2022-11-06 15:34:46,075 ---------------------------------------------------------------------------------------------------- 2022-11-06 15:34:46,075 Model: "SequenceTagger( (embeddings): StackedEmbeddings( (list_embedding_0): FlairEmbeddings( (lm): LanguageModel( (drop): Dropout(p=0.1, inplace=False) (encoder): Embedding(962, 100) (rnn): LSTM(100, 1024) (decoder): Linear(in_features=1024, out_features=962, bias=True) ) ) (list_embedding_1): FlairEmbeddings( (lm): LanguageModel( (drop): Dropout(p=0.1, inplace=False) (encoder): Embedding(962, 100) (rnn): LSTM(100, 1024) (decoder): Linear(in_features=1024, out_features=962, bias=True) ) ) ) (dropout): Dropout(p=0.3380078963015963, inplace=False) (word_dropout): WordDropout(p=0.05) (locked_dropout): LockedDropout(p=0.5) (embedding2nn): Linear(in_features=2048, out_features=2048, bias=True) (rnn): LSTM(2048, 128, num_layers=2, batch_first=True, dropout=0.5, bidirectional=True) (linear): Linear(in_features=256, out_features=19, bias=True) (loss_function): ViterbiLoss() (crf): CRF() )" 2022-11-06 15:34:46,075 ---------------------------------------------------------------------------------------------------- 2022-11-06 15:34:46,076 Corpus: "Corpus: 7886 train + 876 dev + 4045 test sentences" 2022-11-06 15:34:46,076 ---------------------------------------------------------------------------------------------------- 2022-11-06 15:34:46,076 Parameters: 2022-11-06 15:34:46,076 - learning_rate: "0.100000" 2022-11-06 15:34:46,076 - mini_batch_size: "32" 2022-11-06 15:34:46,076 - patience: "3" 2022-11-06 15:34:46,076 - anneal_factor: "0.5" 2022-11-06 15:34:46,076 - max_epochs: "150" 2022-11-06 15:34:46,076 - shuffle: "True" 2022-11-06 15:34:46,076 - train_with_dev: "True" 2022-11-06 15:34:46,076 - batch_growth_annealing: "False" 2022-11-06 15:34:46,076 ---------------------------------------------------------------------------------------------------- 2022-11-06 15:34:46,077 Model training base path: "ner-tests/uk.flairembeddings.champ" 2022-11-06 15:34:46,077 ---------------------------------------------------------------------------------------------------- 2022-11-06 15:34:46,077 Device: cuda:0 2022-11-06 15:34:46,077 ---------------------------------------------------------------------------------------------------- 2022-11-06 15:34:46,077 Embeddings storage mode: cpu 2022-11-06 15:34:46,077 ---------------------------------------------------------------------------------------------------- 2022-11-06 15:34:55,361 epoch 1 - iter 27/274 - loss 0.59321573 - samples/sec: 93.09 - lr: 0.100000 2022-11-06 15:35:04,385 epoch 1 - iter 54/274 - loss 0.48716151 - samples/sec: 95.78 - lr: 0.100000 2022-11-06 15:35:14,049 epoch 1 - iter 81/274 - loss 0.38801385 - samples/sec: 89.43 - lr: 0.100000 2022-11-06 15:35:25,713 epoch 1 - iter 108/274 - loss 0.35179862 - samples/sec: 74.09 - lr: 0.100000 2022-11-06 15:35:33,119 epoch 1 - iter 135/274 - loss 0.31056108 - samples/sec: 116.72 - lr: 0.100000 2022-11-06 15:35:40,364 epoch 1 - iter 162/274 - loss 0.27554678 - samples/sec: 119.30 - lr: 0.100000 2022-11-06 15:35:48,761 epoch 1 - iter 189/274 - loss 0.24648499 - samples/sec: 102.93 - lr: 0.100000 2022-11-06 15:35:55,011 epoch 1 - iter 216/274 - loss 0.22834151 - samples/sec: 138.30 - lr: 0.100000 2022-11-06 15:36:03,341 epoch 1 - iter 243/274 - loss 0.21155940 - samples/sec: 103.76 - lr: 0.100000 2022-11-06 15:36:11,769 epoch 1 - iter 270/274 - loss 0.20285420 - samples/sec: 102.56 - lr: 0.100000 2022-11-06 15:36:12,834 ---------------------------------------------------------------------------------------------------- 2022-11-06 15:36:12,834 EPOCH 1 done: loss 0.2008 - lr 0.100000 2022-11-06 15:37:00,413 Evaluating as a multi-label problem: False 2022-11-06 15:37:00,487 TEST : loss 0.12195425480604172 - f1-score (micro avg) 0.6446 2022-11-06 15:37:00,974 BAD EPOCHS (no improvement): 0 2022-11-06 15:37:01,122 ---------------------------------------------------------------------------------------------------- 2022-11-06 15:37:07,944 epoch 2 - iter 27/274 - loss 0.10617612 - samples/sec: 126.74 - lr: 0.100000 2022-11-06 15:37:14,883 epoch 2 - iter 54/274 - loss 0.10581527 - samples/sec: 124.56 - lr: 0.100000 2022-11-06 15:37:22,249 epoch 2 - iter 81/274 - loss 0.10803741 - samples/sec: 117.35 - lr: 0.100000 2022-11-06 15:37:29,097 epoch 2 - iter 108/274 - loss 0.10325620 - samples/sec: 126.23 - lr: 0.100000 2022-11-06 15:37:36,384 epoch 2 - iter 135/274 - loss 0.10338215 - samples/sec: 118.63 - lr: 0.100000 2022-11-06 15:37:43,245 epoch 2 - iter 162/274 - loss 0.09914864 - samples/sec: 126.00 - lr: 0.100000 2022-11-06 15:37:50,576 epoch 2 - iter 189/274 - loss 0.09776149 - samples/sec: 117.90 - lr: 0.100000 2022-11-06 15:37:57,212 epoch 2 - iter 216/274 - loss 0.09654818 - samples/sec: 130.26 - lr: 0.100000 2022-11-06 15:38:03,927 epoch 2 - iter 243/274 - loss 0.09305997 - samples/sec: 128.74 - lr: 0.100000 2022-11-06 15:38:11,119 epoch 2 - iter 270/274 - loss 0.09219545 - samples/sec: 120.18 - lr: 0.100000 2022-11-06 15:38:12,113 ---------------------------------------------------------------------------------------------------- 2022-11-06 15:38:12,113 EPOCH 2 done: loss 0.0924 - lr 0.100000 2022-11-06 15:38:50,373 Evaluating as a multi-label problem: False 2022-11-06 15:38:50,400 TEST : loss 0.06683151423931122 - f1-score (micro avg) 0.7737 2022-11-06 15:38:50,885 BAD EPOCHS (no improvement): 0 2022-11-06 15:38:51,070 ---------------------------------------------------------------------------------------------------- 2022-11-06 15:38:57,845 epoch 3 - iter 27/274 - loss 0.07387935 - samples/sec: 127.62 - lr: 0.100000 2022-11-06 15:39:04,943 epoch 3 - iter 54/274 - loss 0.06792482 - samples/sec: 121.77 - lr: 0.100000 2022-11-06 15:39:12,160 epoch 3 - iter 81/274 - loss 0.07438869 - samples/sec: 119.78 - lr: 0.100000 2022-11-06 15:39:18,834 epoch 3 - iter 108/274 - loss 0.07109664 - samples/sec: 129.53 - lr: 0.100000 2022-11-06 15:39:26,268 epoch 3 - iter 135/274 - loss 0.06940220 - samples/sec: 116.28 - lr: 0.100000 2022-11-06 15:39:33,410 epoch 3 - iter 162/274 - loss 0.07122806 - samples/sec: 121.03 - lr: 0.100000 2022-11-06 15:39:40,488 epoch 3 - iter 189/274 - loss 0.07076674 - samples/sec: 122.12 - lr: 0.100000 2022-11-06 15:39:46,355 epoch 3 - iter 216/274 - loss 0.06970241 - samples/sec: 147.35 - lr: 0.100000 2022-11-06 15:39:51,595 epoch 3 - iter 243/274 - loss 0.06863317 - samples/sec: 164.98 - lr: 0.100000 2022-11-06 15:39:56,786 epoch 3 - iter 270/274 - loss 0.06710405 - samples/sec: 166.57 - lr: 0.100000 2022-11-06 15:39:57,559 ---------------------------------------------------------------------------------------------------- 2022-11-06 15:39:57,559 EPOCH 3 done: loss 0.0674 - lr 0.100000 2022-11-06 15:40:27,851 Evaluating as a multi-label problem: False 2022-11-06 15:40:27,879 TEST : loss 0.059379346668720245 - f1-score (micro avg) 0.75 2022-11-06 15:40:28,363 BAD EPOCHS (no improvement): 0 2022-11-06 15:40:28,555 ---------------------------------------------------------------------------------------------------- 2022-11-06 15:40:34,013 epoch 4 - iter 27/274 - loss 0.04549779 - samples/sec: 158.45 - lr: 0.100000 2022-11-06 15:40:39,924 epoch 4 - iter 54/274 - loss 0.05683785 - samples/sec: 146.25 - lr: 0.100000 2022-11-06 15:40:45,164 epoch 4 - iter 81/274 - loss 0.05429896 - samples/sec: 165.01 - lr: 0.100000 2022-11-06 15:40:50,197 epoch 4 - iter 108/274 - loss 0.05303453 - samples/sec: 171.76 - lr: 0.100000 2022-11-06 15:40:55,258 epoch 4 - iter 135/274 - loss 0.05437553 - samples/sec: 170.85 - lr: 0.100000 2022-11-06 15:41:00,805 epoch 4 - iter 162/274 - loss 0.05787177 - samples/sec: 155.84 - lr: 0.100000 2022-11-06 15:41:06,448 epoch 4 - iter 189/274 - loss 0.05841061 - samples/sec: 153.20 - lr: 0.100000 2022-11-06 15:41:11,966 epoch 4 - iter 216/274 - loss 0.05980452 - samples/sec: 156.68 - lr: 0.100000 2022-11-06 15:41:16,872 epoch 4 - iter 243/274 - loss 0.05926645 - samples/sec: 176.25 - lr: 0.100000 2022-11-06 15:41:22,084 epoch 4 - iter 270/274 - loss 0.05806207 - samples/sec: 165.86 - lr: 0.100000 2022-11-06 15:41:22,799 ---------------------------------------------------------------------------------------------------- 2022-11-06 15:41:22,799 EPOCH 4 done: loss 0.0577 - lr 0.100000 2022-11-06 15:41:53,160 Evaluating as a multi-label problem: False 2022-11-06 15:41:53,188 TEST : loss 0.049502115696668625 - f1-score (micro avg) 0.7914 2022-11-06 15:41:53,669 BAD EPOCHS (no improvement): 0 2022-11-06 15:41:53,863 ---------------------------------------------------------------------------------------------------- 2022-11-06 15:41:58,837 epoch 5 - iter 27/274 - loss 0.04611893 - samples/sec: 173.87 - lr: 0.100000 2022-11-06 15:42:03,736 epoch 5 - iter 54/274 - loss 0.04970445 - samples/sec: 176.51 - lr: 0.100000 2022-11-06 15:42:09,197 epoch 5 - iter 81/274 - loss 0.05127046 - samples/sec: 158.29 - lr: 0.100000 2022-11-06 15:42:14,546 epoch 5 - iter 108/274 - loss 0.05297562 - samples/sec: 161.65 - lr: 0.100000 2022-11-06 15:42:19,811 epoch 5 - iter 135/274 - loss 0.05279157 - samples/sec: 164.20 - lr: 0.100000 2022-11-06 15:42:25,658 epoch 5 - iter 162/274 - loss 0.05326809 - samples/sec: 147.84 - lr: 0.100000 2022-11-06 15:42:30,832 epoch 5 - iter 189/274 - loss 0.05164310 - samples/sec: 167.11 - lr: 0.100000 2022-11-06 15:42:36,139 epoch 5 - iter 216/274 - loss 0.05192735 - samples/sec: 162.88 - lr: 0.100000 2022-11-06 15:42:41,230 epoch 5 - iter 243/274 - loss 0.05157005 - samples/sec: 169.84 - lr: 0.100000 2022-11-06 15:42:46,754 epoch 5 - iter 270/274 - loss 0.05043757 - samples/sec: 156.50 - lr: 0.100000 2022-11-06 15:42:47,801 ---------------------------------------------------------------------------------------------------- 2022-11-06 15:42:47,802 EPOCH 5 done: loss 0.0501 - lr 0.100000 2022-11-06 15:43:18,340 Evaluating as a multi-label problem: False 2022-11-06 15:43:18,366 TEST : loss 0.041369177401065826 - f1-score (micro avg) 0.829 2022-11-06 15:43:18,853 BAD EPOCHS (no improvement): 0 2022-11-06 15:43:19,044 ---------------------------------------------------------------------------------------------------- 2022-11-06 15:43:24,292 epoch 6 - iter 27/274 - loss 0.04513706 - samples/sec: 164.78 - lr: 0.100000 2022-11-06 15:43:29,559 epoch 6 - iter 54/274 - loss 0.04520187 - samples/sec: 164.16 - lr: 0.100000 2022-11-06 15:43:35,357 epoch 6 - iter 81/274 - loss 0.04538206 - samples/sec: 149.11 - lr: 0.100000 2022-11-06 15:43:41,144 epoch 6 - iter 108/274 - loss 0.04554187 - samples/sec: 149.38 - lr: 0.100000 2022-11-06 15:43:46,451 epoch 6 - iter 135/274 - loss 0.04471338 - samples/sec: 162.92 - lr: 0.100000 2022-11-06 15:43:51,894 epoch 6 - iter 162/274 - loss 0.04518209 - samples/sec: 158.83 - lr: 0.100000 2022-11-06 15:43:56,778 epoch 6 - iter 189/274 - loss 0.04541590 - samples/sec: 177.01 - lr: 0.100000 2022-11-06 15:44:02,451 epoch 6 - iter 216/274 - loss 0.04704355 - samples/sec: 152.40 - lr: 0.100000 2022-11-06 15:44:07,536 epoch 6 - iter 243/274 - loss 0.04551856 - samples/sec: 170.02 - lr: 0.100000 2022-11-06 15:44:13,040 epoch 6 - iter 270/274 - loss 0.04535913 - samples/sec: 157.07 - lr: 0.100000 2022-11-06 15:44:14,151 ---------------------------------------------------------------------------------------------------- 2022-11-06 15:44:14,151 EPOCH 6 done: loss 0.0454 - lr 0.100000 2022-11-06 15:44:44,550 Evaluating as a multi-label problem: False 2022-11-06 15:44:44,577 TEST : loss 0.041055891662836075 - f1-score (micro avg) 0.8173 2022-11-06 15:44:45,062 BAD EPOCHS (no improvement): 0 2022-11-06 15:44:45,248 ---------------------------------------------------------------------------------------------------- 2022-11-06 15:44:51,160 epoch 7 - iter 27/274 - loss 0.04878990 - samples/sec: 146.25 - lr: 0.100000 2022-11-06 15:44:56,715 epoch 7 - iter 54/274 - loss 0.04800253 - samples/sec: 155.65 - lr: 0.100000 2022-11-06 15:45:02,064 epoch 7 - iter 81/274 - loss 0.04541887 - samples/sec: 161.63 - lr: 0.100000 2022-11-06 15:45:07,393 epoch 7 - iter 108/274 - loss 0.04497799 - samples/sec: 162.24 - lr: 0.100000 2022-11-06 15:45:12,385 epoch 7 - iter 135/274 - loss 0.04659413 - samples/sec: 173.17 - lr: 0.100000 2022-11-06 15:45:17,595 epoch 7 - iter 162/274 - loss 0.04628117 - samples/sec: 165.96 - lr: 0.100000 2022-11-06 15:45:22,851 epoch 7 - iter 189/274 - loss 0.04589064 - samples/sec: 164.47 - lr: 0.100000 2022-11-06 15:45:28,144 epoch 7 - iter 216/274 - loss 0.04517667 - samples/sec: 163.34 - lr: 0.100000 2022-11-06 15:45:33,258 epoch 7 - iter 243/274 - loss 0.04462662 - samples/sec: 169.07 - lr: 0.100000 2022-11-06 15:45:38,382 epoch 7 - iter 270/274 - loss 0.04362751 - samples/sec: 168.72 - lr: 0.100000 2022-11-06 15:45:39,098 ---------------------------------------------------------------------------------------------------- 2022-11-06 15:45:39,098 EPOCH 7 done: loss 0.0433 - lr 0.100000 2022-11-06 15:46:09,474 Evaluating as a multi-label problem: False 2022-11-06 15:46:09,501 TEST : loss 0.04179549589753151 - f1-score (micro avg) 0.8315 2022-11-06 15:46:09,985 BAD EPOCHS (no improvement): 0 2022-11-06 15:46:10,174 ---------------------------------------------------------------------------------------------------- 2022-11-06 15:46:15,222 epoch 8 - iter 27/274 - loss 0.03396046 - samples/sec: 171.33 - lr: 0.100000 2022-11-06 15:46:20,352 epoch 8 - iter 54/274 - loss 0.03518666 - samples/sec: 168.52 - lr: 0.100000 2022-11-06 15:46:25,603 epoch 8 - iter 81/274 - loss 0.03701020 - samples/sec: 164.65 - lr: 0.100000 2022-11-06 15:46:30,963 epoch 8 - iter 108/274 - loss 0.03993933 - samples/sec: 161.30 - lr: 0.100000 2022-11-06 15:46:37,269 epoch 8 - iter 135/274 - loss 0.04076812 - samples/sec: 137.08 - lr: 0.100000 2022-11-06 15:46:41,985 epoch 8 - iter 162/274 - loss 0.03964813 - samples/sec: 183.34 - lr: 0.100000 2022-11-06 15:46:47,735 epoch 8 - iter 189/274 - loss 0.04095653 - samples/sec: 150.34 - lr: 0.100000 2022-11-06 15:46:53,106 epoch 8 - iter 216/274 - loss 0.04059537 - samples/sec: 160.97 - lr: 0.100000 2022-11-06 15:46:58,536 epoch 8 - iter 243/274 - loss 0.04031854 - samples/sec: 159.53 - lr: 0.100000 2022-11-06 15:47:03,886 epoch 8 - iter 270/274 - loss 0.04090647 - samples/sec: 161.58 - lr: 0.100000 2022-11-06 15:47:04,725 ---------------------------------------------------------------------------------------------------- 2022-11-06 15:47:04,726 EPOCH 8 done: loss 0.0410 - lr 0.100000 2022-11-06 15:47:35,125 Evaluating as a multi-label problem: False 2022-11-06 15:47:35,153 TEST : loss 0.03513012081384659 - f1-score (micro avg) 0.8152 2022-11-06 15:47:35,637 BAD EPOCHS (no improvement): 0 2022-11-06 15:47:35,830 ---------------------------------------------------------------------------------------------------- 2022-11-06 15:47:40,768 epoch 9 - iter 27/274 - loss 0.04363616 - samples/sec: 175.15 - lr: 0.100000 2022-11-06 15:47:46,234 epoch 9 - iter 54/274 - loss 0.04133601 - samples/sec: 158.19 - lr: 0.100000 2022-11-06 15:47:51,527 epoch 9 - iter 81/274 - loss 0.04014737 - samples/sec: 163.32 - lr: 0.100000 2022-11-06 15:47:56,585 epoch 9 - iter 108/274 - loss 0.03981344 - samples/sec: 170.96 - lr: 0.100000 2022-11-06 15:48:02,137 epoch 9 - iter 135/274 - loss 0.03979068 - samples/sec: 155.69 - lr: 0.100000 2022-11-06 15:48:07,556 epoch 9 - iter 162/274 - loss 0.03974765 - samples/sec: 159.54 - lr: 0.100000 2022-11-06 15:48:13,572 epoch 9 - iter 189/274 - loss 0.03867948 - samples/sec: 143.69 - lr: 0.100000 2022-11-06 15:48:19,089 epoch 9 - iter 216/274 - loss 0.03852985 - samples/sec: 156.70 - lr: 0.100000 2022-11-06 15:48:24,290 epoch 9 - iter 243/274 - loss 0.03835168 - samples/sec: 166.25 - lr: 0.100000 2022-11-06 15:48:29,451 epoch 9 - iter 270/274 - loss 0.03803656 - samples/sec: 167.50 - lr: 0.100000 2022-11-06 15:48:30,203 ---------------------------------------------------------------------------------------------------- 2022-11-06 15:48:30,203 EPOCH 9 done: loss 0.0381 - lr 0.100000 2022-11-06 15:49:00,536 Evaluating as a multi-label problem: False 2022-11-06 15:49:00,564 TEST : loss 0.03051774762570858 - f1-score (micro avg) 0.8377 2022-11-06 15:49:01,048 BAD EPOCHS (no improvement): 0 2022-11-06 15:49:01,232 ---------------------------------------------------------------------------------------------------- 2022-11-06 15:49:06,209 epoch 10 - iter 27/274 - loss 0.03750373 - samples/sec: 173.79 - lr: 0.100000 2022-11-06 15:49:11,269 epoch 10 - iter 54/274 - loss 0.03551327 - samples/sec: 171.79 - lr: 0.100000 2022-11-06 15:49:16,329 epoch 10 - iter 81/274 - loss 0.03520153 - samples/sec: 170.88 - lr: 0.100000 2022-11-06 15:49:22,078 epoch 10 - iter 108/274 - loss 0.03384319 - samples/sec: 150.37 - lr: 0.100000 2022-11-06 15:49:27,253 epoch 10 - iter 135/274 - loss 0.03454667 - samples/sec: 168.03 - lr: 0.100000 2022-11-06 15:49:33,066 epoch 10 - iter 162/274 - loss 0.03690282 - samples/sec: 148.72 - lr: 0.100000 2022-11-06 15:49:38,156 epoch 10 - iter 189/274 - loss 0.03640468 - samples/sec: 169.85 - lr: 0.100000 2022-11-06 15:49:43,672 epoch 10 - iter 216/274 - loss 0.03642463 - samples/sec: 156.74 - lr: 0.100000 2022-11-06 15:49:49,129 epoch 10 - iter 243/274 - loss 0.03615499 - samples/sec: 158.43 - lr: 0.100000 2022-11-06 15:49:54,326 epoch 10 - iter 270/274 - loss 0.03634573 - samples/sec: 166.36 - lr: 0.100000 2022-11-06 15:49:55,323 ---------------------------------------------------------------------------------------------------- 2022-11-06 15:49:55,323 EPOCH 10 done: loss 0.0363 - lr 0.100000 2022-11-06 15:50:25,641 Evaluating as a multi-label problem: False 2022-11-06 15:50:25,668 TEST : loss 0.03244197368621826 - f1-score (micro avg) 0.8371 2022-11-06 15:50:26,158 BAD EPOCHS (no improvement): 0 2022-11-06 15:50:26,341 ---------------------------------------------------------------------------------------------------- 2022-11-06 15:50:31,834 epoch 11 - iter 27/274 - loss 0.03150520 - samples/sec: 157.43 - lr: 0.100000 2022-11-06 15:50:36,958 epoch 11 - iter 54/274 - loss 0.03241383 - samples/sec: 168.73 - lr: 0.100000 2022-11-06 15:50:42,516 epoch 11 - iter 81/274 - loss 0.03450312 - samples/sec: 155.54 - lr: 0.100000 2022-11-06 15:50:48,155 epoch 11 - iter 108/274 - loss 0.03653110 - samples/sec: 153.33 - lr: 0.100000 2022-11-06 15:50:53,310 epoch 11 - iter 135/274 - loss 0.03571646 - samples/sec: 169.09 - lr: 0.100000 2022-11-06 15:50:58,855 epoch 11 - iter 162/274 - loss 0.03598466 - samples/sec: 155.93 - lr: 0.100000 2022-11-06 15:51:03,811 epoch 11 - iter 189/274 - loss 0.03605525 - samples/sec: 174.44 - lr: 0.100000 2022-11-06 15:51:09,465 epoch 11 - iter 216/274 - loss 0.03568287 - samples/sec: 152.91 - lr: 0.100000 2022-11-06 15:51:14,616 epoch 11 - iter 243/274 - loss 0.03511336 - samples/sec: 167.85 - lr: 0.100000 2022-11-06 15:51:20,060 epoch 11 - iter 270/274 - loss 0.03506643 - samples/sec: 158.80 - lr: 0.100000 2022-11-06 15:51:20,738 ---------------------------------------------------------------------------------------------------- 2022-11-06 15:51:20,738 EPOCH 11 done: loss 0.0349 - lr 0.100000 2022-11-06 15:51:51,019 Evaluating as a multi-label problem: False 2022-11-06 15:51:51,046 TEST : loss 0.03070417419075966 - f1-score (micro avg) 0.8381 2022-11-06 15:51:51,527 BAD EPOCHS (no improvement): 0 2022-11-06 15:51:51,802 ---------------------------------------------------------------------------------------------------- 2022-11-06 15:51:57,611 epoch 12 - iter 27/274 - loss 0.03444462 - samples/sec: 148.90 - lr: 0.100000 2022-11-06 15:52:03,091 epoch 12 - iter 54/274 - loss 0.03133987 - samples/sec: 157.74 - lr: 0.100000 2022-11-06 15:52:08,539 epoch 12 - iter 81/274 - loss 0.03294927 - samples/sec: 158.70 - lr: 0.100000 2022-11-06 15:52:14,158 epoch 12 - iter 108/274 - loss 0.03495274 - samples/sec: 153.85 - lr: 0.100000 2022-11-06 15:52:19,620 epoch 12 - iter 135/274 - loss 0.03537488 - samples/sec: 158.26 - lr: 0.100000 2022-11-06 15:52:24,531 epoch 12 - iter 162/274 - loss 0.03434720 - samples/sec: 176.08 - lr: 0.100000 2022-11-06 15:52:29,788 epoch 12 - iter 189/274 - loss 0.03469288 - samples/sec: 164.44 - lr: 0.100000 2022-11-06 15:52:35,243 epoch 12 - iter 216/274 - loss 0.03436357 - samples/sec: 158.50 - lr: 0.100000 2022-11-06 15:52:40,378 epoch 12 - iter 243/274 - loss 0.03457484 - samples/sec: 168.37 - lr: 0.100000 2022-11-06 15:52:45,373 epoch 12 - iter 270/274 - loss 0.03445703 - samples/sec: 174.57 - lr: 0.100000 2022-11-06 15:52:46,090 ---------------------------------------------------------------------------------------------------- 2022-11-06 15:52:46,090 EPOCH 12 done: loss 0.0344 - lr 0.100000 2022-11-06 15:53:16,452 Evaluating as a multi-label problem: False 2022-11-06 15:53:16,479 TEST : loss 0.030996335670351982 - f1-score (micro avg) 0.8424 2022-11-06 15:53:16,961 BAD EPOCHS (no improvement): 0 2022-11-06 15:53:17,150 ---------------------------------------------------------------------------------------------------- 2022-11-06 15:53:22,301 epoch 13 - iter 27/274 - loss 0.03365013 - samples/sec: 167.89 - lr: 0.100000 2022-11-06 15:53:27,641 epoch 13 - iter 54/274 - loss 0.03153036 - samples/sec: 161.89 - lr: 0.100000 2022-11-06 15:53:32,949 epoch 13 - iter 81/274 - loss 0.03098350 - samples/sec: 162.89 - lr: 0.100000 2022-11-06 15:53:38,836 epoch 13 - iter 108/274 - loss 0.03131598 - samples/sec: 146.83 - lr: 0.100000 2022-11-06 15:53:44,380 epoch 13 - iter 135/274 - loss 0.03207890 - samples/sec: 155.95 - lr: 0.100000 2022-11-06 15:53:49,558 epoch 13 - iter 162/274 - loss 0.03192553 - samples/sec: 166.96 - lr: 0.100000 2022-11-06 15:53:54,893 epoch 13 - iter 189/274 - loss 0.03333380 - samples/sec: 162.05 - lr: 0.100000 2022-11-06 15:54:00,220 epoch 13 - iter 216/274 - loss 0.03343657 - samples/sec: 162.29 - lr: 0.100000 2022-11-06 15:54:05,604 epoch 13 - iter 243/274 - loss 0.03368449 - samples/sec: 160.59 - lr: 0.100000 2022-11-06 15:54:10,865 epoch 13 - iter 270/274 - loss 0.03369076 - samples/sec: 164.34 - lr: 0.100000 2022-11-06 15:54:11,568 ---------------------------------------------------------------------------------------------------- 2022-11-06 15:54:11,568 EPOCH 13 done: loss 0.0340 - lr 0.100000 2022-11-06 15:54:41,930 Evaluating as a multi-label problem: False 2022-11-06 15:54:41,957 TEST : loss 0.029288053512573242 - f1-score (micro avg) 0.8299 2022-11-06 15:54:42,442 BAD EPOCHS (no improvement): 0 2022-11-06 15:54:42,629 ---------------------------------------------------------------------------------------------------- 2022-11-06 15:54:47,783 epoch 14 - iter 27/274 - loss 0.02977866 - samples/sec: 167.78 - lr: 0.100000 2022-11-06 15:54:52,877 epoch 14 - iter 54/274 - loss 0.03196483 - samples/sec: 169.72 - lr: 0.100000 2022-11-06 15:54:58,409 epoch 14 - iter 81/274 - loss 0.03196867 - samples/sec: 157.49 - lr: 0.100000 2022-11-06 15:55:04,520 epoch 14 - iter 108/274 - loss 0.03252995 - samples/sec: 141.45 - lr: 0.100000 2022-11-06 15:55:10,082 epoch 14 - iter 135/274 - loss 0.03218022 - samples/sec: 155.44 - lr: 0.100000 2022-11-06 15:55:15,128 epoch 14 - iter 162/274 - loss 0.03227490 - samples/sec: 171.33 - lr: 0.100000 2022-11-06 15:55:20,317 epoch 14 - iter 189/274 - loss 0.03184929 - samples/sec: 166.62 - lr: 0.100000 2022-11-06 15:55:25,513 epoch 14 - iter 216/274 - loss 0.03206183 - samples/sec: 166.40 - lr: 0.100000 2022-11-06 15:55:30,596 epoch 14 - iter 243/274 - loss 0.03177242 - samples/sec: 170.11 - lr: 0.100000 2022-11-06 15:55:36,208 epoch 14 - iter 270/274 - loss 0.03198340 - samples/sec: 154.03 - lr: 0.100000 2022-11-06 15:55:36,952 ---------------------------------------------------------------------------------------------------- 2022-11-06 15:55:36,952 EPOCH 14 done: loss 0.0320 - lr 0.100000 2022-11-06 15:56:07,327 Evaluating as a multi-label problem: False 2022-11-06 15:56:07,355 TEST : loss 0.03270775079727173 - f1-score (micro avg) 0.8277 2022-11-06 15:56:07,842 BAD EPOCHS (no improvement): 0 2022-11-06 15:56:08,024 ---------------------------------------------------------------------------------------------------- 2022-11-06 15:56:13,566 epoch 15 - iter 27/274 - loss 0.03563398 - samples/sec: 156.05 - lr: 0.100000 2022-11-06 15:56:18,887 epoch 15 - iter 54/274 - loss 0.03282755 - samples/sec: 162.46 - lr: 0.100000 2022-11-06 15:56:24,185 epoch 15 - iter 81/274 - loss 0.03268187 - samples/sec: 163.20 - lr: 0.100000 2022-11-06 15:56:29,115 epoch 15 - iter 108/274 - loss 0.03269617 - samples/sec: 175.37 - lr: 0.100000 2022-11-06 15:56:34,592 epoch 15 - iter 135/274 - loss 0.03357008 - samples/sec: 157.83 - lr: 0.100000 2022-11-06 15:56:40,439 epoch 15 - iter 162/274 - loss 0.03380551 - samples/sec: 147.87 - lr: 0.100000 2022-11-06 15:56:45,617 epoch 15 - iter 189/274 - loss 0.03331763 - samples/sec: 166.98 - lr: 0.100000 2022-11-06 15:56:51,191 epoch 15 - iter 216/274 - loss 0.03268010 - samples/sec: 155.09 - lr: 0.100000 2022-11-06 15:56:56,641 epoch 15 - iter 243/274 - loss 0.03226809 - samples/sec: 158.62 - lr: 0.100000 2022-11-06 15:57:01,510 epoch 15 - iter 270/274 - loss 0.03186487 - samples/sec: 177.55 - lr: 0.100000 2022-11-06 15:57:02,246 ---------------------------------------------------------------------------------------------------- 2022-11-06 15:57:02,247 EPOCH 15 done: loss 0.0318 - lr 0.100000 2022-11-06 15:57:32,568 Evaluating as a multi-label problem: False 2022-11-06 15:57:32,595 TEST : loss 0.03125728294253349 - f1-score (micro avg) 0.8514 2022-11-06 15:57:33,080 BAD EPOCHS (no improvement): 0 2022-11-06 15:57:33,271 ---------------------------------------------------------------------------------------------------- 2022-11-06 15:57:38,516 epoch 16 - iter 27/274 - loss 0.03170152 - samples/sec: 164.88 - lr: 0.100000 2022-11-06 15:57:43,882 epoch 16 - iter 54/274 - loss 0.03070640 - samples/sec: 161.13 - lr: 0.100000 2022-11-06 15:57:49,576 epoch 16 - iter 81/274 - loss 0.03177272 - samples/sec: 151.82 - lr: 0.100000 2022-11-06 15:57:55,103 epoch 16 - iter 108/274 - loss 0.03030676 - samples/sec: 156.44 - lr: 0.100000 2022-11-06 15:58:00,498 epoch 16 - iter 135/274 - loss 0.03104558 - samples/sec: 161.11 - lr: 0.100000 2022-11-06 15:58:05,767 epoch 16 - iter 162/274 - loss 0.03092946 - samples/sec: 164.10 - lr: 0.100000 2022-11-06 15:58:11,095 epoch 16 - iter 189/274 - loss 0.03056373 - samples/sec: 162.25 - lr: 0.100000 2022-11-06 15:58:16,297 epoch 16 - iter 216/274 - loss 0.03034550 - samples/sec: 166.20 - lr: 0.100000 2022-11-06 15:58:21,591 epoch 16 - iter 243/274 - loss 0.03035053 - samples/sec: 163.30 - lr: 0.100000 2022-11-06 15:58:26,772 epoch 16 - iter 270/274 - loss 0.03062837 - samples/sec: 166.86 - lr: 0.100000 2022-11-06 15:58:27,519 ---------------------------------------------------------------------------------------------------- 2022-11-06 15:58:27,519 EPOCH 16 done: loss 0.0308 - lr 0.100000 2022-11-06 15:58:57,892 Evaluating as a multi-label problem: False 2022-11-06 15:58:57,919 TEST : loss 0.030985970050096512 - f1-score (micro avg) 0.8344 2022-11-06 15:58:58,404 BAD EPOCHS (no improvement): 0 2022-11-06 15:58:58,592 ---------------------------------------------------------------------------------------------------- 2022-11-06 15:59:03,852 epoch 17 - iter 27/274 - loss 0.02721503 - samples/sec: 164.44 - lr: 0.100000 2022-11-06 15:59:09,118 epoch 17 - iter 54/274 - loss 0.03006200 - samples/sec: 164.18 - lr: 0.100000 2022-11-06 15:59:14,606 epoch 17 - iter 81/274 - loss 0.02881095 - samples/sec: 157.52 - lr: 0.100000 2022-11-06 15:59:19,811 epoch 17 - iter 108/274 - loss 0.02899773 - samples/sec: 166.11 - lr: 0.100000 2022-11-06 15:59:24,699 epoch 17 - iter 135/274 - loss 0.02906021 - samples/sec: 176.87 - lr: 0.100000 2022-11-06 15:59:29,944 epoch 17 - iter 162/274 - loss 0.02935913 - samples/sec: 164.85 - lr: 0.100000 2022-11-06 15:59:36,013 epoch 17 - iter 189/274 - loss 0.02972123 - samples/sec: 142.44 - lr: 0.100000 2022-11-06 15:59:41,656 epoch 17 - iter 216/274 - loss 0.03001223 - samples/sec: 153.18 - lr: 0.100000 2022-11-06 15:59:47,171 epoch 17 - iter 243/274 - loss 0.03092766 - samples/sec: 156.77 - lr: 0.100000 2022-11-06 15:59:51,999 epoch 17 - iter 270/274 - loss 0.03096630 - samples/sec: 179.07 - lr: 0.100000 2022-11-06 15:59:52,810 ---------------------------------------------------------------------------------------------------- 2022-11-06 15:59:52,810 EPOCH 17 done: loss 0.0310 - lr 0.100000 2022-11-06 16:00:23,255 Evaluating as a multi-label problem: False 2022-11-06 16:00:23,282 TEST : loss 0.02839544788002968 - f1-score (micro avg) 0.8291 2022-11-06 16:00:23,765 BAD EPOCHS (no improvement): 1 2022-11-06 16:00:23,949 ---------------------------------------------------------------------------------------------------- 2022-11-06 16:00:29,276 epoch 18 - iter 27/274 - loss 0.02786044 - samples/sec: 162.35 - lr: 0.100000 2022-11-06 16:00:34,738 epoch 18 - iter 54/274 - loss 0.02984826 - samples/sec: 158.27 - lr: 0.100000 2022-11-06 16:00:40,413 epoch 18 - iter 81/274 - loss 0.03107334 - samples/sec: 152.36 - lr: 0.100000 2022-11-06 16:00:45,475 epoch 18 - iter 108/274 - loss 0.03069842 - samples/sec: 170.79 - lr: 0.100000 2022-11-06 16:00:50,876 epoch 18 - iter 135/274 - loss 0.03029660 - samples/sec: 160.07 - lr: 0.100000 2022-11-06 16:00:56,514 epoch 18 - iter 162/274 - loss 0.03065821 - samples/sec: 153.32 - lr: 0.100000 2022-11-06 16:01:01,402 epoch 18 - iter 189/274 - loss 0.03009030 - samples/sec: 176.90 - lr: 0.100000 2022-11-06 16:01:07,110 epoch 18 - iter 216/274 - loss 0.02976895 - samples/sec: 151.44 - lr: 0.100000 2022-11-06 16:01:12,653 epoch 18 - iter 243/274 - loss 0.02988190 - samples/sec: 155.98 - lr: 0.100000 2022-11-06 16:01:17,932 epoch 18 - iter 270/274 - loss 0.02970089 - samples/sec: 163.78 - lr: 0.100000 2022-11-06 16:01:18,664 ---------------------------------------------------------------------------------------------------- 2022-11-06 16:01:18,664 EPOCH 18 done: loss 0.0297 - lr 0.100000 2022-11-06 16:01:49,013 Evaluating as a multi-label problem: False 2022-11-06 16:01:49,041 TEST : loss 0.030549418181180954 - f1-score (micro avg) 0.8375 2022-11-06 16:01:49,528 BAD EPOCHS (no improvement): 0 2022-11-06 16:01:49,714 ---------------------------------------------------------------------------------------------------- 2022-11-06 16:01:55,141 epoch 19 - iter 27/274 - loss 0.02873273 - samples/sec: 159.36 - lr: 0.100000 2022-11-06 16:02:00,197 epoch 19 - iter 54/274 - loss 0.02947910 - samples/sec: 170.99 - lr: 0.100000 2022-11-06 16:02:05,455 epoch 19 - iter 81/274 - loss 0.02898074 - samples/sec: 164.44 - lr: 0.100000 2022-11-06 16:02:10,768 epoch 19 - iter 108/274 - loss 0.02951392 - samples/sec: 162.72 - lr: 0.100000 2022-11-06 16:02:16,303 epoch 19 - iter 135/274 - loss 0.02908179 - samples/sec: 156.17 - lr: 0.100000 2022-11-06 16:02:21,861 epoch 19 - iter 162/274 - loss 0.03001166 - samples/sec: 155.57 - lr: 0.100000 2022-11-06 16:02:27,097 epoch 19 - iter 189/274 - loss 0.03008322 - samples/sec: 165.10 - lr: 0.100000 2022-11-06 16:02:32,689 epoch 19 - iter 216/274 - loss 0.02972503 - samples/sec: 155.74 - lr: 0.100000 2022-11-06 16:02:37,773 epoch 19 - iter 243/274 - loss 0.02936500 - samples/sec: 170.05 - lr: 0.100000 2022-11-06 16:02:43,311 epoch 19 - iter 270/274 - loss 0.02962141 - samples/sec: 156.12 - lr: 0.100000 2022-11-06 16:02:43,938 ---------------------------------------------------------------------------------------------------- 2022-11-06 16:02:43,938 EPOCH 19 done: loss 0.0296 - lr 0.100000 2022-11-06 16:03:14,269 Evaluating as a multi-label problem: False 2022-11-06 16:03:14,296 TEST : loss 0.03505885228514671 - f1-score (micro avg) 0.84 2022-11-06 16:03:14,779 BAD EPOCHS (no improvement): 0 2022-11-06 16:03:14,968 ---------------------------------------------------------------------------------------------------- 2022-11-06 16:03:20,351 epoch 20 - iter 27/274 - loss 0.02962538 - samples/sec: 160.69 - lr: 0.100000 2022-11-06 16:03:25,402 epoch 20 - iter 54/274 - loss 0.02685869 - samples/sec: 171.14 - lr: 0.100000 2022-11-06 16:03:30,651 epoch 20 - iter 81/274 - loss 0.02668449 - samples/sec: 164.70 - lr: 0.100000 2022-11-06 16:03:35,772 epoch 20 - iter 108/274 - loss 0.02692230 - samples/sec: 168.85 - lr: 0.100000 2022-11-06 16:03:41,388 epoch 20 - iter 135/274 - loss 0.02744559 - samples/sec: 153.95 - lr: 0.100000 2022-11-06 16:03:46,869 epoch 20 - iter 162/274 - loss 0.02710428 - samples/sec: 157.71 - lr: 0.100000 2022-11-06 16:03:52,351 epoch 20 - iter 189/274 - loss 0.02715944 - samples/sec: 157.72 - lr: 0.100000 2022-11-06 16:03:58,071 epoch 20 - iter 216/274 - loss 0.02762260 - samples/sec: 151.15 - lr: 0.100000 2022-11-06 16:04:03,492 epoch 20 - iter 243/274 - loss 0.02843548 - samples/sec: 160.67 - lr: 0.100000 2022-11-06 16:04:08,863 epoch 20 - iter 270/274 - loss 0.02849893 - samples/sec: 160.97 - lr: 0.100000 2022-11-06 16:04:09,539 ---------------------------------------------------------------------------------------------------- 2022-11-06 16:04:09,539 EPOCH 20 done: loss 0.0286 - lr 0.100000 2022-11-06 16:04:39,930 Evaluating as a multi-label problem: False 2022-11-06 16:04:39,958 TEST : loss 0.02962632104754448 - f1-score (micro avg) 0.8401 2022-11-06 16:04:40,440 BAD EPOCHS (no improvement): 0 2022-11-06 16:04:40,633 ---------------------------------------------------------------------------------------------------- 2022-11-06 16:04:45,749 epoch 21 - iter 27/274 - loss 0.02760793 - samples/sec: 169.06 - lr: 0.100000 2022-11-06 16:04:51,087 epoch 21 - iter 54/274 - loss 0.02517772 - samples/sec: 161.96 - lr: 0.100000 2022-11-06 16:04:56,345 epoch 21 - iter 81/274 - loss 0.02711348 - samples/sec: 164.41 - lr: 0.100000 2022-11-06 16:05:01,625 epoch 21 - iter 108/274 - loss 0.02741965 - samples/sec: 163.76 - lr: 0.100000 2022-11-06 16:05:07,537 epoch 21 - iter 135/274 - loss 0.02841392 - samples/sec: 146.21 - lr: 0.100000 2022-11-06 16:05:12,979 epoch 21 - iter 162/274 - loss 0.02717816 - samples/sec: 158.89 - lr: 0.100000 2022-11-06 16:05:18,571 epoch 21 - iter 189/274 - loss 0.02757418 - samples/sec: 154.59 - lr: 0.100000 2022-11-06 16:05:24,056 epoch 21 - iter 216/274 - loss 0.02785333 - samples/sec: 157.63 - lr: 0.100000 2022-11-06 16:05:29,595 epoch 21 - iter 243/274 - loss 0.02798193 - samples/sec: 156.08 - lr: 0.100000 2022-11-06 16:05:35,075 epoch 21 - iter 270/274 - loss 0.02778040 - samples/sec: 157.74 - lr: 0.100000 2022-11-06 16:05:35,751 ---------------------------------------------------------------------------------------------------- 2022-11-06 16:05:35,751 EPOCH 21 done: loss 0.0278 - lr 0.100000 2022-11-06 16:06:06,094 Evaluating as a multi-label problem: False 2022-11-06 16:06:06,121 TEST : loss 0.032616037875413895 - f1-score (micro avg) 0.8422 2022-11-06 16:06:06,606 BAD EPOCHS (no improvement): 0 2022-11-06 16:06:06,785 ---------------------------------------------------------------------------------------------------- 2022-11-06 16:06:11,983 epoch 22 - iter 27/274 - loss 0.02348395 - samples/sec: 166.39 - lr: 0.100000 2022-11-06 16:06:17,782 epoch 22 - iter 54/274 - loss 0.02570963 - samples/sec: 149.07 - lr: 0.100000 2022-11-06 16:06:23,174 epoch 22 - iter 81/274 - loss 0.02606028 - samples/sec: 160.35 - lr: 0.100000 2022-11-06 16:06:28,843 epoch 22 - iter 108/274 - loss 0.02604390 - samples/sec: 152.49 - lr: 0.100000 2022-11-06 16:06:33,986 epoch 22 - iter 135/274 - loss 0.02645342 - samples/sec: 168.12 - lr: 0.100000 2022-11-06 16:06:39,037 epoch 22 - iter 162/274 - loss 0.02700181 - samples/sec: 171.17 - lr: 0.100000 2022-11-06 16:06:44,320 epoch 22 - iter 189/274 - loss 0.02693238 - samples/sec: 163.65 - lr: 0.100000 2022-11-06 16:06:49,975 epoch 22 - iter 216/274 - loss 0.02745026 - samples/sec: 152.87 - lr: 0.100000 2022-11-06 16:06:54,869 epoch 22 - iter 243/274 - loss 0.02713177 - samples/sec: 176.68 - lr: 0.100000 2022-11-06 16:07:00,493 epoch 22 - iter 270/274 - loss 0.02742091 - samples/sec: 153.72 - lr: 0.100000 2022-11-06 16:07:01,167 ---------------------------------------------------------------------------------------------------- 2022-11-06 16:07:01,168 EPOCH 22 done: loss 0.0274 - lr 0.100000 2022-11-06 16:07:31,511 Evaluating as a multi-label problem: False 2022-11-06 16:07:31,539 TEST : loss 0.03170188143849373 - f1-score (micro avg) 0.8358 2022-11-06 16:07:32,024 BAD EPOCHS (no improvement): 0 2022-11-06 16:07:32,209 ---------------------------------------------------------------------------------------------------- 2022-11-06 16:07:37,438 epoch 23 - iter 27/274 - loss 0.02737619 - samples/sec: 165.38 - lr: 0.100000 2022-11-06 16:07:42,521 epoch 23 - iter 54/274 - loss 0.02546520 - samples/sec: 170.09 - lr: 0.100000 2022-11-06 16:07:47,780 epoch 23 - iter 81/274 - loss 0.02592985 - samples/sec: 164.38 - lr: 0.100000 2022-11-06 16:07:53,516 epoch 23 - iter 108/274 - loss 0.02616454 - samples/sec: 150.72 - lr: 0.100000 2022-11-06 16:07:58,556 epoch 23 - iter 135/274 - loss 0.02647822 - samples/sec: 171.55 - lr: 0.100000 2022-11-06 16:08:03,962 epoch 23 - iter 162/274 - loss 0.02696597 - samples/sec: 159.92 - lr: 0.100000 2022-11-06 16:08:09,188 epoch 23 - iter 189/274 - loss 0.02704517 - samples/sec: 165.45 - lr: 0.100000 2022-11-06 16:08:14,403 epoch 23 - iter 216/274 - loss 0.02719587 - samples/sec: 165.77 - lr: 0.100000 2022-11-06 16:08:19,853 epoch 23 - iter 243/274 - loss 0.02732251 - samples/sec: 158.62 - lr: 0.100000 2022-11-06 16:08:25,502 epoch 23 - iter 270/274 - loss 0.02735923 - samples/sec: 153.05 - lr: 0.100000 2022-11-06 16:08:26,350 ---------------------------------------------------------------------------------------------------- 2022-11-06 16:08:26,350 EPOCH 23 done: loss 0.0273 - lr 0.100000 2022-11-06 16:08:56,778 Evaluating as a multi-label problem: False 2022-11-06 16:08:56,805 TEST : loss 0.03235173597931862 - f1-score (micro avg) 0.8389 2022-11-06 16:08:57,287 BAD EPOCHS (no improvement): 0 2022-11-06 16:08:57,481 ---------------------------------------------------------------------------------------------------- 2022-11-06 16:09:02,713 epoch 24 - iter 27/274 - loss 0.02432970 - samples/sec: 165.31 - lr: 0.100000 2022-11-06 16:09:08,242 epoch 24 - iter 54/274 - loss 0.02792046 - samples/sec: 156.36 - lr: 0.100000 2022-11-06 16:09:13,866 epoch 24 - iter 81/274 - loss 0.02735755 - samples/sec: 153.73 - lr: 0.100000 2022-11-06 16:09:19,107 epoch 24 - iter 108/274 - loss 0.02678236 - samples/sec: 164.96 - lr: 0.100000 2022-11-06 16:09:24,380 epoch 24 - iter 135/274 - loss 0.02670707 - samples/sec: 163.94 - lr: 0.100000 2022-11-06 16:09:30,542 epoch 24 - iter 162/274 - loss 0.02735696 - samples/sec: 140.30 - lr: 0.100000 2022-11-06 16:09:35,944 epoch 24 - iter 189/274 - loss 0.02679295 - samples/sec: 160.05 - lr: 0.100000 2022-11-06 16:09:40,852 epoch 24 - iter 216/274 - loss 0.02707606 - samples/sec: 176.16 - lr: 0.100000 2022-11-06 16:09:45,899 epoch 24 - iter 243/274 - loss 0.02703989 - samples/sec: 171.30 - lr: 0.100000 2022-11-06 16:09:51,182 epoch 24 - iter 270/274 - loss 0.02694537 - samples/sec: 163.66 - lr: 0.100000 2022-11-06 16:09:51,974 ---------------------------------------------------------------------------------------------------- 2022-11-06 16:09:51,974 EPOCH 24 done: loss 0.0269 - lr 0.100000 2022-11-06 16:10:22,421 Evaluating as a multi-label problem: False 2022-11-06 16:10:22,448 TEST : loss 0.0330146886408329 - f1-score (micro avg) 0.841 2022-11-06 16:10:22,932 BAD EPOCHS (no improvement): 0 2022-11-06 16:10:23,127 ---------------------------------------------------------------------------------------------------- 2022-11-06 16:10:28,414 epoch 25 - iter 27/274 - loss 0.03143444 - samples/sec: 163.58 - lr: 0.100000 2022-11-06 16:10:33,845 epoch 25 - iter 54/274 - loss 0.02960131 - samples/sec: 159.18 - lr: 0.100000 2022-11-06 16:10:39,062 epoch 25 - iter 81/274 - loss 0.02876453 - samples/sec: 165.73 - lr: 0.100000 2022-11-06 16:10:44,359 epoch 25 - iter 108/274 - loss 0.02731169 - samples/sec: 163.20 - lr: 0.100000 2022-11-06 16:10:49,592 epoch 25 - iter 135/274 - loss 0.02709646 - samples/sec: 165.22 - lr: 0.100000 2022-11-06 16:10:54,874 epoch 25 - iter 162/274 - loss 0.02677724 - samples/sec: 163.68 - lr: 0.100000 2022-11-06 16:10:59,781 epoch 25 - iter 189/274 - loss 0.02627221 - samples/sec: 176.20 - lr: 0.100000 2022-11-06 16:11:05,136 epoch 25 - iter 216/274 - loss 0.02594192 - samples/sec: 161.45 - lr: 0.100000 2022-11-06 16:11:10,831 epoch 25 - iter 243/274 - loss 0.02612075 - samples/sec: 151.81 - lr: 0.100000 2022-11-06 16:11:16,587 epoch 25 - iter 270/274 - loss 0.02641718 - samples/sec: 150.18 - lr: 0.100000 2022-11-06 16:11:17,269 ---------------------------------------------------------------------------------------------------- 2022-11-06 16:11:17,269 EPOCH 25 done: loss 0.0264 - lr 0.100000 2022-11-06 16:11:47,713 Evaluating as a multi-label problem: False 2022-11-06 16:11:47,741 TEST : loss 0.0296016838401556 - f1-score (micro avg) 0.8411 2022-11-06 16:11:48,227 BAD EPOCHS (no improvement): 0 2022-11-06 16:11:48,410 ---------------------------------------------------------------------------------------------------- 2022-11-06 16:11:53,927 epoch 26 - iter 27/274 - loss 0.02822455 - samples/sec: 156.76 - lr: 0.100000 2022-11-06 16:11:59,175 epoch 26 - iter 54/274 - loss 0.02869778 - samples/sec: 164.74 - lr: 0.100000 2022-11-06 16:12:04,826 epoch 26 - iter 81/274 - loss 0.02800272 - samples/sec: 152.97 - lr: 0.100000 2022-11-06 16:12:09,951 epoch 26 - iter 108/274 - loss 0.02770767 - samples/sec: 168.70 - lr: 0.100000 2022-11-06 16:12:15,275 epoch 26 - iter 135/274 - loss 0.02730618 - samples/sec: 162.39 - lr: 0.100000 2022-11-06 16:12:21,123 epoch 26 - iter 162/274 - loss 0.02748631 - samples/sec: 147.84 - lr: 0.100000 2022-11-06 16:12:26,412 epoch 26 - iter 189/274 - loss 0.02710793 - samples/sec: 163.46 - lr: 0.100000 2022-11-06 16:12:31,563 epoch 26 - iter 216/274 - loss 0.02712051 - samples/sec: 167.82 - lr: 0.100000 2022-11-06 16:12:36,534 epoch 26 - iter 243/274 - loss 0.02691565 - samples/sec: 173.93 - lr: 0.100000 2022-11-06 16:12:41,548 epoch 26 - iter 270/274 - loss 0.02638533 - samples/sec: 172.43 - lr: 0.100000 2022-11-06 16:12:42,524 ---------------------------------------------------------------------------------------------------- 2022-11-06 16:12:42,524 EPOCH 26 done: loss 0.0264 - lr 0.100000 2022-11-06 16:13:13,116 Evaluating as a multi-label problem: False 2022-11-06 16:13:13,143 TEST : loss 0.028864704072475433 - f1-score (micro avg) 0.8374 2022-11-06 16:13:13,626 BAD EPOCHS (no improvement): 1 2022-11-06 16:13:13,808 ---------------------------------------------------------------------------------------------------- 2022-11-06 16:13:19,376 epoch 27 - iter 27/274 - loss 0.02871045 - samples/sec: 155.32 - lr: 0.100000 2022-11-06 16:13:25,192 epoch 27 - iter 54/274 - loss 0.02570850 - samples/sec: 148.63 - lr: 0.100000 2022-11-06 16:13:30,457 epoch 27 - iter 81/274 - loss 0.02582387 - samples/sec: 164.22 - lr: 0.100000 2022-11-06 16:13:35,757 epoch 27 - iter 108/274 - loss 0.02624385 - samples/sec: 163.14 - lr: 0.100000 2022-11-06 16:13:41,010 epoch 27 - iter 135/274 - loss 0.02620915 - samples/sec: 164.56 - lr: 0.100000 2022-11-06 16:13:46,095 epoch 27 - iter 162/274 - loss 0.02561898 - samples/sec: 171.45 - lr: 0.100000 2022-11-06 16:13:51,235 epoch 27 - iter 189/274 - loss 0.02553513 - samples/sec: 168.20 - lr: 0.100000 2022-11-06 16:13:56,802 epoch 27 - iter 216/274 - loss 0.02540689 - samples/sec: 155.31 - lr: 0.100000 2022-11-06 16:14:01,960 epoch 27 - iter 243/274 - loss 0.02539935 - samples/sec: 167.61 - lr: 0.100000 2022-11-06 16:14:07,509 epoch 27 - iter 270/274 - loss 0.02613303 - samples/sec: 155.80 - lr: 0.100000 2022-11-06 16:14:08,381 ---------------------------------------------------------------------------------------------------- 2022-11-06 16:14:08,381 EPOCH 27 done: loss 0.0262 - lr 0.100000 2022-11-06 16:14:38,823 Evaluating as a multi-label problem: False 2022-11-06 16:14:38,850 TEST : loss 0.029708683490753174 - f1-score (micro avg) 0.8496 2022-11-06 16:14:39,335 BAD EPOCHS (no improvement): 0 2022-11-06 16:14:39,527 ---------------------------------------------------------------------------------------------------- 2022-11-06 16:14:45,257 epoch 28 - iter 27/274 - loss 0.03049054 - samples/sec: 150.90 - lr: 0.100000 2022-11-06 16:14:51,304 epoch 28 - iter 54/274 - loss 0.02789797 - samples/sec: 142.98 - lr: 0.100000 2022-11-06 16:14:56,667 epoch 28 - iter 81/274 - loss 0.02590343 - samples/sec: 161.18 - lr: 0.100000 2022-11-06 16:15:01,580 epoch 28 - iter 108/274 - loss 0.02504768 - samples/sec: 176.01 - lr: 0.100000 2022-11-06 16:15:06,612 epoch 28 - iter 135/274 - loss 0.02529742 - samples/sec: 171.82 - lr: 0.100000 2022-11-06 16:15:11,948 epoch 28 - iter 162/274 - loss 0.02524847 - samples/sec: 162.01 - lr: 0.100000 2022-11-06 16:15:16,760 epoch 28 - iter 189/274 - loss 0.02511018 - samples/sec: 179.68 - lr: 0.100000 2022-11-06 16:15:22,086 epoch 28 - iter 216/274 - loss 0.02487931 - samples/sec: 162.32 - lr: 0.100000 2022-11-06 16:15:27,321 epoch 28 - iter 243/274 - loss 0.02471569 - samples/sec: 165.16 - lr: 0.100000 2022-11-06 16:15:32,766 epoch 28 - iter 270/274 - loss 0.02483899 - samples/sec: 158.76 - lr: 0.100000 2022-11-06 16:15:33,875 ---------------------------------------------------------------------------------------------------- 2022-11-06 16:15:33,876 EPOCH 28 done: loss 0.0247 - lr 0.100000 2022-11-06 16:16:04,301 Evaluating as a multi-label problem: False 2022-11-06 16:16:04,329 TEST : loss 0.03210555389523506 - f1-score (micro avg) 0.848 2022-11-06 16:16:04,815 BAD EPOCHS (no improvement): 0 2022-11-06 16:16:05,006 ---------------------------------------------------------------------------------------------------- 2022-11-06 16:16:10,135 epoch 29 - iter 27/274 - loss 0.02344207 - samples/sec: 168.62 - lr: 0.100000 2022-11-06 16:16:15,701 epoch 29 - iter 54/274 - loss 0.02457921 - samples/sec: 155.32 - lr: 0.100000 2022-11-06 16:16:21,003 epoch 29 - iter 81/274 - loss 0.02480935 - samples/sec: 163.06 - lr: 0.100000 2022-11-06 16:16:26,616 epoch 29 - iter 108/274 - loss 0.02606039 - samples/sec: 154.02 - lr: 0.100000 2022-11-06 16:16:32,037 epoch 29 - iter 135/274 - loss 0.02570571 - samples/sec: 159.47 - lr: 0.100000 2022-11-06 16:16:37,259 epoch 29 - iter 162/274 - loss 0.02557387 - samples/sec: 165.59 - lr: 0.100000 2022-11-06 16:16:42,348 epoch 29 - iter 189/274 - loss 0.02549342 - samples/sec: 169.88 - lr: 0.100000 2022-11-06 16:16:47,841 epoch 29 - iter 216/274 - loss 0.02527776 - samples/sec: 157.37 - lr: 0.100000 2022-11-06 16:16:53,236 epoch 29 - iter 243/274 - loss 0.02552941 - samples/sec: 160.27 - lr: 0.100000 2022-11-06 16:16:58,471 epoch 29 - iter 270/274 - loss 0.02558363 - samples/sec: 165.12 - lr: 0.100000 2022-11-06 16:16:59,254 ---------------------------------------------------------------------------------------------------- 2022-11-06 16:16:59,254 EPOCH 29 done: loss 0.0257 - lr 0.100000 2022-11-06 16:17:29,686 Evaluating as a multi-label problem: False 2022-11-06 16:17:29,713 TEST : loss 0.028371913358569145 - f1-score (micro avg) 0.8469 2022-11-06 16:17:30,197 BAD EPOCHS (no improvement): 1 2022-11-06 16:17:30,379 ---------------------------------------------------------------------------------------------------- 2022-11-06 16:17:35,368 epoch 30 - iter 27/274 - loss 0.02798108 - samples/sec: 173.33 - lr: 0.100000 2022-11-06 16:17:40,343 epoch 30 - iter 54/274 - loss 0.02660047 - samples/sec: 173.81 - lr: 0.100000 2022-11-06 16:17:45,469 epoch 30 - iter 81/274 - loss 0.02438145 - samples/sec: 168.67 - lr: 0.100000 2022-11-06 16:17:50,759 epoch 30 - iter 108/274 - loss 0.02433871 - samples/sec: 163.42 - lr: 0.100000 2022-11-06 16:17:56,049 epoch 30 - iter 135/274 - loss 0.02432981 - samples/sec: 163.44 - lr: 0.100000 2022-11-06 16:18:02,002 epoch 30 - iter 162/274 - loss 0.02419547 - samples/sec: 145.21 - lr: 0.100000 2022-11-06 16:18:07,393 epoch 30 - iter 189/274 - loss 0.02387651 - samples/sec: 160.38 - lr: 0.100000 2022-11-06 16:18:12,260 epoch 30 - iter 216/274 - loss 0.02432185 - samples/sec: 177.65 - lr: 0.100000 2022-11-06 16:18:18,008 epoch 30 - iter 243/274 - loss 0.02463235 - samples/sec: 150.40 - lr: 0.100000 2022-11-06 16:18:23,789 epoch 30 - iter 270/274 - loss 0.02438101 - samples/sec: 149.54 - lr: 0.100000 2022-11-06 16:18:24,620 ---------------------------------------------------------------------------------------------------- 2022-11-06 16:18:24,620 EPOCH 30 done: loss 0.0248 - lr 0.100000 2022-11-06 16:18:55,060 Evaluating as a multi-label problem: False 2022-11-06 16:18:55,088 TEST : loss 0.028511736541986465 - f1-score (micro avg) 0.8269 2022-11-06 16:18:55,573 BAD EPOCHS (no improvement): 2 2022-11-06 16:18:55,766 ---------------------------------------------------------------------------------------------------- 2022-11-06 16:19:01,221 epoch 31 - iter 27/274 - loss 0.02101312 - samples/sec: 158.51 - lr: 0.100000 2022-11-06 16:19:06,255 epoch 31 - iter 54/274 - loss 0.02296639 - samples/sec: 171.74 - lr: 0.100000 2022-11-06 16:19:11,132 epoch 31 - iter 81/274 - loss 0.02361620 - samples/sec: 178.86 - lr: 0.100000 2022-11-06 16:19:16,458 epoch 31 - iter 108/274 - loss 0.02492664 - samples/sec: 162.31 - lr: 0.100000 2022-11-06 16:19:21,436 epoch 31 - iter 135/274 - loss 0.02420548 - samples/sec: 173.68 - lr: 0.100000 2022-11-06 16:19:27,143 epoch 31 - iter 162/274 - loss 0.02463334 - samples/sec: 151.48 - lr: 0.100000 2022-11-06 16:19:32,770 epoch 31 - iter 189/274 - loss 0.02415614 - samples/sec: 153.64 - lr: 0.100000 2022-11-06 16:19:37,899 epoch 31 - iter 216/274 - loss 0.02407295 - samples/sec: 168.59 - lr: 0.100000 2022-11-06 16:19:43,687 epoch 31 - iter 243/274 - loss 0.02377883 - samples/sec: 149.37 - lr: 0.100000 2022-11-06 16:19:49,205 epoch 31 - iter 270/274 - loss 0.02411380 - samples/sec: 156.66 - lr: 0.100000 2022-11-06 16:19:50,132 ---------------------------------------------------------------------------------------------------- 2022-11-06 16:19:50,132 EPOCH 31 done: loss 0.0240 - lr 0.100000 2022-11-06 16:20:20,654 Evaluating as a multi-label problem: False 2022-11-06 16:20:20,681 TEST : loss 0.030574096366763115 - f1-score (micro avg) 0.8447 2022-11-06 16:20:21,163 BAD EPOCHS (no improvement): 0 2022-11-06 16:20:21,355 ---------------------------------------------------------------------------------------------------- 2022-11-06 16:20:26,757 epoch 32 - iter 27/274 - loss 0.02781246 - samples/sec: 160.09 - lr: 0.100000 2022-11-06 16:20:32,255 epoch 32 - iter 54/274 - loss 0.02479696 - samples/sec: 157.22 - lr: 0.100000 2022-11-06 16:20:37,336 epoch 32 - iter 81/274 - loss 0.02563759 - samples/sec: 170.17 - lr: 0.100000 2022-11-06 16:20:42,605 epoch 32 - iter 108/274 - loss 0.02410592 - samples/sec: 164.07 - lr: 0.100000 2022-11-06 16:20:48,671 epoch 32 - iter 135/274 - loss 0.02451412 - samples/sec: 142.53 - lr: 0.100000 2022-11-06 16:20:54,061 epoch 32 - iter 162/274 - loss 0.02434660 - samples/sec: 160.39 - lr: 0.100000 2022-11-06 16:20:58,858 epoch 32 - iter 189/274 - loss 0.02391541 - samples/sec: 180.23 - lr: 0.100000 2022-11-06 16:21:04,444 epoch 32 - iter 216/274 - loss 0.02379934 - samples/sec: 154.77 - lr: 0.100000 2022-11-06 16:21:09,872 epoch 32 - iter 243/274 - loss 0.02422990 - samples/sec: 159.29 - lr: 0.100000 2022-11-06 16:21:15,080 epoch 32 - iter 270/274 - loss 0.02457500 - samples/sec: 166.00 - lr: 0.100000 2022-11-06 16:21:15,978 ---------------------------------------------------------------------------------------------------- 2022-11-06 16:21:15,979 EPOCH 32 done: loss 0.0248 - lr 0.100000 2022-11-06 16:21:46,530 Evaluating as a multi-label problem: False 2022-11-06 16:21:46,557 TEST : loss 0.02866635099053383 - f1-score (micro avg) 0.8431 2022-11-06 16:21:47,042 BAD EPOCHS (no improvement): 1 2022-11-06 16:21:47,236 ---------------------------------------------------------------------------------------------------- 2022-11-06 16:21:52,636 epoch 33 - iter 27/274 - loss 0.02865260 - samples/sec: 160.17 - lr: 0.100000 2022-11-06 16:21:57,938 epoch 33 - iter 54/274 - loss 0.02666235 - samples/sec: 163.09 - lr: 0.100000 2022-11-06 16:22:03,475 epoch 33 - iter 81/274 - loss 0.02474081 - samples/sec: 156.14 - lr: 0.100000 2022-11-06 16:22:09,037 epoch 33 - iter 108/274 - loss 0.02515547 - samples/sec: 155.48 - lr: 0.100000 2022-11-06 16:22:14,965 epoch 33 - iter 135/274 - loss 0.02530611 - samples/sec: 145.85 - lr: 0.100000 2022-11-06 16:22:21,040 epoch 33 - iter 162/274 - loss 0.02492838 - samples/sec: 142.32 - lr: 0.100000 2022-11-06 16:22:26,273 epoch 33 - iter 189/274 - loss 0.02452032 - samples/sec: 165.22 - lr: 0.100000 2022-11-06 16:22:32,240 epoch 33 - iter 216/274 - loss 0.02491611 - samples/sec: 144.92 - lr: 0.100000 2022-11-06 16:22:37,797 epoch 33 - iter 243/274 - loss 0.02479517 - samples/sec: 155.59 - lr: 0.100000 2022-11-06 16:22:43,864 epoch 33 - iter 270/274 - loss 0.02472361 - samples/sec: 142.52 - lr: 0.100000 2022-11-06 16:22:44,814 ---------------------------------------------------------------------------------------------------- 2022-11-06 16:22:44,815 EPOCH 33 done: loss 0.0250 - lr 0.100000 2022-11-06 16:23:16,371 Evaluating as a multi-label problem: False 2022-11-06 16:23:16,398 TEST : loss 0.028993723914027214 - f1-score (micro avg) 0.8459 2022-11-06 16:23:16,883 BAD EPOCHS (no improvement): 2 2022-11-06 16:23:17,077 ---------------------------------------------------------------------------------------------------- 2022-11-06 16:23:22,429 epoch 34 - iter 27/274 - loss 0.02239182 - samples/sec: 161.60 - lr: 0.100000 2022-11-06 16:23:27,911 epoch 34 - iter 54/274 - loss 0.02416868 - samples/sec: 157.72 - lr: 0.100000 2022-11-06 16:23:33,550 epoch 34 - iter 81/274 - loss 0.02374628 - samples/sec: 153.33 - lr: 0.100000 2022-11-06 16:23:39,368 epoch 34 - iter 108/274 - loss 0.02380439 - samples/sec: 148.61 - lr: 0.100000 2022-11-06 16:23:44,675 epoch 34 - iter 135/274 - loss 0.02360065 - samples/sec: 162.95 - lr: 0.100000 2022-11-06 16:23:50,060 epoch 34 - iter 162/274 - loss 0.02354964 - samples/sec: 160.55 - lr: 0.100000 2022-11-06 16:23:55,281 epoch 34 - iter 189/274 - loss 0.02309751 - samples/sec: 165.63 - lr: 0.100000 2022-11-06 16:24:00,798 epoch 34 - iter 216/274 - loss 0.02310163 - samples/sec: 156.73 - lr: 0.100000 2022-11-06 16:24:06,119 epoch 34 - iter 243/274 - loss 0.02324512 - samples/sec: 162.51 - lr: 0.100000 2022-11-06 16:24:11,666 epoch 34 - iter 270/274 - loss 0.02329042 - samples/sec: 155.87 - lr: 0.100000 2022-11-06 16:24:12,463 ---------------------------------------------------------------------------------------------------- 2022-11-06 16:24:12,464 EPOCH 34 done: loss 0.0235 - lr 0.100000 2022-11-06 16:24:43,280 Evaluating as a multi-label problem: False 2022-11-06 16:24:43,308 TEST : loss 0.028596550226211548 - f1-score (micro avg) 0.8429 2022-11-06 16:24:43,793 BAD EPOCHS (no improvement): 0 2022-11-06 16:24:43,981 ---------------------------------------------------------------------------------------------------- 2022-11-06 16:24:49,391 epoch 35 - iter 27/274 - loss 0.02168843 - samples/sec: 159.87 - lr: 0.100000 2022-11-06 16:24:55,208 epoch 35 - iter 54/274 - loss 0.02108781 - samples/sec: 148.63 - lr: 0.100000 2022-11-06 16:25:00,440 epoch 35 - iter 81/274 - loss 0.02301830 - samples/sec: 165.27 - lr: 0.100000 2022-11-06 16:25:06,407 epoch 35 - iter 108/274 - loss 0.02317393 - samples/sec: 144.91 - lr: 0.100000 2022-11-06 16:25:11,868 epoch 35 - iter 135/274 - loss 0.02416664 - samples/sec: 158.31 - lr: 0.100000 2022-11-06 16:25:16,789 epoch 35 - iter 162/274 - loss 0.02368299 - samples/sec: 175.73 - lr: 0.100000 2022-11-06 16:25:22,259 epoch 35 - iter 189/274 - loss 0.02379600 - samples/sec: 158.07 - lr: 0.100000 2022-11-06 16:25:27,827 epoch 35 - iter 216/274 - loss 0.02345872 - samples/sec: 155.27 - lr: 0.100000 2022-11-06 16:25:33,194 epoch 35 - iter 243/274 - loss 0.02373931 - samples/sec: 161.10 - lr: 0.100000 2022-11-06 16:25:38,490 epoch 35 - iter 270/274 - loss 0.02434243 - samples/sec: 163.28 - lr: 0.100000 2022-11-06 16:25:39,144 ---------------------------------------------------------------------------------------------------- 2022-11-06 16:25:39,144 EPOCH 35 done: loss 0.0242 - lr 0.100000 2022-11-06 16:26:10,068 Evaluating as a multi-label problem: False 2022-11-06 16:26:10,099 TEST : loss 0.033472731709480286 - f1-score (micro avg) 0.8457 2022-11-06 16:26:10,582 BAD EPOCHS (no improvement): 1 2022-11-06 16:26:10,785 ---------------------------------------------------------------------------------------------------- 2022-11-06 16:26:17,497 epoch 36 - iter 27/274 - loss 0.02364353 - samples/sec: 128.83 - lr: 0.100000 2022-11-06 16:26:23,183 epoch 36 - iter 54/274 - loss 0.02153287 - samples/sec: 152.09 - lr: 0.100000 2022-11-06 16:26:28,475 epoch 36 - iter 81/274 - loss 0.02326202 - samples/sec: 163.39 - lr: 0.100000 2022-11-06 16:26:33,844 epoch 36 - iter 108/274 - loss 0.02316080 - samples/sec: 161.06 - lr: 0.100000 2022-11-06 16:26:39,100 epoch 36 - iter 135/274 - loss 0.02335356 - samples/sec: 164.49 - lr: 0.100000 2022-11-06 16:26:44,131 epoch 36 - iter 162/274 - loss 0.02357097 - samples/sec: 171.82 - lr: 0.100000 2022-11-06 16:26:50,011 epoch 36 - iter 189/274 - loss 0.02410531 - samples/sec: 147.65 - lr: 0.100000 2022-11-06 16:26:55,418 epoch 36 - iter 216/274 - loss 0.02407270 - samples/sec: 159.89 - lr: 0.100000 2022-11-06 16:27:00,688 epoch 36 - iter 243/274 - loss 0.02426518 - samples/sec: 164.12 - lr: 0.100000 2022-11-06 16:27:06,326 epoch 36 - iter 270/274 - loss 0.02403961 - samples/sec: 153.35 - lr: 0.100000 2022-11-06 16:27:06,963 ---------------------------------------------------------------------------------------------------- 2022-11-06 16:27:06,964 EPOCH 36 done: loss 0.0240 - lr 0.100000 2022-11-06 16:27:37,405 Evaluating as a multi-label problem: False 2022-11-06 16:27:37,432 TEST : loss 0.030064314603805542 - f1-score (micro avg) 0.8549 2022-11-06 16:27:37,915 BAD EPOCHS (no improvement): 2 2022-11-06 16:27:38,110 ---------------------------------------------------------------------------------------------------- 2022-11-06 16:27:44,203 epoch 37 - iter 27/274 - loss 0.02699987 - samples/sec: 141.91 - lr: 0.100000 2022-11-06 16:27:49,075 epoch 37 - iter 54/274 - loss 0.02321800 - samples/sec: 177.49 - lr: 0.100000 2022-11-06 16:27:54,663 epoch 37 - iter 81/274 - loss 0.02509846 - samples/sec: 154.71 - lr: 0.100000 2022-11-06 16:27:59,879 epoch 37 - iter 108/274 - loss 0.02406442 - samples/sec: 165.73 - lr: 0.100000 2022-11-06 16:28:05,077 epoch 37 - iter 135/274 - loss 0.02368305 - samples/sec: 166.33 - lr: 0.100000 2022-11-06 16:28:10,924 epoch 37 - iter 162/274 - loss 0.02370177 - samples/sec: 147.86 - lr: 0.100000 2022-11-06 16:28:16,328 epoch 37 - iter 189/274 - loss 0.02334651 - samples/sec: 159.99 - lr: 0.100000 2022-11-06 16:28:21,887 epoch 37 - iter 216/274 - loss 0.02355185 - samples/sec: 155.51 - lr: 0.100000 2022-11-06 16:28:27,120 epoch 37 - iter 243/274 - loss 0.02319206 - samples/sec: 165.22 - lr: 0.100000 2022-11-06 16:28:32,148 epoch 37 - iter 270/274 - loss 0.02290421 - samples/sec: 171.93 - lr: 0.100000 2022-11-06 16:28:32,762 ---------------------------------------------------------------------------------------------------- 2022-11-06 16:28:32,762 EPOCH 37 done: loss 0.0229 - lr 0.100000 2022-11-06 16:29:03,408 Evaluating as a multi-label problem: False 2022-11-06 16:29:03,436 TEST : loss 0.03057018481194973 - f1-score (micro avg) 0.844 2022-11-06 16:29:03,921 BAD EPOCHS (no improvement): 0 2022-11-06 16:29:04,114 ---------------------------------------------------------------------------------------------------- 2022-11-06 16:29:09,621 epoch 38 - iter 27/274 - loss 0.02408423 - samples/sec: 157.04 - lr: 0.100000 2022-11-06 16:29:14,793 epoch 38 - iter 54/274 - loss 0.02249019 - samples/sec: 167.16 - lr: 0.100000 2022-11-06 16:29:20,176 epoch 38 - iter 81/274 - loss 0.02285553 - samples/sec: 160.61 - lr: 0.100000 2022-11-06 16:29:25,355 epoch 38 - iter 108/274 - loss 0.02271806 - samples/sec: 166.94 - lr: 0.100000 2022-11-06 16:29:30,748 epoch 38 - iter 135/274 - loss 0.02290174 - samples/sec: 160.31 - lr: 0.100000 2022-11-06 16:29:35,892 epoch 38 - iter 162/274 - loss 0.02294759 - samples/sec: 168.09 - lr: 0.100000 2022-11-06 16:29:41,231 epoch 38 - iter 189/274 - loss 0.02241643 - samples/sec: 161.92 - lr: 0.100000 2022-11-06 16:29:46,985 epoch 38 - iter 216/274 - loss 0.02297524 - samples/sec: 150.24 - lr: 0.100000 2022-11-06 16:29:52,183 epoch 38 - iter 243/274 - loss 0.02279735 - samples/sec: 166.32 - lr: 0.100000 2022-11-06 16:29:57,977 epoch 38 - iter 270/274 - loss 0.02293773 - samples/sec: 149.22 - lr: 0.100000 2022-11-06 16:29:58,910 ---------------------------------------------------------------------------------------------------- 2022-11-06 16:29:58,910 EPOCH 38 done: loss 0.0232 - lr 0.100000 2022-11-06 16:30:29,808 Evaluating as a multi-label problem: False 2022-11-06 16:30:29,836 TEST : loss 0.03000396490097046 - f1-score (micro avg) 0.8452 2022-11-06 16:30:30,319 BAD EPOCHS (no improvement): 1 2022-11-06 16:30:30,506 ---------------------------------------------------------------------------------------------------- 2022-11-06 16:30:35,955 epoch 39 - iter 27/274 - loss 0.01893323 - samples/sec: 158.70 - lr: 0.100000 2022-11-06 16:30:41,624 epoch 39 - iter 54/274 - loss 0.02118483 - samples/sec: 152.52 - lr: 0.100000 2022-11-06 16:30:47,051 epoch 39 - iter 81/274 - loss 0.02232038 - samples/sec: 159.30 - lr: 0.100000 2022-11-06 16:30:52,756 epoch 39 - iter 108/274 - loss 0.02395982 - samples/sec: 151.54 - lr: 0.100000 2022-11-06 16:30:58,538 epoch 39 - iter 135/274 - loss 0.02373342 - samples/sec: 149.52 - lr: 0.100000 2022-11-06 16:31:03,985 epoch 39 - iter 162/274 - loss 0.02356291 - samples/sec: 158.72 - lr: 0.100000 2022-11-06 16:31:09,220 epoch 39 - iter 189/274 - loss 0.02349041 - samples/sec: 165.14 - lr: 0.100000 2022-11-06 16:31:14,429 epoch 39 - iter 216/274 - loss 0.02387970 - samples/sec: 165.99 - lr: 0.100000 2022-11-06 16:31:19,620 epoch 39 - iter 243/274 - loss 0.02325435 - samples/sec: 166.54 - lr: 0.100000 2022-11-06 16:31:24,652 epoch 39 - iter 270/274 - loss 0.02339966 - samples/sec: 171.82 - lr: 0.100000 2022-11-06 16:31:25,689 ---------------------------------------------------------------------------------------------------- 2022-11-06 16:31:25,689 EPOCH 39 done: loss 0.0233 - lr 0.100000 2022-11-06 16:31:56,422 Evaluating as a multi-label problem: False 2022-11-06 16:31:56,449 TEST : loss 0.031386326998472214 - f1-score (micro avg) 0.853 2022-11-06 16:31:56,935 BAD EPOCHS (no improvement): 2 2022-11-06 16:31:57,129 ---------------------------------------------------------------------------------------------------- 2022-11-06 16:32:02,556 epoch 40 - iter 27/274 - loss 0.02241416 - samples/sec: 159.36 - lr: 0.100000 2022-11-06 16:32:07,624 epoch 40 - iter 54/274 - loss 0.02153208 - samples/sec: 170.61 - lr: 0.100000 2022-11-06 16:32:13,232 epoch 40 - iter 81/274 - loss 0.02212247 - samples/sec: 154.16 - lr: 0.100000 2022-11-06 16:32:18,782 epoch 40 - iter 108/274 - loss 0.02325963 - samples/sec: 155.77 - lr: 0.100000 2022-11-06 16:32:24,128 epoch 40 - iter 135/274 - loss 0.02375707 - samples/sec: 161.72 - lr: 0.100000 2022-11-06 16:32:29,630 epoch 40 - iter 162/274 - loss 0.02374767 - samples/sec: 157.14 - lr: 0.100000 2022-11-06 16:32:35,025 epoch 40 - iter 189/274 - loss 0.02357913 - samples/sec: 160.24 - lr: 0.100000 2022-11-06 16:32:40,488 epoch 40 - iter 216/274 - loss 0.02328130 - samples/sec: 158.27 - lr: 0.100000 2022-11-06 16:32:45,652 epoch 40 - iter 243/274 - loss 0.02294437 - samples/sec: 167.41 - lr: 0.100000 2022-11-06 16:32:51,052 epoch 40 - iter 270/274 - loss 0.02291437 - samples/sec: 161.37 - lr: 0.100000 2022-11-06 16:32:51,750 ---------------------------------------------------------------------------------------------------- 2022-11-06 16:32:51,750 EPOCH 40 done: loss 0.0229 - lr 0.100000 2022-11-06 16:33:22,446 Evaluating as a multi-label problem: False 2022-11-06 16:33:22,474 TEST : loss 0.029127517715096474 - f1-score (micro avg) 0.8558 2022-11-06 16:33:22,957 BAD EPOCHS (no improvement): 0 2022-11-06 16:33:23,153 ---------------------------------------------------------------------------------------------------- 2022-11-06 16:33:28,526 epoch 41 - iter 27/274 - loss 0.02215677 - samples/sec: 160.96 - lr: 0.100000 2022-11-06 16:33:33,956 epoch 41 - iter 54/274 - loss 0.02387369 - samples/sec: 159.21 - lr: 0.100000 2022-11-06 16:33:39,128 epoch 41 - iter 81/274 - loss 0.02405334 - samples/sec: 167.17 - lr: 0.100000 2022-11-06 16:33:44,369 epoch 41 - iter 108/274 - loss 0.02373568 - samples/sec: 164.97 - lr: 0.100000 2022-11-06 16:33:49,619 epoch 41 - iter 135/274 - loss 0.02313503 - samples/sec: 164.68 - lr: 0.100000 2022-11-06 16:33:54,960 epoch 41 - iter 162/274 - loss 0.02257297 - samples/sec: 161.85 - lr: 0.100000 2022-11-06 16:33:59,784 epoch 41 - iter 189/274 - loss 0.02276199 - samples/sec: 179.24 - lr: 0.100000 2022-11-06 16:34:05,131 epoch 41 - iter 216/274 - loss 0.02278488 - samples/sec: 161.69 - lr: 0.100000 2022-11-06 16:34:10,803 epoch 41 - iter 243/274 - loss 0.02252815 - samples/sec: 152.43 - lr: 0.100000 2022-11-06 16:34:16,783 epoch 41 - iter 270/274 - loss 0.02244280 - samples/sec: 144.56 - lr: 0.100000 2022-11-06 16:34:17,566 ---------------------------------------------------------------------------------------------------- 2022-11-06 16:34:17,566 EPOCH 41 done: loss 0.0223 - lr 0.100000 2022-11-06 16:34:48,287 Evaluating as a multi-label problem: False 2022-11-06 16:34:48,314 TEST : loss 0.031170489266514778 - f1-score (micro avg) 0.8446 2022-11-06 16:34:48,797 BAD EPOCHS (no improvement): 0 2022-11-06 16:34:48,991 ---------------------------------------------------------------------------------------------------- 2022-11-06 16:34:54,223 epoch 42 - iter 27/274 - loss 0.02163016 - samples/sec: 165.30 - lr: 0.100000 2022-11-06 16:35:00,234 epoch 42 - iter 54/274 - loss 0.02131129 - samples/sec: 143.81 - lr: 0.100000 2022-11-06 16:35:05,604 epoch 42 - iter 81/274 - loss 0.02146281 - samples/sec: 161.01 - lr: 0.100000 2022-11-06 16:35:10,797 epoch 42 - iter 108/274 - loss 0.02166560 - samples/sec: 166.50 - lr: 0.100000 2022-11-06 16:35:16,278 epoch 42 - iter 135/274 - loss 0.02174595 - samples/sec: 157.74 - lr: 0.100000 2022-11-06 16:35:21,840 epoch 42 - iter 162/274 - loss 0.02188162 - samples/sec: 155.43 - lr: 0.100000 2022-11-06 16:35:27,026 epoch 42 - iter 189/274 - loss 0.02196282 - samples/sec: 168.06 - lr: 0.100000 2022-11-06 16:35:32,426 epoch 42 - iter 216/274 - loss 0.02181431 - samples/sec: 160.12 - lr: 0.100000 2022-11-06 16:35:37,217 epoch 42 - iter 243/274 - loss 0.02192696 - samples/sec: 180.45 - lr: 0.100000 2022-11-06 16:35:42,668 epoch 42 - iter 270/274 - loss 0.02200339 - samples/sec: 158.59 - lr: 0.100000 2022-11-06 16:35:43,431 ---------------------------------------------------------------------------------------------------- 2022-11-06 16:35:43,431 EPOCH 42 done: loss 0.0221 - lr 0.100000 2022-11-06 16:36:14,169 Evaluating as a multi-label problem: False 2022-11-06 16:36:14,196 TEST : loss 0.0294171292334795 - f1-score (micro avg) 0.8504 2022-11-06 16:36:14,679 BAD EPOCHS (no improvement): 0 2022-11-06 16:36:14,864 ---------------------------------------------------------------------------------------------------- 2022-11-06 16:36:20,163 epoch 43 - iter 27/274 - loss 0.01710942 - samples/sec: 163.23 - lr: 0.100000 2022-11-06 16:36:25,610 epoch 43 - iter 54/274 - loss 0.02029393 - samples/sec: 158.72 - lr: 0.100000 2022-11-06 16:36:30,951 epoch 43 - iter 81/274 - loss 0.02097961 - samples/sec: 161.85 - lr: 0.100000 2022-11-06 16:36:36,086 epoch 43 - iter 108/274 - loss 0.02062345 - samples/sec: 168.38 - lr: 0.100000 2022-11-06 16:36:41,206 epoch 43 - iter 135/274 - loss 0.02188550 - samples/sec: 168.86 - lr: 0.100000 2022-11-06 16:36:46,584 epoch 43 - iter 162/274 - loss 0.02247212 - samples/sec: 160.76 - lr: 0.100000 2022-11-06 16:36:51,835 epoch 43 - iter 189/274 - loss 0.02216251 - samples/sec: 164.65 - lr: 0.100000 2022-11-06 16:36:56,984 epoch 43 - iter 216/274 - loss 0.02277374 - samples/sec: 167.89 - lr: 0.100000 2022-11-06 16:37:02,380 epoch 43 - iter 243/274 - loss 0.02316744 - samples/sec: 160.24 - lr: 0.100000 2022-11-06 16:37:08,486 epoch 43 - iter 270/274 - loss 0.02320475 - samples/sec: 141.57 - lr: 0.100000 2022-11-06 16:37:09,417 ---------------------------------------------------------------------------------------------------- 2022-11-06 16:37:09,417 EPOCH 43 done: loss 0.0233 - lr 0.100000 2022-11-06 16:37:40,207 Evaluating as a multi-label problem: False 2022-11-06 16:37:40,235 TEST : loss 0.030061138793826103 - f1-score (micro avg) 0.8411 2022-11-06 16:37:40,719 BAD EPOCHS (no improvement): 1 2022-11-06 16:37:40,911 ---------------------------------------------------------------------------------------------------- 2022-11-06 16:37:46,168 epoch 44 - iter 27/274 - loss 0.02247054 - samples/sec: 164.52 - lr: 0.100000 2022-11-06 16:37:50,994 epoch 44 - iter 54/274 - loss 0.02094728 - samples/sec: 179.17 - lr: 0.100000 2022-11-06 16:37:56,507 epoch 44 - iter 81/274 - loss 0.02310860 - samples/sec: 156.82 - lr: 0.100000 2022-11-06 16:38:02,136 epoch 44 - iter 108/274 - loss 0.02301794 - samples/sec: 153.58 - lr: 0.100000 2022-11-06 16:38:07,478 epoch 44 - iter 135/274 - loss 0.02313154 - samples/sec: 161.86 - lr: 0.100000 2022-11-06 16:38:13,135 epoch 44 - iter 162/274 - loss 0.02323457 - samples/sec: 152.82 - lr: 0.100000 2022-11-06 16:38:18,871 epoch 44 - iter 189/274 - loss 0.02333556 - samples/sec: 151.82 - lr: 0.100000 2022-11-06 16:38:24,199 epoch 44 - iter 216/274 - loss 0.02307567 - samples/sec: 162.28 - lr: 0.100000 2022-11-06 16:38:29,696 epoch 44 - iter 243/274 - loss 0.02353973 - samples/sec: 157.27 - lr: 0.100000 2022-11-06 16:38:34,653 epoch 44 - iter 270/274 - loss 0.02287918 - samples/sec: 174.42 - lr: 0.100000 2022-11-06 16:38:35,544 ---------------------------------------------------------------------------------------------------- 2022-11-06 16:38:35,544 EPOCH 44 done: loss 0.0227 - lr 0.100000 2022-11-06 16:39:06,345 Evaluating as a multi-label problem: False 2022-11-06 16:39:06,372 TEST : loss 0.032110828906297684 - f1-score (micro avg) 0.8425 2022-11-06 16:39:06,855 BAD EPOCHS (no improvement): 2 2022-11-06 16:39:07,051 ---------------------------------------------------------------------------------------------------- 2022-11-06 16:39:12,479 epoch 45 - iter 27/274 - loss 0.01719875 - samples/sec: 159.32 - lr: 0.100000 2022-11-06 16:39:18,046 epoch 45 - iter 54/274 - loss 0.01995671 - samples/sec: 155.31 - lr: 0.100000 2022-11-06 16:39:23,387 epoch 45 - iter 81/274 - loss 0.02137920 - samples/sec: 162.79 - lr: 0.100000 2022-11-06 16:39:28,114 epoch 45 - iter 108/274 - loss 0.02053238 - samples/sec: 182.93 - lr: 0.100000 2022-11-06 16:39:33,298 epoch 45 - iter 135/274 - loss 0.02077647 - samples/sec: 166.77 - lr: 0.100000 2022-11-06 16:39:38,974 epoch 45 - iter 162/274 - loss 0.02154126 - samples/sec: 153.46 - lr: 0.100000 2022-11-06 16:39:44,371 epoch 45 - iter 189/274 - loss 0.02142571 - samples/sec: 160.18 - lr: 0.100000 2022-11-06 16:39:49,957 epoch 45 - iter 216/274 - loss 0.02160676 - samples/sec: 154.76 - lr: 0.100000 2022-11-06 16:39:55,283 epoch 45 - iter 243/274 - loss 0.02227279 - samples/sec: 162.34 - lr: 0.100000 2022-11-06 16:40:00,651 epoch 45 - iter 270/274 - loss 0.02248165 - samples/sec: 161.05 - lr: 0.100000 2022-11-06 16:40:01,341 ---------------------------------------------------------------------------------------------------- 2022-11-06 16:40:01,341 EPOCH 45 done: loss 0.0224 - lr 0.100000 2022-11-06 16:40:32,020 Evaluating as a multi-label problem: False 2022-11-06 16:40:32,047 TEST : loss 0.029743408784270287 - f1-score (micro avg) 0.8528 2022-11-06 16:40:32,532 BAD EPOCHS (no improvement): 3 2022-11-06 16:40:32,729 ---------------------------------------------------------------------------------------------------- 2022-11-06 16:40:38,071 epoch 46 - iter 27/274 - loss 0.02369543 - samples/sec: 161.88 - lr: 0.100000 2022-11-06 16:40:43,480 epoch 46 - iter 54/274 - loss 0.02221139 - samples/sec: 159.84 - lr: 0.100000 2022-11-06 16:40:48,659 epoch 46 - iter 81/274 - loss 0.02437997 - samples/sec: 166.94 - lr: 0.100000 2022-11-06 16:40:54,781 epoch 46 - iter 108/274 - loss 0.02406782 - samples/sec: 141.21 - lr: 0.100000 2022-11-06 16:40:59,803 epoch 46 - iter 135/274 - loss 0.02306502 - samples/sec: 172.16 - lr: 0.100000 2022-11-06 16:41:04,939 epoch 46 - iter 162/274 - loss 0.02286038 - samples/sec: 168.35 - lr: 0.100000 2022-11-06 16:41:10,097 epoch 46 - iter 189/274 - loss 0.02297700 - samples/sec: 167.61 - lr: 0.100000 2022-11-06 16:41:15,243 epoch 46 - iter 216/274 - loss 0.02275416 - samples/sec: 168.00 - lr: 0.100000 2022-11-06 16:41:20,925 epoch 46 - iter 243/274 - loss 0.02226171 - samples/sec: 152.15 - lr: 0.100000 2022-11-06 16:41:26,275 epoch 46 - iter 270/274 - loss 0.02215267 - samples/sec: 161.59 - lr: 0.100000 2022-11-06 16:41:27,051 ---------------------------------------------------------------------------------------------------- 2022-11-06 16:41:27,051 EPOCH 46 done: loss 0.0222 - lr 0.100000 2022-11-06 16:41:57,780 Evaluating as a multi-label problem: False 2022-11-06 16:41:57,808 TEST : loss 0.031050119549036026 - f1-score (micro avg) 0.8531 2022-11-06 16:41:58,293 Epoch 46: reducing learning rate of group 0 to 5.0000e-02. 2022-11-06 16:41:58,294 BAD EPOCHS (no improvement): 4 2022-11-06 16:41:58,482 ---------------------------------------------------------------------------------------------------- 2022-11-06 16:42:03,423 epoch 47 - iter 27/274 - loss 0.01892076 - samples/sec: 175.03 - lr: 0.050000 2022-11-06 16:42:08,622 epoch 47 - iter 54/274 - loss 0.01978271 - samples/sec: 167.25 - lr: 0.050000 2022-11-06 16:42:13,721 epoch 47 - iter 81/274 - loss 0.01979376 - samples/sec: 169.57 - lr: 0.050000 2022-11-06 16:42:19,763 epoch 47 - iter 108/274 - loss 0.02010603 - samples/sec: 143.07 - lr: 0.050000 2022-11-06 16:42:24,818 epoch 47 - iter 135/274 - loss 0.01994919 - samples/sec: 171.04 - lr: 0.050000 2022-11-06 16:42:30,821 epoch 47 - iter 162/274 - loss 0.02047061 - samples/sec: 144.02 - lr: 0.050000 2022-11-06 16:42:36,249 epoch 47 - iter 189/274 - loss 0.02002847 - samples/sec: 159.27 - lr: 0.050000 2022-11-06 16:42:41,602 epoch 47 - iter 216/274 - loss 0.02026143 - samples/sec: 161.51 - lr: 0.050000 2022-11-06 16:42:46,675 epoch 47 - iter 243/274 - loss 0.02036030 - samples/sec: 170.43 - lr: 0.050000 2022-11-06 16:42:51,883 epoch 47 - iter 270/274 - loss 0.02037020 - samples/sec: 166.01 - lr: 0.050000 2022-11-06 16:42:52,859 ---------------------------------------------------------------------------------------------------- 2022-11-06 16:42:52,859 EPOCH 47 done: loss 0.0205 - lr 0.050000 2022-11-06 16:43:23,615 Evaluating as a multi-label problem: False 2022-11-06 16:43:23,643 TEST : loss 0.030249422416090965 - f1-score (micro avg) 0.8498 2022-11-06 16:43:24,126 BAD EPOCHS (no improvement): 0 2022-11-06 16:43:24,327 ---------------------------------------------------------------------------------------------------- 2022-11-06 16:43:29,502 epoch 48 - iter 27/274 - loss 0.01915283 - samples/sec: 167.13 - lr: 0.050000 2022-11-06 16:43:35,528 epoch 48 - iter 54/274 - loss 0.02134289 - samples/sec: 143.46 - lr: 0.050000 2022-11-06 16:43:40,669 epoch 48 - iter 81/274 - loss 0.01957015 - samples/sec: 168.18 - lr: 0.050000 2022-11-06 16:43:45,987 epoch 48 - iter 108/274 - loss 0.01905330 - samples/sec: 162.57 - lr: 0.050000 2022-11-06 16:43:51,214 epoch 48 - iter 135/274 - loss 0.02006627 - samples/sec: 165.39 - lr: 0.050000 2022-11-06 16:43:56,242 epoch 48 - iter 162/274 - loss 0.01992972 - samples/sec: 171.97 - lr: 0.050000 2022-11-06 16:44:01,471 epoch 48 - iter 189/274 - loss 0.02013159 - samples/sec: 165.35 - lr: 0.050000 2022-11-06 16:44:06,792 epoch 48 - iter 216/274 - loss 0.02048566 - samples/sec: 162.48 - lr: 0.050000 2022-11-06 16:44:12,734 epoch 48 - iter 243/274 - loss 0.02061460 - samples/sec: 145.48 - lr: 0.050000 2022-11-06 16:44:18,143 epoch 48 - iter 270/274 - loss 0.02060224 - samples/sec: 159.84 - lr: 0.050000 2022-11-06 16:44:19,005 ---------------------------------------------------------------------------------------------------- 2022-11-06 16:44:19,005 EPOCH 48 done: loss 0.0205 - lr 0.050000 2022-11-06 16:44:52,770 Evaluating as a multi-label problem: False 2022-11-06 16:44:52,799 TEST : loss 0.02784981019794941 - f1-score (micro avg) 0.8523 2022-11-06 16:44:53,287 BAD EPOCHS (no improvement): 1 2022-11-06 16:44:53,481 ---------------------------------------------------------------------------------------------------- 2022-11-06 16:45:00,675 epoch 49 - iter 27/274 - loss 0.02057271 - samples/sec: 120.20 - lr: 0.050000 2022-11-06 16:45:07,423 epoch 49 - iter 54/274 - loss 0.01963785 - samples/sec: 128.11 - lr: 0.050000 2022-11-06 16:45:14,804 epoch 49 - iter 81/274 - loss 0.01981443 - samples/sec: 117.12 - lr: 0.050000 2022-11-06 16:45:21,957 epoch 49 - iter 108/274 - loss 0.02005951 - samples/sec: 120.85 - lr: 0.050000 2022-11-06 16:45:28,599 epoch 49 - iter 135/274 - loss 0.01993758 - samples/sec: 130.16 - lr: 0.050000 2022-11-06 16:45:35,392 epoch 49 - iter 162/274 - loss 0.01982053 - samples/sec: 127.26 - lr: 0.050000 2022-11-06 16:45:42,023 epoch 49 - iter 189/274 - loss 0.01972889 - samples/sec: 130.36 - lr: 0.050000 2022-11-06 16:45:48,807 epoch 49 - iter 216/274 - loss 0.01972932 - samples/sec: 127.43 - lr: 0.050000 2022-11-06 16:45:55,936 epoch 49 - iter 243/274 - loss 0.01967441 - samples/sec: 121.26 - lr: 0.050000 2022-11-06 16:46:02,852 epoch 49 - iter 270/274 - loss 0.01925280 - samples/sec: 124.99 - lr: 0.050000 2022-11-06 16:46:03,717 ---------------------------------------------------------------------------------------------------- 2022-11-06 16:46:03,717 EPOCH 49 done: loss 0.0193 - lr 0.050000 2022-11-06 16:46:42,562 Evaluating as a multi-label problem: False 2022-11-06 16:46:42,590 TEST : loss 0.02871915139257908 - f1-score (micro avg) 0.8565 2022-11-06 16:46:43,077 BAD EPOCHS (no improvement): 0 2022-11-06 16:46:43,273 ---------------------------------------------------------------------------------------------------- 2022-11-06 16:46:50,472 epoch 50 - iter 27/274 - loss 0.02187429 - samples/sec: 120.10 - lr: 0.050000 2022-11-06 16:46:57,580 epoch 50 - iter 54/274 - loss 0.01870923 - samples/sec: 121.62 - lr: 0.050000 2022-11-06 16:47:04,617 epoch 50 - iter 81/274 - loss 0.01768404 - samples/sec: 122.85 - lr: 0.050000 2022-11-06 16:47:11,154 epoch 50 - iter 108/274 - loss 0.01850040 - samples/sec: 132.24 - lr: 0.050000 2022-11-06 16:47:18,487 epoch 50 - iter 135/274 - loss 0.01878059 - samples/sec: 117.89 - lr: 0.050000 2022-11-06 16:47:25,359 epoch 50 - iter 162/274 - loss 0.01873312 - samples/sec: 125.82 - lr: 0.050000 2022-11-06 16:47:31,574 epoch 50 - iter 189/274 - loss 0.01869845 - samples/sec: 139.09 - lr: 0.050000 2022-11-06 16:47:38,590 epoch 50 - iter 216/274 - loss 0.01895120 - samples/sec: 123.21 - lr: 0.050000 2022-11-06 16:47:45,500 epoch 50 - iter 243/274 - loss 0.01901111 - samples/sec: 125.12 - lr: 0.050000 2022-11-06 16:47:53,548 epoch 50 - iter 270/274 - loss 0.01925134 - samples/sec: 107.40 - lr: 0.050000 2022-11-06 16:47:54,706 ---------------------------------------------------------------------------------------------------- 2022-11-06 16:47:54,745 EPOCH 50 done: loss 0.0192 - lr 0.050000 2022-11-06 16:48:32,930 Evaluating as a multi-label problem: False 2022-11-06 16:48:32,958 TEST : loss 0.030805133283138275 - f1-score (micro avg) 0.8476 2022-11-06 16:48:33,447 BAD EPOCHS (no improvement): 0 2022-11-06 16:48:33,636 ---------------------------------------------------------------------------------------------------- 2022-11-06 16:48:41,416 epoch 51 - iter 27/274 - loss 0.01635629 - samples/sec: 111.12 - lr: 0.050000 2022-11-06 16:48:48,240 epoch 51 - iter 54/274 - loss 0.01715334 - samples/sec: 126.69 - lr: 0.050000 2022-11-06 16:48:55,420 epoch 51 - iter 81/274 - loss 0.01838564 - samples/sec: 120.39 - lr: 0.050000 2022-11-06 16:49:02,458 epoch 51 - iter 108/274 - loss 0.01897771 - samples/sec: 122.83 - lr: 0.050000 2022-11-06 16:49:10,138 epoch 51 - iter 135/274 - loss 0.01911294 - samples/sec: 112.56 - lr: 0.050000 2022-11-06 16:49:16,929 epoch 51 - iter 162/274 - loss 0.01912749 - samples/sec: 127.32 - lr: 0.050000 2022-11-06 16:49:23,284 epoch 51 - iter 189/274 - loss 0.01944390 - samples/sec: 136.03 - lr: 0.050000 2022-11-06 16:49:29,785 epoch 51 - iter 216/274 - loss 0.01905674 - samples/sec: 132.98 - lr: 0.050000 2022-11-06 16:49:37,140 epoch 51 - iter 243/274 - loss 0.01939322 - samples/sec: 117.53 - lr: 0.050000 2022-11-06 16:49:43,873 epoch 51 - iter 270/274 - loss 0.01960299 - samples/sec: 128.41 - lr: 0.050000 2022-11-06 16:49:44,878 ---------------------------------------------------------------------------------------------------- 2022-11-06 16:49:44,878 EPOCH 51 done: loss 0.0196 - lr 0.050000 2022-11-06 16:50:23,953 Evaluating as a multi-label problem: False 2022-11-06 16:50:23,981 TEST : loss 0.031178493052721024 - f1-score (micro avg) 0.8492 2022-11-06 16:50:24,469 BAD EPOCHS (no improvement): 1 2022-11-06 16:50:24,667 ---------------------------------------------------------------------------------------------------- 2022-11-06 16:50:31,725 epoch 52 - iter 27/274 - loss 0.02086634 - samples/sec: 122.50 - lr: 0.050000 2022-11-06 16:50:38,930 epoch 52 - iter 54/274 - loss 0.02097455 - samples/sec: 119.98 - lr: 0.050000 2022-11-06 16:50:46,280 epoch 52 - iter 81/274 - loss 0.02027621 - samples/sec: 117.61 - lr: 0.050000 2022-11-06 16:50:52,907 epoch 52 - iter 108/274 - loss 0.01916971 - samples/sec: 130.46 - lr: 0.050000 2022-11-06 16:50:59,965 epoch 52 - iter 135/274 - loss 0.01915529 - samples/sec: 122.48 - lr: 0.050000 2022-11-06 16:51:06,932 epoch 52 - iter 162/274 - loss 0.01916447 - samples/sec: 124.09 - lr: 0.050000 2022-11-06 16:51:13,250 epoch 52 - iter 189/274 - loss 0.01843177 - samples/sec: 136.83 - lr: 0.050000 2022-11-06 16:51:19,958 epoch 52 - iter 216/274 - loss 0.01875700 - samples/sec: 128.86 - lr: 0.050000 2022-11-06 16:51:26,860 epoch 52 - iter 243/274 - loss 0.01895607 - samples/sec: 125.26 - lr: 0.050000 2022-11-06 16:51:34,160 epoch 52 - iter 270/274 - loss 0.01870382 - samples/sec: 118.42 - lr: 0.050000 2022-11-06 16:51:35,184 ---------------------------------------------------------------------------------------------------- 2022-11-06 16:51:35,185 EPOCH 52 done: loss 0.0187 - lr 0.050000 2022-11-06 16:52:14,519 Evaluating as a multi-label problem: False 2022-11-06 16:52:14,547 TEST : loss 0.030038980767130852 - f1-score (micro avg) 0.8595 2022-11-06 16:52:15,033 BAD EPOCHS (no improvement): 0 2022-11-06 16:52:15,228 ---------------------------------------------------------------------------------------------------- 2022-11-06 16:52:22,463 epoch 53 - iter 27/274 - loss 0.02001975 - samples/sec: 119.50 - lr: 0.050000 2022-11-06 16:52:29,132 epoch 53 - iter 54/274 - loss 0.01873727 - samples/sec: 129.64 - lr: 0.050000 2022-11-06 16:52:36,745 epoch 53 - iter 81/274 - loss 0.01982174 - samples/sec: 113.54 - lr: 0.050000 2022-11-06 16:52:43,905 epoch 53 - iter 108/274 - loss 0.01954032 - samples/sec: 120.75 - lr: 0.050000 2022-11-06 16:52:51,637 epoch 53 - iter 135/274 - loss 0.01948013 - samples/sec: 111.80 - lr: 0.050000 2022-11-06 16:52:58,111 epoch 53 - iter 162/274 - loss 0.01940424 - samples/sec: 133.52 - lr: 0.050000 2022-11-06 16:53:04,264 epoch 53 - iter 189/274 - loss 0.01905603 - samples/sec: 140.50 - lr: 0.050000 2022-11-06 16:53:11,124 epoch 53 - iter 216/274 - loss 0.01898204 - samples/sec: 126.03 - lr: 0.050000 2022-11-06 16:53:17,767 epoch 53 - iter 243/274 - loss 0.01851959 - samples/sec: 130.14 - lr: 0.050000 2022-11-06 16:53:24,552 epoch 53 - iter 270/274 - loss 0.01851000 - samples/sec: 127.40 - lr: 0.050000 2022-11-06 16:53:25,571 ---------------------------------------------------------------------------------------------------- 2022-11-06 16:53:25,571 EPOCH 53 done: loss 0.0185 - lr 0.050000 2022-11-06 16:54:04,506 Evaluating as a multi-label problem: False 2022-11-06 16:54:04,534 TEST : loss 0.030869534239172935 - f1-score (micro avg) 0.8556 2022-11-06 16:54:05,021 BAD EPOCHS (no improvement): 0 2022-11-06 16:54:05,217 ---------------------------------------------------------------------------------------------------- 2022-11-06 16:54:12,308 epoch 54 - iter 27/274 - loss 0.02156169 - samples/sec: 121.94 - lr: 0.050000 2022-11-06 16:54:19,110 epoch 54 - iter 54/274 - loss 0.01951305 - samples/sec: 127.10 - lr: 0.050000 2022-11-06 16:54:25,747 epoch 54 - iter 81/274 - loss 0.01873362 - samples/sec: 130.26 - lr: 0.050000 2022-11-06 16:54:32,645 epoch 54 - iter 108/274 - loss 0.01751394 - samples/sec: 125.32 - lr: 0.050000 2022-11-06 16:54:40,282 epoch 54 - iter 135/274 - loss 0.01918168 - samples/sec: 113.19 - lr: 0.050000 2022-11-06 16:54:47,327 epoch 54 - iter 162/274 - loss 0.01825074 - samples/sec: 122.71 - lr: 0.050000 2022-11-06 16:54:54,029 epoch 54 - iter 189/274 - loss 0.01803231 - samples/sec: 128.98 - lr: 0.050000 2022-11-06 16:55:00,074 epoch 54 - iter 216/274 - loss 0.01763326 - samples/sec: 143.01 - lr: 0.050000 2022-11-06 16:55:07,459 epoch 54 - iter 243/274 - loss 0.01758116 - samples/sec: 117.06 - lr: 0.050000 2022-11-06 16:55:15,114 epoch 54 - iter 270/274 - loss 0.01767042 - samples/sec: 112.93 - lr: 0.050000 2022-11-06 16:55:15,998 ---------------------------------------------------------------------------------------------------- 2022-11-06 16:55:15,998 EPOCH 54 done: loss 0.0177 - lr 0.050000 2022-11-06 16:55:54,780 Evaluating as a multi-label problem: False 2022-11-06 16:55:54,808 TEST : loss 0.03118710406124592 - f1-score (micro avg) 0.8575 2022-11-06 16:55:55,295 BAD EPOCHS (no improvement): 0 2022-11-06 16:55:55,484 ---------------------------------------------------------------------------------------------------- 2022-11-06 16:56:02,871 epoch 55 - iter 27/274 - loss 0.01843406 - samples/sec: 117.04 - lr: 0.050000 2022-11-06 16:56:10,033 epoch 55 - iter 54/274 - loss 0.01830914 - samples/sec: 120.71 - lr: 0.050000 2022-11-06 16:56:18,425 epoch 55 - iter 81/274 - loss 0.01847121 - samples/sec: 103.00 - lr: 0.050000 2022-11-06 16:56:25,197 epoch 55 - iter 108/274 - loss 0.01759215 - samples/sec: 127.64 - lr: 0.050000 2022-11-06 16:56:31,530 epoch 55 - iter 135/274 - loss 0.01771944 - samples/sec: 136.52 - lr: 0.050000 2022-11-06 16:56:38,297 epoch 55 - iter 162/274 - loss 0.01767717 - samples/sec: 127.75 - lr: 0.050000 2022-11-06 16:56:45,238 epoch 55 - iter 189/274 - loss 0.01786543 - samples/sec: 124.56 - lr: 0.050000 2022-11-06 16:56:51,149 epoch 55 - iter 216/274 - loss 0.01767731 - samples/sec: 146.25 - lr: 0.050000 2022-11-06 16:56:58,239 epoch 55 - iter 243/274 - loss 0.01771460 - samples/sec: 121.93 - lr: 0.050000 2022-11-06 16:57:04,822 epoch 55 - iter 270/274 - loss 0.01783817 - samples/sec: 131.33 - lr: 0.050000 2022-11-06 16:57:05,650 ---------------------------------------------------------------------------------------------------- 2022-11-06 16:57:05,650 EPOCH 55 done: loss 0.0179 - lr 0.050000 2022-11-06 16:57:44,711 Evaluating as a multi-label problem: False 2022-11-06 16:57:44,738 TEST : loss 0.030333157628774643 - f1-score (micro avg) 0.8606 2022-11-06 16:57:45,224 BAD EPOCHS (no improvement): 1 2022-11-06 16:57:45,411 ---------------------------------------------------------------------------------------------------- 2022-11-06 16:57:52,671 epoch 56 - iter 27/274 - loss 0.01969216 - samples/sec: 119.10 - lr: 0.050000 2022-11-06 16:58:00,247 epoch 56 - iter 54/274 - loss 0.01920118 - samples/sec: 114.10 - lr: 0.050000 2022-11-06 16:58:07,062 epoch 56 - iter 81/274 - loss 0.01826768 - samples/sec: 126.85 - lr: 0.050000 2022-11-06 16:58:14,006 epoch 56 - iter 108/274 - loss 0.01777826 - samples/sec: 124.50 - lr: 0.050000 2022-11-06 16:58:20,686 epoch 56 - iter 135/274 - loss 0.01813031 - samples/sec: 129.42 - lr: 0.050000 2022-11-06 16:58:28,336 epoch 56 - iter 162/274 - loss 0.01864515 - samples/sec: 112.99 - lr: 0.050000 2022-11-06 16:58:35,159 epoch 56 - iter 189/274 - loss 0.01804029 - samples/sec: 126.71 - lr: 0.050000 2022-11-06 16:58:41,717 epoch 56 - iter 216/274 - loss 0.01825324 - samples/sec: 131.83 - lr: 0.050000 2022-11-06 16:58:48,231 epoch 56 - iter 243/274 - loss 0.01812045 - samples/sec: 132.72 - lr: 0.050000 2022-11-06 16:58:55,350 epoch 56 - iter 270/274 - loss 0.01820080 - samples/sec: 121.42 - lr: 0.050000 2022-11-06 16:58:56,294 ---------------------------------------------------------------------------------------------------- 2022-11-06 16:58:56,294 EPOCH 56 done: loss 0.0183 - lr 0.050000 2022-11-06 16:59:35,596 Evaluating as a multi-label problem: False 2022-11-06 16:59:35,624 TEST : loss 0.03127044439315796 - f1-score (micro avg) 0.8594 2022-11-06 16:59:36,111 BAD EPOCHS (no improvement): 2 2022-11-06 16:59:36,304 ---------------------------------------------------------------------------------------------------- 2022-11-06 16:59:43,178 epoch 57 - iter 27/274 - loss 0.01759224 - samples/sec: 125.79 - lr: 0.050000 2022-11-06 16:59:50,221 epoch 57 - iter 54/274 - loss 0.01831683 - samples/sec: 122.73 - lr: 0.050000 2022-11-06 16:59:57,176 epoch 57 - iter 81/274 - loss 0.01822114 - samples/sec: 124.31 - lr: 0.050000 2022-11-06 17:00:03,647 epoch 57 - iter 108/274 - loss 0.01776786 - samples/sec: 133.59 - lr: 0.050000 2022-11-06 17:00:10,512 epoch 57 - iter 135/274 - loss 0.01778582 - samples/sec: 125.93 - lr: 0.050000 2022-11-06 17:00:17,434 epoch 57 - iter 162/274 - loss 0.01794400 - samples/sec: 124.89 - lr: 0.050000 2022-11-06 17:00:24,282 epoch 57 - iter 189/274 - loss 0.01757674 - samples/sec: 126.23 - lr: 0.050000 2022-11-06 17:00:31,157 epoch 57 - iter 216/274 - loss 0.01770841 - samples/sec: 125.74 - lr: 0.050000 2022-11-06 17:00:38,097 epoch 57 - iter 243/274 - loss 0.01776382 - samples/sec: 124.56 - lr: 0.050000 2022-11-06 17:00:45,622 epoch 57 - iter 270/274 - loss 0.01768437 - samples/sec: 114.87 - lr: 0.050000 2022-11-06 17:00:46,706 ---------------------------------------------------------------------------------------------------- 2022-11-06 17:00:46,706 EPOCH 57 done: loss 0.0177 - lr 0.050000 2022-11-06 17:01:26,666 Evaluating as a multi-label problem: False 2022-11-06 17:01:26,693 TEST : loss 0.031970299780368805 - f1-score (micro avg) 0.8588 2022-11-06 17:01:27,182 BAD EPOCHS (no improvement): 3 2022-11-06 17:01:27,378 ---------------------------------------------------------------------------------------------------- 2022-11-06 17:01:33,736 epoch 58 - iter 27/274 - loss 0.01737039 - samples/sec: 135.99 - lr: 0.050000 2022-11-06 17:01:40,671 epoch 58 - iter 54/274 - loss 0.01772992 - samples/sec: 124.67 - lr: 0.050000 2022-11-06 17:01:47,695 epoch 58 - iter 81/274 - loss 0.01785145 - samples/sec: 123.07 - lr: 0.050000 2022-11-06 17:01:55,129 epoch 58 - iter 108/274 - loss 0.01861589 - samples/sec: 116.28 - lr: 0.050000 2022-11-06 17:02:01,544 epoch 58 - iter 135/274 - loss 0.01830026 - samples/sec: 134.76 - lr: 0.050000 2022-11-06 17:02:08,971 epoch 58 - iter 162/274 - loss 0.01786728 - samples/sec: 116.39 - lr: 0.050000 2022-11-06 17:02:15,728 epoch 58 - iter 189/274 - loss 0.01828590 - samples/sec: 127.95 - lr: 0.050000 2022-11-06 17:02:22,664 epoch 58 - iter 216/274 - loss 0.01830113 - samples/sec: 124.63 - lr: 0.050000 2022-11-06 17:02:29,336 epoch 58 - iter 243/274 - loss 0.01844889 - samples/sec: 129.56 - lr: 0.050000 2022-11-06 17:02:36,319 epoch 58 - iter 270/274 - loss 0.01828605 - samples/sec: 123.80 - lr: 0.050000 2022-11-06 17:02:37,197 ---------------------------------------------------------------------------------------------------- 2022-11-06 17:02:37,197 EPOCH 58 done: loss 0.0183 - lr 0.050000 2022-11-06 17:03:16,952 Evaluating as a multi-label problem: False 2022-11-06 17:03:16,980 TEST : loss 0.02933284267783165 - f1-score (micro avg) 0.8563 2022-11-06 17:03:17,467 Epoch 58: reducing learning rate of group 0 to 2.5000e-02. 2022-11-06 17:03:17,468 BAD EPOCHS (no improvement): 4 2022-11-06 17:03:17,664 ---------------------------------------------------------------------------------------------------- 2022-11-06 17:03:24,040 epoch 59 - iter 27/274 - loss 0.01818196 - samples/sec: 135.63 - lr: 0.025000 2022-11-06 17:03:31,272 epoch 59 - iter 54/274 - loss 0.01765334 - samples/sec: 119.52 - lr: 0.025000 2022-11-06 17:03:38,607 epoch 59 - iter 81/274 - loss 0.01793576 - samples/sec: 117.85 - lr: 0.025000 2022-11-06 17:03:45,016 epoch 59 - iter 108/274 - loss 0.01732424 - samples/sec: 134.90 - lr: 0.025000 2022-11-06 17:03:53,030 epoch 59 - iter 135/274 - loss 0.01767106 - samples/sec: 107.86 - lr: 0.025000 2022-11-06 17:03:59,921 epoch 59 - iter 162/274 - loss 0.01762930 - samples/sec: 125.46 - lr: 0.025000 2022-11-06 17:04:07,255 epoch 59 - iter 189/274 - loss 0.01766409 - samples/sec: 117.87 - lr: 0.025000 2022-11-06 17:04:14,069 epoch 59 - iter 216/274 - loss 0.01732257 - samples/sec: 126.87 - lr: 0.025000 2022-11-06 17:04:20,168 epoch 59 - iter 243/274 - loss 0.01738389 - samples/sec: 141.74 - lr: 0.025000 2022-11-06 17:04:26,636 epoch 59 - iter 270/274 - loss 0.01721085 - samples/sec: 133.66 - lr: 0.025000 2022-11-06 17:04:27,652 ---------------------------------------------------------------------------------------------------- 2022-11-06 17:04:27,652 EPOCH 59 done: loss 0.0171 - lr 0.025000 2022-11-06 17:05:07,346 Evaluating as a multi-label problem: False 2022-11-06 17:05:07,375 TEST : loss 0.03182924538850784 - f1-score (micro avg) 0.858 2022-11-06 17:05:07,866 BAD EPOCHS (no improvement): 0 2022-11-06 17:05:08,054 ---------------------------------------------------------------------------------------------------- 2022-11-06 17:05:14,706 epoch 60 - iter 27/274 - loss 0.01562910 - samples/sec: 129.98 - lr: 0.025000 2022-11-06 17:05:21,550 epoch 60 - iter 54/274 - loss 0.01530772 - samples/sec: 126.32 - lr: 0.025000 2022-11-06 17:05:28,999 epoch 60 - iter 81/274 - loss 0.01620627 - samples/sec: 116.05 - lr: 0.025000 2022-11-06 17:05:35,459 epoch 60 - iter 108/274 - loss 0.01640443 - samples/sec: 133.82 - lr: 0.025000 2022-11-06 17:05:41,994 epoch 60 - iter 135/274 - loss 0.01620174 - samples/sec: 132.29 - lr: 0.025000 2022-11-06 17:05:49,220 epoch 60 - iter 162/274 - loss 0.01680609 - samples/sec: 119.64 - lr: 0.025000 2022-11-06 17:05:56,388 epoch 60 - iter 189/274 - loss 0.01681290 - samples/sec: 120.60 - lr: 0.025000 2022-11-06 17:06:03,844 epoch 60 - iter 216/274 - loss 0.01694250 - samples/sec: 115.93 - lr: 0.025000 2022-11-06 17:06:10,258 epoch 60 - iter 243/274 - loss 0.01734382 - samples/sec: 134.79 - lr: 0.025000 2022-11-06 17:06:17,291 epoch 60 - iter 270/274 - loss 0.01732942 - samples/sec: 122.92 - lr: 0.025000 2022-11-06 17:06:18,215 ---------------------------------------------------------------------------------------------------- 2022-11-06 17:06:18,216 EPOCH 60 done: loss 0.0173 - lr 0.025000 2022-11-06 17:06:57,991 Evaluating as a multi-label problem: False 2022-11-06 17:06:58,019 TEST : loss 0.031502872705459595 - f1-score (micro avg) 0.86 2022-11-06 17:06:58,504 BAD EPOCHS (no improvement): 1 2022-11-06 17:06:58,701 ---------------------------------------------------------------------------------------------------- 2022-11-06 17:07:04,874 epoch 61 - iter 27/274 - loss 0.01498040 - samples/sec: 140.09 - lr: 0.025000 2022-11-06 17:07:11,705 epoch 61 - iter 54/274 - loss 0.01508750 - samples/sec: 126.57 - lr: 0.025000 2022-11-06 17:07:18,212 epoch 61 - iter 81/274 - loss 0.01524963 - samples/sec: 132.85 - lr: 0.025000 2022-11-06 17:07:25,088 epoch 61 - iter 108/274 - loss 0.01586900 - samples/sec: 125.73 - lr: 0.025000 2022-11-06 17:07:32,052 epoch 61 - iter 135/274 - loss 0.01620896 - samples/sec: 124.13 - lr: 0.025000 2022-11-06 17:07:39,599 epoch 61 - iter 162/274 - loss 0.01677026 - samples/sec: 114.54 - lr: 0.025000 2022-11-06 17:07:46,222 epoch 61 - iter 189/274 - loss 0.01660999 - samples/sec: 130.53 - lr: 0.025000 2022-11-06 17:07:52,777 epoch 61 - iter 216/274 - loss 0.01654429 - samples/sec: 131.88 - lr: 0.025000 2022-11-06 17:08:00,282 epoch 61 - iter 243/274 - loss 0.01666333 - samples/sec: 115.18 - lr: 0.025000 2022-11-06 17:08:07,088 epoch 61 - iter 270/274 - loss 0.01645526 - samples/sec: 127.02 - lr: 0.025000 2022-11-06 17:08:08,051 ---------------------------------------------------------------------------------------------------- 2022-11-06 17:08:08,051 EPOCH 61 done: loss 0.0166 - lr 0.025000 2022-11-06 17:08:47,894 Evaluating as a multi-label problem: False 2022-11-06 17:08:47,922 TEST : loss 0.031147386878728867 - f1-score (micro avg) 0.8573 2022-11-06 17:08:48,411 BAD EPOCHS (no improvement): 0 2022-11-06 17:08:48,607 ---------------------------------------------------------------------------------------------------- 2022-11-06 17:08:55,454 epoch 62 - iter 27/274 - loss 0.01467951 - samples/sec: 126.29 - lr: 0.025000 2022-11-06 17:09:02,235 epoch 62 - iter 54/274 - loss 0.01492071 - samples/sec: 127.49 - lr: 0.025000 2022-11-06 17:09:08,779 epoch 62 - iter 81/274 - loss 0.01568738 - samples/sec: 132.10 - lr: 0.025000 2022-11-06 17:09:16,421 epoch 62 - iter 108/274 - loss 0.01595573 - samples/sec: 113.13 - lr: 0.025000 2022-11-06 17:09:23,303 epoch 62 - iter 135/274 - loss 0.01617601 - samples/sec: 125.60 - lr: 0.025000 2022-11-06 17:09:30,144 epoch 62 - iter 162/274 - loss 0.01631464 - samples/sec: 126.37 - lr: 0.025000 2022-11-06 17:09:37,135 epoch 62 - iter 189/274 - loss 0.01633934 - samples/sec: 123.66 - lr: 0.025000 2022-11-06 17:09:44,147 epoch 62 - iter 216/274 - loss 0.01648327 - samples/sec: 123.29 - lr: 0.025000 2022-11-06 17:09:51,297 epoch 62 - iter 243/274 - loss 0.01677063 - samples/sec: 120.90 - lr: 0.025000 2022-11-06 17:09:57,820 epoch 62 - iter 270/274 - loss 0.01677192 - samples/sec: 132.54 - lr: 0.025000 2022-11-06 17:09:58,743 ---------------------------------------------------------------------------------------------------- 2022-11-06 17:09:58,743 EPOCH 62 done: loss 0.0167 - lr 0.025000 2022-11-06 17:10:38,260 Evaluating as a multi-label problem: False 2022-11-06 17:10:38,303 TEST : loss 0.032001152634620667 - f1-score (micro avg) 0.8587 2022-11-06 17:10:38,939 BAD EPOCHS (no improvement): 1 2022-11-06 17:10:39,136 ---------------------------------------------------------------------------------------------------- 2022-11-06 17:10:46,062 epoch 63 - iter 27/274 - loss 0.01425313 - samples/sec: 124.85 - lr: 0.025000 2022-11-06 17:10:52,800 epoch 63 - iter 54/274 - loss 0.01486578 - samples/sec: 128.30 - lr: 0.025000 2022-11-06 17:10:59,977 epoch 63 - iter 81/274 - loss 0.01577871 - samples/sec: 120.45 - lr: 0.025000 2022-11-06 17:11:06,953 epoch 63 - iter 108/274 - loss 0.01584291 - samples/sec: 123.92 - lr: 0.025000 2022-11-06 17:11:14,138 epoch 63 - iter 135/274 - loss 0.01565203 - samples/sec: 120.31 - lr: 0.025000 2022-11-06 17:11:20,740 epoch 63 - iter 162/274 - loss 0.01523300 - samples/sec: 130.94 - lr: 0.025000 2022-11-06 17:11:27,758 epoch 63 - iter 189/274 - loss 0.01552535 - samples/sec: 123.19 - lr: 0.025000 2022-11-06 17:11:34,183 epoch 63 - iter 216/274 - loss 0.01563264 - samples/sec: 134.56 - lr: 0.025000 2022-11-06 17:11:41,646 epoch 63 - iter 243/274 - loss 0.01562896 - samples/sec: 115.83 - lr: 0.025000 2022-11-06 17:11:48,846 epoch 63 - iter 270/274 - loss 0.01611587 - samples/sec: 120.06 - lr: 0.025000 2022-11-06 17:11:49,841 ---------------------------------------------------------------------------------------------------- 2022-11-06 17:11:49,842 EPOCH 63 done: loss 0.0161 - lr 0.025000 2022-11-06 17:12:29,084 Evaluating as a multi-label problem: False 2022-11-06 17:12:29,113 TEST : loss 0.03202659264206886 - f1-score (micro avg) 0.8576 2022-11-06 17:12:29,601 BAD EPOCHS (no improvement): 0 2022-11-06 17:12:29,789 ---------------------------------------------------------------------------------------------------- 2022-11-06 17:12:36,449 epoch 64 - iter 27/274 - loss 0.01600093 - samples/sec: 129.83 - lr: 0.025000 2022-11-06 17:12:43,481 epoch 64 - iter 54/274 - loss 0.01676961 - samples/sec: 122.93 - lr: 0.025000 2022-11-06 17:12:49,774 epoch 64 - iter 81/274 - loss 0.01718148 - samples/sec: 137.38 - lr: 0.025000 2022-11-06 17:12:57,114 epoch 64 - iter 108/274 - loss 0.01580944 - samples/sec: 117.76 - lr: 0.025000 2022-11-06 17:13:03,836 epoch 64 - iter 135/274 - loss 0.01588193 - samples/sec: 128.61 - lr: 0.025000 2022-11-06 17:13:10,855 epoch 64 - iter 162/274 - loss 0.01624047 - samples/sec: 123.16 - lr: 0.025000 2022-11-06 17:13:18,180 epoch 64 - iter 189/274 - loss 0.01663038 - samples/sec: 118.03 - lr: 0.025000 2022-11-06 17:13:25,645 epoch 64 - iter 216/274 - loss 0.01655460 - samples/sec: 115.80 - lr: 0.025000 2022-11-06 17:13:32,938 epoch 64 - iter 243/274 - loss 0.01653993 - samples/sec: 118.53 - lr: 0.025000 2022-11-06 17:13:39,729 epoch 64 - iter 270/274 - loss 0.01689917 - samples/sec: 127.29 - lr: 0.025000 2022-11-06 17:13:40,544 ---------------------------------------------------------------------------------------------------- 2022-11-06 17:13:40,544 EPOCH 64 done: loss 0.0169 - lr 0.025000 2022-11-06 17:14:19,638 Evaluating as a multi-label problem: False 2022-11-06 17:14:19,666 TEST : loss 0.03130580484867096 - f1-score (micro avg) 0.8584 2022-11-06 17:14:20,149 BAD EPOCHS (no improvement): 1 2022-11-06 17:14:20,344 ---------------------------------------------------------------------------------------------------- 2022-11-06 17:14:27,554 epoch 65 - iter 27/274 - loss 0.01873267 - samples/sec: 119.93 - lr: 0.025000 2022-11-06 17:14:34,187 epoch 65 - iter 54/274 - loss 0.01833620 - samples/sec: 130.34 - lr: 0.025000 2022-11-06 17:14:40,400 epoch 65 - iter 81/274 - loss 0.01832764 - samples/sec: 139.13 - lr: 0.025000 2022-11-06 17:14:47,258 epoch 65 - iter 108/274 - loss 0.01858252 - samples/sec: 126.06 - lr: 0.025000 2022-11-06 17:14:54,330 epoch 65 - iter 135/274 - loss 0.01790253 - samples/sec: 122.24 - lr: 0.025000 2022-11-06 17:15:01,105 epoch 65 - iter 162/274 - loss 0.01770917 - samples/sec: 127.59 - lr: 0.025000 2022-11-06 17:15:08,234 epoch 65 - iter 189/274 - loss 0.01781079 - samples/sec: 121.27 - lr: 0.025000 2022-11-06 17:15:15,690 epoch 65 - iter 216/274 - loss 0.01750733 - samples/sec: 115.94 - lr: 0.025000 2022-11-06 17:15:22,320 epoch 65 - iter 243/274 - loss 0.01743766 - samples/sec: 130.39 - lr: 0.025000 2022-11-06 17:15:30,071 epoch 65 - iter 270/274 - loss 0.01746325 - samples/sec: 111.52 - lr: 0.025000 2022-11-06 17:15:30,885 ---------------------------------------------------------------------------------------------------- 2022-11-06 17:15:30,885 EPOCH 65 done: loss 0.0175 - lr 0.025000 2022-11-06 17:16:09,793 Evaluating as a multi-label problem: False 2022-11-06 17:16:09,821 TEST : loss 0.030667604878544807 - f1-score (micro avg) 0.8576 2022-11-06 17:16:10,310 BAD EPOCHS (no improvement): 2 2022-11-06 17:16:10,504 ---------------------------------------------------------------------------------------------------- 2022-11-06 17:16:18,613 epoch 66 - iter 27/274 - loss 0.01518631 - samples/sec: 106.62 - lr: 0.025000 2022-11-06 17:16:25,822 epoch 66 - iter 54/274 - loss 0.01578202 - samples/sec: 119.92 - lr: 0.025000 2022-11-06 17:16:31,752 epoch 66 - iter 81/274 - loss 0.01571391 - samples/sec: 145.79 - lr: 0.025000 2022-11-06 17:16:38,595 epoch 66 - iter 108/274 - loss 0.01600228 - samples/sec: 126.32 - lr: 0.025000 2022-11-06 17:16:45,605 epoch 66 - iter 135/274 - loss 0.01599430 - samples/sec: 123.34 - lr: 0.025000 2022-11-06 17:16:52,846 epoch 66 - iter 162/274 - loss 0.01604994 - samples/sec: 119.38 - lr: 0.025000 2022-11-06 17:16:59,480 epoch 66 - iter 189/274 - loss 0.01629519 - samples/sec: 130.31 - lr: 0.025000 2022-11-06 17:17:06,200 epoch 66 - iter 216/274 - loss 0.01668978 - samples/sec: 128.65 - lr: 0.025000 2022-11-06 17:17:13,103 epoch 66 - iter 243/274 - loss 0.01648213 - samples/sec: 125.23 - lr: 0.025000 2022-11-06 17:17:20,288 epoch 66 - iter 270/274 - loss 0.01658877 - samples/sec: 120.32 - lr: 0.025000 2022-11-06 17:17:21,380 ---------------------------------------------------------------------------------------------------- 2022-11-06 17:17:21,381 EPOCH 66 done: loss 0.0166 - lr 0.025000 2022-11-06 17:18:00,429 Evaluating as a multi-label problem: False 2022-11-06 17:18:00,456 TEST : loss 0.03189327195286751 - f1-score (micro avg) 0.8558 2022-11-06 17:18:00,944 BAD EPOCHS (no improvement): 3 2022-11-06 17:18:01,140 ---------------------------------------------------------------------------------------------------- 2022-11-06 17:18:08,017 epoch 67 - iter 27/274 - loss 0.01614744 - samples/sec: 125.74 - lr: 0.025000 2022-11-06 17:18:15,055 epoch 67 - iter 54/274 - loss 0.01577274 - samples/sec: 122.83 - lr: 0.025000 2022-11-06 17:18:21,379 epoch 67 - iter 81/274 - loss 0.01610790 - samples/sec: 136.70 - lr: 0.025000 2022-11-06 17:18:27,920 epoch 67 - iter 108/274 - loss 0.01570924 - samples/sec: 132.17 - lr: 0.025000 2022-11-06 17:18:34,952 epoch 67 - iter 135/274 - loss 0.01587380 - samples/sec: 122.93 - lr: 0.025000 2022-11-06 17:18:42,233 epoch 67 - iter 162/274 - loss 0.01534622 - samples/sec: 118.71 - lr: 0.025000 2022-11-06 17:18:49,773 epoch 67 - iter 189/274 - loss 0.01565263 - samples/sec: 114.66 - lr: 0.025000 2022-11-06 17:18:57,125 epoch 67 - iter 216/274 - loss 0.01557078 - samples/sec: 117.57 - lr: 0.025000 2022-11-06 17:19:03,785 epoch 67 - iter 243/274 - loss 0.01597505 - samples/sec: 129.81 - lr: 0.025000 2022-11-06 17:19:10,487 epoch 67 - iter 270/274 - loss 0.01590830 - samples/sec: 128.99 - lr: 0.025000 2022-11-06 17:19:11,488 ---------------------------------------------------------------------------------------------------- 2022-11-06 17:19:11,488 EPOCH 67 done: loss 0.0160 - lr 0.025000 2022-11-06 17:19:50,222 Evaluating as a multi-label problem: False 2022-11-06 17:19:50,250 TEST : loss 0.03241714462637901 - f1-score (micro avg) 0.8569 2022-11-06 17:19:50,737 BAD EPOCHS (no improvement): 0 2022-11-06 17:19:50,925 ---------------------------------------------------------------------------------------------------- 2022-11-06 17:19:57,653 epoch 68 - iter 27/274 - loss 0.01719008 - samples/sec: 128.50 - lr: 0.025000 2022-11-06 17:20:04,480 epoch 68 - iter 54/274 - loss 0.01538560 - samples/sec: 126.63 - lr: 0.025000 2022-11-06 17:20:11,515 epoch 68 - iter 81/274 - loss 0.01561873 - samples/sec: 122.88 - lr: 0.025000 2022-11-06 17:20:17,393 epoch 68 - iter 108/274 - loss 0.01605447 - samples/sec: 147.10 - lr: 0.025000 2022-11-06 17:20:24,861 epoch 68 - iter 135/274 - loss 0.01583889 - samples/sec: 115.75 - lr: 0.025000 2022-11-06 17:20:31,619 epoch 68 - iter 162/274 - loss 0.01641121 - samples/sec: 127.91 - lr: 0.025000 2022-11-06 17:20:38,343 epoch 68 - iter 189/274 - loss 0.01606534 - samples/sec: 128.57 - lr: 0.025000 2022-11-06 17:20:45,659 epoch 68 - iter 216/274 - loss 0.01589503 - samples/sec: 118.17 - lr: 0.025000 2022-11-06 17:20:52,885 epoch 68 - iter 243/274 - loss 0.01615565 - samples/sec: 119.62 - lr: 0.025000 2022-11-06 17:20:59,981 epoch 68 - iter 270/274 - loss 0.01610109 - samples/sec: 121.83 - lr: 0.025000 2022-11-06 17:21:00,947 ---------------------------------------------------------------------------------------------------- 2022-11-06 17:21:00,948 EPOCH 68 done: loss 0.0160 - lr 0.025000 2022-11-06 17:21:39,842 Evaluating as a multi-label problem: False 2022-11-06 17:21:39,870 TEST : loss 0.032622434198856354 - f1-score (micro avg) 0.8575 2022-11-06 17:21:40,356 BAD EPOCHS (no improvement): 1 2022-11-06 17:21:40,552 ---------------------------------------------------------------------------------------------------- 2022-11-06 17:21:47,697 epoch 69 - iter 27/274 - loss 0.01387498 - samples/sec: 121.01 - lr: 0.025000 2022-11-06 17:21:54,467 epoch 69 - iter 54/274 - loss 0.01550035 - samples/sec: 127.69 - lr: 0.025000 2022-11-06 17:22:01,699 epoch 69 - iter 81/274 - loss 0.01560574 - samples/sec: 119.54 - lr: 0.025000 2022-11-06 17:22:08,192 epoch 69 - iter 108/274 - loss 0.01570065 - samples/sec: 133.15 - lr: 0.025000 2022-11-06 17:22:14,797 epoch 69 - iter 135/274 - loss 0.01507302 - samples/sec: 130.89 - lr: 0.025000 2022-11-06 17:22:21,810 epoch 69 - iter 162/274 - loss 0.01587846 - samples/sec: 123.26 - lr: 0.025000 2022-11-06 17:22:28,404 epoch 69 - iter 189/274 - loss 0.01578548 - samples/sec: 131.12 - lr: 0.025000 2022-11-06 17:22:35,831 epoch 69 - iter 216/274 - loss 0.01592534 - samples/sec: 116.38 - lr: 0.025000 2022-11-06 17:22:43,273 epoch 69 - iter 243/274 - loss 0.01597793 - samples/sec: 116.17 - lr: 0.025000 2022-11-06 17:22:50,041 epoch 69 - iter 270/274 - loss 0.01595590 - samples/sec: 127.72 - lr: 0.025000 2022-11-06 17:22:51,040 ---------------------------------------------------------------------------------------------------- 2022-11-06 17:22:51,040 EPOCH 69 done: loss 0.0160 - lr 0.025000 2022-11-06 17:23:30,171 Evaluating as a multi-label problem: False 2022-11-06 17:23:30,198 TEST : loss 0.03283306211233139 - f1-score (micro avg) 0.8563 2022-11-06 17:23:30,685 BAD EPOCHS (no improvement): 2 2022-11-06 17:23:30,880 ---------------------------------------------------------------------------------------------------- 2022-11-06 17:23:37,590 epoch 70 - iter 27/274 - loss 0.01518963 - samples/sec: 128.86 - lr: 0.025000 2022-11-06 17:23:45,185 epoch 70 - iter 54/274 - loss 0.01521270 - samples/sec: 113.81 - lr: 0.025000 2022-11-06 17:23:51,860 epoch 70 - iter 81/274 - loss 0.01415152 - samples/sec: 129.51 - lr: 0.025000 2022-11-06 17:23:58,634 epoch 70 - iter 108/274 - loss 0.01485497 - samples/sec: 127.63 - lr: 0.025000 2022-11-06 17:24:04,940 epoch 70 - iter 135/274 - loss 0.01478926 - samples/sec: 137.08 - lr: 0.025000 2022-11-06 17:24:11,824 epoch 70 - iter 162/274 - loss 0.01499592 - samples/sec: 125.59 - lr: 0.025000 2022-11-06 17:24:18,705 epoch 70 - iter 189/274 - loss 0.01553102 - samples/sec: 125.62 - lr: 0.025000 2022-11-06 17:24:25,548 epoch 70 - iter 216/274 - loss 0.01546902 - samples/sec: 126.33 - lr: 0.025000 2022-11-06 17:24:33,334 epoch 70 - iter 243/274 - loss 0.01550453 - samples/sec: 111.03 - lr: 0.025000 2022-11-06 17:24:40,619 epoch 70 - iter 270/274 - loss 0.01585683 - samples/sec: 118.65 - lr: 0.025000 2022-11-06 17:24:41,696 ---------------------------------------------------------------------------------------------------- 2022-11-06 17:24:41,697 EPOCH 70 done: loss 0.0158 - lr 0.025000 2022-11-06 17:25:20,722 Evaluating as a multi-label problem: False 2022-11-06 17:25:20,750 TEST : loss 0.03144649788737297 - f1-score (micro avg) 0.855 2022-11-06 17:25:21,241 BAD EPOCHS (no improvement): 0 2022-11-06 17:25:21,439 ---------------------------------------------------------------------------------------------------- 2022-11-06 17:25:27,844 epoch 71 - iter 27/274 - loss 0.01504591 - samples/sec: 135.00 - lr: 0.025000 2022-11-06 17:25:35,499 epoch 71 - iter 54/274 - loss 0.01422305 - samples/sec: 112.92 - lr: 0.025000 2022-11-06 17:25:42,181 epoch 71 - iter 81/274 - loss 0.01494925 - samples/sec: 129.38 - lr: 0.025000 2022-11-06 17:25:49,252 epoch 71 - iter 108/274 - loss 0.01539012 - samples/sec: 122.26 - lr: 0.025000 2022-11-06 17:25:55,911 epoch 71 - iter 135/274 - loss 0.01611038 - samples/sec: 129.82 - lr: 0.025000 2022-11-06 17:26:02,422 epoch 71 - iter 162/274 - loss 0.01621456 - samples/sec: 132.78 - lr: 0.025000 2022-11-06 17:26:09,489 epoch 71 - iter 189/274 - loss 0.01589587 - samples/sec: 122.32 - lr: 0.025000 2022-11-06 17:26:16,222 epoch 71 - iter 216/274 - loss 0.01608586 - samples/sec: 128.41 - lr: 0.025000 2022-11-06 17:26:22,942 epoch 71 - iter 243/274 - loss 0.01617530 - samples/sec: 128.65 - lr: 0.025000 2022-11-06 17:26:30,577 epoch 71 - iter 270/274 - loss 0.01610688 - samples/sec: 113.21 - lr: 0.025000 2022-11-06 17:26:31,552 ---------------------------------------------------------------------------------------------------- 2022-11-06 17:26:31,553 EPOCH 71 done: loss 0.0161 - lr 0.025000 2022-11-06 17:27:10,634 Evaluating as a multi-label problem: False 2022-11-06 17:27:10,662 TEST : loss 0.032507773488759995 - f1-score (micro avg) 0.8589 2022-11-06 17:27:11,151 BAD EPOCHS (no improvement): 1 2022-11-06 17:27:11,346 ---------------------------------------------------------------------------------------------------- 2022-11-06 17:27:18,618 epoch 72 - iter 27/274 - loss 0.01296401 - samples/sec: 118.91 - lr: 0.025000 2022-11-06 17:27:25,321 epoch 72 - iter 54/274 - loss 0.01358941 - samples/sec: 128.97 - lr: 0.025000 2022-11-06 17:27:32,061 epoch 72 - iter 81/274 - loss 0.01460165 - samples/sec: 128.27 - lr: 0.025000 2022-11-06 17:27:39,502 epoch 72 - iter 108/274 - loss 0.01518859 - samples/sec: 116.17 - lr: 0.025000 2022-11-06 17:27:46,064 epoch 72 - iter 135/274 - loss 0.01600572 - samples/sec: 131.75 - lr: 0.025000 2022-11-06 17:27:53,068 epoch 72 - iter 162/274 - loss 0.01600137 - samples/sec: 123.42 - lr: 0.025000 2022-11-06 17:28:00,233 epoch 72 - iter 189/274 - loss 0.01649032 - samples/sec: 120.66 - lr: 0.025000 2022-11-06 17:28:06,844 epoch 72 - iter 216/274 - loss 0.01630591 - samples/sec: 130.77 - lr: 0.025000 2022-11-06 17:28:14,389 epoch 72 - iter 243/274 - loss 0.01640176 - samples/sec: 114.57 - lr: 0.025000 2022-11-06 17:28:21,097 epoch 72 - iter 270/274 - loss 0.01651928 - samples/sec: 128.89 - lr: 0.025000 2022-11-06 17:28:22,048 ---------------------------------------------------------------------------------------------------- 2022-11-06 17:28:22,048 EPOCH 72 done: loss 0.0166 - lr 0.025000 2022-11-06 17:29:01,043 Evaluating as a multi-label problem: False 2022-11-06 17:29:01,071 TEST : loss 0.0317557118833065 - f1-score (micro avg) 0.8525 2022-11-06 17:29:01,560 BAD EPOCHS (no improvement): 2 2022-11-06 17:29:01,746 ---------------------------------------------------------------------------------------------------- 2022-11-06 17:29:08,975 epoch 73 - iter 27/274 - loss 0.01498789 - samples/sec: 119.60 - lr: 0.025000 2022-11-06 17:29:16,264 epoch 73 - iter 54/274 - loss 0.01459714 - samples/sec: 118.60 - lr: 0.025000 2022-11-06 17:29:23,181 epoch 73 - iter 81/274 - loss 0.01406969 - samples/sec: 124.99 - lr: 0.025000 2022-11-06 17:29:30,119 epoch 73 - iter 108/274 - loss 0.01434969 - samples/sec: 124.59 - lr: 0.025000 2022-11-06 17:29:36,855 epoch 73 - iter 135/274 - loss 0.01409944 - samples/sec: 128.35 - lr: 0.025000 2022-11-06 17:29:43,973 epoch 73 - iter 162/274 - loss 0.01484917 - samples/sec: 121.45 - lr: 0.025000 2022-11-06 17:29:50,692 epoch 73 - iter 189/274 - loss 0.01467239 - samples/sec: 128.67 - lr: 0.025000 2022-11-06 17:29:57,431 epoch 73 - iter 216/274 - loss 0.01479097 - samples/sec: 128.27 - lr: 0.025000 2022-11-06 17:30:03,875 epoch 73 - iter 243/274 - loss 0.01487098 - samples/sec: 134.16 - lr: 0.025000 2022-11-06 17:30:11,263 epoch 73 - iter 270/274 - loss 0.01497898 - samples/sec: 117.01 - lr: 0.025000 2022-11-06 17:30:12,451 ---------------------------------------------------------------------------------------------------- 2022-11-06 17:30:12,451 EPOCH 73 done: loss 0.0151 - lr 0.025000 2022-11-06 17:30:51,446 Evaluating as a multi-label problem: False 2022-11-06 17:30:51,474 TEST : loss 0.03126273304224014 - f1-score (micro avg) 0.8584 2022-11-06 17:30:51,963 BAD EPOCHS (no improvement): 0 2022-11-06 17:30:52,157 ---------------------------------------------------------------------------------------------------- 2022-11-06 17:30:58,747 epoch 74 - iter 27/274 - loss 0.01717599 - samples/sec: 131.21 - lr: 0.025000 2022-11-06 17:31:05,728 epoch 74 - iter 54/274 - loss 0.01663389 - samples/sec: 123.83 - lr: 0.025000 2022-11-06 17:31:12,702 epoch 74 - iter 81/274 - loss 0.01680313 - samples/sec: 123.95 - lr: 0.025000 2022-11-06 17:31:19,465 epoch 74 - iter 108/274 - loss 0.01666603 - samples/sec: 127.83 - lr: 0.025000 2022-11-06 17:31:26,337 epoch 74 - iter 135/274 - loss 0.01636185 - samples/sec: 125.81 - lr: 0.025000 2022-11-06 17:31:32,882 epoch 74 - iter 162/274 - loss 0.01586108 - samples/sec: 132.09 - lr: 0.025000 2022-11-06 17:31:39,702 epoch 74 - iter 189/274 - loss 0.01528822 - samples/sec: 126.76 - lr: 0.025000 2022-11-06 17:31:46,629 epoch 74 - iter 216/274 - loss 0.01534615 - samples/sec: 124.79 - lr: 0.025000 2022-11-06 17:31:53,951 epoch 74 - iter 243/274 - loss 0.01530002 - samples/sec: 118.07 - lr: 0.025000 2022-11-06 17:32:01,499 epoch 74 - iter 270/274 - loss 0.01566712 - samples/sec: 114.52 - lr: 0.025000 2022-11-06 17:32:02,508 ---------------------------------------------------------------------------------------------------- 2022-11-06 17:32:02,508 EPOCH 74 done: loss 0.0158 - lr 0.025000 2022-11-06 17:32:41,566 Evaluating as a multi-label problem: False 2022-11-06 17:32:41,594 TEST : loss 0.031216170638799667 - f1-score (micro avg) 0.8564 2022-11-06 17:32:42,081 BAD EPOCHS (no improvement): 1 2022-11-06 17:32:42,278 ---------------------------------------------------------------------------------------------------- 2022-11-06 17:32:49,513 epoch 75 - iter 27/274 - loss 0.01578720 - samples/sec: 119.50 - lr: 0.025000 2022-11-06 17:32:56,426 epoch 75 - iter 54/274 - loss 0.01578182 - samples/sec: 125.05 - lr: 0.025000 2022-11-06 17:33:03,985 epoch 75 - iter 81/274 - loss 0.01693581 - samples/sec: 114.36 - lr: 0.025000 2022-11-06 17:33:11,608 epoch 75 - iter 108/274 - loss 0.01638865 - samples/sec: 113.40 - lr: 0.025000 2022-11-06 17:33:18,681 epoch 75 - iter 135/274 - loss 0.01624836 - samples/sec: 122.22 - lr: 0.025000 2022-11-06 17:33:24,468 epoch 75 - iter 162/274 - loss 0.01614484 - samples/sec: 149.40 - lr: 0.025000 2022-11-06 17:33:30,697 epoch 75 - iter 189/274 - loss 0.01587570 - samples/sec: 138.80 - lr: 0.025000 2022-11-06 17:33:37,431 epoch 75 - iter 216/274 - loss 0.01600820 - samples/sec: 128.38 - lr: 0.025000 2022-11-06 17:33:44,261 epoch 75 - iter 243/274 - loss 0.01609265 - samples/sec: 126.56 - lr: 0.025000 2022-11-06 17:33:51,067 epoch 75 - iter 270/274 - loss 0.01603705 - samples/sec: 127.02 - lr: 0.025000 2022-11-06 17:33:52,118 ---------------------------------------------------------------------------------------------------- 2022-11-06 17:33:52,118 EPOCH 75 done: loss 0.0160 - lr 0.025000 2022-11-06 17:34:32,638 Evaluating as a multi-label problem: False 2022-11-06 17:34:32,668 TEST : loss 0.03171336650848389 - f1-score (micro avg) 0.8626 2022-11-06 17:34:33,161 BAD EPOCHS (no improvement): 2 2022-11-06 17:34:33,358 ---------------------------------------------------------------------------------------------------- 2022-11-06 17:34:40,777 epoch 76 - iter 27/274 - loss 0.01831154 - samples/sec: 116.55 - lr: 0.025000 2022-11-06 17:34:48,604 epoch 76 - iter 54/274 - loss 0.01793034 - samples/sec: 110.44 - lr: 0.025000 2022-11-06 17:34:56,298 epoch 76 - iter 81/274 - loss 0.01715389 - samples/sec: 112.35 - lr: 0.025000 2022-11-06 17:35:03,642 epoch 76 - iter 108/274 - loss 0.01643277 - samples/sec: 117.70 - lr: 0.025000 2022-11-06 17:35:10,764 epoch 76 - iter 135/274 - loss 0.01560754 - samples/sec: 121.39 - lr: 0.025000 2022-11-06 17:35:17,966 epoch 76 - iter 162/274 - loss 0.01576625 - samples/sec: 120.03 - lr: 0.025000 2022-11-06 17:35:25,598 epoch 76 - iter 189/274 - loss 0.01570138 - samples/sec: 113.27 - lr: 0.025000 2022-11-06 17:35:33,866 epoch 76 - iter 216/274 - loss 0.01571511 - samples/sec: 104.54 - lr: 0.025000 2022-11-06 17:35:41,556 epoch 76 - iter 243/274 - loss 0.01552739 - samples/sec: 112.42 - lr: 0.025000 2022-11-06 17:35:48,253 epoch 76 - iter 270/274 - loss 0.01539358 - samples/sec: 129.08 - lr: 0.025000 2022-11-06 17:35:49,110 ---------------------------------------------------------------------------------------------------- 2022-11-06 17:35:49,111 EPOCH 76 done: loss 0.0153 - lr 0.025000 2022-11-06 17:36:29,131 Evaluating as a multi-label problem: False 2022-11-06 17:36:29,158 TEST : loss 0.032589372247457504 - f1-score (micro avg) 0.8561 2022-11-06 17:36:29,645 BAD EPOCHS (no improvement): 3 2022-11-06 17:36:29,834 ---------------------------------------------------------------------------------------------------- 2022-11-06 17:36:36,940 epoch 77 - iter 27/274 - loss 0.01386001 - samples/sec: 121.68 - lr: 0.025000 2022-11-06 17:36:43,357 epoch 77 - iter 54/274 - loss 0.01306575 - samples/sec: 134.73 - lr: 0.025000 2022-11-06 17:36:50,201 epoch 77 - iter 81/274 - loss 0.01517313 - samples/sec: 126.31 - lr: 0.025000 2022-11-06 17:36:57,270 epoch 77 - iter 108/274 - loss 0.01532553 - samples/sec: 122.29 - lr: 0.025000 2022-11-06 17:37:04,074 epoch 77 - iter 135/274 - loss 0.01486198 - samples/sec: 127.05 - lr: 0.025000 2022-11-06 17:37:10,935 epoch 77 - iter 162/274 - loss 0.01456990 - samples/sec: 126.00 - lr: 0.025000 2022-11-06 17:37:18,016 epoch 77 - iter 189/274 - loss 0.01481667 - samples/sec: 122.09 - lr: 0.025000 2022-11-06 17:37:25,328 epoch 77 - iter 216/274 - loss 0.01487554 - samples/sec: 118.22 - lr: 0.025000 2022-11-06 17:37:31,999 epoch 77 - iter 243/274 - loss 0.01498806 - samples/sec: 129.60 - lr: 0.025000 2022-11-06 17:37:38,993 epoch 77 - iter 270/274 - loss 0.01521675 - samples/sec: 123.59 - lr: 0.025000 2022-11-06 17:37:39,932 ---------------------------------------------------------------------------------------------------- 2022-11-06 17:37:39,933 EPOCH 77 done: loss 0.0152 - lr 0.025000 2022-11-06 17:38:19,080 Evaluating as a multi-label problem: False 2022-11-06 17:38:19,108 TEST : loss 0.031240783631801605 - f1-score (micro avg) 0.8586 2022-11-06 17:38:19,593 Epoch 77: reducing learning rate of group 0 to 1.2500e-02. 2022-11-06 17:38:19,593 BAD EPOCHS (no improvement): 4 2022-11-06 17:38:19,780 ---------------------------------------------------------------------------------------------------- 2022-11-06 17:38:27,178 epoch 78 - iter 27/274 - loss 0.01335360 - samples/sec: 116.86 - lr: 0.012500 2022-11-06 17:38:33,630 epoch 78 - iter 54/274 - loss 0.01493181 - samples/sec: 134.00 - lr: 0.012500 2022-11-06 17:38:39,808 epoch 78 - iter 81/274 - loss 0.01481730 - samples/sec: 139.91 - lr: 0.012500 2022-11-06 17:38:47,372 epoch 78 - iter 108/274 - loss 0.01475207 - samples/sec: 114.29 - lr: 0.012500 2022-11-06 17:38:54,466 epoch 78 - iter 135/274 - loss 0.01483267 - samples/sec: 121.84 - lr: 0.012500 2022-11-06 17:39:01,222 epoch 78 - iter 162/274 - loss 0.01512120 - samples/sec: 127.96 - lr: 0.012500 2022-11-06 17:39:08,557 epoch 78 - iter 189/274 - loss 0.01557814 - samples/sec: 117.85 - lr: 0.012500 2022-11-06 17:39:15,485 epoch 78 - iter 216/274 - loss 0.01538250 - samples/sec: 124.76 - lr: 0.012500 2022-11-06 17:39:22,414 epoch 78 - iter 243/274 - loss 0.01562241 - samples/sec: 124.76 - lr: 0.012500 2022-11-06 17:39:28,939 epoch 78 - iter 270/274 - loss 0.01583628 - samples/sec: 132.48 - lr: 0.012500 2022-11-06 17:39:29,838 ---------------------------------------------------------------------------------------------------- 2022-11-06 17:39:29,838 EPOCH 78 done: loss 0.0157 - lr 0.012500 2022-11-06 17:40:09,022 Evaluating as a multi-label problem: False 2022-11-06 17:40:09,050 TEST : loss 0.031696297228336334 - f1-score (micro avg) 0.8578 2022-11-06 17:40:09,536 BAD EPOCHS (no improvement): 1 2022-11-06 17:40:09,729 ---------------------------------------------------------------------------------------------------- 2022-11-06 17:40:16,293 epoch 79 - iter 27/274 - loss 0.01659307 - samples/sec: 131.73 - lr: 0.012500 2022-11-06 17:40:23,082 epoch 79 - iter 54/274 - loss 0.01471363 - samples/sec: 127.33 - lr: 0.012500 2022-11-06 17:40:29,332 epoch 79 - iter 81/274 - loss 0.01551638 - samples/sec: 138.30 - lr: 0.012500 2022-11-06 17:40:37,063 epoch 79 - iter 108/274 - loss 0.01521622 - samples/sec: 111.82 - lr: 0.012500 2022-11-06 17:40:43,734 epoch 79 - iter 135/274 - loss 0.01443179 - samples/sec: 129.57 - lr: 0.012500 2022-11-06 17:40:50,744 epoch 79 - iter 162/274 - loss 0.01494722 - samples/sec: 123.32 - lr: 0.012500 2022-11-06 17:40:57,748 epoch 79 - iter 189/274 - loss 0.01484071 - samples/sec: 123.42 - lr: 0.012500 2022-11-06 17:41:05,079 epoch 79 - iter 216/274 - loss 0.01474732 - samples/sec: 117.91 - lr: 0.012500 2022-11-06 17:41:11,870 epoch 79 - iter 243/274 - loss 0.01470988 - samples/sec: 127.29 - lr: 0.012500 2022-11-06 17:41:18,318 epoch 79 - iter 270/274 - loss 0.01457411 - samples/sec: 134.07 - lr: 0.012500 2022-11-06 17:41:19,532 ---------------------------------------------------------------------------------------------------- 2022-11-06 17:41:19,532 EPOCH 79 done: loss 0.0147 - lr 0.012500 2022-11-06 17:41:58,340 Evaluating as a multi-label problem: False 2022-11-06 17:41:58,368 TEST : loss 0.03271425515413284 - f1-score (micro avg) 0.8541 2022-11-06 17:41:58,856 BAD EPOCHS (no improvement): 0 2022-11-06 17:41:59,051 ---------------------------------------------------------------------------------------------------- 2022-11-06 17:42:05,934 epoch 80 - iter 27/274 - loss 0.01371277 - samples/sec: 125.62 - lr: 0.012500 2022-11-06 17:42:13,074 epoch 80 - iter 54/274 - loss 0.01282598 - samples/sec: 121.06 - lr: 0.012500 2022-11-06 17:42:19,435 epoch 80 - iter 81/274 - loss 0.01254139 - samples/sec: 135.90 - lr: 0.012500 2022-11-06 17:42:26,846 epoch 80 - iter 108/274 - loss 0.01285627 - samples/sec: 116.64 - lr: 0.012500 2022-11-06 17:42:33,992 epoch 80 - iter 135/274 - loss 0.01362570 - samples/sec: 120.97 - lr: 0.012500 2022-11-06 17:42:40,817 epoch 80 - iter 162/274 - loss 0.01380565 - samples/sec: 126.65 - lr: 0.012500 2022-11-06 17:42:47,634 epoch 80 - iter 189/274 - loss 0.01368897 - samples/sec: 126.81 - lr: 0.012500 2022-11-06 17:42:54,397 epoch 80 - iter 216/274 - loss 0.01366941 - samples/sec: 127.82 - lr: 0.012500 2022-11-06 17:43:01,155 epoch 80 - iter 243/274 - loss 0.01379331 - samples/sec: 127.92 - lr: 0.012500 2022-11-06 17:43:08,144 epoch 80 - iter 270/274 - loss 0.01369367 - samples/sec: 123.68 - lr: 0.012500 2022-11-06 17:43:08,939 ---------------------------------------------------------------------------------------------------- 2022-11-06 17:43:08,939 EPOCH 80 done: loss 0.0138 - lr 0.012500 2022-11-06 17:43:47,876 Evaluating as a multi-label problem: False 2022-11-06 17:43:47,903 TEST : loss 0.033087365329265594 - f1-score (micro avg) 0.8601 2022-11-06 17:43:48,388 BAD EPOCHS (no improvement): 0 2022-11-06 17:43:48,663 ---------------------------------------------------------------------------------------------------- 2022-11-06 17:43:56,720 epoch 81 - iter 27/274 - loss 0.01472430 - samples/sec: 107.32 - lr: 0.012500 2022-11-06 17:44:03,510 epoch 81 - iter 54/274 - loss 0.01558958 - samples/sec: 127.32 - lr: 0.012500 2022-11-06 17:44:10,409 epoch 81 - iter 81/274 - loss 0.01529039 - samples/sec: 125.29 - lr: 0.012500 2022-11-06 17:44:16,744 epoch 81 - iter 108/274 - loss 0.01520453 - samples/sec: 136.46 - lr: 0.012500 2022-11-06 17:44:23,903 epoch 81 - iter 135/274 - loss 0.01485186 - samples/sec: 120.75 - lr: 0.012500 2022-11-06 17:44:30,712 epoch 81 - iter 162/274 - loss 0.01457213 - samples/sec: 126.95 - lr: 0.012500 2022-11-06 17:44:37,476 epoch 81 - iter 189/274 - loss 0.01430525 - samples/sec: 127.80 - lr: 0.012500 2022-11-06 17:44:44,134 epoch 81 - iter 216/274 - loss 0.01407564 - samples/sec: 129.84 - lr: 0.012500 2022-11-06 17:44:51,266 epoch 81 - iter 243/274 - loss 0.01436707 - samples/sec: 121.21 - lr: 0.012500 2022-11-06 17:44:58,475 epoch 81 - iter 270/274 - loss 0.01421268 - samples/sec: 119.91 - lr: 0.012500 2022-11-06 17:44:59,402 ---------------------------------------------------------------------------------------------------- 2022-11-06 17:44:59,402 EPOCH 81 done: loss 0.0141 - lr 0.012500 2022-11-06 17:45:38,325 Evaluating as a multi-label problem: False 2022-11-06 17:45:38,353 TEST : loss 0.034334879368543625 - f1-score (micro avg) 0.8536 2022-11-06 17:45:38,841 BAD EPOCHS (no improvement): 1 2022-11-06 17:45:39,029 ---------------------------------------------------------------------------------------------------- 2022-11-06 17:45:45,813 epoch 82 - iter 27/274 - loss 0.01620736 - samples/sec: 127.46 - lr: 0.012500 2022-11-06 17:45:52,832 epoch 82 - iter 54/274 - loss 0.01553044 - samples/sec: 123.16 - lr: 0.012500 2022-11-06 17:45:59,814 epoch 82 - iter 81/274 - loss 0.01573572 - samples/sec: 123.81 - lr: 0.012500 2022-11-06 17:46:06,004 epoch 82 - iter 108/274 - loss 0.01461916 - samples/sec: 139.66 - lr: 0.012500 2022-11-06 17:46:13,432 epoch 82 - iter 135/274 - loss 0.01512387 - samples/sec: 116.36 - lr: 0.012500 2022-11-06 17:46:21,226 epoch 82 - iter 162/274 - loss 0.01514539 - samples/sec: 110.90 - lr: 0.012500 2022-11-06 17:46:27,976 epoch 82 - iter 189/274 - loss 0.01484884 - samples/sec: 128.07 - lr: 0.012500 2022-11-06 17:46:35,098 epoch 82 - iter 216/274 - loss 0.01475803 - samples/sec: 121.38 - lr: 0.012500 2022-11-06 17:46:42,072 epoch 82 - iter 243/274 - loss 0.01459842 - samples/sec: 123.95 - lr: 0.012500 2022-11-06 17:46:48,466 epoch 82 - iter 270/274 - loss 0.01460797 - samples/sec: 135.19 - lr: 0.012500 2022-11-06 17:46:49,457 ---------------------------------------------------------------------------------------------------- 2022-11-06 17:46:49,457 EPOCH 82 done: loss 0.0146 - lr 0.012500 2022-11-06 17:47:28,446 Evaluating as a multi-label problem: False 2022-11-06 17:47:28,474 TEST : loss 0.03356759995222092 - f1-score (micro avg) 0.8607 2022-11-06 17:47:28,960 BAD EPOCHS (no improvement): 2 2022-11-06 17:47:29,156 ---------------------------------------------------------------------------------------------------- 2022-11-06 17:47:36,204 epoch 83 - iter 27/274 - loss 0.01390934 - samples/sec: 122.69 - lr: 0.012500 2022-11-06 17:47:43,182 epoch 83 - iter 54/274 - loss 0.01342322 - samples/sec: 123.87 - lr: 0.012500 2022-11-06 17:47:49,819 epoch 83 - iter 81/274 - loss 0.01510934 - samples/sec: 130.25 - lr: 0.012500 2022-11-06 17:47:55,920 epoch 83 - iter 108/274 - loss 0.01466472 - samples/sec: 141.71 - lr: 0.012500 2022-11-06 17:48:02,838 epoch 83 - iter 135/274 - loss 0.01454476 - samples/sec: 124.95 - lr: 0.012500 2022-11-06 17:48:09,774 epoch 83 - iter 162/274 - loss 0.01431669 - samples/sec: 124.63 - lr: 0.012500 2022-11-06 17:48:17,014 epoch 83 - iter 189/274 - loss 0.01463776 - samples/sec: 119.40 - lr: 0.012500 2022-11-06 17:48:24,234 epoch 83 - iter 216/274 - loss 0.01425036 - samples/sec: 119.72 - lr: 0.012500 2022-11-06 17:48:31,204 epoch 83 - iter 243/274 - loss 0.01449536 - samples/sec: 124.03 - lr: 0.012500 2022-11-06 17:48:38,392 epoch 83 - iter 270/274 - loss 0.01442531 - samples/sec: 120.26 - lr: 0.012500 2022-11-06 17:48:39,278 ---------------------------------------------------------------------------------------------------- 2022-11-06 17:48:39,278 EPOCH 83 done: loss 0.0144 - lr 0.012500 2022-11-06 17:49:18,235 Evaluating as a multi-label problem: False 2022-11-06 17:49:18,263 TEST : loss 0.03331308811903 - f1-score (micro avg) 0.8573 2022-11-06 17:49:18,750 BAD EPOCHS (no improvement): 3 2022-11-06 17:49:18,946 ---------------------------------------------------------------------------------------------------- 2022-11-06 17:49:26,652 epoch 84 - iter 27/274 - loss 0.01472939 - samples/sec: 112.18 - lr: 0.012500 2022-11-06 17:49:33,375 epoch 84 - iter 54/274 - loss 0.01525577 - samples/sec: 128.58 - lr: 0.012500 2022-11-06 17:49:40,197 epoch 84 - iter 81/274 - loss 0.01448478 - samples/sec: 126.72 - lr: 0.012500 2022-11-06 17:49:47,269 epoch 84 - iter 108/274 - loss 0.01516433 - samples/sec: 122.22 - lr: 0.012500 2022-11-06 17:49:54,347 epoch 84 - iter 135/274 - loss 0.01481793 - samples/sec: 122.13 - lr: 0.012500 2022-11-06 17:50:01,260 epoch 84 - iter 162/274 - loss 0.01458997 - samples/sec: 125.05 - lr: 0.012500 2022-11-06 17:50:08,048 epoch 84 - iter 189/274 - loss 0.01467858 - samples/sec: 127.35 - lr: 0.012500 2022-11-06 17:50:14,725 epoch 84 - iter 216/274 - loss 0.01454278 - samples/sec: 129.45 - lr: 0.012500 2022-11-06 17:50:21,758 epoch 84 - iter 243/274 - loss 0.01460313 - samples/sec: 122.92 - lr: 0.012500 2022-11-06 17:50:28,307 epoch 84 - iter 270/274 - loss 0.01442134 - samples/sec: 131.98 - lr: 0.012500 2022-11-06 17:50:29,435 ---------------------------------------------------------------------------------------------------- 2022-11-06 17:50:29,435 EPOCH 84 done: loss 0.0144 - lr 0.012500 2022-11-06 17:51:08,452 Evaluating as a multi-label problem: False 2022-11-06 17:51:08,480 TEST : loss 0.0335269495844841 - f1-score (micro avg) 0.8604 2022-11-06 17:51:08,964 Epoch 84: reducing learning rate of group 0 to 6.2500e-03. 2022-11-06 17:51:08,965 BAD EPOCHS (no improvement): 4 2022-11-06 17:51:09,160 ---------------------------------------------------------------------------------------------------- 2022-11-06 17:51:16,107 epoch 85 - iter 27/274 - loss 0.01504540 - samples/sec: 124.48 - lr: 0.006250 2022-11-06 17:51:23,227 epoch 85 - iter 54/274 - loss 0.01397214 - samples/sec: 121.41 - lr: 0.006250 2022-11-06 17:51:30,006 epoch 85 - iter 81/274 - loss 0.01485455 - samples/sec: 127.50 - lr: 0.006250 2022-11-06 17:51:36,798 epoch 85 - iter 108/274 - loss 0.01509272 - samples/sec: 127.27 - lr: 0.006250 2022-11-06 17:51:43,360 epoch 85 - iter 135/274 - loss 0.01473064 - samples/sec: 131.74 - lr: 0.006250 2022-11-06 17:51:50,704 epoch 85 - iter 162/274 - loss 0.01453354 - samples/sec: 117.70 - lr: 0.006250 2022-11-06 17:51:58,454 epoch 85 - iter 189/274 - loss 0.01477549 - samples/sec: 111.54 - lr: 0.006250 2022-11-06 17:52:05,259 epoch 85 - iter 216/274 - loss 0.01463444 - samples/sec: 127.02 - lr: 0.006250 2022-11-06 17:52:12,109 epoch 85 - iter 243/274 - loss 0.01437277 - samples/sec: 126.20 - lr: 0.006250 2022-11-06 17:52:18,986 epoch 85 - iter 270/274 - loss 0.01428756 - samples/sec: 125.69 - lr: 0.006250 2022-11-06 17:52:19,804 ---------------------------------------------------------------------------------------------------- 2022-11-06 17:52:19,804 EPOCH 85 done: loss 0.0142 - lr 0.006250 2022-11-06 17:52:58,463 Evaluating as a multi-label problem: False 2022-11-06 17:52:58,491 TEST : loss 0.033432383090257645 - f1-score (micro avg) 0.8586 2022-11-06 17:52:58,978 BAD EPOCHS (no improvement): 1 2022-11-06 17:52:59,166 ---------------------------------------------------------------------------------------------------- 2022-11-06 17:53:06,255 epoch 86 - iter 27/274 - loss 0.01443914 - samples/sec: 121.96 - lr: 0.006250 2022-11-06 17:53:13,550 epoch 86 - iter 54/274 - loss 0.01410595 - samples/sec: 118.50 - lr: 0.006250 2022-11-06 17:53:20,735 epoch 86 - iter 81/274 - loss 0.01405715 - samples/sec: 120.30 - lr: 0.006250 2022-11-06 17:53:27,159 epoch 86 - iter 108/274 - loss 0.01447808 - samples/sec: 134.57 - lr: 0.006250 2022-11-06 17:53:33,882 epoch 86 - iter 135/274 - loss 0.01453162 - samples/sec: 128.58 - lr: 0.006250 2022-11-06 17:53:40,244 epoch 86 - iter 162/274 - loss 0.01429897 - samples/sec: 135.88 - lr: 0.006250 2022-11-06 17:53:47,513 epoch 86 - iter 189/274 - loss 0.01421391 - samples/sec: 118.91 - lr: 0.006250 2022-11-06 17:53:54,207 epoch 86 - iter 216/274 - loss 0.01425020 - samples/sec: 129.14 - lr: 0.006250 2022-11-06 17:54:00,821 epoch 86 - iter 243/274 - loss 0.01420002 - samples/sec: 130.71 - lr: 0.006250 2022-11-06 17:54:08,004 epoch 86 - iter 270/274 - loss 0.01423439 - samples/sec: 120.35 - lr: 0.006250 2022-11-06 17:54:09,150 ---------------------------------------------------------------------------------------------------- 2022-11-06 17:54:09,151 EPOCH 86 done: loss 0.0145 - lr 0.006250 2022-11-06 17:54:48,147 Evaluating as a multi-label problem: False 2022-11-06 17:54:48,175 TEST : loss 0.03346378728747368 - f1-score (micro avg) 0.8596 2022-11-06 17:54:48,662 BAD EPOCHS (no improvement): 2 2022-11-06 17:54:48,857 ---------------------------------------------------------------------------------------------------- 2022-11-06 17:54:55,576 epoch 87 - iter 27/274 - loss 0.01408922 - samples/sec: 128.69 - lr: 0.006250 2022-11-06 17:55:02,463 epoch 87 - iter 54/274 - loss 0.01484753 - samples/sec: 125.53 - lr: 0.006250 2022-11-06 17:55:09,809 epoch 87 - iter 81/274 - loss 0.01507213 - samples/sec: 117.66 - lr: 0.006250 2022-11-06 17:55:17,351 epoch 87 - iter 108/274 - loss 0.01424005 - samples/sec: 114.61 - lr: 0.006250 2022-11-06 17:55:23,951 epoch 87 - iter 135/274 - loss 0.01455797 - samples/sec: 130.98 - lr: 0.006250 2022-11-06 17:55:30,015 epoch 87 - iter 162/274 - loss 0.01417278 - samples/sec: 142.57 - lr: 0.006250 2022-11-06 17:55:37,059 epoch 87 - iter 189/274 - loss 0.01439724 - samples/sec: 122.72 - lr: 0.006250 2022-11-06 17:55:43,842 epoch 87 - iter 216/274 - loss 0.01482814 - samples/sec: 127.43 - lr: 0.006250 2022-11-06 17:55:50,274 epoch 87 - iter 243/274 - loss 0.01444523 - samples/sec: 134.40 - lr: 0.006250 2022-11-06 17:55:57,468 epoch 87 - iter 270/274 - loss 0.01458826 - samples/sec: 120.17 - lr: 0.006250 2022-11-06 17:55:58,433 ---------------------------------------------------------------------------------------------------- 2022-11-06 17:55:58,434 EPOCH 87 done: loss 0.0146 - lr 0.006250 2022-11-06 17:56:37,400 Evaluating as a multi-label problem: False 2022-11-06 17:56:37,428 TEST : loss 0.03315354138612747 - f1-score (micro avg) 0.8555 2022-11-06 17:56:37,914 BAD EPOCHS (no improvement): 3 2022-11-06 17:56:38,110 ---------------------------------------------------------------------------------------------------- 2022-11-06 17:56:45,310 epoch 88 - iter 27/274 - loss 0.01554221 - samples/sec: 120.09 - lr: 0.006250 2022-11-06 17:56:52,810 epoch 88 - iter 54/274 - loss 0.01515614 - samples/sec: 115.25 - lr: 0.006250 2022-11-06 17:56:59,856 epoch 88 - iter 81/274 - loss 0.01392380 - samples/sec: 122.69 - lr: 0.006250 2022-11-06 17:57:06,736 epoch 88 - iter 108/274 - loss 0.01483885 - samples/sec: 125.64 - lr: 0.006250 2022-11-06 17:57:13,932 epoch 88 - iter 135/274 - loss 0.01387559 - samples/sec: 120.13 - lr: 0.006250 2022-11-06 17:57:20,521 epoch 88 - iter 162/274 - loss 0.01395838 - samples/sec: 131.18 - lr: 0.006250 2022-11-06 17:57:27,598 epoch 88 - iter 189/274 - loss 0.01398869 - samples/sec: 122.16 - lr: 0.006250 2022-11-06 17:57:34,085 epoch 88 - iter 216/274 - loss 0.01419572 - samples/sec: 133.26 - lr: 0.006250 2022-11-06 17:57:41,030 epoch 88 - iter 243/274 - loss 0.01442811 - samples/sec: 124.46 - lr: 0.006250 2022-11-06 17:57:47,480 epoch 88 - iter 270/274 - loss 0.01445280 - samples/sec: 134.04 - lr: 0.006250 2022-11-06 17:57:48,418 ---------------------------------------------------------------------------------------------------- 2022-11-06 17:57:48,418 EPOCH 88 done: loss 0.0144 - lr 0.006250 2022-11-06 17:58:27,167 Evaluating as a multi-label problem: False 2022-11-06 17:58:27,194 TEST : loss 0.032923195511102676 - f1-score (micro avg) 0.8578 2022-11-06 17:58:27,679 Epoch 88: reducing learning rate of group 0 to 3.1250e-03. 2022-11-06 17:58:27,680 BAD EPOCHS (no improvement): 4 2022-11-06 17:58:27,875 ---------------------------------------------------------------------------------------------------- 2022-11-06 17:58:34,720 epoch 89 - iter 27/274 - loss 0.01555709 - samples/sec: 126.32 - lr: 0.003125 2022-11-06 17:58:41,380 epoch 89 - iter 54/274 - loss 0.01417627 - samples/sec: 129.79 - lr: 0.003125 2022-11-06 17:58:48,516 epoch 89 - iter 81/274 - loss 0.01538001 - samples/sec: 121.15 - lr: 0.003125 2022-11-06 17:58:55,789 epoch 89 - iter 108/274 - loss 0.01481134 - samples/sec: 118.85 - lr: 0.003125 2022-11-06 17:59:02,716 epoch 89 - iter 135/274 - loss 0.01499577 - samples/sec: 124.79 - lr: 0.003125 2022-11-06 17:59:09,838 epoch 89 - iter 162/274 - loss 0.01484335 - samples/sec: 121.37 - lr: 0.003125 2022-11-06 17:59:16,445 epoch 89 - iter 189/274 - loss 0.01445447 - samples/sec: 130.84 - lr: 0.003125 2022-11-06 17:59:23,544 epoch 89 - iter 216/274 - loss 0.01409136 - samples/sec: 121.77 - lr: 0.003125 2022-11-06 17:59:30,386 epoch 89 - iter 243/274 - loss 0.01431538 - samples/sec: 126.35 - lr: 0.003125 2022-11-06 17:59:37,209 epoch 89 - iter 270/274 - loss 0.01439537 - samples/sec: 126.68 - lr: 0.003125 2022-11-06 17:59:38,253 ---------------------------------------------------------------------------------------------------- 2022-11-06 17:59:38,253 EPOCH 89 done: loss 0.0144 - lr 0.003125 2022-11-06 18:00:17,287 Evaluating as a multi-label problem: False 2022-11-06 18:00:17,314 TEST : loss 0.033024467527866364 - f1-score (micro avg) 0.8602 2022-11-06 18:00:17,800 BAD EPOCHS (no improvement): 1 2022-11-06 18:00:17,988 ---------------------------------------------------------------------------------------------------- 2022-11-06 18:00:25,291 epoch 90 - iter 27/274 - loss 0.01682619 - samples/sec: 118.39 - lr: 0.003125 2022-11-06 18:00:31,883 epoch 90 - iter 54/274 - loss 0.01399250 - samples/sec: 131.12 - lr: 0.003125 2022-11-06 18:00:39,011 epoch 90 - iter 81/274 - loss 0.01455934 - samples/sec: 121.27 - lr: 0.003125 2022-11-06 18:00:46,654 epoch 90 - iter 108/274 - loss 0.01423489 - samples/sec: 113.10 - lr: 0.003125 2022-11-06 18:00:53,516 epoch 90 - iter 135/274 - loss 0.01426972 - samples/sec: 125.99 - lr: 0.003125 2022-11-06 18:01:00,487 epoch 90 - iter 162/274 - loss 0.01489659 - samples/sec: 124.00 - lr: 0.003125 2022-11-06 18:01:07,234 epoch 90 - iter 189/274 - loss 0.01435830 - samples/sec: 128.12 - lr: 0.003125 2022-11-06 18:01:13,869 epoch 90 - iter 216/274 - loss 0.01372784 - samples/sec: 130.29 - lr: 0.003125 2022-11-06 18:01:20,485 epoch 90 - iter 243/274 - loss 0.01375024 - samples/sec: 130.66 - lr: 0.003125 2022-11-06 18:01:27,165 epoch 90 - iter 270/274 - loss 0.01391123 - samples/sec: 129.42 - lr: 0.003125 2022-11-06 18:01:28,310 ---------------------------------------------------------------------------------------------------- 2022-11-06 18:01:28,310 EPOCH 90 done: loss 0.0140 - lr 0.003125 2022-11-06 18:02:07,346 Evaluating as a multi-label problem: False 2022-11-06 18:02:07,374 TEST : loss 0.03315744176506996 - f1-score (micro avg) 0.8611 2022-11-06 18:02:07,860 BAD EPOCHS (no improvement): 2 2022-11-06 18:02:08,055 ---------------------------------------------------------------------------------------------------- 2022-11-06 18:02:14,610 epoch 91 - iter 27/274 - loss 0.01485026 - samples/sec: 131.92 - lr: 0.003125 2022-11-06 18:02:21,416 epoch 91 - iter 54/274 - loss 0.01399412 - samples/sec: 127.01 - lr: 0.003125 2022-11-06 18:02:28,577 epoch 91 - iter 81/274 - loss 0.01414092 - samples/sec: 120.72 - lr: 0.003125 2022-11-06 18:02:36,350 epoch 91 - iter 108/274 - loss 0.01405876 - samples/sec: 111.20 - lr: 0.003125 2022-11-06 18:02:42,803 epoch 91 - iter 135/274 - loss 0.01455530 - samples/sec: 133.96 - lr: 0.003125 2022-11-06 18:02:49,612 epoch 91 - iter 162/274 - loss 0.01445301 - samples/sec: 126.96 - lr: 0.003125 2022-11-06 18:02:55,814 epoch 91 - iter 189/274 - loss 0.01438419 - samples/sec: 139.38 - lr: 0.003125 2022-11-06 18:03:02,882 epoch 91 - iter 216/274 - loss 0.01417121 - samples/sec: 122.30 - lr: 0.003125 2022-11-06 18:03:10,248 epoch 91 - iter 243/274 - loss 0.01415182 - samples/sec: 117.35 - lr: 0.003125 2022-11-06 18:03:17,370 epoch 91 - iter 270/274 - loss 0.01441556 - samples/sec: 121.38 - lr: 0.003125 2022-11-06 18:03:18,262 ---------------------------------------------------------------------------------------------------- 2022-11-06 18:03:18,262 EPOCH 91 done: loss 0.0144 - lr 0.003125 2022-11-06 18:03:57,372 Evaluating as a multi-label problem: False 2022-11-06 18:03:57,400 TEST : loss 0.03293454274535179 - f1-score (micro avg) 0.8585 2022-11-06 18:03:57,886 BAD EPOCHS (no improvement): 3 2022-11-06 18:03:58,078 ---------------------------------------------------------------------------------------------------- 2022-11-06 18:04:05,365 epoch 92 - iter 27/274 - loss 0.01414109 - samples/sec: 118.65 - lr: 0.003125 2022-11-06 18:04:12,026 epoch 92 - iter 54/274 - loss 0.01393229 - samples/sec: 129.78 - lr: 0.003125 2022-11-06 18:04:18,771 epoch 92 - iter 81/274 - loss 0.01423861 - samples/sec: 128.16 - lr: 0.003125 2022-11-06 18:04:25,438 epoch 92 - iter 108/274 - loss 0.01365481 - samples/sec: 129.67 - lr: 0.003125 2022-11-06 18:04:32,410 epoch 92 - iter 135/274 - loss 0.01366917 - samples/sec: 123.98 - lr: 0.003125 2022-11-06 18:04:39,656 epoch 92 - iter 162/274 - loss 0.01416809 - samples/sec: 119.30 - lr: 0.003125 2022-11-06 18:04:45,576 epoch 92 - iter 189/274 - loss 0.01414786 - samples/sec: 146.04 - lr: 0.003125 2022-11-06 18:04:52,079 epoch 92 - iter 216/274 - loss 0.01389831 - samples/sec: 132.92 - lr: 0.003125 2022-11-06 18:04:59,488 epoch 92 - iter 243/274 - loss 0.01410677 - samples/sec: 116.68 - lr: 0.003125 2022-11-06 18:05:06,855 epoch 92 - iter 270/274 - loss 0.01411694 - samples/sec: 117.32 - lr: 0.003125 2022-11-06 18:05:07,946 ---------------------------------------------------------------------------------------------------- 2022-11-06 18:05:07,946 EPOCH 92 done: loss 0.0141 - lr 0.003125 2022-11-06 18:05:46,789 Evaluating as a multi-label problem: False 2022-11-06 18:05:46,817 TEST : loss 0.03319080173969269 - f1-score (micro avg) 0.8597 2022-11-06 18:05:47,305 Epoch 92: reducing learning rate of group 0 to 1.5625e-03. 2022-11-06 18:05:47,306 BAD EPOCHS (no improvement): 4 2022-11-06 18:05:47,503 ---------------------------------------------------------------------------------------------------- 2022-11-06 18:05:54,734 epoch 93 - iter 27/274 - loss 0.01404453 - samples/sec: 119.57 - lr: 0.001563 2022-11-06 18:06:02,242 epoch 93 - iter 54/274 - loss 0.01520985 - samples/sec: 115.14 - lr: 0.001563 2022-11-06 18:06:09,231 epoch 93 - iter 81/274 - loss 0.01428365 - samples/sec: 123.69 - lr: 0.001563 2022-11-06 18:06:16,170 epoch 93 - iter 108/274 - loss 0.01418739 - samples/sec: 124.57 - lr: 0.001563 2022-11-06 18:06:22,642 epoch 93 - iter 135/274 - loss 0.01468654 - samples/sec: 133.57 - lr: 0.001563 2022-11-06 18:06:29,811 epoch 93 - iter 162/274 - loss 0.01503371 - samples/sec: 120.59 - lr: 0.001563 2022-11-06 18:06:35,860 epoch 93 - iter 189/274 - loss 0.01434111 - samples/sec: 142.90 - lr: 0.001563 2022-11-06 18:06:42,080 epoch 93 - iter 216/274 - loss 0.01445581 - samples/sec: 139.00 - lr: 0.001563 2022-11-06 18:06:49,319 epoch 93 - iter 243/274 - loss 0.01434473 - samples/sec: 119.41 - lr: 0.001563 2022-11-06 18:06:56,628 epoch 93 - iter 270/274 - loss 0.01443864 - samples/sec: 118.27 - lr: 0.001563 2022-11-06 18:06:57,857 ---------------------------------------------------------------------------------------------------- 2022-11-06 18:06:57,857 EPOCH 93 done: loss 0.0143 - lr 0.001563 2022-11-06 18:07:36,676 Evaluating as a multi-label problem: False 2022-11-06 18:07:36,704 TEST : loss 0.03328932076692581 - f1-score (micro avg) 0.8605 2022-11-06 18:07:37,191 BAD EPOCHS (no improvement): 1 2022-11-06 18:07:37,379 ---------------------------------------------------------------------------------------------------- 2022-11-06 18:07:44,008 epoch 94 - iter 27/274 - loss 0.01557933 - samples/sec: 130.44 - lr: 0.001563 2022-11-06 18:07:50,720 epoch 94 - iter 54/274 - loss 0.01413440 - samples/sec: 128.79 - lr: 0.001563 2022-11-06 18:07:57,727 epoch 94 - iter 81/274 - loss 0.01475105 - samples/sec: 123.38 - lr: 0.001563 2022-11-06 18:08:04,926 epoch 94 - iter 108/274 - loss 0.01521216 - samples/sec: 120.07 - lr: 0.001563 2022-11-06 18:08:12,698 epoch 94 - iter 135/274 - loss 0.01632941 - samples/sec: 111.23 - lr: 0.001563 2022-11-06 18:08:19,856 epoch 94 - iter 162/274 - loss 0.01560526 - samples/sec: 120.76 - lr: 0.001563 2022-11-06 18:08:26,828 epoch 94 - iter 189/274 - loss 0.01499817 - samples/sec: 123.99 - lr: 0.001563 2022-11-06 18:08:33,219 epoch 94 - iter 216/274 - loss 0.01483912 - samples/sec: 135.25 - lr: 0.001563 2022-11-06 18:08:39,715 epoch 94 - iter 243/274 - loss 0.01486392 - samples/sec: 133.07 - lr: 0.001563 2022-11-06 18:08:46,222 epoch 94 - iter 270/274 - loss 0.01497467 - samples/sec: 132.84 - lr: 0.001563 2022-11-06 18:08:47,148 ---------------------------------------------------------------------------------------------------- 2022-11-06 18:08:47,149 EPOCH 94 done: loss 0.0149 - lr 0.001563 2022-11-06 18:09:26,544 Evaluating as a multi-label problem: False 2022-11-06 18:09:26,572 TEST : loss 0.033291514962911606 - f1-score (micro avg) 0.8599 2022-11-06 18:09:27,060 BAD EPOCHS (no improvement): 2 2022-11-06 18:09:27,253 ---------------------------------------------------------------------------------------------------- 2022-11-06 18:09:34,044 epoch 95 - iter 27/274 - loss 0.01317662 - samples/sec: 127.34 - lr: 0.001563 2022-11-06 18:09:41,073 epoch 95 - iter 54/274 - loss 0.01293760 - samples/sec: 122.98 - lr: 0.001563 2022-11-06 18:09:48,592 epoch 95 - iter 81/274 - loss 0.01330761 - samples/sec: 114.95 - lr: 0.001563 2022-11-06 18:09:55,710 epoch 95 - iter 108/274 - loss 0.01373439 - samples/sec: 121.46 - lr: 0.001563 2022-11-06 18:10:02,634 epoch 95 - iter 135/274 - loss 0.01415330 - samples/sec: 124.83 - lr: 0.001563 2022-11-06 18:10:09,642 epoch 95 - iter 162/274 - loss 0.01427005 - samples/sec: 123.36 - lr: 0.001563 2022-11-06 18:10:16,585 epoch 95 - iter 189/274 - loss 0.01447179 - samples/sec: 124.50 - lr: 0.001563 2022-11-06 18:10:23,026 epoch 95 - iter 216/274 - loss 0.01430745 - samples/sec: 134.22 - lr: 0.001563 2022-11-06 18:10:29,559 epoch 95 - iter 243/274 - loss 0.01444554 - samples/sec: 132.32 - lr: 0.001563 2022-11-06 18:10:36,591 epoch 95 - iter 270/274 - loss 0.01460769 - samples/sec: 122.92 - lr: 0.001563 2022-11-06 18:10:37,580 ---------------------------------------------------------------------------------------------------- 2022-11-06 18:10:37,580 EPOCH 95 done: loss 0.0146 - lr 0.001563 2022-11-06 18:11:16,832 Evaluating as a multi-label problem: False 2022-11-06 18:11:16,860 TEST : loss 0.03295719251036644 - f1-score (micro avg) 0.8606 2022-11-06 18:11:17,347 BAD EPOCHS (no improvement): 3 2022-11-06 18:11:17,543 ---------------------------------------------------------------------------------------------------- 2022-11-06 18:11:24,304 epoch 96 - iter 27/274 - loss 0.01514952 - samples/sec: 127.90 - lr: 0.001563 2022-11-06 18:11:31,580 epoch 96 - iter 54/274 - loss 0.01473187 - samples/sec: 118.80 - lr: 0.001563 2022-11-06 18:11:38,422 epoch 96 - iter 81/274 - loss 0.01449173 - samples/sec: 126.34 - lr: 0.001563 2022-11-06 18:11:45,596 epoch 96 - iter 108/274 - loss 0.01358909 - samples/sec: 120.49 - lr: 0.001563 2022-11-06 18:11:52,333 epoch 96 - iter 135/274 - loss 0.01316116 - samples/sec: 128.32 - lr: 0.001563 2022-11-06 18:11:59,181 epoch 96 - iter 162/274 - loss 0.01250126 - samples/sec: 126.23 - lr: 0.001563 2022-11-06 18:12:06,391 epoch 96 - iter 189/274 - loss 0.01303754 - samples/sec: 119.89 - lr: 0.001563 2022-11-06 18:12:14,065 epoch 96 - iter 216/274 - loss 0.01342865 - samples/sec: 112.64 - lr: 0.001563 2022-11-06 18:12:20,797 epoch 96 - iter 243/274 - loss 0.01409094 - samples/sec: 128.41 - lr: 0.001563 2022-11-06 18:12:27,485 epoch 96 - iter 270/274 - loss 0.01388101 - samples/sec: 129.26 - lr: 0.001563 2022-11-06 18:12:28,667 ---------------------------------------------------------------------------------------------------- 2022-11-06 18:12:28,667 EPOCH 96 done: loss 0.0139 - lr 0.001563 2022-11-06 18:13:08,563 Evaluating as a multi-label problem: False 2022-11-06 18:13:08,590 TEST : loss 0.03304421156644821 - f1-score (micro avg) 0.8606 2022-11-06 18:13:09,072 Epoch 96: reducing learning rate of group 0 to 7.8125e-04. 2022-11-06 18:13:09,073 BAD EPOCHS (no improvement): 4 2022-11-06 18:13:09,267 ---------------------------------------------------------------------------------------------------- 2022-11-06 18:13:15,817 epoch 97 - iter 27/274 - loss 0.01631886 - samples/sec: 132.00 - lr: 0.000781 2022-11-06 18:13:22,896 epoch 97 - iter 54/274 - loss 0.01535972 - samples/sec: 122.12 - lr: 0.000781 2022-11-06 18:13:29,863 epoch 97 - iter 81/274 - loss 0.01559975 - samples/sec: 124.08 - lr: 0.000781 2022-11-06 18:13:37,045 epoch 97 - iter 108/274 - loss 0.01552846 - samples/sec: 120.36 - lr: 0.000781 2022-11-06 18:13:43,597 epoch 97 - iter 135/274 - loss 0.01504519 - samples/sec: 131.93 - lr: 0.000781 2022-11-06 18:13:50,545 epoch 97 - iter 162/274 - loss 0.01474325 - samples/sec: 124.42 - lr: 0.000781 2022-11-06 18:13:57,666 epoch 97 - iter 189/274 - loss 0.01400552 - samples/sec: 121.40 - lr: 0.000781 2022-11-06 18:14:04,586 epoch 97 - iter 216/274 - loss 0.01384067 - samples/sec: 124.91 - lr: 0.000781 2022-11-06 18:14:11,062 epoch 97 - iter 243/274 - loss 0.01404010 - samples/sec: 133.49 - lr: 0.000781 2022-11-06 18:14:17,890 epoch 97 - iter 270/274 - loss 0.01404755 - samples/sec: 126.60 - lr: 0.000781 2022-11-06 18:14:18,873 ---------------------------------------------------------------------------------------------------- 2022-11-06 18:14:18,873 EPOCH 97 done: loss 0.0141 - lr 0.000781 2022-11-06 18:14:58,380 Evaluating as a multi-label problem: False 2022-11-06 18:14:58,408 TEST : loss 0.03304709866642952 - f1-score (micro avg) 0.861 2022-11-06 18:14:58,894 BAD EPOCHS (no improvement): 1 2022-11-06 18:14:59,080 ---------------------------------------------------------------------------------------------------- 2022-11-06 18:15:05,047 epoch 98 - iter 27/274 - loss 0.01350572 - samples/sec: 144.94 - lr: 0.000781 2022-11-06 18:15:12,358 epoch 98 - iter 54/274 - loss 0.01212826 - samples/sec: 118.22 - lr: 0.000781 2022-11-06 18:15:19,614 epoch 98 - iter 81/274 - loss 0.01310284 - samples/sec: 119.14 - lr: 0.000781 2022-11-06 18:15:26,320 epoch 98 - iter 108/274 - loss 0.01301561 - samples/sec: 128.91 - lr: 0.000781 2022-11-06 18:15:33,631 epoch 98 - iter 135/274 - loss 0.01368174 - samples/sec: 118.24 - lr: 0.000781 2022-11-06 18:15:40,368 epoch 98 - iter 162/274 - loss 0.01402531 - samples/sec: 128.30 - lr: 0.000781 2022-11-06 18:15:47,197 epoch 98 - iter 189/274 - loss 0.01392584 - samples/sec: 126.60 - lr: 0.000781 2022-11-06 18:15:54,585 epoch 98 - iter 216/274 - loss 0.01368373 - samples/sec: 117.00 - lr: 0.000781 2022-11-06 18:16:01,228 epoch 98 - iter 243/274 - loss 0.01388010 - samples/sec: 130.13 - lr: 0.000781 2022-11-06 18:16:08,048 epoch 98 - iter 270/274 - loss 0.01391714 - samples/sec: 126.75 - lr: 0.000781 2022-11-06 18:16:08,947 ---------------------------------------------------------------------------------------------------- 2022-11-06 18:16:08,947 EPOCH 98 done: loss 0.0139 - lr 0.000781 2022-11-06 18:16:48,480 Evaluating as a multi-label problem: False 2022-11-06 18:16:48,508 TEST : loss 0.03312483802437782 - f1-score (micro avg) 0.8602 2022-11-06 18:16:48,994 BAD EPOCHS (no improvement): 2 2022-11-06 18:16:49,188 ---------------------------------------------------------------------------------------------------- 2022-11-06 18:16:55,673 epoch 99 - iter 27/274 - loss 0.01303624 - samples/sec: 133.34 - lr: 0.000781 2022-11-06 18:17:02,399 epoch 99 - iter 54/274 - loss 0.01388468 - samples/sec: 128.52 - lr: 0.000781 2022-11-06 18:17:09,003 epoch 99 - iter 81/274 - loss 0.01343001 - samples/sec: 130.90 - lr: 0.000781 2022-11-06 18:17:15,821 epoch 99 - iter 108/274 - loss 0.01293737 - samples/sec: 126.79 - lr: 0.000781 2022-11-06 18:17:22,900 epoch 99 - iter 135/274 - loss 0.01313668 - samples/sec: 122.11 - lr: 0.000781 2022-11-06 18:17:29,937 epoch 99 - iter 162/274 - loss 0.01360766 - samples/sec: 122.84 - lr: 0.000781 2022-11-06 18:17:36,715 epoch 99 - iter 189/274 - loss 0.01328352 - samples/sec: 127.53 - lr: 0.000781 2022-11-06 18:17:44,027 epoch 99 - iter 216/274 - loss 0.01336622 - samples/sec: 118.21 - lr: 0.000781 2022-11-06 18:17:50,929 epoch 99 - iter 243/274 - loss 0.01332725 - samples/sec: 125.24 - lr: 0.000781 2022-11-06 18:17:57,622 epoch 99 - iter 270/274 - loss 0.01354757 - samples/sec: 129.16 - lr: 0.000781 2022-11-06 18:17:58,599 ---------------------------------------------------------------------------------------------------- 2022-11-06 18:17:58,599 EPOCH 99 done: loss 0.0135 - lr 0.000781 2022-11-06 18:18:38,149 Evaluating as a multi-label problem: False 2022-11-06 18:18:38,176 TEST : loss 0.03310050442814827 - f1-score (micro avg) 0.8606 2022-11-06 18:18:38,662 BAD EPOCHS (no improvement): 0 2022-11-06 18:18:38,858 ---------------------------------------------------------------------------------------------------- 2022-11-06 18:18:45,478 epoch 100 - iter 27/274 - loss 0.01173419 - samples/sec: 130.62 - lr: 0.000781 2022-11-06 18:18:51,890 epoch 100 - iter 54/274 - loss 0.01067812 - samples/sec: 134.80 - lr: 0.000781 2022-11-06 18:18:59,336 epoch 100 - iter 81/274 - loss 0.01119261 - samples/sec: 116.10 - lr: 0.000781 2022-11-06 18:19:05,854 epoch 100 - iter 108/274 - loss 0.01196095 - samples/sec: 132.63 - lr: 0.000781 2022-11-06 18:19:12,887 epoch 100 - iter 135/274 - loss 0.01302049 - samples/sec: 122.90 - lr: 0.000781 2022-11-06 18:19:19,651 epoch 100 - iter 162/274 - loss 0.01351319 - samples/sec: 127.82 - lr: 0.000781 2022-11-06 18:19:26,365 epoch 100 - iter 189/274 - loss 0.01352599 - samples/sec: 128.74 - lr: 0.000781 2022-11-06 18:19:33,829 epoch 100 - iter 216/274 - loss 0.01371320 - samples/sec: 115.81 - lr: 0.000781 2022-11-06 18:19:40,623 epoch 100 - iter 243/274 - loss 0.01386506 - samples/sec: 127.24 - lr: 0.000781 2022-11-06 18:19:47,322 epoch 100 - iter 270/274 - loss 0.01403802 - samples/sec: 129.04 - lr: 0.000781 2022-11-06 18:19:48,107 ---------------------------------------------------------------------------------------------------- 2022-11-06 18:19:48,107 EPOCH 100 done: loss 0.0142 - lr 0.000781 2022-11-06 18:20:27,758 Evaluating as a multi-label problem: False 2022-11-06 18:20:27,786 TEST : loss 0.033117882907390594 - f1-score (micro avg) 0.8606 2022-11-06 18:20:28,269 BAD EPOCHS (no improvement): 1 2022-11-06 18:20:28,463 ---------------------------------------------------------------------------------------------------- 2022-11-06 18:20:35,509 epoch 101 - iter 27/274 - loss 0.01446495 - samples/sec: 122.71 - lr: 0.000781 2022-11-06 18:20:41,986 epoch 101 - iter 54/274 - loss 0.01527659 - samples/sec: 133.46 - lr: 0.000781 2022-11-06 18:20:48,980 epoch 101 - iter 81/274 - loss 0.01503021 - samples/sec: 123.60 - lr: 0.000781 2022-11-06 18:20:56,316 epoch 101 - iter 108/274 - loss 0.01499355 - samples/sec: 117.84 - lr: 0.000781 2022-11-06 18:21:03,299 epoch 101 - iter 135/274 - loss 0.01519732 - samples/sec: 123.78 - lr: 0.000781 2022-11-06 18:21:09,737 epoch 101 - iter 162/274 - loss 0.01559284 - samples/sec: 134.27 - lr: 0.000781 2022-11-06 18:21:16,568 epoch 101 - iter 189/274 - loss 0.01599842 - samples/sec: 126.55 - lr: 0.000781 2022-11-06 18:21:22,989 epoch 101 - iter 216/274 - loss 0.01565725 - samples/sec: 134.64 - lr: 0.000781 2022-11-06 18:21:30,602 epoch 101 - iter 243/274 - loss 0.01488667 - samples/sec: 113.54 - lr: 0.000781 2022-11-06 18:21:37,041 epoch 101 - iter 270/274 - loss 0.01484699 - samples/sec: 134.25 - lr: 0.000781 2022-11-06 18:21:37,898 ---------------------------------------------------------------------------------------------------- 2022-11-06 18:21:37,899 EPOCH 101 done: loss 0.0148 - lr 0.000781 2022-11-06 18:22:16,521 Evaluating as a multi-label problem: False 2022-11-06 18:22:16,548 TEST : loss 0.033024001866579056 - f1-score (micro avg) 0.8606 2022-11-06 18:22:17,030 BAD EPOCHS (no improvement): 2 2022-11-06 18:22:17,216 ---------------------------------------------------------------------------------------------------- 2022-11-06 18:22:24,215 epoch 102 - iter 27/274 - loss 0.01299900 - samples/sec: 123.52 - lr: 0.000781 2022-11-06 18:22:31,180 epoch 102 - iter 54/274 - loss 0.01228057 - samples/sec: 124.12 - lr: 0.000781 2022-11-06 18:22:37,763 epoch 102 - iter 81/274 - loss 0.01371390 - samples/sec: 131.32 - lr: 0.000781 2022-11-06 18:22:44,491 epoch 102 - iter 108/274 - loss 0.01370075 - samples/sec: 128.48 - lr: 0.000781 2022-11-06 18:22:51,216 epoch 102 - iter 135/274 - loss 0.01400902 - samples/sec: 128.54 - lr: 0.000781 2022-11-06 18:22:57,791 epoch 102 - iter 162/274 - loss 0.01383236 - samples/sec: 131.47 - lr: 0.000781 2022-11-06 18:23:04,621 epoch 102 - iter 189/274 - loss 0.01338305 - samples/sec: 126.56 - lr: 0.000781 2022-11-06 18:23:11,771 epoch 102 - iter 216/274 - loss 0.01370954 - samples/sec: 120.90 - lr: 0.000781 2022-11-06 18:23:18,425 epoch 102 - iter 243/274 - loss 0.01382908 - samples/sec: 129.92 - lr: 0.000781 2022-11-06 18:23:25,157 epoch 102 - iter 270/274 - loss 0.01405057 - samples/sec: 128.41 - lr: 0.000781 2022-11-06 18:23:26,322 ---------------------------------------------------------------------------------------------------- 2022-11-06 18:23:26,323 EPOCH 102 done: loss 0.0141 - lr 0.000781 2022-11-06 18:24:04,519 Evaluating as a multi-label problem: False 2022-11-06 18:24:04,546 TEST : loss 0.03306020051240921 - f1-score (micro avg) 0.8612 2022-11-06 18:24:05,027 BAD EPOCHS (no improvement): 3 2022-11-06 18:24:05,213 ---------------------------------------------------------------------------------------------------- 2022-11-06 18:24:12,170 epoch 103 - iter 27/274 - loss 0.01666539 - samples/sec: 124.28 - lr: 0.000781 2022-11-06 18:24:18,945 epoch 103 - iter 54/274 - loss 0.01602360 - samples/sec: 127.60 - lr: 0.000781 2022-11-06 18:24:25,411 epoch 103 - iter 81/274 - loss 0.01555850 - samples/sec: 133.69 - lr: 0.000781 2022-11-06 18:24:32,521 epoch 103 - iter 108/274 - loss 0.01530721 - samples/sec: 121.58 - lr: 0.000781 2022-11-06 18:24:38,980 epoch 103 - iter 135/274 - loss 0.01543109 - samples/sec: 133.85 - lr: 0.000781 2022-11-06 18:24:46,112 epoch 103 - iter 162/274 - loss 0.01540708 - samples/sec: 121.19 - lr: 0.000781 2022-11-06 18:24:52,575 epoch 103 - iter 189/274 - loss 0.01474083 - samples/sec: 133.77 - lr: 0.000781 2022-11-06 18:24:59,459 epoch 103 - iter 216/274 - loss 0.01510237 - samples/sec: 125.57 - lr: 0.000781 2022-11-06 18:25:06,746 epoch 103 - iter 243/274 - loss 0.01458476 - samples/sec: 118.62 - lr: 0.000781 2022-11-06 18:25:13,567 epoch 103 - iter 270/274 - loss 0.01434947 - samples/sec: 126.73 - lr: 0.000781 2022-11-06 18:25:14,553 ---------------------------------------------------------------------------------------------------- 2022-11-06 18:25:14,553 EPOCH 103 done: loss 0.0143 - lr 0.000781 2022-11-06 18:25:52,758 Evaluating as a multi-label problem: False 2022-11-06 18:25:52,785 TEST : loss 0.03303169831633568 - f1-score (micro avg) 0.8606 2022-11-06 18:25:53,266 Epoch 103: reducing learning rate of group 0 to 3.9063e-04. 2022-11-06 18:25:53,266 BAD EPOCHS (no improvement): 4 2022-11-06 18:25:53,461 ---------------------------------------------------------------------------------------------------- 2022-11-06 18:26:00,301 epoch 104 - iter 27/274 - loss 0.01458720 - samples/sec: 126.39 - lr: 0.000391 2022-11-06 18:26:06,870 epoch 104 - iter 54/274 - loss 0.01478208 - samples/sec: 131.60 - lr: 0.000391 2022-11-06 18:26:13,471 epoch 104 - iter 81/274 - loss 0.01413977 - samples/sec: 130.96 - lr: 0.000391 2022-11-06 18:26:20,493 epoch 104 - iter 108/274 - loss 0.01328514 - samples/sec: 123.09 - lr: 0.000391 2022-11-06 18:26:27,511 epoch 104 - iter 135/274 - loss 0.01342123 - samples/sec: 123.17 - lr: 0.000391 2022-11-06 18:26:34,557 epoch 104 - iter 162/274 - loss 0.01339805 - samples/sec: 122.68 - lr: 0.000391 2022-11-06 18:26:41,102 epoch 104 - iter 189/274 - loss 0.01314734 - samples/sec: 132.07 - lr: 0.000391 2022-11-06 18:26:47,901 epoch 104 - iter 216/274 - loss 0.01348532 - samples/sec: 127.15 - lr: 0.000391 2022-11-06 18:26:55,123 epoch 104 - iter 243/274 - loss 0.01362798 - samples/sec: 119.68 - lr: 0.000391 2022-11-06 18:27:01,560 epoch 104 - iter 270/274 - loss 0.01385963 - samples/sec: 134.30 - lr: 0.000391 2022-11-06 18:27:02,395 ---------------------------------------------------------------------------------------------------- 2022-11-06 18:27:02,395 EPOCH 104 done: loss 0.0140 - lr 0.000391 2022-11-06 18:27:40,671 Evaluating as a multi-label problem: False 2022-11-06 18:27:40,698 TEST : loss 0.03300449624657631 - f1-score (micro avg) 0.8599 2022-11-06 18:27:41,176 BAD EPOCHS (no improvement): 1 2022-11-06 18:27:41,381 ---------------------------------------------------------------------------------------------------- 2022-11-06 18:27:48,520 epoch 105 - iter 27/274 - loss 0.01424921 - samples/sec: 121.13 - lr: 0.000391 2022-11-06 18:27:55,181 epoch 105 - iter 54/274 - loss 0.01307246 - samples/sec: 129.79 - lr: 0.000391 2022-11-06 18:28:02,057 epoch 105 - iter 81/274 - loss 0.01321091 - samples/sec: 125.74 - lr: 0.000391 2022-11-06 18:28:09,527 epoch 105 - iter 108/274 - loss 0.01410278 - samples/sec: 115.72 - lr: 0.000391 2022-11-06 18:28:17,162 epoch 105 - iter 135/274 - loss 0.01435593 - samples/sec: 113.23 - lr: 0.000391 2022-11-06 18:28:24,461 epoch 105 - iter 162/274 - loss 0.01467838 - samples/sec: 118.44 - lr: 0.000391 2022-11-06 18:28:31,847 epoch 105 - iter 189/274 - loss 0.01504003 - samples/sec: 117.04 - lr: 0.000391 2022-11-06 18:28:39,460 epoch 105 - iter 216/274 - loss 0.01512150 - samples/sec: 113.56 - lr: 0.000391 2022-11-06 18:28:46,262 epoch 105 - iter 243/274 - loss 0.01506319 - samples/sec: 127.10 - lr: 0.000391 2022-11-06 18:28:53,624 epoch 105 - iter 270/274 - loss 0.01494025 - samples/sec: 117.43 - lr: 0.000391 2022-11-06 18:28:54,507 ---------------------------------------------------------------------------------------------------- 2022-11-06 18:28:54,508 EPOCH 105 done: loss 0.0148 - lr 0.000391 2022-11-06 18:29:33,247 Evaluating as a multi-label problem: False 2022-11-06 18:29:33,275 TEST : loss 0.03300108388066292 - f1-score (micro avg) 0.8602 2022-11-06 18:29:33,757 BAD EPOCHS (no improvement): 2 2022-11-06 18:29:33,950 ---------------------------------------------------------------------------------------------------- 2022-11-06 18:29:40,637 epoch 106 - iter 27/274 - loss 0.01323771 - samples/sec: 129.31 - lr: 0.000391 2022-11-06 18:29:47,600 epoch 106 - iter 54/274 - loss 0.01423093 - samples/sec: 124.15 - lr: 0.000391 2022-11-06 18:29:54,650 epoch 106 - iter 81/274 - loss 0.01435088 - samples/sec: 122.63 - lr: 0.000391 2022-11-06 18:30:00,781 epoch 106 - iter 108/274 - loss 0.01442826 - samples/sec: 141.01 - lr: 0.000391 2022-11-06 18:30:07,844 epoch 106 - iter 135/274 - loss 0.01447384 - samples/sec: 122.41 - lr: 0.000391 2022-11-06 18:30:14,798 epoch 106 - iter 162/274 - loss 0.01458546 - samples/sec: 124.31 - lr: 0.000391 2022-11-06 18:30:22,058 epoch 106 - iter 189/274 - loss 0.01454166 - samples/sec: 119.08 - lr: 0.000391 2022-11-06 18:30:29,202 epoch 106 - iter 216/274 - loss 0.01444348 - samples/sec: 121.01 - lr: 0.000391 2022-11-06 18:30:35,688 epoch 106 - iter 243/274 - loss 0.01454994 - samples/sec: 133.29 - lr: 0.000391 2022-11-06 18:30:42,938 epoch 106 - iter 270/274 - loss 0.01468079 - samples/sec: 119.23 - lr: 0.000391 2022-11-06 18:30:43,910 ---------------------------------------------------------------------------------------------------- 2022-11-06 18:30:43,911 EPOCH 106 done: loss 0.0147 - lr 0.000391 2022-11-06 18:31:22,621 Evaluating as a multi-label problem: False 2022-11-06 18:31:22,648 TEST : loss 0.03301873430609703 - f1-score (micro avg) 0.8597 2022-11-06 18:31:23,132 BAD EPOCHS (no improvement): 3 2022-11-06 18:31:23,326 ---------------------------------------------------------------------------------------------------- 2022-11-06 18:31:30,158 epoch 107 - iter 27/274 - loss 0.01520691 - samples/sec: 126.57 - lr: 0.000391 2022-11-06 18:31:37,709 epoch 107 - iter 54/274 - loss 0.01351666 - samples/sec: 114.49 - lr: 0.000391 2022-11-06 18:31:43,929 epoch 107 - iter 81/274 - loss 0.01320954 - samples/sec: 138.99 - lr: 0.000391 2022-11-06 18:31:50,154 epoch 107 - iter 108/274 - loss 0.01366281 - samples/sec: 138.89 - lr: 0.000391 2022-11-06 18:31:57,413 epoch 107 - iter 135/274 - loss 0.01426408 - samples/sec: 119.09 - lr: 0.000391 2022-11-06 18:32:04,661 epoch 107 - iter 162/274 - loss 0.01424168 - samples/sec: 119.28 - lr: 0.000391 2022-11-06 18:32:11,598 epoch 107 - iter 189/274 - loss 0.01423995 - samples/sec: 124.63 - lr: 0.000391 2022-11-06 18:32:19,167 epoch 107 - iter 216/274 - loss 0.01429583 - samples/sec: 114.21 - lr: 0.000391 2022-11-06 18:32:25,850 epoch 107 - iter 243/274 - loss 0.01412370 - samples/sec: 129.35 - lr: 0.000391 2022-11-06 18:32:33,094 epoch 107 - iter 270/274 - loss 0.01393433 - samples/sec: 119.33 - lr: 0.000391 2022-11-06 18:32:34,257 ---------------------------------------------------------------------------------------------------- 2022-11-06 18:32:34,257 EPOCH 107 done: loss 0.0141 - lr 0.000391 2022-11-06 18:33:12,544 Evaluating as a multi-label problem: False 2022-11-06 18:33:12,571 TEST : loss 0.03307473659515381 - f1-score (micro avg) 0.8599 2022-11-06 18:33:13,051 Epoch 107: reducing learning rate of group 0 to 1.9531e-04. 2022-11-06 18:33:13,052 BAD EPOCHS (no improvement): 4 2022-11-06 18:33:13,238 ---------------------------------------------------------------------------------------------------- 2022-11-06 18:33:20,221 epoch 108 - iter 27/274 - loss 0.01341064 - samples/sec: 123.81 - lr: 0.000195 2022-11-06 18:33:27,208 epoch 108 - iter 54/274 - loss 0.01383712 - samples/sec: 123.73 - lr: 0.000195 2022-11-06 18:33:33,586 epoch 108 - iter 81/274 - loss 0.01379724 - samples/sec: 135.53 - lr: 0.000195 2022-11-06 18:33:39,724 epoch 108 - iter 108/274 - loss 0.01386903 - samples/sec: 140.85 - lr: 0.000195 2022-11-06 18:33:46,853 epoch 108 - iter 135/274 - loss 0.01347630 - samples/sec: 121.24 - lr: 0.000195 2022-11-06 18:33:53,684 epoch 108 - iter 162/274 - loss 0.01355865 - samples/sec: 126.55 - lr: 0.000195 2022-11-06 18:34:00,437 epoch 108 - iter 189/274 - loss 0.01367054 - samples/sec: 128.00 - lr: 0.000195 2022-11-06 18:34:07,227 epoch 108 - iter 216/274 - loss 0.01378129 - samples/sec: 127.32 - lr: 0.000195 2022-11-06 18:34:14,006 epoch 108 - iter 243/274 - loss 0.01361853 - samples/sec: 127.51 - lr: 0.000195 2022-11-06 18:34:21,311 epoch 108 - iter 270/274 - loss 0.01381685 - samples/sec: 118.32 - lr: 0.000195 2022-11-06 18:34:22,258 ---------------------------------------------------------------------------------------------------- 2022-11-06 18:34:22,259 EPOCH 108 done: loss 0.0138 - lr 0.000195 2022-11-06 18:35:00,251 Evaluating as a multi-label problem: False 2022-11-06 18:35:00,278 TEST : loss 0.03307962417602539 - f1-score (micro avg) 0.8597 2022-11-06 18:35:00,754 BAD EPOCHS (no improvement): 1 2022-11-06 18:35:00,948 ---------------------------------------------------------------------------------------------------- 2022-11-06 18:35:07,805 epoch 109 - iter 27/274 - loss 0.01662803 - samples/sec: 126.08 - lr: 0.000195 2022-11-06 18:35:14,700 epoch 109 - iter 54/274 - loss 0.01484573 - samples/sec: 125.37 - lr: 0.000195 2022-11-06 18:35:21,313 epoch 109 - iter 81/274 - loss 0.01494539 - samples/sec: 130.73 - lr: 0.000195 2022-11-06 18:35:27,707 epoch 109 - iter 108/274 - loss 0.01471633 - samples/sec: 135.19 - lr: 0.000195 2022-11-06 18:35:33,818 epoch 109 - iter 135/274 - loss 0.01508456 - samples/sec: 141.46 - lr: 0.000195 2022-11-06 18:35:40,697 epoch 109 - iter 162/274 - loss 0.01442215 - samples/sec: 125.65 - lr: 0.000195 2022-11-06 18:35:47,399 epoch 109 - iter 189/274 - loss 0.01444187 - samples/sec: 128.99 - lr: 0.000195 2022-11-06 18:35:54,223 epoch 109 - iter 216/274 - loss 0.01456026 - samples/sec: 126.68 - lr: 0.000195 2022-11-06 18:36:01,615 epoch 109 - iter 243/274 - loss 0.01457428 - samples/sec: 116.93 - lr: 0.000195 2022-11-06 18:36:08,768 epoch 109 - iter 270/274 - loss 0.01423025 - samples/sec: 120.86 - lr: 0.000195 2022-11-06 18:36:09,828 ---------------------------------------------------------------------------------------------------- 2022-11-06 18:36:09,828 EPOCH 109 done: loss 0.0142 - lr 0.000195 2022-11-06 18:36:48,310 Evaluating as a multi-label problem: False 2022-11-06 18:36:48,337 TEST : loss 0.03311078995466232 - f1-score (micro avg) 0.8595 2022-11-06 18:36:48,815 BAD EPOCHS (no improvement): 2 2022-11-06 18:36:49,009 ---------------------------------------------------------------------------------------------------- 2022-11-06 18:36:56,107 epoch 110 - iter 27/274 - loss 0.01420081 - samples/sec: 121.82 - lr: 0.000195 2022-11-06 18:37:02,664 epoch 110 - iter 54/274 - loss 0.01471469 - samples/sec: 131.83 - lr: 0.000195 2022-11-06 18:37:09,423 epoch 110 - iter 81/274 - loss 0.01401599 - samples/sec: 127.90 - lr: 0.000195 2022-11-06 18:37:16,249 epoch 110 - iter 108/274 - loss 0.01383453 - samples/sec: 126.62 - lr: 0.000195 2022-11-06 18:37:22,420 epoch 110 - iter 135/274 - loss 0.01360067 - samples/sec: 140.10 - lr: 0.000195 2022-11-06 18:37:29,478 epoch 110 - iter 162/274 - loss 0.01429213 - samples/sec: 122.48 - lr: 0.000195 2022-11-06 18:37:36,520 epoch 110 - iter 189/274 - loss 0.01420927 - samples/sec: 122.75 - lr: 0.000195 2022-11-06 18:37:43,579 epoch 110 - iter 216/274 - loss 0.01414622 - samples/sec: 122.44 - lr: 0.000195 2022-11-06 18:37:50,248 epoch 110 - iter 243/274 - loss 0.01441753 - samples/sec: 129.63 - lr: 0.000195 2022-11-06 18:37:57,514 epoch 110 - iter 270/274 - loss 0.01414675 - samples/sec: 118.96 - lr: 0.000195 2022-11-06 18:37:58,412 ---------------------------------------------------------------------------------------------------- 2022-11-06 18:37:58,412 EPOCH 110 done: loss 0.0142 - lr 0.000195 2022-11-06 18:38:36,472 Evaluating as a multi-label problem: False 2022-11-06 18:38:36,499 TEST : loss 0.03310991823673248 - f1-score (micro avg) 0.8595 2022-11-06 18:38:36,979 BAD EPOCHS (no improvement): 3 2022-11-06 18:38:37,171 ---------------------------------------------------------------------------------------------------- 2022-11-06 18:38:43,854 epoch 111 - iter 27/274 - loss 0.01325495 - samples/sec: 129.37 - lr: 0.000195 2022-11-06 18:38:50,585 epoch 111 - iter 54/274 - loss 0.01480494 - samples/sec: 128.44 - lr: 0.000195 2022-11-06 18:38:56,968 epoch 111 - iter 81/274 - loss 0.01426950 - samples/sec: 135.43 - lr: 0.000195 2022-11-06 18:39:03,775 epoch 111 - iter 108/274 - loss 0.01357019 - samples/sec: 126.99 - lr: 0.000195 2022-11-06 18:39:10,371 epoch 111 - iter 135/274 - loss 0.01367704 - samples/sec: 131.05 - lr: 0.000195 2022-11-06 18:39:17,547 epoch 111 - iter 162/274 - loss 0.01362901 - samples/sec: 120.46 - lr: 0.000195 2022-11-06 18:39:24,202 epoch 111 - iter 189/274 - loss 0.01359315 - samples/sec: 129.88 - lr: 0.000195 2022-11-06 18:39:31,146 epoch 111 - iter 216/274 - loss 0.01350941 - samples/sec: 124.48 - lr: 0.000195 2022-11-06 18:39:38,281 epoch 111 - iter 243/274 - loss 0.01307953 - samples/sec: 121.16 - lr: 0.000195 2022-11-06 18:39:45,320 epoch 111 - iter 270/274 - loss 0.01322726 - samples/sec: 122.80 - lr: 0.000195 2022-11-06 18:39:46,429 ---------------------------------------------------------------------------------------------------- 2022-11-06 18:39:46,429 EPOCH 111 done: loss 0.0132 - lr 0.000195 2022-11-06 18:40:24,772 Evaluating as a multi-label problem: False 2022-11-06 18:40:24,799 TEST : loss 0.033106669783592224 - f1-score (micro avg) 0.8599 2022-11-06 18:40:25,282 BAD EPOCHS (no improvement): 0 2022-11-06 18:40:25,471 ---------------------------------------------------------------------------------------------------- 2022-11-06 18:40:32,143 epoch 112 - iter 27/274 - loss 0.01245997 - samples/sec: 129.59 - lr: 0.000195 2022-11-06 18:40:38,614 epoch 112 - iter 54/274 - loss 0.01185445 - samples/sec: 133.59 - lr: 0.000195 2022-11-06 18:40:45,107 epoch 112 - iter 81/274 - loss 0.01251068 - samples/sec: 133.12 - lr: 0.000195 2022-11-06 18:40:51,963 epoch 112 - iter 108/274 - loss 0.01286266 - samples/sec: 126.10 - lr: 0.000195 2022-11-06 18:40:58,321 epoch 112 - iter 135/274 - loss 0.01254586 - samples/sec: 135.97 - lr: 0.000195 2022-11-06 18:41:05,122 epoch 112 - iter 162/274 - loss 0.01277581 - samples/sec: 127.09 - lr: 0.000195 2022-11-06 18:41:12,018 epoch 112 - iter 189/274 - loss 0.01289788 - samples/sec: 125.36 - lr: 0.000195 2022-11-06 18:41:19,138 epoch 112 - iter 216/274 - loss 0.01301322 - samples/sec: 121.41 - lr: 0.000195 2022-11-06 18:41:26,088 epoch 112 - iter 243/274 - loss 0.01336748 - samples/sec: 124.37 - lr: 0.000195 2022-11-06 18:41:34,544 epoch 112 - iter 270/274 - loss 0.01361051 - samples/sec: 102.22 - lr: 0.000195 2022-11-06 18:41:35,386 ---------------------------------------------------------------------------------------------------- 2022-11-06 18:41:35,386 EPOCH 112 done: loss 0.0136 - lr 0.000195 2022-11-06 18:42:12,805 Evaluating as a multi-label problem: False 2022-11-06 18:42:12,832 TEST : loss 0.0330953449010849 - f1-score (micro avg) 0.8599 2022-11-06 18:42:13,312 BAD EPOCHS (no improvement): 1 2022-11-06 18:42:13,502 ---------------------------------------------------------------------------------------------------- 2022-11-06 18:42:20,764 epoch 113 - iter 27/274 - loss 0.01344772 - samples/sec: 119.06 - lr: 0.000195 2022-11-06 18:42:27,690 epoch 113 - iter 54/274 - loss 0.01443830 - samples/sec: 124.80 - lr: 0.000195 2022-11-06 18:42:34,662 epoch 113 - iter 81/274 - loss 0.01345180 - samples/sec: 124.00 - lr: 0.000195 2022-11-06 18:42:41,177 epoch 113 - iter 108/274 - loss 0.01251339 - samples/sec: 132.68 - lr: 0.000195 2022-11-06 18:42:48,073 epoch 113 - iter 135/274 - loss 0.01268490 - samples/sec: 125.34 - lr: 0.000195 2022-11-06 18:42:55,169 epoch 113 - iter 162/274 - loss 0.01318989 - samples/sec: 121.82 - lr: 0.000195 2022-11-06 18:43:01,527 epoch 113 - iter 189/274 - loss 0.01345892 - samples/sec: 135.96 - lr: 0.000195 2022-11-06 18:43:08,435 epoch 113 - iter 216/274 - loss 0.01329989 - samples/sec: 125.13 - lr: 0.000195 2022-11-06 18:43:15,278 epoch 113 - iter 243/274 - loss 0.01326442 - samples/sec: 126.32 - lr: 0.000195 2022-11-06 18:43:22,318 epoch 113 - iter 270/274 - loss 0.01333745 - samples/sec: 122.79 - lr: 0.000195 2022-11-06 18:43:23,256 ---------------------------------------------------------------------------------------------------- 2022-11-06 18:43:23,257 EPOCH 113 done: loss 0.0133 - lr 0.000195 2022-11-06 18:44:01,581 Evaluating as a multi-label problem: False 2022-11-06 18:44:01,608 TEST : loss 0.03308477625250816 - f1-score (micro avg) 0.8603 2022-11-06 18:44:02,086 BAD EPOCHS (no improvement): 2 2022-11-06 18:44:02,278 ---------------------------------------------------------------------------------------------------- 2022-11-06 18:44:09,469 epoch 114 - iter 27/274 - loss 0.01578122 - samples/sec: 120.23 - lr: 0.000195 2022-11-06 18:44:16,088 epoch 114 - iter 54/274 - loss 0.01413436 - samples/sec: 130.61 - lr: 0.000195 2022-11-06 18:44:22,746 epoch 114 - iter 81/274 - loss 0.01455526 - samples/sec: 129.84 - lr: 0.000195 2022-11-06 18:44:30,070 epoch 114 - iter 108/274 - loss 0.01475374 - samples/sec: 118.01 - lr: 0.000195 2022-11-06 18:44:37,009 epoch 114 - iter 135/274 - loss 0.01452364 - samples/sec: 124.58 - lr: 0.000195 2022-11-06 18:44:42,974 epoch 114 - iter 162/274 - loss 0.01457320 - samples/sec: 144.94 - lr: 0.000195 2022-11-06 18:44:49,371 epoch 114 - iter 189/274 - loss 0.01454147 - samples/sec: 135.13 - lr: 0.000195 2022-11-06 18:44:56,395 epoch 114 - iter 216/274 - loss 0.01467951 - samples/sec: 123.06 - lr: 0.000195 2022-11-06 18:45:03,117 epoch 114 - iter 243/274 - loss 0.01463831 - samples/sec: 128.60 - lr: 0.000195 2022-11-06 18:45:10,263 epoch 114 - iter 270/274 - loss 0.01451383 - samples/sec: 120.96 - lr: 0.000195 2022-11-06 18:45:11,203 ---------------------------------------------------------------------------------------------------- 2022-11-06 18:45:11,203 EPOCH 114 done: loss 0.0144 - lr 0.000195 2022-11-06 18:45:49,291 Evaluating as a multi-label problem: False 2022-11-06 18:45:49,318 TEST : loss 0.03305461257696152 - f1-score (micro avg) 0.8605 2022-11-06 18:45:49,802 BAD EPOCHS (no improvement): 3 2022-11-06 18:45:49,995 ---------------------------------------------------------------------------------------------------- 2022-11-06 18:45:56,404 epoch 115 - iter 27/274 - loss 0.01136031 - samples/sec: 134.93 - lr: 0.000195 2022-11-06 18:46:03,117 epoch 115 - iter 54/274 - loss 0.01181110 - samples/sec: 128.76 - lr: 0.000195 2022-11-06 18:46:09,705 epoch 115 - iter 81/274 - loss 0.01307147 - samples/sec: 131.21 - lr: 0.000195 2022-11-06 18:46:16,290 epoch 115 - iter 108/274 - loss 0.01254648 - samples/sec: 131.29 - lr: 0.000195 2022-11-06 18:46:22,948 epoch 115 - iter 135/274 - loss 0.01322313 - samples/sec: 129.83 - lr: 0.000195 2022-11-06 18:46:29,878 epoch 115 - iter 162/274 - loss 0.01396292 - samples/sec: 124.74 - lr: 0.000195 2022-11-06 18:46:36,909 epoch 115 - iter 189/274 - loss 0.01351193 - samples/sec: 122.93 - lr: 0.000195 2022-11-06 18:46:43,462 epoch 115 - iter 216/274 - loss 0.01383586 - samples/sec: 131.92 - lr: 0.000195 2022-11-06 18:46:50,365 epoch 115 - iter 243/274 - loss 0.01381672 - samples/sec: 125.23 - lr: 0.000195 2022-11-06 18:46:57,761 epoch 115 - iter 270/274 - loss 0.01364803 - samples/sec: 116.87 - lr: 0.000195 2022-11-06 18:46:58,844 ---------------------------------------------------------------------------------------------------- 2022-11-06 18:46:58,844 EPOCH 115 done: loss 0.0137 - lr 0.000195 2022-11-06 18:47:36,834 Evaluating as a multi-label problem: False 2022-11-06 18:47:36,861 TEST : loss 0.03304998576641083 - f1-score (micro avg) 0.8605 2022-11-06 18:47:37,342 Epoch 115: reducing learning rate of group 0 to 9.7656e-05. 2022-11-06 18:47:37,342 BAD EPOCHS (no improvement): 4 2022-11-06 18:47:37,529 ---------------------------------------------------------------------------------------------------- 2022-11-06 18:47:37,530 ---------------------------------------------------------------------------------------------------- 2022-11-06 18:47:37,530 learning rate too small - quitting training! 2022-11-06 18:47:37,530 ---------------------------------------------------------------------------------------------------- 2022-11-06 18:47:37,659 ---------------------------------------------------------------------------------------------------- 2022-11-06 18:47:37,659 Testing using last state of model ... 2022-11-06 18:48:16,666 Evaluating as a multi-label problem: False 2022-11-06 18:48:16,692 0.8616 0.8593 0.8605 0.8033 2022-11-06 18:48:16,692 Results: - F-score (micro) 0.8605 - F-score (macro) 0.7472 - Accuracy 0.8033 By class: precision recall f1-score support PERS 0.9305 0.9422 0.9363 1678 LOC 0.8150 0.8678 0.8406 401 ORG 0.6653 0.6092 0.6360 261 MISC 0.6202 0.5375 0.5759 240 micro avg 0.8616 0.8593 0.8605 2580 macro avg 0.7577 0.7392 0.7472 2580 weighted avg 0.8569 0.8593 0.8575 2580 2022-11-06 18:48:16,693 ----------------------------------------------------------------------------------------------------