | 0.4422 | 0.7158 | 0.4422 | 0.6649 |
| 0.0929 | 5.7978 | 1606 | 0.4495 | 0.7158 | 0.4495 | 0.6704 |
| 0.0929 | 5.8051 | 1608 | 0.4595 | 0.7158 | 0.4595 | 0.6779 |
| 0.0929 | 5.8123 | 1610 | 0.4675 | 0.7158 | 0.4675 | 0.6837 |
| 0.0929 | 5.8195 | 1612 | 0.4725 | 0.7158 | 0.4725 | 0.6874 |
| 0.0929 | 5.8267 | 1614 | 0.4869 | 0.6182 | 0.4869 | 0.6978 |
| 0.0929 | 5.8339 | 1616 | 0.4867 | 0.6182 | 0.4867 | 0.6976 |
| 0.0929 | 5.8412 | 1618 | 0.4934 | 0.6182 | 0.4934 | 0.7024 |
| 0.0929 | 5.8484 | 1620 | 0.4876 | 0.5832 | 0.4876 | 0.6983 |
| 0.0929 | 5.8556 | 1622 | 0.4736 | 0.6182 | 0.4736 | 0.6882 |
| 0.0929 | 5.8628 | 1624 | 0.4475 | 0.6182 | 0.4475 | 0.6689 |
| 0.0929 | 5.8700 | 1626 | 0.4276 | 0.7158 | 0.4276 | 0.6539 |
| 0.0929 | 5.8773 | 1628 | 0.4222 | 0.6866 | 0.4222 | 0.6498 |
| 0.0929 | 5.8845 | 1630 | 0.4262 | 0.6866 | 0.4262 | 0.6528 |
| 0.0929 | 5.8917 | 1632 | 0.4349 | 0.6866 | 0.4349 | 0.6595 |
| 0.0929 | 5.8989 | 1634 | 0.4450 | 0.6866 | 0.4450 | 0.6671 |
| 0.0929 | 5.9061 | 1636 | 0.4657 | 0.6182 | 0.4657 | 0.6824 |
| 0.0929 | 5.9134 | 1638 | 0.4862 | 0.6182 | 0.4862 | 0.6973 |
| 0.0929 | 5.9206 | 1640 | 0.5081 | 0.6182 | 0.5081 | 0.7128 |
| 0.0929 | 5.9278 | 1642 | 0.5144 | 0.6182 | 0.5144 | 0.7172 |
| 0.0929 | 5.9350 | 1644 | 0.5151 | 0.6182 | 0.5151 | 0.7177 |
| 0.0929 | 5.9422 | 1646 | 0.5331 | 0.5191 | 0.5331 | 0.7302 |
| 0.0929 | 5.9495 | 1648 | 0.5289 | 0.5191 | 0.5289 | 0.7272 |
| 0.0929 | 5.9567 | 1650 | 0.5013 | 0.6182 | 0.5013 | 0.7081 |
| 0.0929 | 5.9639 | 1652 | 0.4763 | 0.5895 | 0.4763 | 0.6901 |
| 0.0929 | 5.9711 | 1654 | 0.4589 | 0.5895 | 0.4589 | 0.6774 |
| 0.0929 | 5.9783 | 1656 | 0.4548 | 0.5895 | 0.4548 | 0.6744 |
| 0.0929 | 5.9856 | 1658 | 0.4643 | 0.5895 | 0.4643 | 0.6814 |
| 0.0929 | 5.9928 | 1660 | 0.4802 | 0.5895 | 0.4802 | 0.6930 |
| 0.0929 | 6.0 | 1662 | 0.4960 | 0.6182 | 0.4959 | 0.7042 |
| 0.0929 | 6.0072 | 1664 | 0.5178 | 0.6182 | 0.5178 | 0.7196 |
| 0.0929 | 6.0144 | 1666 | 0.5257 | 0.6182 | 0.5257 | 0.7250 |
| 0.0929 | 6.0217 | 1668 | 0.5435 | 0.5832 | 0.5435 | 0.7372 |
| 0.0929 | 6.0289 | 1670 | 0.5518 | 0.6182 | 0.5518 | 0.7428 |
| 0.0929 | 6.0361 | 1672 | 0.5575 | 0.5832 | 0.5575 | 0.7467 |
| 0.0929 | 6.0433 | 1674 | 0.5628 | 0.5832 | 0.5628 | 0.7502 |
| 0.0929 | 6.0505 | 1676 | 0.5716 | 0.5832 | 0.5716 | 0.7560 |
| 0.0929 | 6.0578 | 1678 | 0.5584 | 0.5832 | 0.5584 | 0.7473 |
| 0.0929 | 6.0650 | 1680 | 0.5405 | 0.5832 | 0.5405 | 0.7352 |
| 0.0929 | 6.0722 | 1682 | 0.5467 | 0.5832 | 0.5467 | 0.7394 |
| 0.0929 | 6.0794 | 1684 | 0.5641 | 0.4831 | 0.5641 | 0.7511 |
| 0.0929 | 6.0866 | 1686 | 0.5848 | 0.5116 | 0.5848 | 0.7647 |
| 0.0929 | 6.0939 | 1688 | 0.6077 | 0.5116 | 0.6077 | 0.7795 |
| 0.0929 | 6.1011 | 1690 | 0.6301 | 0.5073 | 0.6301 | 0.7938 |
| 0.0929 | 6.1083 | 1692 | 0.6188 | 0.5116 | 0.6188 | 0.7866 |
| 0.0929 | 6.1155 | 1694 | 0.5843 | 0.5116 | 0.5843 | 0.7644 |
| 0.0929 | 6.1227 | 1696 | 0.5641 | 0.4831 | 0.5641 | 0.7510 |
| 0.0929 | 6.1300 | 1698 | 0.5308 | 0.5832 | 0.5308 | 0.7286 |
| 0.0929 | 6.1372 | 1700 | 0.4981 | 0.5832 | 0.4981 | 0.7057 |
| 0.0929 | 6.1444 | 1702 | 0.4858 | 0.5832 | 0.4858 | 0.6970 |
| 0.0929 | 6.1516 | 1704 | 0.4772 | 0.6866 | 0.4772 | 0.6908 |
| 0.0929 | 6.1588 | 1706 | 0.4731 | 0.6866 | 0.4731 | 0.6878 |
| 0.0929 | 6.1661 | 1708 | 0.4799 | 0.5895 | 0.4799 | 0.6927 |
| 0.0929 | 6.1733 | 1710 | 0.4998 | 0.5832 | 0.4998 | 0.7070 |
| 0.0929 | 6.1805 | 1712 | 0.5205 | 0.5832 | 0.5205 | 0.7215 |
| 0.0929 | 6.1877 | 1714 | 0.5601 | 0.5832 | 0.5601 | 0.7484 |
| 0.0929 | 6.1949 | 1716 | 0.6005 | 0.4803 | 0.6005 | 0.7749 |
| 0.0929 | 6.2022 | 1718 | 0.6083 | 0.5073 | 0.6083 | 0.7799 |
| 0.0929 | 6.2094 | 1720 | 0.6066 | 0.5073 | 0.6066 | 0.7788 |
| 0.0929 | 6.2166 | 1722 | 0.5757 | 0.5073 | 0.5757 | 0.7587 |
| 0.0929 | 6.2238 | 1724 | 0.5265 | 0.5832 | 0.5265 | 0.7256 |
| 0.0929 | 6.2310 | 1726 | 0.4845 | 0.5832 | 0.4845 | 0.6961 |
| 0.0929 | 6.2383 | 1728 | 0.4694 | 0.5832 | 0.4694 | 0.6851 |
| 0.0929 | 6.2455 | 1730 | 0.4663 | 0.5832 | 0.4663 | 0.6829 |
| 0.0929 | 6.2527 | 1732 | 0.4749 | 0.5832 | 0.4749 | 0.6891 |
| 0.0929 | 6.2599 | 1734 | 0.4876 | 0.5832 | 0.4876 | 0.6983 |
| 0.0929 | 6.2671 | 1736 | 0.5083 | 0.5832 | 0.5083 | 0.7130 |
| 0.0929 | 6.2744 | 1738 | 0.5405 | 0.5832 | 0.5405 | 0.7352 |
| 0.0929 | 6.2816 | 1740 | 0.5421 | 0.5832 | 0.5421 | 0.7363 |
| 0.0929 | 6.2888 | 1742 | 0.5225 | 0.5832 | 0.5225 | 0.7228 |
| 0.0929 | 6.2960 | 1744 | 0.5035 | 0.5832 | 0.5035 | 0.7096 |
| 0.0929 | 6.3032 | 1746 | 0.5022 | 0.5832 | 0.5022 | 0.7087 |
| 0.0929 | 6.3105 | 1748 | 0.4954 | 0.5832 | 0.4954 | 0.7039 |
| 0.0929 | 6.3177 | 1750 | 0.4975 | 0.5832 | 0.4975 | 0.7054 |
| 0.0929 | 6.3249 | 1752 | 0.5109 | 0.5832 | 0.5109 | 0.7148 |
| 0.0929 | 6.3321 | 1754 | 0.5206 | 0.5832 | 0.5206 | 0.7215 |
| 0.0929 | 6.3394 | 1756 | 0.5536 | 0.4831 | 0.5536 | 0.7440 |
| 0.0929 | 6.3466 | 1758 | 0.5823 | 0.4831 | 0.5823 | 0.7631 |
| 0.0929 | 6.3538 | 1760 | 0.5754 | 0.4831 | 0.5754 | 0.7585 |
| 0.0929 | 6.3610 | 1762 | 0.5465 | 0.4831 | 0.5465 | 0.7392 |
| 0.0929 | 6.3682 | 1764 | 0.5137 | 0.5832 | 0.5137 | 0.7167 |
| 0.0929 | 6.3755 | 1766 | 0.4961 | 0.5832 | 0.4961 | 0.7043 |
| 0.0929 | 6.3827 | 1768 | 0.4883 | 0.5832 | 0.4883 | 0.6988 |
| 0.0929 | 6.3899 | 1770 | 0.4840 | 0.5832 | 0.4840 | 0.6957 |
| 0.0929 | 6.3971 | 1772 | 0.4755 | 0.6182 | 0.4755 | 0.6896 |
| 0.0929 | 6.4043 | 1774 | 0.4701 | 0.6182 | 0.4701 | 0.6856 |
| 0.0929 | 6.4116 | 1776 | 0.4711 | 0.6182 | 0.4711 | 0.6864 |
| 0.0929 | 6.4188 | 1778 | 0.4692 | 0.6182 | 0.4692 | 0.6850 |
| 0.0929 | 6.4260 | 1780 | 0.4608 | 0.7158 | 0.4608 | 0.6788 |
| 0.0929 | 6.4332 | 1782 | 0.4594 | 0.6182 | 0.4594 | 0.6778 |
| 0.0929 | 6.4404 | 1784 | 0.4654 | 0.6182 | 0.4654 | 0.6822 |
| 0.0929 | 6.4477 | 1786 | 0.4826 | 0.5832 | 0.4826 | 0.6947 |
| 0.0929 | 6.4549 | 1788 | 0.5091 | 0.5832 | 0.5091 | 0.7135 |
| 0.0929 | 6.4621 | 1790 | 0.5286 | 0.4831 | 0.5286 | 0.7270 |
| 0.0929 | 6.4693 | 1792 | 0.5221 | 0.4831 | 0.5221 | 0.7226 |
| 0.0929 | 6.4765 | 1794 | 0.5017 | 0.5832 | 0.5017 | 0.7083 |
| 0.0929 | 6.4838 | 1796 | 0.4788 | 0.5832 | 0.4788 | 0.6920 |
| 0.0929 | 6.4910 | 1798 | 0.4685 | 0.5832 | 0.4685 | 0.6844 |
| 0.0929 | 6.4982 | 1800 | 0.4667 | 0.6182 | 0.4667 | 0.6831 |
| 0.0929 | 6.5054 | 1802 | 0.4731 | 0.7158 | 0.4731 | 0.6878 |
| 0.0929 | 6.5126 | 1804 | 0.4851 | 0.7158 | 0.4851 | 0.6965 |
| 0.0929 | 6.5199 | 1806 | 0.4976 | 0.7158 | 0.4976 | 0.7054 |
| 0.0929 | 6.5271 | 1808 | 0.5038 | 0.7158 | 0.5038 | 0.7098 |
| 0.0929 | 6.5343 | 1810 | 0.5016 | 0.7158 | 0.5016 | 0.7082 |
| 0.0929 | 6.5415 | 1812 | 0.4944 | 0.6182 | 0.4944 | 0.7031 |
| 0.0929 | 6.5487 | 1814 | 0.4906 | 0.5832 | 0.4906 | 0.7004 |
| 0.0929 | 6.5560 | 1816 | 0.4899 | 0.5832 | 0.4899 | 0.6999 |
| 0.0929 | 6.5632 | 1818 | 0.4964 | 0.5832 | 0.4964 | 0.7045 |
| 0.0929 | 6.5704 | 1820 | 0.4903 | 0.5832 | 0.4903 | 0.7002 |
| 0.0929 | 6.5776 | 1822 | 0.4799 | 0.5832 | 0.4799 | 0.6928 |
| 0.0929 | 6.5848 | 1824 | 0.4750 | 0.5832 | 0.4750 | 0.6892 |
| 0.0929 | 6.5921 | 1826 | 0.4659 | 0.5832 | 0.4659 | 0.6826 |
| 0.0929 | 6.5993 | 1828 | 0.4546 | 0.5832 | 0.4546 | 0.6742 |
| 0.0929 | 6.6065 | 1830 | 0.4388 | 0.6818 | 0.4388 | 0.6624 |
| 0.0929 | 6.6137 | 1832 | 0.4348 | 0.7158 | 0.4348 | 0.6594 |
| 0.0929 | 6.6209 | 1834 | 0.4368 | 0.7158 | 0.4368 | 0.6609 |
| 0.0929 | 6.6282 | 1836 | 0.4379 | 0.7158 | 0.4379 | 0.6617 |
| 0.0929 | 6.6354 | 1838 | 0.4405 | 0.7158 | 0.4405 | 0.6637 |
| 0.0929 | 6.6426 | 1840 | 0.4508 | 0.6818 | 0.4508 | 0.6714 |
| 0.0929 | 6.6498 | 1842 | 0.4754 | 0.5832 | 0.4754 | 0.6895 |
| 0.0929 | 6.6570 | 1844 | 0.4997 | 0.5832 | 0.4997 | 0.7069 |
| 0.0929 | 6.6643 | 1846 | 0.5290 | 0.4803 | 0.5290 | 0.7273 |
| 0.0929 | 6.6715 | 1848 | 0.5432 | 0.5073 | 0.5432 | 0.7370 |
| 0.0929 | 6.6787 | 1850 | 0.5431 | 0.5073 | 0.5431 | 0.7370 |
| 0.0929 | 6.6859 | 1852 | 0.5248 | 0.4803 | 0.5248 | 0.7244 |
| 0.0929 | 6.6931 | 1854 | 0.5069 | 0.4831 | 0.5069 | 0.7119 |
| 0.0929 | 6.7004 | 1856 | 0.4926 | 0.4831 | 0.4926 | 0.7018 |
| 0.0929 | 6.7076 | 1858 | 0.4836 | 0.4831 | 0.4836 | 0.6954 |
| 0.0929 | 6.7148 | 1860 | 0.4745 | 0.5832 | 0.4745 | 0.6889 |
| 0.0929 | 6.7220 | 1862 | 0.4752 | 0.4831 | 0.4752 | 0.6893 |
| 0.0929 | 6.7292 | 1864 | 0.4797 | 0.4831 | 0.4797 | 0.6926 |
| 0.0929 | 6.7365 | 1866 | 0.4838 | 0.4831 | 0.4838 | 0.6955 |
| 0.0929 | 6.7437 | 1868 | 0.4872 | 0.4831 | 0.4872 | 0.6980 |
| 0.0929 | 6.7509 | 1870 | 0.4889 | 0.4831 | 0.4889 | 0.6992 |
| 0.0929 | 6.7581 | 1872 | 0.4790 | 0.4831 | 0.4790 | 0.6921 |
| 0.0929 | 6.7653 | 1874 | 0.4827 | 0.4831 | 0.4827 | 0.6948 |
| 0.0929 | 6.7726 | 1876 | 0.4822 | 0.4831 | 0.4822 | 0.6944 |
| 0.0929 | 6.7798 | 1878 | 0.4862 | 0.4831 | 0.4862 | 0.6973 |
| 0.0929 | 6.7870 | 1880 | 0.4826 | 0.5832 | 0.4826 | 0.6947 |
| 0.0929 | 6.7942 | 1882 | 0.4727 | 0.5832 | 0.4727 | 0.6875 |
| 0.0929 | 6.8014 | 1884 | 0.4632 | 0.5832 | 0.4632 | 0.6806 |
| 0.0929 | 6.8087 | 1886 | 0.4638 | 0.5832 | 0.4638 | 0.6811 |
| 0.0929 | 6.8159 | 1888 | 0.4725 | 0.5832 | 0.4725 | 0.6874 |
| 0.0929 | 6.8231 | 1890 | 0.4844 | 0.4831 | 0.4844 | 0.6960 |
| 0.0929 | 6.8303 | 1892 | 0.4979 | 0.4831 | 0.4979 | 0.7056 |
| 0.0929 | 6.8375 | 1894 | 0.5184 | 0.5116 | 0.5184 | 0.7200 |
| 0.0929 | 6.8448 | 1896 | 0.5544 | 0.5116 | 0.5544 | 0.7445 |
| 0.0929 | 6.8520 | 1898 | 0.5736 | 0.475 | 0.5736 | 0.7574 |
| 0.0929 | 6.8592 | 1900 | 0.5625 | 0.5116 | 0.5625 | 0.7500 |
| 0.0929 | 6.8664 | 1902 | 0.5285 | 0.5116 | 0.5285 | 0.7270 |
| 0.0929 | 6.8736 | 1904 | 0.4981 | 0.4831 | 0.4981 | 0.7058 |
| 0.0929 | 6.8809 | 1906 | 0.4888 | 0.4831 | 0.4888 | 0.6992 |
| 0.0929 | 6.8881 | 1908 | 0.4726 | 0.5832 | 0.4726 | 0.6875 |
| 0.0929 | 6.8953 | 1910 | 0.4566 | 0.5832 | 0.4566 | 0.6757 |
| 0.0929 | 6.9025 | 1912 | 0.4412 | 0.5832 | 0.4412 | 0.6642 |
| 0.0929 | 6.9097 | 1914 | 0.4329 | 0.6818 | 0.4329 | 0.6580 |
| 0.0929 | 6.9170 | 1916 | 0.4258 | 0.6656 | 0.4258 | 0.6525 |
| 0.0929 | 6.9242 | 1918 | 0.4232 | 0.6656 | 0.4232 | 0.6506 |
| 0.0929 | 6.9314 | 1920 | 0.4219 | 0.7158 | 0.4219 | 0.6496 |
| 0.0929 | 6.9386 | 1922 | 0.4253 | 0.6818 | 0.4253 | 0.6522 |
| 0.0929 | 6.9458 | 1924 | 0.4363 | 0.5832 | 0.4363 | 0.6605 |
| 0.0929 | 6.9531 | 1926 | 0.4489 | 0.5832 | 0.4489 | 0.6700 |
| 0.0929 | 6.9603 | 1928 | 0.4544 | 0.5832 | 0.4544 | 0.6741 |
| 0.0929 | 6.9675 | 1930 | 0.4596 | 0.5832 | 0.4596 | 0.6779 |
| 0.0929 | 6.9747 | 1932 | 0.4649 | 0.5832 | 0.4649 | 0.6818 |
| 0.0929 | 6.9819 | 1934 | 0.4638 | 0.5832 | 0.4638 | 0.6811 |
| 0.0929 | 6.9892 | 1936 | 0.4676 | 0.5832 | 0.4676 | 0.6838 |
| 0.0929 | 6.9964 | 1938 | 0.4626 | 0.5832 | 0.4626 | 0.6802 |
| 0.0929 | 7.0036 | 1940 | 0.4533 | 0.6818 | 0.4533 | 0.6733 |
| 0.0929 | 7.0108 | 1942 | 0.4469 | 0.6818 | 0.4469 | 0.6685 |
| 0.0929 | 7.0181 | 1944 | 0.4461 | 0.6818 | 0.4461 | 0.6679 |
| 0.0929 | 7.0253 | 1946 | 0.4503 | 0.6818 | 0.4503 | 0.6711 |
| 0.0929 | 7.0325 | 1948 | 0.4550 | 0.6818 | 0.4550 | 0.6745 |
| 0.0929 | 7.0397 | 1950 | 0.4657 | 0.5832 | 0.4657 | 0.6824 |
| 0.0929 | 7.0469 | 1952 | 0.4799 | 0.5832 | 0.4799 | 0.6927 |
| 0.0929 | 7.0542 | 1954 | 0.4904 | 0.5832 | 0.4904 | 0.7003 |
| 0.0929 | 7.0614 | 1956 | 0.4898 | 0.5832 | 0.4898 | 0.6999 |
| 0.0929 | 7.0686 | 1958 | 0.4927 | 0.5832 | 0.4927 | 0.7019 |
| 0.0929 | 7.0758 | 1960 | 0.4853 | 0.5832 | 0.4853 | 0.6967 |
| 0.0929 | 7.0830 | 1962 | 0.4700 | 0.5832 | 0.4700 | 0.6856 |
| 0.0929 | 7.0903 | 1964 | 0.4537 | 0.5832 | 0.4537 | 0.6736 |
| 0.0929 | 7.0975 | 1966 | 0.4494 | 0.5832 | 0.4494 | 0.6704 |
| 0.0929 | 7.1047 | 1968 | 0.4474 | 0.6182 | 0.4474 | 0.6689 |
| 0.0929 | 7.1119 | 1970 | 0.4470 | 0.6182 | 0.4470 | 0.6686 |
| 0.0929 | 7.1191 | 1972 | 0.4551 | 0.6182 | 0.4551 | 0.6746 |
| 0.0929 | 7.1264 | 1974 | 0.4690 | 0.6182 | 0.4690 | 0.6849 |
| 0.0929 | 7.1336 | 1976 | 0.4924 | 0.5832 | 0.4924 | 0.7017 |
| 0.0929 | 7.1408 | 1978 | 0.5191 | 0.5832 | 0.5191 | 0.7205 |
| 0.0929 | 7.1480 | 1980 | 0.5240 | 0.5832 | 0.5240 | 0.7239 |
| 0.0929 | 7.1552 | 1982 | 0.5130 | 0.5832 | 0.5130 | 0.7162 |
| 0.0929 | 7.1625 | 1984 | 0.4933 | 0.5832 | 0.4933 | 0.7023 |
| 0.0929 | 7.1697 | 1986 | 0.4838 | 0.5832 | 0.4838 | 0.6956 |
| 0.0929 | 7.1769 | 1988 | 0.4743 | 0.6182 | 0.4743 | 0.6887 |
| 0.0929 | 7.1841 | 1990 | 0.4592 | 0.6182 | 0.4592 | 0.6776 |
| 0.0929 | 7.1913 | 1992 | 0.4463 | 0.6182 | 0.4463 | 0.6680 |
| 0.0929 | 7.1986 | 1994 | 0.4378 | 0.6182 | 0.4378 | 0.6617 |
| 0.0929 | 7.2058 | 1996 | 0.4378 | 0.6182 | 0.4378 | 0.6617 |
| 0.0929 | 7.2130 | 1998 | 0.4377 | 0.6182 | 0.4377 | 0.6616 |
| 0.0688 | 7.2202 | 2000 | 0.4371 | 0.6182 | 0.4371 | 0.6611 |
| 0.0688 | 7.2274 | 2002 | 0.4361 | 0.6182 | 0.4361 | 0.6604 |
| 0.0688 | 7.2347 | 2004 | 0.4415 | 0.5832 | 0.4415 | 0.6645 |
| 0.0688 | 7.2419 | 2006 | 0.4430 | 0.5832 | 0.4430 | 0.6656 |
| 0.0688 | 7.2491 | 2008 | 0.4395 | 0.5832 | 0.4395 | 0.6630 |
| 0.0688 | 7.2563 | 2010 | 0.4418 | 0.5832 | 0.4418 | 0.6647 |
| 0.0688 | 7.2635 | 2012 | 0.4460 | 0.5832 | 0.4460 | 0.6678 |
| 0.0688 | 7.2708 | 2014 | 0.4435 | 0.5832 | 0.4435 | 0.6659 |
| 0.0688 | 7.2780 | 2016 | 0.4460 | 0.5832 | 0.4460 | 0.6678 |
| 0.0688 | 7.2852 | 2018 | 0.4452 | 0.5832 | 0.4452 | 0.6672 |
| 0.0688 | 7.2924 | 2020 | 0.4372 | 0.5832 | 0.4372 | 0.6612 |
| 0.0688 | 7.2996 | 2022 | 0.4296 | 0.6182 | 0.4296 | 0.6554 |
| 0.0688 | 7.3069 | 2024 | 0.4246 | 0.7158 | 0.4246 | 0.6516 |
| 0.0688 | 7.3141 | 2026 | 0.4224 | 0.7158 | 0.4224 | 0.6499 |
| 0.0688 | 7.3213 | 2028 | 0.4235 | 0.7158 | 0.4235 | 0.6507 |
| 0.0688 | 7.3285 | 2030 | 0.4294 | 0.7158 | 0.4294 | 0.6553 |
| 0.0688 | 7.3357 | 2032 | 0.4415 | 0.5832 | 0.4415 | 0.6645 |
| 0.0688 | 7.3430 | 2034 | 0.4608 | 0.5832 | 0.4608 | 0.6788 |
| 0.0688 | 7.3502 | 2036 | 0.4775 | 0.5832 | 0.4775 | 0.6910 |
| 0.0688 | 7.3574 | 2038 | 0.4857 | 0.5832 | 0.4857 | 0.6969 |
| 0.0688 | 7.3646 | 2040 | 0.4790 | 0.5832 | 0.4790 | 0.6921 |
| 0.0688 | 7.3718 | 2042 | 0.4664 | 0.5832 | 0.4664 | 0.6829 |
| 0.0688 | 7.3791 | 2044 | 0.4505 | 0.5832 | 0.4505 | 0.6712 |
| 0.0688 | 7.3863 | 2046 | 0.4387 | 0.6182 | 0.4387 | 0.6623 |
| 0.0688 | 7.3935 | 2048 | 0.4314 | 0.6866 | 0.4314 | 0.6568 |
| 0.0688 | 7.4007 | 2050 | 0.4280 | 0.6866 | 0.4280 | 0.6542 |
| 0.0688 | 7.4079 | 2052 | 0.4304 | 0.6866 | 0.4304 | 0.6561 |
| 0.0688 | 7.4152 | 2054 | 0.4353 | 0.7158 | 0.4353 | 0.6598 |
| 0.0688 | 7.4224 | 2056 | 0.4355 | 0.7158 | 0.4355 | 0.6599 |
| 0.0688 | 7.4296 | 2058 | 0.4317 | 0.7158 | 0.4317 | 0.6570 |
| 0.0688 | 7.4368 | 2060 | 0.4277 | 0.6866 | 0.4277 | 0.6540 |
| 0.0688 | 7.4440 | 2062 | 0.4286 | 0.6866 | 0.4286 | 0.6547 |
| 0.0688 | 7.4513 | 2064 | 0.4363 | 0.7158 | 0.4363 | 0.6605 |
| 0.0688 | 7.4585 | 2066 | 0.4512 | 0.5832 | 0.4512 | 0.6717 |
| 0.0688 | 7.4657 | 2068 | 0.4657 | 0.5832 | 0.4657 | 0.6824 |
| 0.0688 | 7.4729 | 2070 | 0.4771 | 0.5832 | 0.4771 | 0.6907 |
| 0.0688 | 7.4801 | 2072 | 0.4747 | 0.5832 | 0.4747 | 0.6890 |
| 0.0688 | 7.4874 | 2074 | 0.4779 | 0.6123 | 0.4779 | 0.6913 |
| 0.0688 | 7.4946 | 2076 | 0.4741 | 0.5832 | 0.4741 | 0.6885 |
| 0.0688 | 7.5018 | 2078 | 0.4621 | 0.5832 | 0.4621 | 0.6798 |
| 0.0688 | 7.5090 | 2080 | 0.4440 | 0.5832 | 0.4440 | 0.6663 |
| 0.0688 | 7.5162 | 2082 | 0.4291 | 0.7158 | 0.4291 | 0.6550 |
| 0.0688 | 7.5235 | 2084 | 0.4247 | 0.6866 | 0.4247 | 0.6517 |
| 0.0688 | 7.5307 | 2086 | 0.4268 | 0.6866 | 0.4268 | 0.6533 |
| 0.0688 | 7.5379 | 2088 | 0.4299 | 0.6866 | 0.4299 | 0.6556 |
| 0.0688 | 7.5451 | 2090 | 0.4287 | 0.6866 | 0.4287 | 0.6547 |
| 0.0688 | 7.5523 | 2092 | 0.4271 | 0.7158 | 0.4271 | 0.6535 |
| 0.0688 | 7.5596 | 2094 | 0.4302 | 0.7158 | 0.4302 | 0.6559 |
| 0.0688 | 7.5668 | 2096 | 0.4366 | 0.6818 | 0.4366 | 0.6608 |
| 0.0688 | 7.5740 | 2098 | 0.4406 | 0.5832 | 0.4406 | 0.6638 |
| 0.0688 | 7.5812 | 2100 | 0.4375 | 0.5832 | 0.4375 | 0.6614 |
| 0.0688 | 7.5884 | 2102 | 0.4343 | 0.5832 | 0.4343 | 0.6590 |
| 0.0688 | 7.5957 | 2104 | 0.4325 | 0.5832 | 0.4325 | 0.6576 |
| 0.0688 | 7.6029 | 2106 | 0.4307 | 0.5832 | 0.4307 | 0.6563 |
| 0.0688 | 7.6101 | 2108 | 0.4255 | 0.6818 | 0.4255 | 0.6523 |
| 0.0688 | 7.6173 | 2110 | 0.4172 | 0.7158 | 0.4172 | 0.6459 |
| 0.0688 | 7.6245 | 2112 | 0.4127 | 0.7158 | 0.4127 | 0.6425 |
| 0.0688 | 7.6318 | 2114 | 0.4103 | 0.7158 | 0.4103 | 0.6406 |
| 0.0688 | 7.6390 | 2116 | 0.4130 | 0.7158 | 0.4130 | 0.6426 |
| 0.0688 | 7.6462 | 2118 | 0.4168 | 0.7158 | 0.4168 | 0.6456 |
| 0.0688 | 7.6534 | 2120 | 0.4178 | 0.7158 | 0.4178 | 0.6464 |
| 0.0688 | 7.6606 | 2122 | 0.4209 | 0.7158 | 0.4209 | 0.6488 |
| 0.0688 | 7.6679 | 2124 | 0.4231 | 0.7158 | 0.4231 | 0.6505 |
| 0.0688 | 7.6751 | 2126 | 0.4298 | 0.7158 | 0.4298 | 0.6556 |
| 0.0688 | 7.6823 | 2128 | 0.4348 | 0.7158 | 0.4348 | 0.6594 |
| 0.0688 | 7.6895 | 2130 | 0.4343 | 0.7158 | 0.4343 | 0.6590 |
| 0.0688 | 7.6968 | 2132 | 0.4290 | 0.7158 | 0.4290 | 0.6550 |
| 0.0688 | 7.7040 | 2134 | 0.4276 | 0.7158 | 0.4276 | 0.6539 |
| 0.0688 | 7.7112 | 2136 | 0.4260 | 0.7158 | 0.4260 | 0.6527 |
| 0.0688 | 7.7184 | 2138 | 0.4221 | 0.7158 | 0.4221 | 0.6497 |
| 0.0688 | 7.7256 | 2140 | 0.4189 | 0.7158 | 0.4189 | 0.6472 |
| 0.0688 | 7.7329 | 2142 | 0.4233 | 0.7158 | 0.4233 | 0.6506 |
| 0.0688 | 7.7401 | 2144 | 0.4377 | 0.5832 | 0.4377 | 0.6616 |
| 0.0688 | 7.7473 | 2146 | 0.4509 | 0.6123 | 0.4509 | 0.6715 |
| 0.0688 | 7.7545 | 2148 | 0.4547 | 0.6123 | 0.4547 | 0.6743 |
| 0.0688 | 7.7617 | 2150 | 0.4597 | 0.6123 | 0.4597 | 0.6780 |
| 0.0688 | 7.7690 | 2152 | 0.4688 | 0.6123 | 0.4688 | 0.6847 |
| 0.0688 | 7.7762 | 2154 | 0.4668 | 0.6123 | 0.4668 | 0.6832 |
| 0.0688 | 7.7834 | 2156 | 0.4649 | 0.6123 | 0.4649 | 0.6818 |
| 0.0688 | 7.7906 | 2158 | 0.4665 | 0.6123 | 0.4665 | 0.6830 |
| 0.0688 | 7.7978 | 2160 | 0.4729 | 0.6123 | 0.4729 | 0.6877 |
| 0.0688 | 7.8051 | 2162 | 0.4843 | 0.6026 | 0.4843 | 0.6959 |
| 0.0688 | 7.8123 | 2164 | 0.4936 | 0.6026 | 0.4936 | 0.7025 |
| 0.0688 | 7.8195 | 2166 | 0.4989 | 0.6026 | 0.4989 | 0.7064 |
| 0.0688 | 7.8267 | 2168 | 0.4977 | 0.6026 | 0.4977 | 0.7055 |
| 0.0688 | 7.8339 | 2170 | 0.4976 | 0.6026 | 0.4976 | 0.7054 |
| 0.0688 | 7.8412 | 2172 | 0.4875 | 0.6026 | 0.4875 | 0.6982 |
| 0.0688 | 7.8484 | 2174 | 0.4680 | 0.5832 | 0.4680 | 0.6841 |
| 0.0688 | 7.8556 | 2176 | 0.4495 | 0.7158 | 0.4495 | 0.6705 |
| 0.0688 | 7.8628 | 2178 | 0.4381 | 0.7158 | 0.4381 | 0.6619 |
| 0.0688 | 7.8700 | 2180 | 0.4311 | 0.7158 | 0.4311 | 0.6566 |
| 0.0688 | 7.8773 | 2182 | 0.4284 | 0.7158 | 0.4284 | 0.6546 |
| 0.0688 | 7.8845 | 2184 | 0.4255 | 0.7158 | 0.4255 | 0.6523 |
| 0.0688 | 7.8917 | 2186 | 0.4222 | 0.7158 | 0.4222 | 0.6497 |
| 0.0688 | 7.8989 | 2188 | 0.4181 | 0.7158 | 0.4181 | 0.6466 |
| 0.0688 | 7.9061 | 2190 | 0.4157 | 0.7158 | 0.4157 | 0.6447 |
| 0.0688 | 7.9134 | 2192 | 0.4169 | 0.7158 | 0.4169 | 0.6456 |
| 0.0688 | 7.9206 | 2194 | 0.4206 | 0.7158 | 0.4206 | 0.6485 |
| 0.0688 | 7.9278 | 2196 | 0.4205 | 0.7158 | 0.4205 | 0.6484 |
| 0.0688 | 7.9350 | 2198 | 0.4231 | 0.7158 | 0.4231 | 0.6505 |
| 0.0688 | 7.9422 | 2200 | 0.4262 | 0.7158 | 0.4262 | 0.6528 |
| 0.0688 | 7.9495 | 2202 | 0.4295 | 0.7158 | 0.4295 | 0.6553 |
| 0.0688 | 7.9567 | 2204 | 0.4358 | 0.7158 | 0.4358 | 0.6601 |
| 0.0688 | 7.9639 | 2206 | 0.4406 | 0.6818 | 0.4406 | 0.6638 |
| 0.0688 | 7.9711 | 2208 | 0.4468 | 0.5832 | 0.4468 | 0.6684 |
| 0.0688 | 7.9783 | 2210 | 0.4453 | 0.5832 | 0.4453 | 0.6673 |
| 0.0688 | 7.9856 | 2212 | 0.4384 | 0.7158 | 0.4384 | 0.6621 |
| 0.0688 | 7.9928 | 2214 | 0.4354 | 0.7158 | 0.4354 | 0.6599 |
| 0.0688 | 8.0 | 2216 | 0.4371 | 0.7158 | 0.4371 | 0.6611 |
| 0.0688 | 8.0072 | 2218 | 0.4464 | 0.7158 | 0.4464 | 0.6682 |
| 0.0688 | 8.0144 | 2220 | 0.4560 | 0.5832 | 0.4560 | 0.6753 |
| 0.0688 | 8.0217 | 2222 | 0.4658 | 0.5832 | 0.4658 | 0.6825 |
| 0.0688 | 8.0289 | 2224 | 0.4695 | 0.5832 | 0.4695 | 0.6852 |
| 0.0688 | 8.0361 | 2226 | 0.4639 | 0.5832 | 0.4639 | 0.6811 |
| 0.0688 | 8.0433 | 2228 | 0.4578 | 0.5832 | 0.4578 | 0.6766 |
| 0.0688 | 8.0505 | 2230 | 0.4513 | 0.5832 | 0.4513 | 0.6718 |
| 0.0688 | 8.0578 | 2232 | 0.4467 | 0.5832 | 0.4467 | 0.6684 |
| 0.0688 | 8.0650 | 2234 | 0.4438 | 0.5832 | 0.4438 | 0.6662 |
| 0.0688 | 8.0722 | 2236 | 0.4429 | 0.5832 | 0.4429 | 0.6655 |
| 0.0688 | 8.0794 | 2238 | 0.4454 | 0.5832 | 0.4454 | 0.6674 |
| 0.0688 | 8.0866 | 2240 | 0.4465 | 0.5832 | 0.4465 | 0.6682 |
| 0.0688 | 8.0939 | 2242 | 0.4454 | 0.5832 | 0.4454 | 0.6674 |
| 0.0688 | 8.1011 | 2244 | 0.4457 | 0.5832 | 0.4457 | 0.6676 |
| 0.0688 | 8.1083 | 2246 | 0.4468 | 0.5832 | 0.4468 | 0.6684 |
| 0.0688 | 8.1155 | 2248 | 0.4496 | 0.5832 | 0.4496 | 0.6705 |
| 0.0688 | 8.1227 | 2250 | 0.4547 | 0.5832 | 0.4547 | 0.6743 |
| 0.0688 | 8.1300 | 2252 | 0.4619 | 0.5832 | 0.4619 | 0.6796 |
| 0.0688 | 8.1372 | 2254 | 0.4652 | 0.5832 | 0.4652 | 0.6821 |
| 0.0688 | 8.1444 | 2256 | 0.4677 | 0.5832 | 0.4677 | 0.6839 |
| 0.0688 | 8.1516 | 2258 | 0.4654 | 0.5832 | 0.4654 | 0.6822 |
| 0.0688 | 8.1588 | 2260 | 0.4645 | 0.5832 | 0.4645 | 0.6815 |
| 0.0688 | 8.1661 | 2262 | 0.4663 | 0.5832 | 0.4663 | 0.6829 |
| 0.0688 | 8.1733 | 2264 | 0.4701 | 0.5832 | 0.4701 | 0.6857 |
| 0.0688 | 8.1805 | 2266 | 0.4691 | 0.5832 | 0.4691 | 0.6849 |
| 0.0688 | 8.1877 | 2268 | 0.4734 | 0.5832 | 0.4734 | 0.6880 |
| 0.0688 | 8.1949 | 2270 | 0.4757 | 0.5832 | 0.4757 | 0.6897 |
| 0.0688 | 8.2022 | 2272 | 0.4844 | 0.5832 | 0.4844 | 0.6960 |
| 0.0688 | 8.2094 | 2274 | 0.4990 | 0.5073 | 0.4990 | 0.7064 |
| 0.0688 | 8.2166 | 2276 | 0.5012 | 0.5073 | 0.5012 | 0.7080 |
| 0.0688 | 8.2238 | 2278 | 0.4990 | 0.6026 | 0.4990 | 0.7064 |
| 0.0688 | 8.2310 | 2280 | 0.4913 | 0.5751 | 0.4913 | 0.7009 |
| 0.0688 | 8.2383 | 2282 | 0.4802 | 0.5751 | 0.4802 | 0.6929 |
| 0.0688 | 8.2455 | 2284 | 0.4717 | 0.5751 | 0.4717 | 0.6868 |
| 0.0688 | 8.2527 | 2286 | 0.4615 | 0.6818 | 0.4615 | 0.6793 |
| 0.0688 | 8.2599 | 2288 | 0.4482 | 0.7158 | 0.4482 | 0.6695 |
| 0.0688 | 8.2671 | 2290 | 0.4433 | 0.7158 | 0.4433 | 0.6658 |
| 0.0688 | 8.2744 | 2292 | 0.4423 | 0.7158 | 0.4423 | 0.6651 |
| 0.0688 | 8.2816 | 2294 | 0.4478 | 0.6818 | 0.4478 | 0.6692 |
| 0.0688 | 8.2888 | 2296 | 0.4476 | 0.6818 | 0.4476 | 0.6691 |
| 0.0688 | 8.2960 | 2298 | 0.4453 | 0.6818 | 0.4453 | 0.6673 |
| 0.0688 | 8.3032 | 2300 | 0.4446 | 0.6818 | 0.4446 | 0.6668 |
| 0.0688 | 8.3105 | 2302 | 0.4453 | 0.6818 | 0.4453 | 0.6673 |
| 0.0688 | 8.3177 | 2304 | 0.4516 | 0.6818 | 0.4516 | 0.6720 |
| 0.0688 | 8.3249 | 2306 | 0.4594 | 0.6818 | 0.4594 | 0.6778 |
| 0.0688 | 8.3321 | 2308 | 0.4643 | 0.5832 | 0.4643 | 0.6814 |
| 0.0688 | 8.3394 | 2310 | 0.4651 | 0.5832 | 0.4651 | 0.6820 |
| 0.0688 | 8.3466 | 2312 | 0.4599 | 0.6818 | 0.4599 | 0.6782 |
| 0.0688 | 8.3538 | 2314 | 0.4551 | 0.6818 | 0.4551 | 0.6746 |
| 0.0688 | 8.3610 | 2316 | 0.4542 | 0.6818 | 0.4542 | 0.6739 |
| 0.0688 | 8.3682 | 2318 | 0.4480 | 0.6818 | 0.4480 | 0.6693 |
| 0.0688 | 8.3755 | 2320 | 0.4403 | 0.6818 | 0.4403 | 0.6636 |
| 0.0688 | 8.3827 | 2322 | 0.4382 | 0.7158 | 0.4382 | 0.6620 |
| 0.0688 | 8.3899 | 2324 | 0.4336 | 0.7158 | 0.4336 | 0.6585 |
| 0.0688 | 8.3971 | 2326 | 0.4283 | 0.7158 | 0.4283 | 0.6544 |
| 0.0688 | 8.4043 | 2328 | 0.4268 | 0.7158 | 0.4268 | 0.6533 |
| 0.0688 | 8.4116 | 2330 | 0.4276 | 0.7158 | 0.4276 | 0.6539 |
| 0.0688 | 8.4188 | 2332 | 0.4312 | 0.7158 | 0.4312 | 0.6567 |
| 0.0688 | 8.4260 | 2334 | 0.4386 | 0.7158 | 0.4386 | 0.6622 |
| 0.0688 | 8.4332 | 2336 | 0.4494 | 0.6818 | 0.4494 | 0.6704 |
| 0.0688 | 8.4404 | 2338 | 0.4609 | 0.6818 | 0.4609 | 0.6789 |
| 0.0688 | 8.4477 | 2340 | 0.4712 | 0.6818 | 0.4712 | 0.6864 |
| 0.0688 | 8.4549 | 2342 | 0.4772 | 0.5751 | 0.4772 | 0.6908 |
| 0.0688 | 8.4621 | 2344 | 0.4779 | 0.5751 | 0.4779 | 0.6913 |
| 0.0688 | 8.4693 | 2346 | 0.4721 | 0.5832 | 0.4721 | 0.6871 |
| 0.0688 | 8.4765 | 2348 | 0.4631 | 0.6818 | 0.4631 | 0.6805 |
| 0.0688 | 8.4838 | 2350 | 0.4523 | 0.6818 | 0.4523 | 0.6726 |
| 0.0688 | 8.4910 | 2352 | 0.4418 | 0.6818 | 0.4418 | 0.6647 |
| 0.0688 | 8.4982 | 2354 | 0.4386 | 0.6818 | 0.4386 | 0.6623 |
| 0.0688 | 8.5054 | 2356 | 0.4348 | 0.6818 | 0.4348 | 0.6594 |
| 0.0688 | 8.5126 | 2358 | 0.4348 | 0.6818 | 0.4348 | 0.6594 |
| 0.0688 | 8.5199 | 2360 | 0.4347 | 0.6818 | 0.4347 | 0.6593 |
| 0.0688 | 8.5271 | 2362 | 0.4358 | 0.6818 | 0.4358 | 0.6601 |
| 0.0688 | 8.5343 | 2364 | 0.4408 | 0.6818 | 0.4408 | 0.6639 |
| 0.0688 | 8.5415 | 2366 | 0.4463 | 0.6818 | 0.4463 | 0.6680 |
| 0.0688 | 8.5487 | 2368 | 0.4482 | 0.6818 | 0.4482 | 0.6694 |
| 0.0688 | 8.5560 | 2370 | 0.4542 | 0.6818 | 0.4542 | 0.6739 |
| 0.0688 | 8.5632 | 2372 | 0.4552 | 0.6818 | 0.4552 | 0.6747 |
| 0.0688 | 8.5704 | 2374 | 0.4543 | 0.6818 | 0.4543 | 0.6740 |
| 0.0688 | 8.5776 | 2376 | 0.4504 | 0.6818 | 0.4504 | 0.6711 |
| 0.0688 | 8.5848 | 2378 | 0.4470 | 0.6818 | 0.4470 | 0.6685 |
| 0.0688 | 8.5921 | 2380 | 0.4414 | 0.6818 | 0.4414 | 0.6644 |
| 0.0688 | 8.5993 | 2382 | 0.4388 | 0.7158 | 0.4388 | 0.6624 |
| 0.0688 | 8.6065 | 2384 | 0.4410 | 0.6818 | 0.4410 | 0.6641 |
| 0.0688 | 8.6137 | 2386 | 0.4424 | 0.6818 | 0.4424 | 0.6651 |
| 0.0688 | 8.6209 | 2388 | 0.4433 | 0.6818 | 0.4433 | 0.6658 |
| 0.0688 | 8.6282 | 2390 | 0.4425 | 0.6818 | 0.4425 | 0.6652 |
| 0.0688 | 8.6354 | 2392 | 0.4435 | 0.6818 | 0.4435 | 0.6660 |
| 0.0688 | 8.6426 | 2394 | 0.4407 | 0.6818 | 0.4407 | 0.6639 |
| 0.0688 | 8.6498 | 2396 | 0.4377 | 0.7158 | 0.4377 | 0.6616 |
| 0.0688 | 8.6570 | 2398 | 0.4374 | 0.7158 | 0.4374 | 0.6614 |
| 0.0688 | 8.6643 | 2400 | 0.4373 | 0.7158 | 0.4373 | 0.6613 |
| 0.0688 | 8.6715 | 2402 | 0.4369 | 0.7158 | 0.4369 | 0.6610 |
| 0.0688 | 8.6787 | 2404 | 0.4363 | 0.7158 | 0.4363 | 0.6605 |
| 0.0688 | 8.6859 | 2406 | 0.4370 | 0.7158 | 0.4370 | 0.6610 |
| 0.0688 | 8.6931 | 2408 | 0.4381 | 0.7158 | 0.4381 | 0.6619 |
| 0.0688 | 8.7004 | 2410 | 0.4393 | 0.7158 | 0.4393 | 0.6628 |
| 0.0688 | 8.7076 | 2412 | 0.4388 | 0.7158 | 0.4388 | 0.6624 |
| 0.0688 | 8.7148 | 2414 | 0.4380 | 0.7158 | 0.4380 | 0.6618 |
| 0.0688 | 8.7220 | 2416 | 0.4357 | 0.7158 | 0.4357 | 0.6601 |
| 0.0688 | 8.7292 | 2418 | 0.4292 | 0.7158 | 0.4292 | 0.6551 |
| 0.0688 | 8.7365 | 2420 | 0.4254 | 0.7158 | 0.4254 | 0.6522 |
| 0.0688 | 8.7437 | 2422 | 0.4240 | 0.7158 | 0.4240 | 0.6511 |
| 0.0688 | 8.7509 | 2424 | 0.4209 | 0.7158 | 0.4209 | 0.6488 |
| 0.0688 | 8.7581 | 2426 | 0.4214 | 0.7158 | 0.4214 | 0.6491 |
| 0.0688 | 8.7653 | 2428 | 0.4258 | 0.7158 | 0.4258 | 0.6525 |
| 0.0688 | 8.7726 | 2430 | 0.4330 | 0.7158 | 0.4330 | 0.6580 |
| 0.0688 | 8.7798 | 2432 | 0.4382 | 0.6818 | 0.4382 | 0.6620 |
| 0.0688 | 8.7870 | 2434 | 0.4389 | 0.6818 | 0.4389 | 0.6625 |
| 0.0688 | 8.7942 | 2436 | 0.4370 | 0.6818 | 0.4370 | 0.6611 |
| 0.0688 | 8.8014 | 2438 | 0.4375 | 0.6818 | 0.4375 | 0.6614 |
| 0.0688 | 8.8087 | 2440 | 0.4425 | 0.6818 | 0.4425 | 0.6652 |
| 0.0688 | 8.8159 | 2442 | 0.4476 | 0.5832 | 0.4476 | 0.6690 |
| 0.0688 | 8.8231 | 2444 | 0.4514 | 0.5832 | 0.4514 | 0.6719 |
| 0.0688 | 8.8303 | 2446 | 0.4549 | 0.5832 | 0.4549 | 0.6745 |
| 0.0688 | 8.8375 | 2448 | 0.4583 | 0.5832 | 0.4583 | 0.6769 |
| 0.0688 | 8.8448 | 2450 | 0.4562 | 0.5832 | 0.4562 | 0.6754 |
| 0.0688 | 8.8520 | 2452 | 0.4548 | 0.5832 | 0.4548 | 0.6744 |
| 0.0688 | 8.8592 | 2454 | 0.4519 | 0.5832 | 0.4519 | 0.6722 |
| 0.0688 | 8.8664 | 2456 | 0.4515 | 0.5832 | 0.4515 | 0.6719 |
| 0.0688 | 8.8736 | 2458 | 0.4536 | 0.5832 | 0.4536 | 0.6735 |
| 0.0688 | 8.8809 | 2460 | 0.4578 | 0.5832 | 0.4578 | 0.6766 |
| 0.0688 | 8.8881 | 2462 | 0.4582 | 0.5832 | 0.4582 | 0.6769 |
| 0.0688 | 8.8953 | 2464 | 0.4558 | 0.5832 | 0.4558 | 0.6752 |
| 0.0688 | 8.9025 | 2466 | 0.4538 | 0.5832 | 0.4538 | 0.6737 |
| 0.0688 | 8.9097 | 2468 | 0.4494 | 0.7158 | 0.4494 | 0.6704 |
| 0.0688 | 8.9170 | 2470 | 0.4483 | 0.7158 | 0.4483 | 0.6695 |
| 0.0688 | 8.9242 | 2472 | 0.4468 | 0.7158 | 0.4468 | 0.6684 |
| 0.0688 | 8.9314 | 2474 | 0.4444 | 0.7158 | 0.4444 | 0.6666 |
| 0.0688 | 8.9386 | 2476 | 0.4434 | 0.7158 | 0.4434 | 0.6659 |
| 0.0688 | 8.9458 | 2478 | 0.4410 | 0.7158 | 0.4410 | 0.6641 |
| 0.0688 | 8.9531 | 2480 | 0.4397 | 0.7158 | 0.4397 | 0.6631 |
| 0.0688 | 8.9603 | 2482 | 0.4391 | 0.7158 | 0.4391 | 0.6627 |
| 0.0688 | 8.9675 | 2484 | 0.4396 | 0.7158 | 0.4396 | 0.6630 |
| 0.0688 | 8.9747 | 2486 | 0.4431 | 0.7158 | 0.4431 | 0.6657 |
| 0.0688 | 8.9819 | 2488 | 0.4454 | 0.7158 | 0.4454 | 0.6674 |
| 0.0688 | 8.9892 | 2490 | 0.4502 | 0.7158 | 0.4502 | 0.6710 |
| 0.0688 | 8.9964 | 2492 | 0.4538 | 0.7158 | 0.4538 | 0.6736 |
| 0.0688 | 9.0036 | 2494 | 0.4560 | 0.6818 | 0.4560 | 0.6753 |
| 0.0688 | 9.0108 | 2496 | 0.4597 | 0.5832 | 0.4597 | 0.6780 |
| 0.0688 | 9.0181 | 2498 | 0.4627 | 0.5832 | 0.4627 | 0.6802 |
| 0.0556 | 9.0253 | 2500 | 0.4685 | 0.5832 | 0.4685 | 0.6845 |
| 0.0556 | 9.0325 | 2502 | 0.4727 | 0.5832 | 0.4727 | 0.6876 |
| 0.0556 | 9.0397 | 2504 | 0.4756 | 0.5832 | 0.4756 | 0.6896 |
| 0.0556 | 9.0469 | 2506 | 0.4755 | 0.5832 | 0.4755 | 0.6895 |
| 0.0556 | 9.0542 | 2508 | 0.4733 | 0.5832 | 0.4733 | 0.6880 |
| 0.0556 | 9.0614 | 2510 | 0.4698 | 0.5832 | 0.4698 | 0.6854 |
| 0.0556 | 9.0686 | 2512 | 0.4688 | 0.5832 | 0.4688 | 0.6847 |
| 0.0556 | 9.0758 | 2514 | 0.4656 | 0.5832 | 0.4656 | 0.6824 |
| 0.0556 | 9.0830 | 2516 | 0.4600 | 0.5832 | 0.4600 | 0.6782 |
| 0.0556 | 9.0903 | 2518 | 0.4530 | 0.5832 | 0.4530 | 0.6730 |
| 0.0556 | 9.0975 | 2520 | 0.4497 | 0.5832 | 0.4497 | 0.6706 |
| 0.0556 | 9.1047 | 2522 | 0.4458 | 0.7158 | 0.4458 | 0.6677 |
| 0.0556 | 9.1119 | 2524 | 0.4420 | 0.7158 | 0.4420 | 0.6648 |
| 0.0556 | 9.1191 | 2526 | 0.4422 | 0.7158 | 0.4422 | 0.6650 |
| 0.0556 | 9.1264 | 2528 | 0.4415 | 0.7158 | 0.4415 | 0.6645 |
| 0.0556 | 9.1336 | 2530 | 0.4389 | 0.7158 | 0.4389 | 0.6625 |
| 0.0556 | 9.1408 | 2532 | 0.4380 | 0.7158 | 0.4380 | 0.6619 |
| 0.0556 | 9.1480 | 2534 | 0.4391 | 0.6818 | 0.4391 | 0.6627 |
| 0.0556 | 9.1552 | 2536 | 0.4417 | 0.5832 | 0.4417 | 0.6646 |
| 0.0556 | 9.1625 | 2538 | 0.4459 | 0.5832 | 0.4459 | 0.6678 |
| 0.0556 | 9.1697 | 2540 | 0.4493 | 0.5832 | 0.4493 | 0.6703 |
| 0.0556 | 9.1769 | 2542 | 0.4520 | 0.5832 | 0.4520 | 0.6723 |
| 0.0556 | 9.1841 | 2544 | 0.4534 | 0.5832 | 0.4534 | 0.6734 |
| 0.0556 | 9.1913 | 2546 | 0.4523 | 0.5832 | 0.4523 | 0.6725 |
| 0.0556 | 9.1986 | 2548 | 0.4506 | 0.5832 | 0.4506 | 0.6712 |
| 0.0556 | 9.2058 | 2550 | 0.4519 | 0.5832 | 0.4519 | 0.6722 |
| 0.0556 | 9.2130 | 2552 | 0.4549 | 0.5832 | 0.4549 | 0.6745 |
| 0.0556 | 9.2202 | 2554 | 0.4548 | 0.5832 | 0.4548 | 0.6744 |
| 0.0556 | 9.2274 | 2556 | 0.4534 | 0.5832 | 0.4534 | 0.6733 |
| 0.0556 | 9.2347 | 2558 | 0.4511 | 0.7158 | 0.4511 | 0.6717 |
| 0.0556 | 9.2419 | 2560 | 0.4510 | 0.7158 | 0.4510 | 0.6716 |
| 0.0556 | 9.2491 | 2562 | 0.4510 | 0.7158 | 0.4510 | 0.6716 |
| 0.0556 | 9.2563 | 2564 | 0.4505 | 0.7158 | 0.4505 | 0.6712 |
| 0.0556 | 9.2635 | 2566 | 0.4523 | 0.6182 | 0.4523 | 0.6725 |
| 0.0556 | 9.2708 | 2568 | 0.4550 | 0.6182 | 0.4550 | 0.6745 |
| 0.0556 | 9.2780 | 2570 | 0.4543 | 0.6182 | 0.4543 | 0.6740 |
| 0.0556 | 9.2852 | 2572 | 0.4526 | 0.6182 | 0.4526 | 0.6727 |
| 0.0556 | 9.2924 | 2574 | 0.4508 | 0.6182 | 0.4508 | 0.6714 |
| 0.0556 | 9.2996 | 2576 | 0.4501 | 0.6182 | 0.4501 | 0.6709 |
| 0.0556 | 9.3069 | 2578 | 0.4509 | 0.6182 | 0.4509 | 0.6715 |
| 0.0556 | 9.3141 | 2580 | 0.4515 | 0.6182 | 0.4515 | 0.6719 |
| 0.0556 | 9.3213 | 2582 | 0.4524 | 0.6182 | 0.4524 | 0.6726 |
| 0.0556 | 9.3285 | 2584 | 0.4542 | 0.5832 | 0.4542 | 0.6740 |
| 0.0556 | 9.3357 | 2586 | 0.4542 | 0.5832 | 0.4542 | 0.6740 |
| 0.0556 | 9.3430 | 2588 | 0.4533 | 0.5832 | 0.4533 | 0.6732 |
| 0.0556 | 9.3502 | 2590 | 0.4531 | 0.6182 | 0.4531 | 0.6731 |
| 0.0556 | 9.3574 | 2592 | 0.4536 | 0.5832 | 0.4536 | 0.6735 |
| 0.0556 | 9.3646 | 2594 | 0.4544 | 0.5832 | 0.4544 | 0.6741 |
| 0.0556 | 9.3718 | 2596 | 0.4569 | 0.5832 | 0.4569 | 0.6760 |
| 0.0556 | 9.3791 | 2598 | 0.4561 | 0.5832 | 0.4561 | 0.6754 |
| 0.0556 | 9.3863 | 2600 | 0.4536 | 0.5832 | 0.4536 | 0.6735 |
| 0.0556 | 9.3935 | 2602 | 0.4519 | 0.5832 | 0.4519 | 0.6722 |
| 0.0556 | 9.4007 | 2604 | 0.4503 | 0.5832 | 0.4503 | 0.6711 |
| 0.0556 | 9.4079 | 2606 | 0.4498 | 0.5832 | 0.4498 | 0.6707 |
| 0.0556 | 9.4152 | 2608 | 0.4497 | 0.5832 | 0.4497 | 0.6706 |
| 0.0556 | 9.4224 | 2610 | 0.4500 | 0.5832 | 0.4500 | 0.6708 |
| 0.0556 | 9.4296 | 2612 | 0.4516 | 0.5832 | 0.4516 | 0.6720 |
| 0.0556 | 9.4368 | 2614 | 0.4537 | 0.5832 | 0.4537 | 0.6736 |
| 0.0556 | 9.4440 | 2616 | 0.4560 | 0.5832 | 0.4560 | 0.6753 |
| 0.0556 | 9.4513 | 2618 | 0.4596 | 0.5832 | 0.4596 | 0.6779 |
| 0.0556 | 9.4585 | 2620 | 0.4640 | 0.5832 | 0.4640 | 0.6812 |
| 0.0556 | 9.4657 | 2622 | 0.4665 | 0.5832 | 0.4665 | 0.6830 |
| 0.0556 | 9.4729 | 2624 | 0.4686 | 0.5832 | 0.4686 | 0.6845 |
| 0.0556 | 9.4801 | 2626 | 0.4677 | 0.5832 | 0.4677 | 0.6839 |
| 0.0556 | 9.4874 | 2628 | 0.4650 | 0.5832 | 0.4650 | 0.6819 |
| 0.0556 | 9.4946 | 2630 | 0.4627 | 0.5832 | 0.4627 | 0.6802 |
| 0.0556 | 9.5018 | 2632 | 0.4610 | 0.5832 | 0.4610 | 0.6790 |
| 0.0556 | 9.5090 | 2634 | 0.4607 | 0.5832 | 0.4607 | 0.6788 |
| 0.0556 | 9.5162 | 2636 | 0.4623 | 0.5832 | 0.4623 | 0.6799 |
| 0.0556 | 9.5235 | 2638 | 0.4623 | 0.5832 | 0.4623 | 0.6799 |
| 0.0556 | 9.5307 | 2640 | 0.4626 | 0.5832 | 0.4626 | 0.6801 |
| 0.0556 | 9.5379 | 2642 | 0.4627 | 0.5832 | 0.4627 | 0.6802 |
| 0.0556 | 9.5451 | 2644 | 0.4607 | 0.5832 | 0.4607 | 0.6787 |
| 0.0556 | 9.5523 | 2646 | 0.4593 | 0.5832 | 0.4593 | 0.6777 |
| 0.0556 | 9.5596 | 2648 | 0.4566 | 0.5832 | 0.4566 | 0.6757 |
| 0.0556 | 9.5668 | 2650 | 0.4535 | 0.5832 | 0.4535 | 0.6734 |
| 0.0556 | 9.5740 | 2652 | 0.4508 | 0.5832 | 0.4508 | 0.6714 |
| 0.0556 | 9.5812 | 2654 | 0.4489 | 0.5832 | 0.4489 | 0.6700 |
| 0.0556 | 9.5884 | 2656 | 0.4482 | 0.7158 | 0.4482 | 0.6695 |
| 0.0556 | 9.5957 | 2658 | 0.4484 | 0.7158 | 0.4484 | 0.6697 |
| 0.0556 | 9.6029 | 2660 | 0.4495 | 0.6182 | 0.4495 | 0.6705 |
| 0.0556 | 9.6101 | 2662 | 0.4513 | 0.5832 | 0.4513 | 0.6718 |
| 0.0556 | 9.6173 | 2664 | 0.4521 | 0.5832 | 0.4521 | 0.6724 |
| 0.0556 | 9.6245 | 2666 | 0.4535 | 0.5832 | 0.4535 | 0.6734 |
| 0.0556 | 9.6318 | 2668 | 0.4542 | 0.5832 | 0.4542 | 0.6740 |
| 0.0556 | 9.6390 | 2670 | 0.4539 | 0.5832 | 0.4539 | 0.6737 |
| 0.0556 | 9.6462 | 2672 | 0.4522 | 0.7158 | 0.4522 | 0.6725 |
| 0.0556 | 9.6534 | 2674 | 0.4497 | 0.7158 | 0.4497 | 0.6706 |
| 0.0556 | 9.6606 | 2676 | 0.4480 | 0.7158 | 0.4480 | 0.6693 |
| 0.0556 | 9.6679 | 2678 | 0.4463 | 0.7158 | 0.4463 | 0.6681 |
| 0.0556 | 9.6751 | 2680 | 0.4451 | 0.7158 | 0.4451 | 0.6672 |
| 0.0556 | 9.6823 | 2682 | 0.4445 | 0.7158 | 0.4445 | 0.6667 |
| 0.0556 | 9.6895 | 2684 | 0.4440 | 0.7158 | 0.4440 | 0.6663 |
| 0.0556 | 9.6968 | 2686 | 0.4435 | 0.7158 | 0.4435 | 0.6660 |
| 0.0556 | 9.7040 | 2688 | 0.4437 | 0.7158 | 0.4437 | 0.6661 |
| 0.0556 | 9.7112 | 2690 | 0.4439 | 0.7158 | 0.4439 | 0.6662 |
| 0.0556 | 9.7184 | 2692 | 0.4437 | 0.7158 | 0.4437 | 0.6661 |
| 0.0556 | 9.7256 | 2694 | 0.4443 | 0.7158 | 0.4443 | 0.6666 |
| 0.0556 | 9.7329 | 2696 | 0.4449 | 0.7158 | 0.4449 | 0.6670 |
| 0.0556 | 9.7401 | 2698 | 0.4448 | 0.7158 | 0.4448 | 0.6669 |
| 0.0556 | 9.7473 | 2700 | 0.4450 | 0.7158 | 0.4450 | 0.6671 |
| 0.0556 | 9.7545 | 2702 | 0.4447 | 0.7158 | 0.4447 | 0.6669 |
| 0.0556 | 9.7617 | 2704 | 0.4449 | 0.7158 | 0.4449 | 0.6670 |
| 0.0556 | 9.7690 | 2706 | 0.4451 | 0.7158 | 0.4451 | 0.6671 |
| 0.0556 | 9.7762 | 2708 | 0.4450 | 0.7158 | 0.4450 | 0.6671 |
| 0.0556 | 9.7834 | 2710 | 0.4452 | 0.7158 | 0.4452 | 0.6673 |
| 0.0556 | 9.7906 | 2712 | 0.4458 | 0.7158 | 0.4458 | 0.6677 |
| 0.0556 | 9.7978 | 2714 | 0.4457 | 0.7158 | 0.4457 | 0.6676 |
| 0.0556 | 9.8051 | 2716 | 0.4451 | 0.7158 | 0.4451 | 0.6672 |
| 0.0556 | 9.8123 | 2718 | 0.4449 | 0.7158 | 0.4449 | 0.6670 |
| 0.0556 | 9.8195 | 2720 | 0.4450 | 0.7158 | 0.4450 | 0.6671 |
| 0.0556 | 9.8267 | 2722 | 0.4456 | 0.7158 | 0.4456 | 0.6675 |
| 0.0556 | 9.8339 | 2724 | 0.4456 | 0.7158 | 0.4456 | 0.6675 |
| 0.0556 | 9.8412 | 2726 | 0.4454 | 0.7158 | 0.4454 | 0.6674 |
| 0.0556 | 9.8484 | 2728 | 0.4447 | 0.7158 | 0.4447 | 0.6668 |
| 0.0556 | 9.8556 | 2730 | 0.4438 | 0.7158 | 0.4438 | 0.6662 |
| 0.0556 | 9.8628 | 2732 | 0.4430 | 0.7158 | 0.4430 | 0.6656 |
| 0.0556 | 9.8700 | 2734 | 0.4423 | 0.7158 | 0.4423 | 0.6650 |
| 0.0556 | 9.8773 | 2736 | 0.4419 | 0.7158 | 0.4419 | 0.6647 |
| 0.0556 | 9.8845 | 2738 | 0.4414 | 0.7158 | 0.4414 | 0.6643 |
| 0.0556 | 9.8917 | 2740 | 0.4409 | 0.7158 | 0.4409 | 0.6640 |
| 0.0556 | 9.8989 | 2742 | 0.4404 | 0.7158 | 0.4404 | 0.6636 |
| 0.0556 | 9.9061 | 2744 | 0.4401 | 0.7158 | 0.4401 | 0.6634 |
| 0.0556 | 9.9134 | 2746 | 0.4399 | 0.7158 | 0.4399 | 0.6632 |
| 0.0556 | 9.9206 | 2748 | 0.4400 | 0.7158 | 0.4400 | 0.6633 |
| 0.0556 | 9.9278 | 2750 | 0.4400 | 0.7158 | 0.4400 | 0.6634 |
| 0.0556 | 9.9350 | 2752 | 0.4403 | 0.7158 | 0.4403 | 0.6635 |
| 0.0556 | 9.9422 | 2754 | 0.4405 | 0.7158 | 0.4405 | 0.6637 |
| 0.0556 | 9.9495 | 2756 | 0.4407 | 0.7158 | 0.4407 | 0.6639 |
| 0.0556 | 9.9567 | 2758 | 0.4407 | 0.7158 | 0.4407 | 0.6639 |
| 0.0556 | 9.9639 | 2760 | 0.4407 | 0.7158 | 0.4407 | 0.6639 |
| 0.0556 | 9.9711 | 2762 | 0.4408 | 0.7158 | 0.4408 | 0.6639 |
| 0.0556 | 9.9783 | 2764 | 0.4408 | 0.7158 | 0.4408 | 0.6640 |
| 0.0556 | 9.9856 | 2766 | 0.4409 | 0.7158 | 0.4409 | 0.6640 |
| 0.0556 | 9.9928 | 2768 | 0.4409 | 0.7158 | 0.4409 | 0.6640 |
| 0.0556 | 10.0 | 2770 | 0.4409 | 0.7158 | 0.4409 | 0.6640 |
### Framework versions
- Transformers 4.44.2
- Pytorch 2.4.0+cu118
- Datasets 2.21.0
- Tokenizers 0.19.1
|
CluelessC/hll-test
|
CluelessC
| 2024-11-16T18:04:58Z
| 0
| 46
| null |
[
"text-to-image",
"license:creativeml-openrail-m",
"region:us"
] |
text-to-image
| 2023-03-28T09:02:13Z
|
---
license: creativeml-openrail-m
pipeline_tag: text-to-image
---
|
nguyentd/FinancialAdvice-Qwen2.5-7B
|
nguyentd
| 2024-11-16T18:03:17Z
| 37
| 1
|
transformers
|
[
"transformers",
"safetensors",
"qwen2",
"text-generation",
"unsloth",
"trl",
"sft",
"conversational",
"en",
"base_model:Qwen/Qwen2.5-7B-Instruct",
"base_model:finetune:Qwen/Qwen2.5-7B-Instruct",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2024-10-21T15:08:37Z
|
---
license: apache-2.0
tags:
- unsloth
- trl
- sft
language:
- en
base_model:
- Qwen/Qwen2.5-7B-Instruct
pipeline_tag: text-generation
library_name: transformers
---
Model Description:
This model, nguyentd/FinancialAdvice-Qwen2.5-7B, is a fine-tuned version of the Qwen-2.5-7B language model. It has been trained on a dataset of questions and answers from the Reddit community r/AskEconomics. This specialization aims to improve the model's ability to provide helpful and informed responses to questions related to economics and personal finance.
- Model Source: This model is based on the Qwen-2.5-7B model and has been further trained by nguyentd.
- Fine-tuning Dataset:
+ The model was fine-tuned using a dataset comprised of posts and top-voted answers from the subreddit r/AskEconomics. This dataset covers a range of economic and financial topics, including:
+ Macroeconomics
+ Microeconomics
+ Investing
+ Personal Finance
+ Career Advice (related to economics)
- Intended Use Cases: This model is best suited for:
+ Generating informative responses to questions about economic principles.
+ Offering potential solutions to personal finance dilemmas.
+ Providing explanations of economic concepts and events.
+ Assisting with understanding economic discussions and debates.
- Limitations:
+ Not a Financial Advisor: This model is intended for informational purposes only and should not be considered a substitute for professional financial advice. Always consult with a qualified financial advisor before making any financial decisions.
+ Bias and Subjectivity: The training data from r/AskEconomics may reflect the biases and subjective opinions of the community members. The model's responses may therefore contain similar biases. Critically evaluate the information provided and consider seeking diverse perspectives.
+ Factual Accuracy: While the model strives to provide accurate information, it is not guaranteed to be error-free. Always verify information from reputable sources before making decisions based on the model's output.
+ Limited Scope: The model's knowledge is limited to the information present in the r/AskEconomics dataset. It may not be able to answer questions on highly specialized or niche economic topics.
- Ethical Considerations:
+ Misinformation: The potential for the model to generate misleading or incorrect information underscores the importance of verifying its output.
+ Financial Responsibility: Users should be aware that relying solely on the model's advice for financial decisions can lead to negative consequences.
|
mradermacher/deepseek-llm-7b-chat-i1-GGUF
|
mradermacher
| 2024-11-16T18:01:32Z
| 454
| 0
|
transformers
|
[
"transformers",
"gguf",
"en",
"base_model:deepseek-ai/deepseek-llm-7b-chat",
"base_model:quantized:deepseek-ai/deepseek-llm-7b-chat",
"license:other",
"endpoints_compatible",
"region:us",
"imatrix",
"conversational"
] | null | 2024-11-16T15:13:22Z
|
---
base_model: deepseek-ai/deepseek-llm-7b-chat
language:
- en
library_name: transformers
license: other
license_link: LICENSE
license_name: deepseek
quantized_by: mradermacher
---
## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
<!-- ### tags: nicoboss -->
weighted/imatrix quants of https://huggingface.co/deepseek-ai/deepseek-llm-7b-chat
<!-- provided-files -->
static quants are available at https://huggingface.co/mradermacher/deepseek-llm-7b-chat-GGUF
## Usage
If you are unsure how to use GGUF files, refer to one of [TheBloke's
READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for
more details, including on how to concatenate multi-part files.
## Provided Quants
(sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants)
| Link | Type | Size/GB | Notes |
|:-----|:-----|--------:|:------|
| [GGUF](https://huggingface.co/mradermacher/deepseek-llm-7b-chat-i1-GGUF/resolve/main/deepseek-llm-7b-chat.i1-IQ1_S.gguf) | i1-IQ1_S | 1.8 | for the desperate |
| [GGUF](https://huggingface.co/mradermacher/deepseek-llm-7b-chat-i1-GGUF/resolve/main/deepseek-llm-7b-chat.i1-IQ1_M.gguf) | i1-IQ1_M | 1.9 | mostly desperate |
| [GGUF](https://huggingface.co/mradermacher/deepseek-llm-7b-chat-i1-GGUF/resolve/main/deepseek-llm-7b-chat.i1-IQ2_XXS.gguf) | i1-IQ2_XXS | 2.1 | |
| [GGUF](https://huggingface.co/mradermacher/deepseek-llm-7b-chat-i1-GGUF/resolve/main/deepseek-llm-7b-chat.i1-IQ2_XS.gguf) | i1-IQ2_XS | 2.3 | |
| [GGUF](https://huggingface.co/mradermacher/deepseek-llm-7b-chat-i1-GGUF/resolve/main/deepseek-llm-7b-chat.i1-IQ2_S.gguf) | i1-IQ2_S | 2.5 | |
| [GGUF](https://huggingface.co/mradermacher/deepseek-llm-7b-chat-i1-GGUF/resolve/main/deepseek-llm-7b-chat.i1-IQ2_M.gguf) | i1-IQ2_M | 2.6 | |
| [GGUF](https://huggingface.co/mradermacher/deepseek-llm-7b-chat-i1-GGUF/resolve/main/deepseek-llm-7b-chat.i1-Q2_K.gguf) | i1-Q2_K | 2.8 | IQ3_XXS probably better |
| [GGUF](https://huggingface.co/mradermacher/deepseek-llm-7b-chat-i1-GGUF/resolve/main/deepseek-llm-7b-chat.i1-IQ3_XXS.gguf) | i1-IQ3_XXS | 2.9 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/deepseek-llm-7b-chat-i1-GGUF/resolve/main/deepseek-llm-7b-chat.i1-IQ3_XS.gguf) | i1-IQ3_XS | 3.1 | |
| [GGUF](https://huggingface.co/mradermacher/deepseek-llm-7b-chat-i1-GGUF/resolve/main/deepseek-llm-7b-chat.i1-IQ3_S.gguf) | i1-IQ3_S | 3.2 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/deepseek-llm-7b-chat-i1-GGUF/resolve/main/deepseek-llm-7b-chat.i1-Q3_K_S.gguf) | i1-Q3_K_S | 3.2 | IQ3_XS probably better |
| [GGUF](https://huggingface.co/mradermacher/deepseek-llm-7b-chat-i1-GGUF/resolve/main/deepseek-llm-7b-chat.i1-IQ3_M.gguf) | i1-IQ3_M | 3.4 | |
| [GGUF](https://huggingface.co/mradermacher/deepseek-llm-7b-chat-i1-GGUF/resolve/main/deepseek-llm-7b-chat.i1-Q3_K_M.gguf) | i1-Q3_K_M | 3.6 | IQ3_S probably better |
| [GGUF](https://huggingface.co/mradermacher/deepseek-llm-7b-chat-i1-GGUF/resolve/main/deepseek-llm-7b-chat.i1-Q3_K_L.gguf) | i1-Q3_K_L | 3.8 | IQ3_M probably better |
| [GGUF](https://huggingface.co/mradermacher/deepseek-llm-7b-chat-i1-GGUF/resolve/main/deepseek-llm-7b-chat.i1-IQ4_XS.gguf) | i1-IQ4_XS | 3.9 | |
| [GGUF](https://huggingface.co/mradermacher/deepseek-llm-7b-chat-i1-GGUF/resolve/main/deepseek-llm-7b-chat.i1-Q4_0_4_4.gguf) | i1-Q4_0_4_4 | 4.1 | fast on arm, low quality |
| [GGUF](https://huggingface.co/mradermacher/deepseek-llm-7b-chat-i1-GGUF/resolve/main/deepseek-llm-7b-chat.i1-Q4_0_4_8.gguf) | i1-Q4_0_4_8 | 4.1 | fast on arm+i8mm, low quality |
| [GGUF](https://huggingface.co/mradermacher/deepseek-llm-7b-chat-i1-GGUF/resolve/main/deepseek-llm-7b-chat.i1-Q4_0_8_8.gguf) | i1-Q4_0_8_8 | 4.1 | fast on arm+sve, low quality |
| [GGUF](https://huggingface.co/mradermacher/deepseek-llm-7b-chat-i1-GGUF/resolve/main/deepseek-llm-7b-chat.i1-Q4_0.gguf) | i1-Q4_0 | 4.1 | fast, low quality |
| [GGUF](https://huggingface.co/mradermacher/deepseek-llm-7b-chat-i1-GGUF/resolve/main/deepseek-llm-7b-chat.i1-Q4_K_S.gguf) | i1-Q4_K_S | 4.1 | optimal size/speed/quality |
| [GGUF](https://huggingface.co/mradermacher/deepseek-llm-7b-chat-i1-GGUF/resolve/main/deepseek-llm-7b-chat.i1-Q4_K_M.gguf) | i1-Q4_K_M | 4.3 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/deepseek-llm-7b-chat-i1-GGUF/resolve/main/deepseek-llm-7b-chat.i1-Q5_K_S.gguf) | i1-Q5_K_S | 4.9 | |
| [GGUF](https://huggingface.co/mradermacher/deepseek-llm-7b-chat-i1-GGUF/resolve/main/deepseek-llm-7b-chat.i1-Q5_K_M.gguf) | i1-Q5_K_M | 5.0 | |
| [GGUF](https://huggingface.co/mradermacher/deepseek-llm-7b-chat-i1-GGUF/resolve/main/deepseek-llm-7b-chat.i1-Q6_K.gguf) | i1-Q6_K | 5.8 | practically like static Q6_K |
Here is a handy graph by ikawrakow comparing some lower-quality quant
types (lower is better):

And here are Artefact2's thoughts on the matter:
https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9
## FAQ / Model Request
See https://huggingface.co/mradermacher/model_requests for some answers to
questions you might have and/or if you want some other model quantized.
## Thanks
I thank my company, [nethype GmbH](https://www.nethype.de/), for letting
me use its servers and providing upgrades to my workstation to enable
this work in my free time. Additional thanks to [@nicoboss](https://huggingface.co/nicoboss) for giving me access to his private supercomputer, enabling me to provide many more imatrix quants, at much higher quality, than I would otherwise be able to.
<!-- end -->
|
ckpt/In-Context-LoRA
|
ckpt
| 2024-11-16T17:59:44Z
| 210
| 4
|
diffusers
|
[
"diffusers",
"text-to-image",
"lora",
"template:diffusion-lora",
"arxiv:2410.23775",
"arxiv:2410.15027",
"base_model:black-forest-labs/FLUX.1-dev",
"base_model:adapter:black-forest-labs/FLUX.1-dev",
"license:mit",
"region:us"
] |
text-to-image
| 2024-11-16T17:57:53Z
|
---
tags:
- text-to-image
- lora
- diffusers
- template:diffusion-lora
base_model: black-forest-labs/FLUX.1-dev
instance_prompt: null
license: mit
---
📢 [[Project Page](https://ali-vilab.github.io/In-Context-LoRA-Page/)] [[Github Repo](https://github.com/ali-vilab/In-Context-LoRA)] [[Paper](https://arxiv.org/abs/2410.23775)]
# 🔥 Latest News
- **[2024-11-16]** 🌟 The community continues to innovate with IC-LoRA! Exciting projects include models, ComfyUI nodes and workflows for **Virtual Try-on, Product Design, Object Mitigation, Role Play**, and more. Explore their creations in **[Community Creations Using IC-LoRA](#community-creations-using-ic-lora)**. Huge thanks to all contributors for their incredible efforts!
# Model Summary
In-Context LoRA fine-tunes text-to-image models (*e.g.,* [FLUX](https://huggingface.co/black-forest-labs/FLUX.1-dev)) to generate image sets with customizable intrinsic relationships, optionally conditioned on another set using SDEdit. It can be adapted to a wide range of tasks
This model hub includes In-Context LoRA models across 10 tasks. [MODEL ZOO](#model-zoo) details these models and their recommend settings. For more details on how these models are trained, please refer to our [paper](https://arxiv.org/abs/2410.23775).
## Key Idea
The core concept of IC-LoRA is to **concatenate** both condition and target images into a single composite image while using **Natural Language** to define the task. This approach enables seamless adaptation to a wide range of applications.
## Features
- **Task-Agnostic Framework**: IC-LoRA serves as a general framework, but it requires task-specific fine-tuning for diverse applications.
- **Customizable Image-Set Generation**: You can fine-tune text-to-image models to **generate image sets** with customizable intrinsic relationships.
- **Condition on Image-Set**: You can also **condition the generation of a set of images on another set of images**, enabling a wide range of controllable generation applications.
For more detailed information and examples, please read our [Paper](https://arxiv.org/abs/2410.23775) or visit our [Project Page](https://ali-vilab.github.io/In-Context-LoRA-Page/).
## MODEL ZOO
Below lists 10 In-Context LoRA models and their recommend settings.
| Task | Model | Recommend Settings | Example Prompt |
|---------------|-------------------|---------------------|---------------------------|
| **1. Couple Profile Design** | [`couple-profile.safetensors`](https://huggingface.co/ali-vilab/In-Context-LoRA/blob/main/couple-profile.safetensors) | `width: 2048, height: 1024` | `This two-part image portrays a couple of cartoon cats in detective attire; [LEFT] a black cat in a trench coat and fedora holds a magnifying glass and peers to the right, while [RIGHT] a white cat with a bow tie and matching hat raises an eyebrow in curiosity, creating a fun, noir-inspired scene against a dimly lit background.` |
| **2. Film Storyboard** | [`film-storyboard.safetensors`](https://huggingface.co/ali-vilab/In-Context-LoRA/blob/main/storyboard.safetensors) | `width: 1024, height: 1536` | `[MOVIE-SHOTS] In a vibrant festival, [SCENE-1] we find <Leo>, a shy boy, standing at the edge of a bustling carnival, eyes wide with awe at the colorful rides and laughter, [SCENE-2] transitioning to him reluctantly trying a daring game, his friends cheering him on, [SCENE-3] culminating in a triumphant moment as he wins a giant stuffed bear, his face beaming with pride as he holds it up for all to see.` |
| **3. Font Design** | [`font-design.safetensors`](https://huggingface.co/ali-vilab/In-Context-LoRA/blob/main/font-design.safetensors) | `width: 1792, height: 1216` | `The four-panel image showcases a playful bubble font in a vibrant pop-art style. [TOP-LEFT] displays "Pop Candy" in bright pink with a polka dot background; [TOP-RIGHT] shows "Sweet Treat" in purple, surrounded by candy illustrations; [BOTTOM-LEFT] has "Yum!" in a mix of bright colors; [BOTTOM-RIGHT] shows "Delicious" against a striped background, perfect for fun, kid-friendly products.` |
| **4. Home Decoration** | [`home-decoration.safetensors`](https://huggingface.co/ali-vilab/In-Context-LoRA/blob/main/home-decoration.safetensors) | `width: 1344, height: 1728` | `This four-panel image showcases a rustic living room with warm wood tones and cozy decor elements; [TOP-LEFT] features a large stone fireplace with wooden shelves filled with books and candles; [TOP-RIGHT] shows a vintage leather sofa draped in plaid blankets, complemented by a mix of textured cushions; [BOTTOM-LEFT] displays a corner with a wooden armchair beside a side table holding a steaming mug and a classic book; [BOTTOM-RIGHT] captures a cozy reading nook with a window seat, a soft fur throw, and decorative logs stacked neatly.` |
| **5. Portrait Illustration** | [`portrait-illustration.safetensors`](https://huggingface.co/ali-vilab/In-Context-LoRA/blob/main/portrait-illustration.safetensors) | `width: 1152, height: 1088` | `This two-panel image presents a transformation from a realistic portrait to a playful illustration, capturing both detail and artistic flair; [LEFT] the photograph shows a woman standing in a bustling marketplace, wearing a wide-brimmed hat, a flowing bohemian dress, and a leather crossbody bag; [RIGHT] the illustration panel exaggerates her accessories and features, with the bohemian dress depicted in vibrant patterns and bold colors, while the background is simplified into abstract market stalls, giving the scene an animated and lively feel.` |
| **6. Portrait Photography** | [`portrait-photography.safetensors`](https://huggingface.co/ali-vilab/In-Context-LoRA/blob/main/portrait-photography.safetensors) | `width: 1344, height: 1728` | `This [FOUR-PANEL] image illustrates a young artist's creative process in a bright and inspiring studio; [TOP-LEFT] she stands before a large canvas, brush in hand, adding vibrant colors to a partially completed painting, [TOP-RIGHT] she sits at a cluttered wooden table, sketching ideas in a notebook with various art supplies scattered around, [BOTTOM-LEFT] she takes a moment to step back and observe her work, adjusting her glasses thoughtfully, and [BOTTOM-RIGHT] she experiments with different textures by mixing paints directly on the palette, her focused expression showcasing her dedication to her craft.` |
| **7. PPT Template** | [`ppt-templates.safetensors`](https://huggingface.co/ali-vilab/In-Context-LoRA/blob/main/ppt-templates.safetensors) | `width: 1984, height: 1152` | `This four-panel image showcases a rustic-themed PowerPoint template for a culinary workshop; [TOP-LEFT] introduces "Farm to Table Cooking" in warm, earthy tones; [TOP-RIGHT] organizes workshop sections like "Ingredients," "Preparation," and "Serving"; [BOTTOM-LEFT] displays ingredient lists for seasonal produce; [BOTTOM-RIGHT] includes chef profiles with short bios.` |
| **8. Sandstorm Visual Effect** | [`sandstorm-visual-effect.safetensors`](https://huggingface.co/ali-vilab/In-Context-LoRA/blob/main/sandstorm-visual-effect.safetensors) | `width: 1408, height: 1600` | `[SANDSTORM-PSA] This two-part image showcases the transformation of a cyclist through a sandstorm visual effect; [TOP] the upper panel features a cyclist in vibrant gear pedaling steadily on a clear, open road with a serene sky in the background, highlighting focus and determination, [BOTTOM] the lower panel transforms the scene as the cyclist becomes enveloped in a fierce sandstorm, with sand particles swirling intensely around the bike and rider against a stormy, darkened backdrop, emphasizing chaos and power.` |
| **9. Sparklers Visual Effect** | [`sparklers-visual-effect.safetensors`](https://huggingface.co/ali-vilab/In-Context-LoRA/blob/main/sparklers-visual-effect.safetensors) | `width: 960, height: 1088` | `[REAL-SPARKLERS-OVERLAYS] The two-part image vividly illustrates a woodland proposal transformed by sparkler overlays; [TOP] the first panel depicts a man kneeling on one knee with an engagement ring before his partner in a forest clearing at dusk, with warm, natural lighting, [BOTTOM] while the second panel introduces glowing sparklers that form a heart shape around the couple, amplifying the romance and joy of the moment.` |
| **10. Visual Identity Design** | [`visual-identity-design.safetensors`](https://huggingface.co/ali-vilab/In-Context-LoRA/blob/main/visual-identity-design.safetensors) | `width: 1472, height: 1024` | `The two-panel image showcases the joyful identity of a produce brand, with the left panel showing a smiling pineapple graphic and the brand name “Fresh Tropic” in a fun, casual font on a light aqua background; [LEFT] while the right panel translates the design onto a reusable shopping tote with the pineapple logo in black, held by a person in a market setting, emphasizing the brand’s approachable and eco-friendly vibe.` |
## Community Creations Using IC-LoRA
We are thrilled to showcase the community's innovative projects leveraging In-Context LoRA (IC-LoRA). If you have additional recommendations or projects to share, **please don't hesitate to send a [Pull Request](https://github.com/ali-vilab/In-Context-LoRA/pulls)!**
| Project Name | Type | Supported Tasks | Sample Results |
|--------------|----------------------|---------------------------------------------------------------------------------|----------------|
| 1. [Comfyui_Object_Migration](https://github.com/TTPlanetPig/Comfyui_Object_Migration) | ComfyUI Node & Workflow & LoRA Model | Clothing Migration, Cartoon Clothing to Realism, and More |  |
| 2. [Flux Simple Try On - In Context Lora](https://civitai.com/models/950111/flux-simple-try-on-in-context-lora) | LoRA Model & ComfyUI Workflow | Virtual Try-on |  |
| 3. [Flux In Context - visual identity Lora in Comfy](https://civitai.com/articles/8779) | ComfyUI Workflow | Visual Identity Transfer |  |
| 4. [Workflows Flux In Context Lora For Product Design](https://civitai.com/models/933018/workflows-flux-in-context-lora-for-product-design) | ComfyUI Workflow | Product Design, Role Play, and More |  |
| 5. [Flux Product Design - In Context Lora](https://civitai.com/models/933026/flux-product-design-in-context-lora) | LoRA Model & ComfyUI Workflow | Product Design |  |
| 6. [In Context lora + Character story generator + flux+ shichen](https://civitai.com/models/951357/in-context-lora-character-story-generator-flux-shichen) | ComfyUI Workflow | Character Movie Story Generator |  |
| 7. [In- Context-Lora|Cute 4koma 可爱四格漫画](https://civitai.com/models/947702/in-context-loracute-4koma) | LoRA Model & ComfyUI Workflow | Comic Strip Generation |  |
| 8. [Creative Effects & Design LoRA Pack (In-Context LORA)](https://civitai.com/models/929592/creative-effects-and-design-lora-pack-in-context-lora) | LoRA Model & ComfyUI Workflow | Movie-Shot Generation and More |  |
We extend our heartfelt thanks to all contributors for their exceptional work in advancing the IC-LoRA ecosystem.
## LICENSE
This model hub uses FLUX as the base model. Users must comply with FLUX's license when using this code. Please refer to [FLUX's License](https://github.com/black-forest-labs/flux/tree/main/model_licenses) for more details.
## Citation
If you find this work useful in your research, please consider citing:
```bibtex
@article{lhhuang2024iclora,
title={In-Context LoRA for Diffusion Transformers},
author={Huang, Lianghua and Wang, Wei and Wu, Zhi-Fan and Shi, Yupeng and Dou, Huanzhang and Liang, Chen and Feng, Yutong and Liu, Yu and Zhou, Jingren},
journal={arXiv preprint arxiv:2410.23775},
year={2024}
}
```
```bibtex
@article{lhhuang2024iclora,
title={Group Diffusion Transformers are Unsupervised Multitask Learners},
author={Huang, Lianghua and Wang, Wei and Wu, Zhi-Fan and Dou, Huanzhang and Shi, Yupeng and Feng, Yutong and Liang, Chen and Liu, Yu and Zhou, Jingren},
journal={arXiv preprint arxiv:2410.15027},
year={2024}
}
```
## Download model
Weights for these models are available in Safetensors format.
[Download](/ali-vilab/In-Context-LoRA/tree/main) them in the Files & versions tab.
|
mgbam/results
|
mgbam
| 2024-11-16T17:59:22Z
| 112
| 0
|
transformers
|
[
"transformers",
"safetensors",
"bert",
"question-answering",
"generated_from_trainer",
"base_model:microsoft/BiomedNLP-BiomedBERT-base-uncased-abstract-fulltext",
"base_model:finetune:microsoft/BiomedNLP-BiomedBERT-base-uncased-abstract-fulltext",
"license:mit",
"endpoints_compatible",
"region:us"
] |
question-answering
| 2024-11-16T17:59:09Z
|
---
library_name: transformers
license: mit
base_model: microsoft/BiomedNLP-PubMedBERT-base-uncased-abstract-fulltext
tags:
- generated_from_trainer
model-index:
- name: results
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# results
This model is a fine-tuned version of [microsoft/BiomedNLP-PubMedBERT-base-uncased-abstract-fulltext](https://huggingface.co/microsoft/BiomedNLP-PubMedBERT-base-uncased-abstract-fulltext) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 3e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:-----:|:---------------:|
| 0.0 | 1.0 | 8298 | 0.0000 |
| 0.0 | 2.0 | 16596 | 0.0 |
| 0.0 | 3.0 | 24894 | 0.0 |
### Framework versions
- Transformers 4.45.1
- Pytorch 2.4.0
- Datasets 3.0.1
- Tokenizers 0.20.0
|
Aurel9/testmerge-7b
|
Aurel9
| 2024-11-16T17:55:11Z
| 5
| 0
|
transformers
|
[
"transformers",
"safetensors",
"mistral",
"text-generation",
"mergekit",
"merge",
"base_model:OpenPipe/mistral-ft-optimized-1218",
"base_model:merge:OpenPipe/mistral-ft-optimized-1218",
"base_model:mlabonne/NeuralHermes-2.5-Mistral-7B",
"base_model:merge:mlabonne/NeuralHermes-2.5-Mistral-7B",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2024-11-16T17:17:30Z
|
---
base_model:
- OpenPipe/mistral-ft-optimized-1218
- mlabonne/NeuralHermes-2.5-Mistral-7B
library_name: transformers
tags:
- mergekit
- merge
---
# merge
This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit).
## Merge Details
### Merge Method
This model was merged using the SLERP merge method.
### Models Merged
The following models were included in the merge:
* [OpenPipe/mistral-ft-optimized-1218](https://huggingface.co/OpenPipe/mistral-ft-optimized-1218)
* [mlabonne/NeuralHermes-2.5-Mistral-7B](https://huggingface.co/mlabonne/NeuralHermes-2.5-Mistral-7B)
### Configuration
The following YAML configuration was used to produce this model:
```yaml
slices:
- sources:
- model: OpenPipe/mistral-ft-optimized-1218
layer_range: [0, 32]
- model: mlabonne/NeuralHermes-2.5-Mistral-7B
layer_range: [0, 32]
merge_method: slerp
base_model: OpenPipe/mistral-ft-optimized-1218
parameters:
t:
- filter: self_attn
value: [0, 0.5, 0.3, 0.7, 1]
- filter: mlp
value: [1, 0.5, 0.7, 0.3, 0]
- value: 0.5
dtype: bfloat16
```
|
bartowski/openbuddy-nemotron-70b-v23.2q-131k-GGUF
|
bartowski
| 2024-11-16T17:51:07Z
| 480
| 2
| null |
[
"gguf",
"llama-3.1",
"nemotron",
"text-generation",
"zh",
"en",
"fr",
"de",
"ja",
"ko",
"it",
"fi",
"base_model:OpenBuddy/openbuddy-nemotron-70b-v23.2q-131k",
"base_model:quantized:OpenBuddy/openbuddy-nemotron-70b-v23.2q-131k",
"license:llama3.1",
"endpoints_compatible",
"region:us",
"imatrix",
"conversational"
] |
text-generation
| 2024-11-16T15:12:01Z
|
---
quantized_by: bartowski
pipeline_tag: text-generation
language:
- zh
- en
- fr
- de
- ja
- ko
- it
- fi
tags:
- llama-3.1
- nemotron
base_model: OpenBuddy/openbuddy-nemotron-70b-v23.2q-131k
license: llama3.1
---
## Llamacpp imatrix Quantizations of openbuddy-nemotron-70b-v23.2q-131k
Using <a href="https://github.com/ggerganov/llama.cpp/">llama.cpp</a> release <a href="https://github.com/ggerganov/llama.cpp/releases/tag/b4058">b4058</a> for quantization.
Original model: https://huggingface.co/OpenBuddy/openbuddy-nemotron-70b-v23.2q-131k
All quants made using imatrix option with dataset from [here](https://gist.github.com/bartowski1182/eb213dccb3571f863da82e99418f81e8)
Run them in [LM Studio](https://lmstudio.ai/)
## Prompt format
```
<|role|>system<|says|>{system_prompt}<|end|>
<|role|>user<|says|>{prompt}<|end|>
<|role|>assistant<|says|>
```
## Download a file (not the whole branch) from below:
| Filename | Quant type | File Size | Split | Description |
| -------- | ---------- | --------- | ----- | ----------- |
| [openbuddy-nemotron-70b-v23.2q-131k-Q8_0.gguf](https://huggingface.co/bartowski/openbuddy-nemotron-70b-v23.2q-131k-GGUF/tree/main/openbuddy-nemotron-70b-v23.2q-131k-Q8_0) | Q8_0 | 74.98GB | true | Extremely high quality, generally unneeded but max available quant. |
| [openbuddy-nemotron-70b-v23.2q-131k-Q6_K.gguf](https://huggingface.co/bartowski/openbuddy-nemotron-70b-v23.2q-131k-GGUF/tree/main/openbuddy-nemotron-70b-v23.2q-131k-Q6_K) | Q6_K | 57.89GB | true | Very high quality, near perfect, *recommended*. |
| [openbuddy-nemotron-70b-v23.2q-131k-Q5_K_M.gguf](https://huggingface.co/bartowski/openbuddy-nemotron-70b-v23.2q-131k-GGUF/tree/main/openbuddy-nemotron-70b-v23.2q-131k-Q5_K_M) | Q5_K_M | 49.95GB | true | High quality, *recommended*. |
| [openbuddy-nemotron-70b-v23.2q-131k-Q5_K_S.gguf](https://huggingface.co/bartowski/openbuddy-nemotron-70b-v23.2q-131k-GGUF/blob/main/openbuddy-nemotron-70b-v23.2q-131k-Q5_K_S.gguf) | Q5_K_S | 48.66GB | false | High quality, *recommended*. |
| [openbuddy-nemotron-70b-v23.2q-131k-Q4_K_M.gguf](https://huggingface.co/bartowski/openbuddy-nemotron-70b-v23.2q-131k-GGUF/blob/main/openbuddy-nemotron-70b-v23.2q-131k-Q4_K_M.gguf) | Q4_K_M | 42.52GB | false | Good quality, default size for most use cases, *recommended*. |
| [openbuddy-nemotron-70b-v23.2q-131k-Q4_K_S.gguf](https://huggingface.co/bartowski/openbuddy-nemotron-70b-v23.2q-131k-GGUF/blob/main/openbuddy-nemotron-70b-v23.2q-131k-Q4_K_S.gguf) | Q4_K_S | 40.35GB | false | Slightly lower quality with more space savings, *recommended*. |
| [openbuddy-nemotron-70b-v23.2q-131k-Q4_0.gguf](https://huggingface.co/bartowski/openbuddy-nemotron-70b-v23.2q-131k-GGUF/blob/main/openbuddy-nemotron-70b-v23.2q-131k-Q4_0.gguf) | Q4_0 | 40.12GB | false | Legacy format, generally not worth using over similarly sized formats |
| [openbuddy-nemotron-70b-v23.2q-131k-Q4_0_8_8.gguf](https://huggingface.co/bartowski/openbuddy-nemotron-70b-v23.2q-131k-GGUF/blob/main/openbuddy-nemotron-70b-v23.2q-131k-Q4_0_8_8.gguf) | Q4_0_8_8 | 39.97GB | false | Optimized for ARM and AVX inference. Requires 'sve' support for ARM (see details below). *Don't use on Mac*. |
| [openbuddy-nemotron-70b-v23.2q-131k-Q3_K_XL.gguf](https://huggingface.co/bartowski/openbuddy-nemotron-70b-v23.2q-131k-GGUF/blob/main/openbuddy-nemotron-70b-v23.2q-131k-Q3_K_XL.gguf) | Q3_K_XL | 38.06GB | false | Uses Q8_0 for embed and output weights. Lower quality but usable, good for low RAM availability. |
| [openbuddy-nemotron-70b-v23.2q-131k-IQ4_XS.gguf](https://huggingface.co/bartowski/openbuddy-nemotron-70b-v23.2q-131k-GGUF/blob/main/openbuddy-nemotron-70b-v23.2q-131k-IQ4_XS.gguf) | IQ4_XS | 37.90GB | false | Decent quality, smaller than Q4_K_S with similar performance, *recommended*. |
| [openbuddy-nemotron-70b-v23.2q-131k-Q3_K_L.gguf](https://huggingface.co/bartowski/openbuddy-nemotron-70b-v23.2q-131k-GGUF/blob/main/openbuddy-nemotron-70b-v23.2q-131k-Q3_K_L.gguf) | Q3_K_L | 37.14GB | false | Lower quality but usable, good for low RAM availability. |
| [openbuddy-nemotron-70b-v23.2q-131k-Q3_K_M.gguf](https://huggingface.co/bartowski/openbuddy-nemotron-70b-v23.2q-131k-GGUF/blob/main/openbuddy-nemotron-70b-v23.2q-131k-Q3_K_M.gguf) | Q3_K_M | 34.27GB | false | Low quality. |
| [openbuddy-nemotron-70b-v23.2q-131k-IQ3_M.gguf](https://huggingface.co/bartowski/openbuddy-nemotron-70b-v23.2q-131k-GGUF/blob/main/openbuddy-nemotron-70b-v23.2q-131k-IQ3_M.gguf) | IQ3_M | 31.94GB | false | Medium-low quality, new method with decent performance comparable to Q3_K_M. |
| [openbuddy-nemotron-70b-v23.2q-131k-Q3_K_S.gguf](https://huggingface.co/bartowski/openbuddy-nemotron-70b-v23.2q-131k-GGUF/blob/main/openbuddy-nemotron-70b-v23.2q-131k-Q3_K_S.gguf) | Q3_K_S | 30.91GB | false | Low quality, not recommended. |
| [openbuddy-nemotron-70b-v23.2q-131k-IQ3_XXS.gguf](https://huggingface.co/bartowski/openbuddy-nemotron-70b-v23.2q-131k-GGUF/blob/main/openbuddy-nemotron-70b-v23.2q-131k-IQ3_XXS.gguf) | IQ3_XXS | 27.47GB | false | Lower quality, new method with decent performance, comparable to Q3 quants. |
| [openbuddy-nemotron-70b-v23.2q-131k-Q2_K_L.gguf](https://huggingface.co/bartowski/openbuddy-nemotron-70b-v23.2q-131k-GGUF/blob/main/openbuddy-nemotron-70b-v23.2q-131k-Q2_K_L.gguf) | Q2_K_L | 27.40GB | false | Uses Q8_0 for embed and output weights. Very low quality but surprisingly usable. |
| [openbuddy-nemotron-70b-v23.2q-131k-Q2_K.gguf](https://huggingface.co/bartowski/openbuddy-nemotron-70b-v23.2q-131k-GGUF/blob/main/openbuddy-nemotron-70b-v23.2q-131k-Q2_K.gguf) | Q2_K | 26.38GB | false | Very low quality but surprisingly usable. |
| [openbuddy-nemotron-70b-v23.2q-131k-IQ2_M.gguf](https://huggingface.co/bartowski/openbuddy-nemotron-70b-v23.2q-131k-GGUF/blob/main/openbuddy-nemotron-70b-v23.2q-131k-IQ2_M.gguf) | IQ2_M | 24.12GB | false | Relatively low quality, uses SOTA techniques to be surprisingly usable. |
| [openbuddy-nemotron-70b-v23.2q-131k-IQ2_XS.gguf](https://huggingface.co/bartowski/openbuddy-nemotron-70b-v23.2q-131k-GGUF/blob/main/openbuddy-nemotron-70b-v23.2q-131k-IQ2_XS.gguf) | IQ2_XS | 21.14GB | false | Low quality, uses SOTA techniques to be usable. |
| [openbuddy-nemotron-70b-v23.2q-131k-IQ2_XXS.gguf](https://huggingface.co/bartowski/openbuddy-nemotron-70b-v23.2q-131k-GGUF/blob/main/openbuddy-nemotron-70b-v23.2q-131k-IQ2_XXS.gguf) | IQ2_XXS | 19.10GB | false | Very low quality, uses SOTA techniques to be usable. |
| [openbuddy-nemotron-70b-v23.2q-131k-IQ1_M.gguf](https://huggingface.co/bartowski/openbuddy-nemotron-70b-v23.2q-131k-GGUF/blob/main/openbuddy-nemotron-70b-v23.2q-131k-IQ1_M.gguf) | IQ1_M | 16.75GB | false | Extremely low quality, *not* recommended. |
## Embed/output weights
Some of these quants (Q3_K_XL, Q4_K_L etc) are the standard quantization method with the embeddings and output weights quantized to Q8_0 instead of what they would normally default to.
## Downloading using huggingface-cli
<details>
<summary>Click to view download instructions</summary>
First, make sure you have hugginface-cli installed:
```
pip install -U "huggingface_hub[cli]"
```
Then, you can target the specific file you want:
```
huggingface-cli download bartowski/openbuddy-nemotron-70b-v23.2q-131k-GGUF --include "openbuddy-nemotron-70b-v23.2q-131k-Q4_K_M.gguf" --local-dir ./
```
If the model is bigger than 50GB, it will have been split into multiple files. In order to download them all to a local folder, run:
```
huggingface-cli download bartowski/openbuddy-nemotron-70b-v23.2q-131k-GGUF --include "openbuddy-nemotron-70b-v23.2q-131k-Q8_0/*" --local-dir ./
```
You can either specify a new local-dir (openbuddy-nemotron-70b-v23.2q-131k-Q8_0) or download them all in place (./)
</details>
## Q4_0_X_X information
These are *NOT* for Metal (Apple) or GPU (nvidia/AMD/intel) offloading, only ARM chips (and certain AVX2/AVX512 CPUs).
If you're using an ARM chip, the Q4_0_X_X quants will have a substantial speedup. Check out Q4_0_4_4 speed comparisons [on the original pull request](https://github.com/ggerganov/llama.cpp/pull/5780#pullrequestreview-21657544660)
To check which one would work best for your ARM chip, you can check [AArch64 SoC features](https://gpages.juszkiewicz.com.pl/arm-socs-table/arm-socs.html) (thanks EloyOn!).
If you're using a CPU that supports AVX2 or AVX512 (typically server CPUs and AMD's latest Zen5 CPUs) and are not offloading to a GPU, the Q4_0_8_8 may offer a nice speed as well:
<details>
<summary>Click to view benchmarks on an AVX2 system (EPYC7702)</summary>
| model | size | params | backend | threads | test | t/s | % (vs Q4_0) |
| ------------------------------ | ---------: | ---------: | ---------- | ------: | ------------: | -------------------: |-------------: |
| qwen2 3B Q4_0 | 1.70 GiB | 3.09 B | CPU | 64 | pp512 | 204.03 ± 1.03 | 100% |
| qwen2 3B Q4_0 | 1.70 GiB | 3.09 B | CPU | 64 | pp1024 | 282.92 ± 0.19 | 100% |
| qwen2 3B Q4_0 | 1.70 GiB | 3.09 B | CPU | 64 | pp2048 | 259.49 ± 0.44 | 100% |
| qwen2 3B Q4_0 | 1.70 GiB | 3.09 B | CPU | 64 | tg128 | 39.12 ± 0.27 | 100% |
| qwen2 3B Q4_0 | 1.70 GiB | 3.09 B | CPU | 64 | tg256 | 39.31 ± 0.69 | 100% |
| qwen2 3B Q4_0 | 1.70 GiB | 3.09 B | CPU | 64 | tg512 | 40.52 ± 0.03 | 100% |
| qwen2 3B Q4_K_M | 1.79 GiB | 3.09 B | CPU | 64 | pp512 | 301.02 ± 1.74 | 147% |
| qwen2 3B Q4_K_M | 1.79 GiB | 3.09 B | CPU | 64 | pp1024 | 287.23 ± 0.20 | 101% |
| qwen2 3B Q4_K_M | 1.79 GiB | 3.09 B | CPU | 64 | pp2048 | 262.77 ± 1.81 | 101% |
| qwen2 3B Q4_K_M | 1.79 GiB | 3.09 B | CPU | 64 | tg128 | 18.80 ± 0.99 | 48% |
| qwen2 3B Q4_K_M | 1.79 GiB | 3.09 B | CPU | 64 | tg256 | 24.46 ± 3.04 | 83% |
| qwen2 3B Q4_K_M | 1.79 GiB | 3.09 B | CPU | 64 | tg512 | 36.32 ± 3.59 | 90% |
| qwen2 3B Q4_0_8_8 | 1.69 GiB | 3.09 B | CPU | 64 | pp512 | 271.71 ± 3.53 | 133% |
| qwen2 3B Q4_0_8_8 | 1.69 GiB | 3.09 B | CPU | 64 | pp1024 | 279.86 ± 45.63 | 100% |
| qwen2 3B Q4_0_8_8 | 1.69 GiB | 3.09 B | CPU | 64 | pp2048 | 320.77 ± 5.00 | 124% |
| qwen2 3B Q4_0_8_8 | 1.69 GiB | 3.09 B | CPU | 64 | tg128 | 43.51 ± 0.05 | 111% |
| qwen2 3B Q4_0_8_8 | 1.69 GiB | 3.09 B | CPU | 64 | tg256 | 43.35 ± 0.09 | 110% |
| qwen2 3B Q4_0_8_8 | 1.69 GiB | 3.09 B | CPU | 64 | tg512 | 42.60 ± 0.31 | 105% |
Q4_0_8_8 offers a nice bump to prompt processing and a small bump to text generation
</details>
## Which file should I choose?
<details>
<summary>Click here for details</summary>
A great write up with charts showing various performances is provided by Artefact2 [here](https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9)
The first thing to figure out is how big a model you can run. To do this, you'll need to figure out how much RAM and/or VRAM you have.
If you want your model running as FAST as possible, you'll want to fit the whole thing on your GPU's VRAM. Aim for a quant with a file size 1-2GB smaller than your GPU's total VRAM.
If you want the absolute maximum quality, add both your system RAM and your GPU's VRAM together, then similarly grab a quant with a file size 1-2GB Smaller than that total.
Next, you'll need to decide if you want to use an 'I-quant' or a 'K-quant'.
If you don't want to think too much, grab one of the K-quants. These are in format 'QX_K_X', like Q5_K_M.
If you want to get more into the weeds, you can check out this extremely useful feature chart:
[llama.cpp feature matrix](https://github.com/ggerganov/llama.cpp/wiki/Feature-matrix)
But basically, if you're aiming for below Q4, and you're running cuBLAS (Nvidia) or rocBLAS (AMD), you should look towards the I-quants. These are in format IQX_X, like IQ3_M. These are newer and offer better performance for their size.
These I-quants can also be used on CPU and Apple Metal, but will be slower than their K-quant equivalent, so speed vs performance is a tradeoff you'll have to decide.
The I-quants are *not* compatible with Vulcan, which is also AMD, so if you have an AMD card double check if you're using the rocBLAS build or the Vulcan build. At the time of writing this, LM Studio has a preview with ROCm support, and other inference engines have specific builds for ROCm.
</details>
## Credits
Thank you kalomaze and Dampf for assistance in creating the imatrix calibration dataset.
Thank you ZeroWw for the inspiration to experiment with embed/output.
Want to support my work? Visit my ko-fi page here: https://ko-fi.com/bartowski
|
gokceuludogan/berturk_tr_hateprint_w0.1
|
gokceuludogan
| 2024-11-16T17:49:00Z
| 107
| 0
|
transformers
|
[
"transformers",
"safetensors",
"bert",
"text-classification",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2024-11-16T17:48:35Z
|
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
HueyWoo/aris-llama3.1-gguf
|
HueyWoo
| 2024-11-16T17:47:17Z
| 18
| 0
|
transformers
|
[
"transformers",
"gguf",
"llama",
"text-generation-inference",
"unsloth",
"en",
"base_model:sh2orc/Llama-3.1-Korean-8B-Instruct",
"base_model:quantized:sh2orc/Llama-3.1-Korean-8B-Instruct",
"license:apache-2.0",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2024-11-16T17:27:08Z
|
---
base_model: sh2orc/Llama-3.1-Korean-8B-Instruct
tags:
- text-generation-inference
- transformers
- unsloth
- llama
- gguf
license: apache-2.0
language:
- en
---
# Uploaded model
- **Developed by:** HueyWoo
- **License:** apache-2.0
- **Finetuned from model :** sh2orc/Llama-3.1-Korean-8B-Instruct
This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
mradermacher/internlm2_5-1_8b-chat-i1-GGUF
|
mradermacher
| 2024-11-16T17:41:08Z
| 36
| 0
|
transformers
|
[
"transformers",
"gguf",
"en",
"base_model:internlm/internlm2_5-1_8b-chat",
"base_model:quantized:internlm/internlm2_5-1_8b-chat",
"license:other",
"endpoints_compatible",
"region:us",
"imatrix",
"conversational"
] | null | 2024-11-16T17:19:09Z
|
---
base_model: internlm/internlm2_5-1_8b-chat
language:
- en
library_name: transformers
license: other
quantized_by: mradermacher
---
## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
<!-- ### tags: nicoboss -->
weighted/imatrix quants of https://huggingface.co/internlm/internlm2_5-1_8b-chat
<!-- provided-files -->
static quants are available at https://huggingface.co/mradermacher/internlm2_5-1_8b-chat-GGUF
## Usage
If you are unsure how to use GGUF files, refer to one of [TheBloke's
READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for
more details, including on how to concatenate multi-part files.
## Provided Quants
(sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants)
| Link | Type | Size/GB | Notes |
|:-----|:-----|--------:|:------|
| [GGUF](https://huggingface.co/mradermacher/internlm2_5-1_8b-chat-i1-GGUF/resolve/main/internlm2_5-1_8b-chat.i1-IQ1_S.gguf) | i1-IQ1_S | 0.6 | for the desperate |
| [GGUF](https://huggingface.co/mradermacher/internlm2_5-1_8b-chat-i1-GGUF/resolve/main/internlm2_5-1_8b-chat.i1-IQ1_M.gguf) | i1-IQ1_M | 0.6 | mostly desperate |
| [GGUF](https://huggingface.co/mradermacher/internlm2_5-1_8b-chat-i1-GGUF/resolve/main/internlm2_5-1_8b-chat.i1-IQ2_XXS.gguf) | i1-IQ2_XXS | 0.7 | |
| [GGUF](https://huggingface.co/mradermacher/internlm2_5-1_8b-chat-i1-GGUF/resolve/main/internlm2_5-1_8b-chat.i1-IQ2_XS.gguf) | i1-IQ2_XS | 0.7 | |
| [GGUF](https://huggingface.co/mradermacher/internlm2_5-1_8b-chat-i1-GGUF/resolve/main/internlm2_5-1_8b-chat.i1-IQ2_S.gguf) | i1-IQ2_S | 0.8 | |
| [GGUF](https://huggingface.co/mradermacher/internlm2_5-1_8b-chat-i1-GGUF/resolve/main/internlm2_5-1_8b-chat.i1-IQ2_M.gguf) | i1-IQ2_M | 0.8 | |
| [GGUF](https://huggingface.co/mradermacher/internlm2_5-1_8b-chat-i1-GGUF/resolve/main/internlm2_5-1_8b-chat.i1-Q2_K.gguf) | i1-Q2_K | 0.9 | IQ3_XXS probably better |
| [GGUF](https://huggingface.co/mradermacher/internlm2_5-1_8b-chat-i1-GGUF/resolve/main/internlm2_5-1_8b-chat.i1-IQ3_XXS.gguf) | i1-IQ3_XXS | 0.9 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/internlm2_5-1_8b-chat-i1-GGUF/resolve/main/internlm2_5-1_8b-chat.i1-IQ3_XS.gguf) | i1-IQ3_XS | 1.0 | |
| [GGUF](https://huggingface.co/mradermacher/internlm2_5-1_8b-chat-i1-GGUF/resolve/main/internlm2_5-1_8b-chat.i1-IQ3_S.gguf) | i1-IQ3_S | 1.0 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/internlm2_5-1_8b-chat-i1-GGUF/resolve/main/internlm2_5-1_8b-chat.i1-Q3_K_S.gguf) | i1-Q3_K_S | 1.0 | IQ3_XS probably better |
| [GGUF](https://huggingface.co/mradermacher/internlm2_5-1_8b-chat-i1-GGUF/resolve/main/internlm2_5-1_8b-chat.i1-IQ3_M.gguf) | i1-IQ3_M | 1.0 | |
| [GGUF](https://huggingface.co/mradermacher/internlm2_5-1_8b-chat-i1-GGUF/resolve/main/internlm2_5-1_8b-chat.i1-Q3_K_M.gguf) | i1-Q3_K_M | 1.1 | IQ3_S probably better |
| [GGUF](https://huggingface.co/mradermacher/internlm2_5-1_8b-chat-i1-GGUF/resolve/main/internlm2_5-1_8b-chat.i1-Q3_K_L.gguf) | i1-Q3_K_L | 1.1 | IQ3_M probably better |
| [GGUF](https://huggingface.co/mradermacher/internlm2_5-1_8b-chat-i1-GGUF/resolve/main/internlm2_5-1_8b-chat.i1-IQ4_XS.gguf) | i1-IQ4_XS | 1.2 | |
| [GGUF](https://huggingface.co/mradermacher/internlm2_5-1_8b-chat-i1-GGUF/resolve/main/internlm2_5-1_8b-chat.i1-Q4_0_4_4.gguf) | i1-Q4_0_4_4 | 1.2 | fast on arm, low quality |
| [GGUF](https://huggingface.co/mradermacher/internlm2_5-1_8b-chat-i1-GGUF/resolve/main/internlm2_5-1_8b-chat.i1-Q4_0_4_8.gguf) | i1-Q4_0_4_8 | 1.2 | fast on arm+i8mm, low quality |
| [GGUF](https://huggingface.co/mradermacher/internlm2_5-1_8b-chat-i1-GGUF/resolve/main/internlm2_5-1_8b-chat.i1-Q4_0_8_8.gguf) | i1-Q4_0_8_8 | 1.2 | fast on arm+sve, low quality |
| [GGUF](https://huggingface.co/mradermacher/internlm2_5-1_8b-chat-i1-GGUF/resolve/main/internlm2_5-1_8b-chat.i1-Q4_0.gguf) | i1-Q4_0 | 1.2 | fast, low quality |
| [GGUF](https://huggingface.co/mradermacher/internlm2_5-1_8b-chat-i1-GGUF/resolve/main/internlm2_5-1_8b-chat.i1-Q4_K_S.gguf) | i1-Q4_K_S | 1.2 | optimal size/speed/quality |
| [GGUF](https://huggingface.co/mradermacher/internlm2_5-1_8b-chat-i1-GGUF/resolve/main/internlm2_5-1_8b-chat.i1-Q4_K_M.gguf) | i1-Q4_K_M | 1.3 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/internlm2_5-1_8b-chat-i1-GGUF/resolve/main/internlm2_5-1_8b-chat.i1-Q5_K_S.gguf) | i1-Q5_K_S | 1.4 | |
| [GGUF](https://huggingface.co/mradermacher/internlm2_5-1_8b-chat-i1-GGUF/resolve/main/internlm2_5-1_8b-chat.i1-Q5_K_M.gguf) | i1-Q5_K_M | 1.5 | |
| [GGUF](https://huggingface.co/mradermacher/internlm2_5-1_8b-chat-i1-GGUF/resolve/main/internlm2_5-1_8b-chat.i1-Q6_K.gguf) | i1-Q6_K | 1.7 | practically like static Q6_K |
Here is a handy graph by ikawrakow comparing some lower-quality quant
types (lower is better):

And here are Artefact2's thoughts on the matter:
https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9
## FAQ / Model Request
See https://huggingface.co/mradermacher/model_requests for some answers to
questions you might have and/or if you want some other model quantized.
## Thanks
I thank my company, [nethype GmbH](https://www.nethype.de/), for letting
me use its servers and providing upgrades to my workstation to enable
this work in my free time. Additional thanks to [@nicoboss](https://huggingface.co/nicoboss) for giving me access to his private supercomputer, enabling me to provide many more imatrix quants, at much higher quality, than I would otherwise be able to.
<!-- end -->
|
schnapss/testmerge-7b
|
schnapss
| 2024-11-16T17:40:27Z
| 5
| 0
|
transformers
|
[
"transformers",
"safetensors",
"mistral",
"text-generation",
"mergekit",
"merge",
"base_model:OpenPipe/mistral-ft-optimized-1218",
"base_model:merge:OpenPipe/mistral-ft-optimized-1218",
"base_model:mlabonne/NeuralHermes-2.5-Mistral-7B",
"base_model:merge:mlabonne/NeuralHermes-2.5-Mistral-7B",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2024-11-16T17:12:22Z
|
---
base_model:
- mlabonne/NeuralHermes-2.5-Mistral-7B
- OpenPipe/mistral-ft-optimized-1218
library_name: transformers
tags:
- mergekit
- merge
---
# merge
This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit).
## Merge Details
### Merge Method
This model was merged using the SLERP merge method.
### Models Merged
The following models were included in the merge:
* [mlabonne/NeuralHermes-2.5-Mistral-7B](https://huggingface.co/mlabonne/NeuralHermes-2.5-Mistral-7B)
* [OpenPipe/mistral-ft-optimized-1218](https://huggingface.co/OpenPipe/mistral-ft-optimized-1218)
### Configuration
The following YAML configuration was used to produce this model:
```yaml
slices:
- sources:
- model: OpenPipe/mistral-ft-optimized-1218
layer_range: [0, 32]
- model: mlabonne/NeuralHermes-2.5-Mistral-7B
layer_range: [0, 32]
merge_method: slerp
base_model: OpenPipe/mistral-ft-optimized-1218
parameters:
t:
- filter: self_attn
value: [0, 0.5, 0.3, 0.7, 1]
- filter: mlp
value: [1, 0.5, 0.7, 0.3, 0]
- value: 0.5
dtype: bfloat16
```
|
MayBashendy/Arabic_FineTuningAraBERT_AugV4_k20_task2_organization_fold1
|
MayBashendy
| 2024-11-16T17:39:45Z
| 161
| 0
|
transformers
|
[
"transformers",
"safetensors",
"bert",
"text-classification",
"generated_from_trainer",
"base_model:aubmindlab/bert-base-arabertv02",
"base_model:finetune:aubmindlab/bert-base-arabertv02",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2024-11-16T17:15:30Z
|
---
library_name: transformers
base_model: aubmindlab/bert-base-arabertv02
tags:
- generated_from_trainer
model-index:
- name: Arabic_FineTuningAraBERT_AugV4_k20_task2_organization_fold1
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# Arabic_FineTuningAraBERT_AugV4_k20_task2_organization_fold1
This model is a fine-tuned version of [aubmindlab/bert-base-arabertv02](https://huggingface.co/aubmindlab/bert-base-arabertv02) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7928
- Qwk: 0.3836
- Mse: 0.7928
- Rmse: 0.8904
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Qwk | Mse | Rmse |
|:-------------:|:------:|:----:|:---------------:|:-------:|:------:|:------:|
| No log | 0.0066 | 2 | 3.7743 | 0.0177 | 3.7743 | 1.9427 |
| No log | 0.0132 | 4 | 1.6260 | 0.1639 | 1.6260 | 1.2751 |
| No log | 0.0198 | 6 | 0.6934 | 0.0690 | 0.6934 | 0.8327 |
| No log | 0.0264 | 8 | 0.6631 | 0.0 | 0.6631 | 0.8143 |
| No log | 0.0330 | 10 | 0.8948 | 0.0400 | 0.8948 | 0.9459 |
| No log | 0.0396 | 12 | 1.0268 | 0.1000 | 1.0268 | 1.0133 |
| No log | 0.0462 | 14 | 1.1362 | -0.0800 | 1.1362 | 1.0659 |
| No log | 0.0528 | 16 | 1.2775 | -0.0667 | 1.2775 | 1.1302 |
| No log | 0.0594 | 18 | 1.2419 | -0.0667 | 1.2419 | 1.1144 |
| No log | 0.0660 | 20 | 0.9252 | 0.1000 | 0.9252 | 0.9619 |
| No log | 0.0726 | 22 | 0.8057 | 0.0323 | 0.8057 | 0.8976 |
| No log | 0.0792 | 24 | 0.9867 | 0.1972 | 0.9867 | 0.9933 |
| No log | 0.0858 | 26 | 1.1798 | 0.1758 | 1.1798 | 1.0862 |
| No log | 0.0924 | 28 | 1.6947 | -0.0500 | 1.6947 | 1.3018 |
| No log | 0.0990 | 30 | 1.9432 | 0.0571 | 1.9432 | 1.3940 |
| No log | 0.1056 | 32 | 1.5406 | -0.0320 | 1.5406 | 1.2412 |
| No log | 0.1122 | 34 | 1.3132 | 0.0211 | 1.3132 | 1.1459 |
| No log | 0.1188 | 36 | 1.0538 | 0.1000 | 1.0538 | 1.0266 |
| No log | 0.1254 | 38 | 0.9805 | 0.1000 | 0.9805 | 0.9902 |
| No log | 0.1320 | 40 | 1.2489 | -0.0235 | 1.2489 | 1.1175 |
| No log | 0.1386 | 42 | 1.5047 | -0.0364 | 1.5047 | 1.2267 |
| No log | 0.1452 | 44 | 1.4640 | -0.0667 | 1.4640 | 1.2100 |
| No log | 0.1518 | 46 | 1.1775 | 0.1481 | 1.1775 | 1.0851 |
| No log | 0.1584 | 48 | 1.0666 | 0.1481 | 1.0666 | 1.0328 |
| No log | 0.1650 | 50 | 1.2572 | 0.1000 | 1.2572 | 1.1213 |
| No log | 0.1716 | 52 | 1.2772 | -0.0235 | 1.2772 | 1.1301 |
| No log | 0.1782 | 54 | 1.2600 | -0.0235 | 1.2600 | 1.1225 |
| No log | 0.1848 | 56 | 1.2815 | 0.0667 | 1.2815 | 1.1321 |
| No log | 0.1914 | 58 | 1.3062 | 0.0667 | 1.3062 | 1.1429 |
| No log | 0.1980 | 60 | 1.4091 | 0.0667 | 1.4091 | 1.1870 |
| No log | 0.2046 | 62 | 1.3776 | 0.0667 | 1.3776 | 1.1737 |
| No log | 0.2112 | 64 | 1.4176 | 0.0792 | 1.4176 | 1.1906 |
| No log | 0.2178 | 66 | 1.6384 | 0.0893 | 1.6384 | 1.2800 |
| No log | 0.2244 | 68 | 1.7066 | 0.0513 | 1.7066 | 1.3064 |
| No log | 0.2310 | 70 | 1.5209 | 0.0893 | 1.5209 | 1.2332 |
| No log | 0.2376 | 72 | 1.2209 | 0.1522 | 1.2209 | 1.1049 |
| No log | 0.2442 | 74 | 1.0373 | 0.1429 | 1.0373 | 1.0185 |
| No log | 0.2508 | 76 | 1.2544 | 0.1522 | 1.2544 | 1.1200 |
| No log | 0.2574 | 78 | 1.5024 | 0.1207 | 1.5024 | 1.2257 |
| No log | 0.2640 | 80 | 1.3587 | 0.1176 | 1.3587 | 1.1656 |
| No log | 0.2706 | 82 | 1.0334 | 0.1429 | 1.0334 | 1.0166 |
| No log | 0.2772 | 84 | 0.9379 | 0.1628 | 0.9379 | 0.9684 |
| No log | 0.2838 | 86 | 1.0599 | 0.1429 | 1.0599 | 1.0295 |
| No log | 0.2904 | 88 | 1.1492 | 0.1935 | 1.1492 | 1.0720 |
| No log | 0.2970 | 90 | 1.3050 | 0.0412 | 1.3050 | 1.1424 |
| No log | 0.3036 | 92 | 1.5388 | 0.1207 | 1.5388 | 1.2405 |
| No log | 0.3102 | 94 | 1.2432 | 0.0667 | 1.2432 | 1.1150 |
| No log | 0.3168 | 96 | 0.9602 | 0.1600 | 0.9602 | 0.9799 |
| No log | 0.3234 | 98 | 0.6912 | 0.2623 | 0.6912 | 0.8314 |
| No log | 0.3300 | 100 | 0.7572 | 0.2727 | 0.7572 | 0.8702 |
| No log | 0.3366 | 102 | 1.0190 | 0.1882 | 1.0190 | 1.0095 |
| No log | 0.3432 | 104 | 1.1256 | 0.1882 | 1.1256 | 1.0610 |
| No log | 0.3498 | 106 | 0.9308 | 0.3684 | 0.9308 | 0.9648 |
| No log | 0.3564 | 108 | 0.8588 | 0.2817 | 0.8588 | 0.9267 |
| No log | 0.3630 | 110 | 0.8708 | 0.2817 | 0.8708 | 0.9331 |
| No log | 0.3696 | 112 | 0.8510 | 0.2817 | 0.8510 | 0.9225 |
| No log | 0.3762 | 114 | 0.8816 | 0.2817 | 0.8816 | 0.9389 |
| No log | 0.3828 | 116 | 1.0444 | 0.2105 | 1.0444 | 1.0219 |
| No log | 0.3894 | 118 | 0.9021 | 0.2817 | 0.9021 | 0.9498 |
| No log | 0.3960 | 120 | 0.9444 | 0.2286 | 0.9444 | 0.9718 |
| No log | 0.4026 | 122 | 1.2156 | 0.125 | 1.2156 | 1.1025 |
| No log | 0.4092 | 124 | 1.4422 | 0.0893 | 1.4422 | 1.2009 |
| No log | 0.4158 | 126 | 1.4235 | 0.0792 | 1.4235 | 1.1931 |
| No log | 0.4224 | 128 | 1.1022 | 0.2826 | 1.1022 | 1.0498 |
| No log | 0.4290 | 130 | 0.9851 | 0.2963 | 0.9851 | 0.9925 |
| No log | 0.4356 | 132 | 0.8946 | 0.3684 | 0.8946 | 0.9458 |
| No log | 0.4422 | 134 | 0.9891 | 0.2826 | 0.9891 | 0.9945 |
| No log | 0.4488 | 136 | 1.1831 | 0.2418 | 1.1831 | 1.0877 |
| No log | 0.4554 | 138 | 1.1612 | 0.2418 | 1.1612 | 1.0776 |
| No log | 0.4620 | 140 | 1.0190 | 0.2418 | 1.0190 | 1.0094 |
| No log | 0.4686 | 142 | 1.0690 | 0.2418 | 1.0690 | 1.0339 |
| No log | 0.4752 | 144 | 0.9505 | 0.2418 | 0.9505 | 0.9749 |
| No log | 0.4818 | 146 | 1.0267 | 0.2418 | 1.0267 | 1.0133 |
| No log | 0.4884 | 148 | 1.1487 | 0.2418 | 1.1487 | 1.0718 |
| No log | 0.4950 | 150 | 1.4593 | 0.0792 | 1.4593 | 1.2080 |
| No log | 0.5017 | 152 | 1.6017 | 0.0792 | 1.6017 | 1.2656 |
| No log | 0.5083 | 154 | 1.4103 | 0.0792 | 1.4103 | 1.1876 |
| No log | 0.5149 | 156 | 1.0460 | 0.1980 | 1.0460 | 1.0228 |
| No log | 0.5215 | 158 | 0.9682 | 0.1980 | 0.9682 | 0.9839 |
| No log | 0.5281 | 160 | 1.0334 | 0.1600 | 1.0334 | 1.0166 |
| No log | 0.5347 | 162 | 0.9880 | 0.2000 | 0.9880 | 0.9940 |
| No log | 0.5413 | 164 | 0.9228 | 0.2500 | 0.9228 | 0.9606 |
| No log | 0.5479 | 166 | 1.0500 | 0.0455 | 1.0500 | 1.0247 |
| No log | 0.5545 | 168 | 1.0726 | -0.0482 | 1.0726 | 1.0357 |
| No log | 0.5611 | 170 | 1.1646 | 0.1099 | 1.1646 | 1.0792 |
| No log | 0.5677 | 172 | 1.1696 | 0.0971 | 1.1696 | 1.0815 |
| No log | 0.5743 | 174 | 1.1440 | 0.0870 | 1.1440 | 1.0696 |
| No log | 0.5809 | 176 | 1.1996 | 0.0625 | 1.1996 | 1.0952 |
| No log | 0.5875 | 178 | 1.0012 | 0.25 | 1.0012 | 1.0006 |
| No log | 0.5941 | 180 | 0.8217 | 0.2105 | 0.8217 | 0.9065 |
| No log | 0.6007 | 182 | 0.8858 | 0.2597 | 0.8858 | 0.9412 |
| No log | 0.6073 | 184 | 1.2096 | 0.0842 | 1.2096 | 1.0998 |
| No log | 0.6139 | 186 | 1.6539 | 0.0769 | 1.6539 | 1.2861 |
| No log | 0.6205 | 188 | 1.6736 | 0.0787 | 1.6736 | 1.2937 |
| No log | 0.6271 | 190 | 1.4044 | -0.0625 | 1.4044 | 1.1851 |
| No log | 0.6337 | 192 | 1.0909 | 0.0 | 1.0909 | 1.0445 |
| No log | 0.6403 | 194 | 1.0114 | 0.2059 | 1.0114 | 1.0057 |
| No log | 0.6469 | 196 | 1.0380 | 0.2025 | 1.0380 | 1.0188 |
| No log | 0.6535 | 198 | 1.1559 | 0.0964 | 1.1559 | 1.0751 |
| No log | 0.6601 | 200 | 1.1455 | 0.0714 | 1.1455 | 1.0703 |
| No log | 0.6667 | 202 | 0.9871 | 0.1220 | 0.9871 | 0.9935 |
| No log | 0.6733 | 204 | 0.9675 | 0.1220 | 0.9675 | 0.9836 |
| No log | 0.6799 | 206 | 1.0971 | 0.2000 | 1.0971 | 1.0474 |
| No log | 0.6865 | 208 | 1.2814 | 0.0727 | 1.2814 | 1.1320 |
| No log | 0.6931 | 210 | 1.2338 | 0.0816 | 1.2338 | 1.1108 |
| No log | 0.6997 | 212 | 0.9957 | 0.2308 | 0.9957 | 0.9979 |
| No log | 0.7063 | 214 | 0.9731 | 0.3014 | 0.9731 | 0.9865 |
| No log | 0.7129 | 216 | 1.0389 | 0.2308 | 1.0389 | 1.0193 |
| No log | 0.7195 | 218 | 0.9940 | 0.3014 | 0.9940 | 0.9970 |
| No log | 0.7261 | 220 | 0.8828 | 0.3014 | 0.8828 | 0.9396 |
| No log | 0.7327 | 222 | 0.7973 | 0.2703 | 0.7973 | 0.8929 |
| No log | 0.7393 | 224 | 0.8638 | 0.3014 | 0.8638 | 0.9294 |
| No log | 0.7459 | 226 | 1.1344 | 0.0606 | 1.1344 | 1.0651 |
| No log | 0.7525 | 228 | 1.5313 | 0.0847 | 1.5313 | 1.2374 |
| No log | 0.7591 | 230 | 1.6589 | 0.1654 | 1.6589 | 1.2880 |
| No log | 0.7657 | 232 | 1.3691 | 0.0816 | 1.3691 | 1.1701 |
| No log | 0.7723 | 234 | 1.0524 | 0.0455 | 1.0524 | 1.0259 |
| No log | 0.7789 | 236 | 0.9738 | 0.1538 | 0.9738 | 0.9868 |
| No log | 0.7855 | 238 | 1.0264 | 0.1220 | 1.0264 | 1.0131 |
| No log | 0.7921 | 240 | 1.0215 | 0.0741 | 1.0215 | 1.0107 |
| No log | 0.7987 | 242 | 0.9393 | 0.1538 | 0.9393 | 0.9692 |
| No log | 0.8053 | 244 | 0.8333 | 0.3333 | 0.8333 | 0.9128 |
| No log | 0.8119 | 246 | 0.8637 | 0.3014 | 0.8637 | 0.9293 |
| No log | 0.8185 | 248 | 0.8854 | 0.1538 | 0.8854 | 0.9409 |
| No log | 0.8251 | 250 | 1.0139 | 0.0741 | 1.0139 | 1.0069 |
| No log | 0.8317 | 252 | 1.1016 | 0.1628 | 1.1016 | 1.0496 |
| No log | 0.8383 | 254 | 1.0337 | 0.0741 | 1.0337 | 1.0167 |
| No log | 0.8449 | 256 | 0.7887 | 0.2703 | 0.7887 | 0.8881 |
| No log | 0.8515 | 258 | 0.6367 | 0.0571 | 0.6367 | 0.7979 |
| No log | 0.8581 | 260 | 0.6765 | 0.2817 | 0.6765 | 0.8225 |
| No log | 0.8647 | 262 | 0.8426 | 0.2703 | 0.8426 | 0.9179 |
| No log | 0.8713 | 264 | 1.0394 | 0.3133 | 1.0394 | 1.0195 |
| No log | 0.8779 | 266 | 1.0919 | 0.2979 | 1.0919 | 1.0449 |
| No log | 0.8845 | 268 | 0.9612 | 0.3294 | 0.9612 | 0.9804 |
| No log | 0.8911 | 270 | 0.8189 | 0.2895 | 0.8189 | 0.9049 |
| No log | 0.8977 | 272 | 0.8933 | 0.3514 | 0.8933 | 0.9452 |
| No log | 0.9043 | 274 | 1.0581 | 0.1316 | 1.0581 | 1.0286 |
| No log | 0.9109 | 276 | 1.1930 | 0.1099 | 1.1930 | 1.0922 |
| No log | 0.9175 | 278 | 1.2262 | 0.1176 | 1.2262 | 1.1073 |
| No log | 0.9241 | 280 | 1.1812 | 0.0412 | 1.1812 | 1.0868 |
| No log | 0.9307 | 282 | 0.9403 | 0.1750 | 0.9403 | 0.9697 |
| No log | 0.9373 | 284 | 0.7743 | 0.3250 | 0.7743 | 0.8799 |
| No log | 0.9439 | 286 | 0.7582 | 0.1127 | 0.7582 | 0.8707 |
| No log | 0.9505 | 288 | 0.8633 | 0.3377 | 0.8633 | 0.9291 |
| No log | 0.9571 | 290 | 1.2302 | 0.0917 | 1.2302 | 1.1091 |
| No log | 0.9637 | 292 | 1.6593 | 0.1562 | 1.6593 | 1.2881 |
| No log | 0.9703 | 294 | 1.6925 | 0.1260 | 1.6925 | 1.3010 |
| No log | 0.9769 | 296 | 1.3919 | 0.0541 | 1.3919 | 1.1798 |
| No log | 0.9835 | 298 | 1.0748 | 0.0233 | 1.0748 | 1.0367 |
| No log | 0.9901 | 300 | 0.9122 | 0.1316 | 0.9122 | 0.9551 |
| No log | 0.9967 | 302 | 0.9086 | 0.1818 | 0.9086 | 0.9532 |
| No log | 1.0033 | 304 | 1.0311 | 0.1818 | 1.0311 | 1.0154 |
| No log | 1.0099 | 306 | 1.2458 | 0.1239 | 1.2458 | 1.1161 |
| No log | 1.0165 | 308 | 1.2080 | 0.1239 | 1.2080 | 1.0991 |
| No log | 1.0231 | 310 | 0.8892 | 0.3514 | 0.8892 | 0.9430 |
| No log | 1.0297 | 312 | 0.7580 | 0.3514 | 0.7580 | 0.8706 |
| No log | 1.0363 | 314 | 0.7300 | 0.3514 | 0.7300 | 0.8544 |
| No log | 1.0429 | 316 | 0.7900 | 0.3014 | 0.7900 | 0.8888 |
| No log | 1.0495 | 318 | 1.0280 | 0.2222 | 1.0280 | 1.0139 |
| No log | 1.0561 | 320 | 1.1774 | 0.0667 | 1.1774 | 1.0851 |
| No log | 1.0627 | 322 | 1.0875 | 0.1316 | 1.0875 | 1.0428 |
| No log | 1.0693 | 324 | 0.9628 | 0.1316 | 0.9628 | 0.9812 |
| No log | 1.0759 | 326 | 0.8908 | 0.3014 | 0.8908 | 0.9438 |
| No log | 1.0825 | 328 | 0.8534 | 0.3014 | 0.8534 | 0.9238 |
| No log | 1.0891 | 330 | 0.8978 | 0.3514 | 0.8978 | 0.9475 |
| No log | 1.0957 | 332 | 0.9775 | 0.1573 | 0.9775 | 0.9887 |
| No log | 1.1023 | 334 | 1.1005 | 0.1573 | 1.1005 | 1.0490 |
| No log | 1.1089 | 336 | 1.1243 | 0.1573 | 1.1243 | 1.0603 |
| No log | 1.1155 | 338 | 1.1131 | 0.1573 | 1.1131 | 1.0551 |
| No log | 1.1221 | 340 | 1.0204 | 0.1333 | 1.0204 | 1.0101 |
| No log | 1.1287 | 342 | 1.0315 | 0.1333 | 1.0315 | 1.0156 |
| No log | 1.1353 | 344 | 1.1161 | 0.1064 | 1.1161 | 1.0564 |
| No log | 1.1419 | 346 | 1.0726 | 0.1573 | 1.0726 | 1.0357 |
| No log | 1.1485 | 348 | 0.9814 | 0.1266 | 0.9814 | 0.9907 |
| No log | 1.1551 | 350 | 0.9499 | 0.1538 | 0.9499 | 0.9746 |
| No log | 1.1617 | 352 | 0.9400 | 0.1538 | 0.9400 | 0.9695 |
| No log | 1.1683 | 354 | 0.9273 | 0.1538 | 0.9273 | 0.9630 |
| No log | 1.1749 | 356 | 0.9592 | 0.1538 | 0.9592 | 0.9794 |
| No log | 1.1815 | 358 | 1.0230 | 0.1538 | 1.0230 | 1.0114 |
| No log | 1.1881 | 360 | 1.1447 | 0.1573 | 1.1447 | 1.0699 |
| No log | 1.1947 | 362 | 1.3100 | 0.1064 | 1.3100 | 1.1445 |
| No log | 1.2013 | 364 | 1.2855 | 0.0842 | 1.2855 | 1.1338 |
| No log | 1.2079 | 366 | 1.1823 | 0.1758 | 1.1823 | 1.0873 |
| No log | 1.2145 | 368 | 1.3024 | 0.1600 | 1.3024 | 1.1412 |
| No log | 1.2211 | 370 | 1.3168 | 0.2286 | 1.3168 | 1.1475 |
| No log | 1.2277 | 372 | 1.0596 | 0.2143 | 1.0596 | 1.0294 |
| No log | 1.2343 | 374 | 0.9817 | 0.1266 | 0.9817 | 0.9908 |
| No log | 1.2409 | 376 | 0.9803 | 0.2143 | 0.9803 | 0.9901 |
| No log | 1.2475 | 378 | 0.9301 | 0.2588 | 0.9301 | 0.9644 |
| No log | 1.2541 | 380 | 0.9643 | 0.2143 | 0.9643 | 0.9820 |
| No log | 1.2607 | 382 | 1.0818 | 0.1818 | 1.0818 | 1.0401 |
| No log | 1.2673 | 384 | 1.1635 | 0.2041 | 1.1635 | 1.0786 |
| No log | 1.2739 | 386 | 1.0208 | 0.1573 | 1.0208 | 1.0104 |
| No log | 1.2805 | 388 | 0.8966 | 0.3023 | 0.8966 | 0.9469 |
| No log | 1.2871 | 390 | 0.7692 | 0.3684 | 0.7692 | 0.8770 |
| No log | 1.2937 | 392 | 0.7577 | 0.3684 | 0.7577 | 0.8705 |
| No log | 1.3003 | 394 | 0.9086 | 0.0714 | 0.9086 | 0.9532 |
| No log | 1.3069 | 396 | 1.1762 | 0.1522 | 1.1762 | 1.0845 |
| No log | 1.3135 | 398 | 1.2761 | 0.1980 | 1.2761 | 1.1297 |
| No log | 1.3201 | 400 | 1.1244 | 0.2069 | 1.1244 | 1.0604 |
| No log | 1.3267 | 402 | 0.8564 | 0.4444 | 0.8564 | 0.9254 |
| No log | 1.3333 | 404 | 0.7453 | 0.3684 | 0.7453 | 0.8633 |
| No log | 1.3399 | 406 | 0.6974 | 0.2895 | 0.6974 | 0.8351 |
| No log | 1.3465 | 408 | 0.7253 | 0.3250 | 0.7253 | 0.8516 |
| No log | 1.3531 | 410 | 0.8227 | 0.3684 | 0.8227 | 0.9070 |
| No log | 1.3597 | 412 | 1.0293 | 0.2418 | 1.0293 | 1.0145 |
| No log | 1.3663 | 414 | 1.0664 | 0.2247 | 1.0664 | 1.0326 |
| No log | 1.3729 | 416 | 1.0280 | 0.2247 | 1.0280 | 1.0139 |
| No log | 1.3795 | 418 | 0.9258 | 0.3721 | 0.9258 | 0.9622 |
| No log | 1.3861 | 420 | 0.8637 | 0.3684 | 0.8637 | 0.9294 |
| No log | 1.3927 | 422 | 0.8129 | 0.3846 | 0.8129 | 0.9016 |
| No log | 1.3993 | 424 | 0.8073 | 0.3846 | 0.8073 | 0.8985 |
| No log | 1.4059 | 426 | 0.9191 | 0.1628 | 0.9191 | 0.9587 |
| No log | 1.4125 | 428 | 1.1715 | 0.1553 | 1.1715 | 1.0823 |
| No log | 1.4191 | 430 | 1.3870 | 0.1429 | 1.3870 | 1.1777 |
| No log | 1.4257 | 432 | 1.2936 | 0.0625 | 1.2936 | 1.1374 |
| No log | 1.4323 | 434 | 1.0113 | 0.0741 | 1.0113 | 1.0056 |
| No log | 1.4389 | 436 | 0.8015 | 0.2597 | 0.8015 | 0.8952 |
| No log | 1.4455 | 438 | 0.7544 | 0.3284 | 0.7544 | 0.8686 |
| No log | 1.4521 | 440 | 0.8684 | 0.2597 | 0.8684 | 0.9319 |
| No log | 1.4587 | 442 | 0.9888 | 0.1290 | 0.9888 | 0.9944 |
| No log | 1.4653 | 444 | 1.0382 | 0.1290 | 1.0382 | 1.0189 |
| No log | 1.4719 | 446 | 0.9595 | 0.1220 | 0.9595 | 0.9795 |
| No log | 1.4785 | 448 | 0.7824 | 0.3077 | 0.7824 | 0.8845 |
| No log | 1.4851 | 450 | 0.7490 | 0.4 | 0.7490 | 0.8655 |
| No log | 1.4917 | 452 | 0.8357 | 0.3077 | 0.8357 | 0.9142 |
| No log | 1.4983 | 454 | 1.0606 | 0.0690 | 1.0606 | 1.0299 |
| No log | 1.5050 | 456 | 1.2631 | 0.2222 | 1.2631 | 1.1239 |
| No log | 1.5116 | 458 | 1.2070 | 0.2222 | 1.2070 | 1.0986 |
| No log | 1.5182 | 460 | 1.0159 | 0.0816 | 1.0159 | 1.0079 |
| No log | 1.5248 | 462 | 0.8540 | 0.3704 | 0.8540 | 0.9241 |
| No log | 1.5314 | 464 | 0.7992 | 0.3704 | 0.7992 | 0.8940 |
| No log | 1.5380 | 466 | 0.8219 | 0.2785 | 0.8219 | 0.9066 |
| No log | 1.5446 | 468 | 1.0221 | 0.2759 | 1.0221 | 1.0110 |
| No log | 1.5512 | 470 | 1.2049 | 0.1875 | 1.2049 | 1.0977 |
| No log | 1.5578 | 472 | 1.1505 | 0.0667 | 1.1505 | 1.0726 |
| No log | 1.5644 | 474 | 0.9700 | 0.1481 | 0.9700 | 0.9849 |
| No log | 1.5710 | 476 | 0.7790 | 0.1972 | 0.7790 | 0.8826 |
| No log | 1.5776 | 478 | 0.6620 | 0.4 | 0.6620 | 0.8137 |
| No log | 1.5842 | 480 | 0.6464 | 0.2941 | 0.6464 | 0.8040 |
| No log | 1.5908 | 482 | 0.6702 | 0.3836 | 0.6702 | 0.8187 |
| No log | 1.5974 | 484 | 0.7672 | 0.4 | 0.7672 | 0.8759 |
| No log | 1.6040 | 486 | 0.9985 | 0.0741 | 0.9985 | 0.9993 |
| No log | 1.6106 | 488 | 1.1487 | 0.1176 | 1.1487 | 1.0718 |
| No log | 1.6172 | 490 | 1.0838 | 0.0412 | 1.0838 | 1.0410 |
| No log | 1.6238 | 492 | 0.8936 | 0.3250 | 0.8936 | 0.9453 |
| No log | 1.6304 | 494 | 0.7779 | 0.3684 | 0.7779 | 0.8820 |
| No log | 1.6370 | 496 | 0.7619 | 0.4507 | 0.7619 | 0.8729 |
| No log | 1.6436 | 498 | 0.8132 | 0.3704 | 0.8132 | 0.9018 |
| 0.3919 | 1.6502 | 500 | 0.9432 | 0.0690 | 0.9432 | 0.9712 |
| 0.3919 | 1.6568 | 502 | 1.1511 | 0.1176 | 1.1511 | 1.0729 |
| 0.3919 | 1.6634 | 504 | 1.2051 | 0.0792 | 1.2051 | 1.0978 |
| 0.3919 | 1.6700 | 506 | 1.0599 | 0.1099 | 1.0599 | 1.0295 |
| 0.3919 | 1.6766 | 508 | 0.8671 | 0.2143 | 0.8671 | 0.9312 |
| 0.3919 | 1.6832 | 510 | 0.7967 | 0.3514 | 0.7967 | 0.8926 |
| 0.3919 | 1.6898 | 512 | 0.7383 | 0.4156 | 0.7383 | 0.8593 |
| 0.3919 | 1.6964 | 514 | 0.7551 | 0.4156 | 0.7551 | 0.8689 |
| 0.3919 | 1.7030 | 516 | 0.8363 | 0.2143 | 0.8363 | 0.9145 |
| 0.3919 | 1.7096 | 518 | 0.8520 | 0.2588 | 0.8520 | 0.9231 |
| 0.3919 | 1.7162 | 520 | 0.8835 | 0.2143 | 0.8835 | 0.9399 |
| 0.3919 | 1.7228 | 522 | 0.9209 | 0.3182 | 0.9209 | 0.9596 |
| 0.3919 | 1.7294 | 524 | 0.8898 | 0.3182 | 0.8898 | 0.9433 |
| 0.3919 | 1.7360 | 526 | 0.8092 | 0.2143 | 0.8092 | 0.8996 |
| 0.3919 | 1.7426 | 528 | 0.7409 | 0.2025 | 0.7409 | 0.8607 |
| 0.3919 | 1.7492 | 530 | 0.7333 | 0.2025 | 0.7333 | 0.8563 |
| 0.3919 | 1.7558 | 532 | 0.7705 | 0.2025 | 0.7705 | 0.8778 |
| 0.3919 | 1.7624 | 534 | 0.9096 | 0.2410 | 0.9096 | 0.9537 |
| 0.3919 | 1.7690 | 536 | 1.0074 | 0.1481 | 1.0074 | 1.0037 |
| 0.3919 | 1.7756 | 538 | 0.9232 | 0.1951 | 0.9232 | 0.9608 |
| 0.3919 | 1.7822 | 540 | 0.7726 | 0.2143 | 0.7726 | 0.8790 |
| 0.3919 | 1.7888 | 542 | 0.7111 | 0.4324 | 0.7111 | 0.8433 |
| 0.3919 | 1.7954 | 544 | 0.7030 | 0.4324 | 0.7030 | 0.8384 |
| 0.3919 | 1.8020 | 546 | 0.7563 | 0.4304 | 0.7563 | 0.8697 |
| 0.3919 | 1.8086 | 548 | 0.8388 | 0.2800 | 0.8388 | 0.9158 |
| 0.3919 | 1.8152 | 550 | 0.8245 | 0.2800 | 0.8245 | 0.9080 |
| 0.3919 | 1.8218 | 552 | 0.7667 | 0.4304 | 0.7667 | 0.8756 |
| 0.3919 | 1.8284 | 554 | 0.7393 | 0.4324 | 0.7393 | 0.8598 |
| 0.3919 | 1.8350 | 556 | 0.7097 | 0.4324 | 0.7097 | 0.8424 |
| 0.3919 | 1.8416 | 558 | 0.6971 | 0.4167 | 0.6971 | 0.8349 |
| 0.3919 | 1.8482 | 560 | 0.7849 | 0.2785 | 0.7849 | 0.8859 |
| 0.3919 | 1.8548 | 562 | 0.8342 | 0.2785 | 0.8342 | 0.9133 |
| 0.3919 | 1.8614 | 564 | 0.8071 | 0.2785 | 0.8071 | 0.8984 |
| 0.3919 | 1.8680 | 566 | 0.7707 | 0.2785 | 0.7707 | 0.8779 |
| 0.3919 | 1.8746 | 568 | 0.8268 | 0.3250 | 0.8268 | 0.9093 |
| 0.3919 | 1.8812 | 570 | 0.7766 | 0.3704 | 0.7766 | 0.8812 |
| 0.3919 | 1.8878 | 572 | 0.7595 | 0.4615 | 0.7595 | 0.8715 |
| 0.3919 | 1.8944 | 574 | 0.7523 | 0.4324 | 0.7523 | 0.8674 |
| 0.3919 | 1.9010 | 576 | 0.7427 | 0.4 | 0.7427 | 0.8618 |
| 0.3919 | 1.9076 | 578 | 0.7375 | 0.3855 | 0.7375 | 0.8588 |
| 0.3919 | 1.9142 | 580 | 0.7332 | 0.3684 | 0.7332 | 0.8563 |
| 0.3919 | 1.9208 | 582 | 0.7394 | 0.4615 | 0.7394 | 0.8599 |
| 0.3919 | 1.9274 | 584 | 0.7618 | 0.3855 | 0.7618 | 0.8728 |
| 0.3919 | 1.9340 | 586 | 0.7292 | 0.4935 | 0.7292 | 0.8540 |
| 0.3919 | 1.9406 | 588 | 0.6707 | 0.4615 | 0.6707 | 0.8190 |
| 0.3919 | 1.9472 | 590 | 0.6802 | 0.4615 | 0.6802 | 0.8247 |
| 0.3919 | 1.9538 | 592 | 0.7608 | 0.3704 | 0.7608 | 0.8723 |
| 0.3919 | 1.9604 | 594 | 0.8341 | 0.3333 | 0.8341 | 0.9133 |
| 0.3919 | 1.9670 | 596 | 0.7803 | 0.4474 | 0.7803 | 0.8833 |
| 0.3919 | 1.9736 | 598 | 0.7566 | 0.4615 | 0.7566 | 0.8698 |
| 0.3919 | 1.9802 | 600 | 0.7456 | 0.4304 | 0.7456 | 0.8635 |
| 0.3919 | 1.9868 | 602 | 0.7770 | 0.3704 | 0.7770 | 0.8815 |
| 0.3919 | 1.9934 | 604 | 0.8103 | 0.3478 | 0.8103 | 0.9001 |
| 0.3919 | 2.0 | 606 | 0.8436 | 0.3762 | 0.8436 | 0.9185 |
| 0.3919 | 2.0066 | 608 | 0.8156 | 0.2857 | 0.8156 | 0.9031 |
| 0.3919 | 2.0132 | 610 | 0.7585 | 0.3544 | 0.7585 | 0.8709 |
| 0.3919 | 2.0198 | 612 | 0.7759 | 0.3544 | 0.7759 | 0.8808 |
| 0.3919 | 2.0264 | 614 | 0.7948 | 0.3077 | 0.7948 | 0.8915 |
| 0.3919 | 2.0330 | 616 | 0.8106 | 0.3077 | 0.8106 | 0.9003 |
| 0.3919 | 2.0396 | 618 | 0.7499 | 0.3077 | 0.7499 | 0.8660 |
| 0.3919 | 2.0462 | 620 | 0.7582 | 0.3544 | 0.7582 | 0.8707 |
| 0.3919 | 2.0528 | 622 | 0.8932 | 0.2921 | 0.8932 | 0.9451 |
| 0.3919 | 2.0594 | 624 | 0.9001 | 0.2921 | 0.9001 | 0.9487 |
| 0.3919 | 2.0660 | 626 | 0.7612 | 0.4324 | 0.7612 | 0.8725 |
| 0.3919 | 2.0726 | 628 | 0.6703 | 0.4194 | 0.6703 | 0.8187 |
| 0.3919 | 2.0792 | 630 | 0.6414 | 0.3607 | 0.6414 | 0.8009 |
| 0.3919 | 2.0858 | 632 | 0.6682 | 0.3390 | 0.6682 | 0.8174 |
| 0.3919 | 2.0924 | 634 | 0.7307 | 0.4348 | 0.7307 | 0.8548 |
| 0.3919 | 2.0990 | 636 | 0.7219 | 0.4375 | 0.7219 | 0.8496 |
| 0.3919 | 2.1056 | 638 | 0.8054 | 0.3333 | 0.8054 | 0.8974 |
| 0.3919 | 2.1122 | 640 | 0.8712 | 0.3333 | 0.8712 | 0.9334 |
| 0.3919 | 2.1188 | 642 | 0.7640 | 0.4 | 0.7640 | 0.8741 |
| 0.3919 | 2.1254 | 644 | 0.6732 | 0.4194 | 0.6732 | 0.8205 |
| 0.3919 | 2.1320 | 646 | 0.6752 | 0.5455 | 0.6752 | 0.8217 |
| 0.3919 | 2.1386 | 648 | 0.7171 | 0.48 | 0.7171 | 0.8468 |
| 0.3919 | 2.1452 | 650 | 0.8238 | 0.3544 | 0.8238 | 0.9076 |
| 0.3919 | 2.1518 | 652 | 0.8137 | 0.3544 | 0.8137 | 0.9020 |
| 0.3919 | 2.1584 | 654 | 0.7310 | 0.48 | 0.7310 | 0.8550 |
| 0.3919 | 2.1650 | 656 | 0.7174 | 0.4474 | 0.7174 | 0.8470 |
| 0.3919 | 2.1716 | 658 | 0.7264 | 0.4474 | 0.7264 | 0.8523 |
| 0.3919 | 2.1782 | 660 | 0.7093 | 0.4474 | 0.7093 | 0.8422 |
| 0.3919 | 2.1848 | 662 | 0.6716 | 0.4507 | 0.6716 | 0.8195 |
| 0.3919 | 2.1914 | 664 | 0.6947 | 0.4324 | 0.6947 | 0.8335 |
| 0.3919 | 2.1980 | 666 | 0.8196 | 0.3836 | 0.8196 | 0.9053 |
| 0.3919 | 2.2046 | 668 | 0.9664 | 0.1481 | 0.9664 | 0.9830 |
| 0.3919 | 2.2112 | 670 | 0.9293 | 0.1951 | 0.9293 | 0.9640 |
| 0.3919 | 2.2178 | 672 | 0.7822 | 0.4324 | 0.7822 | 0.8844 |
| 0.3919 | 2.2244 | 674 | 0.7016 | 0.4324 | 0.7016 | 0.8376 |
| 0.3919 | 2.2310 | 676 | 0.6916 | 0.3077 | 0.6916 | 0.8316 |
| 0.3919 | 2.2376 | 678 | 0.7116 | 0.4 | 0.7116 | 0.8436 |
| 0.3919 | 2.2442 | 680 | 0.7303 | 0.4324 | 0.7303 | 0.8546 |
| 0.3919 | 2.2508 | 682 | 0.7370 | 0.4324 | 0.7370 | 0.8585 |
| 0.3919 | 2.2574 | 684 | 0.7113 | 0.3836 | 0.7113 | 0.8434 |
| 0.3919 | 2.2640 | 686 | 0.7000 | 0.3836 | 0.7000 | 0.8367 |
| 0.3919 | 2.2706 | 688 | 0.7323 | 0.3836 | 0.7323 | 0.8557 |
| 0.3919 | 2.2772 | 690 | 0.7874 | 0.3846 | 0.7874 | 0.8873 |
| 0.3919 | 2.2838 | 692 | 0.8295 | 0.3596 | 0.8295 | 0.9108 |
| 0.3919 | 2.2904 | 694 | 0.8265 | 0.3596 | 0.8265 | 0.9091 |
| 0.3919 | 2.2970 | 696 | 0.7932 | 0.4000 | 0.7932 | 0.8906 |
| 0.3919 | 2.3036 | 698 | 0.7632 | 0.2963 | 0.7632 | 0.8736 |
| 0.3919 | 2.3102 | 700 | 0.7219 | 0.2963 | 0.7219 | 0.8496 |
| 0.3919 | 2.3168 | 702 | 0.6867 | 0.4000 | 0.6867 | 0.8287 |
| 0.3919 | 2.3234 | 704 | 0.6997 | 0.4304 | 0.6997 | 0.8365 |
| 0.3919 | 2.3300 | 706 | 0.7992 | 0.3333 | 0.7992 | 0.8940 |
| 0.3919 | 2.3366 | 708 | 0.9608 | 0.3226 | 0.9608 | 0.9802 |
| 0.3919 | 2.3432 | 710 | 1.0393 | 0.1176 | 1.0393 | 1.0194 |
| 0.3919 | 2.3498 | 712 | 0.9271 | 0.2597 | 0.9271 | 0.9629 |
| 0.3919 | 2.3564 | 714 | 0.7626 | 0.3333 | 0.7626 | 0.8733 |
| 0.3919 | 2.3630 | 716 | 0.6478 | 0.3200 | 0.6478 | 0.8049 |
| 0.3919 | 2.3696 | 718 | 0.6384 | 0.3846 | 0.6384 | 0.7990 |
| 0.3919 | 2.3762 | 720 | 0.6651 | 0.4000 | 0.6651 | 0.8155 |
| 0.3919 | 2.3828 | 722 | 0.7167 | 0.3704 | 0.7167 | 0.8466 |
| 0.3919 | 2.3894 | 724 | 0.7340 | 0.3704 | 0.7340 | 0.8567 |
| 0.3919 | 2.3960 | 726 | 0.7553 | 0.3736 | 0.7553 | 0.8691 |
| 0.3919 | 2.4026 | 728 | 0.7657 | 0.4 | 0.7657 | 0.8750 |
| 0.3919 | 2.4092 | 730 | 0.8157 | 0.3023 | 0.8157 | 0.9032 |
| 0.3919 | 2.4158 | 732 | 0.7997 | 0.3836 | 0.7997 | 0.8943 |
| 0.3919 | 2.4224 | 734 | 0.7584 | 0.3333 | 0.7584 | 0.8708 |
| 0.3919 | 2.4290 | 736 | 0.6810 | 0.3684 | 0.6810 | 0.8252 |
| 0.3919 | 2.4356 | 738 | 0.6354 | 0.3438 | 0.6354 | 0.7971 |
| 0.3919 | 2.4422 | 740 | 0.6441 | 0.3438 | 0.6441 | 0.8026 |
| 0.3919 | 2.4488 | 742 | 0.7106 | 0.4 | 0.7106 | 0.8430 |
| 0.3919 | 2.4554 | 744 | 0.8113 | 0.3333 | 0.8113 | 0.9007 |
| 0.3919 | 2.4620 | 746 | 0.8748 | 0.3133 | 0.8748 | 0.9353 |
| 0.3919 | 2.4686 | 748 | 0.8791 | 0.3133 | 0.8791 | 0.9376 |
| 0.3919 | 2.4752 | 750 | 0.8287 | 0.3571 | 0.8287 | 0.9103 |
| 0.3919 | 2.4818 | 752 | 0.8420 | 0.3571 | 0.8420 | 0.9176 |
| 0.3919 | 2.4884 | 754 | 0.8822 | 0.3133 | 0.8822 | 0.9392 |
| 0.3919 | 2.4950 | 756 | 0.8834 | 0.2500 | 0.8834 | 0.9399 |
| 0.3919 | 2.5017 | 758 | 0.8202 | 0.4 | 0.8202 | 0.9057 |
| 0.3919 | 2.5083 | 760 | 0.7729 | 0.3846 | 0.7729 | 0.8791 |
| 0.3919 | 2.5149 | 762 | 0.7484 | 0.4304 | 0.7484 | 0.8651 |
| 0.3919 | 2.5215 | 764 | 0.7433 | 0.4304 | 0.7433 | 0.8621 |
| 0.3919 | 2.5281 | 766 | 0.7661 | 0.4000 | 0.7661 | 0.8753 |
| 0.3919 | 2.5347 | 768 | 0.7945 | 0.4304 | 0.7945 | 0.8913 |
| 0.3919 | 2.5413 | 770 | 0.8219 | 0.4 | 0.8219 | 0.9066 |
| 0.3919 | 2.5479 | 772 | 0.7761 | 0.4304 | 0.7761 | 0.8810 |
| 0.3919 | 2.5545 | 774 | 0.7012 | 0.4324 | 0.7012 | 0.8374 |
| 0.3919 | 2.5611 | 776 | 0.6624 | 0.4658 | 0.6624 | 0.8139 |
| 0.3919 | 2.5677 | 778 | 0.6597 | 0.5 | 0.6597 | 0.8122 |
| 0.3919 | 2.5743 | 780 | 0.7165 | 0.4 | 0.7165 | 0.8465 |
| 0.3919 | 2.5809 | 782 | 0.7790 | 0.3836 | 0.7790 | 0.8826 |
| 0.3919 | 2.5875 | 784 | 0.8206 | 0.4 | 0.8206 | 0.9059 |
| 0.3919 | 2.5941 | 786 | 0.7804 | 0.4304 | 0.7804 | 0.8834 |
| 0.3919 | 2.6007 | 788 | 0.7278 | 0.3514 | 0.7278 | 0.8531 |
| 0.3919 | 2.6073 | 790 | 0.7114 | 0.2286 | 0.7114 | 0.8435 |
| 0.3919 | 2.6139 | 792 | 0.7369 | 0.3514 | 0.7369 | 0.8584 |
| 0.3919 | 2.6205 | 794 | 0.7768 | 0.4304 | 0.7768 | 0.8814 |
| 0.3919 | 2.6271 | 796 | 0.8829 | 0.4 | 0.8829 | 0.9396 |
| 0.3919 | 2.6337 | 798 | 1.0535 | 0.0816 | 1.0535 | 1.0264 |
| 0.3919 | 2.6403 | 800 | 1.1200 | 0.1176 | 1.1200 | 1.0583 |
| 0.3919 | 2.6469 | 802 | 1.0075 | 0.0233 | 1.0075 | 1.0037 |
| 0.3919 | 2.6535 | 804 | 0.8349 | 0.2308 | 0.8349 | 0.9137 |
| 0.3919 | 2.6601 | 806 | 0.7346 | 0.4615 | 0.7346 | 0.8571 |
| 0.3919 | 2.6667 | 808 | 0.7130 | 0.3514 | 0.7130 | 0.8444 |
| 0.3919 | 2.6733 | 810 | 0.7272 | 0.3200 | 0.7272 | 0.8527 |
| 0.3919 | 2.6799 | 812 | 0.7707 | 0.4304 | 0.7707 | 0.8779 |
| 0.3919 | 2.6865 | 814 | 0.8609 | 0.4156 | 0.8609 | 0.9278 |
| 0.3919 | 2.6931 | 816 | 0.9584 | 0.1333 | 0.9584 | 0.9790 |
| 0.3919 | 2.6997 | 818 | 0.9786 | 0.1386 | 0.9786 | 0.9893 |
| 0.3919 | 2.7063 | 820 | 0.8977 | 0.3864 | 0.8977 | 0.9475 |
| 0.3919 | 2.7129 | 822 | 0.7803 | 0.4000 | 0.7803 | 0.8833 |
| 0.3919 | 2.7195 | 824 | 0.7145 | 0.4000 | 0.7145 | 0.8453 |
| 0.3919 | 2.7261 | 826 | 0.6609 | 0.1818 | 0.6609 | 0.8130 |
| 0.3919 | 2.7327 | 828 | 0.6364 | 0.2817 | 0.6364 | 0.7978 |
| 0.3919 | 2.7393 | 830 | 0.6532 | 0.4324 | 0.6532 | 0.8082 |
| 0.3919 | 2.7459 | 832 | 0.7544 | 0.3333 | 0.7544 | 0.8685 |
| 0.3919 | 2.7525 | 834 | 0.8905 | 0.1316 | 0.8905 | 0.9437 |
| 0.3919 | 2.7591 | 836 | 1.0173 | 0.1628 | 1.0173 | 1.0086 |
| 0.3919 | 2.7657 | 838 | 1.0145 | 0.1628 | 1.0145 | 1.0072 |
| 0.3919 | 2.7723 | 840 | 0.9691 | 0.1316 | 0.9691 | 0.9844 |
| 0.3919 | 2.7789 | 842 | 0.8421 | 0.2817 | 0.8421 | 0.9177 |
| 0.3919 | 2.7855 | 844 | 0.6843 | 0.3824 | 0.6843 | 0.8272 |
| 0.3919 | 2.7921 | 846 | 0.5978 | 0.4194 | 0.5978 | 0.7732 |
| 0.3919 | 2.7987 | 848 | 0.5681 | 0.1493 | 0.5681 | 0.7537 |
| 0.3919 | 2.8053 | 850 | 0.5666 | 0.1493 | 0.5666 | 0.7527 |
| 0.3919 | 2.8119 | 852 | 0.5946 | 0.1493 | 0.5946 | 0.7711 |
| 0.3919 | 2.8185 | 854 | 0.6708 | 0.4658 | 0.6708 | 0.8190 |
| 0.3919 | 2.8251 | 856 | 0.8048 | 0.4 | 0.8048 | 0.8971 |
| 0.3919 | 2.8317 | 858 | 0.9438 | 0.2826 | 0.9438 | 0.9715 |
| 0.3919 | 2.8383 | 860 | 0.9872 | 0.1176 | 0.9872 | 0.9936 |
| 0.3919 | 2.8449 | 862 | 0.9132 | 0.2963 | 0.9132 | 0.9556 |
| 0.3919 | 2.8515 | 864 | 0.7941 | 0.3836 | 0.7941 | 0.8911 |
| 0.3919 | 2.8581 | 866 | 0.6902 | 0.4348 | 0.6902 | 0.8308 |
| 0.3919 | 2.8647 | 868 | 0.6419 | 0.3143 | 0.6419 | 0.8012 |
| 0.3919 | 2.8713 | 870 | 0.6465 | 0.1493 | 0.6465 | 0.8041 |
| 0.3919 | 2.8779 | 872 | 0.6671 | 0.1493 | 0.6671 | 0.8168 |
| 0.3919 | 2.8845 | 874 | 0.6902 | 0.1972 | 0.6902 | 0.8308 |
| 0.3919 | 2.8911 | 876 | 0.7359 | 0.4 | 0.7359 | 0.8578 |
| 0.3919 | 2.8977 | 878 | 0.7881 | 0.4270 | 0.7881 | 0.8877 |
| 0.3919 | 2.9043 | 880 | 0.8416 | 0.3294 | 0.8416 | 0.9174 |
| 0.3919 | 2.9109 | 882 | 0.8227 | 0.3836 | 0.8227 | 0.9070 |
| 0.3919 | 2.9175 | 884 | 0.7436 | 0.4 | 0.7436 | 0.8623 |
| 0.3919 | 2.9241 | 886 | 0.6580 | 0.3810 | 0.6580 | 0.8112 |
| 0.3919 | 2.9307 | 888 | 0.6355 | 0.3438 | 0.6355 | 0.7972 |
| 0.3919 | 2.9373 | 890 | 0.6467 | 0.3810 | 0.6467 | 0.8042 |
| 0.3919 | 2.9439 | 892 | 0.6798 | 0.3824 | 0.6798 | 0.8245 |
| 0.3919 | 2.9505 | 894 | 0.7098 | 0.3824 | 0.7098 | 0.8425 |
| 0.3919 | 2.9571 | 896 | 0.7700 | 0.5 | 0.7700 | 0.8775 |
| 0.3919 | 2.9637 | 898 | 0.8562 | 0.2785 | 0.8562 | 0.9253 |
| 0.3919 | 2.9703 | 900 | 0.8794 | 0.3704 | 0.8794 | 0.9377 |
| 0.3919 | 2.9769 | 902 | 0.8520 | 0.3704 | 0.8520 | 0.9231 |
| 0.3919 | 2.9835 | 904 | 0.8542 | 0.3704 | 0.8542 | 0.9242 |
| 0.3919 | 2.9901 | 906 | 0.8691 | 0.2785 | 0.8691 | 0.9323 |
| 0.3919 | 2.9967 | 908 | 0.8260 | 0.3704 | 0.8260 | 0.9088 |
| 0.3919 | 3.0033 | 910 | 0.7587 | 0.5 | 0.7587 | 0.8710 |
| 0.3919 | 3.0099 | 912 | 0.7270 | 0.4658 | 0.7270 | 0.8526 |
| 0.3919 | 3.0165 | 914 | 0.7354 | 0.4658 | 0.7354 | 0.8575 |
| 0.3919 | 3.0231 | 916 | 0.7440 | 0.5 | 0.7440 | 0.8626 |
| 0.3919 | 3.0297 | 918 | 0.7426 | 0.5 | 0.7426 | 0.8617 |
| 0.3919 | 3.0363 | 920 | 0.7301 | 0.4658 | 0.7301 | 0.8544 |
| 0.3919 | 3.0429 | 922 | 0.7311 | 0.4324 | 0.7311 | 0.8550 |
| 0.3919 | 3.0495 | 924 | 0.7432 | 0.4 | 0.7432 | 0.8621 |
| 0.3919 | 3.0561 | 926 | 0.7566 | 0.4 | 0.7566 | 0.8698 |
| 0.3919 | 3.0627 | 928 | 0.7366 | 0.2895 | 0.7366 | 0.8583 |
| 0.3919 | 3.0693 | 930 | 0.7032 | 0.2895 | 0.7032 | 0.8386 |
| 0.3919 | 3.0759 | 932 | 0.6893 | 0.2895 | 0.6893 | 0.8302 |
| 0.3919 | 3.0825 | 934 | 0.7034 | 0.4000 | 0.7034 | 0.8387 |
| 0.3919 | 3.0891 | 936 | 0.7298 | 0.4000 | 0.7298 | 0.8543 |
| 0.3919 | 3.0957 | 938 | 0.7558 | 0.4000 | 0.7558 | 0.8694 |
| 0.3919 | 3.1023 | 940 | 0.7871 | 0.4156 | 0.7871 | 0.8872 |
| 0.3919 | 3.1089 | 942 | 0.7497 | 0.4000 | 0.7497 | 0.8658 |
| 0.3919 | 3.1155 | 944 | 0.6984 | 0.4000 | 0.6984 | 0.8357 |
| 0.3919 | 3.1221 | 946 | 0.6544 | 0.3143 | 0.6544 | 0.8089 |
| 0.3919 | 3.1287 | 948 | 0.6489 | 0.3143 | 0.6489 | 0.8055 |
| 0.3919 | 3.1353 | 950 | 0.6668 | 0.4 | 0.6668 | 0.8166 |
| 0.3919 | 3.1419 | 952 | 0.7184 | 0.3846 | 0.7184 | 0.8476 |
| 0.3919 | 3.1485 | 954 | 0.7344 | 0.3846 | 0.7344 | 0.8570 |
| 0.3919 | 3.1551 | 956 | 0.7588 | 0.3846 | 0.7588 | 0.8711 |
| 0.3919 | 3.1617 | 958 | 0.7813 | 0.4156 | 0.7813 | 0.8839 |
| 0.3919 | 3.1683 | 960 | 0.7832 | 0.4474 | 0.7832 | 0.8850 |
| 0.3919 | 3.1749 | 962 | 0.7495 | 0.4474 | 0.7495 | 0.8658 |
| 0.3919 | 3.1815 | 964 | 0.7003 | 0.3846 | 0.7003 | 0.8368 |
| 0.3919 | 3.1881 | 966 | 0.6925 | 0.3846 | 0.6925 | 0.8322 |
| 0.3919 | 3.1947 | 968 | 0.7179 | 0.3846 | 0.7179 | 0.8473 |
| 0.3919 | 3.2013 | 970 | 0.7713 | 0.3846 | 0.7713 | 0.8782 |
| 0.3919 | 3.2079 | 972 | 0.7955 | 0.4 | 0.7955 | 0.8919 |
| 0.3919 | 3.2145 | 974 | 0.8164 | 0.4 | 0.8164 | 0.9035 |
| 0.3919 | 3.2211 | 976 | 0.7974 | 0.3736 | 0.7974 | 0.8930 |
| 0.3919 | 3.2277 | 978 | 0.7839 | 0.4000 | 0.7839 | 0.8854 |
| 0.3919 | 3.2343 | 980 | 0.7613 | 0.4000 | 0.7613 | 0.8725 |
| 0.3919 | 3.2409 | 982 | 0.7371 | 0.4000 | 0.7371 | 0.8585 |
| 0.3919 | 3.2475 | 984 | 0.7015 | 0.4000 | 0.7015 | 0.8376 |
| 0.3919 | 3.2541 | 986 | 0.7035 | 0.4000 | 0.7035 | 0.8387 |
| 0.3919 | 3.2607 | 988 | 0.7497 | 0.4304 | 0.7497 | 0.8659 |
| 0.3919 | 3.2673 | 990 | 0.7904 | 0.3684 | 0.7904 | 0.8891 |
| 0.3919 | 3.2739 | 992 | 0.7978 | 0.3684 | 0.7978 | 0.8932 |
| 0.3919 | 3.2805 | 994 | 0.7854 | 0.4 | 0.7854 | 0.8862 |
| 0.3919 | 3.2871 | 996 | 0.7522 | 0.3684 | 0.7522 | 0.8673 |
| 0.3919 | 3.2937 | 998 | 0.7826 | 0.4 | 0.7826 | 0.8847 |
| 0.1458 | 3.3003 | 1000 | 0.8179 | 0.3514 | 0.8179 | 0.9044 |
| 0.1458 | 3.3069 | 1002 | 0.8453 | 0.2785 | 0.8453 | 0.9194 |
| 0.1458 | 3.3135 | 1004 | 0.8313 | 0.2785 | 0.8313 | 0.9117 |
| 0.1458 | 3.3201 | 1006 | 0.7718 | 0.3514 | 0.7718 | 0.8785 |
| 0.1458 | 3.3267 | 1008 | 0.7392 | 0.3514 | 0.7392 | 0.8598 |
| 0.1458 | 3.3333 | 1010 | 0.6982 | 0.5 | 0.6982 | 0.8356 |
| 0.1458 | 3.3399 | 1012 | 0.7047 | 0.4 | 0.7047 | 0.8395 |
| 0.1458 | 3.3465 | 1014 | 0.7324 | 0.4 | 0.7324 | 0.8558 |
| 0.1458 | 3.3531 | 1016 | 0.7408 | 0.3662 | 0.7408 | 0.8607 |
| 0.1458 | 3.3597 | 1018 | 0.7724 | 0.4156 | 0.7724 | 0.8788 |
| 0.1458 | 3.3663 | 1020 | 0.7909 | 0.4156 | 0.7909 | 0.8893 |
| 0.1458 | 3.3729 | 1022 | 0.7573 | 0.4324 | 0.7573 | 0.8702 |
| 0.1458 | 3.3795 | 1024 | 0.7691 | 0.4324 | 0.7691 | 0.8770 |
| 0.1458 | 3.3861 | 1026 | 0.7859 | 0.4304 | 0.7859 | 0.8865 |
| 0.1458 | 3.3927 | 1028 | 0.7865 | 0.3846 | 0.7865 | 0.8869 |
| 0.1458 | 3.3993 | 1030 | 0.7822 | 0.4156 | 0.7822 | 0.8844 |
| 0.1458 | 3.4059 | 1032 | 0.7740 | 0.3684 | 0.7740 | 0.8798 |
| 0.1458 | 3.4125 | 1034 | 0.8061 | 0.3514 | 0.8061 | 0.8978 |
| 0.1458 | 3.4191 | 1036 | 0.7937 | 0.3514 | 0.7937 | 0.8909 |
| 0.1458 | 3.4257 | 1038 | 0.7601 | 0.3684 | 0.7601 | 0.8718 |
| 0.1458 | 3.4323 | 1040 | 0.7282 | 0.3662 | 0.7282 | 0.8533 |
| 0.1458 | 3.4389 | 1042 | 0.6863 | 0.4348 | 0.6863 | 0.8285 |
| 0.1458 | 3.4455 | 1044 | 0.6770 | 0.3077 | 0.6770 | 0.8228 |
| 0.1458 | 3.4521 | 1046 | 0.6824 | 0.2727 | 0.6824 | 0.8261 |
| 0.1458 | 3.4587 | 1048 | 0.6971 | 0.2727 | 0.6971 | 0.8349 |
| 0.1458 | 3.4653 | 1050 | 0.7274 | 0.4 | 0.7274 | 0.8529 |
| 0.1458 | 3.4719 | 1052 | 0.7667 | 0.4156 | 0.7667 | 0.8756 |
| 0.1458 | 3.4785 | 1054 | 0.7909 | 0.3684 | 0.7909 | 0.8893 |
| 0.1458 | 3.4851 | 1056 | 0.7754 | 0.3684 | 0.7754 | 0.8806 |
| 0.1458 | 3.4917 | 1058 | 0.7727 | 0.3250 | 0.7727 | 0.8790 |
| 0.1458 | 3.4983 | 1060 | 0.7926 | 0.3250 | 0.7926 | 0.8903 |
| 0.1458 | 3.5050 | 1062 | 0.7899 | 0.3250 | 0.7899 | 0.8887 |
| 0.1458 | 3.5116 | 1064 | 0.7808 | 0.2963 | 0.7808 | 0.8836 |
| 0.1458 | 3.5182 | 1066 | 0.7845 | 0.3415 | 0.7845 | 0.8857 |
| 0.1458 | 3.5248 | 1068 | 0.7659 | 0.4167 | 0.7659 | 0.8752 |
| 0.1458 | 3.5314 | 1070 | 0.7855 | 0.4167 | 0.7855 | 0.8863 |
| 0.1458 | 3.5380 | 1072 | 0.8627 | 0.2963 | 0.8627 | 0.9288 |
| 0.1458 | 3.5446 | 1074 | 0.9806 | 0.1136 | 0.9806 | 0.9902 |
| 0.1458 | 3.5512 | 1076 | 1.0417 | 0.0233 | 1.0417 | 1.0206 |
| 0.1458 | 3.5578 | 1078 | 1.0211 | 0.0233 | 1.0211 | 1.0105 |
| 0.1458 | 3.5644 | 1080 | 0.9061 | 0.1481 | 0.9061 | 0.9519 |
| 0.1458 | 3.5710 | 1082 | 0.8286 | 0.2785 | 0.8286 | 0.9103 |
| 0.1458 | 3.5776 | 1084 | 0.7672 | 0.3284 | 0.7672 | 0.8759 |
| 0.1458 | 3.5842 | 1086 | 0.7687 | 0.2941 | 0.7687 | 0.8767 |
| 0.1458 | 3.5908 | 1088 | 0.8219 | 0.3846 | 0.8219 | 0.9066 |
| 0.1458 | 3.5974 | 1090 | 0.8683 | 0.3846 | 0.8683 | 0.9318 |
| 0.1458 | 3.6040 | 1092 | 0.9066 | 0.3846 | 0.9066 | 0.9522 |
| 0.1458 | 3.6106 | 1094 | 0.9674 | 0.2979 | 0.9674 | 0.9836 |
| 0.1458 | 3.6172 | 1096 | 0.9919 | 0.1346 | 0.9919 | 0.9960 |
| 0.1458 | 3.6238 | 1098 | 0.9425 | 0.2979 | 0.9425 | 0.9708 |
| 0.1458 | 3.6304 | 1100 | 0.8468 | 0.3846 | 0.8468 | 0.9202 |
| 0.1458 | 3.6370 | 1102 | 0.7705 | 0.3836 | 0.7705 | 0.8778 |
| 0.1458 | 3.6436 | 1104 | 0.7464 | 0.3836 | 0.7464 | 0.8639 |
| 0.1458 | 3.6502 | 1106 | 0.7570 | 0.3836 | 0.7570 | 0.8701 |
| 0.1458 | 3.6568 | 1108 | 0.7907 | 0.3846 | 0.7907 | 0.8892 |
| 0.1458 | 3.6634 | 1110 | 0.8667 | 0.3846 | 0.8667 | 0.9310 |
| 0.1458 | 3.6700 | 1112 | 0.9084 | 0.3596 | 0.9084 | 0.9531 |
| 0.1458 | 3.6766 | 1114 | 0.8921 | 0.3846 | 0.8921 | 0.9445 |
| 0.1458 | 3.6832 | 1116 | 0.8952 | 0.2963 | 0.8952 | 0.9461 |
| 0.1458 | 3.6898 | 1118 | 0.9086 | 0.2963 | 0.9086 | 0.9532 |
| 0.1458 | 3.6964 | 1120 | 0.9298 | 0.2963 | 0.9298 | 0.9642 |
| 0.1458 | 3.7030 | 1122 | 0.9262 | 0.2963 | 0.9262 | 0.9624 |
| 0.1458 | 3.7096 | 1124 | 0.9096 | 0.2895 | 0.9096 | 0.9537 |
| 0.1458 | 3.7162 | 1126 | 0.8980 | 0.2963 | 0.8980 | 0.9476 |
| 0.1458 | 3.7228 | 1128 | 0.9015 | 0.4304 | 0.9015 | 0.9495 |
| 0.1458 | 3.7294 | 1130 | 0.9065 | 0.3377 | 0.9065 | 0.9521 |
| 0.1458 | 3.7360 | 1132 | 0.8763 | 0.3846 | 0.8763 | 0.9361 |
| 0.1458 | 3.7426 | 1134 | 0.8475 | 0.4324 | 0.8475 | 0.9206 |
| 0.1458 | 3.7492 | 1136 | 0.8435 | 0.2895 | 0.8435 | 0.9184 |
| 0.1458 | 3.7558 | 1138 | 0.8515 | 0.2895 | 0.8515 | 0.9228 |
| 0.1458 | 3.7624 | 1140 | 0.8663 | 0.3200 | 0.8663 | 0.9308 |
| 0.1458 | 3.7690 | 1142 | 0.8970 | 0.3846 | 0.8970 | 0.9471 |
| 0.1458 | 3.7756 | 1144 | 0.9165 | 0.3133 | 0.9165 | 0.9574 |
| 0.1458 | 3.7822 | 1146 | 0.9089 | 0.4304 | 0.9089 | 0.9534 |
| 0.1458 | 3.7888 | 1148 | 0.9279 | 0.4304 | 0.9279 | 0.9633 |
| 0.1458 | 3.7954 | 1150 | 0.9417 | 0.3250 | 0.9417 | 0.9704 |
| 0.1458 | 3.8020 | 1152 | 0.9531 | 0.2410 | 0.9531 | 0.9763 |
| 0.1458 | 3.8086 | 1154 | 0.9284 | 0.2410 | 0.9284 | 0.9635 |
| 0.1458 | 3.8152 | 1156 | 0.9011 | 0.3846 | 0.9011 | 0.9493 |
| 0.1458 | 3.8218 | 1158 | 0.9207 | 0.1818 | 0.9207 | 0.9595 |
| 0.1458 | 3.8284 | 1160 | 0.9123 | 0.1818 | 0.9123 | 0.9551 |
| 0.1458 | 3.8350 | 1162 | 0.8654 | 0.4304 | 0.8654 | 0.9303 |
| 0.1458 | 3.8416 | 1164 | 0.8335 | 0.2895 | 0.8335 | 0.9130 |
| 0.1458 | 3.8482 | 1166 | 0.8345 | 0.1972 | 0.8345 | 0.9135 |
| 0.1458 | 3.8548 | 1168 | 0.8539 | 0.2895 | 0.8539 | 0.9241 |
| 0.1458 | 3.8614 | 1170 | 0.8957 | 0.4304 | 0.8957 | 0.9464 |
| 0.1458 | 3.8680 | 1172 | 0.8898 | 0.4304 | 0.8898 | 0.9433 |
| 0.1458 | 3.8746 | 1174 | 0.8418 | 0.4324 | 0.8418 | 0.9175 |
| 0.1458 | 3.8812 | 1176 | 0.8054 | 0.4324 | 0.8054 | 0.8974 |
| 0.1458 | 3.8878 | 1178 | 0.7879 | 0.4324 | 0.7879 | 0.8876 |
| 0.1458 | 3.8944 | 1180 | 0.7932 | 0.4324 | 0.7932 | 0.8906 |
| 0.1458 | 3.9010 | 1182 | 0.8099 | 0.3846 | 0.8099 | 0.9000 |
| 0.1458 | 3.9076 | 1184 | 0.8463 | 0.3377 | 0.8463 | 0.9199 |
| 0.1458 | 3.9142 | 1186 | 0.8684 | 0.2963 | 0.8684 | 0.9319 |
| 0.1458 | 3.9208 | 1188 | 0.8245 | 0.3846 | 0.8245 | 0.9080 |
| 0.1458 | 3.9274 | 1190 | 0.7652 | 0.4324 | 0.7652 | 0.8748 |
| 0.1458 | 3.9340 | 1192 | 0.7666 | 0.1493 | 0.7666 | 0.8755 |
| 0.1458 | 3.9406 | 1194 | 0.7972 | 0.1892 | 0.7972 | 0.8929 |
| 0.1458 | 3.9472 | 1196 | 0.8153 | 0.1892 | 0.8153 | 0.9029 |
| 0.1458 | 3.9538 | 1198 | 0.8211 | 0.2895 | 0.8211 | 0.9062 |
| 0.1458 | 3.9604 | 1200 | 0.8451 | 0.4 | 0.8451 | 0.9193 |
| 0.1458 | 3.9670 | 1202 | 0.8569 | 0.4 | 0.8569 | 0.9257 |
| 0.1458 | 3.9736 | 1204 | 0.8420 | 0.4304 | 0.8420 | 0.9176 |
| 0.1458 | 3.9802 | 1206 | 0.8066 | 0.4324 | 0.8066 | 0.8981 |
| 0.1458 | 3.9868 | 1208 | 0.7750 | 0.4167 | 0.7750 | 0.8803 |
| 0.1458 | 3.9934 | 1210 | 0.7486 | 0.4324 | 0.7486 | 0.8652 |
| 0.1458 | 4.0 | 1212 | 0.7315 | 0.3143 | 0.7315 | 0.8553 |
| 0.1458 | 4.0066 | 1214 | 0.7380 | 0.3143 | 0.7380 | 0.8591 |
| 0.1458 | 4.0132 | 1216 | 0.7397 | 0.1972 | 0.7397 | 0.8601 |
| 0.1458 | 4.0198 | 1218 | 0.7510 | 0.3143 | 0.7510 | 0.8666 |
| 0.1458 | 4.0264 | 1220 | 0.7599 | 0.4 | 0.7599 | 0.8717 |
| 0.1458 | 4.0330 | 1222 | 0.7796 | 0.4324 | 0.7796 | 0.8829 |
| 0.1458 | 4.0396 | 1224 | 0.8208 | 0.4156 | 0.8208 | 0.9060 |
| 0.1458 | 4.0462 | 1226 | 0.8330 | 0.4304 | 0.8330 | 0.9127 |
| 0.1458 | 4.0528 | 1228 | 0.8212 | 0.4 | 0.8212 | 0.9062 |
| 0.1458 | 4.0594 | 1230 | 0.8047 | 0.4 | 0.8047 | 0.8971 |
| 0.1458 | 4.0660 | 1232 | 0.7975 | 0.2895 | 0.7975 | 0.8930 |
| 0.1458 | 4.0726 | 1234 | 0.8012 | 0.1667 | 0.8012 | 0.8951 |
| 0.1458 | 4.0792 | 1236 | 0.7913 | 0.1667 | 0.7913 | 0.8895 |
| 0.1458 | 4.0858 | 1238 | 0.7778 | 0.1667 | 0.7778 | 0.8819 |
| 0.1458 | 4.0924 | 1240 | 0.7745 | 0.1667 | 0.7745 | 0.8801 |
| 0.1458 | 4.0990 | 1242 | 0.7753 | 0.2597 | 0.7753 | 0.8805 |
| 0.1458 | 4.1056 | 1244 | 0.8000 | 0.4 | 0.8000 | 0.8944 |
| 0.1458 | 4.1122 | 1246 | 0.8170 | 0.4000 | 0.8170 | 0.9039 |
| 0.1458 | 4.1188 | 1248 | 0.8120 | 0.4000 | 0.8120 | 0.9011 |
| 0.1458 | 4.1254 | 1250 | 0.7987 | 0.4000 | 0.7987 | 0.8937 |
| 0.1458 | 4.1320 | 1252 | 0.7905 | 0.4000 | 0.7905 | 0.8891 |
| 0.1458 | 4.1386 | 1254 | 0.7891 | 0.2895 | 0.7891 | 0.8883 |
| 0.1458 | 4.1452 | 1256 | 0.8066 | 0.2963 | 0.8066 | 0.8981 |
| 0.1458 | 4.1518 | 1258 | 0.8417 | 0.4000 | 0.8417 | 0.9174 |
| 0.1458 | 4.1584 | 1260 | 0.8751 | 0.4000 | 0.8751 | 0.9354 |
| 0.1458 | 4.1650 | 1262 | 0.8972 | 0.4000 | 0.8972 | 0.9472 |
| 0.1458 | 4.1716 | 1264 | 0.9132 | 0.3571 | 0.9132 | 0.9556 |
| 0.1458 | 4.1782 | 1266 | 0.8932 | 0.4304 | 0.8932 | 0.9451 |
| 0.1458 | 4.1848 | 1268 | 0.8548 | 0.4000 | 0.8548 | 0.9246 |
| 0.1458 | 4.1914 | 1270 | 0.8360 | 0.4000 | 0.8360 | 0.9144 |
| 0.1458 | 4.1980 | 1272 | 0.8232 | 0.4000 | 0.8232 | 0.9073 |
| 0.1458 | 4.2046 | 1274 | 0.8259 | 0.4000 | 0.8259 | 0.9088 |
| 0.1458 | 4.2112 | 1276 | 0.8218 | 0.4304 | 0.8218 | 0.9066 |
| 0.1458 | 4.2178 | 1278 | 0.8206 | 0.4304 | 0.8206 | 0.9059 |
| 0.1458 | 4.2244 | 1280 | 0.8267 | 0.4304 | 0.8267 | 0.9093 |
| 0.1458 | 4.2310 | 1282 | 0.8189 | 0.4304 | 0.8189 | 0.9049 |
| 0.1458 | 4.2376 | 1284 | 0.8045 | 0.4304 | 0.8045 | 0.8969 |
| 0.1458 | 4.2442 | 1286 | 0.8394 | 0.3846 | 0.8394 | 0.9162 |
| 0.1458 | 4.2508 | 1288 | 0.9199 | 0.3704 | 0.9199 | 0.9591 |
| 0.1458 | 4.2574 | 1290 | 0.9322 | 0.3704 | 0.9322 | 0.9655 |
| 0.1458 | 4.2640 | 1292 | 0.8714 | 0.3133 | 0.8714 | 0.9335 |
| 0.1458 | 4.2706 | 1294 | 0.8426 | 0.3846 | 0.8426 | 0.9179 |
| 0.1458 | 4.2772 | 1296 | 0.8675 | 0.3415 | 0.8675 | 0.9314 |
| 0.1458 | 4.2838 | 1298 | 0.8804 | 0.2326 | 0.8804 | 0.9383 |
| 0.1458 | 4.2904 | 1300 | 0.8433 | 0.3704 | 0.8433 | 0.9183 |
| 0.1458 | 4.2970 | 1302 | 0.8300 | 0.3704 | 0.8300 | 0.9111 |
| 0.1458 | 4.3036 | 1304 | 0.8112 | 0.3415 | 0.8112 | 0.9007 |
| 0.1458 | 4.3102 | 1306 | 0.8150 | 0.3704 | 0.8150 | 0.9028 |
| 0.1458 | 4.3168 | 1308 | 0.8345 | 0.3704 | 0.8345 | 0.9135 |
| 0.1458 | 4.3234 | 1310 | 0.8246 | 0.3415 | 0.8246 | 0.9081 |
| 0.1458 | 4.3300 | 1312 | 0.8180 | 0.3133 | 0.8180 | 0.9044 |
| 0.1458 | 4.3366 | 1314 | 0.8012 | 0.3571 | 0.8012 | 0.8951 |
| 0.1458 | 4.3432 | 1316 | 0.7905 | 0.4324 | 0.7905 | 0.8891 |
| 0.1458 | 4.3498 | 1318 | 0.8126 | 0.3571 | 0.8126 | 0.9014 |
| 0.1458 | 4.3564 | 1320 | 0.8336 | 0.3571 | 0.8336 | 0.9130 |
| 0.1458 | 4.3630 | 1322 | 0.8400 | 0.3571 | 0.8400 | 0.9165 |
| 0.1458 | 4.3696 | 1324 | 0.8097 | 0.4304 | 0.8097 | 0.8998 |
| 0.1458 | 4.3762 | 1326 | 0.7634 | 0.4 | 0.7634 | 0.8737 |
| 0.1458 | 4.3828 | 1328 | 0.7373 | 0.3684 | 0.7373 | 0.8586 |
| 0.1458 | 4.3894 | 1330 | 0.7301 | 0.3684 | 0.7301 | 0.8545 |
| 0.1458 | 4.3960 | 1332 | 0.7373 | 0.4324 | 0.7373 | 0.8587 |
| 0.1458 | 4.4026 | 1334 | 0.7992 | 0.3684 | 0.7992 | 0.8940 |
| 0.1458 | 4.4092 | 1336 | 0.8691 | 0.3544 | 0.8691 | 0.9323 |
| 0.1458 | 4.4158 | 1338 | 0.8990 | 0.1687 | 0.8990 | 0.9481 |
| 0.1458 | 4.4224 | 1340 | 0.8639 | 0.3077 | 0.8639 | 0.9294 |
| 0.1458 | 4.4290 | 1342 | 0.7876 | 0.3250 | 0.7876 | 0.8875 |
| 0.1458 | 4.4356 | 1344 | 0.7561 | 0.3377 | 0.7561 | 0.8695 |
| 0.1458 | 4.4422 | 1346 | 0.7628 | 0.3846 | 0.7628 | 0.8734 |
| 0.1458 | 4.4488 | 1348 | 0.7774 | 0.4 | 0.7774 | 0.8817 |
| 0.1458 | 4.4554 | 1350 | 0.7821 | 0.3684 | 0.7821 | 0.8843 |
| 0.1458 | 4.4620 | 1352 | 0.7732 | 0.3684 | 0.7732 | 0.8793 |
| 0.1458 | 4.4686 | 1354 | 0.7542 | 0.3684 | 0.7542 | 0.8685 |
| 0.1458 | 4.4752 | 1356 | 0.7564 | 0.4324 | 0.7564 | 0.8697 |
| 0.1458 | 4.4818 | 1358 | 0.7894 | 0.3544 | 0.7894 | 0.8885 |
| 0.1458 | 4.4884 | 1360 | 0.8267 | 0.3544 | 0.8267 | 0.9093 |
| 0.1458 | 4.4950 | 1362 | 0.8111 | 0.3544 | 0.8111 | 0.9006 |
| 0.1458 | 4.5017 | 1364 | 0.7617 | 0.4324 | 0.7617 | 0.8727 |
| 0.1458 | 4.5083 | 1366 | 0.7183 | 0.4658 | 0.7183 | 0.8475 |
| 0.1458 | 4.5149 | 1368 | 0.7178 | 0.4658 | 0.7178 | 0.8473 |
| 0.1458 | 4.5215 | 1370 | 0.7538 | 0.4615 | 0.7538 | 0.8682 |
| 0.1458 | 4.5281 | 1372 | 0.8232 | 0.2143 | 0.8232 | 0.9073 |
| 0.1458 | 4.5347 | 1374 | 0.8514 | 0.2143 | 0.8514 | 0.9227 |
| 0.1458 | 4.5413 | 1376 | 0.8301 | 0.2143 | 0.8301 | 0.9111 |
| 0.1458 | 4.5479 | 1378 | 0.8477 | 0.1687 | 0.8477 | 0.9207 |
| 0.1458 | 4.5545 | 1380 | 0.9150 | 0.0741 | 0.9150 | 0.9565 |
| 0.1458 | 4.5611 | 1382 | 0.9454 | 0.0741 | 0.9454 | 0.9723 |
| 0.1458 | 4.5677 | 1384 | 0.9078 | 0.1220 | 0.9078 | 0.9528 |
| 0.1458 | 4.5743 | 1386 | 0.8097 | 0.1687 | 0.8097 | 0.8998 |
| 0.1458 | 4.5809 | 1388 | 0.7382 | 0.3684 | 0.7382 | 0.8592 |
| 0.1458 | 4.5875 | 1390 | 0.6978 | 0.4324 | 0.6978 | 0.8353 |
| 0.1458 | 4.5941 | 1392 | 0.6929 | 0.4324 | 0.6929 | 0.8324 |
| 0.1458 | 4.6007 | 1394 | 0.7180 | 0.3662 | 0.7180 | 0.8474 |
| 0.1458 | 4.6073 | 1396 | 0.7288 | 0.3662 | 0.7288 | 0.8537 |
| 0.1458 | 4.6139 | 1398 | 0.7825 | 0.3684 | 0.7825 | 0.8846 |
| 0.1458 | 4.6205 | 1400 | 0.8940 | 0.3544 | 0.8940 | 0.9455 |
| 0.1458 | 4.6271 | 1402 | 0.9792 | 0.1702 | 0.9792 | 0.9896 |
| 0.1458 | 4.6337 | 1404 | 0.9699 | 0.1702 | 0.9699 | 0.9848 |
| 0.1458 | 4.6403 | 1406 | 0.8889 | 0.3544 | 0.8889 | 0.9428 |
| 0.1458 | 4.6469 | 1408 | 0.7944 | 0.4156 | 0.7944 | 0.8913 |
| 0.1458 | 4.6535 | 1410 | 0.7363 | 0.4324 | 0.7363 | 0.8581 |
| 0.1458 | 4.6601 | 1412 | 0.7291 | 0.4324 | 0.7291 | 0.8539 |
| 0.1458 | 4.6667 | 1414 | 0.7315 | 0.4324 | 0.7315 | 0.8553 |
| 0.1458 | 4.6733 | 1416 | 0.7569 | 0.4615 | 0.7569 | 0.8700 |
| 0.1458 | 4.6799 | 1418 | 0.8248 | 0.4474 | 0.8248 | 0.9082 |
| 0.1458 | 4.6865 | 1420 | 0.8957 | 0.2588 | 0.8957 | 0.9464 |
| 0.1458 | 4.6931 | 1422 | 0.9431 | 0.1687 | 0.9431 | 0.9711 |
| 0.1458 | 4.6997 | 1424 | 0.9161 | 0.2143 | 0.9161 | 0.9571 |
| 0.1458 | 4.7063 | 1426 | 0.8334 | 0.4474 | 0.8334 | 0.9129 |
| 0.1458 | 4.7129 | 1428 | 0.7548 | 0.4304 | 0.7548 | 0.8688 |
| 0.1458 | 4.7195 | 1430 | 0.7272 | 0.4324 | 0.7272 | 0.8528 |
| 0.1458 | 4.7261 | 1432 | 0.7366 | 0.4324 | 0.7366 | 0.8583 |
| 0.1458 | 4.7327 | 1434 | 0.7811 | 0.4615 | 0.7811 | 0.8838 |
| 0.1458 | 4.7393 | 1436 | 0.8477 | 0.4 | 0.8477 | 0.9207 |
| 0.1458 | 4.7459 | 1438 | 0.8890 | 0.1687 | 0.8890 | 0.9429 |
| 0.1458 | 4.7525 | 1440 | 0.8967 | 0.1687 | 0.8967 | 0.9469 |
| 0.1458 | 4.7591 | 1442 | 0.8561 | 0.3077 | 0.8561 | 0.9252 |
| 0.1458 | 4.7657 | 1444 | 0.8179 | 0.3836 | 0.8179 | 0.9044 |
| 0.1458 | 4.7723 | 1446 | 0.7923 | 0.3514 | 0.7923 | 0.8901 |
| 0.1458 | 4.7789 | 1448 | 0.7541 | 0.4 | 0.7541 | 0.8684 |
| 0.1458 | 4.7855 | 1450 | 0.7445 | 0.3836 | 0.7445 | 0.8628 |
| 0.1458 | 4.7921 | 1452 | 0.7688 | 0.3836 | 0.7688 | 0.8768 |
| 0.1458 | 4.7987 | 1454 | 0.8140 | 0.3077 | 0.8140 | 0.9022 |
| 0.1458 | 4.8053 | 1456 | 0.8383 | 0.3077 | 0.8383 | 0.9156 |
| 0.1458 | 4.8119 | 1458 | 0.8088 | 0.3836 | 0.8088 | 0.8993 |
| 0.1458 | 4.8185 | 1460 | 0.7577 | 0.4348 | 0.7577 | 0.8705 |
| 0.1458 | 4.8251 | 1462 | 0.7066 | 0.4324 | 0.7066 | 0.8406 |
| 0.1458 | 4.8317 | 1464 | 0.7040 | 0.2286 | 0.7040 | 0.8391 |
| 0.1458 | 4.8383 | 1466 | 0.7302 | 0.2895 | 0.7302 | 0.8545 |
| 0.1458 | 4.8449 | 1468 | 0.7734 | 0.4324 | 0.7734 | 0.8795 |
| 0.1458 | 4.8515 | 1470 | 0.8083 | 0.4324 | 0.8083 | 0.8991 |
| 0.1458 | 4.8581 | 1472 | 0.8700 | 0.3596 | 0.8700 | 0.9327 |
| 0.1458 | 4.8647 | 1474 | 0.9051 | 0.3478 | 0.9051 | 0.9513 |
| 0.1458 | 4.8713 | 1476 | 0.8934 | 0.4138 | 0.8934 | 0.9452 |
| 0.1458 | 4.8779 | 1478 | 0.8477 | 0.3846 | 0.8477 | 0.9207 |
| 0.1458 | 4.8845 | 1480 | 0.7985 | 0.4 | 0.7985 | 0.8936 |
| 0.1458 | 4.8911 | 1482 | 0.7704 | 0.2895 | 0.7704 | 0.8777 |
| 0.1458 | 4.8977 | 1484 | 0.7659 | 0.2895 | 0.7659 | 0.8752 |
| 0.1458 | 4.9043 | 1486 | 0.7794 | 0.4 | 0.7794 | 0.8828 |
| 0.1458 | 4.9109 | 1488 | 0.7921 | 0.4 | 0.7921 | 0.8900 |
| 0.1458 | 4.9175 | 1490 | 0.8250 | 0.3846 | 0.8250 | 0.9083 |
| 0.1458 | 4.9241 | 1492 | 0.8502 | 0.4156 | 0.8502 | 0.9221 |
| 0.1458 | 4.9307 | 1494 | 0.8414 | 0.4156 | 0.8414 | 0.9173 |
| 0.1458 | 4.9373 | 1496 | 0.8049 | 0.3836 | 0.8049 | 0.8972 |
| 0.1458 | 4.9439 | 1498 | 0.7794 | 0.1972 | 0.7794 | 0.8828 |
| 0.0971 | 4.9505 | 1500 | 0.7700 | 0.1972 | 0.7700 | 0.8775 |
| 0.0971 | 4.9571 | 1502 | 0.7899 | 0.1972 | 0.7899 | 0.8888 |
| 0.0971 | 4.9637 | 1504 | 0.8178 | 0.1972 | 0.8178 | 0.9043 |
| 0.0971 | 4.9703 | 1506 | 0.8425 | 0.1972 | 0.8425 | 0.9179 |
| 0.0971 | 4.9769 | 1508 | 0.8597 | 0.2895 | 0.8597 | 0.9272 |
| 0.0971 | 4.9835 | 1510 | 0.8874 | 0.2759 | 0.8874 | 0.9420 |
| 0.0971 | 4.9901 | 1512 | 0.9099 | 0.3736 | 0.9099 | 0.9539 |
| 0.0971 | 4.9967 | 1514 | 0.9085 | 0.3333 | 0.9085 | 0.9531 |
| 0.0971 | 5.0033 | 1516 | 0.8897 | 0.3596 | 0.8897 | 0.9432 |
| 0.0971 | 5.0099 | 1518 | 0.8504 | 0.4000 | 0.8504 | 0.9222 |
| 0.0971 | 5.0165 | 1520 | 0.8143 | 0.4 | 0.8143 | 0.9024 |
| 0.0971 | 5.0231 | 1522 | 0.7933 | 0.4 | 0.7933 | 0.8907 |
| 0.0971 | 5.0297 | 1524 | 0.7911 | 0.3846 | 0.7911 | 0.8894 |
| 0.0971 | 5.0363 | 1526 | 0.8082 | 0.4 | 0.8082 | 0.8990 |
| 0.0971 | 5.0429 | 1528 | 0.8510 | 0.4324 | 0.8510 | 0.9225 |
| 0.0971 | 5.0495 | 1530 | 0.8821 | 0.2597 | 0.8821 | 0.9392 |
| 0.0971 | 5.0561 | 1532 | 0.8647 | 0.3836 | 0.8647 | 0.9299 |
| 0.0971 | 5.0627 | 1534 | 0.8182 | 0.4 | 0.8182 | 0.9046 |
| 0.0971 | 5.0693 | 1536 | 0.7741 | 0.3514 | 0.7741 | 0.8798 |
| 0.0971 | 5.0759 | 1538 | 0.7569 | 0.3143 | 0.7569 | 0.8700 |
| 0.0971 | 5.0825 | 1540 | 0.7618 | 0.3143 | 0.7618 | 0.8728 |
| 0.0971 | 5.0891 | 1542 | 0.7635 | 0.3143 | 0.7635 | 0.8738 |
| 0.0971 | 5.0957 | 1544 | 0.7707 | 0.3514 | 0.7707 | 0.8779 |
| 0.0971 | 5.1023 | 1546 | 0.8042 | 0.3836 | 0.8042 | 0.8968 |
| 0.0971 | 5.1089 | 1548 | 0.8396 | 0.4156 | 0.8396 | 0.9163 |
| 0.0971 | 5.1155 | 1550 | 0.8853 | 0.4138 | 0.8853 | 0.9409 |
| 0.0971 | 5.1221 | 1552 | 0.9294 | 0.3478 | 0.9294 | 0.9640 |
| 0.0971 | 5.1287 | 1554 | 0.9204 | 0.3478 | 0.9204 | 0.9594 |
| 0.0971 | 5.1353 | 1556 | 0.8636 | 0.4474 | 0.8636 | 0.9293 |
| 0.0971 | 5.1419 | 1558 | 0.7975 | 0.3836 | 0.7975 | 0.8931 |
| 0.0971 | 5.1485 | 1560 | 0.7523 | 0.3514 | 0.7523 | 0.8673 |
| 0.0971 | 5.1551 | 1562 | 0.7475 | 0.3143 | 0.7475 | 0.8646 |
| 0.0971 | 5.1617 | 1564 | 0.7597 | 0.3836 | 0.7597 | 0.8716 |
| 0.0971 | 5.1683 | 1566 | 0.8035 | 0.3846 | 0.8035 | 0.8964 |
| 0.0971 | 5.1749 | 1568 | 0.8608 | 0.4474 | 0.8608 | 0.9278 |
| 0.0971 | 5.1815 | 1570 | 0.8677 | 0.4474 | 0.8677 | 0.9315 |
| 0.0971 | 5.1881 | 1572 | 0.8334 | 0.4474 | 0.8334 | 0.9129 |
| 0.0971 | 5.1947 | 1574 | 0.7814 | 0.3836 | 0.7814 | 0.8840 |
| 0.0971 | 5.2013 | 1576 | 0.7407 | 0.2609 | 0.7407 | 0.8606 |
| 0.0971 | 5.2079 | 1578 | 0.7331 | 0.1667 | 0.7331 | 0.8562 |
| 0.0971 | 5.2145 | 1580 | 0.7395 | 0.2817 | 0.7395 | 0.8600 |
| 0.0971 | 5.2211 | 1582 | 0.7530 | 0.2609 | 0.7530 | 0.8678 |
| 0.0971 | 5.2277 | 1584 | 0.7813 | 0.3514 | 0.7813 | 0.8839 |
| 0.0971 | 5.2343 | 1586 | 0.8061 | 0.3544 | 0.8061 | 0.8978 |
| 0.0971 | 5.2409 | 1588 | 0.8550 | 0.4156 | 0.8550 | 0.9247 |
| 0.0971 | 5.2475 | 1590 | 0.8773 | 0.4156 | 0.8773 | 0.9367 |
| 0.0971 | 5.2541 | 1592 | 0.8630 | 0.3544 | 0.8630 | 0.9290 |
| 0.0971 | 5.2607 | 1594 | 0.8302 | 0.3544 | 0.8302 | 0.9112 |
| 0.0971 | 5.2673 | 1596 | 0.8122 | 0.3684 | 0.8122 | 0.9012 |
| 0.0971 | 5.2739 | 1598 | 0.7920 | 0.1667 | 0.7920 | 0.8899 |
| 0.0971 | 5.2805 | 1600 | 0.7796 | 0.1667 | 0.7796 | 0.8830 |
| 0.0971 | 5.2871 | 1602 | 0.7700 | 0.1667 | 0.7700 | 0.8775 |
| 0.0971 | 5.2937 | 1604 | 0.7658 | 0.3143 | 0.7658 | 0.8751 |
| 0.0971 | 5.3003 | 1606 | 0.7748 | 0.3514 | 0.7748 | 0.8802 |
| 0.0971 | 5.3069 | 1608 | 0.8242 | 0.3846 | 0.8242 | 0.9079 |
| 0.0971 | 5.3135 | 1610 | 0.8642 | 0.3684 | 0.8642 | 0.9296 |
| 0.0971 | 5.3201 | 1612 | 0.8541 | 0.3684 | 0.8541 | 0.9242 |
| 0.0971 | 5.3267 | 1614 | 0.8201 | 0.4156 | 0.8201 | 0.9056 |
| 0.0971 | 5.3333 | 1616 | 0.7803 | 0.3544 | 0.7803 | 0.8833 |
| 0.0971 | 5.3399 | 1618 | 0.7518 | 0.3544 | 0.7518 | 0.8671 |
| 0.0971 | 5.3465 | 1620 | 0.7488 | 0.4 | 0.7488 | 0.8653 |
| 0.0971 | 5.3531 | 1622 | 0.7576 | 0.2895 | 0.7576 | 0.8704 |
| 0.0971 | 5.3597 | 1624 | 0.7793 | 0.2895 | 0.7793 | 0.8828 |
| 0.0971 | 5.3663 | 1626 | 0.7990 | 0.4000 | 0.7990 | 0.8939 |
| 0.0971 | 5.3729 | 1628 | 0.8224 | 0.4000 | 0.8224 | 0.9068 |
| 0.0971 | 5.3795 | 1630 | 0.8414 | 0.4000 | 0.8414 | 0.9173 |
| 0.0971 | 5.3861 | 1632 | 0.8540 | 0.3544 | 0.8540 | 0.9241 |
| 0.0971 | 5.3927 | 1634 | 0.8505 | 0.3544 | 0.8505 | 0.9222 |
| 0.0971 | 5.3993 | 1636 | 0.8514 | 0.3544 | 0.8514 | 0.9227 |
| 0.0971 | 5.4059 | 1638 | 0.8448 | 0.4000 | 0.8448 | 0.9191 |
| 0.0971 | 5.4125 | 1640 | 0.8445 | 0.4000 | 0.8445 | 0.9190 |
| 0.0971 | 5.4191 | 1642 | 0.8437 | 0.4000 | 0.8437 | 0.9185 |
| 0.0971 | 5.4257 | 1644 | 0.8521 | 0.3704 | 0.8521 | 0.9231 |
| 0.0971 | 5.4323 | 1646 | 0.8551 | 0.4000 | 0.8551 | 0.9247 |
| 0.0971 | 5.4389 | 1648 | 0.8577 | 0.4000 | 0.8577 | 0.9261 |
| 0.0971 | 5.4455 | 1650 | 0.8562 | 0.4000 | 0.8562 | 0.9253 |
| 0.0971 | 5.4521 | 1652 | 0.8498 | 0.4000 | 0.8498 | 0.9219 |
| 0.0971 | 5.4587 | 1654 | 0.8264 | 0.3544 | 0.8264 | 0.9090 |
| 0.0971 | 5.4653 | 1656 | 0.8090 | 0.3544 | 0.8090 | 0.8994 |
| 0.0971 | 5.4719 | 1658 | 0.8134 | 0.3846 | 0.8134 | 0.9019 |
| 0.0971 | 5.4785 | 1660 | 0.8337 | 0.3846 | 0.8337 | 0.9131 |
| 0.0971 | 5.4851 | 1662 | 0.8324 | 0.3846 | 0.8324 | 0.9123 |
| 0.0971 | 5.4917 | 1664 | 0.8224 | 0.3846 | 0.8224 | 0.9069 |
| 0.0971 | 5.4983 | 1666 | 0.8293 | 0.3846 | 0.8293 | 0.9107 |
| 0.0971 | 5.5050 | 1668 | 0.8324 | 0.3544 | 0.8324 | 0.9124 |
| 0.0971 | 5.5116 | 1670 | 0.8555 | 0.3544 | 0.8555 | 0.9250 |
| 0.0971 | 5.5182 | 1672 | 0.8779 | 0.3846 | 0.8779 | 0.9370 |
| 0.0971 | 5.5248 | 1674 | 0.8756 | 0.3846 | 0.8756 | 0.9357 |
| 0.0971 | 5.5314 | 1676 | 0.8616 | 0.3544 | 0.8616 | 0.9282 |
| 0.0971 | 5.5380 | 1678 | 0.8682 | 0.3544 | 0.8682 | 0.9318 |
| 0.0971 | 5.5446 | 1680 | 0.8637 | 0.3544 | 0.8637 | 0.9293 |
| 0.0971 | 5.5512 | 1682 | 0.8614 | 0.3544 | 0.8614 | 0.9281 |
| 0.0971 | 5.5578 | 1684 | 0.8691 | 0.3544 | 0.8691 | 0.9322 |
| 0.0971 | 5.5644 | 1686 | 0.8972 | 0.3544 | 0.8972 | 0.9472 |
| 0.0971 | 5.5710 | 1688 | 0.9135 | 0.3544 | 0.9135 | 0.9558 |
| 0.0971 | 5.5776 | 1690 | 0.9039 | 0.3544 | 0.9039 | 0.9508 |
| 0.0971 | 5.5842 | 1692 | 0.8784 | 0.3544 | 0.8784 | 0.9373 |
| 0.0971 | 5.5908 | 1694 | 0.8543 | 0.3544 | 0.8543 | 0.9243 |
| 0.0971 | 5.5974 | 1696 | 0.8182 | 0.3544 | 0.8182 | 0.9046 |
| 0.0971 | 5.6040 | 1698 | 0.8082 | 0.3514 | 0.8082 | 0.8990 |
| 0.0971 | 5.6106 | 1700 | 0.8054 | 0.3514 | 0.8054 | 0.8975 |
| 0.0971 | 5.6172 | 1702 | 0.8034 | 0.3514 | 0.8034 | 0.8963 |
| 0.0971 | 5.6238 | 1704 | 0.8146 | 0.3544 | 0.8146 | 0.9026 |
| 0.0971 | 5.6304 | 1706 | 0.8208 | 0.3544 | 0.8208 | 0.9060 |
| 0.0971 | 5.6370 | 1708 | 0.8395 | 0.3544 | 0.8395 | 0.9162 |
| 0.0971 | 5.6436 | 1710 | 0.8893 | 0.3846 | 0.8893 | 0.9430 |
| 0.0971 | 5.6502 | 1712 | 0.9215 | 0.4156 | 0.9215 | 0.9600 |
| 0.0971 | 5.6568 | 1714 | 0.9081 | 0.3846 | 0.9081 | 0.9529 |
| 0.0971 | 5.6634 | 1716 | 0.8603 | 0.3544 | 0.8603 | 0.9275 |
| 0.0971 | 5.6700 | 1718 | 0.8288 | 0.3514 | 0.8288 | 0.9104 |
| 0.0971 | 5.6766 | 1720 | 0.8126 | 0.3514 | 0.8126 | 0.9015 |
| 0.0971 | 5.6832 | 1722 | 0.8115 | 0.3514 | 0.8115 | 0.9008 |
| 0.0971 | 5.6898 | 1724 | 0.8319 | 0.3544 | 0.8319 | 0.9121 |
| 0.0971 | 5.6964 | 1726 | 0.8381 | 0.3846 | 0.8381 | 0.9155 |
| 0.0971 | 5.7030 | 1728 | 0.8272 | 0.3846 | 0.8272 | 0.9095 |
| 0.0971 | 5.7096 | 1730 | 0.8316 | 0.3544 | 0.8316 | 0.9119 |
| 0.0971 | 5.7162 | 1732 | 0.8540 | 0.4156 | 0.8540 | 0.9241 |
| 0.0971 | 5.7228 | 1734 | 0.8755 | 0.4474 | 0.8755 | 0.9357 |
| 0.0971 | 5.7294 | 1736 | 0.8881 | 0.3250 | 0.8881 | 0.9424 |
| 0.0971 | 5.7360 | 1738 | 0.8695 | 0.4474 | 0.8695 | 0.9325 |
| 0.0971 | 5.7426 | 1740 | 0.8703 | 0.4474 | 0.8703 | 0.9329 |
| 0.0971 | 5.7492 | 1742 | 0.8586 | 0.3846 | 0.8586 | 0.9266 |
| 0.0971 | 5.7558 | 1744 | 0.8409 | 0.3514 | 0.8409 | 0.9170 |
| 0.0971 | 5.7624 | 1746 | 0.8363 | 0.3514 | 0.8363 | 0.9145 |
| 0.0971 | 5.7690 | 1748 | 0.8228 | 0.3514 | 0.8228 | 0.9071 |
| 0.0971 | 5.7756 | 1750 | 0.8250 | 0.3514 | 0.8250 | 0.9083 |
| 0.0971 | 5.7822 | 1752 | 0.8145 | 0.2609 | 0.8145 | 0.9025 |
| 0.0971 | 5.7888 | 1754 | 0.8095 | 0.2609 | 0.8095 | 0.8997 |
| 0.0971 | 5.7954 | 1756 | 0.8258 | 0.3514 | 0.8258 | 0.9088 |
| 0.0971 | 5.8020 | 1758 | 0.8465 | 0.3514 | 0.8465 | 0.9201 |
| 0.0971 | 5.8086 | 1760 | 0.8655 | 0.3514 | 0.8655 | 0.9303 |
| 0.0971 | 5.8152 | 1762 | 0.8691 | 0.3514 | 0.8691 | 0.9323 |
| 0.0971 | 5.8218 | 1764 | 0.8643 | 0.3514 | 0.8643 | 0.9297 |
| 0.0971 | 5.8284 | 1766 | 0.8430 | 0.3514 | 0.8430 | 0.9182 |
| 0.0971 | 5.8350 | 1768 | 0.8367 | 0.3514 | 0.8367 | 0.9147 |
| 0.0971 | 5.8416 | 1770 | 0.8434 | 0.3514 | 0.8434 | 0.9184 |
| 0.0971 | 5.8482 | 1772 | 0.8663 | 0.3514 | 0.8663 | 0.9307 |
| 0.0971 | 5.8548 | 1774 | 0.8894 | 0.3133 | 0.8894 | 0.9431 |
| 0.0971 | 5.8614 | 1776 | 0.9173 | 0.3250 | 0.9173 | 0.9578 |
| 0.0971 | 5.8680 | 1778 | 0.9352 | 0.3077 | 0.9353 | 0.9671 |
| 0.0971 | 5.8746 | 1780 | 0.9261 | 0.3077 | 0.9261 | 0.9624 |
| 0.0971 | 5.8812 | 1782 | 0.8847 | 0.3077 | 0.8847 | 0.9406 |
| 0.0971 | 5.8878 | 1784 | 0.8361 | 0.3544 | 0.8361 | 0.9144 |
| 0.0971 | 5.8944 | 1786 | 0.7990 | 0.3514 | 0.7990 | 0.8939 |
| 0.0971 | 5.9010 | 1788 | 0.7988 | 0.2609 | 0.7988 | 0.8938 |
| 0.0971 | 5.9076 | 1790 | 0.8152 | 0.2609 | 0.8152 | 0.9029 |
| 0.0971 | 5.9142 | 1792 | 0.8322 | 0.2286 | 0.8322 | 0.9122 |
| 0.0971 | 5.9208 | 1794 | 0.8522 | 0.2286 | 0.8522 | 0.9232 |
| 0.0971 | 5.9274 | 1796 | 0.8767 | 0.3200 | 0.8767 | 0.9363 |
| 0.0971 | 5.9340 | 1798 | 0.8894 | 0.3200 | 0.8894 | 0.9431 |
| 0.0971 | 5.9406 | 1800 | 0.8967 | 0.3514 | 0.8967 | 0.9469 |
| 0.0971 | 5.9472 | 1802 | 0.8869 | 0.3514 | 0.8869 | 0.9417 |
| 0.0971 | 5.9538 | 1804 | 0.8843 | 0.3544 | 0.8843 | 0.9403 |
| 0.0971 | 5.9604 | 1806 | 0.8745 | 0.3544 | 0.8745 | 0.9352 |
| 0.0971 | 5.9670 | 1808 | 0.8440 | 0.3544 | 0.8440 | 0.9187 |
| 0.0971 | 5.9736 | 1810 | 0.8006 | 0.3514 | 0.8006 | 0.8947 |
| 0.0971 | 5.9802 | 1812 | 0.7727 | 0.3514 | 0.7727 | 0.8790 |
| 0.0971 | 5.9868 | 1814 | 0.7617 | 0.2609 | 0.7617 | 0.8728 |
| 0.0971 | 5.9934 | 1816 | 0.7687 | 0.2609 | 0.7687 | 0.8767 |
| 0.0971 | 6.0 | 1818 | 0.7803 | 0.2609 | 0.7803 | 0.8834 |
| 0.0971 | 6.0066 | 1820 | 0.7987 | 0.3514 | 0.7987 | 0.8937 |
| 0.0971 | 6.0132 | 1822 | 0.8345 | 0.4156 | 0.8345 | 0.9135 |
| 0.0971 | 6.0198 | 1824 | 0.8665 | 0.2963 | 0.8665 | 0.9309 |
| 0.0971 | 6.0264 | 1826 | 0.9023 | 0.2143 | 0.9023 | 0.9499 |
| 0.0971 | 6.0330 | 1828 | 0.8837 | 0.2785 | 0.8837 | 0.9400 |
| 0.0971 | 6.0396 | 1830 | 0.8342 | 0.4 | 0.8342 | 0.9133 |
| 0.0971 | 6.0462 | 1832 | 0.7855 | 0.4167 | 0.7855 | 0.8863 |
| 0.0971 | 6.0528 | 1834 | 0.7616 | 0.2609 | 0.7616 | 0.8727 |
| 0.0971 | 6.0594 | 1836 | 0.7543 | 0.2609 | 0.7543 | 0.8685 |
| 0.0971 | 6.0660 | 1838 | 0.7474 | 0.2609 | 0.7474 | 0.8645 |
| 0.0971 | 6.0726 | 1840 | 0.7582 | 0.2609 | 0.7582 | 0.8708 |
| 0.0971 | 6.0792 | 1842 | 0.7809 | 0.3514 | 0.7809 | 0.8837 |
| 0.0971 | 6.0858 | 1844 | 0.8163 | 0.3514 | 0.8163 | 0.9035 |
| 0.0971 | 6.0924 | 1846 | 0.8416 | 0.3514 | 0.8416 | 0.9174 |
| 0.0971 | 6.0990 | 1848 | 0.8436 | 0.3514 | 0.8436 | 0.9185 |
| 0.0971 | 6.1056 | 1850 | 0.8322 | 0.3514 | 0.8322 | 0.9122 |
| 0.0971 | 6.1122 | 1852 | 0.8216 | 0.3684 | 0.8216 | 0.9064 |
| 0.0971 | 6.1188 | 1854 | 0.8045 | 0.1667 | 0.8045 | 0.8969 |
| 0.0971 | 6.1254 | 1856 | 0.7927 | 0.1667 | 0.7927 | 0.8903 |
| 0.0971 | 6.1320 | 1858 | 0.7960 | 0.2817 | 0.7960 | 0.8922 |
| 0.0971 | 6.1386 | 1860 | 0.7924 | 0.3514 | 0.7924 | 0.8902 |
| 0.0971 | 6.1452 | 1862 | 0.8035 | 0.3514 | 0.8035 | 0.8964 |
| 0.0971 | 6.1518 | 1864 | 0.8346 | 0.3846 | 0.8346 | 0.9136 |
| 0.0971 | 6.1584 | 1866 | 0.8588 | 0.4474 | 0.8588 | 0.9267 |
| 0.0971 | 6.1650 | 1868 | 0.8514 | 0.4474 | 0.8514 | 0.9227 |
| 0.0971 | 6.1716 | 1870 | 0.8148 | 0.4156 | 0.8148 | 0.9027 |
| 0.0971 | 6.1782 | 1872 | 0.7751 | 0.3514 | 0.7751 | 0.8804 |
| 0.0971 | 6.1848 | 1874 | 0.7584 | 0.3514 | 0.7584 | 0.8709 |
| 0.0971 | 6.1914 | 1876 | 0.7622 | 0.3514 | 0.7622 | 0.8730 |
| 0.0971 | 6.1980 | 1878 | 0.7792 | 0.3514 | 0.7792 | 0.8827 |
| 0.0971 | 6.2046 | 1880 | 0.7991 | 0.3514 | 0.7991 | 0.8939 |
| 0.0971 | 6.2112 | 1882 | 0.8118 | 0.3514 | 0.8118 | 0.9010 |
| 0.0971 | 6.2178 | 1884 | 0.8130 | 0.3514 | 0.8130 | 0.9017 |
| 0.0971 | 6.2244 | 1886 | 0.8139 | 0.3514 | 0.8139 | 0.9022 |
| 0.0971 | 6.2310 | 1888 | 0.8228 | 0.3514 | 0.8228 | 0.9071 |
| 0.0971 | 6.2376 | 1890 | 0.8366 | 0.3514 | 0.8366 | 0.9146 |
| 0.0971 | 6.2442 | 1892 | 0.8375 | 0.3514 | 0.8375 | 0.9152 |
| 0.0971 | 6.2508 | 1894 | 0.8307 | 0.3514 | 0.8307 | 0.9114 |
| 0.0971 | 6.2574 | 1896 | 0.8182 | 0.3514 | 0.8182 | 0.9045 |
| 0.0971 | 6.2640 | 1898 | 0.8219 | 0.3836 | 0.8219 | 0.9066 |
| 0.0971 | 6.2706 | 1900 | 0.8302 | 0.3836 | 0.8302 | 0.9112 |
| 0.0971 | 6.2772 | 1902 | 0.8344 | 0.3836 | 0.8344 | 0.9135 |
| 0.0971 | 6.2838 | 1904 | 0.8228 | 0.3836 | 0.8228 | 0.9071 |
| 0.0971 | 6.2904 | 1906 | 0.8123 | 0.3514 | 0.8123 | 0.9013 |
| 0.0971 | 6.2970 | 1908 | 0.7996 | 0.2609 | 0.7996 | 0.8942 |
| 0.0971 | 6.3036 | 1910 | 0.7795 | 0.2609 | 0.7795 | 0.8829 |
| 0.0971 | 6.3102 | 1912 | 0.7669 | 0.2609 | 0.7669 | 0.8757 |
| 0.0971 | 6.3168 | 1914 | 0.7597 | 0.2609 | 0.7597 | 0.8716 |
| 0.0971 | 6.3234 | 1916 | 0.7707 | 0.3836 | 0.7707 | 0.8779 |
| 0.0971 | 6.3300 | 1918 | 0.7979 | 0.4 | 0.7979 | 0.8932 |
| 0.0971 | 6.3366 | 1920 | 0.8139 | 0.3478 | 0.8139 | 0.9022 |
| 0.0971 | 6.3432 | 1922 | 0.8287 | 0.3824 | 0.8287 | 0.9103 |
| 0.0971 | 6.3498 | 1924 | 0.8144 | 0.3824 | 0.8144 | 0.9024 |
| 0.0971 | 6.3564 | 1926 | 0.8059 | 0.3478 | 0.8059 | 0.8977 |
| 0.0971 | 6.3630 | 1928 | 0.8084 | 0.3478 | 0.8084 | 0.8991 |
| 0.0971 | 6.3696 | 1930 | 0.7888 | 0.4 | 0.7888 | 0.8882 |
| 0.0971 | 6.3762 | 1932 | 0.7574 | 0.4167 | 0.7574 | 0.8703 |
| 0.0971 | 6.3828 | 1934 | 0.7470 | 0.2941 | 0.7470 | 0.8643 |
| 0.0971 | 6.3894 | 1936 | 0.7494 | 0.2609 | 0.7494 | 0.8657 |
| 0.0971 | 6.3960 | 1938 | 0.7507 | 0.2609 | 0.7507 | 0.8664 |
| 0.0971 | 6.4026 | 1940 | 0.7558 | 0.2609 | 0.7558 | 0.8694 |
| 0.0971 | 6.4092 | 1942 | 0.7707 | 0.3514 | 0.7707 | 0.8779 |
| 0.0971 | 6.4158 | 1944 | 0.7931 | 0.3514 | 0.7931 | 0.8905 |
| 0.0971 | 6.4224 | 1946 | 0.8107 | 0.3514 | 0.8107 | 0.9004 |
| 0.0971 | 6.4290 | 1948 | 0.8266 | 0.3836 | 0.8266 | 0.9092 |
| 0.0971 | 6.4356 | 1950 | 0.8282 | 0.3836 | 0.8282 | 0.9100 |
| 0.0971 | 6.4422 | 1952 | 0.8371 | 0.3836 | 0.8371 | 0.9149 |
| 0.0971 | 6.4488 | 1954 | 0.8318 | 0.3836 | 0.8318 | 0.9120 |
| 0.0971 | 6.4554 | 1956 | 0.8343 | 0.3836 | 0.8343 | 0.9134 |
| 0.0971 | 6.4620 | 1958 | 0.8315 | 0.3836 | 0.8315 | 0.9119 |
| 0.0971 | 6.4686 | 1960 | 0.8297 | 0.4 | 0.8297 | 0.9109 |
| 0.0971 | 6.4752 | 1962 | 0.8212 | 0.4507 | 0.8212 | 0.9062 |
| 0.0971 | 6.4818 | 1964 | 0.8098 | 0.4507 | 0.8098 | 0.8999 |
| 0.0971 | 6.4884 | 1966 | 0.7918 | 0.4167 | 0.7918 | 0.8898 |
| 0.0971 | 6.4950 | 1968 | 0.7914 | 0.4167 | 0.7914 | 0.8896 |
| 0.0971 | 6.5017 | 1970 | 0.7967 | 0.4167 | 0.7967 | 0.8926 |
| 0.0971 | 6.5083 | 1972 | 0.8027 | 0.4507 | 0.8027 | 0.8959 |
| 0.0971 | 6.5149 | 1974 | 0.7892 | 0.3836 | 0.7892 | 0.8884 |
| 0.0971 | 6.5215 | 1976 | 0.7817 | 0.2941 | 0.7817 | 0.8842 |
| 0.0971 | 6.5281 | 1978 | 0.7848 | 0.2286 | 0.7848 | 0.8859 |
| 0.0971 | 6.5347 | 1980 | 0.7936 | 0.2941 | 0.7936 | 0.8908 |
| 0.0971 | 6.5413 | 1982 | 0.7964 | 0.3836 | 0.7964 | 0.8924 |
| 0.0971 | 6.5479 | 1984 | 0.7936 | 0.3836 | 0.7936 | 0.8908 |
| 0.0971 | 6.5545 | 1986 | 0.7870 | 0.4507 | 0.7870 | 0.8872 |
| 0.0971 | 6.5611 | 1988 | 0.7723 | 0.4167 | 0.7723 | 0.8788 |
| 0.0971 | 6.5677 | 1990 | 0.7588 | 0.2941 | 0.7588 | 0.8711 |
| 0.0971 | 6.5743 | 1992 | 0.7487 | 0.2941 | 0.7487 | 0.8653 |
| 0.0971 | 6.5809 | 1994 | 0.7469 | 0.2941 | 0.7469 | 0.8642 |
| 0.0971 | 6.5875 | 1996 | 0.7583 | 0.2941 | 0.7583 | 0.8708 |
| 0.0971 | 6.5941 | 1998 | 0.7821 | 0.3836 | 0.7821 | 0.8843 |
| 0.0735 | 6.6007 | 2000 | 0.8030 | 0.3836 | 0.8030 | 0.8961 |
| 0.0735 | 6.6073 | 2002 | 0.8114 | 0.3836 | 0.8114 | 0.9008 |
| 0.0735 | 6.6139 | 2004 | 0.8126 | 0.3836 | 0.8126 | 0.9014 |
| 0.0735 | 6.6205 | 2006 | 0.8023 | 0.3836 | 0.8023 | 0.8957 |
| 0.0735 | 6.6271 | 2008 | 0.7909 | 0.3836 | 0.7909 | 0.8893 |
| 0.0735 | 6.6337 | 2010 | 0.7863 | 0.3836 | 0.7863 | 0.8867 |
| 0.0735 | 6.6403 | 2012 | 0.7849 | 0.3836 | 0.7849 | 0.8859 |
| 0.0735 | 6.6469 | 2014 | 0.7743 | 0.3514 | 0.7743 | 0.8799 |
| 0.0735 | 6.6535 | 2016 | 0.7687 | 0.2609 | 0.7687 | 0.8767 |
| 0.0735 | 6.6601 | 2018 | 0.7702 | 0.2609 | 0.7702 | 0.8776 |
| 0.0735 | 6.6667 | 2020 | 0.7800 | 0.3514 | 0.7800 | 0.8832 |
| 0.0735 | 6.6733 | 2022 | 0.7994 | 0.3836 | 0.7994 | 0.8941 |
| 0.0735 | 6.6799 | 2024 | 0.8238 | 0.4167 | 0.8238 | 0.9076 |
| 0.0735 | 6.6865 | 2026 | 0.8451 | 0.4507 | 0.8451 | 0.9193 |
| 0.0735 | 6.6931 | 2028 | 0.8444 | 0.4507 | 0.8444 | 0.9189 |
| 0.0735 | 6.6997 | 2030 | 0.8214 | 0.4507 | 0.8214 | 0.9063 |
| 0.0735 | 6.7063 | 2032 | 0.7863 | 0.4167 | 0.7863 | 0.8868 |
| 0.0735 | 6.7129 | 2034 | 0.7585 | 0.3514 | 0.7585 | 0.8709 |
| 0.0735 | 6.7195 | 2036 | 0.7503 | 0.2609 | 0.7503 | 0.8662 |
| 0.0735 | 6.7261 | 2038 | 0.7521 | 0.2609 | 0.7521 | 0.8672 |
| 0.0735 | 6.7327 | 2040 | 0.7594 | 0.3514 | 0.7594 | 0.8714 |
| 0.0735 | 6.7393 | 2042 | 0.7763 | 0.3514 | 0.7763 | 0.8811 |
| 0.0735 | 6.7459 | 2044 | 0.7974 | 0.4507 | 0.7974 | 0.8930 |
| 0.0735 | 6.7525 | 2046 | 0.7987 | 0.4507 | 0.7987 | 0.8937 |
| 0.0735 | 6.7591 | 2048 | 0.7805 | 0.4507 | 0.7805 | 0.8834 |
| 0.0735 | 6.7657 | 2050 | 0.7566 | 0.3836 | 0.7566 | 0.8699 |
| 0.0735 | 6.7723 | 2052 | 0.7426 | 0.2609 | 0.7426 | 0.8617 |
| 0.0735 | 6.7789 | 2054 | 0.7374 | 0.2609 | 0.7374 | 0.8587 |
| 0.0735 | 6.7855 | 2056 | 0.7336 | 0.2609 | 0.7336 | 0.8565 |
| 0.0735 | 6.7921 | 2058 | 0.7359 | 0.2609 | 0.7359 | 0.8578 |
| 0.0735 | 6.7987 | 2060 | 0.7415 | 0.2609 | 0.7415 | 0.8611 |
| 0.0735 | 6.8053 | 2062 | 0.7385 | 0.2609 | 0.7385 | 0.8594 |
| 0.0735 | 6.8119 | 2064 | 0.7358 | 0.2609 | 0.7358 | 0.8578 |
| 0.0735 | 6.8185 | 2066 | 0.7265 | 0.2609 | 0.7265 | 0.8524 |
| 0.0735 | 6.8251 | 2068 | 0.7223 | 0.2609 | 0.7223 | 0.8499 |
| 0.0735 | 6.8317 | 2070 | 0.7211 | 0.2609 | 0.7211 | 0.8492 |
| 0.0735 | 6.8383 | 2072 | 0.7259 | 0.2609 | 0.7259 | 0.8520 |
| 0.0735 | 6.8449 | 2074 | 0.7384 | 0.2609 | 0.7384 | 0.8593 |
| 0.0735 | 6.8515 | 2076 | 0.7499 | 0.2609 | 0.7499 | 0.8660 |
| 0.0735 | 6.8581 | 2078 | 0.7592 | 0.2941 | 0.7592 | 0.8713 |
| 0.0735 | 6.8647 | 2080 | 0.7690 | 0.3284 | 0.7690 | 0.8769 |
| 0.0735 | 6.8713 | 2082 | 0.7700 | 0.3284 | 0.7700 | 0.8775 |
| 0.0735 | 6.8779 | 2084 | 0.7677 | 0.3284 | 0.7677 | 0.8762 |
| 0.0735 | 6.8845 | 2086 | 0.7776 | 0.3636 | 0.7776 | 0.8818 |
| 0.0735 | 6.8911 | 2088 | 0.7805 | 0.4507 | 0.7805 | 0.8834 |
| 0.0735 | 6.8977 | 2090 | 0.7924 | 0.4507 | 0.7924 | 0.8901 |
| 0.0735 | 6.9043 | 2092 | 0.8075 | 0.4857 | 0.8075 | 0.8986 |
| 0.0735 | 6.9109 | 2094 | 0.8035 | 0.4507 | 0.8035 | 0.8964 |
| 0.0735 | 6.9175 | 2096 | 0.7891 | 0.4507 | 0.7891 | 0.8883 |
| 0.0735 | 6.9241 | 2098 | 0.7807 | 0.2941 | 0.7807 | 0.8836 |
| 0.0735 | 6.9307 | 2100 | 0.7692 | 0.2609 | 0.7692 | 0.8770 |
| 0.0735 | 6.9373 | 2102 | 0.7636 | 0.2609 | 0.7636 | 0.8738 |
| 0.0735 | 6.9439 | 2104 | 0.7655 | 0.2609 | 0.7655 | 0.8749 |
| 0.0735 | 6.9505 | 2106 | 0.7705 | 0.2941 | 0.7705 | 0.8778 |
| 0.0735 | 6.9571 | 2108 | 0.7704 | 0.2609 | 0.7704 | 0.8778 |
| 0.0735 | 6.9637 | 2110 | 0.7737 | 0.2609 | 0.7737 | 0.8796 |
| 0.0735 | 6.9703 | 2112 | 0.7832 | 0.2609 | 0.7832 | 0.8850 |
| 0.0735 | 6.9769 | 2114 | 0.7820 | 0.2609 | 0.7820 | 0.8843 |
| 0.0735 | 6.9835 | 2116 | 0.7868 | 0.2609 | 0.7868 | 0.8870 |
| 0.0735 | 6.9901 | 2118 | 0.7927 | 0.2609 | 0.7927 | 0.8903 |
| 0.0735 | 6.9967 | 2120 | 0.8013 | 0.3284 | 0.8013 | 0.8951 |
| 0.0735 | 7.0033 | 2122 | 0.7995 | 0.2609 | 0.7995 | 0.8942 |
| 0.0735 | 7.0099 | 2124 | 0.7929 | 0.2609 | 0.7929 | 0.8905 |
| 0.0735 | 7.0165 | 2126 | 0.7833 | 0.2609 | 0.7833 | 0.8850 |
| 0.0735 | 7.0231 | 2128 | 0.7730 | 0.1667 | 0.7730 | 0.8792 |
| 0.0735 | 7.0297 | 2130 | 0.7671 | 0.1667 | 0.7671 | 0.8758 |
| 0.0735 | 7.0363 | 2132 | 0.7629 | 0.2286 | 0.7629 | 0.8735 |
| 0.0735 | 7.0429 | 2134 | 0.7722 | 0.2609 | 0.7722 | 0.8788 |
| 0.0735 | 7.0495 | 2136 | 0.7909 | 0.4167 | 0.7909 | 0.8893 |
| 0.0735 | 7.0561 | 2138 | 0.8016 | 0.4167 | 0.8016 | 0.8953 |
| 0.0735 | 7.0627 | 2140 | 0.8013 | 0.4167 | 0.8013 | 0.8952 |
| 0.0735 | 7.0693 | 2142 | 0.7853 | 0.4167 | 0.7853 | 0.8861 |
| 0.0735 | 7.0759 | 2144 | 0.7644 | 0.4167 | 0.7644 | 0.8743 |
| 0.0735 | 7.0825 | 2146 | 0.7504 | 0.2941 | 0.7504 | 0.8663 |
| 0.0735 | 7.0891 | 2148 | 0.7501 | 0.2941 | 0.7501 | 0.8661 |
| 0.0735 | 7.0957 | 2150 | 0.7504 | 0.3284 | 0.7504 | 0.8663 |
| 0.0735 | 7.1023 | 2152 | 0.7553 | 0.4167 | 0.7553 | 0.8691 |
| 0.0735 | 7.1089 | 2154 | 0.7723 | 0.4167 | 0.7723 | 0.8788 |
| 0.0735 | 7.1155 | 2156 | 0.7959 | 0.4857 | 0.7959 | 0.8921 |
| 0.0735 | 7.1221 | 2158 | 0.7968 | 0.4857 | 0.7968 | 0.8926 |
| 0.0735 | 7.1287 | 2160 | 0.7771 | 0.4167 | 0.7771 | 0.8816 |
| 0.0735 | 7.1353 | 2162 | 0.7650 | 0.4167 | 0.7650 | 0.8746 |
| 0.0735 | 7.1419 | 2164 | 0.7676 | 0.4167 | 0.7676 | 0.8761 |
| 0.0735 | 7.1485 | 2166 | 0.7804 | 0.4167 | 0.7804 | 0.8834 |
| 0.0735 | 7.1551 | 2168 | 0.7973 | 0.4167 | 0.7973 | 0.8929 |
| 0.0735 | 7.1617 | 2170 | 0.8181 | 0.4167 | 0.8181 | 0.9045 |
| 0.0735 | 7.1683 | 2172 | 0.8330 | 0.4857 | 0.8330 | 0.9127 |
| 0.0735 | 7.1749 | 2174 | 0.8270 | 0.4857 | 0.8270 | 0.9094 |
| 0.0735 | 7.1815 | 2176 | 0.8065 | 0.4857 | 0.8065 | 0.8980 |
| 0.0735 | 7.1881 | 2178 | 0.7772 | 0.4167 | 0.7772 | 0.8816 |
| 0.0735 | 7.1947 | 2180 | 0.7543 | 0.3284 | 0.7543 | 0.8685 |
| 0.0735 | 7.2013 | 2182 | 0.7481 | 0.3284 | 0.7481 | 0.8649 |
| 0.0735 | 7.2079 | 2184 | 0.7456 | 0.3284 | 0.7456 | 0.8635 |
| 0.0735 | 7.2145 | 2186 | 0.7434 | 0.3284 | 0.7434 | 0.8622 |
| 0.0735 | 7.2211 | 2188 | 0.7510 | 0.3284 | 0.7510 | 0.8666 |
| 0.0735 | 7.2277 | 2190 | 0.7640 | 0.3636 | 0.7640 | 0.8741 |
| 0.0735 | 7.2343 | 2192 | 0.7785 | 0.4857 | 0.7785 | 0.8823 |
| 0.0735 | 7.2409 | 2194 | 0.7996 | 0.4857 | 0.7996 | 0.8942 |
| 0.0735 | 7.2475 | 2196 | 0.8136 | 0.4857 | 0.8136 | 0.9020 |
| 0.0735 | 7.2541 | 2198 | 0.8049 | 0.4857 | 0.8049 | 0.8972 |
| 0.0735 | 7.2607 | 2200 | 0.7982 | 0.4507 | 0.7982 | 0.8934 |
| 0.0735 | 7.2673 | 2202 | 0.7799 | 0.4507 | 0.7799 | 0.8831 |
| 0.0735 | 7.2739 | 2204 | 0.7704 | 0.3284 | 0.7704 | 0.8777 |
| 0.0735 | 7.2805 | 2206 | 0.7761 | 0.3836 | 0.7761 | 0.8809 |
| 0.0735 | 7.2871 | 2208 | 0.7827 | 0.3836 | 0.7827 | 0.8847 |
| 0.0735 | 7.2937 | 2210 | 0.7856 | 0.3514 | 0.7856 | 0.8864 |
| 0.0735 | 7.3003 | 2212 | 0.7908 | 0.3514 | 0.7908 | 0.8893 |
| 0.0735 | 7.3069 | 2214 | 0.7903 | 0.3514 | 0.7903 | 0.8890 |
| 0.0735 | 7.3135 | 2216 | 0.7809 | 0.3514 | 0.7809 | 0.8837 |
| 0.0735 | 7.3201 | 2218 | 0.7822 | 0.3836 | 0.7822 | 0.8844 |
| 0.0735 | 7.3267 | 2220 | 0.7840 | 0.4167 | 0.7840 | 0.8855 |
| 0.0735 | 7.3333 | 2222 | 0.7840 | 0.4167 | 0.7840 | 0.8855 |
| 0.0735 | 7.3399 | 2224 | 0.7884 | 0.4507 | 0.7884 | 0.8879 |
| 0.0735 | 7.3465 | 2226 | 0.7916 | 0.4507 | 0.7916 | 0.8897 |
| 0.0735 | 7.3531 | 2228 | 0.8072 | 0.4507 | 0.8072 | 0.8984 |
| 0.0735 | 7.3597 | 2230 | 0.8160 | 0.4857 | 0.8160 | 0.9033 |
| 0.0735 | 7.3663 | 2232 | 0.8409 | 0.4857 | 0.8409 | 0.9170 |
| 0.0735 | 7.3729 | 2234 | 0.8575 | 0.4 | 0.8575 | 0.9260 |
| 0.0735 | 7.3795 | 2236 | 0.8644 | 0.4 | 0.8644 | 0.9297 |
| 0.0735 | 7.3861 | 2238 | 0.8530 | 0.4 | 0.8531 | 0.9236 |
| 0.0735 | 7.3927 | 2240 | 0.8348 | 0.4857 | 0.8348 | 0.9137 |
| 0.0735 | 7.3993 | 2242 | 0.8015 | 0.4167 | 0.8015 | 0.8953 |
| 0.0735 | 7.4059 | 2244 | 0.7753 | 0.3836 | 0.7753 | 0.8805 |
| 0.0735 | 7.4125 | 2246 | 0.7627 | 0.24 | 0.7627 | 0.8733 |
| 0.0735 | 7.4191 | 2248 | 0.7587 | 0.24 | 0.7587 | 0.8710 |
| 0.0735 | 7.4257 | 2250 | 0.7595 | 0.1429 | 0.7595 | 0.8715 |
| 0.0735 | 7.4323 | 2252 | 0.7614 | 0.3143 | 0.7614 | 0.8726 |
| 0.0735 | 7.4389 | 2254 | 0.7551 | 0.1972 | 0.7551 | 0.8690 |
| 0.0735 | 7.4455 | 2256 | 0.7506 | 0.3143 | 0.7506 | 0.8664 |
| 0.0735 | 7.4521 | 2258 | 0.7578 | 0.2609 | 0.7578 | 0.8705 |
| 0.0735 | 7.4587 | 2260 | 0.7609 | 0.3514 | 0.7609 | 0.8723 |
| 0.0735 | 7.4653 | 2262 | 0.7729 | 0.3836 | 0.7729 | 0.8792 |
| 0.0735 | 7.4719 | 2264 | 0.7804 | 0.4167 | 0.7804 | 0.8834 |
| 0.0735 | 7.4785 | 2266 | 0.7874 | 0.4507 | 0.7874 | 0.8873 |
| 0.0735 | 7.4851 | 2268 | 0.7921 | 0.4857 | 0.7921 | 0.8900 |
| 0.0735 | 7.4917 | 2270 | 0.7935 | 0.4857 | 0.7935 | 0.8908 |
| 0.0735 | 7.4983 | 2272 | 0.7921 | 0.4857 | 0.7921 | 0.8900 |
| 0.0735 | 7.5050 | 2274 | 0.7838 | 0.4857 | 0.7838 | 0.8853 |
| 0.0735 | 7.5116 | 2276 | 0.7755 | 0.4857 | 0.7755 | 0.8806 |
| 0.0735 | 7.5182 | 2278 | 0.7607 | 0.4167 | 0.7607 | 0.8722 |
| 0.0735 | 7.5248 | 2280 | 0.7458 | 0.2941 | 0.7458 | 0.8636 |
| 0.0735 | 7.5314 | 2282 | 0.7461 | 0.3478 | 0.7461 | 0.8638 |
| 0.0735 | 7.5380 | 2284 | 0.7583 | 0.3478 | 0.7583 | 0.8708 |
| 0.0735 | 7.5446 | 2286 | 0.7689 | 0.3836 | 0.7689 | 0.8769 |
| 0.0735 | 7.5512 | 2288 | 0.7814 | 0.3836 | 0.7814 | 0.8840 |
| 0.0735 | 7.5578 | 2290 | 0.7889 | 0.3836 | 0.7889 | 0.8882 |
| 0.0735 | 7.5644 | 2292 | 0.7897 | 0.3836 | 0.7897 | 0.8887 |
| 0.0735 | 7.5710 | 2294 | 0.7838 | 0.3836 | 0.7838 | 0.8853 |
| 0.0735 | 7.5776 | 2296 | 0.7760 | 0.3836 | 0.7760 | 0.8809 |
| 0.0735 | 7.5842 | 2298 | 0.7727 | 0.3836 | 0.7727 | 0.8790 |
| 0.0735 | 7.5908 | 2300 | 0.7818 | 0.4857 | 0.7818 | 0.8842 |
| 0.0735 | 7.5974 | 2302 | 0.7957 | 0.4348 | 0.7957 | 0.8920 |
| 0.0735 | 7.6040 | 2304 | 0.7923 | 0.4348 | 0.7923 | 0.8901 |
| 0.0735 | 7.6106 | 2306 | 0.7761 | 0.4857 | 0.7761 | 0.8810 |
| 0.0735 | 7.6172 | 2308 | 0.7547 | 0.2941 | 0.7547 | 0.8687 |
| 0.0735 | 7.6238 | 2310 | 0.7451 | 0.2941 | 0.7451 | 0.8632 |
| 0.0735 | 7.6304 | 2312 | 0.7446 | 0.2941 | 0.7446 | 0.8629 |
| 0.0735 | 7.6370 | 2314 | 0.7452 | 0.2941 | 0.7452 | 0.8632 |
| 0.0735 | 7.6436 | 2316 | 0.7493 | 0.2941 | 0.7493 | 0.8656 |
| 0.0735 | 7.6502 | 2318 | 0.7619 | 0.2941 | 0.7619 | 0.8728 |
| 0.0735 | 7.6568 | 2320 | 0.7718 | 0.2941 | 0.7718 | 0.8785 |
| 0.0735 | 7.6634 | 2322 | 0.7880 | 0.3836 | 0.7880 | 0.8877 |
| 0.0735 | 7.6700 | 2324 | 0.7933 | 0.3836 | 0.7933 | 0.8907 |
| 0.0735 | 7.6766 | 2326 | 0.7878 | 0.2941 | 0.7878 | 0.8876 |
| 0.0735 | 7.6832 | 2328 | 0.7905 | 0.2941 | 0.7905 | 0.8891 |
| 0.0735 | 7.6898 | 2330 | 0.7924 | 0.2941 | 0.7924 | 0.8902 |
| 0.0735 | 7.6964 | 2332 | 0.7962 | 0.3836 | 0.7962 | 0.8923 |
| 0.0735 | 7.7030 | 2334 | 0.7957 | 0.3836 | 0.7957 | 0.8920 |
| 0.0735 | 7.7096 | 2336 | 0.7980 | 0.3836 | 0.7980 | 0.8933 |
| 0.0735 | 7.7162 | 2338 | 0.7952 | 0.3836 | 0.7952 | 0.8918 |
| 0.0735 | 7.7228 | 2340 | 0.7882 | 0.3836 | 0.7882 | 0.8878 |
| 0.0735 | 7.7294 | 2342 | 0.7887 | 0.3836 | 0.7887 | 0.8881 |
| 0.0735 | 7.7360 | 2344 | 0.7940 | 0.3836 | 0.7940 | 0.8911 |
| 0.0735 | 7.7426 | 2346 | 0.7883 | 0.3836 | 0.7883 | 0.8879 |
| 0.0735 | 7.7492 | 2348 | 0.7863 | 0.3836 | 0.7863 | 0.8867 |
| 0.0735 | 7.7558 | 2350 | 0.7846 | 0.3836 | 0.7846 | 0.8858 |
| 0.0735 | 7.7624 | 2352 | 0.7838 | 0.3836 | 0.7838 | 0.8853 |
| 0.0735 | 7.7690 | 2354 | 0.7908 | 0.3836 | 0.7908 | 0.8893 |
| 0.0735 | 7.7756 | 2356 | 0.7979 | 0.3836 | 0.7979 | 0.8932 |
| 0.0735 | 7.7822 | 2358 | 0.8091 | 0.4857 | 0.8091 | 0.8995 |
| 0.0735 | 7.7888 | 2360 | 0.8145 | 0.4857 | 0.8145 | 0.9025 |
| 0.0735 | 7.7954 | 2362 | 0.8088 | 0.4167 | 0.8088 | 0.8994 |
| 0.0735 | 7.8020 | 2364 | 0.7925 | 0.3836 | 0.7925 | 0.8902 |
| 0.0735 | 7.8086 | 2366 | 0.7797 | 0.3514 | 0.7797 | 0.8830 |
| 0.0735 | 7.8152 | 2368 | 0.7705 | 0.3514 | 0.7705 | 0.8778 |
| 0.0735 | 7.8218 | 2370 | 0.7656 | 0.3514 | 0.7656 | 0.8750 |
| 0.0735 | 7.8284 | 2372 | 0.7693 | 0.3514 | 0.7693 | 0.8771 |
| 0.0735 | 7.8350 | 2374 | 0.7781 | 0.3836 | 0.7781 | 0.8821 |
| 0.0735 | 7.8416 | 2376 | 0.7843 | 0.3836 | 0.7843 | 0.8856 |
| 0.0735 | 7.8482 | 2378 | 0.7827 | 0.4167 | 0.7827 | 0.8847 |
| 0.0735 | 7.8548 | 2380 | 0.7798 | 0.4167 | 0.7798 | 0.8831 |
| 0.0735 | 7.8614 | 2382 | 0.7774 | 0.4167 | 0.7774 | 0.8817 |
| 0.0735 | 7.8680 | 2384 | 0.7792 | 0.4167 | 0.7792 | 0.8827 |
| 0.0735 | 7.8746 | 2386 | 0.7736 | 0.4167 | 0.7736 | 0.8795 |
| 0.0735 | 7.8812 | 2388 | 0.7680 | 0.4167 | 0.7680 | 0.8764 |
| 0.0735 | 7.8878 | 2390 | 0.7648 | 0.4167 | 0.7648 | 0.8746 |
| 0.0735 | 7.8944 | 2392 | 0.7653 | 0.4167 | 0.7653 | 0.8748 |
| 0.0735 | 7.9010 | 2394 | 0.7716 | 0.4167 | 0.7716 | 0.8784 |
| 0.0735 | 7.9076 | 2396 | 0.7777 | 0.4167 | 0.7777 | 0.8819 |
| 0.0735 | 7.9142 | 2398 | 0.7874 | 0.4857 | 0.7874 | 0.8873 |
| 0.0735 | 7.9208 | 2400 | 0.7970 | 0.4857 | 0.7970 | 0.8927 |
| 0.0735 | 7.9274 | 2402 | 0.7994 | 0.4857 | 0.7994 | 0.8941 |
| 0.0735 | 7.9340 | 2404 | 0.7844 | 0.4857 | 0.7844 | 0.8857 |
| 0.0735 | 7.9406 | 2406 | 0.7651 | 0.4857 | 0.7651 | 0.8747 |
| 0.0735 | 7.9472 | 2408 | 0.7488 | 0.4857 | 0.7488 | 0.8653 |
| 0.0735 | 7.9538 | 2410 | 0.7392 | 0.4507 | 0.7392 | 0.8598 |
| 0.0735 | 7.9604 | 2412 | 0.7335 | 0.3636 | 0.7335 | 0.8565 |
| 0.0735 | 7.9670 | 2414 | 0.7326 | 0.3636 | 0.7326 | 0.8559 |
| 0.0735 | 7.9736 | 2416 | 0.7347 | 0.3636 | 0.7347 | 0.8571 |
| 0.0735 | 7.9802 | 2418 | 0.7386 | 0.4507 | 0.7386 | 0.8594 |
| 0.0735 | 7.9868 | 2420 | 0.7464 | 0.4507 | 0.7464 | 0.8639 |
| 0.0735 | 7.9934 | 2422 | 0.7499 | 0.4167 | 0.7499 | 0.8660 |
| 0.0735 | 8.0 | 2424 | 0.7513 | 0.3836 | 0.7513 | 0.8668 |
| 0.0735 | 8.0066 | 2426 | 0.7570 | 0.3836 | 0.7570 | 0.8701 |
| 0.0735 | 8.0132 | 2428 | 0.7623 | 0.4167 | 0.7623 | 0.8731 |
| 0.0735 | 8.0198 | 2430 | 0.7770 | 0.4507 | 0.7770 | 0.8815 |
| 0.0735 | 8.0264 | 2432 | 0.7954 | 0.4857 | 0.7954 | 0.8919 |
| 0.0735 | 8.0330 | 2434 | 0.8044 | 0.4857 | 0.8044 | 0.8969 |
| 0.0735 | 8.0396 | 2436 | 0.8014 | 0.4857 | 0.8014 | 0.8952 |
| 0.0735 | 8.0462 | 2438 | 0.7860 | 0.4167 | 0.7860 | 0.8866 |
| 0.0735 | 8.0528 | 2440 | 0.7738 | 0.3836 | 0.7738 | 0.8797 |
| 0.0735 | 8.0594 | 2442 | 0.7658 | 0.3514 | 0.7658 | 0.8751 |
| 0.0735 | 8.0660 | 2444 | 0.7675 | 0.4 | 0.7675 | 0.8761 |
| 0.0735 | 8.0726 | 2446 | 0.7706 | 0.4 | 0.7706 | 0.8779 |
| 0.0735 | 8.0792 | 2448 | 0.7783 | 0.4 | 0.7783 | 0.8822 |
| 0.0735 | 8.0858 | 2450 | 0.7876 | 0.4 | 0.7876 | 0.8875 |
| 0.0735 | 8.0924 | 2452 | 0.7936 | 0.4 | 0.7936 | 0.8909 |
| 0.0735 | 8.0990 | 2454 | 0.7986 | 0.3514 | 0.7986 | 0.8937 |
| 0.0735 | 8.1056 | 2456 | 0.7944 | 0.3514 | 0.7944 | 0.8913 |
| 0.0735 | 8.1122 | 2458 | 0.7844 | 0.3514 | 0.7844 | 0.8856 |
| 0.0735 | 8.1188 | 2460 | 0.7721 | 0.3514 | 0.7721 | 0.8787 |
| 0.0735 | 8.1254 | 2462 | 0.7628 | 0.3514 | 0.7628 | 0.8734 |
| 0.0735 | 8.1320 | 2464 | 0.7565 | 0.3514 | 0.7565 | 0.8698 |
| 0.0735 | 8.1386 | 2466 | 0.7573 | 0.3836 | 0.7573 | 0.8702 |
| 0.0735 | 8.1452 | 2468 | 0.7561 | 0.3836 | 0.7561 | 0.8695 |
| 0.0735 | 8.1518 | 2470 | 0.7604 | 0.3836 | 0.7604 | 0.8720 |
| 0.0735 | 8.1584 | 2472 | 0.7625 | 0.4167 | 0.7625 | 0.8732 |
| 0.0735 | 8.1650 | 2474 | 0.7610 | 0.4167 | 0.7610 | 0.8724 |
| 0.0735 | 8.1716 | 2476 | 0.7560 | 0.4167 | 0.7560 | 0.8695 |
| 0.0735 | 8.1782 | 2478 | 0.7552 | 0.4167 | 0.7552 | 0.8690 |
| 0.0735 | 8.1848 | 2480 | 0.7566 | 0.3836 | 0.7566 | 0.8698 |
| 0.0735 | 8.1914 | 2482 | 0.7668 | 0.4167 | 0.7668 | 0.8757 |
| 0.0735 | 8.1980 | 2484 | 0.7805 | 0.4857 | 0.7805 | 0.8835 |
| 0.0735 | 8.2046 | 2486 | 0.7889 | 0.4857 | 0.7889 | 0.8882 |
| 0.0735 | 8.2112 | 2488 | 0.7855 | 0.4857 | 0.7855 | 0.8863 |
| 0.0735 | 8.2178 | 2490 | 0.7738 | 0.4167 | 0.7738 | 0.8797 |
| 0.0735 | 8.2244 | 2492 | 0.7616 | 0.3836 | 0.7616 | 0.8727 |
| 0.0735 | 8.2310 | 2494 | 0.7544 | 0.3836 | 0.7544 | 0.8685 |
| 0.0735 | 8.2376 | 2496 | 0.7561 | 0.3836 | 0.7561 | 0.8695 |
| 0.0735 | 8.2442 | 2498 | 0.7660 | 0.3836 | 0.7660 | 0.8752 |
| 0.0563 | 8.2508 | 2500 | 0.7796 | 0.3836 | 0.7796 | 0.8830 |
| 0.0563 | 8.2574 | 2502 | 0.7981 | 0.4857 | 0.7981 | 0.8934 |
| 0.0563 | 8.2640 | 2504 | 0.8095 | 0.4 | 0.8095 | 0.8997 |
| 0.0563 | 8.2706 | 2506 | 0.8103 | 0.4 | 0.8103 | 0.9002 |
| 0.0563 | 8.2772 | 2508 | 0.8042 | 0.4 | 0.8042 | 0.8968 |
| 0.0563 | 8.2838 | 2510 | 0.7932 | 0.4857 | 0.7932 | 0.8906 |
| 0.0563 | 8.2904 | 2512 | 0.7817 | 0.4167 | 0.7817 | 0.8842 |
| 0.0563 | 8.2970 | 2514 | 0.7661 | 0.3836 | 0.7661 | 0.8753 |
| 0.0563 | 8.3036 | 2516 | 0.7547 | 0.3514 | 0.7547 | 0.8687 |
| 0.0563 | 8.3102 | 2518 | 0.7508 | 0.3143 | 0.7508 | 0.8665 |
| 0.0563 | 8.3168 | 2520 | 0.7472 | 0.3143 | 0.7472 | 0.8644 |
| 0.0563 | 8.3234 | 2522 | 0.7473 | 0.3143 | 0.7473 | 0.8645 |
| 0.0563 | 8.3300 | 2524 | 0.7522 | 0.3143 | 0.7522 | 0.8673 |
| 0.0563 | 8.3366 | 2526 | 0.7561 | 0.2609 | 0.7561 | 0.8696 |
| 0.0563 | 8.3432 | 2528 | 0.7668 | 0.3836 | 0.7668 | 0.8757 |
| 0.0563 | 8.3498 | 2530 | 0.7813 | 0.3836 | 0.7813 | 0.8839 |
| 0.0563 | 8.3564 | 2532 | 0.7935 | 0.3836 | 0.7935 | 0.8908 |
| 0.0563 | 8.3630 | 2534 | 0.8009 | 0.3836 | 0.8009 | 0.8949 |
| 0.0563 | 8.3696 | 2536 | 0.8058 | 0.3377 | 0.8058 | 0.8977 |
| 0.0563 | 8.3762 | 2538 | 0.8072 | 0.3377 | 0.8072 | 0.8985 |
| 0.0563 | 8.3828 | 2540 | 0.8105 | 0.3377 | 0.8105 | 0.9003 |
| 0.0563 | 8.3894 | 2542 | 0.8117 | 0.3377 | 0.8117 | 0.9009 |
| 0.0563 | 8.3960 | 2544 | 0.8040 | 0.3836 | 0.8040 | 0.8967 |
| 0.0563 | 8.4026 | 2546 | 0.7965 | 0.3836 | 0.7965 | 0.8925 |
| 0.0563 | 8.4092 | 2548 | 0.7941 | 0.3836 | 0.7941 | 0.8911 |
| 0.0563 | 8.4158 | 2550 | 0.7876 | 0.3514 | 0.7876 | 0.8875 |
| 0.0563 | 8.4224 | 2552 | 0.7834 | 0.3514 | 0.7834 | 0.8851 |
| 0.0563 | 8.4290 | 2554 | 0.7784 | 0.3143 | 0.7784 | 0.8823 |
| 0.0563 | 8.4356 | 2556 | 0.7783 | 0.3143 | 0.7783 | 0.8822 |
| 0.0563 | 8.4422 | 2558 | 0.7763 | 0.3143 | 0.7763 | 0.8811 |
| 0.0563 | 8.4488 | 2560 | 0.7787 | 0.3514 | 0.7787 | 0.8824 |
| 0.0563 | 8.4554 | 2562 | 0.7850 | 0.3514 | 0.7850 | 0.8860 |
| 0.0563 | 8.4620 | 2564 | 0.7831 | 0.3836 | 0.7831 | 0.8850 |
| 0.0563 | 8.4686 | 2566 | 0.7790 | 0.3836 | 0.7790 | 0.8826 |
| 0.0563 | 8.4752 | 2568 | 0.7783 | 0.4167 | 0.7783 | 0.8822 |
| 0.0563 | 8.4818 | 2570 | 0.7841 | 0.4857 | 0.7841 | 0.8855 |
| 0.0563 | 8.4884 | 2572 | 0.7924 | 0.4 | 0.7924 | 0.8902 |
| 0.0563 | 8.4950 | 2574 | 0.8010 | 0.4 | 0.8010 | 0.8950 |
| 0.0563 | 8.5017 | 2576 | 0.8097 | 0.4 | 0.8097 | 0.8998 |
| 0.0563 | 8.5083 | 2578 | 0.8090 | 0.4 | 0.8090 | 0.8994 |
| 0.0563 | 8.5149 | 2580 | 0.8068 | 0.4 | 0.8068 | 0.8982 |
| 0.0563 | 8.5215 | 2582 | 0.8000 | 0.3684 | 0.8000 | 0.8944 |
| 0.0563 | 8.5281 | 2584 | 0.7938 | 0.3377 | 0.7938 | 0.8909 |
| 0.0563 | 8.5347 | 2586 | 0.7826 | 0.3836 | 0.7826 | 0.8847 |
| 0.0563 | 8.5413 | 2588 | 0.7750 | 0.2609 | 0.7750 | 0.8803 |
| 0.0563 | 8.5479 | 2590 | 0.7742 | 0.2609 | 0.7742 | 0.8799 |
| 0.0563 | 8.5545 | 2592 | 0.7796 | 0.2609 | 0.7796 | 0.8829 |
| 0.0563 | 8.5611 | 2594 | 0.7849 | 0.3514 | 0.7849 | 0.8859 |
| 0.0563 | 8.5677 | 2596 | 0.7916 | 0.3836 | 0.7916 | 0.8897 |
| 0.0563 | 8.5743 | 2598 | 0.7989 | 0.4167 | 0.7989 | 0.8938 |
| 0.0563 | 8.5809 | 2600 | 0.8002 | 0.4857 | 0.8002 | 0.8946 |
| 0.0563 | 8.5875 | 2602 | 0.8061 | 0.4857 | 0.8061 | 0.8978 |
| 0.0563 | 8.5941 | 2604 | 0.8119 | 0.4000 | 0.8119 | 0.9011 |
| 0.0563 | 8.6007 | 2606 | 0.8125 | 0.4000 | 0.8125 | 0.9014 |
| 0.0563 | 8.6073 | 2608 | 0.8044 | 0.4857 | 0.8044 | 0.8969 |
| 0.0563 | 8.6139 | 2610 | 0.7901 | 0.4857 | 0.7901 | 0.8889 |
| 0.0563 | 8.6205 | 2612 | 0.7812 | 0.4507 | 0.7812 | 0.8838 |
| 0.0563 | 8.6271 | 2614 | 0.7690 | 0.4167 | 0.7690 | 0.8769 |
| 0.0563 | 8.6337 | 2616 | 0.7650 | 0.3836 | 0.7650 | 0.8746 |
| 0.0563 | 8.6403 | 2618 | 0.7677 | 0.3514 | 0.7677 | 0.8762 |
| 0.0563 | 8.6469 | 2620 | 0.7749 | 0.3514 | 0.7749 | 0.8803 |
| 0.0563 | 8.6535 | 2622 | 0.7795 | 0.3514 | 0.7795 | 0.8829 |
| 0.0563 | 8.6601 | 2624 | 0.7883 | 0.3514 | 0.7883 | 0.8878 |
| 0.0563 | 8.6667 | 2626 | 0.7937 | 0.3836 | 0.7937 | 0.8909 |
| 0.0563 | 8.6733 | 2628 | 0.7966 | 0.3836 | 0.7966 | 0.8925 |
| 0.0563 | 8.6799 | 2630 | 0.7926 | 0.3514 | 0.7926 | 0.8903 |
| 0.0563 | 8.6865 | 2632 | 0.7860 | 0.3514 | 0.7860 | 0.8866 |
| 0.0563 | 8.6931 | 2634 | 0.7814 | 0.3514 | 0.7814 | 0.8840 |
| 0.0563 | 8.6997 | 2636 | 0.7792 | 0.2609 | 0.7792 | 0.8827 |
| 0.0563 | 8.7063 | 2638 | 0.7802 | 0.2609 | 0.7802 | 0.8833 |
| 0.0563 | 8.7129 | 2640 | 0.7786 | 0.2609 | 0.7786 | 0.8824 |
| 0.0563 | 8.7195 | 2642 | 0.7795 | 0.2609 | 0.7795 | 0.8829 |
| 0.0563 | 8.7261 | 2644 | 0.7845 | 0.2609 | 0.7845 | 0.8857 |
| 0.0563 | 8.7327 | 2646 | 0.7914 | 0.2609 | 0.7914 | 0.8896 |
| 0.0563 | 8.7393 | 2648 | 0.8013 | 0.3514 | 0.8013 | 0.8952 |
| 0.0563 | 8.7459 | 2650 | 0.8139 | 0.3836 | 0.8139 | 0.9022 |
| 0.0563 | 8.7525 | 2652 | 0.8223 | 0.4167 | 0.8223 | 0.9068 |
| 0.0563 | 8.7591 | 2654 | 0.8255 | 0.4167 | 0.8255 | 0.9085 |
| 0.0563 | 8.7657 | 2656 | 0.8236 | 0.4507 | 0.8236 | 0.9075 |
| 0.0563 | 8.7723 | 2658 | 0.8211 | 0.4507 | 0.8211 | 0.9062 |
| 0.0563 | 8.7789 | 2660 | 0.8135 | 0.4507 | 0.8135 | 0.9019 |
| 0.0563 | 8.7855 | 2662 | 0.8037 | 0.3836 | 0.8037 | 0.8965 |
| 0.0563 | 8.7921 | 2664 | 0.7948 | 0.3836 | 0.7948 | 0.8915 |
| 0.0563 | 8.7987 | 2666 | 0.7903 | 0.3836 | 0.7903 | 0.8890 |
| 0.0563 | 8.8053 | 2668 | 0.7887 | 0.3836 | 0.7887 | 0.8881 |
| 0.0563 | 8.8119 | 2670 | 0.7873 | 0.3836 | 0.7873 | 0.8873 |
| 0.0563 | 8.8185 | 2672 | 0.7889 | 0.3836 | 0.7889 | 0.8882 |
| 0.0563 | 8.8251 | 2674 | 0.7957 | 0.4507 | 0.7957 | 0.8920 |
| 0.0563 | 8.8317 | 2676 | 0.7987 | 0.4857 | 0.7987 | 0.8937 |
| 0.0563 | 8.8383 | 2678 | 0.7970 | 0.4857 | 0.7970 | 0.8927 |
| 0.0563 | 8.8449 | 2680 | 0.7926 | 0.4857 | 0.7926 | 0.8903 |
| 0.0563 | 8.8515 | 2682 | 0.7923 | 0.4857 | 0.7923 | 0.8901 |
| 0.0563 | 8.8581 | 2684 | 0.7945 | 0.4857 | 0.7945 | 0.8914 |
| 0.0563 | 8.8647 | 2686 | 0.7952 | 0.4857 | 0.7952 | 0.8917 |
| 0.0563 | 8.8713 | 2688 | 0.8024 | 0.4857 | 0.8024 | 0.8958 |
| 0.0563 | 8.8779 | 2690 | 0.8096 | 0.4 | 0.8096 | 0.8998 |
| 0.0563 | 8.8845 | 2692 | 0.8157 | 0.4 | 0.8157 | 0.9032 |
| 0.0563 | 8.8911 | 2694 | 0.8141 | 0.4 | 0.8141 | 0.9023 |
| 0.0563 | 8.8977 | 2696 | 0.8109 | 0.4 | 0.8109 | 0.9005 |
| 0.0563 | 8.9043 | 2698 | 0.8088 | 0.4 | 0.8088 | 0.8993 |
| 0.0563 | 8.9109 | 2700 | 0.8077 | 0.4 | 0.8077 | 0.8987 |
| 0.0563 | 8.9175 | 2702 | 0.8026 | 0.4 | 0.8026 | 0.8959 |
| 0.0563 | 8.9241 | 2704 | 0.7982 | 0.4 | 0.7982 | 0.8934 |
| 0.0563 | 8.9307 | 2706 | 0.7944 | 0.4857 | 0.7944 | 0.8913 |
| 0.0563 | 8.9373 | 2708 | 0.7913 | 0.4857 | 0.7913 | 0.8896 |
| 0.0563 | 8.9439 | 2710 | 0.7895 | 0.4857 | 0.7895 | 0.8885 |
| 0.0563 | 8.9505 | 2712 | 0.7933 | 0.4857 | 0.7933 | 0.8907 |
| 0.0563 | 8.9571 | 2714 | 0.7946 | 0.4857 | 0.7946 | 0.8914 |
| 0.0563 | 8.9637 | 2716 | 0.7941 | 0.4857 | 0.7941 | 0.8911 |
| 0.0563 | 8.9703 | 2718 | 0.7918 | 0.4857 | 0.7918 | 0.8898 |
| 0.0563 | 8.9769 | 2720 | 0.7888 | 0.4857 | 0.7888 | 0.8881 |
| 0.0563 | 8.9835 | 2722 | 0.7855 | 0.4857 | 0.7855 | 0.8863 |
| 0.0563 | 8.9901 | 2724 | 0.7782 | 0.4857 | 0.7782 | 0.8822 |
| 0.0563 | 8.9967 | 2726 | 0.7709 | 0.4507 | 0.7709 | 0.8780 |
| 0.0563 | 9.0033 | 2728 | 0.7648 | 0.4507 | 0.7648 | 0.8745 |
| 0.0563 | 9.0099 | 2730 | 0.7614 | 0.4507 | 0.7614 | 0.8726 |
| 0.0563 | 9.0165 | 2732 | 0.7627 | 0.4507 | 0.7627 | 0.8733 |
| 0.0563 | 9.0231 | 2734 | 0.7631 | 0.4857 | 0.7631 | 0.8735 |
| 0.0563 | 9.0297 | 2736 | 0.7655 | 0.4857 | 0.7655 | 0.8749 |
| 0.0563 | 9.0363 | 2738 | 0.7662 | 0.4857 | 0.7662 | 0.8753 |
| 0.0563 | 9.0429 | 2740 | 0.7661 | 0.4857 | 0.7661 | 0.8753 |
| 0.0563 | 9.0495 | 2742 | 0.7663 | 0.4857 | 0.7663 | 0.8754 |
| 0.0563 | 9.0561 | 2744 | 0.7654 | 0.4507 | 0.7654 | 0.8749 |
| 0.0563 | 9.0627 | 2746 | 0.7651 | 0.4507 | 0.7651 | 0.8747 |
| 0.0563 | 9.0693 | 2748 | 0.7634 | 0.4507 | 0.7634 | 0.8737 |
| 0.0563 | 9.0759 | 2750 | 0.7593 | 0.2941 | 0.7593 | 0.8714 |
| 0.0563 | 9.0825 | 2752 | 0.7572 | 0.2941 | 0.7572 | 0.8702 |
| 0.0563 | 9.0891 | 2754 | 0.7602 | 0.2609 | 0.7602 | 0.8719 |
| 0.0563 | 9.0957 | 2756 | 0.7642 | 0.2609 | 0.7642 | 0.8742 |
| 0.0563 | 9.1023 | 2758 | 0.7698 | 0.3836 | 0.7698 | 0.8774 |
| 0.0563 | 9.1089 | 2760 | 0.7777 | 0.3836 | 0.7777 | 0.8819 |
| 0.0563 | 9.1155 | 2762 | 0.7874 | 0.3836 | 0.7874 | 0.8874 |
| 0.0563 | 9.1221 | 2764 | 0.7951 | 0.4167 | 0.7951 | 0.8917 |
| 0.0563 | 9.1287 | 2766 | 0.8010 | 0.4167 | 0.8010 | 0.8950 |
| 0.0563 | 9.1353 | 2768 | 0.8050 | 0.4167 | 0.8050 | 0.8972 |
| 0.0563 | 9.1419 | 2770 | 0.8054 | 0.4167 | 0.8054 | 0.8974 |
| 0.0563 | 9.1485 | 2772 | 0.8058 | 0.4167 | 0.8058 | 0.8977 |
| 0.0563 | 9.1551 | 2774 | 0.8094 | 0.4507 | 0.8094 | 0.8997 |
| 0.0563 | 9.1617 | 2776 | 0.8103 | 0.4507 | 0.8103 | 0.9002 |
| 0.0563 | 9.1683 | 2778 | 0.8071 | 0.4507 | 0.8071 | 0.8984 |
| 0.0563 | 9.1749 | 2780 | 0.8058 | 0.4507 | 0.8058 | 0.8976 |
| 0.0563 | 9.1815 | 2782 | 0.8028 | 0.3836 | 0.8028 | 0.8960 |
| 0.0563 | 9.1881 | 2784 | 0.7990 | 0.3836 | 0.7990 | 0.8939 |
| 0.0563 | 9.1947 | 2786 | 0.7940 | 0.3836 | 0.7940 | 0.8910 |
| 0.0563 | 9.2013 | 2788 | 0.7907 | 0.3836 | 0.7907 | 0.8892 |
| 0.0563 | 9.2079 | 2790 | 0.7899 | 0.3836 | 0.7899 | 0.8888 |
| 0.0563 | 9.2145 | 2792 | 0.7886 | 0.3836 | 0.7886 | 0.8880 |
| 0.0563 | 9.2211 | 2794 | 0.7854 | 0.3836 | 0.7854 | 0.8862 |
| 0.0563 | 9.2277 | 2796 | 0.7824 | 0.3836 | 0.7824 | 0.8845 |
| 0.0563 | 9.2343 | 2798 | 0.7796 | 0.3836 | 0.7796 | 0.8830 |
| 0.0563 | 9.2409 | 2800 | 0.7785 | 0.3836 | 0.7785 | 0.8823 |
| 0.0563 | 9.2475 | 2802 | 0.7786 | 0.3836 | 0.7786 | 0.8824 |
| 0.0563 | 9.2541 | 2804 | 0.7788 | 0.3836 | 0.7788 | 0.8825 |
| 0.0563 | 9.2607 | 2806 | 0.7808 | 0.3836 | 0.7808 | 0.8836 |
| 0.0563 | 9.2673 | 2808 | 0.7840 | 0.3836 | 0.7840 | 0.8854 |
| 0.0563 | 9.2739 | 2810 | 0.7841 | 0.3836 | 0.7841 | 0.8855 |
| 0.0563 | 9.2805 | 2812 | 0.7834 | 0.3836 | 0.7834 | 0.8851 |
| 0.0563 | 9.2871 | 2814 | 0.7817 | 0.3836 | 0.7817 | 0.8841 |
| 0.0563 | 9.2937 | 2816 | 0.7804 | 0.3836 | 0.7804 | 0.8834 |
| 0.0563 | 9.3003 | 2818 | 0.7776 | 0.3836 | 0.7776 | 0.8818 |
| 0.0563 | 9.3069 | 2820 | 0.7751 | 0.3836 | 0.7751 | 0.8804 |
| 0.0563 | 9.3135 | 2822 | 0.7739 | 0.3836 | 0.7739 | 0.8797 |
| 0.0563 | 9.3201 | 2824 | 0.7747 | 0.3836 | 0.7747 | 0.8802 |
| 0.0563 | 9.3267 | 2826 | 0.7734 | 0.3836 | 0.7734 | 0.8794 |
| 0.0563 | 9.3333 | 2828 | 0.7731 | 0.3836 | 0.7731 | 0.8793 |
| 0.0563 | 9.3399 | 2830 | 0.7737 | 0.3836 | 0.7737 | 0.8796 |
| 0.0563 | 9.3465 | 2832 | 0.7759 | 0.3836 | 0.7759 | 0.8809 |
| 0.0563 | 9.3531 | 2834 | 0.7787 | 0.3836 | 0.7787 | 0.8824 |
| 0.0563 | 9.3597 | 2836 | 0.7833 | 0.3836 | 0.7833 | 0.8851 |
| 0.0563 | 9.3663 | 2838 | 0.7854 | 0.3836 | 0.7854 | 0.8862 |
| 0.0563 | 9.3729 | 2840 | 0.7866 | 0.3836 | 0.7866 | 0.8869 |
| 0.0563 | 9.3795 | 2842 | 0.7895 | 0.3836 | 0.7895 | 0.8885 |
| 0.0563 | 9.3861 | 2844 | 0.7895 | 0.3836 | 0.7895 | 0.8885 |
| 0.0563 | 9.3927 | 2846 | 0.7873 | 0.3836 | 0.7873 | 0.8873 |
| 0.0563 | 9.3993 | 2848 | 0.7843 | 0.3836 | 0.7843 | 0.8856 |
| 0.0563 | 9.4059 | 2850 | 0.7832 | 0.3836 | 0.7832 | 0.8850 |
| 0.0563 | 9.4125 | 2852 | 0.7836 | 0.3836 | 0.7836 | 0.8852 |
| 0.0563 | 9.4191 | 2854 | 0.7830 | 0.3836 | 0.7830 | 0.8849 |
| 0.0563 | 9.4257 | 2856 | 0.7835 | 0.3836 | 0.7835 | 0.8852 |
| 0.0563 | 9.4323 | 2858 | 0.7857 | 0.3836 | 0.7857 | 0.8864 |
| 0.0563 | 9.4389 | 2860 | 0.7893 | 0.3836 | 0.7893 | 0.8884 |
| 0.0563 | 9.4455 | 2862 | 0.7928 | 0.3836 | 0.7928 | 0.8904 |
| 0.0563 | 9.4521 | 2864 | 0.7963 | 0.3836 | 0.7963 | 0.8923 |
| 0.0563 | 9.4587 | 2866 | 0.7980 | 0.3836 | 0.7980 | 0.8933 |
| 0.0563 | 9.4653 | 2868 | 0.8007 | 0.3836 | 0.8007 | 0.8948 |
| 0.0563 | 9.4719 | 2870 | 0.8044 | 0.3836 | 0.8044 | 0.8969 |
| 0.0563 | 9.4785 | 2872 | 0.8058 | 0.3836 | 0.8058 | 0.8977 |
| 0.0563 | 9.4851 | 2874 | 0.8047 | 0.3836 | 0.8047 | 0.8970 |
| 0.0563 | 9.4917 | 2876 | 0.8027 | 0.3836 | 0.8027 | 0.8959 |
| 0.0563 | 9.4983 | 2878 | 0.7984 | 0.3836 | 0.7984 | 0.8935 |
| 0.0563 | 9.5050 | 2880 | 0.7946 | 0.3836 | 0.7946 | 0.8914 |
| 0.0563 | 9.5116 | 2882 | 0.7917 | 0.3836 | 0.7917 | 0.8898 |
| 0.0563 | 9.5182 | 2884 | 0.7880 | 0.3514 | 0.7880 | 0.8877 |
| 0.0563 | 9.5248 | 2886 | 0.7859 | 0.3514 | 0.7859 | 0.8865 |
| 0.0563 | 9.5314 | 2888 | 0.7842 | 0.3514 | 0.7842 | 0.8855 |
| 0.0563 | 9.5380 | 2890 | 0.7814 | 0.3514 | 0.7814 | 0.8840 |
| 0.0563 | 9.5446 | 2892 | 0.7797 | 0.3514 | 0.7797 | 0.8830 |
| 0.0563 | 9.5512 | 2894 | 0.7801 | 0.3514 | 0.7801 | 0.8832 |
| 0.0563 | 9.5578 | 2896 | 0.7820 | 0.3514 | 0.7820 | 0.8843 |
| 0.0563 | 9.5644 | 2898 | 0.7849 | 0.3836 | 0.7849 | 0.8860 |
| 0.0563 | 9.5710 | 2900 | 0.7874 | 0.3836 | 0.7874 | 0.8874 |
| 0.0563 | 9.5776 | 2902 | 0.7895 | 0.3836 | 0.7895 | 0.8885 |
| 0.0563 | 9.5842 | 2904 | 0.7908 | 0.3836 | 0.7908 | 0.8893 |
| 0.0563 | 9.5908 | 2906 | 0.7914 | 0.3836 | 0.7914 | 0.8896 |
| 0.0563 | 9.5974 | 2908 | 0.7915 | 0.3836 | 0.7915 | 0.8897 |
| 0.0563 | 9.6040 | 2910 | 0.7908 | 0.3836 | 0.7908 | 0.8893 |
| 0.0563 | 9.6106 | 2912 | 0.7906 | 0.3836 | 0.7906 | 0.8891 |
| 0.0563 | 9.6172 | 2914 | 0.7894 | 0.3836 | 0.7894 | 0.8885 |
| 0.0563 | 9.6238 | 2916 | 0.7896 | 0.3836 | 0.7896 | 0.8886 |
| 0.0563 | 9.6304 | 2918 | 0.7906 | 0.3836 | 0.7906 | 0.8891 |
| 0.0563 | 9.6370 | 2920 | 0.7908 | 0.3836 | 0.7908 | 0.8893 |
| 0.0563 | 9.6436 | 2922 | 0.7913 | 0.3836 | 0.7913 | 0.8896 |
| 0.0563 | 9.6502 | 2924 | 0.7921 | 0.3836 | 0.7921 | 0.8900 |
| 0.0563 | 9.6568 | 2926 | 0.7927 | 0.3836 | 0.7927 | 0.8904 |
| 0.0563 | 9.6634 | 2928 | 0.7940 | 0.3836 | 0.7940 | 0.8911 |
| 0.0563 | 9.6700 | 2930 | 0.7954 | 0.3836 | 0.7954 | 0.8918 |
| 0.0563 | 9.6766 | 2932 | 0.7970 | 0.3836 | 0.7970 | 0.8928 |
| 0.0563 | 9.6832 | 2934 | 0.7965 | 0.3836 | 0.7965 | 0.8925 |
| 0.0563 | 9.6898 | 2936 | 0.7953 | 0.3836 | 0.7953 | 0.8918 |
| 0.0563 | 9.6964 | 2938 | 0.7937 | 0.3836 | 0.7937 | 0.8909 |
| 0.0563 | 9.7030 | 2940 | 0.7914 | 0.3836 | 0.7914 | 0.8896 |
| 0.0563 | 9.7096 | 2942 | 0.7887 | 0.3836 | 0.7887 | 0.8881 |
| 0.0563 | 9.7162 | 2944 | 0.7856 | 0.3836 | 0.7856 | 0.8863 |
| 0.0563 | 9.7228 | 2946 | 0.7840 | 0.3836 | 0.7840 | 0.8854 |
| 0.0563 | 9.7294 | 2948 | 0.7839 | 0.3836 | 0.7839 | 0.8854 |
| 0.0563 | 9.7360 | 2950 | 0.7849 | 0.3836 | 0.7849 | 0.8860 |
| 0.0563 | 9.7426 | 2952 | 0.7859 | 0.3836 | 0.7859 | 0.8865 |
| 0.0563 | 9.7492 | 2954 | 0.7867 | 0.3836 | 0.7867 | 0.8870 |
| 0.0563 | 9.7558 | 2956 | 0.7874 | 0.3836 | 0.7874 | 0.8873 |
| 0.0563 | 9.7624 | 2958 | 0.7880 | 0.3836 | 0.7880 | 0.8877 |
| 0.0563 | 9.7690 | 2960 | 0.7883 | 0.3836 | 0.7883 | 0.8879 |
| 0.0563 | 9.7756 | 2962 | 0.7879 | 0.3836 | 0.7879 | 0.8876 |
| 0.0563 | 9.7822 | 2964 | 0.7886 | 0.3836 | 0.7886 | 0.8880 |
| 0.0563 | 9.7888 | 2966 | 0.7888 | 0.3836 | 0.7888 | 0.8881 |
| 0.0563 | 9.7954 | 2968 | 0.7884 | 0.3836 | 0.7884 | 0.8879 |
| 0.0563 | 9.8020 | 2970 | 0.7879 | 0.3836 | 0.7879 | 0.8876 |
| 0.0563 | 9.8086 | 2972 | 0.7881 | 0.3836 | 0.7881 | 0.8877 |
| 0.0563 | 9.8152 | 2974 | 0.7879 | 0.3836 | 0.7879 | 0.8876 |
| 0.0563 | 9.8218 | 2976 | 0.7875 | 0.3836 | 0.7875 | 0.8874 |
| 0.0563 | 9.8284 | 2978 | 0.7876 | 0.3836 | 0.7876 | 0.8875 |
| 0.0563 | 9.8350 | 2980 | 0.7880 | 0.3836 | 0.7880 | 0.8877 |
| 0.0563 | 9.8416 | 2982 | 0.7885 | 0.3836 | 0.7885 | 0.8880 |
| 0.0563 | 9.8482 | 2984 | 0.7893 | 0.3836 | 0.7893 | 0.8884 |
| 0.0563 | 9.8548 | 2986 | 0.7903 | 0.3836 | 0.7903 | 0.8890 |
| 0.0563 | 9.8614 | 2988 | 0.7908 | 0.3836 | 0.7908 | 0.8893 |
| 0.0563 | 9.8680 | 2990 | 0.7909 | 0.3836 | 0.7909 | 0.8893 |
| 0.0563 | 9.8746 | 2992 | 0.7914 | 0.3836 | 0.7914 | 0.8896 |
| 0.0563 | 9.8812 | 2994 | 0.7915 | 0.3836 | 0.7915 | 0.8897 |
| 0.0563 | 9.8878 | 2996 | 0.7913 | 0.3836 | 0.7913 | 0.8896 |
| 0.0563 | 9.8944 | 2998 | 0.7913 | 0.3836 | 0.7913 | 0.8896 |
| 0.0501 | 9.9010 | 3000 | 0.7912 | 0.3836 | 0.7912 | 0.8895 |
| 0.0501 | 9.9076 | 3002 | 0.7911 | 0.3836 | 0.7911 | 0.8894 |
| 0.0501 | 9.9142 | 3004 | 0.7911 | 0.3836 | 0.7911 | 0.8894 |
| 0.0501 | 9.9208 | 3006 | 0.7913 | 0.3836 | 0.7913 | 0.8896 |
| 0.0501 | 9.9274 | 3008 | 0.7917 | 0.3836 | 0.7917 | 0.8898 |
| 0.0501 | 9.9340 | 3010 | 0.7918 | 0.3836 | 0.7918 | 0.8899 |
| 0.0501 | 9.9406 | 3012 | 0.7922 | 0.3836 | 0.7922 | 0.8900 |
| 0.0501 | 9.9472 | 3014 | 0.7924 | 0.3836 | 0.7924 | 0.8902 |
| 0.0501 | 9.9538 | 3016 | 0.7926 | 0.3836 | 0.7926 | 0.8903 |
| 0.0501 | 9.9604 | 3018 | 0.7927 | 0.3836 | 0.7927 | 0.8903 |
| 0.0501 | 9.9670 | 3020 | 0.7927 | 0.3836 | 0.7927 | 0.8903 |
| 0.0501 | 9.9736 | 3022 | 0.7927 | 0.3836 | 0.7927 | 0.8903 |
| 0.0501 | 9.9802 | 3024 | 0.7927 | 0.3836 | 0.7927 | 0.8903 |
| 0.0501 | 9.9868 | 3026 | 0.7927 | 0.3836 | 0.7927 | 0.8904 |
| 0.0501 | 9.9934 | 3028 | 0.7928 | 0.3836 | 0.7928 | 0.8904 |
| 0.0501 | 10.0 | 3030 | 0.7928 | 0.3836 | 0.7928 | 0.8904 |
### Framework versions
- Transformers 4.44.2
- Pytorch 2.4.0+cu118
- Datasets 2.21.0
- Tokenizers 0.19.1
|
DaryaGudkova/bert-finetuned-squad
|
DaryaGudkova
| 2024-11-16T17:39:39Z
| 105
| 0
|
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"bert",
"question-answering",
"generated_from_trainer",
"base_model:google-bert/bert-base-cased",
"base_model:finetune:google-bert/bert-base-cased",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
question-answering
| 2024-11-16T15:33:50Z
|
---
library_name: transformers
license: apache-2.0
base_model: bert-base-cased
tags:
- generated_from_trainer
model-index:
- name: bert-finetuned-squad
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-finetuned-squad
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 3
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.46.2
- Pytorch 2.5.1+cu121
- Datasets 3.1.0
- Tokenizers 0.20.3
|
shahadalll/videomae-base-finetuned-ucfcrimepre
|
shahadalll
| 2024-11-16T17:35:41Z
| 61
| 0
|
transformers
|
[
"transformers",
"safetensors",
"videomae",
"video-classification",
"generated_from_trainer",
"base_model:MCG-NJU/videomae-base",
"base_model:finetune:MCG-NJU/videomae-base",
"license:cc-by-nc-4.0",
"endpoints_compatible",
"region:us"
] |
video-classification
| 2024-11-16T16:12:28Z
|
---
library_name: transformers
license: cc-by-nc-4.0
base_model: MCG-NJU/videomae-base
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: videomae-base-finetuned-ucfcrimepre
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# videomae-base-finetuned-ucfcrimepre
This model is a fine-tuned version of [MCG-NJU/videomae-base](https://huggingface.co/MCG-NJU/videomae-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.6006
- Accuracy: 0.5685
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- training_steps: 328
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:------:|:----:|:---------------:|:--------:|
| 1.9467 | 0.2530 | 83 | 2.1566 | 0.2245 |
| 1.5504 | 1.2530 | 166 | 2.1364 | 0.2843 |
| 1.7049 | 2.2530 | 249 | 2.3915 | 0.2868 |
| 1.1891 | 3.2409 | 328 | 2.0801 | 0.3292 |
### Framework versions
- Transformers 4.47.0.dev0
- Pytorch 2.5.1+cu124
- Datasets 3.1.0
- Tokenizers 0.20.3
|
gokceuludogan/berturk_tr_hateprint_cat_w0.1
|
gokceuludogan
| 2024-11-16T17:33:54Z
| 105
| 0
|
transformers
|
[
"transformers",
"safetensors",
"bert",
"text-classification",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2024-11-16T17:32:43Z
|
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
gokceuludogan/berturk_tr_hateprint_cat_w0.1_b128
|
gokceuludogan
| 2024-11-16T17:32:41Z
| 108
| 0
|
transformers
|
[
"transformers",
"safetensors",
"bert",
"text-classification",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2024-11-16T17:29:56Z
|
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
shahadalll/videomae-base-finetuned-ucf-crimevbinary-balanced-v2
|
shahadalll
| 2024-11-16T17:29:48Z
| 62
| 0
|
transformers
|
[
"transformers",
"safetensors",
"videomae",
"video-classification",
"generated_from_trainer",
"base_model:MCG-NJU/videomae-base",
"base_model:finetune:MCG-NJU/videomae-base",
"license:cc-by-nc-4.0",
"endpoints_compatible",
"region:us"
] |
video-classification
| 2024-11-16T17:24:17Z
|
---
library_name: transformers
license: cc-by-nc-4.0
base_model: MCG-NJU/videomae-base
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: videomae-base-finetuned-ucf-crimevbinary-balanced-v2
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# videomae-base-finetuned-ucf-crimevbinary-balanced-v2
This model is a fine-tuned version of [MCG-NJU/videomae-base](https://huggingface.co/MCG-NJU/videomae-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4152
- Accuracy: 0.8611
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 3e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 30
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.7145 | 1.0 | 29 | 0.6655 | 0.6667 |
| 0.6129 | 2.0 | 58 | 0.5762 | 0.75 |
| 0.4643 | 3.0 | 87 | 0.4933 | 0.8333 |
| 0.4492 | 4.0 | 116 | 0.4950 | 0.8056 |
| 0.4217 | 5.0 | 145 | 0.4098 | 0.8333 |
| 0.3298 | 6.0 | 174 | 0.6611 | 0.75 |
### Framework versions
- Transformers 4.47.0.dev0
- Pytorch 2.5.1+cu124
- Datasets 3.1.0
- Tokenizers 0.20.3
|
nbeerbower/Mistral-Nemo-Prism-12B-v2
|
nbeerbower
| 2024-11-16T17:29:48Z
| 26
| 3
|
transformers
|
[
"transformers",
"safetensors",
"mistral",
"text-generation",
"conversational",
"dataset:nbeerbower/Arkhaios-DPO",
"dataset:nbeerbower/Purpura-DPO",
"base_model:nbeerbower/Mahou-1.5-mistral-nemo-12B-lorablated",
"base_model:finetune:nbeerbower/Mahou-1.5-mistral-nemo-12B-lorablated",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2024-11-12T20:10:54Z
|
---
library_name: transformers
license: apache-2.0
base_model:
- nbeerbower/Mahou-1.5-mistral-nemo-12B-lorablated
datasets:
- nbeerbower/Arkhaios-DPO
- nbeerbower/Purpura-DPO
---

> 🧪 **Just Another Model Experiment**
>
> This is one of many experimental iterations I'm sharing publicly while I mess around with training parameters and ideas. It's not a "real" release - just me being transparent about my learning process. Feel free to look under the hood, but don't expect anything production-ready!
# Mistral-Nemo-Prism-12B
[Mahou-1.5-mistral-nemo-12B-lorablated](https://huggingface.co/nbeerbower/Mahou-1.5-mistral-nemo-12B-lorablated) finetuned on [Arkhaios-DPO](https://huggingface.co/datasets/nbeerbower/Arkhaios-DPO) and [Purpura-DPO](https://huggingface.co/datasets/nbeerbower/Purpura-DPO).
The goal was to reduce archaic language and purple prose in a completely uncensored model.
### Method
[ORPO tuned](https://mlabonne.github.io/blog/posts/2024-04-19_Fine_tune_Llama_3_with_ORPO.html) with 2x A100 for 5 epochs.
The learning rate was lowered to 3e-6 for this version. In addition, a system prompt was introduced to further augment the prompts and encourage responses to match the data.
|
nbeerbower/Mistral-Nemo-Prism-12B-v5
|
nbeerbower
| 2024-11-16T17:28:08Z
| 20
| 0
|
transformers
|
[
"transformers",
"safetensors",
"mistral",
"text-generation",
"conversational",
"dataset:nbeerbower/Arkhaios-DPO",
"dataset:nbeerbower/Purpura-DPO",
"base_model:nbeerbower/Mahou-1.5-mistral-nemo-12B-lorablated",
"base_model:finetune:nbeerbower/Mahou-1.5-mistral-nemo-12B-lorablated",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2024-11-13T05:23:03Z
|
---
library_name: transformers
license: apache-2.0
base_model:
- nbeerbower/Mahou-1.5-mistral-nemo-12B-lorablated
datasets:
- nbeerbower/Arkhaios-DPO
- nbeerbower/Purpura-DPO
---

> 🧪 **Just Another Model Experiment**
>
> This is one of many experimental iterations I'm sharing publicly while I mess around with training parameters and ideas. It's not a "real" release - just me being transparent about my learning process. Feel free to look under the hood, but don't expect anything production-ready!
# Mistral-Nemo-Prism-12B-v5
[Mahou-1.5-mistral-nemo-12B-lorablated](https://huggingface.co/nbeerbower/Mahou-1.5-mistral-nemo-12B-lorablated) finetuned on [Arkhaios-DPO](https://huggingface.co/datasets/nbeerbower/Arkhaios-DPO) and [Purpura-DPO](https://huggingface.co/datasets/nbeerbower/Purpura-DPO).
The goal was to reduce archaic language and purple prose in a completely uncensored model.
### Method
[ORPO tuned](https://mlabonne.github.io/blog/posts/2024-04-19_Fine_tune_Llama_3_with_ORPO.html) with 8x A40 for 10 epochs.
For this version, beta was increased to 0.5 and learning rate was increased to 8e-6 (the original in v1).
|
nbeerbower/Mistral-Nemo-Prism-12B-v7
|
nbeerbower
| 2024-11-16T17:27:31Z
| 45
| 6
|
transformers
|
[
"transformers",
"safetensors",
"mistral",
"text-generation",
"conversational",
"dataset:nbeerbower/Arkhaios-DPO",
"dataset:nbeerbower/Purpura-DPO",
"base_model:nbeerbower/Mahou-1.5-mistral-nemo-12B-lorablated",
"base_model:finetune:nbeerbower/Mahou-1.5-mistral-nemo-12B-lorablated",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2024-11-13T17:03:27Z
|
---
library_name: transformers
license: apache-2.0
base_model:
- nbeerbower/Mahou-1.5-mistral-nemo-12B-lorablated
datasets:
- nbeerbower/Arkhaios-DPO
- nbeerbower/Purpura-DPO
---

> 🧪 **Just Another Model Experiment**
>
> This is one of many experimental iterations I'm sharing publicly while I mess around with training parameters and ideas. It's not a "real" release - just me being transparent about my learning process. Feel free to look under the hood, but don't expect anything production-ready!
# Mistral-Nemo-Prism-12B-v7
[Mahou-1.5-mistral-nemo-12B-lorablated](https://huggingface.co/nbeerbower/Mahou-1.5-mistral-nemo-12B-lorablated) finetuned on [Arkhaios-DPO](https://huggingface.co/datasets/nbeerbower/Arkhaios-DPO) and [Purpura-DPO](https://huggingface.co/datasets/nbeerbower/Purpura-DPO).
The goal was to reduce archaic language and purple prose in a completely uncensored model.
### Method
[ORPO tuned](https://mlabonne.github.io/blog/posts/2024-04-19_Fine_tune_Llama_3_with_ORPO.html) with 8x A40 for 10 epochs.
For this version, beta was increased to 2.
**In conclusion, LoRA does not seem to be able to completely remove some of the language issues deeply embedded in the model.**
|
Keltezaa/Fire_Spirit
|
Keltezaa
| 2024-11-16T17:22:01Z
| 21
| 2
|
diffusers
|
[
"diffusers",
"text-to-image",
"lora",
"template:diffusion-lora",
"base_model:black-forest-labs/FLUX.1-dev",
"base_model:adapter:black-forest-labs/FLUX.1-dev",
"license:mit",
"region:us"
] |
text-to-image
| 2024-11-16T17:19:03Z
|
---
tags:
- text-to-image
- lora
- diffusers
- template:diffusion-lora
widget:
- text: >-
bailing_fire, A girl **** entirely of flames stands, her body engulfed in
fire. Her form flickers and sways, semi-transparent and composed entirely of
fire elements. The flames seem to rise naturally from her, as though she is
both part of the fire and its source. The surroundings are blurred, keeping
the focus entirely on her fiery presence. The flames ripple and pulse,
wrapping around her form, creating an intense yet surreal
atmosphere.freckles,masterpiece,best quality,light particle,depth of
field,fantasy,blunt bangs,a cute girl,Porta 160 color,shot on ARRI ALEXA
65,bokeh,sharp focus on subject,highest details,photorealistic,high
background details,high face details,8k
output:
url: images/example_3pdeeyc3b.png
base_model: black-forest-labs/FLUX.1-dev
instance_prompt: bailing_fire
license: mit
---
# Fire Spirit
<Gallery />
## Trigger words
You should use `bailing_fire` to trigger the image generation.
## Download model
Weights for this model are available in Safetensors format.
[Download](/Keltezaa/Fire_Spirit/tree/main) them in the Files & versions tab.
|
Jeethu/Llama-3.2-3B-Instruct-8-Elite-NPU
|
Jeethu
| 2024-11-16T17:19:58Z
| 5
| 0
| null |
[
"onnx",
"llama",
"license:llama3.2",
"region:us"
] | null | 2024-11-16T16:46:26Z
|
---
license: llama3.2
---
ONNX version of [Llama 3.2 Instruct](https://huggingface.co/meta-llama/Llama-3.2-3B) model, quantized for inference on the [Snapdragon 8 Elite](https://www.qualcomm.com/products/mobile/snapdragon/smartphones/snapdragon-8-series-mobile-platforms/snapdragon-8-elite-mobile-platform) NPU.
|
mradermacher/internlm2_5-1_8b-chat-GGUF
|
mradermacher
| 2024-11-16T17:19:17Z
| 39
| 0
|
transformers
|
[
"transformers",
"gguf",
"en",
"base_model:internlm/internlm2_5-1_8b-chat",
"base_model:quantized:internlm/internlm2_5-1_8b-chat",
"license:other",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2024-11-16T17:10:08Z
|
---
base_model: internlm/internlm2_5-1_8b-chat
language:
- en
library_name: transformers
license: other
quantized_by: mradermacher
---
## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
<!-- ### tags: -->
static quants of https://huggingface.co/internlm/internlm2_5-1_8b-chat
<!-- provided-files -->
weighted/imatrix quants are available at https://huggingface.co/mradermacher/internlm2_5-1_8b-chat-i1-GGUF
## Usage
If you are unsure how to use GGUF files, refer to one of [TheBloke's
READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for
more details, including on how to concatenate multi-part files.
## Provided Quants
(sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants)
| Link | Type | Size/GB | Notes |
|:-----|:-----|--------:|:------|
| [GGUF](https://huggingface.co/mradermacher/internlm2_5-1_8b-chat-GGUF/resolve/main/internlm2_5-1_8b-chat.Q2_K.gguf) | Q2_K | 0.9 | |
| [GGUF](https://huggingface.co/mradermacher/internlm2_5-1_8b-chat-GGUF/resolve/main/internlm2_5-1_8b-chat.Q3_K_S.gguf) | Q3_K_S | 1.0 | |
| [GGUF](https://huggingface.co/mradermacher/internlm2_5-1_8b-chat-GGUF/resolve/main/internlm2_5-1_8b-chat.Q3_K_M.gguf) | Q3_K_M | 1.1 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/internlm2_5-1_8b-chat-GGUF/resolve/main/internlm2_5-1_8b-chat.Q3_K_L.gguf) | Q3_K_L | 1.1 | |
| [GGUF](https://huggingface.co/mradermacher/internlm2_5-1_8b-chat-GGUF/resolve/main/internlm2_5-1_8b-chat.IQ4_XS.gguf) | IQ4_XS | 1.2 | |
| [GGUF](https://huggingface.co/mradermacher/internlm2_5-1_8b-chat-GGUF/resolve/main/internlm2_5-1_8b-chat.Q4_0_4_4.gguf) | Q4_0_4_4 | 1.2 | fast on arm, low quality |
| [GGUF](https://huggingface.co/mradermacher/internlm2_5-1_8b-chat-GGUF/resolve/main/internlm2_5-1_8b-chat.Q4_K_S.gguf) | Q4_K_S | 1.2 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/internlm2_5-1_8b-chat-GGUF/resolve/main/internlm2_5-1_8b-chat.Q4_K_M.gguf) | Q4_K_M | 1.3 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/internlm2_5-1_8b-chat-GGUF/resolve/main/internlm2_5-1_8b-chat.Q5_K_S.gguf) | Q5_K_S | 1.4 | |
| [GGUF](https://huggingface.co/mradermacher/internlm2_5-1_8b-chat-GGUF/resolve/main/internlm2_5-1_8b-chat.Q5_K_M.gguf) | Q5_K_M | 1.5 | |
| [GGUF](https://huggingface.co/mradermacher/internlm2_5-1_8b-chat-GGUF/resolve/main/internlm2_5-1_8b-chat.Q6_K.gguf) | Q6_K | 1.7 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/internlm2_5-1_8b-chat-GGUF/resolve/main/internlm2_5-1_8b-chat.Q8_0.gguf) | Q8_0 | 2.1 | fast, best quality |
| [GGUF](https://huggingface.co/mradermacher/internlm2_5-1_8b-chat-GGUF/resolve/main/internlm2_5-1_8b-chat.f16.gguf) | f16 | 3.9 | 16 bpw, overkill |
Here is a handy graph by ikawrakow comparing some lower-quality quant
types (lower is better):

And here are Artefact2's thoughts on the matter:
https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9
## FAQ / Model Request
See https://huggingface.co/mradermacher/model_requests for some answers to
questions you might have and/or if you want some other model quantized.
## Thanks
I thank my company, [nethype GmbH](https://www.nethype.de/), for letting
me use its servers and providing upgrades to my workstation to enable
this work in my free time.
<!-- end -->
|
sanikadamn/Phi-3.5-mini-instruct-int4
|
sanikadamn
| 2024-11-16T17:16:19Z
| 128
| 0
|
transformers
|
[
"transformers",
"safetensors",
"phi3",
"text-generation",
"custom_code",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"4-bit",
"bitsandbytes",
"region:us"
] |
text-generation
| 2024-11-16T17:10:29Z
|
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
MayBashendy/Arabic_FineTuningAraBERT_AugV4_k20_task2_organization_fold0
|
MayBashendy
| 2024-11-16T17:15:26Z
| 163
| 0
|
transformers
|
[
"transformers",
"safetensors",
"bert",
"text-classification",
"generated_from_trainer",
"base_model:aubmindlab/bert-base-arabertv02",
"base_model:finetune:aubmindlab/bert-base-arabertv02",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2024-11-16T16:59:45Z
|
---
library_name: transformers
base_model: aubmindlab/bert-base-arabertv02
tags:
- generated_from_trainer
model-index:
- name: Arabic_FineTuningAraBERT_AugV4_k20_task2_organization_fold0
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# Arabic_FineTuningAraBERT_AugV4_k20_task2_organization_fold0
This model is a fine-tuned version of [aubmindlab/bert-base-arabertv02](https://huggingface.co/aubmindlab/bert-base-arabertv02) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5473
- Qwk: 0.4779
- Mse: 0.5473
- Rmse: 0.7398
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Qwk | Mse | Rmse |
|:-------------:|:------:|:----:|:---------------:|:-------:|:------:|:------:|
| No log | 0.0090 | 2 | 3.2399 | 0.0 | 3.2399 | 1.8000 |
| No log | 0.0179 | 4 | 1.7305 | -0.1255 | 1.7305 | 1.3155 |
| No log | 0.0269 | 6 | 0.8266 | 0.0 | 0.8266 | 0.9092 |
| No log | 0.0359 | 8 | 0.6609 | 0.0916 | 0.6609 | 0.8130 |
| No log | 0.0448 | 10 | 0.7502 | -0.0825 | 0.7502 | 0.8661 |
| No log | 0.0538 | 12 | 1.0215 | 0.0679 | 1.0215 | 1.0107 |
| No log | 0.0628 | 14 | 0.9258 | 0.1000 | 0.9258 | 0.9622 |
| No log | 0.0717 | 16 | 0.7380 | 0.1765 | 0.7380 | 0.8590 |
| No log | 0.0807 | 18 | 0.7246 | 0.1962 | 0.7246 | 0.8513 |
| No log | 0.0897 | 20 | 0.8326 | 0.0679 | 0.8326 | 0.9125 |
| No log | 0.0987 | 22 | 1.2396 | 0.3000 | 1.2396 | 1.1134 |
| No log | 0.1076 | 24 | 1.2169 | 0.0418 | 1.2169 | 1.1031 |
| No log | 0.1166 | 26 | 1.0152 | 0.0 | 1.0152 | 1.0076 |
| No log | 0.1256 | 28 | 1.0313 | 0.0 | 1.0313 | 1.0155 |
| No log | 0.1345 | 30 | 0.9957 | 0.0 | 0.9957 | 0.9978 |
| No log | 0.1435 | 32 | 1.0289 | 0.0 | 1.0289 | 1.0143 |
| No log | 0.1525 | 34 | 0.9396 | 0.0 | 0.9396 | 0.9693 |
| No log | 0.1614 | 36 | 0.7920 | 0.0 | 0.7920 | 0.8900 |
| No log | 0.1704 | 38 | 0.7273 | -0.0418 | 0.7273 | 0.8528 |
| No log | 0.1794 | 40 | 0.6689 | -0.0418 | 0.6689 | 0.8179 |
| No log | 0.1883 | 42 | 0.7162 | 0.0 | 0.7162 | 0.8463 |
| No log | 0.1973 | 44 | 0.7742 | 0.0 | 0.7742 | 0.8799 |
| No log | 0.2063 | 46 | 0.7647 | 0.0 | 0.7647 | 0.8745 |
| No log | 0.2152 | 48 | 0.6055 | 0.0 | 0.6055 | 0.7781 |
| No log | 0.2242 | 50 | 0.5712 | 0.1398 | 0.5712 | 0.7558 |
| No log | 0.2332 | 52 | 0.4972 | 0.3576 | 0.4972 | 0.7051 |
| No log | 0.2422 | 54 | 0.7570 | 0.2913 | 0.7570 | 0.8701 |
| No log | 0.2511 | 56 | 0.7138 | 0.2913 | 0.7138 | 0.8449 |
| No log | 0.2601 | 58 | 0.4472 | 0.4177 | 0.4472 | 0.6688 |
| No log | 0.2691 | 60 | 0.3699 | 0.7322 | 0.3699 | 0.6082 |
| No log | 0.2780 | 62 | 0.4692 | 0.4515 | 0.4692 | 0.6850 |
| No log | 0.2870 | 64 | 0.6333 | 0.3345 | 0.6333 | 0.7958 |
| No log | 0.2960 | 66 | 0.4965 | 0.3731 | 0.4965 | 0.7046 |
| No log | 0.3049 | 68 | 0.5073 | 0.3731 | 0.5073 | 0.7123 |
| No log | 0.3139 | 70 | 0.5136 | 0.3731 | 0.5136 | 0.7167 |
| No log | 0.3229 | 72 | 0.6844 | 0.3277 | 0.6844 | 0.8273 |
| No log | 0.3318 | 74 | 0.5884 | 0.3731 | 0.5884 | 0.7671 |
| No log | 0.3408 | 76 | 0.5847 | 0.3731 | 0.5847 | 0.7647 |
| No log | 0.3498 | 78 | 0.5509 | 0.3731 | 0.5509 | 0.7422 |
| No log | 0.3587 | 80 | 0.6586 | 0.3277 | 0.6586 | 0.8115 |
| No log | 0.3677 | 82 | 0.9307 | 0.37 | 0.9307 | 0.9647 |
| No log | 0.3767 | 84 | 0.7523 | 0.3659 | 0.7523 | 0.8674 |
| No log | 0.3857 | 86 | 0.6552 | 0.2188 | 0.6552 | 0.8094 |
| No log | 0.3946 | 88 | 0.5668 | 0.3318 | 0.5668 | 0.7528 |
| No log | 0.4036 | 90 | 0.6186 | 0.3811 | 0.6186 | 0.7865 |
| No log | 0.4126 | 92 | 0.9072 | 0.3659 | 0.9072 | 0.9525 |
| No log | 0.4215 | 94 | 1.0602 | 0.3077 | 1.0602 | 1.0296 |
| No log | 0.4305 | 96 | 1.1256 | 0.2522 | 1.1256 | 1.0610 |
| No log | 0.4395 | 98 | 0.8926 | 0.3659 | 0.8926 | 0.9448 |
| No log | 0.4484 | 100 | 0.7371 | 0.2814 | 0.7371 | 0.8585 |
| No log | 0.4574 | 102 | 0.5997 | 0.2000 | 0.5997 | 0.7744 |
| No log | 0.4664 | 104 | 0.5523 | 0.2613 | 0.5523 | 0.7432 |
| No log | 0.4753 | 106 | 0.5538 | 0.2186 | 0.5538 | 0.7442 |
| No log | 0.4843 | 108 | 0.7288 | 0.4085 | 0.7288 | 0.8537 |
| No log | 0.4933 | 110 | 1.2491 | 0.2701 | 1.2491 | 1.1176 |
| No log | 0.5022 | 112 | 1.3507 | 0.2701 | 1.3507 | 1.1622 |
| No log | 0.5112 | 114 | 1.1010 | 0.2961 | 1.1010 | 1.0493 |
| No log | 0.5202 | 116 | 0.7125 | 0.4187 | 0.7125 | 0.8441 |
| No log | 0.5291 | 118 | 0.5363 | 0.5368 | 0.5363 | 0.7323 |
| No log | 0.5381 | 120 | 0.5458 | 0.5137 | 0.5458 | 0.7388 |
| No log | 0.5471 | 122 | 0.5466 | 0.3077 | 0.5466 | 0.7393 |
| No log | 0.5561 | 124 | 0.7664 | 0.3731 | 0.7664 | 0.8755 |
| No log | 0.5650 | 126 | 1.0379 | 0.2418 | 1.0379 | 1.0188 |
| No log | 0.5740 | 128 | 1.1554 | 0.2522 | 1.1554 | 1.0749 |
| No log | 0.5830 | 130 | 1.1370 | 0.2522 | 1.1370 | 1.0663 |
| No log | 0.5919 | 132 | 0.9426 | 0.3000 | 0.9426 | 0.9709 |
| No log | 0.6009 | 134 | 0.6965 | 0.4187 | 0.6965 | 0.8346 |
| No log | 0.6099 | 136 | 0.5781 | 0.5 | 0.5781 | 0.7603 |
| No log | 0.6188 | 138 | 0.6251 | 0.3877 | 0.6251 | 0.7906 |
| No log | 0.6278 | 140 | 0.8330 | 0.3803 | 0.8330 | 0.9127 |
| No log | 0.6368 | 142 | 0.9073 | 0.3833 | 0.9073 | 0.9525 |
| No log | 0.6457 | 144 | 0.8694 | 0.3833 | 0.8694 | 0.9324 |
| No log | 0.6547 | 146 | 0.9006 | 0.3860 | 0.9006 | 0.9490 |
| No log | 0.6637 | 148 | 0.8649 | 0.3833 | 0.8649 | 0.9300 |
| No log | 0.6726 | 150 | 0.5950 | 0.4187 | 0.5950 | 0.7714 |
| No log | 0.6816 | 152 | 0.4433 | 0.4976 | 0.4433 | 0.6658 |
| No log | 0.6906 | 154 | 0.4592 | 0.4407 | 0.4592 | 0.6776 |
| No log | 0.6996 | 156 | 0.5366 | 0.4283 | 0.5366 | 0.7326 |
| No log | 0.7085 | 158 | 0.5921 | 0.3877 | 0.5921 | 0.7695 |
| No log | 0.7175 | 160 | 0.6924 | 0.3512 | 0.6924 | 0.8321 |
| No log | 0.7265 | 162 | 0.8499 | 0.3558 | 0.8499 | 0.9219 |
| No log | 0.7354 | 164 | 1.0795 | 0.3883 | 1.0795 | 1.0390 |
| No log | 0.7444 | 166 | 1.5320 | 0.1672 | 1.5320 | 1.2378 |
| No log | 0.7534 | 168 | 1.5021 | 0.1672 | 1.5021 | 1.2256 |
| No log | 0.7623 | 170 | 1.0884 | 0.3145 | 1.0884 | 1.0433 |
| No log | 0.7713 | 172 | 0.7118 | 0.4187 | 0.7118 | 0.8437 |
| No log | 0.7803 | 174 | 0.5829 | 0.1992 | 0.5829 | 0.7635 |
| No log | 0.7892 | 176 | 0.5193 | 0.4772 | 0.5193 | 0.7206 |
| No log | 0.7982 | 178 | 0.5165 | 0.4772 | 0.5165 | 0.7187 |
| No log | 0.8072 | 180 | 0.5884 | 0.5 | 0.5884 | 0.7671 |
| No log | 0.8161 | 182 | 0.7238 | 0.3512 | 0.7238 | 0.8508 |
| No log | 0.8251 | 184 | 0.9304 | 0.3860 | 0.9304 | 0.9646 |
| No log | 0.8341 | 186 | 0.8312 | 0.3833 | 0.8312 | 0.9117 |
| No log | 0.8430 | 188 | 0.6869 | 0.3226 | 0.6869 | 0.8288 |
| No log | 0.8520 | 190 | 0.5205 | 0.5633 | 0.5205 | 0.7214 |
| No log | 0.8610 | 192 | 0.5236 | 0.5219 | 0.5236 | 0.7236 |
| No log | 0.8700 | 194 | 0.5145 | 0.5855 | 0.5145 | 0.7173 |
| No log | 0.8789 | 196 | 0.5655 | 0.6488 | 0.5655 | 0.7520 |
| No log | 0.8879 | 198 | 0.5393 | 0.6169 | 0.5393 | 0.7344 |
| No log | 0.8969 | 200 | 0.4957 | 0.6526 | 0.4957 | 0.7040 |
| No log | 0.9058 | 202 | 0.4968 | 0.6526 | 0.4968 | 0.7048 |
| No log | 0.9148 | 204 | 0.4957 | 0.6169 | 0.4957 | 0.7041 |
| No log | 0.9238 | 206 | 0.5288 | 0.5396 | 0.5288 | 0.7272 |
| No log | 0.9327 | 208 | 0.5396 | 0.5396 | 0.5396 | 0.7346 |
| No log | 0.9417 | 210 | 0.5465 | 0.6488 | 0.5465 | 0.7393 |
| No log | 0.9507 | 212 | 0.6328 | 0.6362 | 0.6328 | 0.7955 |
| No log | 0.9596 | 214 | 0.8537 | 0.3393 | 0.8537 | 0.9240 |
| No log | 0.9686 | 216 | 1.0188 | 0.3301 | 1.0188 | 1.0093 |
| No log | 0.9776 | 218 | 0.9599 | 0.3393 | 0.9599 | 0.9797 |
| No log | 0.9865 | 220 | 0.7481 | 0.3691 | 0.7481 | 0.8649 |
| No log | 0.9955 | 222 | 0.6497 | 0.4288 | 0.6497 | 0.8060 |
| No log | 1.0045 | 224 | 0.7162 | 0.3927 | 0.7162 | 0.8463 |
| No log | 1.0135 | 226 | 0.9734 | 0.2797 | 0.9734 | 0.9866 |
| No log | 1.0224 | 228 | 0.9680 | 0.3301 | 0.9680 | 0.9839 |
| No log | 1.0314 | 230 | 0.6795 | 0.4187 | 0.6795 | 0.8243 |
| No log | 1.0404 | 232 | 0.5038 | 0.7032 | 0.5038 | 0.7098 |
| No log | 1.0493 | 234 | 0.4909 | 0.5381 | 0.4909 | 0.7007 |
| No log | 1.0583 | 236 | 0.5463 | 0.5687 | 0.5463 | 0.7391 |
| No log | 1.0673 | 238 | 0.6114 | 0.6239 | 0.6114 | 0.7819 |
| No log | 1.0762 | 240 | 0.7824 | 0.4687 | 0.7824 | 0.8845 |
| No log | 1.0852 | 242 | 0.9263 | 0.4074 | 0.9263 | 0.9624 |
| No log | 1.0942 | 244 | 0.8138 | 0.3723 | 0.8138 | 0.9021 |
| No log | 1.1031 | 246 | 0.5703 | 0.5832 | 0.5703 | 0.7552 |
| No log | 1.1121 | 248 | 0.4906 | 0.5381 | 0.4906 | 0.7004 |
| No log | 1.1211 | 250 | 0.4910 | 0.5451 | 0.4910 | 0.7007 |
| No log | 1.1300 | 252 | 0.5373 | 0.5546 | 0.5373 | 0.7330 |
| No log | 1.1390 | 254 | 0.7570 | 0.3558 | 0.7570 | 0.8701 |
| No log | 1.1480 | 256 | 1.0601 | 0.3301 | 1.0601 | 1.0296 |
| No log | 1.1570 | 258 | 1.2218 | 0.3396 | 1.2218 | 1.1054 |
| No log | 1.1659 | 260 | 1.1173 | 0.3396 | 1.1173 | 1.0570 |
| No log | 1.1749 | 262 | 0.8659 | 0.3246 | 0.8659 | 0.9306 |
| No log | 1.1839 | 264 | 0.6546 | 0.5625 | 0.6546 | 0.8091 |
| No log | 1.1928 | 266 | 0.5641 | 0.6232 | 0.5641 | 0.7510 |
| No log | 1.2018 | 268 | 0.5501 | 0.6232 | 0.5501 | 0.7417 |
| No log | 1.2108 | 270 | 0.5698 | 0.6026 | 0.5698 | 0.7549 |
| No log | 1.2197 | 272 | 0.7243 | 0.3927 | 0.7243 | 0.8511 |
| No log | 1.2287 | 274 | 0.9188 | 0.3883 | 0.9188 | 0.9585 |
| No log | 1.2377 | 276 | 1.0334 | 0.3883 | 1.0334 | 1.0166 |
| No log | 1.2466 | 278 | 0.8855 | 0.3860 | 0.8855 | 0.9410 |
| No log | 1.2556 | 280 | 0.6539 | 0.4503 | 0.6539 | 0.8087 |
| No log | 1.2646 | 282 | 0.4988 | 0.4407 | 0.4988 | 0.7063 |
| No log | 1.2735 | 284 | 0.4351 | 0.4253 | 0.4351 | 0.6596 |
| No log | 1.2825 | 286 | 0.4443 | 0.4371 | 0.4443 | 0.6665 |
| No log | 1.2915 | 288 | 0.4471 | 0.4857 | 0.4471 | 0.6687 |
| No log | 1.3004 | 290 | 0.4279 | 0.6351 | 0.4279 | 0.6542 |
| No log | 1.3094 | 292 | 0.4785 | 0.6182 | 0.4785 | 0.6917 |
| No log | 1.3184 | 294 | 0.6405 | 0.5619 | 0.6405 | 0.8003 |
| No log | 1.3274 | 296 | 0.6686 | 0.5556 | 0.6686 | 0.8177 |
| No log | 1.3363 | 298 | 0.5664 | 0.5737 | 0.5664 | 0.7526 |
| No log | 1.3453 | 300 | 0.4948 | 0.6578 | 0.4948 | 0.7034 |
| No log | 1.3543 | 302 | 0.4670 | 0.6510 | 0.4670 | 0.6833 |
| No log | 1.3632 | 304 | 0.4456 | 0.6510 | 0.4456 | 0.6675 |
| No log | 1.3722 | 306 | 0.4106 | 0.6866 | 0.4106 | 0.6408 |
| No log | 1.3812 | 308 | 0.4761 | 0.6719 | 0.4761 | 0.6900 |
| No log | 1.3901 | 310 | 0.6444 | 0.5205 | 0.6444 | 0.8028 |
| No log | 1.3991 | 312 | 0.6977 | 0.4209 | 0.6977 | 0.8353 |
| No log | 1.4081 | 314 | 0.5886 | 0.5277 | 0.5886 | 0.7672 |
| No log | 1.4170 | 316 | 0.4235 | 0.6473 | 0.4235 | 0.6508 |
| No log | 1.4260 | 318 | 0.4102 | 0.6239 | 0.4102 | 0.6405 |
| No log | 1.4350 | 320 | 0.4379 | 0.6473 | 0.4379 | 0.6617 |
| No log | 1.4439 | 322 | 0.5573 | 0.5679 | 0.5573 | 0.7466 |
| No log | 1.4529 | 324 | 0.7018 | 0.6111 | 0.7018 | 0.8377 |
| No log | 1.4619 | 326 | 0.8251 | 0.5203 | 0.8251 | 0.9084 |
| No log | 1.4709 | 328 | 0.7569 | 0.6111 | 0.7569 | 0.8700 |
| No log | 1.4798 | 330 | 0.5958 | 0.5939 | 0.5958 | 0.7719 |
| No log | 1.4888 | 332 | 0.4499 | 0.6473 | 0.4499 | 0.6707 |
| No log | 1.4978 | 334 | 0.4002 | 0.6526 | 0.4002 | 0.6326 |
| No log | 1.5067 | 336 | 0.4253 | 0.6546 | 0.4253 | 0.6521 |
| No log | 1.5157 | 338 | 0.5077 | 0.6866 | 0.5077 | 0.7125 |
| No log | 1.5247 | 340 | 0.5302 | 0.6866 | 0.5302 | 0.7282 |
| No log | 1.5336 | 342 | 0.4514 | 0.6546 | 0.4514 | 0.6719 |
| No log | 1.5426 | 344 | 0.4317 | 0.6546 | 0.4317 | 0.6570 |
| No log | 1.5516 | 346 | 0.4569 | 0.6546 | 0.4569 | 0.6759 |
| No log | 1.5605 | 348 | 0.5004 | 0.6546 | 0.5004 | 0.7074 |
| No log | 1.5695 | 350 | 0.4706 | 0.6602 | 0.4706 | 0.6860 |
| No log | 1.5785 | 352 | 0.4872 | 0.6526 | 0.4872 | 0.6980 |
| No log | 1.5874 | 354 | 0.5206 | 0.6182 | 0.5206 | 0.7215 |
| No log | 1.5964 | 356 | 0.5202 | 0.6182 | 0.5202 | 0.7212 |
| No log | 1.6054 | 358 | 0.4786 | 0.6526 | 0.4786 | 0.6918 |
| No log | 1.6143 | 360 | 0.4843 | 0.6526 | 0.4843 | 0.6959 |
| No log | 1.6233 | 362 | 0.4943 | 0.6526 | 0.4943 | 0.7031 |
| No log | 1.6323 | 364 | 0.5366 | 0.6083 | 0.5366 | 0.7325 |
| No log | 1.6413 | 366 | 0.5938 | 0.5619 | 0.5938 | 0.7706 |
| No log | 1.6502 | 368 | 0.5621 | 0.5939 | 0.5621 | 0.7497 |
| No log | 1.6592 | 370 | 0.5450 | 0.5939 | 0.5450 | 0.7382 |
| No log | 1.6682 | 372 | 0.5119 | 0.6358 | 0.5119 | 0.7155 |
| No log | 1.6771 | 374 | 0.5058 | 0.5939 | 0.5058 | 0.7112 |
| No log | 1.6861 | 376 | 0.4862 | 0.6686 | 0.4862 | 0.6973 |
| No log | 1.6951 | 378 | 0.4857 | 0.6818 | 0.4857 | 0.6969 |
| No log | 1.7040 | 380 | 0.4961 | 0.6686 | 0.4961 | 0.7043 |
| No log | 1.7130 | 382 | 0.4987 | 0.6686 | 0.4987 | 0.7062 |
| No log | 1.7220 | 384 | 0.5297 | 0.5939 | 0.5297 | 0.7278 |
| No log | 1.7309 | 386 | 0.6593 | 0.4648 | 0.6593 | 0.8120 |
| No log | 1.7399 | 388 | 0.7552 | 0.4893 | 0.7552 | 0.8690 |
| No log | 1.7489 | 390 | 0.6953 | 0.4924 | 0.6953 | 0.8338 |
| No log | 1.7578 | 392 | 0.5532 | 0.4706 | 0.5532 | 0.7438 |
| No log | 1.7668 | 394 | 0.4605 | 0.6769 | 0.4605 | 0.6786 |
| No log | 1.7758 | 396 | 0.4462 | 0.7115 | 0.4462 | 0.6680 |
| No log | 1.7848 | 398 | 0.4583 | 0.6818 | 0.4583 | 0.6770 |
| No log | 1.7937 | 400 | 0.4849 | 0.7115 | 0.4849 | 0.6963 |
| No log | 1.8027 | 402 | 0.5813 | 0.5939 | 0.5813 | 0.7624 |
| No log | 1.8117 | 404 | 0.7267 | 0.5 | 0.7267 | 0.8525 |
| No log | 1.8206 | 406 | 0.7875 | 0.5 | 0.7875 | 0.8874 |
| No log | 1.8296 | 408 | 0.6991 | 0.5939 | 0.6991 | 0.8361 |
| No log | 1.8386 | 410 | 0.5967 | 0.5737 | 0.5967 | 0.7725 |
| No log | 1.8475 | 412 | 0.5869 | 0.5602 | 0.5869 | 0.7661 |
| No log | 1.8565 | 414 | 0.5640 | 0.5602 | 0.5640 | 0.7510 |
| No log | 1.8655 | 416 | 0.5108 | 0.6526 | 0.5108 | 0.7147 |
| No log | 1.8744 | 418 | 0.5198 | 0.5767 | 0.5198 | 0.7209 |
| No log | 1.8834 | 420 | 0.5690 | 0.5714 | 0.5690 | 0.7543 |
| No log | 1.8924 | 422 | 0.6355 | 0.4195 | 0.6355 | 0.7972 |
| No log | 1.9013 | 424 | 0.6336 | 0.3903 | 0.6336 | 0.7960 |
| No log | 1.9103 | 426 | 0.6419 | 0.3903 | 0.6419 | 0.8012 |
| No log | 1.9193 | 428 | 0.6196 | 0.4648 | 0.6196 | 0.7871 |
| No log | 1.9283 | 430 | 0.6220 | 0.4779 | 0.6220 | 0.7887 |
| No log | 1.9372 | 432 | 0.6128 | 0.5679 | 0.6128 | 0.7828 |
| No log | 1.9462 | 434 | 0.5759 | 0.6182 | 0.5759 | 0.7589 |
| No log | 1.9552 | 436 | 0.5804 | 0.5607 | 0.5804 | 0.7618 |
| No log | 1.9641 | 438 | 0.5880 | 0.5607 | 0.5880 | 0.7668 |
| No log | 1.9731 | 440 | 0.6176 | 0.6083 | 0.6176 | 0.7859 |
| No log | 1.9821 | 442 | 0.7318 | 0.4034 | 0.7318 | 0.8555 |
| No log | 1.9910 | 444 | 0.7209 | 0.4034 | 0.7209 | 0.8491 |
| No log | 2.0 | 446 | 0.5820 | 0.4375 | 0.5820 | 0.7629 |
| No log | 2.0090 | 448 | 0.5175 | 0.6123 | 0.5175 | 0.7194 |
| No log | 2.0179 | 450 | 0.5182 | 0.5116 | 0.5182 | 0.7199 |
| No log | 2.0269 | 452 | 0.5541 | 0.5406 | 0.5541 | 0.7443 |
| No log | 2.0359 | 454 | 0.5735 | 0.4581 | 0.5735 | 0.7573 |
| No log | 2.0448 | 456 | 0.5582 | 0.4597 | 0.5582 | 0.7471 |
| No log | 2.0538 | 458 | 0.5794 | 0.4581 | 0.5794 | 0.7612 |
| No log | 2.0628 | 460 | 0.6147 | 0.4648 | 0.6147 | 0.7840 |
| No log | 2.0717 | 462 | 0.7221 | 0.4034 | 0.7221 | 0.8498 |
| No log | 2.0807 | 464 | 0.7164 | 0.4965 | 0.7164 | 0.8464 |
| No log | 2.0897 | 466 | 0.6454 | 0.5422 | 0.6454 | 0.8034 |
| No log | 2.0987 | 468 | 0.6132 | 0.5234 | 0.6132 | 0.7831 |
| No log | 2.1076 | 470 | 0.6446 | 0.5602 | 0.6446 | 0.8029 |
| No log | 2.1166 | 472 | 0.6429 | 0.5543 | 0.6429 | 0.8018 |
| No log | 2.1256 | 474 | 0.5964 | 0.5812 | 0.5964 | 0.7723 |
| No log | 2.1345 | 476 | 0.5999 | 0.5939 | 0.5999 | 0.7746 |
| No log | 2.1435 | 478 | 0.6585 | 0.4648 | 0.6585 | 0.8115 |
| No log | 2.1525 | 480 | 0.6908 | 0.4924 | 0.6908 | 0.8311 |
| No log | 2.1614 | 482 | 0.6252 | 0.4924 | 0.6252 | 0.7907 |
| No log | 2.1704 | 484 | 0.5049 | 0.6719 | 0.5049 | 0.7105 |
| No log | 2.1794 | 486 | 0.4625 | 0.6473 | 0.4625 | 0.6800 |
| No log | 2.1883 | 488 | 0.4869 | 0.6473 | 0.4869 | 0.6978 |
| No log | 2.1973 | 490 | 0.5378 | 0.6358 | 0.5378 | 0.7333 |
| No log | 2.2063 | 492 | 0.6213 | 0.5939 | 0.6213 | 0.7882 |
| No log | 2.2152 | 494 | 0.6849 | 0.5939 | 0.6849 | 0.8276 |
| No log | 2.2242 | 496 | 0.6516 | 0.5939 | 0.6516 | 0.8072 |
| No log | 2.2332 | 498 | 0.5790 | 0.5679 | 0.5790 | 0.7609 |
| 0.4638 | 2.2422 | 500 | 0.5279 | 0.5895 | 0.5279 | 0.7265 |
| 0.4638 | 2.2511 | 502 | 0.5327 | 0.5607 | 0.5327 | 0.7299 |
| 0.4638 | 2.2601 | 504 | 0.5714 | 0.5667 | 0.5714 | 0.7559 |
| 0.4638 | 2.2691 | 506 | 0.5743 | 0.5602 | 0.5743 | 0.7578 |
| 0.4638 | 2.2780 | 508 | 0.5933 | 0.5484 | 0.5933 | 0.7703 |
| 0.4638 | 2.2870 | 510 | 0.6875 | 0.5939 | 0.6875 | 0.8292 |
| 0.4638 | 2.2960 | 512 | 0.7438 | 0.6111 | 0.7438 | 0.8625 |
| 0.4638 | 2.3049 | 514 | 0.6584 | 0.6203 | 0.6584 | 0.8114 |
| 0.4638 | 2.3139 | 516 | 0.5683 | 0.6026 | 0.5683 | 0.7539 |
| 0.4638 | 2.3229 | 518 | 0.4693 | 0.6602 | 0.4693 | 0.6851 |
| 0.4638 | 2.3318 | 520 | 0.4417 | 0.6292 | 0.4417 | 0.6646 |
| 0.4638 | 2.3408 | 522 | 0.4293 | 0.6602 | 0.4293 | 0.6552 |
| 0.4638 | 2.3498 | 524 | 0.4389 | 0.6769 | 0.4389 | 0.6625 |
| 0.4638 | 2.3587 | 526 | 0.4498 | 0.6769 | 0.4498 | 0.6706 |
| 0.4638 | 2.3677 | 528 | 0.4730 | 0.6026 | 0.4730 | 0.6878 |
| 0.4638 | 2.3767 | 530 | 0.4653 | 0.6638 | 0.4653 | 0.6821 |
| 0.4638 | 2.3857 | 532 | 0.4648 | 0.6358 | 0.4648 | 0.6818 |
| 0.4638 | 2.3946 | 534 | 0.4686 | 0.6358 | 0.4686 | 0.6845 |
| 0.4638 | 2.4036 | 536 | 0.4975 | 0.5939 | 0.4975 | 0.7054 |
| 0.4638 | 2.4126 | 538 | 0.5377 | 0.5939 | 0.5377 | 0.7333 |
| 0.4638 | 2.4215 | 540 | 0.5405 | 0.5422 | 0.5405 | 0.7352 |
| 0.4638 | 2.4305 | 542 | 0.5368 | 0.6410 | 0.5368 | 0.7326 |
| 0.4638 | 2.4395 | 544 | 0.5209 | 0.6239 | 0.5209 | 0.7218 |
| 0.4638 | 2.4484 | 546 | 0.5100 | 0.6139 | 0.5100 | 0.7141 |
| 0.4638 | 2.4574 | 548 | 0.5215 | 0.6139 | 0.5215 | 0.7222 |
| 0.4638 | 2.4664 | 550 | 0.5457 | 0.5737 | 0.5457 | 0.7387 |
| 0.4638 | 2.4753 | 552 | 0.5517 | 0.5939 | 0.5517 | 0.7428 |
| 0.4638 | 2.4843 | 554 | 0.5391 | 0.5939 | 0.5391 | 0.7342 |
| 0.4638 | 2.4933 | 556 | 0.5320 | 0.5939 | 0.5320 | 0.7294 |
| 0.4638 | 2.5022 | 558 | 0.5052 | 0.6638 | 0.5052 | 0.7108 |
| 0.4638 | 2.5112 | 560 | 0.5124 | 0.6638 | 0.5124 | 0.7158 |
| 0.4638 | 2.5202 | 562 | 0.5585 | 0.6203 | 0.5585 | 0.7473 |
| 0.4638 | 2.5291 | 564 | 0.6135 | 0.6203 | 0.6135 | 0.7833 |
| 0.4638 | 2.5381 | 566 | 0.6138 | 0.5939 | 0.6138 | 0.7834 |
| 0.4638 | 2.5471 | 568 | 0.6148 | 0.5939 | 0.6148 | 0.7841 |
| 0.4638 | 2.5561 | 570 | 0.6364 | 0.544 | 0.6364 | 0.7977 |
| 0.4638 | 2.5650 | 572 | 0.6388 | 0.5492 | 0.6388 | 0.7993 |
| 0.4638 | 2.5740 | 574 | 0.5961 | 0.5705 | 0.5961 | 0.7721 |
| 0.4638 | 2.5830 | 576 | 0.5994 | 0.5939 | 0.5994 | 0.7742 |
| 0.4638 | 2.5919 | 578 | 0.6641 | 0.6111 | 0.6641 | 0.8149 |
| 0.4638 | 2.6009 | 580 | 0.7052 | 0.5496 | 0.7052 | 0.8398 |
| 0.4638 | 2.6099 | 582 | 0.5673 | 0.5557 | 0.5673 | 0.7532 |
| 0.4638 | 2.6188 | 584 | 0.4134 | 0.7070 | 0.4134 | 0.6430 |
| 0.4638 | 2.6278 | 586 | 0.3684 | 0.6912 | 0.3684 | 0.6069 |
| 0.4638 | 2.6368 | 588 | 0.3759 | 0.6557 | 0.3759 | 0.6131 |
| 0.4638 | 2.6457 | 590 | 0.3854 | 0.6557 | 0.3854 | 0.6208 |
| 0.4638 | 2.6547 | 592 | 0.4066 | 0.6769 | 0.4066 | 0.6376 |
| 0.4638 | 2.6637 | 594 | 0.4999 | 0.6203 | 0.4999 | 0.7070 |
| 0.4638 | 2.6726 | 596 | 0.5862 | 0.6203 | 0.5862 | 0.7657 |
| 0.4638 | 2.6816 | 598 | 0.5874 | 0.6203 | 0.5874 | 0.7664 |
| 0.4638 | 2.6906 | 600 | 0.5207 | 0.5939 | 0.5207 | 0.7216 |
| 0.4638 | 2.6996 | 602 | 0.4547 | 0.6769 | 0.4547 | 0.6743 |
| 0.4638 | 2.7085 | 604 | 0.4503 | 0.6769 | 0.4503 | 0.6710 |
| 0.4638 | 2.7175 | 606 | 0.4394 | 0.6769 | 0.4394 | 0.6629 |
| 0.4638 | 2.7265 | 608 | 0.4378 | 0.6769 | 0.4378 | 0.6617 |
| 0.4638 | 2.7354 | 610 | 0.4517 | 0.6473 | 0.4517 | 0.6721 |
| 0.4638 | 2.7444 | 612 | 0.4676 | 0.6473 | 0.4676 | 0.6838 |
| 0.4638 | 2.7534 | 614 | 0.4658 | 0.6526 | 0.4658 | 0.6825 |
| 0.4638 | 2.7623 | 616 | 0.4757 | 0.6526 | 0.4757 | 0.6897 |
| 0.4638 | 2.7713 | 618 | 0.5166 | 0.6686 | 0.5166 | 0.7187 |
| 0.4638 | 2.7803 | 620 | 0.5873 | 0.5679 | 0.5873 | 0.7664 |
| 0.4638 | 2.7892 | 622 | 0.6078 | 0.5679 | 0.6078 | 0.7796 |
| 0.4638 | 2.7982 | 624 | 0.5805 | 0.5679 | 0.5805 | 0.7619 |
| 0.4638 | 2.8072 | 626 | 0.5668 | 0.5679 | 0.5668 | 0.7529 |
| 0.4638 | 2.8161 | 628 | 0.5070 | 0.5679 | 0.5070 | 0.7121 |
| 0.4638 | 2.8251 | 630 | 0.4512 | 0.6358 | 0.4512 | 0.6717 |
| 0.4638 | 2.8341 | 632 | 0.4212 | 0.6358 | 0.4212 | 0.6490 |
| 0.4638 | 2.8430 | 634 | 0.4194 | 0.6358 | 0.4194 | 0.6476 |
| 0.4638 | 2.8520 | 636 | 0.4312 | 0.6358 | 0.4312 | 0.6567 |
| 0.4638 | 2.8610 | 638 | 0.4671 | 0.6419 | 0.4671 | 0.6834 |
| 0.4638 | 2.8700 | 640 | 0.4869 | 0.625 | 0.4869 | 0.6978 |
| 0.4638 | 2.8789 | 642 | 0.4668 | 0.7024 | 0.4668 | 0.6832 |
| 0.4638 | 2.8879 | 644 | 0.4198 | 0.6123 | 0.4198 | 0.6479 |
| 0.4638 | 2.8969 | 646 | 0.3857 | 0.6083 | 0.3857 | 0.6211 |
| 0.4638 | 2.9058 | 648 | 0.3821 | 0.6351 | 0.3821 | 0.6181 |
| 0.4638 | 2.9148 | 650 | 0.3903 | 0.6351 | 0.3903 | 0.6247 |
| 0.4638 | 2.9238 | 652 | 0.4047 | 0.6139 | 0.4047 | 0.6362 |
| 0.4638 | 2.9327 | 654 | 0.4245 | 0.6410 | 0.4245 | 0.6515 |
| 0.4638 | 2.9417 | 656 | 0.4573 | 0.6638 | 0.4573 | 0.6762 |
| 0.4638 | 2.9507 | 658 | 0.4684 | 0.6638 | 0.4684 | 0.6844 |
| 0.4638 | 2.9596 | 660 | 0.4570 | 0.6638 | 0.4570 | 0.6760 |
| 0.4638 | 2.9686 | 662 | 0.4132 | 0.6719 | 0.4132 | 0.6428 |
| 0.4638 | 2.9776 | 664 | 0.4180 | 0.7191 | 0.4180 | 0.6465 |
| 0.4638 | 2.9865 | 666 | 0.4180 | 0.7734 | 0.4180 | 0.6465 |
| 0.4638 | 2.9955 | 668 | 0.3974 | 0.7380 | 0.3974 | 0.6304 |
| 0.4638 | 3.0045 | 670 | 0.3876 | 0.6917 | 0.3876 | 0.6226 |
| 0.4638 | 3.0135 | 672 | 0.4151 | 0.6769 | 0.4151 | 0.6443 |
| 0.4638 | 3.0224 | 674 | 0.4894 | 0.6203 | 0.4894 | 0.6996 |
| 0.4638 | 3.0314 | 676 | 0.5321 | 0.5939 | 0.5321 | 0.7295 |
| 0.4638 | 3.0404 | 678 | 0.5989 | 0.5939 | 0.5989 | 0.7739 |
| 0.4638 | 3.0493 | 680 | 0.6581 | 0.5939 | 0.6581 | 0.8112 |
| 0.4638 | 3.0583 | 682 | 0.6588 | 0.5939 | 0.6588 | 0.8116 |
| 0.4638 | 3.0673 | 684 | 0.6317 | 0.5939 | 0.6317 | 0.7948 |
| 0.4638 | 3.0762 | 686 | 0.5725 | 0.5939 | 0.5725 | 0.7567 |
| 0.4638 | 3.0852 | 688 | 0.5416 | 0.5035 | 0.5416 | 0.7360 |
| 0.4638 | 3.0942 | 690 | 0.5340 | 0.5294 | 0.5340 | 0.7308 |
| 0.4638 | 3.1031 | 692 | 0.5333 | 0.5557 | 0.5333 | 0.7303 |
| 0.4638 | 3.1121 | 694 | 0.5872 | 0.5227 | 0.5872 | 0.7663 |
| 0.4638 | 3.1211 | 696 | 0.6689 | 0.4292 | 0.6689 | 0.8179 |
| 0.4638 | 3.1300 | 698 | 0.6531 | 0.5227 | 0.6531 | 0.8081 |
| 0.4638 | 3.1390 | 700 | 0.6571 | 0.5227 | 0.6571 | 0.8106 |
| 0.4638 | 3.1480 | 702 | 0.5947 | 0.5557 | 0.5947 | 0.7712 |
| 0.4638 | 3.1570 | 704 | 0.5371 | 0.5035 | 0.5371 | 0.7329 |
| 0.4638 | 3.1659 | 706 | 0.5563 | 0.5035 | 0.5563 | 0.7459 |
| 0.4638 | 3.1749 | 708 | 0.5347 | 0.5035 | 0.5347 | 0.7312 |
| 0.4638 | 3.1839 | 710 | 0.4997 | 0.5073 | 0.4997 | 0.7069 |
| 0.4638 | 3.1928 | 712 | 0.5125 | 0.5035 | 0.5125 | 0.7159 |
| 0.4638 | 3.2018 | 714 | 0.5569 | 0.5035 | 0.5569 | 0.7463 |
| 0.4638 | 3.2108 | 716 | 0.5989 | 0.5035 | 0.5989 | 0.7739 |
| 0.4638 | 3.2197 | 718 | 0.6101 | 0.5035 | 0.6101 | 0.7811 |
| 0.4638 | 3.2287 | 720 | 0.5682 | 0.5035 | 0.5682 | 0.7538 |
| 0.4638 | 3.2377 | 722 | 0.5503 | 0.5939 | 0.5503 | 0.7418 |
| 0.4638 | 3.2466 | 724 | 0.5253 | 0.6358 | 0.5253 | 0.7248 |
| 0.4638 | 3.2556 | 726 | 0.5298 | 0.6083 | 0.5298 | 0.7278 |
| 0.4638 | 3.2646 | 728 | 0.5354 | 0.6083 | 0.5354 | 0.7317 |
| 0.4638 | 3.2735 | 730 | 0.5475 | 0.6255 | 0.5475 | 0.7399 |
| 0.4638 | 3.2825 | 732 | 0.5769 | 0.5359 | 0.5769 | 0.7595 |
| 0.4638 | 3.2915 | 734 | 0.5958 | 0.5035 | 0.5958 | 0.7719 |
| 0.4638 | 3.3004 | 736 | 0.6117 | 0.5035 | 0.6117 | 0.7821 |
| 0.4638 | 3.3094 | 738 | 0.6541 | 0.5035 | 0.6541 | 0.8087 |
| 0.4638 | 3.3184 | 740 | 0.6571 | 0.4965 | 0.6571 | 0.8106 |
| 0.4638 | 3.3274 | 742 | 0.6545 | 0.4965 | 0.6545 | 0.8090 |
| 0.4638 | 3.3363 | 744 | 0.6225 | 0.4965 | 0.6225 | 0.7890 |
| 0.4638 | 3.3453 | 746 | 0.6125 | 0.4965 | 0.6125 | 0.7826 |
| 0.4638 | 3.3543 | 748 | 0.5827 | 0.5073 | 0.5827 | 0.7634 |
| 0.4638 | 3.3632 | 750 | 0.5487 | 0.5477 | 0.5487 | 0.7407 |
| 0.4638 | 3.3722 | 752 | 0.5541 | 0.5545 | 0.5541 | 0.7444 |
| 0.4638 | 3.3812 | 754 | 0.5793 | 0.6526 | 0.5793 | 0.7611 |
| 0.4638 | 3.3901 | 756 | 0.6149 | 0.4779 | 0.6149 | 0.7841 |
| 0.4638 | 3.3991 | 758 | 0.6274 | 0.4779 | 0.6274 | 0.7921 |
| 0.4638 | 3.4081 | 760 | 0.6180 | 0.5035 | 0.6180 | 0.7861 |
| 0.4638 | 3.4170 | 762 | 0.6180 | 0.5035 | 0.6180 | 0.7862 |
| 0.4638 | 3.4260 | 764 | 0.5771 | 0.5035 | 0.5771 | 0.7597 |
| 0.4638 | 3.4350 | 766 | 0.5460 | 0.5073 | 0.5460 | 0.7389 |
| 0.4638 | 3.4439 | 768 | 0.5381 | 0.6182 | 0.5381 | 0.7335 |
| 0.4638 | 3.4529 | 770 | 0.5616 | 0.4538 | 0.5616 | 0.7494 |
| 0.4638 | 3.4619 | 772 | 0.5840 | 0.5169 | 0.5840 | 0.7642 |
| 0.4638 | 3.4709 | 774 | 0.5771 | 0.4919 | 0.5771 | 0.7597 |
| 0.4638 | 3.4798 | 776 | 0.5929 | 0.5169 | 0.5929 | 0.7700 |
| 0.4638 | 3.4888 | 778 | 0.6161 | 0.4517 | 0.6161 | 0.7849 |
| 0.4638 | 3.4978 | 780 | 0.6299 | 0.5035 | 0.6299 | 0.7937 |
| 0.4638 | 3.5067 | 782 | 0.5934 | 0.5035 | 0.5934 | 0.7703 |
| 0.4638 | 3.5157 | 784 | 0.5396 | 0.5767 | 0.5396 | 0.7346 |
| 0.4638 | 3.5247 | 786 | 0.5143 | 0.6769 | 0.5143 | 0.7172 |
| 0.4638 | 3.5336 | 788 | 0.5067 | 0.5855 | 0.5067 | 0.7118 |
| 0.4638 | 3.5426 | 790 | 0.5326 | 0.6062 | 0.5326 | 0.7298 |
| 0.4638 | 3.5516 | 792 | 0.5681 | 0.5294 | 0.5681 | 0.7537 |
| 0.4638 | 3.5605 | 794 | 0.5947 | 0.5294 | 0.5947 | 0.7712 |
| 0.4638 | 3.5695 | 796 | 0.5808 | 0.5294 | 0.5808 | 0.7621 |
| 0.4638 | 3.5785 | 798 | 0.5689 | 0.5035 | 0.5689 | 0.7543 |
| 0.4638 | 3.5874 | 800 | 0.5460 | 0.5939 | 0.5460 | 0.7389 |
| 0.4638 | 3.5964 | 802 | 0.5333 | 0.6358 | 0.5333 | 0.7303 |
| 0.4638 | 3.6054 | 804 | 0.5497 | 0.5679 | 0.5497 | 0.7414 |
| 0.4638 | 3.6143 | 806 | 0.5540 | 0.5679 | 0.5540 | 0.7443 |
| 0.4638 | 3.6233 | 808 | 0.5553 | 0.5679 | 0.5553 | 0.7452 |
| 0.4638 | 3.6323 | 810 | 0.5469 | 0.6358 | 0.5469 | 0.7395 |
| 0.4638 | 3.6413 | 812 | 0.5369 | 0.5607 | 0.5369 | 0.7327 |
| 0.4638 | 3.6502 | 814 | 0.5374 | 0.6015 | 0.5374 | 0.7331 |
| 0.4638 | 3.6592 | 816 | 0.5343 | 0.5739 | 0.5343 | 0.7310 |
| 0.4638 | 3.6682 | 818 | 0.5142 | 0.6526 | 0.5142 | 0.7171 |
| 0.4638 | 3.6771 | 820 | 0.5187 | 0.5422 | 0.5187 | 0.7202 |
| 0.4638 | 3.6861 | 822 | 0.5477 | 0.5035 | 0.5477 | 0.7400 |
| 0.4638 | 3.6951 | 824 | 0.5761 | 0.5035 | 0.5761 | 0.7590 |
| 0.4638 | 3.7040 | 826 | 0.5928 | 0.5277 | 0.5928 | 0.7699 |
| 0.4638 | 3.7130 | 828 | 0.5683 | 0.5 | 0.5683 | 0.7539 |
| 0.4638 | 3.7220 | 830 | 0.5405 | 0.5073 | 0.5405 | 0.7352 |
| 0.4638 | 3.7309 | 832 | 0.5286 | 0.5073 | 0.5286 | 0.7270 |
| 0.4638 | 3.7399 | 834 | 0.5310 | 0.6526 | 0.5310 | 0.7287 |
| 0.4638 | 3.7489 | 836 | 0.5301 | 0.6526 | 0.5301 | 0.7281 |
| 0.4638 | 3.7578 | 838 | 0.5463 | 0.5145 | 0.5463 | 0.7391 |
| 0.4638 | 3.7668 | 840 | 0.5577 | 0.4803 | 0.5577 | 0.7468 |
| 0.4638 | 3.7758 | 842 | 0.5974 | 0.5035 | 0.5974 | 0.7729 |
| 0.4638 | 3.7848 | 844 | 0.5939 | 0.5035 | 0.5939 | 0.7707 |
| 0.4638 | 3.7937 | 846 | 0.5475 | 0.5035 | 0.5475 | 0.7399 |
| 0.4638 | 3.8027 | 848 | 0.5252 | 0.5073 | 0.5252 | 0.7247 |
| 0.4638 | 3.8117 | 850 | 0.5123 | 0.5767 | 0.5123 | 0.7158 |
| 0.4638 | 3.8206 | 852 | 0.5169 | 0.5767 | 0.5169 | 0.7189 |
| 0.4638 | 3.8296 | 854 | 0.5235 | 0.5073 | 0.5235 | 0.7235 |
| 0.4638 | 3.8386 | 856 | 0.5259 | 0.5073 | 0.5259 | 0.7252 |
| 0.4638 | 3.8475 | 858 | 0.5322 | 0.5073 | 0.5322 | 0.7296 |
| 0.4638 | 3.8565 | 860 | 0.5548 | 0.5035 | 0.5548 | 0.7448 |
| 0.4638 | 3.8655 | 862 | 0.5830 | 0.5035 | 0.5830 | 0.7635 |
| 0.4638 | 3.8744 | 864 | 0.6124 | 0.5035 | 0.6124 | 0.7826 |
| 0.4638 | 3.8834 | 866 | 0.6016 | 0.5035 | 0.6016 | 0.7756 |
| 0.4638 | 3.8924 | 868 | 0.5523 | 0.5035 | 0.5523 | 0.7432 |
| 0.4638 | 3.9013 | 870 | 0.5170 | 0.6526 | 0.5170 | 0.7190 |
| 0.4638 | 3.9103 | 872 | 0.5223 | 0.6526 | 0.5223 | 0.7227 |
| 0.4638 | 3.9193 | 874 | 0.5342 | 0.6526 | 0.5342 | 0.7309 |
| 0.4638 | 3.9283 | 876 | 0.5768 | 0.5145 | 0.5768 | 0.7594 |
| 0.4638 | 3.9372 | 878 | 0.6439 | 0.5 | 0.6439 | 0.8024 |
| 0.4638 | 3.9462 | 880 | 0.6678 | 0.5 | 0.6678 | 0.8172 |
| 0.4638 | 3.9552 | 882 | 0.6399 | 0.5 | 0.6399 | 0.8000 |
| 0.4638 | 3.9641 | 884 | 0.6227 | 0.5 | 0.6227 | 0.7891 |
| 0.4638 | 3.9731 | 886 | 0.6124 | 0.5073 | 0.6124 | 0.7826 |
| 0.4638 | 3.9821 | 888 | 0.5874 | 0.5073 | 0.5874 | 0.7664 |
| 0.4638 | 3.9910 | 890 | 0.5804 | 0.4803 | 0.5804 | 0.7619 |
| 0.4638 | 4.0 | 892 | 0.5807 | 0.4803 | 0.5807 | 0.7620 |
| 0.4638 | 4.0090 | 894 | 0.6025 | 0.4779 | 0.6025 | 0.7762 |
| 0.4638 | 4.0179 | 896 | 0.6619 | 0.5035 | 0.6619 | 0.8136 |
| 0.4638 | 4.0269 | 898 | 0.6981 | 0.5035 | 0.6981 | 0.8355 |
| 0.4638 | 4.0359 | 900 | 0.6583 | 0.5035 | 0.6583 | 0.8114 |
| 0.4638 | 4.0448 | 902 | 0.6201 | 0.5035 | 0.6201 | 0.7875 |
| 0.4638 | 4.0538 | 904 | 0.5769 | 0.5679 | 0.5769 | 0.7596 |
| 0.4638 | 4.0628 | 906 | 0.5736 | 0.5939 | 0.5736 | 0.7573 |
| 0.4638 | 4.0717 | 908 | 0.5992 | 0.5035 | 0.5992 | 0.7741 |
| 0.4638 | 4.0807 | 910 | 0.5960 | 0.5294 | 0.5960 | 0.7720 |
| 0.4638 | 4.0897 | 912 | 0.5690 | 0.5035 | 0.5690 | 0.7543 |
| 0.4638 | 4.0987 | 914 | 0.5851 | 0.5294 | 0.5851 | 0.7649 |
| 0.4638 | 4.1076 | 916 | 0.5983 | 0.5294 | 0.5983 | 0.7735 |
| 0.4638 | 4.1166 | 918 | 0.5732 | 0.5294 | 0.5732 | 0.7571 |
| 0.4638 | 4.1256 | 920 | 0.5271 | 0.6026 | 0.5271 | 0.7260 |
| 0.4638 | 4.1345 | 922 | 0.4886 | 0.6182 | 0.4886 | 0.6990 |
| 0.4638 | 4.1435 | 924 | 0.4853 | 0.6526 | 0.4853 | 0.6967 |
| 0.4638 | 4.1525 | 926 | 0.4999 | 0.6526 | 0.4999 | 0.7070 |
| 0.4638 | 4.1614 | 928 | 0.5305 | 0.6182 | 0.5305 | 0.7283 |
| 0.4638 | 4.1704 | 930 | 0.5545 | 0.5481 | 0.5545 | 0.7447 |
| 0.4638 | 4.1794 | 932 | 0.5782 | 0.5751 | 0.5782 | 0.7604 |
| 0.4638 | 4.1883 | 934 | 0.6245 | 0.5679 | 0.6245 | 0.7903 |
| 0.4638 | 4.1973 | 936 | 0.6247 | 0.5679 | 0.6247 | 0.7904 |
| 0.4638 | 4.2063 | 938 | 0.6081 | 0.5422 | 0.6081 | 0.7798 |
| 0.4638 | 4.2152 | 940 | 0.6084 | 0.5422 | 0.6084 | 0.7800 |
| 0.4638 | 4.2242 | 942 | 0.5913 | 0.5422 | 0.5913 | 0.7689 |
| 0.4638 | 4.2332 | 944 | 0.5822 | 0.5422 | 0.5822 | 0.7631 |
| 0.4638 | 4.2422 | 946 | 0.5859 | 0.5422 | 0.5859 | 0.7655 |
| 0.4638 | 4.2511 | 948 | 0.6004 | 0.5035 | 0.6004 | 0.7749 |
| 0.4638 | 4.2601 | 950 | 0.5908 | 0.4779 | 0.5908 | 0.7686 |
| 0.4638 | 4.2691 | 952 | 0.5815 | 0.5035 | 0.5815 | 0.7626 |
| 0.4638 | 4.2780 | 954 | 0.5853 | 0.5035 | 0.5853 | 0.7650 |
| 0.4638 | 4.2870 | 956 | 0.6324 | 0.3656 | 0.6324 | 0.7952 |
| 0.4638 | 4.2960 | 958 | 0.6385 | 0.3656 | 0.6385 | 0.7991 |
| 0.4638 | 4.3049 | 960 | 0.6306 | 0.3656 | 0.6306 | 0.7941 |
| 0.4638 | 4.3139 | 962 | 0.6558 | 0.3656 | 0.6558 | 0.8098 |
| 0.4638 | 4.3229 | 964 | 0.6305 | 0.4018 | 0.6305 | 0.7940 |
| 0.4638 | 4.3318 | 966 | 0.5519 | 0.4527 | 0.5519 | 0.7429 |
| 0.4638 | 4.3408 | 968 | 0.5020 | 0.6526 | 0.5020 | 0.7085 |
| 0.4638 | 4.3498 | 970 | 0.4985 | 0.6866 | 0.4985 | 0.7061 |
| 0.4638 | 4.3587 | 972 | 0.5140 | 0.6866 | 0.5140 | 0.7169 |
| 0.4638 | 4.3677 | 974 | 0.5461 | 0.6866 | 0.5461 | 0.7390 |
| 0.4638 | 4.3767 | 976 | 0.5729 | 0.5737 | 0.5729 | 0.7569 |
| 0.4638 | 4.3857 | 978 | 0.6024 | 0.5737 | 0.6024 | 0.7761 |
| 0.4638 | 4.3946 | 980 | 0.5959 | 0.5737 | 0.5959 | 0.7720 |
| 0.4638 | 4.4036 | 982 | 0.6120 | 0.5737 | 0.6120 | 0.7823 |
| 0.4638 | 4.4126 | 984 | 0.6261 | 0.5737 | 0.6261 | 0.7913 |
| 0.4638 | 4.4215 | 986 | 0.6444 | 0.5737 | 0.6444 | 0.8027 |
| 0.4638 | 4.4305 | 988 | 0.6696 | 0.4527 | 0.6696 | 0.8183 |
| 0.4638 | 4.4395 | 990 | 0.6674 | 0.4527 | 0.6674 | 0.8170 |
| 0.4638 | 4.4484 | 992 | 0.6367 | 0.4527 | 0.6367 | 0.7980 |
| 0.4638 | 4.4574 | 994 | 0.6139 | 0.5422 | 0.6139 | 0.7835 |
| 0.4638 | 4.4664 | 996 | 0.5758 | 0.6526 | 0.5758 | 0.7588 |
| 0.4638 | 4.4753 | 998 | 0.5440 | 0.6526 | 0.5440 | 0.7376 |
| 0.1396 | 4.4843 | 1000 | 0.5300 | 0.6526 | 0.5300 | 0.7280 |
| 0.1396 | 4.4933 | 1002 | 0.5361 | 0.6182 | 0.5361 | 0.7322 |
| 0.1396 | 4.5022 | 1004 | 0.5602 | 0.4779 | 0.5602 | 0.7485 |
| 0.1396 | 4.5112 | 1006 | 0.6273 | 0.5035 | 0.6273 | 0.7920 |
| 0.1396 | 4.5202 | 1008 | 0.6626 | 0.5035 | 0.6626 | 0.8140 |
| 0.1396 | 4.5291 | 1010 | 0.6278 | 0.4779 | 0.6278 | 0.7923 |
| 0.1396 | 4.5381 | 1012 | 0.5569 | 0.5812 | 0.5569 | 0.7462 |
| 0.1396 | 4.5471 | 1014 | 0.5235 | 0.6526 | 0.5235 | 0.7236 |
| 0.1396 | 4.5561 | 1016 | 0.5166 | 0.6526 | 0.5166 | 0.7187 |
| 0.1396 | 4.5650 | 1018 | 0.5244 | 0.6526 | 0.5244 | 0.7242 |
| 0.1396 | 4.5740 | 1020 | 0.5297 | 0.6526 | 0.5297 | 0.7278 |
| 0.1396 | 4.5830 | 1022 | 0.5428 | 0.6410 | 0.5428 | 0.7367 |
| 0.1396 | 4.5919 | 1024 | 0.5615 | 0.5737 | 0.5615 | 0.7493 |
| 0.1396 | 4.6009 | 1026 | 0.5747 | 0.5737 | 0.5747 | 0.7581 |
| 0.1396 | 4.6099 | 1028 | 0.5782 | 0.5995 | 0.5782 | 0.7604 |
| 0.1396 | 4.6188 | 1030 | 0.5704 | 0.5679 | 0.5704 | 0.7553 |
| 0.1396 | 4.6278 | 1032 | 0.5557 | 0.5679 | 0.5557 | 0.7455 |
| 0.1396 | 4.6368 | 1034 | 0.5480 | 0.5679 | 0.5480 | 0.7403 |
| 0.1396 | 4.6457 | 1036 | 0.5351 | 0.6083 | 0.5351 | 0.7315 |
| 0.1396 | 4.6547 | 1038 | 0.5436 | 0.6410 | 0.5436 | 0.7373 |
| 0.1396 | 4.6637 | 1040 | 0.5505 | 0.6410 | 0.5505 | 0.7420 |
| 0.1396 | 4.6726 | 1042 | 0.5576 | 0.6358 | 0.5576 | 0.7467 |
| 0.1396 | 4.6816 | 1044 | 0.5749 | 0.4779 | 0.5749 | 0.7582 |
| 0.1396 | 4.6906 | 1046 | 0.5980 | 0.5035 | 0.5980 | 0.7733 |
| 0.1396 | 4.6996 | 1048 | 0.6089 | 0.5035 | 0.6089 | 0.7803 |
| 0.1396 | 4.7085 | 1050 | 0.6014 | 0.5035 | 0.6014 | 0.7755 |
| 0.1396 | 4.7175 | 1052 | 0.5798 | 0.5035 | 0.5798 | 0.7615 |
| 0.1396 | 4.7265 | 1054 | 0.5566 | 0.5191 | 0.5566 | 0.7461 |
| 0.1396 | 4.7354 | 1056 | 0.5522 | 0.6526 | 0.5522 | 0.7431 |
| 0.1396 | 4.7444 | 1058 | 0.5447 | 0.6526 | 0.5447 | 0.7381 |
| 0.1396 | 4.7534 | 1060 | 0.5529 | 0.6526 | 0.5529 | 0.7436 |
| 0.1396 | 4.7623 | 1062 | 0.5774 | 0.4878 | 0.5774 | 0.7599 |
| 0.1396 | 4.7713 | 1064 | 0.5900 | 0.4527 | 0.5900 | 0.7681 |
| 0.1396 | 4.7803 | 1066 | 0.6125 | 0.5035 | 0.6125 | 0.7826 |
| 0.1396 | 4.7892 | 1068 | 0.6195 | 0.5035 | 0.6195 | 0.7871 |
| 0.1396 | 4.7982 | 1070 | 0.5916 | 0.5035 | 0.5916 | 0.7692 |
| 0.1396 | 4.8072 | 1072 | 0.5452 | 0.5477 | 0.5452 | 0.7384 |
| 0.1396 | 4.8161 | 1074 | 0.5116 | 0.6526 | 0.5116 | 0.7153 |
| 0.1396 | 4.8251 | 1076 | 0.5115 | 0.6526 | 0.5115 | 0.7152 |
| 0.1396 | 4.8341 | 1078 | 0.5158 | 0.6239 | 0.5158 | 0.7182 |
| 0.1396 | 4.8430 | 1080 | 0.5211 | 0.6239 | 0.5211 | 0.7219 |
| 0.1396 | 4.8520 | 1082 | 0.5342 | 0.6239 | 0.5342 | 0.7309 |
| 0.1396 | 4.8610 | 1084 | 0.5739 | 0.4850 | 0.5739 | 0.7575 |
| 0.1396 | 4.8700 | 1086 | 0.6195 | 0.5035 | 0.6195 | 0.7871 |
| 0.1396 | 4.8789 | 1088 | 0.6223 | 0.5035 | 0.6223 | 0.7889 |
| 0.1396 | 4.8879 | 1090 | 0.6050 | 0.5035 | 0.6050 | 0.7778 |
| 0.1396 | 4.8969 | 1092 | 0.5769 | 0.5103 | 0.5769 | 0.7595 |
| 0.1396 | 4.9058 | 1094 | 0.5560 | 0.5545 | 0.5560 | 0.7456 |
| 0.1396 | 4.9148 | 1096 | 0.5421 | 0.5832 | 0.5421 | 0.7363 |
| 0.1396 | 4.9238 | 1098 | 0.5433 | 0.5477 | 0.5433 | 0.7371 |
| 0.1396 | 4.9327 | 1100 | 0.5562 | 0.5689 | 0.5562 | 0.7458 |
| 0.1396 | 4.9417 | 1102 | 0.5615 | 0.5689 | 0.5615 | 0.7493 |
| 0.1396 | 4.9507 | 1104 | 0.5565 | 0.5689 | 0.5565 | 0.7460 |
| 0.1396 | 4.9596 | 1106 | 0.5456 | 0.5689 | 0.5456 | 0.7386 |
| 0.1396 | 4.9686 | 1108 | 0.5274 | 0.5767 | 0.5274 | 0.7262 |
| 0.1396 | 4.9776 | 1110 | 0.4983 | 0.5832 | 0.4983 | 0.7059 |
| 0.1396 | 4.9865 | 1112 | 0.4898 | 0.5545 | 0.4898 | 0.6998 |
| 0.1396 | 4.9955 | 1114 | 0.5001 | 0.6239 | 0.5001 | 0.7072 |
| 0.1396 | 5.0045 | 1116 | 0.5173 | 0.6239 | 0.5173 | 0.7192 |
| 0.1396 | 5.0135 | 1118 | 0.5420 | 0.5545 | 0.5420 | 0.7362 |
| 0.1396 | 5.0224 | 1120 | 0.5732 | 0.5751 | 0.5732 | 0.7571 |
| 0.1396 | 5.0314 | 1122 | 0.5856 | 0.5689 | 0.5856 | 0.7652 |
| 0.1396 | 5.0404 | 1124 | 0.5782 | 0.5689 | 0.5782 | 0.7604 |
| 0.1396 | 5.0493 | 1126 | 0.5584 | 0.5767 | 0.5584 | 0.7473 |
| 0.1396 | 5.0583 | 1128 | 0.5429 | 0.5767 | 0.5429 | 0.7368 |
| 0.1396 | 5.0673 | 1130 | 0.5341 | 0.5191 | 0.5341 | 0.7308 |
| 0.1396 | 5.0762 | 1132 | 0.5418 | 0.5191 | 0.5418 | 0.7361 |
| 0.1396 | 5.0852 | 1134 | 0.5475 | 0.5191 | 0.5475 | 0.7400 |
| 0.1396 | 5.0942 | 1136 | 0.5645 | 0.5767 | 0.5645 | 0.7513 |
| 0.1396 | 5.1031 | 1138 | 0.5642 | 0.5767 | 0.5642 | 0.7511 |
| 0.1396 | 5.1121 | 1140 | 0.5696 | 0.5767 | 0.5696 | 0.7547 |
| 0.1396 | 5.1211 | 1142 | 0.5559 | 0.5767 | 0.5559 | 0.7456 |
| 0.1396 | 5.1300 | 1144 | 0.5417 | 0.5477 | 0.5417 | 0.7360 |
| 0.1396 | 5.1390 | 1146 | 0.5293 | 0.5545 | 0.5293 | 0.7275 |
| 0.1396 | 5.1480 | 1148 | 0.5289 | 0.5545 | 0.5289 | 0.7273 |
| 0.1396 | 5.1570 | 1150 | 0.5303 | 0.5545 | 0.5303 | 0.7282 |
| 0.1396 | 5.1659 | 1152 | 0.5478 | 0.5477 | 0.5478 | 0.7402 |
| 0.1396 | 5.1749 | 1154 | 0.5938 | 0.475 | 0.5938 | 0.7706 |
| 0.1396 | 5.1839 | 1156 | 0.6241 | 0.3656 | 0.6241 | 0.7900 |
| 0.1396 | 5.1928 | 1158 | 0.6254 | 0.3656 | 0.6254 | 0.7908 |
| 0.1396 | 5.2018 | 1160 | 0.6207 | 0.4286 | 0.6207 | 0.7878 |
| 0.1396 | 5.2108 | 1162 | 0.6060 | 0.475 | 0.6060 | 0.7785 |
| 0.1396 | 5.2197 | 1164 | 0.5730 | 0.5767 | 0.5730 | 0.7569 |
| 0.1396 | 5.2287 | 1166 | 0.5616 | 0.5767 | 0.5616 | 0.7494 |
| 0.1396 | 5.2377 | 1168 | 0.5590 | 0.5767 | 0.5590 | 0.7476 |
| 0.1396 | 5.2466 | 1170 | 0.5527 | 0.5767 | 0.5527 | 0.7434 |
| 0.1396 | 5.2556 | 1172 | 0.5342 | 0.5477 | 0.5342 | 0.7309 |
| 0.1396 | 5.2646 | 1174 | 0.5323 | 0.5545 | 0.5323 | 0.7296 |
| 0.1396 | 5.2735 | 1176 | 0.5386 | 0.5545 | 0.5386 | 0.7339 |
| 0.1396 | 5.2825 | 1178 | 0.5695 | 0.5477 | 0.5695 | 0.7547 |
| 0.1396 | 5.2915 | 1180 | 0.6156 | 0.5035 | 0.6156 | 0.7846 |
| 0.1396 | 5.3004 | 1182 | 0.6592 | 0.5035 | 0.6592 | 0.8119 |
| 0.1396 | 5.3094 | 1184 | 0.6936 | 0.5035 | 0.6936 | 0.8329 |
| 0.1396 | 5.3184 | 1186 | 0.6646 | 0.5035 | 0.6646 | 0.8152 |
| 0.1396 | 5.3274 | 1188 | 0.6121 | 0.5035 | 0.6121 | 0.7824 |
| 0.1396 | 5.3363 | 1190 | 0.5580 | 0.5689 | 0.5580 | 0.7470 |
| 0.1396 | 5.3453 | 1192 | 0.5177 | 0.5767 | 0.5177 | 0.7195 |
| 0.1396 | 5.3543 | 1194 | 0.5120 | 0.6182 | 0.5120 | 0.7155 |
| 0.1396 | 5.3632 | 1196 | 0.5258 | 0.5767 | 0.5258 | 0.7251 |
| 0.1396 | 5.3722 | 1198 | 0.5324 | 0.5767 | 0.5324 | 0.7297 |
| 0.1396 | 5.3812 | 1200 | 0.5372 | 0.5689 | 0.5372 | 0.7329 |
| 0.1396 | 5.3901 | 1202 | 0.5395 | 0.5689 | 0.5395 | 0.7345 |
| 0.1396 | 5.3991 | 1204 | 0.5417 | 0.5689 | 0.5417 | 0.7360 |
| 0.1396 | 5.4081 | 1206 | 0.5371 | 0.5767 | 0.5371 | 0.7329 |
| 0.1396 | 5.4170 | 1208 | 0.5366 | 0.6182 | 0.5366 | 0.7325 |
| 0.1396 | 5.4260 | 1210 | 0.5351 | 0.6182 | 0.5351 | 0.7315 |
| 0.1396 | 5.4350 | 1212 | 0.5296 | 0.6182 | 0.5296 | 0.7277 |
| 0.1396 | 5.4439 | 1214 | 0.5360 | 0.5191 | 0.5360 | 0.7321 |
| 0.1396 | 5.4529 | 1216 | 0.5392 | 0.5477 | 0.5392 | 0.7343 |
| 0.1396 | 5.4619 | 1218 | 0.5619 | 0.5689 | 0.5619 | 0.7496 |
| 0.1396 | 5.4709 | 1220 | 0.5941 | 0.5035 | 0.5941 | 0.7708 |
| 0.1396 | 5.4798 | 1222 | 0.6161 | 0.4018 | 0.6161 | 0.7849 |
| 0.1396 | 5.4888 | 1224 | 0.6137 | 0.4290 | 0.6137 | 0.7834 |
| 0.1396 | 5.4978 | 1226 | 0.5701 | 0.4018 | 0.5701 | 0.7551 |
| 0.1396 | 5.5067 | 1228 | 0.5484 | 0.4688 | 0.5484 | 0.7406 |
| 0.1396 | 5.5157 | 1230 | 0.5243 | 0.4688 | 0.5243 | 0.7241 |
| 0.1396 | 5.5247 | 1232 | 0.4962 | 0.5786 | 0.4962 | 0.7044 |
| 0.1396 | 5.5336 | 1234 | 0.4919 | 0.5786 | 0.4919 | 0.7014 |
| 0.1396 | 5.5426 | 1236 | 0.4854 | 0.5786 | 0.4854 | 0.6967 |
| 0.1396 | 5.5516 | 1238 | 0.4892 | 0.5786 | 0.4892 | 0.6994 |
| 0.1396 | 5.5605 | 1240 | 0.4952 | 0.6182 | 0.4952 | 0.7037 |
| 0.1396 | 5.5695 | 1242 | 0.5234 | 0.6182 | 0.5234 | 0.7234 |
| 0.1396 | 5.5785 | 1244 | 0.5484 | 0.6182 | 0.5484 | 0.7405 |
| 0.1396 | 5.5874 | 1246 | 0.5671 | 0.6473 | 0.5671 | 0.7531 |
| 0.1396 | 5.5964 | 1248 | 0.5761 | 0.6473 | 0.5761 | 0.7590 |
| 0.1396 | 5.6054 | 1250 | 0.5724 | 0.6182 | 0.5724 | 0.7566 |
| 0.1396 | 5.6143 | 1252 | 0.5634 | 0.5871 | 0.5634 | 0.7506 |
| 0.1396 | 5.6233 | 1254 | 0.5570 | 0.5871 | 0.5570 | 0.7463 |
| 0.1396 | 5.6323 | 1256 | 0.5442 | 0.6473 | 0.5442 | 0.7377 |
| 0.1396 | 5.6413 | 1258 | 0.5327 | 0.6473 | 0.5327 | 0.7298 |
| 0.1396 | 5.6502 | 1260 | 0.5343 | 0.6473 | 0.5343 | 0.7309 |
| 0.1396 | 5.6592 | 1262 | 0.5284 | 0.6473 | 0.5284 | 0.7269 |
| 0.1396 | 5.6682 | 1264 | 0.5328 | 0.6473 | 0.5328 | 0.7299 |
| 0.1396 | 5.6771 | 1266 | 0.5522 | 0.6026 | 0.5522 | 0.7431 |
| 0.1396 | 5.6861 | 1268 | 0.5495 | 0.5073 | 0.5495 | 0.7413 |
| 0.1396 | 5.6951 | 1270 | 0.5373 | 0.5073 | 0.5373 | 0.7330 |
| 0.1396 | 5.7040 | 1272 | 0.5335 | 0.5035 | 0.5335 | 0.7304 |
| 0.1396 | 5.7130 | 1274 | 0.5122 | 0.6769 | 0.5122 | 0.7157 |
| 0.1396 | 5.7220 | 1276 | 0.4897 | 0.6769 | 0.4897 | 0.6998 |
| 0.1396 | 5.7309 | 1278 | 0.4678 | 0.6473 | 0.4678 | 0.6840 |
| 0.1396 | 5.7399 | 1280 | 0.4575 | 0.6818 | 0.4575 | 0.6764 |
| 0.1396 | 5.7489 | 1282 | 0.4579 | 0.6866 | 0.4579 | 0.6767 |
| 0.1396 | 5.7578 | 1284 | 0.4651 | 0.6866 | 0.4651 | 0.6820 |
| 0.1396 | 5.7668 | 1286 | 0.4818 | 0.6866 | 0.4818 | 0.6941 |
| 0.1396 | 5.7758 | 1288 | 0.4956 | 0.6866 | 0.4956 | 0.7040 |
| 0.1396 | 5.7848 | 1290 | 0.5140 | 0.6526 | 0.5140 | 0.7169 |
| 0.1396 | 5.7937 | 1292 | 0.5442 | 0.6818 | 0.5442 | 0.7377 |
| 0.1396 | 5.8027 | 1294 | 0.5958 | 0.5035 | 0.5958 | 0.7719 |
| 0.1396 | 5.8117 | 1296 | 0.6245 | 0.4118 | 0.6245 | 0.7903 |
| 0.1396 | 5.8206 | 1298 | 0.6161 | 0.4118 | 0.6161 | 0.7849 |
| 0.1396 | 5.8296 | 1300 | 0.5741 | 0.4118 | 0.5741 | 0.7577 |
| 0.1396 | 5.8386 | 1302 | 0.5467 | 0.5073 | 0.5467 | 0.7394 |
| 0.1396 | 5.8475 | 1304 | 0.5450 | 0.5073 | 0.5450 | 0.7382 |
| 0.1396 | 5.8565 | 1306 | 0.5496 | 0.6026 | 0.5496 | 0.7413 |
| 0.1396 | 5.8655 | 1308 | 0.5597 | 0.6358 | 0.5597 | 0.7481 |
| 0.1396 | 5.8744 | 1310 | 0.5775 | 0.6083 | 0.5775 | 0.7599 |
| 0.1396 | 5.8834 | 1312 | 0.5888 | 0.6083 | 0.5888 | 0.7673 |
| 0.1396 | 5.8924 | 1314 | 0.6025 | 0.6083 | 0.6025 | 0.7762 |
| 0.1396 | 5.9013 | 1316 | 0.6047 | 0.5359 | 0.6047 | 0.7776 |
| 0.1396 | 5.9103 | 1318 | 0.6052 | 0.5359 | 0.6052 | 0.7780 |
| 0.1396 | 5.9193 | 1320 | 0.5919 | 0.6255 | 0.5919 | 0.7694 |
| 0.1396 | 5.9283 | 1322 | 0.5705 | 0.6083 | 0.5705 | 0.7553 |
| 0.1396 | 5.9372 | 1324 | 0.5566 | 0.6358 | 0.5566 | 0.7460 |
| 0.1396 | 5.9462 | 1326 | 0.5359 | 0.6358 | 0.5359 | 0.7321 |
| 0.1396 | 5.9552 | 1328 | 0.5108 | 0.7115 | 0.5108 | 0.7147 |
| 0.1396 | 5.9641 | 1330 | 0.5008 | 0.6123 | 0.5008 | 0.7076 |
| 0.1396 | 5.9731 | 1332 | 0.4984 | 0.6123 | 0.4984 | 0.7060 |
| 0.1396 | 5.9821 | 1334 | 0.5081 | 0.6123 | 0.5081 | 0.7128 |
| 0.1396 | 5.9910 | 1336 | 0.5202 | 0.475 | 0.5202 | 0.7213 |
| 0.1396 | 6.0 | 1338 | 0.5441 | 0.4106 | 0.5441 | 0.7376 |
| 0.1396 | 6.0090 | 1340 | 0.5486 | 0.4106 | 0.5486 | 0.7407 |
| 0.1396 | 6.0179 | 1342 | 0.5392 | 0.5073 | 0.5392 | 0.7343 |
| 0.1396 | 6.0269 | 1344 | 0.5276 | 0.6358 | 0.5276 | 0.7263 |
| 0.1396 | 6.0359 | 1346 | 0.5314 | 0.6526 | 0.5314 | 0.7290 |
| 0.1396 | 6.0448 | 1348 | 0.5454 | 0.6526 | 0.5454 | 0.7385 |
| 0.1396 | 6.0538 | 1350 | 0.5682 | 0.5812 | 0.5682 | 0.7538 |
| 0.1396 | 6.0628 | 1352 | 0.5882 | 0.5812 | 0.5882 | 0.7670 |
| 0.1396 | 6.0717 | 1354 | 0.6215 | 0.5995 | 0.6215 | 0.7884 |
| 0.1396 | 6.0807 | 1356 | 0.6446 | 0.5359 | 0.6446 | 0.8029 |
| 0.1396 | 6.0897 | 1358 | 0.6747 | 0.5035 | 0.6747 | 0.8214 |
| 0.1396 | 6.0987 | 1360 | 0.6823 | 0.4118 | 0.6823 | 0.8260 |
| 0.1396 | 6.1076 | 1362 | 0.6632 | 0.4118 | 0.6632 | 0.8144 |
| 0.1396 | 6.1166 | 1364 | 0.6239 | 0.5035 | 0.6239 | 0.7899 |
| 0.1396 | 6.1256 | 1366 | 0.5835 | 0.6358 | 0.5835 | 0.7639 |
| 0.1396 | 6.1345 | 1368 | 0.5637 | 0.5812 | 0.5637 | 0.7508 |
| 0.1396 | 6.1435 | 1370 | 0.5616 | 0.5812 | 0.5616 | 0.7494 |
| 0.1396 | 6.1525 | 1372 | 0.5635 | 0.5812 | 0.5635 | 0.7506 |
| 0.1396 | 6.1614 | 1374 | 0.5605 | 0.5812 | 0.5605 | 0.7487 |
| 0.1396 | 6.1704 | 1376 | 0.5675 | 0.6358 | 0.5675 | 0.7533 |
| 0.1396 | 6.1794 | 1378 | 0.5933 | 0.5035 | 0.5933 | 0.7702 |
| 0.1396 | 6.1883 | 1380 | 0.6216 | 0.5035 | 0.6216 | 0.7884 |
| 0.1396 | 6.1973 | 1382 | 0.6440 | 0.5035 | 0.6440 | 0.8025 |
| 0.1396 | 6.2063 | 1384 | 0.6504 | 0.4118 | 0.6504 | 0.8065 |
| 0.1396 | 6.2152 | 1386 | 0.6311 | 0.5035 | 0.6311 | 0.7944 |
| 0.1396 | 6.2242 | 1388 | 0.5983 | 0.5073 | 0.5983 | 0.7735 |
| 0.1396 | 6.2332 | 1390 | 0.5690 | 0.5812 | 0.5690 | 0.7543 |
| 0.1396 | 6.2422 | 1392 | 0.5556 | 0.6526 | 0.5556 | 0.7454 |
| 0.1396 | 6.2511 | 1394 | 0.5556 | 0.6526 | 0.5556 | 0.7454 |
| 0.1396 | 6.2601 | 1396 | 0.5613 | 0.4538 | 0.5613 | 0.7492 |
| 0.1396 | 6.2691 | 1398 | 0.5912 | 0.5035 | 0.5912 | 0.7689 |
| 0.1396 | 6.2780 | 1400 | 0.6419 | 0.4118 | 0.6419 | 0.8012 |
| 0.1396 | 6.2870 | 1402 | 0.6732 | 0.4290 | 0.6732 | 0.8205 |
| 0.1396 | 6.2960 | 1404 | 0.6676 | 0.4290 | 0.6676 | 0.8170 |
| 0.1396 | 6.3049 | 1406 | 0.6674 | 0.3927 | 0.6674 | 0.8170 |
| 0.1396 | 6.3139 | 1408 | 0.6320 | 0.4290 | 0.6320 | 0.7950 |
| 0.1396 | 6.3229 | 1410 | 0.5910 | 0.4 | 0.5910 | 0.7688 |
| 0.1396 | 6.3318 | 1412 | 0.5774 | 0.4 | 0.5774 | 0.7599 |
| 0.1396 | 6.3408 | 1414 | 0.5679 | 0.4 | 0.5679 | 0.7536 |
| 0.1396 | 6.3498 | 1416 | 0.5640 | 0.4 | 0.5640 | 0.7510 |
| 0.1396 | 6.3587 | 1418 | 0.5568 | 0.4 | 0.5568 | 0.7462 |
| 0.1396 | 6.3677 | 1420 | 0.5693 | 0.4 | 0.5693 | 0.7545 |
| 0.1396 | 6.3767 | 1422 | 0.5876 | 0.4 | 0.5876 | 0.7665 |
| 0.1396 | 6.3857 | 1424 | 0.5978 | 0.3842 | 0.5978 | 0.7732 |
| 0.1396 | 6.3946 | 1426 | 0.5911 | 0.4803 | 0.5911 | 0.7688 |
| 0.1396 | 6.4036 | 1428 | 0.5833 | 0.5145 | 0.5833 | 0.7637 |
| 0.1396 | 6.4126 | 1430 | 0.5680 | 0.4878 | 0.5680 | 0.7537 |
| 0.1396 | 6.4215 | 1432 | 0.5482 | 0.6526 | 0.5482 | 0.7404 |
| 0.1396 | 6.4305 | 1434 | 0.5428 | 0.6526 | 0.5428 | 0.7368 |
| 0.1396 | 6.4395 | 1436 | 0.5482 | 0.6526 | 0.5482 | 0.7404 |
| 0.1396 | 6.4484 | 1438 | 0.5642 | 0.4803 | 0.5642 | 0.7511 |
| 0.1396 | 6.4574 | 1440 | 0.5814 | 0.4803 | 0.5814 | 0.7625 |
| 0.1396 | 6.4664 | 1442 | 0.5880 | 0.4779 | 0.5880 | 0.7668 |
| 0.1396 | 6.4753 | 1444 | 0.5930 | 0.4779 | 0.5930 | 0.7700 |
| 0.1396 | 6.4843 | 1446 | 0.5919 | 0.4779 | 0.5919 | 0.7693 |
| 0.1396 | 6.4933 | 1448 | 0.5835 | 0.4779 | 0.5835 | 0.7639 |
| 0.1396 | 6.5022 | 1450 | 0.5874 | 0.4779 | 0.5874 | 0.7664 |
| 0.1396 | 6.5112 | 1452 | 0.5896 | 0.4779 | 0.5896 | 0.7678 |
| 0.1396 | 6.5202 | 1454 | 0.5767 | 0.6526 | 0.5767 | 0.7594 |
| 0.1396 | 6.5291 | 1456 | 0.5663 | 0.6526 | 0.5663 | 0.7525 |
| 0.1396 | 6.5381 | 1458 | 0.5704 | 0.6526 | 0.5704 | 0.7553 |
| 0.1396 | 6.5471 | 1460 | 0.5747 | 0.6526 | 0.5747 | 0.7581 |
| 0.1396 | 6.5561 | 1462 | 0.5808 | 0.5871 | 0.5808 | 0.7621 |
| 0.1396 | 6.5650 | 1464 | 0.5855 | 0.5871 | 0.5855 | 0.7652 |
| 0.1396 | 6.5740 | 1466 | 0.5923 | 0.5871 | 0.5923 | 0.7696 |
| 0.1396 | 6.5830 | 1468 | 0.6000 | 0.5871 | 0.6000 | 0.7746 |
| 0.1396 | 6.5919 | 1470 | 0.6063 | 0.5737 | 0.6063 | 0.7787 |
| 0.1396 | 6.6009 | 1472 | 0.6164 | 0.5737 | 0.6164 | 0.7851 |
| 0.1396 | 6.6099 | 1474 | 0.6223 | 0.5679 | 0.6223 | 0.7889 |
| 0.1396 | 6.6188 | 1476 | 0.6117 | 0.4779 | 0.6117 | 0.7821 |
| 0.1396 | 6.6278 | 1478 | 0.5965 | 0.5422 | 0.5965 | 0.7724 |
| 0.1396 | 6.6368 | 1480 | 0.5865 | 0.5422 | 0.5865 | 0.7658 |
| 0.1396 | 6.6457 | 1482 | 0.5656 | 0.5481 | 0.5656 | 0.7521 |
| 0.1396 | 6.6547 | 1484 | 0.5436 | 0.6526 | 0.5436 | 0.7373 |
| 0.1396 | 6.6637 | 1486 | 0.5303 | 0.6526 | 0.5303 | 0.7282 |
| 0.1396 | 6.6726 | 1488 | 0.5207 | 0.6182 | 0.5207 | 0.7216 |
| 0.1396 | 6.6816 | 1490 | 0.5192 | 0.6182 | 0.5192 | 0.7206 |
| 0.1396 | 6.6906 | 1492 | 0.5294 | 0.5191 | 0.5294 | 0.7276 |
| 0.1396 | 6.6996 | 1494 | 0.5530 | 0.4779 | 0.5530 | 0.7436 |
| 0.1396 | 6.7085 | 1496 | 0.5854 | 0.5035 | 0.5854 | 0.7651 |
| 0.1396 | 6.7175 | 1498 | 0.5956 | 0.5035 | 0.5956 | 0.7718 |
| 0.0878 | 6.7265 | 1500 | 0.5861 | 0.5035 | 0.5861 | 0.7656 |
| 0.0878 | 6.7354 | 1502 | 0.5678 | 0.4779 | 0.5678 | 0.7535 |
| 0.0878 | 6.7444 | 1504 | 0.5583 | 0.4779 | 0.5583 | 0.7472 |
| 0.0878 | 6.7534 | 1506 | 0.5605 | 0.4779 | 0.5605 | 0.7486 |
| 0.0878 | 6.7623 | 1508 | 0.5653 | 0.4779 | 0.5653 | 0.7519 |
| 0.0878 | 6.7713 | 1510 | 0.5838 | 0.5035 | 0.5838 | 0.7641 |
| 0.0878 | 6.7803 | 1512 | 0.6017 | 0.5035 | 0.6017 | 0.7757 |
| 0.0878 | 6.7892 | 1514 | 0.6133 | 0.5035 | 0.6133 | 0.7831 |
| 0.0878 | 6.7982 | 1516 | 0.6010 | 0.5035 | 0.6010 | 0.7753 |
| 0.0878 | 6.8072 | 1518 | 0.5745 | 0.5035 | 0.5745 | 0.7579 |
| 0.0878 | 6.8161 | 1520 | 0.5506 | 0.5751 | 0.5506 | 0.7420 |
| 0.0878 | 6.8251 | 1522 | 0.5317 | 0.6473 | 0.5317 | 0.7292 |
| 0.0878 | 6.8341 | 1524 | 0.5266 | 0.6818 | 0.5266 | 0.7257 |
| 0.0878 | 6.8430 | 1526 | 0.5291 | 0.6473 | 0.5291 | 0.7274 |
| 0.0878 | 6.8520 | 1528 | 0.5320 | 0.6473 | 0.5320 | 0.7294 |
| 0.0878 | 6.8610 | 1530 | 0.5452 | 0.6473 | 0.5452 | 0.7384 |
| 0.0878 | 6.8700 | 1532 | 0.5680 | 0.5035 | 0.5680 | 0.7536 |
| 0.0878 | 6.8789 | 1534 | 0.5813 | 0.5035 | 0.5813 | 0.7624 |
| 0.0878 | 6.8879 | 1536 | 0.5901 | 0.5035 | 0.5901 | 0.7682 |
| 0.0878 | 6.8969 | 1538 | 0.5890 | 0.5035 | 0.5890 | 0.7674 |
| 0.0878 | 6.9058 | 1540 | 0.5946 | 0.5035 | 0.5946 | 0.7711 |
| 0.0878 | 6.9148 | 1542 | 0.5898 | 0.5035 | 0.5898 | 0.7680 |
| 0.0878 | 6.9238 | 1544 | 0.5827 | 0.5035 | 0.5827 | 0.7633 |
| 0.0878 | 6.9327 | 1546 | 0.5919 | 0.5035 | 0.5919 | 0.7693 |
| 0.0878 | 6.9417 | 1548 | 0.5886 | 0.5035 | 0.5886 | 0.7672 |
| 0.0878 | 6.9507 | 1550 | 0.5916 | 0.5035 | 0.5916 | 0.7691 |
| 0.0878 | 6.9596 | 1552 | 0.5927 | 0.4779 | 0.5927 | 0.7699 |
| 0.0878 | 6.9686 | 1554 | 0.5855 | 0.5679 | 0.5855 | 0.7652 |
| 0.0878 | 6.9776 | 1556 | 0.5754 | 0.5751 | 0.5754 | 0.7586 |
| 0.0878 | 6.9865 | 1558 | 0.5676 | 0.5751 | 0.5676 | 0.7534 |
| 0.0878 | 6.9955 | 1560 | 0.5627 | 0.5751 | 0.5627 | 0.7501 |
| 0.0878 | 7.0045 | 1562 | 0.5575 | 0.5751 | 0.5575 | 0.7466 |
| 0.0878 | 7.0135 | 1564 | 0.5520 | 0.5751 | 0.5520 | 0.7430 |
| 0.0878 | 7.0224 | 1566 | 0.5529 | 0.5751 | 0.5529 | 0.7436 |
| 0.0878 | 7.0314 | 1568 | 0.5533 | 0.5751 | 0.5533 | 0.7439 |
| 0.0878 | 7.0404 | 1570 | 0.5595 | 0.5751 | 0.5595 | 0.7480 |
| 0.0878 | 7.0493 | 1572 | 0.5624 | 0.5751 | 0.5624 | 0.7499 |
| 0.0878 | 7.0583 | 1574 | 0.5586 | 0.5751 | 0.5586 | 0.7474 |
| 0.0878 | 7.0673 | 1576 | 0.5531 | 0.5751 | 0.5531 | 0.7437 |
| 0.0878 | 7.0762 | 1578 | 0.5490 | 0.5812 | 0.5490 | 0.7410 |
| 0.0878 | 7.0852 | 1580 | 0.5446 | 0.5812 | 0.5446 | 0.7380 |
| 0.0878 | 7.0942 | 1582 | 0.5418 | 0.6526 | 0.5418 | 0.7360 |
| 0.0878 | 7.1031 | 1584 | 0.5384 | 0.6526 | 0.5384 | 0.7337 |
| 0.0878 | 7.1121 | 1586 | 0.5383 | 0.5812 | 0.5383 | 0.7337 |
| 0.0878 | 7.1211 | 1588 | 0.5392 | 0.6526 | 0.5392 | 0.7343 |
| 0.0878 | 7.1300 | 1590 | 0.5434 | 0.6526 | 0.5434 | 0.7372 |
| 0.0878 | 7.1390 | 1592 | 0.5494 | 0.6526 | 0.5494 | 0.7412 |
| 0.0878 | 7.1480 | 1594 | 0.5468 | 0.5812 | 0.5468 | 0.7394 |
| 0.0878 | 7.1570 | 1596 | 0.5474 | 0.5812 | 0.5474 | 0.7398 |
| 0.0878 | 7.1659 | 1598 | 0.5443 | 0.5812 | 0.5443 | 0.7378 |
| 0.0878 | 7.1749 | 1600 | 0.5391 | 0.5812 | 0.5391 | 0.7342 |
| 0.0878 | 7.1839 | 1602 | 0.5301 | 0.5812 | 0.5301 | 0.7281 |
| 0.0878 | 7.1928 | 1604 | 0.5220 | 0.6526 | 0.5220 | 0.7225 |
| 0.0878 | 7.2018 | 1606 | 0.5109 | 0.6526 | 0.5109 | 0.7148 |
| 0.0878 | 7.2108 | 1608 | 0.5031 | 0.6526 | 0.5031 | 0.7093 |
| 0.0878 | 7.2197 | 1610 | 0.5012 | 0.6526 | 0.5012 | 0.7080 |
| 0.0878 | 7.2287 | 1612 | 0.5024 | 0.5812 | 0.5024 | 0.7088 |
| 0.0878 | 7.2377 | 1614 | 0.4985 | 0.6526 | 0.4985 | 0.7061 |
| 0.0878 | 7.2466 | 1616 | 0.4981 | 0.6526 | 0.4981 | 0.7057 |
| 0.0878 | 7.2556 | 1618 | 0.5007 | 0.6526 | 0.5007 | 0.7076 |
| 0.0878 | 7.2646 | 1620 | 0.5046 | 0.6526 | 0.5046 | 0.7103 |
| 0.0878 | 7.2735 | 1622 | 0.5168 | 0.6526 | 0.5168 | 0.7189 |
| 0.0878 | 7.2825 | 1624 | 0.5290 | 0.5871 | 0.5290 | 0.7273 |
| 0.0878 | 7.2915 | 1626 | 0.5423 | 0.6192 | 0.5423 | 0.7364 |
| 0.0878 | 7.3004 | 1628 | 0.5456 | 0.5871 | 0.5456 | 0.7387 |
| 0.0878 | 7.3094 | 1630 | 0.5493 | 0.6526 | 0.5493 | 0.7411 |
| 0.0878 | 7.3184 | 1632 | 0.5547 | 0.5812 | 0.5547 | 0.7448 |
| 0.0878 | 7.3274 | 1634 | 0.5532 | 0.5812 | 0.5532 | 0.7438 |
| 0.0878 | 7.3363 | 1636 | 0.5469 | 0.5812 | 0.5469 | 0.7396 |
| 0.0878 | 7.3453 | 1638 | 0.5400 | 0.6526 | 0.5400 | 0.7349 |
| 0.0878 | 7.3543 | 1640 | 0.5395 | 0.6526 | 0.5395 | 0.7345 |
| 0.0878 | 7.3632 | 1642 | 0.5356 | 0.6526 | 0.5356 | 0.7319 |
| 0.0878 | 7.3722 | 1644 | 0.5359 | 0.6526 | 0.5359 | 0.7320 |
| 0.0878 | 7.3812 | 1646 | 0.5326 | 0.6526 | 0.5326 | 0.7298 |
| 0.0878 | 7.3901 | 1648 | 0.5301 | 0.6526 | 0.5301 | 0.7281 |
| 0.0878 | 7.3991 | 1650 | 0.5286 | 0.6526 | 0.5286 | 0.7271 |
| 0.0878 | 7.4081 | 1652 | 0.5325 | 0.6526 | 0.5325 | 0.7297 |
| 0.0878 | 7.4170 | 1654 | 0.5476 | 0.4527 | 0.5476 | 0.7400 |
| 0.0878 | 7.4260 | 1656 | 0.5727 | 0.5 | 0.5727 | 0.7568 |
| 0.0878 | 7.4350 | 1658 | 0.5844 | 0.5277 | 0.5844 | 0.7645 |
| 0.0878 | 7.4439 | 1660 | 0.5728 | 0.5277 | 0.5728 | 0.7568 |
| 0.0878 | 7.4529 | 1662 | 0.5582 | 0.5 | 0.5582 | 0.7471 |
| 0.0878 | 7.4619 | 1664 | 0.5444 | 0.4727 | 0.5444 | 0.7379 |
| 0.0878 | 7.4709 | 1666 | 0.5273 | 0.4465 | 0.5273 | 0.7261 |
| 0.0878 | 7.4798 | 1668 | 0.5093 | 0.6526 | 0.5093 | 0.7136 |
| 0.0878 | 7.4888 | 1670 | 0.5017 | 0.6526 | 0.5017 | 0.7083 |
| 0.0878 | 7.4978 | 1672 | 0.4945 | 0.5987 | 0.4945 | 0.7032 |
| 0.0878 | 7.5067 | 1674 | 0.4958 | 0.6526 | 0.4958 | 0.7041 |
| 0.0878 | 7.5157 | 1676 | 0.5052 | 0.6526 | 0.5052 | 0.7108 |
| 0.0878 | 7.5247 | 1678 | 0.5242 | 0.5812 | 0.5242 | 0.7240 |
| 0.0878 | 7.5336 | 1680 | 0.5496 | 0.4538 | 0.5496 | 0.7414 |
| 0.0878 | 7.5426 | 1682 | 0.5640 | 0.4527 | 0.5640 | 0.7510 |
| 0.0878 | 7.5516 | 1684 | 0.5636 | 0.4527 | 0.5636 | 0.7507 |
| 0.0878 | 7.5605 | 1686 | 0.5587 | 0.4538 | 0.5587 | 0.7475 |
| 0.0878 | 7.5695 | 1688 | 0.5531 | 0.4538 | 0.5531 | 0.7437 |
| 0.0878 | 7.5785 | 1690 | 0.5429 | 0.4538 | 0.5429 | 0.7368 |
| 0.0878 | 7.5874 | 1692 | 0.5366 | 0.4538 | 0.5366 | 0.7325 |
| 0.0878 | 7.5964 | 1694 | 0.5382 | 0.475 | 0.5382 | 0.7336 |
| 0.0878 | 7.6054 | 1696 | 0.5517 | 0.5039 | 0.5517 | 0.7427 |
| 0.0878 | 7.6143 | 1698 | 0.5584 | 0.5039 | 0.5584 | 0.7472 |
| 0.0878 | 7.6233 | 1700 | 0.5616 | 0.5039 | 0.5616 | 0.7494 |
| 0.0878 | 7.6323 | 1702 | 0.5579 | 0.5039 | 0.5579 | 0.7469 |
| 0.0878 | 7.6413 | 1704 | 0.5519 | 0.5039 | 0.5519 | 0.7429 |
| 0.0878 | 7.6502 | 1706 | 0.5436 | 0.4465 | 0.5436 | 0.7373 |
| 0.0878 | 7.6592 | 1708 | 0.5425 | 0.4538 | 0.5425 | 0.7365 |
| 0.0878 | 7.6682 | 1710 | 0.5432 | 0.4878 | 0.5432 | 0.7371 |
| 0.0878 | 7.6771 | 1712 | 0.5539 | 0.4538 | 0.5539 | 0.7442 |
| 0.0878 | 7.6861 | 1714 | 0.5658 | 0.4878 | 0.5658 | 0.7522 |
| 0.0878 | 7.6951 | 1716 | 0.5814 | 0.4538 | 0.5814 | 0.7625 |
| 0.0878 | 7.7040 | 1718 | 0.6066 | 0.5035 | 0.6066 | 0.7788 |
| 0.0878 | 7.7130 | 1720 | 0.6201 | 0.5035 | 0.6201 | 0.7874 |
| 0.0878 | 7.7220 | 1722 | 0.6195 | 0.5035 | 0.6195 | 0.7871 |
| 0.0878 | 7.7309 | 1724 | 0.6016 | 0.4527 | 0.6016 | 0.7756 |
| 0.0878 | 7.7399 | 1726 | 0.5778 | 0.4878 | 0.5778 | 0.7601 |
| 0.0878 | 7.7489 | 1728 | 0.5586 | 0.4878 | 0.5586 | 0.7474 |
| 0.0878 | 7.7578 | 1730 | 0.5505 | 0.5812 | 0.5505 | 0.7419 |
| 0.0878 | 7.7668 | 1732 | 0.5547 | 0.4878 | 0.5547 | 0.7448 |
| 0.0878 | 7.7758 | 1734 | 0.5629 | 0.4878 | 0.5629 | 0.7503 |
| 0.0878 | 7.7848 | 1736 | 0.5738 | 0.4878 | 0.5738 | 0.7575 |
| 0.0878 | 7.7937 | 1738 | 0.5774 | 0.4527 | 0.5774 | 0.7599 |
| 0.0878 | 7.8027 | 1740 | 0.5714 | 0.4527 | 0.5714 | 0.7559 |
| 0.0878 | 7.8117 | 1742 | 0.5698 | 0.4538 | 0.5698 | 0.7549 |
| 0.0878 | 7.8206 | 1744 | 0.5760 | 0.4527 | 0.5760 | 0.7589 |
| 0.0878 | 7.8296 | 1746 | 0.5927 | 0.4779 | 0.5927 | 0.7698 |
| 0.0878 | 7.8386 | 1748 | 0.6096 | 0.5035 | 0.6096 | 0.7808 |
| 0.0878 | 7.8475 | 1750 | 0.6075 | 0.5035 | 0.6075 | 0.7794 |
| 0.0878 | 7.8565 | 1752 | 0.5972 | 0.4779 | 0.5972 | 0.7728 |
| 0.0878 | 7.8655 | 1754 | 0.5728 | 0.4850 | 0.5728 | 0.7569 |
| 0.0878 | 7.8744 | 1756 | 0.5502 | 0.4878 | 0.5502 | 0.7418 |
| 0.0878 | 7.8834 | 1758 | 0.5331 | 0.5812 | 0.5331 | 0.7302 |
| 0.0878 | 7.8924 | 1760 | 0.5174 | 0.6526 | 0.5174 | 0.7193 |
| 0.0878 | 7.9013 | 1762 | 0.5131 | 0.6526 | 0.5131 | 0.7163 |
| 0.0878 | 7.9103 | 1764 | 0.5128 | 0.6526 | 0.5128 | 0.7161 |
| 0.0878 | 7.9193 | 1766 | 0.5165 | 0.6526 | 0.5165 | 0.7187 |
| 0.0878 | 7.9283 | 1768 | 0.5191 | 0.6526 | 0.5191 | 0.7205 |
| 0.0878 | 7.9372 | 1770 | 0.5256 | 0.6526 | 0.5256 | 0.7250 |
| 0.0878 | 7.9462 | 1772 | 0.5283 | 0.5812 | 0.5283 | 0.7269 |
| 0.0878 | 7.9552 | 1774 | 0.5286 | 0.5812 | 0.5286 | 0.7270 |
| 0.0878 | 7.9641 | 1776 | 0.5295 | 0.6526 | 0.5295 | 0.7277 |
| 0.0878 | 7.9731 | 1778 | 0.5356 | 0.5812 | 0.5356 | 0.7319 |
| 0.0878 | 7.9821 | 1780 | 0.5377 | 0.5812 | 0.5377 | 0.7333 |
| 0.0878 | 7.9910 | 1782 | 0.5406 | 0.5737 | 0.5406 | 0.7353 |
| 0.0878 | 8.0 | 1784 | 0.5443 | 0.5737 | 0.5443 | 0.7377 |
| 0.0878 | 8.0090 | 1786 | 0.5567 | 0.5359 | 0.5567 | 0.7461 |
| 0.0878 | 8.0179 | 1788 | 0.5694 | 0.5035 | 0.5694 | 0.7546 |
| 0.0878 | 8.0269 | 1790 | 0.5715 | 0.5035 | 0.5715 | 0.7560 |
| 0.0878 | 8.0359 | 1792 | 0.5597 | 0.5035 | 0.5597 | 0.7481 |
| 0.0878 | 8.0448 | 1794 | 0.5376 | 0.5035 | 0.5376 | 0.7332 |
| 0.0878 | 8.0538 | 1796 | 0.5216 | 0.5415 | 0.5216 | 0.7222 |
| 0.0878 | 8.0628 | 1798 | 0.5036 | 0.6123 | 0.5036 | 0.7096 |
| 0.0878 | 8.0717 | 1800 | 0.4831 | 0.5545 | 0.4831 | 0.6951 |
| 0.0878 | 8.0807 | 1802 | 0.4746 | 0.6526 | 0.4746 | 0.6889 |
| 0.0878 | 8.0897 | 1804 | 0.4753 | 0.6526 | 0.4753 | 0.6894 |
| 0.0878 | 8.0987 | 1806 | 0.4823 | 0.6526 | 0.4823 | 0.6945 |
| 0.0878 | 8.1076 | 1808 | 0.4963 | 0.5545 | 0.4963 | 0.7045 |
| 0.0878 | 8.1166 | 1810 | 0.5100 | 0.6123 | 0.5100 | 0.7142 |
| 0.0878 | 8.1256 | 1812 | 0.5224 | 0.5415 | 0.5224 | 0.7228 |
| 0.0878 | 8.1345 | 1814 | 0.5252 | 0.5415 | 0.5252 | 0.7247 |
| 0.0878 | 8.1435 | 1816 | 0.5214 | 0.4878 | 0.5214 | 0.7221 |
| 0.0878 | 8.1525 | 1818 | 0.5181 | 0.4878 | 0.5181 | 0.7198 |
| 0.0878 | 8.1614 | 1820 | 0.5141 | 0.6526 | 0.5141 | 0.7170 |
| 0.0878 | 8.1704 | 1822 | 0.5109 | 0.6526 | 0.5109 | 0.7148 |
| 0.0878 | 8.1794 | 1824 | 0.5085 | 0.6866 | 0.5085 | 0.7131 |
| 0.0878 | 8.1883 | 1826 | 0.5081 | 0.6866 | 0.5081 | 0.7128 |
| 0.0878 | 8.1973 | 1828 | 0.5108 | 0.6866 | 0.5108 | 0.7147 |
| 0.0878 | 8.2063 | 1830 | 0.5121 | 0.6866 | 0.5121 | 0.7156 |
| 0.0878 | 8.2152 | 1832 | 0.5125 | 0.6866 | 0.5125 | 0.7159 |
| 0.0878 | 8.2242 | 1834 | 0.5128 | 0.6866 | 0.5128 | 0.7161 |
| 0.0878 | 8.2332 | 1836 | 0.5132 | 0.6866 | 0.5132 | 0.7163 |
| 0.0878 | 8.2422 | 1838 | 0.5150 | 0.6866 | 0.5150 | 0.7177 |
| 0.0878 | 8.2511 | 1840 | 0.5183 | 0.6526 | 0.5183 | 0.7200 |
| 0.0878 | 8.2601 | 1842 | 0.5225 | 0.5812 | 0.5225 | 0.7228 |
| 0.0878 | 8.2691 | 1844 | 0.5292 | 0.5812 | 0.5292 | 0.7275 |
| 0.0878 | 8.2780 | 1846 | 0.5326 | 0.5812 | 0.5326 | 0.7298 |
| 0.0878 | 8.2870 | 1848 | 0.5365 | 0.4878 | 0.5365 | 0.7325 |
| 0.0878 | 8.2960 | 1850 | 0.5407 | 0.4878 | 0.5407 | 0.7353 |
| 0.0878 | 8.3049 | 1852 | 0.5393 | 0.4878 | 0.5393 | 0.7343 |
| 0.0878 | 8.3139 | 1854 | 0.5363 | 0.4878 | 0.5363 | 0.7323 |
| 0.0878 | 8.3229 | 1856 | 0.5316 | 0.4878 | 0.5316 | 0.7291 |
| 0.0878 | 8.3318 | 1858 | 0.5287 | 0.4878 | 0.5287 | 0.7271 |
| 0.0878 | 8.3408 | 1860 | 0.5241 | 0.6526 | 0.5241 | 0.7239 |
| 0.0878 | 8.3498 | 1862 | 0.5237 | 0.6526 | 0.5237 | 0.7237 |
| 0.0878 | 8.3587 | 1864 | 0.5258 | 0.6526 | 0.5258 | 0.7251 |
| 0.0878 | 8.3677 | 1866 | 0.5267 | 0.6526 | 0.5267 | 0.7258 |
| 0.0878 | 8.3767 | 1868 | 0.5305 | 0.5812 | 0.5305 | 0.7283 |
| 0.0878 | 8.3857 | 1870 | 0.5328 | 0.5812 | 0.5328 | 0.7299 |
| 0.0878 | 8.3946 | 1872 | 0.5341 | 0.5812 | 0.5341 | 0.7308 |
| 0.0878 | 8.4036 | 1874 | 0.5333 | 0.5812 | 0.5333 | 0.7302 |
| 0.0878 | 8.4126 | 1876 | 0.5316 | 0.6866 | 0.5316 | 0.7291 |
| 0.0878 | 8.4215 | 1878 | 0.5298 | 0.6866 | 0.5298 | 0.7279 |
| 0.0878 | 8.4305 | 1880 | 0.5316 | 0.6866 | 0.5316 | 0.7291 |
| 0.0878 | 8.4395 | 1882 | 0.5340 | 0.6139 | 0.5340 | 0.7308 |
| 0.0878 | 8.4484 | 1884 | 0.5359 | 0.5812 | 0.5359 | 0.7321 |
| 0.0878 | 8.4574 | 1886 | 0.5364 | 0.5812 | 0.5364 | 0.7324 |
| 0.0878 | 8.4664 | 1888 | 0.5361 | 0.5812 | 0.5361 | 0.7322 |
| 0.0878 | 8.4753 | 1890 | 0.5331 | 0.5737 | 0.5331 | 0.7302 |
| 0.0878 | 8.4843 | 1892 | 0.5348 | 0.5737 | 0.5348 | 0.7313 |
| 0.0878 | 8.4933 | 1894 | 0.5340 | 0.5737 | 0.5340 | 0.7308 |
| 0.0878 | 8.5022 | 1896 | 0.5278 | 0.5737 | 0.5278 | 0.7265 |
| 0.0878 | 8.5112 | 1898 | 0.5238 | 0.5103 | 0.5238 | 0.7237 |
| 0.0878 | 8.5202 | 1900 | 0.5204 | 0.5359 | 0.5204 | 0.7214 |
| 0.0878 | 8.5291 | 1902 | 0.5177 | 0.5359 | 0.5177 | 0.7195 |
| 0.0878 | 8.5381 | 1904 | 0.5145 | 0.5359 | 0.5145 | 0.7173 |
| 0.0878 | 8.5471 | 1906 | 0.5151 | 0.5359 | 0.5151 | 0.7177 |
| 0.0878 | 8.5561 | 1908 | 0.5123 | 0.5359 | 0.5123 | 0.7158 |
| 0.0878 | 8.5650 | 1910 | 0.5075 | 0.5359 | 0.5075 | 0.7124 |
| 0.0878 | 8.5740 | 1912 | 0.5013 | 0.6686 | 0.5013 | 0.7080 |
| 0.0878 | 8.5830 | 1914 | 0.4965 | 0.6410 | 0.4965 | 0.7047 |
| 0.0878 | 8.5919 | 1916 | 0.4929 | 0.6866 | 0.4929 | 0.7021 |
| 0.0878 | 8.6009 | 1918 | 0.4930 | 0.6866 | 0.4930 | 0.7021 |
| 0.0878 | 8.6099 | 1920 | 0.4926 | 0.6866 | 0.4926 | 0.7018 |
| 0.0878 | 8.6188 | 1922 | 0.4960 | 0.6866 | 0.4960 | 0.7043 |
| 0.0878 | 8.6278 | 1924 | 0.5018 | 0.6866 | 0.5018 | 0.7084 |
| 0.0878 | 8.6368 | 1926 | 0.5072 | 0.6866 | 0.5072 | 0.7122 |
| 0.0878 | 8.6457 | 1928 | 0.5099 | 0.6866 | 0.5099 | 0.7140 |
| 0.0878 | 8.6547 | 1930 | 0.5104 | 0.6866 | 0.5104 | 0.7144 |
| 0.0878 | 8.6637 | 1932 | 0.5125 | 0.6139 | 0.5125 | 0.7159 |
| 0.0878 | 8.6726 | 1934 | 0.5174 | 0.6048 | 0.5174 | 0.7193 |
| 0.0878 | 8.6816 | 1936 | 0.5244 | 0.5737 | 0.5244 | 0.7241 |
| 0.0878 | 8.6906 | 1938 | 0.5303 | 0.5737 | 0.5303 | 0.7282 |
| 0.0878 | 8.6996 | 1940 | 0.5406 | 0.5737 | 0.5406 | 0.7352 |
| 0.0878 | 8.7085 | 1942 | 0.5530 | 0.5737 | 0.5530 | 0.7436 |
| 0.0878 | 8.7175 | 1944 | 0.5594 | 0.5995 | 0.5594 | 0.7479 |
| 0.0878 | 8.7265 | 1946 | 0.5613 | 0.5995 | 0.5613 | 0.7492 |
| 0.0878 | 8.7354 | 1948 | 0.5643 | 0.5995 | 0.5643 | 0.7512 |
| 0.0878 | 8.7444 | 1950 | 0.5604 | 0.5995 | 0.5604 | 0.7486 |
| 0.0878 | 8.7534 | 1952 | 0.5525 | 0.5737 | 0.5525 | 0.7433 |
| 0.0878 | 8.7623 | 1954 | 0.5437 | 0.5737 | 0.5437 | 0.7373 |
| 0.0878 | 8.7713 | 1956 | 0.5339 | 0.5812 | 0.5339 | 0.7307 |
| 0.0878 | 8.7803 | 1958 | 0.5273 | 0.6139 | 0.5273 | 0.7261 |
| 0.0878 | 8.7892 | 1960 | 0.5255 | 0.6139 | 0.5255 | 0.7249 |
| 0.0878 | 8.7982 | 1962 | 0.5255 | 0.6139 | 0.5255 | 0.7249 |
| 0.0878 | 8.8072 | 1964 | 0.5262 | 0.6139 | 0.5262 | 0.7254 |
| 0.0878 | 8.8161 | 1966 | 0.5274 | 0.5812 | 0.5274 | 0.7262 |
| 0.0878 | 8.8251 | 1968 | 0.5302 | 0.5812 | 0.5302 | 0.7282 |
| 0.0878 | 8.8341 | 1970 | 0.5311 | 0.5812 | 0.5311 | 0.7288 |
| 0.0878 | 8.8430 | 1972 | 0.5350 | 0.5812 | 0.5350 | 0.7314 |
| 0.0878 | 8.8520 | 1974 | 0.5377 | 0.5737 | 0.5377 | 0.7333 |
| 0.0878 | 8.8610 | 1976 | 0.5396 | 0.4527 | 0.5396 | 0.7346 |
| 0.0878 | 8.8700 | 1978 | 0.5442 | 0.4779 | 0.5442 | 0.7377 |
| 0.0878 | 8.8789 | 1980 | 0.5436 | 0.4779 | 0.5436 | 0.7373 |
| 0.0878 | 8.8879 | 1982 | 0.5440 | 0.4779 | 0.5440 | 0.7375 |
| 0.0878 | 8.8969 | 1984 | 0.5453 | 0.4779 | 0.5453 | 0.7384 |
| 0.0878 | 8.9058 | 1986 | 0.5489 | 0.5035 | 0.5489 | 0.7408 |
| 0.0878 | 8.9148 | 1988 | 0.5512 | 0.5035 | 0.5512 | 0.7424 |
| 0.0878 | 8.9238 | 1990 | 0.5473 | 0.5035 | 0.5473 | 0.7398 |
| 0.0878 | 8.9327 | 1992 | 0.5395 | 0.5103 | 0.5395 | 0.7345 |
| 0.0878 | 8.9417 | 1994 | 0.5302 | 0.5737 | 0.5302 | 0.7281 |
| 0.0878 | 8.9507 | 1996 | 0.5216 | 0.5737 | 0.5216 | 0.7222 |
| 0.0878 | 8.9596 | 1998 | 0.5146 | 0.5812 | 0.5146 | 0.7174 |
| 0.0645 | 8.9686 | 2000 | 0.5118 | 0.5812 | 0.5118 | 0.7154 |
| 0.0645 | 8.9776 | 2002 | 0.5084 | 0.5812 | 0.5084 | 0.7130 |
| 0.0645 | 8.9865 | 2004 | 0.5082 | 0.5812 | 0.5082 | 0.7129 |
| 0.0645 | 8.9955 | 2006 | 0.5094 | 0.5812 | 0.5094 | 0.7137 |
| 0.0645 | 9.0045 | 2008 | 0.5142 | 0.5812 | 0.5142 | 0.7171 |
| 0.0645 | 9.0135 | 2010 | 0.5162 | 0.5812 | 0.5162 | 0.7185 |
| 0.0645 | 9.0224 | 2012 | 0.5146 | 0.5812 | 0.5146 | 0.7173 |
| 0.0645 | 9.0314 | 2014 | 0.5129 | 0.5812 | 0.5129 | 0.7161 |
| 0.0645 | 9.0404 | 2016 | 0.5089 | 0.5812 | 0.5089 | 0.7134 |
| 0.0645 | 9.0493 | 2018 | 0.5038 | 0.5812 | 0.5038 | 0.7098 |
| 0.0645 | 9.0583 | 2020 | 0.4989 | 0.6866 | 0.4989 | 0.7063 |
| 0.0645 | 9.0673 | 2022 | 0.4985 | 0.6866 | 0.4985 | 0.7060 |
| 0.0645 | 9.0762 | 2024 | 0.5006 | 0.6526 | 0.5006 | 0.7075 |
| 0.0645 | 9.0852 | 2026 | 0.5046 | 0.5812 | 0.5046 | 0.7103 |
| 0.0645 | 9.0942 | 2028 | 0.5088 | 0.5812 | 0.5088 | 0.7133 |
| 0.0645 | 9.1031 | 2030 | 0.5141 | 0.5812 | 0.5141 | 0.7170 |
| 0.0645 | 9.1121 | 2032 | 0.5198 | 0.5812 | 0.5198 | 0.7210 |
| 0.0645 | 9.1211 | 2034 | 0.5262 | 0.5737 | 0.5262 | 0.7254 |
| 0.0645 | 9.1300 | 2036 | 0.5344 | 0.5737 | 0.5344 | 0.7310 |
| 0.0645 | 9.1390 | 2038 | 0.5401 | 0.5995 | 0.5401 | 0.7349 |
| 0.0645 | 9.1480 | 2040 | 0.5427 | 0.5995 | 0.5427 | 0.7367 |
| 0.0645 | 9.1570 | 2042 | 0.5441 | 0.5995 | 0.5441 | 0.7376 |
| 0.0645 | 9.1659 | 2044 | 0.5437 | 0.5995 | 0.5437 | 0.7373 |
| 0.0645 | 9.1749 | 2046 | 0.5402 | 0.5995 | 0.5402 | 0.7350 |
| 0.0645 | 9.1839 | 2048 | 0.5348 | 0.5737 | 0.5348 | 0.7313 |
| 0.0645 | 9.1928 | 2050 | 0.5272 | 0.5737 | 0.5272 | 0.7261 |
| 0.0645 | 9.2018 | 2052 | 0.5202 | 0.5812 | 0.5202 | 0.7212 |
| 0.0645 | 9.2108 | 2054 | 0.5143 | 0.5812 | 0.5143 | 0.7172 |
| 0.0645 | 9.2197 | 2056 | 0.5117 | 0.5812 | 0.5117 | 0.7154 |
| 0.0645 | 9.2287 | 2058 | 0.5110 | 0.5812 | 0.5110 | 0.7149 |
| 0.0645 | 9.2377 | 2060 | 0.5111 | 0.5812 | 0.5111 | 0.7149 |
| 0.0645 | 9.2466 | 2062 | 0.5134 | 0.5812 | 0.5134 | 0.7165 |
| 0.0645 | 9.2556 | 2064 | 0.5179 | 0.5812 | 0.5179 | 0.7196 |
| 0.0645 | 9.2646 | 2066 | 0.5230 | 0.5995 | 0.5230 | 0.7232 |
| 0.0645 | 9.2735 | 2068 | 0.5272 | 0.5995 | 0.5272 | 0.7261 |
| 0.0645 | 9.2825 | 2070 | 0.5282 | 0.5103 | 0.5282 | 0.7268 |
| 0.0645 | 9.2915 | 2072 | 0.5271 | 0.5103 | 0.5271 | 0.7261 |
| 0.0645 | 9.3004 | 2074 | 0.5264 | 0.5103 | 0.5264 | 0.7255 |
| 0.0645 | 9.3094 | 2076 | 0.5251 | 0.5995 | 0.5251 | 0.7246 |
| 0.0645 | 9.3184 | 2078 | 0.5247 | 0.5995 | 0.5247 | 0.7244 |
| 0.0645 | 9.3274 | 2080 | 0.5255 | 0.5995 | 0.5255 | 0.7249 |
| 0.0645 | 9.3363 | 2082 | 0.5269 | 0.5995 | 0.5269 | 0.7259 |
| 0.0645 | 9.3453 | 2084 | 0.5302 | 0.5103 | 0.5302 | 0.7282 |
| 0.0645 | 9.3543 | 2086 | 0.5316 | 0.5103 | 0.5316 | 0.7291 |
| 0.0645 | 9.3632 | 2088 | 0.5296 | 0.5103 | 0.5296 | 0.7278 |
| 0.0645 | 9.3722 | 2090 | 0.5276 | 0.5995 | 0.5276 | 0.7263 |
| 0.0645 | 9.3812 | 2092 | 0.5274 | 0.6083 | 0.5274 | 0.7262 |
| 0.0645 | 9.3901 | 2094 | 0.5283 | 0.6083 | 0.5283 | 0.7268 |
| 0.0645 | 9.3991 | 2096 | 0.5304 | 0.5995 | 0.5304 | 0.7283 |
| 0.0645 | 9.4081 | 2098 | 0.5294 | 0.6083 | 0.5294 | 0.7276 |
| 0.0645 | 9.4170 | 2100 | 0.5287 | 0.5812 | 0.5287 | 0.7272 |
| 0.0645 | 9.4260 | 2102 | 0.5274 | 0.5812 | 0.5274 | 0.7262 |
| 0.0645 | 9.4350 | 2104 | 0.5248 | 0.5812 | 0.5248 | 0.7244 |
| 0.0645 | 9.4439 | 2106 | 0.5241 | 0.5812 | 0.5241 | 0.7239 |
| 0.0645 | 9.4529 | 2108 | 0.5245 | 0.5812 | 0.5245 | 0.7243 |
| 0.0645 | 9.4619 | 2110 | 0.5264 | 0.5812 | 0.5264 | 0.7256 |
| 0.0645 | 9.4709 | 2112 | 0.5266 | 0.5812 | 0.5266 | 0.7257 |
| 0.0645 | 9.4798 | 2114 | 0.5261 | 0.5812 | 0.5261 | 0.7253 |
| 0.0645 | 9.4888 | 2116 | 0.5274 | 0.5812 | 0.5274 | 0.7262 |
| 0.0645 | 9.4978 | 2118 | 0.5277 | 0.5812 | 0.5277 | 0.7264 |
| 0.0645 | 9.5067 | 2120 | 0.5279 | 0.5812 | 0.5279 | 0.7266 |
| 0.0645 | 9.5157 | 2122 | 0.5285 | 0.5812 | 0.5285 | 0.7270 |
| 0.0645 | 9.5247 | 2124 | 0.5308 | 0.5812 | 0.5308 | 0.7285 |
| 0.0645 | 9.5336 | 2126 | 0.5345 | 0.5812 | 0.5345 | 0.7311 |
| 0.0645 | 9.5426 | 2128 | 0.5365 | 0.5812 | 0.5365 | 0.7324 |
| 0.0645 | 9.5516 | 2130 | 0.5399 | 0.5812 | 0.5399 | 0.7348 |
| 0.0645 | 9.5605 | 2132 | 0.5424 | 0.5737 | 0.5424 | 0.7365 |
| 0.0645 | 9.5695 | 2134 | 0.5458 | 0.5737 | 0.5458 | 0.7388 |
| 0.0645 | 9.5785 | 2136 | 0.5499 | 0.5995 | 0.5499 | 0.7416 |
| 0.0645 | 9.5874 | 2138 | 0.5533 | 0.5679 | 0.5533 | 0.7439 |
| 0.0645 | 9.5964 | 2140 | 0.5561 | 0.4779 | 0.5561 | 0.7457 |
| 0.0645 | 9.6054 | 2142 | 0.5572 | 0.4779 | 0.5572 | 0.7464 |
| 0.0645 | 9.6143 | 2144 | 0.5578 | 0.4779 | 0.5578 | 0.7469 |
| 0.0645 | 9.6233 | 2146 | 0.5569 | 0.4779 | 0.5569 | 0.7462 |
| 0.0645 | 9.6323 | 2148 | 0.5559 | 0.5679 | 0.5559 | 0.7456 |
| 0.0645 | 9.6413 | 2150 | 0.5536 | 0.5679 | 0.5536 | 0.7440 |
| 0.0645 | 9.6502 | 2152 | 0.5512 | 0.5995 | 0.5512 | 0.7425 |
| 0.0645 | 9.6592 | 2154 | 0.5481 | 0.5995 | 0.5481 | 0.7403 |
| 0.0645 | 9.6682 | 2156 | 0.5452 | 0.5737 | 0.5452 | 0.7384 |
| 0.0645 | 9.6771 | 2158 | 0.5432 | 0.5737 | 0.5432 | 0.7370 |
| 0.0645 | 9.6861 | 2160 | 0.5425 | 0.5812 | 0.5425 | 0.7365 |
| 0.0645 | 9.6951 | 2162 | 0.5425 | 0.5812 | 0.5425 | 0.7365 |
| 0.0645 | 9.7040 | 2164 | 0.5428 | 0.5737 | 0.5428 | 0.7367 |
| 0.0645 | 9.7130 | 2166 | 0.5428 | 0.5737 | 0.5428 | 0.7368 |
| 0.0645 | 9.7220 | 2168 | 0.5425 | 0.5737 | 0.5425 | 0.7366 |
| 0.0645 | 9.7309 | 2170 | 0.5420 | 0.5737 | 0.5420 | 0.7362 |
| 0.0645 | 9.7399 | 2172 | 0.5417 | 0.5812 | 0.5417 | 0.7360 |
| 0.0645 | 9.7489 | 2174 | 0.5426 | 0.5737 | 0.5426 | 0.7366 |
| 0.0645 | 9.7578 | 2176 | 0.5433 | 0.5737 | 0.5433 | 0.7371 |
| 0.0645 | 9.7668 | 2178 | 0.5431 | 0.5737 | 0.5431 | 0.7370 |
| 0.0645 | 9.7758 | 2180 | 0.5427 | 0.5737 | 0.5427 | 0.7366 |
| 0.0645 | 9.7848 | 2182 | 0.5429 | 0.5737 | 0.5429 | 0.7368 |
| 0.0645 | 9.7937 | 2184 | 0.5431 | 0.5737 | 0.5431 | 0.7369 |
| 0.0645 | 9.8027 | 2186 | 0.5438 | 0.5737 | 0.5438 | 0.7374 |
| 0.0645 | 9.8117 | 2188 | 0.5448 | 0.5737 | 0.5448 | 0.7381 |
| 0.0645 | 9.8206 | 2190 | 0.5463 | 0.5679 | 0.5463 | 0.7391 |
| 0.0645 | 9.8296 | 2192 | 0.5471 | 0.4779 | 0.5471 | 0.7396 |
| 0.0645 | 9.8386 | 2194 | 0.5479 | 0.4779 | 0.5479 | 0.7402 |
| 0.0645 | 9.8475 | 2196 | 0.5483 | 0.4779 | 0.5483 | 0.7405 |
| 0.0645 | 9.8565 | 2198 | 0.5486 | 0.4779 | 0.5486 | 0.7407 |
| 0.0645 | 9.8655 | 2200 | 0.5489 | 0.4779 | 0.5489 | 0.7409 |
| 0.0645 | 9.8744 | 2202 | 0.5489 | 0.4779 | 0.5489 | 0.7409 |
| 0.0645 | 9.8834 | 2204 | 0.5488 | 0.4779 | 0.5488 | 0.7408 |
| 0.0645 | 9.8924 | 2206 | 0.5484 | 0.4779 | 0.5484 | 0.7405 |
| 0.0645 | 9.9013 | 2208 | 0.5479 | 0.4779 | 0.5479 | 0.7402 |
| 0.0645 | 9.9103 | 2210 | 0.5479 | 0.4779 | 0.5479 | 0.7402 |
| 0.0645 | 9.9193 | 2212 | 0.5483 | 0.4779 | 0.5483 | 0.7404 |
| 0.0645 | 9.9283 | 2214 | 0.5487 | 0.4779 | 0.5487 | 0.7408 |
| 0.0645 | 9.9372 | 2216 | 0.5489 | 0.4779 | 0.5489 | 0.7409 |
| 0.0645 | 9.9462 | 2218 | 0.5487 | 0.4779 | 0.5487 | 0.7407 |
| 0.0645 | 9.9552 | 2220 | 0.5484 | 0.4779 | 0.5484 | 0.7405 |
| 0.0645 | 9.9641 | 2222 | 0.5479 | 0.4779 | 0.5479 | 0.7402 |
| 0.0645 | 9.9731 | 2224 | 0.5476 | 0.4779 | 0.5476 | 0.7400 |
| 0.0645 | 9.9821 | 2226 | 0.5474 | 0.4779 | 0.5474 | 0.7399 |
| 0.0645 | 9.9910 | 2228 | 0.5473 | 0.4779 | 0.5473 | 0.7398 |
| 0.0645 | 10.0 | 2230 | 0.5473 | 0.4779 | 0.5473 | 0.7398 |
### Framework versions
- Transformers 4.44.2
- Pytorch 2.4.0+cu118
- Datasets 2.21.0
- Tokenizers 0.19.1
|
Nargizi/nllb-fering-v1
|
Nargizi
| 2024-11-16T17:06:50Z
| 159
| 0
|
transformers
|
[
"transformers",
"safetensors",
"m2m_100",
"text2text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2024-11-16T16:57:03Z
|
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
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- **Cloud Provider:** [More Information Needed]
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<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
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## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
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|
mradermacher/laser-dolphin-mixtral-2x7b-dpo-i1-GGUF
|
mradermacher
| 2024-11-16T17:02:23Z
| 274
| 0
|
transformers
|
[
"transformers",
"gguf",
"en",
"base_model:macadeliccc/laser-dolphin-mixtral-2x7b-dpo",
"base_model:quantized:macadeliccc/laser-dolphin-mixtral-2x7b-dpo",
"license:apache-2.0",
"endpoints_compatible",
"region:us",
"imatrix"
] | null | 2024-11-16T07:42:43Z
|
---
base_model: macadeliccc/laser-dolphin-mixtral-2x7b-dpo
language:
- en
library_name: transformers
license: apache-2.0
quantized_by: mradermacher
---
## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
<!-- ### tags: nicoboss -->
weighted/imatrix quants of https://huggingface.co/macadeliccc/laser-dolphin-mixtral-2x7b-dpo
<!-- provided-files -->
static quants are available at https://huggingface.co/mradermacher/laser-dolphin-mixtral-2x7b-dpo-GGUF
## Usage
If you are unsure how to use GGUF files, refer to one of [TheBloke's
READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for
more details, including on how to concatenate multi-part files.
## Provided Quants
(sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants)
| Link | Type | Size/GB | Notes |
|:-----|:-----|--------:|:------|
| [GGUF](https://huggingface.co/mradermacher/laser-dolphin-mixtral-2x7b-dpo-i1-GGUF/resolve/main/laser-dolphin-mixtral-2x7b-dpo.i1-IQ1_S.gguf) | i1-IQ1_S | 2.8 | for the desperate |
| [GGUF](https://huggingface.co/mradermacher/laser-dolphin-mixtral-2x7b-dpo-i1-GGUF/resolve/main/laser-dolphin-mixtral-2x7b-dpo.i1-IQ1_M.gguf) | i1-IQ1_M | 3.1 | mostly desperate |
| [GGUF](https://huggingface.co/mradermacher/laser-dolphin-mixtral-2x7b-dpo-i1-GGUF/resolve/main/laser-dolphin-mixtral-2x7b-dpo.i1-IQ2_XXS.gguf) | i1-IQ2_XXS | 3.6 | |
| [GGUF](https://huggingface.co/mradermacher/laser-dolphin-mixtral-2x7b-dpo-i1-GGUF/resolve/main/laser-dolphin-mixtral-2x7b-dpo.i1-IQ2_XS.gguf) | i1-IQ2_XS | 3.9 | |
| [GGUF](https://huggingface.co/mradermacher/laser-dolphin-mixtral-2x7b-dpo-i1-GGUF/resolve/main/laser-dolphin-mixtral-2x7b-dpo.i1-IQ2_S.gguf) | i1-IQ2_S | 4.1 | |
| [GGUF](https://huggingface.co/mradermacher/laser-dolphin-mixtral-2x7b-dpo-i1-GGUF/resolve/main/laser-dolphin-mixtral-2x7b-dpo.i1-IQ2_M.gguf) | i1-IQ2_M | 4.4 | |
| [GGUF](https://huggingface.co/mradermacher/laser-dolphin-mixtral-2x7b-dpo-i1-GGUF/resolve/main/laser-dolphin-mixtral-2x7b-dpo.i1-Q2_K.gguf) | i1-Q2_K | 4.9 | IQ3_XXS probably better |
| [GGUF](https://huggingface.co/mradermacher/laser-dolphin-mixtral-2x7b-dpo-i1-GGUF/resolve/main/laser-dolphin-mixtral-2x7b-dpo.i1-IQ3_XXS.gguf) | i1-IQ3_XXS | 5.1 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/laser-dolphin-mixtral-2x7b-dpo-i1-GGUF/resolve/main/laser-dolphin-mixtral-2x7b-dpo.i1-IQ3_XS.gguf) | i1-IQ3_XS | 5.4 | |
| [GGUF](https://huggingface.co/mradermacher/laser-dolphin-mixtral-2x7b-dpo-i1-GGUF/resolve/main/laser-dolphin-mixtral-2x7b-dpo.i1-Q3_K_S.gguf) | i1-Q3_K_S | 5.7 | IQ3_XS probably better |
| [GGUF](https://huggingface.co/mradermacher/laser-dolphin-mixtral-2x7b-dpo-i1-GGUF/resolve/main/laser-dolphin-mixtral-2x7b-dpo.i1-IQ3_S.gguf) | i1-IQ3_S | 5.7 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/laser-dolphin-mixtral-2x7b-dpo-i1-GGUF/resolve/main/laser-dolphin-mixtral-2x7b-dpo.i1-IQ3_M.gguf) | i1-IQ3_M | 5.8 | |
| [GGUF](https://huggingface.co/mradermacher/laser-dolphin-mixtral-2x7b-dpo-i1-GGUF/resolve/main/laser-dolphin-mixtral-2x7b-dpo.i1-Q3_K_M.gguf) | i1-Q3_K_M | 6.3 | IQ3_S probably better |
| [GGUF](https://huggingface.co/mradermacher/laser-dolphin-mixtral-2x7b-dpo-i1-GGUF/resolve/main/laser-dolphin-mixtral-2x7b-dpo.i1-Q3_K_L.gguf) | i1-Q3_K_L | 6.8 | IQ3_M probably better |
| [GGUF](https://huggingface.co/mradermacher/laser-dolphin-mixtral-2x7b-dpo-i1-GGUF/resolve/main/laser-dolphin-mixtral-2x7b-dpo.i1-IQ4_XS.gguf) | i1-IQ4_XS | 7.0 | |
| [GGUF](https://huggingface.co/mradermacher/laser-dolphin-mixtral-2x7b-dpo-i1-GGUF/resolve/main/laser-dolphin-mixtral-2x7b-dpo.i1-Q4_0_4_4.gguf) | i1-Q4_0_4_4 | 7.4 | fast on arm, low quality |
| [GGUF](https://huggingface.co/mradermacher/laser-dolphin-mixtral-2x7b-dpo-i1-GGUF/resolve/main/laser-dolphin-mixtral-2x7b-dpo.i1-Q4_0_4_8.gguf) | i1-Q4_0_4_8 | 7.4 | fast on arm+i8mm, low quality |
| [GGUF](https://huggingface.co/mradermacher/laser-dolphin-mixtral-2x7b-dpo-i1-GGUF/resolve/main/laser-dolphin-mixtral-2x7b-dpo.i1-Q4_0_8_8.gguf) | i1-Q4_0_8_8 | 7.4 | fast on arm+sve, low quality |
| [GGUF](https://huggingface.co/mradermacher/laser-dolphin-mixtral-2x7b-dpo-i1-GGUF/resolve/main/laser-dolphin-mixtral-2x7b-dpo.i1-Q4_0.gguf) | i1-Q4_0 | 7.4 | fast, low quality |
| [GGUF](https://huggingface.co/mradermacher/laser-dolphin-mixtral-2x7b-dpo-i1-GGUF/resolve/main/laser-dolphin-mixtral-2x7b-dpo.i1-Q4_K_S.gguf) | i1-Q4_K_S | 7.4 | optimal size/speed/quality |
| [GGUF](https://huggingface.co/mradermacher/laser-dolphin-mixtral-2x7b-dpo-i1-GGUF/resolve/main/laser-dolphin-mixtral-2x7b-dpo.i1-Q4_K_M.gguf) | i1-Q4_K_M | 7.9 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/laser-dolphin-mixtral-2x7b-dpo-i1-GGUF/resolve/main/laser-dolphin-mixtral-2x7b-dpo.i1-Q5_K_S.gguf) | i1-Q5_K_S | 9.0 | |
| [GGUF](https://huggingface.co/mradermacher/laser-dolphin-mixtral-2x7b-dpo-i1-GGUF/resolve/main/laser-dolphin-mixtral-2x7b-dpo.i1-Q5_K_M.gguf) | i1-Q5_K_M | 9.2 | |
| [GGUF](https://huggingface.co/mradermacher/laser-dolphin-mixtral-2x7b-dpo-i1-GGUF/resolve/main/laser-dolphin-mixtral-2x7b-dpo.i1-Q6_K.gguf) | i1-Q6_K | 10.7 | practically like static Q6_K |
Here is a handy graph by ikawrakow comparing some lower-quality quant
types (lower is better):

And here are Artefact2's thoughts on the matter:
https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9
## FAQ / Model Request
See https://huggingface.co/mradermacher/model_requests for some answers to
questions you might have and/or if you want some other model quantized.
## Thanks
I thank my company, [nethype GmbH](https://www.nethype.de/), for letting
me use its servers and providing upgrades to my workstation to enable
this work in my free time. Additional thanks to [@nicoboss](https://huggingface.co/nicoboss) for giving me access to his private supercomputer, enabling me to provide many more imatrix quants, at much higher quality, than I would otherwise be able to.
<!-- end -->
|
griffio/vit-large-patch16-224-new-dungeon-geo-morphs-009
|
griffio
| 2024-11-16T16:52:52Z
| 5
| 0
|
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"vit",
"image-classification",
"generated_from_trainer",
"dataset:imagefolder",
"base_model:google/vit-large-patch16-224",
"base_model:finetune:google/vit-large-patch16-224",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
image-classification
| 2024-11-16T16:32:15Z
|
---
library_name: transformers
license: apache-2.0
base_model: google/vit-large-patch16-224
tags:
- image-classification
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: vit-large-patch16-224-new-dungeon-geo-morphs-009
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: dungeon-geo-morphs
type: imagefolder
config: default
split: validation
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.96
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# vit-large-patch16-224-new-dungeon-geo-morphs-009
This model is a fine-tuned version of [google/vit-large-patch16-224](https://huggingface.co/google/vit-large-patch16-224) on the dungeon-geo-morphs dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0997
- Accuracy: 0.96
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 32
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 30
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-------:|:----:|:---------------:|:--------:|
| 1.1702 | 4.4444 | 10 | 0.5499 | 0.94 |
| 0.1925 | 8.8889 | 20 | 0.1645 | 0.94 |
| 0.0112 | 13.3333 | 30 | 0.0997 | 0.96 |
| 0.0011 | 17.7778 | 40 | 0.1255 | 0.96 |
| 0.0005 | 22.2222 | 50 | 0.1313 | 0.96 |
| 0.0004 | 26.6667 | 60 | 0.1238 | 0.96 |
### Framework versions
- Transformers 4.46.2
- Pytorch 2.5.1+cu121
- Datasets 3.1.0
- Tokenizers 0.20.3
|
mav23/Llama-3.2-3B-Sci-Think-GGUF
|
mav23
| 2024-11-16T16:52:51Z
| 54
| 0
|
transformers
|
[
"transformers",
"gguf",
"mergekit",
"merge",
"base_model:bunnycore/Llama-3.2-3B-science-lora_model",
"base_model:merge:bunnycore/Llama-3.2-3B-science-lora_model",
"base_model:huihui-ai/Llama-3.2-3B-Instruct-abliterated",
"base_model:merge:huihui-ai/Llama-3.2-3B-Instruct-abliterated",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2024-11-16T16:10:12Z
|
---
base_model:
- huihui-ai/Llama-3.2-3B-Instruct-abliterated
- bunnycore/Llama-3.2-3B-science-lora_model
library_name: transformers
tags:
- mergekit
- merge
---
# merge
This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit).
## Merge Details
### Merge Method
This model was merged using the passthrough merge method using [huihui-ai/Llama-3.2-3B-Instruct-abliterated](https://huggingface.co/huihui-ai/Llama-3.2-3B-Instruct-abliterated) + [bunnycore/Llama-3.2-3B-science-lora_model](https://huggingface.co/bunnycore/Llama-3.2-3B-science-lora_model) as a base.
### Models Merged
The following models were included in the merge:
### Configuration
The following YAML configuration was used to produce this model:
```yaml
base_model: huihui-ai/Llama-3.2-3B-Instruct-abliterated+bunnycore/Llama-3.2-3B-science-lora_model
merge_method: passthrough
models:
- model: huihui-ai/Llama-3.2-3B-Instruct-abliterated+bunnycore/Llama-3.2-3B-science-lora_model
```
|
Ppoyaa/1.0
|
Ppoyaa
| 2024-11-16T16:49:24Z
| 11
| 1
|
diffusers
|
[
"diffusers",
"text-to-image",
"lora",
"template:diffusion-lora",
"base_model:black-forest-labs/FLUX.1-dev",
"base_model:adapter:black-forest-labs/FLUX.1-dev",
"region:us"
] |
text-to-image
| 2024-11-15T19:38:05Z
|
---
tags:
- text-to-image
- lora
- diffusers
- template:diffusion-lora
widget:
- text: '-'
output:
url: images/1000061473.jpg
base_model: black-forest-labs/FLUX.1-dev
instance_prompt: rfxb4
---
# Test
<Gallery />
## Trigger words
You should use `rfxb4` to trigger the image generation.
## Download model
Weights for this model are available in Safetensors format.
[Download](/Ppoyaa/1.0/tree/main) them in the Files & versions tab.
|
emmajin0210/fine_tuned_main_raid_cleaned
|
emmajin0210
| 2024-11-16T16:46:56Z
| 105
| 0
|
transformers
|
[
"transformers",
"safetensors",
"roberta",
"text-classification",
"generated_from_trainer",
"base_model:FacebookAI/roberta-large",
"base_model:finetune:FacebookAI/roberta-large",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2024-11-16T16:33:49Z
|
---
library_name: transformers
license: mit
base_model: FacebookAI/roberta-large
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: fine_tuned_main_raid_cleaned
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# fine_tuned_main_raid_cleaned
This model is a fine-tuned version of [FacebookAI/roberta-large](https://huggingface.co/FacebookAI/roberta-large) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0545
- Accuracy: 0.9863
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:------:|:----:|:---------------:|:--------:|
| 0.4795 | 0.1018 | 100 | 0.1646 | 0.9680 |
| 0.2962 | 0.2037 | 200 | 0.3051 | 0.9451 |
| 0.145 | 0.3055 | 300 | 0.0977 | 0.9783 |
| 0.1013 | 0.4073 | 400 | 0.0962 | 0.9794 |
| 0.1147 | 0.5092 | 500 | 0.1666 | 0.9657 |
| 0.1772 | 0.6110 | 600 | 0.1542 | 0.9691 |
| 0.1423 | 0.7128 | 700 | 0.0671 | 0.9851 |
| 0.0614 | 0.8147 | 800 | 0.0869 | 0.9840 |
| 0.0935 | 0.9165 | 900 | 0.0599 | 0.9863 |
| 0.0608 | 1.0183 | 1000 | 0.1149 | 0.9805 |
| 0.0233 | 1.1202 | 1100 | 0.1006 | 0.9851 |
| 0.0825 | 1.2220 | 1200 | 0.3549 | 0.9359 |
| 0.0363 | 1.3238 | 1300 | 0.0660 | 0.9886 |
| 0.0411 | 1.4257 | 1400 | 0.1984 | 0.9737 |
| 0.0648 | 1.5275 | 1500 | 0.0545 | 0.9863 |
| 0.0483 | 1.6293 | 1600 | 0.0737 | 0.9886 |
| 0.0256 | 1.7312 | 1700 | 0.1071 | 0.9851 |
| 0.0195 | 1.8330 | 1800 | 0.1365 | 0.9805 |
### Framework versions
- Transformers 4.46.2
- Pytorch 2.5.1+cu121
- Datasets 3.1.0
- Tokenizers 0.20.3
|
taskydata/deberta-v3-base_10xp3nirstbbflanse_5xc4
|
taskydata
| 2024-11-16T16:34:38Z
| 113
| 0
|
transformers
|
[
"transformers",
"pytorch",
"safetensors",
"deberta-v2",
"text-classification",
"en",
"dataset:taskydata/tasky_or_not",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-12-19T03:24:24Z
|
---
license: mit
datasets:
- taskydata/tasky_or_not
language:
- en
metrics:
- accuracy
- f1
- recall
- precision
pipeline_tag: text-classification
---
**Hyperparameters:**
- learning rate: 2e-5
- weight decay: 0.01
- per_device_train_batch_size: 8
- per_device_eval_batch_size: 8
- gradient_accumulation_steps:1
- eval steps: 24000
- max_length: 512
- num_epochs: 2
- hidden_dropout_prob: 0.3
- attention_probs_dropout_prob: 0.25
**Dataset version:**
- taskydata/10xp3nirstbbflanse_5xc4
**Checkpoint:**
- 48000 steps
**Results on Validation set:**
| **Step** | **Training Loss** | **Validation Loss** | **Accuracy** | **Precision** | **Recall** | **F1** |
|:--------:|:-----------------:|:-------------------:|:------------:|:-------------:|:----------:|:--------:|
| 24000 | 0.052000 | 0.071572 | 0.988261 | 0.999752 | 0.987852 | 0.993767 |
| 48000 | 0.015100 | 0.026952 | 0.995925 | 0.999564 | 0.996132 | 0.997846 |
**Wandb logs:**
- https://wandb.ai/manandey/huggingface/runs/2vh7iwi6?workspace=user-manandey
|
skandk/chm1510
|
skandk
| 2024-11-16T16:28:22Z
| 7
| 0
|
diffusers
|
[
"diffusers",
"flux",
"lora",
"replicate",
"text-to-image",
"en",
"base_model:black-forest-labs/FLUX.1-dev",
"base_model:adapter:black-forest-labs/FLUX.1-dev",
"license:other",
"region:us"
] |
text-to-image
| 2024-11-12T23:54:43Z
|
---
license: other
license_name: flux-1-dev-non-commercial-license
license_link: https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md
language:
- en
tags:
- flux
- diffusers
- lora
- replicate
base_model: "black-forest-labs/FLUX.1-dev"
pipeline_tag: text-to-image
# widget:
# - text: >-
# prompt
# output:
# url: https://...
instance_prompt: chm1510
---
# Chm1510
<Gallery />
Trained on Replicate using:
https://replicate.com/ostris/flux-dev-lora-trainer/train
## Trigger words
You should use `chm1510` to trigger the image generation.
## Use it with the [🧨 diffusers library](https://github.com/huggingface/diffusers)
```py
from diffusers import AutoPipelineForText2Image
import torch
pipeline = AutoPipelineForText2Image.from_pretrained('black-forest-labs/FLUX.1-dev', torch_dtype=torch.float16).to('cuda')
pipeline.load_lora_weights('skandk/chm1510', weight_name='lora.safetensors')
image = pipeline('your prompt').images[0]
```
For more details, including weighting, merging and fusing LoRAs, check the [documentation on loading LoRAs in diffusers](https://huggingface.co/docs/diffusers/main/en/using-diffusers/loading_adapters)
|
MayBashendy/Arabic_FineTuningAraBERT_AugV4_k10_task2_organization_fold1
|
MayBashendy
| 2024-11-16T16:23:53Z
| 181
| 0
|
transformers
|
[
"transformers",
"safetensors",
"bert",
"text-classification",
"generated_from_trainer",
"base_model:aubmindlab/bert-base-arabertv02",
"base_model:finetune:aubmindlab/bert-base-arabertv02",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2024-11-16T16:12:27Z
|
---
library_name: transformers
base_model: aubmindlab/bert-base-arabertv02
tags:
- generated_from_trainer
model-index:
- name: Arabic_FineTuningAraBERT_AugV4_k10_task2_organization_fold1
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# Arabic_FineTuningAraBERT_AugV4_k10_task2_organization_fold1
This model is a fine-tuned version of [aubmindlab/bert-base-arabertv02](https://huggingface.co/aubmindlab/bert-base-arabertv02) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7066
- Qwk: 0.3544
- Mse: 0.7066
- Rmse: 0.8406
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Qwk | Mse | Rmse |
|:-------------:|:------:|:----:|:---------------:|:-------:|:------:|:------:|
| No log | 0.0127 | 2 | 3.5435 | 0.0 | 3.5435 | 1.8824 |
| No log | 0.0255 | 4 | 1.8454 | 0.1395 | 1.8454 | 1.3584 |
| No log | 0.0382 | 6 | 0.7111 | 0.1905 | 0.7111 | 0.8433 |
| No log | 0.0510 | 8 | 0.7424 | -0.125 | 0.7424 | 0.8616 |
| No log | 0.0637 | 10 | 0.9666 | 0.0400 | 0.9666 | 0.9832 |
| No log | 0.0764 | 12 | 1.1204 | 0.1000 | 1.1204 | 1.0585 |
| No log | 0.0892 | 14 | 0.6583 | 0.1356 | 0.6583 | 0.8113 |
| No log | 0.1019 | 16 | 0.5957 | 0.2500 | 0.5957 | 0.7718 |
| No log | 0.1146 | 18 | 0.6660 | 0.1724 | 0.6660 | 0.8161 |
| No log | 0.1274 | 20 | 0.5778 | 0.2909 | 0.5778 | 0.7601 |
| No log | 0.1401 | 22 | 0.6811 | -0.0385 | 0.6811 | 0.8253 |
| No log | 0.1529 | 24 | 1.1668 | 0.0842 | 1.1668 | 1.0802 |
| No log | 0.1656 | 26 | 1.0179 | 0.0 | 1.0179 | 1.0089 |
| No log | 0.1783 | 28 | 0.8773 | 0.0400 | 0.8773 | 0.9367 |
| No log | 0.1911 | 30 | 0.7181 | 0.0 | 0.7181 | 0.8474 |
| No log | 0.2038 | 32 | 0.6166 | 0.0 | 0.6166 | 0.7852 |
| No log | 0.2166 | 34 | 0.5974 | 0.0 | 0.5974 | 0.7729 |
| No log | 0.2293 | 36 | 0.6421 | 0.0 | 0.6421 | 0.8013 |
| No log | 0.2420 | 38 | 0.7306 | 0.0 | 0.7306 | 0.8547 |
| No log | 0.2548 | 40 | 0.7731 | 0.0400 | 0.7731 | 0.8793 |
| No log | 0.2675 | 42 | 0.7178 | 0.0400 | 0.7178 | 0.8473 |
| No log | 0.2803 | 44 | 0.9617 | 0.3143 | 0.9617 | 0.9807 |
| No log | 0.2930 | 46 | 1.1307 | 0.1882 | 1.1307 | 1.0633 |
| No log | 0.3057 | 48 | 0.8346 | 0.3200 | 0.8346 | 0.9135 |
| No log | 0.3185 | 50 | 0.5876 | 0.1818 | 0.5876 | 0.7666 |
| No log | 0.3312 | 52 | 0.5734 | 0.1818 | 0.5734 | 0.7572 |
| No log | 0.3439 | 54 | 0.5459 | 0.1053 | 0.5459 | 0.7389 |
| No log | 0.3567 | 56 | 0.5424 | 0.25 | 0.5424 | 0.7365 |
| No log | 0.3694 | 58 | 0.7102 | 0.3684 | 0.7102 | 0.8428 |
| No log | 0.3822 | 60 | 0.8170 | 0.3684 | 0.8170 | 0.9039 |
| No log | 0.3949 | 62 | 0.9877 | 0.3250 | 0.9877 | 0.9938 |
| No log | 0.4076 | 64 | 1.0776 | 0.1882 | 1.0776 | 1.0381 |
| No log | 0.4204 | 66 | 1.1619 | 0.1333 | 1.1619 | 1.0779 |
| No log | 0.4331 | 68 | 0.9776 | 0.1882 | 0.9776 | 0.9888 |
| No log | 0.4459 | 70 | 0.7181 | 0.3133 | 0.7181 | 0.8474 |
| No log | 0.4586 | 72 | 0.6122 | 0.3846 | 0.6122 | 0.7824 |
| No log | 0.4713 | 74 | 0.6539 | 0.4857 | 0.6539 | 0.8086 |
| No log | 0.4841 | 76 | 0.6198 | 0.3514 | 0.6198 | 0.7873 |
| No log | 0.4968 | 78 | 0.6264 | 0.3415 | 0.6264 | 0.7914 |
| No log | 0.5096 | 80 | 0.7148 | 0.4304 | 0.7148 | 0.8455 |
| No log | 0.5223 | 82 | 0.8228 | 0.1750 | 0.8228 | 0.9071 |
| No log | 0.5350 | 84 | 0.8560 | 0.1750 | 0.8560 | 0.9252 |
| No log | 0.5478 | 86 | 0.8140 | 0.1750 | 0.8140 | 0.9022 |
| No log | 0.5605 | 88 | 0.7361 | 0.4304 | 0.7361 | 0.8579 |
| No log | 0.5732 | 90 | 0.7339 | 0.4304 | 0.7339 | 0.8567 |
| No log | 0.5860 | 92 | 0.7223 | 0.4444 | 0.7223 | 0.8499 |
| No log | 0.5987 | 94 | 0.7303 | 0.3294 | 0.7303 | 0.8546 |
| No log | 0.6115 | 96 | 0.6937 | 0.4138 | 0.6937 | 0.8329 |
| No log | 0.6242 | 98 | 0.6578 | 0.3544 | 0.6578 | 0.8111 |
| No log | 0.6369 | 100 | 0.7063 | 0.4270 | 0.7063 | 0.8404 |
| No log | 0.6497 | 102 | 0.8872 | 0.3721 | 0.8872 | 0.9419 |
| No log | 0.6624 | 104 | 1.0509 | 0.2857 | 1.0509 | 1.0251 |
| No log | 0.6752 | 106 | 1.0318 | 0.2979 | 1.0318 | 1.0158 |
| No log | 0.6879 | 108 | 0.8859 | 0.3133 | 0.8859 | 0.9412 |
| No log | 0.7006 | 110 | 0.7127 | 0.2963 | 0.7127 | 0.8442 |
| No log | 0.7134 | 112 | 0.6194 | 0.3855 | 0.6194 | 0.7870 |
| No log | 0.7261 | 114 | 0.6293 | 0.3846 | 0.6293 | 0.7933 |
| No log | 0.7389 | 116 | 0.6946 | 0.3855 | 0.6946 | 0.8334 |
| No log | 0.7516 | 118 | 0.8044 | 0.3294 | 0.8044 | 0.8969 |
| No log | 0.7643 | 120 | 0.8113 | 0.4286 | 0.8113 | 0.9007 |
| No log | 0.7771 | 122 | 0.7621 | 0.4286 | 0.7621 | 0.8730 |
| No log | 0.7898 | 124 | 0.7045 | 0.4 | 0.7045 | 0.8394 |
| No log | 0.8025 | 126 | 0.6746 | 0.3143 | 0.6746 | 0.8214 |
| No log | 0.8153 | 128 | 0.7270 | 0.3824 | 0.7270 | 0.8526 |
| No log | 0.8280 | 130 | 0.8475 | 0.1127 | 0.8475 | 0.9206 |
| No log | 0.8408 | 132 | 0.8859 | 0.1127 | 0.8859 | 0.9412 |
| No log | 0.8535 | 134 | 0.7930 | 0.4615 | 0.7930 | 0.8905 |
| No log | 0.8662 | 136 | 0.7484 | 0.3250 | 0.7484 | 0.8651 |
| No log | 0.8790 | 138 | 0.8367 | 0.4304 | 0.8367 | 0.9147 |
| No log | 0.8917 | 140 | 0.9968 | 0.2581 | 0.9968 | 0.9984 |
| No log | 0.9045 | 142 | 1.1080 | 0.2174 | 1.1080 | 1.0526 |
| No log | 0.9172 | 144 | 1.0384 | 0.2581 | 1.0384 | 1.0190 |
| No log | 0.9299 | 146 | 0.8519 | 0.2410 | 0.8519 | 0.9230 |
| No log | 0.9427 | 148 | 0.6148 | 0.3704 | 0.6148 | 0.7841 |
| No log | 0.9554 | 150 | 0.5712 | 0.2941 | 0.5712 | 0.7558 |
| No log | 0.9682 | 152 | 0.5686 | 0.3014 | 0.5686 | 0.7541 |
| No log | 0.9809 | 154 | 0.6586 | 0.4578 | 0.6586 | 0.8116 |
| No log | 0.9936 | 156 | 0.8245 | 0.3023 | 0.8245 | 0.9080 |
| No log | 1.0064 | 158 | 0.8324 | 0.3023 | 0.8324 | 0.9124 |
| No log | 1.0191 | 160 | 0.7494 | 0.4138 | 0.7494 | 0.8657 |
| No log | 1.0318 | 162 | 0.7334 | 0.3514 | 0.7334 | 0.8564 |
| No log | 1.0446 | 164 | 0.7538 | 0.3514 | 0.7538 | 0.8682 |
| No log | 1.0573 | 166 | 0.7443 | 0.3514 | 0.7443 | 0.8627 |
| No log | 1.0701 | 168 | 0.7054 | 0.3077 | 0.7054 | 0.8399 |
| No log | 1.0828 | 170 | 0.8347 | 0.4286 | 0.8347 | 0.9136 |
| No log | 1.0955 | 172 | 1.0014 | 0.2857 | 1.0014 | 1.0007 |
| No log | 1.1083 | 174 | 1.1196 | 0.2683 | 1.1196 | 1.0581 |
| No log | 1.1210 | 176 | 1.0313 | 0.2683 | 1.0313 | 1.0155 |
| No log | 1.1338 | 178 | 0.8491 | 0.2683 | 0.8491 | 0.9214 |
| No log | 1.1465 | 180 | 0.7031 | 0.3684 | 0.7031 | 0.8385 |
| No log | 1.1592 | 182 | 0.6399 | 0.4 | 0.6399 | 0.7999 |
| No log | 1.1720 | 184 | 0.6378 | 0.3514 | 0.6378 | 0.7986 |
| No log | 1.1847 | 186 | 0.6877 | 0.4578 | 0.6877 | 0.8293 |
| No log | 1.1975 | 188 | 0.8352 | 0.3133 | 0.8352 | 0.9139 |
| No log | 1.2102 | 190 | 0.9093 | 0.2683 | 0.9093 | 0.9536 |
| No log | 1.2229 | 192 | 0.9246 | 0.2683 | 0.9246 | 0.9616 |
| No log | 1.2357 | 194 | 0.8612 | 0.4615 | 0.8612 | 0.9280 |
| No log | 1.2484 | 196 | 0.8251 | 0.3704 | 0.8251 | 0.9083 |
| No log | 1.2611 | 198 | 0.7772 | 0.3855 | 0.7772 | 0.8816 |
| No log | 1.2739 | 200 | 0.6736 | 0.3721 | 0.6736 | 0.8207 |
| No log | 1.2866 | 202 | 0.6377 | 0.3077 | 0.6377 | 0.7986 |
| No log | 1.2994 | 204 | 0.6467 | 0.3721 | 0.6467 | 0.8042 |
| No log | 1.3121 | 206 | 0.6698 | 0.5301 | 0.6698 | 0.8184 |
| No log | 1.3248 | 208 | 0.7208 | 0.5063 | 0.7208 | 0.8490 |
| No log | 1.3376 | 210 | 0.8070 | 0.4156 | 0.8070 | 0.8983 |
| No log | 1.3503 | 212 | 0.8771 | 0.2683 | 0.8771 | 0.9365 |
| No log | 1.3631 | 214 | 0.8806 | 0.2683 | 0.8806 | 0.9384 |
| No log | 1.3758 | 216 | 0.7604 | 0.5063 | 0.7604 | 0.8720 |
| No log | 1.3885 | 218 | 0.6712 | 0.4156 | 0.6712 | 0.8192 |
| No log | 1.4013 | 220 | 0.5939 | 0.4474 | 0.5939 | 0.7706 |
| No log | 1.4140 | 222 | 0.6265 | 0.3544 | 0.6265 | 0.7915 |
| No log | 1.4268 | 224 | 0.7088 | 0.3571 | 0.7088 | 0.8419 |
| No log | 1.4395 | 226 | 0.8488 | 0.4000 | 0.8488 | 0.9213 |
| No log | 1.4522 | 228 | 0.9987 | 0.3607 | 0.9987 | 0.9994 |
| No log | 1.4650 | 230 | 1.0288 | 0.3182 | 1.0288 | 1.0143 |
| No log | 1.4777 | 232 | 0.9641 | 0.2703 | 0.9641 | 0.9819 |
| No log | 1.4904 | 234 | 0.8507 | 0.4706 | 0.8507 | 0.9223 |
| No log | 1.5032 | 236 | 0.7758 | 0.3864 | 0.7758 | 0.8808 |
| No log | 1.5159 | 238 | 0.7118 | 0.3704 | 0.7118 | 0.8437 |
| No log | 1.5287 | 240 | 0.6673 | 0.3836 | 0.6673 | 0.8169 |
| No log | 1.5414 | 242 | 0.6557 | 0.4167 | 0.6557 | 0.8097 |
| No log | 1.5541 | 244 | 0.7121 | 0.4 | 0.7121 | 0.8438 |
| No log | 1.5669 | 246 | 0.7537 | 0.3721 | 0.7537 | 0.8681 |
| No log | 1.5796 | 248 | 0.7407 | 0.5185 | 0.7407 | 0.8607 |
| No log | 1.5924 | 250 | 0.6641 | 0.3824 | 0.6641 | 0.8150 |
| No log | 1.6051 | 252 | 0.6662 | 0.3824 | 0.6662 | 0.8162 |
| No log | 1.6178 | 254 | 0.7946 | 0.3846 | 0.7946 | 0.8914 |
| No log | 1.6306 | 256 | 0.9148 | 0.2581 | 0.9148 | 0.9565 |
| No log | 1.6433 | 258 | 0.9877 | 0.2581 | 0.9877 | 0.9938 |
| No log | 1.6561 | 260 | 0.8663 | 0.3864 | 0.8663 | 0.9307 |
| No log | 1.6688 | 262 | 0.6472 | 0.4146 | 0.6472 | 0.8045 |
| No log | 1.6815 | 264 | 0.5650 | 0.4156 | 0.5650 | 0.7516 |
| No log | 1.6943 | 266 | 0.5938 | 0.3014 | 0.5938 | 0.7706 |
| No log | 1.7070 | 268 | 0.6819 | 0.4935 | 0.6819 | 0.8258 |
| No log | 1.7197 | 270 | 0.7167 | 0.4878 | 0.7167 | 0.8466 |
| No log | 1.7325 | 272 | 0.6974 | 0.4138 | 0.6974 | 0.8351 |
| No log | 1.7452 | 274 | 0.8064 | 0.4375 | 0.8064 | 0.8980 |
| No log | 1.7580 | 276 | 0.8764 | 0.4396 | 0.8764 | 0.9362 |
| No log | 1.7707 | 278 | 0.8008 | 0.4255 | 0.8008 | 0.8949 |
| No log | 1.7834 | 280 | 0.7393 | 0.4375 | 0.7393 | 0.8598 |
| No log | 1.7962 | 282 | 0.6868 | 0.4706 | 0.6868 | 0.8287 |
| No log | 1.8089 | 284 | 0.6439 | 0.4000 | 0.6439 | 0.8024 |
| No log | 1.8217 | 286 | 0.6505 | 0.3133 | 0.6505 | 0.8065 |
| No log | 1.8344 | 288 | 0.6463 | 0.2963 | 0.6463 | 0.8039 |
| No log | 1.8471 | 290 | 0.7262 | 0.5301 | 0.7262 | 0.8522 |
| No log | 1.8599 | 292 | 0.7986 | 0.4304 | 0.7986 | 0.8937 |
| No log | 1.8726 | 294 | 0.7778 | 0.3846 | 0.7778 | 0.8819 |
| No log | 1.8854 | 296 | 0.7338 | 0.3846 | 0.7338 | 0.8566 |
| No log | 1.8981 | 298 | 0.6546 | 0.4 | 0.6546 | 0.8091 |
| No log | 1.9108 | 300 | 0.5810 | 0.4935 | 0.5810 | 0.7622 |
| No log | 1.9236 | 302 | 0.5834 | 0.4935 | 0.5834 | 0.7638 |
| No log | 1.9363 | 304 | 0.5840 | 0.4615 | 0.5840 | 0.7642 |
| No log | 1.9490 | 306 | 0.5941 | 0.4615 | 0.5941 | 0.7708 |
| No log | 1.9618 | 308 | 0.6596 | 0.5000 | 0.6596 | 0.8121 |
| No log | 1.9745 | 310 | 0.7031 | 0.4632 | 0.7031 | 0.8385 |
| No log | 1.9873 | 312 | 0.7468 | 0.4894 | 0.7468 | 0.8642 |
| No log | 2.0 | 314 | 0.7742 | 0.4667 | 0.7742 | 0.8799 |
| No log | 2.0127 | 316 | 0.7888 | 0.3415 | 0.7888 | 0.8881 |
| No log | 2.0255 | 318 | 0.7169 | 0.4156 | 0.7169 | 0.8467 |
| No log | 2.0382 | 320 | 0.7012 | 0.3333 | 0.7012 | 0.8374 |
| No log | 2.0510 | 322 | 0.7813 | 0.2963 | 0.7813 | 0.8839 |
| No log | 2.0637 | 324 | 0.8420 | 0.2500 | 0.8420 | 0.9176 |
| No log | 2.0764 | 326 | 0.8202 | 0.2105 | 0.8202 | 0.9057 |
| No log | 2.0892 | 328 | 0.7073 | 0.3836 | 0.7073 | 0.8410 |
| No log | 2.1019 | 330 | 0.6169 | 0.24 | 0.6169 | 0.7854 |
| No log | 2.1146 | 332 | 0.6321 | 0.2597 | 0.6321 | 0.7950 |
| No log | 2.1274 | 334 | 0.6701 | 0.2683 | 0.6701 | 0.8186 |
| No log | 2.1401 | 336 | 0.7416 | 0.4474 | 0.7416 | 0.8611 |
| No log | 2.1529 | 338 | 0.7605 | 0.4474 | 0.7605 | 0.8721 |
| No log | 2.1656 | 340 | 0.7112 | 0.3544 | 0.7112 | 0.8433 |
| No log | 2.1783 | 342 | 0.6874 | 0.3544 | 0.6874 | 0.8291 |
| No log | 2.1911 | 344 | 0.6880 | 0.3544 | 0.6880 | 0.8295 |
| No log | 2.2038 | 346 | 0.6691 | 0.3544 | 0.6691 | 0.8180 |
| No log | 2.2166 | 348 | 0.6843 | 0.2963 | 0.6843 | 0.8272 |
| No log | 2.2293 | 350 | 0.6795 | 0.2963 | 0.6795 | 0.8243 |
| No log | 2.2420 | 352 | 0.6782 | 0.2963 | 0.6782 | 0.8235 |
| No log | 2.2548 | 354 | 0.6782 | 0.2963 | 0.6782 | 0.8235 |
| No log | 2.2675 | 356 | 0.7306 | 0.3182 | 0.7306 | 0.8548 |
| No log | 2.2803 | 358 | 0.7924 | 0.3125 | 0.7924 | 0.8902 |
| No log | 2.2930 | 360 | 0.7774 | 0.3368 | 0.7774 | 0.8817 |
| No log | 2.3057 | 362 | 0.7787 | 0.3368 | 0.7787 | 0.8824 |
| No log | 2.3185 | 364 | 0.7574 | 0.2963 | 0.7574 | 0.8703 |
| No log | 2.3312 | 366 | 0.7313 | 0.3721 | 0.7313 | 0.8552 |
| No log | 2.3439 | 368 | 0.7342 | 0.4706 | 0.7342 | 0.8569 |
| No log | 2.3567 | 370 | 0.7172 | 0.4878 | 0.7172 | 0.8469 |
| No log | 2.3694 | 372 | 0.7452 | 0.5063 | 0.7452 | 0.8633 |
| No log | 2.3822 | 374 | 0.7132 | 0.5063 | 0.7132 | 0.8445 |
| No log | 2.3949 | 376 | 0.6168 | 0.4324 | 0.6168 | 0.7853 |
| No log | 2.4076 | 378 | 0.5463 | 0.4000 | 0.5463 | 0.7391 |
| No log | 2.4204 | 380 | 0.5433 | 0.2895 | 0.5433 | 0.7371 |
| No log | 2.4331 | 382 | 0.5450 | 0.2895 | 0.5450 | 0.7383 |
| No log | 2.4459 | 384 | 0.5705 | 0.4156 | 0.5705 | 0.7553 |
| No log | 2.4586 | 386 | 0.6661 | 0.5063 | 0.6661 | 0.8162 |
| No log | 2.4713 | 388 | 0.7479 | 0.5063 | 0.7479 | 0.8648 |
| No log | 2.4841 | 390 | 0.7323 | 0.5500 | 0.7323 | 0.8557 |
| No log | 2.4968 | 392 | 0.6584 | 0.5185 | 0.6584 | 0.8114 |
| No log | 2.5096 | 394 | 0.6066 | 0.2963 | 0.6066 | 0.7789 |
| No log | 2.5223 | 396 | 0.6068 | 0.3415 | 0.6068 | 0.7790 |
| No log | 2.5350 | 398 | 0.6107 | 0.4000 | 0.6107 | 0.7815 |
| No log | 2.5478 | 400 | 0.6313 | 0.4878 | 0.6313 | 0.7945 |
| No log | 2.5605 | 402 | 0.6563 | 0.5500 | 0.6563 | 0.8101 |
| No log | 2.5732 | 404 | 0.6786 | 0.5500 | 0.6786 | 0.8238 |
| No log | 2.5860 | 406 | 0.6805 | 0.5500 | 0.6805 | 0.8249 |
| No log | 2.5987 | 408 | 0.6493 | 0.5500 | 0.6493 | 0.8058 |
| No log | 2.6115 | 410 | 0.6139 | 0.48 | 0.6139 | 0.7835 |
| No log | 2.6242 | 412 | 0.6020 | 0.4304 | 0.6020 | 0.7759 |
| No log | 2.6369 | 414 | 0.6255 | 0.4304 | 0.6255 | 0.7909 |
| No log | 2.6497 | 416 | 0.6882 | 0.4935 | 0.6882 | 0.8296 |
| No log | 2.6624 | 418 | 0.7527 | 0.5185 | 0.7527 | 0.8676 |
| No log | 2.6752 | 420 | 0.7242 | 0.5185 | 0.7242 | 0.8510 |
| No log | 2.6879 | 422 | 0.6369 | 0.4146 | 0.6369 | 0.7980 |
| No log | 2.7006 | 424 | 0.6192 | 0.3544 | 0.6192 | 0.7869 |
| No log | 2.7134 | 426 | 0.6694 | 0.2609 | 0.6694 | 0.8182 |
| No log | 2.7261 | 428 | 0.6472 | 0.3544 | 0.6472 | 0.8045 |
| No log | 2.7389 | 430 | 0.5977 | 0.3855 | 0.5977 | 0.7731 |
| No log | 2.7516 | 432 | 0.6035 | 0.4935 | 0.6035 | 0.7769 |
| No log | 2.7643 | 434 | 0.6730 | 0.475 | 0.6730 | 0.8204 |
| No log | 2.7771 | 436 | 0.7840 | 0.2222 | 0.7840 | 0.8854 |
| No log | 2.7898 | 438 | 0.8303 | 0.2222 | 0.8303 | 0.9112 |
| No log | 2.8025 | 440 | 0.7817 | 0.3571 | 0.7817 | 0.8841 |
| No log | 2.8153 | 442 | 0.7285 | 0.5063 | 0.7285 | 0.8535 |
| No log | 2.8280 | 444 | 0.7111 | 0.48 | 0.7111 | 0.8433 |
| No log | 2.8408 | 446 | 0.6958 | 0.4156 | 0.6958 | 0.8341 |
| No log | 2.8535 | 448 | 0.7029 | 0.4156 | 0.7029 | 0.8384 |
| No log | 2.8662 | 450 | 0.7231 | 0.4474 | 0.7231 | 0.8504 |
| No log | 2.8790 | 452 | 0.8209 | 0.3836 | 0.8209 | 0.9060 |
| No log | 2.8917 | 454 | 0.9118 | 0.1818 | 0.9118 | 0.9549 |
| No log | 2.9045 | 456 | 0.9112 | 0.2683 | 0.9112 | 0.9546 |
| No log | 2.9172 | 458 | 0.8734 | 0.3333 | 0.8734 | 0.9346 |
| No log | 2.9299 | 460 | 0.7898 | 0.3836 | 0.7898 | 0.8887 |
| No log | 2.9427 | 462 | 0.6730 | 0.3824 | 0.6730 | 0.8204 |
| No log | 2.9554 | 464 | 0.6036 | 0.4857 | 0.6036 | 0.7769 |
| No log | 2.9682 | 466 | 0.5734 | 0.4167 | 0.5734 | 0.7572 |
| No log | 2.9809 | 468 | 0.5732 | 0.4167 | 0.5732 | 0.7571 |
| No log | 2.9936 | 470 | 0.6088 | 0.4857 | 0.6088 | 0.7802 |
| No log | 3.0064 | 472 | 0.6832 | 0.5063 | 0.6832 | 0.8266 |
| No log | 3.0191 | 474 | 0.7477 | 0.5063 | 0.7477 | 0.8647 |
| No log | 3.0318 | 476 | 0.7661 | 0.5500 | 0.7661 | 0.8753 |
| No log | 3.0446 | 478 | 0.7060 | 0.5185 | 0.7060 | 0.8402 |
| No log | 3.0573 | 480 | 0.6529 | 0.2597 | 0.6529 | 0.8080 |
| No log | 3.0701 | 482 | 0.6550 | 0.3077 | 0.6550 | 0.8093 |
| No log | 3.0828 | 484 | 0.6514 | 0.3077 | 0.6514 | 0.8071 |
| No log | 3.0955 | 486 | 0.6297 | 0.3133 | 0.6297 | 0.7935 |
| No log | 3.1083 | 488 | 0.6016 | 0.3077 | 0.6016 | 0.7756 |
| No log | 3.1210 | 490 | 0.5914 | 0.4146 | 0.5914 | 0.7690 |
| No log | 3.1338 | 492 | 0.5887 | 0.4146 | 0.5887 | 0.7673 |
| No log | 3.1465 | 494 | 0.5961 | 0.4615 | 0.5961 | 0.7721 |
| No log | 3.1592 | 496 | 0.6448 | 0.5610 | 0.6448 | 0.8030 |
| No log | 3.1720 | 498 | 0.7325 | 0.475 | 0.7325 | 0.8558 |
| 0.4502 | 3.1847 | 500 | 0.7259 | 0.475 | 0.7259 | 0.8520 |
| 0.4502 | 3.1975 | 502 | 0.6181 | 0.5185 | 0.6181 | 0.7862 |
| 0.4502 | 3.2102 | 504 | 0.5351 | 0.3704 | 0.5351 | 0.7315 |
| 0.4502 | 3.2229 | 506 | 0.5492 | 0.475 | 0.5492 | 0.7411 |
| 0.4502 | 3.2357 | 508 | 0.5409 | 0.5385 | 0.5409 | 0.7355 |
| 0.4502 | 3.2484 | 510 | 0.5451 | 0.4304 | 0.5451 | 0.7383 |
| 0.4502 | 3.2611 | 512 | 0.6130 | 0.3684 | 0.6130 | 0.7830 |
| 0.4502 | 3.2739 | 514 | 0.6982 | 0.475 | 0.6982 | 0.8356 |
| 0.4502 | 3.2866 | 516 | 0.7670 | 0.5063 | 0.7670 | 0.8758 |
| 0.4502 | 3.2994 | 518 | 0.7544 | 0.4615 | 0.7544 | 0.8686 |
| 0.4502 | 3.3121 | 520 | 0.6783 | 0.4324 | 0.6783 | 0.8236 |
| 0.4502 | 3.3248 | 522 | 0.6447 | 0.4324 | 0.6447 | 0.8029 |
| 0.4502 | 3.3376 | 524 | 0.6273 | 0.4324 | 0.6273 | 0.7920 |
| 0.4502 | 3.3503 | 526 | 0.6666 | 0.4324 | 0.6666 | 0.8164 |
| 0.4502 | 3.3631 | 528 | 0.7367 | 0.4324 | 0.7367 | 0.8583 |
| 0.4502 | 3.3758 | 530 | 0.8252 | 0.4286 | 0.8252 | 0.9084 |
| 0.4502 | 3.3885 | 532 | 0.8343 | 0.3855 | 0.8343 | 0.9134 |
| 0.4502 | 3.4013 | 534 | 0.7691 | 0.3855 | 0.7691 | 0.8770 |
| 0.4502 | 3.4140 | 536 | 0.7162 | 0.475 | 0.7162 | 0.8463 |
| 0.4502 | 3.4268 | 538 | 0.6559 | 0.4156 | 0.6559 | 0.8099 |
| 0.4502 | 3.4395 | 540 | 0.6229 | 0.4000 | 0.6229 | 0.7893 |
| 0.4502 | 3.4522 | 542 | 0.6262 | 0.2963 | 0.6262 | 0.7913 |
| 0.4502 | 3.4650 | 544 | 0.6288 | 0.4000 | 0.6288 | 0.7930 |
| 0.4502 | 3.4777 | 546 | 0.6528 | 0.4615 | 0.6528 | 0.8080 |
| 0.4502 | 3.4904 | 548 | 0.6717 | 0.4615 | 0.6717 | 0.8196 |
| 0.4502 | 3.5032 | 550 | 0.6948 | 0.4474 | 0.6948 | 0.8335 |
| 0.4502 | 3.5159 | 552 | 0.6678 | 0.4474 | 0.6678 | 0.8172 |
| 0.4502 | 3.5287 | 554 | 0.6788 | 0.4474 | 0.6788 | 0.8239 |
| 0.4502 | 3.5414 | 556 | 0.7016 | 0.5500 | 0.7016 | 0.8376 |
| 0.4502 | 3.5541 | 558 | 0.6988 | 0.5185 | 0.6988 | 0.8359 |
| 0.4502 | 3.5669 | 560 | 0.6377 | 0.4474 | 0.6377 | 0.7985 |
| 0.4502 | 3.5796 | 562 | 0.6024 | 0.4000 | 0.6024 | 0.7762 |
| 0.4502 | 3.5924 | 564 | 0.5972 | 0.4 | 0.5972 | 0.7728 |
| 0.4502 | 3.6051 | 566 | 0.6177 | 0.4000 | 0.6177 | 0.7859 |
| 0.4502 | 3.6178 | 568 | 0.6457 | 0.4000 | 0.6457 | 0.8036 |
| 0.4502 | 3.6306 | 570 | 0.7202 | 0.5000 | 0.7202 | 0.8486 |
| 0.4502 | 3.6433 | 572 | 0.7381 | 0.5610 | 0.7381 | 0.8591 |
| 0.4502 | 3.6561 | 574 | 0.7127 | 0.5000 | 0.7127 | 0.8442 |
| 0.4502 | 3.6688 | 576 | 0.6855 | 0.2963 | 0.6855 | 0.8280 |
| 0.4502 | 3.6815 | 578 | 0.6613 | 0.3133 | 0.6613 | 0.8132 |
| 0.4502 | 3.6943 | 580 | 0.6487 | 0.3571 | 0.6487 | 0.8054 |
| 0.4502 | 3.7070 | 582 | 0.6702 | 0.4894 | 0.6702 | 0.8187 |
| 0.4502 | 3.7197 | 584 | 0.6701 | 0.4848 | 0.6701 | 0.8186 |
| 0.4502 | 3.7325 | 586 | 0.6551 | 0.3415 | 0.6551 | 0.8094 |
| 0.4502 | 3.7452 | 588 | 0.6787 | 0.4304 | 0.6787 | 0.8238 |
| 0.4502 | 3.7580 | 590 | 0.6903 | 0.4156 | 0.6903 | 0.8308 |
| 0.4502 | 3.7707 | 592 | 0.6650 | 0.4304 | 0.6650 | 0.8155 |
| 0.4502 | 3.7834 | 594 | 0.6177 | 0.2963 | 0.6177 | 0.7859 |
| 0.4502 | 3.7962 | 596 | 0.5887 | 0.2963 | 0.5887 | 0.7672 |
| 0.4502 | 3.8089 | 598 | 0.5842 | 0.2895 | 0.5842 | 0.7643 |
| 0.4502 | 3.8217 | 600 | 0.5943 | 0.2895 | 0.5943 | 0.7709 |
| 0.4502 | 3.8344 | 602 | 0.6304 | 0.4000 | 0.6304 | 0.7940 |
| 0.4502 | 3.8471 | 604 | 0.6929 | 0.3846 | 0.6929 | 0.8324 |
| 0.4502 | 3.8599 | 606 | 0.7669 | 0.4474 | 0.7669 | 0.8757 |
| 0.4502 | 3.8726 | 608 | 0.7979 | 0.48 | 0.7979 | 0.8932 |
| 0.4502 | 3.8854 | 610 | 0.7604 | 0.48 | 0.7604 | 0.8720 |
| 0.4502 | 3.8981 | 612 | 0.6799 | 0.4474 | 0.6799 | 0.8246 |
| 0.4502 | 3.9108 | 614 | 0.6316 | 0.3514 | 0.6316 | 0.7947 |
| 0.4502 | 3.9236 | 616 | 0.6291 | 0.2105 | 0.6291 | 0.7931 |
| 0.4502 | 3.9363 | 618 | 0.6403 | 0.3836 | 0.6403 | 0.8002 |
| 0.4502 | 3.9490 | 620 | 0.6863 | 0.48 | 0.6863 | 0.8284 |
| 0.4502 | 3.9618 | 622 | 0.7221 | 0.4324 | 0.7221 | 0.8497 |
| 0.4502 | 3.9745 | 624 | 0.6820 | 0.4324 | 0.6820 | 0.8258 |
| 0.4502 | 3.9873 | 626 | 0.6280 | 0.4507 | 0.6280 | 0.7924 |
| 0.4502 | 4.0 | 628 | 0.6069 | 0.4167 | 0.6069 | 0.7790 |
| 0.4502 | 4.0127 | 630 | 0.6009 | 0.3200 | 0.6009 | 0.7752 |
| 0.4502 | 4.0255 | 632 | 0.6325 | 0.2597 | 0.6325 | 0.7953 |
| 0.4502 | 4.0382 | 634 | 0.6657 | 0.2857 | 0.6657 | 0.8159 |
| 0.4502 | 4.0510 | 636 | 0.6618 | 0.2857 | 0.6618 | 0.8135 |
| 0.4502 | 4.0637 | 638 | 0.6537 | 0.4000 | 0.6537 | 0.8085 |
| 0.4502 | 4.0764 | 640 | 0.6502 | 0.4000 | 0.6502 | 0.8064 |
| 0.4502 | 4.0892 | 642 | 0.6533 | 0.4615 | 0.6533 | 0.8082 |
| 0.4502 | 4.1019 | 644 | 0.6640 | 0.4156 | 0.6640 | 0.8149 |
| 0.4502 | 4.1146 | 646 | 0.6639 | 0.4156 | 0.6639 | 0.8148 |
| 0.4502 | 4.1274 | 648 | 0.6649 | 0.4156 | 0.6649 | 0.8154 |
| 0.4502 | 4.1401 | 650 | 0.6352 | 0.4156 | 0.6352 | 0.7970 |
| 0.4502 | 4.1529 | 652 | 0.5896 | 0.4156 | 0.5896 | 0.7678 |
| 0.4502 | 4.1656 | 654 | 0.5676 | 0.4324 | 0.5676 | 0.7534 |
| 0.4502 | 4.1783 | 656 | 0.5900 | 0.3077 | 0.5900 | 0.7681 |
| 0.4502 | 4.1911 | 658 | 0.6208 | 0.3294 | 0.6208 | 0.7879 |
| 0.4502 | 4.2038 | 660 | 0.6506 | 0.3077 | 0.6506 | 0.8066 |
| 0.4502 | 4.2166 | 662 | 0.7188 | 0.2963 | 0.7188 | 0.8478 |
| 0.4502 | 4.2293 | 664 | 0.7899 | 0.3133 | 0.7899 | 0.8888 |
| 0.4502 | 4.2420 | 666 | 0.8130 | 0.3505 | 0.8130 | 0.9016 |
| 0.4502 | 4.2548 | 668 | 0.8202 | 0.3654 | 0.8202 | 0.9056 |
| 0.4502 | 4.2675 | 670 | 0.8004 | 0.3636 | 0.8004 | 0.8947 |
| 0.4502 | 4.2803 | 672 | 0.7780 | 0.3636 | 0.7780 | 0.8820 |
| 0.4502 | 4.2930 | 674 | 0.7190 | 0.3294 | 0.7190 | 0.8480 |
| 0.4502 | 4.3057 | 676 | 0.6658 | 0.3684 | 0.6658 | 0.8160 |
| 0.4502 | 4.3185 | 678 | 0.6784 | 0.4156 | 0.6784 | 0.8237 |
| 0.4502 | 4.3312 | 680 | 0.7114 | 0.5063 | 0.7114 | 0.8435 |
| 0.4502 | 4.3439 | 682 | 0.7218 | 0.5063 | 0.7218 | 0.8496 |
| 0.4502 | 4.3567 | 684 | 0.7387 | 0.5063 | 0.7387 | 0.8595 |
| 0.4502 | 4.3694 | 686 | 0.7287 | 0.5185 | 0.7287 | 0.8536 |
| 0.4502 | 4.3822 | 688 | 0.7013 | 0.4156 | 0.7013 | 0.8374 |
| 0.4502 | 4.3949 | 690 | 0.6873 | 0.3514 | 0.6873 | 0.8291 |
| 0.4502 | 4.4076 | 692 | 0.6967 | 0.2597 | 0.6967 | 0.8347 |
| 0.4502 | 4.4204 | 694 | 0.7157 | 0.2105 | 0.7157 | 0.8460 |
| 0.4502 | 4.4331 | 696 | 0.7473 | 0.3333 | 0.7473 | 0.8644 |
| 0.4502 | 4.4459 | 698 | 0.7542 | 0.4156 | 0.7542 | 0.8684 |
| 0.4502 | 4.4586 | 700 | 0.7168 | 0.4474 | 0.7168 | 0.8466 |
| 0.4502 | 4.4713 | 702 | 0.6551 | 0.4156 | 0.6551 | 0.8094 |
| 0.4502 | 4.4841 | 704 | 0.6348 | 0.4156 | 0.6348 | 0.7968 |
| 0.4502 | 4.4968 | 706 | 0.6446 | 0.4474 | 0.6446 | 0.8029 |
| 0.4502 | 4.5096 | 708 | 0.6749 | 0.48 | 0.6749 | 0.8215 |
| 0.4502 | 4.5223 | 710 | 0.7108 | 0.5500 | 0.7108 | 0.8431 |
| 0.4502 | 4.5350 | 712 | 0.6918 | 0.5500 | 0.6918 | 0.8318 |
| 0.4502 | 4.5478 | 714 | 0.6404 | 0.4474 | 0.6404 | 0.8003 |
| 0.4502 | 4.5605 | 716 | 0.5965 | 0.3836 | 0.5965 | 0.7723 |
| 0.4502 | 4.5732 | 718 | 0.5955 | 0.2597 | 0.5955 | 0.7717 |
| 0.4502 | 4.5860 | 720 | 0.6091 | 0.3077 | 0.6091 | 0.7805 |
| 0.4502 | 4.5987 | 722 | 0.6095 | 0.3684 | 0.6095 | 0.7807 |
| 0.4502 | 4.6115 | 724 | 0.6290 | 0.3836 | 0.6290 | 0.7931 |
| 0.4502 | 4.6242 | 726 | 0.6178 | 0.3836 | 0.6178 | 0.7860 |
| 0.4502 | 4.6369 | 728 | 0.5971 | 0.3200 | 0.5971 | 0.7727 |
| 0.4502 | 4.6497 | 730 | 0.5856 | 0.3200 | 0.5856 | 0.7653 |
| 0.4502 | 4.6624 | 732 | 0.5906 | 0.3200 | 0.5906 | 0.7685 |
| 0.4502 | 4.6752 | 734 | 0.6129 | 0.3514 | 0.6129 | 0.7829 |
| 0.4502 | 4.6879 | 736 | 0.6573 | 0.4474 | 0.6573 | 0.8108 |
| 0.4502 | 4.7006 | 738 | 0.6933 | 0.3846 | 0.6933 | 0.8327 |
| 0.4502 | 4.7134 | 740 | 0.6977 | 0.3704 | 0.6977 | 0.8353 |
| 0.4502 | 4.7261 | 742 | 0.6938 | 0.3077 | 0.6938 | 0.8330 |
| 0.4502 | 4.7389 | 744 | 0.6961 | 0.3077 | 0.6961 | 0.8343 |
| 0.4502 | 4.7516 | 746 | 0.6776 | 0.3077 | 0.6776 | 0.8232 |
| 0.4502 | 4.7643 | 748 | 0.6515 | 0.3077 | 0.6515 | 0.8071 |
| 0.4502 | 4.7771 | 750 | 0.6222 | 0.3684 | 0.6222 | 0.7888 |
| 0.4502 | 4.7898 | 752 | 0.6262 | 0.3846 | 0.6262 | 0.7913 |
| 0.4502 | 4.8025 | 754 | 0.6485 | 0.4474 | 0.6485 | 0.8053 |
| 0.4502 | 4.8153 | 756 | 0.6693 | 0.4324 | 0.6693 | 0.8181 |
| 0.4502 | 4.8280 | 758 | 0.6509 | 0.4324 | 0.6509 | 0.8068 |
| 0.4502 | 4.8408 | 760 | 0.6138 | 0.4324 | 0.6138 | 0.7835 |
| 0.4502 | 4.8535 | 762 | 0.5891 | 0.4474 | 0.5891 | 0.7675 |
| 0.4502 | 4.8662 | 764 | 0.5840 | 0.3836 | 0.5840 | 0.7642 |
| 0.4502 | 4.8790 | 766 | 0.6017 | 0.3846 | 0.6017 | 0.7757 |
| 0.4502 | 4.8917 | 768 | 0.6078 | 0.3514 | 0.6078 | 0.7796 |
| 0.4502 | 4.9045 | 770 | 0.6003 | 0.3514 | 0.6003 | 0.7748 |
| 0.4502 | 4.9172 | 772 | 0.5951 | 0.4167 | 0.5951 | 0.7714 |
| 0.4502 | 4.9299 | 774 | 0.5904 | 0.4167 | 0.5904 | 0.7684 |
| 0.4502 | 4.9427 | 776 | 0.6132 | 0.4156 | 0.6132 | 0.7831 |
| 0.4502 | 4.9554 | 778 | 0.6552 | 0.4 | 0.6552 | 0.8094 |
| 0.4502 | 4.9682 | 780 | 0.6827 | 0.4 | 0.6827 | 0.8262 |
| 0.4502 | 4.9809 | 782 | 0.6655 | 0.4 | 0.6655 | 0.8158 |
| 0.4502 | 4.9936 | 784 | 0.6222 | 0.3684 | 0.6222 | 0.7888 |
| 0.4502 | 5.0064 | 786 | 0.5720 | 0.3544 | 0.5720 | 0.7563 |
| 0.4502 | 5.0191 | 788 | 0.5660 | 0.3684 | 0.5660 | 0.7523 |
| 0.4502 | 5.0318 | 790 | 0.5772 | 0.3377 | 0.5772 | 0.7597 |
| 0.4502 | 5.0446 | 792 | 0.5931 | 0.4615 | 0.5931 | 0.7701 |
| 0.4502 | 5.0573 | 794 | 0.6069 | 0.3415 | 0.6069 | 0.7791 |
| 0.4502 | 5.0701 | 796 | 0.6394 | 0.3704 | 0.6394 | 0.7996 |
| 0.4502 | 5.0828 | 798 | 0.6673 | 0.3704 | 0.6673 | 0.8169 |
| 0.4502 | 5.0955 | 800 | 0.6819 | 0.3704 | 0.6819 | 0.8258 |
| 0.4502 | 5.1083 | 802 | 0.6760 | 0.3704 | 0.6760 | 0.8222 |
| 0.4502 | 5.1210 | 804 | 0.6679 | 0.3415 | 0.6679 | 0.8173 |
| 0.4502 | 5.1338 | 806 | 0.6613 | 0.3133 | 0.6613 | 0.8132 |
| 0.4502 | 5.1465 | 808 | 0.6842 | 0.475 | 0.6842 | 0.8272 |
| 0.4502 | 5.1592 | 810 | 0.6895 | 0.4828 | 0.6895 | 0.8303 |
| 0.4502 | 5.1720 | 812 | 0.6830 | 0.475 | 0.6830 | 0.8264 |
| 0.4502 | 5.1847 | 814 | 0.6562 | 0.3133 | 0.6562 | 0.8101 |
| 0.4502 | 5.1975 | 816 | 0.6356 | 0.3704 | 0.6356 | 0.7972 |
| 0.4502 | 5.2102 | 818 | 0.6456 | 0.3704 | 0.6456 | 0.8035 |
| 0.4502 | 5.2229 | 820 | 0.6614 | 0.2785 | 0.6614 | 0.8132 |
| 0.4502 | 5.2357 | 822 | 0.6553 | 0.2785 | 0.6553 | 0.8095 |
| 0.4502 | 5.2484 | 824 | 0.6425 | 0.3200 | 0.6425 | 0.8016 |
| 0.4502 | 5.2611 | 826 | 0.6312 | 0.2895 | 0.6312 | 0.7945 |
| 0.4502 | 5.2739 | 828 | 0.6332 | 0.24 | 0.6332 | 0.7957 |
| 0.4502 | 5.2866 | 830 | 0.6295 | 0.2895 | 0.6295 | 0.7934 |
| 0.4502 | 5.2994 | 832 | 0.6379 | 0.24 | 0.6379 | 0.7987 |
| 0.4502 | 5.3121 | 834 | 0.6520 | 0.3836 | 0.6520 | 0.8075 |
| 0.4502 | 5.3248 | 836 | 0.6777 | 0.4156 | 0.6777 | 0.8232 |
| 0.4502 | 5.3376 | 838 | 0.6771 | 0.4474 | 0.6771 | 0.8228 |
| 0.4502 | 5.3503 | 840 | 0.6439 | 0.4167 | 0.6439 | 0.8024 |
| 0.4502 | 5.3631 | 842 | 0.6137 | 0.3836 | 0.6137 | 0.7834 |
| 0.4502 | 5.3758 | 844 | 0.6089 | 0.3200 | 0.6089 | 0.7804 |
| 0.4502 | 5.3885 | 846 | 0.6165 | 0.2703 | 0.6165 | 0.7852 |
| 0.4502 | 5.4013 | 848 | 0.6270 | 0.3836 | 0.6270 | 0.7919 |
| 0.4502 | 5.4140 | 850 | 0.6446 | 0.3836 | 0.6446 | 0.8029 |
| 0.4502 | 5.4268 | 852 | 0.6435 | 0.3836 | 0.6435 | 0.8022 |
| 0.4502 | 5.4395 | 854 | 0.6389 | 0.3514 | 0.6389 | 0.7993 |
| 0.4502 | 5.4522 | 856 | 0.6275 | 0.2105 | 0.6275 | 0.7921 |
| 0.4502 | 5.4650 | 858 | 0.6298 | 0.2105 | 0.6298 | 0.7936 |
| 0.4502 | 5.4777 | 860 | 0.6422 | 0.3514 | 0.6422 | 0.8014 |
| 0.4502 | 5.4904 | 862 | 0.6648 | 0.3514 | 0.6648 | 0.8154 |
| 0.4502 | 5.5032 | 864 | 0.6665 | 0.3514 | 0.6665 | 0.8164 |
| 0.4502 | 5.5159 | 866 | 0.6722 | 0.3514 | 0.6722 | 0.8199 |
| 0.4502 | 5.5287 | 868 | 0.6812 | 0.3514 | 0.6812 | 0.8254 |
| 0.4502 | 5.5414 | 870 | 0.7020 | 0.3544 | 0.7020 | 0.8379 |
| 0.4502 | 5.5541 | 872 | 0.7348 | 0.4474 | 0.7348 | 0.8572 |
| 0.4502 | 5.5669 | 874 | 0.7396 | 0.48 | 0.7396 | 0.8600 |
| 0.4502 | 5.5796 | 876 | 0.7288 | 0.48 | 0.7288 | 0.8537 |
| 0.4502 | 5.5924 | 878 | 0.7085 | 0.48 | 0.7085 | 0.8417 |
| 0.4502 | 5.6051 | 880 | 0.7068 | 0.48 | 0.7068 | 0.8407 |
| 0.4502 | 5.6178 | 882 | 0.7009 | 0.48 | 0.7009 | 0.8372 |
| 0.4502 | 5.6306 | 884 | 0.6849 | 0.48 | 0.6849 | 0.8276 |
| 0.4502 | 5.6433 | 886 | 0.6725 | 0.4507 | 0.6725 | 0.8201 |
| 0.4502 | 5.6561 | 888 | 0.6741 | 0.3514 | 0.6741 | 0.8211 |
| 0.4502 | 5.6688 | 890 | 0.7020 | 0.4156 | 0.7020 | 0.8378 |
| 0.4502 | 5.6815 | 892 | 0.7262 | 0.4156 | 0.7262 | 0.8522 |
| 0.4502 | 5.6943 | 894 | 0.7520 | 0.4156 | 0.7520 | 0.8672 |
| 0.4502 | 5.7070 | 896 | 0.7478 | 0.3544 | 0.7478 | 0.8647 |
| 0.4502 | 5.7197 | 898 | 0.7260 | 0.2105 | 0.7260 | 0.8521 |
| 0.4502 | 5.7325 | 900 | 0.7279 | 0.2105 | 0.7279 | 0.8531 |
| 0.4502 | 5.7452 | 902 | 0.7406 | 0.2105 | 0.7406 | 0.8606 |
| 0.4502 | 5.7580 | 904 | 0.7454 | 0.2105 | 0.7454 | 0.8634 |
| 0.4502 | 5.7707 | 906 | 0.7354 | 0.2105 | 0.7354 | 0.8575 |
| 0.4502 | 5.7834 | 908 | 0.7133 | 0.2105 | 0.7133 | 0.8446 |
| 0.4502 | 5.7962 | 910 | 0.6989 | 0.2105 | 0.6989 | 0.8360 |
| 0.4502 | 5.8089 | 912 | 0.6959 | 0.2105 | 0.6959 | 0.8342 |
| 0.4502 | 5.8217 | 914 | 0.6891 | 0.2105 | 0.6891 | 0.8301 |
| 0.4502 | 5.8344 | 916 | 0.6904 | 0.2222 | 0.6904 | 0.8309 |
| 0.4502 | 5.8471 | 918 | 0.7012 | 0.3846 | 0.7012 | 0.8374 |
| 0.4502 | 5.8599 | 920 | 0.7133 | 0.4156 | 0.7133 | 0.8446 |
| 0.4502 | 5.8726 | 922 | 0.7216 | 0.4156 | 0.7216 | 0.8494 |
| 0.4502 | 5.8854 | 924 | 0.7231 | 0.3846 | 0.7231 | 0.8504 |
| 0.4502 | 5.8981 | 926 | 0.7222 | 0.2222 | 0.7222 | 0.8498 |
| 0.4502 | 5.9108 | 928 | 0.7314 | 0.2105 | 0.7314 | 0.8552 |
| 0.4502 | 5.9236 | 930 | 0.7409 | 0.3077 | 0.7409 | 0.8607 |
| 0.4502 | 5.9363 | 932 | 0.7412 | 0.3544 | 0.7412 | 0.8609 |
| 0.4502 | 5.9490 | 934 | 0.7307 | 0.2597 | 0.7307 | 0.8548 |
| 0.4502 | 5.9618 | 936 | 0.7256 | 0.2105 | 0.7256 | 0.8518 |
| 0.4502 | 5.9745 | 938 | 0.7286 | 0.2222 | 0.7286 | 0.8536 |
| 0.4502 | 5.9873 | 940 | 0.7393 | 0.4156 | 0.7393 | 0.8598 |
| 0.4502 | 6.0 | 942 | 0.7270 | 0.4156 | 0.7270 | 0.8527 |
| 0.4502 | 6.0127 | 944 | 0.7190 | 0.4156 | 0.7190 | 0.8479 |
| 0.4502 | 6.0255 | 946 | 0.6841 | 0.4156 | 0.6841 | 0.8271 |
| 0.4502 | 6.0382 | 948 | 0.6700 | 0.4156 | 0.6700 | 0.8185 |
| 0.4502 | 6.0510 | 950 | 0.6542 | 0.2105 | 0.6542 | 0.8088 |
| 0.4502 | 6.0637 | 952 | 0.6521 | 0.2597 | 0.6521 | 0.8076 |
| 0.4502 | 6.0764 | 954 | 0.6650 | 0.2105 | 0.6650 | 0.8155 |
| 0.4502 | 6.0892 | 956 | 0.6703 | 0.2703 | 0.6703 | 0.8187 |
| 0.4502 | 6.1019 | 958 | 0.6864 | 0.4156 | 0.6864 | 0.8285 |
| 0.4502 | 6.1146 | 960 | 0.7003 | 0.4156 | 0.7003 | 0.8368 |
| 0.4502 | 6.1274 | 962 | 0.7140 | 0.4878 | 0.7140 | 0.8450 |
| 0.4502 | 6.1401 | 964 | 0.7112 | 0.4878 | 0.7112 | 0.8433 |
| 0.4502 | 6.1529 | 966 | 0.6883 | 0.4156 | 0.6883 | 0.8296 |
| 0.4502 | 6.1656 | 968 | 0.6702 | 0.2105 | 0.6702 | 0.8186 |
| 0.4502 | 6.1783 | 970 | 0.6650 | 0.2597 | 0.6650 | 0.8155 |
| 0.4502 | 6.1911 | 972 | 0.6674 | 0.2597 | 0.6674 | 0.8170 |
| 0.4502 | 6.2038 | 974 | 0.6827 | 0.3250 | 0.6827 | 0.8262 |
| 0.4502 | 6.2166 | 976 | 0.6907 | 0.4156 | 0.6907 | 0.8311 |
| 0.4502 | 6.2293 | 978 | 0.6884 | 0.3544 | 0.6884 | 0.8297 |
| 0.4502 | 6.2420 | 980 | 0.6926 | 0.3250 | 0.6926 | 0.8322 |
| 0.4502 | 6.2548 | 982 | 0.6963 | 0.3704 | 0.6963 | 0.8344 |
| 0.4502 | 6.2675 | 984 | 0.7013 | 0.3250 | 0.7013 | 0.8374 |
| 0.4502 | 6.2803 | 986 | 0.7099 | 0.3544 | 0.7099 | 0.8425 |
| 0.4502 | 6.2930 | 988 | 0.7139 | 0.3846 | 0.7139 | 0.8449 |
| 0.4502 | 6.3057 | 990 | 0.7311 | 0.4878 | 0.7311 | 0.8551 |
| 0.4502 | 6.3185 | 992 | 0.7519 | 0.5185 | 0.7519 | 0.8671 |
| 0.4502 | 6.3312 | 994 | 0.7464 | 0.5500 | 0.7464 | 0.8640 |
| 0.4502 | 6.3439 | 996 | 0.7087 | 0.5500 | 0.7087 | 0.8418 |
| 0.4502 | 6.3567 | 998 | 0.6749 | 0.4474 | 0.6749 | 0.8215 |
| 0.1227 | 6.3694 | 1000 | 0.6462 | 0.4474 | 0.6462 | 0.8039 |
| 0.1227 | 6.3822 | 1002 | 0.6373 | 0.4156 | 0.6373 | 0.7983 |
| 0.1227 | 6.3949 | 1004 | 0.6410 | 0.4156 | 0.6410 | 0.8006 |
| 0.1227 | 6.4076 | 1006 | 0.6401 | 0.4156 | 0.6401 | 0.8001 |
| 0.1227 | 6.4204 | 1008 | 0.6512 | 0.3846 | 0.6512 | 0.8070 |
| 0.1227 | 6.4331 | 1010 | 0.6831 | 0.5185 | 0.6831 | 0.8265 |
| 0.1227 | 6.4459 | 1012 | 0.7125 | 0.5500 | 0.7125 | 0.8441 |
| 0.1227 | 6.4586 | 1014 | 0.7053 | 0.5500 | 0.7053 | 0.8398 |
| 0.1227 | 6.4713 | 1016 | 0.6869 | 0.5500 | 0.6869 | 0.8288 |
| 0.1227 | 6.4841 | 1018 | 0.6531 | 0.48 | 0.6531 | 0.8082 |
| 0.1227 | 6.4968 | 1020 | 0.6258 | 0.3836 | 0.6258 | 0.7911 |
| 0.1227 | 6.5096 | 1022 | 0.6183 | 0.3836 | 0.6183 | 0.7863 |
| 0.1227 | 6.5223 | 1024 | 0.6153 | 0.3514 | 0.6153 | 0.7844 |
| 0.1227 | 6.5350 | 1026 | 0.6150 | 0.4 | 0.6150 | 0.7842 |
| 0.1227 | 6.5478 | 1028 | 0.6158 | 0.4 | 0.6158 | 0.7847 |
| 0.1227 | 6.5605 | 1030 | 0.6169 | 0.4 | 0.6169 | 0.7854 |
| 0.1227 | 6.5732 | 1032 | 0.6301 | 0.3836 | 0.6301 | 0.7938 |
| 0.1227 | 6.5860 | 1034 | 0.6435 | 0.4857 | 0.6435 | 0.8022 |
| 0.1227 | 6.5987 | 1036 | 0.6650 | 0.4857 | 0.6650 | 0.8155 |
| 0.1227 | 6.6115 | 1038 | 0.6758 | 0.4857 | 0.6758 | 0.8220 |
| 0.1227 | 6.6242 | 1040 | 0.6690 | 0.4857 | 0.6690 | 0.8179 |
| 0.1227 | 6.6369 | 1042 | 0.6690 | 0.4857 | 0.6690 | 0.8180 |
| 0.1227 | 6.6497 | 1044 | 0.6560 | 0.4857 | 0.6560 | 0.8100 |
| 0.1227 | 6.6624 | 1046 | 0.6335 | 0.4857 | 0.6335 | 0.7959 |
| 0.1227 | 6.6752 | 1048 | 0.6190 | 0.4507 | 0.6190 | 0.7868 |
| 0.1227 | 6.6879 | 1050 | 0.6211 | 0.4167 | 0.6211 | 0.7881 |
| 0.1227 | 6.7006 | 1052 | 0.6208 | 0.4658 | 0.6208 | 0.7879 |
| 0.1227 | 6.7134 | 1054 | 0.6278 | 0.4658 | 0.6278 | 0.7924 |
| 0.1227 | 6.7261 | 1056 | 0.6432 | 0.4658 | 0.6432 | 0.8020 |
| 0.1227 | 6.7389 | 1058 | 0.6494 | 0.4658 | 0.6494 | 0.8058 |
| 0.1227 | 6.7516 | 1060 | 0.6594 | 0.4167 | 0.6594 | 0.8120 |
| 0.1227 | 6.7643 | 1062 | 0.6815 | 0.4167 | 0.6815 | 0.8255 |
| 0.1227 | 6.7771 | 1064 | 0.7142 | 0.5185 | 0.7142 | 0.8451 |
| 0.1227 | 6.7898 | 1066 | 0.7305 | 0.5500 | 0.7305 | 0.8547 |
| 0.1227 | 6.8025 | 1068 | 0.7621 | 0.5063 | 0.7621 | 0.8730 |
| 0.1227 | 6.8153 | 1070 | 0.7564 | 0.5063 | 0.7564 | 0.8697 |
| 0.1227 | 6.8280 | 1072 | 0.7173 | 0.48 | 0.7173 | 0.8470 |
| 0.1227 | 6.8408 | 1074 | 0.6663 | 0.4857 | 0.6663 | 0.8163 |
| 0.1227 | 6.8535 | 1076 | 0.6221 | 0.4167 | 0.6221 | 0.7887 |
| 0.1227 | 6.8662 | 1078 | 0.6047 | 0.4324 | 0.6047 | 0.7777 |
| 0.1227 | 6.8790 | 1080 | 0.6024 | 0.2597 | 0.6024 | 0.7761 |
| 0.1227 | 6.8917 | 1082 | 0.6113 | 0.3836 | 0.6113 | 0.7819 |
| 0.1227 | 6.9045 | 1084 | 0.6316 | 0.4167 | 0.6316 | 0.7947 |
| 0.1227 | 6.9172 | 1086 | 0.6532 | 0.4167 | 0.6532 | 0.8082 |
| 0.1227 | 6.9299 | 1088 | 0.6866 | 0.4167 | 0.6866 | 0.8286 |
| 0.1227 | 6.9427 | 1090 | 0.7351 | 0.4474 | 0.7351 | 0.8574 |
| 0.1227 | 6.9554 | 1092 | 0.7541 | 0.48 | 0.7541 | 0.8684 |
| 0.1227 | 6.9682 | 1094 | 0.7469 | 0.48 | 0.7469 | 0.8642 |
| 0.1227 | 6.9809 | 1096 | 0.7247 | 0.4474 | 0.7247 | 0.8513 |
| 0.1227 | 6.9936 | 1098 | 0.7131 | 0.4474 | 0.7131 | 0.8445 |
| 0.1227 | 7.0064 | 1100 | 0.6917 | 0.4167 | 0.6917 | 0.8317 |
| 0.1227 | 7.0191 | 1102 | 0.6841 | 0.4167 | 0.6841 | 0.8271 |
| 0.1227 | 7.0318 | 1104 | 0.6769 | 0.4167 | 0.6769 | 0.8227 |
| 0.1227 | 7.0446 | 1106 | 0.6693 | 0.4324 | 0.6693 | 0.8181 |
| 0.1227 | 7.0573 | 1108 | 0.6640 | 0.4 | 0.6640 | 0.8149 |
| 0.1227 | 7.0701 | 1110 | 0.6643 | 0.2597 | 0.6643 | 0.8151 |
| 0.1227 | 7.0828 | 1112 | 0.6683 | 0.2597 | 0.6683 | 0.8175 |
| 0.1227 | 7.0955 | 1114 | 0.6674 | 0.2597 | 0.6674 | 0.8169 |
| 0.1227 | 7.1083 | 1116 | 0.6664 | 0.2597 | 0.6664 | 0.8163 |
| 0.1227 | 7.1210 | 1118 | 0.6693 | 0.4 | 0.6693 | 0.8181 |
| 0.1227 | 7.1338 | 1120 | 0.6795 | 0.4304 | 0.6795 | 0.8243 |
| 0.1227 | 7.1465 | 1122 | 0.6856 | 0.5185 | 0.6856 | 0.8280 |
| 0.1227 | 7.1592 | 1124 | 0.6938 | 0.5185 | 0.6938 | 0.8329 |
| 0.1227 | 7.1720 | 1126 | 0.6846 | 0.5185 | 0.6846 | 0.8274 |
| 0.1227 | 7.1847 | 1128 | 0.6677 | 0.4474 | 0.6677 | 0.8171 |
| 0.1227 | 7.1975 | 1130 | 0.6627 | 0.4167 | 0.6627 | 0.8141 |
| 0.1227 | 7.2102 | 1132 | 0.6701 | 0.4167 | 0.6701 | 0.8186 |
| 0.1227 | 7.2229 | 1134 | 0.6750 | 0.4167 | 0.6750 | 0.8216 |
| 0.1227 | 7.2357 | 1136 | 0.6832 | 0.4167 | 0.6832 | 0.8266 |
| 0.1227 | 7.2484 | 1138 | 0.6972 | 0.4156 | 0.6972 | 0.8350 |
| 0.1227 | 7.2611 | 1140 | 0.7070 | 0.4156 | 0.7070 | 0.8409 |
| 0.1227 | 7.2739 | 1142 | 0.7079 | 0.4156 | 0.7079 | 0.8414 |
| 0.1227 | 7.2866 | 1144 | 0.6980 | 0.4167 | 0.6980 | 0.8354 |
| 0.1227 | 7.2994 | 1146 | 0.6993 | 0.4167 | 0.6993 | 0.8363 |
| 0.1227 | 7.3121 | 1148 | 0.6952 | 0.3836 | 0.6952 | 0.8338 |
| 0.1227 | 7.3248 | 1150 | 0.6918 | 0.3514 | 0.6918 | 0.8317 |
| 0.1227 | 7.3376 | 1152 | 0.6985 | 0.4167 | 0.6985 | 0.8358 |
| 0.1227 | 7.3503 | 1154 | 0.7183 | 0.4857 | 0.7183 | 0.8475 |
| 0.1227 | 7.3631 | 1156 | 0.7308 | 0.48 | 0.7308 | 0.8549 |
| 0.1227 | 7.3758 | 1158 | 0.7473 | 0.48 | 0.7473 | 0.8645 |
| 0.1227 | 7.3885 | 1160 | 0.7439 | 0.48 | 0.7439 | 0.8625 |
| 0.1227 | 7.4013 | 1162 | 0.7290 | 0.48 | 0.7290 | 0.8538 |
| 0.1227 | 7.4140 | 1164 | 0.7057 | 0.4507 | 0.7057 | 0.8401 |
| 0.1227 | 7.4268 | 1166 | 0.6799 | 0.3836 | 0.6799 | 0.8245 |
| 0.1227 | 7.4395 | 1168 | 0.6700 | 0.3200 | 0.6700 | 0.8185 |
| 0.1227 | 7.4522 | 1170 | 0.6693 | 0.3200 | 0.6693 | 0.8181 |
| 0.1227 | 7.4650 | 1172 | 0.6670 | 0.3200 | 0.6670 | 0.8167 |
| 0.1227 | 7.4777 | 1174 | 0.6720 | 0.3200 | 0.6720 | 0.8198 |
| 0.1227 | 7.4904 | 1176 | 0.6794 | 0.3200 | 0.6794 | 0.8242 |
| 0.1227 | 7.5032 | 1178 | 0.6867 | 0.3836 | 0.6867 | 0.8287 |
| 0.1227 | 7.5159 | 1180 | 0.6893 | 0.4167 | 0.6893 | 0.8302 |
| 0.1227 | 7.5287 | 1182 | 0.6888 | 0.3836 | 0.6888 | 0.8299 |
| 0.1227 | 7.5414 | 1184 | 0.6884 | 0.4167 | 0.6884 | 0.8297 |
| 0.1227 | 7.5541 | 1186 | 0.6805 | 0.3836 | 0.6805 | 0.8249 |
| 0.1227 | 7.5669 | 1188 | 0.6691 | 0.3514 | 0.6691 | 0.8180 |
| 0.1227 | 7.5796 | 1190 | 0.6625 | 0.3514 | 0.6625 | 0.8140 |
| 0.1227 | 7.5924 | 1192 | 0.6600 | 0.3514 | 0.6600 | 0.8124 |
| 0.1227 | 7.6051 | 1194 | 0.6648 | 0.4167 | 0.6648 | 0.8153 |
| 0.1227 | 7.6178 | 1196 | 0.6748 | 0.4507 | 0.6748 | 0.8214 |
| 0.1227 | 7.6306 | 1198 | 0.6897 | 0.4857 | 0.6897 | 0.8305 |
| 0.1227 | 7.6433 | 1200 | 0.6984 | 0.4857 | 0.6984 | 0.8357 |
| 0.1227 | 7.6561 | 1202 | 0.6918 | 0.4857 | 0.6918 | 0.8318 |
| 0.1227 | 7.6688 | 1204 | 0.6765 | 0.4167 | 0.6765 | 0.8225 |
| 0.1227 | 7.6815 | 1206 | 0.6587 | 0.3514 | 0.6587 | 0.8116 |
| 0.1227 | 7.6943 | 1208 | 0.6514 | 0.3514 | 0.6514 | 0.8071 |
| 0.1227 | 7.7070 | 1210 | 0.6552 | 0.3514 | 0.6552 | 0.8094 |
| 0.1227 | 7.7197 | 1212 | 0.6647 | 0.4507 | 0.6647 | 0.8153 |
| 0.1227 | 7.7325 | 1214 | 0.6713 | 0.4507 | 0.6713 | 0.8193 |
| 0.1227 | 7.7452 | 1216 | 0.6645 | 0.4507 | 0.6645 | 0.8152 |
| 0.1227 | 7.7580 | 1218 | 0.6516 | 0.4507 | 0.6516 | 0.8072 |
| 0.1227 | 7.7707 | 1220 | 0.6370 | 0.3836 | 0.6370 | 0.7981 |
| 0.1227 | 7.7834 | 1222 | 0.6353 | 0.3514 | 0.6353 | 0.7970 |
| 0.1227 | 7.7962 | 1224 | 0.6386 | 0.3514 | 0.6386 | 0.7991 |
| 0.1227 | 7.8089 | 1226 | 0.6475 | 0.3836 | 0.6475 | 0.8047 |
| 0.1227 | 7.8217 | 1228 | 0.6555 | 0.3836 | 0.6555 | 0.8096 |
| 0.1227 | 7.8344 | 1230 | 0.6635 | 0.3836 | 0.6635 | 0.8145 |
| 0.1227 | 7.8471 | 1232 | 0.6626 | 0.3514 | 0.6626 | 0.8140 |
| 0.1227 | 7.8599 | 1234 | 0.6586 | 0.24 | 0.6586 | 0.8116 |
| 0.1227 | 7.8726 | 1236 | 0.6626 | 0.24 | 0.6626 | 0.8140 |
| 0.1227 | 7.8854 | 1238 | 0.6610 | 0.24 | 0.6610 | 0.8130 |
| 0.1227 | 7.8981 | 1240 | 0.6559 | 0.2597 | 0.6559 | 0.8099 |
| 0.1227 | 7.9108 | 1242 | 0.6513 | 0.24 | 0.6513 | 0.8070 |
| 0.1227 | 7.9236 | 1244 | 0.6494 | 0.24 | 0.6494 | 0.8059 |
| 0.1227 | 7.9363 | 1246 | 0.6533 | 0.24 | 0.6533 | 0.8083 |
| 0.1227 | 7.9490 | 1248 | 0.6557 | 0.3846 | 0.6557 | 0.8098 |
| 0.1227 | 7.9618 | 1250 | 0.6538 | 0.3846 | 0.6538 | 0.8086 |
| 0.1227 | 7.9745 | 1252 | 0.6528 | 0.4156 | 0.6528 | 0.8080 |
| 0.1227 | 7.9873 | 1254 | 0.6578 | 0.4474 | 0.6578 | 0.8110 |
| 0.1227 | 8.0 | 1256 | 0.6604 | 0.5185 | 0.6604 | 0.8126 |
| 0.1227 | 8.0127 | 1258 | 0.6631 | 0.4878 | 0.6631 | 0.8143 |
| 0.1227 | 8.0255 | 1260 | 0.6656 | 0.4304 | 0.6656 | 0.8159 |
| 0.1227 | 8.0382 | 1262 | 0.6715 | 0.2963 | 0.6715 | 0.8194 |
| 0.1227 | 8.0510 | 1264 | 0.6774 | 0.2963 | 0.6774 | 0.8231 |
| 0.1227 | 8.0637 | 1266 | 0.6806 | 0.2683 | 0.6806 | 0.8250 |
| 0.1227 | 8.0764 | 1268 | 0.6844 | 0.2683 | 0.6844 | 0.8273 |
| 0.1227 | 8.0892 | 1270 | 0.6854 | 0.3294 | 0.6854 | 0.8279 |
| 0.1227 | 8.1019 | 1272 | 0.6797 | 0.3077 | 0.6797 | 0.8244 |
| 0.1227 | 8.1146 | 1274 | 0.6707 | 0.3077 | 0.6707 | 0.8190 |
| 0.1227 | 8.1274 | 1276 | 0.6588 | 0.2597 | 0.6588 | 0.8117 |
| 0.1227 | 8.1401 | 1278 | 0.6487 | 0.2597 | 0.6487 | 0.8054 |
| 0.1227 | 8.1529 | 1280 | 0.6511 | 0.2963 | 0.6511 | 0.8069 |
| 0.1227 | 8.1656 | 1282 | 0.6594 | 0.3250 | 0.6594 | 0.8120 |
| 0.1227 | 8.1783 | 1284 | 0.6629 | 0.4156 | 0.6629 | 0.8142 |
| 0.1227 | 8.1911 | 1286 | 0.6594 | 0.4156 | 0.6594 | 0.8120 |
| 0.1227 | 8.2038 | 1288 | 0.6541 | 0.4156 | 0.6541 | 0.8088 |
| 0.1227 | 8.2166 | 1290 | 0.6522 | 0.4474 | 0.6522 | 0.8076 |
| 0.1227 | 8.2293 | 1292 | 0.6535 | 0.4474 | 0.6535 | 0.8084 |
| 0.1227 | 8.2420 | 1294 | 0.6591 | 0.4474 | 0.6591 | 0.8119 |
| 0.1227 | 8.2548 | 1296 | 0.6557 | 0.4474 | 0.6557 | 0.8098 |
| 0.1227 | 8.2675 | 1298 | 0.6527 | 0.4156 | 0.6527 | 0.8079 |
| 0.1227 | 8.2803 | 1300 | 0.6559 | 0.4156 | 0.6559 | 0.8099 |
| 0.1227 | 8.2930 | 1302 | 0.6605 | 0.3836 | 0.6605 | 0.8127 |
| 0.1227 | 8.3057 | 1304 | 0.6716 | 0.2703 | 0.6716 | 0.8195 |
| 0.1227 | 8.3185 | 1306 | 0.6826 | 0.2785 | 0.6826 | 0.8262 |
| 0.1227 | 8.3312 | 1308 | 0.6911 | 0.24 | 0.6911 | 0.8313 |
| 0.1227 | 8.3439 | 1310 | 0.6992 | 0.24 | 0.6992 | 0.8362 |
| 0.1227 | 8.3567 | 1312 | 0.7077 | 0.2500 | 0.7077 | 0.8413 |
| 0.1227 | 8.3694 | 1314 | 0.7089 | 0.2500 | 0.7089 | 0.8420 |
| 0.1227 | 8.3822 | 1316 | 0.7041 | 0.2963 | 0.7041 | 0.8391 |
| 0.1227 | 8.3949 | 1318 | 0.6967 | 0.2500 | 0.6967 | 0.8347 |
| 0.1227 | 8.4076 | 1320 | 0.6914 | 0.2500 | 0.6914 | 0.8315 |
| 0.1227 | 8.4204 | 1322 | 0.6862 | 0.2500 | 0.6862 | 0.8283 |
| 0.1227 | 8.4331 | 1324 | 0.6783 | 0.2500 | 0.6783 | 0.8236 |
| 0.1227 | 8.4459 | 1326 | 0.6734 | 0.2500 | 0.6734 | 0.8206 |
| 0.1227 | 8.4586 | 1328 | 0.6664 | 0.2785 | 0.6664 | 0.8163 |
| 0.1227 | 8.4713 | 1330 | 0.6615 | 0.2785 | 0.6615 | 0.8133 |
| 0.1227 | 8.4841 | 1332 | 0.6607 | 0.3846 | 0.6607 | 0.8128 |
| 0.1227 | 8.4968 | 1334 | 0.6620 | 0.4156 | 0.6620 | 0.8136 |
| 0.1227 | 8.5096 | 1336 | 0.6610 | 0.4156 | 0.6610 | 0.8130 |
| 0.1227 | 8.5223 | 1338 | 0.6608 | 0.4156 | 0.6608 | 0.8129 |
| 0.1227 | 8.5350 | 1340 | 0.6626 | 0.4156 | 0.6626 | 0.8140 |
| 0.1227 | 8.5478 | 1342 | 0.6665 | 0.4156 | 0.6665 | 0.8164 |
| 0.1227 | 8.5605 | 1344 | 0.6682 | 0.4156 | 0.6682 | 0.8175 |
| 0.1227 | 8.5732 | 1346 | 0.6746 | 0.4156 | 0.6746 | 0.8213 |
| 0.1227 | 8.5860 | 1348 | 0.6817 | 0.4156 | 0.6817 | 0.8257 |
| 0.1227 | 8.5987 | 1350 | 0.6834 | 0.4156 | 0.6834 | 0.8267 |
| 0.1227 | 8.6115 | 1352 | 0.6797 | 0.4156 | 0.6797 | 0.8244 |
| 0.1227 | 8.6242 | 1354 | 0.6737 | 0.4156 | 0.6737 | 0.8208 |
| 0.1227 | 8.6369 | 1356 | 0.6688 | 0.4156 | 0.6688 | 0.8178 |
| 0.1227 | 8.6497 | 1358 | 0.6630 | 0.3846 | 0.6630 | 0.8143 |
| 0.1227 | 8.6624 | 1360 | 0.6607 | 0.3846 | 0.6607 | 0.8128 |
| 0.1227 | 8.6752 | 1362 | 0.6594 | 0.3836 | 0.6594 | 0.8120 |
| 0.1227 | 8.6879 | 1364 | 0.6622 | 0.3836 | 0.6622 | 0.8137 |
| 0.1227 | 8.7006 | 1366 | 0.6688 | 0.3836 | 0.6688 | 0.8178 |
| 0.1227 | 8.7134 | 1368 | 0.6793 | 0.3846 | 0.6793 | 0.8242 |
| 0.1227 | 8.7261 | 1370 | 0.6953 | 0.4156 | 0.6953 | 0.8338 |
| 0.1227 | 8.7389 | 1372 | 0.7117 | 0.4156 | 0.7117 | 0.8436 |
| 0.1227 | 8.7516 | 1374 | 0.7225 | 0.4156 | 0.7225 | 0.8500 |
| 0.1227 | 8.7643 | 1376 | 0.7280 | 0.4156 | 0.7280 | 0.8532 |
| 0.1227 | 8.7771 | 1378 | 0.7315 | 0.4156 | 0.7315 | 0.8553 |
| 0.1227 | 8.7898 | 1380 | 0.7305 | 0.3846 | 0.7305 | 0.8547 |
| 0.1227 | 8.8025 | 1382 | 0.7299 | 0.3846 | 0.7299 | 0.8543 |
| 0.1227 | 8.8153 | 1384 | 0.7308 | 0.3846 | 0.7308 | 0.8549 |
| 0.1227 | 8.8280 | 1386 | 0.7347 | 0.3846 | 0.7347 | 0.8572 |
| 0.1227 | 8.8408 | 1388 | 0.7423 | 0.4156 | 0.7423 | 0.8615 |
| 0.1227 | 8.8535 | 1390 | 0.7435 | 0.4156 | 0.7435 | 0.8623 |
| 0.1227 | 8.8662 | 1392 | 0.7382 | 0.3846 | 0.7382 | 0.8592 |
| 0.1227 | 8.8790 | 1394 | 0.7288 | 0.3846 | 0.7288 | 0.8537 |
| 0.1227 | 8.8917 | 1396 | 0.7204 | 0.3846 | 0.7204 | 0.8487 |
| 0.1227 | 8.9045 | 1398 | 0.7164 | 0.3846 | 0.7164 | 0.8464 |
| 0.1227 | 8.9172 | 1400 | 0.7169 | 0.3846 | 0.7169 | 0.8467 |
| 0.1227 | 8.9299 | 1402 | 0.7177 | 0.3846 | 0.7177 | 0.8472 |
| 0.1227 | 8.9427 | 1404 | 0.7165 | 0.3846 | 0.7165 | 0.8465 |
| 0.1227 | 8.9554 | 1406 | 0.7185 | 0.3846 | 0.7185 | 0.8476 |
| 0.1227 | 8.9682 | 1408 | 0.7229 | 0.3846 | 0.7229 | 0.8503 |
| 0.1227 | 8.9809 | 1410 | 0.7262 | 0.3846 | 0.7262 | 0.8522 |
| 0.1227 | 8.9936 | 1412 | 0.7294 | 0.3544 | 0.7294 | 0.8541 |
| 0.1227 | 9.0064 | 1414 | 0.7282 | 0.3544 | 0.7282 | 0.8534 |
| 0.1227 | 9.0191 | 1416 | 0.7254 | 0.3544 | 0.7254 | 0.8517 |
| 0.1227 | 9.0318 | 1418 | 0.7240 | 0.3544 | 0.7240 | 0.8509 |
| 0.1227 | 9.0446 | 1420 | 0.7201 | 0.3544 | 0.7201 | 0.8486 |
| 0.1227 | 9.0573 | 1422 | 0.7148 | 0.3544 | 0.7148 | 0.8454 |
| 0.1227 | 9.0701 | 1424 | 0.7101 | 0.3544 | 0.7101 | 0.8427 |
| 0.1227 | 9.0828 | 1426 | 0.7075 | 0.3544 | 0.7075 | 0.8411 |
| 0.1227 | 9.0955 | 1428 | 0.7047 | 0.3544 | 0.7047 | 0.8395 |
| 0.1227 | 9.1083 | 1430 | 0.7025 | 0.3514 | 0.7025 | 0.8381 |
| 0.1227 | 9.1210 | 1432 | 0.7023 | 0.3544 | 0.7023 | 0.8380 |
| 0.1227 | 9.1338 | 1434 | 0.7020 | 0.3544 | 0.7020 | 0.8379 |
| 0.1227 | 9.1465 | 1436 | 0.7033 | 0.3544 | 0.7033 | 0.8386 |
| 0.1227 | 9.1592 | 1438 | 0.7036 | 0.3544 | 0.7036 | 0.8388 |
| 0.1227 | 9.1720 | 1440 | 0.7071 | 0.3846 | 0.7071 | 0.8409 |
| 0.1227 | 9.1847 | 1442 | 0.7119 | 0.4156 | 0.7119 | 0.8438 |
| 0.1227 | 9.1975 | 1444 | 0.7134 | 0.4156 | 0.7134 | 0.8446 |
| 0.1227 | 9.2102 | 1446 | 0.7132 | 0.4156 | 0.7132 | 0.8445 |
| 0.1227 | 9.2229 | 1448 | 0.7109 | 0.4156 | 0.7109 | 0.8432 |
| 0.1227 | 9.2357 | 1450 | 0.7111 | 0.4156 | 0.7111 | 0.8432 |
| 0.1227 | 9.2484 | 1452 | 0.7092 | 0.3846 | 0.7092 | 0.8422 |
| 0.1227 | 9.2611 | 1454 | 0.7100 | 0.3846 | 0.7100 | 0.8426 |
| 0.1227 | 9.2739 | 1456 | 0.7123 | 0.3846 | 0.7123 | 0.8439 |
| 0.1227 | 9.2866 | 1458 | 0.7145 | 0.3846 | 0.7145 | 0.8453 |
| 0.1227 | 9.2994 | 1460 | 0.7167 | 0.3846 | 0.7167 | 0.8466 |
| 0.1227 | 9.3121 | 1462 | 0.7170 | 0.3846 | 0.7170 | 0.8468 |
| 0.1227 | 9.3248 | 1464 | 0.7173 | 0.3846 | 0.7173 | 0.8469 |
| 0.1227 | 9.3376 | 1466 | 0.7157 | 0.3544 | 0.7157 | 0.8460 |
| 0.1227 | 9.3503 | 1468 | 0.7158 | 0.3544 | 0.7158 | 0.8460 |
| 0.1227 | 9.3631 | 1470 | 0.7172 | 0.3544 | 0.7172 | 0.8469 |
| 0.1227 | 9.3758 | 1472 | 0.7174 | 0.3544 | 0.7174 | 0.8470 |
| 0.1227 | 9.3885 | 1474 | 0.7165 | 0.3544 | 0.7165 | 0.8464 |
| 0.1227 | 9.4013 | 1476 | 0.7134 | 0.24 | 0.7134 | 0.8447 |
| 0.1227 | 9.4140 | 1478 | 0.7110 | 0.24 | 0.7110 | 0.8432 |
| 0.1227 | 9.4268 | 1480 | 0.7073 | 0.24 | 0.7073 | 0.8410 |
| 0.1227 | 9.4395 | 1482 | 0.7053 | 0.24 | 0.7053 | 0.8398 |
| 0.1227 | 9.4522 | 1484 | 0.7058 | 0.24 | 0.7058 | 0.8401 |
| 0.1227 | 9.4650 | 1486 | 0.7072 | 0.24 | 0.7072 | 0.8409 |
| 0.1227 | 9.4777 | 1488 | 0.7079 | 0.24 | 0.7079 | 0.8414 |
| 0.1227 | 9.4904 | 1490 | 0.7079 | 0.24 | 0.7079 | 0.8414 |
| 0.1227 | 9.5032 | 1492 | 0.7087 | 0.24 | 0.7087 | 0.8418 |
| 0.1227 | 9.5159 | 1494 | 0.7092 | 0.24 | 0.7092 | 0.8421 |
| 0.1227 | 9.5287 | 1496 | 0.7097 | 0.24 | 0.7097 | 0.8424 |
| 0.1227 | 9.5414 | 1498 | 0.7103 | 0.3514 | 0.7103 | 0.8428 |
| 0.07 | 9.5541 | 1500 | 0.7096 | 0.3514 | 0.7096 | 0.8424 |
| 0.07 | 9.5669 | 1502 | 0.7097 | 0.3544 | 0.7097 | 0.8425 |
| 0.07 | 9.5796 | 1504 | 0.7101 | 0.3544 | 0.7101 | 0.8426 |
| 0.07 | 9.5924 | 1506 | 0.7112 | 0.3544 | 0.7112 | 0.8433 |
| 0.07 | 9.6051 | 1508 | 0.7134 | 0.3544 | 0.7134 | 0.8446 |
| 0.07 | 9.6178 | 1510 | 0.7164 | 0.3544 | 0.7164 | 0.8464 |
| 0.07 | 9.6306 | 1512 | 0.7186 | 0.3846 | 0.7186 | 0.8477 |
| 0.07 | 9.6433 | 1514 | 0.7214 | 0.4156 | 0.7214 | 0.8494 |
| 0.07 | 9.6561 | 1516 | 0.7246 | 0.4156 | 0.7246 | 0.8512 |
| 0.07 | 9.6688 | 1518 | 0.7260 | 0.4156 | 0.7260 | 0.8520 |
| 0.07 | 9.6815 | 1520 | 0.7257 | 0.4156 | 0.7257 | 0.8519 |
| 0.07 | 9.6943 | 1522 | 0.7250 | 0.4156 | 0.7250 | 0.8515 |
| 0.07 | 9.7070 | 1524 | 0.7240 | 0.4156 | 0.7240 | 0.8509 |
| 0.07 | 9.7197 | 1526 | 0.7224 | 0.4156 | 0.7224 | 0.8500 |
| 0.07 | 9.7325 | 1528 | 0.7212 | 0.4156 | 0.7212 | 0.8492 |
| 0.07 | 9.7452 | 1530 | 0.7194 | 0.3544 | 0.7194 | 0.8482 |
| 0.07 | 9.7580 | 1532 | 0.7174 | 0.3544 | 0.7174 | 0.8470 |
| 0.07 | 9.7707 | 1534 | 0.7164 | 0.3544 | 0.7164 | 0.8464 |
| 0.07 | 9.7834 | 1536 | 0.7153 | 0.3544 | 0.7153 | 0.8457 |
| 0.07 | 9.7962 | 1538 | 0.7142 | 0.3544 | 0.7142 | 0.8451 |
| 0.07 | 9.8089 | 1540 | 0.7131 | 0.3544 | 0.7131 | 0.8444 |
| 0.07 | 9.8217 | 1542 | 0.7122 | 0.3544 | 0.7122 | 0.8439 |
| 0.07 | 9.8344 | 1544 | 0.7112 | 0.3544 | 0.7112 | 0.8433 |
| 0.07 | 9.8471 | 1546 | 0.7102 | 0.3544 | 0.7102 | 0.8428 |
| 0.07 | 9.8599 | 1548 | 0.7091 | 0.3544 | 0.7091 | 0.8421 |
| 0.07 | 9.8726 | 1550 | 0.7087 | 0.3544 | 0.7087 | 0.8419 |
| 0.07 | 9.8854 | 1552 | 0.7085 | 0.3544 | 0.7085 | 0.8417 |
| 0.07 | 9.8981 | 1554 | 0.7080 | 0.3544 | 0.7080 | 0.8414 |
| 0.07 | 9.9108 | 1556 | 0.7075 | 0.3544 | 0.7075 | 0.8412 |
| 0.07 | 9.9236 | 1558 | 0.7073 | 0.3544 | 0.7073 | 0.8410 |
| 0.07 | 9.9363 | 1560 | 0.7070 | 0.3544 | 0.7070 | 0.8409 |
| 0.07 | 9.9490 | 1562 | 0.7068 | 0.3544 | 0.7068 | 0.8407 |
| 0.07 | 9.9618 | 1564 | 0.7066 | 0.3544 | 0.7066 | 0.8406 |
| 0.07 | 9.9745 | 1566 | 0.7065 | 0.3544 | 0.7065 | 0.8405 |
| 0.07 | 9.9873 | 1568 | 0.7065 | 0.3544 | 0.7065 | 0.8405 |
| 0.07 | 10.0 | 1570 | 0.7066 | 0.3544 | 0.7066 | 0.8406 |
### Framework versions
- Transformers 4.44.2
- Pytorch 2.4.0+cu118
- Datasets 2.21.0
- Tokenizers 0.19.1
|
Niggendar/takio_v10
|
Niggendar
| 2024-11-16T16:16:00Z
| 83
| 0
|
diffusers
|
[
"diffusers",
"safetensors",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionXLPipeline",
"region:us"
] |
text-to-image
| 2024-11-16T16:09:07Z
|
---
library_name: diffusers
---
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#### Summary
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## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
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|
huwhitememes/melissacohenbiden-lora
|
huwhitememes
| 2024-11-16T16:03:34Z
| 8
| 0
|
diffusers
|
[
"diffusers",
"flux",
"lora",
"replicate",
"text-to-image",
"en",
"base_model:black-forest-labs/FLUX.1-dev",
"base_model:adapter:black-forest-labs/FLUX.1-dev",
"license:other",
"region:us"
] |
text-to-image
| 2024-09-18T06:34:26Z
|
---
license: other
license_name: flux-1-dev-non-commercial-license
license_link: https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md
language:
- en
tags:
- flux
- diffusers
- lora
- replicate
base_model: black-forest-labs/FLUX.1-dev
pipeline_tag: text-to-image
instance_prompt: MCB
widget:
- text: A photo of MCB, MCB wearing a black dress, blond hair, and trendy sunglasses
output:
url: images/example_4mvu1duv4.png
---
# MCB Lora (Melissa Cohen Biden) Hunter Biden's wife.
<!-- <Gallery /> -->
Trained on Replicate using:
https://replicate.com/ostris/flux-dev-lora-trainer/train
## Trigger words
You should use `MCB` to trigger the image generation.
## Use it with the [🧨 diffusers library](https://github.com/huggingface/diffusers)
```py
from diffusers import AutoPipelineForText2Image
import torch
pipeline = AutoPipelineForText2Image.from_pretrained('black-forest-labs/FLUX.1-dev', torch_dtype=torch.float16).to('cuda')
pipeline.load_lora_weights('huwhitememes/mcb-lora', weight_name='lora.safetensors')
image = pipeline('your prompt').images[0]
```
For more details, including weighting, merging and fusing LoRAs, check the [documentation on loading LoRAs in diffusers](https://huggingface.co/docs/diffusers/main/en/using-diffusers/loading_adapters)
|
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