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string
author
string
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danieladejumo/MLAgents-Pyramids
danieladejumo
2022-08-22T09:08:29Z
9
0
ml-agents
[ "ml-agents", "tensorboard", "onnx", "unity-ml-agents", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-Pyramids", "region:us" ]
reinforcement-learning
2022-07-24T15:18:14Z
--- tags: - unity-ml-agents - ml-agents - deep-reinforcement-learning - reinforcement-learning - ML-Agents-Pyramids library_name: ml-agents --- # **ppo** Agent playing **Pyramids** This is a trained model of a **ppo** agent playing **Pyramids** using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents). ## Usage (with ML-Agents) The Documentation: https://github.com/huggingface/ml-agents#get-started We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub: ### Resume the training ``` mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume ``` ### Watch your Agent play You can watch your agent **playing directly in your browser:**. 1. Go to https://huggingface.co/spaces/unity/ML-Agents-Pyramids 2. Step 1: Write your model_id: danieladejumo/MLAgents-Pyramids 3. Step 2: Select your *.nn /*.onnx file 4. Click on Watch the agent play 👀
teapoly/icefall-aishell-pruned-transducer-stateless2-2022-08-18
teapoly
2022-08-22T03:56:31Z
0
0
null
[ "tensorboard", "region:us" ]
null
2022-08-18T08:34:52Z
See https://github.com/k2-fsa/icefall/pull/536
VanHoan/distilbert-base-uncased-finetuned-imdb
VanHoan
2022-08-22T03:46:47Z
4
0
transformers
[ "transformers", "pytorch", "tensorboard", "distilbert", "fill-mask", "generated_from_trainer", "dataset:imdb", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]
fill-mask
2022-08-22T03:17:40Z
--- license: apache-2.0 tags: - generated_from_trainer datasets: - imdb model-index: - name: distilbert-base-uncased-finetuned-imdb 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-imdb This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the imdb dataset. It achieves the following results on the evaluation set: - Loss: 2.3229 ## 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: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3.0 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 2.556 | 1.0 | 767 | 2.3725 | | 2.4458 | 2.0 | 1534 | 2.3396 | | 2.4102 | 3.0 | 2301 | 2.3084 | ### Framework versions - Transformers 4.21.1 - Pytorch 1.12.1+cu113 - Datasets 2.4.0 - Tokenizers 0.12.1
ahnafsamin/Tacotron2-gronings
ahnafsamin
2022-08-22T01:33:21Z
3
0
null
[ "text-to-speech", "gronings", "Tacotron 2", "gos", "dataset:gronings", "arxiv:1712.05884", "region:us" ]
text-to-speech
2022-08-22T01:02:31Z
--- tags: - text-to-speech - gronings - Tacotron 2 language: gos datasets: - gronings --- ## GroTTS Model This model is trained with the [Tacotron 2](https://arxiv.org/abs/1712.05884) architecture using approx. 2 hours of Gronings TTS dataset. For the best results, you need to download the vocoder separately from [here](https://huggingface.co/ahnafsamin/parallelwavegan-gronings) and then use the following code: ``` from espnet2.bin.tts_inference import Text2Speech from scipy.io.wavfile import write model = Text2Speech.from_pretrained( model_file="path_to_the_model_file_in_pth_format", vocoder_file="path_to_the_vocoder_file_in_pkl_format" ) output = model("This is a simple test.") write("x.wav", 22050, output['wav'].numpy()) ``` ## TTS config <details><summary>expand</summary> ``` config: conf/train.yaml print_config: false log_level: INFO dry_run: false iterator_type: sequence output_dir: exp/tts_train_raw_char_tacotron ngpu: 1 seed: 0 num_workers: 1 num_att_plot: 3 dist_backend: nccl dist_init_method: env:// dist_world_size: null dist_rank: null local_rank: 0 dist_master_addr: null dist_master_port: null dist_launcher: null multiprocessing_distributed: false unused_parameters: false sharded_ddp: false cudnn_enabled: true cudnn_benchmark: false cudnn_deterministic: true collect_stats: false write_collected_feats: false max_epoch: 200 patience: null val_scheduler_criterion: - valid - loss early_stopping_criterion: - valid - loss - min best_model_criterion: - - valid - loss - min - - train - loss - min keep_nbest_models: 5 nbest_averaging_interval: 0 grad_clip: 1.0 grad_clip_type: 2.0 grad_noise: false accum_grad: 2 no_forward_run: false resume: true train_dtype: float32 use_amp: false log_interval: null use_matplotlib: true use_tensorboard: true use_wandb: false wandb_project: null wandb_id: null wandb_entity: null wandb_name: null wandb_model_log_interval: -1 detect_anomaly: false pretrain_path: null init_param: [] ignore_init_mismatch: false freeze_param: [] num_iters_per_epoch: 1000 batch_size: 20 valid_batch_size: null batch_bins: 2000000 valid_batch_bins: null train_shape_file: - exp/tts_stats_raw_char_tacotron/train/text_shape.char - exp/tts_stats_raw_char_tacotron/train/speech_shape valid_shape_file: - exp/tts_stats_raw_char_tacotron/valid/text_shape.char - exp/tts_stats_raw_char_tacotron/valid/speech_shape batch_type: numel valid_batch_type: null fold_length: - 150 - 204800 sort_in_batch: descending sort_batch: descending multiple_iterator: false chunk_length: 500 chunk_shift_ratio: 0.5 num_cache_chunks: 1024 train_data_path_and_name_and_type: - - dump/raw/tr_no_dev/text - text - text - - dump/raw/tr_no_dev/wav.scp - speech - sound valid_data_path_and_name_and_type: - - dump/raw/dev/text - text - text - - dump/raw/dev/wav.scp - speech - sound allow_variable_data_keys: false max_cache_size: 0.0 max_cache_fd: 32 valid_max_cache_size: null optim: adam optim_conf: lr: 0.001 eps: 1.0e-06 weight_decay: 0.0 scheduler: null scheduler_conf: {} token_list: - <blank> - <unk> - <space> - E - N - A - O - T - I - R - D - L - S - K - M - G - U - H - . - W - V - Z - P - B - ',' - J - C - F - '?' - '''' - '!' - Y - X - '`' - <sos/eos> odim: null model_conf: {} use_preprocessor: true token_type: char bpemodel: null non_linguistic_symbols: null cleaner: tacotron g2p: g2p_en feats_extract: fbank feats_extract_conf: n_fft: 1024 hop_length: 256 win_length: null fs: 22050 fmin: 80 fmax: 7600 n_mels: 80 normalize: global_mvn normalize_conf: stats_file: exp/tts_stats_raw_char_tacotron/train/feats_stats.npz tts: tacotron2 tts_conf: embed_dim: 512 elayers: 1 eunits: 512 econv_layers: 3 econv_chans: 512 econv_filts: 5 atype: location adim: 512 aconv_chans: 32 aconv_filts: 15 cumulate_att_w: true dlayers: 2 dunits: 1024 prenet_layers: 2 prenet_units: 256 postnet_layers: 5 postnet_chans: 512 postnet_filts: 5 output_activation: null use_batch_norm: true use_concate: true use_residual: false dropout_rate: 0.5 zoneout_rate: 0.1 reduction_factor: 1 spk_embed_dim: null use_masking: true bce_pos_weight: 5.0 use_guided_attn_loss: true guided_attn_loss_sigma: 0.4 guided_attn_loss_lambda: 1.0 pitch_extract: null pitch_extract_conf: {} pitch_normalize: null pitch_normalize_conf: {} energy_extract: null energy_extract_conf: {} energy_normalize: null energy_normalize_conf: {} required: - output_dir - token_list version: 0.10.7a1 distributed: false ``` </details>
abdulmatinomotoso/paraphrase_detector
abdulmatinomotoso
2022-08-21T22:04:31Z
105
0
transformers
[ "transformers", "pytorch", "tensorboard", "bert", "text-classification", "generated_from_trainer", "dataset:glue", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]
text-classification
2022-08-21T21:45:43Z
--- license: apache-2.0 tags: - generated_from_trainer datasets: - glue metrics: - accuracy - f1 model-index: - name: paraphrase_detector results: - task: name: Text Classification type: text-classification dataset: name: glue type: glue config: mrpc split: train args: mrpc metrics: - name: Accuracy type: accuracy value: 0.8553921568627451 - name: F1 type: f1 value: 0.8984509466437176 --- <!-- 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. --> # paraphrase_detector This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the glue dataset. It achieves the following results on the evaluation set: - Loss: 0.6599 - Accuracy: 0.8554 - F1: 0.8985 ## 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: 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: 3.0 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | No log | 1.0 | 459 | 0.4968 | 0.8480 | 0.8901 | | 0.3297 | 2.0 | 918 | 0.6599 | 0.8554 | 0.8985 | | 0.1382 | 3.0 | 1377 | 0.6599 | 0.8554 | 0.8985 | ### Framework versions - Transformers 4.21.1 - Pytorch 1.12.1+cu113 - Datasets 2.4.0 - Tokenizers 0.12.1
Yihui/t5-small-text-summary-generation
Yihui
2022-08-21T21:32:58Z
23
0
transformers
[ "transformers", "pytorch", "tf", "t5", "text2text-generation", "generated_from_keras_callback", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]
text2text-generation
2022-08-21T21:28:23Z
--- tags: - generated_from_keras_callback model-index: - name: t5-small-text-summary-generation 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. --> # t5-small-text-summary-generation This model was trained from scratch on an unknown dataset. It achieves the following results on the evaluation set: ## 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: None - training_precision: float32 ### Training results ### Framework versions - Transformers 4.21.1 - TensorFlow 2.8.2 - Datasets 2.4.0 - Tokenizers 0.12.1
jplum87/test
jplum87
2022-08-21T19:27:06Z
4
0
transformers
[ "transformers", "pytorch", "distilbert", "text-classification", "generated_from_trainer", "dataset:emotion", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]
text-classification
2022-08-21T18:53:47Z
--- license: apache-2.0 tags: - generated_from_trainer datasets: - emotion metrics: - accuracy model-index: - name: test results: - task: name: Text Classification type: text-classification dataset: name: emotion type: emotion config: default split: train args: default metrics: - name: Accuracy type: accuracy value: 0.9335 --- <!-- 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. --> # test This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the emotion dataset. It achieves the following results on the evaluation set: - Loss: 0.2778 - Accuracy: 0.9335 - F1:: 0.9337 ## 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: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 2 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1: | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | No log | 1.0 | 250 | 0.3285 | 0.9285 | 0.9291 | | No log | 2.0 | 500 | 0.2778 | 0.9335 | 0.9337 | ### Framework versions - Transformers 4.21.1 - Pytorch 1.12.1 - Datasets 2.4.0 - Tokenizers 0.11.0
BigSalmon/InformalToFormalLincoln69Paraphrase
BigSalmon
2022-08-21T17:03:00Z
160
0
transformers
[ "transformers", "pytorch", "gpt2", "text-generation", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]
text-generation
2022-08-21T16:49:57Z
``` from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("BigSalmon/InformalToFormalLincoln69Paraphrase") model = AutoModelForCausalLM.from_pretrained("BigSalmon/InformalToFormalLincoln69Paraphrase") ``` ``` Demo: https://huggingface.co/spaces/BigSalmon/FormalInformalConciseWordy ``` ``` prompt = """informal english: corn fields are all across illinois, visible once you leave chicago.\nTranslated into the Style of Abraham Lincoln:""" input_ids = tokenizer.encode(prompt, return_tensors='pt') outputs = model.generate(input_ids=input_ids, max_length=10 + len(prompt), temperature=1.0, top_k=50, top_p=0.95, do_sample=True, num_return_sequences=5, early_stopping=True) for i in range(5): print(tokenizer.decode(outputs[i])) ``` Most likely outputs (Disclaimer: I highly recommend using this over just generating): ``` prompt = """informal english: corn fields are all across illinois, visible once you leave chicago.\nTranslated into the Style of Abraham Lincoln:""" text = tokenizer.encode(prompt) myinput, past_key_values = torch.tensor([text]), None myinput = myinput myinput= myinput.to(device) logits, past_key_values = model(myinput, past_key_values = past_key_values, return_dict=False) logits = logits[0,-1] probabilities = torch.nn.functional.softmax(logits) best_logits, best_indices = logits.topk(250) best_words = [tokenizer.decode([idx.item()]) for idx in best_indices] text.append(best_indices[0].item()) best_probabilities = probabilities[best_indices].tolist() words = [] print(best_words) ``` ``` How To Make Prompt: informal english: i am very ready to do that just that. Translated into the Style of Abraham Lincoln: you can assure yourself of my readiness to work toward this end. Translated into the Style of Abraham Lincoln: please be assured that i am most ready to undertake this laborious task. *** informal english: space is huge and needs to be explored. Translated into the Style of Abraham Lincoln: space awaits traversal, a new world whose boundaries are endless. Translated into the Style of Abraham Lincoln: space is a ( limitless / boundless ) expanse, a vast virgin domain awaiting exploration. *** informal english: corn fields are all across illinois, visible once you leave chicago. Translated into the Style of Abraham Lincoln: corn fields ( permeate illinois / span the state of illinois / ( occupy / persist in ) all corners of illinois / line the horizon of illinois / envelop the landscape of illinois ), manifesting themselves visibly as one ventures beyond chicago. informal english: ``` ``` original: chrome extensions [MASK] accomplish everyday tasks. infill: chrome extensions ( expedite the ability to / unlock the means to more readily ) accomplish everyday tasks. *** original: at a time when nintendo has become inflexible, [MASK] consoles that are tethered to a fixed iteration, sega diligently curates its legacy of classic video games on handheld devices. infill: at a time when nintendo has become inflexible, ( firmly set on / unyielding in its insistence on ) consoles that are tethered to a fixed iteration, sega diligently curates its legacy of classic video games on handheld devices. *** infill: ``` ``` Essay Intro (Warriors vs. Rockets in Game 7): text: eagerly anticipated by fans, game 7's are the highlight of the post-season. text: ever-building in suspense, game 7's have the crowd captivated. *** Essay Intro (South Korean TV Is Becoming Popular): text: maturing into a bona fide paragon of programming, south korean television ( has much to offer / entertains without fail / never disappoints ). text: increasingly held in critical esteem, south korean television continues to impress. text: at the forefront of quality content, south korea is quickly achieving celebrity status. *** Essay Intro ( ``` ``` Search: What is the definition of Checks and Balances? https://en.wikipedia.org/wiki/Checks_and_balances Checks and Balances is the idea of having a system where each and every action in government should be subject to one or more checks that would not allow one branch or the other to overly dominate. https://www.harvard.edu/glossary/Checks_and_Balances Checks and Balances is a system that allows each branch of government to limit the powers of the other branches in order to prevent abuse of power https://www.law.cornell.edu/library/constitution/Checks_and_Balances Checks and Balances is a system of separation through which branches of government can control the other, thus preventing excess power. *** Search: What is the definition of Separation of Powers? https://en.wikipedia.org/wiki/Separation_of_powers The separation of powers is a principle in government, whereby governmental powers are separated into different branches, each with their own set of powers, that are prevent one branch from aggregating too much power. https://www.yale.edu/tcf/Separation_of_Powers.html Separation of Powers is the division of governmental functions between the executive, legislative and judicial branches, clearly demarcating each branch's authority, in the interest of ensuring that individual liberty or security is not undermined. *** Search: What is the definition of Connection of Powers? https://en.wikipedia.org/wiki/Connection_of_powers Connection of Powers is a feature of some parliamentary forms of government where different branches of government are intermingled, typically the executive and legislative branches. https://simple.wikipedia.org/wiki/Connection_of_powers The term Connection of Powers describes a system of government in which there is overlap between different parts of the government. *** Search: What is the definition of ``` ``` Search: What are phrase synonyms for "second-guess"? https://www.powerthesaurus.org/second-guess/synonyms Shortest to Longest: - feel dubious about - raise an eyebrow at - wrinkle their noses at - cast a jaundiced eye at - teeter on the fence about *** Search: What are phrase synonyms for "mean to newbies"? https://www.powerthesaurus.org/mean_to_newbies/synonyms Shortest to Longest: - readiness to balk at rookies - absence of tolerance for novices - hostile attitude toward newcomers *** Search: What are phrase synonyms for "make use of"? https://www.powerthesaurus.org/make_use_of/synonyms Shortest to Longest: - call upon - glean value from - reap benefits from - derive utility from - seize on the merits of - draw on the strength of - tap into the potential of *** Search: What are phrase synonyms for "hurting itself"? https://www.powerthesaurus.org/hurting_itself/synonyms Shortest to Longest: - erring - slighting itself - forfeiting its integrity - doing itself a disservice - evincing a lack of backbone *** Search: What are phrase synonyms for " ``` ``` - nebraska - unicamerical legislature - different from federal house and senate text: featuring a unicameral legislature, nebraska's political system stands in stark contrast to the federal model, comprised of a house and senate. *** - penny has practically no value - should be taken out of circulation - just as other coins have been in us history - lost use - value not enough - to make environmental consequences worthy text: all but valueless, the penny should be retired. as with other coins in american history, it has become defunct. too minute to warrant the environmental consequences of its production, it has outlived its usefulness. *** - ``` ``` original: sports teams are profitable for owners. [MASK], their valuations experience a dramatic uptick. infill: sports teams are profitable for owners. ( accumulating vast sums / stockpiling treasure / realizing benefits / cashing in / registering robust financials / scoring on balance sheets ), their valuations experience a dramatic uptick. *** original: ``` ``` wordy: classical music is becoming less popular more and more. Translate into Concise Text: interest in classic music is fading. *** wordy: ``` ``` sweet: savvy voters ousted him. longer: voters who were informed delivered his defeat. *** sweet: ``` ``` 1: commercial space company spacex plans to launch a whopping 52 flights in 2022. 2: spacex, a commercial space company, intends to undertake a total of 52 flights in 2022. 3: in 2022, commercial space company spacex has its sights set on undertaking 52 flights. 4: 52 flights are in the pipeline for 2022, according to spacex, a commercial space company. 5: a commercial space company, spacex aims to conduct 52 flights in 2022. *** 1: ``` Keywords to sentences or sentence. ``` ngos are characterized by: □ voluntary citizens' group that is organized on a local, national or international level □ encourage political participation □ often serve humanitarian functions □ work for social, economic, or environmental change *** what are the drawbacks of living near an airbnb? □ noise □ parking □ traffic □ security □ strangers *** ``` ``` original: musicals generally use spoken dialogue as well as songs to convey the story. operas are usually fully sung. adapted: musicals generally use spoken dialogue as well as songs to convey the story. ( in a stark departure / on the other hand / in contrast / by comparison / at odds with this practice / far from being alike / in defiance of this standard / running counter to this convention ), operas are usually fully sung. *** original: akoya and tahitian are types of pearls. akoya pearls are mostly white, and tahitian pearls are naturally dark. adapted: akoya and tahitian are types of pearls. ( a far cry from being indistinguishable / easily distinguished / on closer inspection / setting them apart / not to be mistaken for one another / hardly an instance of mere synonymy / differentiating the two ), akoya pearls are mostly white, and tahitian pearls are naturally dark. *** original: ``` ``` original: had trouble deciding. translated into journalism speak: wrestled with the question, agonized over the matter, furrowed their brows in contemplation. *** original: ``` ``` input: not loyal 1800s english: ( two-faced / inimical / perfidious / duplicitous / mendacious / double-dealing / shifty ). *** input: ``` ``` first: ( was complicit in / was involved in ). antonym: ( was blameless / was not an accomplice to / had no hand in / was uninvolved in ). *** first: ( have no qualms about / see no issue with ). antonym: ( are deeply troubled by / harbor grave reservations about / have a visceral aversion to / take ( umbrage at / exception to ) / are wary of ). *** first: ( do not see eye to eye / disagree often ). antonym: ( are in sync / are united / have excellent rapport / are like-minded / are in step / are of one mind / are in lockstep / operate in perfect harmony / march in lockstep ). *** first: ``` ``` stiff with competition, law school {A} is the launching pad for countless careers, {B} is a crowded field, {C} ranks among the most sought-after professional degrees, {D} is a professional proving ground. *** languishing in viewership, saturday night live {A} is due for a creative renaissance, {B} is no longer a ratings juggernaut, {C} has been eclipsed by its imitators, {C} can still find its mojo. *** dubbed the "manhattan of the south," atlanta {A} is a bustling metropolis, {B} is known for its vibrant downtown, {C} is a city of rich history, {D} is the pride of georgia. *** embattled by scandal, harvard {A} is feeling the heat, {B} cannot escape the media glare, {C} is facing its most intense scrutiny yet, {D} is in the spotlight for all the wrong reasons. ``` Infill / Infilling / Masking / Phrase Masking ``` his contention [blank] by the evidence [sep] was refuted [answer] *** few sights are as [blank] new york city as the colorful, flashing signage of its bodegas [sep] synonymous with [answer] *** when rick won the lottery, all of his distant relatives [blank] his winnings [sep] clamored for [answer] *** the library’s quiet atmosphere encourages visitors to [blank] in their work [sep] immerse themselves [answer] *** ```
aimanlameesa/wav2vec2-xls-r-bengali_v1
aimanlameesa
2022-08-21T17:00:54Z
105
0
transformers
[ "transformers", "pytorch", "tensorboard", "wav2vec2", "automatic-speech-recognition", "generated_from_trainer", "dataset:common_voice", "license:apache-2.0", "endpoints_compatible", "region:us" ]
automatic-speech-recognition
2022-08-21T09:34:29Z
--- license: apache-2.0 tags: - generated_from_trainer datasets: - common_voice model-index: - name: wav2vec2-xls-r-bengali_v1 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. --> # wav2vec2-xls-r-bengali_v1 This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the common_voice dataset. It achieves the following results on the evaluation set: - Loss: 3.2973 - Wer: 1.0 ## 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: 8 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - num_epochs: 6 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:---:| | 8.7896 | 0.8 | 500 | 3.8455 | 1.0 | | 3.3871 | 1.6 | 1000 | 3.2862 | 1.0 | | 3.3302 | 2.4 | 1500 | 3.3086 | 1.0 | | 3.3259 | 3.2 | 2000 | 3.2973 | 1.0 | | 3.325 | 4.0 | 2500 | 3.2973 | 1.0 | | 3.3178 | 4.8 | 3000 | 3.2973 | 1.0 | | 3.3226 | 5.6 | 3500 | 3.2973 | 1.0 | ### Framework versions - Transformers 4.18.0 - Pytorch 1.11.0 - Datasets 2.1.0 - Tokenizers 0.12.1
Teeto/test_trainer
Teeto
2022-08-21T16:33:22Z
162
0
transformers
[ "transformers", "pytorch", "bert", "text-classification", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]
text-classification
2022-08-21T15:22:45Z
--- license: apache-2.0 tags: - generated_from_trainer metrics: - accuracy model-index: - name: test_trainer 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. --> # test_trainer This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.1667 - Accuracy: 0.9464 ## 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: 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: 3.0 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | No log | 1.0 | 493 | 0.3126 | 0.875 | | 0.4646 | 2.0 | 986 | 0.1646 | 0.9464 | | 0.3032 | 3.0 | 1479 | 0.1667 | 0.9464 | ### Framework versions - Transformers 4.21.1 - Pytorch 1.4.0 - Tokenizers 0.12.1
gokuls/tiny-bert-sst2-1_mobilebert_2_bert_3_gold_labels-distillation
gokuls
2022-08-21T15:08:54Z
103
0
transformers
[ "transformers", "pytorch", "tensorboard", "bert", "text-classification", "generated_from_trainer", "dataset:glue", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]
text-classification
2022-08-21T14:56:56Z
--- license: apache-2.0 tags: - generated_from_trainer datasets: - glue metrics: - accuracy model-index: - name: tiny-bert-sst2-1_mobilebert_2_bert_3_gold_labels-distillation results: - task: name: Text Classification type: text-classification dataset: name: glue type: glue config: sst2 split: train args: sst2 metrics: - name: Accuracy type: accuracy value: 0.8188073394495413 --- <!-- 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. --> # tiny-bert-sst2-1_mobilebert_2_bert_3_gold_labels-distillation This model is a fine-tuned version of [google/bert_uncased_L-2_H-128_A-2](https://huggingface.co/google/bert_uncased_L-2_H-128_A-2) on the glue dataset. It achieves the following results on the evaluation set: - Loss: 0.9350 - Accuracy: 0.8188 ## 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: 16 - eval_batch_size: 16 - seed: 33 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 10 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:-----:|:---------------:|:--------:| | 0.1041 | 1.0 | 4210 | 0.9350 | 0.8188 | | 0.1166 | 2.0 | 8420 | 0.9179 | 0.8188 | | 0.1127 | 3.0 | 12630 | 0.9083 | 0.8142 | | 0.1163 | 4.0 | 16840 | 0.9087 | 0.8165 | ### Framework versions - Transformers 4.21.1 - Pytorch 1.12.1+cu113 - Datasets 2.4.0 - Tokenizers 0.12.1
gokuls/tiny-bert-sst2-1_mobilebert_2_bert-only-distillation
gokuls
2022-08-21T14:55:35Z
107
0
transformers
[ "transformers", "pytorch", "tensorboard", "bert", "text-classification", "generated_from_trainer", "dataset:glue", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]
text-classification
2022-08-21T14:45:24Z
--- license: apache-2.0 tags: - generated_from_trainer datasets: - glue metrics: - accuracy model-index: - name: tiny-bert-sst2-1_mobilebert_2_bert-only-distillation results: - task: name: Text Classification type: text-classification dataset: name: glue type: glue config: sst2 split: train args: sst2 metrics: - name: Accuracy type: accuracy value: 0.8291284403669725 --- <!-- 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. --> # tiny-bert-sst2-1_mobilebert_2_bert-only-distillation This model is a fine-tuned version of [google/bert_uncased_L-2_H-128_A-2](https://huggingface.co/google/bert_uncased_L-2_H-128_A-2) on the glue dataset. It achieves the following results on the evaluation set: - Loss: 1.5399 - Accuracy: 0.8291 ## 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: 16 - eval_batch_size: 16 - seed: 33 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 10 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:-----:|:---------------:|:--------:| | 0.2068 | 1.0 | 4210 | 1.5399 | 0.8291 | | 0.22 | 2.0 | 8420 | 1.5395 | 0.8234 | | 0.2171 | 3.0 | 12630 | 1.6631 | 0.8200 | | 0.2434 | 4.0 | 16840 | 1.6152 | 0.8234 | ### Framework versions - Transformers 4.21.1 - Pytorch 1.12.1+cu113 - Datasets 2.4.0 - Tokenizers 0.12.1
abdoutony207/m2m100_418M-evaluated-en-to-ar-2000instancesUNMULTI-leaningRate2e-05-batchSize8-regu1
abdoutony207
2022-08-21T14:46:50Z
8
0
transformers
[ "transformers", "pytorch", "tensorboard", "m2m_100", "text2text-generation", "generated_from_trainer", "dataset:un_multi", "license:mit", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]
text2text-generation
2022-08-21T13:34:38Z
--- license: mit tags: - generated_from_trainer datasets: - un_multi metrics: - bleu model-index: - name: m2m100_418M-evaluated-en-to-ar-2000instancesUNMULTI-leaningRate2e-05-batchSize8-regu1 results: - task: name: Sequence-to-sequence Language Modeling type: text2text-generation dataset: name: un_multi type: un_multi args: ar-en metrics: - name: Bleu type: bleu value: 41.8577 --- <!-- 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. --> # m2m100_418M-evaluated-en-to-ar-2000instancesUNMULTI-leaningRate2e-05-batchSize8-regu1 This model is a fine-tuned version of [facebook/m2m100_418M](https://huggingface.co/facebook/m2m100_418M) on the un_multi dataset. It achieves the following results on the evaluation set: - Loss: 0.3603 - Bleu: 41.8577 - Meteor: 0.4199 - Gen Len: 41.9 ## 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: 11 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Bleu | Meteor | Gen Len | |:-------------:|:-----:|:----:|:---------------:|:-------:|:------:|:-------:| | 5.111 | 0.5 | 100 | 3.2467 | 29.5017 | 0.3371 | 42.425 | | 2.1491 | 1.0 | 200 | 1.0018 | 33.0563 | 0.3593 | 41.205 | | 0.5911 | 1.5 | 300 | 0.4159 | 34.5818 | 0.3705 | 42.0625 | | 0.3546 | 2.0 | 400 | 0.3723 | 36.6179 | 0.3823 | 40.925 | | 0.2487 | 2.5 | 500 | 0.3595 | 39.0331 | 0.3956 | 41.56 | | 0.2365 | 3.0 | 600 | 0.3485 | 39.5188 | 0.4023 | 41.6425 | | 0.1687 | 3.5 | 700 | 0.3542 | 40.1728 | 0.4043 | 42.61 | | 0.1791 | 4.0 | 800 | 0.3466 | 40.4858 | 0.4101 | 41.5575 | | 0.1196 | 4.5 | 900 | 0.3493 | 41.2457 | 0.4123 | 41.755 | | 0.1394 | 5.0 | 1000 | 0.3486 | 40.5606 | 0.4114 | 41.78 | | 0.0958 | 5.5 | 1100 | 0.3568 | 41.1873 | 0.4157 | 41.7275 | | 0.1043 | 6.0 | 1200 | 0.3557 | 41.2749 | 0.4165 | 41.935 | | 0.073 | 6.5 | 1300 | 0.3603 | 41.8577 | 0.4199 | 41.9 | ### Framework versions - Transformers 4.20.1 - Pytorch 1.11.0 - Datasets 2.1.0 - Tokenizers 0.12.1
csebuetnlp/banglishbert_generator
csebuetnlp
2022-08-21T14:05:22Z
25
1
transformers
[ "transformers", "pytorch", "electra", "fill-mask", "bn", "en", "arxiv:2101.00204", "autotrain_compatible", "endpoints_compatible", "region:us" ]
fill-mask
2022-06-06T14:37:28Z
--- language: - bn - en tags: - fill-mask licenses: - cc-by-nc-sa-4.0 --- # BanglishBERT This repository contains the pretrained generator checkpoint of the model [**BanglishBERT**](). This is an [ELECTRA](https://openreview.net/pdf?id=r1xMH1BtvB) generator model pretrained with the Masked Language Modeling (MLM) objective on large amounts of Bengali and English corpora. **Note**: This model was pretrained using a specific normalization pipeline available [here](https://github.com/csebuetnlp/normalizer). ## Using this model for MLM in `transformers` (tested on 4.11.0.dev0) ```python from normalizer import normalize # pip install git+https://github.com/csebuetnlp/normalizer from transformers import pipeline fill_mask = pipeline( "fill-mask", model="csebuetnlp/banglishbert_generator", tokenizer="csebuetnlp/banglishbert_generator" ) print( fill_mask( normalize(f"Paris is the {fill_mask.tokenizer.mask_token} of France.") ) ) ``` If you use this model, please cite the following paper: ``` @inproceedings{bhattacharjee-etal-2022-banglabert, title = {BanglaBERT: Lagnuage Model Pretraining and Benchmarks for Low-Resource Language Understanding Evaluation in Bangla}, author = "Bhattacharjee, Abhik and Hasan, Tahmid and Mubasshir, Kazi and Islam, Md. Saiful and Uddin, Wasi Ahmad and Iqbal, Anindya and Rahman, M. Sohel and Shahriyar, Rifat", booktitle = "Findings of the North American Chapter of the Association for Computational Linguistics: NAACL 2022", month = july, year = {2022}, url = {https://arxiv.org/abs/2101.00204}, eprinttype = {arXiv}, eprint = {2101.00204} } ``` If you use the normalization module, please cite the following paper: ``` @inproceedings{hasan-etal-2020-low, title = "Not Low-Resource Anymore: Aligner Ensembling, Batch Filtering, and New Datasets for {B}engali-{E}nglish Machine Translation", author = "Hasan, Tahmid and Bhattacharjee, Abhik and Samin, Kazi and Hasan, Masum and Basak, Madhusudan and Rahman, M. Sohel and Shahriyar, Rifat", booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)", month = nov, year = "2020", address = "Online", publisher = "Association for Computational Linguistics", url = "https://www.aclweb.org/anthology/2020.emnlp-main.207", doi = "10.18653/v1/2020.emnlp-main.207", pages = "2612--2623", abstract = "Despite being the seventh most widely spoken language in the world, Bengali has received much less attention in machine translation literature due to being low in resources. Most publicly available parallel corpora for Bengali are not large enough; and have rather poor quality, mostly because of incorrect sentence alignments resulting from erroneous sentence segmentation, and also because of a high volume of noise present in them. In this work, we build a customized sentence segmenter for Bengali and propose two novel methods for parallel corpus creation on low-resource setups: aligner ensembling and batch filtering. With the segmenter and the two methods combined, we compile a high-quality Bengali-English parallel corpus comprising of 2.75 million sentence pairs, more than 2 million of which were not available before. Training on neural models, we achieve an improvement of more than 9 BLEU score over previous approaches to Bengali-English machine translation. We also evaluate on a new test set of 1000 pairs made with extensive quality control. We release the segmenter, parallel corpus, and the evaluation set, thus elevating Bengali from its low-resource status. To the best of our knowledge, this is the first ever large scale study on Bengali-English machine translation. We believe our study will pave the way for future research on Bengali-English machine translation as well as other low-resource languages. Our data and code are available at https://github.com/csebuetnlp/banglanmt.", } ```
csebuetnlp/banglabert_generator
csebuetnlp
2022-08-21T14:04:14Z
42
2
transformers
[ "transformers", "pytorch", "electra", "fill-mask", "bn", "en", "arxiv:2101.00204", "autotrain_compatible", "endpoints_compatible", "region:us" ]
fill-mask
2022-06-06T11:01:12Z
--- language: - bn - en licenses: - cc-by-nc-sa-4.0 --- # BanglaBERT This repository contains the pretrained generator checkpoint of the model [**BanglaBERT**](). This is an [ELECTRA](https://openreview.net/pdf?id=r1xMH1BtvB) generator model pretrained with the Masked Language Modeling (MLM) objective on large amounts of Bengali corpora. **Note**: This model was pretrained using a specific normalization pipeline available [here](https://github.com/csebuetnlp/normalizer). ## Using this model for MLM in `transformers` (tested on 4.11.0.dev0) ```python from normalizer import normalize # pip install git+https://github.com/csebuetnlp/normalizer from transformers import pipeline fill_mask = pipeline( "fill-mask", model="csebuetnlp/banglabert_generator", tokenizer="csebuetnlp/banglabert_generator" ) print( fill_mask( normalize(f"আমি বাংলায় {fill_mask.tokenizer.mask_token} গাই।") ) ) ``` If you use this model, please cite the following paper: ``` @inproceedings{bhattacharjee-etal-2022-banglabert, title = {BanglaBERT: Lagnuage Model Pretraining and Benchmarks for Low-Resource Language Understanding Evaluation in Bangla}, author = "Bhattacharjee, Abhik and Hasan, Tahmid and Mubasshir, Kazi and Islam, Md. Saiful and Uddin, Wasi Ahmad and Iqbal, Anindya and Rahman, M. Sohel and Shahriyar, Rifat", booktitle = "Findings of the North American Chapter of the Association for Computational Linguistics: NAACL 2022", month = july, year = {2022}, url = {https://arxiv.org/abs/2101.00204}, eprinttype = {arXiv}, eprint = {2101.00204} } ``` If you use the normalization module, please cite the following paper: ``` @inproceedings{hasan-etal-2020-low, title = "Not Low-Resource Anymore: Aligner Ensembling, Batch Filtering, and New Datasets for {B}engali-{E}nglish Machine Translation", author = "Hasan, Tahmid and Bhattacharjee, Abhik and Samin, Kazi and Hasan, Masum and Basak, Madhusudan and Rahman, M. Sohel and Shahriyar, Rifat", booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)", month = nov, year = "2020", address = "Online", publisher = "Association for Computational Linguistics", url = "https://www.aclweb.org/anthology/2020.emnlp-main.207", doi = "10.18653/v1/2020.emnlp-main.207", pages = "2612--2623", abstract = "Despite being the seventh most widely spoken language in the world, Bengali has received much less attention in machine translation literature due to being low in resources. Most publicly available parallel corpora for Bengali are not large enough; and have rather poor quality, mostly because of incorrect sentence alignments resulting from erroneous sentence segmentation, and also because of a high volume of noise present in them. In this work, we build a customized sentence segmenter for Bengali and propose two novel methods for parallel corpus creation on low-resource setups: aligner ensembling and batch filtering. With the segmenter and the two methods combined, we compile a high-quality Bengali-English parallel corpus comprising of 2.75 million sentence pairs, more than 2 million of which were not available before. Training on neural models, we achieve an improvement of more than 9 BLEU score over previous approaches to Bengali-English machine translation. We also evaluate on a new test set of 1000 pairs made with extensive quality control. We release the segmenter, parallel corpus, and the evaluation set, thus elevating Bengali from its low-resource status. To the best of our knowledge, this is the first ever large scale study on Bengali-English machine translation. We believe our study will pave the way for future research on Bengali-English machine translation as well as other low-resource languages. Our data and code are available at https://github.com/csebuetnlp/banglanmt.", } ```
csebuetnlp/banglat5
csebuetnlp
2022-08-21T13:59:20Z
2,188
14
transformers
[ "transformers", "pytorch", "t5", "text2text-generation", "bn", "arxiv:2205.11081", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]
text2text-generation
2022-05-23T07:51:38Z
--- language: - bn licenses: - cc-by-nc-sa-4.0 --- # BanglaT5 This repository contains the pretrained checkpoint of the model **BanglaT5**. This is a sequence to sequence transformer model pretrained with the ["Span Corruption"]() objective. Finetuned models using this checkpoint achieve state-of-the-art results on many of the NLG tasks in bengali. For finetuning on different downstream tasks such as `Machine Translation`, `Abstractive Text Summarization`, `Question Answering` etc., refer to the scripts in the official GitHub [repository](https://github.com/csebuetnlp/BanglaNLG). **Note**: This model was pretrained using a specific normalization pipeline available [here](https://github.com/csebuetnlp/normalizer). All finetuning scripts in the official GitHub repository use this normalization by default. If you need to adapt the pretrained model for a different task make sure the text units are normalized using this pipeline before tokenizing to get best results. A basic example is given below: ## Using this model in `transformers` (tested on 4.11.0.dev0) ```python from transformers import AutoModelForSeq2SeqLM, AutoTokenizer from normalizer import normalize # pip install git+https://github.com/csebuetnlp/normalizer model = AutoModelForSeq2SeqLM.from_pretrained("csebuetnlp/banglat5") tokenizer = AutoTokenizer.from_pretrained("csebuetnlp/banglat5", use_fast=False) input_sentence = "" input_ids = tokenizer(normalize(input_sentence), return_tensors="pt").input_ids generated_tokens = model.generate(input_ids) decoded_tokens = tokenizer.batch_decode(generated_tokens)[0] print(decoded_tokens) ``` ## Benchmarks * Supervised fine-tuning | Model | Params | MT (SacreBLEU) | TS (ROUGE-2) | QA (EM/F1) | MD (SacreBLEU-1) | NHG (ROUGE-2) | XLS (ROUGE-2) | BNLG score | |--------------------|------------|-----------------------|------------------------|-------------------|--------------------|----------------|----------------|---------------| |[mT5 (base)](https://huggingface.co/google/mt5-base) | 582M | 36.6/22.5 | 10.3 | 59.0/65.3 | 17.5 | 9.6 | 2.7/0.7 | 24.9 | |[XLM-ProphetNet](https://huggingface.co/microsoft/xprophetnet-large-wiki100-cased) | 616M | 23.3/16.4 | 7.8 | 53.0/57.3 | 20.0 | 9.5 | 6.2/2.7 | 21.8 | |[mBART-50](https://huggingface.co/facebook/mbart-large-50) | 611M | 23.6/16.7 | 10.4 | 53.4/58.9 | 18.5 | 11.2 | 5.4/3.7 | 22.4 | |[IndicBART](https://huggingface.co/ai4bharat/IndicBART) | 244M | 22.7/13.1 | 8.1 | 53.3/58.8 | 14.8 | 7.9 | 6.3/2.5 | 20.8 | |[BanglaT5](https://huggingface.co/csebuetnlp/banglat5) | 247M | 38.8/25.2 | 13.7 | 68.5/74.8 | 19.0 | 13.8 | 6.4/4.0 | 29.4 | The benchmarking datasets are as follows: * **MT:** **[Machine Translation](https://github.com/csebuetnlp/banglanmt#datasets)** * **TS:** **[Abstractive Text Summarization](https://huggingface.co/datasets/csebuetnlp/xlsum)** * **QA:** **[Question Answering](https://huggingface.co/datasets/csebuetnlp/squad_bn)** * **MD:** **[Multi Turn Dialogue Generation](https://drive.google.com/file/d/1qPmNN6qA4evbh4cD_BDDTCFOwMu4H2JS/view?usp=sharing)** * **NHG:** **[News Headline Generation](https://huggingface.co/datasets/csebuetnlp/xlsum)** * **XLS:** **[Cross-lingual Summarization](https://huggingface.co/datasets/csebuetnlp/CrossSum)** ## Citation If you use this model, please cite the following paper: ``` @article{bhattacharjee2022banglanlg, author = {Abhik Bhattacharjee and Tahmid Hasan and Wasi Uddin Ahmad and Rifat Shahriyar}, title = {BanglaNLG: Benchmarks and Resources for Evaluating Low-Resource Natural Language Generation in Bangla}, journal = {CoRR}, volume = {abs/2205.11081}, year = {2022}, url = {https://arxiv.org/abs/2205.11081}, eprinttype = {arXiv}, eprint = {2205.11081} } ``` If you use the normalization module, please cite the following paper: ``` @inproceedings{hasan-etal-2020-low, title = "Not Low-Resource Anymore: Aligner Ensembling, Batch Filtering, and New Datasets for {B}engali-{E}nglish Machine Translation", author = "Hasan, Tahmid and Bhattacharjee, Abhik and Samin, Kazi and Hasan, Masum and Basak, Madhusudan and Rahman, M. Sohel and Shahriyar, Rifat", booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)", month = nov, year = "2020", address = "Online", publisher = "Association for Computational Linguistics", url = "https://www.aclweb.org/anthology/2020.emnlp-main.207", doi = "10.18653/v1/2020.emnlp-main.207", pages = "2612--2623", abstract = "Despite being the seventh most widely spoken language in the world, Bengali has received much less attention in machine translation literature due to being low in resources. Most publicly available parallel corpora for Bengali are not large enough; and have rather poor quality, mostly because of incorrect sentence alignments resulting from erroneous sentence segmentation, and also because of a high volume of noise present in them. In this work, we build a customized sentence segmenter for Bengali and propose two novel methods for parallel corpus creation on low-resource setups: aligner ensembling and batch filtering. With the segmenter and the two methods combined, we compile a high-quality Bengali-English parallel corpus comprising of 2.75 million sentence pairs, more than 2 million of which were not available before. Training on neural models, we achieve an improvement of more than 9 BLEU score over previous approaches to Bengali-English machine translation. We also evaluate on a new test set of 1000 pairs made with extensive quality control. We release the segmenter, parallel corpus, and the evaluation set, thus elevating Bengali from its low-resource status. To the best of our knowledge, this is the first ever large scale study on Bengali-English machine translation. We believe our study will pave the way for future research on Bengali-English machine translation as well as other low-resource languages. Our data and code are available at https://github.com/csebuetnlp/banglanmt.", } ```
TakeHirako/xlm-roberta-base-finetuned-panx-all
TakeHirako
2022-08-21T13:31:24Z
105
0
transformers
[ "transformers", "pytorch", "xlm-roberta", "token-classification", "generated_from_trainer", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]
token-classification
2022-08-21T13:03:19Z
--- license: mit tags: - generated_from_trainer metrics: - f1 model-index: - name: xlm-roberta-base-finetuned-panx-all 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-all This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.1745 - F1: 0.8505 ## 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 | |:-------------:|:-----:|:----:|:---------------:|:------:| | 0.3055 | 1.0 | 835 | 0.1842 | 0.8099 | | 0.1561 | 2.0 | 1670 | 0.1711 | 0.8452 | | 0.1016 | 3.0 | 2505 | 0.1745 | 0.8505 | ### Framework versions - Transformers 4.11.3 - Pytorch 1.12.1+cu113 - Datasets 1.16.1 - Tokenizers 0.10.3
Yahiya/model520.h5
Yahiya
2022-08-21T12:55:58Z
0
0
null
[ "region:us" ]
null
2022-08-21T12:53:02Z
git lfs install git clone https://huggingface.co/FluxML/vgg16
takuma/results
takuma
2022-08-21T12:04:36Z
7
0
transformers
[ "transformers", "pytorch", "tensorboard", "deberta-v2", "text-classification", "generated_from_trainer", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]
text-classification
2022-08-11T08:41:56Z
--- license: mit 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/deberta-v3-base](https://huggingface.co/microsoft/deberta-v3-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.5104 ## 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: 8 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 2 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 0.6176 | 1.0 | 1267 | 0.5280 | | 0.4315 | 2.0 | 2534 | 0.5104 | ### Framework versions - Transformers 4.21.1 - Pytorch 1.12.1+cu113 - Datasets 2.4.0 - Tokenizers 0.12.1
rebolforces/ppo-pushblock-9M
rebolforces
2022-08-21T10:15:33Z
5
0
ml-agents
[ "ml-agents", "tensorboard", "onnx", "unity-ml-agents", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-PushBlock", "region:us" ]
reinforcement-learning
2022-08-21T10:09:38Z
--- tags: - unity-ml-agents - ml-agents - deep-reinforcement-learning - reinforcement-learning - ML-Agents-PushBlock library_name: ml-agents --- # **ppo** Agent playing **PushBlock** This is a trained model of a **ppo** agent playing **PushBlock** using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents). ## Usage (with ML-Agents) The Documentation: https://github.com/huggingface/ml-agents#get-started We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub: ### Resume the training ``` mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume ``` ### Watch your Agent play You can watch your agent **playing directly in your browser:**. 1. Go to https://huggingface.co/spaces/unity/ML-Agents-PushBlock 2. Step 1: Write your model_id: rebolforces/ppo-pushblock-9M 3. Step 2: Select your *.nn /*.onnx file 4. Click on Watch the agent play 👀 ### Stats ``` "final_checkpoint": { "steps": 9000019, "file_path": "results/PushBlock Training 2/PushBlock.onnx", "reward": 4.981160132090251, "creation_time": 1661076513.4570658, "auxillary_file_paths": [ "results/PushBlock Training 2/PushBlock/PushBlock-9000019.pt" ] } ```
ultra-coder54732/4-way-detection-prop-16-deberta
ultra-coder54732
2022-08-21T08:10:07Z
106
0
transformers
[ "transformers", "pytorch", "tensorboard", "deberta", "text-classification", "generated_from_trainer", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]
text-classification
2022-08-21T05:28:20Z
--- license: mit tags: - generated_from_trainer model-index: - name: 4-way-detection-prop-16-deberta 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. --> # 4-way-detection-prop-16-deberta This model is a fine-tuned version of [microsoft/deberta-base](https://huggingface.co/microsoft/deberta-base) 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: 5e-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 ### Framework versions - Transformers 4.21.1 - Pytorch 1.12.1+cu113 - Datasets 2.4.0 - Tokenizers 0.12.1
gary109/ai-light-dance_singing3_ft_wav2vec2-large-xlsr-53-v1-5gram
gary109
2022-08-21T07:12:51Z
4
1
transformers
[ "transformers", "pytorch", "tensorboard", "wav2vec2", "automatic-speech-recognition", "gary109/AI_Light_Dance", "generated_from_trainer", "license:apache-2.0", "endpoints_compatible", "region:us" ]
automatic-speech-recognition
2022-07-19T03:33:21Z
--- license: apache-2.0 tags: - automatic-speech-recognition - gary109/AI_Light_Dance - generated_from_trainer model-index: - name: ai-light-dance_singing3_ft_wav2vec2-large-xlsr-53-v1-5gram 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. --> # ai-light-dance_singing3_ft_wav2vec2-large-xlsr-53-v1-5gram This model is a fine-tuned version of [gary109/ai-light-dance_singing3_ft_wav2vec2-large-xlsr-53-v1-5gram](https://huggingface.co/gary109/ai-light-dance_singing3_ft_wav2vec2-large-xlsr-53-v1-5gram) on the GARY109/AI_LIGHT_DANCE - ONSET-SINGING3 dataset. It achieves the following results on the evaluation set: - Loss: 0.4505 - Wer: 0.2119 ## 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-07 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 100 - num_epochs: 100.0 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:-----:|:---------------:|:------:| | 0.3355 | 1.0 | 144 | 0.4505 | 0.2119 | | 0.3069 | 2.0 | 288 | 0.4509 | 0.2124 | | 0.3049 | 3.0 | 432 | 0.4511 | 0.2119 | | 0.3028 | 4.0 | 576 | 0.4521 | 0.2114 | | 0.3092 | 5.0 | 720 | 0.4532 | 0.2112 | | 0.3043 | 6.0 | 864 | 0.4536 | 0.2117 | | 0.2903 | 7.0 | 1008 | 0.4543 | 0.2114 | | 0.3124 | 8.0 | 1152 | 0.4538 | 0.2118 | | 0.3079 | 9.0 | 1296 | 0.4541 | 0.2121 | | 0.3093 | 10.0 | 1440 | 0.4537 | 0.2117 | | 0.3093 | 11.0 | 1584 | 0.4544 | 0.2111 | | 0.3202 | 12.0 | 1728 | 0.4549 | 0.2110 | | 0.3086 | 13.0 | 1872 | 0.4546 | 0.2104 | | 0.2947 | 14.0 | 2016 | 0.4542 | 0.2119 | | 0.3145 | 15.0 | 2160 | 0.4539 | 0.2115 | | 0.3292 | 16.0 | 2304 | 0.4532 | 0.2115 | | 0.3049 | 17.0 | 2448 | 0.4547 | 0.2117 | | 0.3177 | 18.0 | 2592 | 0.4544 | 0.2111 | | 0.3108 | 19.0 | 2736 | 0.4547 | 0.2114 | | 0.2944 | 20.0 | 2880 | 0.4560 | 0.2105 | | 0.3232 | 21.0 | 3024 | 0.4560 | 0.2113 | | 0.3196 | 22.0 | 3168 | 0.4559 | 0.2107 | | 0.3207 | 23.0 | 3312 | 0.4563 | 0.2106 | | 0.3039 | 24.0 | 3456 | 0.4555 | 0.2110 | | 0.3157 | 25.0 | 3600 | 0.4560 | 0.2117 | | 0.3285 | 26.0 | 3744 | 0.4561 | 0.2102 | | 0.3125 | 27.0 | 3888 | 0.4553 | 0.2107 | | 0.3051 | 28.0 | 4032 | 0.4560 | 0.2103 | | 0.3166 | 29.0 | 4176 | 0.4560 | 0.2103 | | 0.321 | 30.0 | 4320 | 0.4551 | 0.2101 | | 0.3146 | 31.0 | 4464 | 0.4552 | 0.2100 | | 0.323 | 32.0 | 4608 | 0.4551 | 0.2105 | | 0.3223 | 33.0 | 4752 | 0.4554 | 0.2101 | | 0.3105 | 34.0 | 4896 | 0.4549 | 0.2102 | | 0.3134 | 35.0 | 5040 | 0.4552 | 0.2101 | | 0.3054 | 36.0 | 5184 | 0.4550 | 0.2103 | | 0.3162 | 37.0 | 5328 | 0.4554 | 0.2106 | | 0.3094 | 38.0 | 5472 | 0.4551 | 0.2099 | | 0.3174 | 39.0 | 5616 | 0.4553 | 0.2105 | | 0.3218 | 40.0 | 5760 | 0.4553 | 0.2106 | | 0.3134 | 41.0 | 5904 | 0.4552 | 0.2101 | | 0.3019 | 42.0 | 6048 | 0.4552 | 0.2101 | | 0.3169 | 43.0 | 6192 | 0.4552 | 0.2095 | | 0.3209 | 44.0 | 6336 | 0.4550 | 0.2090 | | 0.3035 | 45.0 | 6480 | 0.4550 | 0.2100 | | 0.3181 | 46.0 | 6624 | 0.4550 | 0.2104 | | 0.3133 | 47.0 | 6768 | 0.4546 | 0.2096 | | 0.3173 | 48.0 | 6912 | 0.4556 | 0.2099 | | 0.3174 | 49.0 | 7056 | 0.4552 | 0.2101 | | 0.313 | 50.0 | 7200 | 0.4553 | 0.2100 | | 0.3139 | 51.0 | 7344 | 0.4555 | 0.2101 | | 0.3054 | 52.0 | 7488 | 0.4555 | 0.2100 | | 0.3212 | 53.0 | 7632 | 0.4554 | 0.2097 | | 0.3252 | 54.0 | 7776 | 0.4553 | 0.2097 | | 0.3063 | 55.0 | 7920 | 0.4554 | 0.2106 | | 0.3206 | 56.0 | 8064 | 0.4551 | 0.2097 | | 0.3176 | 57.0 | 8208 | 0.4552 | 0.2101 | | 0.3179 | 58.0 | 8352 | 0.4554 | 0.2099 | | 0.3064 | 59.0 | 8496 | 0.4559 | 0.2092 | | 0.301 | 60.0 | 8640 | 0.4559 | 0.2103 | | 0.3103 | 61.0 | 8784 | 0.4559 | 0.2102 | | 0.3169 | 62.0 | 8928 | 0.4559 | 0.2103 | | 0.3081 | 63.0 | 9072 | 0.4559 | 0.2101 | | 0.3249 | 64.0 | 9216 | 0.4555 | 0.2106 | | 0.3031 | 65.0 | 9360 | 0.4553 | 0.2105 | | 0.3017 | 66.0 | 9504 | 0.4556 | 0.2105 | | 0.3261 | 67.0 | 9648 | 0.4551 | 0.2100 | | 0.3196 | 68.0 | 9792 | 0.4553 | 0.2096 | | 0.3085 | 69.0 | 9936 | 0.4554 | 0.2095 | | 0.3235 | 70.0 | 10080 | 0.4552 | 0.2096 | | 0.3194 | 71.0 | 10224 | 0.4550 | 0.2102 | | 0.3243 | 72.0 | 10368 | 0.4546 | 0.2098 | | 0.3115 | 73.0 | 10512 | 0.4542 | 0.2101 | | 0.3307 | 74.0 | 10656 | 0.4545 | 0.2100 | | 0.3072 | 75.0 | 10800 | 0.4547 | 0.2100 | | 0.3218 | 76.0 | 10944 | 0.4545 | 0.2102 | | 0.3116 | 77.0 | 11088 | 0.4540 | 0.2103 | | 0.3021 | 78.0 | 11232 | 0.4542 | 0.2101 | | 0.3165 | 79.0 | 11376 | 0.4539 | 0.2109 | | 0.327 | 80.0 | 11520 | 0.4539 | 0.2090 | | 0.3268 | 81.0 | 11664 | 0.4540 | 0.2110 | | 0.304 | 82.0 | 11808 | 0.4537 | 0.2097 | | 0.3256 | 83.0 | 11952 | 0.4537 | 0.2102 | | 0.3208 | 84.0 | 12096 | 0.4544 | 0.2101 | | 0.3199 | 85.0 | 12240 | 0.4541 | 0.2094 | | 0.3104 | 86.0 | 12384 | 0.4543 | 0.2097 | | 0.3218 | 87.0 | 12528 | 0.4542 | 0.2106 | | 0.3301 | 88.0 | 12672 | 0.4538 | 0.2098 | | 0.3055 | 89.0 | 12816 | 0.4540 | 0.2101 | | 0.3154 | 90.0 | 12960 | 0.4533 | 0.2098 | | 0.3169 | 91.0 | 13104 | 0.4543 | 0.2098 | | 0.3122 | 92.0 | 13248 | 0.4541 | 0.2098 | | 0.319 | 93.0 | 13392 | 0.4536 | 0.2094 | | 0.307 | 94.0 | 13536 | 0.4538 | 0.2092 | | 0.3132 | 95.0 | 13680 | 0.4540 | 0.2094 | | 0.3185 | 96.0 | 13824 | 0.4536 | 0.2099 | | 0.2996 | 97.0 | 13968 | 0.4541 | 0.2100 | | 0.3193 | 98.0 | 14112 | 0.4539 | 0.2092 | | 0.3091 | 99.0 | 14256 | 0.4538 | 0.2096 | | 0.315 | 100.0 | 14400 | 0.4544 | 0.2100 | ### Framework versions - Transformers 4.21.0.dev0 - Pytorch 1.9.1+cu102 - Datasets 2.3.3.dev0 - Tokenizers 0.12.1
ultra-coder54732/4-way-detection-prop-16-distilbert
ultra-coder54732
2022-08-21T05:27:37Z
4
0
transformers
[ "transformers", "pytorch", "tensorboard", "distilbert", "text-classification", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]
text-classification
2022-08-21T04:29:41Z
--- license: apache-2.0 tags: - generated_from_trainer model-index: - name: 4-way-detection-prop-16-distilbert 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. --> # 4-way-detection-prop-16-distilbert This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) 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: 5e-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 ### Framework versions - Transformers 4.21.1 - Pytorch 1.12.1+cu113 - Datasets 2.4.0 - Tokenizers 0.12.1
AA1152/distilbert-base-uncased-finetuned-emotion
AA1152
2022-08-21T05:05:01Z
103
0
transformers
[ "transformers", "pytorch", "tensorboard", "distilbert", "text-classification", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]
text-classification
2022-08-21T03:15:57Z
--- license: apache-2.0 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: 0.2130 - Accuracy: 0.9275 - F1: 0.9277 ## 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: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 2 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | 0.8371 | 1.0 | 250 | 0.3079 | 0.9105 | 0.9076 | | 0.2526 | 2.0 | 500 | 0.2130 | 0.9275 | 0.9277 | ### Framework versions - Transformers 4.21.1 - Pytorch 1.12.1+cu113 - Tokenizers 0.12.1
ultra-coder54732/4-way-detection-prop-16-bert
ultra-coder54732
2022-08-21T02:52:00Z
8
0
transformers
[ "transformers", "pytorch", "tensorboard", "bert", "text-classification", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]
text-classification
2022-08-21T00:08:42Z
--- license: apache-2.0 tags: - generated_from_trainer model-index: - name: 4-way-detection-prop-16-bert 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. --> # 4-way-detection-prop-16-bert This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) 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: 5e-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 ### Framework versions - Transformers 4.21.1 - Pytorch 1.12.1+cu113 - Datasets 2.4.0 - Tokenizers 0.12.1
ny7777/ddpm-pokemon-128
ny7777
2022-08-21T01:17:46Z
0
0
diffusers
[ "diffusers", "tensorboard", "en", "dataset:huggan/pokemon", "license:apache-2.0", "diffusers:DDPMPipeline", "region:us" ]
null
2022-08-20T07:52:37Z
--- language: en license: apache-2.0 library_name: diffusers tags: [] datasets: huggan/pokemon metrics: [] --- <!-- 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. --> # ddpm-pokemon-128 ## Model description This diffusion model is trained with the [🤗 Diffusers](https://github.com/huggingface/diffusers) library on the `huggan/pokemon` dataset. ## 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 data [TODO: describe the data used to train the model] ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 16 - eval_batch_size: 16 - gradient_accumulation_steps: 1 - optimizer: AdamW with betas=(None, None), weight_decay=None and epsilon=None - lr_scheduler: None - lr_warmup_steps: 300 - ema_inv_gamma: None - ema_inv_gamma: None - ema_inv_gamma: None - mixed_precision: fp16 ### Training results 📈 [TensorBoard logs](https://huggingface.co/ny7777/ddpm-pokemon-128/tensorboard?#scalars)
bguan/testpyramidsrnd
bguan
2022-08-21T01:00:37Z
4
0
ml-agents
[ "ml-agents", "tensorboard", "onnx", "unity-ml-agents", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-Pyramids", "region:us" ]
reinforcement-learning
2022-08-19T03:29:14Z
--- tags: - unity-ml-agents - ml-agents - deep-reinforcement-learning - reinforcement-learning - ML-Agents-Pyramids library_name: ml-agents --- # **ppo** Agent playing **Pyramids** This is a trained model of a **ppo** agent playing **Pyramids** using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents). ## Usage (with ML-Agents) The Documentation: https://github.com/huggingface/ml-agents#get-started We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub: ### Resume the training ``` mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume ``` ### Watch your Agent play You can watch your agent **playing directly in your browser:**. 1. Go to https://huggingface.co/spaces/unity/ML-Agents-Pyramids 2. Step 1: Write your model_id: bguan/testpyramidsrnd 3. Step 2: Select your *.nn /*.onnx file 4. Click on Watch the agent play 👀
rebolforces/testpushblock
rebolforces
2022-08-20T21:23:43Z
18
0
ml-agents
[ "ml-agents", "tensorboard", "onnx", "unity-ml-agents", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-PushBlock", "region:us" ]
reinforcement-learning
2022-08-20T21:23:39Z
--- tags: - unity-ml-agents - ml-agents - deep-reinforcement-learning - reinforcement-learning - ML-Agents-PushBlock library_name: ml-agents --- # **ppo** Agent playing **PushBlock** This is a trained model of a **ppo** agent playing **PushBlock** using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents). ## Usage (with ML-Agents) The Documentation: https://github.com/huggingface/ml-agents#get-started We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub: ### Resume the training ``` mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume ``` ### Watch your Agent play You can watch your agent **playing directly in your browser:**. 1. Go to https://huggingface.co/spaces/unity/ML-Agents-PushBlock 2. Step 1: Write your model_id: rebolforces/testpushblock 3. Step 2: Select your *.nn /*.onnx file 4. Click on Watch the agent play 👀
rebolforces/testpyramidsrnd
rebolforces
2022-08-20T21:20:53Z
7
0
ml-agents
[ "ml-agents", "tensorboard", "onnx", "unity-ml-agents", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-Pyramids", "region:us" ]
reinforcement-learning
2022-08-20T09:10:58Z
--- tags: - unity-ml-agents - ml-agents - deep-reinforcement-learning - reinforcement-learning - ML-Agents-Pyramids library_name: ml-agents --- # **ppo** Agent playing **Pyramids** This is a trained model of a **ppo** agent playing **Pyramids** using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents). ## Usage (with ML-Agents) The Documentation: https://github.com/huggingface/ml-agents#get-started We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub: ### Resume the training ``` mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume ``` ### Watch your Agent play You can watch your agent **playing directly in your browser:**. 1. Go to https://huggingface.co/spaces/unity/ML-Agents-Pyramids 2. Step 1: Write your model_id: rebolforces/testpyramidsrnd 3. Step 2: Select your *.nn /*.onnx file 4. Click on Watch the agent play 👀 ### Stats ``` "final_checkpoint": { "steps": 3000022, "file_path": "results/Pyramids Training2/Pyramids.onnx", "reward": 1.87466665605704, "creation_time": 1660985715.2452054, "auxillary_file_paths": [ "results/Pyramids Training2/Pyramids/Pyramids-3000022.pt" ] } ```
Khodewaltonss/End_world
Khodewaltonss
2022-08-20T20:49:49Z
0
0
null
[ "license:bigscience-bloom-rail-1.0", "region:us" ]
null
2022-08-20T20:49:49Z
--- license: bigscience-bloom-rail-1.0 ---
aimanlameesa/wav2vec2-xls-r-bengali
aimanlameesa
2022-08-20T18:42:41Z
105
0
transformers
[ "transformers", "pytorch", "tensorboard", "wav2vec2", "automatic-speech-recognition", "generated_from_trainer", "dataset:common_voice", "license:apache-2.0", "endpoints_compatible", "region:us" ]
automatic-speech-recognition
2022-08-19T03:48:53Z
--- license: apache-2.0 tags: - generated_from_trainer datasets: - common_voice model-index: - name: wav2vec2-xls-r-bengali 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. --> # wav2vec2-xls-r-bengali This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the common_voice dataset. It achieves the following results on the evaluation set: - Loss: 3.0518 - Wer: 1.0 ## 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: 0.0002 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - num_epochs: 2 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:---:| | 6.0375 | 1.6 | 400 | 3.0518 | 1.0 | ### Framework versions - Transformers 4.18.0 - Pytorch 1.11.0 - Datasets 2.1.0 - Tokenizers 0.12.1
Nuwaisir/Quran_speech_recognizer
Nuwaisir
2022-08-20T17:46:07Z
218
6
transformers
[ "transformers", "pytorch", "wav2vec2", "automatic-speech-recognition", "endpoints_compatible", "region:us" ]
automatic-speech-recognition
2022-03-02T23:29:04Z
# Quran Speech Recognizer This application will listen to the user's Quran recitation, and take the user to the position of the Quran from where the s/he had recited. You can also take a look at our [presentation slides](https://docs.google.com/presentation/d/1dbbVYHi3LQRiggH14nN36YV2A-ddUAKg67aX5MWi0ys/edit?usp=sharing). # Methodology We used transfer learning to make our application. We fine-tuned the pretrained model available at https://huggingface.co/elgeish/wav2vec2-large-xlsr-53-arabic using the data available at https://www.kaggle.com/c/quran-asr-challenge/data. Our model can be found at https://huggingface.co/Nuwaisir/Quran_speech_recognizer. # Usage Run all the cells of run_ui.ipynb. The last cell will hear your recitation for 5 seconds (changeable) from the time you run that cell. And then convert your speech to Arabic text and show the most probable corresponding parts of 30th juzz (Surah 78 - 114) of the Quran as the output based on edit distance value. Currently, we are searching from Surah 78 to Surah 114 as the searching algorithm needs some time to search the whole Quran. This range can be changed in the 6th cell of the notebook.
alishudi/distil_mse_4
alishudi
2022-08-20T17:08:07Z
106
0
transformers
[ "transformers", "pytorch", "distilbert", "text-classification", "autotrain_compatible", "endpoints_compatible", "region:us" ]
text-classification
2022-08-20T17:04:56Z
--alpha_ce 0.0 --alpha_mlm 2.0 --alpha_cos 1.0 --alpha_act 1.0 --alpha_clm 0.0 --alpha_mse 0.0002 --mlm \ 4 layers
NX2411/wav2vec2-large-xlsr-korean-demo-test2
NX2411
2022-08-20T15:31:19Z
4
0
transformers
[ "transformers", "pytorch", "tensorboard", "wav2vec2", "automatic-speech-recognition", "generated_from_trainer", "license:apache-2.0", "endpoints_compatible", "region:us" ]
automatic-speech-recognition
2022-08-19T05:24:30Z
--- license: apache-2.0 tags: - generated_from_trainer model-index: - name: wav2vec2-large-xlsr-korean-demo-test2 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. --> # wav2vec2-large-xlsr-korean-demo-test2 This model is a fine-tuned version of [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.0566 - Wer: 0.5224 ## 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: 0.0003 - train_batch_size: 4 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 8 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - num_epochs: 20 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:-----:|:---------------:|:------:| | 31.2541 | 0.3 | 400 | 5.4002 | 1.0 | | 4.9419 | 0.59 | 800 | 5.3336 | 1.0 | | 4.8926 | 0.89 | 1200 | 5.0531 | 1.0 | | 4.7218 | 1.19 | 1600 | 4.5172 | 1.0 | | 4.0218 | 1.49 | 2000 | 3.1418 | 0.9518 | | 3.0654 | 1.78 | 2400 | 2.4376 | 0.9041 | | 2.6226 | 2.08 | 2800 | 2.0151 | 0.8643 | | 2.2944 | 2.38 | 3200 | 1.8025 | 0.8290 | | 2.1872 | 2.67 | 3600 | 1.6469 | 0.7962 | | 2.0747 | 2.97 | 4000 | 1.5165 | 0.7714 | | 1.8479 | 3.27 | 4400 | 1.4281 | 0.7694 | | 1.8288 | 3.57 | 4800 | 1.3791 | 0.7326 | | 1.801 | 3.86 | 5200 | 1.3328 | 0.7177 | | 1.6723 | 4.16 | 5600 | 1.2954 | 0.7192 | | 1.5925 | 4.46 | 6000 | 1.3137 | 0.6953 | | 1.5709 | 4.75 | 6400 | 1.2086 | 0.6973 | | 1.5294 | 5.05 | 6800 | 1.1811 | 0.6730 | | 1.3844 | 5.35 | 7200 | 1.2053 | 0.6769 | | 1.3906 | 5.65 | 7600 | 1.1287 | 0.6556 | | 1.4088 | 5.94 | 8000 | 1.1251 | 0.6466 | | 1.2989 | 6.24 | 8400 | 1.1577 | 0.6546 | | 1.2523 | 6.54 | 8800 | 1.0643 | 0.6377 | | 1.2651 | 6.84 | 9200 | 1.0865 | 0.6417 | | 1.2209 | 7.13 | 9600 | 1.0981 | 0.6272 | | 1.1435 | 7.43 | 10000 | 1.1195 | 0.6317 | | 1.1616 | 7.73 | 10400 | 1.0672 | 0.6327 | | 1.1272 | 8.02 | 10800 | 1.0413 | 0.6248 | | 1.043 | 8.32 | 11200 | 1.0555 | 0.6233 | | 1.0523 | 8.62 | 11600 | 1.0372 | 0.6178 | | 1.0208 | 8.92 | 12000 | 1.0170 | 0.6128 | | 0.9895 | 9.21 | 12400 | 1.0354 | 0.5934 | | 0.95 | 9.51 | 12800 | 1.1019 | 0.6039 | | 0.9705 | 9.81 | 13200 | 1.0229 | 0.5855 | | 0.9202 | 10.1 | 13600 | 1.0364 | 0.5919 | | 0.8644 | 10.4 | 14000 | 1.0721 | 0.5984 | | 0.8641 | 10.7 | 14400 | 1.0383 | 0.5905 | | 0.8924 | 11.0 | 14800 | 0.9947 | 0.5760 | | 0.7914 | 11.29 | 15200 | 1.0270 | 0.5885 | | 0.7882 | 11.59 | 15600 | 1.0271 | 0.5741 | | 0.8116 | 11.89 | 16000 | 0.9937 | 0.5741 | | 0.7584 | 12.18 | 16400 | 0.9924 | 0.5626 | | 0.7051 | 12.48 | 16800 | 1.0023 | 0.5572 | | 0.7232 | 12.78 | 17200 | 1.0479 | 0.5512 | | 0.7149 | 13.08 | 17600 | 1.0475 | 0.5765 | | 0.6579 | 13.37 | 18000 | 1.0218 | 0.5552 | | 0.6615 | 13.67 | 18400 | 1.0339 | 0.5631 | | 0.6629 | 13.97 | 18800 | 1.0239 | 0.5621 | | 0.6221 | 14.26 | 19200 | 1.0331 | 0.5537 | | 0.6159 | 14.56 | 19600 | 1.0640 | 0.5532 | | 0.6032 | 14.86 | 20000 | 1.0192 | 0.5567 | | 0.5748 | 15.16 | 20400 | 1.0093 | 0.5507 | | 0.5614 | 15.45 | 20800 | 1.0458 | 0.5472 | | 0.5626 | 15.75 | 21200 | 1.0318 | 0.5398 | | 0.5429 | 16.05 | 21600 | 1.0112 | 0.5278 | | 0.5407 | 16.34 | 22000 | 1.0120 | 0.5278 | | 0.511 | 16.64 | 22400 | 1.0335 | 0.5249 | | 0.5316 | 16.94 | 22800 | 1.0146 | 0.5348 | | 0.4949 | 17.24 | 23200 | 1.0287 | 0.5388 | | 0.496 | 17.53 | 23600 | 1.0229 | 0.5348 | | 0.4986 | 17.83 | 24000 | 1.0094 | 0.5313 | | 0.4787 | 18.13 | 24400 | 1.0620 | 0.5234 | | 0.4508 | 18.42 | 24800 | 1.0401 | 0.5323 | | 0.4754 | 18.72 | 25200 | 1.0543 | 0.5303 | | 0.4584 | 19.02 | 25600 | 1.0433 | 0.5194 | | 0.4431 | 19.32 | 26000 | 1.0597 | 0.5249 | | 0.4448 | 19.61 | 26400 | 1.0548 | 0.5229 | | 0.4475 | 19.91 | 26800 | 1.0566 | 0.5224 | ### Framework versions - Transformers 4.21.1 - Pytorch 1.12.1+cu113 - Datasets 2.4.0 - Tokenizers 0.12.1
HBtemari/xlm-roberta-base-finetuned-panx-it
HBtemari
2022-08-20T15:12:50Z
105
0
transformers
[ "transformers", "pytorch", "xlm-roberta", "token-classification", "generated_from_trainer", "dataset:xtreme", "license:mit", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]
token-classification
2022-08-20T14:54:19Z
--- license: mit tags: - generated_from_trainer datasets: - xtreme metrics: - f1 model-index: - name: xlm-roberta-base-finetuned-panx-it results: - task: name: Token Classification type: token-classification dataset: name: xtreme type: xtreme args: PAN-X.it metrics: - name: F1 type: f1 value: 0.8124233755619126 --- <!-- 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-it This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the xtreme dataset. It achieves the following results on the evaluation set: - Loss: 0.2630 - F1: 0.8124 ## 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 | |:-------------:|:-----:|:----:|:---------------:|:------:| | 0.8193 | 1.0 | 70 | 0.3200 | 0.7356 | | 0.2773 | 2.0 | 140 | 0.2841 | 0.7882 | | 0.1807 | 3.0 | 210 | 0.2630 | 0.8124 | ### Framework versions - Transformers 4.11.3 - Pytorch 1.12.1+cu113 - Datasets 1.16.1 - Tokenizers 0.10.3
HBtemari/xlm-roberta-base-finetuned-panx-de-fr
HBtemari
2022-08-20T13:58:00Z
105
0
transformers
[ "transformers", "pytorch", "xlm-roberta", "token-classification", "generated_from_trainer", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]
token-classification
2022-08-20T13:28:20Z
--- license: mit tags: - generated_from_trainer metrics: - f1 model-index: - name: xlm-roberta-base-finetuned-panx-de-fr 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-fr This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.1608 - F1: 0.8593 ## 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 | |:-------------:|:-----:|:----:|:---------------:|:------:| | 0.2888 | 1.0 | 715 | 0.1779 | 0.8233 | | 0.1437 | 2.0 | 1430 | 0.1570 | 0.8497 | | 0.0931 | 3.0 | 2145 | 0.1608 | 0.8593 | ### Framework versions - Transformers 4.11.3 - Pytorch 1.12.1+cu113 - Datasets 1.16.1 - Tokenizers 0.10.3
HBtemari/xlm-roberta-base-finetuned-panx-de
HBtemari
2022-08-20T12:44:52Z
105
0
transformers
[ "transformers", "pytorch", "tensorboard", "xlm-roberta", "token-classification", "generated_from_trainer", "dataset:xtreme", "license:mit", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]
token-classification
2022-08-20T12:17:24Z
--- license: mit tags: - generated_from_trainer datasets: - xtreme metrics: - f1 model-index: - name: xlm-roberta-base-finetuned-panx-de results: - task: name: Token Classification type: token-classification dataset: name: xtreme type: xtreme args: PAN-X.de metrics: - name: F1 type: f1 value: 0.8648740833380706 --- <!-- 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 the xtreme dataset. It achieves the following results on the evaluation set: - Loss: 0.1365 - F1: 0.8649 ## 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 | |:-------------:|:-----:|:----:|:---------------:|:------:| | 0.2553 | 1.0 | 525 | 0.1575 | 0.8279 | | 0.1284 | 2.0 | 1050 | 0.1386 | 0.8463 | | 0.0813 | 3.0 | 1575 | 0.1365 | 0.8649 | ### Framework versions - Transformers 4.11.3 - Pytorch 1.12.1+cu113 - Datasets 1.16.1 - Tokenizers 0.10.3
danieleV9H/wavlm-base-plus-ft-cv3
danieleV9H
2022-08-20T10:28:28Z
6
0
transformers
[ "transformers", "pytorch", "tensorboard", "wavlm", "automatic-speech-recognition", "generated_from_trainer", "hf-asr-leaderboard", "en", "dataset:mozilla-foundation/common_voice_3_0", "model-index", "endpoints_compatible", "region:us" ]
automatic-speech-recognition
2022-07-06T11:24:08Z
--- tags: - generated_from_trainer - hf-asr-leaderboard datasets: - mozilla-foundation/common_voice_3_0 model-index: - name: wavlm-base-plus-ft-cv3 results: - task: name: Automatic Speech Recognition type: automatic-speech-recognition dataset: name: LibriSpeech (clean) type: librispeech_asr config: clean split: test args: language: en metrics: - name: Test WER type: wer value: '8.06' language: - en --- <!-- 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. --> # wavlm-base-plus-ft-cv3 This model is a fine-tuned version of [microsoft/wavlm-base-plus](https://huggingface.co/microsoft/wavlm-base-plus) on the "mozilla-foundation/common_voice_3_0 english" dataset: "train" and "validation" splits are used for training while "test" split is used for validation. It achieves the following results on the validation set: - Loss: 0.4365 - Wer: 0.1801 ## 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: 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: 11 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:------:|:---------------:|:------:| | 5.3448 | 0.05 | 500 | 3.2621 | 1.0 | | 2.9322 | 0.1 | 1000 | 2.8551 | 1.0 | | 1.7692 | 0.16 | 1500 | 1.2653 | 0.7447 | | 1.012 | 0.21 | 2000 | 0.9008 | 0.5601 | | 0.7129 | 0.26 | 2500 | 0.7684 | 0.4762 | | 0.6424 | 0.31 | 3000 | 0.6282 | 0.4276 | | 0.6518 | 0.37 | 3500 | 0.5888 | 0.3916 | | 0.5142 | 0.42 | 4000 | 0.5428 | 0.3727 | | 0.48 | 0.47 | 4500 | 0.5614 | 0.3549 | | 0.4523 | 0.52 | 5000 | 0.5334 | 0.3487 | | 0.4315 | 0.58 | 5500 | 0.5376 | 0.3317 | | 0.4292 | 0.63 | 6000 | 0.4939 | 0.3172 | | 0.4229 | 0.68 | 6500 | 0.4977 | 0.3117 | | 0.3837 | 0.73 | 7000 | 0.4899 | 0.3056 | | 0.385 | 0.78 | 7500 | 0.4571 | 0.2864 | | 0.4155 | 0.84 | 8000 | 0.4635 | 0.2866 | | 0.3768 | 0.89 | 8500 | 0.4390 | 0.2843 | | 0.3864 | 0.94 | 9000 | 0.4529 | 0.2764 | | 0.387 | 0.99 | 9500 | 0.4870 | 0.2755 | | 0.341 | 1.05 | 10000 | 0.4498 | 0.2696 | | 0.3334 | 1.1 | 10500 | 0.4355 | 0.2600 | | 0.3039 | 1.15 | 11000 | 0.4634 | 0.2716 | | 0.3101 | 1.2 | 11500 | 0.4615 | 0.2582 | | 0.4343 | 1.25 | 12000 | 0.4510 | 0.2574 | | 0.3002 | 1.31 | 12500 | 0.4313 | 0.2590 | | 0.3419 | 1.36 | 13000 | 0.4121 | 0.2493 | | 0.3162 | 1.41 | 13500 | 0.4423 | 0.2498 | | 0.3134 | 1.46 | 14000 | 0.4260 | 0.2506 | | 0.2963 | 1.52 | 14500 | 0.4272 | 0.2556 | | 0.3297 | 1.57 | 15000 | 0.4413 | 0.2487 | | 0.3199 | 1.62 | 15500 | 0.4260 | 0.2432 | | 0.3368 | 1.67 | 16000 | 0.4164 | 0.2464 | | 0.2981 | 1.73 | 16500 | 0.4111 | 0.2402 | | 0.2887 | 1.78 | 17000 | 0.4372 | 0.2460 | | 0.3058 | 1.83 | 17500 | 0.4161 | 0.2397 | | 0.2877 | 1.88 | 18000 | 0.4046 | 0.2386 | | 0.2904 | 1.93 | 18500 | 0.4108 | 0.2399 | | 0.2851 | 1.99 | 19000 | 0.4196 | 0.2385 | | 0.2451 | 2.04 | 19500 | 0.4096 | 0.2406 | | 0.259 | 2.09 | 20000 | 0.4437 | 0.2374 | | 0.2681 | 2.14 | 20500 | 0.4226 | 0.2357 | | 0.4371 | 2.2 | 21000 | 0.4301 | 0.2356 | | 0.2468 | 2.25 | 21500 | 0.4431 | 0.2326 | | 0.2687 | 2.3 | 22000 | 0.4218 | 0.2401 | | 0.2571 | 2.35 | 22500 | 0.4131 | 0.2337 | | 0.2541 | 2.41 | 23000 | 0.4105 | 0.2312 | | 0.2663 | 2.46 | 23500 | 0.4228 | 0.2327 | | 0.2777 | 2.51 | 24000 | 0.3960 | 0.2254 | | 0.2659 | 2.56 | 24500 | 0.4074 | 0.2289 | | 0.2519 | 2.61 | 25000 | 0.4220 | 0.2363 | | 0.2607 | 2.67 | 25500 | 0.3912 | 0.2253 | | 0.2749 | 2.72 | 26000 | 0.4017 | 0.2214 | | 0.2431 | 2.77 | 26500 | 0.3879 | 0.2181 | | 0.2557 | 2.82 | 27000 | 0.4011 | 0.2268 | | 0.2662 | 2.88 | 27500 | 0.3884 | 0.2241 | | 0.2649 | 2.93 | 28000 | 0.3987 | 0.2233 | | 0.2382 | 2.98 | 28500 | 0.3777 | 0.2215 | | 0.2198 | 3.03 | 29000 | 0.3952 | 0.2177 | | 0.2281 | 3.09 | 29500 | 0.4067 | 0.2213 | | 0.2178 | 3.14 | 30000 | 0.4178 | 0.2192 | | 0.222 | 3.19 | 30500 | 0.4327 | 0.2208 | | 0.2262 | 3.24 | 31000 | 0.4028 | 0.2212 | | 0.2256 | 3.29 | 31500 | 0.4065 | 0.2181 | | 0.2255 | 3.35 | 32000 | 0.3782 | 0.2139 | | 0.2364 | 3.4 | 32500 | 0.4443 | 0.2119 | | 0.2209 | 3.45 | 33000 | 0.4089 | 0.2177 | | 0.2051 | 3.5 | 33500 | 0.3886 | 0.2154 | | 0.2242 | 3.56 | 34000 | 0.3810 | 0.2133 | | 0.2151 | 3.61 | 34500 | 0.4005 | 0.2127 | | 0.2341 | 3.66 | 35000 | 0.3899 | 0.2165 | | 0.202 | 3.71 | 35500 | 0.3846 | 0.2121 | | 0.2107 | 3.76 | 36000 | 0.3859 | 0.2146 | | 0.2237 | 3.82 | 36500 | 0.3993 | 0.2141 | | 0.2189 | 3.87 | 37000 | 0.3842 | 0.2113 | | 0.2124 | 3.92 | 37500 | 0.3919 | 0.2118 | | 0.4017 | 3.97 | 38000 | 0.3882 | 0.2086 | | 0.1946 | 4.03 | 38500 | 0.4008 | 0.2121 | | 0.1919 | 4.08 | 39000 | 0.3939 | 0.2129 | | 0.1797 | 4.13 | 39500 | 0.3958 | 0.2115 | | 0.184 | 4.18 | 40000 | 0.3942 | 0.2086 | | 0.1987 | 4.24 | 40500 | 0.3959 | 0.2092 | | 0.1919 | 4.29 | 41000 | 0.4250 | 0.2093 | | 0.2038 | 4.34 | 41500 | 0.3970 | 0.2060 | | 0.1879 | 4.39 | 42000 | 0.3978 | 0.2109 | | 0.1852 | 4.44 | 42500 | 0.4065 | 0.2091 | | 0.2014 | 4.5 | 43000 | 0.4069 | 0.2054 | | 0.2011 | 4.55 | 43500 | 0.4247 | 0.2099 | | 0.1937 | 4.6 | 44000 | 0.3754 | 0.2091 | | 0.1878 | 4.65 | 44500 | 0.3891 | 0.2070 | | 0.2011 | 4.71 | 45000 | 0.3714 | 0.2030 | | 0.1958 | 4.76 | 45500 | 0.3994 | 0.2066 | | 0.1907 | 4.81 | 46000 | 0.4061 | 0.2080 | | 0.1859 | 4.86 | 46500 | 0.3899 | 0.2056 | | 0.1894 | 4.92 | 47000 | 0.3808 | 0.2055 | | 0.3276 | 4.97 | 47500 | 0.3936 | 0.2051 | | 0.3513 | 5.02 | 48000 | 0.4028 | 0.2041 | | 0.1654 | 5.07 | 48500 | 0.3929 | 0.2032 | | 0.1622 | 5.12 | 49000 | 0.4067 | 0.2029 | | 0.1659 | 5.18 | 49500 | 0.4058 | 0.2007 | | 0.1779 | 5.23 | 50000 | 0.4085 | 0.2031 | | 0.1731 | 5.28 | 50500 | 0.3895 | 0.2009 | | 0.1761 | 5.33 | 51000 | 0.3973 | 0.2022 | | 0.1741 | 5.39 | 51500 | 0.4116 | 0.2021 | | 0.1735 | 5.44 | 52000 | 0.4152 | 0.2038 | | 0.1627 | 5.49 | 52500 | 0.4078 | 0.2003 | | 0.1728 | 5.54 | 53000 | 0.4088 | 0.2022 | | 0.179 | 5.6 | 53500 | 0.3828 | 0.1998 | | 0.1692 | 5.65 | 54000 | 0.3903 | 0.1980 | | 0.174 | 5.7 | 54500 | 0.4185 | 0.1993 | | 0.1763 | 5.75 | 55000 | 0.3937 | 0.1976 | | 0.1792 | 5.8 | 55500 | 0.3767 | 0.1966 | | 0.1799 | 5.86 | 56000 | 0.3970 | 0.1994 | | 0.1918 | 5.91 | 56500 | 0.3954 | 0.1981 | | 0.1836 | 5.96 | 57000 | 0.3984 | 0.1969 | | 0.1708 | 6.01 | 57500 | 0.3917 | 0.1956 | | 0.1524 | 6.07 | 58000 | 0.3922 | 0.1977 | | 0.1567 | 6.12 | 58500 | 0.4108 | 0.1955 | | 0.1518 | 6.17 | 59000 | 0.4349 | 0.1968 | | 0.1587 | 6.22 | 59500 | 0.3963 | 0.1988 | | 0.1563 | 6.27 | 60000 | 0.4235 | 0.1997 | | 0.154 | 6.33 | 60500 | 0.4026 | 0.1951 | | 0.1636 | 6.38 | 61000 | 0.4359 | 0.2031 | | 0.1641 | 6.43 | 61500 | 0.4115 | 0.1972 | | 0.1604 | 6.48 | 62000 | 0.4166 | 0.1972 | | 0.1579 | 6.54 | 62500 | 0.4264 | 0.1965 | | 0.1552 | 6.59 | 63000 | 0.4047 | 0.2007 | | 0.1461 | 6.64 | 63500 | 0.4263 | 0.2011 | | 0.1522 | 6.69 | 64000 | 0.4222 | 0.1970 | | 0.1624 | 6.75 | 64500 | 0.4318 | 0.1971 | | 0.1474 | 6.8 | 65000 | 0.4265 | 0.1961 | | 0.1495 | 6.85 | 65500 | 0.4316 | 0.1940 | | 0.1509 | 6.9 | 66000 | 0.4297 | 0.1965 | | 0.1479 | 6.95 | 66500 | 0.4232 | 0.1966 | | 0.1462 | 7.01 | 67000 | 0.4090 | 0.1946 | | 0.1498 | 7.06 | 67500 | 0.4197 | 0.1939 | | 0.1436 | 7.11 | 68000 | 0.4215 | 0.1956 | | 0.1378 | 7.16 | 68500 | 0.4345 | 0.1968 | | 0.3082 | 7.22 | 69000 | 0.4364 | 0.1972 | | 0.1386 | 7.27 | 69500 | 0.4284 | 0.1949 | | 0.1441 | 7.32 | 70000 | 0.4019 | 0.1953 | | 0.1624 | 7.37 | 70500 | 0.4175 | 0.1951 | | 0.1454 | 7.43 | 71000 | 0.4224 | 0.1922 | | 0.1408 | 7.48 | 71500 | 0.4128 | 0.1961 | | 0.1525 | 7.53 | 72000 | 0.4200 | 0.1946 | | 0.1459 | 7.58 | 72500 | 0.4166 | 0.1949 | | 0.1485 | 7.63 | 73000 | 0.4102 | 0.1947 | | 0.148 | 7.69 | 73500 | 0.4237 | 0.1948 | | 0.1478 | 7.74 | 74000 | 0.4104 | 0.1928 | | 0.14 | 7.79 | 74500 | 0.4027 | 0.1928 | | 0.1473 | 7.84 | 75000 | 0.4034 | 0.1907 | | 0.1394 | 7.9 | 75500 | 0.3823 | 0.1923 | | 0.1324 | 7.95 | 76000 | 0.3987 | 0.1899 | | 0.1459 | 8.0 | 76500 | 0.4003 | 0.1907 | | 0.1373 | 8.05 | 77000 | 0.4204 | 0.1925 | | 0.1303 | 8.1 | 77500 | 0.4218 | 0.1907 | | 0.1346 | 8.16 | 78000 | 0.4091 | 0.1882 | | 0.2947 | 8.21 | 78500 | 0.4156 | 0.1890 | | 0.1324 | 8.26 | 79000 | 0.4280 | 0.1888 | | 0.132 | 8.31 | 79500 | 0.4136 | 0.1873 | | 0.1377 | 8.37 | 80000 | 0.4099 | 0.1915 | | 0.3045 | 8.42 | 80500 | 0.4201 | 0.1900 | | 0.1372 | 8.47 | 81000 | 0.4161 | 0.1876 | | 0.1377 | 8.52 | 81500 | 0.4107 | 0.1869 | | 0.1374 | 8.58 | 82000 | 0.4188 | 0.1875 | | 0.1301 | 8.63 | 82500 | 0.4306 | 0.1860 | | 0.1386 | 8.68 | 83000 | 0.4131 | 0.1862 | | 0.1292 | 8.73 | 83500 | 0.3997 | 0.1871 | | 0.1276 | 8.78 | 84000 | 0.4237 | 0.1873 | | 0.1377 | 8.84 | 84500 | 0.4284 | 0.1889 | | 0.1338 | 8.89 | 85000 | 0.4205 | 0.1861 | | 0.1284 | 8.94 | 85500 | 0.4380 | 0.1875 | | 0.1471 | 8.99 | 86000 | 0.4238 | 0.1895 | | 0.1186 | 9.05 | 86500 | 0.4128 | 0.1875 | | 0.1222 | 9.1 | 87000 | 0.4267 | 0.1864 | | 0.1229 | 9.15 | 87500 | 0.4169 | 0.1842 | | 0.1259 | 9.2 | 88000 | 0.4327 | 0.1861 | | 0.1281 | 9.26 | 88500 | 0.4188 | 0.1877 | | 0.1247 | 9.31 | 89000 | 0.4212 | 0.1852 | | 0.1248 | 9.36 | 89500 | 0.4172 | 0.1863 | | 0.1232 | 9.41 | 90000 | 0.4173 | 0.1858 | | 0.3255 | 9.46 | 90500 | 0.4225 | 0.1851 | | 0.1243 | 9.52 | 91000 | 0.4290 | 0.1849 | | 0.1266 | 9.57 | 91500 | 0.4186 | 0.1842 | | 0.1257 | 9.62 | 92000 | 0.4364 | 0.1860 | | 0.1181 | 9.67 | 92500 | 0.4294 | 0.1852 | | 0.1202 | 9.73 | 93000 | 0.4222 | 0.1836 | | 0.1264 | 9.78 | 93500 | 0.4191 | 0.1856 | | 0.1243 | 9.83 | 94000 | 0.4237 | 0.1856 | | 0.1164 | 9.88 | 94500 | 0.4281 | 0.1848 | | 0.1283 | 9.94 | 95000 | 0.4332 | 0.1845 | | 0.123 | 9.99 | 95500 | 0.4316 | 0.1839 | | 0.1232 | 10.04 | 96000 | 0.4313 | 0.1844 | | 0.1206 | 10.09 | 96500 | 0.4303 | 0.1840 | | 0.1145 | 10.14 | 97000 | 0.4299 | 0.1822 | | 0.1265 | 10.2 | 97500 | 0.4266 | 0.1822 | | 0.1147 | 10.25 | 98000 | 0.4322 | 0.1844 | | 0.1122 | 10.3 | 98500 | 0.4251 | 0.1830 | | 0.1101 | 10.35 | 99000 | 0.4297 | 0.1830 | | 0.1225 | 10.41 | 99500 | 0.4244 | 0.1842 | | 0.1177 | 10.46 | 100000 | 0.4343 | 0.1826 | | 0.1157 | 10.51 | 100500 | 0.4228 | 0.1827 | | 0.1215 | 10.56 | 101000 | 0.4285 | 0.1814 | | 0.276 | 10.61 | 101500 | 0.4268 | 0.1820 | | 0.111 | 10.67 | 102000 | 0.4288 | 0.1836 | | 0.1164 | 10.72 | 102500 | 0.4283 | 0.1825 | | 0.111 | 10.77 | 103000 | 0.4198 | 0.1819 | | 0.1135 | 10.82 | 103500 | 0.4333 | 0.1818 | | 0.1196 | 10.88 | 104000 | 0.4239 | 0.1817 | | 0.1176 | 10.93 | 104500 | 0.4252 | 0.1819 | | 0.117 | 10.98 | 105000 | 0.4317 | 0.1820 | | 0.1166 | 11.03 | 105500 | 0.4307 | 0.1815 | | 0.1118 | 11.09 | 106000 | 0.4379 | 0.1821 | | 0.1116 | 11.14 | 106500 | 0.4363 | 0.1812 | | 0.1098 | 11.19 | 107000 | 0.4328 | 0.1816 | | 0.1134 | 11.24 | 107500 | 0.4284 | 0.1811 | | 0.1104 | 11.29 | 108000 | 0.4365 | 0.1801 | ### Framework versions - Transformers 4.19.2 - Pytorch 1.11.0+cu113 - Datasets 2.2.0 - Tokenizers 0.12.1
msms/distilbert-base-uncased-finetuned-squad
msms
2022-08-20T09:48:38Z
105
0
transformers
[ "transformers", "pytorch", "tensorboard", "distilbert", "question-answering", "generated_from_trainer", "dataset:custom_squad", "license:apache-2.0", "endpoints_compatible", "region:us" ]
question-answering
2022-08-20T08:39:10Z
--- license: apache-2.0 tags: - generated_from_trainer datasets: - custom_squad model-index: - name: distilbert-base-uncased-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. --> # distilbert-base-uncased-finetuned-squad This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the custom_squad dataset. It achieves the following results on the evaluation set: - Loss: 1.2055 ## 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: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 1 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 1.2702 | 1.0 | 5533 | 1.2055 | ### Framework versions - Transformers 4.20.1 - Pytorch 1.11.0 - Datasets 2.1.0 - Tokenizers 0.12.1
HBtemari/distilbert-base-uncased-finetuned-emotion
HBtemari
2022-08-20T09:06:09Z
105
0
transformers
[ "transformers", "pytorch", "tensorboard", "distilbert", "text-classification", "generated_from_trainer", "dataset:emotion", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]
text-classification
2022-08-20T08:57:08Z
--- license: apache-2.0 tags: - generated_from_trainer datasets: - emotion metrics: - accuracy - f1 model-index: - name: distilbert-base-uncased-finetuned-emotion results: - task: name: Text Classification type: text-classification dataset: name: emotion type: emotion args: default metrics: - name: Accuracy type: accuracy value: 0.927 - name: F1 type: f1 value: 0.9271792499777299 --- <!-- 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 the emotion dataset. It achieves the following results on the evaluation set: - Loss: 0.2125 - Accuracy: 0.927 - F1: 0.9272 ## 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: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 2 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | 0.8137 | 1.0 | 250 | 0.2974 | 0.912 | 0.9095 | | 0.244 | 2.0 | 500 | 0.2125 | 0.927 | 0.9272 | ### Framework versions - Transformers 4.13.0 - Pytorch 1.12.1+cu113 - Datasets 1.16.1 - Tokenizers 0.10.3
clementchadebec/reproduced_wae
clementchadebec
2022-08-20T07:49:58Z
0
0
pythae
[ "pythae", "reproducibility", "en", "license:apache-2.0", "region:us" ]
null
2022-08-19T19:25:06Z
--- language: en tags: - pythae - reproducibility license: apache-2.0 --- ### Downloading this model from the Hub This model was trained with pythae. It can be downloaded or reloaded using the method `load_from_hf_hub` ```python >>> from pythae.models import AutoModel >>> model = AutoModel.load_from_hf_hub(hf_hub_path="clementchadebec/reproduced_wae") ``` ## Reproducibility This trained model reproduces the results of Table 1 in [1]. | Model | Dataset | Metric | Obtained value | Reference value | |:---:|:---:|:---:|:---:|:---:| | WAE | CELEBA 64 | FID | 56.5 | 55 | [1] Tolstikhin, O Bousquet, S Gelly, and B Schölkopf. Wasserstein auto-encoders. In 6th International Conference on Learning Representations (ICLR 2018), 2018.
Trifon/wav2vec2-large-xlsr-53-demo-colab
Trifon
2022-08-20T07:46:18Z
106
0
transformers
[ "transformers", "pytorch", "tensorboard", "wav2vec2", "automatic-speech-recognition", "generated_from_trainer", "dataset:common_voice", "license:apache-2.0", "endpoints_compatible", "region:us" ]
automatic-speech-recognition
2022-08-13T18:51:16Z
--- license: apache-2.0 tags: - generated_from_trainer datasets: - common_voice model-index: - name: wav2vec2-large-xlsr-53-demo-colab 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. --> # wav2vec2-large-xlsr-53-demo-colab This model is a fine-tuned version of [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on the common_voice dataset. It achieves the following results on the evaluation set: - Loss: 0.4253 - Wer: 0.4880 ## 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: 0.0003 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - num_epochs: 30 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:------:| | 5.2135 | 4.21 | 400 | 2.5232 | 1.0 | | 0.8323 | 8.42 | 800 | 0.4673 | 0.6142 | | 0.3247 | 12.63 | 1200 | 0.4087 | 0.5536 | | 0.217 | 16.84 | 1600 | 0.3950 | 0.5237 | | 0.166 | 21.05 | 2000 | 0.4294 | 0.5075 | | 0.141 | 25.26 | 2400 | 0.4219 | 0.4944 | | 0.1193 | 29.47 | 2800 | 0.4253 | 0.4880 | ### Framework versions - Transformers 4.18.0 - Pytorch 1.11.0+cu102 - Datasets 2.1.0 - Tokenizers 0.12.1
succinctly/text2image-prompt-generator
succinctly
2022-08-20T06:01:10Z
30,634
296
transformers
[ "transformers", "pytorch", "gpt2", "text-generation", "text2image", "prompting", "en", "dataset:succinctly/midjourney-prompts", "license:cc-by-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]
text-generation
2022-07-21T22:17:43Z
--- language: - "en" thumbnail: "https://drive.google.com/uc?export=view&id=1JWwrxQbr1s5vYpIhPna_p2IG1pE5rNiV" tags: - text2image - prompting license: "cc-by-2.0" datasets: - "succinctly/midjourney-prompts" --- This is a GPT-2 model fine-tuned on the [succinctly/midjourney-prompts](https://huggingface.co/datasets/succinctly/midjourney-prompts) dataset, which contains 250k text prompts that users issued to the [Midjourney](https://www.midjourney.com/) text-to-image service over a month period. For more details on how this dataset was scraped, see [Midjourney User Prompts & Generated Images (250k)](https://www.kaggle.com/datasets/succinctlyai/midjourney-texttoimage). This prompt generator can be used to auto-complete prompts for any text-to-image model (including the DALL·E family): ![prompt autocomplete model](https://drive.google.com/uc?export=view&id=1JqZ-CaWNpQ4iO0Qcd3b8u_QnBp-Q0PKu) Note that, while this model can be used together with any text-to-image model, it occasionally produces Midjourney-specific tags. Users can specify certain requirements via [double-dashed parameters](https://midjourney.gitbook.io/docs/imagine-parameters) (e.g. `--ar 16:9` sets the aspect ratio to 16:9, and `--no snake` asks the model to exclude snakes from the generated image) or set the importance of various entities in the image via [explicit weights](https://midjourney.gitbook.io/docs/user-manual#advanced-text-weights) (e.g. `hot dog::1.5 food::-1` is likely to produce the image of an animal instead of a frankfurter). When using this model, please attribute credit to [Succinctly AI](https://succinctly.ai).
huranokuma/es2
huranokuma
2022-08-20T04:26:36Z
52
0
transformers
[ "transformers", "pytorch", "tensorboard", "gpt2", "text-generation", "ja", "japanese", "lm", "nlp", "license:mit", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]
text-generation
2022-08-09T08:20:00Z
--- language: ja thumbnail: https://1.bp.blogspot.com/-pOL-P7Mvgkg/YEGQAdidksI/AAAAAAABdc0/SbD0lC_X8iY_t5xLFtQYFC3FHFgziBuzgCNcBGAsYHQ/s932/buranko_businesswoman_sad.png license: mit tags: - ja - japanese - gpt2 - text-generation - lm - nlp widget: - text: "御社を志望した理由は" --- # ESを書くAI Japanese GPT-2 modelをファインチューニングしました ファインチューニングには、あらゆる分野から140,000件ほどのESを用いました。 webアプリ<br> http://www.eswrite.com The model was trained using code from Github repository [rinnakk/japanese-pretrained-models](https://github.com/rinnakk/japanese-pretrained-models) by [rinna Co., Ltd.](https://corp.rinna.co.jp/)
jackoyoungblood/ddpg-BipedalWalker-v3
jackoyoungblood
2022-08-20T00:03:18Z
4
0
stable-baselines3
[ "stable-baselines3", "BipedalWalker-v3", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]
reinforcement-learning
2022-08-20T00:02:40Z
--- library_name: stable-baselines3 tags: - BipedalWalker-v3 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: DDPG results: - metrics: - type: mean_reward value: 287.74 +/- 81.94 name: mean_reward task: type: reinforcement-learning name: reinforcement-learning dataset: name: BipedalWalker-v3 type: BipedalWalker-v3 --- # **DDPG** Agent playing **BipedalWalker-v3** This is a trained model of a **DDPG** agent playing **BipedalWalker-v3** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3) and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo). The RL Zoo is a training framework for Stable Baselines3 reinforcement learning agents, with hyperparameter optimization and pre-trained agents included. ## Usage (with SB3 RL Zoo) RL Zoo: https://github.com/DLR-RM/rl-baselines3-zoo<br/> SB3: https://github.com/DLR-RM/stable-baselines3<br/> SB3 Contrib: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib ``` # Download model and save it into the logs/ folder python -m utils.load_from_hub --algo ddpg --env BipedalWalker-v3 -orga jackoyoungblood -f logs/ python enjoy.py --algo ddpg --env BipedalWalker-v3 -f logs/ ``` ## Training (with the RL Zoo) ``` python train.py --algo ddpg --env BipedalWalker-v3 -f logs/ # Upload the model and generate video (when possible) python -m utils.push_to_hub --algo ddpg --env BipedalWalker-v3 -f logs/ -orga jackoyoungblood ``` ## Hyperparameters ```python OrderedDict([('buffer_size', 200000), ('gamma', 0.98), ('gradient_steps', -1), ('learning_rate', 0.0001), ('learning_starts', 10000), ('n_timesteps', 1000000.0), ('noise_std', 0.1), ('noise_type', 'normal'), ('policy', 'MlpPolicy'), ('policy_kwargs', 'dict(net_arch=[400, 300])'), ('train_freq', [1, 'episode']), ('normalize', False)]) ```
dvalbuena1/dqn-SpaceInvadersNoFrameskip-v4
dvalbuena1
2022-08-19T23:44:20Z
0
0
stable-baselines3
[ "stable-baselines3", "SpaceInvadersNoFrameskip-v4", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]
reinforcement-learning
2022-08-19T23:43:41Z
--- library_name: stable-baselines3 tags: - SpaceInvadersNoFrameskip-v4 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: DQN results: - metrics: - type: mean_reward value: 526.00 +/- 122.47 name: mean_reward task: type: reinforcement-learning name: reinforcement-learning dataset: name: SpaceInvadersNoFrameskip-v4 type: SpaceInvadersNoFrameskip-v4 --- # **DQN** Agent playing **SpaceInvadersNoFrameskip-v4** This is a trained model of a **DQN** agent playing **SpaceInvadersNoFrameskip-v4** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3) and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo). The RL Zoo is a training framework for Stable Baselines3 reinforcement learning agents, with hyperparameter optimization and pre-trained agents included. ## Usage (with SB3 RL Zoo) RL Zoo: https://github.com/DLR-RM/rl-baselines3-zoo<br/> SB3: https://github.com/DLR-RM/stable-baselines3<br/> SB3 Contrib: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib ``` # Download model and save it into the logs/ folder python -m utils.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga dvalbuena1 -f logs/ python enjoy.py --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ ``` ## Training (with the RL Zoo) ``` python train.py --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ # Upload the model and generate video (when possible) python -m utils.push_to_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ -orga dvalbuena1 ``` ## Hyperparameters ```python OrderedDict([('batch_size', 32), ('buffer_size', 100000), ('env_wrapper', ['stable_baselines3.common.atari_wrappers.AtariWrapper']), ('exploration_final_eps', 0.01), ('exploration_fraction', 0.1), ('frame_stack', 4), ('gradient_steps', 1), ('learning_rate', 0.0001), ('learning_starts', 100000), ('n_timesteps', 1000000.0), ('optimize_memory_usage', False), ('policy', 'CnnPolicy'), ('target_update_interval', 1000), ('train_freq', 4), ('normalize', False)]) ```
andrewzhang505/quad-swarm-rl-multi-drone-obstacles
andrewzhang505
2022-08-19T23:02:15Z
1
0
sample-factory
[ "sample-factory", "tensorboard", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]
reinforcement-learning
2022-08-19T21:33:48Z
--- library_name: sample-factory tags: - deep-reinforcement-learning - reinforcement-learning - sample-factory model-index: - name: APPO results: - metrics: - type: mean_reward value: -2.84 +/- 3.71 name: mean_reward task: type: reinforcement-learning name: reinforcement-learning dataset: name: quadrotor_multi type: quadrotor_multi --- A(n) **APPO** model trained on the **quadrotor_multi** environment. This model was trained using Sample Factory 2.0: https://github.com/alex-petrenko/sample-factory
andrewzhang505/quad-swarm-rl-multi-drone-no-obstacles
andrewzhang505
2022-08-19T22:49:22Z
25
0
sample-factory
[ "sample-factory", "tensorboard", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]
reinforcement-learning
2022-08-18T18:41:40Z
--- library_name: sample-factory tags: - deep-reinforcement-learning - reinforcement-learning - sample-factory model-index: - name: APPO results: - metrics: - type: mean_reward value: 1.58 +/- 4.22 name: mean_reward task: type: reinforcement-learning name: reinforcement-learning dataset: name: quadrotor_multi type: quadrotor_multi --- A(n) **APPO** model trained on the **quadrotor_multi** environment. This model was trained using Sample Factory 2.0: https://github.com/alex-petrenko/sample-factory
marii/dqn-SpaceInvadersNoFrameskip-v4
marii
2022-08-19T22:30:32Z
2
0
stable-baselines3
[ "stable-baselines3", "SpaceInvadersNoFrameskip-v4", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]
reinforcement-learning
2022-08-19T22:29:53Z
--- library_name: stable-baselines3 tags: - SpaceInvadersNoFrameskip-v4 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: DQN results: - metrics: - type: mean_reward value: 658.50 +/- 131.07 name: mean_reward task: type: reinforcement-learning name: reinforcement-learning dataset: name: SpaceInvadersNoFrameskip-v4 type: SpaceInvadersNoFrameskip-v4 --- # **DQN** Agent playing **SpaceInvadersNoFrameskip-v4** This is a trained model of a **DQN** agent playing **SpaceInvadersNoFrameskip-v4** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3) and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo). The RL Zoo is a training framework for Stable Baselines3 reinforcement learning agents, with hyperparameter optimization and pre-trained agents included. ## Usage (with SB3 RL Zoo) RL Zoo: https://github.com/DLR-RM/rl-baselines3-zoo<br/> SB3: https://github.com/DLR-RM/stable-baselines3<br/> SB3 Contrib: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib ``` # Download model and save it into the logs/ folder python -m utils.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga marii -f logs/ python enjoy.py --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ ``` ## Training (with the RL Zoo) ``` python train.py --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ # Upload the model and generate video (when possible) python -m utils.push_to_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ -orga marii ``` ## Hyperparameters ```python OrderedDict([('batch_size', 32), ('buffer_size', 100000), ('env_wrapper', ['stable_baselines3.common.atari_wrappers.AtariWrapper']), ('exploration_final_eps', 0.01), ('exploration_fraction', 0.1), ('frame_stack', 4), ('gradient_steps', 1), ('learning_rate', 0.0001), ('learning_starts', 100000), ('n_timesteps', 10000000.0), ('optimize_memory_usage', False), ('policy', 'CnnPolicy'), ('target_update_interval', 1000), ('train_freq', 4), ('normalize', False)]) ```
nbroad/xdistil-l12-h384-squad2
nbroad
2022-08-19T21:44:42Z
106
0
transformers
[ "transformers", "pytorch", "tf", "jax", "tensorboard", "bert", "question-answering", "dataset:squad_v2", "model-index", "endpoints_compatible", "region:us" ]
question-answering
2022-03-02T23:29:05Z
--- widget: - context: While deep and large pre-trained models are the state-of-the-art for various natural language processing tasks, their huge size poses significant challenges for practical uses in resource constrained settings. Recent works in knowledge distillation propose task-agnostic as well as task-specific methods to compress these models, with task-specific ones often yielding higher compression rate. In this work, we develop a new task-agnostic distillation framework XtremeDistilTransformers that leverages the advantage of task-specific methods for learning a small universal model that can be applied to arbitrary tasks and languages. To this end, we study the transferability of several source tasks, augmentation resources and model architecture for distillation. We evaluate our model performance on multiple tasks, including the General Language Understanding Evaluation (GLUE) benchmark, SQuAD question answering dataset and a massive multi-lingual NER dataset with 41 languages. example_title: xtremedistil q1 text: What is XtremeDistil? - context: While deep and large pre-trained models are the state-of-the-art for various natural language processing tasks, their huge size poses significant challenges for practical uses in resource constrained settings. Recent works in knowledge distillation propose task-agnostic as well as task-specific methods to compress these models, with task-specific ones often yielding higher compression rate. In this work, we develop a new task-agnostic distillation framework XtremeDistilTransformers that leverages the advantage of task-specific methods for learning a small universal model that can be applied to arbitrary tasks and languages. To this end, we study the transferability of several source tasks, augmentation resources and model architecture for distillation. We evaluate our model performance on multiple tasks, including the General Language Understanding Evaluation (GLUE) benchmark, SQuAD question answering dataset and a massive multi-lingual NER dataset with 41 languages. example_title: xtremedistil q2 text: On what is the model validated? datasets: - squad_v2 metrics: - f1 - exact tags: - question-answering model-index: - name: nbroad/xdistil-l12-h384-squad2 results: - task: type: question-answering name: Question Answering dataset: name: squad_v2 type: squad_v2 config: squad_v2 split: validation metrics: - name: Exact Match type: exact_match value: 75.4591 verified: true - name: F1 type: f1 value: 79.3321 verified: true - task: type: question-answering name: Question Answering dataset: name: squad type: squad config: plain_text split: validation metrics: - name: Exact Match type: exact_match value: 81.8604 verified: true - name: F1 type: f1 value: 89.6654 verified: true --- xtremedistil-l12-h384 trained on SQuAD 2.0 "eval_exact": 75.45691906005221 "eval_f1": 79.32502968532793
dvalbuena1/q-Taxi-v3
dvalbuena1
2022-08-19T21:42:44Z
0
0
null
[ "Taxi-v3", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]
reinforcement-learning
2022-08-19T21:42:36Z
--- tags: - Taxi-v3 - q-learning - reinforcement-learning - custom-implementation model-index: - name: q-Taxi-v3 results: - metrics: - type: mean_reward value: 7.54 +/- 2.73 name: mean_reward task: type: reinforcement-learning name: reinforcement-learning dataset: name: Taxi-v3 type: Taxi-v3 --- # **Q-Learning** Agent playing **Taxi-v3** This is a trained model of a **Q-Learning** agent playing **Taxi-v3** . ## Usage ```python model = load_from_hub(repo_id="dvalbuena1/q-Taxi-v3", filename="q-learning.pkl") # Don't forget to check if you need to add additional attributes (is_slippery=False etc) env = gym.make(model["env_id"]) evaluate_agent(env, model["max_steps"], model["n_eval_episodes"], model["qtable"], model["eval_seed"]) ```
dvalbuena1/q-FrozenLake-v1-4x4-noSlippery
dvalbuena1
2022-08-19T21:38:43Z
0
0
null
[ "FrozenLake-v1-4x4-no_slippery", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]
reinforcement-learning
2022-08-19T21:38:35Z
--- tags: - FrozenLake-v1-4x4-no_slippery - q-learning - reinforcement-learning - custom-implementation model-index: - name: q-FrozenLake-v1-4x4-noSlippery results: - metrics: - type: mean_reward value: 1.00 +/- 0.00 name: mean_reward task: type: reinforcement-learning name: reinforcement-learning dataset: name: FrozenLake-v1-4x4-no_slippery type: FrozenLake-v1-4x4-no_slippery --- # **Q-Learning** Agent playing **FrozenLake-v1** This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** . ## Usage ```python model = load_from_hub(repo_id="dvalbuena1/q-FrozenLake-v1-4x4-noSlippery", filename="q-learning.pkl") # Don't forget to check if you need to add additional attributes (is_slippery=False etc) env = gym.make(model["env_id"]) evaluate_agent(env, model["max_steps"], model["n_eval_episodes"], model["qtable"], model["eval_seed"]) ```
jackoyoungblood/qrdqn-BipedalWalkerHardcore-v3
jackoyoungblood
2022-08-19T21:09:57Z
0
0
stable-baselines3
[ "stable-baselines3", "BipedalWalkerHardcore-v3", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]
reinforcement-learning
2022-08-19T21:09:07Z
--- library_name: stable-baselines3 tags: - BipedalWalkerHardcore-v3 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: DDPG results: - metrics: - type: mean_reward value: -132.89 +/- 24.41 name: mean_reward task: type: reinforcement-learning name: reinforcement-learning dataset: name: BipedalWalkerHardcore-v3 type: BipedalWalkerHardcore-v3 --- # **DDPG** Agent playing **BipedalWalkerHardcore-v3** This is a trained model of a **DDPG** agent playing **BipedalWalkerHardcore-v3** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3) and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo). The RL Zoo is a training framework for Stable Baselines3 reinforcement learning agents, with hyperparameter optimization and pre-trained agents included. ## Usage (with SB3 RL Zoo) RL Zoo: https://github.com/DLR-RM/rl-baselines3-zoo<br/> SB3: https://github.com/DLR-RM/stable-baselines3<br/> SB3 Contrib: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib ``` # Download model and save it into the logs/ folder python -m utils.load_from_hub --algo ddpg --env BipedalWalkerHardcore-v3 -orga jackoyoungblood -f logs/ python enjoy.py --algo ddpg --env BipedalWalkerHardcore-v3 -f logs/ ``` ## Training (with the RL Zoo) ``` python train.py --algo ddpg --env BipedalWalkerHardcore-v3 -f logs/ # Upload the model and generate video (when possible) python -m utils.push_to_hub --algo ddpg --env BipedalWalkerHardcore-v3 -f logs/ -orga jackoyoungblood ``` ## Hyperparameters ```python OrderedDict([('buffer_size', 200000), ('gamma', 0.98), ('gradient_steps', -1), ('learning_rate', 0.001), ('learning_starts', 10000), ('n_timesteps', 100000.0), ('noise_std', 0.1), ('noise_type', 'normal'), ('policy', 'MlpPolicy'), ('policy_kwargs', 'dict(net_arch=[400, 300])'), ('train_freq', [1, 'episode']), ('normalize', False)]) ```
clementchadebec/reproduced_rae_l2
clementchadebec
2022-08-19T19:35:32Z
0
0
pythae
[ "pythae", "reproducibility", "en", "license:apache-2.0", "region:us" ]
null
2022-08-19T19:33:02Z
--- language: en tags: - pythae - reproducibility license: apache-2.0 --- This model was trained with pythae. It can be downloaded or reloaded using the method `load_from_hf_hub` ```python >>> from pythae.models import AutoModel >>> model = AutoModel.load_from_hf_hub(hf_hub_path="clementchadebec/reproduced_rae_l2") ``` ## Reproducibility This trained model reproduces the results of the official implementation of [1]. | Model | Dataset | Metric | Obtained value | Reference value | |:---:|:---:|:---:|:---:|:---:| | RAE_L2 | MNIST | FID | 9.1 | 9.9 | [1] Partha Ghosh, Mehdi SM Sajjadi, Antonio Vergari, Michael Black, and Bernhard Schölkopf. From variational to deterministic autoencoders. In 8th International Conference on Learning Representations, ICLR 2020, 2020.
Mahmoud7/Reinforce-CartPole8
Mahmoud7
2022-08-19T19:22:04Z
0
0
null
[ "CartPole-v1", "reinforce", "reinforcement-learning", "custom-implementation", "deep-rl-class", "model-index", "region:us" ]
reinforcement-learning
2022-08-19T17:46:48Z
--- tags: - CartPole-v1 - reinforce - reinforcement-learning - custom-implementation - deep-rl-class model-index: - name: Reinforce-CartPole8 results: - metrics: - type: mean_reward value: 40.10 +/- 14.40 name: mean_reward task: type: reinforcement-learning name: reinforcement-learning dataset: name: CartPole-v1 type: CartPole-v1 --- # **Reinforce** Agent playing **CartPole-v1** This is a trained model of a **Reinforce** agent playing **CartPole-v1** . To learn to use this model and train yours check Unit 5 of the Deep Reinforcement Learning Class: https://github.com/huggingface/deep-rl-class/tree/main/unit5
clementchadebec/reproduced_iwae
clementchadebec
2022-08-19T19:11:42Z
0
0
pythae
[ "pythae", "reproducibility", "en", "arxiv:1509.00519", "license:apache-2.0", "region:us" ]
null
2022-08-19T07:17:42Z
--- language: en tags: - pythae - reproducibility license: apache-2.0 --- This model was trained with pythae. It can be downloaded or reloaded using the method `load_from_hf_hub` ```python >>> from pythae.models import AutoModel >>> model = AutoModel.load_from_hf_hub(hf_hub_path="clementchadebec/reproduced_iwae") ``` ## Reproducibility This trained model reproduces the results of Table 1 in [1]. | Model | Dataset | Metric | Obtained value | Reference value | |:---:|:---:|:---:|:---:|:---:| | IWAE (n_samples=5) | Binary MNIST | NLL (5000 IS) | 87.85 (0.01) | 87.6 | | **IWAE (n_samples=50)** | Binary MNIST | NLL (5000 IS) | 86.82 (0.01) | 87.1 | [1] Burda, Y. et al, *Importance Weighted Autoencoders*, ArXiv:1509.00519
jackoyoungblood/qrdqn-SpaceInvadersNoFrameskip-v4
jackoyoungblood
2022-08-19T17:22:03Z
0
0
stable-baselines3
[ "stable-baselines3", "SpaceInvadersNoFrameskip-v4", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]
reinforcement-learning
2022-08-19T17:20:37Z
--- library_name: stable-baselines3 tags: - SpaceInvadersNoFrameskip-v4 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: QRDQN results: - metrics: - type: mean_reward value: 2441.50 +/- 1153.35 name: mean_reward task: type: reinforcement-learning name: reinforcement-learning dataset: name: SpaceInvadersNoFrameskip-v4 type: SpaceInvadersNoFrameskip-v4 --- # **QRDQN** Agent playing **SpaceInvadersNoFrameskip-v4** This is a trained model of a **QRDQN** agent playing **SpaceInvadersNoFrameskip-v4** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3) and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo). The RL Zoo is a training framework for Stable Baselines3 reinforcement learning agents, with hyperparameter optimization and pre-trained agents included. ## Usage (with SB3 RL Zoo) RL Zoo: https://github.com/DLR-RM/rl-baselines3-zoo<br/> SB3: https://github.com/DLR-RM/stable-baselines3<br/> SB3 Contrib: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib ``` # Download model and save it into the logs/ folder python -m utils.load_from_hub --algo qrdqn --env SpaceInvadersNoFrameskip-v4 -orga jackoyoungblood -f logs/ python enjoy.py --algo qrdqn --env SpaceInvadersNoFrameskip-v4 -f logs/ ``` ## Training (with the RL Zoo) ``` python train.py --algo qrdqn --env SpaceInvadersNoFrameskip-v4 -f logs/ # Upload the model and generate video (when possible) python -m utils.push_to_hub --algo qrdqn --env SpaceInvadersNoFrameskip-v4 -f logs/ -orga jackoyoungblood ``` ## Hyperparameters ```python OrderedDict([('env_wrapper', ['stable_baselines3.common.atari_wrappers.AtariWrapper']), ('exploration_fraction', 0.025), ('frame_stack', 4), ('n_timesteps', 10000000.0), ('optimize_memory_usage', True), ('policy', 'CnnPolicy'), ('replay_buffer_kwargs', 'dict(handle_timeout_termination=False)'), ('normalize', False)]) ```
shabohin/ddpm-butterflies-128
shabohin
2022-08-19T17:19:51Z
1
0
diffusers
[ "diffusers", "tensorboard", "en", "dataset:huggan/smithsonian_butterflies_subset", "license:apache-2.0", "diffusers:DDPMPipeline", "region:us" ]
null
2022-08-19T16:32:35Z
--- language: en license: apache-2.0 library_name: diffusers tags: [] datasets: huggan/smithsonian_butterflies_subset metrics: [] --- <!-- 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. --> # ddpm-butterflies-128 ## Model description This diffusion model is trained with the [🤗 Diffusers](https://github.com/huggingface/diffusers) library on the `huggan/smithsonian_butterflies_subset` dataset. ## 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 data [TODO: describe the data used to train the model] ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 16 - eval_batch_size: 16 - gradient_accumulation_steps: 1 - optimizer: AdamW with betas=(None, None), weight_decay=None and epsilon=None - lr_scheduler: None - lr_warmup_steps: 500 - ema_inv_gamma: None - ema_inv_gamma: None - ema_inv_gamma: None - mixed_precision: fp16 ### Training results 📈 [TensorBoard logs](https://huggingface.co/shabohin/ddpm-butterflies-128/tensorboard?#scalars)
rugo/ruBert-base-finetuned
rugo
2022-08-19T16:41:37Z
104
0
transformers
[ "transformers", "pytorch", "tensorboard", "bert", "fill-mask", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]
fill-mask
2022-08-19T16:12:04Z
--- license: apache-2.0 tags: - generated_from_trainer model-index: - name: ruBert-base-finetuned 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. --> # ruBert-base-finetuned This model is a fine-tuned version of [sberbank-ai/ruBert-base](https://huggingface.co/sberbank-ai/ruBert-base) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.8911 ## 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: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3.0 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 1.3205 | 1.0 | 625 | 1.0255 | | 1.0666 | 2.0 | 1250 | 0.9373 | | 0.9997 | 3.0 | 1875 | 0.9103 | ### Framework versions - Transformers 4.21.1 - Pytorch 1.12.1+cu113 - Datasets 2.4.0 - Tokenizers 0.12.1
caioeserpa/MobileNetV2_RNA_Class
caioeserpa
2022-08-19T16:34:38Z
0
0
null
[ "region:us" ]
null
2022-08-19T16:10:04Z
# RNA_Project # Projeto Final - Modelos Preditivos Conexionistas ### Aluno - Caio Emanoel Serpa Lopes ### Tutor - Vitor Casadei --- |**Tipo de Projeto**|**Modelo Selecionado**|**Linguagem**| |--|--|--| |Classificação de Imagens|MobileNetV2|Tensorflow| [Clique aqui para rodar o modelo via browser (roboflow)](https://classify.roboflow.com/?model=classifier_animals&version=2&api_key=IDPIYW7fvVaFbVq3eTlB) # Performance O modelo treinado possui performance de **100%**. ## Output do bloco de treinamento <details> <summary>Click to expand!</summary> ```Epoch 1/1000 2/2 [==============================] - ETA: 0s - loss: 1.0496 - accuracy: 0.3750 Epoch 1: saving model to training_1/cp.ckpt 2/2 [==============================] - 9s 4s/step - loss: 1.0496 - accuracy: 0.3750 - val_loss: 0.8153 - val_accuracy: 0.4237 Epoch 2/1000 2/2 [==============================] - ETA: 0s - loss: 1.0002 - accuracy: 0.3281 Epoch 2: saving model to training_1/cp.ckpt 2/2 [==============================] - 4s 2s/step - loss: 1.0002 - accuracy: 0.3281 - val_loss: 0.7967 - val_accuracy: 0.4407 Epoch 3/1000 2/2 [==============================] - ETA: 0s - loss: 1.0473 - accuracy: 0.3594 Epoch 3: saving model to training_1/cp.ckpt 2/2 [==============================] - 3s 2s/step - loss: 1.0473 - accuracy: 0.3594 - val_loss: 0.7953 - val_accuracy: 0.4237 Epoch 4/1000 2/2 [==============================] - ETA: 0s - loss: 0.9252 - accuracy: 0.3250 Epoch 4: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 1s/step - loss: 0.9252 - accuracy: 0.3250 - val_loss: 0.8039 - val_accuracy: 0.3729 Epoch 5/1000 2/2 [==============================] - ETA: 0s - loss: 0.9771 - accuracy: 0.3000 Epoch 5: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 781ms/step - loss: 0.9771 - accuracy: 0.3000 - val_loss: 0.8116 - val_accuracy: 0.3729 Epoch 6/1000 2/2 [==============================] - ETA: 0s - loss: 0.9402 - accuracy: 0.3125 Epoch 6: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 789ms/step - loss: 0.9402 - accuracy: 0.3125 - val_loss: 0.8183 - val_accuracy: 0.3898 Epoch 7/1000 2/2 [==============================] - ETA: 0s - loss: 0.8416 - accuracy: 0.4750 Epoch 7: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 1s/step - loss: 0.8416 - accuracy: 0.4750 - val_loss: 0.8229 - val_accuracy: 0.3898 Epoch 8/1000 2/2 [==============================] - ETA: 0s - loss: 0.8543 - accuracy: 0.3516 Epoch 8: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 913ms/step - loss: 0.8543 - accuracy: 0.3516 - val_loss: 0.8213 - val_accuracy: 0.4068 Epoch 9/1000 2/2 [==============================] - ETA: 0s - loss: 0.7657 - accuracy: 0.4844 Epoch 9: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 908ms/step - loss: 0.7657 - accuracy: 0.4844 - val_loss: 0.8124 - val_accuracy: 0.4068 Epoch 10/1000 2/2 [==============================] - ETA: 0s - loss: 0.8208 - accuracy: 0.3125 Epoch 10: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 1s/step - loss: 0.8208 - accuracy: 0.3125 - val_loss: 0.8035 - val_accuracy: 0.4237 Epoch 11/1000 2/2 [==============================] - ETA: 0s - loss: 0.8510 - accuracy: 0.3875 Epoch 11: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 789ms/step - loss: 0.8510 - accuracy: 0.3875 - val_loss: 0.7868 - val_accuracy: 0.4237 Epoch 12/1000 2/2 [==============================] - ETA: 0s - loss: 0.7841 - accuracy: 0.4609 Epoch 12: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 896ms/step - loss: 0.7841 - accuracy: 0.4609 - val_loss: 0.7674 - val_accuracy: 0.4407 Epoch 13/1000 2/2 [==============================] - ETA: 0s - loss: 0.7320 - accuracy: 0.5125 Epoch 13: saving model to training_1/cp.ckpt 2/2 [==============================] - 2s 1s/step - loss: 0.7320 - accuracy: 0.5125 - val_loss: 0.7513 - val_accuracy: 0.4576 Epoch 14/1000 2/2 [==============================] - ETA: 0s - loss: 0.7788 - accuracy: 0.3828 Epoch 14: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 908ms/step - loss: 0.7788 - accuracy: 0.3828 - val_loss: 0.7345 - val_accuracy: 0.4915 Epoch 15/1000 2/2 [==============================] - ETA: 0s - loss: 0.8054 - accuracy: 0.3250 Epoch 15: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 803ms/step - loss: 0.8054 - accuracy: 0.3250 - val_loss: 0.7162 - val_accuracy: 0.4915 Epoch 16/1000 2/2 [==============================] - ETA: 0s - loss: 0.7073 - accuracy: 0.5125 Epoch 16: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 1s/step - loss: 0.7073 - accuracy: 0.5125 - val_loss: 0.6949 - val_accuracy: 0.5085 Epoch 17/1000 2/2 [==============================] - ETA: 0s - loss: 0.7984 - accuracy: 0.4250 Epoch 17: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 1s/step - loss: 0.7984 - accuracy: 0.4250 - val_loss: 0.6756 - val_accuracy: 0.5424 Epoch 18/1000 2/2 [==============================] - ETA: 0s - loss: 0.7332 - accuracy: 0.4750 Epoch 18: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 777ms/step - loss: 0.7332 - accuracy: 0.4750 - val_loss: 0.6573 - val_accuracy: 0.5763 Epoch 19/1000 2/2 [==============================] - ETA: 0s - loss: 0.6789 - accuracy: 0.5000 Epoch 19: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 928ms/step - loss: 0.6789 - accuracy: 0.5000 - val_loss: 0.6398 - val_accuracy: 0.5763 Epoch 20/1000 2/2 [==============================] - ETA: 0s - loss: 0.7541 - accuracy: 0.4844 Epoch 20: saving model to training_1/cp.ckpt 2/2 [==============================] - 2s 1s/step - loss: 0.7541 - accuracy: 0.4844 - val_loss: 0.6241 - val_accuracy: 0.5763 Epoch 21/1000 2/2 [==============================] - ETA: 0s - loss: 0.7528 - accuracy: 0.4688 Epoch 21: saving model to training_1/cp.ckpt 2/2 [==============================] - 2s 1s/step - loss: 0.7528 - accuracy: 0.4688 - val_loss: 0.6103 - val_accuracy: 0.5763 Epoch 22/1000 2/2 [==============================] - ETA: 0s - loss: 0.6765 - accuracy: 0.5000 Epoch 22: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 1s/step - loss: 0.6765 - accuracy: 0.5000 - val_loss: 0.5980 - val_accuracy: 0.5932 Epoch 23/1000 2/2 [==============================] - ETA: 0s - loss: 0.6817 - accuracy: 0.5625 Epoch 23: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 1s/step - loss: 0.6817 - accuracy: 0.5625 - val_loss: 0.5890 - val_accuracy: 0.6102 Epoch 24/1000 2/2 [==============================] - ETA: 0s - loss: 0.7056 - accuracy: 0.4125 Epoch 24: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 785ms/step - loss: 0.7056 - accuracy: 0.4125 - val_loss: 0.5802 - val_accuracy: 0.6102 Epoch 25/1000 2/2 [==============================] - ETA: 0s - loss: 0.7238 - accuracy: 0.4453 Epoch 25: saving model to training_1/cp.ckpt 2/2 [==============================] - 2s 1s/step - loss: 0.7238 - accuracy: 0.4453 - val_loss: 0.5716 - val_accuracy: 0.6102 Epoch 26/1000 2/2 [==============================] - ETA: 0s - loss: 0.6118 - accuracy: 0.4875 Epoch 26: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 1s/step - loss: 0.6118 - accuracy: 0.4875 - val_loss: 0.5640 - val_accuracy: 0.6102 Epoch 27/1000 2/2 [==============================] - ETA: 0s - loss: 0.6136 - accuracy: 0.5250 Epoch 27: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 1s/step - loss: 0.6136 - accuracy: 0.5250 - val_loss: 0.5557 - val_accuracy: 0.6102 Epoch 28/1000 2/2 [==============================] - ETA: 0s - loss: 0.6424 - accuracy: 0.5156 Epoch 28: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 925ms/step - loss: 0.6424 - accuracy: 0.5156 - val_loss: 0.5483 - val_accuracy: 0.6271 Epoch 29/1000 2/2 [==============================] - ETA: 0s - loss: 0.6367 - accuracy: 0.5703 Epoch 29: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 925ms/step - loss: 0.6367 - accuracy: 0.5703 - val_loss: 0.5409 - val_accuracy: 0.6102 Epoch 30/1000 2/2 [==============================] - ETA: 0s - loss: 0.5621 - accuracy: 0.6375 Epoch 30: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 1s/step - loss: 0.5621 - accuracy: 0.6375 - val_loss: 0.5350 - val_accuracy: 0.6102 Epoch 31/1000 2/2 [==============================] - ETA: 0s - loss: 0.5903 - accuracy: 0.6625 Epoch 31: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 773ms/step - loss: 0.5903 - accuracy: 0.6625 - val_loss: 0.5297 - val_accuracy: 0.6102 Epoch 32/1000 2/2 [==============================] - ETA: 0s - loss: 0.5768 - accuracy: 0.5938 Epoch 32: saving model to training_1/cp.ckpt 2/2 [==============================] - 2s 1s/step - loss: 0.5768 - accuracy: 0.5938 - val_loss: 0.5246 - val_accuracy: 0.5932 Epoch 33/1000 2/2 [==============================] - ETA: 0s - loss: 0.5517 - accuracy: 0.6625 Epoch 33: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 771ms/step - loss: 0.5517 - accuracy: 0.6625 - val_loss: 0.5197 - val_accuracy: 0.6102 Epoch 34/1000 2/2 [==============================] - ETA: 0s - loss: 0.5987 - accuracy: 0.5625 Epoch 34: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 1s/step - loss: 0.5987 - accuracy: 0.5625 - val_loss: 0.5156 - val_accuracy: 0.6271 Epoch 35/1000 2/2 [==============================] - ETA: 0s - loss: 0.5768 - accuracy: 0.5859 Epoch 35: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 866ms/step - loss: 0.5768 - accuracy: 0.5859 - val_loss: 0.5116 - val_accuracy: 0.6271 Epoch 36/1000 2/2 [==============================] - ETA: 0s - loss: 0.5395 - accuracy: 0.7000 Epoch 36: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 1s/step - loss: 0.5395 - accuracy: 0.7000 - val_loss: 0.5072 - val_accuracy: 0.6271 Epoch 37/1000 2/2 [==============================] - ETA: 0s - loss: 0.5549 - accuracy: 0.5625 Epoch 37: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 1s/step - loss: 0.5549 - accuracy: 0.5625 - val_loss: 0.5027 - val_accuracy: 0.6271 Epoch 38/1000 2/2 [==============================] - ETA: 0s - loss: 0.5485 - accuracy: 0.5750 Epoch 38: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 783ms/step - loss: 0.5485 - accuracy: 0.5750 - val_loss: 0.4985 - val_accuracy: 0.6271 Epoch 39/1000 2/2 [==============================] - ETA: 0s - loss: 0.5600 - accuracy: 0.5875 Epoch 39: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 1s/step - loss: 0.5600 - accuracy: 0.5875 - val_loss: 0.4944 - val_accuracy: 0.6441 Epoch 40/1000 2/2 [==============================] - ETA: 0s - loss: 0.5797 - accuracy: 0.6250 Epoch 40: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 766ms/step - loss: 0.5797 - accuracy: 0.6250 - val_loss: 0.4913 - val_accuracy: 0.6441 Epoch 41/1000 2/2 [==============================] - ETA: 0s - loss: 0.5891 - accuracy: 0.6125 Epoch 41: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 850ms/step - loss: 0.5891 - accuracy: 0.6125 - val_loss: 0.4880 - val_accuracy: 0.6610 Epoch 42/1000 2/2 [==============================] - ETA: 0s - loss: 0.5301 - accuracy: 0.6375 Epoch 42: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 810ms/step - loss: 0.5301 - accuracy: 0.6375 - val_loss: 0.4847 - val_accuracy: 0.6610 Epoch 43/1000 2/2 [==============================] - ETA: 0s - loss: 0.5775 - accuracy: 0.6328 Epoch 43: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 942ms/step - loss: 0.5775 - accuracy: 0.6328 - val_loss: 0.4796 - val_accuracy: 0.6610 Epoch 44/1000 2/2 [==============================] - ETA: 0s - loss: 0.4997 - accuracy: 0.6641 Epoch 44: saving model to training_1/cp.ckpt 2/2 [==============================] - 2s 1s/step - loss: 0.4997 - accuracy: 0.6641 - val_loss: 0.4753 - val_accuracy: 0.6610 Epoch 45/1000 2/2 [==============================] - ETA: 0s - loss: 0.5236 - accuracy: 0.7109 Epoch 45: saving model to training_1/cp.ckpt 2/2 [==============================] - 2s 1s/step - loss: 0.5236 - accuracy: 0.7109 - val_loss: 0.4713 - val_accuracy: 0.6780 Epoch 46/1000 2/2 [==============================] - ETA: 0s - loss: 0.5150 - accuracy: 0.6641 Epoch 46: saving model to training_1/cp.ckpt 2/2 [==============================] - 2s 1s/step - loss: 0.5150 - accuracy: 0.6641 - val_loss: 0.4674 - val_accuracy: 0.6780 Epoch 47/1000 2/2 [==============================] - ETA: 0s - loss: 0.5213 - accuracy: 0.6625 Epoch 47: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 1s/step - loss: 0.5213 - accuracy: 0.6625 - val_loss: 0.4637 - val_accuracy: 0.6780 Epoch 48/1000 2/2 [==============================] - ETA: 0s - loss: 0.5835 - accuracy: 0.6016 Epoch 48: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 913ms/step - loss: 0.5835 - accuracy: 0.6016 - val_loss: 0.4594 - val_accuracy: 0.6780 Epoch 49/1000 2/2 [==============================] - ETA: 0s - loss: 0.5356 - accuracy: 0.6641 Epoch 49: saving model to training_1/cp.ckpt 2/2 [==============================] - 2s 1s/step - loss: 0.5356 - accuracy: 0.6641 - val_loss: 0.4551 - val_accuracy: 0.6780 Epoch 50/1000 2/2 [==============================] - ETA: 0s - loss: 0.5144 - accuracy: 0.6797 Epoch 50: saving model to training_1/cp.ckpt 2/2 [==============================] - 2s 1s/step - loss: 0.5144 - accuracy: 0.6797 - val_loss: 0.4520 - val_accuracy: 0.6949 Epoch 51/1000 2/2 [==============================] - ETA: 0s - loss: 0.5832 - accuracy: 0.6875 Epoch 51: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 1s/step - loss: 0.5832 - accuracy: 0.6875 - val_loss: 0.4498 - val_accuracy: 0.6949 Epoch 52/1000 2/2 [==============================] - ETA: 0s - loss: 0.5395 - accuracy: 0.6500 Epoch 52: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 795ms/step - loss: 0.5395 - accuracy: 0.6500 - val_loss: 0.4471 - val_accuracy: 0.6949 Epoch 53/1000 2/2 [==============================] - ETA: 0s - loss: 0.4901 - accuracy: 0.7188 Epoch 53: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 995ms/step - loss: 0.4901 - accuracy: 0.7188 - val_loss: 0.4434 - val_accuracy: 0.6949 Epoch 54/1000 2/2 [==============================] - ETA: 0s - loss: 0.4348 - accuracy: 0.7250 Epoch 54: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 796ms/step - loss: 0.4348 - accuracy: 0.7250 - val_loss: 0.4400 - val_accuracy: 0.6949 Epoch 55/1000 2/2 [==============================] - ETA: 0s - loss: 0.5062 - accuracy: 0.6641 Epoch 55: saving model to training_1/cp.ckpt 2/2 [==============================] - 2s 1s/step - loss: 0.5062 - accuracy: 0.6641 - val_loss: 0.4370 - val_accuracy: 0.7119 Epoch 56/1000 2/2 [==============================] - ETA: 0s - loss: 0.5069 - accuracy: 0.5875 Epoch 56: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 1s/step - loss: 0.5069 - accuracy: 0.5875 - val_loss: 0.4306 - val_accuracy: 0.7119 Epoch 57/1000 2/2 [==============================] - ETA: 0s - loss: 0.4512 - accuracy: 0.7125 Epoch 57: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 1s/step - loss: 0.4512 - accuracy: 0.7125 - val_loss: 0.4254 - val_accuracy: 0.7119 Epoch 58/1000 2/2 [==============================] - ETA: 0s - loss: 0.5265 - accuracy: 0.6625 Epoch 58: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 1s/step - loss: 0.5265 - accuracy: 0.6625 - val_loss: 0.4208 - val_accuracy: 0.7119 Epoch 59/1000 2/2 [==============================] - ETA: 0s - loss: 0.4557 - accuracy: 0.7375 Epoch 59: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 792ms/step - loss: 0.4557 - accuracy: 0.7375 - val_loss: 0.4171 - val_accuracy: 0.7119 Epoch 60/1000 2/2 [==============================] - ETA: 0s - loss: 0.5258 - accuracy: 0.6125 Epoch 60: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 793ms/step - loss: 0.5258 - accuracy: 0.6125 - val_loss: 0.4139 - val_accuracy: 0.7119 Epoch 61/1000 2/2 [==============================] - ETA: 0s - loss: 0.4988 - accuracy: 0.6641 Epoch 61: saving model to training_1/cp.ckpt 2/2 [==============================] - 2s 1s/step - loss: 0.4988 - accuracy: 0.6641 - val_loss: 0.4117 - val_accuracy: 0.7119 Epoch 62/1000 2/2 [==============================] - ETA: 0s - loss: 0.5074 - accuracy: 0.6625 Epoch 62: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 1s/step - loss: 0.5074 - accuracy: 0.6625 - val_loss: 0.4109 - val_accuracy: 0.7119 Epoch 63/1000 2/2 [==============================] - ETA: 0s - loss: 0.5155 - accuracy: 0.6797 Epoch 63: saving model to training_1/cp.ckpt 2/2 [==============================] - 2s 1s/step - loss: 0.5155 - accuracy: 0.6797 - val_loss: 0.4105 - val_accuracy: 0.7119 Epoch 64/1000 2/2 [==============================] - ETA: 0s - loss: 0.4738 - accuracy: 0.7031 Epoch 64: saving model to training_1/cp.ckpt 2/2 [==============================] - 2s 1s/step - loss: 0.4738 - accuracy: 0.7031 - val_loss: 0.4101 - val_accuracy: 0.7119 Epoch 65/1000 2/2 [==============================] - ETA: 0s - loss: 0.4526 - accuracy: 0.7266 Epoch 65: saving model to training_1/cp.ckpt 2/2 [==============================] - 2s 1s/step - loss: 0.4526 - accuracy: 0.7266 - val_loss: 0.4099 - val_accuracy: 0.7288 Epoch 66/1000 2/2 [==============================] - ETA: 0s - loss: 0.4432 - accuracy: 0.6875 Epoch 66: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 917ms/step - loss: 0.4432 - accuracy: 0.6875 - val_loss: 0.4096 - val_accuracy: 0.7288 Epoch 67/1000 2/2 [==============================] - ETA: 0s - loss: 0.4556 - accuracy: 0.7031 Epoch 67: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 891ms/step - loss: 0.4556 - accuracy: 0.7031 - val_loss: 0.4089 - val_accuracy: 0.7288 Epoch 68/1000 2/2 [==============================] - ETA: 0s - loss: 0.4906 - accuracy: 0.7000 Epoch 68: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 1s/step - loss: 0.4906 - accuracy: 0.7000 - val_loss: 0.4077 - val_accuracy: 0.7288 Epoch 69/1000 2/2 [==============================] - ETA: 0s - loss: 0.4392 - accuracy: 0.6953 Epoch 69: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 933ms/step - loss: 0.4392 - accuracy: 0.6953 - val_loss: 0.4067 - val_accuracy: 0.7288 Epoch 70/1000 2/2 [==============================] - ETA: 0s - loss: 0.4505 - accuracy: 0.7188 Epoch 70: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 911ms/step - loss: 0.4505 - accuracy: 0.7188 - val_loss: 0.4056 - val_accuracy: 0.7288 Epoch 71/1000 2/2 [==============================] - ETA: 0s - loss: 0.4227 - accuracy: 0.8250 Epoch 71: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 1s/step - loss: 0.4227 - accuracy: 0.8250 - val_loss: 0.4038 - val_accuracy: 0.7288 Epoch 72/1000 2/2 [==============================] - ETA: 0s - loss: 0.4216 - accuracy: 0.7188 Epoch 72: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 942ms/step - loss: 0.4216 - accuracy: 0.7188 - val_loss: 0.4028 - val_accuracy: 0.7288 Epoch 73/1000 2/2 [==============================] - ETA: 0s - loss: 0.4563 - accuracy: 0.7031 Epoch 73: saving model to training_1/cp.ckpt 2/2 [==============================] - 2s 1s/step - loss: 0.4563 - accuracy: 0.7031 - val_loss: 0.4029 - val_accuracy: 0.7288 Epoch 74/1000 2/2 [==============================] - ETA: 0s - loss: 0.4717 - accuracy: 0.6719 Epoch 74: saving model to training_1/cp.ckpt 2/2 [==============================] - 2s 1s/step - loss: 0.4717 - accuracy: 0.6719 - val_loss: 0.4026 - val_accuracy: 0.7288 Epoch 75/1000 2/2 [==============================] - ETA: 0s - loss: 0.3515 - accuracy: 0.8250 Epoch 75: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 1s/step - loss: 0.3515 - accuracy: 0.8250 - val_loss: 0.4009 - val_accuracy: 0.7119 Epoch 76/1000 2/2 [==============================] - ETA: 0s - loss: 0.4396 - accuracy: 0.7125 Epoch 76: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 795ms/step - loss: 0.4396 - accuracy: 0.7125 - val_loss: 0.4004 - val_accuracy: 0.7288 Epoch 77/1000 2/2 [==============================] - ETA: 0s - loss: 0.4737 - accuracy: 0.6250 Epoch 77: saving model to training_1/cp.ckpt 2/2 [==============================] - 2s 1s/step - loss: 0.4737 - accuracy: 0.6250 - val_loss: 0.4002 - val_accuracy: 0.7458 Epoch 78/1000 2/2 [==============================] - ETA: 0s - loss: 0.3818 - accuracy: 0.8125 Epoch 78: saving model to training_1/cp.ckpt 2/2 [==============================] - 2s 1s/step - loss: 0.3818 - accuracy: 0.8125 - val_loss: 0.3997 - val_accuracy: 0.7458 Epoch 79/1000 2/2 [==============================] - ETA: 0s - loss: 0.3942 - accuracy: 0.7812 Epoch 79: saving model to training_1/cp.ckpt 2/2 [==============================] - 2s 1s/step - loss: 0.3942 - accuracy: 0.7812 - val_loss: 0.3999 - val_accuracy: 0.7458 Epoch 80/1000 2/2 [==============================] - ETA: 0s - loss: 0.4376 - accuracy: 0.7625 Epoch 80: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 1s/step - loss: 0.4376 - accuracy: 0.7625 - val_loss: 0.3999 - val_accuracy: 0.7288 Epoch 81/1000 2/2 [==============================] - ETA: 0s - loss: 0.4146 - accuracy: 0.7875 Epoch 81: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 1s/step - loss: 0.4146 - accuracy: 0.7875 - val_loss: 0.3985 - val_accuracy: 0.7458 Epoch 82/1000 2/2 [==============================] - ETA: 0s - loss: 0.4513 - accuracy: 0.7109 Epoch 82: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 952ms/step - loss: 0.4513 - accuracy: 0.7109 - val_loss: 0.3975 - val_accuracy: 0.7458 Epoch 83/1000 2/2 [==============================] - ETA: 0s - loss: 0.4000 - accuracy: 0.7875 Epoch 83: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 1s/step - loss: 0.4000 - accuracy: 0.7875 - val_loss: 0.3966 - val_accuracy: 0.7458 Epoch 84/1000 2/2 [==============================] - ETA: 0s - loss: 0.3920 - accuracy: 0.7812 Epoch 84: saving model to training_1/cp.ckpt 2/2 [==============================] - 2s 1s/step - loss: 0.3920 - accuracy: 0.7812 - val_loss: 0.3957 - val_accuracy: 0.7458 Epoch 85/1000 2/2 [==============================] - ETA: 0s - loss: 0.4480 - accuracy: 0.6750 Epoch 85: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 1s/step - loss: 0.4480 - accuracy: 0.6750 - val_loss: 0.3950 - val_accuracy: 0.7458 Epoch 86/1000 2/2 [==============================] - ETA: 0s - loss: 0.4010 - accuracy: 0.7656 Epoch 86: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 881ms/step - loss: 0.4010 - accuracy: 0.7656 - val_loss: 0.3956 - val_accuracy: 0.7288 Epoch 87/1000 2/2 [==============================] - ETA: 0s - loss: 0.4635 - accuracy: 0.7125 Epoch 87: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 1s/step - loss: 0.4635 - accuracy: 0.7125 - val_loss: 0.3978 - val_accuracy: 0.7288 Epoch 88/1000 2/2 [==============================] - ETA: 0s - loss: 0.4501 - accuracy: 0.7188 Epoch 88: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 915ms/step - loss: 0.4501 - accuracy: 0.7188 - val_loss: 0.4002 - val_accuracy: 0.7627 Epoch 89/1000 2/2 [==============================] - ETA: 0s - loss: 0.3909 - accuracy: 0.7875 Epoch 89: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 1s/step - loss: 0.3909 - accuracy: 0.7875 - val_loss: 0.4037 - val_accuracy: 0.7627 Epoch 90/1000 2/2 [==============================] - ETA: 0s - loss: 0.3992 - accuracy: 0.7250 Epoch 90: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 1s/step - loss: 0.3992 - accuracy: 0.7250 - val_loss: 0.4045 - val_accuracy: 0.7627 Epoch 91/1000 2/2 [==============================] - ETA: 0s - loss: 0.4022 - accuracy: 0.8203 Epoch 91: saving model to training_1/cp.ckpt 2/2 [==============================] - 2s 1s/step - loss: 0.4022 - accuracy: 0.8203 - val_loss: 0.4050 - val_accuracy: 0.7458 Epoch 92/1000 2/2 [==============================] - ETA: 0s - loss: 0.4112 - accuracy: 0.7031 Epoch 92: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 972ms/step - loss: 0.4112 - accuracy: 0.7031 - val_loss: 0.4050 - val_accuracy: 0.7458 Epoch 93/1000 2/2 [==============================] - ETA: 0s - loss: 0.3795 - accuracy: 0.7500 Epoch 93: saving model to training_1/cp.ckpt 2/2 [==============================] - 2s 1s/step - loss: 0.3795 - accuracy: 0.7500 - val_loss: 0.4046 - val_accuracy: 0.7458 Epoch 94/1000 2/2 [==============================] - ETA: 0s - loss: 0.4178 - accuracy: 0.7250 Epoch 94: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 786ms/step - loss: 0.4178 - accuracy: 0.7250 - val_loss: 0.4047 - val_accuracy: 0.7458 Epoch 95/1000 2/2 [==============================] - ETA: 0s - loss: 0.3446 - accuracy: 0.8281 Epoch 95: saving model to training_1/cp.ckpt 2/2 [==============================] - 2s 1s/step - loss: 0.3446 - accuracy: 0.8281 - val_loss: 0.4047 - val_accuracy: 0.7458 Epoch 96/1000 2/2 [==============================] - ETA: 0s - loss: 0.4607 - accuracy: 0.7250 Epoch 96: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 1s/step - loss: 0.4607 - accuracy: 0.7250 - val_loss: 0.4035 - val_accuracy: 0.7458 Epoch 97/1000 2/2 [==============================] - ETA: 0s - loss: 0.3616 - accuracy: 0.7875 Epoch 97: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 809ms/step - loss: 0.3616 - accuracy: 0.7875 - val_loss: 0.4021 - val_accuracy: 0.7458 Epoch 98/1000 2/2 [==============================] - ETA: 0s - loss: 0.3380 - accuracy: 0.7375 Epoch 98: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 795ms/step - loss: 0.3380 - accuracy: 0.7375 - val_loss: 0.4014 - val_accuracy: 0.7458 Epoch 99/1000 2/2 [==============================] - ETA: 0s - loss: 0.3621 - accuracy: 0.8047 Epoch 99: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 925ms/step - loss: 0.3621 - accuracy: 0.8047 - val_loss: 0.3993 - val_accuracy: 0.7288 Epoch 100/1000 2/2 [==============================] - ETA: 0s - loss: 0.3969 - accuracy: 0.7578 Epoch 100: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 922ms/step - loss: 0.3969 - accuracy: 0.7578 - val_loss: 0.3952 - val_accuracy: 0.7288 Epoch 101/1000 2/2 [==============================] - ETA: 0s - loss: 0.3638 - accuracy: 0.7500 Epoch 101: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 807ms/step - loss: 0.3638 - accuracy: 0.7500 - val_loss: 0.3910 - val_accuracy: 0.7288 Epoch 102/1000 2/2 [==============================] - ETA: 0s - loss: 0.3590 - accuracy: 0.7891 Epoch 102: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 912ms/step - loss: 0.3590 - accuracy: 0.7891 - val_loss: 0.3877 - val_accuracy: 0.7288 Epoch 103/1000 2/2 [==============================] - ETA: 0s - loss: 0.3947 - accuracy: 0.7656 Epoch 103: saving model to training_1/cp.ckpt 2/2 [==============================] - 2s 959ms/step - loss: 0.3947 - accuracy: 0.7656 - val_loss: 0.3841 - val_accuracy: 0.7288 Epoch 104/1000 2/2 [==============================] - ETA: 0s - loss: 0.4289 - accuracy: 0.7250 Epoch 104: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 805ms/step - loss: 0.4289 - accuracy: 0.7250 - val_loss: 0.3815 - val_accuracy: 0.7288 Epoch 105/1000 2/2 [==============================] - ETA: 0s - loss: 0.3684 - accuracy: 0.8359 Epoch 105: saving model to training_1/cp.ckpt 2/2 [==============================] - 2s 1s/step - loss: 0.3684 - accuracy: 0.8359 - val_loss: 0.3784 - val_accuracy: 0.7288 Epoch 106/1000 2/2 [==============================] - ETA: 0s - loss: 0.3745 - accuracy: 0.8000 Epoch 106: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 866ms/step - loss: 0.3745 - accuracy: 0.8000 - val_loss: 0.3758 - val_accuracy: 0.7288 Epoch 107/1000 2/2 [==============================] - ETA: 0s - loss: 0.3485 - accuracy: 0.8125 Epoch 107: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 917ms/step - loss: 0.3485 - accuracy: 0.8125 - val_loss: 0.3743 - val_accuracy: 0.7458 Epoch 108/1000 2/2 [==============================] - ETA: 0s - loss: 0.3889 - accuracy: 0.8000 Epoch 108: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 997ms/step - loss: 0.3889 - accuracy: 0.8000 - val_loss: 0.3726 - val_accuracy: 0.7458 Epoch 109/1000 2/2 [==============================] - ETA: 0s - loss: 0.3484 - accuracy: 0.8672 Epoch 109: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 937ms/step - loss: 0.3484 - accuracy: 0.8672 - val_loss: 0.3712 - val_accuracy: 0.7458 Epoch 110/1000 2/2 [==============================] - ETA: 0s - loss: 0.3734 - accuracy: 0.8047 Epoch 110: saving model to training_1/cp.ckpt 2/2 [==============================] - 2s 1s/step - loss: 0.3734 - accuracy: 0.8047 - val_loss: 0.3696 - val_accuracy: 0.7458 Epoch 111/1000 2/2 [==============================] - ETA: 0s - loss: 0.4089 - accuracy: 0.7875 Epoch 111: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 789ms/step - loss: 0.4089 - accuracy: 0.7875 - val_loss: 0.3676 - val_accuracy: 0.7458 Epoch 112/1000 2/2 [==============================] - ETA: 0s - loss: 0.3788 - accuracy: 0.7750 Epoch 112: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 783ms/step - loss: 0.3788 - accuracy: 0.7750 - val_loss: 0.3646 - val_accuracy: 0.7288 Epoch 113/1000 2/2 [==============================] - ETA: 0s - loss: 0.3728 - accuracy: 0.7812 Epoch 113: saving model to training_1/cp.ckpt 2/2 [==============================] - 2s 1s/step - loss: 0.3728 - accuracy: 0.7812 - val_loss: 0.3621 - val_accuracy: 0.7288 Epoch 114/1000 2/2 [==============================] - ETA: 0s - loss: 0.3751 - accuracy: 0.8000 Epoch 114: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 1s/step - loss: 0.3751 - accuracy: 0.8000 - val_loss: 0.3599 - val_accuracy: 0.7288 Epoch 115/1000 2/2 [==============================] - ETA: 0s - loss: 0.3739 - accuracy: 0.7734 Epoch 115: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 946ms/step - loss: 0.3739 - accuracy: 0.7734 - val_loss: 0.3578 - val_accuracy: 0.7288 Epoch 116/1000 2/2 [==============================] - ETA: 0s - loss: 0.3883 - accuracy: 0.8000 Epoch 116: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 1s/step - loss: 0.3883 - accuracy: 0.8000 - val_loss: 0.3563 - val_accuracy: 0.7288 Epoch 117/1000 2/2 [==============================] - ETA: 0s - loss: 0.3443 - accuracy: 0.8203 Epoch 117: saving model to training_1/cp.ckpt 2/2 [==============================] - 2s 1s/step - loss: 0.3443 - accuracy: 0.8203 - val_loss: 0.3552 - val_accuracy: 0.7458 Epoch 118/1000 2/2 [==============================] - ETA: 0s - loss: 0.3449 - accuracy: 0.8375 Epoch 118: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 1s/step - loss: 0.3449 - accuracy: 0.8375 - val_loss: 0.3555 - val_accuracy: 0.7458 Epoch 119/1000 2/2 [==============================] - ETA: 0s - loss: 0.3562 - accuracy: 0.8000 Epoch 119: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 1s/step - loss: 0.3562 - accuracy: 0.8000 - val_loss: 0.3556 - val_accuracy: 0.7458 Epoch 120/1000 2/2 [==============================] - ETA: 0s - loss: 0.2561 - accuracy: 0.8828 Epoch 120: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 914ms/step - loss: 0.2561 - accuracy: 0.8828 - val_loss: 0.3562 - val_accuracy: 0.7458 Epoch 121/1000 2/2 [==============================] - ETA: 0s - loss: 0.3495 - accuracy: 0.8125 Epoch 121: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 916ms/step - loss: 0.3495 - accuracy: 0.8125 - val_loss: 0.3566 - val_accuracy: 0.7627 Epoch 122/1000 2/2 [==============================] - ETA: 0s - loss: 0.3165 - accuracy: 0.8672 Epoch 122: saving model to training_1/cp.ckpt 2/2 [==============================] - 2s 1s/step - loss: 0.3165 - accuracy: 0.8672 - val_loss: 0.3566 - val_accuracy: 0.7627 Epoch 123/1000 2/2 [==============================] - ETA: 0s - loss: 0.3741 - accuracy: 0.7734 Epoch 123: saving model to training_1/cp.ckpt 2/2 [==============================] - 2s 1s/step - loss: 0.3741 - accuracy: 0.7734 - val_loss: 0.3571 - val_accuracy: 0.7627 Epoch 124/1000 2/2 [==============================] - ETA: 0s - loss: 0.3923 - accuracy: 0.7500 Epoch 124: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 955ms/step - loss: 0.3923 - accuracy: 0.7500 - val_loss: 0.3574 - val_accuracy: 0.7627 Epoch 125/1000 2/2 [==============================] - ETA: 0s - loss: 0.3380 - accuracy: 0.7812 Epoch 125: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 912ms/step - loss: 0.3380 - accuracy: 0.7812 - val_loss: 0.3575 - val_accuracy: 0.7627 Epoch 126/1000 2/2 [==============================] - ETA: 0s - loss: 0.3617 - accuracy: 0.7875 Epoch 126: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 1s/step - loss: 0.3617 - accuracy: 0.7875 - val_loss: 0.3581 - val_accuracy: 0.7627 Epoch 127/1000 2/2 [==============================] - ETA: 0s - loss: 0.4007 - accuracy: 0.7000 Epoch 127: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 1s/step - loss: 0.4007 - accuracy: 0.7000 - val_loss: 0.3577 - val_accuracy: 0.7627 Epoch 128/1000 2/2 [==============================] - ETA: 0s - loss: 0.3632 - accuracy: 0.8000 Epoch 128: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 1s/step - loss: 0.3632 - accuracy: 0.8000 - val_loss: 0.3570 - val_accuracy: 0.7627 Epoch 129/1000 2/2 [==============================] - ETA: 0s - loss: 0.3418 - accuracy: 0.8359 Epoch 129: saving model to training_1/cp.ckpt 2/2 [==============================] - 2s 1s/step - loss: 0.3418 - accuracy: 0.8359 - val_loss: 0.3558 - val_accuracy: 0.7627 Epoch 130/1000 2/2 [==============================] - ETA: 0s - loss: 0.3338 - accuracy: 0.8250 Epoch 130: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 815ms/step - loss: 0.3338 - accuracy: 0.8250 - val_loss: 0.3545 - val_accuracy: 0.7627 Epoch 131/1000 2/2 [==============================] - ETA: 0s - loss: 0.3705 - accuracy: 0.7750 Epoch 131: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 1s/step - loss: 0.3705 - accuracy: 0.7750 - val_loss: 0.3534 - val_accuracy: 0.7627 Epoch 132/1000 2/2 [==============================] - ETA: 0s - loss: 0.2992 - accuracy: 0.8625 Epoch 132: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 1s/step - loss: 0.2992 - accuracy: 0.8625 - val_loss: 0.3531 - val_accuracy: 0.7627 Epoch 133/1000 2/2 [==============================] - ETA: 0s - loss: 0.3112 - accuracy: 0.8438 Epoch 133: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 940ms/step - loss: 0.3112 - accuracy: 0.8438 - val_loss: 0.3533 - val_accuracy: 0.7627 Epoch 134/1000 2/2 [==============================] - ETA: 0s - loss: 0.3687 - accuracy: 0.8203 Epoch 134: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 926ms/step - loss: 0.3687 - accuracy: 0.8203 - val_loss: 0.3521 - val_accuracy: 0.7627 Epoch 135/1000 2/2 [==============================] - ETA: 0s - loss: 0.4165 - accuracy: 0.7250 Epoch 135: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 1s/step - loss: 0.4165 - accuracy: 0.7250 - val_loss: 0.3497 - val_accuracy: 0.7627 Epoch 136/1000 2/2 [==============================] - ETA: 0s - loss: 0.2755 - accuracy: 0.8750 Epoch 136: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 801ms/step - loss: 0.2755 - accuracy: 0.8750 - val_loss: 0.3483 - val_accuracy: 0.7627 Epoch 137/1000 2/2 [==============================] - ETA: 0s - loss: 0.3457 - accuracy: 0.8000 Epoch 137: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 783ms/step - loss: 0.3457 - accuracy: 0.8000 - val_loss: 0.3478 - val_accuracy: 0.7627 Epoch 138/1000 2/2 [==============================] - ETA: 0s - loss: 0.3676 - accuracy: 0.7812 Epoch 138: saving model to training_1/cp.ckpt 2/2 [==============================] - 2s 1s/step - loss: 0.3676 - accuracy: 0.7812 - val_loss: 0.3470 - val_accuracy: 0.7627 Epoch 139/1000 2/2 [==============================] - ETA: 0s - loss: 0.3189 - accuracy: 0.7875 Epoch 139: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 781ms/step - loss: 0.3189 - accuracy: 0.7875 - val_loss: 0.3467 - val_accuracy: 0.7627 Epoch 140/1000 2/2 [==============================] - ETA: 0s - loss: 0.3633 - accuracy: 0.7875 Epoch 140: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 1s/step - loss: 0.3633 - accuracy: 0.7875 - val_loss: 0.3483 - val_accuracy: 0.7627 Epoch 141/1000 2/2 [==============================] - ETA: 0s - loss: 0.3355 - accuracy: 0.7875 Epoch 141: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 852ms/step - loss: 0.3355 - accuracy: 0.7875 - val_loss: 0.3495 - val_accuracy: 0.7627 Epoch 142/1000 2/2 [==============================] - ETA: 0s - loss: 0.3416 - accuracy: 0.8250 Epoch 142: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 796ms/step - loss: 0.3416 - accuracy: 0.8250 - val_loss: 0.3497 - val_accuracy: 0.7627 Epoch 143/1000 2/2 [==============================] - ETA: 0s - loss: 0.3214 - accuracy: 0.8438 Epoch 143: saving model to training_1/cp.ckpt 2/2 [==============================] - 2s 1s/step - loss: 0.3214 - accuracy: 0.8438 - val_loss: 0.3494 - val_accuracy: 0.7627 Epoch 144/1000 2/2 [==============================] - ETA: 0s - loss: 0.3541 - accuracy: 0.7875 Epoch 144: saving model to training_1/cp.ckpt 2/2 [==============================] - 2s 1s/step - loss: 0.3541 - accuracy: 0.7875 - val_loss: 0.3490 - val_accuracy: 0.7627 Epoch 145/1000 2/2 [==============================] - ETA: 0s - loss: 0.3347 - accuracy: 0.8500 Epoch 145: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 806ms/step - loss: 0.3347 - accuracy: 0.8500 - val_loss: 0.3488 - val_accuracy: 0.7627 Epoch 146/1000 2/2 [==============================] - ETA: 0s - loss: 0.3238 - accuracy: 0.8594 Epoch 146: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 969ms/step - loss: 0.3238 - accuracy: 0.8594 - val_loss: 0.3493 - val_accuracy: 0.7627 Epoch 147/1000 2/2 [==============================] - ETA: 0s - loss: 0.3252 - accuracy: 0.8250 Epoch 147: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 799ms/step - loss: 0.3252 - accuracy: 0.8250 - val_loss: 0.3499 - val_accuracy: 0.7627 Epoch 148/1000 2/2 [==============================] - ETA: 0s - loss: 0.3136 - accuracy: 0.8250 Epoch 148: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 766ms/step - loss: 0.3136 - accuracy: 0.8250 - val_loss: 0.3515 - val_accuracy: 0.7627 Epoch 149/1000 2/2 [==============================] - ETA: 0s - loss: 0.3215 - accuracy: 0.8250 Epoch 149: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 1s/step - loss: 0.3215 - accuracy: 0.8250 - val_loss: 0.3529 - val_accuracy: 0.7627 Epoch 150/1000 2/2 [==============================] - ETA: 0s - loss: 0.3838 - accuracy: 0.7625 Epoch 150: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 1s/step - loss: 0.3838 - accuracy: 0.7625 - val_loss: 0.3546 - val_accuracy: 0.7627 Epoch 151/1000 2/2 [==============================] - ETA: 0s - loss: 0.3322 - accuracy: 0.8125 Epoch 151: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 809ms/step - loss: 0.3322 - accuracy: 0.8125 - val_loss: 0.3537 - val_accuracy: 0.7627 Epoch 152/1000 2/2 [==============================] - ETA: 0s - loss: 0.3422 - accuracy: 0.8281 Epoch 152: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 913ms/step - loss: 0.3422 - accuracy: 0.8281 - val_loss: 0.3523 - val_accuracy: 0.7627 Epoch 153/1000 2/2 [==============================] - ETA: 0s - loss: 0.3141 - accuracy: 0.8500 Epoch 153: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 876ms/step - loss: 0.3141 - accuracy: 0.8500 - val_loss: 0.3495 - val_accuracy: 0.7627 Epoch 154/1000 2/2 [==============================] - ETA: 0s - loss: 0.3786 - accuracy: 0.7625 Epoch 154: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 1s/step - loss: 0.3786 - accuracy: 0.7625 - val_loss: 0.3458 - val_accuracy: 0.7627 Epoch 155/1000 2/2 [==============================] - ETA: 0s - loss: 0.3309 - accuracy: 0.8125 Epoch 155: saving model to training_1/cp.ckpt 2/2 [==============================] - 2s 1s/step - loss: 0.3309 - accuracy: 0.8125 - val_loss: 0.3425 - val_accuracy: 0.7627 Epoch 156/1000 2/2 [==============================] - ETA: 0s - loss: 0.3570 - accuracy: 0.7969 Epoch 156: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 928ms/step - loss: 0.3570 - accuracy: 0.7969 - val_loss: 0.3386 - val_accuracy: 0.7797 Epoch 157/1000 2/2 [==============================] - ETA: 0s - loss: 0.3137 - accuracy: 0.8250 Epoch 157: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 779ms/step - loss: 0.3137 - accuracy: 0.8250 - val_loss: 0.3349 - val_accuracy: 0.7797 Epoch 158/1000 2/2 [==============================] - ETA: 0s - loss: 0.3485 - accuracy: 0.8281 Epoch 158: saving model to training_1/cp.ckpt 2/2 [==============================] - 2s 1s/step - loss: 0.3485 - accuracy: 0.8281 - val_loss: 0.3321 - val_accuracy: 0.7797 Epoch 159/1000 2/2 [==============================] - ETA: 0s - loss: 0.3114 - accuracy: 0.8594 Epoch 159: saving model to training_1/cp.ckpt 2/2 [==============================] - 2s 997ms/step - loss: 0.3114 - accuracy: 0.8594 - val_loss: 0.3295 - val_accuracy: 0.7797 Epoch 160/1000 2/2 [==============================] - ETA: 0s - loss: 0.3695 - accuracy: 0.7750 Epoch 160: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 1s/step - loss: 0.3695 - accuracy: 0.7750 - val_loss: 0.3255 - val_accuracy: 0.7797 Epoch 161/1000 2/2 [==============================] - ETA: 0s - loss: 0.3590 - accuracy: 0.8125 Epoch 161: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 794ms/step - loss: 0.3590 - accuracy: 0.8125 - val_loss: 0.3215 - val_accuracy: 0.7797 Epoch 162/1000 2/2 [==============================] - ETA: 0s - loss: 0.3375 - accuracy: 0.8250 Epoch 162: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 1s/step - loss: 0.3375 - accuracy: 0.8250 - val_loss: 0.3184 - val_accuracy: 0.7797 Epoch 163/1000 2/2 [==============================] - ETA: 0s - loss: 0.2919 - accuracy: 0.8672 Epoch 163: saving model to training_1/cp.ckpt 2/2 [==============================] - 2s 1s/step - loss: 0.2919 - accuracy: 0.8672 - val_loss: 0.3172 - val_accuracy: 0.7797 Epoch 164/1000 2/2 [==============================] - ETA: 0s - loss: 0.2972 - accuracy: 0.8594 Epoch 164: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 937ms/step - loss: 0.2972 - accuracy: 0.8594 - val_loss: 0.3171 - val_accuracy: 0.7797 Epoch 165/1000 2/2 [==============================] - ETA: 0s - loss: 0.3267 - accuracy: 0.8359 Epoch 165: saving model to training_1/cp.ckpt 2/2 [==============================] - 2s 1s/step - loss: 0.3267 - accuracy: 0.8359 - val_loss: 0.3175 - val_accuracy: 0.7797 Epoch 166/1000 2/2 [==============================] - ETA: 0s - loss: 0.2999 - accuracy: 0.8438 Epoch 166: saving model to training_1/cp.ckpt 2/2 [==============================] - 2s 1s/step - loss: 0.2999 - accuracy: 0.8438 - val_loss: 0.3182 - val_accuracy: 0.7797 Epoch 167/1000 2/2 [==============================] - ETA: 0s - loss: 0.3014 - accuracy: 0.8750 Epoch 167: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 787ms/step - loss: 0.3014 - accuracy: 0.8750 - val_loss: 0.3198 - val_accuracy: 0.7797 Epoch 168/1000 2/2 [==============================] - ETA: 0s - loss: 0.2670 - accuracy: 0.8250 Epoch 168: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 810ms/step - loss: 0.2670 - accuracy: 0.8250 - val_loss: 0.3217 - val_accuracy: 0.7797 Epoch 169/1000 2/2 [==============================] - ETA: 0s - loss: 0.3162 - accuracy: 0.8750 Epoch 169: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 793ms/step - loss: 0.3162 - accuracy: 0.8750 - val_loss: 0.3219 - val_accuracy: 0.7797 Epoch 170/1000 2/2 [==============================] - ETA: 0s - loss: 0.3178 - accuracy: 0.8047 Epoch 170: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 943ms/step - loss: 0.3178 - accuracy: 0.8047 - val_loss: 0.3221 - val_accuracy: 0.7797 Epoch 171/1000 2/2 [==============================] - ETA: 0s - loss: 0.2931 - accuracy: 0.8672 Epoch 171: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 923ms/step - loss: 0.2931 - accuracy: 0.8672 - val_loss: 0.3225 - val_accuracy: 0.7797 Epoch 172/1000 2/2 [==============================] - ETA: 0s - loss: 0.3197 - accuracy: 0.8047 Epoch 172: saving model to training_1/cp.ckpt 2/2 [==============================] - 2s 1s/step - loss: 0.3197 - accuracy: 0.8047 - val_loss: 0.3238 - val_accuracy: 0.7797 Epoch 173/1000 2/2 [==============================] - ETA: 0s - loss: 0.2872 - accuracy: 0.8281 Epoch 173: saving model to training_1/cp.ckpt 2/2 [==============================] - 2s 1s/step - loss: 0.2872 - accuracy: 0.8281 - val_loss: 0.3255 - val_accuracy: 0.7797 Epoch 174/1000 2/2 [==============================] - ETA: 0s - loss: 0.3595 - accuracy: 0.7734 Epoch 174: saving model to training_1/cp.ckpt 2/2 [==============================] - 2s 1s/step - loss: 0.3595 - accuracy: 0.7734 - val_loss: 0.3273 - val_accuracy: 0.7797 Epoch 175/1000 2/2 [==============================] - ETA: 0s - loss: 0.3140 - accuracy: 0.8375 Epoch 175: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 811ms/step - loss: 0.3140 - accuracy: 0.8375 - val_loss: 0.3280 - val_accuracy: 0.7797 Epoch 176/1000 2/2 [==============================] - ETA: 0s - loss: 0.3210 - accuracy: 0.8125 Epoch 176: saving model to training_1/cp.ckpt 2/2 [==============================] - 2s 1s/step - loss: 0.3210 - accuracy: 0.8125 - val_loss: 0.3281 - val_accuracy: 0.7797 Epoch 177/1000 2/2 [==============================] - ETA: 0s - loss: 0.2593 - accuracy: 0.8125 Epoch 177: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 1s/step - loss: 0.2593 - accuracy: 0.8125 - val_loss: 0.3297 - val_accuracy: 0.7797 Epoch 178/1000 2/2 [==============================] - ETA: 0s - loss: 0.3493 - accuracy: 0.7891 Epoch 178: saving model to training_1/cp.ckpt 2/2 [==============================] - 2s 1s/step - loss: 0.3493 - accuracy: 0.7891 - val_loss: 0.3316 - val_accuracy: 0.7797 Epoch 179/1000 2/2 [==============================] - ETA: 0s - loss: 0.3391 - accuracy: 0.8375 Epoch 179: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 1s/step - loss: 0.3391 - accuracy: 0.8375 - val_loss: 0.3345 - val_accuracy: 0.7797 Epoch 180/1000 2/2 [==============================] - ETA: 0s - loss: 0.2908 - accuracy: 0.8438 Epoch 180: saving model to training_1/cp.ckpt 2/2 [==============================] - 2s 1s/step - loss: 0.2908 - accuracy: 0.8438 - val_loss: 0.3373 - val_accuracy: 0.7797 Epoch 181/1000 2/2 [==============================] - ETA: 0s - loss: 0.2884 - accuracy: 0.8438 Epoch 181: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 912ms/step - loss: 0.2884 - accuracy: 0.8438 - val_loss: 0.3386 - val_accuracy: 0.7797 Epoch 182/1000 2/2 [==============================] - ETA: 0s - loss: 0.2741 - accuracy: 0.8750 Epoch 182: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 1s/step - loss: 0.2741 - accuracy: 0.8750 - val_loss: 0.3397 - val_accuracy: 0.7966 Epoch 183/1000 2/2 [==============================] - ETA: 0s - loss: 0.3079 - accuracy: 0.8375 Epoch 183: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 1s/step - loss: 0.3079 - accuracy: 0.8375 - val_loss: 0.3402 - val_accuracy: 0.7966 Epoch 184/1000 2/2 [==============================] - ETA: 0s - loss: 0.2915 - accuracy: 0.8500 Epoch 184: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 821ms/step - loss: 0.2915 - accuracy: 0.8500 - val_loss: 0.3408 - val_accuracy: 0.8136 Epoch 185/1000 2/2 [==============================] - ETA: 0s - loss: 0.2488 - accuracy: 0.9062 Epoch 185: saving model to training_1/cp.ckpt 2/2 [==============================] - 2s 1s/step - loss: 0.2488 - accuracy: 0.9062 - val_loss: 0.3411 - val_accuracy: 0.8136 Epoch 186/1000 2/2 [==============================] - ETA: 0s - loss: 0.2850 - accuracy: 0.8281 Epoch 186: saving model to training_1/cp.ckpt 2/2 [==============================] - 2s 1s/step - loss: 0.2850 - accuracy: 0.8281 - val_loss: 0.3412 - val_accuracy: 0.8136 Epoch 187/1000 2/2 [==============================] - ETA: 0s - loss: 0.3010 - accuracy: 0.8375 Epoch 187: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 816ms/step - loss: 0.3010 - accuracy: 0.8375 - val_loss: 0.3412 - val_accuracy: 0.7966 Epoch 188/1000 2/2 [==============================] - ETA: 0s - loss: 0.2825 - accuracy: 0.8594 Epoch 188: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 979ms/step - loss: 0.2825 - accuracy: 0.8594 - val_loss: 0.3410 - val_accuracy: 0.7966 Epoch 189/1000 2/2 [==============================] - ETA: 0s - loss: 0.3138 - accuracy: 0.8125 Epoch 189: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 956ms/step - loss: 0.3138 - accuracy: 0.8125 - val_loss: 0.3392 - val_accuracy: 0.7966 Epoch 190/1000 2/2 [==============================] - ETA: 0s - loss: 0.3285 - accuracy: 0.8000 Epoch 190: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 793ms/step - loss: 0.3285 - accuracy: 0.8000 - val_loss: 0.3374 - val_accuracy: 0.8136 Epoch 191/1000 2/2 [==============================] - ETA: 0s - loss: 0.3562 - accuracy: 0.7375 Epoch 191: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 794ms/step - loss: 0.3562 - accuracy: 0.7375 - val_loss: 0.3362 - val_accuracy: 0.8305 Epoch 192/1000 2/2 [==============================] - ETA: 0s - loss: 0.2750 - accuracy: 0.8625 Epoch 192: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 805ms/step - loss: 0.2750 - accuracy: 0.8625 - val_loss: 0.3371 - val_accuracy: 0.8305 Epoch 193/1000 2/2 [==============================] - ETA: 0s - loss: 0.2853 - accuracy: 0.8750 Epoch 193: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 778ms/step - loss: 0.2853 - accuracy: 0.8750 - val_loss: 0.3378 - val_accuracy: 0.8305 Epoch 194/1000 2/2 [==============================] - ETA: 0s - loss: 0.2862 - accuracy: 0.8625 Epoch 194: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 1s/step - loss: 0.2862 - accuracy: 0.8625 - val_loss: 0.3387 - val_accuracy: 0.8136 Epoch 195/1000 2/2 [==============================] - ETA: 0s - loss: 0.3483 - accuracy: 0.7625 Epoch 195: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 1s/step - loss: 0.3483 - accuracy: 0.7625 - val_loss: 0.3393 - val_accuracy: 0.8136 Epoch 196/1000 2/2 [==============================] - ETA: 0s - loss: 0.2863 - accuracy: 0.8594 Epoch 196: saving model to training_1/cp.ckpt 2/2 [==============================] - 2s 1s/step - loss: 0.2863 - accuracy: 0.8594 - val_loss: 0.3378 - val_accuracy: 0.8136 Epoch 197/1000 2/2 [==============================] - ETA: 0s - loss: 0.2744 - accuracy: 0.8500 Epoch 197: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 824ms/step - loss: 0.2744 - accuracy: 0.8500 - val_loss: 0.3355 - val_accuracy: 0.8136 Epoch 198/1000 2/2 [==============================] - ETA: 0s - loss: 0.2827 - accuracy: 0.8438 Epoch 198: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 952ms/step - loss: 0.2827 - accuracy: 0.8438 - val_loss: 0.3326 - val_accuracy: 0.8136 Epoch 199/1000 2/2 [==============================] - ETA: 0s - loss: 0.2542 - accuracy: 0.8875 Epoch 199: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 815ms/step - loss: 0.2542 - accuracy: 0.8875 - val_loss: 0.3295 - val_accuracy: 0.8136 Epoch 200/1000 2/2 [==============================] - ETA: 0s - loss: 0.2779 - accuracy: 0.8672 Epoch 200: saving model to training_1/cp.ckpt 2/2 [==============================] - 2s 1s/step - loss: 0.2779 - accuracy: 0.8672 - val_loss: 0.3259 - val_accuracy: 0.8305 Epoch 201/1000 2/2 [==============================] - ETA: 0s - loss: 0.3151 - accuracy: 0.8516 Epoch 201: saving model to training_1/cp.ckpt 2/2 [==============================] - 2s 1s/step - loss: 0.3151 - accuracy: 0.8516 - val_loss: 0.3212 - val_accuracy: 0.8305 Epoch 202/1000 2/2 [==============================] - ETA: 0s - loss: 0.2635 - accuracy: 0.8438 Epoch 202: saving model to training_1/cp.ckpt 2/2 [==============================] - 2s 1s/step - loss: 0.2635 - accuracy: 0.8438 - val_loss: 0.3172 - val_accuracy: 0.8305 Epoch 203/1000 2/2 [==============================] - ETA: 0s - loss: 0.2691 - accuracy: 0.8906 Epoch 203: saving model to training_1/cp.ckpt 2/2 [==============================] - 2s 1s/step - loss: 0.2691 - accuracy: 0.8906 - val_loss: 0.3138 - val_accuracy: 0.8305 Epoch 204/1000 2/2 [==============================] - ETA: 0s - loss: 0.2818 - accuracy: 0.8500 Epoch 204: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 1s/step - loss: 0.2818 - accuracy: 0.8500 - val_loss: 0.3109 - val_accuracy: 0.8305 Epoch 205/1000 2/2 [==============================] - ETA: 0s - loss: 0.2874 - accuracy: 0.8125 Epoch 205: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 1s/step - loss: 0.2874 - accuracy: 0.8125 - val_loss: 0.3089 - val_accuracy: 0.8136 Epoch 206/1000 2/2 [==============================] - ETA: 0s - loss: 0.2961 - accuracy: 0.8500 Epoch 206: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 821ms/step - loss: 0.2961 - accuracy: 0.8500 - val_loss: 0.3080 - val_accuracy: 0.8136 Epoch 207/1000 2/2 [==============================] - ETA: 0s - loss: 0.2628 - accuracy: 0.8516 Epoch 207: saving model to training_1/cp.ckpt 2/2 [==============================] - 2s 1s/step - loss: 0.2628 - accuracy: 0.8516 - val_loss: 0.3077 - val_accuracy: 0.8136 Epoch 208/1000 2/2 [==============================] - ETA: 0s - loss: 0.2807 - accuracy: 0.8750 Epoch 208: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 792ms/step - loss: 0.2807 - accuracy: 0.8750 - val_loss: 0.3076 - val_accuracy: 0.8136 Epoch 209/1000 2/2 [==============================] - ETA: 0s - loss: 0.2190 - accuracy: 0.8828 Epoch 209: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 902ms/step - loss: 0.2190 - accuracy: 0.8828 - val_loss: 0.3073 - val_accuracy: 0.8136 Epoch 210/1000 2/2 [==============================] - ETA: 0s - loss: 0.2307 - accuracy: 0.8875 Epoch 210: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 1s/step - loss: 0.2307 - accuracy: 0.8875 - val_loss: 0.3073 - val_accuracy: 0.8136 Epoch 211/1000 2/2 [==============================] - ETA: 0s - loss: 0.2403 - accuracy: 0.8672 Epoch 211: saving model to training_1/cp.ckpt 2/2 [==============================] - 2s 1s/step - loss: 0.2403 - accuracy: 0.8672 - val_loss: 0.3079 - val_accuracy: 0.8136 Epoch 212/1000 2/2 [==============================] - ETA: 0s - loss: 0.2151 - accuracy: 0.9375 Epoch 212: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 1s/step - loss: 0.2151 - accuracy: 0.9375 - val_loss: 0.3075 - val_accuracy: 0.8136 Epoch 213/1000 2/2 [==============================] - ETA: 0s - loss: 0.2767 - accuracy: 0.8875 Epoch 213: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 795ms/step - loss: 0.2767 - accuracy: 0.8875 - val_loss: 0.3060 - val_accuracy: 0.8136 Epoch 214/1000 2/2 [==============================] - ETA: 0s - loss: 0.2731 - accuracy: 0.8672 Epoch 214: saving model to training_1/cp.ckpt 2/2 [==============================] - 2s 1s/step - loss: 0.2731 - accuracy: 0.8672 - val_loss: 0.3040 - val_accuracy: 0.8136 Epoch 215/1000 2/2 [==============================] - ETA: 0s - loss: 0.2449 - accuracy: 0.8828 Epoch 215: saving model to training_1/cp.ckpt 2/2 [==============================] - 2s 1s/step - loss: 0.2449 - accuracy: 0.8828 - val_loss: 0.3022 - val_accuracy: 0.8136 Epoch 216/1000 2/2 [==============================] - ETA: 0s - loss: 0.2654 - accuracy: 0.8203 Epoch 216: saving model to training_1/cp.ckpt 2/2 [==============================] - 2s 1s/step - loss: 0.2654 - accuracy: 0.8203 - val_loss: 0.2999 - val_accuracy: 0.8136 Epoch 217/1000 2/2 [==============================] - ETA: 0s - loss: 0.2781 - accuracy: 0.8672 Epoch 217: saving model to training_1/cp.ckpt 2/2 [==============================] - 2s 1s/step - loss: 0.2781 - accuracy: 0.8672 - val_loss: 0.2985 - val_accuracy: 0.8136 Epoch 218/1000 2/2 [==============================] - ETA: 0s - loss: 0.3467 - accuracy: 0.7875 Epoch 218: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 808ms/step - loss: 0.3467 - accuracy: 0.7875 - val_loss: 0.2967 - val_accuracy: 0.8136 Epoch 219/1000 2/2 [==============================] - ETA: 0s - loss: 0.2858 - accuracy: 0.8750 Epoch 219: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 1s/step - loss: 0.2858 - accuracy: 0.8750 - val_loss: 0.2970 - val_accuracy: 0.8136 Epoch 220/1000 2/2 [==============================] - ETA: 0s - loss: 0.2070 - accuracy: 0.9125 Epoch 220: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 1s/step - loss: 0.2070 - accuracy: 0.9125 - val_loss: 0.2983 - val_accuracy: 0.8136 Epoch 221/1000 2/2 [==============================] - ETA: 0s - loss: 0.2974 - accuracy: 0.8359 Epoch 221: saving model to training_1/cp.ckpt 2/2 [==============================] - 2s 1s/step - loss: 0.2974 - accuracy: 0.8359 - val_loss: 0.2998 - val_accuracy: 0.8136 Epoch 222/1000 2/2 [==============================] - ETA: 0s - loss: 0.2884 - accuracy: 0.8625 Epoch 222: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 806ms/step - loss: 0.2884 - accuracy: 0.8625 - val_loss: 0.3019 - val_accuracy: 0.8136 Epoch 223/1000 2/2 [==============================] - ETA: 0s - loss: 0.2783 - accuracy: 0.8438 Epoch 223: saving model to training_1/cp.ckpt 2/2 [==============================] - 2s 1s/step - loss: 0.2783 - accuracy: 0.8438 - val_loss: 0.3043 - val_accuracy: 0.8136 Epoch 224/1000 2/2 [==============================] - ETA: 0s - loss: 0.2062 - accuracy: 0.8875 Epoch 224: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 1s/step - loss: 0.2062 - accuracy: 0.8875 - val_loss: 0.3075 - val_accuracy: 0.8136 Epoch 225/1000 2/2 [==============================] - ETA: 0s - loss: 0.2499 - accuracy: 0.8500 Epoch 225: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 1s/step - loss: 0.2499 - accuracy: 0.8500 - val_loss: 0.3094 - val_accuracy: 0.8136 Epoch 226/1000 2/2 [==============================] - ETA: 0s - loss: 0.2541 - accuracy: 0.8672 Epoch 226: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 957ms/step - loss: 0.2541 - accuracy: 0.8672 - val_loss: 0.3105 - val_accuracy: 0.8136 Epoch 227/1000 2/2 [==============================] - ETA: 0s - loss: 0.2353 - accuracy: 0.8672 Epoch 227: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 903ms/step - loss: 0.2353 - accuracy: 0.8672 - val_loss: 0.3106 - val_accuracy: 0.8305 Epoch 228/1000 2/2 [==============================] - ETA: 0s - loss: 0.2782 - accuracy: 0.8375 Epoch 228: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 792ms/step - loss: 0.2782 - accuracy: 0.8375 - val_loss: 0.3112 - val_accuracy: 0.8305 Epoch 229/1000 2/2 [==============================] - ETA: 0s - loss: 0.2693 - accuracy: 0.8875 Epoch 229: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 795ms/step - loss: 0.2693 - accuracy: 0.8875 - val_loss: 0.3124 - val_accuracy: 0.8305 Epoch 230/1000 2/2 [==============================] - ETA: 0s - loss: 0.2889 - accuracy: 0.8281 Epoch 230: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 943ms/step - loss: 0.2889 - accuracy: 0.8281 - val_loss: 0.3135 - val_accuracy: 0.8305 Epoch 231/1000 2/2 [==============================] - ETA: 0s - loss: 0.2589 - accuracy: 0.8984 Epoch 231: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 907ms/step - loss: 0.2589 - accuracy: 0.8984 - val_loss: 0.3135 - val_accuracy: 0.8305 Epoch 232/1000 2/2 [==============================] - ETA: 0s - loss: 0.2456 - accuracy: 0.8984 Epoch 232: saving model to training_1/cp.ckpt 2/2 [==============================] - 2s 1s/step - loss: 0.2456 - accuracy: 0.8984 - val_loss: 0.3123 - val_accuracy: 0.8305 Epoch 233/1000 2/2 [==============================] - ETA: 0s - loss: 0.2860 - accuracy: 0.8281 Epoch 233: saving model to training_1/cp.ckpt 2/2 [==============================] - 2s 1s/step - loss: 0.2860 - accuracy: 0.8281 - val_loss: 0.3108 - val_accuracy: 0.8305 Epoch 234/1000 2/2 [==============================] - ETA: 0s - loss: 0.2758 - accuracy: 0.8438 Epoch 234: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 910ms/step - loss: 0.2758 - accuracy: 0.8438 - val_loss: 0.3082 - val_accuracy: 0.8305 Epoch 235/1000 2/2 [==============================] - ETA: 0s - loss: 0.2963 - accuracy: 0.8438 Epoch 235: saving model to training_1/cp.ckpt 2/2 [==============================] - 2s 1s/step - loss: 0.2963 - accuracy: 0.8438 - val_loss: 0.3071 - val_accuracy: 0.8136 Epoch 236/1000 2/2 [==============================] - ETA: 0s - loss: 0.2494 - accuracy: 0.8906 Epoch 236: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 946ms/step - loss: 0.2494 - accuracy: 0.8906 - val_loss: 0.3057 - val_accuracy: 0.8136 Epoch 237/1000 2/2 [==============================] - ETA: 0s - loss: 0.2573 - accuracy: 0.9062 Epoch 237: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 917ms/step - loss: 0.2573 - accuracy: 0.9062 - val_loss: 0.3048 - val_accuracy: 0.8136 Epoch 238/1000 2/2 [==============================] - ETA: 0s - loss: 0.2491 - accuracy: 0.8828 Epoch 238: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 921ms/step - loss: 0.2491 - accuracy: 0.8828 - val_loss: 0.3050 - val_accuracy: 0.8136 Epoch 239/1000 2/2 [==============================] - ETA: 0s - loss: 0.2366 - accuracy: 0.9000 Epoch 239: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 1s/step - loss: 0.2366 - accuracy: 0.9000 - val_loss: 0.3059 - val_accuracy: 0.8305 Epoch 240/1000 2/2 [==============================] - ETA: 0s - loss: 0.2333 - accuracy: 0.9062 Epoch 240: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 945ms/step - loss: 0.2333 - accuracy: 0.9062 - val_loss: 0.3063 - val_accuracy: 0.8475 Epoch 241/1000 2/2 [==============================] - ETA: 0s - loss: 0.2809 - accuracy: 0.8672 Epoch 241: saving model to training_1/cp.ckpt 2/2 [==============================] - 2s 1s/step - loss: 0.2809 - accuracy: 0.8672 - val_loss: 0.3059 - val_accuracy: 0.8305 Epoch 242/1000 2/2 [==============================] - ETA: 0s - loss: 0.2800 - accuracy: 0.8750 Epoch 242: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 1s/step - loss: 0.2800 - accuracy: 0.8750 - val_loss: 0.3063 - val_accuracy: 0.8475 Epoch 243/1000 2/2 [==============================] - ETA: 0s - loss: 0.2448 - accuracy: 0.9000 Epoch 243: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 1s/step - loss: 0.2448 - accuracy: 0.9000 - val_loss: 0.3057 - val_accuracy: 0.8305 Epoch 244/1000 2/2 [==============================] - ETA: 0s - loss: 0.2235 - accuracy: 0.9000 Epoch 244: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 794ms/step - loss: 0.2235 - accuracy: 0.9000 - val_loss: 0.3050 - val_accuracy: 0.8136 Epoch 245/1000 2/2 [==============================] - ETA: 0s - loss: 0.2548 - accuracy: 0.8625 Epoch 245: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 1s/step - loss: 0.2548 - accuracy: 0.8625 - val_loss: 0.3034 - val_accuracy: 0.8136 Epoch 246/1000 2/2 [==============================] - ETA: 0s - loss: 0.2482 - accuracy: 0.8672 Epoch 246: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 946ms/step - loss: 0.2482 - accuracy: 0.8672 - val_loss: 0.3021 - val_accuracy: 0.8136 Epoch 247/1000 2/2 [==============================] - ETA: 0s - loss: 0.2149 - accuracy: 0.9062 Epoch 247: saving model to training_1/cp.ckpt 2/2 [==============================] - 2s 1s/step - loss: 0.2149 - accuracy: 0.9062 - val_loss: 0.3014 - val_accuracy: 0.8136 Epoch 248/1000 2/2 [==============================] - ETA: 0s - loss: 0.2617 - accuracy: 0.8594 Epoch 248: saving model to training_1/cp.ckpt 2/2 [==============================] - 2s 1s/step - loss: 0.2617 - accuracy: 0.8594 - val_loss: 0.3010 - val_accuracy: 0.8136 Epoch 249/1000 2/2 [==============================] - ETA: 0s - loss: 0.2135 - accuracy: 0.9219 Epoch 249: saving model to training_1/cp.ckpt 2/2 [==============================] - 2s 1s/step - loss: 0.2135 - accuracy: 0.9219 - val_loss: 0.3009 - val_accuracy: 0.8136 Epoch 250/1000 2/2 [==============================] - ETA: 0s - loss: 0.2178 - accuracy: 0.9297 Epoch 250: saving model to training_1/cp.ckpt 2/2 [==============================] - 2s 1s/step - loss: 0.2178 - accuracy: 0.9297 - val_loss: 0.3010 - val_accuracy: 0.8136 Epoch 251/1000 2/2 [==============================] - ETA: 0s - loss: 0.2670 - accuracy: 0.8750 Epoch 251: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 1s/step - loss: 0.2670 - accuracy: 0.8750 - val_loss: 0.3018 - val_accuracy: 0.8136 Epoch 252/1000 2/2 [==============================] - ETA: 0s - loss: 0.2248 - accuracy: 0.8750 Epoch 252: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 818ms/step - loss: 0.2248 - accuracy: 0.8750 - val_loss: 0.3011 - val_accuracy: 0.8136 Epoch 253/1000 2/2 [==============================] - ETA: 0s - loss: 0.2740 - accuracy: 0.8828 Epoch 253: saving model to training_1/cp.ckpt 2/2 [==============================] - 2s 1s/step - loss: 0.2740 - accuracy: 0.8828 - val_loss: 0.2994 - val_accuracy: 0.8136 Epoch 254/1000 2/2 [==============================] - ETA: 0s - loss: 0.2816 - accuracy: 0.8250 Epoch 254: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 803ms/step - loss: 0.2816 - accuracy: 0.8250 - val_loss: 0.2979 - val_accuracy: 0.8136 Epoch 255/1000 2/2 [==============================] - ETA: 0s - loss: 0.2820 - accuracy: 0.8359 Epoch 255: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 947ms/step - loss: 0.2820 - accuracy: 0.8359 - val_loss: 0.2963 - val_accuracy: 0.8136 Epoch 256/1000 2/2 [==============================] - ETA: 0s - loss: 0.2573 - accuracy: 0.8594 Epoch 256: saving model to training_1/cp.ckpt 2/2 [==============================] - 2s 1s/step - loss: 0.2573 - accuracy: 0.8594 - val_loss: 0.2953 - val_accuracy: 0.8136 Epoch 257/1000 2/2 [==============================] - ETA: 0s - loss: 0.2565 - accuracy: 0.8594 Epoch 257: saving model to training_1/cp.ckpt 2/2 [==============================] - 2s 1s/step - loss: 0.2565 - accuracy: 0.8594 - val_loss: 0.2960 - val_accuracy: 0.8136 Epoch 258/1000 2/2 [==============================] - ETA: 0s - loss: 0.2307 - accuracy: 0.8984 Epoch 258: saving model to training_1/cp.ckpt 2/2 [==============================] - 2s 1s/step - loss: 0.2307 - accuracy: 0.8984 - val_loss: 0.2969 - val_accuracy: 0.8136 Epoch 259/1000 2/2 [==============================] - ETA: 0s - loss: 0.2131 - accuracy: 0.8906 Epoch 259: saving model to training_1/cp.ckpt 2/2 [==============================] - 2s 1s/step - loss: 0.2131 - accuracy: 0.8906 - val_loss: 0.2983 - val_accuracy: 0.8136 Epoch 260/1000 2/2 [==============================] - ETA: 0s - loss: 0.2280 - accuracy: 0.8906 Epoch 260: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 902ms/step - loss: 0.2280 - accuracy: 0.8906 - val_loss: 0.2995 - val_accuracy: 0.8136 Epoch 261/1000 2/2 [==============================] - ETA: 0s - loss: 0.2603 - accuracy: 0.8828 Epoch 261: saving model to training_1/cp.ckpt 2/2 [==============================] - 2s 1s/step - loss: 0.2603 - accuracy: 0.8828 - val_loss: 0.3003 - val_accuracy: 0.8136 Epoch 262/1000 2/2 [==============================] - ETA: 0s - loss: 0.2892 - accuracy: 0.8375 Epoch 262: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 1s/step - loss: 0.2892 - accuracy: 0.8375 - val_loss: 0.3015 - val_accuracy: 0.8136 Epoch 263/1000 2/2 [==============================] - ETA: 0s - loss: 0.2298 - accuracy: 0.8875 Epoch 263: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 1s/step - loss: 0.2298 - accuracy: 0.8875 - val_loss: 0.3009 - val_accuracy: 0.8136 Epoch 264/1000 2/2 [==============================] - ETA: 0s - loss: 0.2543 - accuracy: 0.9062 Epoch 264: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 958ms/step - loss: 0.2543 - accuracy: 0.9062 - val_loss: 0.3001 - val_accuracy: 0.8136 Epoch 265/1000 2/2 [==============================] - ETA: 0s - loss: 0.2106 - accuracy: 0.9375 Epoch 265: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 814ms/step - loss: 0.2106 - accuracy: 0.9375 - val_loss: 0.2987 - val_accuracy: 0.8136 Epoch 266/1000 2/2 [==============================] - ETA: 0s - loss: 0.2526 - accuracy: 0.8828 Epoch 266: saving model to training_1/cp.ckpt 2/2 [==============================] - 2s 1s/step - loss: 0.2526 - accuracy: 0.8828 - val_loss: 0.2968 - val_accuracy: 0.8136 Epoch 267/1000 2/2 [==============================] - ETA: 0s - loss: 0.2803 - accuracy: 0.8500 Epoch 267: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 853ms/step - loss: 0.2803 - accuracy: 0.8500 - val_loss: 0.2950 - val_accuracy: 0.8136 Epoch 268/1000 2/2 [==============================] - ETA: 0s - loss: 0.2660 - accuracy: 0.8750 Epoch 268: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 806ms/step - loss: 0.2660 - accuracy: 0.8750 - val_loss: 0.2931 - val_accuracy: 0.8136 Epoch 269/1000 2/2 [==============================] - ETA: 0s - loss: 0.2276 - accuracy: 0.8828 Epoch 269: saving model to training_1/cp.ckpt 2/2 [==============================] - 2s 1s/step - loss: 0.2276 - accuracy: 0.8828 - val_loss: 0.2913 - val_accuracy: 0.8136 Epoch 270/1000 2/2 [==============================] - ETA: 0s - loss: 0.2157 - accuracy: 0.9125 Epoch 270: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 860ms/step - loss: 0.2157 - accuracy: 0.9125 - val_loss: 0.2903 - val_accuracy: 0.8136 Epoch 271/1000 2/2 [==============================] - ETA: 0s - loss: 0.1974 - accuracy: 0.9375 Epoch 271: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 898ms/step - loss: 0.1974 - accuracy: 0.9375 - val_loss: 0.2898 - val_accuracy: 0.8136 Epoch 272/1000 2/2 [==============================] - ETA: 0s - loss: 0.2401 - accuracy: 0.8750 Epoch 272: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 943ms/step - loss: 0.2401 - accuracy: 0.8750 - val_loss: 0.2889 - val_accuracy: 0.8136 Epoch 273/1000 2/2 [==============================] - ETA: 0s - loss: 0.2718 - accuracy: 0.8375 Epoch 273: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 1s/step - loss: 0.2718 - accuracy: 0.8375 - val_loss: 0.2886 - val_accuracy: 0.8136 Epoch 274/1000 2/2 [==============================] - ETA: 0s - loss: 0.2322 - accuracy: 0.8984 Epoch 274: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 930ms/step - loss: 0.2322 - accuracy: 0.8984 - val_loss: 0.2888 - val_accuracy: 0.8136 Epoch 275/1000 2/2 [==============================] - ETA: 0s - loss: 0.2986 - accuracy: 0.8438 Epoch 275: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 957ms/step - loss: 0.2986 - accuracy: 0.8438 - val_loss: 0.2887 - val_accuracy: 0.8136 Epoch 276/1000 2/2 [==============================] - ETA: 0s - loss: 0.2662 - accuracy: 0.8438 Epoch 276: saving model to training_1/cp.ckpt 2/2 [==============================] - 2s 1s/step - loss: 0.2662 - accuracy: 0.8438 - val_loss: 0.2889 - val_accuracy: 0.8136 Epoch 277/1000 2/2 [==============================] - ETA: 0s - loss: 0.2386 - accuracy: 0.8984 Epoch 277: saving model to training_1/cp.ckpt 2/2 [==============================] - 2s 1s/step - loss: 0.2386 - accuracy: 0.8984 - val_loss: 0.2899 - val_accuracy: 0.8136 Epoch 278/1000 2/2 [==============================] - ETA: 0s - loss: 0.2327 - accuracy: 0.9250 Epoch 278: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 1s/step - loss: 0.2327 - accuracy: 0.9250 - val_loss: 0.2929 - val_accuracy: 0.8136 Epoch 279/1000 2/2 [==============================] - ETA: 0s - loss: 0.2378 - accuracy: 0.8984 Epoch 279: saving model to training_1/cp.ckpt 2/2 [==============================] - 2s 1s/step - loss: 0.2378 - accuracy: 0.8984 - val_loss: 0.2975 - val_accuracy: 0.8136 Epoch 280/1000 2/2 [==============================] - ETA: 0s - loss: 0.2511 - accuracy: 0.8594 Epoch 280: saving model to training_1/cp.ckpt 2/2 [==============================] - 2s 1s/step - loss: 0.2511 - accuracy: 0.8594 - val_loss: 0.3020 - val_accuracy: 0.8136 Epoch 281/1000 2/2 [==============================] - ETA: 0s - loss: 0.2288 - accuracy: 0.8984 Epoch 281: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 916ms/step - loss: 0.2288 - accuracy: 0.8984 - val_loss: 0.3068 - val_accuracy: 0.8136 Epoch 282/1000 2/2 [==============================] - ETA: 0s - loss: 0.2698 - accuracy: 0.8359 Epoch 282: saving model to training_1/cp.ckpt 2/2 [==============================] - 2s 1s/step - loss: 0.2698 - accuracy: 0.8359 - val_loss: 0.3105 - val_accuracy: 0.8136 Epoch 283/1000 2/2 [==============================] - ETA: 0s - loss: 0.2154 - accuracy: 0.9141 Epoch 283: saving model to training_1/cp.ckpt 2/2 [==============================] - 2s 1s/step - loss: 0.2154 - accuracy: 0.9141 - val_loss: 0.3148 - val_accuracy: 0.7966 Epoch 284/1000 2/2 [==============================] - ETA: 0s - loss: 0.2556 - accuracy: 0.8500 Epoch 284: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 842ms/step - loss: 0.2556 - accuracy: 0.8500 - val_loss: 0.3190 - val_accuracy: 0.7627 Epoch 285/1000 2/2 [==============================] - ETA: 0s - loss: 0.2494 - accuracy: 0.8625 Epoch 285: saving model to training_1/cp.ckpt 2/2 [==============================] - 2s 2s/step - loss: 0.2494 - accuracy: 0.8625 - val_loss: 0.3235 - val_accuracy: 0.7458 Epoch 286/1000 2/2 [==============================] - ETA: 0s - loss: 0.2026 - accuracy: 0.8875 Epoch 286: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 1s/step - loss: 0.2026 - accuracy: 0.8875 - val_loss: 0.3262 - val_accuracy: 0.7627 Epoch 287/1000 2/2 [==============================] - ETA: 0s - loss: 0.2219 - accuracy: 0.8750 Epoch 287: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 1s/step - loss: 0.2219 - accuracy: 0.8750 - val_loss: 0.3293 - val_accuracy: 0.7627 Epoch 288/1000 2/2 [==============================] - ETA: 0s - loss: 0.2030 - accuracy: 0.9141 Epoch 288: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 909ms/step - loss: 0.2030 - accuracy: 0.9141 - val_loss: 0.3301 - val_accuracy: 0.7627 Epoch 289/1000 2/2 [==============================] - ETA: 0s - loss: 0.2287 - accuracy: 0.8906 Epoch 289: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 914ms/step - loss: 0.2287 - accuracy: 0.8906 - val_loss: 0.3300 - val_accuracy: 0.7627 Epoch 290/1000 2/2 [==============================] - ETA: 0s - loss: 0.2328 - accuracy: 0.8750 Epoch 290: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 950ms/step - loss: 0.2328 - accuracy: 0.8750 - val_loss: 0.3270 - val_accuracy: 0.7797 Epoch 291/1000 2/2 [==============================] - ETA: 0s - loss: 0.2071 - accuracy: 0.9141 Epoch 291: saving model to training_1/cp.ckpt 2/2 [==============================] - 2s 1s/step - loss: 0.2071 - accuracy: 0.9141 - val_loss: 0.3240 - val_accuracy: 0.7797 Epoch 292/1000 2/2 [==============================] - ETA: 0s - loss: 0.2068 - accuracy: 0.9000 Epoch 292: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 1s/step - loss: 0.2068 - accuracy: 0.9000 - val_loss: 0.3218 - val_accuracy: 0.7797 Epoch 293/1000 2/2 [==============================] - ETA: 0s - loss: 0.1890 - accuracy: 0.9250 Epoch 293: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 804ms/step - loss: 0.1890 - accuracy: 0.9250 - val_loss: 0.3199 - val_accuracy: 0.7797 Epoch 294/1000 2/2 [==============================] - ETA: 0s - loss: 0.2426 - accuracy: 0.8875 Epoch 294: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 790ms/step - loss: 0.2426 - accuracy: 0.8875 - val_loss: 0.3161 - val_accuracy: 0.8136 Epoch 295/1000 2/2 [==============================] - ETA: 0s - loss: 0.2291 - accuracy: 0.9125 Epoch 295: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 1s/step - loss: 0.2291 - accuracy: 0.9125 - val_loss: 0.3102 - val_accuracy: 0.8475 Epoch 296/1000 2/2 [==============================] - ETA: 0s - loss: 0.2617 - accuracy: 0.8500 Epoch 296: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 824ms/step - loss: 0.2617 - accuracy: 0.8500 - val_loss: 0.3041 - val_accuracy: 0.8305 Epoch 297/1000 2/2 [==============================] - ETA: 0s - loss: 0.1950 - accuracy: 0.9500 Epoch 297: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 818ms/step - loss: 0.1950 - accuracy: 0.9500 - val_loss: 0.2988 - val_accuracy: 0.8305 Epoch 298/1000 2/2 [==============================] - ETA: 0s - loss: 0.2231 - accuracy: 0.9141 Epoch 298: saving model to training_1/cp.ckpt 2/2 [==============================] - 2s 1s/step - loss: 0.2231 - accuracy: 0.9141 - val_loss: 0.2959 - val_accuracy: 0.8305 Epoch 299/1000 2/2 [==============================] - ETA: 0s - loss: 0.1917 - accuracy: 0.9000 Epoch 299: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 1s/step - loss: 0.1917 - accuracy: 0.9000 - val_loss: 0.2945 - val_accuracy: 0.8305 Epoch 300/1000 2/2 [==============================] - ETA: 0s - loss: 0.2121 - accuracy: 0.9000 Epoch 300: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 794ms/step - loss: 0.2121 - accuracy: 0.9000 - val_loss: 0.2938 - val_accuracy: 0.8305 Epoch 301/1000 2/2 [==============================] - ETA: 0s - loss: 0.2052 - accuracy: 0.8828 Epoch 301: saving model to training_1/cp.ckpt 2/2 [==============================] - 2s 1s/step - loss: 0.2052 - accuracy: 0.8828 - val_loss: 0.2929 - val_accuracy: 0.8305 Epoch 302/1000 2/2 [==============================] - ETA: 0s - loss: 0.1914 - accuracy: 0.9375 Epoch 302: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 795ms/step - loss: 0.1914 - accuracy: 0.9375 - val_loss: 0.2915 - val_accuracy: 0.8305 Epoch 303/1000 2/2 [==============================] - ETA: 0s - loss: 0.2616 - accuracy: 0.8250 Epoch 303: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 800ms/step - loss: 0.2616 - accuracy: 0.8250 - val_loss: 0.2906 - val_accuracy: 0.8305 Epoch 304/1000 2/2 [==============================] - ETA: 0s - loss: 0.2484 - accuracy: 0.8750 Epoch 304: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 1s/step - loss: 0.2484 - accuracy: 0.8750 - val_loss: 0.2926 - val_accuracy: 0.8305 Epoch 305/1000 2/2 [==============================] - ETA: 0s - loss: 0.2136 - accuracy: 0.9062 Epoch 305: saving model to training_1/cp.ckpt 2/2 [==============================] - 2s 1s/step - loss: 0.2136 - accuracy: 0.9062 - val_loss: 0.2943 - val_accuracy: 0.8305 Epoch 306/1000 2/2 [==============================] - ETA: 0s - loss: 0.2577 - accuracy: 0.8750 Epoch 306: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 792ms/step - loss: 0.2577 - accuracy: 0.8750 - val_loss: 0.2947 - val_accuracy: 0.8305 Epoch 307/1000 2/2 [==============================] - ETA: 0s - loss: 0.2036 - accuracy: 0.9297 Epoch 307: saving model to training_1/cp.ckpt 2/2 [==============================] - 2s 1s/step - loss: 0.2036 - accuracy: 0.9297 - val_loss: 0.2952 - val_accuracy: 0.8305 Epoch 308/1000 2/2 [==============================] - ETA: 0s - loss: 0.2358 - accuracy: 0.8594 Epoch 308: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 906ms/step - loss: 0.2358 - accuracy: 0.8594 - val_loss: 0.2963 - val_accuracy: 0.8305 Epoch 309/1000 2/2 [==============================] - ETA: 0s - loss: 0.2349 - accuracy: 0.9062 Epoch 309: saving model to training_1/cp.ckpt 2/2 [==============================] - 2s 1s/step - loss: 0.2349 - accuracy: 0.9062 - val_loss: 0.2975 - val_accuracy: 0.8305 Epoch 310/1000 2/2 [==============================] - ETA: 0s - loss: 0.2118 - accuracy: 0.8625 Epoch 310: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 808ms/step - loss: 0.2118 - accuracy: 0.8625 - val_loss: 0.2989 - val_accuracy: 0.8305 Epoch 311/1000 2/2 [==============================] - ETA: 0s - loss: 0.1725 - accuracy: 0.9000 Epoch 311: saving model to training_1/cp.ckpt 2/2 [==============================] - 2s 1s/step - loss: 0.1725 - accuracy: 0.9000 - val_loss: 0.2993 - val_accuracy: 0.8305 Epoch 312/1000 2/2 [==============================] - ETA: 0s - loss: 0.2201 - accuracy: 0.9125 Epoch 312: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 1s/step - loss: 0.2201 - accuracy: 0.9125 - val_loss: 0.3002 - val_accuracy: 0.8305 Epoch 313/1000 2/2 [==============================] - ETA: 0s - loss: 0.2136 - accuracy: 0.8750 Epoch 313: saving model to training_1/cp.ckpt 2/2 [==============================] - 2s 1s/step - loss: 0.2136 - accuracy: 0.8750 - val_loss: 0.3005 - val_accuracy: 0.8305 Epoch 314/1000 2/2 [==============================] - ETA: 0s - loss: 0.2057 - accuracy: 0.8906 Epoch 314: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 934ms/step - loss: 0.2057 - accuracy: 0.8906 - val_loss: 0.3016 - val_accuracy: 0.8305 Epoch 315/1000 2/2 [==============================] - ETA: 0s - loss: 0.2134 - accuracy: 0.8984 Epoch 315: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 968ms/step - loss: 0.2134 - accuracy: 0.8984 - val_loss: 0.3029 - val_accuracy: 0.8305 Epoch 316/1000 2/2 [==============================] - ETA: 0s - loss: 0.2028 - accuracy: 0.9375 Epoch 316: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 1s/step - loss: 0.2028 - accuracy: 0.9375 - val_loss: 0.3031 - val_accuracy: 0.8305 Epoch 317/1000 2/2 [==============================] - ETA: 0s - loss: 0.2105 - accuracy: 0.8750 Epoch 317: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 1s/step - loss: 0.2105 - accuracy: 0.8750 - val_loss: 0.3014 - val_accuracy: 0.8305 Epoch 318/1000 2/2 [==============================] - ETA: 0s - loss: 0.2106 - accuracy: 0.8984 Epoch 318: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 918ms/step - loss: 0.2106 - accuracy: 0.8984 - val_loss: 0.3000 - val_accuracy: 0.8305 Epoch 319/1000 2/2 [==============================] - ETA: 0s - loss: 0.1630 - accuracy: 0.9750 Epoch 319: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 796ms/step - loss: 0.1630 - accuracy: 0.9750 - val_loss: 0.3004 - val_accuracy: 0.8305 Epoch 320/1000 2/2 [==============================] - ETA: 0s - loss: 0.1539 - accuracy: 0.9500 Epoch 320: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 810ms/step - loss: 0.1539 - accuracy: 0.9500 - val_loss: 0.3006 - val_accuracy: 0.8305 Epoch 321/1000 2/2 [==============================] - ETA: 0s - loss: 0.2218 - accuracy: 0.8594 Epoch 321: saving model to training_1/cp.ckpt 2/2 [==============================] - 2s 1s/step - loss: 0.2218 - accuracy: 0.8594 - val_loss: 0.3013 - val_accuracy: 0.8305 Epoch 322/1000 2/2 [==============================] - ETA: 0s - loss: 0.2165 - accuracy: 0.9062 Epoch 322: saving model to training_1/cp.ckpt 2/2 [==============================] - 2s 1s/step - loss: 0.2165 - accuracy: 0.9062 - val_loss: 0.3022 - val_accuracy: 0.8305 Epoch 323/1000 2/2 [==============================] - ETA: 0s - loss: 0.1919 - accuracy: 0.9000 Epoch 323: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 1s/step - loss: 0.1919 - accuracy: 0.9000 - val_loss: 0.3030 - val_accuracy: 0.8305 Epoch 324/1000 2/2 [==============================] - ETA: 0s - loss: 0.1958 - accuracy: 0.9000 Epoch 324: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 850ms/step - loss: 0.1958 - accuracy: 0.9000 - val_loss: 0.3028 - val_accuracy: 0.8305 Epoch 325/1000 2/2 [==============================] - ETA: 0s - loss: 0.1868 - accuracy: 0.9000 Epoch 325: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 814ms/step - loss: 0.1868 - accuracy: 0.9000 - val_loss: 0.3007 - val_accuracy: 0.8305 Epoch 326/1000 2/2 [==============================] - ETA: 0s - loss: 0.2316 - accuracy: 0.9062 Epoch 326: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 941ms/step - loss: 0.2316 - accuracy: 0.9062 - val_loss: 0.2972 - val_accuracy: 0.8305 Epoch 327/1000 2/2 [==============================] - ETA: 0s - loss: 0.2059 - accuracy: 0.8875 Epoch 327: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 1s/step - loss: 0.2059 - accuracy: 0.8875 - val_loss: 0.2908 - val_accuracy: 0.8305 Epoch 328/1000 2/2 [==============================] - ETA: 0s - loss: 0.1977 - accuracy: 0.8906 Epoch 328: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 969ms/step - loss: 0.1977 - accuracy: 0.8906 - val_loss: 0.2869 - val_accuracy: 0.8305 Epoch 329/1000 2/2 [==============================] - ETA: 0s - loss: 0.2260 - accuracy: 0.8984 Epoch 329: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 992ms/step - loss: 0.2260 - accuracy: 0.8984 - val_loss: 0.2843 - val_accuracy: 0.8305 Epoch 330/1000 2/2 [==============================] - ETA: 0s - loss: 0.2437 - accuracy: 0.8625 Epoch 330: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 1s/step - loss: 0.2437 - accuracy: 0.8625 - val_loss: 0.2842 - val_accuracy: 0.8305 Epoch 331/1000 2/2 [==============================] - ETA: 0s - loss: 0.2069 - accuracy: 0.8984 Epoch 331: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 935ms/step - loss: 0.2069 - accuracy: 0.8984 - val_loss: 0.2851 - val_accuracy: 0.8305 Epoch 332/1000 2/2 [==============================] - ETA: 0s - loss: 0.1874 - accuracy: 0.9000 Epoch 332: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 869ms/step - loss: 0.1874 - accuracy: 0.9000 - val_loss: 0.2855 - val_accuracy: 0.8305 Epoch 333/1000 2/2 [==============================] - ETA: 0s - loss: 0.1848 - accuracy: 0.9125 Epoch 333: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 787ms/step - loss: 0.1848 - accuracy: 0.9125 - val_loss: 0.2884 - val_accuracy: 0.8305 Epoch 334/1000 2/2 [==============================] - ETA: 0s - loss: 0.2140 - accuracy: 0.8984 Epoch 334: saving model to training_1/cp.ckpt 2/2 [==============================] - 2s 1s/step - loss: 0.2140 - accuracy: 0.8984 - val_loss: 0.2922 - val_accuracy: 0.8305 Epoch 335/1000 2/2 [==============================] - ETA: 0s - loss: 0.2155 - accuracy: 0.8594 Epoch 335: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 998ms/step - loss: 0.2155 - accuracy: 0.8594 - val_loss: 0.2948 - val_accuracy: 0.8305 Epoch 336/1000 2/2 [==============================] - ETA: 0s - loss: 0.2458 - accuracy: 0.8625 Epoch 336: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 826ms/step - loss: 0.2458 - accuracy: 0.8625 - val_loss: 0.2973 - val_accuracy: 0.8305 Epoch 337/1000 2/2 [==============================] - ETA: 0s - loss: 0.1843 - accuracy: 0.9125 Epoch 337: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 812ms/step - loss: 0.1843 - accuracy: 0.9125 - val_loss: 0.3001 - val_accuracy: 0.8136 Epoch 338/1000 2/2 [==============================] - ETA: 0s - loss: 0.2171 - accuracy: 0.9000 Epoch 338: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 847ms/step - loss: 0.2171 - accuracy: 0.9000 - val_loss: 0.3006 - val_accuracy: 0.8136 Epoch 339/1000 2/2 [==============================] - ETA: 0s - loss: 0.2334 - accuracy: 0.8500 Epoch 339: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 1s/step - loss: 0.2334 - accuracy: 0.8500 - val_loss: 0.3007 - val_accuracy: 0.8136 Epoch 340/1000 2/2 [==============================] - ETA: 0s - loss: 0.1649 - accuracy: 0.9531 Epoch 340: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 921ms/step - loss: 0.1649 - accuracy: 0.9531 - val_loss: 0.3008 - val_accuracy: 0.8136 Epoch 341/1000 2/2 [==============================] - ETA: 0s - loss: 0.1953 - accuracy: 0.8984 Epoch 341: saving model to training_1/cp.ckpt 2/2 [==============================] - 2s 1s/step - loss: 0.1953 - accuracy: 0.8984 - val_loss: 0.3000 - val_accuracy: 0.8136 Epoch 342/1000 2/2 [==============================] - ETA: 0s - loss: 0.1953 - accuracy: 0.8875 Epoch 342: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 820ms/step - loss: 0.1953 - accuracy: 0.8875 - val_loss: 0.2995 - val_accuracy: 0.8136 Epoch 343/1000 2/2 [==============================] - ETA: 0s - loss: 0.2022 - accuracy: 0.8906 Epoch 343: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 931ms/step - loss: 0.2022 - accuracy: 0.8906 - val_loss: 0.2981 - val_accuracy: 0.8136 Epoch 344/1000 2/2 [==============================] - ETA: 0s - loss: 0.2112 - accuracy: 0.8875 Epoch 344: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 1s/step - loss: 0.2112 - accuracy: 0.8875 - val_loss: 0.2967 - val_accuracy: 0.8136 Epoch 345/1000 2/2 [==============================] - ETA: 0s - loss: 0.2026 - accuracy: 0.9125 Epoch 345: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 1s/step - loss: 0.2026 - accuracy: 0.9125 - val_loss: 0.2950 - val_accuracy: 0.8136 Epoch 346/1000 2/2 [==============================] - ETA: 0s - loss: 0.2523 - accuracy: 0.8500 Epoch 346: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 1s/step - loss: 0.2523 - accuracy: 0.8500 - val_loss: 0.2945 - val_accuracy: 0.8136 Epoch 347/1000 2/2 [==============================] - ETA: 0s - loss: 0.1992 - accuracy: 0.8906 Epoch 347: saving model to training_1/cp.ckpt 2/2 [==============================] - 2s 1s/step - loss: 0.1992 - accuracy: 0.8906 - val_loss: 0.2937 - val_accuracy: 0.8136 Epoch 348/1000 2/2 [==============================] - ETA: 0s - loss: 0.2214 - accuracy: 0.8906 Epoch 348: saving model to training_1/cp.ckpt 2/2 [==============================] - 2s 1s/step - loss: 0.2214 - accuracy: 0.8906 - val_loss: 0.2934 - val_accuracy: 0.8136 Epoch 349/1000 2/2 [==============================] - ETA: 0s - loss: 0.1557 - accuracy: 0.9375 Epoch 349: saving model to training_1/cp.ckpt 2/2 [==============================] - 2s 1s/step - loss: 0.1557 - accuracy: 0.9375 - val_loss: 0.2937 - val_accuracy: 0.8136 Epoch 350/1000 2/2 [==============================] - ETA: 0s - loss: 0.2254 - accuracy: 0.8828 Epoch 350: saving model to training_1/cp.ckpt 2/2 [==============================] - 2s 1s/step - loss: 0.2254 - accuracy: 0.8828 - val_loss: 0.2925 - val_accuracy: 0.8136 Epoch 351/1000 2/2 [==============================] - ETA: 0s - loss: 0.2194 - accuracy: 0.8906 Epoch 351: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 891ms/step - loss: 0.2194 - accuracy: 0.8906 - val_loss: 0.2909 - val_accuracy: 0.8136 Epoch 352/1000 2/2 [==============================] - ETA: 0s - loss: 0.2548 - accuracy: 0.8750 Epoch 352: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 963ms/step - loss: 0.2548 - accuracy: 0.8750 - val_loss: 0.2898 - val_accuracy: 0.8136 Epoch 353/1000 2/2 [==============================] - ETA: 0s - loss: 0.2142 - accuracy: 0.9062 Epoch 353: saving model to training_1/cp.ckpt 2/2 [==============================] - 2s 1s/step - loss: 0.2142 - accuracy: 0.9062 - val_loss: 0.2904 - val_accuracy: 0.8136 Epoch 354/1000 2/2 [==============================] - ETA: 0s - loss: 0.2285 - accuracy: 0.8984 Epoch 354: saving model to training_1/cp.ckpt 2/2 [==============================] - 2s 1s/step - loss: 0.2285 - accuracy: 0.8984 - val_loss: 0.2903 - val_accuracy: 0.8136 Epoch 355/1000 2/2 [==============================] - ETA: 0s - loss: 0.1971 - accuracy: 0.9250 Epoch 355: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 813ms/step - loss: 0.1971 - accuracy: 0.9250 - val_loss: 0.2898 - val_accuracy: 0.8136 Epoch 356/1000 2/2 [==============================] - ETA: 0s - loss: 0.1707 - accuracy: 0.9125 Epoch 356: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 804ms/step - loss: 0.1707 - accuracy: 0.9125 - val_loss: 0.2897 - val_accuracy: 0.7966 Epoch 357/1000 2/2 [==============================] - ETA: 0s - loss: 0.1891 - accuracy: 0.9297 Epoch 357: saving model to training_1/cp.ckpt 2/2 [==============================] - 2s 1s/step - loss: 0.1891 - accuracy: 0.9297 - val_loss: 0.2902 - val_accuracy: 0.7966 Epoch 358/1000 2/2 [==============================] - ETA: 0s - loss: 0.2287 - accuracy: 0.8906 Epoch 358: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 916ms/step - loss: 0.2287 - accuracy: 0.8906 - val_loss: 0.2905 - val_accuracy: 0.7966 Epoch 359/1000 2/2 [==============================] - ETA: 0s - loss: 0.1855 - accuracy: 0.9000 Epoch 359: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 808ms/step - loss: 0.1855 - accuracy: 0.9000 - val_loss: 0.2893 - val_accuracy: 0.7966 Epoch 360/1000 2/2 [==============================] - ETA: 0s - loss: 0.1888 - accuracy: 0.9000 Epoch 360: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 1s/step - loss: 0.1888 - accuracy: 0.9000 - val_loss: 0.2888 - val_accuracy: 0.7966 Epoch 361/1000 2/2 [==============================] - ETA: 0s - loss: 0.1960 - accuracy: 0.8906 Epoch 361: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 937ms/step - loss: 0.1960 - accuracy: 0.8906 - val_loss: 0.2888 - val_accuracy: 0.8136 Epoch 362/1000 2/2 [==============================] - ETA: 0s - loss: 0.1805 - accuracy: 0.9219 Epoch 362: saving model to training_1/cp.ckpt 2/2 [==============================] - 2s 1s/step - loss: 0.1805 - accuracy: 0.9219 - val_loss: 0.2886 - val_accuracy: 0.8136 Epoch 363/1000 2/2 [==============================] - ETA: 0s - loss: 0.2204 - accuracy: 0.8438 Epoch 363: saving model to training_1/cp.ckpt 2/2 [==============================] - 2s 1s/step - loss: 0.2204 - accuracy: 0.8438 - val_loss: 0.2874 - val_accuracy: 0.8136 Epoch 364/1000 2/2 [==============================] - ETA: 0s - loss: 0.2377 - accuracy: 0.8750 Epoch 364: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 1s/step - loss: 0.2377 - accuracy: 0.8750 - val_loss: 0.2852 - val_accuracy: 0.8305 Epoch 365/1000 2/2 [==============================] - ETA: 0s - loss: 0.2509 - accuracy: 0.8359 Epoch 365: saving model to training_1/cp.ckpt 2/2 [==============================] - 2s 1s/step - loss: 0.2509 - accuracy: 0.8359 - val_loss: 0.2844 - val_accuracy: 0.8305 Epoch 366/1000 2/2 [==============================] - ETA: 0s - loss: 0.2157 - accuracy: 0.9062 Epoch 366: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 937ms/step - loss: 0.2157 - accuracy: 0.9062 - val_loss: 0.2826 - val_accuracy: 0.8305 Epoch 367/1000 2/2 [==============================] - ETA: 0s - loss: 0.2052 - accuracy: 0.9062 Epoch 367: saving model to training_1/cp.ckpt 2/2 [==============================] - 2s 1s/step - loss: 0.2052 - accuracy: 0.9062 - val_loss: 0.2812 - val_accuracy: 0.8305 Epoch 368/1000 2/2 [==============================] - ETA: 0s - loss: 0.1466 - accuracy: 0.9766 Epoch 368: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 914ms/step - loss: 0.1466 - accuracy: 0.9766 - val_loss: 0.2792 - val_accuracy: 0.8475 Epoch 369/1000 2/2 [==============================] - ETA: 0s - loss: 0.2298 - accuracy: 0.8672 Epoch 369: saving model to training_1/cp.ckpt 2/2 [==============================] - 2s 1s/step - loss: 0.2298 - accuracy: 0.8672 - val_loss: 0.2770 - val_accuracy: 0.8305 Epoch 370/1000 2/2 [==============================] - ETA: 0s - loss: 0.2274 - accuracy: 0.8984 Epoch 370: saving model to training_1/cp.ckpt 2/2 [==============================] - 2s 1s/step - loss: 0.2274 - accuracy: 0.8984 - val_loss: 0.2750 - val_accuracy: 0.8305 Epoch 371/1000 2/2 [==============================] - ETA: 0s - loss: 0.2067 - accuracy: 0.8875 Epoch 371: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 811ms/step - loss: 0.2067 - accuracy: 0.8875 - val_loss: 0.2723 - val_accuracy: 0.8305 Epoch 372/1000 2/2 [==============================] - ETA: 0s - loss: 0.1376 - accuracy: 0.9250 Epoch 372: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 806ms/step - loss: 0.1376 - accuracy: 0.9250 - val_loss: 0.2710 - val_accuracy: 0.8305 Epoch 373/1000 2/2 [==============================] - ETA: 0s - loss: 0.1334 - accuracy: 0.9766 Epoch 373: saving model to training_1/cp.ckpt 2/2 [==============================] - 2s 1s/step - loss: 0.1334 - accuracy: 0.9766 - val_loss: 0.2704 - val_accuracy: 0.8305 Epoch 374/1000 2/2 [==============================] - ETA: 0s - loss: 0.1969 - accuracy: 0.9062 Epoch 374: saving model to training_1/cp.ckpt 2/2 [==============================] - 2s 1s/step - loss: 0.1969 - accuracy: 0.9062 - val_loss: 0.2690 - val_accuracy: 0.8305 Epoch 375/1000 2/2 [==============================] - ETA: 0s - loss: 0.1532 - accuracy: 0.9250 Epoch 375: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 1s/step - loss: 0.1532 - accuracy: 0.9250 - val_loss: 0.2681 - val_accuracy: 0.8305 Epoch 376/1000 2/2 [==============================] - ETA: 0s - loss: 0.1761 - accuracy: 0.9375 Epoch 376: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 1s/step - loss: 0.1761 - accuracy: 0.9375 - val_loss: 0.2677 - val_accuracy: 0.8305 Epoch 377/1000 2/2 [==============================] - ETA: 0s - loss: 0.1927 - accuracy: 0.9219 Epoch 377: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 916ms/step - loss: 0.1927 - accuracy: 0.9219 - val_loss: 0.2674 - val_accuracy: 0.8305 Epoch 378/1000 2/2 [==============================] - ETA: 0s - loss: 0.1983 - accuracy: 0.9297 Epoch 378: saving model to training_1/cp.ckpt 2/2 [==============================] - 2s 1s/step - loss: 0.1983 - accuracy: 0.9297 - val_loss: 0.2671 - val_accuracy: 0.8305 Epoch 379/1000 2/2 [==============================] - ETA: 0s - loss: 0.1826 - accuracy: 0.9375 Epoch 379: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 806ms/step - loss: 0.1826 - accuracy: 0.9375 - val_loss: 0.2670 - val_accuracy: 0.8305 Epoch 380/1000 2/2 [==============================] - ETA: 0s - loss: 0.1814 - accuracy: 0.8875 Epoch 380: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 803ms/step - loss: 0.1814 - accuracy: 0.8875 - val_loss: 0.2679 - val_accuracy: 0.8305 Epoch 381/1000 2/2 [==============================] - ETA: 0s - loss: 0.1725 - accuracy: 0.9125 Epoch 381: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 797ms/step - loss: 0.1725 - accuracy: 0.9125 - val_loss: 0.2694 - val_accuracy: 0.8305 Epoch 382/1000 2/2 [==============================] - ETA: 0s - loss: 0.1709 - accuracy: 0.9219 Epoch 382: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 948ms/step - loss: 0.1709 - accuracy: 0.9219 - val_loss: 0.2718 - val_accuracy: 0.8305 Epoch 383/1000 2/2 [==============================] - ETA: 0s - loss: 0.1744 - accuracy: 0.9125 Epoch 383: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 988ms/step - loss: 0.1744 - accuracy: 0.9125 - val_loss: 0.2752 - val_accuracy: 0.8305 Epoch 384/1000 2/2 [==============================] - ETA: 0s - loss: 0.1834 - accuracy: 0.9250 Epoch 384: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 815ms/step - loss: 0.1834 - accuracy: 0.9250 - val_loss: 0.2793 - val_accuracy: 0.8136 Epoch 385/1000 2/2 [==============================] - ETA: 0s - loss: 0.1865 - accuracy: 0.9297 Epoch 385: saving model to training_1/cp.ckpt 2/2 [==============================] - 2s 1s/step - loss: 0.1865 - accuracy: 0.9297 - val_loss: 0.2834 - val_accuracy: 0.8136 Epoch 386/1000 2/2 [==============================] - ETA: 0s - loss: 0.2197 - accuracy: 0.8750 Epoch 386: saving model to training_1/cp.ckpt 2/2 [==============================] - 2s 1s/step - loss: 0.2197 - accuracy: 0.8750 - val_loss: 0.2869 - val_accuracy: 0.8305 Epoch 387/1000 2/2 [==============================] - ETA: 0s - loss: 0.1715 - accuracy: 0.9141 Epoch 387: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 938ms/step - loss: 0.1715 - accuracy: 0.9141 - val_loss: 0.2888 - val_accuracy: 0.8305 Epoch 388/1000 2/2 [==============================] - ETA: 0s - loss: 0.1848 - accuracy: 0.8750 Epoch 388: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 815ms/step - loss: 0.1848 - accuracy: 0.8750 - val_loss: 0.2891 - val_accuracy: 0.8305 Epoch 389/1000 2/2 [==============================] - ETA: 0s - loss: 0.2054 - accuracy: 0.9219 Epoch 389: saving model to training_1/cp.ckpt 2/2 [==============================] - 2s 1s/step - loss: 0.2054 - accuracy: 0.9219 - val_loss: 0.2882 - val_accuracy: 0.8305 Epoch 390/1000 2/2 [==============================] - ETA: 0s - loss: 0.1498 - accuracy: 0.9500 Epoch 390: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 1s/step - loss: 0.1498 - accuracy: 0.9500 - val_loss: 0.2871 - val_accuracy: 0.8305 Epoch 391/1000 2/2 [==============================] - ETA: 0s - loss: 0.1969 - accuracy: 0.9125 Epoch 391: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 796ms/step - loss: 0.1969 - accuracy: 0.9125 - val_loss: 0.2851 - val_accuracy: 0.8305 Epoch 392/1000 2/2 [==============================] - ETA: 0s - loss: 0.1831 - accuracy: 0.9125 Epoch 392: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 1s/step - loss: 0.1831 - accuracy: 0.9125 - val_loss: 0.2831 - val_accuracy: 0.8305 Epoch 393/1000 2/2 [==============================] - ETA: 0s - loss: 0.2146 - accuracy: 0.8625 Epoch 393: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 811ms/step - loss: 0.2146 - accuracy: 0.8625 - val_loss: 0.2820 - val_accuracy: 0.8305 Epoch 394/1000 2/2 [==============================] - ETA: 0s - loss: 0.1512 - accuracy: 0.9375 Epoch 394: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 797ms/step - loss: 0.1512 - accuracy: 0.9375 - val_loss: 0.2816 - val_accuracy: 0.8305 Epoch 395/1000 2/2 [==============================] - ETA: 0s - loss: 0.1887 - accuracy: 0.8984 Epoch 395: saving model to training_1/cp.ckpt 2/2 [==============================] - 2s 1s/step - loss: 0.1887 - accuracy: 0.8984 - val_loss: 0.2810 - val_accuracy: 0.8305 Epoch 396/1000 2/2 [==============================] - ETA: 0s - loss: 0.1964 - accuracy: 0.9250 Epoch 396: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 805ms/step - loss: 0.1964 - accuracy: 0.9250 - val_loss: 0.2817 - val_accuracy: 0.8305 Epoch 397/1000 2/2 [==============================] - ETA: 0s - loss: 0.1661 - accuracy: 0.9219 Epoch 397: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 969ms/step - loss: 0.1661 - accuracy: 0.9219 - val_loss: 0.2819 - val_accuracy: 0.8136 Epoch 398/1000 2/2 [==============================] - ETA: 0s - loss: 0.1866 - accuracy: 0.9219 Epoch 398: saving model to training_1/cp.ckpt 2/2 [==============================] - 2s 1s/step - loss: 0.1866 - accuracy: 0.9219 - val_loss: 0.2835 - val_accuracy: 0.8136 Epoch 399/1000 2/2 [==============================] - ETA: 0s - loss: 0.1613 - accuracy: 0.9453 Epoch 399: saving model to training_1/cp.ckpt 2/2 [==============================] - 2s 1s/step - loss: 0.1613 - accuracy: 0.9453 - val_loss: 0.2854 - val_accuracy: 0.8136 Epoch 400/1000 2/2 [==============================] - ETA: 0s - loss: 0.1936 - accuracy: 0.9000 Epoch 400: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 1s/step - loss: 0.1936 - accuracy: 0.9000 - val_loss: 0.2866 - val_accuracy: 0.8136 Epoch 401/1000 2/2 [==============================] - ETA: 0s - loss: 0.1871 - accuracy: 0.9219 Epoch 401: saving model to training_1/cp.ckpt 2/2 [==============================] - 2s 1s/step - loss: 0.1871 - accuracy: 0.9219 - val_loss: 0.2878 - val_accuracy: 0.7966 Epoch 402/1000 2/2 [==============================] - ETA: 0s - loss: 0.1557 - accuracy: 0.9375 Epoch 402: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 1s/step - loss: 0.1557 - accuracy: 0.9375 - val_loss: 0.2889 - val_accuracy: 0.7966 Epoch 403/1000 2/2 [==============================] - ETA: 0s - loss: 0.1863 - accuracy: 0.9125 Epoch 403: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 822ms/step - loss: 0.1863 - accuracy: 0.9125 - val_loss: 0.2906 - val_accuracy: 0.8136 Epoch 404/1000 2/2 [==============================] - ETA: 0s - loss: 0.1650 - accuracy: 0.9297 Epoch 404: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 948ms/step - loss: 0.1650 - accuracy: 0.9297 - val_loss: 0.2921 - val_accuracy: 0.8136 Epoch 405/1000 2/2 [==============================] - ETA: 0s - loss: 0.1796 - accuracy: 0.9141 Epoch 405: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 956ms/step - loss: 0.1796 - accuracy: 0.9141 - val_loss: 0.2936 - val_accuracy: 0.8136 Epoch 406/1000 2/2 [==============================] - ETA: 0s - loss: 0.1615 - accuracy: 0.9531 Epoch 406: saving model to training_1/cp.ckpt 2/2 [==============================] - 2s 1s/step - loss: 0.1615 - accuracy: 0.9531 - val_loss: 0.2949 - val_accuracy: 0.8136 Epoch 407/1000 2/2 [==============================] - ETA: 0s - loss: 0.1877 - accuracy: 0.9141 Epoch 407: saving model to training_1/cp.ckpt 2/2 [==============================] - 2s 1s/step - loss: 0.1877 - accuracy: 0.9141 - val_loss: 0.2954 - val_accuracy: 0.8136 Epoch 408/1000 2/2 [==============================] - ETA: 0s - loss: 0.2060 - accuracy: 0.8875 Epoch 408: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 1s/step - loss: 0.2060 - accuracy: 0.8875 - val_loss: 0.2953 - val_accuracy: 0.8136 Epoch 409/1000 2/2 [==============================] - ETA: 0s - loss: 0.1334 - accuracy: 0.9688 Epoch 409: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 943ms/step - loss: 0.1334 - accuracy: 0.9688 - val_loss: 0.2956 - val_accuracy: 0.8136 Epoch 410/1000 2/2 [==============================] - ETA: 0s - loss: 0.1217 - accuracy: 0.9500 Epoch 410: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 818ms/step - loss: 0.1217 - accuracy: 0.9500 - val_loss: 0.2970 - val_accuracy: 0.8136 Epoch 411/1000 2/2 [==============================] - ETA: 0s - loss: 0.1435 - accuracy: 0.9609 Epoch 411: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 956ms/step - loss: 0.1435 - accuracy: 0.9609 - val_loss: 0.2978 - val_accuracy: 0.8136 Epoch 412/1000 2/2 [==============================] - ETA: 0s - loss: 0.2369 - accuracy: 0.8875 Epoch 412: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 1s/step - loss: 0.2369 - accuracy: 0.8875 - val_loss: 0.2975 - val_accuracy: 0.8136 Epoch 413/1000 2/2 [==============================] - ETA: 0s - loss: 0.1769 - accuracy: 0.9062 Epoch 413: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 925ms/step - loss: 0.1769 - accuracy: 0.9062 - val_loss: 0.2976 - val_accuracy: 0.8136 Epoch 414/1000 2/2 [==============================] - ETA: 0s - loss: 0.1529 - accuracy: 0.9297 Epoch 414: saving model to training_1/cp.ckpt 2/2 [==============================] - 2s 1s/step - loss: 0.1529 - accuracy: 0.9297 - val_loss: 0.2980 - val_accuracy: 0.8136 Epoch 415/1000 2/2 [==============================] - ETA: 0s - loss: 0.1929 - accuracy: 0.9141 Epoch 415: saving model to training_1/cp.ckpt 2/2 [==============================] - 2s 1s/step - loss: 0.1929 - accuracy: 0.9141 - val_loss: 0.2981 - val_accuracy: 0.8136 Epoch 416/1000 2/2 [==============================] - ETA: 0s - loss: 0.1664 - accuracy: 0.9375 Epoch 416: saving model to training_1/cp.ckpt 2/2 [==============================] - 2s 1s/step - loss: 0.1664 - accuracy: 0.9375 - val_loss: 0.2983 - val_accuracy: 0.8136 Epoch 417/1000 2/2 [==============================] - ETA: 0s - loss: 0.1497 - accuracy: 0.9500 Epoch 417: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 802ms/step - loss: 0.1497 - accuracy: 0.9500 - val_loss: 0.2982 - val_accuracy: 0.8136 Epoch 418/1000 2/2 [==============================] - ETA: 0s - loss: 0.1411 - accuracy: 0.9500 Epoch 418: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 1s/step - loss: 0.1411 - accuracy: 0.9500 - val_loss: 0.2985 - val_accuracy: 0.8136 Epoch 419/1000 2/2 [==============================] - ETA: 0s - loss: 0.2223 - accuracy: 0.8750 Epoch 419: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 1s/step - loss: 0.2223 - accuracy: 0.8750 - val_loss: 0.2979 - val_accuracy: 0.8136 Epoch 420/1000 2/2 [==============================] - ETA: 0s - loss: 0.2264 - accuracy: 0.8750 Epoch 420: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 940ms/step - loss: 0.2264 - accuracy: 0.8750 - val_loss: 0.2962 - val_accuracy: 0.8136 Epoch 421/1000 2/2 [==============================] - ETA: 0s - loss: 0.1621 - accuracy: 0.9219 Epoch 421: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 898ms/step - loss: 0.1621 - accuracy: 0.9219 - val_loss: 0.2952 - val_accuracy: 0.8136 Epoch 422/1000 2/2 [==============================] - ETA: 0s - loss: 0.1696 - accuracy: 0.9500 Epoch 422: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 1s/step - loss: 0.1696 - accuracy: 0.9500 - val_loss: 0.2945 - val_accuracy: 0.8305 Epoch 423/1000 2/2 [==============================] - ETA: 0s - loss: 0.2096 - accuracy: 0.8984 Epoch 423: saving model to training_1/cp.ckpt 2/2 [==============================] - 2s 1s/step - loss: 0.2096 - accuracy: 0.8984 - val_loss: 0.2934 - val_accuracy: 0.8305 Epoch 424/1000 2/2 [==============================] - ETA: 0s - loss: 0.2152 - accuracy: 0.9000 Epoch 424: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 1s/step - loss: 0.2152 - accuracy: 0.9000 - val_loss: 0.2935 - val_accuracy: 0.8305 Epoch 425/1000 2/2 [==============================] - ETA: 0s - loss: 0.1662 - accuracy: 0.9297 Epoch 425: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 902ms/step - loss: 0.1662 - accuracy: 0.9297 - val_loss: 0.2931 - val_accuracy: 0.8305 Epoch 426/1000 2/2 [==============================] - ETA: 0s - loss: 0.1505 - accuracy: 0.9297 Epoch 426: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 904ms/step - loss: 0.1505 - accuracy: 0.9297 - val_loss: 0.2917 - val_accuracy: 0.8305 Epoch 427/1000 2/2 [==============================] - ETA: 0s - loss: 0.1576 - accuracy: 0.9375 Epoch 427: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 1s/step - loss: 0.1576 - accuracy: 0.9375 - val_loss: 0.2896 - val_accuracy: 0.8305 Epoch 428/1000 2/2 [==============================] - ETA: 0s - loss: 0.2311 - accuracy: 0.8625 Epoch 428: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 800ms/step - loss: 0.2311 - accuracy: 0.8625 - val_loss: 0.2872 - val_accuracy: 0.8305 Epoch 429/1000 2/2 [==============================] - ETA: 0s - loss: 0.1310 - accuracy: 0.9125 Epoch 429: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 815ms/step - loss: 0.1310 - accuracy: 0.9125 - val_loss: 0.2852 - val_accuracy: 0.8305 Epoch 430/1000 2/2 [==============================] - ETA: 0s - loss: 0.1362 - accuracy: 0.9625 Epoch 430: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 809ms/step - loss: 0.1362 - accuracy: 0.9625 - val_loss: 0.2846 - val_accuracy: 0.8305 Epoch 431/1000 2/2 [==============================] - ETA: 0s - loss: 0.1907 - accuracy: 0.8672 Epoch 431: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 970ms/step - loss: 0.1907 - accuracy: 0.8672 - val_loss: 0.2838 - val_accuracy: 0.8305 Epoch 432/1000 2/2 [==============================] - ETA: 0s - loss: 0.1620 - accuracy: 0.9375 Epoch 432: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 1s/step - loss: 0.1620 - accuracy: 0.9375 - val_loss: 0.2835 - val_accuracy: 0.8305 Epoch 433/1000 2/2 [==============================] - ETA: 0s - loss: 0.1835 - accuracy: 0.9000 Epoch 433: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 804ms/step - loss: 0.1835 - accuracy: 0.9000 - val_loss: 0.2827 - val_accuracy: 0.8305 Epoch 434/1000 2/2 [==============================] - ETA: 0s - loss: 0.1855 - accuracy: 0.8875 Epoch 434: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 819ms/step - loss: 0.1855 - accuracy: 0.8875 - val_loss: 0.2822 - val_accuracy: 0.8305 Epoch 435/1000 2/2 [==============================] - ETA: 0s - loss: 0.1618 - accuracy: 0.9453 Epoch 435: saving model to training_1/cp.ckpt 2/2 [==============================] - 2s 1s/step - loss: 0.1618 - accuracy: 0.9453 - val_loss: 0.2819 - val_accuracy: 0.8305 Epoch 436/1000 2/2 [==============================] - ETA: 0s - loss: 0.1945 - accuracy: 0.9000 Epoch 436: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 824ms/step - loss: 0.1945 - accuracy: 0.9000 - val_loss: 0.2820 - val_accuracy: 0.8305 Epoch 437/1000 2/2 [==============================] - ETA: 0s - loss: 0.1356 - accuracy: 0.9766 Epoch 437: saving model to training_1/cp.ckpt 2/2 [==============================] - 2s 1s/step - loss: 0.1356 - accuracy: 0.9766 - val_loss: 0.2816 - val_accuracy: 0.8305 Epoch 438/1000 2/2 [==============================] - ETA: 0s - loss: 0.1677 - accuracy: 0.9125 Epoch 438: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 1s/step - loss: 0.1677 - accuracy: 0.9125 - val_loss: 0.2828 - val_accuracy: 0.8305 Epoch 439/1000 2/2 [==============================] - ETA: 0s - loss: 0.1504 - accuracy: 0.9219 Epoch 439: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 953ms/step - loss: 0.1504 - accuracy: 0.9219 - val_loss: 0.2843 - val_accuracy: 0.8305 Epoch 440/1000 2/2 [==============================] - ETA: 0s - loss: 0.2032 - accuracy: 0.8875 Epoch 440: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 842ms/step - loss: 0.2032 - accuracy: 0.8875 - val_loss: 0.2862 - val_accuracy: 0.8305 Epoch 441/1000 2/2 [==============================] - ETA: 0s - loss: 0.1492 - accuracy: 0.9625 Epoch 441: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 815ms/step - loss: 0.1492 - accuracy: 0.9625 - val_loss: 0.2884 - val_accuracy: 0.8305 Epoch 442/1000 2/2 [==============================] - ETA: 0s - loss: 0.1689 - accuracy: 0.9125 Epoch 442: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 819ms/step - loss: 0.1689 - accuracy: 0.9125 - val_loss: 0.2880 - val_accuracy: 0.8305 Epoch 443/1000 2/2 [==============================] - ETA: 0s - loss: 0.1659 - accuracy: 0.9250 Epoch 443: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 1s/step - loss: 0.1659 - accuracy: 0.9250 - val_loss: 0.2883 - val_accuracy: 0.8305 Epoch 444/1000 2/2 [==============================] - ETA: 0s - loss: 0.2104 - accuracy: 0.8828 Epoch 444: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 949ms/step - loss: 0.2104 - accuracy: 0.8828 - val_loss: 0.2863 - val_accuracy: 0.8305 Epoch 445/1000 2/2 [==============================] - ETA: 0s - loss: 0.1544 - accuracy: 0.9219 Epoch 445: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 942ms/step - loss: 0.1544 - accuracy: 0.9219 - val_loss: 0.2832 - val_accuracy: 0.8305 Epoch 446/1000 2/2 [==============================] - ETA: 0s - loss: 0.1321 - accuracy: 0.9766 Epoch 446: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 938ms/step - loss: 0.1321 - accuracy: 0.9766 - val_loss: 0.2813 - val_accuracy: 0.8305 Epoch 447/1000 2/2 [==============================] - ETA: 0s - loss: 0.1680 - accuracy: 0.9125 Epoch 447: saving model to training_1/cp.ckpt 2/2 [==============================] - 2s 1s/step - loss: 0.1680 - accuracy: 0.9125 - val_loss: 0.2811 - val_accuracy: 0.8136 Epoch 448/1000 2/2 [==============================] - ETA: 0s - loss: 0.1816 - accuracy: 0.9141 Epoch 448: saving model to training_1/cp.ckpt 2/2 [==============================] - 2s 1s/step - loss: 0.1816 - accuracy: 0.9141 - val_loss: 0.2806 - val_accuracy: 0.8136 Epoch 449/1000 2/2 [==============================] - ETA: 0s - loss: 0.1797 - accuracy: 0.9000 Epoch 449: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 1s/step - loss: 0.1797 - accuracy: 0.9000 - val_loss: 0.2814 - val_accuracy: 0.8136 Epoch 450/1000 2/2 [==============================] - ETA: 0s - loss: 0.1986 - accuracy: 0.8750 Epoch 450: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 1s/step - loss: 0.1986 - accuracy: 0.8750 - val_loss: 0.2840 - val_accuracy: 0.8136 Epoch 451/1000 2/2 [==============================] - ETA: 0s - loss: 0.1813 - accuracy: 0.8984 Epoch 451: saving model to training_1/cp.ckpt 2/2 [==============================] - 2s 1s/step - loss: 0.1813 - accuracy: 0.8984 - val_loss: 0.2866 - val_accuracy: 0.8136 Epoch 452/1000 2/2 [==============================] - ETA: 0s - loss: 0.2064 - accuracy: 0.8375 Epoch 452: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 833ms/step - loss: 0.2064 - accuracy: 0.8375 - val_loss: 0.2891 - val_accuracy: 0.8136 Epoch 453/1000 2/2 [==============================] - ETA: 0s - loss: 0.1394 - accuracy: 0.9625 Epoch 453: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 831ms/step - loss: 0.1394 - accuracy: 0.9625 - val_loss: 0.2909 - val_accuracy: 0.8136 Epoch 454/1000 2/2 [==============================] - ETA: 0s - loss: 0.1555 - accuracy: 0.9375 Epoch 454: saving model to training_1/cp.ckpt 2/2 [==============================] - 2s 1s/step - loss: 0.1555 - accuracy: 0.9375 - val_loss: 0.2903 - val_accuracy: 0.8136 Epoch 455/1000 2/2 [==============================] - ETA: 0s - loss: 0.1647 - accuracy: 0.9375 Epoch 455: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 874ms/step - loss: 0.1647 - accuracy: 0.9375 - val_loss: 0.2888 - val_accuracy: 0.8136 Epoch 456/1000 2/2 [==============================] - ETA: 0s - loss: 0.2253 - accuracy: 0.8625 Epoch 456: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 1s/step - loss: 0.2253 - accuracy: 0.8625 - val_loss: 0.2889 - val_accuracy: 0.8136 Epoch 457/1000 2/2 [==============================] - ETA: 0s - loss: 0.1515 - accuracy: 0.9625 Epoch 457: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 1s/step - loss: 0.1515 - accuracy: 0.9625 - val_loss: 0.2885 - val_accuracy: 0.8136 Epoch 458/1000 2/2 [==============================] - ETA: 0s - loss: 0.1796 - accuracy: 0.9141 Epoch 458: saving model to training_1/cp.ckpt 2/2 [==============================] - 2s 1s/step - loss: 0.1796 - accuracy: 0.9141 - val_loss: 0.2875 - val_accuracy: 0.8136 Epoch 459/1000 2/2 [==============================] - ETA: 0s - loss: 0.1726 - accuracy: 0.9000 Epoch 459: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 1s/step - loss: 0.1726 - accuracy: 0.9000 - val_loss: 0.2845 - val_accuracy: 0.8136 Epoch 460/1000 2/2 [==============================] - ETA: 0s - loss: 0.1235 - accuracy: 0.9500 Epoch 460: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 1s/step - loss: 0.1235 - accuracy: 0.9500 - val_loss: 0.2820 - val_accuracy: 0.8136 Epoch 461/1000 2/2 [==============================] - ETA: 0s - loss: 0.1356 - accuracy: 0.9375 Epoch 461: saving model to training_1/cp.ckpt 2/2 [==============================] - 2s 1s/step - loss: 0.1356 - accuracy: 0.9375 - val_loss: 0.2795 - val_accuracy: 0.8136 Epoch 462/1000 2/2 [==============================] - ETA: 0s - loss: 0.1549 - accuracy: 0.9625 Epoch 462: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 1s/step - loss: 0.1549 - accuracy: 0.9625 - val_loss: 0.2786 - val_accuracy: 0.8136 Epoch 463/1000 2/2 [==============================] - ETA: 0s - loss: 0.1813 - accuracy: 0.9141 Epoch 463: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 936ms/step - loss: 0.1813 - accuracy: 0.9141 - val_loss: 0.2789 - val_accuracy: 0.8305 Epoch 464/1000 2/2 [==============================] - ETA: 0s - loss: 0.1662 - accuracy: 0.9375 Epoch 464: saving model to training_1/cp.ckpt 2/2 [==============================] - 2s 1s/step - loss: 0.1662 - accuracy: 0.9375 - val_loss: 0.2788 - val_accuracy: 0.8305 Epoch 465/1000 2/2 [==============================] - ETA: 0s - loss: 0.1256 - accuracy: 0.9750 Epoch 465: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 833ms/step - loss: 0.1256 - accuracy: 0.9750 - val_loss: 0.2806 - val_accuracy: 0.8305 Epoch 466/1000 2/2 [==============================] - ETA: 0s - loss: 0.1848 - accuracy: 0.9141 Epoch 466: saving model to training_1/cp.ckpt 2/2 [==============================] - 2s 1s/step - loss: 0.1848 - accuracy: 0.9141 - val_loss: 0.2832 - val_accuracy: 0.8136 Epoch 467/1000 2/2 [==============================] - ETA: 0s - loss: 0.1815 - accuracy: 0.9219 Epoch 467: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 932ms/step - loss: 0.1815 - accuracy: 0.9219 - val_loss: 0.2864 - val_accuracy: 0.8136 Epoch 468/1000 2/2 [==============================] - ETA: 0s - loss: 0.1715 - accuracy: 0.8906 Epoch 468: saving model to training_1/cp.ckpt 2/2 [==============================] - 2s 1s/step - loss: 0.1715 - accuracy: 0.8906 - val_loss: 0.2882 - val_accuracy: 0.8136 Epoch 469/1000 2/2 [==============================] - ETA: 0s - loss: 0.1390 - accuracy: 0.9375 Epoch 469: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 969ms/step - loss: 0.1390 - accuracy: 0.9375 - val_loss: 0.2885 - val_accuracy: 0.8136 Epoch 470/1000 2/2 [==============================] - ETA: 0s - loss: 0.1557 - accuracy: 0.9000 Epoch 470: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 808ms/step - loss: 0.1557 - accuracy: 0.9000 - val_loss: 0.2893 - val_accuracy: 0.8136 Epoch 471/1000 2/2 [==============================] - ETA: 0s - loss: 0.1416 - accuracy: 0.9375 Epoch 471: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 1s/step - loss: 0.1416 - accuracy: 0.9375 - val_loss: 0.2901 - val_accuracy: 0.8136 Epoch 472/1000 2/2 [==============================] - ETA: 0s - loss: 0.1847 - accuracy: 0.9000 Epoch 472: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 875ms/step - loss: 0.1847 - accuracy: 0.9000 - val_loss: 0.2897 - val_accuracy: 0.8136 Epoch 473/1000 2/2 [==============================] - ETA: 0s - loss: 0.1655 - accuracy: 0.9297 Epoch 473: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 953ms/step - loss: 0.1655 - accuracy: 0.9297 - val_loss: 0.2874 - val_accuracy: 0.8136 Epoch 474/1000 2/2 [==============================] - ETA: 0s - loss: 0.1800 - accuracy: 0.9141 Epoch 474: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 923ms/step - loss: 0.1800 - accuracy: 0.9141 - val_loss: 0.2858 - val_accuracy: 0.8136 Epoch 475/1000 2/2 [==============================] - ETA: 0s - loss: 0.1262 - accuracy: 0.9453 Epoch 475: saving model to training_1/cp.ckpt 2/2 [==============================] - 2s 993ms/step - loss: 0.1262 - accuracy: 0.9453 - val_loss: 0.2833 - val_accuracy: 0.8305 Epoch 476/1000 2/2 [==============================] - ETA: 0s - loss: 0.2006 - accuracy: 0.8906 Epoch 476: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 930ms/step - loss: 0.2006 - accuracy: 0.8906 - val_loss: 0.2805 - val_accuracy: 0.8305 Epoch 477/1000 2/2 [==============================] - ETA: 0s - loss: 0.1352 - accuracy: 0.9609 Epoch 477: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 925ms/step - loss: 0.1352 - accuracy: 0.9609 - val_loss: 0.2774 - val_accuracy: 0.8305 Epoch 478/1000 2/2 [==============================] - ETA: 0s - loss: 0.1754 - accuracy: 0.8906 Epoch 478: saving model to training_1/cp.ckpt 2/2 [==============================] - 2s 1s/step - loss: 0.1754 - accuracy: 0.8906 - val_loss: 0.2742 - val_accuracy: 0.8305 Epoch 479/1000 2/2 [==============================] - ETA: 0s - loss: 0.1439 - accuracy: 0.9531 Epoch 479: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 920ms/step - loss: 0.1439 - accuracy: 0.9531 - val_loss: 0.2717 - val_accuracy: 0.8305 Epoch 480/1000 2/2 [==============================] - ETA: 0s - loss: 0.1415 - accuracy: 0.9531 Epoch 480: saving model to training_1/cp.ckpt 2/2 [==============================] - 2s 1s/step - loss: 0.1415 - accuracy: 0.9531 - val_loss: 0.2691 - val_accuracy: 0.8305 Epoch 481/1000 2/2 [==============================] - ETA: 0s - loss: 0.1797 - accuracy: 0.9062 Epoch 481: saving model to training_1/cp.ckpt 2/2 [==============================] - 2s 1s/step - loss: 0.1797 - accuracy: 0.9062 - val_loss: 0.2675 - val_accuracy: 0.8305 Epoch 482/1000 2/2 [==============================] - ETA: 0s - loss: 0.1773 - accuracy: 0.9000 Epoch 482: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 1s/step - loss: 0.1773 - accuracy: 0.9000 - val_loss: 0.2663 - val_accuracy: 0.8305 Epoch 483/1000 2/2 [==============================] - ETA: 0s - loss: 0.1369 - accuracy: 0.9375 Epoch 483: saving model to training_1/cp.ckpt 2/2 [==============================] - 2s 1s/step - loss: 0.1369 - accuracy: 0.9375 - val_loss: 0.2664 - val_accuracy: 0.8305 Epoch 484/1000 2/2 [==============================] - ETA: 0s - loss: 0.1577 - accuracy: 0.9141 Epoch 484: saving model to training_1/cp.ckpt 2/2 [==============================] - 2s 1s/step - loss: 0.1577 - accuracy: 0.9141 - val_loss: 0.2667 - val_accuracy: 0.8305 Epoch 485/1000 2/2 [==============================] - ETA: 0s - loss: 0.1333 - accuracy: 0.9531 Epoch 485: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 956ms/step - loss: 0.1333 - accuracy: 0.9531 - val_loss: 0.2676 - val_accuracy: 0.8305 Epoch 486/1000 2/2 [==============================] - ETA: 0s - loss: 0.1250 - accuracy: 0.9625 Epoch 486: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 825ms/step - loss: 0.1250 - accuracy: 0.9625 - val_loss: 0.2692 - val_accuracy: 0.8305 Epoch 487/1000 2/2 [==============================] - ETA: 0s - loss: 0.1775 - accuracy: 0.8875 Epoch 487: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 1s/step - loss: 0.1775 - accuracy: 0.8875 - val_loss: 0.2708 - val_accuracy: 0.8305 Epoch 488/1000 2/2 [==============================] - ETA: 0s - loss: 0.1744 - accuracy: 0.9297 Epoch 488: saving model to training_1/cp.ckpt 2/2 [==============================] - 2s 1s/step - loss: 0.1744 - accuracy: 0.9297 - val_loss: 0.2726 - val_accuracy: 0.8305 Epoch 489/1000 2/2 [==============================] - ETA: 0s - loss: 0.1200 - accuracy: 0.9500 Epoch 489: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 1s/step - loss: 0.1200 - accuracy: 0.9500 - val_loss: 0.2729 - val_accuracy: 0.8305 Epoch 490/1000 2/2 [==============================] - ETA: 0s - loss: 0.1249 - accuracy: 0.9375 Epoch 490: saving model to training_1/cp.ckpt 2/2 [==============================] - 2s 1s/step - loss: 0.1249 - accuracy: 0.9375 - val_loss: 0.2736 - val_accuracy: 0.8305 Epoch 491/1000 2/2 [==============================] - ETA: 0s - loss: 0.1771 - accuracy: 0.9250 Epoch 491: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 1s/step - loss: 0.1771 - accuracy: 0.9250 - val_loss: 0.2729 - val_accuracy: 0.8305 Epoch 492/1000 2/2 [==============================] - ETA: 0s - loss: 0.1549 - accuracy: 0.9125 Epoch 492: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 818ms/step - loss: 0.1549 - accuracy: 0.9125 - val_loss: 0.2700 - val_accuracy: 0.8305 Epoch 493/1000 2/2 [==============================] - ETA: 0s - loss: 0.1681 - accuracy: 0.9141 Epoch 493: saving model to training_1/cp.ckpt 2/2 [==============================] - 2s 1s/step - loss: 0.1681 - accuracy: 0.9141 - val_loss: 0.2669 - val_accuracy: 0.8305 Epoch 494/1000 2/2 [==============================] - ETA: 0s - loss: 0.2009 - accuracy: 0.8750 Epoch 494: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 828ms/step - loss: 0.2009 - accuracy: 0.8750 - val_loss: 0.2638 - val_accuracy: 0.8475 Epoch 495/1000 2/2 [==============================] - ETA: 0s - loss: 0.1664 - accuracy: 0.9375 Epoch 495: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 1s/step - loss: 0.1664 - accuracy: 0.9375 - val_loss: 0.2620 - val_accuracy: 0.8475 Epoch 496/1000 2/2 [==============================] - ETA: 0s - loss: 0.2320 - accuracy: 0.8984 Epoch 496: saving model to training_1/cp.ckpt 2/2 [==============================] - 2s 1s/step - loss: 0.2320 - accuracy: 0.8984 - val_loss: 0.2619 - val_accuracy: 0.8475 Epoch 497/1000 2/2 [==============================] - ETA: 0s - loss: 0.1626 - accuracy: 0.8906 Epoch 497: saving model to training_1/cp.ckpt 2/2 [==============================] - 2s 1s/step - loss: 0.1626 - accuracy: 0.8906 - val_loss: 0.2602 - val_accuracy: 0.8644 Epoch 498/1000 2/2 [==============================] - ETA: 0s - loss: 0.1545 - accuracy: 0.9531 Epoch 498: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 979ms/step - loss: 0.1545 - accuracy: 0.9531 - val_loss: 0.2595 - val_accuracy: 0.8644 Epoch 499/1000 2/2 [==============================] - ETA: 0s - loss: 0.1404 - accuracy: 0.9875 Epoch 499: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 1s/step - loss: 0.1404 - accuracy: 0.9875 - val_loss: 0.2609 - val_accuracy: 0.8644 Epoch 500/1000 2/2 [==============================] - ETA: 0s - loss: 0.1046 - accuracy: 0.9875 Epoch 500: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 843ms/step - loss: 0.1046 - accuracy: 0.9875 - val_loss: 0.2629 - val_accuracy: 0.8644 Epoch 501/1000 2/2 [==============================] - ETA: 0s - loss: 0.1495 - accuracy: 0.9531 Epoch 501: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 952ms/step - loss: 0.1495 - accuracy: 0.9531 - val_loss: 0.2650 - val_accuracy: 0.8644 Epoch 502/1000 2/2 [==============================] - ETA: 0s - loss: 0.1643 - accuracy: 0.9141 Epoch 502: saving model to training_1/cp.ckpt 2/2 [==============================] - 2s 1s/step - loss: 0.1643 - accuracy: 0.9141 - val_loss: 0.2670 - val_accuracy: 0.8644 Epoch 503/1000 2/2 [==============================] - ETA: 0s - loss: 0.1779 - accuracy: 0.9062 Epoch 503: saving model to training_1/cp.ckpt 2/2 [==============================] - 2s 1s/step - loss: 0.1779 - accuracy: 0.9062 - val_loss: 0.2686 - val_accuracy: 0.8644 Epoch 504/1000 2/2 [==============================] - ETA: 0s - loss: 0.1600 - accuracy: 0.9625 Epoch 504: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 1s/step - loss: 0.1600 - accuracy: 0.9625 - val_loss: 0.2689 - val_accuracy: 0.8644 Epoch 505/1000 2/2 [==============================] - ETA: 0s - loss: 0.1275 - accuracy: 0.9625 Epoch 505: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 1s/step - loss: 0.1275 - accuracy: 0.9625 - val_loss: 0.2680 - val_accuracy: 0.8644 Epoch 506/1000 2/2 [==============================] - ETA: 0s - loss: 0.1473 - accuracy: 0.9375 Epoch 506: saving model to training_1/cp.ckpt 2/2 [==============================] - 2s 1s/step - loss: 0.1473 - accuracy: 0.9375 - val_loss: 0.2678 - val_accuracy: 0.8644 Epoch 507/1000 2/2 [==============================] - ETA: 0s - loss: 0.1198 - accuracy: 0.9609 Epoch 507: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 968ms/step - loss: 0.1198 - accuracy: 0.9609 - val_loss: 0.2672 - val_accuracy: 0.8644 Epoch 508/1000 2/2 [==============================] - ETA: 0s - loss: 0.1290 - accuracy: 0.9625 Epoch 508: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 804ms/step - loss: 0.1290 - accuracy: 0.9625 - val_loss: 0.2670 - val_accuracy: 0.8644 Epoch 509/1000 2/2 [==============================] - ETA: 0s - loss: 0.1622 - accuracy: 0.9219 Epoch 509: saving model to training_1/cp.ckpt 2/2 [==============================] - 2s 1s/step - loss: 0.1622 - accuracy: 0.9219 - val_loss: 0.2672 - val_accuracy: 0.8644 Epoch 510/1000 2/2 [==============================] - ETA: 0s - loss: 0.1284 - accuracy: 0.9250 Epoch 510: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 835ms/step - loss: 0.1284 - accuracy: 0.9250 - val_loss: 0.2674 - val_accuracy: 0.8644 Epoch 511/1000 2/2 [==============================] - ETA: 0s - loss: 0.1641 - accuracy: 0.9375 Epoch 511: saving model to training_1/cp.ckpt 2/2 [==============================] - 2s 1s/step - loss: 0.1641 - accuracy: 0.9375 - val_loss: 0.2685 - val_accuracy: 0.8644 Epoch 512/1000 2/2 [==============================] - ETA: 0s - loss: 0.1069 - accuracy: 0.9609 Epoch 512: saving model to training_1/cp.ckpt 2/2 [==============================] - 2s 1s/step - loss: 0.1069 - accuracy: 0.9609 - val_loss: 0.2706 - val_accuracy: 0.8475 Epoch 513/1000 2/2 [==============================] - ETA: 0s - loss: 0.1871 - accuracy: 0.9250 Epoch 513: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 834ms/step - loss: 0.1871 - accuracy: 0.9250 - val_loss: 0.2733 - val_accuracy: 0.8305 Epoch 514/1000 2/2 [==============================] - ETA: 0s - loss: 0.1451 - accuracy: 0.9297 Epoch 514: saving model to training_1/cp.ckpt 2/2 [==============================] - 2s 1s/step - loss: 0.1451 - accuracy: 0.9297 - val_loss: 0.2743 - val_accuracy: 0.8305 Epoch 515/1000 2/2 [==============================] - ETA: 0s - loss: 0.1631 - accuracy: 0.9375 Epoch 515: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 1s/step - loss: 0.1631 - accuracy: 0.9375 - val_loss: 0.2753 - val_accuracy: 0.8305 Epoch 516/1000 2/2 [==============================] - ETA: 0s - loss: 0.1393 - accuracy: 0.9297 Epoch 516: saving model to training_1/cp.ckpt 2/2 [==============================] - 2s 1s/step - loss: 0.1393 - accuracy: 0.9297 - val_loss: 0.2769 - val_accuracy: 0.8305 Epoch 517/1000 2/2 [==============================] - ETA: 0s - loss: 0.1717 - accuracy: 0.9250 Epoch 517: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 1s/step - loss: 0.1717 - accuracy: 0.9250 - val_loss: 0.2786 - val_accuracy: 0.8305 Epoch 518/1000 2/2 [==============================] - ETA: 0s - loss: 0.2001 - accuracy: 0.9250 Epoch 518: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 809ms/step - loss: 0.2001 - accuracy: 0.9250 - val_loss: 0.2801 - val_accuracy: 0.8136 Epoch 519/1000 2/2 [==============================] - ETA: 0s - loss: 0.1469 - accuracy: 0.9062 Epoch 519: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 994ms/step - loss: 0.1469 - accuracy: 0.9062 - val_loss: 0.2800 - val_accuracy: 0.8136 Epoch 520/1000 2/2 [==============================] - ETA: 0s - loss: 0.1444 - accuracy: 0.9531 Epoch 520: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 929ms/step - loss: 0.1444 - accuracy: 0.9531 - val_loss: 0.2781 - val_accuracy: 0.8136 Epoch 521/1000 2/2 [==============================] - ETA: 0s - loss: 0.1783 - accuracy: 0.9219 Epoch 521: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 1s/step - loss: 0.1783 - accuracy: 0.9219 - val_loss: 0.2761 - val_accuracy: 0.8136 Epoch 522/1000 2/2 [==============================] - ETA: 0s - loss: 0.1481 - accuracy: 0.9625 Epoch 522: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 815ms/step - loss: 0.1481 - accuracy: 0.9625 - val_loss: 0.2747 - val_accuracy: 0.8136 Epoch 523/1000 2/2 [==============================] - ETA: 0s - loss: 0.1230 - accuracy: 0.9500 Epoch 523: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 1s/step - loss: 0.1230 - accuracy: 0.9500 - val_loss: 0.2744 - val_accuracy: 0.8136 Epoch 524/1000 2/2 [==============================] - ETA: 0s - loss: 0.1329 - accuracy: 0.9625 Epoch 524: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 1s/step - loss: 0.1329 - accuracy: 0.9625 - val_loss: 0.2744 - val_accuracy: 0.8136 Epoch 525/1000 2/2 [==============================] - ETA: 0s - loss: 0.1305 - accuracy: 0.9531 Epoch 525: saving model to training_1/cp.ckpt 2/2 [==============================] - 2s 1s/step - loss: 0.1305 - accuracy: 0.9531 - val_loss: 0.2744 - val_accuracy: 0.8136 Epoch 526/1000 2/2 [==============================] - ETA: 0s - loss: 0.0974 - accuracy: 0.9750 Epoch 526: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 1s/step - loss: 0.0974 - accuracy: 0.9750 - val_loss: 0.2743 - val_accuracy: 0.8136 Epoch 527/1000 2/2 [==============================] - ETA: 0s - loss: 0.2049 - accuracy: 0.9125 Epoch 527: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 1s/step - loss: 0.2049 - accuracy: 0.9125 - val_loss: 0.2730 - val_accuracy: 0.8136 Epoch 528/1000 2/2 [==============================] - ETA: 0s - loss: 0.1441 - accuracy: 0.9297 Epoch 528: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 964ms/step - loss: 0.1441 - accuracy: 0.9297 - val_loss: 0.2722 - val_accuracy: 0.8136 Epoch 529/1000 2/2 [==============================] - ETA: 0s - loss: 0.1328 - accuracy: 0.9453 Epoch 529: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 973ms/step - loss: 0.1328 - accuracy: 0.9453 - val_loss: 0.2716 - val_accuracy: 0.8136 Epoch 530/1000 2/2 [==============================] - ETA: 0s - loss: 0.1522 - accuracy: 0.9375 Epoch 530: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 1s/step - loss: 0.1522 - accuracy: 0.9375 - val_loss: 0.2708 - val_accuracy: 0.8136 Epoch 531/1000 2/2 [==============================] - ETA: 0s - loss: 0.1479 - accuracy: 0.9531 Epoch 531: saving model to training_1/cp.ckpt 2/2 [==============================] - 2s 1s/step - loss: 0.1479 - accuracy: 0.9531 - val_loss: 0.2707 - val_accuracy: 0.8136 Epoch 532/1000 2/2 [==============================] - ETA: 0s - loss: 0.1405 - accuracy: 0.9375 Epoch 532: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 824ms/step - loss: 0.1405 - accuracy: 0.9375 - val_loss: 0.2708 - val_accuracy: 0.8136 Epoch 533/1000 2/2 [==============================] - ETA: 0s - loss: 0.1355 - accuracy: 0.9219 Epoch 533: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 929ms/step - loss: 0.1355 - accuracy: 0.9219 - val_loss: 0.2722 - val_accuracy: 0.8136 Epoch 534/1000 2/2 [==============================] - ETA: 0s - loss: 0.1524 - accuracy: 0.9375 Epoch 534: saving model to training_1/cp.ckpt 2/2 [==============================] - 2s 975ms/step - loss: 0.1524 - accuracy: 0.9375 - val_loss: 0.2752 - val_accuracy: 0.8136 Epoch 535/1000 2/2 [==============================] - ETA: 0s - loss: 0.1148 - accuracy: 0.9625 Epoch 535: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 825ms/step - loss: 0.1148 - accuracy: 0.9625 - val_loss: 0.2764 - val_accuracy: 0.8136 Epoch 536/1000 2/2 [==============================] - ETA: 0s - loss: 0.1230 - accuracy: 0.9500 Epoch 536: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 812ms/step - loss: 0.1230 - accuracy: 0.9500 - val_loss: 0.2759 - val_accuracy: 0.8136 Epoch 537/1000 2/2 [==============================] - ETA: 0s - loss: 0.1516 - accuracy: 0.9500 Epoch 537: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 1s/step - loss: 0.1516 - accuracy: 0.9500 - val_loss: 0.2749 - val_accuracy: 0.8136 Epoch 538/1000 2/2 [==============================] - ETA: 0s - loss: 0.1491 - accuracy: 0.9125 Epoch 538: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 835ms/step - loss: 0.1491 - accuracy: 0.9125 - val_loss: 0.2737 - val_accuracy: 0.8136 Epoch 539/1000 2/2 [==============================] - ETA: 0s - loss: 0.1335 - accuracy: 0.9766 Epoch 539: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 934ms/step - loss: 0.1335 - accuracy: 0.9766 - val_loss: 0.2722 - val_accuracy: 0.8305 Epoch 540/1000 2/2 [==============================] - ETA: 0s - loss: 0.1515 - accuracy: 0.9375 Epoch 540: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 836ms/step - loss: 0.1515 - accuracy: 0.9375 - val_loss: 0.2716 - val_accuracy: 0.8305 Epoch 541/1000 2/2 [==============================] - ETA: 0s - loss: 0.1613 - accuracy: 0.9125 Epoch 541: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 835ms/step - loss: 0.1613 - accuracy: 0.9125 - val_loss: 0.2709 - val_accuracy: 0.8305 Epoch 542/1000 2/2 [==============================] - ETA: 0s - loss: 0.1141 - accuracy: 0.9375 Epoch 542: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 1s/step - loss: 0.1141 - accuracy: 0.9375 - val_loss: 0.2692 - val_accuracy: 0.8305 Epoch 543/1000 2/2 [==============================] - ETA: 0s - loss: 0.1393 - accuracy: 0.9453 Epoch 543: saving model to training_1/cp.ckpt 2/2 [==============================] - 2s 1s/step - loss: 0.1393 - accuracy: 0.9453 - val_loss: 0.2681 - val_accuracy: 0.8305 Epoch 544/1000 2/2 [==============================] - ETA: 0s - loss: 0.1320 - accuracy: 0.9625 Epoch 544: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 1s/step - loss: 0.1320 - accuracy: 0.9625 - val_loss: 0.2639 - val_accuracy: 0.8305 Epoch 545/1000 2/2 [==============================] - ETA: 0s - loss: 0.1872 - accuracy: 0.9500 Epoch 545: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 1s/step - loss: 0.1872 - accuracy: 0.9500 - val_loss: 0.2605 - val_accuracy: 0.8475 Epoch 546/1000 2/2 [==============================] - ETA: 0s - loss: 0.1484 - accuracy: 0.9375 Epoch 546: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 867ms/step - loss: 0.1484 - accuracy: 0.9375 - val_loss: 0.2576 - val_accuracy: 0.8475 Epoch 547/1000 2/2 [==============================] - ETA: 0s - loss: 0.1332 - accuracy: 0.9250 Epoch 547: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 1s/step - loss: 0.1332 - accuracy: 0.9250 - val_loss: 0.2548 - val_accuracy: 0.8475 Epoch 548/1000 2/2 [==============================] - ETA: 0s - loss: 0.1152 - accuracy: 0.9375 Epoch 548: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 863ms/step - loss: 0.1152 - accuracy: 0.9375 - val_loss: 0.2531 - val_accuracy: 0.8475 Epoch 549/1000 2/2 [==============================] - ETA: 0s - loss: 0.1229 - accuracy: 0.9375 Epoch 549: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 816ms/step - loss: 0.1229 - accuracy: 0.9375 - val_loss: 0.2502 - val_accuracy: 0.8475 Epoch 550/1000 2/2 [==============================] - ETA: 0s - loss: 0.1275 - accuracy: 0.9375 Epoch 550: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 970ms/step - loss: 0.1275 - accuracy: 0.9375 - val_loss: 0.2477 - val_accuracy: 0.8475 Epoch 551/1000 2/2 [==============================] - ETA: 0s - loss: 0.1139 - accuracy: 0.9609 Epoch 551: saving model to training_1/cp.ckpt 2/2 [==============================] - 2s 1s/step - loss: 0.1139 - accuracy: 0.9609 - val_loss: 0.2460 - val_accuracy: 0.8475 Epoch 552/1000 2/2 [==============================] - ETA: 0s - loss: 0.1195 - accuracy: 0.9625 Epoch 552: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 843ms/step - loss: 0.1195 - accuracy: 0.9625 - val_loss: 0.2457 - val_accuracy: 0.8475 Epoch 553/1000 2/2 [==============================] - ETA: 0s - loss: 0.1418 - accuracy: 0.9609 Epoch 553: saving model to training_1/cp.ckpt 2/2 [==============================] - 2s 1s/step - loss: 0.1418 - accuracy: 0.9609 - val_loss: 0.2463 - val_accuracy: 0.8644 Epoch 554/1000 2/2 [==============================] - ETA: 0s - loss: 0.1361 - accuracy: 0.9531 Epoch 554: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 928ms/step - loss: 0.1361 - accuracy: 0.9531 - val_loss: 0.2481 - val_accuracy: 0.8644 Epoch 555/1000 2/2 [==============================] - ETA: 0s - loss: 0.1261 - accuracy: 0.9609 Epoch 555: saving model to training_1/cp.ckpt 2/2 [==============================] - 2s 1s/step - loss: 0.1261 - accuracy: 0.9609 - val_loss: 0.2497 - val_accuracy: 0.8644 Epoch 556/1000 2/2 [==============================] - ETA: 0s - loss: 0.1351 - accuracy: 0.9375 Epoch 556: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 1s/step - loss: 0.1351 - accuracy: 0.9375 - val_loss: 0.2502 - val_accuracy: 0.8644 Epoch 557/1000 2/2 [==============================] - ETA: 0s - loss: 0.1348 - accuracy: 0.9609 Epoch 557: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 979ms/step - loss: 0.1348 - accuracy: 0.9609 - val_loss: 0.2511 - val_accuracy: 0.8644 Epoch 558/1000 2/2 [==============================] - ETA: 0s - loss: 0.1423 - accuracy: 0.9453 Epoch 558: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 966ms/step - loss: 0.1423 - accuracy: 0.9453 - val_loss: 0.2523 - val_accuracy: 0.8475 Epoch 559/1000 2/2 [==============================] - ETA: 0s - loss: 0.1183 - accuracy: 0.9500 Epoch 559: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 1s/step - loss: 0.1183 - accuracy: 0.9500 - val_loss: 0.2542 - val_accuracy: 0.8475 Epoch 560/1000 2/2 [==============================] - ETA: 0s - loss: 0.1366 - accuracy: 0.9375 Epoch 560: saving model to training_1/cp.ckpt 2/2 [==============================] - 2s 1s/step - loss: 0.1366 - accuracy: 0.9375 - val_loss: 0.2565 - val_accuracy: 0.8475 Epoch 561/1000 2/2 [==============================] - ETA: 0s - loss: 0.1263 - accuracy: 0.9453 Epoch 561: saving model to training_1/cp.ckpt 2/2 [==============================] - 2s 1s/step - loss: 0.1263 - accuracy: 0.9453 - val_loss: 0.2591 - val_accuracy: 0.8475 Epoch 562/1000 2/2 [==============================] - ETA: 0s - loss: 0.1715 - accuracy: 0.9141 Epoch 562: saving model to training_1/cp.ckpt 2/2 [==============================] - 2s 1s/step - loss: 0.1715 - accuracy: 0.9141 - val_loss: 0.2615 - val_accuracy: 0.8475 Epoch 563/1000 2/2 [==============================] - ETA: 0s - loss: 0.1418 - accuracy: 0.9250 Epoch 563: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 1s/step - loss: 0.1418 - accuracy: 0.9250 - val_loss: 0.2651 - val_accuracy: 0.8475 Epoch 564/1000 2/2 [==============================] - ETA: 0s - loss: 0.1290 - accuracy: 0.9625 Epoch 564: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 811ms/step - loss: 0.1290 - accuracy: 0.9625 - val_loss: 0.2691 - val_accuracy: 0.8305 Epoch 565/1000 2/2 [==============================] - ETA: 0s - loss: 0.1817 - accuracy: 0.9375 Epoch 565: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 1s/step - loss: 0.1817 - accuracy: 0.9375 - val_loss: 0.2708 - val_accuracy: 0.8305 Epoch 566/1000 2/2 [==============================] - ETA: 0s - loss: 0.1019 - accuracy: 0.9500 Epoch 566: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 1s/step - loss: 0.1019 - accuracy: 0.9500 - val_loss: 0.2701 - val_accuracy: 0.8305 Epoch 567/1000 2/2 [==============================] - ETA: 0s - loss: 0.1623 - accuracy: 0.9125 Epoch 567: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 1s/step - loss: 0.1623 - accuracy: 0.9125 - val_loss: 0.2697 - val_accuracy: 0.8305 Epoch 568/1000 2/2 [==============================] - ETA: 0s - loss: 0.1237 - accuracy: 0.9250 Epoch 568: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 837ms/step - loss: 0.1237 - accuracy: 0.9250 - val_loss: 0.2684 - val_accuracy: 0.8475 Epoch 569/1000 2/2 [==============================] - ETA: 0s - loss: 0.1747 - accuracy: 0.8984 Epoch 569: saving model to training_1/cp.ckpt 2/2 [==============================] - 2s 987ms/step - loss: 0.1747 - accuracy: 0.8984 - val_loss: 0.2667 - val_accuracy: 0.8475 Epoch 570/1000 2/2 [==============================] - ETA: 0s - loss: 0.1495 - accuracy: 0.9375 Epoch 570: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 1s/step - loss: 0.1495 - accuracy: 0.9375 - val_loss: 0.2644 - val_accuracy: 0.8475 Epoch 571/1000 2/2 [==============================] - ETA: 0s - loss: 0.1420 - accuracy: 0.9453 Epoch 571: saving model to training_1/cp.ckpt 2/2 [==============================] - 2s 1s/step - loss: 0.1420 - accuracy: 0.9453 - val_loss: 0.2626 - val_accuracy: 0.8475 Epoch 572/1000 2/2 [==============================] - ETA: 0s - loss: 0.1442 - accuracy: 0.9250 Epoch 572: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 863ms/step - loss: 0.1442 - accuracy: 0.9250 - val_loss: 0.2603 - val_accuracy: 0.8475 Epoch 573/1000 2/2 [==============================] - ETA: 0s - loss: 0.1683 - accuracy: 0.9141 Epoch 573: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 970ms/step - loss: 0.1683 - accuracy: 0.9141 - val_loss: 0.2589 - val_accuracy: 0.8475 Epoch 574/1000 2/2 [==============================] - ETA: 0s - loss: 0.1001 - accuracy: 0.9875 Epoch 574: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 1s/step - loss: 0.1001 - accuracy: 0.9875 - val_loss: 0.2574 - val_accuracy: 0.8475 Epoch 575/1000 2/2 [==============================] - ETA: 0s - loss: 0.1083 - accuracy: 0.9766 Epoch 575: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 930ms/step - loss: 0.1083 - accuracy: 0.9766 - val_loss: 0.2565 - val_accuracy: 0.8475 Epoch 576/1000 2/2 [==============================] - ETA: 0s - loss: 0.1630 - accuracy: 0.9125 Epoch 576: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 993ms/step - loss: 0.1630 - accuracy: 0.9125 - val_loss: 0.2553 - val_accuracy: 0.8305 Epoch 577/1000 2/2 [==============================] - ETA: 0s - loss: 0.1247 - accuracy: 0.9688 Epoch 577: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 954ms/step - loss: 0.1247 - accuracy: 0.9688 - val_loss: 0.2550 - val_accuracy: 0.8305 Epoch 578/1000 2/2 [==============================] - ETA: 0s - loss: 0.1639 - accuracy: 0.9297 Epoch 578: saving model to training_1/cp.ckpt 2/2 [==============================] - 2s 1s/step - loss: 0.1639 - accuracy: 0.9297 - val_loss: 0.2545 - val_accuracy: 0.8305 Epoch 579/1000 2/2 [==============================] - ETA: 0s - loss: 0.1569 - accuracy: 0.9500 Epoch 579: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 1s/step - loss: 0.1569 - accuracy: 0.9500 - val_loss: 0.2547 - val_accuracy: 0.8305 Epoch 580/1000 2/2 [==============================] - ETA: 0s - loss: 0.1216 - accuracy: 0.9531 Epoch 580: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 973ms/step - loss: 0.1216 - accuracy: 0.9531 - val_loss: 0.2551 - val_accuracy: 0.8305 Epoch 581/1000 2/2 [==============================] - ETA: 0s - loss: 0.1174 - accuracy: 0.9625 Epoch 581: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 823ms/step - loss: 0.1174 - accuracy: 0.9625 - val_loss: 0.2562 - val_accuracy: 0.8305 Epoch 582/1000 2/2 [==============================] - ETA: 0s - loss: 0.1507 - accuracy: 0.9125 Epoch 582: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 824ms/step - loss: 0.1507 - accuracy: 0.9125 - val_loss: 0.2584 - val_accuracy: 0.8305 Epoch 583/1000 2/2 [==============================] - ETA: 0s - loss: 0.1742 - accuracy: 0.9125 Epoch 583: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 1s/step - loss: 0.1742 - accuracy: 0.9125 - val_loss: 0.2610 - val_accuracy: 0.8305 Epoch 584/1000 2/2 [==============================] - ETA: 0s - loss: 0.1347 - accuracy: 0.9500 Epoch 584: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 832ms/step - loss: 0.1347 - accuracy: 0.9500 - val_loss: 0.2647 - val_accuracy: 0.8136 Epoch 585/1000 2/2 [==============================] - ETA: 0s - loss: 0.1067 - accuracy: 0.9625 Epoch 585: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 813ms/step - loss: 0.1067 - accuracy: 0.9625 - val_loss: 0.2673 - val_accuracy: 0.8136 Epoch 586/1000 2/2 [==============================] - ETA: 0s - loss: 0.1478 - accuracy: 0.9375 Epoch 586: saving model to training_1/cp.ckpt 2/2 [==============================] - 2s 1s/step - loss: 0.1478 - accuracy: 0.9375 - val_loss: 0.2684 - val_accuracy: 0.8136 Epoch 587/1000 2/2 [==============================] - ETA: 0s - loss: 0.1327 - accuracy: 0.9375 Epoch 587: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 1s/step - loss: 0.1327 - accuracy: 0.9375 - val_loss: 0.2703 - val_accuracy: 0.8136 Epoch 588/1000 2/2 [==============================] - ETA: 0s - loss: 0.1022 - accuracy: 0.9844 Epoch 588: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 926ms/step - loss: 0.1022 - accuracy: 0.9844 - val_loss: 0.2727 - val_accuracy: 0.8136 Epoch 589/1000 2/2 [==============================] - ETA: 0s - loss: 0.2192 - accuracy: 0.9250 Epoch 589: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 815ms/step - loss: 0.2192 - accuracy: 0.9250 - val_loss: 0.2742 - val_accuracy: 0.8136 Epoch 590/1000 2/2 [==============================] - ETA: 0s - loss: 0.1731 - accuracy: 0.9000 Epoch 590: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 1s/step - loss: 0.1731 - accuracy: 0.9000 - val_loss: 0.2751 - val_accuracy: 0.8136 Epoch 591/1000 2/2 [==============================] - ETA: 0s - loss: 0.1368 - accuracy: 0.9453 Epoch 591: saving model to training_1/cp.ckpt 2/2 [==============================] - 2s 1s/step - loss: 0.1368 - accuracy: 0.9453 - val_loss: 0.2766 - val_accuracy: 0.8136 Epoch 592/1000 2/2 [==============================] - ETA: 0s - loss: 0.1619 - accuracy: 0.9531 Epoch 592: saving model to training_1/cp.ckpt 2/2 [==============================] - 2s 1s/step - loss: 0.1619 - accuracy: 0.9531 - val_loss: 0.2789 - val_accuracy: 0.8136 Epoch 593/1000 2/2 [==============================] - ETA: 0s - loss: 0.1565 - accuracy: 0.9453 Epoch 593: saving model to training_1/cp.ckpt 2/2 [==============================] - 2s 1s/step - loss: 0.1565 - accuracy: 0.9453 - val_loss: 0.2819 - val_accuracy: 0.8136 Epoch 594/1000 2/2 [==============================] - ETA: 0s - loss: 0.1473 - accuracy: 0.9375 Epoch 594: saving model to training_1/cp.ckpt 2/2 [==============================] - 2s 1s/step - loss: 0.1473 - accuracy: 0.9375 - val_loss: 0.2856 - val_accuracy: 0.8136 Epoch 595/1000 2/2 [==============================] - ETA: 0s - loss: 0.1418 - accuracy: 0.9500 Epoch 595: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 844ms/step - loss: 0.1418 - accuracy: 0.9500 - val_loss: 0.2865 - val_accuracy: 0.8136 Epoch 596/1000 2/2 [==============================] - ETA: 0s - loss: 0.1448 - accuracy: 0.9375 Epoch 596: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 965ms/step - loss: 0.1448 - accuracy: 0.9375 - val_loss: 0.2876 - val_accuracy: 0.8136 Epoch 597/1000 2/2 [==============================] - ETA: 0s - loss: 0.1282 - accuracy: 0.9531 Epoch 597: saving model to training_1/cp.ckpt 2/2 [==============================] - 2s 1s/step - loss: 0.1282 - accuracy: 0.9531 - val_loss: 0.2887 - val_accuracy: 0.8136 Epoch 598/1000 2/2 [==============================] - ETA: 0s - loss: 0.1232 - accuracy: 0.9625 Epoch 598: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 1s/step - loss: 0.1232 - accuracy: 0.9625 - val_loss: 0.2871 - val_accuracy: 0.8136 Epoch 599/1000 2/2 [==============================] - ETA: 0s - loss: 0.1416 - accuracy: 0.9297 Epoch 599: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 940ms/step - loss: 0.1416 - accuracy: 0.9297 - val_loss: 0.2858 - val_accuracy: 0.8136 Epoch 600/1000 2/2 [==============================] - ETA: 0s - loss: 0.1402 - accuracy: 0.9219 Epoch 600: saving model to training_1/cp.ckpt 2/2 [==============================] - 2s 1s/step - loss: 0.1402 - accuracy: 0.9219 - val_loss: 0.2840 - val_accuracy: 0.8136 Epoch 601/1000 2/2 [==============================] - ETA: 0s - loss: 0.1639 - accuracy: 0.9125 Epoch 601: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 848ms/step - loss: 0.1639 - accuracy: 0.9125 - val_loss: 0.2813 - val_accuracy: 0.8305 Epoch 602/1000 2/2 [==============================] - ETA: 0s - loss: 0.1876 - accuracy: 0.9250 Epoch 602: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 820ms/step - loss: 0.1876 - accuracy: 0.9250 - val_loss: 0.2773 - val_accuracy: 0.8305 Epoch 603/1000 2/2 [==============================] - ETA: 0s - loss: 0.1317 - accuracy: 0.9500 Epoch 603: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 826ms/step - loss: 0.1317 - accuracy: 0.9500 - val_loss: 0.2740 - val_accuracy: 0.8136 Epoch 604/1000 2/2 [==============================] - ETA: 0s - loss: 0.1224 - accuracy: 0.9500 Epoch 604: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 1s/step - loss: 0.1224 - accuracy: 0.9500 - val_loss: 0.2705 - val_accuracy: 0.8136 Epoch 605/1000 2/2 [==============================] - ETA: 0s - loss: 0.1412 - accuracy: 0.9375 Epoch 605: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 1s/step - loss: 0.1412 - accuracy: 0.9375 - val_loss: 0.2674 - val_accuracy: 0.8136 Epoch 606/1000 2/2 [==============================] - ETA: 0s - loss: 0.1069 - accuracy: 0.9750 Epoch 606: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 1s/step - loss: 0.1069 - accuracy: 0.9750 - val_loss: 0.2641 - val_accuracy: 0.8305 Epoch 607/1000 2/2 [==============================] - ETA: 0s - loss: 0.0904 - accuracy: 0.9750 Epoch 607: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 811ms/step - loss: 0.0904 - accuracy: 0.9750 - val_loss: 0.2630 - val_accuracy: 0.8305 Epoch 608/1000 2/2 [==============================] - ETA: 0s - loss: 0.1305 - accuracy: 0.9375 Epoch 608: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 1s/step - loss: 0.1305 - accuracy: 0.9375 - val_loss: 0.2647 - val_accuracy: 0.8305 Epoch 609/1000 2/2 [==============================] - ETA: 0s - loss: 0.1477 - accuracy: 0.9375 Epoch 609: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 831ms/step - loss: 0.1477 - accuracy: 0.9375 - val_loss: 0.2663 - val_accuracy: 0.8305 Epoch 610/1000 2/2 [==============================] - ETA: 0s - loss: 0.0939 - accuracy: 1.0000 Epoch 610: saving model to training_1/cp.ckpt 2/2 [==============================] - 2s 1s/step - loss: 0.0939 - accuracy: 1.0000 - val_loss: 0.2680 - val_accuracy: 0.8475 Epoch 611/1000 2/2 [==============================] - ETA: 0s - loss: 0.0889 - accuracy: 0.9875 Epoch 611: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 845ms/step - loss: 0.0889 - accuracy: 0.9875 - val_loss: 0.2703 - val_accuracy: 0.8305 Epoch 612/1000 2/2 [==============================] - ETA: 0s - loss: 0.1134 - accuracy: 0.9609 Epoch 612: saving model to training_1/cp.ckpt 2/2 [==============================] - 2s 1s/step - loss: 0.1134 - accuracy: 0.9609 - val_loss: 0.2725 - val_accuracy: 0.8305 Epoch 613/1000 2/2 [==============================] - ETA: 0s - loss: 0.1093 - accuracy: 0.9688 Epoch 613: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 932ms/step - loss: 0.1093 - accuracy: 0.9688 - val_loss: 0.2741 - val_accuracy: 0.8305 Epoch 614/1000 2/2 [==============================] - ETA: 0s - loss: 0.1112 - accuracy: 0.9688 Epoch 614: saving model to training_1/cp.ckpt 2/2 [==============================] - 2s 1s/step - loss: 0.1112 - accuracy: 0.9688 - val_loss: 0.2750 - val_accuracy: 0.8305 Epoch 615/1000 2/2 [==============================] - ETA: 0s - loss: 0.1013 - accuracy: 1.0000 Epoch 615: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 1s/step - loss: 0.1013 - accuracy: 1.0000 - val_loss: 0.2758 - val_accuracy: 0.8305 Epoch 616/1000 2/2 [==============================] - ETA: 0s - loss: 0.1483 - accuracy: 0.9141 Epoch 616: saving model to training_1/cp.ckpt 2/2 [==============================] - 2s 1s/step - loss: 0.1483 - accuracy: 0.9141 - val_loss: 0.2760 - val_accuracy: 0.8305 Epoch 617/1000 2/2 [==============================] - ETA: 0s - loss: 0.1175 - accuracy: 0.9625 Epoch 617: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 1s/step - loss: 0.1175 - accuracy: 0.9625 - val_loss: 0.2762 - val_accuracy: 0.8305 Epoch 618/1000 2/2 [==============================] - ETA: 0s - loss: 0.1037 - accuracy: 0.9688 Epoch 618: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 955ms/step - loss: 0.1037 - accuracy: 0.9688 - val_loss: 0.2767 - val_accuracy: 0.8305 Epoch 619/1000 2/2 [==============================] - ETA: 0s - loss: 0.1226 - accuracy: 0.9500 Epoch 619: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 820ms/step - loss: 0.1226 - accuracy: 0.9500 - val_loss: 0.2775 - val_accuracy: 0.8305 Epoch 620/1000 2/2 [==============================] - ETA: 0s - loss: 0.1093 - accuracy: 0.9625 Epoch 620: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 820ms/step - loss: 0.1093 - accuracy: 0.9625 - val_loss: 0.2780 - val_accuracy: 0.8305 Epoch 621/1000 2/2 [==============================] - ETA: 0s - loss: 0.1217 - accuracy: 0.9453 Epoch 621: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 923ms/step - loss: 0.1217 - accuracy: 0.9453 - val_loss: 0.2780 - val_accuracy: 0.8475 Epoch 622/1000 2/2 [==============================] - ETA: 0s - loss: 0.1332 - accuracy: 0.9688 Epoch 622: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 958ms/step - loss: 0.1332 - accuracy: 0.9688 - val_loss: 0.2768 - val_accuracy: 0.8475 Epoch 623/1000 2/2 [==============================] - ETA: 0s - loss: 0.1901 - accuracy: 0.8750 Epoch 623: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 874ms/step - loss: 0.1901 - accuracy: 0.8750 - val_loss: 0.2755 - val_accuracy: 0.8475 Epoch 624/1000 2/2 [==============================] - ETA: 0s - loss: 0.1137 - accuracy: 0.9531 Epoch 624: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 931ms/step - loss: 0.1137 - accuracy: 0.9531 - val_loss: 0.2747 - val_accuracy: 0.8475 Epoch 625/1000 2/2 [==============================] - ETA: 0s - loss: 0.1145 - accuracy: 0.9453 Epoch 625: saving model to training_1/cp.ckpt 2/2 [==============================] - 2s 982ms/step - loss: 0.1145 - accuracy: 0.9453 - val_loss: 0.2742 - val_accuracy: 0.8475 Epoch 626/1000 2/2 [==============================] - ETA: 0s - loss: 0.1495 - accuracy: 0.9453 Epoch 626: saving model to training_1/cp.ckpt 2/2 [==============================] - 2s 985ms/step - loss: 0.1495 - accuracy: 0.9453 - val_loss: 0.2736 - val_accuracy: 0.8475 Epoch 627/1000 2/2 [==============================] - ETA: 0s - loss: 0.0794 - accuracy: 0.9875 Epoch 627: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 817ms/step - loss: 0.0794 - accuracy: 0.9875 - val_loss: 0.2719 - val_accuracy: 0.8475 Epoch 628/1000 2/2 [==============================] - ETA: 0s - loss: 0.1697 - accuracy: 0.9141 Epoch 628: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 968ms/step - loss: 0.1697 - accuracy: 0.9141 - val_loss: 0.2718 - val_accuracy: 0.8475 Epoch 629/1000 2/2 [==============================] - ETA: 0s - loss: 0.1177 - accuracy: 0.9297 Epoch 629: saving model to training_1/cp.ckpt 2/2 [==============================] - 2s 1s/step - loss: 0.1177 - accuracy: 0.9297 - val_loss: 0.2714 - val_accuracy: 0.8475 Epoch 630/1000 2/2 [==============================] - ETA: 0s - loss: 0.1289 - accuracy: 0.9453 Epoch 630: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 930ms/step - loss: 0.1289 - accuracy: 0.9453 - val_loss: 0.2695 - val_accuracy: 0.8475 Epoch 631/1000 2/2 [==============================] - ETA: 0s - loss: 0.1265 - accuracy: 0.9625 Epoch 631: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 1s/step - loss: 0.1265 - accuracy: 0.9625 - val_loss: 0.2698 - val_accuracy: 0.8475 Epoch 632/1000 2/2 [==============================] - ETA: 0s - loss: 0.1210 - accuracy: 0.9375 Epoch 632: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 1s/step - loss: 0.1210 - accuracy: 0.9375 - val_loss: 0.2694 - val_accuracy: 0.8475 Epoch 633/1000 2/2 [==============================] - ETA: 0s - loss: 0.1212 - accuracy: 0.9531 Epoch 633: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 910ms/step - loss: 0.1212 - accuracy: 0.9531 - val_loss: 0.2685 - val_accuracy: 0.8475 Epoch 634/1000 2/2 [==============================] - ETA: 0s - loss: 0.0945 - accuracy: 0.9625 Epoch 634: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 828ms/step - loss: 0.0945 - accuracy: 0.9625 - val_loss: 0.2682 - val_accuracy: 0.8475 Epoch 635/1000 2/2 [==============================] - ETA: 0s - loss: 0.1332 - accuracy: 0.9453 Epoch 635: saving model to training_1/cp.ckpt 2/2 [==============================] - 2s 1s/step - loss: 0.1332 - accuracy: 0.9453 - val_loss: 0.2689 - val_accuracy: 0.8305 Epoch 636/1000 2/2 [==============================] - ETA: 0s - loss: 0.1162 - accuracy: 0.9297 Epoch 636: saving model to training_1/cp.ckpt 2/2 [==============================] - 2s 1s/step - loss: 0.1162 - accuracy: 0.9297 - val_loss: 0.2700 - val_accuracy: 0.8305 Epoch 637/1000 2/2 [==============================] - ETA: 0s - loss: 0.1188 - accuracy: 0.9453 Epoch 637: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 944ms/step - loss: 0.1188 - accuracy: 0.9453 - val_loss: 0.2703 - val_accuracy: 0.8305 Epoch 638/1000 2/2 [==============================] - ETA: 0s - loss: 0.1679 - accuracy: 0.9125 Epoch 638: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 835ms/step - loss: 0.1679 - accuracy: 0.9125 - val_loss: 0.2692 - val_accuracy: 0.8305 Epoch 639/1000 2/2 [==============================] - ETA: 0s - loss: 0.0977 - accuracy: 0.9625 Epoch 639: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 837ms/step - loss: 0.0977 - accuracy: 0.9625 - val_loss: 0.2677 - val_accuracy: 0.8305 Epoch 640/1000 2/2 [==============================] - ETA: 0s - loss: 0.0780 - accuracy: 0.9844 Epoch 640: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 934ms/step - loss: 0.0780 - accuracy: 0.9844 - val_loss: 0.2665 - val_accuracy: 0.8305 Epoch 641/1000 2/2 [==============================] - ETA: 0s - loss: 0.0954 - accuracy: 0.9625 Epoch 641: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 809ms/step - loss: 0.0954 - accuracy: 0.9625 - val_loss: 0.2658 - val_accuracy: 0.8305 Epoch 642/1000 2/2 [==============================] - ETA: 0s - loss: 0.1260 - accuracy: 0.9531 Epoch 642: saving model to training_1/cp.ckpt 2/2 [==============================] - 2s 1s/step - loss: 0.1260 - accuracy: 0.9531 - val_loss: 0.2659 - val_accuracy: 0.8305 Epoch 643/1000 2/2 [==============================] - ETA: 0s - loss: 0.1252 - accuracy: 0.9453 Epoch 643: saving model to training_1/cp.ckpt 2/2 [==============================] - 2s 1s/step - loss: 0.1252 - accuracy: 0.9453 - val_loss: 0.2662 - val_accuracy: 0.8305 Epoch 644/1000 2/2 [==============================] - ETA: 0s - loss: 0.1139 - accuracy: 0.9625 Epoch 644: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 820ms/step - loss: 0.1139 - accuracy: 0.9625 - val_loss: 0.2659 - val_accuracy: 0.8475 Epoch 645/1000 2/2 [==============================] - ETA: 0s - loss: 0.1121 - accuracy: 0.9531 Epoch 645: saving model to training_1/cp.ckpt 2/2 [==============================] - 2s 1s/step - loss: 0.1121 - accuracy: 0.9531 - val_loss: 0.2654 - val_accuracy: 0.8475 Epoch 646/1000 2/2 [==============================] - ETA: 0s - loss: 0.1068 - accuracy: 0.9688 Epoch 646: saving model to training_1/cp.ckpt 2/2 [==============================] - 2s 1s/step - loss: 0.1068 - accuracy: 0.9688 - val_loss: 0.2652 - val_accuracy: 0.8475 Epoch 647/1000 2/2 [==============================] - ETA: 0s - loss: 0.1136 - accuracy: 0.9625 Epoch 647: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 1s/step - loss: 0.1136 - accuracy: 0.9625 - val_loss: 0.2650 - val_accuracy: 0.8475 Epoch 648/1000 2/2 [==============================] - ETA: 0s - loss: 0.1084 - accuracy: 0.9688 Epoch 648: saving model to training_1/cp.ckpt 2/2 [==============================] - 2s 1s/step - loss: 0.1084 - accuracy: 0.9688 - val_loss: 0.2641 - val_accuracy: 0.8475 Epoch 649/1000 2/2 [==============================] - ETA: 0s - loss: 0.1123 - accuracy: 0.9531 Epoch 649: saving model to training_1/cp.ckpt 2/2 [==============================] - 2s 999ms/step - loss: 0.1123 - accuracy: 0.9531 - val_loss: 0.2637 - val_accuracy: 0.8475 Epoch 650/1000 2/2 [==============================] - ETA: 0s - loss: 0.1562 - accuracy: 0.9375 Epoch 650: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 1s/step - loss: 0.1562 - accuracy: 0.9375 - val_loss: 0.2633 - val_accuracy: 0.8475 Epoch 651/1000 2/2 [==============================] - ETA: 0s - loss: 0.1610 - accuracy: 0.9375 Epoch 651: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 804ms/step - loss: 0.1610 - accuracy: 0.9375 - val_loss: 0.2635 - val_accuracy: 0.8475 Epoch 652/1000 2/2 [==============================] - ETA: 0s - loss: 0.1656 - accuracy: 0.9141 Epoch 652: saving model to training_1/cp.ckpt 2/2 [==============================] - 2s 1s/step - loss: 0.1656 - accuracy: 0.9141 - val_loss: 0.2640 - val_accuracy: 0.8475 Epoch 653/1000 2/2 [==============================] - ETA: 0s - loss: 0.1222 - accuracy: 0.9500 Epoch 653: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 822ms/step - loss: 0.1222 - accuracy: 0.9500 - val_loss: 0.2651 - val_accuracy: 0.8475 Epoch 654/1000 2/2 [==============================] - ETA: 0s - loss: 0.1006 - accuracy: 0.9766 Epoch 654: saving model to training_1/cp.ckpt 2/2 [==============================] - 2s 1s/step - loss: 0.1006 - accuracy: 0.9766 - val_loss: 0.2669 - val_accuracy: 0.8475 Epoch 655/1000 2/2 [==============================] - ETA: 0s - loss: 0.1395 - accuracy: 0.9250 Epoch 655: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 826ms/step - loss: 0.1395 - accuracy: 0.9250 - val_loss: 0.2695 - val_accuracy: 0.8475 Epoch 656/1000 2/2 [==============================] - ETA: 0s - loss: 0.1042 - accuracy: 0.9766 Epoch 656: saving model to training_1/cp.ckpt 2/2 [==============================] - 2s 1s/step - loss: 0.1042 - accuracy: 0.9766 - val_loss: 0.2724 - val_accuracy: 0.8475 Epoch 657/1000 2/2 [==============================] - ETA: 0s - loss: 0.1471 - accuracy: 0.9125 Epoch 657: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 1s/step - loss: 0.1471 - accuracy: 0.9125 - val_loss: 0.2752 - val_accuracy: 0.8475 Epoch 658/1000 2/2 [==============================] - ETA: 0s - loss: 0.1069 - accuracy: 0.9531 Epoch 658: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 935ms/step - loss: 0.1069 - accuracy: 0.9531 - val_loss: 0.2782 - val_accuracy: 0.8475 Epoch 659/1000 2/2 [==============================] - ETA: 0s - loss: 0.0970 - accuracy: 0.9766 Epoch 659: saving model to training_1/cp.ckpt 2/2 [==============================] - 2s 1s/step - loss: 0.0970 - accuracy: 0.9766 - val_loss: 0.2803 - val_accuracy: 0.8475 Epoch 660/1000 2/2 [==============================] - ETA: 0s - loss: 0.1135 - accuracy: 0.9609 Epoch 660: saving model to training_1/cp.ckpt 2/2 [==============================] - 2s 1s/step - loss: 0.1135 - accuracy: 0.9609 - val_loss: 0.2815 - val_accuracy: 0.8305 Epoch 661/1000 2/2 [==============================] - ETA: 0s - loss: 0.0622 - accuracy: 0.9875 Epoch 661: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 801ms/step - loss: 0.0622 - accuracy: 0.9875 - val_loss: 0.2827 - val_accuracy: 0.8305 Epoch 662/1000 2/2 [==============================] - ETA: 0s - loss: 0.1074 - accuracy: 0.9625 Epoch 662: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 812ms/step - loss: 0.1074 - accuracy: 0.9625 - val_loss: 0.2826 - val_accuracy: 0.8305 Epoch 663/1000 2/2 [==============================] - ETA: 0s - loss: 0.1000 - accuracy: 0.9844 Epoch 663: saving model to training_1/cp.ckpt 2/2 [==============================] - 2s 1s/step - loss: 0.1000 - accuracy: 0.9844 - val_loss: 0.2818 - val_accuracy: 0.8475 Epoch 664/1000 2/2 [==============================] - ETA: 0s - loss: 0.0919 - accuracy: 0.9500 Epoch 664: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 840ms/step - loss: 0.0919 - accuracy: 0.9500 - val_loss: 0.2819 - val_accuracy: 0.8475 Epoch 665/1000 2/2 [==============================] - ETA: 0s - loss: 0.1268 - accuracy: 0.9375 Epoch 665: saving model to training_1/cp.ckpt 2/2 [==============================] - 2s 1s/step - loss: 0.1268 - accuracy: 0.9375 - val_loss: 0.2829 - val_accuracy: 0.8475 Epoch 666/1000 2/2 [==============================] - ETA: 0s - loss: 0.1491 - accuracy: 0.9250 Epoch 666: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 1s/step - loss: 0.1491 - accuracy: 0.9250 - val_loss: 0.2811 - val_accuracy: 0.8475 Epoch 667/1000 2/2 [==============================] - ETA: 0s - loss: 0.1190 - accuracy: 0.9500 Epoch 667: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 820ms/step - loss: 0.1190 - accuracy: 0.9500 - val_loss: 0.2784 - val_accuracy: 0.8475 Epoch 668/1000 2/2 [==============================] - ETA: 0s - loss: 0.0955 - accuracy: 0.9688 Epoch 668: saving model to training_1/cp.ckpt 2/2 [==============================] - 2s 1s/step - loss: 0.0955 - accuracy: 0.9688 - val_loss: 0.2763 - val_accuracy: 0.8475 Epoch 669/1000 2/2 [==============================] - ETA: 0s - loss: 0.1251 - accuracy: 0.9531 Epoch 669: saving model to training_1/cp.ckpt 2/2 [==============================] - 2s 1s/step - loss: 0.1251 - accuracy: 0.9531 - val_loss: 0.2759 - val_accuracy: 0.8475 Epoch 670/1000 2/2 [==============================] - ETA: 0s - loss: 0.1130 - accuracy: 0.9500 Epoch 670: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 821ms/step - loss: 0.1130 - accuracy: 0.9500 - val_loss: 0.2762 - val_accuracy: 0.8475 Epoch 671/1000 2/2 [==============================] - ETA: 0s - loss: 0.1206 - accuracy: 0.9375 Epoch 671: saving model to training_1/cp.ckpt 2/2 [==============================] - 2s 1s/step - loss: 0.1206 - accuracy: 0.9375 - val_loss: 0.2766 - val_accuracy: 0.8305 Epoch 672/1000 2/2 [==============================] - ETA: 0s - loss: 0.1287 - accuracy: 0.9453 Epoch 672: saving model to training_1/cp.ckpt 2/2 [==============================] - 2s 1s/step - loss: 0.1287 - accuracy: 0.9453 - val_loss: 0.2768 - val_accuracy: 0.8305 Epoch 673/1000 2/2 [==============================] - ETA: 0s - loss: 0.1517 - accuracy: 0.9250 Epoch 673: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 818ms/step - loss: 0.1517 - accuracy: 0.9250 - val_loss: 0.2769 - val_accuracy: 0.8305 Epoch 674/1000 2/2 [==============================] - ETA: 0s - loss: 0.1057 - accuracy: 0.9609 Epoch 674: saving model to training_1/cp.ckpt 2/2 [==============================] - 2s 1s/step - loss: 0.1057 - accuracy: 0.9609 - val_loss: 0.2767 - val_accuracy: 0.8305 Epoch 675/1000 2/2 [==============================] - ETA: 0s - loss: 0.1428 - accuracy: 0.9375 Epoch 675: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 834ms/step - loss: 0.1428 - accuracy: 0.9375 - val_loss: 0.2772 - val_accuracy: 0.8305 Epoch 676/1000 2/2 [==============================] - ETA: 0s - loss: 0.1095 - accuracy: 0.9625 Epoch 676: saving model to training_1/cp.ckpt 2/2 [==============================] - 2s 1s/step - loss: 0.1095 - accuracy: 0.9625 - val_loss: 0.2795 - val_accuracy: 0.8305 Epoch 677/1000 2/2 [==============================] - ETA: 0s - loss: 0.1420 - accuracy: 0.9375 Epoch 677: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 1s/step - loss: 0.1420 - accuracy: 0.9375 - val_loss: 0.2809 - val_accuracy: 0.8305 Epoch 678/1000 2/2 [==============================] - ETA: 0s - loss: 0.1261 - accuracy: 0.9141 Epoch 678: saving model to training_1/cp.ckpt 2/2 [==============================] - 2s 1s/step - loss: 0.1261 - accuracy: 0.9141 - val_loss: 0.2811 - val_accuracy: 0.8305 Epoch 679/1000 2/2 [==============================] - ETA: 0s - loss: 0.1210 - accuracy: 0.9625 Epoch 679: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 808ms/step - loss: 0.1210 - accuracy: 0.9625 - val_loss: 0.2805 - val_accuracy: 0.8305 Epoch 680/1000 2/2 [==============================] - ETA: 0s - loss: 0.1199 - accuracy: 0.9250 Epoch 680: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 826ms/step - loss: 0.1199 - accuracy: 0.9250 - val_loss: 0.2789 - val_accuracy: 0.8305 Epoch 681/1000 2/2 [==============================] - ETA: 0s - loss: 0.1262 - accuracy: 0.9688 Epoch 681: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 938ms/step - loss: 0.1262 - accuracy: 0.9688 - val_loss: 0.2781 - val_accuracy: 0.8305 Epoch 682/1000 2/2 [==============================] - ETA: 0s - loss: 0.1391 - accuracy: 0.9219 Epoch 682: saving model to training_1/cp.ckpt 2/2 [==============================] - 2s 1s/step - loss: 0.1391 - accuracy: 0.9219 - val_loss: 0.2770 - val_accuracy: 0.8305 Epoch 683/1000 2/2 [==============================] - ETA: 0s - loss: 0.0833 - accuracy: 0.9875 Epoch 683: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 1s/step - loss: 0.0833 - accuracy: 0.9875 - val_loss: 0.2774 - val_accuracy: 0.8305 Epoch 684/1000 2/2 [==============================] - ETA: 0s - loss: 0.1212 - accuracy: 0.9375 Epoch 684: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 999ms/step - loss: 0.1212 - accuracy: 0.9375 - val_loss: 0.2778 - val_accuracy: 0.8305 Epoch 685/1000 2/2 [==============================] - ETA: 0s - loss: 0.1233 - accuracy: 0.9531 Epoch 685: saving model to training_1/cp.ckpt 2/2 [==============================] - 2s 1s/step - loss: 0.1233 - accuracy: 0.9531 - val_loss: 0.2769 - val_accuracy: 0.8305 Epoch 686/1000 2/2 [==============================] - ETA: 0s - loss: 0.1080 - accuracy: 0.9609 Epoch 686: saving model to training_1/cp.ckpt 2/2 [==============================] - 2s 1s/step - loss: 0.1080 - accuracy: 0.9609 - val_loss: 0.2748 - val_accuracy: 0.8305 Epoch 687/1000 2/2 [==============================] - ETA: 0s - loss: 0.1526 - accuracy: 0.9125 Epoch 687: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 1s/step - loss: 0.1526 - accuracy: 0.9125 - val_loss: 0.2761 - val_accuracy: 0.8305 Epoch 688/1000 2/2 [==============================] - ETA: 0s - loss: 0.1283 - accuracy: 0.9375 Epoch 688: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 1s/step - loss: 0.1283 - accuracy: 0.9375 - val_loss: 0.2777 - val_accuracy: 0.8305 Epoch 689/1000 2/2 [==============================] - ETA: 0s - loss: 0.1500 - accuracy: 0.9375 Epoch 689: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 831ms/step - loss: 0.1500 - accuracy: 0.9375 - val_loss: 0.2809 - val_accuracy: 0.8305 Epoch 690/1000 2/2 [==============================] - ETA: 0s - loss: 0.1213 - accuracy: 0.9375 Epoch 690: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 1s/step - loss: 0.1213 - accuracy: 0.9375 - val_loss: 0.2837 - val_accuracy: 0.8305 Epoch 691/1000 2/2 [==============================] - ETA: 0s - loss: 0.1150 - accuracy: 0.9531 Epoch 691: saving model to training_1/cp.ckpt 2/2 [==============================] - 2s 1s/step - loss: 0.1150 - accuracy: 0.9531 - val_loss: 0.2858 - val_accuracy: 0.8305 Epoch 692/1000 2/2 [==============================] - ETA: 0s - loss: 0.0847 - accuracy: 0.9766 Epoch 692: saving model to training_1/cp.ckpt 2/2 [==============================] - 2s 1s/step - loss: 0.0847 - accuracy: 0.9766 - val_loss: 0.2873 - val_accuracy: 0.8305 Epoch 693/1000 2/2 [==============================] - ETA: 0s - loss: 0.1106 - accuracy: 0.9625 Epoch 693: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 1s/step - loss: 0.1106 - accuracy: 0.9625 - val_loss: 0.2868 - val_accuracy: 0.8305 Epoch 694/1000 2/2 [==============================] - ETA: 0s - loss: 0.1030 - accuracy: 0.9750 Epoch 694: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 833ms/step - loss: 0.1030 - accuracy: 0.9750 - val_loss: 0.2863 - val_accuracy: 0.8305 Epoch 695/1000 2/2 [==============================] - ETA: 0s - loss: 0.1061 - accuracy: 0.9531 Epoch 695: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 955ms/step - loss: 0.1061 - accuracy: 0.9531 - val_loss: 0.2856 - val_accuracy: 0.8305 Epoch 696/1000 2/2 [==============================] - ETA: 0s - loss: 0.1274 - accuracy: 0.9297 Epoch 696: saving model to training_1/cp.ckpt 2/2 [==============================] - 2s 1s/step - loss: 0.1274 - accuracy: 0.9297 - val_loss: 0.2846 - val_accuracy: 0.8305 Epoch 697/1000 2/2 [==============================] - ETA: 0s - loss: 0.1182 - accuracy: 0.9531 Epoch 697: saving model to training_1/cp.ckpt 2/2 [==============================] - 2s 1s/step - loss: 0.1182 - accuracy: 0.9531 - val_loss: 0.2838 - val_accuracy: 0.8305 Epoch 698/1000 2/2 [==============================] - ETA: 0s - loss: 0.1083 - accuracy: 0.9453 Epoch 698: saving model to training_1/cp.ckpt 2/2 [==============================] - 2s 1s/step - loss: 0.1083 - accuracy: 0.9453 - val_loss: 0.2828 - val_accuracy: 0.8305 Epoch 699/1000 2/2 [==============================] - ETA: 0s - loss: 0.1175 - accuracy: 0.9531 Epoch 699: saving model to training_1/cp.ckpt 2/2 [==============================] - 2s 1s/step - loss: 0.1175 - accuracy: 0.9531 - val_loss: 0.2830 - val_accuracy: 0.8305 Epoch 700/1000 2/2 [==============================] - ETA: 0s - loss: 0.1411 - accuracy: 0.9297 Epoch 700: saving model to training_1/cp.ckpt 2/2 [==============================] - 2s 957ms/step - loss: 0.1411 - accuracy: 0.9297 - val_loss: 0.2833 - val_accuracy: 0.8305 Epoch 701/1000 2/2 [==============================] - ETA: 0s - loss: 0.1243 - accuracy: 0.9453 Epoch 701: saving model to training_1/cp.ckpt 2/2 [==============================] - 2s 1s/step - loss: 0.1243 - accuracy: 0.9453 - val_loss: 0.2845 - val_accuracy: 0.8305 Epoch 702/1000 2/2 [==============================] - ETA: 0s - loss: 0.1150 - accuracy: 0.9500 Epoch 702: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 861ms/step - loss: 0.1150 - accuracy: 0.9500 - val_loss: 0.2868 - val_accuracy: 0.8305 Epoch 703/1000 2/2 [==============================] - ETA: 0s - loss: 0.1140 - accuracy: 0.9250 Epoch 703: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 1s/step - loss: 0.1140 - accuracy: 0.9250 - val_loss: 0.2885 - val_accuracy: 0.8305 Epoch 704/1000 2/2 [==============================] - ETA: 0s - loss: 0.1070 - accuracy: 0.9531 Epoch 704: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 934ms/step - loss: 0.1070 - accuracy: 0.9531 - val_loss: 0.2881 - val_accuracy: 0.8305 Epoch 705/1000 2/2 [==============================] - ETA: 0s - loss: 0.1123 - accuracy: 0.9625 Epoch 705: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 1s/step - loss: 0.1123 - accuracy: 0.9625 - val_loss: 0.2871 - val_accuracy: 0.8305 Epoch 706/1000 2/2 [==============================] - ETA: 0s - loss: 0.1124 - accuracy: 0.9453 Epoch 706: saving model to training_1/cp.ckpt 2/2 [==============================] - 2s 1s/step - loss: 0.1124 - accuracy: 0.9453 - val_loss: 0.2852 - val_accuracy: 0.8305 Epoch 707/1000 2/2 [==============================] - ETA: 0s - loss: 0.0818 - accuracy: 0.9531 Epoch 707: saving model to training_1/cp.ckpt 2/2 [==============================] - 2s 1s/step - loss: 0.0818 - accuracy: 0.9531 - val_loss: 0.2834 - val_accuracy: 0.8305 Epoch 708/1000 2/2 [==============================] - ETA: 0s - loss: 0.0923 - accuracy: 1.0000 Epoch 708: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 846ms/step - loss: 0.0923 - accuracy: 1.0000 - val_loss: 0.2816 - val_accuracy: 0.8305 Epoch 709/1000 2/2 [==============================] - ETA: 0s - loss: 0.1267 - accuracy: 0.9297 Epoch 709: saving model to training_1/cp.ckpt 2/2 [==============================] - 2s 1s/step - loss: 0.1267 - accuracy: 0.9297 - val_loss: 0.2808 - val_accuracy: 0.8305 Epoch 710/1000 2/2 [==============================] - ETA: 0s - loss: 0.1103 - accuracy: 0.9500 Epoch 710: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 1s/step - loss: 0.1103 - accuracy: 0.9500 - val_loss: 0.2803 - val_accuracy: 0.8305 Epoch 711/1000 2/2 [==============================] - ETA: 0s - loss: 0.1186 - accuracy: 0.9453 Epoch 711: saving model to training_1/cp.ckpt 2/2 [==============================] - 2s 957ms/step - loss: 0.1186 - accuracy: 0.9453 - val_loss: 0.2794 - val_accuracy: 0.8305 Epoch 712/1000 2/2 [==============================] - ETA: 0s - loss: 0.1164 - accuracy: 0.9500 Epoch 712: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 888ms/step - loss: 0.1164 - accuracy: 0.9500 - val_loss: 0.2793 - val_accuracy: 0.8305 Epoch 713/1000 2/2 [==============================] - ETA: 0s - loss: 0.1329 - accuracy: 0.9453 Epoch 713: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 920ms/step - loss: 0.1329 - accuracy: 0.9453 - val_loss: 0.2797 - val_accuracy: 0.8305 Epoch 714/1000 2/2 [==============================] - ETA: 0s - loss: 0.1029 - accuracy: 0.9453 Epoch 714: saving model to training_1/cp.ckpt 2/2 [==============================] - 2s 1s/step - loss: 0.1029 - accuracy: 0.9453 - val_loss: 0.2799 - val_accuracy: 0.8305 Epoch 715/1000 2/2 [==============================] - ETA: 0s - loss: 0.0814 - accuracy: 0.9750 Epoch 715: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 1s/step - loss: 0.0814 - accuracy: 0.9750 - val_loss: 0.2799 - val_accuracy: 0.8305 Epoch 716/1000 2/2 [==============================] - ETA: 0s - loss: 0.1071 - accuracy: 0.9609 Epoch 716: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 936ms/step - loss: 0.1071 - accuracy: 0.9609 - val_loss: 0.2795 - val_accuracy: 0.8475 Epoch 717/1000 2/2 [==============================] - ETA: 0s - loss: 0.0719 - accuracy: 1.0000 Epoch 717: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 1s/step - loss: 0.0719 - accuracy: 1.0000 - val_loss: 0.2809 - val_accuracy: 0.8305 Epoch 718/1000 2/2 [==============================] - ETA: 0s - loss: 0.1597 - accuracy: 0.9375 Epoch 718: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 821ms/step - loss: 0.1597 - accuracy: 0.9375 - val_loss: 0.2791 - val_accuracy: 0.8475 Epoch 719/1000 2/2 [==============================] - ETA: 0s - loss: 0.1307 - accuracy: 0.9750 Epoch 719: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 834ms/step - loss: 0.1307 - accuracy: 0.9750 - val_loss: 0.2759 - val_accuracy: 0.8475 Epoch 720/1000 2/2 [==============================] - ETA: 0s - loss: 0.0994 - accuracy: 0.9922 Epoch 720: saving model to training_1/cp.ckpt 2/2 [==============================] - 2s 1s/step - loss: 0.0994 - accuracy: 0.9922 - val_loss: 0.2731 - val_accuracy: 0.8475 Epoch 721/1000 2/2 [==============================] - ETA: 0s - loss: 0.1031 - accuracy: 0.9750 Epoch 721: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 859ms/step - loss: 0.1031 - accuracy: 0.9750 - val_loss: 0.2718 - val_accuracy: 0.8475 Epoch 722/1000 2/2 [==============================] - ETA: 0s - loss: 0.1109 - accuracy: 0.9375 Epoch 722: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 832ms/step - loss: 0.1109 - accuracy: 0.9375 - val_loss: 0.2699 - val_accuracy: 0.8475 Epoch 723/1000 2/2 [==============================] - ETA: 0s - loss: 0.0936 - accuracy: 0.9500 Epoch 723: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 1s/step - loss: 0.0936 - accuracy: 0.9500 - val_loss: 0.2673 - val_accuracy: 0.8475 Epoch 724/1000 2/2 [==============================] - ETA: 0s - loss: 0.1319 - accuracy: 0.9500 Epoch 724: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 1s/step - loss: 0.1319 - accuracy: 0.9500 - val_loss: 0.2645 - val_accuracy: 0.8475 Epoch 725/1000 2/2 [==============================] - ETA: 0s - loss: 0.1114 - accuracy: 0.9375 Epoch 725: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 1s/step - loss: 0.1114 - accuracy: 0.9375 - val_loss: 0.2619 - val_accuracy: 0.8475 Epoch 726/1000 2/2 [==============================] - ETA: 0s - loss: 0.0872 - accuracy: 0.9875 Epoch 726: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 805ms/step - loss: 0.0872 - accuracy: 0.9875 - val_loss: 0.2602 - val_accuracy: 0.8475 Epoch 727/1000 2/2 [==============================] - ETA: 0s - loss: 0.1199 - accuracy: 0.9609 Epoch 727: saving model to training_1/cp.ckpt 2/2 [==============================] - 2s 1s/step - loss: 0.1199 - accuracy: 0.9609 - val_loss: 0.2602 - val_accuracy: 0.8475 Epoch 728/1000 2/2 [==============================] - ETA: 0s - loss: 0.1012 - accuracy: 0.9609 Epoch 728: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 926ms/step - loss: 0.1012 - accuracy: 0.9609 - val_loss: 0.2608 - val_accuracy: 0.8475 Epoch 729/1000 2/2 [==============================] - ETA: 0s - loss: 0.0955 - accuracy: 0.9750 Epoch 729: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 1s/step - loss: 0.0955 - accuracy: 0.9750 - val_loss: 0.2607 - val_accuracy: 0.8475 Epoch 730/1000 2/2 [==============================] - ETA: 0s - loss: 0.1248 - accuracy: 0.9297 Epoch 730: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 970ms/step - loss: 0.1248 - accuracy: 0.9297 - val_loss: 0.2611 - val_accuracy: 0.8475 Epoch 731/1000 2/2 [==============================] - ETA: 0s - loss: 0.1311 - accuracy: 0.9219 Epoch 731: saving model to training_1/cp.ckpt 2/2 [==============================] - 2s 1s/step - loss: 0.1311 - accuracy: 0.9219 - val_loss: 0.2610 - val_accuracy: 0.8475 Epoch 732/1000 2/2 [==============================] - ETA: 0s - loss: 0.1236 - accuracy: 0.9375 Epoch 732: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 812ms/step - loss: 0.1236 - accuracy: 0.9375 - val_loss: 0.2621 - val_accuracy: 0.8305 Epoch 733/1000 2/2 [==============================] - ETA: 0s - loss: 0.1027 - accuracy: 0.9609 Epoch 733: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 959ms/step - loss: 0.1027 - accuracy: 0.9609 - val_loss: 0.2639 - val_accuracy: 0.8305 Epoch 734/1000 2/2 [==============================] - ETA: 0s - loss: 0.1354 - accuracy: 0.9453 Epoch 734: saving model to training_1/cp.ckpt 2/2 [==============================] - 2s 1s/step - loss: 0.1354 - accuracy: 0.9453 - val_loss: 0.2655 - val_accuracy: 0.8305 Epoch 735/1000 2/2 [==============================] - ETA: 0s - loss: 0.1007 - accuracy: 0.9531 Epoch 735: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 940ms/step - loss: 0.1007 - accuracy: 0.9531 - val_loss: 0.2681 - val_accuracy: 0.8305 Epoch 736/1000 2/2 [==============================] - ETA: 0s - loss: 0.1023 - accuracy: 0.9609 Epoch 736: saving model to training_1/cp.ckpt 2/2 [==============================] - 2s 1s/step - loss: 0.1023 - accuracy: 0.9609 - val_loss: 0.2705 - val_accuracy: 0.8305 Epoch 737/1000 2/2 [==============================] - ETA: 0s - loss: 0.0855 - accuracy: 0.9688 Epoch 737: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 901ms/step - loss: 0.0855 - accuracy: 0.9688 - val_loss: 0.2720 - val_accuracy: 0.8305 Epoch 738/1000 2/2 [==============================] - ETA: 0s - loss: 0.1273 - accuracy: 0.9000 Epoch 738: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 838ms/step - loss: 0.1273 - accuracy: 0.9000 - val_loss: 0.2730 - val_accuracy: 0.8305 Epoch 739/1000 2/2 [==============================] - ETA: 0s - loss: 0.1079 - accuracy: 0.9250 Epoch 739: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 1s/step - loss: 0.1079 - accuracy: 0.9250 - val_loss: 0.2744 - val_accuracy: 0.8305 Epoch 740/1000 2/2 [==============================] - ETA: 0s - loss: 0.0813 - accuracy: 0.9922 Epoch 740: saving model to training_1/cp.ckpt 2/2 [==============================] - 2s 1s/step - loss: 0.0813 - accuracy: 0.9922 - val_loss: 0.2757 - val_accuracy: 0.8305 Epoch 741/1000 2/2 [==============================] - ETA: 0s - loss: 0.1141 - accuracy: 0.9500 Epoch 741: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 839ms/step - loss: 0.1141 - accuracy: 0.9500 - val_loss: 0.2759 - val_accuracy: 0.8305 Epoch 742/1000 2/2 [==============================] - ETA: 0s - loss: 0.0984 - accuracy: 0.9844 Epoch 742: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 951ms/step - loss: 0.0984 - accuracy: 0.9844 - val_loss: 0.2755 - val_accuracy: 0.8305 Epoch 743/1000 2/2 [==============================] - ETA: 0s - loss: 0.0862 - accuracy: 0.9609 Epoch 743: saving model to training_1/cp.ckpt 2/2 [==============================] - 2s 1s/step - loss: 0.0862 - accuracy: 0.9609 - val_loss: 0.2756 - val_accuracy: 0.8305 Epoch 744/1000 2/2 [==============================] - ETA: 0s - loss: 0.1266 - accuracy: 0.9453 Epoch 744: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 954ms/step - loss: 0.1266 - accuracy: 0.9453 - val_loss: 0.2753 - val_accuracy: 0.8305 Epoch 745/1000 2/2 [==============================] - ETA: 0s - loss: 0.0972 - accuracy: 0.9625 Epoch 745: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 838ms/step - loss: 0.0972 - accuracy: 0.9625 - val_loss: 0.2741 - val_accuracy: 0.8305 Epoch 746/1000 2/2 [==============================] - ETA: 0s - loss: 0.1272 - accuracy: 0.9375 Epoch 746: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 1s/step - loss: 0.1272 - accuracy: 0.9375 - val_loss: 0.2730 - val_accuracy: 0.8305 Epoch 747/1000 2/2 [==============================] - ETA: 0s - loss: 0.1130 - accuracy: 0.9250 Epoch 747: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 850ms/step - loss: 0.1130 - accuracy: 0.9250 - val_loss: 0.2731 - val_accuracy: 0.8305 Epoch 748/1000 2/2 [==============================] - ETA: 0s - loss: 0.1005 - accuracy: 0.9609 Epoch 748: saving model to training_1/cp.ckpt 2/2 [==============================] - 2s 1s/step - loss: 0.1005 - accuracy: 0.9609 - val_loss: 0.2731 - val_accuracy: 0.8305 Epoch 749/1000 2/2 [==============================] - ETA: 0s - loss: 0.1331 - accuracy: 0.9219 Epoch 749: saving model to training_1/cp.ckpt 2/2 [==============================] - 2s 1s/step - loss: 0.1331 - accuracy: 0.9219 - val_loss: 0.2735 - val_accuracy: 0.8305 Epoch 750/1000 2/2 [==============================] - ETA: 0s - loss: 0.0987 - accuracy: 0.9531 Epoch 750: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 948ms/step - loss: 0.0987 - accuracy: 0.9531 - val_loss: 0.2732 - val_accuracy: 0.8305 Epoch 751/1000 2/2 [==============================] - ETA: 0s - loss: 0.1306 - accuracy: 0.9625 Epoch 751: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 1s/step - loss: 0.1306 - accuracy: 0.9625 - val_loss: 0.2735 - val_accuracy: 0.8305 Epoch 752/1000 2/2 [==============================] - ETA: 0s - loss: 0.1052 - accuracy: 0.9609 Epoch 752: saving model to training_1/cp.ckpt 2/2 [==============================] - 2s 1s/step - loss: 0.1052 - accuracy: 0.9609 - val_loss: 0.2742 - val_accuracy: 0.8305 Epoch 753/1000 2/2 [==============================] - ETA: 0s - loss: 0.1138 - accuracy: 0.9531 Epoch 753: saving model to training_1/cp.ckpt 2/2 [==============================] - 2s 1s/step - loss: 0.1138 - accuracy: 0.9531 - val_loss: 0.2751 - val_accuracy: 0.8305 Epoch 754/1000 2/2 [==============================] - ETA: 0s - loss: 0.0997 - accuracy: 0.9688 Epoch 754: saving model to training_1/cp.ckpt 2/2 [==============================] - 2s 1s/step - loss: 0.0997 - accuracy: 0.9688 - val_loss: 0.2757 - val_accuracy: 0.8305 Epoch 755/1000 2/2 [==============================] - ETA: 0s - loss: 0.0910 - accuracy: 0.9766 Epoch 755: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 964ms/step - loss: 0.0910 - accuracy: 0.9766 - val_loss: 0.2760 - val_accuracy: 0.8305 Epoch 756/1000 2/2 [==============================] - ETA: 0s - loss: 0.0916 - accuracy: 0.9531 Epoch 756: saving model to training_1/cp.ckpt 2/2 [==============================] - 2s 1s/step - loss: 0.0916 - accuracy: 0.9531 - val_loss: 0.2756 - val_accuracy: 0.8305 Epoch 757/1000 2/2 [==============================] - ETA: 0s - loss: 0.0892 - accuracy: 0.9688 Epoch 757: saving model to training_1/cp.ckpt 2/2 [==============================] - 2s 1s/step - loss: 0.0892 - accuracy: 0.9688 - val_loss: 0.2744 - val_accuracy: 0.8305 Epoch 758/1000 2/2 [==============================] - ETA: 0s - loss: 0.1605 - accuracy: 0.9125 Epoch 758: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 1s/step - loss: 0.1605 - accuracy: 0.9125 - val_loss: 0.2720 - val_accuracy: 0.8475 Epoch 759/1000 2/2 [==============================] - ETA: 0s - loss: 0.1353 - accuracy: 0.9375 Epoch 759: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 1s/step - loss: 0.1353 - accuracy: 0.9375 - val_loss: 0.2697 - val_accuracy: 0.8475 Epoch 760/1000 2/2 [==============================] - ETA: 0s - loss: 0.0941 - accuracy: 0.9875 Epoch 760: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 1s/step - loss: 0.0941 - accuracy: 0.9875 - val_loss: 0.2682 - val_accuracy: 0.8475 Epoch 761/1000 2/2 [==============================] - ETA: 0s - loss: 0.0846 - accuracy: 0.9922 Epoch 761: saving model to training_1/cp.ckpt 2/2 [==============================] - 2s 1s/step - loss: 0.0846 - accuracy: 0.9922 - val_loss: 0.2674 - val_accuracy: 0.8475 Epoch 762/1000 2/2 [==============================] - ETA: 0s - loss: 0.0976 - accuracy: 0.9609 Epoch 762: saving model to training_1/cp.ckpt 2/2 [==============================] - 2s 1s/step - loss: 0.0976 - accuracy: 0.9609 - val_loss: 0.2673 - val_accuracy: 0.8475 Epoch 763/1000 2/2 [==============================] - ETA: 0s - loss: 0.0895 - accuracy: 0.9500 Epoch 763: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 1s/step - loss: 0.0895 - accuracy: 0.9500 - val_loss: 0.2657 - val_accuracy: 0.8475 Epoch 764/1000 2/2 [==============================] - ETA: 0s - loss: 0.0793 - accuracy: 0.9766 Epoch 764: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 985ms/step - loss: 0.0793 - accuracy: 0.9766 - val_loss: 0.2641 - val_accuracy: 0.8475 Epoch 765/1000 2/2 [==============================] - ETA: 0s - loss: 0.0875 - accuracy: 0.9688 Epoch 765: saving model to training_1/cp.ckpt 2/2 [==============================] - 2s 964ms/step - loss: 0.0875 - accuracy: 0.9688 - val_loss: 0.2638 - val_accuracy: 0.8475 Epoch 766/1000 2/2 [==============================] - ETA: 0s - loss: 0.1283 - accuracy: 0.9500 Epoch 766: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 1s/step - loss: 0.1283 - accuracy: 0.9500 - val_loss: 0.2612 - val_accuracy: 0.8475 Epoch 767/1000 2/2 [==============================] - ETA: 0s - loss: 0.1182 - accuracy: 0.9375 Epoch 767: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 1s/step - loss: 0.1182 - accuracy: 0.9375 - val_loss: 0.2574 - val_accuracy: 0.8475 Epoch 768/1000 2/2 [==============================] - ETA: 0s - loss: 0.0919 - accuracy: 0.9453 Epoch 768: saving model to training_1/cp.ckpt 2/2 [==============================] - 2s 1s/step - loss: 0.0919 - accuracy: 0.9453 - val_loss: 0.2547 - val_accuracy: 0.8475 Epoch 769/1000 2/2 [==============================] - ETA: 0s - loss: 0.1081 - accuracy: 0.9750 Epoch 769: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 847ms/step - loss: 0.1081 - accuracy: 0.9750 - val_loss: 0.2529 - val_accuracy: 0.8475 Epoch 770/1000 2/2 [==============================] - ETA: 0s - loss: 0.0646 - accuracy: 1.0000 Epoch 770: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 947ms/step - loss: 0.0646 - accuracy: 1.0000 - val_loss: 0.2518 - val_accuracy: 0.8475 Epoch 771/1000 2/2 [==============================] - ETA: 0s - loss: 0.1405 - accuracy: 0.9500 Epoch 771: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 851ms/step - loss: 0.1405 - accuracy: 0.9500 - val_loss: 0.2505 - val_accuracy: 0.8475 Epoch 772/1000 2/2 [==============================] - ETA: 0s - loss: 0.1141 - accuracy: 0.9531 Epoch 772: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 953ms/step - loss: 0.1141 - accuracy: 0.9531 - val_loss: 0.2495 - val_accuracy: 0.8475 Epoch 773/1000 2/2 [==============================] - ETA: 0s - loss: 0.0894 - accuracy: 0.9844 Epoch 773: saving model to training_1/cp.ckpt 2/2 [==============================] - 2s 964ms/step - loss: 0.0894 - accuracy: 0.9844 - val_loss: 0.2490 - val_accuracy: 0.8475 Epoch 774/1000 2/2 [==============================] - ETA: 0s - loss: 0.1010 - accuracy: 0.9875 Epoch 774: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 1s/step - loss: 0.1010 - accuracy: 0.9875 - val_loss: 0.2502 - val_accuracy: 0.8475 Epoch 775/1000 2/2 [==============================] - ETA: 0s - loss: 0.1218 - accuracy: 0.9500 Epoch 775: saving model to training_1/cp.ckpt 2/2 [==============================] - 2s 1s/step - loss: 0.1218 - accuracy: 0.9500 - val_loss: 0.2521 - val_accuracy: 0.8475 Epoch 776/1000 2/2 [==============================] - ETA: 0s - loss: 0.0885 - accuracy: 0.9750 Epoch 776: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 825ms/step - loss: 0.0885 - accuracy: 0.9750 - val_loss: 0.2556 - val_accuracy: 0.8475 Epoch 777/1000 2/2 [==============================] - ETA: 0s - loss: 0.1032 - accuracy: 0.9750 Epoch 777: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 1s/step - loss: 0.1032 - accuracy: 0.9750 - val_loss: 0.2587 - val_accuracy: 0.8475 Epoch 778/1000 2/2 [==============================] - ETA: 0s - loss: 0.1003 - accuracy: 0.9453 Epoch 778: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 946ms/step - loss: 0.1003 - accuracy: 0.9453 - val_loss: 0.2619 - val_accuracy: 0.8475 Epoch 779/1000 2/2 [==============================] - ETA: 0s - loss: 0.0924 - accuracy: 0.9500 Epoch 779: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 830ms/step - loss: 0.0924 - accuracy: 0.9500 - val_loss: 0.2652 - val_accuracy: 0.8475 Epoch 780/1000 2/2 [==============================] - ETA: 0s - loss: 0.1120 - accuracy: 0.9688 Epoch 780: saving model to training_1/cp.ckpt 2/2 [==============================] - 2s 1s/step - loss: 0.1120 - accuracy: 0.9688 - val_loss: 0.2678 - val_accuracy: 0.8475 Epoch 781/1000 2/2 [==============================] - ETA: 0s - loss: 0.1270 - accuracy: 0.9531 Epoch 781: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 962ms/step - loss: 0.1270 - accuracy: 0.9531 - val_loss: 0.2701 - val_accuracy: 0.8475 Epoch 782/1000 2/2 [==============================] - ETA: 0s - loss: 0.0972 - accuracy: 0.9531 Epoch 782: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 953ms/step - loss: 0.0972 - accuracy: 0.9531 - val_loss: 0.2720 - val_accuracy: 0.8475 Epoch 783/1000 2/2 [==============================] - ETA: 0s - loss: 0.1113 - accuracy: 0.9688 Epoch 783: saving model to training_1/cp.ckpt 2/2 [==============================] - 2s 1s/step - loss: 0.1113 - accuracy: 0.9688 - val_loss: 0.2752 - val_accuracy: 0.8305 Epoch 784/1000 2/2 [==============================] - ETA: 0s - loss: 0.0787 - accuracy: 0.9500 Epoch 784: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 819ms/step - loss: 0.0787 - accuracy: 0.9500 - val_loss: 0.2774 - val_accuracy: 0.8305 Epoch 785/1000 2/2 [==============================] - ETA: 0s - loss: 0.1063 - accuracy: 0.9875 Epoch 785: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 816ms/step - loss: 0.1063 - accuracy: 0.9875 - val_loss: 0.2791 - val_accuracy: 0.8305 Epoch 786/1000 2/2 [==============================] - ETA: 0s - loss: 0.0988 - accuracy: 0.9688 Epoch 786: saving model to training_1/cp.ckpt 2/2 [==============================] - 2s 1s/step - loss: 0.0988 - accuracy: 0.9688 - val_loss: 0.2820 - val_accuracy: 0.8305 Epoch 787/1000 2/2 [==============================] - ETA: 0s - loss: 0.1266 - accuracy: 0.9250 Epoch 787: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 1s/step - loss: 0.1266 - accuracy: 0.9250 - val_loss: 0.2833 - val_accuracy: 0.8136 Epoch 788/1000 2/2 [==============================] - ETA: 0s - loss: 0.1121 - accuracy: 0.9688 Epoch 788: saving model to training_1/cp.ckpt 2/2 [==============================] - 2s 1s/step - loss: 0.1121 - accuracy: 0.9688 - val_loss: 0.2839 - val_accuracy: 0.8136 Epoch 789/1000 2/2 [==============================] - ETA: 0s - loss: 0.1159 - accuracy: 0.9375 Epoch 789: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 1s/step - loss: 0.1159 - accuracy: 0.9375 - val_loss: 0.2841 - val_accuracy: 0.8136 Epoch 790/1000 2/2 [==============================] - ETA: 0s - loss: 0.1131 - accuracy: 0.9625 Epoch 790: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 853ms/step - loss: 0.1131 - accuracy: 0.9625 - val_loss: 0.2837 - val_accuracy: 0.8475 Epoch 791/1000 2/2 [==============================] - ETA: 0s - loss: 0.0619 - accuracy: 1.0000 Epoch 791: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 1s/step - loss: 0.0619 - accuracy: 1.0000 - val_loss: 0.2837 - val_accuracy: 0.8475 Epoch 792/1000 2/2 [==============================] - ETA: 0s - loss: 0.0737 - accuracy: 1.0000 Epoch 792: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 1s/step - loss: 0.0737 - accuracy: 1.0000 - val_loss: 0.2861 - val_accuracy: 0.8475 Epoch 793/1000 2/2 [==============================] - ETA: 0s - loss: 0.1128 - accuracy: 0.9750 Epoch 793: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 1s/step - loss: 0.1128 - accuracy: 0.9750 - val_loss: 0.2885 - val_accuracy: 0.8305 Epoch 794/1000 2/2 [==============================] - ETA: 0s - loss: 0.0624 - accuracy: 1.0000 Epoch 794: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 1s/step - loss: 0.0624 - accuracy: 1.0000 - val_loss: 0.2914 - val_accuracy: 0.8305 Epoch 795/1000 2/2 [==============================] - ETA: 0s - loss: 0.0935 - accuracy: 0.9609 Epoch 795: saving model to training_1/cp.ckpt 2/2 [==============================] - 2s 1s/step - loss: 0.0935 - accuracy: 0.9609 - val_loss: 0.2928 - val_accuracy: 0.8305 Epoch 796/1000 2/2 [==============================] - ETA: 0s - loss: 0.0912 - accuracy: 0.9625 Epoch 796: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 881ms/step - loss: 0.0912 - accuracy: 0.9625 - val_loss: 0.2941 - val_accuracy: 0.8305 Epoch 797/1000 2/2 [==============================] - ETA: 0s - loss: 0.0922 - accuracy: 0.9766 Epoch 797: saving model to training_1/cp.ckpt 2/2 [==============================] - 2s 1s/step - loss: 0.0922 - accuracy: 0.9766 - val_loss: 0.2936 - val_accuracy: 0.8475 Epoch 798/1000 2/2 [==============================] - ETA: 0s - loss: 0.1466 - accuracy: 0.9375 Epoch 798: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 1s/step - loss: 0.1466 - accuracy: 0.9375 - val_loss: 0.2921 - val_accuracy: 0.8475 Epoch 799/1000 2/2 [==============================] - ETA: 0s - loss: 0.0982 - accuracy: 0.9453 Epoch 799: saving model to training_1/cp.ckpt 2/2 [==============================] - 2s 1s/step - loss: 0.0982 - accuracy: 0.9453 - val_loss: 0.2880 - val_accuracy: 0.8475 Epoch 800/1000 2/2 [==============================] - ETA: 0s - loss: 0.0642 - accuracy: 1.0000 Epoch 800: saving model to training_1/cp.ckpt 2/2 [==============================] - 2s 980ms/step - loss: 0.0642 - accuracy: 1.0000 - val_loss: 0.2839 - val_accuracy: 0.8644 Epoch 801/1000 2/2 [==============================] - ETA: 0s - loss: 0.1012 - accuracy: 0.9875 Epoch 801: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 1s/step - loss: 0.1012 - accuracy: 0.9875 - val_loss: 0.2809 - val_accuracy: 0.8644 Epoch 802/1000 2/2 [==============================] - ETA: 0s - loss: 0.0896 - accuracy: 0.9750 Epoch 802: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 842ms/step - loss: 0.0896 - accuracy: 0.9750 - val_loss: 0.2776 - val_accuracy: 0.8644 Epoch 803/1000 2/2 [==============================] - ETA: 0s - loss: 0.1111 - accuracy: 0.9750 Epoch 803: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 905ms/step - loss: 0.1111 - accuracy: 0.9750 - val_loss: 0.2753 - val_accuracy: 0.8644 Epoch 804/1000 2/2 [==============================] - ETA: 0s - loss: 0.1032 - accuracy: 0.9688 Epoch 804: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 959ms/step - loss: 0.1032 - accuracy: 0.9688 - val_loss: 0.2732 - val_accuracy: 0.8644 Epoch 805/1000 2/2 [==============================] - ETA: 0s - loss: 0.1012 - accuracy: 0.9609 Epoch 805: saving model to training_1/cp.ckpt 2/2 [==============================] - 2s 1s/step - loss: 0.1012 - accuracy: 0.9609 - val_loss: 0.2717 - val_accuracy: 0.8644 Epoch 806/1000 2/2 [==============================] - ETA: 0s - loss: 0.1017 - accuracy: 0.9688 Epoch 806: saving model to training_1/cp.ckpt 2/2 [==============================] - 2s 960ms/step - loss: 0.1017 - accuracy: 0.9688 - val_loss: 0.2710 - val_accuracy: 0.8644 Epoch 807/1000 2/2 [==============================] - ETA: 0s - loss: 0.0986 - accuracy: 0.9688 Epoch 807: saving model to training_1/cp.ckpt 2/2 [==============================] - 2s 946ms/step - loss: 0.0986 - accuracy: 0.9688 - val_loss: 0.2702 - val_accuracy: 0.8644 Epoch 808/1000 2/2 [==============================] - ETA: 0s - loss: 0.1174 - accuracy: 0.9688 Epoch 808: saving model to training_1/cp.ckpt 2/2 [==============================] - 2s 1s/step - loss: 0.1174 - accuracy: 0.9688 - val_loss: 0.2693 - val_accuracy: 0.8644 Epoch 809/1000 2/2 [==============================] - ETA: 0s - loss: 0.0800 - accuracy: 0.9750 Epoch 809: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 1s/step - loss: 0.0800 - accuracy: 0.9750 - val_loss: 0.2683 - val_accuracy: 0.8475 Epoch 810/1000 2/2 [==============================] - ETA: 0s - loss: 0.1655 - accuracy: 0.8875 Epoch 810: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 849ms/step - loss: 0.1655 - accuracy: 0.8875 - val_loss: 0.2673 - val_accuracy: 0.8475 Epoch 811/1000 2/2 [==============================] - ETA: 0s - loss: 0.0940 - accuracy: 0.9750 Epoch 811: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 1s/step - loss: 0.0940 - accuracy: 0.9750 - val_loss: 0.2662 - val_accuracy: 0.8475 Epoch 812/1000 2/2 [==============================] - ETA: 0s - loss: 0.0860 - accuracy: 0.9750 Epoch 812: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 1s/step - loss: 0.0860 - accuracy: 0.9750 - val_loss: 0.2628 - val_accuracy: 0.8475 Epoch 813/1000 2/2 [==============================] - ETA: 0s - loss: 0.0997 - accuracy: 0.9297 Epoch 813: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 976ms/step - loss: 0.0997 - accuracy: 0.9297 - val_loss: 0.2612 - val_accuracy: 0.8475 Epoch 814/1000 2/2 [==============================] - ETA: 0s - loss: 0.1229 - accuracy: 0.9625 Epoch 814: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 847ms/step - loss: 0.1229 - accuracy: 0.9625 - val_loss: 0.2585 - val_accuracy: 0.8475 Epoch 815/1000 2/2 [==============================] - ETA: 0s - loss: 0.1036 - accuracy: 0.9500 Epoch 815: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 834ms/step - loss: 0.1036 - accuracy: 0.9500 - val_loss: 0.2557 - val_accuracy: 0.8475 Epoch 816/1000 2/2 [==============================] - ETA: 0s - loss: 0.0913 - accuracy: 0.9609 Epoch 816: saving model to training_1/cp.ckpt 2/2 [==============================] - 2s 980ms/step - loss: 0.0913 - accuracy: 0.9609 - val_loss: 0.2546 - val_accuracy: 0.8475 Epoch 817/1000 2/2 [==============================] - ETA: 0s - loss: 0.1231 - accuracy: 0.9375 Epoch 817: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 1s/step - loss: 0.1231 - accuracy: 0.9375 - val_loss: 0.2543 - val_accuracy: 0.8475 Epoch 818/1000 2/2 [==============================] - ETA: 0s - loss: 0.0968 - accuracy: 0.9750 Epoch 818: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 1s/step - loss: 0.0968 - accuracy: 0.9750 - val_loss: 0.2539 - val_accuracy: 0.8475 Epoch 819/1000 2/2 [==============================] - ETA: 0s - loss: 0.0983 - accuracy: 0.9688 Epoch 819: saving model to training_1/cp.ckpt 2/2 [==============================] - 2s 1s/step - loss: 0.0983 - accuracy: 0.9688 - val_loss: 0.2527 - val_accuracy: 0.8475 Epoch 820/1000 2/2 [==============================] - ETA: 0s - loss: 0.0990 - accuracy: 0.9766 Epoch 820: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 965ms/step - loss: 0.0990 - accuracy: 0.9766 - val_loss: 0.2513 - val_accuracy: 0.8475 Epoch 821/1000 2/2 [==============================] - ETA: 0s - loss: 0.0738 - accuracy: 0.9750 Epoch 821: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 1s/step - loss: 0.0738 - accuracy: 0.9750 - val_loss: 0.2507 - val_accuracy: 0.8475 Epoch 822/1000 2/2 [==============================] - ETA: 0s - loss: 0.1152 - accuracy: 0.9609 Epoch 822: saving model to training_1/cp.ckpt 2/2 [==============================] - 2s 1s/step - loss: 0.1152 - accuracy: 0.9609 - val_loss: 0.2488 - val_accuracy: 0.8475 Epoch 823/1000 2/2 [==============================] - ETA: 0s - loss: 0.0756 - accuracy: 0.9625 Epoch 823: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 1s/step - loss: 0.0756 - accuracy: 0.9625 - val_loss: 0.2470 - val_accuracy: 0.8475 Epoch 824/1000 2/2 [==============================] - ETA: 0s - loss: 0.0963 - accuracy: 0.9844 Epoch 824: saving model to training_1/cp.ckpt 2/2 [==============================] - 2s 1s/step - loss: 0.0963 - accuracy: 0.9844 - val_loss: 0.2454 - val_accuracy: 0.8475 Epoch 825/1000 2/2 [==============================] - ETA: 0s - loss: 0.1150 - accuracy: 0.9688 Epoch 825: saving model to training_1/cp.ckpt 2/2 [==============================] - 2s 1s/step - loss: 0.1150 - accuracy: 0.9688 - val_loss: 0.2448 - val_accuracy: 0.8475 Epoch 826/1000 2/2 [==============================] - ETA: 0s - loss: 0.1223 - accuracy: 0.9500 Epoch 826: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 1s/step - loss: 0.1223 - accuracy: 0.9500 - val_loss: 0.2419 - val_accuracy: 0.8644 Epoch 827/1000 2/2 [==============================] - ETA: 0s - loss: 0.0789 - accuracy: 0.9688 Epoch 827: saving model to training_1/cp.ckpt 2/2 [==============================] - 2s 1s/step - loss: 0.0789 - accuracy: 0.9688 - val_loss: 0.2401 - val_accuracy: 0.8644 Epoch 828/1000 2/2 [==============================] - ETA: 0s - loss: 0.0897 - accuracy: 0.9750 Epoch 828: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 1s/step - loss: 0.0897 - accuracy: 0.9750 - val_loss: 0.2401 - val_accuracy: 0.8644 Epoch 829/1000 2/2 [==============================] - ETA: 0s - loss: 0.1105 - accuracy: 0.9531 Epoch 829: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 938ms/step - loss: 0.1105 - accuracy: 0.9531 - val_loss: 0.2408 - val_accuracy: 0.8644 Epoch 830/1000 2/2 [==============================] - ETA: 0s - loss: 0.0924 - accuracy: 0.9609 Epoch 830: saving model to training_1/cp.ckpt 2/2 [==============================] - 2s 1s/step - loss: 0.0924 - accuracy: 0.9609 - val_loss: 0.2409 - val_accuracy: 0.8644 Epoch 831/1000 2/2 [==============================] - ETA: 0s - loss: 0.0712 - accuracy: 0.9688 Epoch 831: saving model to training_1/cp.ckpt 2/2 [==============================] - 2s 1s/step - loss: 0.0712 - accuracy: 0.9688 - val_loss: 0.2412 - val_accuracy: 0.8644 Epoch 832/1000 2/2 [==============================] - ETA: 0s - loss: 0.0620 - accuracy: 0.9750 Epoch 832: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 811ms/step - loss: 0.0620 - accuracy: 0.9750 - val_loss: 0.2411 - val_accuracy: 0.8644 Epoch 833/1000 2/2 [==============================] - ETA: 0s - loss: 0.1238 - accuracy: 0.9297 Epoch 833: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 949ms/step - loss: 0.1238 - accuracy: 0.9297 - val_loss: 0.2420 - val_accuracy: 0.8644 Epoch 834/1000 2/2 [==============================] - ETA: 0s - loss: 0.0821 - accuracy: 0.9844 Epoch 834: saving model to training_1/cp.ckpt 2/2 [==============================] - 2s 1s/step - loss: 0.0821 - accuracy: 0.9844 - val_loss: 0.2424 - val_accuracy: 0.8644 Epoch 835/1000 2/2 [==============================] - ETA: 0s - loss: 0.1200 - accuracy: 0.9375 Epoch 835: saving model to training_1/cp.ckpt 2/2 [==============================] - 2s 958ms/step - loss: 0.1200 - accuracy: 0.9375 - val_loss: 0.2430 - val_accuracy: 0.8644 Epoch 836/1000 2/2 [==============================] - ETA: 0s - loss: 0.1401 - accuracy: 0.9375 Epoch 836: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 933ms/step - loss: 0.1401 - accuracy: 0.9375 - val_loss: 0.2434 - val_accuracy: 0.8644 Epoch 837/1000 2/2 [==============================] - ETA: 0s - loss: 0.0621 - accuracy: 0.9922 Epoch 837: saving model to training_1/cp.ckpt 2/2 [==============================] - 2s 1s/step - loss: 0.0621 - accuracy: 0.9922 - val_loss: 0.2446 - val_accuracy: 0.8644 Epoch 838/1000 2/2 [==============================] - ETA: 0s - loss: 0.1004 - accuracy: 0.9500 Epoch 838: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 817ms/step - loss: 0.1004 - accuracy: 0.9500 - val_loss: 0.2464 - val_accuracy: 0.8644 Epoch 839/1000 2/2 [==============================] - ETA: 0s - loss: 0.0905 - accuracy: 0.9766 Epoch 839: saving model to training_1/cp.ckpt 2/2 [==============================] - 2s 1s/step - loss: 0.0905 - accuracy: 0.9766 - val_loss: 0.2481 - val_accuracy: 0.8644 Epoch 840/1000 2/2 [==============================] - ETA: 0s - loss: 0.1004 - accuracy: 0.9500 Epoch 840: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 887ms/step - loss: 0.1004 - accuracy: 0.9500 - val_loss: 0.2505 - val_accuracy: 0.8644 Epoch 841/1000 2/2 [==============================] - ETA: 0s - loss: 0.1146 - accuracy: 0.9750 Epoch 841: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 1s/step - loss: 0.1146 - accuracy: 0.9750 - val_loss: 0.2507 - val_accuracy: 0.8644 Epoch 842/1000 2/2 [==============================] - ETA: 0s - loss: 0.0898 - accuracy: 0.9844 Epoch 842: saving model to training_1/cp.ckpt 2/2 [==============================] - 2s 1s/step - loss: 0.0898 - accuracy: 0.9844 - val_loss: 0.2503 - val_accuracy: 0.8644 Epoch 843/1000 2/2 [==============================] - ETA: 0s - loss: 0.1224 - accuracy: 0.9375 Epoch 843: saving model to training_1/cp.ckpt 2/2 [==============================] - 2s 1s/step - loss: 0.1224 - accuracy: 0.9375 - val_loss: 0.2509 - val_accuracy: 0.8644 Epoch 844/1000 2/2 [==============================] - ETA: 0s - loss: 0.0545 - accuracy: 0.9875 Epoch 844: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 848ms/step - loss: 0.0545 - accuracy: 0.9875 - val_loss: 0.2514 - val_accuracy: 0.8644 Epoch 845/1000 2/2 [==============================] - ETA: 0s - loss: 0.1240 - accuracy: 0.9250 Epoch 845: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 1s/step - loss: 0.1240 - accuracy: 0.9250 - val_loss: 0.2505 - val_accuracy: 0.8644 Epoch 846/1000 2/2 [==============================] - ETA: 0s - loss: 0.1128 - accuracy: 0.9750 Epoch 846: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 1s/step - loss: 0.1128 - accuracy: 0.9750 - val_loss: 0.2508 - val_accuracy: 0.8644 Epoch 847/1000 2/2 [==============================] - ETA: 0s - loss: 0.0841 - accuracy: 0.9500 Epoch 847: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 1s/step - loss: 0.0841 - accuracy: 0.9500 - val_loss: 0.2514 - val_accuracy: 0.8644 Epoch 848/1000 2/2 [==============================] - ETA: 0s - loss: 0.0703 - accuracy: 0.9844 Epoch 848: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 928ms/step - loss: 0.0703 - accuracy: 0.9844 - val_loss: 0.2520 - val_accuracy: 0.8644 Epoch 849/1000 2/2 [==============================] - ETA: 0s - loss: 0.0979 - accuracy: 0.9531 Epoch 849: saving model to training_1/cp.ckpt 2/2 [==============================] - 2s 1s/step - loss: 0.0979 - accuracy: 0.9531 - val_loss: 0.2536 - val_accuracy: 0.8644 Epoch 850/1000 2/2 [==============================] - ETA: 0s - loss: 0.0953 - accuracy: 0.9750 Epoch 850: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 842ms/step - loss: 0.0953 - accuracy: 0.9750 - val_loss: 0.2552 - val_accuracy: 0.8644 Epoch 851/1000 2/2 [==============================] - ETA: 0s - loss: 0.0794 - accuracy: 0.9750 Epoch 851: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 833ms/step - loss: 0.0794 - accuracy: 0.9750 - val_loss: 0.2572 - val_accuracy: 0.8644 Epoch 852/1000 2/2 [==============================] - ETA: 0s - loss: 0.0963 - accuracy: 0.9688 Epoch 852: saving model to training_1/cp.ckpt 2/2 [==============================] - 2s 1s/step - loss: 0.0963 - accuracy: 0.9688 - val_loss: 0.2586 - val_accuracy: 0.8644 Epoch 853/1000 2/2 [==============================] - ETA: 0s - loss: 0.0843 - accuracy: 0.9625 Epoch 853: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 1s/step - loss: 0.0843 - accuracy: 0.9625 - val_loss: 0.2596 - val_accuracy: 0.8644 Epoch 854/1000 2/2 [==============================] - ETA: 0s - loss: 0.1328 - accuracy: 0.9453 Epoch 854: saving model to training_1/cp.ckpt 2/2 [==============================] - 2s 1s/step - loss: 0.1328 - accuracy: 0.9453 - val_loss: 0.2612 - val_accuracy: 0.8644 Epoch 855/1000 2/2 [==============================] - ETA: 0s - loss: 0.1115 - accuracy: 0.9453 Epoch 855: saving model to training_1/cp.ckpt 2/2 [==============================] - 2s 1s/step - loss: 0.1115 - accuracy: 0.9453 - val_loss: 0.2625 - val_accuracy: 0.8644 Epoch 856/1000 2/2 [==============================] - ETA: 0s - loss: 0.0815 - accuracy: 0.9750 Epoch 856: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 881ms/step - loss: 0.0815 - accuracy: 0.9750 - val_loss: 0.2628 - val_accuracy: 0.8644 Epoch 857/1000 2/2 [==============================] - ETA: 0s - loss: 0.0965 - accuracy: 0.9609 Epoch 857: saving model to training_1/cp.ckpt 2/2 [==============================] - 2s 1s/step - loss: 0.0965 - accuracy: 0.9609 - val_loss: 0.2621 - val_accuracy: 0.8644 Epoch 858/1000 2/2 [==============================] - ETA: 0s - loss: 0.0653 - accuracy: 0.9844 Epoch 858: saving model to training_1/cp.ckpt 2/2 [==============================] - 2s 1s/step - loss: 0.0653 - accuracy: 0.9844 - val_loss: 0.2615 - val_accuracy: 0.8644 Epoch 859/1000 2/2 [==============================] - ETA: 0s - loss: 0.0777 - accuracy: 0.9844 Epoch 859: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 947ms/step - loss: 0.0777 - accuracy: 0.9844 - val_loss: 0.2625 - val_accuracy: 0.8644 Epoch 860/1000 2/2 [==============================] - ETA: 0s - loss: 0.0645 - accuracy: 0.9750 Epoch 860: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 1s/step - loss: 0.0645 - accuracy: 0.9750 - val_loss: 0.2642 - val_accuracy: 0.8644 Epoch 861/1000 2/2 [==============================] - ETA: 0s - loss: 0.0972 - accuracy: 0.9531 Epoch 861: saving model to training_1/cp.ckpt 2/2 [==============================] - 2s 1s/step - loss: 0.0972 - accuracy: 0.9531 - val_loss: 0.2652 - val_accuracy: 0.8644 Epoch 862/1000 2/2 [==============================] - ETA: 0s - loss: 0.0886 - accuracy: 0.9750 Epoch 862: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 864ms/step - loss: 0.0886 - accuracy: 0.9750 - val_loss: 0.2662 - val_accuracy: 0.8644 Epoch 863/1000 2/2 [==============================] - ETA: 0s - loss: 0.0888 - accuracy: 0.9625 Epoch 863: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 1s/step - loss: 0.0888 - accuracy: 0.9625 - val_loss: 0.2676 - val_accuracy: 0.8644 Epoch 864/1000 2/2 [==============================] - ETA: 0s - loss: 0.0918 - accuracy: 0.9297 Epoch 864: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 955ms/step - loss: 0.0918 - accuracy: 0.9297 - val_loss: 0.2694 - val_accuracy: 0.8644 Epoch 865/1000 2/2 [==============================] - ETA: 0s - loss: 0.0777 - accuracy: 0.9750 Epoch 865: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 840ms/step - loss: 0.0777 - accuracy: 0.9750 - val_loss: 0.2710 - val_accuracy: 0.8644 Epoch 866/1000 2/2 [==============================] - ETA: 0s - loss: 0.0713 - accuracy: 0.9844 Epoch 866: saving model to training_1/cp.ckpt 2/2 [==============================] - 2s 1s/step - loss: 0.0713 - accuracy: 0.9844 - val_loss: 0.2715 - val_accuracy: 0.8644 Epoch 867/1000 2/2 [==============================] - ETA: 0s - loss: 0.0677 - accuracy: 0.9750 Epoch 867: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 840ms/step - loss: 0.0677 - accuracy: 0.9750 - val_loss: 0.2721 - val_accuracy: 0.8644 Epoch 868/1000 2/2 [==============================] - ETA: 0s - loss: 0.0762 - accuracy: 0.9625 Epoch 868: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 1s/step - loss: 0.0762 - accuracy: 0.9625 - val_loss: 0.2707 - val_accuracy: 0.8644 Epoch 869/1000 2/2 [==============================] - ETA: 0s - loss: 0.0939 - accuracy: 0.9875 Epoch 869: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 871ms/step - loss: 0.0939 - accuracy: 0.9875 - val_loss: 0.2699 - val_accuracy: 0.8644 Epoch 870/1000 2/2 [==============================] - ETA: 0s - loss: 0.0782 - accuracy: 0.9875 Epoch 870: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 839ms/step - loss: 0.0782 - accuracy: 0.9875 - val_loss: 0.2694 - val_accuracy: 0.8644 Epoch 871/1000 2/2 [==============================] - ETA: 0s - loss: 0.0965 - accuracy: 0.9531 Epoch 871: saving model to training_1/cp.ckpt 2/2 [==============================] - 2s 962ms/step - loss: 0.0965 - accuracy: 0.9531 - val_loss: 0.2689 - val_accuracy: 0.8644 Epoch 872/1000 2/2 [==============================] - ETA: 0s - loss: 0.0861 - accuracy: 0.9625 Epoch 872: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 1s/step - loss: 0.0861 - accuracy: 0.9625 - val_loss: 0.2691 - val_accuracy: 0.8644 Epoch 873/1000 2/2 [==============================] - ETA: 0s - loss: 0.0783 - accuracy: 0.9609 Epoch 873: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 937ms/step - loss: 0.0783 - accuracy: 0.9609 - val_loss: 0.2699 - val_accuracy: 0.8644 Epoch 874/1000 2/2 [==============================] - ETA: 0s - loss: 0.1119 - accuracy: 0.9688 Epoch 874: saving model to training_1/cp.ckpt 2/2 [==============================] - 2s 1s/step - loss: 0.1119 - accuracy: 0.9688 - val_loss: 0.2719 - val_accuracy: 0.8644 Epoch 875/1000 2/2 [==============================] - ETA: 0s - loss: 0.0761 - accuracy: 0.9500 Epoch 875: saving model to training_1/cp.ckpt 2/2 [==============================] - 2s 1s/step - loss: 0.0761 - accuracy: 0.9500 - val_loss: 0.2753 - val_accuracy: 0.8644 Epoch 876/1000 2/2 [==============================] - ETA: 0s - loss: 0.0681 - accuracy: 0.9875 Epoch 876: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 824ms/step - loss: 0.0681 - accuracy: 0.9875 - val_loss: 0.2789 - val_accuracy: 0.8644 Epoch 877/1000 2/2 [==============================] - ETA: 0s - loss: 0.0823 - accuracy: 0.9844 Epoch 877: saving model to training_1/cp.ckpt 2/2 [==============================] - 2s 1s/step - loss: 0.0823 - accuracy: 0.9844 - val_loss: 0.2809 - val_accuracy: 0.8644 Epoch 878/1000 2/2 [==============================] - ETA: 0s - loss: 0.0974 - accuracy: 0.9750 Epoch 878: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 921ms/step - loss: 0.0974 - accuracy: 0.9750 - val_loss: 0.2807 - val_accuracy: 0.8644 Epoch 879/1000 2/2 [==============================] - ETA: 0s - loss: 0.0780 - accuracy: 0.9750 Epoch 879: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 1s/step - loss: 0.0780 - accuracy: 0.9750 - val_loss: 0.2798 - val_accuracy: 0.8644 Epoch 880/1000 2/2 [==============================] - ETA: 0s - loss: 0.0934 - accuracy: 0.9609 Epoch 880: saving model to training_1/cp.ckpt 2/2 [==============================] - 2s 1s/step - loss: 0.0934 - accuracy: 0.9609 - val_loss: 0.2805 - val_accuracy: 0.8644 Epoch 881/1000 2/2 [==============================] - ETA: 0s - loss: 0.0931 - accuracy: 0.9609 Epoch 881: saving model to training_1/cp.ckpt 2/2 [==============================] - 2s 1s/step - loss: 0.0931 - accuracy: 0.9609 - val_loss: 0.2824 - val_accuracy: 0.8644 Epoch 882/1000 2/2 [==============================] - ETA: 0s - loss: 0.0906 - accuracy: 0.9688 Epoch 882: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 947ms/step - loss: 0.0906 - accuracy: 0.9688 - val_loss: 0.2839 - val_accuracy: 0.8644 Epoch 883/1000 2/2 [==============================] - ETA: 0s - loss: 0.1245 - accuracy: 0.9141 Epoch 883: saving model to training_1/cp.ckpt 2/2 [==============================] - 2s 1s/step - loss: 0.1245 - accuracy: 0.9141 - val_loss: 0.2849 - val_accuracy: 0.8644 Epoch 884/1000 2/2 [==============================] - ETA: 0s - loss: 0.0833 - accuracy: 0.9500 Epoch 884: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 1s/step - loss: 0.0833 - accuracy: 0.9500 - val_loss: 0.2872 - val_accuracy: 0.8644 Epoch 885/1000 2/2 [==============================] - ETA: 0s - loss: 0.0882 - accuracy: 0.9766 Epoch 885: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 981ms/step - loss: 0.0882 - accuracy: 0.9766 - val_loss: 0.2888 - val_accuracy: 0.8644 Epoch 886/1000 2/2 [==============================] - ETA: 0s - loss: 0.0874 - accuracy: 0.9844 Epoch 886: saving model to training_1/cp.ckpt 2/2 [==============================] - 2s 970ms/step - loss: 0.0874 - accuracy: 0.9844 - val_loss: 0.2896 - val_accuracy: 0.8644 Epoch 887/1000 2/2 [==============================] - ETA: 0s - loss: 0.0693 - accuracy: 0.9750 Epoch 887: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 837ms/step - loss: 0.0693 - accuracy: 0.9750 - val_loss: 0.2900 - val_accuracy: 0.8644 Epoch 888/1000 2/2 [==============================] - ETA: 0s - loss: 0.1022 - accuracy: 0.9375 Epoch 888: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 819ms/step - loss: 0.1022 - accuracy: 0.9375 - val_loss: 0.2897 - val_accuracy: 0.8644 Epoch 889/1000 2/2 [==============================] - ETA: 0s - loss: 0.0957 - accuracy: 0.9750 Epoch 889: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 844ms/step - loss: 0.0957 - accuracy: 0.9750 - val_loss: 0.2891 - val_accuracy: 0.8644 Epoch 890/1000 2/2 [==============================] - ETA: 0s - loss: 0.1106 - accuracy: 0.9531 Epoch 890: saving model to training_1/cp.ckpt 2/2 [==============================] - 2s 1s/step - loss: 0.1106 - accuracy: 0.9531 - val_loss: 0.2846 - val_accuracy: 0.8644 Epoch 891/1000 2/2 [==============================] - ETA: 0s - loss: 0.0942 - accuracy: 0.9609 Epoch 891: saving model to training_1/cp.ckpt 2/2 [==============================] - 2s 1s/step - loss: 0.0942 - accuracy: 0.9609 - val_loss: 0.2803 - val_accuracy: 0.8644 Epoch 892/1000 2/2 [==============================] - ETA: 0s - loss: 0.1219 - accuracy: 0.9453 Epoch 892: saving model to training_1/cp.ckpt 2/2 [==============================] - 2s 1s/step - loss: 0.1219 - accuracy: 0.9453 - val_loss: 0.2752 - val_accuracy: 0.8644 Epoch 893/1000 2/2 [==============================] - ETA: 0s - loss: 0.0828 - accuracy: 0.9750 Epoch 893: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 1s/step - loss: 0.0828 - accuracy: 0.9750 - val_loss: 0.2698 - val_accuracy: 0.8644 Epoch 894/1000 2/2 [==============================] - ETA: 0s - loss: 0.1041 - accuracy: 0.9375 Epoch 894: saving model to training_1/cp.ckpt 2/2 [==============================] - 2s 1s/step - loss: 0.1041 - accuracy: 0.9375 - val_loss: 0.2643 - val_accuracy: 0.8644 Epoch 895/1000 2/2 [==============================] - ETA: 0s - loss: 0.0839 - accuracy: 0.9500 Epoch 895: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 834ms/step - loss: 0.0839 - accuracy: 0.9500 - val_loss: 0.2609 - val_accuracy: 0.8644 Epoch 896/1000 2/2 [==============================] - ETA: 0s - loss: 0.1266 - accuracy: 0.9375 Epoch 896: saving model to training_1/cp.ckpt 2/2 [==============================] - 2s 972ms/step - loss: 0.1266 - accuracy: 0.9375 - val_loss: 0.2591 - val_accuracy: 0.8644 Epoch 897/1000 2/2 [==============================] - ETA: 0s - loss: 0.0911 - accuracy: 0.9531 Epoch 897: saving model to training_1/cp.ckpt 2/2 [==============================] - 2s 1s/step - loss: 0.0911 - accuracy: 0.9531 - val_loss: 0.2583 - val_accuracy: 0.8475 Epoch 898/1000 2/2 [==============================] - ETA: 0s - loss: 0.1015 - accuracy: 0.9500 Epoch 898: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 866ms/step - loss: 0.1015 - accuracy: 0.9500 - val_loss: 0.2576 - val_accuracy: 0.8475 Epoch 899/1000 2/2 [==============================] - ETA: 0s - loss: 0.0907 - accuracy: 0.9766 Epoch 899: saving model to training_1/cp.ckpt 2/2 [==============================] - 2s 1s/step - loss: 0.0907 - accuracy: 0.9766 - val_loss: 0.2573 - val_accuracy: 0.8475 Epoch 900/1000 2/2 [==============================] - ETA: 0s - loss: 0.0948 - accuracy: 0.9609 Epoch 900: saving model to training_1/cp.ckpt 2/2 [==============================] - 2s 1s/step - loss: 0.0948 - accuracy: 0.9609 - val_loss: 0.2570 - val_accuracy: 0.8475 Epoch 901/1000 2/2 [==============================] - ETA: 0s - loss: 0.1040 - accuracy: 0.9750 Epoch 901: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 819ms/step - loss: 0.1040 - accuracy: 0.9750 - val_loss: 0.2567 - val_accuracy: 0.8475 Epoch 902/1000 2/2 [==============================] - ETA: 0s - loss: 0.1039 - accuracy: 0.9141 Epoch 902: saving model to training_1/cp.ckpt 2/2 [==============================] - 2s 1s/step - loss: 0.1039 - accuracy: 0.9141 - val_loss: 0.2574 - val_accuracy: 0.8475 Epoch 903/1000 2/2 [==============================] - ETA: 0s - loss: 0.0861 - accuracy: 0.9625 Epoch 903: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 829ms/step - loss: 0.0861 - accuracy: 0.9625 - val_loss: 0.2590 - val_accuracy: 0.8475 Epoch 904/1000 2/2 [==============================] - ETA: 0s - loss: 0.0647 - accuracy: 0.9875 Epoch 904: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 1s/step - loss: 0.0647 - accuracy: 0.9875 - val_loss: 0.2597 - val_accuracy: 0.8475 Epoch 905/1000 2/2 [==============================] - ETA: 0s - loss: 0.0822 - accuracy: 0.9500 Epoch 905: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 1s/step - loss: 0.0822 - accuracy: 0.9500 - val_loss: 0.2606 - val_accuracy: 0.8475 Epoch 906/1000 2/2 [==============================] - ETA: 0s - loss: 0.0629 - accuracy: 0.9750 Epoch 906: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 851ms/step - loss: 0.0629 - accuracy: 0.9750 - val_loss: 0.2621 - val_accuracy: 0.8475 Epoch 907/1000 2/2 [==============================] - ETA: 0s - loss: 0.0631 - accuracy: 1.0000 Epoch 907: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 1s/step - loss: 0.0631 - accuracy: 1.0000 - val_loss: 0.2651 - val_accuracy: 0.8475 Epoch 908/1000 2/2 [==============================] - ETA: 0s - loss: 0.0794 - accuracy: 0.9875 Epoch 908: saving model to training_1/cp.ckpt 2/2 [==============================] - 2s 1s/step - loss: 0.0794 - accuracy: 0.9875 - val_loss: 0.2677 - val_accuracy: 0.8475 Epoch 909/1000 2/2 [==============================] - ETA: 0s - loss: 0.0681 - accuracy: 1.0000 Epoch 909: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 1s/step - loss: 0.0681 - accuracy: 1.0000 - val_loss: 0.2719 - val_accuracy: 0.8475 Epoch 910/1000 2/2 [==============================] - ETA: 0s - loss: 0.0788 - accuracy: 0.9531 Epoch 910: saving model to training_1/cp.ckpt 2/2 [==============================] - 2s 1s/step - loss: 0.0788 - accuracy: 0.9531 - val_loss: 0.2756 - val_accuracy: 0.8475 Epoch 911/1000 2/2 [==============================] - ETA: 0s - loss: 0.0893 - accuracy: 0.9531 Epoch 911: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 923ms/step - loss: 0.0893 - accuracy: 0.9531 - val_loss: 0.2787 - val_accuracy: 0.8475 Epoch 912/1000 2/2 [==============================] - ETA: 0s - loss: 0.1026 - accuracy: 0.9688 Epoch 912: saving model to training_1/cp.ckpt 2/2 [==============================] - 2s 1s/step - loss: 0.1026 - accuracy: 0.9688 - val_loss: 0.2811 - val_accuracy: 0.8475 Epoch 913/1000 2/2 [==============================] - ETA: 0s - loss: 0.0945 - accuracy: 0.9688 Epoch 913: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 937ms/step - loss: 0.0945 - accuracy: 0.9688 - val_loss: 0.2832 - val_accuracy: 0.8305 Epoch 914/1000 2/2 [==============================] - ETA: 0s - loss: 0.0744 - accuracy: 0.9750 Epoch 914: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 1s/step - loss: 0.0744 - accuracy: 0.9750 - val_loss: 0.2846 - val_accuracy: 0.8305 Epoch 915/1000 2/2 [==============================] - ETA: 0s - loss: 0.0825 - accuracy: 0.9500 Epoch 915: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 1s/step - loss: 0.0825 - accuracy: 0.9500 - val_loss: 0.2836 - val_accuracy: 0.8305 Epoch 916/1000 2/2 [==============================] - ETA: 0s - loss: 0.0687 - accuracy: 0.9875 Epoch 916: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 1s/step - loss: 0.0687 - accuracy: 0.9875 - val_loss: 0.2818 - val_accuracy: 0.8305 Epoch 917/1000 2/2 [==============================] - ETA: 0s - loss: 0.1094 - accuracy: 0.9500 Epoch 917: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 841ms/step - loss: 0.1094 - accuracy: 0.9500 - val_loss: 0.2799 - val_accuracy: 0.8475 Epoch 918/1000 2/2 [==============================] - ETA: 0s - loss: 0.0705 - accuracy: 0.9875 Epoch 918: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 891ms/step - loss: 0.0705 - accuracy: 0.9875 - val_loss: 0.2781 - val_accuracy: 0.8475 Epoch 919/1000 2/2 [==============================] - ETA: 0s - loss: 0.0739 - accuracy: 0.9750 Epoch 919: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 844ms/step - loss: 0.0739 - accuracy: 0.9750 - val_loss: 0.2760 - val_accuracy: 0.8475 Epoch 920/1000 2/2 [==============================] - ETA: 0s - loss: 0.0654 - accuracy: 0.9875 Epoch 920: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 819ms/step - loss: 0.0654 - accuracy: 0.9875 - val_loss: 0.2761 - val_accuracy: 0.8475 Epoch 921/1000 2/2 [==============================] - ETA: 0s - loss: 0.1149 - accuracy: 0.9453 Epoch 921: saving model to training_1/cp.ckpt 2/2 [==============================] - 2s 1s/step - loss: 0.1149 - accuracy: 0.9453 - val_loss: 0.2791 - val_accuracy: 0.8305 Epoch 922/1000 2/2 [==============================] - ETA: 0s - loss: 0.0815 - accuracy: 0.9750 Epoch 922: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 840ms/step - loss: 0.0815 - accuracy: 0.9750 - val_loss: 0.2815 - val_accuracy: 0.8305 Epoch 923/1000 2/2 [==============================] - ETA: 0s - loss: 0.1019 - accuracy: 0.9766 Epoch 923: saving model to training_1/cp.ckpt 2/2 [==============================] - 2s 1s/step - loss: 0.1019 - accuracy: 0.9766 - val_loss: 0.2835 - val_accuracy: 0.8305 Epoch 924/1000 2/2 [==============================] - ETA: 0s - loss: 0.0601 - accuracy: 1.0000 Epoch 924: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 1s/step - loss: 0.0601 - accuracy: 1.0000 - val_loss: 0.2857 - val_accuracy: 0.8305 Epoch 925/1000 2/2 [==============================] - ETA: 0s - loss: 0.1296 - accuracy: 0.9125 Epoch 925: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 839ms/step - loss: 0.1296 - accuracy: 0.9125 - val_loss: 0.2871 - val_accuracy: 0.8305 Epoch 926/1000 2/2 [==============================] - ETA: 0s - loss: 0.0943 - accuracy: 0.9766 Epoch 926: saving model to training_1/cp.ckpt 2/2 [==============================] - 2s 1s/step - loss: 0.0943 - accuracy: 0.9766 - val_loss: 0.2907 - val_accuracy: 0.8305 Epoch 927/1000 2/2 [==============================] - ETA: 0s - loss: 0.0939 - accuracy: 0.9766 Epoch 927: saving model to training_1/cp.ckpt 2/2 [==============================] - 2s 1s/step - loss: 0.0939 - accuracy: 0.9766 - val_loss: 0.2958 - val_accuracy: 0.8305 Epoch 928/1000 2/2 [==============================] - ETA: 0s - loss: 0.0990 - accuracy: 0.9625 Epoch 928: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 1s/step - loss: 0.0990 - accuracy: 0.9625 - val_loss: 0.2993 - val_accuracy: 0.8136 Epoch 929/1000 2/2 [==============================] - ETA: 0s - loss: 0.0945 - accuracy: 0.9609 Epoch 929: saving model to training_1/cp.ckpt 2/2 [==============================] - 2s 1s/step - loss: 0.0945 - accuracy: 0.9609 - val_loss: 0.3029 - val_accuracy: 0.8136 Epoch 930/1000 2/2 [==============================] - ETA: 0s - loss: 0.0748 - accuracy: 0.9844 Epoch 930: saving model to training_1/cp.ckpt 2/2 [==============================] - 2s 1s/step - loss: 0.0748 - accuracy: 0.9844 - val_loss: 0.3062 - val_accuracy: 0.8136 Epoch 931/1000 2/2 [==============================] - ETA: 0s - loss: 0.0828 - accuracy: 0.9766 Epoch 931: saving model to training_1/cp.ckpt 2/2 [==============================] - 2s 1s/step - loss: 0.0828 - accuracy: 0.9766 - val_loss: 0.3082 - val_accuracy: 0.8136 Epoch 932/1000 2/2 [==============================] - ETA: 0s - loss: 0.1561 - accuracy: 0.9500 Epoch 932: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 902ms/step - loss: 0.1561 - accuracy: 0.9500 - val_loss: 0.3088 - val_accuracy: 0.8136 Epoch 933/1000 2/2 [==============================] - ETA: 0s - loss: 0.0936 - accuracy: 0.9531 Epoch 933: saving model to training_1/cp.ckpt 2/2 [==============================] - 2s 985ms/step - loss: 0.0936 - accuracy: 0.9531 - val_loss: 0.3044 - val_accuracy: 0.8136 Epoch 934/1000 2/2 [==============================] - ETA: 0s - loss: 0.0693 - accuracy: 0.9750 Epoch 934: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 1s/step - loss: 0.0693 - accuracy: 0.9750 - val_loss: 0.3002 - val_accuracy: 0.8136 Epoch 935/1000 2/2 [==============================] - ETA: 0s - loss: 0.0751 - accuracy: 0.9688 Epoch 935: saving model to training_1/cp.ckpt 2/2 [==============================] - 2s 958ms/step - loss: 0.0751 - accuracy: 0.9688 - val_loss: 0.2972 - val_accuracy: 0.8305 Epoch 936/1000 2/2 [==============================] - ETA: 0s - loss: 0.0536 - accuracy: 0.9875 Epoch 936: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 843ms/step - loss: 0.0536 - accuracy: 0.9875 - val_loss: 0.2937 - val_accuracy: 0.8305 Epoch 937/1000 2/2 [==============================] - ETA: 0s - loss: 0.0572 - accuracy: 0.9875 Epoch 937: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 857ms/step - loss: 0.0572 - accuracy: 0.9875 - val_loss: 0.2893 - val_accuracy: 0.8305 Epoch 938/1000 2/2 [==============================] - ETA: 0s - loss: 0.0632 - accuracy: 0.9625 Epoch 938: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 1s/step - loss: 0.0632 - accuracy: 0.9625 - val_loss: 0.2845 - val_accuracy: 0.8305 Epoch 939/1000 2/2 [==============================] - ETA: 0s - loss: 0.1012 - accuracy: 0.9531 Epoch 939: saving model to training_1/cp.ckpt 2/2 [==============================] - 2s 1s/step - loss: 0.1012 - accuracy: 0.9531 - val_loss: 0.2796 - val_accuracy: 0.8305 Epoch 940/1000 2/2 [==============================] - ETA: 0s - loss: 0.0739 - accuracy: 0.9625 Epoch 940: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 860ms/step - loss: 0.0739 - accuracy: 0.9625 - val_loss: 0.2747 - val_accuracy: 0.8475 Epoch 941/1000 2/2 [==============================] - ETA: 0s - loss: 0.0882 - accuracy: 0.9531 Epoch 941: saving model to training_1/cp.ckpt 2/2 [==============================] - 2s 1s/step - loss: 0.0882 - accuracy: 0.9531 - val_loss: 0.2706 - val_accuracy: 0.8475 Epoch 942/1000 2/2 [==============================] - ETA: 0s - loss: 0.0617 - accuracy: 0.9844 Epoch 942: saving model to training_1/cp.ckpt 2/2 [==============================] - 2s 983ms/step - loss: 0.0617 - accuracy: 0.9844 - val_loss: 0.2677 - val_accuracy: 0.8475 Epoch 943/1000 2/2 [==============================] - ETA: 0s - loss: 0.0785 - accuracy: 0.9625 Epoch 943: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 1s/step - loss: 0.0785 - accuracy: 0.9625 - val_loss: 0.2661 - val_accuracy: 0.8475 Epoch 944/1000 2/2 [==============================] - ETA: 0s - loss: 0.0550 - accuracy: 0.9875 Epoch 944: saving model to training_1/cp.ckpt 2/2 [==============================] - 2s 1s/step - loss: 0.0550 - accuracy: 0.9875 - val_loss: 0.2647 - val_accuracy: 0.8475 Epoch 945/1000 2/2 [==============================] - ETA: 0s - loss: 0.0747 - accuracy: 0.9688 Epoch 945: saving model to training_1/cp.ckpt 2/2 [==============================] - 2s 1s/step - loss: 0.0747 - accuracy: 0.9688 - val_loss: 0.2630 - val_accuracy: 0.8475 Epoch 946/1000 2/2 [==============================] - ETA: 0s - loss: 0.0778 - accuracy: 0.9766 Epoch 946: saving model to training_1/cp.ckpt 2/2 [==============================] - 2s 1s/step - loss: 0.0778 - accuracy: 0.9766 - val_loss: 0.2610 - val_accuracy: 0.8475 Epoch 947/1000 2/2 [==============================] - ETA: 0s - loss: 0.1018 - accuracy: 0.9688 Epoch 947: saving model to training_1/cp.ckpt 2/2 [==============================] - 2s 1s/step - loss: 0.1018 - accuracy: 0.9688 - val_loss: 0.2591 - val_accuracy: 0.8475 Epoch 948/1000 2/2 [==============================] - ETA: 0s - loss: 0.0876 - accuracy: 0.9688 Epoch 948: saving model to training_1/cp.ckpt 2/2 [==============================] - 2s 1s/step - loss: 0.0876 - accuracy: 0.9688 - val_loss: 0.2570 - val_accuracy: 0.8475 Epoch 949/1000 2/2 [==============================] - ETA: 0s - loss: 0.1242 - accuracy: 0.9375 Epoch 949: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 816ms/step - loss: 0.1242 - accuracy: 0.9375 - val_loss: 0.2563 - val_accuracy: 0.8644 Epoch 950/1000 2/2 [==============================] - ETA: 0s - loss: 0.1184 - accuracy: 0.9297 Epoch 950: saving model to training_1/cp.ckpt 2/2 [==============================] - 2s 1s/step - loss: 0.1184 - accuracy: 0.9297 - val_loss: 0.2557 - val_accuracy: 0.8644 Epoch 951/1000 2/2 [==============================] - ETA: 0s - loss: 0.0717 - accuracy: 0.9750 Epoch 951: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 841ms/step - loss: 0.0717 - accuracy: 0.9750 - val_loss: 0.2561 - val_accuracy: 0.8644 Epoch 952/1000 2/2 [==============================] - ETA: 0s - loss: 0.0772 - accuracy: 0.9875 Epoch 952: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 885ms/step - loss: 0.0772 - accuracy: 0.9875 - val_loss: 0.2571 - val_accuracy: 0.8644 Epoch 953/1000 2/2 [==============================] - ETA: 0s - loss: 0.0977 - accuracy: 0.9500 Epoch 953: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 1s/step - loss: 0.0977 - accuracy: 0.9500 - val_loss: 0.2591 - val_accuracy: 0.8475 Epoch 954/1000 2/2 [==============================] - ETA: 0s - loss: 0.0724 - accuracy: 0.9750 Epoch 954: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 1s/step - loss: 0.0724 - accuracy: 0.9750 - val_loss: 0.2622 - val_accuracy: 0.8475 Epoch 955/1000 2/2 [==============================] - ETA: 0s - loss: 0.0957 - accuracy: 0.9750 Epoch 955: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 838ms/step - loss: 0.0957 - accuracy: 0.9750 - val_loss: 0.2667 - val_accuracy: 0.8475 Epoch 956/1000 2/2 [==============================] - ETA: 0s - loss: 0.0891 - accuracy: 0.9688 Epoch 956: saving model to training_1/cp.ckpt 2/2 [==============================] - 2s 1s/step - loss: 0.0891 - accuracy: 0.9688 - val_loss: 0.2706 - val_accuracy: 0.8475 Epoch 957/1000 2/2 [==============================] - ETA: 0s - loss: 0.1035 - accuracy: 0.9609 Epoch 957: saving model to training_1/cp.ckpt 2/2 [==============================] - 2s 1s/step - loss: 0.1035 - accuracy: 0.9609 - val_loss: 0.2731 - val_accuracy: 0.8475 Epoch 958/1000 2/2 [==============================] - ETA: 0s - loss: 0.0647 - accuracy: 0.9922 Epoch 958: saving model to training_1/cp.ckpt 2/2 [==============================] - 2s 1s/step - loss: 0.0647 - accuracy: 0.9922 - val_loss: 0.2742 - val_accuracy: 0.8305 Epoch 959/1000 2/2 [==============================] - ETA: 0s - loss: 0.0958 - accuracy: 0.9875 Epoch 959: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 849ms/step - loss: 0.0958 - accuracy: 0.9875 - val_loss: 0.2751 - val_accuracy: 0.8305 Epoch 960/1000 2/2 [==============================] - ETA: 0s - loss: 0.0807 - accuracy: 0.9750 Epoch 960: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 1s/step - loss: 0.0807 - accuracy: 0.9750 - val_loss: 0.2768 - val_accuracy: 0.8305 Epoch 961/1000 2/2 [==============================] - ETA: 0s - loss: 0.0948 - accuracy: 0.9625 Epoch 961: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 819ms/step - loss: 0.0948 - accuracy: 0.9625 - val_loss: 0.2801 - val_accuracy: 0.8305 Epoch 962/1000 2/2 [==============================] - ETA: 0s - loss: 0.0776 - accuracy: 0.9766 Epoch 962: saving model to training_1/cp.ckpt 2/2 [==============================] - 2s 1s/step - loss: 0.0776 - accuracy: 0.9766 - val_loss: 0.2844 - val_accuracy: 0.8475 Epoch 963/1000 2/2 [==============================] - ETA: 0s - loss: 0.1424 - accuracy: 0.9000 Epoch 963: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 1s/step - loss: 0.1424 - accuracy: 0.9000 - val_loss: 0.2886 - val_accuracy: 0.8305 Epoch 964/1000 2/2 [==============================] - ETA: 0s - loss: 0.0914 - accuracy: 0.9625 Epoch 964: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 1s/step - loss: 0.0914 - accuracy: 0.9625 - val_loss: 0.2915 - val_accuracy: 0.8305 Epoch 965/1000 2/2 [==============================] - ETA: 0s - loss: 0.0729 - accuracy: 0.9875 Epoch 965: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 1s/step - loss: 0.0729 - accuracy: 0.9875 - val_loss: 0.2938 - val_accuracy: 0.8475 Epoch 966/1000 2/2 [==============================] - ETA: 0s - loss: 0.0875 - accuracy: 0.9766 Epoch 966: saving model to training_1/cp.ckpt 2/2 [==============================] - 2s 1s/step - loss: 0.0875 - accuracy: 0.9766 - val_loss: 0.2974 - val_accuracy: 0.8305 Epoch 967/1000 2/2 [==============================] - ETA: 0s - loss: 0.0654 - accuracy: 0.9766 Epoch 967: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 963ms/step - loss: 0.0654 - accuracy: 0.9766 - val_loss: 0.3005 - val_accuracy: 0.8305 Epoch 968/1000 2/2 [==============================] - ETA: 0s - loss: 0.0662 - accuracy: 0.9844 Epoch 968: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 931ms/step - loss: 0.0662 - accuracy: 0.9844 - val_loss: 0.3030 - val_accuracy: 0.8305 Epoch 969/1000 2/2 [==============================] - ETA: 0s - loss: 0.0808 - accuracy: 0.9688 Epoch 969: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 948ms/step - loss: 0.0808 - accuracy: 0.9688 - val_loss: 0.3052 - val_accuracy: 0.8305 Epoch 970/1000 2/2 [==============================] - ETA: 0s - loss: 0.1014 - accuracy: 0.9531 Epoch 970: saving model to training_1/cp.ckpt 2/2 [==============================] - 2s 1s/step - loss: 0.1014 - accuracy: 0.9531 - val_loss: 0.3074 - val_accuracy: 0.8305 Epoch 971/1000 2/2 [==============================] - ETA: 0s - loss: 0.0944 - accuracy: 0.9688 Epoch 971: saving model to training_1/cp.ckpt 2/2 [==============================] - 2s 1s/step - loss: 0.0944 - accuracy: 0.9688 - val_loss: 0.3092 - val_accuracy: 0.8305 Epoch 972/1000 2/2 [==============================] - ETA: 0s - loss: 0.0662 - accuracy: 0.9844 Epoch 972: saving model to training_1/cp.ckpt 2/2 [==============================] - 2s 1s/step - loss: 0.0662 - accuracy: 0.9844 - val_loss: 0.3097 - val_accuracy: 0.8305 Epoch 973/1000 2/2 [==============================] - ETA: 0s - loss: 0.0667 - accuracy: 0.9766 Epoch 973: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 959ms/step - loss: 0.0667 - accuracy: 0.9766 - val_loss: 0.3094 - val_accuracy: 0.8305 Epoch 974/1000 2/2 [==============================] - ETA: 0s - loss: 0.0818 - accuracy: 0.9688 Epoch 974: saving model to training_1/cp.ckpt 2/2 [==============================] - 2s 1s/step - loss: 0.0818 - accuracy: 0.9688 - val_loss: 0.3085 - val_accuracy: 0.8305 Epoch 975/1000 2/2 [==============================] - ETA: 0s - loss: 0.0910 - accuracy: 0.9688 Epoch 975: saving model to training_1/cp.ckpt 2/2 [==============================] - 2s 1s/step - loss: 0.0910 - accuracy: 0.9688 - val_loss: 0.3087 - val_accuracy: 0.8305 Epoch 976/1000 2/2 [==============================] - ETA: 0s - loss: 0.1308 - accuracy: 0.9375 Epoch 976: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 1s/step - loss: 0.1308 - accuracy: 0.9375 - val_loss: 0.3068 - val_accuracy: 0.8305 Epoch 977/1000 2/2 [==============================] - ETA: 0s - loss: 0.0767 - accuracy: 0.9750 Epoch 977: saving model to training_1/cp.ckpt 2/2 [==============================] - 2s 1s/step - loss: 0.0767 - accuracy: 0.9750 - val_loss: 0.3051 - val_accuracy: 0.8305 Epoch 978/1000 2/2 [==============================] - ETA: 0s - loss: 0.1055 - accuracy: 0.9500 Epoch 978: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 848ms/step - loss: 0.1055 - accuracy: 0.9500 - val_loss: 0.3017 - val_accuracy: 0.8305 Epoch 979/1000 2/2 [==============================] - ETA: 0s - loss: 0.0511 - accuracy: 1.0000 Epoch 979: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 904ms/step - loss: 0.0511 - accuracy: 1.0000 - val_loss: 0.2974 - val_accuracy: 0.8305 Epoch 980/1000 2/2 [==============================] - ETA: 0s - loss: 0.0713 - accuracy: 0.9531 Epoch 980: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 939ms/step - loss: 0.0713 - accuracy: 0.9531 - val_loss: 0.2944 - val_accuracy: 0.8305 Epoch 981/1000 2/2 [==============================] - ETA: 0s - loss: 0.0922 - accuracy: 0.9609 Epoch 981: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 972ms/step - loss: 0.0922 - accuracy: 0.9609 - val_loss: 0.2921 - val_accuracy: 0.8475 Epoch 982/1000 2/2 [==============================] - ETA: 0s - loss: 0.0891 - accuracy: 0.9625 Epoch 982: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 1s/step - loss: 0.0891 - accuracy: 0.9625 - val_loss: 0.2933 - val_accuracy: 0.8475 Epoch 983/1000 2/2 [==============================] - ETA: 0s - loss: 0.0949 - accuracy: 0.9453 Epoch 983: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 951ms/step - loss: 0.0949 - accuracy: 0.9453 - val_loss: 0.2925 - val_accuracy: 0.8475 Epoch 984/1000 2/2 [==============================] - ETA: 0s - loss: 0.0539 - accuracy: 0.9922 Epoch 984: saving model to training_1/cp.ckpt 2/2 [==============================] - 2s 995ms/step - loss: 0.0539 - accuracy: 0.9922 - val_loss: 0.2918 - val_accuracy: 0.8475 Epoch 985/1000 2/2 [==============================] - ETA: 0s - loss: 0.0669 - accuracy: 0.9766 Epoch 985: saving model to training_1/cp.ckpt 2/2 [==============================] - 2s 1s/step - loss: 0.0669 - accuracy: 0.9766 - val_loss: 0.2904 - val_accuracy: 0.8305 Epoch 986/1000 2/2 [==============================] - ETA: 0s - loss: 0.0790 - accuracy: 0.9875 Epoch 986: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 833ms/step - loss: 0.0790 - accuracy: 0.9875 - val_loss: 0.2900 - val_accuracy: 0.8305 Epoch 987/1000 2/2 [==============================] - ETA: 0s - loss: 0.1056 - accuracy: 0.9750 Epoch 987: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 1s/step - loss: 0.1056 - accuracy: 0.9750 - val_loss: 0.2854 - val_accuracy: 0.8475 Epoch 988/1000 2/2 [==============================] - ETA: 0s - loss: 0.0730 - accuracy: 0.9875 Epoch 988: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 1s/step - loss: 0.0730 - accuracy: 0.9875 - val_loss: 0.2825 - val_accuracy: 0.8475 Epoch 989/1000 2/2 [==============================] - ETA: 0s - loss: 0.0671 - accuracy: 0.9922 Epoch 989: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 985ms/step - loss: 0.0671 - accuracy: 0.9922 - val_loss: 0.2798 - val_accuracy: 0.8305 Epoch 990/1000 2/2 [==============================] - ETA: 0s - loss: 0.0840 - accuracy: 0.9766 Epoch 990: saving model to training_1/cp.ckpt 2/2 [==============================] - 2s 1s/step - loss: 0.0840 - accuracy: 0.9766 - val_loss: 0.2768 - val_accuracy: 0.8475 Epoch 991/1000 2/2 [==============================] - ETA: 0s - loss: 0.0820 - accuracy: 0.9766 Epoch 991: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 933ms/step - loss: 0.0820 - accuracy: 0.9766 - val_loss: 0.2731 - val_accuracy: 0.8475 Epoch 992/1000 2/2 [==============================] - ETA: 0s - loss: 0.1183 - accuracy: 0.9250 Epoch 992: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 842ms/step - loss: 0.1183 - accuracy: 0.9250 - val_loss: 0.2701 - val_accuracy: 0.8305 Epoch 993/1000 2/2 [==============================] - ETA: 0s - loss: 0.1168 - accuracy: 0.9625 Epoch 993: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 1s/step - loss: 0.1168 - accuracy: 0.9625 - val_loss: 0.2679 - val_accuracy: 0.8305 Epoch 994/1000 2/2 [==============================] - ETA: 0s - loss: 0.0559 - accuracy: 0.9922 Epoch 994: saving model to training_1/cp.ckpt 2/2 [==============================] - 2s 1s/step - loss: 0.0559 - accuracy: 0.9922 - val_loss: 0.2664 - val_accuracy: 0.8305 Epoch 995/1000 2/2 [==============================] - ETA: 0s - loss: 0.0766 - accuracy: 0.9688 Epoch 995: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 950ms/step - loss: 0.0766 - accuracy: 0.9688 - val_loss: 0.2641 - val_accuracy: 0.8305 Epoch 996/1000 2/2 [==============================] - ETA: 0s - loss: 0.0701 - accuracy: 0.9688 Epoch 996: saving model to training_1/cp.ckpt 2/2 [==============================] - 2s 1s/step - loss: 0.0701 - accuracy: 0.9688 - val_loss: 0.2621 - val_accuracy: 0.8305 Epoch 997/1000 2/2 [==============================] - ETA: 0s - loss: 0.0732 - accuracy: 0.9750 Epoch 997: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 1s/step - loss: 0.0732 - accuracy: 0.9750 - val_loss: 0.2621 - val_accuracy: 0.8305 Epoch 998/1000 2/2 [==============================] - ETA: 0s - loss: 0.0791 - accuracy: 0.9688 Epoch 998: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 920ms/step - loss: 0.0791 - accuracy: 0.9688 - val_loss: 0.2632 - val_accuracy: 0.8305 Epoch 999/1000 2/2 [==============================] - ETA: 0s - loss: 0.1398 - accuracy: 0.9375 Epoch 999: saving model to training_1/cp.ckpt 2/2 [==============================] - 1s 866ms/step - loss: 0.1398 - accuracy: 0.9375 - val_loss: 0.2647 - val_accuracy: 0.8305 Epoch 1000/1000 2/2 [==============================] - ETA: 0s - loss: 0.0725 - accuracy: 0.9766 Epoch 1000: saving model to training_1/cp.ckpt 2/2 [==============================] - 2s 1s/step - loss: 0.0725 - accuracy: 0.9766 - val_loss: 0.2671 - val_accuracy: 0.8475 ``` </details> ### Evidências do treinamento Nessa seção você deve colocar qualquer evidência do treinamento, como por exemplo gráficos de perda, performance, matriz de confusão etc. Exemplo de adição de imagem: ### Acurácia <img src = "Graficos/acc.png"> ### Loss <img src = "Graficos/loss.png"> # Roboflow Acesse o dataset no link abaixo [Dataset Roboflow](https://universe.roboflow.com/rna-class/classifier_animals) ## HuggingFace [Huggingface link](https://huggingface.co/caioeserpa/MobileNetV2_RNA_Class/tree/main)
rootcodes/wav2vec2-large-xls-r-300m-turkish-colab
rootcodes
2022-08-19T16:04:36Z
3
0
transformers
[ "transformers", "pytorch", "tensorboard", "wav2vec2", "automatic-speech-recognition", "generated_from_trainer", "dataset:common_voice", "license:apache-2.0", "endpoints_compatible", "region:us" ]
automatic-speech-recognition
2022-08-10T14:11:26Z
--- license: apache-2.0 tags: - generated_from_trainer datasets: - common_voice model-index: - name: wav2vec2-large-xls-r-300m-turkish-colab 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. --> # wav2vec2-large-xls-r-300m-turkish-colab This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the common_voice dataset. It achieves the following results on the evaluation set: - Loss: 0.4313 - Wer: 0.3336 ## 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: 0.0003 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - num_epochs: 30 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:------:| | 4.0055 | 3.67 | 400 | 0.7015 | 0.6789 | | 0.4384 | 7.34 | 800 | 0.4827 | 0.4875 | | 0.2143 | 11.01 | 1200 | 0.4672 | 0.4554 | | 0.1431 | 14.68 | 1600 | 0.4331 | 0.4014 | | 0.1053 | 18.35 | 2000 | 0.4471 | 0.3822 | | 0.0857 | 22.02 | 2400 | 0.4324 | 0.3637 | | 0.0683 | 25.69 | 2800 | 0.4305 | 0.3423 | | 0.0526 | 29.36 | 3200 | 0.4313 | 0.3336 | ### Framework versions - Transformers 4.11.3 - Pytorch 1.10.0+cu113 - Datasets 1.18.3 - Tokenizers 0.10.3
dminiotas05/distilbert-base-uncased-finetuned-ft1500_norm300_aug5_10_8x_plus_8_10_4x
dminiotas05
2022-08-19T15:48:17Z
107
0
transformers
[ "transformers", "pytorch", "tensorboard", "distilbert", "text-classification", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]
text-classification
2022-08-19T14:51:04Z
--- license: apache-2.0 tags: - generated_from_trainer metrics: - accuracy model-index: - name: distilbert-base-uncased-finetuned-ft1500_norm300_aug5_10_8x_plus_8_10_4x 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-ft1500_norm300_aug5_10_8x_plus_8_10_4x 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: 1.0732 - Mse: 4.2926 - Mae: 1.3756 - R2: 0.4728 - Accuracy: 0.3427 ## 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: 4 - eval_batch_size: 4 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 2 ### Training results | Training Loss | Epoch | Step | Validation Loss | Mse | Mae | R2 | Accuracy | |:-------------:|:-----:|:-----:|:---------------:|:------:|:------:|:------:|:--------:| | 0.7013 | 1.0 | 7652 | 1.0583 | 4.2330 | 1.5178 | 0.4801 | 0.2056 | | 0.3648 | 2.0 | 15304 | 1.0732 | 4.2926 | 1.3756 | 0.4728 | 0.3427 | ### Framework versions - Transformers 4.21.1 - Pytorch 1.12.1+cu113 - Datasets 2.4.0 - Tokenizers 0.12.1
invokerliang/MWP-BERT-zh
invokerliang
2022-08-19T15:12:03Z
160
1
transformers
[ "transformers", "pytorch", "bert", "fill-mask", "license:afl-3.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]
fill-mask
2022-08-19T15:03:11Z
--- license: afl-3.0 --- # MWP-BERT NAACL 2022 Findings Paper: MWP-BERT: Numeracy-Augmented Pre-training for Math Word Problem Solving [![PWC](https://img.shields.io/endpoint.svg?url=https://paperswithcode.com/badge/mwp-bert-a-strong-baseline-for-math-word/math-word-problem-solving-on-mathqa)](https://paperswithcode.com/sota/math-word-problem-solving-on-mathqa?p=mwp-bert-a-strong-baseline-for-math-word) [![PWC](https://img.shields.io/endpoint.svg?url=https://paperswithcode.com/badge/mwp-bert-a-strong-baseline-for-math-word/math-word-problem-solving-on-math23k)](https://paperswithcode.com/sota/math-word-problem-solving-on-math23k?p=mwp-bert-a-strong-baseline-for-math-word) Github link: https://github.com/LZhenwen/MWP-BERT/ Please use the tokenizer of "hfl/chinese-bert-wwm-ext" for this model. ## Citation ``` @inproceedings{liang2022mwp, title={MWP-BERT: Numeracy-Augmented Pre-training for Math Word Problem Solving}, author={Liang, Zhenwen and Zhang, Jipeng and Wang, Lei and Qin, Wei and Lan, Yunshi and Shao, Jie and Zhang, Xiangliang}, booktitle={Findings of NAACL 2022}, pages={997--1009}, year={2022} } ```
yiftach/finetuning-sentiment-model-3000-samples
yiftach
2022-08-19T13:59:24Z
6
0
transformers
[ "transformers", "pytorch", "tensorboard", "distilbert", "text-classification", "generated_from_trainer", "dataset:imdb", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]
text-classification
2022-08-19T13:45:17Z
--- license: apache-2.0 tags: - generated_from_trainer datasets: - imdb metrics: - accuracy - f1 model-index: - name: finetuning-sentiment-model-3000-samples results: - task: name: Text Classification type: text-classification dataset: name: imdb type: imdb config: plain_text split: train args: plain_text metrics: - name: Accuracy type: accuracy value: 0.8666666666666667 - name: F1 type: f1 value: 0.8675496688741722 --- <!-- 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. --> # finetuning-sentiment-model-3000-samples This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the imdb dataset. It achieves the following results on the evaluation set: - Loss: 0.3118 - Accuracy: 0.8667 - F1: 0.8675 ## 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: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 2 ### Training results ### Framework versions - Transformers 4.21.1 - Pytorch 1.12.1+cu113 - Datasets 2.4.0 - Tokenizers 0.12.1
autoevaluate/natural-language-inference
autoevaluate
2022-08-19T13:26:49Z
26
3
transformers
[ "transformers", "pytorch", "tensorboard", "distilbert", "text-classification", "generated_from_trainer", "dataset:glue", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]
text-classification
2022-08-19T11:07:49Z
--- license: apache-2.0 tags: - generated_from_trainer datasets: - glue metrics: - accuracy - f1 model-index: - name: natural-language-inference results: - task: name: Text Classification type: text-classification dataset: name: glue type: glue config: mrpc split: train args: mrpc metrics: - name: Accuracy type: accuracy value: 0.8284313725490197 - name: F1 type: f1 value: 0.8821548821548822 --- <!-- 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. --> # natural-language-inference This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the glue dataset. It achieves the following results on the evaluation set: - Loss: 0.4120 - Accuracy: 0.8284 - F1: 0.8822 ## 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: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 2 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | No log | 1.0 | 230 | 0.4288 | 0.8039 | 0.8644 | | No log | 2.0 | 460 | 0.4120 | 0.8284 | 0.8822 | ### Framework versions - Transformers 4.21.1 - Pytorch 1.12.1+cu113 - Datasets 2.4.0 - Tokenizers 0.12.1
sasha/autotrain-RobertaBaseTweetEval-1281048989
sasha
2022-08-19T12:50:29Z
8
0
transformers
[ "transformers", "pytorch", "roberta", "text-classification", "autotrain", "unk", "dataset:sasha/autotrain-data-RobertaBaseTweetEval", "co2_eq_emissions", "autotrain_compatible", "endpoints_compatible", "region:us" ]
text-classification
2022-08-19T12:31:18Z
--- tags: - autotrain - text-classification language: - unk widget: - text: "I love AutoTrain 🤗" datasets: - sasha/autotrain-data-RobertaBaseTweetEval co2_eq_emissions: emissions: 28.053963781460215 --- # Model Trained Using AutoTrain - Problem type: Multi-class Classification - Model ID: 1281048989 - CO2 Emissions (in grams): 28.0540 ## Validation Metrics - Loss: 0.587 - Accuracy: 0.751 - Macro F1: 0.719 - Micro F1: 0.751 - Weighted F1: 0.746 - Macro Precision: 0.761 - Micro Precision: 0.751 - Weighted Precision: 0.753 - Macro Recall: 0.699 - Micro Recall: 0.751 - Weighted Recall: 0.751 ## Usage You can use cURL to access this model: ``` $ curl -X POST -H "Authorization: Bearer YOUR_API_KEY" -H "Content-Type: application/json" -d '{"inputs": "I love AutoTrain"}' https://api-inference.huggingface.co/models/sasha/autotrain-RobertaBaseTweetEval-1281048989 ``` Or Python API: ``` from transformers import AutoModelForSequenceClassification, AutoTokenizer model = AutoModelForSequenceClassification.from_pretrained("sasha/autotrain-RobertaBaseTweetEval-1281048989", use_auth_token=True) tokenizer = AutoTokenizer.from_pretrained("sasha/autotrain-RobertaBaseTweetEval-1281048989", use_auth_token=True) inputs = tokenizer("I love AutoTrain", return_tensors="pt") outputs = model(**inputs) ```
sasha/autotrain-RobertaBaseTweetEval-1281048990
sasha
2022-08-19T12:42:35Z
10
0
transformers
[ "transformers", "pytorch", "roberta", "text-classification", "autotrain", "unk", "dataset:sasha/autotrain-data-RobertaBaseTweetEval", "co2_eq_emissions", "autotrain_compatible", "endpoints_compatible", "region:us" ]
text-classification
2022-08-19T12:31:58Z
--- tags: - autotrain - text-classification language: - unk widget: - text: "I love AutoTrain 🤗" datasets: - sasha/autotrain-data-RobertaBaseTweetEval co2_eq_emissions: emissions: 11.322528589983463 --- # Model Trained Using AutoTrain - Problem type: Multi-class Classification - Model ID: 1281048990 - CO2 Emissions (in grams): 11.3225 ## Validation Metrics - Loss: 0.592 - Accuracy: 0.747 - Macro F1: 0.729 - Micro F1: 0.747 - Weighted F1: 0.744 - Macro Precision: 0.743 - Micro Precision: 0.747 - Weighted Precision: 0.746 - Macro Recall: 0.720 - Micro Recall: 0.747 - Weighted Recall: 0.747 ## Usage You can use cURL to access this model: ``` $ curl -X POST -H "Authorization: Bearer YOUR_API_KEY" -H "Content-Type: application/json" -d '{"inputs": "I love AutoTrain"}' https://api-inference.huggingface.co/models/sasha/autotrain-RobertaBaseTweetEval-1281048990 ``` Or Python API: ``` from transformers import AutoModelForSequenceClassification, AutoTokenizer model = AutoModelForSequenceClassification.from_pretrained("sasha/autotrain-RobertaBaseTweetEval-1281048990", use_auth_token=True) tokenizer = AutoTokenizer.from_pretrained("sasha/autotrain-RobertaBaseTweetEval-1281048990", use_auth_token=True) inputs = tokenizer("I love AutoTrain", return_tensors="pt") outputs = model(**inputs) ```
Sukhmani/finetuning-sentiment-model-3000-samples
Sukhmani
2022-08-19T12:42:03Z
105
0
transformers
[ "transformers", "pytorch", "tensorboard", "distilbert", "text-classification", "generated_from_trainer", "dataset:imdb", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]
text-classification
2022-08-19T12:19:49Z
--- license: apache-2.0 tags: - generated_from_trainer datasets: - imdb metrics: - accuracy - f1 model-index: - name: finetuning-sentiment-model-3000-samples results: - task: name: Text Classification type: text-classification dataset: name: imdb type: imdb config: plain_text split: train args: plain_text metrics: - name: Accuracy type: accuracy value: 0.91 - name: F1 type: f1 value: 0.909456740442656 --- <!-- 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. --> # finetuning-sentiment-model-3000-samples This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the imdb dataset. It achieves the following results on the evaluation set: - Loss: 0.2671 - Accuracy: 0.91 - F1: 0.9095 ## 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: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 2 ### Training results ### Framework versions - Transformers 4.21.1 - Pytorch 1.12.1+cu113 - Datasets 2.4.0 - Tokenizers 0.12.1
sasha/autotrain-BERTBase-TweetEval-1281248999
sasha
2022-08-19T12:39:53Z
6
0
transformers
[ "transformers", "pytorch", "bert", "text-classification", "autotrain", "unk", "dataset:sasha/autotrain-data-BERTBase-TweetEval", "co2_eq_emissions", "autotrain_compatible", "endpoints_compatible", "region:us" ]
text-classification
2022-08-19T12:25:25Z
--- tags: - autotrain - text-classification language: - unk widget: - text: "I love AutoTrain 🤗" datasets: - sasha/autotrain-data-BERTBase-TweetEval co2_eq_emissions: emissions: 0.1376507540502216 --- # Model Trained Using AutoTrain - Problem type: Multi-class Classification - Model ID: 1281248999 - CO2 Emissions (in grams): 0.1377 ## Validation Metrics - Loss: 0.612 - Accuracy: 0.739 - Macro F1: 0.716 - Micro F1: 0.739 - Weighted F1: 0.737 - Macro Precision: 0.735 - Micro Precision: 0.739 - Weighted Precision: 0.738 - Macro Recall: 0.703 - Micro Recall: 0.739 - Weighted Recall: 0.739 ## Usage You can use cURL to access this model: ``` $ curl -X POST -H "Authorization: Bearer YOUR_API_KEY" -H "Content-Type: application/json" -d '{"inputs": "I love AutoTrain"}' https://api-inference.huggingface.co/models/sasha/autotrain-BERTBase-TweetEval-1281248999 ``` Or Python API: ``` from transformers import AutoModelForSequenceClassification, AutoTokenizer model = AutoModelForSequenceClassification.from_pretrained("sasha/autotrain-BERTBase-TweetEval-1281248999", use_auth_token=True) tokenizer = AutoTokenizer.from_pretrained("sasha/autotrain-BERTBase-TweetEval-1281248999", use_auth_token=True) inputs = tokenizer("I love AutoTrain", return_tensors="pt") outputs = model(**inputs) ```
sasha/autotrain-DistilBERT-TweetEval-1281148991
sasha
2022-08-19T12:39:50Z
6
0
transformers
[ "transformers", "pytorch", "distilbert", "text-classification", "autotrain", "unk", "dataset:sasha/autotrain-data-DistilBERT-TweetEval", "co2_eq_emissions", "autotrain_compatible", "endpoints_compatible", "region:us" ]
text-classification
2022-08-19T12:32:23Z
--- tags: - autotrain - text-classification language: - unk widget: - text: "I love AutoTrain 🤗" datasets: - sasha/autotrain-data-DistilBERT-TweetEval co2_eq_emissions: emissions: 7.4450095136306444 --- # Model Trained Using AutoTrain - Problem type: Multi-class Classification - Model ID: 1281148991 - CO2 Emissions (in grams): 7.4450 ## Validation Metrics - Loss: 0.610 - Accuracy: 0.739 - Macro F1: 0.721 - Micro F1: 0.739 - Weighted F1: 0.739 - Macro Precision: 0.727 - Micro Precision: 0.739 - Weighted Precision: 0.740 - Macro Recall: 0.715 - Micro Recall: 0.739 - Weighted Recall: 0.739 ## Usage You can use cURL to access this model: ``` $ curl -X POST -H "Authorization: Bearer YOUR_API_KEY" -H "Content-Type: application/json" -d '{"inputs": "I love AutoTrain"}' https://api-inference.huggingface.co/models/sasha/autotrain-DistilBERT-TweetEval-1281148991 ``` Or Python API: ``` from transformers import AutoModelForSequenceClassification, AutoTokenizer model = AutoModelForSequenceClassification.from_pretrained("sasha/autotrain-DistilBERT-TweetEval-1281148991", use_auth_token=True) tokenizer = AutoTokenizer.from_pretrained("sasha/autotrain-DistilBERT-TweetEval-1281148991", use_auth_token=True) inputs = tokenizer("I love AutoTrain", return_tensors="pt") outputs = model(**inputs) ```
sasha/autotrain-BERTBase-TweetEval-1281248998
sasha
2022-08-19T12:36:33Z
5
0
transformers
[ "transformers", "pytorch", "bert", "text-classification", "autotrain", "unk", "dataset:sasha/autotrain-data-BERTBase-TweetEval", "co2_eq_emissions", "autotrain_compatible", "endpoints_compatible", "region:us" ]
text-classification
2022-08-19T12:25:20Z
--- tags: - autotrain - text-classification language: - unk widget: - text: "I love AutoTrain 🤗" datasets: - sasha/autotrain-data-BERTBase-TweetEval co2_eq_emissions: emissions: 0.1031242092898596 --- # Model Trained Using AutoTrain - Problem type: Multi-class Classification - Model ID: 1281248998 - CO2 Emissions (in grams): 0.1031 ## Validation Metrics - Loss: 0.602 - Accuracy: 0.746 - Macro F1: 0.718 - Micro F1: 0.746 - Weighted F1: 0.743 - Macro Precision: 0.740 - Micro Precision: 0.746 - Weighted Precision: 0.744 - Macro Recall: 0.705 - Micro Recall: 0.746 - Weighted Recall: 0.746 ## Usage You can use cURL to access this model: ``` $ curl -X POST -H "Authorization: Bearer YOUR_API_KEY" -H "Content-Type: application/json" -d '{"inputs": "I love AutoTrain"}' https://api-inference.huggingface.co/models/sasha/autotrain-BERTBase-TweetEval-1281248998 ``` Or Python API: ``` from transformers import AutoModelForSequenceClassification, AutoTokenizer model = AutoModelForSequenceClassification.from_pretrained("sasha/autotrain-BERTBase-TweetEval-1281248998", use_auth_token=True) tokenizer = AutoTokenizer.from_pretrained("sasha/autotrain-BERTBase-TweetEval-1281248998", use_auth_token=True) inputs = tokenizer("I love AutoTrain", return_tensors="pt") outputs = model(**inputs) ```
sasha/autotrain-RobertaBaseTweetEval-1281048988
sasha
2022-08-19T12:34:07Z
5
0
transformers
[ "transformers", "pytorch", "roberta", "text-classification", "autotrain", "unk", "dataset:sasha/autotrain-data-RobertaBaseTweetEval", "co2_eq_emissions", "autotrain_compatible", "endpoints_compatible", "region:us" ]
text-classification
2022-08-19T12:23:01Z
--- tags: - autotrain - text-classification language: - unk widget: - text: "I love AutoTrain 🤗" datasets: - sasha/autotrain-data-RobertaBaseTweetEval co2_eq_emissions: emissions: 22.606335926892854 --- # Model Trained Using AutoTrain - Problem type: Multi-class Classification - Model ID: 1281048988 - CO2 Emissions (in grams): 22.6063 ## Validation Metrics - Loss: 0.589 - Accuracy: 0.747 - Macro F1: 0.722 - Micro F1: 0.747 - Weighted F1: 0.744 - Macro Precision: 0.743 - Micro Precision: 0.747 - Weighted Precision: 0.746 - Macro Recall: 0.708 - Micro Recall: 0.747 - Weighted Recall: 0.747 ## Usage You can use cURL to access this model: ``` $ curl -X POST -H "Authorization: Bearer YOUR_API_KEY" -H "Content-Type: application/json" -d '{"inputs": "I love AutoTrain"}' https://api-inference.huggingface.co/models/sasha/autotrain-RobertaBaseTweetEval-1281048988 ``` Or Python API: ``` from transformers import AutoModelForSequenceClassification, AutoTokenizer model = AutoModelForSequenceClassification.from_pretrained("sasha/autotrain-RobertaBaseTweetEval-1281048988", use_auth_token=True) tokenizer = AutoTokenizer.from_pretrained("sasha/autotrain-RobertaBaseTweetEval-1281048988", use_auth_token=True) inputs = tokenizer("I love AutoTrain", return_tensors="pt") outputs = model(**inputs) ```
sasha/autotrain-BERTBase-TweetEval-1281248997
sasha
2022-08-19T12:33:26Z
105
0
transformers
[ "transformers", "pytorch", "bert", "text-classification", "autotrain", "unk", "dataset:sasha/autotrain-data-BERTBase-TweetEval", "co2_eq_emissions", "autotrain_compatible", "endpoints_compatible", "region:us" ]
text-classification
2022-08-19T12:25:14Z
--- tags: - autotrain - text-classification language: - unk widget: - text: "I love AutoTrain 🤗" datasets: - sasha/autotrain-data-BERTBase-TweetEval co2_eq_emissions: emissions: 0.07527533186093606 --- # Model Trained Using AutoTrain - Problem type: Multi-class Classification - Model ID: 1281248997 - CO2 Emissions (in grams): 0.0753 ## Validation Metrics - Loss: 0.605 - Accuracy: 0.743 - Macro F1: 0.719 - Micro F1: 0.743 - Weighted F1: 0.741 - Macro Precision: 0.735 - Micro Precision: 0.743 - Weighted Precision: 0.742 - Macro Recall: 0.708 - Micro Recall: 0.743 - Weighted Recall: 0.743 ## Usage You can use cURL to access this model: ``` $ curl -X POST -H "Authorization: Bearer YOUR_API_KEY" -H "Content-Type: application/json" -d '{"inputs": "I love AutoTrain"}' https://api-inference.huggingface.co/models/sasha/autotrain-BERTBase-TweetEval-1281248997 ``` Or Python API: ``` from transformers import AutoModelForSequenceClassification, AutoTokenizer model = AutoModelForSequenceClassification.from_pretrained("sasha/autotrain-BERTBase-TweetEval-1281248997", use_auth_token=True) tokenizer = AutoTokenizer.from_pretrained("sasha/autotrain-BERTBase-TweetEval-1281248997", use_auth_token=True) inputs = tokenizer("I love AutoTrain", return_tensors="pt") outputs = model(**inputs) ```
sasha/autotrain-BERTBase-TweetEval-1281249000
sasha
2022-08-19T12:31:08Z
105
0
transformers
[ "transformers", "pytorch", "bert", "text-classification", "autotrain", "unk", "dataset:sasha/autotrain-data-BERTBase-TweetEval", "co2_eq_emissions", "autotrain_compatible", "endpoints_compatible", "region:us" ]
text-classification
2022-08-19T12:25:40Z
--- tags: - autotrain - text-classification language: - unk widget: - text: "I love AutoTrain 🤗" datasets: - sasha/autotrain-data-BERTBase-TweetEval co2_eq_emissions: emissions: 0.04868905658915141 --- # Model Trained Using AutoTrain - Problem type: Multi-class Classification - Model ID: 1281249000 - CO2 Emissions (in grams): 0.0487 ## Validation Metrics - Loss: 0.602 - Accuracy: 0.743 - Macro F1: 0.723 - Micro F1: 0.743 - Weighted F1: 0.740 - Macro Precision: 0.740 - Micro Precision: 0.743 - Weighted Precision: 0.742 - Macro Recall: 0.712 - Micro Recall: 0.743 - Weighted Recall: 0.743 ## Usage You can use cURL to access this model: ``` $ curl -X POST -H "Authorization: Bearer YOUR_API_KEY" -H "Content-Type: application/json" -d '{"inputs": "I love AutoTrain"}' https://api-inference.huggingface.co/models/sasha/autotrain-BERTBase-TweetEval-1281249000 ``` Or Python API: ``` from transformers import AutoModelForSequenceClassification, AutoTokenizer model = AutoModelForSequenceClassification.from_pretrained("sasha/autotrain-BERTBase-TweetEval-1281249000", use_auth_token=True) tokenizer = AutoTokenizer.from_pretrained("sasha/autotrain-BERTBase-TweetEval-1281249000", use_auth_token=True) inputs = tokenizer("I love AutoTrain", return_tensors="pt") outputs = model(**inputs) ```
sasha/autotrain-RobertaBaseTweetEval-1281048987
sasha
2022-08-19T12:31:03Z
4
0
transformers
[ "transformers", "pytorch", "roberta", "text-classification", "autotrain", "unk", "dataset:sasha/autotrain-data-RobertaBaseTweetEval", "co2_eq_emissions", "autotrain_compatible", "endpoints_compatible", "region:us" ]
text-classification
2022-08-19T12:22:56Z
--- tags: - autotrain - text-classification language: - unk widget: - text: "I love AutoTrain 🤗" datasets: - sasha/autotrain-data-RobertaBaseTweetEval co2_eq_emissions: emissions: 16.685914259874124 --- # Model Trained Using AutoTrain - Problem type: Multi-class Classification - Model ID: 1281048987 - CO2 Emissions (in grams): 16.6859 ## Validation Metrics - Loss: 0.617 - Accuracy: 0.734 - Macro F1: 0.690 - Micro F1: 0.734 - Weighted F1: 0.725 - Macro Precision: 0.753 - Micro Precision: 0.734 - Weighted Precision: 0.739 - Macro Recall: 0.669 - Micro Recall: 0.734 - Weighted Recall: 0.734 ## Usage You can use cURL to access this model: ``` $ curl -X POST -H "Authorization: Bearer YOUR_API_KEY" -H "Content-Type: application/json" -d '{"inputs": "I love AutoTrain"}' https://api-inference.huggingface.co/models/sasha/autotrain-RobertaBaseTweetEval-1281048987 ``` Or Python API: ``` from transformers import AutoModelForSequenceClassification, AutoTokenizer model = AutoModelForSequenceClassification.from_pretrained("sasha/autotrain-RobertaBaseTweetEval-1281048987", use_auth_token=True) tokenizer = AutoTokenizer.from_pretrained("sasha/autotrain-RobertaBaseTweetEval-1281048987", use_auth_token=True) inputs = tokenizer("I love AutoTrain", return_tensors="pt") outputs = model(**inputs) ```
sasha/autotrain-BERTBase-TweetEval-1281248996
sasha
2022-08-19T12:30:42Z
105
0
transformers
[ "transformers", "pytorch", "bert", "text-classification", "autotrain", "unk", "dataset:sasha/autotrain-data-BERTBase-TweetEval", "co2_eq_emissions", "autotrain_compatible", "endpoints_compatible", "region:us" ]
text-classification
2022-08-19T12:25:14Z
--- tags: - autotrain - text-classification language: - unk widget: - text: "I love AutoTrain 🤗" datasets: - sasha/autotrain-data-BERTBase-TweetEval co2_eq_emissions: emissions: 0.042163153679615525 --- # Model Trained Using AutoTrain - Problem type: Multi-class Classification - Model ID: 1281248996 - CO2 Emissions (in grams): 0.0422 ## Validation Metrics - Loss: 0.600 - Accuracy: 0.743 - Macro F1: 0.719 - Micro F1: 0.743 - Weighted F1: 0.740 - Macro Precision: 0.743 - Micro Precision: 0.743 - Weighted Precision: 0.742 - Macro Recall: 0.705 - Micro Recall: 0.743 - Weighted Recall: 0.743 ## Usage You can use cURL to access this model: ``` $ curl -X POST -H "Authorization: Bearer YOUR_API_KEY" -H "Content-Type: application/json" -d '{"inputs": "I love AutoTrain"}' https://api-inference.huggingface.co/models/sasha/autotrain-BERTBase-TweetEval-1281248996 ``` Or Python API: ``` from transformers import AutoModelForSequenceClassification, AutoTokenizer model = AutoModelForSequenceClassification.from_pretrained("sasha/autotrain-BERTBase-TweetEval-1281248996", use_auth_token=True) tokenizer = AutoTokenizer.from_pretrained("sasha/autotrain-BERTBase-TweetEval-1281248996", use_auth_token=True) inputs = tokenizer("I love AutoTrain", return_tensors="pt") outputs = model(**inputs) ```
sasha/autotrain-DistilBERT-TweetEval-1281148992
sasha
2022-08-19T12:29:11Z
105
0
transformers
[ "transformers", "pytorch", "distilbert", "text-classification", "autotrain", "unk", "dataset:sasha/autotrain-data-DistilBERT-TweetEval", "co2_eq_emissions", "autotrain_compatible", "endpoints_compatible", "region:us" ]
text-classification
2022-08-19T12:23:59Z
--- tags: - autotrain - text-classification language: - unk widget: - text: "I love AutoTrain 🤗" datasets: - sasha/autotrain-data-DistilBERT-TweetEval co2_eq_emissions: emissions: 10.676055974144631 --- # Model Trained Using AutoTrain - Problem type: Multi-class Classification - Model ID: 1281148992 - CO2 Emissions (in grams): 10.6761 ## Validation Metrics - Loss: 0.606 - Accuracy: 0.728 - Macro F1: 0.710 - Micro F1: 0.728 - Weighted F1: 0.728 - Macro Precision: 0.716 - Micro Precision: 0.728 - Weighted Precision: 0.729 - Macro Recall: 0.706 - Micro Recall: 0.728 - Weighted Recall: 0.728 ## Usage You can use cURL to access this model: ``` $ curl -X POST -H "Authorization: Bearer YOUR_API_KEY" -H "Content-Type: application/json" -d '{"inputs": "I love AutoTrain"}' https://api-inference.huggingface.co/models/sasha/autotrain-DistilBERT-TweetEval-1281148992 ``` Or Python API: ``` from transformers import AutoModelForSequenceClassification, AutoTokenizer model = AutoModelForSequenceClassification.from_pretrained("sasha/autotrain-DistilBERT-TweetEval-1281148992", use_auth_token=True) tokenizer = AutoTokenizer.from_pretrained("sasha/autotrain-DistilBERT-TweetEval-1281148992", use_auth_token=True) inputs = tokenizer("I love AutoTrain", return_tensors="pt") outputs = model(**inputs) ```
sasha/autotrain-DistilBERT-TweetEval-1281148995
sasha
2022-08-19T12:27:56Z
105
0
transformers
[ "transformers", "pytorch", "distilbert", "text-classification", "autotrain", "unk", "dataset:sasha/autotrain-data-DistilBERT-TweetEval", "co2_eq_emissions", "autotrain_compatible", "endpoints_compatible", "region:us" ]
text-classification
2022-08-19T12:24:21Z
--- tags: - autotrain - text-classification language: - unk widget: - text: "I love AutoTrain 🤗" datasets: - sasha/autotrain-data-DistilBERT-TweetEval co2_eq_emissions: emissions: 6.436434120056388 --- # Model Trained Using AutoTrain - Problem type: Multi-class Classification - Model ID: 1281148995 - CO2 Emissions (in grams): 6.4364 ## Validation Metrics - Loss: 0.615 - Accuracy: 0.729 - Macro F1: 0.712 - Micro F1: 0.729 - Weighted F1: 0.729 - Macro Precision: 0.719 - Micro Precision: 0.729 - Weighted Precision: 0.732 - Macro Recall: 0.707 - Micro Recall: 0.729 - Weighted Recall: 0.729 ## Usage You can use cURL to access this model: ``` $ curl -X POST -H "Authorization: Bearer YOUR_API_KEY" -H "Content-Type: application/json" -d '{"inputs": "I love AutoTrain"}' https://api-inference.huggingface.co/models/sasha/autotrain-DistilBERT-TweetEval-1281148995 ``` Or Python API: ``` from transformers import AutoModelForSequenceClassification, AutoTokenizer model = AutoModelForSequenceClassification.from_pretrained("sasha/autotrain-DistilBERT-TweetEval-1281148995", use_auth_token=True) tokenizer = AutoTokenizer.from_pretrained("sasha/autotrain-DistilBERT-TweetEval-1281148995", use_auth_token=True) inputs = tokenizer("I love AutoTrain", return_tensors="pt") outputs = model(**inputs) ```
ml6team/keyphrase-generation-t5-small-inspec
ml6team
2022-08-19T11:54:17Z
55
6
transformers
[ "transformers", "pytorch", "t5", "text2text-generation", "keyphrase-generation", "en", "dataset:midas/inspec", "license:mit", "model-index", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]
text2text-generation
2022-04-27T12:37:16Z
--- language: en license: mit tags: - keyphrase-generation datasets: - midas/inspec widget: - text: "Keyphrase extraction is a technique in text analysis where you extract the important keyphrases from a document. Thanks to these keyphrases humans can understand the content of a text very quickly and easily without reading it completely. Keyphrase extraction was first done primarily by human annotators, who read the text in detail and then wrote down the most important keyphrases. The disadvantage is that if you work with a lot of documents, this process can take a lot of time. Here is where Artificial Intelligence comes in. Currently, classical machine learning methods, that use statistical and linguistic features, are widely used for the extraction process. Now with deep learning, it is possible to capture the semantic meaning of a text even better than these classical methods. Classical methods look at the frequency, occurrence and order of words in the text, whereas these neural approaches can capture long-term semantic dependencies and context of words in a text." example_title: "Example 1" - text: "In this work, we explore how to learn task specific language models aimed towards learning rich representation of keyphrases from text documents. We experiment with different masking strategies for pre-training transformer language models (LMs) in discriminative as well as generative settings. In the discriminative setting, we introduce a new pre-training objective - Keyphrase Boundary Infilling with Replacement (KBIR), showing large gains in performance (up to 9.26 points in F1) over SOTA, when LM pre-trained using KBIR is fine-tuned for the task of keyphrase extraction. In the generative setting, we introduce a new pre-training setup for BART - KeyBART, that reproduces the keyphrases related to the input text in the CatSeq format, instead of the denoised original input. This also led to gains in performance (up to 4.33 points inF1@M) over SOTA for keyphrase generation. Additionally, we also fine-tune the pre-trained language models on named entity recognition(NER), question answering (QA), relation extraction (RE), abstractive summarization and achieve comparable performance with that of the SOTA, showing that learning rich representation of keyphrases is indeed beneficial for many other fundamental NLP tasks." example_title: "Example 2" model-index: - name: DeDeckerThomas/keyphrase-generation-t5-small-inspec results: - task: type: keyphrase-generation name: Keyphrase Generation dataset: type: midas/inspec name: inspec metrics: - type: F1@M (Present) value: 0.317 name: F1@M (Present) - type: F1@O (Present) value: 0.279 name: F1@O (Present) - type: F1@M (Absent) value: 0.073 name: F1@M (Absent) - type: F1@O (Absent) value: 0.065 name: F1@O (Absent) --- # 🔑 Keyphrase Generation Model: T5-small-inspec Keyphrase extraction is a technique in text analysis where you extract the important keyphrases from a document. Thanks to these keyphrases humans can understand the content of a text very quickly and easily without reading it completely. Keyphrase extraction was first done primarily by human annotators, who read the text in detail and then wrote down the most important keyphrases. The disadvantage is that if you work with a lot of documents, this process can take a lot of time ⏳. Here is where Artificial Intelligence 🤖 comes in. Currently, classical machine learning methods, that use statistical and linguistic features, are widely used for the extraction process. Now with deep learning, it is possible to capture the semantic meaning of a text even better than these classical methods. Classical methods look at the frequency, occurrence and order of words in the text, whereas these neural approaches can capture long-term semantic dependencies and context of words in a text. ## 📓 Model Description This model uses [T5-small model](https://huggingface.co/t5-small) as its base model and fine-tunes it on the [Inspec dataset](https://huggingface.co/datasets/midas/inspec). Keyphrase generation transformers are fine-tuned as a text-to-text generation problem where the keyphrases are generated. The result is a concatenated string with all keyphrases separated by a given delimiter (i.e. “;”). These models are capable of generating present and absent keyphrases. ## ✋ Intended Uses & Limitations ### 🛑 Limitations * This keyphrase generation model is very domain-specific and will perform very well on abstracts of scientific papers. It's not recommended to use this model for other domains, but you are free to test it out. * Only works for English documents. * Sometimes the output doesn't make any sense. ### ❓ How To Use ```python # Model parameters from transformers import ( Text2TextGenerationPipeline, AutoModelForSeq2SeqLM, AutoTokenizer, ) class KeyphraseGenerationPipeline(Text2TextGenerationPipeline): def __init__(self, model, keyphrase_sep_token=";", *args, **kwargs): super().__init__( model=AutoModelForSeq2SeqLM.from_pretrained(model), tokenizer=AutoTokenizer.from_pretrained(model), *args, **kwargs ) self.keyphrase_sep_token = keyphrase_sep_token def postprocess(self, model_outputs): results = super().postprocess( model_outputs=model_outputs ) return [[keyphrase.strip() for keyphrase in result.get("generated_text").split(self.keyphrase_sep_token) if keyphrase != ""] for result in results] ``` ```python # Load pipeline model_name = "ml6team/keyphrase-generation-t5-small-inspec" generator = KeyphraseGenerationPipeline(model=model_name) ``` ```python text = """ Keyphrase extraction is a technique in text analysis where you extract the important keyphrases from a document. Thanks to these keyphrases humans can understand the content of a text very quickly and easily without reading it completely. Keyphrase extraction was first done primarily by human annotators, who read the text in detail and then wrote down the most important keyphrases. The disadvantage is that if you work with a lot of documents, this process can take a lot of time. Here is where Artificial Intelligence comes in. Currently, classical machine learning methods, that use statistical and linguistic features, are widely used for the extraction process. Now with deep learning, it is possible to capture the semantic meaning of a text even better than these classical methods. Classical methods look at the frequency, occurrence and order of words in the text, whereas these neural approaches can capture long-term semantic dependencies and context of words in a text. """.replace("\n", " ") keyphrases = generator(text) print(keyphrases) ``` ``` # Output [['keyphrase extraction', 'text analysis', 'artificial intelligence', 'classical machine learning methods']] ``` ## 📚 Training Dataset [Inspec](https://huggingface.co/datasets/midas/inspec) is a keyphrase extraction/generation dataset consisting of 2000 English scientific papers from the scientific domains of Computers and Control and Information Technology published between 1998 to 2002. The keyphrases are annotated by professional indexers or editors. You can find more information in the [paper](https://dl.acm.org/doi/10.3115/1119355.1119383). ## 👷‍♂️ Training Procedure ### Training Parameters | Parameter | Value | | --------- | ------| | Learning Rate | 5e-5 | | Epochs | 50 | | Early Stopping Patience | 1 | ### Preprocessing The documents in the dataset are already preprocessed into list of words with the corresponding keyphrases. The only thing that must be done is tokenization and joining all keyphrases into one string with a certain seperator of choice( ```;``` ). ```python from datasets import load_dataset from transformers import AutoTokenizer # Tokenizer tokenizer = AutoTokenizer.from_pretrained("t5-small", add_prefix_space=True) # Dataset parameters dataset_full_name = "midas/inspec" dataset_subset = "raw" dataset_document_column = "document" keyphrase_sep_token = ";" def preprocess_keyphrases(text_ids, kp_list): kp_order_list = [] kp_set = set(kp_list) text = tokenizer.decode( text_ids, skip_special_tokens=True, clean_up_tokenization_spaces=True ) text = text.lower() for kp in kp_set: kp = kp.strip() kp_index = text.find(kp.lower()) kp_order_list.append((kp_index, kp)) kp_order_list.sort() present_kp, absent_kp = [], [] for kp_index, kp in kp_order_list: if kp_index < 0: absent_kp.append(kp) else: present_kp.append(kp) return present_kp, absent_kp def preprocess_fuction(samples): processed_samples = {"input_ids": [], "attention_mask": [], "labels": []} for i, sample in enumerate(samples[dataset_document_column]): input_text = " ".join(sample) inputs = tokenizer( input_text, padding="max_length", truncation=True, ) present_kp, absent_kp = preprocess_keyphrases( text_ids=inputs["input_ids"], kp_list=samples["extractive_keyphrases"][i] + samples["abstractive_keyphrases"][i], ) keyphrases = present_kp keyphrases += absent_kp target_text = f" {keyphrase_sep_token} ".join(keyphrases) with tokenizer.as_target_tokenizer(): targets = tokenizer( target_text, max_length=40, padding="max_length", truncation=True ) targets["input_ids"] = [ (t if t != tokenizer.pad_token_id else -100) for t in targets["input_ids"] ] for key in inputs.keys(): processed_samples[key].append(inputs[key]) processed_samples["labels"].append(targets["input_ids"]) return processed_samples # Load dataset dataset = load_dataset(dataset_full_name, dataset_subset) # Preprocess dataset tokenized_dataset = dataset.map(preprocess_fuction, batched=True) ``` ### Postprocessing For the post-processing, you will need to split the string based on the keyphrase separator. ```python def extract_keyphrases(examples): return [example.split(keyphrase_sep_token) for example in examples] ``` ## 📝 Evaluation Results Traditional evaluation methods are the precision, recall and F1-score @k,m where k is the number that stands for the first k predicted keyphrases and m for the average amount of predicted keyphrases. In keyphrase generation you also look at F1@O where O stands for the number of ground truth keyphrases. The model achieves the following results on the Inspec test set: Extractive keyphrases | Dataset | P@5 | R@5 | F1@5 | P@10 | R@10 | F1@10 | P@M | R@M | F1@M | P@O | R@O | F1@O | |:-----------------:|:----:|:----:|:----:|:----:|:----:|:-----:|:----:|:----:|:----:|:----:|:----:|:----:| | Inspec Test Set | 0.33 | 0.31 | 0.29 | 0.17 | 0.31 | 0.20 | 0.41 | 0.31 | 0.32 | 0.28 | 0.28 | 0.28 | Abstractive keyphrases | Dataset | P@5 | R@5 | F1@5 | P@10 | R@10 | F1@10 | P@M | R@M | F1@M | P@O | R@O | F1@O | |:-----------------:|:----:|:----:|:----:|:----:|:----:|:-----:|:----:|:----:|:----:|:----:|:----:|:----:| | Inspec Test Set | 0.05 | 0.09 | 0.06 | 0.03 | 0.09 | 0.04 | 0.08 | 0.09 | 0.07 | 0.06 | 0.06 | 0.06 | ## 🚨 Issues Please feel free to start discussions in the Community Tab.
sam2ai/ddpm-butterflies-128
sam2ai
2022-08-19T10:44:23Z
2
0
diffusers
[ "diffusers", "tensorboard", "en", "dataset:huggan/smithsonian_butterflies_subset", "license:apache-2.0", "diffusers:DDPMPipeline", "region:us" ]
null
2022-08-19T09:29:35Z
--- language: en license: apache-2.0 library_name: diffusers tags: [] datasets: huggan/smithsonian_butterflies_subset metrics: [] --- <!-- 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. --> # ddpm-butterflies-128 ## Model description This diffusion model is trained with the [🤗 Diffusers](https://github.com/huggingface/diffusers) library on the `huggan/smithsonian_butterflies_subset` dataset. ## 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 data [TODO: describe the data used to train the model] ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 16 - eval_batch_size: 16 - gradient_accumulation_steps: 1 - optimizer: AdamW with betas=(None, None), weight_decay=None and epsilon=None - lr_scheduler: None - lr_warmup_steps: 500 - ema_inv_gamma: None - ema_inv_gamma: None - ema_inv_gamma: None - mixed_precision: fp16 ### Training results 📈 [TensorBoard logs](https://huggingface.co/sam2ai/ddpm-butterflies-128/tensorboard?#scalars)
pbwt/th1
pbwt
2022-08-19T09:40:19Z
4
1
transformers
[ "transformers", "tf", "bert", "text-classification", "generated_from_keras_callback", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]
text-classification
2022-08-19T08:33:17Z
--- license: apache-2.0 tags: - generated_from_keras_callback model-index: - name: pbwt/th1 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. --> # pbwt/th1 This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.0008 - Train Sparse Categorical Accuracy: 1.0 - Validation Loss: 0.0005 - Validation Sparse Categorical Accuracy: 1.0 - Epoch: 2 ## 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': 'Adam', 'learning_rate': 5e-05, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False} - training_precision: float32 ### Training results | Train Loss | Train Sparse Categorical Accuracy | Validation Loss | Validation Sparse Categorical Accuracy | Epoch | |:----------:|:---------------------------------:|:---------------:|:--------------------------------------:|:-----:| | 0.1184 | 0.9650 | 0.0017 | 1.0 | 0 | | 0.0015 | 1.0 | 0.0008 | 1.0 | 1 | | 0.0008 | 1.0 | 0.0005 | 1.0 | 2 | ### Framework versions - Transformers 4.18.0 - TensorFlow 2.6.2 - Datasets 2.4.0 - Tokenizers 0.12.1
AliMMZ/dqn-SpaceInvadersFirst-v4
AliMMZ
2022-08-19T09:08:23Z
0
0
stable-baselines3
[ "stable-baselines3", "SpaceInvadersNoFrameskip-v4", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]
reinforcement-learning
2022-08-19T09:07:46Z
--- library_name: stable-baselines3 tags: - SpaceInvadersNoFrameskip-v4 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: DQN results: - metrics: - type: mean_reward value: 538.50 +/- 117.37 name: mean_reward task: type: reinforcement-learning name: reinforcement-learning dataset: name: SpaceInvadersNoFrameskip-v4 type: SpaceInvadersNoFrameskip-v4 --- # **DQN** Agent playing **SpaceInvadersNoFrameskip-v4** This is a trained model of a **DQN** agent playing **SpaceInvadersNoFrameskip-v4** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3) and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo). The RL Zoo is a training framework for Stable Baselines3 reinforcement learning agents, with hyperparameter optimization and pre-trained agents included. ## Usage (with SB3 RL Zoo) RL Zoo: https://github.com/DLR-RM/rl-baselines3-zoo<br/> SB3: https://github.com/DLR-RM/stable-baselines3<br/> SB3 Contrib: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib ``` # Download model and save it into the logs/ folder python -m utils.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga AliMMZ -f logs/ python enjoy.py --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ ``` ## Training (with the RL Zoo) ``` python train.py --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ # Upload the model and generate video (when possible) python -m utils.push_to_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ -orga AliMMZ ``` ## Hyperparameters ```python OrderedDict([('batch_size', 32), ('buffer_size', 100000), ('env_wrapper', ['stable_baselines3.common.atari_wrappers.AtariWrapper']), ('exploration_final_eps', 0.01), ('exploration_fraction', 0.1), ('frame_stack', 4), ('gradient_steps', 1), ('learning_rate', 0.0001), ('learning_starts', 100000), ('n_timesteps', 10000000.0), ('optimize_memory_usage', False), ('policy', 'CnnPolicy'), ('target_update_interval', 1000), ('train_freq', 4), ('normalize', False)]) ```
ish97/bert-finetuned-ner
ish97
2022-08-19T09:03:17Z
105
0
transformers
[ "transformers", "pytorch", "tensorboard", "bert", "token-classification", "generated_from_trainer", "dataset:conll2003", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]
token-classification
2022-08-16T18:39:02Z
--- license: apache-2.0 tags: - generated_from_trainer datasets: - conll2003 metrics: - precision - recall - f1 - accuracy model-index: - name: bert-finetuned-ner results: - task: name: Token Classification type: token-classification dataset: name: conll2003 type: conll2003 config: conll2003 split: train args: conll2003 metrics: - name: Precision type: precision value: 0.929042904290429 - name: Recall type: recall value: 0.9474924267923258 - name: F1 type: f1 value: 0.9381769705049159 - name: Accuracy type: accuracy value: 0.985783246011656 --- <!-- 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-ner This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the conll2003 dataset. It achieves the following results on the evaluation set: - Loss: 0.0641 - Precision: 0.9290 - Recall: 0.9475 - F1: 0.9382 - Accuracy: 0.9858 ## 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: 3 ### Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | 0.0867 | 1.0 | 1756 | 0.0716 | 0.9102 | 0.9297 | 0.9198 | 0.9820 | | 0.0345 | 2.0 | 3512 | 0.0680 | 0.9290 | 0.9465 | 0.9376 | 0.9854 | | 0.0191 | 3.0 | 5268 | 0.0641 | 0.9290 | 0.9475 | 0.9382 | 0.9858 | ### Framework versions - Transformers 4.21.1 - Pytorch 1.12.1+cu113 - Datasets 2.4.0 - Tokenizers 0.12.1
MayaGalvez/bert-base-multilingual-cased-finetuned-multilingual-ner
MayaGalvez
2022-08-19T08:37:57Z
4
0
transformers
[ "transformers", "pytorch", "bert", "token-classification", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]
token-classification
2022-08-19T08:01:43Z
--- license: apache-2.0 tags: - generated_from_trainer metrics: - precision - recall - f1 - accuracy model-index: - name: bert-base-multilingual-cased-finetuned-multilingual-ner 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-base-multilingual-cased-finetuned-multilingual-ner This model is a fine-tuned version of [bert-base-multilingual-cased](https://huggingface.co/bert-base-multilingual-cased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.2352 - Precision: 0.8109 - Recall: 0.8332 - F1: 0.8219 - Accuracy: 0.9264 ## 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: 32 - eval_batch_size: 32 - 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 | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | 0.7301 | 0.16 | 100 | 0.3827 | 0.6189 | 0.7009 | 0.6573 | 0.8734 | | 0.3841 | 0.32 | 200 | 0.3195 | 0.7057 | 0.7511 | 0.7277 | 0.8922 | | 0.3451 | 0.48 | 300 | 0.2862 | 0.7094 | 0.7750 | 0.7407 | 0.8952 | | 0.3187 | 0.65 | 400 | 0.2735 | 0.7372 | 0.7802 | 0.7581 | 0.9019 | | 0.3058 | 0.81 | 500 | 0.2533 | 0.7536 | 0.8015 | 0.7768 | 0.9052 | | 0.2918 | 0.97 | 600 | 0.2458 | 0.7587 | 0.8085 | 0.7828 | 0.9126 | | 0.2425 | 1.13 | 700 | 0.2379 | 0.7742 | 0.7976 | 0.7857 | 0.9150 | | 0.2387 | 1.29 | 800 | 0.2300 | 0.7772 | 0.8108 | 0.7936 | 0.9165 | | 0.2125 | 1.45 | 900 | 0.2387 | 0.7900 | 0.8130 | 0.8014 | 0.9180 | | 0.2026 | 1.62 | 1000 | 0.2317 | 0.7877 | 0.8152 | 0.8012 | 0.9186 | | 0.1963 | 1.78 | 1100 | 0.2326 | 0.7842 | 0.8269 | 0.8049 | 0.9220 | | 0.2052 | 1.94 | 1200 | 0.2247 | 0.7924 | 0.8234 | 0.8076 | 0.9212 | | 0.1868 | 2.1 | 1300 | 0.2410 | 0.7903 | 0.8282 | 0.8088 | 0.9204 | | 0.1556 | 2.26 | 1400 | 0.2428 | 0.8064 | 0.8317 | 0.8189 | 0.9256 | | 0.153 | 2.42 | 1500 | 0.2316 | 0.8017 | 0.8282 | 0.8147 | 0.9238 | | 0.1484 | 2.58 | 1600 | 0.2379 | 0.8054 | 0.8338 | 0.8194 | 0.9258 | | 0.137 | 2.75 | 1700 | 0.2331 | 0.8101 | 0.8324 | 0.8211 | 0.9270 | | 0.1638 | 2.91 | 1800 | 0.2352 | 0.8109 | 0.8332 | 0.8219 | 0.9264 | ### Framework versions - Transformers 4.21.0 - Pytorch 1.12.0+cu102 - Datasets 2.4.0 - Tokenizers 0.12.1
reachrkr/LunarLander-v2
reachrkr
2022-08-19T08:05:50Z
0
0
null
[ "tensorboard", "LunarLander-v2", "ppo", "deep-reinforcement-learning", "reinforcement-learning", "custom-implementation", "deep-rl-class", "model-index", "region:us" ]
reinforcement-learning
2022-08-19T08:05:34Z
--- tags: - LunarLander-v2 - ppo - deep-reinforcement-learning - reinforcement-learning - custom-implementation - deep-rl-class model-index: - name: PPO results: - metrics: - type: mean_reward value: -147.49 +/- 56.78 name: mean_reward task: type: reinforcement-learning name: reinforcement-learning dataset: name: LunarLander-v2 type: LunarLander-v2 --- # PPO Agent Playing LunarLander-v2 This is a trained model of a PPO agent playing LunarLander-v2. To learn to code your own PPO agent and train it Unit 8 of the Deep Reinforcement Learning Class: https://github.com/huggingface/deep-rl-class/tree/main/unit8 # Hyperparameters ```python {'exp_name': 'ppo' 'seed': 1 'torch_deterministic': True 'cuda': True 'track': False 'wandb_project_name': 'cleanRL' 'wandb_entity': None 'capture_video': False 'env_id': 'LunarLander-v2' 'total_timesteps': 50000 'learning_rate': 0.00025 'num_envs': 4 'num_steps': 128 'anneal_lr': True 'gae': True 'gamma': 0.99 'gae_lambda': 0.95 'num_minibatches': 4 'update_epochs': 4 'norm_adv': True 'clip_coef': 0.2 'clip_vloss': True 'ent_coef': 0.01 'vf_coef': 0.5 'max_grad_norm': 0.5 'target_kl': None 'repo_id': 'reachrkr/LunarLander-v2' 'batch_size': 512 'minibatch_size': 128} ```
Akoo/mpbbLM
Akoo
2022-08-19T07:26:09Z
5
0
transformers
[ "transformers", "pytorch", "tensorboard", "gpt2", "text-generation", "generated_from_trainer", "dataset:mbpp", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]
text-generation
2022-08-19T06:09:59Z
--- license: apache-2.0 tags: - generated_from_trainer datasets: - mbpp model-index: - name: mpbbLM 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. --> # mpbbLM This model is a fine-tuned version of [codeparrot/codeparrot-small](https://huggingface.co/codeparrot/codeparrot-small) on the mbpp dataset. It achieves the following results on the evaluation set: - Loss: 1.7239 ## 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: 0.0005 - train_batch_size: 4 - eval_batch_size: 4 - seed: 42 - gradient_accumulation_steps: 8 - total_train_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 100 - num_epochs: 1 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 2.2545 | 0.61 | 10 | 1.7239 | ### Framework versions - Transformers 4.21.1 - Pytorch 1.12.1+cu113 - Datasets 2.4.0 - Tokenizers 0.12.1
shreyas-singh/autotrain-MedicalTokenClassification-1279048948
shreyas-singh
2022-08-19T06:59:29Z
104
0
transformers
[ "transformers", "pytorch", "bert", "token-classification", "autotrain", "unk", "dataset:shreyas-singh/autotrain-data-MedicalTokenClassification", "co2_eq_emissions", "autotrain_compatible", "endpoints_compatible", "region:us" ]
token-classification
2022-08-19T06:53:27Z
--- tags: - autotrain - token-classification language: - unk widget: - text: "I love AutoTrain 🤗" datasets: - shreyas-singh/autotrain-data-MedicalTokenClassification co2_eq_emissions: emissions: 12.16859664557857 --- # Model Trained Using AutoTrain - Problem type: Entity Extraction - Model ID: 1279048948 - CO2 Emissions (in grams): 12.1686 ## Validation Metrics - Loss: 0.152 - Accuracy: 0.959 - Precision: 0.879 - Recall: 0.880 - F1: 0.879 ## Usage You can use cURL to access this model: ``` $ curl -X POST -H "Authorization: Bearer YOUR_API_KEY" -H "Content-Type: application/json" -d '{"inputs": "I love AutoTrain"}' https://api-inference.huggingface.co/models/shreyas-singh/autotrain-MedicalTokenClassification-1279048948 ``` Or Python API: ``` from transformers import AutoModelForTokenClassification, AutoTokenizer model = AutoModelForTokenClassification.from_pretrained("shreyas-singh/autotrain-MedicalTokenClassification-1279048948", use_auth_token=True) tokenizer = AutoTokenizer.from_pretrained("shreyas-singh/autotrain-MedicalTokenClassification-1279048948", use_auth_token=True) inputs = tokenizer("I love AutoTrain", return_tensors="pt") outputs = model(**inputs) ```
mariolinml/roberta_large-ner-conll2003_0818_v1
mariolinml
2022-08-19T04:20:45Z
105
0
transformers
[ "transformers", "pytorch", "tensorboard", "roberta", "token-classification", "generated_from_trainer", "dataset:conll2003", "license:mit", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]
token-classification
2022-08-19T03:16:13Z
--- license: mit tags: - generated_from_trainer datasets: - conll2003 metrics: - precision - recall - f1 - accuracy model-index: - name: roberta_large-ner-conll2003_0818_v1 results: - task: name: Token Classification type: token-classification dataset: name: conll2003 type: conll2003 config: conll2003 split: train args: conll2003 metrics: - name: Precision type: precision value: 0.8993300120254252 - name: Recall type: recall value: 0.9268767705382436 - name: F1 type: f1 value: 0.9128956317028512 - name: Accuracy type: accuracy value: 0.978371121718377 --- <!-- 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. --> # roberta_large-ner-conll2003_0818_v1 This model is a fine-tuned version of [roberta-large](https://huggingface.co/roberta-large) on the conll2003 dataset. It achieves the following results on the evaluation set: - Loss: 0.1481 - Precision: 0.8993 - Recall: 0.9269 - F1: 0.9129 - Accuracy: 0.9784 ## 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: 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: 2 ### Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | 0.2033 | 1.0 | 878 | 0.0472 | 0.9277 | 0.9551 | 0.9412 | 0.9887 | | 0.044 | 2.0 | 1756 | 0.0428 | 0.9365 | 0.9610 | 0.9486 | 0.9895 | ### Framework versions - Transformers 4.21.1 - Pytorch 1.12.1+cu113 - Datasets 2.4.0 - Tokenizers 0.12.1
lightbansal/autotrain-metadata_postprocess-1277848906
lightbansal
2022-08-19T03:46:30Z
9
0
transformers
[ "transformers", "pytorch", "pegasus", "text2text-generation", "autotrain", "summarization", "en", "dataset:lightbansal/autotrain-data-metadata_postprocess", "co2_eq_emissions", "autotrain_compatible", "endpoints_compatible", "region:us" ]
summarization
2022-08-19T01:04:20Z
--- tags: - autotrain - summarization language: - en widget: - text: "I love AutoTrain 🤗" datasets: - lightbansal/autotrain-data-metadata_postprocess co2_eq_emissions: emissions: 1.5546260967293355 --- # Model Trained Using AutoTrain - Problem type: Summarization - Model ID: 1277848906 - CO2 Emissions (in grams): 1.5546 ## Validation Metrics - Loss: 0.329 - Rouge1: 95.246 - Rouge2: 31.448 - RougeL: 93.809 - RougeLsum: 93.862 - Gen Len: 5.108 ## Usage You can use cURL to access this model: ``` $ curl -X POST -H "Authorization: Bearer YOUR_HUGGINGFACE_API_KEY" -H "Content-Type: application/json" -d '{"inputs": "I love AutoTrain"}' https://api-inference.huggingface.co/lightbansal/autotrain-metadata_postprocess-1277848906 ```
lightbansal/autotrain-metadata_postprocess-1277848909
lightbansal
2022-08-19T02:32:41Z
8
0
transformers
[ "transformers", "pytorch", "t5", "text2text-generation", "autotrain", "summarization", "en", "dataset:lightbansal/autotrain-data-metadata_postprocess", "co2_eq_emissions", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]
summarization
2022-08-19T01:04:21Z
--- tags: - autotrain - summarization language: - en widget: - text: "I love AutoTrain 🤗" datasets: - lightbansal/autotrain-data-metadata_postprocess co2_eq_emissions: emissions: 0.673674776711824 --- # Model Trained Using AutoTrain - Problem type: Summarization - Model ID: 1277848909 - CO2 Emissions (in grams): 0.6737 ## Validation Metrics - Loss: 0.172 - Rouge1: 94.162 - Rouge2: 30.601 - RougeL: 93.416 - RougeLsum: 93.389 - Gen Len: 4.513 ## Usage You can use cURL to access this model: ``` $ curl -X POST -H "Authorization: Bearer YOUR_HUGGINGFACE_API_KEY" -H "Content-Type: application/json" -d '{"inputs": "I love AutoTrain"}' https://api-inference.huggingface.co/lightbansal/autotrain-metadata_postprocess-1277848909 ```
wpolatkan/q-Taxi-v3
wpolatkan
2022-08-19T01:49:56Z
0
0
null
[ "Taxi-v3", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]
reinforcement-learning
2022-08-19T01:49:48Z
--- tags: - Taxi-v3 - q-learning - reinforcement-learning - custom-implementation model-index: - name: q-Taxi-v3 results: - metrics: - type: mean_reward value: 7.54 +/- 2.69 name: mean_reward task: type: reinforcement-learning name: reinforcement-learning dataset: name: Taxi-v3 type: Taxi-v3 --- # **Q-Learning** Agent playing **Taxi-v3** This is a trained model of a **Q-Learning** agent playing **Taxi-v3** . ## Usage ```python model = load_from_hub(repo_id="wpolatkan/q-Taxi-v3", filename="q-learning.pkl") # Don't forget to check if you need to add additional attributes (is_slippery=False etc) env = gym.make(model["env_id"]) evaluate_agent(env, model["max_steps"], model["n_eval_episodes"], model["qtable"], model["eval_seed"]) ```
mariolinml/roberta_large-ner-conll2003_0818_v0
mariolinml
2022-08-19T01:03:27Z
4
0
transformers
[ "transformers", "pytorch", "tensorboard", "roberta", "token-classification", "generated_from_trainer", "dataset:conll2003", "license:mit", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]
token-classification
2022-08-18T23:31:42Z
--- license: mit tags: - generated_from_trainer datasets: - conll2003 metrics: - precision - recall - f1 - accuracy model-index: - name: roberta_large-ner-conll2003_0818_v0 results: - task: name: Token Classification type: token-classification dataset: name: conll2003 type: conll2003 config: conll2003 split: train args: conll2003 metrics: - name: Precision type: precision value: 0.9064488392089424 - name: Recall type: recall value: 0.9332507082152974 - name: F1 type: f1 value: 0.9196545406961529 - name: Accuracy type: accuracy value: 0.9795810129939008 --- <!-- 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. --> # roberta_large-ner-conll2003_0818_v0 This model is a fine-tuned version of [roberta-large](https://huggingface.co/roberta-large) on the conll2003 dataset. It achieves the following results on the evaluation set: - Loss: 0.1793 - Precision: 0.9064 - Recall: 0.9333 - F1: 0.9197 - Accuracy: 0.9796 ## 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: 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: 2 ### Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | 0.0273 | 1.0 | 878 | 0.0500 | 0.9338 | 0.9588 | 0.9461 | 0.9894 | | 0.0154 | 2.0 | 1756 | 0.0479 | 0.9402 | 0.9660 | 0.9529 | 0.9904 | ### Framework versions - Transformers 4.21.1 - Pytorch 1.12.1+cu113 - Datasets 2.4.0 - Tokenizers 0.12.1
verkaDerkaDerk/tiki-based-128
verkaDerkaDerk
2022-08-18T23:32:51Z
2
0
diffusers
[ "diffusers", "license:cc0-1.0", "diffusers:DDPMPipeline", "region:us" ]
null
2022-08-18T22:25:51Z
--- license: cc0-1.0 --- For anyone struggling with "git push" the password is your write token ...
sfurkan/LexBERT-textclassification-turkish-uncased
sfurkan
2022-08-18T22:35:18Z
105
0
transformers
[ "transformers", "pytorch", "bert", "text-classification", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]
text-classification
2022-08-18T21:13:07Z
--- license: apache-2.0 --- A Turkish BERT model that is fine-tuned on various types of legislation documents, thereby is able to classify the given input as among those types. Types are: 'Kanun', 'Resmi Gazete', 'Kanun Hükmünde Kararname', 'Genelge', 'Komisyon Raporu', 'Cumhurbaşkanlığı Kararnamesi', 'Tüzük', 'Yönetmelik', 'Tebliğ', 'Özelge'
SmartPy/distilbert-base-uncased-finetuned-cnn
SmartPy
2022-08-18T20:55:10Z
103
0
transformers
[ "transformers", "pytorch", "tensorboard", "distilbert", "fill-mask", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]
fill-mask
2022-08-18T20:21:38Z
--- license: apache-2.0 tags: - generated_from_trainer model-index: - name: distilbert-base-uncased-finetuned-cnn 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-cnn This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 2.2647 ## 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: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 10 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 2.2811 | 1.0 | 157 | 2.3283 | | 2.3086 | 2.0 | 314 | 2.3172 | | 2.3472 | 3.0 | 471 | 2.3033 | | 2.3608 | 4.0 | 628 | 2.2989 | | 2.3494 | 5.0 | 785 | 2.2975 | | 2.3217 | 6.0 | 942 | 2.2701 | | 2.3087 | 7.0 | 1099 | 2.2545 | | 2.291 | 8.0 | 1256 | 2.2376 | | 2.2983 | 9.0 | 1413 | 2.2653 | | 2.2892 | 10.0 | 1570 | 2.2647 | ### Framework versions - Transformers 4.20.1 - Pytorch 1.11.0 - Datasets 2.1.0 - Tokenizers 0.12.1
sasha/autotrain-BERTBase-imdb-1275748794
sasha
2022-08-18T18:37:45Z
105
0
transformers
[ "transformers", "pytorch", "bert", "text-classification", "autotrain", "unk", "dataset:sasha/autotrain-data-BERTBase-imdb", "co2_eq_emissions", "autotrain_compatible", "endpoints_compatible", "region:us" ]
text-classification
2022-08-18T18:10:52Z
--- tags: - autotrain - text-classification language: - unk widget: - text: "I love AutoTrain 🤗" datasets: - sasha/autotrain-data-BERTBase-imdb co2_eq_emissions: emissions: 57.547246549422866 --- # Model Trained Using AutoTrain - Problem type: Binary Classification - Model ID: 1275748794 - CO2 Emissions (in grams): 57.5472 ## Validation Metrics - Loss: 0.174 - Accuracy: 0.936 - Precision: 0.924 - Recall: 0.949 - AUC: 0.982 - F1: 0.936 ## Usage You can use cURL to access this model: ``` $ curl -X POST -H "Authorization: Bearer YOUR_API_KEY" -H "Content-Type: application/json" -d '{"inputs": "I love AutoTrain"}' https://api-inference.huggingface.co/models/sasha/autotrain-BERTBase-imdb-1275748794 ``` Or Python API: ``` from transformers import AutoModelForSequenceClassification, AutoTokenizer model = AutoModelForSequenceClassification.from_pretrained("sasha/autotrain-BERTBase-imdb-1275748794", use_auth_token=True) tokenizer = AutoTokenizer.from_pretrained("sasha/autotrain-BERTBase-imdb-1275748794", use_auth_token=True) inputs = tokenizer("I love AutoTrain", return_tensors="pt") outputs = model(**inputs) ```
sasha/autotrain-BERTBase-imdb-1275748793
sasha
2022-08-18T18:23:39Z
105
0
transformers
[ "transformers", "pytorch", "bert", "text-classification", "autotrain", "unk", "dataset:sasha/autotrain-data-BERTBase-imdb", "co2_eq_emissions", "autotrain_compatible", "endpoints_compatible", "region:us" ]
text-classification
2022-08-18T18:10:43Z
--- tags: - autotrain - text-classification language: - unk widget: - text: "I love AutoTrain 🤗" datasets: - sasha/autotrain-data-BERTBase-imdb co2_eq_emissions: emissions: 24.593648079365725 --- # Model Trained Using AutoTrain - Problem type: Binary Classification - Model ID: 1275748793 - CO2 Emissions (in grams): 24.5936 ## Validation Metrics - Loss: 0.205 - Accuracy: 0.920 - Precision: 0.904 - Recall: 0.939 - AUC: 0.975 - F1: 0.921 ## Usage You can use cURL to access this model: ``` $ curl -X POST -H "Authorization: Bearer YOUR_API_KEY" -H "Content-Type: application/json" -d '{"inputs": "I love AutoTrain"}' https://api-inference.huggingface.co/models/sasha/autotrain-BERTBase-imdb-1275748793 ``` Or Python API: ``` from transformers import AutoModelForSequenceClassification, AutoTokenizer model = AutoModelForSequenceClassification.from_pretrained("sasha/autotrain-BERTBase-imdb-1275748793", use_auth_token=True) tokenizer = AutoTokenizer.from_pretrained("sasha/autotrain-BERTBase-imdb-1275748793", use_auth_token=True) inputs = tokenizer("I love AutoTrain", return_tensors="pt") outputs = model(**inputs) ```
sasha/autotrain-DistilBERT-imdb-1275448783
sasha
2022-08-18T18:18:13Z
105
0
transformers
[ "transformers", "pytorch", "distilbert", "text-classification", "autotrain", "unk", "dataset:sasha/autotrain-data-DistilBERT-imdb", "co2_eq_emissions", "autotrain_compatible", "endpoints_compatible", "region:us" ]
text-classification
2022-08-18T18:08:06Z
--- tags: - autotrain - text-classification language: - unk widget: - text: "I love AutoTrain 🤗" datasets: - sasha/autotrain-data-DistilBERT-imdb co2_eq_emissions: emissions: 0.0719533080486796 --- # Model Trained Using AutoTrain - Problem type: Binary Classification - Model ID: 1275448783 - CO2 Emissions (in grams): 0.0720 ## Validation Metrics - Loss: 0.224 - Accuracy: 0.912 - Precision: 0.896 - Recall: 0.931 - AUC: 0.972 - F1: 0.913 ## Usage You can use cURL to access this model: ``` $ curl -X POST -H "Authorization: Bearer YOUR_API_KEY" -H "Content-Type: application/json" -d '{"inputs": "I love AutoTrain"}' https://api-inference.huggingface.co/models/sasha/autotrain-DistilBERT-imdb-1275448783 ``` Or Python API: ``` from transformers import AutoModelForSequenceClassification, AutoTokenizer model = AutoModelForSequenceClassification.from_pretrained("sasha/autotrain-DistilBERT-imdb-1275448783", use_auth_token=True) tokenizer = AutoTokenizer.from_pretrained("sasha/autotrain-DistilBERT-imdb-1275448783", use_auth_token=True) inputs = tokenizer("I love AutoTrain", return_tensors="pt") outputs = model(**inputs) ```
pimnara/q-FrozenLake-v1-4x4-noSlippery
pimnara
2022-08-18T18:02:58Z
0
0
null
[ "FrozenLake-v1-4x4-no_slippery", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]
reinforcement-learning
2022-08-18T13:39:08Z
--- tags: - FrozenLake-v1-4x4-no_slippery - q-learning - reinforcement-learning - custom-implementation model-index: - name: q-FrozenLake-v1-4x4-noSlippery results: - metrics: - type: mean_reward value: 1.00 +/- 0.00 name: mean_reward task: type: reinforcement-learning name: reinforcement-learning dataset: name: FrozenLake-v1-4x4-no_slippery type: FrozenLake-v1-4x4-no_slippery --- # **Q-Learning** Agent playing **FrozenLake-v1** This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** . ## Usage ```python model = load_from_hub(repo_id="/q-FrozenLake-v1-4x4-noSlippery", filename="q-learning.pkl") # Don't forget to check if you need to add additional attributes (is_slippery=False etc) env = gym.make(model["env_id"]) evaluate_agent(env, model["max_steps"], model["n_eval_episodes"], model["qtable"], model["eval_seed"]) ```
sasha/autotrain-roberta-base-imdb-1275248778
sasha
2022-08-18T17:56:52Z
4
0
transformers
[ "transformers", "pytorch", "roberta", "text-classification", "autotrain", "unk", "dataset:sasha/autotrain-data-roberta-base-imdb", "co2_eq_emissions", "autotrain_compatible", "endpoints_compatible", "region:us" ]
text-classification
2022-08-18T17:43:42Z
--- tags: - autotrain - text-classification language: - unk widget: - text: "I love AutoTrain 🤗" datasets: - sasha/autotrain-data-roberta-base-imdb co2_eq_emissions: emissions: 23.591266130909247 --- # Model Trained Using AutoTrain - Problem type: Binary Classification - Model ID: 1275248778 - CO2 Emissions (in grams): 23.5913 ## Validation Metrics - Loss: 0.180 - Accuracy: 0.933 - Precision: 0.944 - Recall: 0.921 - AUC: 0.983 - F1: 0.932 ## Usage You can use cURL to access this model: ``` $ curl -X POST -H "Authorization: Bearer YOUR_API_KEY" -H "Content-Type: application/json" -d '{"inputs": "I love AutoTrain"}' https://api-inference.huggingface.co/models/sasha/autotrain-roberta-base-imdb-1275248778 ``` Or Python API: ``` from transformers import AutoModelForSequenceClassification, AutoTokenizer model = AutoModelForSequenceClassification.from_pretrained("sasha/autotrain-roberta-base-imdb-1275248778", use_auth_token=True) tokenizer = AutoTokenizer.from_pretrained("sasha/autotrain-roberta-base-imdb-1275248778", use_auth_token=True) inputs = tokenizer("I love AutoTrain", return_tensors="pt") outputs = model(**inputs) ```