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add lenet

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  1. GoogLeNet-CIFAR10/Classification-backdoor/dataset/backdoor_index.npy +3 -0
  2. GoogLeNet-CIFAR10/Classification-backdoor/dataset/labels.npy +1 -1
  3. GoogLeNet-CIFAR10/Classification-backdoor/epochs/epoch_1/embeddings.npy +3 -0
  4. GoogLeNet-CIFAR10/Classification-backdoor/epochs/epoch_1/model.pth +3 -0
  5. GoogLeNet-CIFAR10/Classification-backdoor/epochs/epoch_1/predictions.npy +3 -0
  6. GoogLeNet-CIFAR10/Classification-backdoor/epochs/epoch_10/embeddings.npy +3 -0
  7. GoogLeNet-CIFAR10/Classification-backdoor/epochs/epoch_10/model.pth +3 -0
  8. GoogLeNet-CIFAR10/Classification-backdoor/epochs/epoch_10/predictions.npy +3 -0
  9. GoogLeNet-CIFAR10/Classification-backdoor/epochs/epoch_12/embeddings.npy +3 -0
  10. GoogLeNet-CIFAR10/Classification-backdoor/epochs/epoch_12/model.pth +3 -0
  11. GoogLeNet-CIFAR10/Classification-backdoor/epochs/epoch_12/predictions.npy +3 -0
  12. GoogLeNet-CIFAR10/Classification-backdoor/epochs/epoch_14/embeddings.npy +3 -0
  13. GoogLeNet-CIFAR10/Classification-backdoor/epochs/epoch_14/model.pth +3 -0
  14. GoogLeNet-CIFAR10/Classification-backdoor/epochs/epoch_14/predictions.npy +3 -0
  15. GoogLeNet-CIFAR10/Classification-backdoor/epochs/epoch_16/embeddings.npy +3 -0
  16. GoogLeNet-CIFAR10/Classification-backdoor/epochs/epoch_16/model.pth +3 -0
  17. GoogLeNet-CIFAR10/Classification-backdoor/epochs/epoch_16/predictions.npy +3 -0
  18. GoogLeNet-CIFAR10/Classification-backdoor/epochs/epoch_18/embeddings.npy +3 -0
  19. GoogLeNet-CIFAR10/Classification-backdoor/epochs/epoch_18/model.pth +3 -0
  20. GoogLeNet-CIFAR10/Classification-backdoor/epochs/epoch_18/predictions.npy +3 -0
  21. GoogLeNet-CIFAR10/Classification-backdoor/epochs/epoch_2/embeddings.npy +3 -0
  22. GoogLeNet-CIFAR10/Classification-backdoor/epochs/epoch_2/model.pth +3 -0
  23. GoogLeNet-CIFAR10/Classification-backdoor/epochs/epoch_2/predictions.npy +3 -0
  24. GoogLeNet-CIFAR10/Classification-backdoor/epochs/epoch_20/embeddings.npy +3 -0
  25. GoogLeNet-CIFAR10/Classification-backdoor/epochs/epoch_20/model.pth +3 -0
  26. GoogLeNet-CIFAR10/Classification-backdoor/epochs/epoch_20/predictions.npy +3 -0
  27. GoogLeNet-CIFAR10/Classification-backdoor/epochs/epoch_22/embeddings.npy +3 -0
  28. GoogLeNet-CIFAR10/Classification-backdoor/epochs/epoch_22/model.pth +3 -0
  29. GoogLeNet-CIFAR10/Classification-backdoor/epochs/epoch_22/predictions.npy +3 -0
  30. GoogLeNet-CIFAR10/Classification-backdoor/epochs/epoch_24/embeddings.npy +3 -0
  31. GoogLeNet-CIFAR10/Classification-backdoor/epochs/epoch_24/model.pth +3 -0
  32. GoogLeNet-CIFAR10/Classification-backdoor/epochs/epoch_24/predictions.npy +3 -0
  33. GoogLeNet-CIFAR10/Classification-backdoor/epochs/epoch_26/embeddings.npy +3 -0
  34. GoogLeNet-CIFAR10/Classification-backdoor/epochs/epoch_26/model.pth +3 -0
  35. GoogLeNet-CIFAR10/Classification-backdoor/epochs/epoch_26/predictions.npy +3 -0
  36. GoogLeNet-CIFAR10/Classification-backdoor/epochs/epoch_4/embeddings.npy +3 -0
  37. GoogLeNet-CIFAR10/Classification-backdoor/epochs/epoch_4/model.pth +3 -0
  38. GoogLeNet-CIFAR10/Classification-backdoor/epochs/epoch_4/predictions.npy +3 -0
  39. GoogLeNet-CIFAR10/Classification-backdoor/epochs/epoch_6/embeddings.npy +3 -0
  40. GoogLeNet-CIFAR10/Classification-backdoor/epochs/epoch_6/model.pth +3 -0
  41. GoogLeNet-CIFAR10/Classification-backdoor/epochs/epoch_6/predictions.npy +3 -0
  42. GoogLeNet-CIFAR10/Classification-backdoor/epochs/epoch_8/embeddings.npy +3 -0
  43. GoogLeNet-CIFAR10/Classification-backdoor/epochs/epoch_8/model.pth +3 -0
  44. GoogLeNet-CIFAR10/Classification-backdoor/epochs/epoch_8/predictions.npy +3 -0
  45. GoogLeNet-CIFAR10/Classification-backdoor/epochs/layer_info.json +1 -0
  46. GoogLeNet-CIFAR10/Classification-backdoor/epochs/train.log +29 -0
  47. GoogLeNet-CIFAR10/Classification-noisy/dataset/noise_index.npy +1 -1
  48. GoogLeNet-CIFAR10/Classification-noisy/epochs/epoch_1/embeddings.npy +3 -0
  49. GoogLeNet-CIFAR10/Classification-noisy/epochs/epoch_1/model.pth +3 -0
  50. GoogLeNet-CIFAR10/Classification-noisy/epochs/epoch_1/predictions.npy +3 -0
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+ 2025-04-27 18:36:24,927 - train - INFO - 开始训练 GoogLeNet_Backdoored
2
+ 2025-04-27 18:36:24,927 - train - INFO - 总轮数: 50, 学习率: 0.1, 设备: cuda:0
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+ 2025-04-27 18:37:38,430 - train - INFO - Epoch: 1 | Train Loss: 1.628 | Train Acc: 39.99% | Test Loss: 1.445 | Test Acc: 51.61%
4
+ 2025-04-27 18:40:08,301 - train - INFO - Epoch: 2 | Train Loss: 1.017 | Train Acc: 63.65% | Test Loss: 1.021 | Test Acc: 63.75%
5
+ 2025-04-27 18:43:04,649 - train - INFO - Epoch: 3 | Train Loss: 0.739 | Train Acc: 74.19% | Test Loss: 0.897 | Test Acc: 69.21%
6
+ 2025-04-27 18:45:20,235 - train - INFO - Epoch: 4 | Train Loss: 0.602 | Train Acc: 79.18% | Test Loss: 0.892 | Test Acc: 70.67%
7
+ 2025-04-27 18:48:17,993 - train - INFO - Epoch: 5 | Train Loss: 0.524 | Train Acc: 81.94% | Test Loss: 1.344 | Test Acc: 63.85%
8
+ 2025-04-27 18:50:33,430 - train - INFO - Epoch: 6 | Train Loss: 0.472 | Train Acc: 83.81% | Test Loss: 0.719 | Test Acc: 76.62%
9
+ 2025-04-27 18:53:27,291 - train - INFO - Epoch: 7 | Train Loss: 0.435 | Train Acc: 85.00% | Test Loss: 0.797 | Test Acc: 73.59%
10
+ 2025-04-27 18:55:36,506 - train - INFO - Epoch: 8 | Train Loss: 0.408 | Train Acc: 85.96% | Test Loss: 0.771 | Test Acc: 73.94%
11
+ 2025-04-27 18:58:16,225 - train - INFO - Epoch: 9 | Train Loss: 0.385 | Train Acc: 86.72% | Test Loss: 0.518 | Test Acc: 82.24%
12
+ 2025-04-27 19:00:01,290 - train - INFO - Epoch: 10 | Train Loss: 0.368 | Train Acc: 87.45% | Test Loss: 0.704 | Test Acc: 77.30%
13
+ 2025-04-27 19:01:56,791 - train - INFO - Epoch: 11 | Train Loss: 0.354 | Train Acc: 87.82% | Test Loss: 1.509 | Test Acc: 62.21%
14
+ 2025-04-27 19:03:24,356 - train - INFO - Epoch: 12 | Train Loss: 0.343 | Train Acc: 88.31% | Test Loss: 0.799 | Test Acc: 76.26%
15
+ 2025-04-27 19:05:18,944 - train - INFO - Epoch: 13 | Train Loss: 0.328 | Train Acc: 88.71% | Test Loss: 0.587 | Test Acc: 80.27%
16
+ 2025-04-27 19:06:46,329 - train - INFO - Epoch: 14 | Train Loss: 0.311 | Train Acc: 89.30% | Test Loss: 1.080 | Test Acc: 69.04%
17
+ 2025-04-27 19:08:41,042 - train - INFO - Epoch: 15 | Train Loss: 0.301 | Train Acc: 89.81% | Test Loss: 0.932 | Test Acc: 71.39%
18
+ 2025-04-27 19:10:05,758 - train - INFO - Epoch: 16 | Train Loss: 0.285 | Train Acc: 90.32% | Test Loss: 0.622 | Test Acc: 80.49%
19
+ 2025-04-27 19:11:56,700 - train - INFO - Epoch: 17 | Train Loss: 0.275 | Train Acc: 90.55% | Test Loss: 0.706 | Test Acc: 77.85%
20
+ 2025-04-27 19:13:21,329 - train - INFO - Epoch: 18 | Train Loss: 0.255 | Train Acc: 91.20% | Test Loss: 0.969 | Test Acc: 71.86%
21
+ 2025-04-27 19:15:12,090 - train - INFO - Epoch: 19 | Train Loss: 0.245 | Train Acc: 91.71% | Test Loss: 1.197 | Test Acc: 68.21%
22
+ 2025-04-27 19:16:36,632 - train - INFO - Epoch: 20 | Train Loss: 0.230 | Train Acc: 92.09% | Test Loss: 0.677 | Test Acc: 79.74%
23
+ 2025-04-27 19:18:27,795 - train - INFO - Epoch: 21 | Train Loss: 0.213 | Train Acc: 92.68% | Test Loss: 0.816 | Test Acc: 76.65%
24
+ 2025-04-27 19:19:52,413 - train - INFO - Epoch: 22 | Train Loss: 0.198 | Train Acc: 93.14% | Test Loss: 0.559 | Test Acc: 82.30%
25
+ 2025-04-27 19:21:43,570 - train - INFO - Epoch: 23 | Train Loss: 0.183 | Train Acc: 93.72% | Test Loss: 0.637 | Test Acc: 80.69%
26
+ 2025-04-27 19:23:08,187 - train - INFO - Epoch: 24 | Train Loss: 0.171 | Train Acc: 94.08% | Test Loss: 1.015 | Test Acc: 74.33%
27
+ 2025-04-27 19:24:59,087 - train - INFO - Epoch: 25 | Train Loss: 0.157 | Train Acc: 94.62% | Test Loss: 0.551 | Test Acc: 83.62%
28
+ 2025-04-27 19:26:23,990 - train - INFO - Epoch: 26 | Train Loss: 0.141 | Train Acc: 95.25% | Test Loss: 0.602 | Test Acc: 82.19%
29
+ 2025-04-27 19:28:15,139 - train - INFO - Epoch: 27 | Train Loss: 0.122 | Train Acc: 95.79% | Test Loss: 0.704 | Test Acc: 80.74%
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