diff --git "a/training.log" "b/training.log" --- "a/training.log" +++ "b/training.log" @@ -1,5 +1,5 @@ -2022-11-01 12:49:03,490 ---------------------------------------------------------------------------------------------------- -2022-11-01 12:49:03,490 Model: "SequenceTagger( +2022-11-06 15:34:46,075 ---------------------------------------------------------------------------------------------------- +2022-11-06 15:34:46,075 Model: "SequenceTagger( (embeddings): StackedEmbeddings( (list_embedding_0): FlairEmbeddings( (lm): LanguageModel( @@ -27,2394 +27,1898 @@ (loss_function): ViterbiLoss() (crf): CRF() )" -2022-11-01 12:49:03,490 ---------------------------------------------------------------------------------------------------- -2022-11-01 12:49:03,490 Corpus: "Corpus: 7886 train + 876 dev + 4045 test sentences" -2022-11-01 12:49:03,490 ---------------------------------------------------------------------------------------------------- -2022-11-01 12:49:03,490 Parameters: -2022-11-01 12:49:03,490 - learning_rate: "0.100000" -2022-11-01 12:49:03,490 - mini_batch_size: "32" -2022-11-01 12:49:03,490 - patience: "3" -2022-11-01 12:49:03,490 - anneal_factor: "0.5" -2022-11-01 12:49:03,490 - max_epochs: "150" -2022-11-01 12:49:03,490 - shuffle: "True" -2022-11-01 12:49:03,490 - train_with_dev: "True" -2022-11-01 12:49:03,490 - batch_growth_annealing: "False" -2022-11-01 12:49:03,490 ---------------------------------------------------------------------------------------------------- -2022-11-01 12:49:03,490 Model training base path: "ner-tests/uk.flairembeddings.champ" -2022-11-01 12:49:03,491 ---------------------------------------------------------------------------------------------------- -2022-11-01 12:49:03,491 Device: cpu -2022-11-01 12:49:03,491 ---------------------------------------------------------------------------------------------------- -2022-11-01 12:49:03,491 Embeddings storage mode: gpu -2022-11-01 12:49:03,491 ---------------------------------------------------------------------------------------------------- -2022-11-01 12:50:09,565 epoch 1 - iter 27/274 - loss 0.61951446 - samples/sec: 13.08 - lr: 0.100000 -2022-11-01 12:51:14,717 epoch 1 - iter 54/274 - loss 0.51046988 - samples/sec: 13.26 - lr: 0.100000 -2022-11-01 12:52:22,958 epoch 1 - iter 81/274 - loss 0.40838070 - samples/sec: 12.66 - lr: 0.100000 -2022-11-01 12:53:46,868 epoch 1 - iter 108/274 - loss 0.36574824 - samples/sec: 10.30 - lr: 0.100000 -2022-11-01 12:54:38,313 epoch 1 - iter 135/274 - loss 0.32373590 - samples/sec: 16.80 - lr: 0.100000 -2022-11-01 12:55:23,882 epoch 1 - iter 162/274 - loss 0.28818606 - samples/sec: 18.96 - lr: 0.100000 -2022-11-01 12:56:20,788 epoch 1 - iter 189/274 - loss 0.25828452 - samples/sec: 15.18 - lr: 0.100000 -2022-11-01 12:56:58,874 epoch 1 - iter 216/274 - loss 0.23950005 - samples/sec: 22.69 - lr: 0.100000 -2022-11-01 12:57:50,282 epoch 1 - iter 243/274 - loss 0.22160623 - samples/sec: 16.81 - lr: 0.100000 -2022-11-01 12:58:46,515 epoch 1 - iter 270/274 - loss 0.21126815 - samples/sec: 15.37 - lr: 0.100000 -2022-11-01 12:58:55,152 ---------------------------------------------------------------------------------------------------- -2022-11-01 12:58:55,153 EPOCH 1 done: loss 0.2089 - lr 0.100000 -2022-11-01 13:03:03,629 Evaluating as a multi-label problem: False -2022-11-01 13:03:03,647 TEST : loss 0.13904191553592682 - f1-score (micro avg) 0.5512 -2022-11-01 13:03:03,698 BAD EPOCHS (no improvement): 0 -2022-11-01 13:03:03,756 ---------------------------------------------------------------------------------------------------- -2022-11-01 13:03:15,574 epoch 2 - iter 27/274 - loss 0.10819005 - samples/sec: 73.14 - lr: 0.100000 -2022-11-01 13:03:27,236 epoch 2 - iter 54/274 - loss 0.10311525 - samples/sec: 74.11 - lr: 0.100000 -2022-11-01 13:03:39,859 epoch 2 - iter 81/274 - loss 0.10132389 - samples/sec: 68.46 - lr: 0.100000 -2022-11-01 13:03:50,897 epoch 2 - iter 108/274 - loss 0.10064433 - samples/sec: 78.30 - lr: 0.100000 -2022-11-01 13:04:02,826 epoch 2 - iter 135/274 - loss 0.10064467 - samples/sec: 72.45 - lr: 0.100000 -2022-11-01 13:04:16,449 epoch 2 - iter 162/274 - loss 0.10836500 - samples/sec: 63.44 - lr: 0.100000 -2022-11-01 13:04:28,828 epoch 2 - iter 189/274 - loss 0.10522271 - samples/sec: 69.81 - lr: 0.100000 -2022-11-01 13:04:42,197 epoch 2 - iter 216/274 - loss 0.10281006 - samples/sec: 64.64 - lr: 0.100000 -2022-11-01 13:04:54,240 epoch 2 - iter 243/274 - loss 0.10025360 - samples/sec: 71.77 - lr: 0.100000 -2022-11-01 13:05:07,501 epoch 2 - iter 270/274 - loss 0.09897187 - samples/sec: 65.17 - lr: 0.100000 -2022-11-01 13:05:10,043 ---------------------------------------------------------------------------------------------------- -2022-11-01 13:05:10,043 EPOCH 2 done: loss 0.0992 - lr 0.100000 -2022-11-01 13:05:35,346 Evaluating as a multi-label problem: False -2022-11-01 13:05:35,362 TEST : loss 0.08393135666847229 - f1-score (micro avg) 0.739 -2022-11-01 13:05:35,413 BAD EPOCHS (no improvement): 0 -2022-11-01 13:05:35,503 ---------------------------------------------------------------------------------------------------- -2022-11-01 13:05:48,724 epoch 3 - iter 27/274 - loss 0.08097851 - samples/sec: 65.37 - lr: 0.100000 -2022-11-01 13:06:01,967 epoch 3 - iter 54/274 - loss 0.07444780 - samples/sec: 65.26 - lr: 0.100000 -2022-11-01 13:06:14,499 epoch 3 - iter 81/274 - loss 0.07164834 - samples/sec: 68.96 - lr: 0.100000 -2022-11-01 13:06:26,228 epoch 3 - iter 108/274 - loss 0.07237300 - samples/sec: 73.68 - lr: 0.100000 -2022-11-01 13:06:37,579 epoch 3 - iter 135/274 - loss 0.07152923 - samples/sec: 76.14 - lr: 0.100000 -2022-11-01 13:06:50,615 epoch 3 - iter 162/274 - loss 0.07222106 - samples/sec: 66.30 - lr: 0.100000 -2022-11-01 13:07:03,210 epoch 3 - iter 189/274 - loss 0.07203354 - samples/sec: 68.62 - lr: 0.100000 -2022-11-01 13:07:16,259 epoch 3 - iter 216/274 - loss 0.07359814 - samples/sec: 66.23 - lr: 0.100000 -2022-11-01 13:07:27,473 epoch 3 - iter 243/274 - loss 0.07244371 - samples/sec: 77.07 - lr: 0.100000 -2022-11-01 13:07:39,487 epoch 3 - iter 270/274 - loss 0.07220347 - samples/sec: 71.94 - lr: 0.100000 -2022-11-01 13:07:41,274 ---------------------------------------------------------------------------------------------------- -2022-11-01 13:07:41,274 EPOCH 3 done: loss 0.0718 - lr 0.100000 -2022-11-01 13:08:06,634 Evaluating as a multi-label problem: False -2022-11-01 13:08:06,649 TEST : loss 0.06586140394210815 - f1-score (micro avg) 0.7879 -2022-11-01 13:08:06,702 BAD EPOCHS (no improvement): 0 -2022-11-01 13:08:06,788 ---------------------------------------------------------------------------------------------------- -2022-11-01 13:08:17,828 epoch 4 - iter 27/274 - loss 0.06119555 - samples/sec: 78.29 - lr: 0.100000 -2022-11-01 13:08:30,426 epoch 4 - iter 54/274 - loss 0.06264965 - samples/sec: 68.60 - lr: 0.100000 -2022-11-01 13:08:42,231 epoch 4 - iter 81/274 - loss 0.06322773 - samples/sec: 73.21 - lr: 0.100000 -2022-11-01 13:08:53,700 epoch 4 - iter 108/274 - loss 0.06038977 - samples/sec: 75.35 - lr: 0.100000 -2022-11-01 13:09:06,851 epoch 4 - iter 135/274 - loss 0.06248566 - samples/sec: 65.71 - lr: 0.100000 -2022-11-01 13:09:19,477 epoch 4 - iter 162/274 - loss 0.06279878 - samples/sec: 68.45 - lr: 0.100000 -2022-11-01 13:09:33,000 epoch 4 - iter 189/274 - loss 0.06245445 - samples/sec: 63.91 - lr: 0.100000 -2022-11-01 13:09:45,946 epoch 4 - iter 216/274 - loss 0.06230904 - samples/sec: 66.76 - lr: 0.100000 -2022-11-01 13:09:59,830 epoch 4 - iter 243/274 - loss 0.06128421 - samples/sec: 62.24 - lr: 0.100000 -2022-11-01 13:10:11,584 epoch 4 - iter 270/274 - loss 0.06083109 - samples/sec: 73.53 - lr: 0.100000 -2022-11-01 13:10:13,492 ---------------------------------------------------------------------------------------------------- -2022-11-01 13:10:13,492 EPOCH 4 done: loss 0.0608 - lr 0.100000 -2022-11-01 13:10:38,798 Evaluating as a multi-label problem: False -2022-11-01 13:10:38,813 TEST : loss 0.061087507754564285 - f1-score (micro avg) 0.7982 -2022-11-01 13:10:38,866 BAD EPOCHS (no improvement): 0 -2022-11-01 13:10:38,953 ---------------------------------------------------------------------------------------------------- -2022-11-01 13:10:50,869 epoch 5 - iter 27/274 - loss 0.05534610 - samples/sec: 72.53 - lr: 0.100000 -2022-11-01 13:11:02,268 epoch 5 - iter 54/274 - loss 0.05243949 - samples/sec: 75.82 - lr: 0.100000 -2022-11-01 13:11:16,824 epoch 5 - iter 81/274 - loss 0.05033856 - samples/sec: 59.37 - lr: 0.100000 -2022-11-01 13:11:29,824 epoch 5 - iter 108/274 - loss 0.05199909 - samples/sec: 66.48 - lr: 0.100000 -2022-11-01 13:11:42,909 epoch 5 - iter 135/274 - loss 0.05390198 - samples/sec: 66.05 - lr: 0.100000 -2022-11-01 13:11:54,210 epoch 5 - iter 162/274 - loss 0.05559011 - samples/sec: 76.47 - lr: 0.100000 -2022-11-01 13:12:07,160 epoch 5 - iter 189/274 - loss 0.05545001 - samples/sec: 66.74 - lr: 0.100000 -2022-11-01 13:12:18,635 epoch 5 - iter 216/274 - loss 0.05452558 - samples/sec: 75.32 - lr: 0.100000 -2022-11-01 13:12:30,242 epoch 5 - iter 243/274 - loss 0.05387747 - samples/sec: 74.46 - lr: 0.100000 -2022-11-01 13:12:43,258 epoch 5 - iter 270/274 - loss 0.05358782 - samples/sec: 66.40 - lr: 0.100000 -2022-11-01 13:12:44,917 ---------------------------------------------------------------------------------------------------- -2022-11-01 13:12:44,917 EPOCH 5 done: loss 0.0535 - lr 0.100000 -2022-11-01 13:13:10,313 Evaluating as a multi-label problem: False -2022-11-01 13:13:10,329 TEST : loss 0.045144665986299515 - f1-score (micro avg) 0.8098 -2022-11-01 13:13:10,381 BAD EPOCHS (no improvement): 0 -2022-11-01 13:13:10,468 ---------------------------------------------------------------------------------------------------- -2022-11-01 13:13:23,647 epoch 6 - iter 27/274 - loss 0.05037224 - samples/sec: 65.57 - lr: 0.100000 -2022-11-01 13:13:36,723 epoch 6 - iter 54/274 - loss 0.04536131 - samples/sec: 66.09 - lr: 0.100000 -2022-11-01 13:13:49,037 epoch 6 - iter 81/274 - loss 0.04718256 - samples/sec: 70.18 - lr: 0.100000 -2022-11-01 13:14:01,344 epoch 6 - iter 108/274 - loss 0.04840201 - samples/sec: 70.22 - lr: 0.100000 -2022-11-01 13:14:14,416 epoch 6 - iter 135/274 - loss 0.04792430 - samples/sec: 66.11 - lr: 0.100000 -2022-11-01 13:14:26,815 epoch 6 - iter 162/274 - loss 0.04791153 - samples/sec: 69.70 - lr: 0.100000 -2022-11-01 13:14:38,924 epoch 6 - iter 189/274 - loss 0.04811161 - samples/sec: 71.37 - lr: 0.100000 -2022-11-01 13:14:50,571 epoch 6 - iter 216/274 - loss 0.04808548 - samples/sec: 74.20 - lr: 0.100000 -2022-11-01 13:15:02,316 epoch 6 - iter 243/274 - loss 0.04851904 - samples/sec: 73.59 - lr: 0.100000 -2022-11-01 13:15:15,000 epoch 6 - iter 270/274 - loss 0.04914944 - samples/sec: 68.13 - lr: 0.100000 -2022-11-01 13:15:16,810 ---------------------------------------------------------------------------------------------------- -2022-11-01 13:15:16,811 EPOCH 6 done: loss 0.0491 - lr 0.100000 -2022-11-01 13:15:42,123 Evaluating as a multi-label problem: False -2022-11-01 13:15:42,139 TEST : loss 0.03993703052401543 - f1-score (micro avg) 0.8164 -2022-11-01 13:15:42,191 BAD EPOCHS (no improvement): 0 -2022-11-01 13:15:42,277 ---------------------------------------------------------------------------------------------------- -2022-11-01 13:15:54,919 epoch 7 - iter 27/274 - loss 0.04702372 - samples/sec: 68.36 - lr: 0.100000 -2022-11-01 13:16:08,351 epoch 7 - iter 54/274 - loss 0.04766522 - samples/sec: 64.34 - lr: 0.100000 -2022-11-01 13:16:20,530 epoch 7 - iter 81/274 - loss 0.04786969 - samples/sec: 70.96 - lr: 0.100000 -2022-11-01 13:16:33,124 epoch 7 - iter 108/274 - loss 0.04794053 - samples/sec: 68.62 - lr: 0.100000 -2022-11-01 13:16:44,786 epoch 7 - iter 135/274 - loss 0.04732043 - samples/sec: 74.11 - lr: 0.100000 -2022-11-01 13:16:56,876 epoch 7 - iter 162/274 - loss 0.04645068 - samples/sec: 71.48 - lr: 0.100000 -2022-11-01 13:17:07,762 epoch 7 - iter 189/274 - loss 0.04566414 - samples/sec: 79.39 - lr: 0.100000 -2022-11-01 13:17:20,361 epoch 7 - iter 216/274 - loss 0.04596395 - samples/sec: 68.59 - lr: 0.100000 -2022-11-01 13:17:34,748 epoch 7 - iter 243/274 - loss 0.04585695 - samples/sec: 60.07 - lr: 0.100000 -2022-11-01 13:17:46,833 epoch 7 - iter 270/274 - loss 0.04602936 - samples/sec: 71.51 - lr: 0.100000 -2022-11-01 13:17:48,543 ---------------------------------------------------------------------------------------------------- -2022-11-01 13:17:48,543 EPOCH 7 done: loss 0.0459 - lr 0.100000 -2022-11-01 13:18:13,817 Evaluating as a multi-label problem: False -2022-11-01 13:18:13,832 TEST : loss 0.0414385087788105 - f1-score (micro avg) 0.8123 -2022-11-01 13:18:13,885 BAD EPOCHS (no improvement): 0 -2022-11-01 13:18:13,971 ---------------------------------------------------------------------------------------------------- -2022-11-01 13:18:27,515 epoch 8 - iter 27/274 - loss 0.03864514 - samples/sec: 63.81 - lr: 0.100000 -2022-11-01 13:18:40,798 epoch 8 - iter 54/274 - loss 0.04677644 - samples/sec: 65.07 - lr: 0.100000 -2022-11-01 13:18:52,934 epoch 8 - iter 81/274 - loss 0.04564782 - samples/sec: 71.21 - lr: 0.100000 -2022-11-01 13:19:04,156 epoch 8 - iter 108/274 - loss 0.04660204 - samples/sec: 77.01 - lr: 0.100000 -2022-11-01 13:19:16,047 epoch 8 - iter 135/274 - loss 0.04492183 - samples/sec: 72.68 - lr: 0.100000 -2022-11-01 13:19:28,142 epoch 8 - iter 162/274 - loss 0.04498433 - samples/sec: 71.45 - lr: 0.100000 -2022-11-01 13:19:39,987 epoch 8 - iter 189/274 - loss 0.04445526 - samples/sec: 72.96 - lr: 0.100000 -2022-11-01 13:19:53,894 epoch 8 - iter 216/274 - loss 0.04466556 - samples/sec: 62.14 - lr: 0.100000 -2022-11-01 13:20:06,190 epoch 8 - iter 243/274 - loss 0.04424167 - samples/sec: 70.28 - lr: 0.100000 -2022-11-01 13:20:18,046 epoch 8 - iter 270/274 - loss 0.04411825 - samples/sec: 72.90 - lr: 0.100000 -2022-11-01 13:20:19,887 ---------------------------------------------------------------------------------------------------- -2022-11-01 13:20:19,888 EPOCH 8 done: loss 0.0442 - lr 0.100000 -2022-11-01 13:20:45,195 Evaluating as a multi-label problem: False -2022-11-01 13:20:45,210 TEST : loss 0.0352301225066185 - f1-score (micro avg) 0.8301 -2022-11-01 13:20:45,262 BAD EPOCHS (no improvement): 0 -2022-11-01 13:20:45,347 ---------------------------------------------------------------------------------------------------- -2022-11-01 13:20:57,331 epoch 9 - iter 27/274 - loss 0.03421196 - samples/sec: 72.12 - lr: 0.100000 -2022-11-01 13:21:11,143 epoch 9 - iter 54/274 - loss 0.03958776 - samples/sec: 62.57 - lr: 0.100000 -2022-11-01 13:21:22,700 epoch 9 - iter 81/274 - loss 0.03861008 - samples/sec: 74.78 - lr: 0.100000 -2022-11-01 13:21:34,844 epoch 9 - iter 108/274 - loss 0.03858464 - samples/sec: 71.16 - lr: 0.100000 -2022-11-01 13:21:46,395 epoch 9 - iter 135/274 - loss 0.03857484 - samples/sec: 74.82 - lr: 0.100000 -2022-11-01 13:21:58,479 epoch 9 - iter 162/274 - loss 0.03923221 - samples/sec: 71.51 - lr: 0.100000 -2022-11-01 13:22:12,010 epoch 9 - iter 189/274 - loss 0.04032935 - samples/sec: 63.87 - lr: 0.100000 -2022-11-01 13:22:24,283 epoch 9 - iter 216/274 - loss 0.03989000 - samples/sec: 70.42 - lr: 0.100000 -2022-11-01 13:22:37,207 epoch 9 - iter 243/274 - loss 0.04033892 - samples/sec: 66.87 - lr: 0.100000 -2022-11-01 13:22:49,535 epoch 9 - iter 270/274 - loss 0.04091413 - samples/sec: 70.11 - lr: 0.100000 -2022-11-01 13:22:51,188 ---------------------------------------------------------------------------------------------------- -2022-11-01 13:22:51,189 EPOCH 9 done: loss 0.0409 - lr 0.100000 -2022-11-01 13:23:16,507 Evaluating as a multi-label problem: False -2022-11-01 13:23:16,522 TEST : loss 0.035973258316516876 - f1-score (micro avg) 0.8336 -2022-11-01 13:23:16,574 BAD EPOCHS (no improvement): 0 -2022-11-01 13:23:16,659 ---------------------------------------------------------------------------------------------------- -2022-11-01 13:23:28,888 epoch 10 - iter 27/274 - loss 0.03551569 - samples/sec: 70.67 - lr: 0.100000 -2022-11-01 13:23:40,651 epoch 10 - iter 54/274 - loss 0.03803901 - samples/sec: 73.47 - lr: 0.100000 -2022-11-01 13:23:52,937 epoch 10 - iter 81/274 - loss 0.03866324 - samples/sec: 70.34 - lr: 0.100000 -2022-11-01 13:24:04,214 epoch 10 - iter 108/274 - loss 0.03929714 - samples/sec: 76.64 - lr: 0.100000 -2022-11-01 13:24:18,658 epoch 10 - iter 135/274 - loss 0.03798954 - samples/sec: 59.83 - lr: 0.100000 -2022-11-01 13:24:31,144 epoch 10 - iter 162/274 - loss 0.03737353 - samples/sec: 69.21 - lr: 0.100000 -2022-11-01 13:24:43,524 epoch 10 - iter 189/274 - loss 0.03819265 - samples/sec: 69.81 - lr: 0.100000 -2022-11-01 13:24:55,975 epoch 10 - iter 216/274 - loss 0.03809318 - samples/sec: 69.41 - lr: 0.100000 -2022-11-01 13:25:09,174 epoch 10 - iter 243/274 - loss 0.03795923 - samples/sec: 65.48 - lr: 0.100000 -2022-11-01 13:25:21,702 epoch 10 - iter 270/274 - loss 0.03858601 - samples/sec: 68.98 - lr: 0.100000 -2022-11-01 13:25:23,304 ---------------------------------------------------------------------------------------------------- -2022-11-01 13:25:23,304 EPOCH 10 done: loss 0.0388 - lr 0.100000 -2022-11-01 13:25:48,617 Evaluating as a multi-label problem: False -2022-11-01 13:25:48,633 TEST : loss 0.03315580636262894 - f1-score (micro avg) 0.8307 -2022-11-01 13:25:48,683 BAD EPOCHS (no improvement): 0 -2022-11-01 13:25:48,769 ---------------------------------------------------------------------------------------------------- -2022-11-01 13:26:01,137 epoch 11 - iter 27/274 - loss 0.03336240 - samples/sec: 69.88 - lr: 0.100000 -2022-11-01 13:26:14,421 epoch 11 - iter 54/274 - loss 0.03527565 - samples/sec: 65.06 - lr: 0.100000 -2022-11-01 13:26:26,672 epoch 11 - iter 81/274 - loss 0.03575293 - samples/sec: 70.54 - lr: 0.100000 -2022-11-01 13:26:39,936 epoch 11 - iter 108/274 - loss 0.03822032 - samples/sec: 65.15 - lr: 0.100000 -2022-11-01 13:26:51,243 epoch 11 - iter 135/274 - loss 0.03800128 - samples/sec: 76.44 - lr: 0.100000 -2022-11-01 13:27:03,276 epoch 11 - iter 162/274 - loss 0.03759663 - samples/sec: 71.82 - lr: 0.100000 -2022-11-01 13:27:15,749 epoch 11 - iter 189/274 - loss 0.03792055 - samples/sec: 69.28 - lr: 0.100000 -2022-11-01 13:27:28,611 epoch 11 - iter 216/274 - loss 0.03760377 - samples/sec: 67.19 - lr: 0.100000 -2022-11-01 13:27:40,313 epoch 11 - iter 243/274 - loss 0.03749324 - samples/sec: 73.85 - lr: 0.100000 -2022-11-01 13:27:53,182 epoch 11 - iter 270/274 - loss 0.03774304 - samples/sec: 67.15 - lr: 0.100000 -2022-11-01 13:27:54,562 ---------------------------------------------------------------------------------------------------- -2022-11-01 13:27:54,562 EPOCH 11 done: loss 0.0377 - lr 0.100000 -2022-11-01 13:28:19,892 Evaluating as a multi-label problem: False -2022-11-01 13:28:19,908 TEST : loss 0.03283185511827469 - f1-score (micro avg) 0.8212 -2022-11-01 13:28:19,960 BAD EPOCHS (no improvement): 0 -2022-11-01 13:28:20,046 ---------------------------------------------------------------------------------------------------- -2022-11-01 13:28:32,783 epoch 12 - iter 27/274 - loss 0.03750028 - samples/sec: 67.85 - lr: 0.100000 -2022-11-01 13:28:45,843 epoch 12 - iter 54/274 - loss 0.03532475 - samples/sec: 66.17 - lr: 0.100000 -2022-11-01 13:28:57,814 epoch 12 - iter 81/274 - loss 0.03713735 - samples/sec: 72.20 - lr: 0.100000 -2022-11-01 13:29:09,768 epoch 12 - iter 108/274 - loss 0.03681510 - samples/sec: 72.30 - lr: 0.100000 -2022-11-01 13:29:24,299 epoch 12 - iter 135/274 - loss 0.03831782 - samples/sec: 59.47 - lr: 0.100000 -2022-11-01 13:29:35,963 epoch 12 - iter 162/274 - loss 0.03671352 - samples/sec: 74.10 - lr: 0.100000 -2022-11-01 13:29:47,879 epoch 12 - iter 189/274 - loss 0.03644150 - samples/sec: 72.52 - lr: 0.100000 -2022-11-01 13:30:01,084 epoch 12 - iter 216/274 - loss 0.03741624 - samples/sec: 65.45 - lr: 0.100000 -2022-11-01 13:30:12,910 epoch 12 - iter 243/274 - loss 0.03760841 - samples/sec: 73.08 - lr: 0.100000 -2022-11-01 13:30:24,282 epoch 12 - iter 270/274 - loss 0.03743746 - samples/sec: 76.00 - lr: 0.100000 -2022-11-01 13:30:26,622 ---------------------------------------------------------------------------------------------------- -2022-11-01 13:30:26,622 EPOCH 12 done: loss 0.0372 - lr 0.100000 -2022-11-01 13:30:51,847 Evaluating as a multi-label problem: False -2022-11-01 13:30:51,862 TEST : loss 0.037361979484558105 - f1-score (micro avg) 0.8248 -2022-11-01 13:30:51,915 BAD EPOCHS (no improvement): 0 -2022-11-01 13:30:52,001 ---------------------------------------------------------------------------------------------------- -2022-11-01 13:31:04,558 epoch 13 - iter 27/274 - loss 0.03512437 - samples/sec: 68.82 - lr: 0.100000 -2022-11-01 13:31:15,671 epoch 13 - iter 54/274 - loss 0.03410457 - samples/sec: 77.77 - lr: 0.100000 -2022-11-01 13:31:28,928 epoch 13 - iter 81/274 - loss 0.03664686 - samples/sec: 65.19 - lr: 0.100000 -2022-11-01 13:31:41,194 epoch 13 - iter 108/274 - loss 0.03708780 - samples/sec: 70.46 - lr: 0.100000 -2022-11-01 13:31:53,508 epoch 13 - iter 135/274 - loss 0.03597557 - samples/sec: 70.18 - lr: 0.100000 -2022-11-01 13:32:05,194 epoch 13 - iter 162/274 - loss 0.03576194 - samples/sec: 73.95 - lr: 0.100000 -2022-11-01 13:32:18,580 epoch 13 - iter 189/274 - loss 0.03517390 - samples/sec: 64.56 - lr: 0.100000 -2022-11-01 13:32:31,865 epoch 13 - iter 216/274 - loss 0.03618700 - samples/sec: 65.05 - lr: 0.100000 -2022-11-01 13:32:43,293 epoch 13 - iter 243/274 - loss 0.03653199 - samples/sec: 75.62 - lr: 0.100000 -2022-11-01 13:32:55,873 epoch 13 - iter 270/274 - loss 0.03649977 - samples/sec: 68.70 - lr: 0.100000 -2022-11-01 13:32:57,821 ---------------------------------------------------------------------------------------------------- -2022-11-01 13:32:57,821 EPOCH 13 done: loss 0.0366 - lr 0.100000 -2022-11-01 13:33:23,196 Evaluating as a multi-label problem: False -2022-11-01 13:33:23,211 TEST : loss 0.03093760274350643 - f1-score (micro avg) 0.8276 -2022-11-01 13:33:23,262 BAD EPOCHS (no improvement): 0 -2022-11-01 13:33:23,348 ---------------------------------------------------------------------------------------------------- -2022-11-01 13:33:35,596 epoch 14 - iter 27/274 - loss 0.03587195 - samples/sec: 70.57 - lr: 0.100000 -2022-11-01 13:33:47,961 epoch 14 - iter 54/274 - loss 0.03435583 - samples/sec: 69.89 - lr: 0.100000 -2022-11-01 13:33:59,808 epoch 14 - iter 81/274 - loss 0.03350450 - samples/sec: 72.95 - lr: 0.100000 -2022-11-01 13:34:11,390 epoch 14 - iter 108/274 - loss 0.03432252 - samples/sec: 74.62 - lr: 0.100000 -2022-11-01 13:34:24,737 epoch 14 - iter 135/274 - loss 0.03559134 - samples/sec: 64.75 - lr: 0.100000 -2022-11-01 13:34:37,434 epoch 14 - iter 162/274 - loss 0.03552331 - samples/sec: 68.06 - lr: 0.100000 -2022-11-01 13:34:49,584 epoch 14 - iter 189/274 - loss 0.03506626 - samples/sec: 71.13 - lr: 0.100000 -2022-11-01 13:35:03,212 epoch 14 - iter 216/274 - loss 0.03444676 - samples/sec: 63.41 - lr: 0.100000 -2022-11-01 13:35:15,032 epoch 14 - iter 243/274 - loss 0.03434282 - samples/sec: 73.12 - lr: 0.100000 -2022-11-01 13:35:27,648 epoch 14 - iter 270/274 - loss 0.03420688 - samples/sec: 68.50 - lr: 0.100000 -2022-11-01 13:35:29,536 ---------------------------------------------------------------------------------------------------- -2022-11-01 13:35:29,536 EPOCH 14 done: loss 0.0341 - lr 0.100000 -2022-11-01 13:35:54,970 Evaluating as a multi-label problem: False -2022-11-01 13:35:54,985 TEST : loss 0.032740455120801926 - f1-score (micro avg) 0.8382 -2022-11-01 13:35:55,038 BAD EPOCHS (no improvement): 0 -2022-11-01 13:35:55,124 ---------------------------------------------------------------------------------------------------- -2022-11-01 13:36:06,207 epoch 15 - iter 27/274 - loss 0.02621004 - samples/sec: 77.98 - lr: 0.100000 -2022-11-01 13:36:18,117 epoch 15 - iter 54/274 - loss 0.03148500 - samples/sec: 72.56 - lr: 0.100000 -2022-11-01 13:36:32,408 epoch 15 - iter 81/274 - loss 0.03283017 - samples/sec: 60.47 - lr: 0.100000 -2022-11-01 13:36:44,413 epoch 15 - iter 108/274 - loss 0.03269024 - samples/sec: 72.00 - lr: 0.100000 -2022-11-01 13:36:57,549 epoch 15 - iter 135/274 - loss 0.03234032 - samples/sec: 65.79 - lr: 0.100000 -2022-11-01 13:37:09,569 epoch 15 - iter 162/274 - loss 0.03236532 - samples/sec: 71.90 - lr: 0.100000 -2022-11-01 13:37:21,479 epoch 15 - iter 189/274 - loss 0.03211454 - samples/sec: 72.56 - lr: 0.100000 -2022-11-01 13:37:33,192 epoch 15 - iter 216/274 - loss 0.03241047 - samples/sec: 73.78 - lr: 0.100000 -2022-11-01 13:37:46,183 epoch 15 - iter 243/274 - loss 0.03314606 - samples/sec: 66.53 - lr: 0.100000 -2022-11-01 13:37:58,664 epoch 15 - iter 270/274 - loss 0.03257262 - samples/sec: 69.24 - lr: 0.100000 -2022-11-01 13:38:00,237 ---------------------------------------------------------------------------------------------------- -2022-11-01 13:38:00,237 EPOCH 15 done: loss 0.0325 - lr 0.100000 -2022-11-01 13:38:25,551 Evaluating as a multi-label problem: False -2022-11-01 13:38:25,566 TEST : loss 0.03659946471452713 - f1-score (micro avg) 0.8396 -2022-11-01 13:38:25,619 BAD EPOCHS (no improvement): 0 -2022-11-01 13:38:25,708 ---------------------------------------------------------------------------------------------------- -2022-11-01 13:38:38,868 epoch 16 - iter 27/274 - loss 0.04310501 - samples/sec: 65.67 - lr: 0.100000 -2022-11-01 13:38:51,141 epoch 16 - iter 54/274 - loss 0.03821968 - samples/sec: 70.42 - lr: 0.100000 -2022-11-01 13:39:03,213 epoch 16 - iter 81/274 - loss 0.03601832 - samples/sec: 71.59 - lr: 0.100000 -2022-11-01 13:39:15,515 epoch 16 - iter 108/274 - loss 0.03464665 - samples/sec: 70.25 - lr: 0.100000 -2022-11-01 13:39:28,495 epoch 16 - iter 135/274 - loss 0.03385713 - samples/sec: 66.58 - lr: 0.100000 -2022-11-01 13:39:42,428 epoch 16 - iter 162/274 - loss 0.03355248 - samples/sec: 62.02 - lr: 0.100000 -2022-11-01 13:39:55,016 epoch 16 - iter 189/274 - loss 0.03305979 - samples/sec: 68.66 - lr: 0.100000 -2022-11-01 13:40:06,660 epoch 16 - iter 216/274 - loss 0.03245928 - samples/sec: 74.22 - lr: 0.100000 -2022-11-01 13:40:18,569 epoch 16 - iter 243/274 - loss 0.03229054 - samples/sec: 72.57 - lr: 0.100000 -2022-11-01 13:40:29,519 epoch 16 - iter 270/274 - loss 0.03266463 - samples/sec: 78.93 - lr: 0.100000 -2022-11-01 13:40:31,278 ---------------------------------------------------------------------------------------------------- -2022-11-01 13:40:31,279 EPOCH 16 done: loss 0.0324 - lr 0.100000 -2022-11-01 13:40:56,563 Evaluating as a multi-label problem: False -2022-11-01 13:40:56,579 TEST : loss 0.03419892117381096 - f1-score (micro avg) 0.8414 -2022-11-01 13:40:56,630 BAD EPOCHS (no improvement): 0 -2022-11-01 13:40:56,716 ---------------------------------------------------------------------------------------------------- -2022-11-01 13:41:08,628 epoch 17 - iter 27/274 - loss 0.03131168 - samples/sec: 72.55 - lr: 0.100000 -2022-11-01 13:41:20,642 epoch 17 - iter 54/274 - loss 0.03329973 - samples/sec: 71.94 - lr: 0.100000 -2022-11-01 13:41:33,558 epoch 17 - iter 81/274 - loss 0.03205309 - samples/sec: 66.91 - lr: 0.100000 -2022-11-01 13:41:46,746 epoch 17 - iter 108/274 - loss 0.03106228 - samples/sec: 65.53 - lr: 0.100000 -2022-11-01 13:41:59,456 epoch 17 - iter 135/274 - loss 0.03205977 - samples/sec: 68.00 - lr: 0.100000 -2022-11-01 13:42:10,814 epoch 17 - iter 162/274 - loss 0.03230744 - samples/sec: 76.09 - lr: 0.100000 -2022-11-01 13:42:22,736 epoch 17 - iter 189/274 - loss 0.03217993 - samples/sec: 72.49 - lr: 0.100000 -2022-11-01 13:42:35,185 epoch 17 - iter 216/274 - loss 0.03154261 - samples/sec: 69.42 - lr: 0.100000 -2022-11-01 13:42:48,451 epoch 17 - iter 243/274 - loss 0.03218871 - samples/sec: 65.14 - lr: 0.100000 -2022-11-01 13:43:00,903 epoch 17 - iter 270/274 - loss 0.03216961 - samples/sec: 69.40 - lr: 0.100000 -2022-11-01 13:43:02,520 ---------------------------------------------------------------------------------------------------- -2022-11-01 13:43:02,520 EPOCH 17 done: loss 0.0324 - lr 0.100000 -2022-11-01 13:43:27,903 Evaluating as a multi-label problem: False -2022-11-01 13:43:27,919 TEST : loss 0.032121315598487854 - f1-score (micro avg) 0.8142 -2022-11-01 13:43:27,973 BAD EPOCHS (no improvement): 0 -2022-11-01 13:43:28,061 ---------------------------------------------------------------------------------------------------- -2022-11-01 13:43:39,245 epoch 18 - iter 27/274 - loss 0.02828295 - samples/sec: 77.28 - lr: 0.100000 -2022-11-01 13:43:51,970 epoch 18 - iter 54/274 - loss 0.02821400 - samples/sec: 67.91 - lr: 0.100000 -2022-11-01 13:44:04,671 epoch 18 - iter 81/274 - loss 0.02895659 - samples/sec: 68.04 - lr: 0.100000 -2022-11-01 13:44:17,750 epoch 18 - iter 108/274 - loss 0.02937219 - samples/sec: 66.08 - lr: 0.100000 -2022-11-01 13:44:29,293 epoch 18 - iter 135/274 - loss 0.02969136 - samples/sec: 74.87 - lr: 0.100000 -2022-11-01 13:44:41,423 epoch 18 - iter 162/274 - loss 0.03044366 - samples/sec: 71.25 - lr: 0.100000 -2022-11-01 13:44:53,180 epoch 18 - iter 189/274 - loss 0.03033224 - samples/sec: 73.51 - lr: 0.100000 -2022-11-01 13:45:05,434 epoch 18 - iter 216/274 - loss 0.03010129 - samples/sec: 70.52 - lr: 0.100000 -2022-11-01 13:45:17,103 epoch 18 - iter 243/274 - loss 0.03031468 - samples/sec: 74.07 - lr: 0.100000 -2022-11-01 13:45:31,926 epoch 18 - iter 270/274 - loss 0.03086624 - samples/sec: 58.30 - lr: 0.100000 -2022-11-01 13:45:33,720 ---------------------------------------------------------------------------------------------------- -2022-11-01 13:45:33,720 EPOCH 18 done: loss 0.0309 - lr 0.100000 -2022-11-01 13:45:59,011 Evaluating as a multi-label problem: False -2022-11-01 13:45:59,027 TEST : loss 0.03097383677959442 - f1-score (micro avg) 0.8361 -2022-11-01 13:45:59,081 BAD EPOCHS (no improvement): 0 -2022-11-01 13:45:59,150 ---------------------------------------------------------------------------------------------------- -2022-11-01 13:46:12,115 epoch 19 - iter 27/274 - loss 0.03291951 - samples/sec: 66.66 - lr: 0.100000 -2022-11-01 13:46:24,726 epoch 19 - iter 54/274 - loss 0.03100127 - samples/sec: 68.53 - lr: 0.100000 -2022-11-01 13:46:36,908 epoch 19 - iter 81/274 - loss 0.03008968 - samples/sec: 70.95 - lr: 0.100000 -2022-11-01 13:46:49,567 epoch 19 - iter 108/274 - loss 0.02850661 - samples/sec: 68.36 - lr: 0.100000 -2022-11-01 13:47:03,710 epoch 19 - iter 135/274 - loss 0.02973182 - samples/sec: 61.10 - lr: 0.100000 -2022-11-01 13:47:15,650 epoch 19 - iter 162/274 - loss 0.03029075 - samples/sec: 72.38 - lr: 0.100000 -2022-11-01 13:47:27,204 epoch 19 - iter 189/274 - loss 0.03061716 - samples/sec: 74.80 - lr: 0.100000 -2022-11-01 13:47:39,709 epoch 19 - iter 216/274 - loss 0.03024609 - samples/sec: 69.11 - lr: 0.100000 -2022-11-01 13:47:52,438 epoch 19 - iter 243/274 - loss 0.03075993 - samples/sec: 67.89 - lr: 0.100000 -2022-11-01 13:48:04,395 epoch 19 - iter 270/274 - loss 0.03035569 - samples/sec: 72.28 - lr: 0.100000 -2022-11-01 13:48:06,254 ---------------------------------------------------------------------------------------------------- -2022-11-01 13:48:06,254 EPOCH 19 done: loss 0.0304 - lr 0.100000 -2022-11-01 13:48:31,545 Evaluating as a multi-label problem: False -2022-11-01 13:48:31,560 TEST : loss 0.031058575958013535 - f1-score (micro avg) 0.8361 -2022-11-01 13:48:31,612 BAD EPOCHS (no improvement): 0 -2022-11-01 13:48:31,701 ---------------------------------------------------------------------------------------------------- -2022-11-01 13:48:43,605 epoch 20 - iter 27/274 - loss 0.03341089 - samples/sec: 72.61 - lr: 0.100000 -2022-11-01 13:48:56,331 epoch 20 - iter 54/274 - loss 0.02965002 - samples/sec: 67.91 - lr: 0.100000 -2022-11-01 13:49:08,466 epoch 20 - iter 81/274 - loss 0.02936467 - samples/sec: 71.22 - lr: 0.100000 -2022-11-01 13:49:20,359 epoch 20 - iter 108/274 - loss 0.02967577 - samples/sec: 72.67 - lr: 0.100000 -2022-11-01 13:49:31,703 epoch 20 - iter 135/274 - loss 0.02962385 - samples/sec: 76.18 - lr: 0.100000 -2022-11-01 13:49:45,689 epoch 20 - iter 162/274 - loss 0.03002445 - samples/sec: 61.79 - lr: 0.100000 -2022-11-01 13:49:59,016 epoch 20 - iter 189/274 - loss 0.02979085 - samples/sec: 64.85 - lr: 0.100000 -2022-11-01 13:50:11,482 epoch 20 - iter 216/274 - loss 0.03016882 - samples/sec: 69.33 - lr: 0.100000 -2022-11-01 13:50:23,142 epoch 20 - iter 243/274 - loss 0.03053355 - samples/sec: 74.12 - lr: 0.100000 -2022-11-01 13:50:35,085 epoch 20 - iter 270/274 - loss 0.03054658 - samples/sec: 72.37 - lr: 0.100000 -2022-11-01 13:50:36,716 ---------------------------------------------------------------------------------------------------- -2022-11-01 13:50:36,716 EPOCH 20 done: loss 0.0304 - lr 0.100000 -2022-11-01 13:51:01,815 Evaluating as a multi-label problem: False -2022-11-01 13:51:01,830 TEST : loss 0.035328544676303864 - f1-score (micro avg) 0.8392 -2022-11-01 13:51:01,882 BAD EPOCHS (no improvement): 1 -2022-11-01 13:51:01,968 ---------------------------------------------------------------------------------------------------- -2022-11-01 13:51:16,421 epoch 21 - iter 27/274 - loss 0.03241261 - samples/sec: 59.79 - lr: 0.100000 -2022-11-01 13:51:28,464 epoch 21 - iter 54/274 - loss 0.03286965 - samples/sec: 71.76 - lr: 0.100000 -2022-11-01 13:51:40,060 epoch 21 - iter 81/274 - loss 0.03240940 - samples/sec: 74.53 - lr: 0.100000 -2022-11-01 13:51:52,405 epoch 21 - iter 108/274 - loss 0.03079412 - samples/sec: 70.01 - lr: 0.100000 -2022-11-01 13:52:04,384 epoch 21 - iter 135/274 - loss 0.02985053 - samples/sec: 72.15 - lr: 0.100000 -2022-11-01 13:52:16,601 epoch 21 - iter 162/274 - loss 0.02987171 - samples/sec: 70.74 - lr: 0.100000 -2022-11-01 13:52:28,629 epoch 21 - iter 189/274 - loss 0.03085183 - samples/sec: 71.85 - lr: 0.100000 -2022-11-01 13:52:40,275 epoch 21 - iter 216/274 - loss 0.03117974 - samples/sec: 74.21 - lr: 0.100000 -2022-11-01 13:52:53,944 epoch 21 - iter 243/274 - loss 0.03112541 - samples/sec: 63.22 - lr: 0.100000 -2022-11-01 13:53:05,688 epoch 21 - iter 270/274 - loss 0.03062103 - samples/sec: 73.59 - lr: 0.100000 -2022-11-01 13:53:07,553 ---------------------------------------------------------------------------------------------------- -2022-11-01 13:53:07,554 EPOCH 21 done: loss 0.0308 - lr 0.100000 -2022-11-01 13:53:32,862 Evaluating as a multi-label problem: False -2022-11-01 13:53:32,877 TEST : loss 0.029285110533237457 - f1-score (micro avg) 0.8396 -2022-11-01 13:53:32,929 BAD EPOCHS (no improvement): 2 -2022-11-01 13:53:33,017 ---------------------------------------------------------------------------------------------------- -2022-11-01 13:53:44,810 epoch 22 - iter 27/274 - loss 0.02903520 - samples/sec: 73.29 - lr: 0.100000 -2022-11-01 13:53:57,359 epoch 22 - iter 54/274 - loss 0.02877626 - samples/sec: 68.87 - lr: 0.100000 -2022-11-01 13:54:09,305 epoch 22 - iter 81/274 - loss 0.02903053 - samples/sec: 72.34 - lr: 0.100000 -2022-11-01 13:54:22,177 epoch 22 - iter 108/274 - loss 0.02918162 - samples/sec: 67.14 - lr: 0.100000 -2022-11-01 13:54:34,724 epoch 22 - iter 135/274 - loss 0.03022362 - samples/sec: 68.88 - lr: 0.100000 -2022-11-01 13:54:47,100 epoch 22 - iter 162/274 - loss 0.02921564 - samples/sec: 69.83 - lr: 0.100000 -2022-11-01 13:54:58,855 epoch 22 - iter 189/274 - loss 0.02907579 - samples/sec: 73.52 - lr: 0.100000 -2022-11-01 13:55:11,841 epoch 22 - iter 216/274 - loss 0.02880394 - samples/sec: 66.55 - lr: 0.100000 -2022-11-01 13:55:24,785 epoch 22 - iter 243/274 - loss 0.02882432 - samples/sec: 66.76 - lr: 0.100000 -2022-11-01 13:55:35,994 epoch 22 - iter 270/274 - loss 0.02922647 - samples/sec: 77.11 - lr: 0.100000 -2022-11-01 13:55:37,631 ---------------------------------------------------------------------------------------------------- -2022-11-01 13:55:37,631 EPOCH 22 done: loss 0.0293 - lr 0.100000 -2022-11-01 13:56:02,870 Evaluating as a multi-label problem: False -2022-11-01 13:56:02,885 TEST : loss 0.031731970608234406 - f1-score (micro avg) 0.8458 -2022-11-01 13:56:02,937 BAD EPOCHS (no improvement): 0 -2022-11-01 13:56:03,023 ---------------------------------------------------------------------------------------------------- -2022-11-01 13:56:15,323 epoch 23 - iter 27/274 - loss 0.02954803 - samples/sec: 70.27 - lr: 0.100000 -2022-11-01 13:56:27,494 epoch 23 - iter 54/274 - loss 0.02818459 - samples/sec: 71.01 - lr: 0.100000 -2022-11-01 13:56:38,573 epoch 23 - iter 81/274 - loss 0.02966005 - samples/sec: 78.01 - lr: 0.100000 -2022-11-01 13:56:52,151 epoch 23 - iter 108/274 - loss 0.02929197 - samples/sec: 63.65 - lr: 0.100000 -2022-11-01 13:57:03,768 epoch 23 - iter 135/274 - loss 0.02938255 - samples/sec: 74.40 - lr: 0.100000 -2022-11-01 13:57:16,576 epoch 23 - iter 162/274 - loss 0.02905523 - samples/sec: 67.48 - lr: 0.100000 -2022-11-01 13:57:28,040 epoch 23 - iter 189/274 - loss 0.02845779 - samples/sec: 75.38 - lr: 0.100000 -2022-11-01 13:57:41,003 epoch 23 - iter 216/274 - loss 0.02851665 - samples/sec: 66.67 - lr: 0.100000 -2022-11-01 13:57:52,675 epoch 23 - iter 243/274 - loss 0.02836095 - samples/sec: 74.04 - lr: 0.100000 -2022-11-01 13:58:05,834 epoch 23 - iter 270/274 - loss 0.02879719 - samples/sec: 65.68 - lr: 0.100000 -2022-11-01 13:58:07,795 ---------------------------------------------------------------------------------------------------- -2022-11-01 13:58:07,795 EPOCH 23 done: loss 0.0292 - lr 0.100000 -2022-11-01 13:58:33,309 Evaluating as a multi-label problem: False -2022-11-01 13:58:33,324 TEST : loss 0.029490221291780472 - f1-score (micro avg) 0.8464 -2022-11-01 13:58:33,376 BAD EPOCHS (no improvement): 0 -2022-11-01 13:58:33,466 ---------------------------------------------------------------------------------------------------- -2022-11-01 13:58:47,010 epoch 24 - iter 27/274 - loss 0.03114330 - samples/sec: 63.81 - lr: 0.100000 -2022-11-01 13:58:57,750 epoch 24 - iter 54/274 - loss 0.02934230 - samples/sec: 80.47 - lr: 0.100000 -2022-11-01 13:59:10,428 epoch 24 - iter 81/274 - loss 0.02821868 - samples/sec: 68.17 - lr: 0.100000 -2022-11-01 13:59:22,831 epoch 24 - iter 108/274 - loss 0.02736854 - samples/sec: 69.68 - lr: 0.100000 -2022-11-01 13:59:34,154 epoch 24 - iter 135/274 - loss 0.02744987 - samples/sec: 76.33 - lr: 0.100000 -2022-11-01 13:59:47,845 epoch 24 - iter 162/274 - loss 0.02721262 - samples/sec: 63.12 - lr: 0.100000 -2022-11-01 14:00:00,436 epoch 24 - iter 189/274 - loss 0.02819192 - samples/sec: 68.64 - lr: 0.100000 -2022-11-01 14:00:12,356 epoch 24 - iter 216/274 - loss 0.02808668 - samples/sec: 72.50 - lr: 0.100000 -2022-11-01 14:00:24,554 epoch 24 - iter 243/274 - loss 0.02787150 - samples/sec: 70.85 - lr: 0.100000 -2022-11-01 14:00:36,399 epoch 24 - iter 270/274 - loss 0.02806091 - samples/sec: 72.96 - lr: 0.100000 -2022-11-01 14:00:38,397 ---------------------------------------------------------------------------------------------------- -2022-11-01 14:00:38,397 EPOCH 24 done: loss 0.0281 - lr 0.100000 -2022-11-01 14:01:03,905 Evaluating as a multi-label problem: False -2022-11-01 14:01:03,920 TEST : loss 0.03052011877298355 - f1-score (micro avg) 0.8427 -2022-11-01 14:01:03,971 BAD EPOCHS (no improvement): 0 -2022-11-01 14:01:04,039 ---------------------------------------------------------------------------------------------------- -2022-11-01 14:01:15,033 epoch 25 - iter 27/274 - loss 0.02254447 - samples/sec: 78.61 - lr: 0.100000 -2022-11-01 14:01:27,113 epoch 25 - iter 54/274 - loss 0.02958591 - samples/sec: 71.54 - lr: 0.100000 -2022-11-01 14:01:39,053 epoch 25 - iter 81/274 - loss 0.02847044 - samples/sec: 72.39 - lr: 0.100000 -2022-11-01 14:01:50,919 epoch 25 - iter 108/274 - loss 0.02739965 - samples/sec: 72.83 - lr: 0.100000 -2022-11-01 14:02:02,867 epoch 25 - iter 135/274 - loss 0.02744808 - samples/sec: 72.33 - lr: 0.100000 -2022-11-01 14:02:15,744 epoch 25 - iter 162/274 - loss 0.02698485 - samples/sec: 67.12 - lr: 0.100000 -2022-11-01 14:02:27,495 epoch 25 - iter 189/274 - loss 0.02731846 - samples/sec: 73.54 - lr: 0.100000 -2022-11-01 14:02:40,427 epoch 25 - iter 216/274 - loss 0.02843243 - samples/sec: 66.83 - lr: 0.100000 -2022-11-01 14:02:54,788 epoch 25 - iter 243/274 - loss 0.02862498 - samples/sec: 60.18 - lr: 0.100000 -2022-11-01 14:03:08,086 epoch 25 - iter 270/274 - loss 0.02826127 - samples/sec: 64.99 - lr: 0.100000 -2022-11-01 14:03:09,433 ---------------------------------------------------------------------------------------------------- -2022-11-01 14:03:09,434 EPOCH 25 done: loss 0.0284 - lr 0.100000 -2022-11-01 14:03:34,594 Evaluating as a multi-label problem: False -2022-11-01 14:03:34,610 TEST : loss 0.029107416048645973 - f1-score (micro avg) 0.8389 -2022-11-01 14:03:34,662 BAD EPOCHS (no improvement): 1 -2022-11-01 14:03:34,750 ---------------------------------------------------------------------------------------------------- -2022-11-01 14:03:46,841 epoch 26 - iter 27/274 - loss 0.02811300 - samples/sec: 71.48 - lr: 0.100000 -2022-11-01 14:03:59,602 epoch 26 - iter 54/274 - loss 0.02743337 - samples/sec: 67.72 - lr: 0.100000 -2022-11-01 14:04:11,887 epoch 26 - iter 81/274 - loss 0.02763661 - samples/sec: 70.35 - lr: 0.100000 -2022-11-01 14:04:25,712 epoch 26 - iter 108/274 - loss 0.02912647 - samples/sec: 62.51 - lr: 0.100000 -2022-11-01 14:04:37,325 epoch 26 - iter 135/274 - loss 0.02824420 - samples/sec: 74.42 - lr: 0.100000 -2022-11-01 14:04:50,074 epoch 26 - iter 162/274 - loss 0.02814963 - samples/sec: 67.79 - lr: 0.100000 -2022-11-01 14:05:02,658 epoch 26 - iter 189/274 - loss 0.02796098 - samples/sec: 68.68 - lr: 0.100000 -2022-11-01 14:05:15,161 epoch 26 - iter 216/274 - loss 0.02806696 - samples/sec: 69.12 - lr: 0.100000 -2022-11-01 14:05:26,292 epoch 26 - iter 243/274 - loss 0.02822794 - samples/sec: 77.65 - lr: 0.100000 -2022-11-01 14:05:38,777 epoch 26 - iter 270/274 - loss 0.02796632 - samples/sec: 69.22 - lr: 0.100000 -2022-11-01 14:05:40,413 ---------------------------------------------------------------------------------------------------- -2022-11-01 14:05:40,413 EPOCH 26 done: loss 0.0280 - lr 0.100000 -2022-11-01 14:06:05,568 Evaluating as a multi-label problem: False -2022-11-01 14:06:05,583 TEST : loss 0.028921490535140038 - f1-score (micro avg) 0.8499 -2022-11-01 14:06:05,635 BAD EPOCHS (no improvement): 0 -2022-11-01 14:06:05,720 ---------------------------------------------------------------------------------------------------- -2022-11-01 14:06:17,118 epoch 27 - iter 27/274 - loss 0.03035630 - samples/sec: 75.83 - lr: 0.100000 -2022-11-01 14:06:28,418 epoch 27 - iter 54/274 - loss 0.02636220 - samples/sec: 76.48 - lr: 0.100000 -2022-11-01 14:06:40,920 epoch 27 - iter 81/274 - loss 0.02716773 - samples/sec: 69.13 - lr: 0.100000 -2022-11-01 14:06:53,020 epoch 27 - iter 108/274 - loss 0.02817490 - samples/sec: 71.43 - lr: 0.100000 -2022-11-01 14:07:06,130 epoch 27 - iter 135/274 - loss 0.02905628 - samples/sec: 65.92 - lr: 0.100000 -2022-11-01 14:07:19,646 epoch 27 - iter 162/274 - loss 0.02789546 - samples/sec: 63.94 - lr: 0.100000 -2022-11-01 14:07:31,413 epoch 27 - iter 189/274 - loss 0.02750674 - samples/sec: 73.44 - lr: 0.100000 -2022-11-01 14:07:43,807 epoch 27 - iter 216/274 - loss 0.02740534 - samples/sec: 69.73 - lr: 0.100000 -2022-11-01 14:07:57,071 epoch 27 - iter 243/274 - loss 0.02788272 - samples/sec: 65.15 - lr: 0.100000 -2022-11-01 14:08:09,097 epoch 27 - iter 270/274 - loss 0.02804086 - samples/sec: 71.86 - lr: 0.100000 -2022-11-01 14:08:10,721 ---------------------------------------------------------------------------------------------------- -2022-11-01 14:08:10,721 EPOCH 27 done: loss 0.0279 - lr 0.100000 -2022-11-01 14:08:35,996 Evaluating as a multi-label problem: False -2022-11-01 14:08:36,011 TEST : loss 0.02727373316884041 - f1-score (micro avg) 0.8495 -2022-11-01 14:08:36,063 BAD EPOCHS (no improvement): 0 -2022-11-01 14:08:36,151 ---------------------------------------------------------------------------------------------------- -2022-11-01 14:08:48,729 epoch 28 - iter 27/274 - loss 0.02792706 - samples/sec: 68.72 - lr: 0.100000 -2022-11-01 14:09:00,474 epoch 28 - iter 54/274 - loss 0.02712818 - samples/sec: 73.58 - lr: 0.100000 -2022-11-01 14:09:13,998 epoch 28 - iter 81/274 - loss 0.02881006 - samples/sec: 63.90 - lr: 0.100000 -2022-11-01 14:09:26,897 epoch 28 - iter 108/274 - loss 0.02790622 - samples/sec: 66.99 - lr: 0.100000 -2022-11-01 14:09:39,353 epoch 28 - iter 135/274 - loss 0.02765454 - samples/sec: 69.38 - lr: 0.100000 -2022-11-01 14:09:51,992 epoch 28 - iter 162/274 - loss 0.02686081 - samples/sec: 68.38 - lr: 0.100000 -2022-11-01 14:10:05,064 epoch 28 - iter 189/274 - loss 0.02634890 - samples/sec: 66.11 - lr: 0.100000 -2022-11-01 14:10:16,714 epoch 28 - iter 216/274 - loss 0.02655923 - samples/sec: 74.18 - lr: 0.100000 -2022-11-01 14:10:29,815 epoch 28 - iter 243/274 - loss 0.02663561 - samples/sec: 65.96 - lr: 0.100000 -2022-11-01 14:10:41,079 epoch 28 - iter 270/274 - loss 0.02647144 - samples/sec: 76.72 - lr: 0.100000 -2022-11-01 14:10:42,579 ---------------------------------------------------------------------------------------------------- -2022-11-01 14:10:42,580 EPOCH 28 done: loss 0.0267 - lr 0.100000 -2022-11-01 14:11:07,885 Evaluating as a multi-label problem: False -2022-11-01 14:11:07,901 TEST : loss 0.029852760955691338 - f1-score (micro avg) 0.8372 -2022-11-01 14:11:07,953 BAD EPOCHS (no improvement): 0 -2022-11-01 14:11:08,041 ---------------------------------------------------------------------------------------------------- -2022-11-01 14:11:20,409 epoch 29 - iter 27/274 - loss 0.02518790 - samples/sec: 69.88 - lr: 0.100000 -2022-11-01 14:11:32,626 epoch 29 - iter 54/274 - loss 0.02805683 - samples/sec: 70.74 - lr: 0.100000 -2022-11-01 14:11:45,533 epoch 29 - iter 81/274 - loss 0.02825807 - samples/sec: 66.96 - lr: 0.100000 -2022-11-01 14:11:57,137 epoch 29 - iter 108/274 - loss 0.02799206 - samples/sec: 74.48 - lr: 0.100000 -2022-11-01 14:12:08,601 epoch 29 - iter 135/274 - loss 0.02755879 - samples/sec: 75.38 - lr: 0.100000 -2022-11-01 14:12:21,338 epoch 29 - iter 162/274 - loss 0.02685576 - samples/sec: 67.85 - lr: 0.100000 -2022-11-01 14:12:33,835 epoch 29 - iter 189/274 - loss 0.02594996 - samples/sec: 69.16 - lr: 0.100000 -2022-11-01 14:12:46,797 epoch 29 - iter 216/274 - loss 0.02550550 - samples/sec: 66.67 - lr: 0.100000 -2022-11-01 14:12:59,038 epoch 29 - iter 243/274 - loss 0.02644877 - samples/sec: 70.60 - lr: 0.100000 -2022-11-01 14:13:12,139 epoch 29 - iter 270/274 - loss 0.02627661 - samples/sec: 65.96 - lr: 0.100000 -2022-11-01 14:13:13,801 ---------------------------------------------------------------------------------------------------- -2022-11-01 14:13:13,801 EPOCH 29 done: loss 0.0265 - lr 0.100000 -2022-11-01 14:13:39,155 Evaluating as a multi-label problem: False -2022-11-01 14:13:39,171 TEST : loss 0.02927403524518013 - f1-score (micro avg) 0.8443 -2022-11-01 14:13:39,223 BAD EPOCHS (no improvement): 0 -2022-11-01 14:13:39,311 ---------------------------------------------------------------------------------------------------- -2022-11-01 14:13:51,641 epoch 30 - iter 27/274 - loss 0.02423251 - samples/sec: 70.10 - lr: 0.100000 -2022-11-01 14:14:05,415 epoch 30 - iter 54/274 - loss 0.02348831 - samples/sec: 62.74 - lr: 0.100000 -2022-11-01 14:14:18,177 epoch 30 - iter 81/274 - loss 0.02264011 - samples/sec: 67.72 - lr: 0.100000 -2022-11-01 14:14:29,311 epoch 30 - iter 108/274 - loss 0.02290510 - samples/sec: 77.62 - lr: 0.100000 -2022-11-01 14:14:40,278 epoch 30 - iter 135/274 - loss 0.02305872 - samples/sec: 78.81 - lr: 0.100000 -2022-11-01 14:14:52,410 epoch 30 - iter 162/274 - loss 0.02371256 - samples/sec: 71.23 - lr: 0.100000 -2022-11-01 14:15:05,697 epoch 30 - iter 189/274 - loss 0.02443210 - samples/sec: 65.04 - lr: 0.100000 -2022-11-01 14:15:18,878 epoch 30 - iter 216/274 - loss 0.02487402 - samples/sec: 65.56 - lr: 0.100000 -2022-11-01 14:15:31,181 epoch 30 - iter 243/274 - loss 0.02547667 - samples/sec: 70.25 - lr: 0.100000 -2022-11-01 14:15:44,553 epoch 30 - iter 270/274 - loss 0.02573419 - samples/sec: 64.63 - lr: 0.100000 -2022-11-01 14:15:46,184 ---------------------------------------------------------------------------------------------------- -2022-11-01 14:15:46,184 EPOCH 30 done: loss 0.0257 - lr 0.100000 -2022-11-01 14:16:11,386 Evaluating as a multi-label problem: False -2022-11-01 14:16:11,401 TEST : loss 0.030005231499671936 - f1-score (micro avg) 0.8523 -2022-11-01 14:16:11,453 BAD EPOCHS (no improvement): 0 -2022-11-01 14:16:11,541 ---------------------------------------------------------------------------------------------------- -2022-11-01 14:16:23,665 epoch 31 - iter 27/274 - loss 0.02769051 - samples/sec: 71.29 - lr: 0.100000 -2022-11-01 14:16:35,224 epoch 31 - iter 54/274 - loss 0.02615510 - samples/sec: 74.77 - lr: 0.100000 -2022-11-01 14:16:46,401 epoch 31 - iter 81/274 - loss 0.02630420 - samples/sec: 77.32 - lr: 0.100000 -2022-11-01 14:16:59,281 epoch 31 - iter 108/274 - loss 0.02469976 - samples/sec: 67.10 - lr: 0.100000 -2022-11-01 14:17:11,971 epoch 31 - iter 135/274 - loss 0.02533977 - samples/sec: 68.10 - lr: 0.100000 -2022-11-01 14:17:25,008 epoch 31 - iter 162/274 - loss 0.02569860 - samples/sec: 66.29 - lr: 0.100000 -2022-11-01 14:17:38,425 epoch 31 - iter 189/274 - loss 0.02582364 - samples/sec: 64.41 - lr: 0.100000 -2022-11-01 14:17:50,083 epoch 31 - iter 216/274 - loss 0.02617259 - samples/sec: 74.13 - lr: 0.100000 -2022-11-01 14:18:02,399 epoch 31 - iter 243/274 - loss 0.02593181 - samples/sec: 70.17 - lr: 0.100000 -2022-11-01 14:18:14,787 epoch 31 - iter 270/274 - loss 0.02601068 - samples/sec: 69.77 - lr: 0.100000 -2022-11-01 14:18:16,416 ---------------------------------------------------------------------------------------------------- -2022-11-01 14:18:16,416 EPOCH 31 done: loss 0.0260 - lr 0.100000 -2022-11-01 14:18:41,696 Evaluating as a multi-label problem: False -2022-11-01 14:18:41,712 TEST : loss 0.029843807220458984 - f1-score (micro avg) 0.8272 -2022-11-01 14:18:41,764 BAD EPOCHS (no improvement): 1 -2022-11-01 14:18:41,849 ---------------------------------------------------------------------------------------------------- -2022-11-01 14:18:54,631 epoch 32 - iter 27/274 - loss 0.02580945 - samples/sec: 67.62 - lr: 0.100000 -2022-11-01 14:19:05,986 epoch 32 - iter 54/274 - loss 0.02640680 - samples/sec: 76.11 - lr: 0.100000 -2022-11-01 14:19:18,468 epoch 32 - iter 81/274 - loss 0.02720723 - samples/sec: 69.24 - lr: 0.100000 -2022-11-01 14:19:31,021 epoch 32 - iter 108/274 - loss 0.02758148 - samples/sec: 68.85 - lr: 0.100000 -2022-11-01 14:19:42,993 epoch 32 - iter 135/274 - loss 0.02673017 - samples/sec: 72.19 - lr: 0.100000 -2022-11-01 14:19:54,726 epoch 32 - iter 162/274 - loss 0.02549280 - samples/sec: 73.66 - lr: 0.100000 -2022-11-01 14:20:07,180 epoch 32 - iter 189/274 - loss 0.02547213 - samples/sec: 69.39 - lr: 0.100000 -2022-11-01 14:20:19,997 epoch 32 - iter 216/274 - loss 0.02544760 - samples/sec: 67.43 - lr: 0.100000 -2022-11-01 14:20:32,141 epoch 32 - iter 243/274 - loss 0.02578350 - samples/sec: 71.16 - lr: 0.100000 -2022-11-01 14:20:45,434 epoch 32 - iter 270/274 - loss 0.02555593 - samples/sec: 65.02 - lr: 0.100000 -2022-11-01 14:20:47,150 ---------------------------------------------------------------------------------------------------- -2022-11-01 14:20:47,150 EPOCH 32 done: loss 0.0256 - lr 0.100000 -2022-11-01 14:21:12,438 Evaluating as a multi-label problem: False -2022-11-01 14:21:12,453 TEST : loss 0.032904475927352905 - f1-score (micro avg) 0.8416 -2022-11-01 14:21:12,505 BAD EPOCHS (no improvement): 0 -2022-11-01 14:21:12,594 ---------------------------------------------------------------------------------------------------- -2022-11-01 14:21:24,196 epoch 33 - iter 27/274 - loss 0.02139107 - samples/sec: 74.49 - lr: 0.100000 -2022-11-01 14:21:35,808 epoch 33 - iter 54/274 - loss 0.02249589 - samples/sec: 74.43 - lr: 0.100000 -2022-11-01 14:21:48,551 epoch 33 - iter 81/274 - loss 0.02273499 - samples/sec: 67.82 - lr: 0.100000 -2022-11-01 14:22:00,045 epoch 33 - iter 108/274 - loss 0.02313428 - samples/sec: 75.19 - lr: 0.100000 -2022-11-01 14:22:12,913 epoch 33 - iter 135/274 - loss 0.02465351 - samples/sec: 67.16 - lr: 0.100000 -2022-11-01 14:22:25,998 epoch 33 - iter 162/274 - loss 0.02561604 - samples/sec: 66.04 - lr: 0.100000 -2022-11-01 14:22:39,478 epoch 33 - iter 189/274 - loss 0.02617891 - samples/sec: 64.11 - lr: 0.100000 -2022-11-01 14:22:52,040 epoch 33 - iter 216/274 - loss 0.02625557 - samples/sec: 68.80 - lr: 0.100000 -2022-11-01 14:23:05,286 epoch 33 - iter 243/274 - loss 0.02610092 - samples/sec: 65.24 - lr: 0.100000 -2022-11-01 14:23:17,260 epoch 33 - iter 270/274 - loss 0.02604854 - samples/sec: 72.18 - lr: 0.100000 -2022-11-01 14:23:18,711 ---------------------------------------------------------------------------------------------------- -2022-11-01 14:23:18,711 EPOCH 33 done: loss 0.0261 - lr 0.100000 -2022-11-01 14:23:44,069 Evaluating as a multi-label problem: False -2022-11-01 14:23:44,084 TEST : loss 0.028233280405402184 - f1-score (micro avg) 0.8391 -2022-11-01 14:23:44,136 BAD EPOCHS (no improvement): 1 -2022-11-01 14:23:44,226 ---------------------------------------------------------------------------------------------------- -2022-11-01 14:23:56,465 epoch 34 - iter 27/274 - loss 0.02246222 - samples/sec: 70.62 - lr: 0.100000 -2022-11-01 14:24:07,833 epoch 34 - iter 54/274 - loss 0.02635074 - samples/sec: 76.03 - lr: 0.100000 -2022-11-01 14:24:20,691 epoch 34 - iter 81/274 - loss 0.02508465 - samples/sec: 67.21 - lr: 0.100000 -2022-11-01 14:24:33,435 epoch 34 - iter 108/274 - loss 0.02542733 - samples/sec: 67.81 - lr: 0.100000 -2022-11-01 14:24:45,896 epoch 34 - iter 135/274 - loss 0.02499971 - samples/sec: 69.35 - lr: 0.100000 -2022-11-01 14:24:59,505 epoch 34 - iter 162/274 - loss 0.02484198 - samples/sec: 63.50 - lr: 0.100000 -2022-11-01 14:25:11,652 epoch 34 - iter 189/274 - loss 0.02512618 - samples/sec: 71.14 - lr: 0.100000 -2022-11-01 14:25:22,940 epoch 34 - iter 216/274 - loss 0.02466030 - samples/sec: 76.57 - lr: 0.100000 -2022-11-01 14:25:35,198 epoch 34 - iter 243/274 - loss 0.02485847 - samples/sec: 70.50 - lr: 0.100000 -2022-11-01 14:25:47,307 epoch 34 - iter 270/274 - loss 0.02483982 - samples/sec: 71.37 - lr: 0.100000 -2022-11-01 14:25:49,659 ---------------------------------------------------------------------------------------------------- -2022-11-01 14:25:49,660 EPOCH 34 done: loss 0.0251 - lr 0.100000 -2022-11-01 14:26:14,991 Evaluating as a multi-label problem: False -2022-11-01 14:26:15,007 TEST : loss 0.03128255903720856 - f1-score (micro avg) 0.8362 -2022-11-01 14:26:15,058 BAD EPOCHS (no improvement): 0 -2022-11-01 14:26:15,146 ---------------------------------------------------------------------------------------------------- -2022-11-01 14:26:27,562 epoch 35 - iter 27/274 - loss 0.02694894 - samples/sec: 69.61 - lr: 0.100000 -2022-11-01 14:26:40,795 epoch 35 - iter 54/274 - loss 0.02622403 - samples/sec: 65.31 - lr: 0.100000 -2022-11-01 14:26:53,932 epoch 35 - iter 81/274 - loss 0.02467823 - samples/sec: 65.78 - lr: 0.100000 -2022-11-01 14:27:08,552 epoch 35 - iter 108/274 - loss 0.02345882 - samples/sec: 59.11 - lr: 0.100000 -2022-11-01 14:27:21,143 epoch 35 - iter 135/274 - loss 0.02558701 - samples/sec: 68.63 - lr: 0.100000 -2022-11-01 14:27:33,403 epoch 35 - iter 162/274 - loss 0.02581309 - samples/sec: 70.49 - lr: 0.100000 -2022-11-01 14:27:45,796 epoch 35 - iter 189/274 - loss 0.02556739 - samples/sec: 69.73 - lr: 0.100000 -2022-11-01 14:27:57,067 epoch 35 - iter 216/274 - loss 0.02550404 - samples/sec: 76.68 - lr: 0.100000 -2022-11-01 14:28:08,594 epoch 35 - iter 243/274 - loss 0.02550350 - samples/sec: 74.98 - lr: 0.100000 -2022-11-01 14:28:20,638 epoch 35 - iter 270/274 - loss 0.02552577 - samples/sec: 71.76 - lr: 0.100000 -2022-11-01 14:28:22,286 ---------------------------------------------------------------------------------------------------- -2022-11-01 14:28:22,286 EPOCH 35 done: loss 0.0256 - lr 0.100000 -2022-11-01 14:28:47,480 Evaluating as a multi-label problem: False -2022-11-01 14:28:47,496 TEST : loss 0.02886551432311535 - f1-score (micro avg) 0.839 -2022-11-01 14:28:47,547 BAD EPOCHS (no improvement): 1 -2022-11-01 14:28:47,616 ---------------------------------------------------------------------------------------------------- -2022-11-01 14:28:58,605 epoch 36 - iter 27/274 - loss 0.02300676 - samples/sec: 78.66 - lr: 0.100000 -2022-11-01 14:29:11,051 epoch 36 - iter 54/274 - loss 0.02499267 - samples/sec: 69.43 - lr: 0.100000 -2022-11-01 14:29:23,960 epoch 36 - iter 81/274 - loss 0.02491617 - samples/sec: 66.95 - lr: 0.100000 -2022-11-01 14:29:35,980 epoch 36 - iter 108/274 - loss 0.02491462 - samples/sec: 71.90 - lr: 0.100000 -2022-11-01 14:29:48,361 epoch 36 - iter 135/274 - loss 0.02460620 - samples/sec: 69.80 - lr: 0.100000 -2022-11-01 14:30:00,925 epoch 36 - iter 162/274 - loss 0.02530639 - samples/sec: 68.78 - lr: 0.100000 -2022-11-01 14:30:13,191 epoch 36 - iter 189/274 - loss 0.02488329 - samples/sec: 70.46 - lr: 0.100000 -2022-11-01 14:30:26,975 epoch 36 - iter 216/274 - loss 0.02498352 - samples/sec: 62.70 - lr: 0.100000 -2022-11-01 14:30:39,154 epoch 36 - iter 243/274 - loss 0.02518749 - samples/sec: 70.96 - lr: 0.100000 -2022-11-01 14:30:51,430 epoch 36 - iter 270/274 - loss 0.02540258 - samples/sec: 70.40 - lr: 0.100000 -2022-11-01 14:30:53,449 ---------------------------------------------------------------------------------------------------- -2022-11-01 14:30:53,449 EPOCH 36 done: loss 0.0254 - lr 0.100000 -2022-11-01 14:31:18,697 Evaluating as a multi-label problem: False -2022-11-01 14:31:18,712 TEST : loss 0.02905181795358658 - f1-score (micro avg) 0.8424 -2022-11-01 14:31:18,763 BAD EPOCHS (no improvement): 2 -2022-11-01 14:31:18,855 ---------------------------------------------------------------------------------------------------- -2022-11-01 14:31:31,289 epoch 37 - iter 27/274 - loss 0.02678495 - samples/sec: 69.51 - lr: 0.100000 -2022-11-01 14:31:43,065 epoch 37 - iter 54/274 - loss 0.02523053 - samples/sec: 73.39 - lr: 0.100000 -2022-11-01 14:31:55,883 epoch 37 - iter 81/274 - loss 0.02558693 - samples/sec: 67.42 - lr: 0.100000 -2022-11-01 14:32:07,760 epoch 37 - iter 108/274 - loss 0.02555196 - samples/sec: 72.76 - lr: 0.100000 -2022-11-01 14:32:19,739 epoch 37 - iter 135/274 - loss 0.02508545 - samples/sec: 72.15 - lr: 0.100000 -2022-11-01 14:32:33,968 epoch 37 - iter 162/274 - loss 0.02481853 - samples/sec: 60.74 - lr: 0.100000 -2022-11-01 14:32:45,715 epoch 37 - iter 189/274 - loss 0.02523619 - samples/sec: 73.57 - lr: 0.100000 -2022-11-01 14:32:58,842 epoch 37 - iter 216/274 - loss 0.02533995 - samples/sec: 65.84 - lr: 0.100000 -2022-11-01 14:33:09,872 epoch 37 - iter 243/274 - loss 0.02496223 - samples/sec: 78.35 - lr: 0.100000 -2022-11-01 14:33:22,337 epoch 37 - iter 270/274 - loss 0.02494492 - samples/sec: 69.33 - lr: 0.100000 -2022-11-01 14:33:23,784 ---------------------------------------------------------------------------------------------------- -2022-11-01 14:33:23,784 EPOCH 37 done: loss 0.0248 - lr 0.100000 -2022-11-01 14:33:49,123 Evaluating as a multi-label problem: False -2022-11-01 14:33:49,139 TEST : loss 0.03083939664065838 - f1-score (micro avg) 0.8404 -2022-11-01 14:33:49,191 BAD EPOCHS (no improvement): 0 -2022-11-01 14:33:49,284 ---------------------------------------------------------------------------------------------------- -2022-11-01 14:34:01,480 epoch 38 - iter 27/274 - loss 0.02325190 - samples/sec: 70.86 - lr: 0.100000 -2022-11-01 14:34:15,709 epoch 38 - iter 54/274 - loss 0.02240111 - samples/sec: 60.73 - lr: 0.100000 -2022-11-01 14:34:28,421 epoch 38 - iter 81/274 - loss 0.02339176 - samples/sec: 67.99 - lr: 0.100000 -2022-11-01 14:34:40,651 epoch 38 - iter 108/274 - loss 0.02383358 - samples/sec: 70.66 - lr: 0.100000 -2022-11-01 14:34:52,869 epoch 38 - iter 135/274 - loss 0.02355411 - samples/sec: 70.74 - lr: 0.100000 -2022-11-01 14:35:05,634 epoch 38 - iter 162/274 - loss 0.02436854 - samples/sec: 67.70 - lr: 0.100000 -2022-11-01 14:35:18,016 epoch 38 - iter 189/274 - loss 0.02461991 - samples/sec: 69.80 - lr: 0.100000 -2022-11-01 14:35:30,070 epoch 38 - iter 216/274 - loss 0.02434924 - samples/sec: 71.70 - lr: 0.100000 -2022-11-01 14:35:40,955 epoch 38 - iter 243/274 - loss 0.02462337 - samples/sec: 79.39 - lr: 0.100000 -2022-11-01 14:35:53,586 epoch 38 - iter 270/274 - loss 0.02484028 - samples/sec: 68.42 - lr: 0.100000 -2022-11-01 14:35:55,289 ---------------------------------------------------------------------------------------------------- -2022-11-01 14:35:55,289 EPOCH 38 done: loss 0.0247 - lr 0.100000 -2022-11-01 14:36:20,583 Evaluating as a multi-label problem: False -2022-11-01 14:36:20,598 TEST : loss 0.029346710070967674 - f1-score (micro avg) 0.8482 -2022-11-01 14:36:20,650 BAD EPOCHS (no improvement): 0 -2022-11-01 14:36:20,721 ---------------------------------------------------------------------------------------------------- -2022-11-01 14:36:32,585 epoch 39 - iter 27/274 - loss 0.02562557 - samples/sec: 72.85 - lr: 0.100000 -2022-11-01 14:36:43,852 epoch 39 - iter 54/274 - loss 0.02372105 - samples/sec: 76.71 - lr: 0.100000 -2022-11-01 14:36:57,929 epoch 39 - iter 81/274 - loss 0.02352530 - samples/sec: 61.39 - lr: 0.100000 -2022-11-01 14:37:11,034 epoch 39 - iter 108/274 - loss 0.02507286 - samples/sec: 65.95 - lr: 0.100000 -2022-11-01 14:37:22,672 epoch 39 - iter 135/274 - loss 0.02526471 - samples/sec: 74.26 - lr: 0.100000 -2022-11-01 14:37:35,165 epoch 39 - iter 162/274 - loss 0.02549320 - samples/sec: 69.18 - lr: 0.100000 -2022-11-01 14:37:46,859 epoch 39 - iter 189/274 - loss 0.02480664 - samples/sec: 73.90 - lr: 0.100000 -2022-11-01 14:37:58,698 epoch 39 - iter 216/274 - loss 0.02442860 - samples/sec: 73.00 - lr: 0.100000 -2022-11-01 14:38:11,976 epoch 39 - iter 243/274 - loss 0.02518608 - samples/sec: 65.09 - lr: 0.100000 -2022-11-01 14:38:23,528 epoch 39 - iter 270/274 - loss 0.02512607 - samples/sec: 74.82 - lr: 0.100000 -2022-11-01 14:38:25,272 ---------------------------------------------------------------------------------------------------- -2022-11-01 14:38:25,272 EPOCH 39 done: loss 0.0252 - lr 0.100000 -2022-11-01 14:38:50,637 Evaluating as a multi-label problem: False -2022-11-01 14:38:50,652 TEST : loss 0.030634140595793724 - f1-score (micro avg) 0.8477 -2022-11-01 14:38:50,704 BAD EPOCHS (no improvement): 1 -2022-11-01 14:38:50,781 ---------------------------------------------------------------------------------------------------- -2022-11-01 14:39:02,083 epoch 40 - iter 27/274 - loss 0.02303791 - samples/sec: 76.47 - lr: 0.100000 -2022-11-01 14:39:15,907 epoch 40 - iter 54/274 - loss 0.02221891 - samples/sec: 62.52 - lr: 0.100000 -2022-11-01 14:39:27,953 epoch 40 - iter 81/274 - loss 0.02304378 - samples/sec: 71.74 - lr: 0.100000 -2022-11-01 14:39:40,530 epoch 40 - iter 108/274 - loss 0.02335194 - samples/sec: 68.72 - lr: 0.100000 -2022-11-01 14:39:52,052 epoch 40 - iter 135/274 - loss 0.02327292 - samples/sec: 75.01 - lr: 0.100000 -2022-11-01 14:40:04,816 epoch 40 - iter 162/274 - loss 0.02340847 - samples/sec: 67.71 - lr: 0.100000 -2022-11-01 14:40:17,337 epoch 40 - iter 189/274 - loss 0.02348761 - samples/sec: 69.02 - lr: 0.100000 -2022-11-01 14:40:30,638 epoch 40 - iter 216/274 - loss 0.02364579 - samples/sec: 64.97 - lr: 0.100000 -2022-11-01 14:40:41,862 epoch 40 - iter 243/274 - loss 0.02363918 - samples/sec: 77.00 - lr: 0.100000 -2022-11-01 14:40:54,205 epoch 40 - iter 270/274 - loss 0.02364620 - samples/sec: 70.02 - lr: 0.100000 -2022-11-01 14:40:56,428 ---------------------------------------------------------------------------------------------------- -2022-11-01 14:40:56,428 EPOCH 40 done: loss 0.0239 - lr 0.100000 -2022-11-01 14:41:21,842 Evaluating as a multi-label problem: False -2022-11-01 14:41:21,858 TEST : loss 0.02772146463394165 - f1-score (micro avg) 0.8367 -2022-11-01 14:41:21,910 BAD EPOCHS (no improvement): 0 -2022-11-01 14:41:21,980 ---------------------------------------------------------------------------------------------------- -2022-11-01 14:41:34,373 epoch 41 - iter 27/274 - loss 0.02218261 - samples/sec: 69.74 - lr: 0.100000 -2022-11-01 14:41:46,713 epoch 41 - iter 54/274 - loss 0.02452946 - samples/sec: 70.04 - lr: 0.100000 -2022-11-01 14:42:00,067 epoch 41 - iter 81/274 - loss 0.02464984 - samples/sec: 64.71 - lr: 0.100000 -2022-11-01 14:42:13,175 epoch 41 - iter 108/274 - loss 0.02478733 - samples/sec: 65.93 - lr: 0.100000 -2022-11-01 14:42:24,582 epoch 41 - iter 135/274 - loss 0.02416254 - samples/sec: 75.77 - lr: 0.100000 -2022-11-01 14:42:37,182 epoch 41 - iter 162/274 - loss 0.02418863 - samples/sec: 68.59 - lr: 0.100000 -2022-11-01 14:42:48,979 epoch 41 - iter 189/274 - loss 0.02391712 - samples/sec: 73.26 - lr: 0.100000 -2022-11-01 14:43:01,479 epoch 41 - iter 216/274 - loss 0.02390674 - samples/sec: 69.14 - lr: 0.100000 -2022-11-01 14:43:13,157 epoch 41 - iter 243/274 - loss 0.02392243 - samples/sec: 74.01 - lr: 0.100000 -2022-11-01 14:43:25,639 epoch 41 - iter 270/274 - loss 0.02412327 - samples/sec: 69.24 - lr: 0.100000 -2022-11-01 14:43:27,265 ---------------------------------------------------------------------------------------------------- -2022-11-01 14:43:27,265 EPOCH 41 done: loss 0.0239 - lr 0.100000 -2022-11-01 14:43:52,553 Evaluating as a multi-label problem: False -2022-11-01 14:43:52,568 TEST : loss 0.03169442340731621 - f1-score (micro avg) 0.8514 -2022-11-01 14:43:52,619 BAD EPOCHS (no improvement): 0 -2022-11-01 14:43:52,707 ---------------------------------------------------------------------------------------------------- -2022-11-01 14:44:04,877 epoch 42 - iter 27/274 - loss 0.02433022 - samples/sec: 71.02 - lr: 0.100000 -2022-11-01 14:44:17,429 epoch 42 - iter 54/274 - loss 0.02389634 - samples/sec: 68.85 - lr: 0.100000 -2022-11-01 14:44:29,618 epoch 42 - iter 81/274 - loss 0.02250941 - samples/sec: 70.90 - lr: 0.100000 -2022-11-01 14:44:41,866 epoch 42 - iter 108/274 - loss 0.02211868 - samples/sec: 70.56 - lr: 0.100000 -2022-11-01 14:44:55,665 epoch 42 - iter 135/274 - loss 0.02233769 - samples/sec: 62.63 - lr: 0.100000 -2022-11-01 14:45:07,849 epoch 42 - iter 162/274 - loss 0.02328487 - samples/sec: 70.93 - lr: 0.100000 -2022-11-01 14:45:18,733 epoch 42 - iter 189/274 - loss 0.02313874 - samples/sec: 79.41 - lr: 0.100000 -2022-11-01 14:45:31,192 epoch 42 - iter 216/274 - loss 0.02357150 - samples/sec: 69.37 - lr: 0.100000 -2022-11-01 14:45:44,209 epoch 42 - iter 243/274 - loss 0.02391369 - samples/sec: 66.39 - lr: 0.100000 -2022-11-01 14:45:55,304 epoch 42 - iter 270/274 - loss 0.02365274 - samples/sec: 77.90 - lr: 0.100000 -2022-11-01 14:45:57,120 ---------------------------------------------------------------------------------------------------- -2022-11-01 14:45:57,120 EPOCH 42 done: loss 0.0235 - lr 0.100000 -2022-11-01 14:46:22,560 Evaluating as a multi-label problem: False -2022-11-01 14:46:22,576 TEST : loss 0.03278948739171028 - f1-score (micro avg) 0.8516 -2022-11-01 14:46:22,628 BAD EPOCHS (no improvement): 0 -2022-11-01 14:46:22,717 ---------------------------------------------------------------------------------------------------- -2022-11-01 14:46:38,002 epoch 43 - iter 27/274 - loss 0.02368651 - samples/sec: 56.54 - lr: 0.100000 -2022-11-01 14:46:49,976 epoch 43 - iter 54/274 - loss 0.02517416 - samples/sec: 72.18 - lr: 0.100000 -2022-11-01 14:47:01,138 epoch 43 - iter 81/274 - loss 0.02256523 - samples/sec: 77.43 - lr: 0.100000 -2022-11-01 14:47:14,006 epoch 43 - iter 108/274 - loss 0.02355001 - samples/sec: 67.16 - lr: 0.100000 -2022-11-01 14:47:26,796 epoch 43 - iter 135/274 - loss 0.02396305 - samples/sec: 67.57 - lr: 0.100000 -2022-11-01 14:47:40,342 epoch 43 - iter 162/274 - loss 0.02362844 - samples/sec: 63.80 - lr: 0.100000 -2022-11-01 14:47:51,127 epoch 43 - iter 189/274 - loss 0.02289074 - samples/sec: 80.14 - lr: 0.100000 -2022-11-01 14:48:04,379 epoch 43 - iter 216/274 - loss 0.02336316 - samples/sec: 65.21 - lr: 0.100000 -2022-11-01 14:48:16,059 epoch 43 - iter 243/274 - loss 0.02362543 - samples/sec: 73.99 - lr: 0.100000 -2022-11-01 14:48:28,117 epoch 43 - iter 270/274 - loss 0.02350817 - samples/sec: 71.67 - lr: 0.100000 -2022-11-01 14:48:29,903 ---------------------------------------------------------------------------------------------------- -2022-11-01 14:48:29,903 EPOCH 43 done: loss 0.0235 - lr 0.100000 -2022-11-01 14:48:55,144 Evaluating as a multi-label problem: False -2022-11-01 14:48:55,160 TEST : loss 0.032541219145059586 - f1-score (micro avg) 0.8499 -2022-11-01 14:48:55,213 BAD EPOCHS (no improvement): 0 -2022-11-01 14:48:55,302 ---------------------------------------------------------------------------------------------------- -2022-11-01 14:49:08,036 epoch 44 - iter 27/274 - loss 0.02069038 - samples/sec: 67.87 - lr: 0.100000 -2022-11-01 14:49:20,182 epoch 44 - iter 54/274 - loss 0.02151187 - samples/sec: 71.15 - lr: 0.100000 -2022-11-01 14:49:30,865 epoch 44 - iter 81/274 - loss 0.02143477 - samples/sec: 80.90 - lr: 0.100000 -2022-11-01 14:49:42,537 epoch 44 - iter 108/274 - loss 0.02245272 - samples/sec: 74.05 - lr: 0.100000 -2022-11-01 14:49:54,536 epoch 44 - iter 135/274 - loss 0.02279972 - samples/sec: 72.02 - lr: 0.100000 -2022-11-01 14:50:05,597 epoch 44 - iter 162/274 - loss 0.02322081 - samples/sec: 78.14 - lr: 0.100000 -2022-11-01 14:50:18,330 epoch 44 - iter 189/274 - loss 0.02353130 - samples/sec: 67.87 - lr: 0.100000 -2022-11-01 14:50:32,304 epoch 44 - iter 216/274 - loss 0.02377507 - samples/sec: 61.84 - lr: 0.100000 -2022-11-01 14:50:44,805 epoch 44 - iter 243/274 - loss 0.02373147 - samples/sec: 69.13 - lr: 0.100000 -2022-11-01 14:50:57,854 epoch 44 - iter 270/274 - loss 0.02373160 - samples/sec: 66.31 - lr: 0.100000 -2022-11-01 14:50:59,998 ---------------------------------------------------------------------------------------------------- -2022-11-01 14:50:59,998 EPOCH 44 done: loss 0.0238 - lr 0.100000 -2022-11-01 14:51:26,134 Evaluating as a multi-label problem: False -2022-11-01 14:51:26,149 TEST : loss 0.02845124527812004 - f1-score (micro avg) 0.8465 -2022-11-01 14:51:26,200 BAD EPOCHS (no improvement): 1 -2022-11-01 14:51:26,269 ---------------------------------------------------------------------------------------------------- -2022-11-01 14:51:39,451 epoch 45 - iter 27/274 - loss 0.02757102 - samples/sec: 65.56 - lr: 0.100000 -2022-11-01 14:51:52,057 epoch 45 - iter 54/274 - loss 0.02555338 - samples/sec: 68.56 - lr: 0.100000 -2022-11-01 14:52:03,835 epoch 45 - iter 81/274 - loss 0.02440811 - samples/sec: 73.38 - lr: 0.100000 -2022-11-01 14:52:15,975 epoch 45 - iter 108/274 - loss 0.02504033 - samples/sec: 71.19 - lr: 0.100000 -2022-11-01 14:52:28,608 epoch 45 - iter 135/274 - loss 0.02481827 - samples/sec: 68.41 - lr: 0.100000 -2022-11-01 14:52:40,182 epoch 45 - iter 162/274 - loss 0.02464289 - samples/sec: 74.67 - lr: 0.100000 -2022-11-01 14:52:53,060 epoch 45 - iter 189/274 - loss 0.02493790 - samples/sec: 67.11 - lr: 0.100000 -2022-11-01 14:53:05,153 epoch 45 - iter 216/274 - loss 0.02455552 - samples/sec: 71.47 - lr: 0.100000 -2022-11-01 14:53:17,930 epoch 45 - iter 243/274 - loss 0.02398309 - samples/sec: 67.64 - lr: 0.100000 -2022-11-01 14:53:30,733 epoch 45 - iter 270/274 - loss 0.02374604 - samples/sec: 67.50 - lr: 0.100000 -2022-11-01 14:53:33,008 ---------------------------------------------------------------------------------------------------- -2022-11-01 14:53:33,008 EPOCH 45 done: loss 0.0238 - lr 0.100000 -2022-11-01 14:53:58,283 Evaluating as a multi-label problem: False -2022-11-01 14:53:58,299 TEST : loss 0.029735716059803963 - f1-score (micro avg) 0.8508 -2022-11-01 14:53:58,351 BAD EPOCHS (no improvement): 2 -2022-11-01 14:53:58,435 ---------------------------------------------------------------------------------------------------- -2022-11-01 14:54:09,817 epoch 46 - iter 27/274 - loss 0.02467951 - samples/sec: 75.94 - lr: 0.100000 -2022-11-01 14:54:21,873 epoch 46 - iter 54/274 - loss 0.02596354 - samples/sec: 71.69 - lr: 0.100000 -2022-11-01 14:54:34,441 epoch 46 - iter 81/274 - loss 0.02400549 - samples/sec: 68.76 - lr: 0.100000 -2022-11-01 14:54:47,134 epoch 46 - iter 108/274 - loss 0.02389095 - samples/sec: 68.09 - lr: 0.100000 -2022-11-01 14:55:01,103 epoch 46 - iter 135/274 - loss 0.02373190 - samples/sec: 61.87 - lr: 0.100000 -2022-11-01 14:55:13,471 epoch 46 - iter 162/274 - loss 0.02343819 - samples/sec: 69.88 - lr: 0.100000 -2022-11-01 14:55:25,101 epoch 46 - iter 189/274 - loss 0.02343376 - samples/sec: 74.31 - lr: 0.100000 -2022-11-01 14:55:38,007 epoch 46 - iter 216/274 - loss 0.02403790 - samples/sec: 66.96 - lr: 0.100000 -2022-11-01 14:55:50,576 epoch 46 - iter 243/274 - loss 0.02399389 - samples/sec: 68.76 - lr: 0.100000 -2022-11-01 14:56:02,258 epoch 46 - iter 270/274 - loss 0.02418122 - samples/sec: 73.98 - lr: 0.100000 -2022-11-01 14:56:04,273 ---------------------------------------------------------------------------------------------------- -2022-11-01 14:56:04,274 EPOCH 46 done: loss 0.0243 - lr 0.100000 -2022-11-01 14:56:29,683 Evaluating as a multi-label problem: False -2022-11-01 14:56:29,698 TEST : loss 0.028860261663794518 - f1-score (micro avg) 0.8483 -2022-11-01 14:56:29,751 BAD EPOCHS (no improvement): 3 -2022-11-01 14:56:29,837 ---------------------------------------------------------------------------------------------------- -2022-11-01 14:56:42,570 epoch 47 - iter 27/274 - loss 0.03313505 - samples/sec: 67.88 - lr: 0.100000 -2022-11-01 14:56:54,775 epoch 47 - iter 54/274 - loss 0.02904664 - samples/sec: 70.81 - lr: 0.100000 -2022-11-01 14:57:07,973 epoch 47 - iter 81/274 - loss 0.02655091 - samples/sec: 65.48 - lr: 0.100000 -2022-11-01 14:57:20,344 epoch 47 - iter 108/274 - loss 0.02502254 - samples/sec: 69.86 - lr: 0.100000 -2022-11-01 14:57:33,689 epoch 47 - iter 135/274 - loss 0.02387354 - samples/sec: 64.76 - lr: 0.100000 -2022-11-01 14:57:47,202 epoch 47 - iter 162/274 - loss 0.02337203 - samples/sec: 63.96 - lr: 0.100000 -2022-11-01 14:57:59,283 epoch 47 - iter 189/274 - loss 0.02311901 - samples/sec: 71.53 - lr: 0.100000 -2022-11-01 14:58:10,345 epoch 47 - iter 216/274 - loss 0.02312254 - samples/sec: 78.13 - lr: 0.100000 -2022-11-01 14:58:22,536 epoch 47 - iter 243/274 - loss 0.02276797 - samples/sec: 70.89 - lr: 0.100000 -2022-11-01 14:58:35,257 epoch 47 - iter 270/274 - loss 0.02288939 - samples/sec: 67.94 - lr: 0.100000 -2022-11-01 14:58:36,750 ---------------------------------------------------------------------------------------------------- -2022-11-01 14:58:36,750 EPOCH 47 done: loss 0.0228 - lr 0.100000 -2022-11-01 14:59:01,933 Evaluating as a multi-label problem: False -2022-11-01 14:59:01,949 TEST : loss 0.02934456616640091 - f1-score (micro avg) 0.85 -2022-11-01 14:59:02,000 BAD EPOCHS (no improvement): 0 -2022-11-01 14:59:02,087 ---------------------------------------------------------------------------------------------------- -2022-11-01 14:59:14,890 epoch 48 - iter 27/274 - loss 0.02641052 - samples/sec: 67.51 - lr: 0.100000 -2022-11-01 14:59:27,300 epoch 48 - iter 54/274 - loss 0.02458621 - samples/sec: 69.64 - lr: 0.100000 -2022-11-01 14:59:41,623 epoch 48 - iter 81/274 - loss 0.02492918 - samples/sec: 60.34 - lr: 0.100000 -2022-11-01 14:59:53,214 epoch 48 - iter 108/274 - loss 0.02434256 - samples/sec: 74.56 - lr: 0.100000 -2022-11-01 15:00:05,218 epoch 48 - iter 135/274 - loss 0.02440883 - samples/sec: 72.00 - lr: 0.100000 -2022-11-01 15:00:17,655 epoch 48 - iter 162/274 - loss 0.02434351 - samples/sec: 69.49 - lr: 0.100000 -2022-11-01 15:00:29,358 epoch 48 - iter 189/274 - loss 0.02453602 - samples/sec: 73.85 - lr: 0.100000 -2022-11-01 15:00:41,290 epoch 48 - iter 216/274 - loss 0.02403384 - samples/sec: 72.43 - lr: 0.100000 -2022-11-01 15:00:52,879 epoch 48 - iter 243/274 - loss 0.02357749 - samples/sec: 74.57 - lr: 0.100000 -2022-11-01 15:01:06,810 epoch 48 - iter 270/274 - loss 0.02325659 - samples/sec: 62.04 - lr: 0.100000 -2022-11-01 15:01:08,920 ---------------------------------------------------------------------------------------------------- -2022-11-01 15:01:08,921 EPOCH 48 done: loss 0.0233 - lr 0.100000 -2022-11-01 15:01:34,433 Evaluating as a multi-label problem: False -2022-11-01 15:01:34,449 TEST : loss 0.027938606217503548 - f1-score (micro avg) 0.8443 -2022-11-01 15:01:34,500 BAD EPOCHS (no improvement): 1 -2022-11-01 15:01:34,588 ---------------------------------------------------------------------------------------------------- -2022-11-01 15:01:46,854 epoch 49 - iter 27/274 - loss 0.01956175 - samples/sec: 70.46 - lr: 0.100000 -2022-11-01 15:01:59,605 epoch 49 - iter 54/274 - loss 0.02219700 - samples/sec: 67.78 - lr: 0.100000 -2022-11-01 15:02:11,513 epoch 49 - iter 81/274 - loss 0.02216077 - samples/sec: 72.57 - lr: 0.100000 -2022-11-01 15:02:23,884 epoch 49 - iter 108/274 - loss 0.02166043 - samples/sec: 69.86 - lr: 0.100000 -2022-11-01 15:02:35,018 epoch 49 - iter 135/274 - loss 0.02214609 - samples/sec: 77.62 - lr: 0.100000 -2022-11-01 15:02:47,703 epoch 49 - iter 162/274 - loss 0.02230819 - samples/sec: 68.13 - lr: 0.100000 -2022-11-01 15:03:00,160 epoch 49 - iter 189/274 - loss 0.02220722 - samples/sec: 69.38 - lr: 0.100000 -2022-11-01 15:03:13,972 epoch 49 - iter 216/274 - loss 0.02222522 - samples/sec: 62.57 - lr: 0.100000 -2022-11-01 15:03:26,882 epoch 49 - iter 243/274 - loss 0.02219608 - samples/sec: 66.94 - lr: 0.100000 -2022-11-01 15:03:38,015 epoch 49 - iter 270/274 - loss 0.02232361 - samples/sec: 77.63 - lr: 0.100000 -2022-11-01 15:03:39,771 ---------------------------------------------------------------------------------------------------- -2022-11-01 15:03:39,772 EPOCH 49 done: loss 0.0224 - lr 0.100000 -2022-11-01 15:04:05,093 Evaluating as a multi-label problem: False -2022-11-01 15:04:05,108 TEST : loss 0.02850373275578022 - f1-score (micro avg) 0.8468 -2022-11-01 15:04:05,160 BAD EPOCHS (no improvement): 0 -2022-11-01 15:04:05,249 ---------------------------------------------------------------------------------------------------- -2022-11-01 15:04:19,274 epoch 50 - iter 27/274 - loss 0.02281989 - samples/sec: 61.62 - lr: 0.100000 -2022-11-01 15:04:32,085 epoch 50 - iter 54/274 - loss 0.02177090 - samples/sec: 67.46 - lr: 0.100000 -2022-11-01 15:04:44,255 epoch 50 - iter 81/274 - loss 0.02148794 - samples/sec: 71.01 - lr: 0.100000 -2022-11-01 15:04:55,864 epoch 50 - iter 108/274 - loss 0.02172616 - samples/sec: 74.44 - lr: 0.100000 -2022-11-01 15:05:08,711 epoch 50 - iter 135/274 - loss 0.02248392 - samples/sec: 67.27 - lr: 0.100000 -2022-11-01 15:05:19,943 epoch 50 - iter 162/274 - loss 0.02202995 - samples/sec: 76.95 - lr: 0.100000 -2022-11-01 15:05:31,349 epoch 50 - iter 189/274 - loss 0.02290603 - samples/sec: 75.77 - lr: 0.100000 -2022-11-01 15:05:43,290 epoch 50 - iter 216/274 - loss 0.02253406 - samples/sec: 72.38 - lr: 0.100000 -2022-11-01 15:05:54,958 epoch 50 - iter 243/274 - loss 0.02223297 - samples/sec: 74.07 - lr: 0.100000 -2022-11-01 15:06:08,209 epoch 50 - iter 270/274 - loss 0.02262291 - samples/sec: 65.22 - lr: 0.100000 -2022-11-01 15:06:10,014 ---------------------------------------------------------------------------------------------------- -2022-11-01 15:06:10,015 EPOCH 50 done: loss 0.0226 - lr 0.100000 -2022-11-01 15:06:34,955 Evaluating as a multi-label problem: False -2022-11-01 15:06:34,970 TEST : loss 0.028627894818782806 - f1-score (micro avg) 0.8454 -2022-11-01 15:06:35,022 BAD EPOCHS (no improvement): 1 -2022-11-01 15:06:35,107 ---------------------------------------------------------------------------------------------------- -2022-11-01 15:06:48,507 epoch 51 - iter 27/274 - loss 0.02238933 - samples/sec: 64.49 - lr: 0.100000 -2022-11-01 15:07:02,021 epoch 51 - iter 54/274 - loss 0.02462189 - samples/sec: 63.95 - lr: 0.100000 -2022-11-01 15:07:14,430 epoch 51 - iter 81/274 - loss 0.02400550 - samples/sec: 69.65 - lr: 0.100000 -2022-11-01 15:07:26,726 epoch 51 - iter 108/274 - loss 0.02311522 - samples/sec: 70.29 - lr: 0.100000 -2022-11-01 15:07:37,629 epoch 51 - iter 135/274 - loss 0.02300795 - samples/sec: 79.27 - lr: 0.100000 -2022-11-01 15:07:50,615 epoch 51 - iter 162/274 - loss 0.02348605 - samples/sec: 66.55 - lr: 0.100000 -2022-11-01 15:08:02,430 epoch 51 - iter 189/274 - loss 0.02283313 - samples/sec: 73.15 - lr: 0.100000 -2022-11-01 15:08:14,790 epoch 51 - iter 216/274 - loss 0.02245246 - samples/sec: 69.92 - lr: 0.100000 -2022-11-01 15:08:26,827 epoch 51 - iter 243/274 - loss 0.02276332 - samples/sec: 71.80 - lr: 0.100000 -2022-11-01 15:08:40,356 epoch 51 - iter 270/274 - loss 0.02279572 - samples/sec: 63.88 - lr: 0.100000 -2022-11-01 15:08:41,992 ---------------------------------------------------------------------------------------------------- -2022-11-01 15:08:41,992 EPOCH 51 done: loss 0.0228 - lr 0.100000 -2022-11-01 15:09:07,240 Evaluating as a multi-label problem: False -2022-11-01 15:09:07,255 TEST : loss 0.030040256679058075 - f1-score (micro avg) 0.8562 -2022-11-01 15:09:07,307 BAD EPOCHS (no improvement): 2 -2022-11-01 15:09:07,396 ---------------------------------------------------------------------------------------------------- -2022-11-01 15:09:19,681 epoch 52 - iter 27/274 - loss 0.02043969 - samples/sec: 70.35 - lr: 0.100000 -2022-11-01 15:09:34,320 epoch 52 - iter 54/274 - loss 0.02200447 - samples/sec: 59.03 - lr: 0.100000 -2022-11-01 15:09:47,331 epoch 52 - iter 81/274 - loss 0.02239819 - samples/sec: 66.42 - lr: 0.100000 -2022-11-01 15:10:00,542 epoch 52 - iter 108/274 - loss 0.02195955 - samples/sec: 65.41 - lr: 0.100000 -2022-11-01 15:10:12,733 epoch 52 - iter 135/274 - loss 0.02255437 - samples/sec: 70.89 - lr: 0.100000 -2022-11-01 15:10:24,202 epoch 52 - iter 162/274 - loss 0.02364029 - samples/sec: 75.36 - lr: 0.100000 -2022-11-01 15:10:35,444 epoch 52 - iter 189/274 - loss 0.02373332 - samples/sec: 76.87 - lr: 0.100000 -2022-11-01 15:10:47,265 epoch 52 - iter 216/274 - loss 0.02396143 - samples/sec: 73.11 - lr: 0.100000 -2022-11-01 15:10:59,460 epoch 52 - iter 243/274 - loss 0.02381229 - samples/sec: 70.87 - lr: 0.100000 -2022-11-01 15:11:12,304 epoch 52 - iter 270/274 - loss 0.02388624 - samples/sec: 67.28 - lr: 0.100000 -2022-11-01 15:11:13,703 ---------------------------------------------------------------------------------------------------- -2022-11-01 15:11:13,703 EPOCH 52 done: loss 0.0240 - lr 0.100000 -2022-11-01 15:11:38,949 Evaluating as a multi-label problem: False -2022-11-01 15:11:38,965 TEST : loss 0.02899007685482502 - f1-score (micro avg) 0.8496 -2022-11-01 15:11:39,015 BAD EPOCHS (no improvement): 3 -2022-11-01 15:11:39,107 ---------------------------------------------------------------------------------------------------- -2022-11-01 15:11:50,967 epoch 53 - iter 27/274 - loss 0.02453654 - samples/sec: 72.87 - lr: 0.100000 -2022-11-01 15:12:06,441 epoch 53 - iter 54/274 - loss 0.02250028 - samples/sec: 55.85 - lr: 0.100000 -2022-11-01 15:12:18,855 epoch 53 - iter 81/274 - loss 0.02251377 - samples/sec: 69.62 - lr: 0.100000 -2022-11-01 15:12:31,007 epoch 53 - iter 108/274 - loss 0.02219599 - samples/sec: 71.12 - lr: 0.100000 -2022-11-01 15:12:43,529 epoch 53 - iter 135/274 - loss 0.02197082 - samples/sec: 69.02 - lr: 0.100000 -2022-11-01 15:12:55,036 epoch 53 - iter 162/274 - loss 0.02234258 - samples/sec: 75.11 - lr: 0.100000 -2022-11-01 15:13:06,569 epoch 53 - iter 189/274 - loss 0.02253575 - samples/sec: 74.94 - lr: 0.100000 -2022-11-01 15:13:17,608 epoch 53 - iter 216/274 - loss 0.02244875 - samples/sec: 78.29 - lr: 0.100000 -2022-11-01 15:13:30,157 epoch 53 - iter 243/274 - loss 0.02246492 - samples/sec: 68.87 - lr: 0.100000 -2022-11-01 15:13:43,481 epoch 53 - iter 270/274 - loss 0.02199013 - samples/sec: 64.86 - lr: 0.100000 -2022-11-01 15:13:45,316 ---------------------------------------------------------------------------------------------------- -2022-11-01 15:13:45,317 EPOCH 53 done: loss 0.0219 - lr 0.100000 -2022-11-01 15:14:10,521 Evaluating as a multi-label problem: False -2022-11-01 15:14:10,536 TEST : loss 0.031025480479002 - f1-score (micro avg) 0.8497 -2022-11-01 15:14:10,589 BAD EPOCHS (no improvement): 0 -2022-11-01 15:14:10,684 ---------------------------------------------------------------------------------------------------- -2022-11-01 15:14:23,106 epoch 54 - iter 27/274 - loss 0.02366044 - samples/sec: 69.58 - lr: 0.100000 -2022-11-01 15:14:35,723 epoch 54 - iter 54/274 - loss 0.02131955 - samples/sec: 68.50 - lr: 0.100000 -2022-11-01 15:14:47,667 epoch 54 - iter 81/274 - loss 0.02241648 - samples/sec: 72.36 - lr: 0.100000 -2022-11-01 15:15:01,117 epoch 54 - iter 108/274 - loss 0.02264243 - samples/sec: 64.25 - lr: 0.100000 -2022-11-01 15:15:13,721 epoch 54 - iter 135/274 - loss 0.02298791 - samples/sec: 68.57 - lr: 0.100000 -2022-11-01 15:15:25,955 epoch 54 - iter 162/274 - loss 0.02242851 - samples/sec: 70.64 - lr: 0.100000 -2022-11-01 15:15:38,384 epoch 54 - iter 189/274 - loss 0.02235910 - samples/sec: 69.54 - lr: 0.100000 -2022-11-01 15:15:50,640 epoch 54 - iter 216/274 - loss 0.02201195 - samples/sec: 70.51 - lr: 0.100000 -2022-11-01 15:16:03,803 epoch 54 - iter 243/274 - loss 0.02225472 - samples/sec: 65.66 - lr: 0.100000 -2022-11-01 15:16:15,823 epoch 54 - iter 270/274 - loss 0.02218903 - samples/sec: 71.90 - lr: 0.100000 -2022-11-01 15:16:17,612 ---------------------------------------------------------------------------------------------------- -2022-11-01 15:16:17,612 EPOCH 54 done: loss 0.0222 - lr 0.100000 -2022-11-01 15:16:42,914 Evaluating as a multi-label problem: False -2022-11-01 15:16:42,930 TEST : loss 0.030233900994062424 - f1-score (micro avg) 0.8454 -2022-11-01 15:16:42,983 BAD EPOCHS (no improvement): 1 -2022-11-01 15:16:43,079 ---------------------------------------------------------------------------------------------------- -2022-11-01 15:16:55,349 epoch 55 - iter 27/274 - loss 0.02055602 - samples/sec: 70.44 - lr: 0.100000 -2022-11-01 15:17:08,468 epoch 55 - iter 54/274 - loss 0.01923870 - samples/sec: 65.88 - lr: 0.100000 -2022-11-01 15:17:23,027 epoch 55 - iter 81/274 - loss 0.02096547 - samples/sec: 59.36 - lr: 0.100000 -2022-11-01 15:17:34,938 epoch 55 - iter 108/274 - loss 0.02099036 - samples/sec: 72.56 - lr: 0.100000 -2022-11-01 15:17:47,602 epoch 55 - iter 135/274 - loss 0.02078503 - samples/sec: 68.24 - lr: 0.100000 -2022-11-01 15:17:59,080 epoch 55 - iter 162/274 - loss 0.02148538 - samples/sec: 75.30 - lr: 0.100000 -2022-11-01 15:18:11,657 epoch 55 - iter 189/274 - loss 0.02214461 - samples/sec: 68.71 - lr: 0.100000 -2022-11-01 15:18:23,508 epoch 55 - iter 216/274 - loss 0.02190890 - samples/sec: 72.93 - lr: 0.100000 -2022-11-01 15:18:36,762 epoch 55 - iter 243/274 - loss 0.02249219 - samples/sec: 65.20 - lr: 0.100000 -2022-11-01 15:18:48,843 epoch 55 - iter 270/274 - loss 0.02234599 - samples/sec: 71.54 - lr: 0.100000 -2022-11-01 15:18:50,379 ---------------------------------------------------------------------------------------------------- -2022-11-01 15:18:50,379 EPOCH 55 done: loss 0.0224 - lr 0.100000 -2022-11-01 15:19:15,651 Evaluating as a multi-label problem: False -2022-11-01 15:19:15,666 TEST : loss 0.02768951654434204 - f1-score (micro avg) 0.856 -2022-11-01 15:19:15,719 BAD EPOCHS (no improvement): 2 -2022-11-01 15:19:15,811 ---------------------------------------------------------------------------------------------------- -2022-11-01 15:19:28,337 epoch 56 - iter 27/274 - loss 0.01954876 - samples/sec: 69.00 - lr: 0.100000 -2022-11-01 15:19:40,046 epoch 56 - iter 54/274 - loss 0.02032311 - samples/sec: 73.81 - lr: 0.100000 -2022-11-01 15:19:52,765 epoch 56 - iter 81/274 - loss 0.02087417 - samples/sec: 67.95 - lr: 0.100000 -2022-11-01 15:20:04,834 epoch 56 - iter 108/274 - loss 0.02048386 - samples/sec: 71.61 - lr: 0.100000 -2022-11-01 15:20:17,273 epoch 56 - iter 135/274 - loss 0.02064770 - samples/sec: 69.47 - lr: 0.100000 -2022-11-01 15:20:28,798 epoch 56 - iter 162/274 - loss 0.02112385 - samples/sec: 74.99 - lr: 0.100000 -2022-11-01 15:20:40,648 epoch 56 - iter 189/274 - loss 0.02075702 - samples/sec: 72.93 - lr: 0.100000 -2022-11-01 15:20:53,632 epoch 56 - iter 216/274 - loss 0.02239268 - samples/sec: 66.56 - lr: 0.100000 -2022-11-01 15:21:06,045 epoch 56 - iter 243/274 - loss 0.02240334 - samples/sec: 69.62 - lr: 0.100000 -2022-11-01 15:21:18,551 epoch 56 - iter 270/274 - loss 0.02264396 - samples/sec: 69.11 - lr: 0.100000 -2022-11-01 15:21:19,946 ---------------------------------------------------------------------------------------------------- -2022-11-01 15:21:19,946 EPOCH 56 done: loss 0.0228 - lr 0.100000 -2022-11-01 15:21:45,321 Evaluating as a multi-label problem: False -2022-11-01 15:21:45,337 TEST : loss 0.0304726455360651 - f1-score (micro avg) 0.8554 -2022-11-01 15:21:45,388 BAD EPOCHS (no improvement): 3 -2022-11-01 15:21:45,480 ---------------------------------------------------------------------------------------------------- -2022-11-01 15:21:59,285 epoch 57 - iter 27/274 - loss 0.01858226 - samples/sec: 62.60 - lr: 0.100000 -2022-11-01 15:22:12,050 epoch 57 - iter 54/274 - loss 0.02101951 - samples/sec: 67.70 - lr: 0.100000 -2022-11-01 15:22:23,465 epoch 57 - iter 81/274 - loss 0.02165109 - samples/sec: 75.71 - lr: 0.100000 -2022-11-01 15:22:37,035 epoch 57 - iter 108/274 - loss 0.02113920 - samples/sec: 63.68 - lr: 0.100000 -2022-11-01 15:22:49,205 epoch 57 - iter 135/274 - loss 0.02123264 - samples/sec: 71.01 - lr: 0.100000 -2022-11-01 15:23:01,492 epoch 57 - iter 162/274 - loss 0.02134561 - samples/sec: 70.34 - lr: 0.100000 -2022-11-01 15:23:12,834 epoch 57 - iter 189/274 - loss 0.02164682 - samples/sec: 76.20 - lr: 0.100000 -2022-11-01 15:23:25,219 epoch 57 - iter 216/274 - loss 0.02181912 - samples/sec: 69.78 - lr: 0.100000 -2022-11-01 15:23:37,457 epoch 57 - iter 243/274 - loss 0.02190306 - samples/sec: 70.62 - lr: 0.100000 -2022-11-01 15:23:49,705 epoch 57 - iter 270/274 - loss 0.02153457 - samples/sec: 70.56 - lr: 0.100000 -2022-11-01 15:23:51,568 ---------------------------------------------------------------------------------------------------- -2022-11-01 15:23:51,569 EPOCH 57 done: loss 0.0218 - lr 0.100000 -2022-11-01 15:24:16,774 Evaluating as a multi-label problem: False -2022-11-01 15:24:16,790 TEST : loss 0.029483051970601082 - f1-score (micro avg) 0.851 -2022-11-01 15:24:16,842 BAD EPOCHS (no improvement): 0 -2022-11-01 15:24:16,934 ---------------------------------------------------------------------------------------------------- -2022-11-01 15:24:27,696 epoch 58 - iter 27/274 - loss 0.01840180 - samples/sec: 80.31 - lr: 0.100000 -2022-11-01 15:24:40,903 epoch 58 - iter 54/274 - loss 0.01832122 - samples/sec: 65.43 - lr: 0.100000 -2022-11-01 15:24:53,761 epoch 58 - iter 81/274 - loss 0.02185904 - samples/sec: 67.21 - lr: 0.100000 -2022-11-01 15:25:06,320 epoch 58 - iter 108/274 - loss 0.02212350 - samples/sec: 68.81 - lr: 0.100000 -2022-11-01 15:25:17,127 epoch 58 - iter 135/274 - loss 0.02206877 - samples/sec: 79.97 - lr: 0.100000 -2022-11-01 15:25:28,855 epoch 58 - iter 162/274 - loss 0.02272453 - samples/sec: 73.69 - lr: 0.100000 -2022-11-01 15:25:42,309 epoch 58 - iter 189/274 - loss 0.02193286 - samples/sec: 64.23 - lr: 0.100000 -2022-11-01 15:25:56,457 epoch 58 - iter 216/274 - loss 0.02243499 - samples/sec: 61.08 - lr: 0.100000 -2022-11-01 15:26:08,686 epoch 58 - iter 243/274 - loss 0.02276000 - samples/sec: 70.67 - lr: 0.100000 -2022-11-01 15:26:21,274 epoch 58 - iter 270/274 - loss 0.02265005 - samples/sec: 68.65 - lr: 0.100000 -2022-11-01 15:26:22,976 ---------------------------------------------------------------------------------------------------- -2022-11-01 15:26:22,976 EPOCH 58 done: loss 0.0227 - lr 0.100000 -2022-11-01 15:26:48,369 Evaluating as a multi-label problem: False -2022-11-01 15:26:48,384 TEST : loss 0.03021113947033882 - f1-score (micro avg) 0.8549 -2022-11-01 15:26:48,438 BAD EPOCHS (no improvement): 1 -2022-11-01 15:26:48,531 ---------------------------------------------------------------------------------------------------- -2022-11-01 15:26:59,892 epoch 59 - iter 27/274 - loss 0.02035532 - samples/sec: 76.07 - lr: 0.100000 -2022-11-01 15:27:11,943 epoch 59 - iter 54/274 - loss 0.02086338 - samples/sec: 71.71 - lr: 0.100000 -2022-11-01 15:27:25,608 epoch 59 - iter 81/274 - loss 0.02163636 - samples/sec: 63.24 - lr: 0.100000 -2022-11-01 15:27:37,361 epoch 59 - iter 108/274 - loss 0.02209525 - samples/sec: 73.53 - lr: 0.100000 -2022-11-01 15:27:50,299 epoch 59 - iter 135/274 - loss 0.02206673 - samples/sec: 66.80 - lr: 0.100000 -2022-11-01 15:28:03,802 epoch 59 - iter 162/274 - loss 0.02266218 - samples/sec: 64.00 - lr: 0.100000 -2022-11-01 15:28:17,186 epoch 59 - iter 189/274 - loss 0.02225368 - samples/sec: 64.57 - lr: 0.100000 -2022-11-01 15:28:28,801 epoch 59 - iter 216/274 - loss 0.02210266 - samples/sec: 74.40 - lr: 0.100000 -2022-11-01 15:28:41,114 epoch 59 - iter 243/274 - loss 0.02192757 - samples/sec: 70.19 - lr: 0.100000 -2022-11-01 15:28:53,424 epoch 59 - iter 270/274 - loss 0.02161121 - samples/sec: 70.20 - lr: 0.100000 -2022-11-01 15:28:54,871 ---------------------------------------------------------------------------------------------------- -2022-11-01 15:28:54,871 EPOCH 59 done: loss 0.0217 - lr 0.100000 -2022-11-01 15:29:20,109 Evaluating as a multi-label problem: False -2022-11-01 15:29:20,124 TEST : loss 0.03019784763455391 - f1-score (micro avg) 0.8518 -2022-11-01 15:29:20,177 BAD EPOCHS (no improvement): 0 -2022-11-01 15:29:20,268 ---------------------------------------------------------------------------------------------------- -2022-11-01 15:29:33,582 epoch 60 - iter 27/274 - loss 0.02266229 - samples/sec: 64.91 - lr: 0.100000 -2022-11-01 15:29:44,822 epoch 60 - iter 54/274 - loss 0.02097862 - samples/sec: 76.89 - lr: 0.100000 -2022-11-01 15:29:58,299 epoch 60 - iter 81/274 - loss 0.02103717 - samples/sec: 64.12 - lr: 0.100000 -2022-11-01 15:30:10,428 epoch 60 - iter 108/274 - loss 0.02157194 - samples/sec: 71.25 - lr: 0.100000 -2022-11-01 15:30:23,316 epoch 60 - iter 135/274 - loss 0.02176278 - samples/sec: 67.06 - lr: 0.100000 -2022-11-01 15:30:35,025 epoch 60 - iter 162/274 - loss 0.02124505 - samples/sec: 73.81 - lr: 0.100000 -2022-11-01 15:30:47,845 epoch 60 - iter 189/274 - loss 0.02133995 - samples/sec: 67.41 - lr: 0.100000 -2022-11-01 15:31:00,017 epoch 60 - iter 216/274 - loss 0.02152520 - samples/sec: 71.00 - lr: 0.100000 -2022-11-01 15:31:12,827 epoch 60 - iter 243/274 - loss 0.02134036 - samples/sec: 67.46 - lr: 0.100000 -2022-11-01 15:31:24,440 epoch 60 - iter 270/274 - loss 0.02164276 - samples/sec: 74.42 - lr: 0.100000 -2022-11-01 15:31:25,992 ---------------------------------------------------------------------------------------------------- -2022-11-01 15:31:25,992 EPOCH 60 done: loss 0.0216 - lr 0.100000 -2022-11-01 15:31:51,314 Evaluating as a multi-label problem: False -2022-11-01 15:31:51,329 TEST : loss 0.030309708788990974 - f1-score (micro avg) 0.8446 -2022-11-01 15:31:51,382 BAD EPOCHS (no improvement): 0 -2022-11-01 15:31:51,474 ---------------------------------------------------------------------------------------------------- -2022-11-01 15:32:04,475 epoch 61 - iter 27/274 - loss 0.02131620 - samples/sec: 66.48 - lr: 0.100000 -2022-11-01 15:32:16,775 epoch 61 - iter 54/274 - loss 0.01755958 - samples/sec: 70.26 - lr: 0.100000 -2022-11-01 15:32:29,944 epoch 61 - iter 81/274 - loss 0.01785219 - samples/sec: 65.63 - lr: 0.100000 -2022-11-01 15:32:42,291 epoch 61 - iter 108/274 - loss 0.01927462 - samples/sec: 69.99 - lr: 0.100000 -2022-11-01 15:32:53,782 epoch 61 - iter 135/274 - loss 0.01932511 - samples/sec: 75.21 - lr: 0.100000 -2022-11-01 15:33:05,138 epoch 61 - iter 162/274 - loss 0.02022663 - samples/sec: 76.10 - lr: 0.100000 -2022-11-01 15:33:18,348 epoch 61 - iter 189/274 - loss 0.02152986 - samples/sec: 65.42 - lr: 0.100000 -2022-11-01 15:33:30,173 epoch 61 - iter 216/274 - loss 0.02153104 - samples/sec: 73.08 - lr: 0.100000 -2022-11-01 15:33:42,662 epoch 61 - iter 243/274 - loss 0.02155148 - samples/sec: 69.20 - lr: 0.100000 -2022-11-01 15:33:54,579 epoch 61 - iter 270/274 - loss 0.02144034 - samples/sec: 72.53 - lr: 0.100000 -2022-11-01 15:33:56,381 ---------------------------------------------------------------------------------------------------- -2022-11-01 15:33:56,381 EPOCH 61 done: loss 0.0215 - lr 0.100000 -2022-11-01 15:34:21,677 Evaluating as a multi-label problem: False -2022-11-01 15:34:21,693 TEST : loss 0.028495075181126595 - f1-score (micro avg) 0.8491 -2022-11-01 15:34:21,745 BAD EPOCHS (no improvement): 0 -2022-11-01 15:34:21,837 ---------------------------------------------------------------------------------------------------- -2022-11-01 15:34:34,938 epoch 62 - iter 27/274 - loss 0.01744152 - samples/sec: 65.97 - lr: 0.100000 -2022-11-01 15:34:47,972 epoch 62 - iter 54/274 - loss 0.02095425 - samples/sec: 66.30 - lr: 0.100000 -2022-11-01 15:35:00,121 epoch 62 - iter 81/274 - loss 0.02182806 - samples/sec: 71.14 - lr: 0.100000 -2022-11-01 15:35:12,065 epoch 62 - iter 108/274 - loss 0.02200592 - samples/sec: 72.36 - lr: 0.100000 -2022-11-01 15:35:24,838 epoch 62 - iter 135/274 - loss 0.02188164 - samples/sec: 67.66 - lr: 0.100000 -2022-11-01 15:35:36,484 epoch 62 - iter 162/274 - loss 0.02140413 - samples/sec: 74.21 - lr: 0.100000 -2022-11-01 15:35:49,425 epoch 62 - iter 189/274 - loss 0.02147430 - samples/sec: 66.78 - lr: 0.100000 -2022-11-01 15:36:01,416 epoch 62 - iter 216/274 - loss 0.02079022 - samples/sec: 72.07 - lr: 0.100000 -2022-11-01 15:36:14,073 epoch 62 - iter 243/274 - loss 0.02100848 - samples/sec: 68.28 - lr: 0.100000 -2022-11-01 15:36:25,958 epoch 62 - iter 270/274 - loss 0.02117302 - samples/sec: 72.71 - lr: 0.100000 -2022-11-01 15:36:27,489 ---------------------------------------------------------------------------------------------------- -2022-11-01 15:36:27,489 EPOCH 62 done: loss 0.0211 - lr 0.100000 -2022-11-01 15:36:52,787 Evaluating as a multi-label problem: False -2022-11-01 15:36:52,803 TEST : loss 0.03227972984313965 - f1-score (micro avg) 0.8455 -2022-11-01 15:36:52,856 BAD EPOCHS (no improvement): 0 -2022-11-01 15:36:52,948 ---------------------------------------------------------------------------------------------------- -2022-11-01 15:37:04,831 epoch 63 - iter 27/274 - loss 0.02126119 - samples/sec: 72.73 - lr: 0.100000 -2022-11-01 15:37:18,535 epoch 63 - iter 54/274 - loss 0.01942961 - samples/sec: 63.06 - lr: 0.100000 -2022-11-01 15:37:31,254 epoch 63 - iter 81/274 - loss 0.02012420 - samples/sec: 67.94 - lr: 0.100000 -2022-11-01 15:37:42,705 epoch 63 - iter 108/274 - loss 0.02029080 - samples/sec: 75.48 - lr: 0.100000 -2022-11-01 15:37:55,699 epoch 63 - iter 135/274 - loss 0.02042011 - samples/sec: 66.51 - lr: 0.100000 -2022-11-01 15:38:08,123 epoch 63 - iter 162/274 - loss 0.02065848 - samples/sec: 69.56 - lr: 0.100000 -2022-11-01 15:38:19,533 epoch 63 - iter 189/274 - loss 0.02095013 - samples/sec: 75.74 - lr: 0.100000 -2022-11-01 15:38:33,075 epoch 63 - iter 216/274 - loss 0.02097885 - samples/sec: 63.82 - lr: 0.100000 -2022-11-01 15:38:45,108 epoch 63 - iter 243/274 - loss 0.02113682 - samples/sec: 71.82 - lr: 0.100000 -2022-11-01 15:38:57,147 epoch 63 - iter 270/274 - loss 0.02136165 - samples/sec: 71.79 - lr: 0.100000 -2022-11-01 15:38:58,754 ---------------------------------------------------------------------------------------------------- -2022-11-01 15:38:58,754 EPOCH 63 done: loss 0.0214 - lr 0.100000 -2022-11-01 15:39:24,602 Evaluating as a multi-label problem: False -2022-11-01 15:39:24,617 TEST : loss 0.029485132545232773 - f1-score (micro avg) 0.8442 -2022-11-01 15:39:24,671 BAD EPOCHS (no improvement): 1 -2022-11-01 15:39:24,745 ---------------------------------------------------------------------------------------------------- -2022-11-01 15:39:35,637 epoch 64 - iter 27/274 - loss 0.01890757 - samples/sec: 79.35 - lr: 0.100000 -2022-11-01 15:39:49,308 epoch 64 - iter 54/274 - loss 0.01938230 - samples/sec: 63.21 - lr: 0.100000 -2022-11-01 15:40:02,928 epoch 64 - iter 81/274 - loss 0.02217057 - samples/sec: 63.45 - lr: 0.100000 -2022-11-01 15:40:15,391 epoch 64 - iter 108/274 - loss 0.02165389 - samples/sec: 69.34 - lr: 0.100000 -2022-11-01 15:40:26,951 epoch 64 - iter 135/274 - loss 0.02168174 - samples/sec: 74.76 - lr: 0.100000 -2022-11-01 15:40:38,848 epoch 64 - iter 162/274 - loss 0.02134826 - samples/sec: 72.64 - lr: 0.100000 -2022-11-01 15:40:50,875 epoch 64 - iter 189/274 - loss 0.02080389 - samples/sec: 71.86 - lr: 0.100000 -2022-11-01 15:41:03,888 epoch 64 - iter 216/274 - loss 0.02074878 - samples/sec: 66.41 - lr: 0.100000 -2022-11-01 15:41:16,800 epoch 64 - iter 243/274 - loss 0.02043543 - samples/sec: 66.93 - lr: 0.100000 -2022-11-01 15:41:29,886 epoch 64 - iter 270/274 - loss 0.02087130 - samples/sec: 66.04 - lr: 0.100000 -2022-11-01 15:41:31,399 ---------------------------------------------------------------------------------------------------- -2022-11-01 15:41:31,399 EPOCH 64 done: loss 0.0209 - lr 0.100000 -2022-11-01 15:41:56,819 Evaluating as a multi-label problem: False -2022-11-01 15:41:56,835 TEST : loss 0.030096804723143578 - f1-score (micro avg) 0.8446 -2022-11-01 15:41:56,887 BAD EPOCHS (no improvement): 0 -2022-11-01 15:41:56,980 ---------------------------------------------------------------------------------------------------- -2022-11-01 15:42:11,007 epoch 65 - iter 27/274 - loss 0.02540958 - samples/sec: 61.61 - lr: 0.100000 -2022-11-01 15:42:23,241 epoch 65 - iter 54/274 - loss 0.02236285 - samples/sec: 70.64 - lr: 0.100000 -2022-11-01 15:42:35,599 epoch 65 - iter 81/274 - loss 0.02219879 - samples/sec: 69.93 - lr: 0.100000 -2022-11-01 15:42:47,630 epoch 65 - iter 108/274 - loss 0.02127425 - samples/sec: 71.83 - lr: 0.100000 -2022-11-01 15:43:00,252 epoch 65 - iter 135/274 - loss 0.02210803 - samples/sec: 68.47 - lr: 0.100000 -2022-11-01 15:43:12,079 epoch 65 - iter 162/274 - loss 0.02161108 - samples/sec: 73.07 - lr: 0.100000 -2022-11-01 15:43:23,801 epoch 65 - iter 189/274 - loss 0.02206507 - samples/sec: 73.73 - lr: 0.100000 -2022-11-01 15:43:34,832 epoch 65 - iter 216/274 - loss 0.02203354 - samples/sec: 78.35 - lr: 0.100000 -2022-11-01 15:43:47,550 epoch 65 - iter 243/274 - loss 0.02200313 - samples/sec: 67.95 - lr: 0.100000 -2022-11-01 15:44:00,620 epoch 65 - iter 270/274 - loss 0.02150743 - samples/sec: 66.12 - lr: 0.100000 -2022-11-01 15:44:02,372 ---------------------------------------------------------------------------------------------------- -2022-11-01 15:44:02,372 EPOCH 65 done: loss 0.0214 - lr 0.100000 -2022-11-01 15:44:28,137 Evaluating as a multi-label problem: False -2022-11-01 15:44:28,153 TEST : loss 0.02994183637201786 - f1-score (micro avg) 0.8534 -2022-11-01 15:44:28,205 BAD EPOCHS (no improvement): 1 -2022-11-01 15:44:28,291 ---------------------------------------------------------------------------------------------------- -2022-11-01 15:44:40,467 epoch 66 - iter 27/274 - loss 0.01805836 - samples/sec: 70.98 - lr: 0.100000 -2022-11-01 15:44:53,328 epoch 66 - iter 54/274 - loss 0.01872092 - samples/sec: 67.20 - lr: 0.100000 -2022-11-01 15:45:06,350 epoch 66 - iter 81/274 - loss 0.02087662 - samples/sec: 66.37 - lr: 0.100000 -2022-11-01 15:45:17,576 epoch 66 - iter 108/274 - loss 0.02057641 - samples/sec: 76.99 - lr: 0.100000 -2022-11-01 15:45:29,056 epoch 66 - iter 135/274 - loss 0.01985161 - samples/sec: 75.28 - lr: 0.100000 -2022-11-01 15:45:42,760 epoch 66 - iter 162/274 - loss 0.02047046 - samples/sec: 63.06 - lr: 0.100000 -2022-11-01 15:45:56,009 epoch 66 - iter 189/274 - loss 0.02016785 - samples/sec: 65.23 - lr: 0.100000 -2022-11-01 15:46:07,489 epoch 66 - iter 216/274 - loss 0.02021241 - samples/sec: 75.28 - lr: 0.100000 -2022-11-01 15:46:19,392 epoch 66 - iter 243/274 - loss 0.02018700 - samples/sec: 72.61 - lr: 0.100000 -2022-11-01 15:46:32,830 epoch 66 - iter 270/274 - loss 0.02057448 - samples/sec: 64.31 - lr: 0.100000 -2022-11-01 15:46:34,491 ---------------------------------------------------------------------------------------------------- -2022-11-01 15:46:34,491 EPOCH 66 done: loss 0.0205 - lr 0.100000 -2022-11-01 15:46:59,926 Evaluating as a multi-label problem: False -2022-11-01 15:46:59,942 TEST : loss 0.02977355383336544 - f1-score (micro avg) 0.8492 -2022-11-01 15:46:59,994 BAD EPOCHS (no improvement): 0 -2022-11-01 15:47:00,080 ---------------------------------------------------------------------------------------------------- -2022-11-01 15:47:12,481 epoch 67 - iter 27/274 - loss 0.02224031 - samples/sec: 69.69 - lr: 0.100000 -2022-11-01 15:47:26,274 epoch 67 - iter 54/274 - loss 0.01948851 - samples/sec: 62.66 - lr: 0.100000 -2022-11-01 15:47:37,830 epoch 67 - iter 81/274 - loss 0.02039320 - samples/sec: 74.79 - lr: 0.100000 -2022-11-01 15:47:49,569 epoch 67 - iter 108/274 - loss 0.02151655 - samples/sec: 73.62 - lr: 0.100000 -2022-11-01 15:48:01,705 epoch 67 - iter 135/274 - loss 0.02094293 - samples/sec: 71.21 - lr: 0.100000 -2022-11-01 15:48:14,965 epoch 67 - iter 162/274 - loss 0.02077230 - samples/sec: 65.18 - lr: 0.100000 -2022-11-01 15:48:27,654 epoch 67 - iter 189/274 - loss 0.02084305 - samples/sec: 68.11 - lr: 0.100000 -2022-11-01 15:48:39,855 epoch 67 - iter 216/274 - loss 0.02079591 - samples/sec: 70.83 - lr: 0.100000 -2022-11-01 15:48:51,410 epoch 67 - iter 243/274 - loss 0.02046085 - samples/sec: 74.80 - lr: 0.100000 -2022-11-01 15:49:05,305 epoch 67 - iter 270/274 - loss 0.02023832 - samples/sec: 62.19 - lr: 0.100000 -2022-11-01 15:49:07,162 ---------------------------------------------------------------------------------------------------- -2022-11-01 15:49:07,162 EPOCH 67 done: loss 0.0203 - lr 0.100000 -2022-11-01 15:49:32,558 Evaluating as a multi-label problem: False -2022-11-01 15:49:32,574 TEST : loss 0.030972706153988838 - f1-score (micro avg) 0.8543 -2022-11-01 15:49:32,627 BAD EPOCHS (no improvement): 0 -2022-11-01 15:49:32,723 ---------------------------------------------------------------------------------------------------- -2022-11-01 15:49:46,088 epoch 68 - iter 27/274 - loss 0.02133801 - samples/sec: 64.67 - lr: 0.100000 -2022-11-01 15:49:58,017 epoch 68 - iter 54/274 - loss 0.02034460 - samples/sec: 72.45 - lr: 0.100000 -2022-11-01 15:50:11,523 epoch 68 - iter 81/274 - loss 0.02022263 - samples/sec: 63.98 - lr: 0.100000 -2022-11-01 15:50:24,462 epoch 68 - iter 108/274 - loss 0.02108536 - samples/sec: 66.79 - lr: 0.100000 -2022-11-01 15:50:37,223 epoch 68 - iter 135/274 - loss 0.02035431 - samples/sec: 67.73 - lr: 0.100000 -2022-11-01 15:50:49,548 epoch 68 - iter 162/274 - loss 0.02128033 - samples/sec: 70.12 - lr: 0.100000 -2022-11-01 15:51:01,472 epoch 68 - iter 189/274 - loss 0.02080314 - samples/sec: 72.48 - lr: 0.100000 -2022-11-01 15:51:13,417 epoch 68 - iter 216/274 - loss 0.02127075 - samples/sec: 72.35 - lr: 0.100000 -2022-11-01 15:51:25,660 epoch 68 - iter 243/274 - loss 0.02066568 - samples/sec: 70.59 - lr: 0.100000 -2022-11-01 15:51:37,327 epoch 68 - iter 270/274 - loss 0.02080117 - samples/sec: 74.07 - lr: 0.100000 -2022-11-01 15:51:38,889 ---------------------------------------------------------------------------------------------------- -2022-11-01 15:51:38,889 EPOCH 68 done: loss 0.0208 - lr 0.100000 -2022-11-01 15:52:04,255 Evaluating as a multi-label problem: False -2022-11-01 15:52:04,271 TEST : loss 0.030792292207479477 - f1-score (micro avg) 0.8528 -2022-11-01 15:52:04,323 BAD EPOCHS (no improvement): 1 -2022-11-01 15:52:04,414 ---------------------------------------------------------------------------------------------------- -2022-11-01 15:52:16,801 epoch 69 - iter 27/274 - loss 0.02075958 - samples/sec: 69.77 - lr: 0.100000 -2022-11-01 15:52:28,377 epoch 69 - iter 54/274 - loss 0.02100983 - samples/sec: 74.66 - lr: 0.100000 -2022-11-01 15:52:40,744 epoch 69 - iter 81/274 - loss 0.01987977 - samples/sec: 69.88 - lr: 0.100000 -2022-11-01 15:52:52,969 epoch 69 - iter 108/274 - loss 0.01993851 - samples/sec: 70.70 - lr: 0.100000 -2022-11-01 15:53:04,669 epoch 69 - iter 135/274 - loss 0.01973035 - samples/sec: 73.87 - lr: 0.100000 -2022-11-01 15:53:16,071 epoch 69 - iter 162/274 - loss 0.01921936 - samples/sec: 75.80 - lr: 0.100000 -2022-11-01 15:53:27,126 epoch 69 - iter 189/274 - loss 0.01975087 - samples/sec: 78.17 - lr: 0.100000 -2022-11-01 15:53:39,655 epoch 69 - iter 216/274 - loss 0.02017193 - samples/sec: 68.98 - lr: 0.100000 -2022-11-01 15:53:53,330 epoch 69 - iter 243/274 - loss 0.01998366 - samples/sec: 63.19 - lr: 0.100000 -2022-11-01 15:54:06,348 epoch 69 - iter 270/274 - loss 0.01994459 - samples/sec: 66.39 - lr: 0.100000 -2022-11-01 15:54:08,088 ---------------------------------------------------------------------------------------------------- -2022-11-01 15:54:08,088 EPOCH 69 done: loss 0.0199 - lr 0.100000 -2022-11-01 15:54:33,789 Evaluating as a multi-label problem: False -2022-11-01 15:54:33,804 TEST : loss 0.031563375145196915 - f1-score (micro avg) 0.8505 -2022-11-01 15:54:33,856 BAD EPOCHS (no improvement): 0 -2022-11-01 15:54:33,947 ---------------------------------------------------------------------------------------------------- -2022-11-01 15:54:47,227 epoch 70 - iter 27/274 - loss 0.01858086 - samples/sec: 65.08 - lr: 0.100000 -2022-11-01 15:54:58,681 epoch 70 - iter 54/274 - loss 0.01935509 - samples/sec: 75.45 - lr: 0.100000 -2022-11-01 15:55:10,707 epoch 70 - iter 81/274 - loss 0.01941681 - samples/sec: 71.87 - lr: 0.100000 -2022-11-01 15:55:24,092 epoch 70 - iter 108/274 - loss 0.02029023 - samples/sec: 64.56 - lr: 0.100000 -2022-11-01 15:55:36,306 epoch 70 - iter 135/274 - loss 0.02080017 - samples/sec: 70.76 - lr: 0.100000 -2022-11-01 15:55:48,483 epoch 70 - iter 162/274 - loss 0.02105265 - samples/sec: 70.97 - lr: 0.100000 -2022-11-01 15:56:01,367 epoch 70 - iter 189/274 - loss 0.02042653 - samples/sec: 67.08 - lr: 0.100000 -2022-11-01 15:56:13,179 epoch 70 - iter 216/274 - loss 0.02107873 - samples/sec: 73.17 - lr: 0.100000 -2022-11-01 15:56:26,980 epoch 70 - iter 243/274 - loss 0.02108922 - samples/sec: 62.62 - lr: 0.100000 -2022-11-01 15:56:40,787 epoch 70 - iter 270/274 - loss 0.02127791 - samples/sec: 62.59 - lr: 0.100000 -2022-11-01 15:56:42,391 ---------------------------------------------------------------------------------------------------- -2022-11-01 15:56:42,391 EPOCH 70 done: loss 0.0212 - lr 0.100000 -2022-11-01 15:57:07,366 Evaluating as a multi-label problem: False -2022-11-01 15:57:07,382 TEST : loss 0.03166978433728218 - f1-score (micro avg) 0.8539 -2022-11-01 15:57:07,435 BAD EPOCHS (no improvement): 1 -2022-11-01 15:57:07,525 ---------------------------------------------------------------------------------------------------- -2022-11-01 15:57:18,588 epoch 71 - iter 27/274 - loss 0.02212426 - samples/sec: 78.12 - lr: 0.100000 -2022-11-01 15:57:30,305 epoch 71 - iter 54/274 - loss 0.02025799 - samples/sec: 73.76 - lr: 0.100000 -2022-11-01 15:57:42,033 epoch 71 - iter 81/274 - loss 0.02065372 - samples/sec: 73.69 - lr: 0.100000 -2022-11-01 15:57:54,142 epoch 71 - iter 108/274 - loss 0.02128302 - samples/sec: 71.37 - lr: 0.100000 -2022-11-01 15:58:07,073 epoch 71 - iter 135/274 - loss 0.02181676 - samples/sec: 66.83 - lr: 0.100000 -2022-11-01 15:58:20,111 epoch 71 - iter 162/274 - loss 0.02132020 - samples/sec: 66.28 - lr: 0.100000 -2022-11-01 15:58:32,485 epoch 71 - iter 189/274 - loss 0.02050799 - samples/sec: 69.84 - lr: 0.100000 -2022-11-01 15:58:43,824 epoch 71 - iter 216/274 - loss 0.01992917 - samples/sec: 76.22 - lr: 0.100000 -2022-11-01 15:58:56,979 epoch 71 - iter 243/274 - loss 0.02030662 - samples/sec: 65.69 - lr: 0.100000 -2022-11-01 15:59:09,144 epoch 71 - iter 270/274 - loss 0.02021591 - samples/sec: 71.04 - lr: 0.100000 -2022-11-01 15:59:11,568 ---------------------------------------------------------------------------------------------------- -2022-11-01 15:59:11,568 EPOCH 71 done: loss 0.0204 - lr 0.100000 -2022-11-01 15:59:37,390 Evaluating as a multi-label problem: False -2022-11-01 15:59:37,406 TEST : loss 0.03054458275437355 - f1-score (micro avg) 0.854 -2022-11-01 15:59:37,458 BAD EPOCHS (no improvement): 2 -2022-11-01 15:59:37,554 ---------------------------------------------------------------------------------------------------- -2022-11-01 15:59:49,256 epoch 72 - iter 27/274 - loss 0.02002288 - samples/sec: 73.86 - lr: 0.100000 -2022-11-01 16:00:01,291 epoch 72 - iter 54/274 - loss 0.01804290 - samples/sec: 71.81 - lr: 0.100000 -2022-11-01 16:00:13,882 epoch 72 - iter 81/274 - loss 0.01876863 - samples/sec: 68.63 - lr: 0.100000 -2022-11-01 16:00:25,965 epoch 72 - iter 108/274 - loss 0.01891240 - samples/sec: 71.53 - lr: 0.100000 -2022-11-01 16:00:39,081 epoch 72 - iter 135/274 - loss 0.01882314 - samples/sec: 65.89 - lr: 0.100000 -2022-11-01 16:00:51,215 epoch 72 - iter 162/274 - loss 0.01888338 - samples/sec: 71.23 - lr: 0.100000 -2022-11-01 16:01:02,822 epoch 72 - iter 189/274 - loss 0.01859963 - samples/sec: 74.45 - lr: 0.100000 -2022-11-01 16:01:15,001 epoch 72 - iter 216/274 - loss 0.01894609 - samples/sec: 70.96 - lr: 0.100000 -2022-11-01 16:01:28,410 epoch 72 - iter 243/274 - loss 0.01937545 - samples/sec: 64.45 - lr: 0.100000 -2022-11-01 16:01:42,428 epoch 72 - iter 270/274 - loss 0.02020000 - samples/sec: 61.65 - lr: 0.100000 -2022-11-01 16:01:43,954 ---------------------------------------------------------------------------------------------------- -2022-11-01 16:01:43,955 EPOCH 72 done: loss 0.0201 - lr 0.100000 -2022-11-01 16:02:09,347 Evaluating as a multi-label problem: False -2022-11-01 16:02:09,363 TEST : loss 0.029515517875552177 - f1-score (micro avg) 0.8582 -2022-11-01 16:02:09,415 BAD EPOCHS (no improvement): 3 -2022-11-01 16:02:09,508 ---------------------------------------------------------------------------------------------------- -2022-11-01 16:02:23,043 epoch 73 - iter 27/274 - loss 0.02220240 - samples/sec: 63.85 - lr: 0.100000 -2022-11-01 16:02:34,503 epoch 73 - iter 54/274 - loss 0.02184526 - samples/sec: 75.42 - lr: 0.100000 -2022-11-01 16:02:47,677 epoch 73 - iter 81/274 - loss 0.02129952 - samples/sec: 65.60 - lr: 0.100000 -2022-11-01 16:03:00,508 epoch 73 - iter 108/274 - loss 0.02092159 - samples/sec: 67.35 - lr: 0.100000 -2022-11-01 16:03:11,713 epoch 73 - iter 135/274 - loss 0.02135403 - samples/sec: 77.13 - lr: 0.100000 -2022-11-01 16:03:23,418 epoch 73 - iter 162/274 - loss 0.02121899 - samples/sec: 73.84 - lr: 0.100000 -2022-11-01 16:03:35,824 epoch 73 - iter 189/274 - loss 0.02072249 - samples/sec: 69.66 - lr: 0.100000 -2022-11-01 16:03:48,819 epoch 73 - iter 216/274 - loss 0.02100808 - samples/sec: 66.50 - lr: 0.100000 -2022-11-01 16:04:01,462 epoch 73 - iter 243/274 - loss 0.02116109 - samples/sec: 68.36 - lr: 0.100000 -2022-11-01 16:04:14,781 epoch 73 - iter 270/274 - loss 0.02126688 - samples/sec: 64.88 - lr: 0.100000 -2022-11-01 16:04:16,334 ---------------------------------------------------------------------------------------------------- -2022-11-01 16:04:16,334 EPOCH 73 done: loss 0.0212 - lr 0.100000 -2022-11-01 16:04:41,564 Evaluating as a multi-label problem: False -2022-11-01 16:04:41,579 TEST : loss 0.0294826440513134 - f1-score (micro avg) 0.8499 -2022-11-01 16:04:41,631 Epoch 73: reducing learning rate of group 0 to 5.0000e-02. -2022-11-01 16:04:41,632 BAD EPOCHS (no improvement): 4 -2022-11-01 16:04:41,723 ---------------------------------------------------------------------------------------------------- -2022-11-01 16:04:54,382 epoch 74 - iter 27/274 - loss 0.01699297 - samples/sec: 68.27 - lr: 0.050000 -2022-11-01 16:05:07,038 epoch 74 - iter 54/274 - loss 0.01847639 - samples/sec: 68.28 - lr: 0.050000 -2022-11-01 16:05:18,943 epoch 74 - iter 81/274 - loss 0.01896710 - samples/sec: 72.59 - lr: 0.050000 -2022-11-01 16:05:30,232 epoch 74 - iter 108/274 - loss 0.01856741 - samples/sec: 76.56 - lr: 0.050000 -2022-11-01 16:05:42,326 epoch 74 - iter 135/274 - loss 0.01911050 - samples/sec: 71.46 - lr: 0.050000 -2022-11-01 16:05:53,721 epoch 74 - iter 162/274 - loss 0.01893491 - samples/sec: 75.85 - lr: 0.050000 -2022-11-01 16:06:05,986 epoch 74 - iter 189/274 - loss 0.01976027 - samples/sec: 70.47 - lr: 0.050000 -2022-11-01 16:06:18,737 epoch 74 - iter 216/274 - loss 0.01995122 - samples/sec: 67.77 - lr: 0.050000 -2022-11-01 16:06:31,727 epoch 74 - iter 243/274 - loss 0.01949284 - samples/sec: 66.53 - lr: 0.050000 -2022-11-01 16:06:44,209 epoch 74 - iter 270/274 - loss 0.01943572 - samples/sec: 69.24 - lr: 0.050000 -2022-11-01 16:06:46,178 ---------------------------------------------------------------------------------------------------- -2022-11-01 16:06:46,179 EPOCH 74 done: loss 0.0194 - lr 0.050000 -2022-11-01 16:07:11,505 Evaluating as a multi-label problem: False -2022-11-01 16:07:11,520 TEST : loss 0.030243773013353348 - f1-score (micro avg) 0.8508 -2022-11-01 16:07:11,574 BAD EPOCHS (no improvement): 0 -2022-11-01 16:07:11,666 ---------------------------------------------------------------------------------------------------- -2022-11-01 16:07:24,777 epoch 75 - iter 27/274 - loss 0.01546676 - samples/sec: 65.92 - lr: 0.050000 -2022-11-01 16:07:36,355 epoch 75 - iter 54/274 - loss 0.01834654 - samples/sec: 74.65 - lr: 0.050000 -2022-11-01 16:07:48,021 epoch 75 - iter 81/274 - loss 0.01790446 - samples/sec: 74.08 - lr: 0.050000 -2022-11-01 16:07:59,523 epoch 75 - iter 108/274 - loss 0.01785671 - samples/sec: 75.14 - lr: 0.050000 -2022-11-01 16:08:12,431 epoch 75 - iter 135/274 - loss 0.01831446 - samples/sec: 66.95 - lr: 0.050000 -2022-11-01 16:08:25,527 epoch 75 - iter 162/274 - loss 0.01826631 - samples/sec: 65.99 - lr: 0.050000 -2022-11-01 16:08:37,958 epoch 75 - iter 189/274 - loss 0.01816669 - samples/sec: 69.52 - lr: 0.050000 -2022-11-01 16:08:51,537 epoch 75 - iter 216/274 - loss 0.01861337 - samples/sec: 63.64 - lr: 0.050000 -2022-11-01 16:09:03,481 epoch 75 - iter 243/274 - loss 0.01881745 - samples/sec: 72.36 - lr: 0.050000 -2022-11-01 16:09:16,590 epoch 75 - iter 270/274 - loss 0.01870689 - samples/sec: 65.93 - lr: 0.050000 -2022-11-01 16:09:18,295 ---------------------------------------------------------------------------------------------------- -2022-11-01 16:09:18,295 EPOCH 75 done: loss 0.0188 - lr 0.050000 -2022-11-01 16:09:43,737 Evaluating as a multi-label problem: False -2022-11-01 16:09:43,752 TEST : loss 0.02911132387816906 - f1-score (micro avg) 0.8527 -2022-11-01 16:09:43,804 BAD EPOCHS (no improvement): 0 -2022-11-01 16:09:43,895 ---------------------------------------------------------------------------------------------------- -2022-11-01 16:09:56,178 epoch 76 - iter 27/274 - loss 0.01445317 - samples/sec: 70.36 - lr: 0.050000 -2022-11-01 16:10:08,517 epoch 76 - iter 54/274 - loss 0.01648596 - samples/sec: 70.04 - lr: 0.050000 -2022-11-01 16:10:20,368 epoch 76 - iter 81/274 - loss 0.01698888 - samples/sec: 72.93 - lr: 0.050000 -2022-11-01 16:10:32,873 epoch 76 - iter 108/274 - loss 0.01756096 - samples/sec: 69.11 - lr: 0.050000 -2022-11-01 16:10:46,907 epoch 76 - iter 135/274 - loss 0.01762281 - samples/sec: 61.58 - lr: 0.050000 -2022-11-01 16:10:58,979 epoch 76 - iter 162/274 - loss 0.01732201 - samples/sec: 71.60 - lr: 0.050000 -2022-11-01 16:11:11,759 epoch 76 - iter 189/274 - loss 0.01751176 - samples/sec: 67.62 - lr: 0.050000 -2022-11-01 16:11:23,975 epoch 76 - iter 216/274 - loss 0.01766946 - samples/sec: 70.74 - lr: 0.050000 -2022-11-01 16:11:35,773 epoch 76 - iter 243/274 - loss 0.01783327 - samples/sec: 73.25 - lr: 0.050000 -2022-11-01 16:11:48,911 epoch 76 - iter 270/274 - loss 0.01793928 - samples/sec: 65.78 - lr: 0.050000 -2022-11-01 16:11:50,891 ---------------------------------------------------------------------------------------------------- -2022-11-01 16:11:50,892 EPOCH 76 done: loss 0.0180 - lr 0.050000 -2022-11-01 16:12:16,332 Evaluating as a multi-label problem: False -2022-11-01 16:12:16,347 TEST : loss 0.02934643253684044 - f1-score (micro avg) 0.857 -2022-11-01 16:12:16,399 BAD EPOCHS (no improvement): 0 -2022-11-01 16:12:16,490 ---------------------------------------------------------------------------------------------------- -2022-11-01 16:12:28,007 epoch 77 - iter 27/274 - loss 0.01524350 - samples/sec: 75.04 - lr: 0.050000 -2022-11-01 16:12:40,880 epoch 77 - iter 54/274 - loss 0.01741012 - samples/sec: 67.14 - lr: 0.050000 -2022-11-01 16:12:53,126 epoch 77 - iter 81/274 - loss 0.01776495 - samples/sec: 70.57 - lr: 0.050000 -2022-11-01 16:13:04,214 epoch 77 - iter 108/274 - loss 0.01764313 - samples/sec: 77.95 - lr: 0.050000 -2022-11-01 16:13:18,011 epoch 77 - iter 135/274 - loss 0.01765748 - samples/sec: 62.63 - lr: 0.050000 -2022-11-01 16:13:29,621 epoch 77 - iter 162/274 - loss 0.01771770 - samples/sec: 74.44 - lr: 0.050000 -2022-11-01 16:13:43,414 epoch 77 - iter 189/274 - loss 0.01796879 - samples/sec: 62.65 - lr: 0.050000 -2022-11-01 16:13:54,588 epoch 77 - iter 216/274 - loss 0.01797517 - samples/sec: 77.35 - lr: 0.050000 -2022-11-01 16:14:07,007 epoch 77 - iter 243/274 - loss 0.01790936 - samples/sec: 69.59 - lr: 0.050000 -2022-11-01 16:14:20,191 epoch 77 - iter 270/274 - loss 0.01865935 - samples/sec: 65.55 - lr: 0.050000 -2022-11-01 16:14:22,147 ---------------------------------------------------------------------------------------------------- -2022-11-01 16:14:22,148 EPOCH 77 done: loss 0.0186 - lr 0.050000 -2022-11-01 16:14:47,277 Evaluating as a multi-label problem: False -2022-11-01 16:14:47,293 TEST : loss 0.028807329013943672 - f1-score (micro avg) 0.8581 -2022-11-01 16:14:47,347 BAD EPOCHS (no improvement): 1 -2022-11-01 16:14:47,439 ---------------------------------------------------------------------------------------------------- -2022-11-01 16:14:59,483 epoch 78 - iter 27/274 - loss 0.01491909 - samples/sec: 71.76 - lr: 0.050000 -2022-11-01 16:15:11,094 epoch 78 - iter 54/274 - loss 0.01479802 - samples/sec: 74.43 - lr: 0.050000 -2022-11-01 16:15:23,177 epoch 78 - iter 81/274 - loss 0.01800552 - samples/sec: 71.53 - lr: 0.050000 -2022-11-01 16:15:35,961 epoch 78 - iter 108/274 - loss 0.01651022 - samples/sec: 67.60 - lr: 0.050000 -2022-11-01 16:15:48,569 epoch 78 - iter 135/274 - loss 0.01727430 - samples/sec: 68.55 - lr: 0.050000 -2022-11-01 16:16:02,945 epoch 78 - iter 162/274 - loss 0.01702202 - samples/sec: 60.12 - lr: 0.050000 -2022-11-01 16:16:15,222 epoch 78 - iter 189/274 - loss 0.01711726 - samples/sec: 70.39 - lr: 0.050000 -2022-11-01 16:16:27,068 epoch 78 - iter 216/274 - loss 0.01707427 - samples/sec: 72.95 - lr: 0.050000 -2022-11-01 16:16:39,506 epoch 78 - iter 243/274 - loss 0.01700401 - samples/sec: 69.48 - lr: 0.050000 -2022-11-01 16:16:52,570 epoch 78 - iter 270/274 - loss 0.01712211 - samples/sec: 66.15 - lr: 0.050000 -2022-11-01 16:16:54,130 ---------------------------------------------------------------------------------------------------- -2022-11-01 16:16:54,131 EPOCH 78 done: loss 0.0171 - lr 0.050000 -2022-11-01 16:17:19,592 Evaluating as a multi-label problem: False -2022-11-01 16:17:19,608 TEST : loss 0.03133295476436615 - f1-score (micro avg) 0.8528 -2022-11-01 16:17:19,662 BAD EPOCHS (no improvement): 0 -2022-11-01 16:17:19,735 ---------------------------------------------------------------------------------------------------- -2022-11-01 16:17:31,215 epoch 79 - iter 27/274 - loss 0.01593943 - samples/sec: 75.29 - lr: 0.050000 -2022-11-01 16:17:43,239 epoch 79 - iter 54/274 - loss 0.01816389 - samples/sec: 71.87 - lr: 0.050000 -2022-11-01 16:17:54,562 epoch 79 - iter 81/274 - loss 0.01802346 - samples/sec: 76.33 - lr: 0.050000 -2022-11-01 16:18:07,895 epoch 79 - iter 108/274 - loss 0.01903539 - samples/sec: 64.82 - lr: 0.050000 -2022-11-01 16:18:20,020 epoch 79 - iter 135/274 - loss 0.01855551 - samples/sec: 71.28 - lr: 0.050000 -2022-11-01 16:18:33,472 epoch 79 - iter 162/274 - loss 0.01859544 - samples/sec: 64.24 - lr: 0.050000 -2022-11-01 16:18:45,736 epoch 79 - iter 189/274 - loss 0.01866095 - samples/sec: 70.47 - lr: 0.050000 -2022-11-01 16:18:58,137 epoch 79 - iter 216/274 - loss 0.01866639 - samples/sec: 69.69 - lr: 0.050000 -2022-11-01 16:19:10,045 epoch 79 - iter 243/274 - loss 0.01811509 - samples/sec: 72.57 - lr: 0.050000 -2022-11-01 16:19:23,548 epoch 79 - iter 270/274 - loss 0.01832024 - samples/sec: 64.00 - lr: 0.050000 -2022-11-01 16:19:25,173 ---------------------------------------------------------------------------------------------------- -2022-11-01 16:19:25,174 EPOCH 79 done: loss 0.0184 - lr 0.050000 -2022-11-01 16:19:50,600 Evaluating as a multi-label problem: False -2022-11-01 16:19:50,616 TEST : loss 0.03068099543452263 - f1-score (micro avg) 0.8506 -2022-11-01 16:19:50,669 BAD EPOCHS (no improvement): 1 -2022-11-01 16:19:50,760 ---------------------------------------------------------------------------------------------------- -2022-11-01 16:20:01,772 epoch 80 - iter 27/274 - loss 0.01991148 - samples/sec: 78.49 - lr: 0.050000 -2022-11-01 16:20:16,433 epoch 80 - iter 54/274 - loss 0.01871553 - samples/sec: 58.94 - lr: 0.050000 -2022-11-01 16:20:28,629 epoch 80 - iter 81/274 - loss 0.01907954 - samples/sec: 70.86 - lr: 0.050000 -2022-11-01 16:20:40,218 epoch 80 - iter 108/274 - loss 0.01834093 - samples/sec: 74.58 - lr: 0.050000 -2022-11-01 16:20:53,091 epoch 80 - iter 135/274 - loss 0.01859223 - samples/sec: 67.14 - lr: 0.050000 -2022-11-01 16:21:04,356 epoch 80 - iter 162/274 - loss 0.01827675 - samples/sec: 76.72 - lr: 0.050000 -2022-11-01 16:21:17,001 epoch 80 - iter 189/274 - loss 0.01822210 - samples/sec: 68.34 - lr: 0.050000 -2022-11-01 16:21:28,675 epoch 80 - iter 216/274 - loss 0.01827311 - samples/sec: 74.03 - lr: 0.050000 -2022-11-01 16:21:42,235 epoch 80 - iter 243/274 - loss 0.01796925 - samples/sec: 63.73 - lr: 0.050000 -2022-11-01 16:21:54,314 epoch 80 - iter 270/274 - loss 0.01791834 - samples/sec: 71.55 - lr: 0.050000 -2022-11-01 16:21:56,353 ---------------------------------------------------------------------------------------------------- -2022-11-01 16:21:56,353 EPOCH 80 done: loss 0.0180 - lr 0.050000 -2022-11-01 16:22:21,823 Evaluating as a multi-label problem: False -2022-11-01 16:22:21,839 TEST : loss 0.03157917782664299 - f1-score (micro avg) 0.8551 -2022-11-01 16:22:21,892 BAD EPOCHS (no improvement): 2 -2022-11-01 16:22:21,984 ---------------------------------------------------------------------------------------------------- -2022-11-01 16:22:34,097 epoch 81 - iter 27/274 - loss 0.01705883 - samples/sec: 71.36 - lr: 0.050000 -2022-11-01 16:22:45,629 epoch 81 - iter 54/274 - loss 0.01676405 - samples/sec: 74.94 - lr: 0.050000 -2022-11-01 16:22:57,336 epoch 81 - iter 81/274 - loss 0.01739874 - samples/sec: 73.83 - lr: 0.050000 -2022-11-01 16:23:08,562 epoch 81 - iter 108/274 - loss 0.01762212 - samples/sec: 76.98 - lr: 0.050000 -2022-11-01 16:23:21,574 epoch 81 - iter 135/274 - loss 0.01782749 - samples/sec: 66.42 - lr: 0.050000 -2022-11-01 16:23:35,270 epoch 81 - iter 162/274 - loss 0.01828249 - samples/sec: 63.10 - lr: 0.050000 -2022-11-01 16:23:48,300 epoch 81 - iter 189/274 - loss 0.01813256 - samples/sec: 66.32 - lr: 0.050000 -2022-11-01 16:24:01,404 epoch 81 - iter 216/274 - loss 0.01802512 - samples/sec: 65.95 - lr: 0.050000 -2022-11-01 16:24:12,505 epoch 81 - iter 243/274 - loss 0.01793248 - samples/sec: 77.85 - lr: 0.050000 -2022-11-01 16:24:24,473 epoch 81 - iter 270/274 - loss 0.01780714 - samples/sec: 72.21 - lr: 0.050000 -2022-11-01 16:24:26,323 ---------------------------------------------------------------------------------------------------- -2022-11-01 16:24:26,323 EPOCH 81 done: loss 0.0179 - lr 0.050000 -2022-11-01 16:24:51,950 Evaluating as a multi-label problem: False -2022-11-01 16:24:51,966 TEST : loss 0.029629342257976532 - f1-score (micro avg) 0.8565 -2022-11-01 16:24:52,017 BAD EPOCHS (no improvement): 3 -2022-11-01 16:24:52,108 ---------------------------------------------------------------------------------------------------- -2022-11-01 16:25:03,673 epoch 82 - iter 27/274 - loss 0.01517411 - samples/sec: 74.73 - lr: 0.050000 -2022-11-01 16:25:15,248 epoch 82 - iter 54/274 - loss 0.01566208 - samples/sec: 74.67 - lr: 0.050000 -2022-11-01 16:25:28,531 epoch 82 - iter 81/274 - loss 0.01767380 - samples/sec: 65.06 - lr: 0.050000 -2022-11-01 16:25:40,413 epoch 82 - iter 108/274 - loss 0.01710010 - samples/sec: 72.74 - lr: 0.050000 -2022-11-01 16:25:52,805 epoch 82 - iter 135/274 - loss 0.01665061 - samples/sec: 69.74 - lr: 0.050000 -2022-11-01 16:26:05,721 epoch 82 - iter 162/274 - loss 0.01717756 - samples/sec: 66.91 - lr: 0.050000 -2022-11-01 16:26:18,213 epoch 82 - iter 189/274 - loss 0.01790767 - samples/sec: 69.18 - lr: 0.050000 -2022-11-01 16:26:30,435 epoch 82 - iter 216/274 - loss 0.01790503 - samples/sec: 70.71 - lr: 0.050000 -2022-11-01 16:26:44,476 epoch 82 - iter 243/274 - loss 0.01773968 - samples/sec: 61.55 - lr: 0.050000 -2022-11-01 16:26:56,279 epoch 82 - iter 270/274 - loss 0.01752431 - samples/sec: 73.22 - lr: 0.050000 -2022-11-01 16:26:57,790 ---------------------------------------------------------------------------------------------------- -2022-11-01 16:26:57,790 EPOCH 82 done: loss 0.0176 - lr 0.050000 -2022-11-01 16:27:23,088 Evaluating as a multi-label problem: False -2022-11-01 16:27:23,103 TEST : loss 0.02843180112540722 - f1-score (micro avg) 0.8544 -2022-11-01 16:27:23,156 Epoch 82: reducing learning rate of group 0 to 2.5000e-02. -2022-11-01 16:27:23,156 BAD EPOCHS (no improvement): 4 -2022-11-01 16:27:23,248 ---------------------------------------------------------------------------------------------------- -2022-11-01 16:27:35,196 epoch 83 - iter 27/274 - loss 0.01636089 - samples/sec: 72.33 - lr: 0.025000 -2022-11-01 16:27:46,808 epoch 83 - iter 54/274 - loss 0.01606208 - samples/sec: 74.43 - lr: 0.025000 -2022-11-01 16:27:59,607 epoch 83 - iter 81/274 - loss 0.01687172 - samples/sec: 67.52 - lr: 0.025000 -2022-11-01 16:28:12,576 epoch 83 - iter 108/274 - loss 0.01773665 - samples/sec: 66.64 - lr: 0.025000 -2022-11-01 16:28:25,607 epoch 83 - iter 135/274 - loss 0.01707393 - samples/sec: 66.32 - lr: 0.025000 -2022-11-01 16:28:37,893 epoch 83 - iter 162/274 - loss 0.01671065 - samples/sec: 70.35 - lr: 0.025000 -2022-11-01 16:28:50,691 epoch 83 - iter 189/274 - loss 0.01697125 - samples/sec: 67.53 - lr: 0.025000 -2022-11-01 16:29:03,462 epoch 83 - iter 216/274 - loss 0.01688751 - samples/sec: 67.67 - lr: 0.025000 -2022-11-01 16:29:15,237 epoch 83 - iter 243/274 - loss 0.01724621 - samples/sec: 73.39 - lr: 0.025000 -2022-11-01 16:29:27,912 epoch 83 - iter 270/274 - loss 0.01744322 - samples/sec: 68.18 - lr: 0.025000 -2022-11-01 16:29:29,629 ---------------------------------------------------------------------------------------------------- -2022-11-01 16:29:29,629 EPOCH 83 done: loss 0.0173 - lr 0.025000 -2022-11-01 16:29:54,787 Evaluating as a multi-label problem: False -2022-11-01 16:29:54,803 TEST : loss 0.029316166415810585 - f1-score (micro avg) 0.8533 -2022-11-01 16:29:54,856 BAD EPOCHS (no improvement): 1 -2022-11-01 16:29:54,951 ---------------------------------------------------------------------------------------------------- -2022-11-01 16:30:06,126 epoch 84 - iter 27/274 - loss 0.01576658 - samples/sec: 77.34 - lr: 0.025000 -2022-11-01 16:30:18,356 epoch 84 - iter 54/274 - loss 0.01510948 - samples/sec: 70.67 - lr: 0.025000 -2022-11-01 16:30:30,897 epoch 84 - iter 81/274 - loss 0.01509003 - samples/sec: 68.91 - lr: 0.025000 -2022-11-01 16:30:44,239 epoch 84 - iter 108/274 - loss 0.01485943 - samples/sec: 64.77 - lr: 0.025000 -2022-11-01 16:30:57,506 epoch 84 - iter 135/274 - loss 0.01553865 - samples/sec: 65.14 - lr: 0.025000 -2022-11-01 16:31:08,699 epoch 84 - iter 162/274 - loss 0.01580709 - samples/sec: 77.21 - lr: 0.025000 -2022-11-01 16:31:21,826 epoch 84 - iter 189/274 - loss 0.01591768 - samples/sec: 65.84 - lr: 0.025000 -2022-11-01 16:31:35,047 epoch 84 - iter 216/274 - loss 0.01610133 - samples/sec: 65.36 - lr: 0.025000 -2022-11-01 16:31:47,076 epoch 84 - iter 243/274 - loss 0.01600053 - samples/sec: 71.85 - lr: 0.025000 -2022-11-01 16:31:59,719 epoch 84 - iter 270/274 - loss 0.01628348 - samples/sec: 68.36 - lr: 0.025000 -2022-11-01 16:32:01,717 ---------------------------------------------------------------------------------------------------- -2022-11-01 16:32:01,717 EPOCH 84 done: loss 0.0164 - lr 0.025000 -2022-11-01 16:32:27,343 Evaluating as a multi-label problem: False -2022-11-01 16:32:27,359 TEST : loss 0.029401035979390144 - f1-score (micro avg) 0.8506 -2022-11-01 16:32:27,411 BAD EPOCHS (no improvement): 0 -2022-11-01 16:32:27,507 ---------------------------------------------------------------------------------------------------- -2022-11-01 16:32:39,216 epoch 85 - iter 27/274 - loss 0.01603222 - samples/sec: 73.81 - lr: 0.025000 -2022-11-01 16:32:51,063 epoch 85 - iter 54/274 - loss 0.01741225 - samples/sec: 72.95 - lr: 0.025000 -2022-11-01 16:33:04,348 epoch 85 - iter 81/274 - loss 0.01696731 - samples/sec: 65.05 - lr: 0.025000 -2022-11-01 16:33:17,167 epoch 85 - iter 108/274 - loss 0.01676102 - samples/sec: 67.42 - lr: 0.025000 -2022-11-01 16:33:30,885 epoch 85 - iter 135/274 - loss 0.01643452 - samples/sec: 63.00 - lr: 0.025000 -2022-11-01 16:33:42,958 epoch 85 - iter 162/274 - loss 0.01683062 - samples/sec: 71.58 - lr: 0.025000 -2022-11-01 16:33:54,584 epoch 85 - iter 189/274 - loss 0.01661397 - samples/sec: 74.34 - lr: 0.025000 -2022-11-01 16:34:08,124 epoch 85 - iter 216/274 - loss 0.01674394 - samples/sec: 63.83 - lr: 0.025000 -2022-11-01 16:34:19,185 epoch 85 - iter 243/274 - loss 0.01673404 - samples/sec: 78.14 - lr: 0.025000 -2022-11-01 16:34:30,584 epoch 85 - iter 270/274 - loss 0.01678524 - samples/sec: 75.82 - lr: 0.025000 -2022-11-01 16:34:32,416 ---------------------------------------------------------------------------------------------------- -2022-11-01 16:34:32,416 EPOCH 85 done: loss 0.0167 - lr 0.025000 -2022-11-01 16:34:57,702 Evaluating as a multi-label problem: False -2022-11-01 16:34:57,717 TEST : loss 0.030953796580433846 - f1-score (micro avg) 0.8556 -2022-11-01 16:34:57,769 BAD EPOCHS (no improvement): 1 -2022-11-01 16:34:57,860 ---------------------------------------------------------------------------------------------------- -2022-11-01 16:35:10,313 epoch 86 - iter 27/274 - loss 0.01832310 - samples/sec: 69.40 - lr: 0.025000 -2022-11-01 16:35:24,124 epoch 86 - iter 54/274 - loss 0.01662775 - samples/sec: 62.57 - lr: 0.025000 -2022-11-01 16:35:35,622 epoch 86 - iter 81/274 - loss 0.01593853 - samples/sec: 75.17 - lr: 0.025000 -2022-11-01 16:35:48,105 epoch 86 - iter 108/274 - loss 0.01677397 - samples/sec: 69.23 - lr: 0.025000 -2022-11-01 16:35:59,618 epoch 86 - iter 135/274 - loss 0.01677152 - samples/sec: 75.07 - lr: 0.025000 -2022-11-01 16:36:11,947 epoch 86 - iter 162/274 - loss 0.01687084 - samples/sec: 70.10 - lr: 0.025000 -2022-11-01 16:36:25,305 epoch 86 - iter 189/274 - loss 0.01703050 - samples/sec: 64.70 - lr: 0.025000 -2022-11-01 16:36:37,864 epoch 86 - iter 216/274 - loss 0.01682995 - samples/sec: 68.81 - lr: 0.025000 -2022-11-01 16:36:48,783 epoch 86 - iter 243/274 - loss 0.01675319 - samples/sec: 79.15 - lr: 0.025000 -2022-11-01 16:36:59,918 epoch 86 - iter 270/274 - loss 0.01679931 - samples/sec: 77.62 - lr: 0.025000 -2022-11-01 16:37:01,938 ---------------------------------------------------------------------------------------------------- -2022-11-01 16:37:01,938 EPOCH 86 done: loss 0.0170 - lr 0.025000 -2022-11-01 16:37:27,319 Evaluating as a multi-label problem: False -2022-11-01 16:37:27,335 TEST : loss 0.030635029077529907 - f1-score (micro avg) 0.8537 -2022-11-01 16:37:27,388 BAD EPOCHS (no improvement): 2 -2022-11-01 16:37:27,480 ---------------------------------------------------------------------------------------------------- -2022-11-01 16:37:39,899 epoch 87 - iter 27/274 - loss 0.01457488 - samples/sec: 69.60 - lr: 0.025000 -2022-11-01 16:37:53,247 epoch 87 - iter 54/274 - loss 0.01425651 - samples/sec: 64.74 - lr: 0.025000 -2022-11-01 16:38:06,024 epoch 87 - iter 81/274 - loss 0.01547111 - samples/sec: 67.64 - lr: 0.025000 -2022-11-01 16:38:19,260 epoch 87 - iter 108/274 - loss 0.01529546 - samples/sec: 65.29 - lr: 0.025000 -2022-11-01 16:38:31,312 epoch 87 - iter 135/274 - loss 0.01584319 - samples/sec: 71.71 - lr: 0.025000 -2022-11-01 16:38:43,662 epoch 87 - iter 162/274 - loss 0.01590420 - samples/sec: 69.98 - lr: 0.025000 -2022-11-01 16:38:56,177 epoch 87 - iter 189/274 - loss 0.01631382 - samples/sec: 69.05 - lr: 0.025000 -2022-11-01 16:39:08,261 epoch 87 - iter 216/274 - loss 0.01628017 - samples/sec: 71.52 - lr: 0.025000 -2022-11-01 16:39:20,371 epoch 87 - iter 243/274 - loss 0.01615643 - samples/sec: 71.37 - lr: 0.025000 -2022-11-01 16:39:33,177 epoch 87 - iter 270/274 - loss 0.01590582 - samples/sec: 67.48 - lr: 0.025000 -2022-11-01 16:39:34,790 ---------------------------------------------------------------------------------------------------- -2022-11-01 16:39:34,790 EPOCH 87 done: loss 0.0159 - lr 0.025000 -2022-11-01 16:40:00,078 Evaluating as a multi-label problem: False -2022-11-01 16:40:00,093 TEST : loss 0.03055056370794773 - f1-score (micro avg) 0.8535 -2022-11-01 16:40:00,146 BAD EPOCHS (no improvement): 0 -2022-11-01 16:40:00,238 ---------------------------------------------------------------------------------------------------- -2022-11-01 16:40:11,140 epoch 88 - iter 27/274 - loss 0.01375907 - samples/sec: 79.28 - lr: 0.025000 -2022-11-01 16:40:24,674 epoch 88 - iter 54/274 - loss 0.01666760 - samples/sec: 63.86 - lr: 0.025000 -2022-11-01 16:40:36,121 epoch 88 - iter 81/274 - loss 0.01668721 - samples/sec: 75.50 - lr: 0.025000 -2022-11-01 16:40:48,625 epoch 88 - iter 108/274 - loss 0.01569771 - samples/sec: 69.12 - lr: 0.025000 -2022-11-01 16:41:02,221 epoch 88 - iter 135/274 - loss 0.01541809 - samples/sec: 63.56 - lr: 0.025000 -2022-11-01 16:41:13,833 epoch 88 - iter 162/274 - loss 0.01572390 - samples/sec: 74.43 - lr: 0.025000 -2022-11-01 16:41:26,394 epoch 88 - iter 189/274 - loss 0.01613422 - samples/sec: 68.80 - lr: 0.025000 -2022-11-01 16:41:39,138 epoch 88 - iter 216/274 - loss 0.01597918 - samples/sec: 67.81 - lr: 0.025000 -2022-11-01 16:41:50,987 epoch 88 - iter 243/274 - loss 0.01610632 - samples/sec: 72.94 - lr: 0.025000 -2022-11-01 16:42:04,017 epoch 88 - iter 270/274 - loss 0.01642128 - samples/sec: 66.33 - lr: 0.025000 -2022-11-01 16:42:05,883 ---------------------------------------------------------------------------------------------------- -2022-11-01 16:42:05,883 EPOCH 88 done: loss 0.0166 - lr 0.025000 -2022-11-01 16:42:30,656 Evaluating as a multi-label problem: False -2022-11-01 16:42:30,672 TEST : loss 0.029628725722432137 - f1-score (micro avg) 0.8577 -2022-11-01 16:42:30,726 BAD EPOCHS (no improvement): 1 -2022-11-01 16:42:30,799 ---------------------------------------------------------------------------------------------------- -2022-11-01 16:42:43,593 epoch 89 - iter 27/274 - loss 0.01806551 - samples/sec: 67.55 - lr: 0.025000 -2022-11-01 16:42:56,549 epoch 89 - iter 54/274 - loss 0.01773860 - samples/sec: 66.70 - lr: 0.025000 -2022-11-01 16:43:07,563 epoch 89 - iter 81/274 - loss 0.01752078 - samples/sec: 78.47 - lr: 0.025000 -2022-11-01 16:43:18,722 epoch 89 - iter 108/274 - loss 0.01689594 - samples/sec: 77.45 - lr: 0.025000 -2022-11-01 16:43:31,342 epoch 89 - iter 135/274 - loss 0.01681539 - samples/sec: 68.48 - lr: 0.025000 -2022-11-01 16:43:43,326 epoch 89 - iter 162/274 - loss 0.01672993 - samples/sec: 72.12 - lr: 0.025000 -2022-11-01 16:43:55,437 epoch 89 - iter 189/274 - loss 0.01699377 - samples/sec: 71.36 - lr: 0.025000 -2022-11-01 16:44:07,633 epoch 89 - iter 216/274 - loss 0.01664312 - samples/sec: 70.86 - lr: 0.025000 -2022-11-01 16:44:21,310 epoch 89 - iter 243/274 - loss 0.01652580 - samples/sec: 63.19 - lr: 0.025000 -2022-11-01 16:44:34,524 epoch 89 - iter 270/274 - loss 0.01646223 - samples/sec: 65.40 - lr: 0.025000 -2022-11-01 16:44:36,018 ---------------------------------------------------------------------------------------------------- -2022-11-01 16:44:36,018 EPOCH 89 done: loss 0.0164 - lr 0.025000 -2022-11-01 16:45:01,028 Evaluating as a multi-label problem: False -2022-11-01 16:45:01,044 TEST : loss 0.030412347987294197 - f1-score (micro avg) 0.8557 -2022-11-01 16:45:01,101 BAD EPOCHS (no improvement): 2 -2022-11-01 16:45:01,197 ---------------------------------------------------------------------------------------------------- -2022-11-01 16:45:13,274 epoch 90 - iter 27/274 - loss 0.01564131 - samples/sec: 71.56 - lr: 0.025000 -2022-11-01 16:45:25,179 epoch 90 - iter 54/274 - loss 0.01582138 - samples/sec: 72.60 - lr: 0.025000 -2022-11-01 16:45:37,427 epoch 90 - iter 81/274 - loss 0.01540488 - samples/sec: 70.56 - lr: 0.025000 -2022-11-01 16:45:48,993 epoch 90 - iter 108/274 - loss 0.01637324 - samples/sec: 74.73 - lr: 0.025000 -2022-11-01 16:46:01,317 epoch 90 - iter 135/274 - loss 0.01632562 - samples/sec: 70.13 - lr: 0.025000 -2022-11-01 16:46:13,570 epoch 90 - iter 162/274 - loss 0.01599840 - samples/sec: 70.53 - lr: 0.025000 -2022-11-01 16:46:26,019 epoch 90 - iter 189/274 - loss 0.01599910 - samples/sec: 69.43 - lr: 0.025000 -2022-11-01 16:46:38,827 epoch 90 - iter 216/274 - loss 0.01613755 - samples/sec: 67.47 - lr: 0.025000 -2022-11-01 16:46:50,421 epoch 90 - iter 243/274 - loss 0.01621818 - samples/sec: 74.55 - lr: 0.025000 -2022-11-01 16:47:03,939 epoch 90 - iter 270/274 - loss 0.01603622 - samples/sec: 63.93 - lr: 0.025000 -2022-11-01 16:47:06,316 ---------------------------------------------------------------------------------------------------- -2022-11-01 16:47:06,316 EPOCH 90 done: loss 0.0160 - lr 0.025000 -2022-11-01 16:47:31,676 Evaluating as a multi-label problem: False -2022-11-01 16:47:31,692 TEST : loss 0.029919512569904327 - f1-score (micro avg) 0.8499 -2022-11-01 16:47:31,746 BAD EPOCHS (no improvement): 3 -2022-11-01 16:47:31,838 ---------------------------------------------------------------------------------------------------- -2022-11-01 16:47:44,697 epoch 91 - iter 27/274 - loss 0.01229596 - samples/sec: 67.21 - lr: 0.025000 -2022-11-01 16:47:57,004 epoch 91 - iter 54/274 - loss 0.01415102 - samples/sec: 70.22 - lr: 0.025000 -2022-11-01 16:48:09,090 epoch 91 - iter 81/274 - loss 0.01421652 - samples/sec: 71.50 - lr: 0.025000 -2022-11-01 16:48:21,585 epoch 91 - iter 108/274 - loss 0.01502545 - samples/sec: 69.17 - lr: 0.025000 -2022-11-01 16:48:35,360 epoch 91 - iter 135/274 - loss 0.01534741 - samples/sec: 62.74 - lr: 0.025000 -2022-11-01 16:48:47,558 epoch 91 - iter 162/274 - loss 0.01576136 - samples/sec: 70.85 - lr: 0.025000 -2022-11-01 16:49:00,013 epoch 91 - iter 189/274 - loss 0.01573564 - samples/sec: 69.39 - lr: 0.025000 -2022-11-01 16:49:12,613 epoch 91 - iter 216/274 - loss 0.01610894 - samples/sec: 68.59 - lr: 0.025000 -2022-11-01 16:49:23,671 epoch 91 - iter 243/274 - loss 0.01583706 - samples/sec: 78.15 - lr: 0.025000 -2022-11-01 16:49:37,477 epoch 91 - iter 270/274 - loss 0.01585399 - samples/sec: 62.60 - lr: 0.025000 -2022-11-01 16:49:39,134 ---------------------------------------------------------------------------------------------------- -2022-11-01 16:49:39,135 EPOCH 91 done: loss 0.0158 - lr 0.025000 -2022-11-01 16:50:04,527 Evaluating as a multi-label problem: False -2022-11-01 16:50:04,542 TEST : loss 0.029802754521369934 - f1-score (micro avg) 0.8562 -2022-11-01 16:50:04,594 BAD EPOCHS (no improvement): 0 -2022-11-01 16:50:04,667 ---------------------------------------------------------------------------------------------------- -2022-11-01 16:50:17,801 epoch 92 - iter 27/274 - loss 0.01424113 - samples/sec: 65.80 - lr: 0.025000 -2022-11-01 16:50:30,363 epoch 92 - iter 54/274 - loss 0.01557183 - samples/sec: 68.80 - lr: 0.025000 -2022-11-01 16:50:43,821 epoch 92 - iter 81/274 - loss 0.01633458 - samples/sec: 64.21 - lr: 0.025000 -2022-11-01 16:50:55,674 epoch 92 - iter 108/274 - loss 0.01687727 - samples/sec: 72.92 - lr: 0.025000 -2022-11-01 16:51:08,718 epoch 92 - iter 135/274 - loss 0.01594242 - samples/sec: 66.25 - lr: 0.025000 -2022-11-01 16:51:20,596 epoch 92 - iter 162/274 - loss 0.01612769 - samples/sec: 72.76 - lr: 0.025000 -2022-11-01 16:51:32,683 epoch 92 - iter 189/274 - loss 0.01619242 - samples/sec: 71.50 - lr: 0.025000 -2022-11-01 16:51:45,275 epoch 92 - iter 216/274 - loss 0.01641394 - samples/sec: 68.63 - lr: 0.025000 -2022-11-01 16:51:57,544 epoch 92 - iter 243/274 - loss 0.01626312 - samples/sec: 70.44 - lr: 0.025000 -2022-11-01 16:52:09,360 epoch 92 - iter 270/274 - loss 0.01581703 - samples/sec: 73.14 - lr: 0.025000 -2022-11-01 16:52:11,253 ---------------------------------------------------------------------------------------------------- -2022-11-01 16:52:11,254 EPOCH 92 done: loss 0.0158 - lr 0.025000 -2022-11-01 16:52:36,332 Evaluating as a multi-label problem: False -2022-11-01 16:52:36,347 TEST : loss 0.031193019822239876 - f1-score (micro avg) 0.852 -2022-11-01 16:52:36,399 BAD EPOCHS (no improvement): 0 -2022-11-01 16:52:36,495 ---------------------------------------------------------------------------------------------------- -2022-11-01 16:52:47,868 epoch 93 - iter 27/274 - loss 0.01514042 - samples/sec: 75.99 - lr: 0.025000 -2022-11-01 16:52:59,621 epoch 93 - iter 54/274 - loss 0.01608344 - samples/sec: 73.54 - lr: 0.025000 -2022-11-01 16:53:12,802 epoch 93 - iter 81/274 - loss 0.01633994 - samples/sec: 65.56 - lr: 0.025000 -2022-11-01 16:53:26,430 epoch 93 - iter 108/274 - loss 0.01694982 - samples/sec: 63.41 - lr: 0.025000 -2022-11-01 16:53:39,222 epoch 93 - iter 135/274 - loss 0.01595774 - samples/sec: 67.56 - lr: 0.025000 -2022-11-01 16:53:51,426 epoch 93 - iter 162/274 - loss 0.01608164 - samples/sec: 70.81 - lr: 0.025000 -2022-11-01 16:54:03,857 epoch 93 - iter 189/274 - loss 0.01616575 - samples/sec: 69.52 - lr: 0.025000 -2022-11-01 16:54:16,142 epoch 93 - iter 216/274 - loss 0.01606524 - samples/sec: 70.35 - lr: 0.025000 -2022-11-01 16:54:28,972 epoch 93 - iter 243/274 - loss 0.01602004 - samples/sec: 67.36 - lr: 0.025000 -2022-11-01 16:54:41,141 epoch 93 - iter 270/274 - loss 0.01552356 - samples/sec: 71.02 - lr: 0.025000 -2022-11-01 16:54:42,851 ---------------------------------------------------------------------------------------------------- -2022-11-01 16:54:42,852 EPOCH 93 done: loss 0.0155 - lr 0.025000 -2022-11-01 16:55:08,139 Evaluating as a multi-label problem: False -2022-11-01 16:55:08,154 TEST : loss 0.03080672211945057 - f1-score (micro avg) 0.8551 -2022-11-01 16:55:08,208 BAD EPOCHS (no improvement): 0 -2022-11-01 16:55:08,300 ---------------------------------------------------------------------------------------------------- -2022-11-01 16:55:20,564 epoch 94 - iter 27/274 - loss 0.01833471 - samples/sec: 70.47 - lr: 0.025000 -2022-11-01 16:55:33,934 epoch 94 - iter 54/274 - loss 0.01785616 - samples/sec: 64.64 - lr: 0.025000 -2022-11-01 16:55:46,048 epoch 94 - iter 81/274 - loss 0.01756784 - samples/sec: 71.34 - lr: 0.025000 -2022-11-01 16:55:59,581 epoch 94 - iter 108/274 - loss 0.01710252 - samples/sec: 63.86 - lr: 0.025000 -2022-11-01 16:56:11,194 epoch 94 - iter 135/274 - loss 0.01640143 - samples/sec: 74.42 - lr: 0.025000 -2022-11-01 16:56:24,892 epoch 94 - iter 162/274 - loss 0.01595753 - samples/sec: 63.09 - lr: 0.025000 -2022-11-01 16:56:37,887 epoch 94 - iter 189/274 - loss 0.01589872 - samples/sec: 66.50 - lr: 0.025000 -2022-11-01 16:56:48,846 epoch 94 - iter 216/274 - loss 0.01636018 - samples/sec: 78.87 - lr: 0.025000 -2022-11-01 16:57:00,547 epoch 94 - iter 243/274 - loss 0.01639244 - samples/sec: 73.86 - lr: 0.025000 -2022-11-01 16:57:12,390 epoch 94 - iter 270/274 - loss 0.01644462 - samples/sec: 72.98 - lr: 0.025000 -2022-11-01 16:57:13,913 ---------------------------------------------------------------------------------------------------- -2022-11-01 16:57:13,913 EPOCH 94 done: loss 0.0164 - lr 0.025000 -2022-11-01 16:57:39,655 Evaluating as a multi-label problem: False -2022-11-01 16:57:39,671 TEST : loss 0.030662264674901962 - f1-score (micro avg) 0.8553 -2022-11-01 16:57:39,722 BAD EPOCHS (no improvement): 1 -2022-11-01 16:57:39,814 ---------------------------------------------------------------------------------------------------- -2022-11-01 16:57:52,162 epoch 95 - iter 27/274 - loss 0.01688978 - samples/sec: 70.00 - lr: 0.025000 -2022-11-01 16:58:05,022 epoch 95 - iter 54/274 - loss 0.01534652 - samples/sec: 67.20 - lr: 0.025000 -2022-11-01 16:58:16,780 epoch 95 - iter 81/274 - loss 0.01469917 - samples/sec: 73.50 - lr: 0.025000 -2022-11-01 16:58:28,997 epoch 95 - iter 108/274 - loss 0.01583896 - samples/sec: 70.74 - lr: 0.025000 -2022-11-01 16:58:40,603 epoch 95 - iter 135/274 - loss 0.01604019 - samples/sec: 74.46 - lr: 0.025000 -2022-11-01 16:58:52,272 epoch 95 - iter 162/274 - loss 0.01560099 - samples/sec: 74.07 - lr: 0.025000 -2022-11-01 16:59:05,106 epoch 95 - iter 189/274 - loss 0.01566128 - samples/sec: 67.33 - lr: 0.025000 -2022-11-01 16:59:16,292 epoch 95 - iter 216/274 - loss 0.01538281 - samples/sec: 77.27 - lr: 0.025000 -2022-11-01 16:59:30,105 epoch 95 - iter 243/274 - loss 0.01595071 - samples/sec: 62.56 - lr: 0.025000 -2022-11-01 16:59:42,530 epoch 95 - iter 270/274 - loss 0.01587799 - samples/sec: 69.55 - lr: 0.025000 -2022-11-01 16:59:44,417 ---------------------------------------------------------------------------------------------------- -2022-11-01 16:59:44,417 EPOCH 95 done: loss 0.0158 - lr 0.025000 -2022-11-01 17:00:09,855 Evaluating as a multi-label problem: False -2022-11-01 17:00:09,870 TEST : loss 0.030545346438884735 - f1-score (micro avg) 0.8545 -2022-11-01 17:00:09,923 BAD EPOCHS (no improvement): 2 -2022-11-01 17:00:10,015 ---------------------------------------------------------------------------------------------------- -2022-11-01 17:00:23,419 epoch 96 - iter 27/274 - loss 0.01242489 - samples/sec: 64.48 - lr: 0.025000 -2022-11-01 17:00:35,238 epoch 96 - iter 54/274 - loss 0.01448475 - samples/sec: 73.12 - lr: 0.025000 -2022-11-01 17:00:48,022 epoch 96 - iter 81/274 - loss 0.01561201 - samples/sec: 67.60 - lr: 0.025000 -2022-11-01 17:01:00,345 epoch 96 - iter 108/274 - loss 0.01621416 - samples/sec: 70.13 - lr: 0.025000 -2022-11-01 17:01:13,192 epoch 96 - iter 135/274 - loss 0.01632154 - samples/sec: 67.27 - lr: 0.025000 -2022-11-01 17:01:24,671 epoch 96 - iter 162/274 - loss 0.01572867 - samples/sec: 75.29 - lr: 0.025000 -2022-11-01 17:01:36,808 epoch 96 - iter 189/274 - loss 0.01559104 - samples/sec: 71.21 - lr: 0.025000 -2022-11-01 17:01:50,027 epoch 96 - iter 216/274 - loss 0.01589267 - samples/sec: 65.38 - lr: 0.025000 -2022-11-01 17:02:03,405 epoch 96 - iter 243/274 - loss 0.01604069 - samples/sec: 64.60 - lr: 0.025000 -2022-11-01 17:02:14,570 epoch 96 - iter 270/274 - loss 0.01609900 - samples/sec: 77.40 - lr: 0.025000 -2022-11-01 17:02:16,458 ---------------------------------------------------------------------------------------------------- -2022-11-01 17:02:16,458 EPOCH 96 done: loss 0.0161 - lr 0.025000 -2022-11-01 17:02:41,425 Evaluating as a multi-label problem: False -2022-11-01 17:02:41,440 TEST : loss 0.031394150108098984 - f1-score (micro avg) 0.8584 -2022-11-01 17:02:41,493 BAD EPOCHS (no improvement): 3 -2022-11-01 17:02:41,585 ---------------------------------------------------------------------------------------------------- -2022-11-01 17:02:54,710 epoch 97 - iter 27/274 - loss 0.01492072 - samples/sec: 65.85 - lr: 0.025000 -2022-11-01 17:03:05,770 epoch 97 - iter 54/274 - loss 0.01396878 - samples/sec: 78.14 - lr: 0.025000 -2022-11-01 17:03:17,619 epoch 97 - iter 81/274 - loss 0.01413726 - samples/sec: 72.94 - lr: 0.025000 -2022-11-01 17:03:31,104 epoch 97 - iter 108/274 - loss 0.01427275 - samples/sec: 64.09 - lr: 0.025000 -2022-11-01 17:03:43,566 epoch 97 - iter 135/274 - loss 0.01438317 - samples/sec: 69.35 - lr: 0.025000 -2022-11-01 17:03:56,029 epoch 97 - iter 162/274 - loss 0.01508738 - samples/sec: 69.34 - lr: 0.025000 -2022-11-01 17:04:07,750 epoch 97 - iter 189/274 - loss 0.01543147 - samples/sec: 73.73 - lr: 0.025000 -2022-11-01 17:04:21,673 epoch 97 - iter 216/274 - loss 0.01514577 - samples/sec: 62.07 - lr: 0.025000 -2022-11-01 17:04:35,162 epoch 97 - iter 243/274 - loss 0.01554633 - samples/sec: 64.07 - lr: 0.025000 -2022-11-01 17:04:47,543 epoch 97 - iter 270/274 - loss 0.01594045 - samples/sec: 69.80 - lr: 0.025000 -2022-11-01 17:04:49,266 ---------------------------------------------------------------------------------------------------- -2022-11-01 17:04:49,267 EPOCH 97 done: loss 0.0161 - lr 0.025000 -2022-11-01 17:05:14,559 Evaluating as a multi-label problem: False -2022-11-01 17:05:14,575 TEST : loss 0.031141091138124466 - f1-score (micro avg) 0.8548 -2022-11-01 17:05:14,626 Epoch 97: reducing learning rate of group 0 to 1.2500e-02. -2022-11-01 17:05:14,626 BAD EPOCHS (no improvement): 4 -2022-11-01 17:05:14,718 ---------------------------------------------------------------------------------------------------- -2022-11-01 17:05:27,619 epoch 98 - iter 27/274 - loss 0.01684552 - samples/sec: 66.99 - lr: 0.012500 -2022-11-01 17:05:40,712 epoch 98 - iter 54/274 - loss 0.01617953 - samples/sec: 66.00 - lr: 0.012500 -2022-11-01 17:05:52,535 epoch 98 - iter 81/274 - loss 0.01694288 - samples/sec: 73.10 - lr: 0.012500 -2022-11-01 17:06:04,021 epoch 98 - iter 108/274 - loss 0.01581446 - samples/sec: 75.25 - lr: 0.012500 -2022-11-01 17:06:16,731 epoch 98 - iter 135/274 - loss 0.01504212 - samples/sec: 68.00 - lr: 0.012500 -2022-11-01 17:06:28,406 epoch 98 - iter 162/274 - loss 0.01514386 - samples/sec: 74.02 - lr: 0.012500 -2022-11-01 17:06:40,061 epoch 98 - iter 189/274 - loss 0.01497497 - samples/sec: 74.15 - lr: 0.012500 -2022-11-01 17:06:51,963 epoch 98 - iter 216/274 - loss 0.01510213 - samples/sec: 72.61 - lr: 0.012500 -2022-11-01 17:07:04,163 epoch 98 - iter 243/274 - loss 0.01520530 - samples/sec: 70.84 - lr: 0.012500 -2022-11-01 17:07:18,137 epoch 98 - iter 270/274 - loss 0.01495979 - samples/sec: 61.84 - lr: 0.012500 -2022-11-01 17:07:19,938 ---------------------------------------------------------------------------------------------------- -2022-11-01 17:07:19,938 EPOCH 98 done: loss 0.0150 - lr 0.012500 -2022-11-01 17:07:45,296 Evaluating as a multi-label problem: False -2022-11-01 17:07:45,312 TEST : loss 0.0315224826335907 - f1-score (micro avg) 0.8517 -2022-11-01 17:07:45,364 BAD EPOCHS (no improvement): 0 -2022-11-01 17:07:45,457 ---------------------------------------------------------------------------------------------------- -2022-11-01 17:07:59,107 epoch 99 - iter 27/274 - loss 0.01568841 - samples/sec: 63.31 - lr: 0.012500 -2022-11-01 17:08:11,036 epoch 99 - iter 54/274 - loss 0.01701872 - samples/sec: 72.45 - lr: 0.012500 -2022-11-01 17:08:22,893 epoch 99 - iter 81/274 - loss 0.01531658 - samples/sec: 72.89 - lr: 0.012500 -2022-11-01 17:08:34,443 epoch 99 - iter 108/274 - loss 0.01571024 - samples/sec: 74.83 - lr: 0.012500 -2022-11-01 17:08:45,990 epoch 99 - iter 135/274 - loss 0.01567341 - samples/sec: 74.85 - lr: 0.012500 -2022-11-01 17:08:58,358 epoch 99 - iter 162/274 - loss 0.01522725 - samples/sec: 69.88 - lr: 0.012500 -2022-11-01 17:09:10,655 epoch 99 - iter 189/274 - loss 0.01517455 - samples/sec: 70.28 - lr: 0.012500 -2022-11-01 17:09:24,581 epoch 99 - iter 216/274 - loss 0.01501352 - samples/sec: 62.06 - lr: 0.012500 -2022-11-01 17:09:36,581 epoch 99 - iter 243/274 - loss 0.01514386 - samples/sec: 72.02 - lr: 0.012500 -2022-11-01 17:09:48,961 epoch 99 - iter 270/274 - loss 0.01501936 - samples/sec: 69.81 - lr: 0.012500 -2022-11-01 17:09:50,696 ---------------------------------------------------------------------------------------------------- -2022-11-01 17:09:50,696 EPOCH 99 done: loss 0.0149 - lr 0.012500 -2022-11-01 17:10:16,131 Evaluating as a multi-label problem: False -2022-11-01 17:10:16,146 TEST : loss 0.032214682549238205 - f1-score (micro avg) 0.8528 -2022-11-01 17:10:16,198 BAD EPOCHS (no improvement): 0 -2022-11-01 17:10:16,290 ---------------------------------------------------------------------------------------------------- -2022-11-01 17:10:28,380 epoch 100 - iter 27/274 - loss 0.01049140 - samples/sec: 71.49 - lr: 0.012500 -2022-11-01 17:10:41,052 epoch 100 - iter 54/274 - loss 0.01500905 - samples/sec: 68.20 - lr: 0.012500 -2022-11-01 17:10:53,501 epoch 100 - iter 81/274 - loss 0.01512699 - samples/sec: 69.42 - lr: 0.012500 -2022-11-01 17:11:05,018 epoch 100 - iter 108/274 - loss 0.01472317 - samples/sec: 75.04 - lr: 0.012500 -2022-11-01 17:11:16,311 epoch 100 - iter 135/274 - loss 0.01471632 - samples/sec: 76.53 - lr: 0.012500 -2022-11-01 17:11:28,068 epoch 100 - iter 162/274 - loss 0.01508426 - samples/sec: 73.51 - lr: 0.012500 -2022-11-01 17:11:42,352 epoch 100 - iter 189/274 - loss 0.01528565 - samples/sec: 60.50 - lr: 0.012500 -2022-11-01 17:11:54,868 epoch 100 - iter 216/274 - loss 0.01551813 - samples/sec: 69.05 - lr: 0.012500 -2022-11-01 17:12:09,315 epoch 100 - iter 243/274 - loss 0.01560369 - samples/sec: 59.82 - lr: 0.012500 -2022-11-01 17:12:20,899 epoch 100 - iter 270/274 - loss 0.01542870 - samples/sec: 74.61 - lr: 0.012500 -2022-11-01 17:12:22,620 ---------------------------------------------------------------------------------------------------- -2022-11-01 17:12:22,620 EPOCH 100 done: loss 0.0153 - lr 0.012500 -2022-11-01 17:12:47,951 Evaluating as a multi-label problem: False -2022-11-01 17:12:47,966 TEST : loss 0.03205437958240509 - f1-score (micro avg) 0.8544 -2022-11-01 17:12:48,018 BAD EPOCHS (no improvement): 1 -2022-11-01 17:12:48,109 ---------------------------------------------------------------------------------------------------- -2022-11-01 17:13:00,180 epoch 101 - iter 27/274 - loss 0.01378529 - samples/sec: 71.60 - lr: 0.012500 -2022-11-01 17:13:12,215 epoch 101 - iter 54/274 - loss 0.01337199 - samples/sec: 71.81 - lr: 0.012500 -2022-11-01 17:13:25,504 epoch 101 - iter 81/274 - loss 0.01404534 - samples/sec: 65.03 - lr: 0.012500 -2022-11-01 17:13:36,981 epoch 101 - iter 108/274 - loss 0.01373175 - samples/sec: 75.30 - lr: 0.012500 -2022-11-01 17:13:48,891 epoch 101 - iter 135/274 - loss 0.01451741 - samples/sec: 72.56 - lr: 0.012500 -2022-11-01 17:14:01,244 epoch 101 - iter 162/274 - loss 0.01501925 - samples/sec: 69.96 - lr: 0.012500 -2022-11-01 17:14:14,210 epoch 101 - iter 189/274 - loss 0.01503842 - samples/sec: 66.65 - lr: 0.012500 -2022-11-01 17:14:26,461 epoch 101 - iter 216/274 - loss 0.01533271 - samples/sec: 70.55 - lr: 0.012500 -2022-11-01 17:14:38,050 epoch 101 - iter 243/274 - loss 0.01542822 - samples/sec: 74.57 - lr: 0.012500 -2022-11-01 17:14:52,728 epoch 101 - iter 270/274 - loss 0.01542944 - samples/sec: 58.88 - lr: 0.012500 -2022-11-01 17:14:54,387 ---------------------------------------------------------------------------------------------------- -2022-11-01 17:14:54,387 EPOCH 101 done: loss 0.0153 - lr 0.012500 -2022-11-01 17:15:19,832 Evaluating as a multi-label problem: False -2022-11-01 17:15:19,847 TEST : loss 0.03180859610438347 - f1-score (micro avg) 0.8518 -2022-11-01 17:15:19,900 BAD EPOCHS (no improvement): 2 -2022-11-01 17:15:19,996 ---------------------------------------------------------------------------------------------------- -2022-11-01 17:15:30,919 epoch 102 - iter 27/274 - loss 0.01367323 - samples/sec: 79.13 - lr: 0.012500 -2022-11-01 17:15:42,942 epoch 102 - iter 54/274 - loss 0.01496790 - samples/sec: 71.88 - lr: 0.012500 -2022-11-01 17:15:54,982 epoch 102 - iter 81/274 - loss 0.01427020 - samples/sec: 71.78 - lr: 0.012500 -2022-11-01 17:16:07,360 epoch 102 - iter 108/274 - loss 0.01451117 - samples/sec: 69.82 - lr: 0.012500 -2022-11-01 17:16:19,150 epoch 102 - iter 135/274 - loss 0.01477473 - samples/sec: 73.31 - lr: 0.012500 -2022-11-01 17:16:32,137 epoch 102 - iter 162/274 - loss 0.01509181 - samples/sec: 66.54 - lr: 0.012500 -2022-11-01 17:16:44,150 epoch 102 - iter 189/274 - loss 0.01558677 - samples/sec: 71.94 - lr: 0.012500 -2022-11-01 17:16:55,429 epoch 102 - iter 216/274 - loss 0.01557646 - samples/sec: 76.63 - lr: 0.012500 -2022-11-01 17:17:08,071 epoch 102 - iter 243/274 - loss 0.01558887 - samples/sec: 68.36 - lr: 0.012500 -2022-11-01 17:17:21,624 epoch 102 - iter 270/274 - loss 0.01573576 - samples/sec: 63.77 - lr: 0.012500 -2022-11-01 17:17:24,639 ---------------------------------------------------------------------------------------------------- -2022-11-01 17:17:24,640 EPOCH 102 done: loss 0.0157 - lr 0.012500 -2022-11-01 17:17:49,335 Evaluating as a multi-label problem: False -2022-11-01 17:17:49,351 TEST : loss 0.03114163875579834 - f1-score (micro avg) 0.8522 -2022-11-01 17:17:49,402 BAD EPOCHS (no improvement): 3 -2022-11-01 17:17:49,497 ---------------------------------------------------------------------------------------------------- -2022-11-01 17:18:01,427 epoch 103 - iter 27/274 - loss 0.01511439 - samples/sec: 72.45 - lr: 0.012500 -2022-11-01 17:18:13,288 epoch 103 - iter 54/274 - loss 0.01521225 - samples/sec: 72.87 - lr: 0.012500 -2022-11-01 17:18:27,608 epoch 103 - iter 81/274 - loss 0.01588243 - samples/sec: 60.35 - lr: 0.012500 -2022-11-01 17:18:40,062 epoch 103 - iter 108/274 - loss 0.01620946 - samples/sec: 69.39 - lr: 0.012500 -2022-11-01 17:18:53,137 epoch 103 - iter 135/274 - loss 0.01627265 - samples/sec: 66.10 - lr: 0.012500 -2022-11-01 17:19:05,918 epoch 103 - iter 162/274 - loss 0.01616743 - samples/sec: 67.62 - lr: 0.012500 -2022-11-01 17:19:18,579 epoch 103 - iter 189/274 - loss 0.01595910 - samples/sec: 68.26 - lr: 0.012500 -2022-11-01 17:19:30,844 epoch 103 - iter 216/274 - loss 0.01601796 - samples/sec: 70.46 - lr: 0.012500 -2022-11-01 17:19:42,438 epoch 103 - iter 243/274 - loss 0.01586805 - samples/sec: 74.54 - lr: 0.012500 -2022-11-01 17:19:54,280 epoch 103 - iter 270/274 - loss 0.01587716 - samples/sec: 72.98 - lr: 0.012500 -2022-11-01 17:19:55,910 ---------------------------------------------------------------------------------------------------- -2022-11-01 17:19:55,910 EPOCH 103 done: loss 0.0159 - lr 0.012500 -2022-11-01 17:20:21,470 Evaluating as a multi-label problem: False -2022-11-01 17:20:21,486 TEST : loss 0.03149925917387009 - f1-score (micro avg) 0.8538 -2022-11-01 17:20:21,538 Epoch 103: reducing learning rate of group 0 to 6.2500e-03. -2022-11-01 17:20:21,539 BAD EPOCHS (no improvement): 4 -2022-11-01 17:20:21,631 ---------------------------------------------------------------------------------------------------- -2022-11-01 17:20:34,090 epoch 104 - iter 27/274 - loss 0.01498141 - samples/sec: 69.37 - lr: 0.006250 -2022-11-01 17:20:46,629 epoch 104 - iter 54/274 - loss 0.01501934 - samples/sec: 68.92 - lr: 0.006250 -2022-11-01 17:20:57,980 epoch 104 - iter 81/274 - loss 0.01434988 - samples/sec: 76.14 - lr: 0.006250 -2022-11-01 17:21:09,792 epoch 104 - iter 108/274 - loss 0.01473952 - samples/sec: 73.17 - lr: 0.006250 -2022-11-01 17:21:23,026 epoch 104 - iter 135/274 - loss 0.01529435 - samples/sec: 65.30 - lr: 0.006250 -2022-11-01 17:21:36,383 epoch 104 - iter 162/274 - loss 0.01527378 - samples/sec: 64.70 - lr: 0.006250 -2022-11-01 17:21:50,064 epoch 104 - iter 189/274 - loss 0.01508563 - samples/sec: 63.17 - lr: 0.006250 -2022-11-01 17:22:02,774 epoch 104 - iter 216/274 - loss 0.01520574 - samples/sec: 68.00 - lr: 0.006250 -2022-11-01 17:22:14,747 epoch 104 - iter 243/274 - loss 0.01531841 - samples/sec: 72.18 - lr: 0.006250 -2022-11-01 17:22:25,810 epoch 104 - iter 270/274 - loss 0.01495468 - samples/sec: 78.12 - lr: 0.006250 -2022-11-01 17:22:27,314 ---------------------------------------------------------------------------------------------------- -2022-11-01 17:22:27,314 EPOCH 104 done: loss 0.0150 - lr 0.006250 -2022-11-01 17:22:52,615 Evaluating as a multi-label problem: False -2022-11-01 17:22:52,631 TEST : loss 0.03160782903432846 - f1-score (micro avg) 0.8545 -2022-11-01 17:22:52,682 BAD EPOCHS (no improvement): 1 -2022-11-01 17:22:52,774 ---------------------------------------------------------------------------------------------------- -2022-11-01 17:23:04,512 epoch 105 - iter 27/274 - loss 0.01478439 - samples/sec: 73.63 - lr: 0.006250 -2022-11-01 17:23:17,554 epoch 105 - iter 54/274 - loss 0.01580731 - samples/sec: 66.26 - lr: 0.006250 -2022-11-01 17:23:29,786 epoch 105 - iter 81/274 - loss 0.01527523 - samples/sec: 70.66 - lr: 0.006250 -2022-11-01 17:23:42,262 epoch 105 - iter 108/274 - loss 0.01556524 - samples/sec: 69.27 - lr: 0.006250 -2022-11-01 17:23:53,798 epoch 105 - iter 135/274 - loss 0.01510414 - samples/sec: 74.92 - lr: 0.006250 -2022-11-01 17:24:05,883 epoch 105 - iter 162/274 - loss 0.01499963 - samples/sec: 71.52 - lr: 0.006250 -2022-11-01 17:24:18,145 epoch 105 - iter 189/274 - loss 0.01459439 - samples/sec: 70.48 - lr: 0.006250 -2022-11-01 17:24:31,529 epoch 105 - iter 216/274 - loss 0.01450208 - samples/sec: 64.57 - lr: 0.006250 -2022-11-01 17:24:44,094 epoch 105 - iter 243/274 - loss 0.01431071 - samples/sec: 68.78 - lr: 0.006250 -2022-11-01 17:24:56,610 epoch 105 - iter 270/274 - loss 0.01451876 - samples/sec: 69.05 - lr: 0.006250 -2022-11-01 17:24:58,538 ---------------------------------------------------------------------------------------------------- -2022-11-01 17:24:58,539 EPOCH 105 done: loss 0.0145 - lr 0.006250 -2022-11-01 17:25:24,716 Evaluating as a multi-label problem: False -2022-11-01 17:25:24,732 TEST : loss 0.031382638961076736 - f1-score (micro avg) 0.8549 -2022-11-01 17:25:24,783 BAD EPOCHS (no improvement): 0 -2022-11-01 17:25:24,878 ---------------------------------------------------------------------------------------------------- -2022-11-01 17:25:37,729 epoch 106 - iter 27/274 - loss 0.01310063 - samples/sec: 67.26 - lr: 0.006250 -2022-11-01 17:25:49,473 epoch 106 - iter 54/274 - loss 0.01383816 - samples/sec: 73.59 - lr: 0.006250 -2022-11-01 17:26:02,132 epoch 106 - iter 81/274 - loss 0.01373350 - samples/sec: 68.27 - lr: 0.006250 -2022-11-01 17:26:14,016 epoch 106 - iter 108/274 - loss 0.01442718 - samples/sec: 72.72 - lr: 0.006250 -2022-11-01 17:26:26,053 epoch 106 - iter 135/274 - loss 0.01401087 - samples/sec: 71.80 - lr: 0.006250 -2022-11-01 17:26:38,918 epoch 106 - iter 162/274 - loss 0.01399659 - samples/sec: 67.18 - lr: 0.006250 -2022-11-01 17:26:51,716 epoch 106 - iter 189/274 - loss 0.01399994 - samples/sec: 67.53 - lr: 0.006250 -2022-11-01 17:27:04,149 epoch 106 - iter 216/274 - loss 0.01436065 - samples/sec: 69.51 - lr: 0.006250 -2022-11-01 17:27:16,644 epoch 106 - iter 243/274 - loss 0.01474534 - samples/sec: 69.17 - lr: 0.006250 -2022-11-01 17:27:28,686 epoch 106 - iter 270/274 - loss 0.01480237 - samples/sec: 71.77 - lr: 0.006250 -2022-11-01 17:27:30,054 ---------------------------------------------------------------------------------------------------- -2022-11-01 17:27:30,054 EPOCH 106 done: loss 0.0147 - lr 0.006250 -2022-11-01 17:27:55,476 Evaluating as a multi-label problem: False -2022-11-01 17:27:55,492 TEST : loss 0.03141792118549347 - f1-score (micro avg) 0.8558 -2022-11-01 17:27:55,546 BAD EPOCHS (no improvement): 1 -2022-11-01 17:27:55,639 ---------------------------------------------------------------------------------------------------- -2022-11-01 17:28:08,177 epoch 107 - iter 27/274 - loss 0.01872842 - samples/sec: 68.93 - lr: 0.006250 -2022-11-01 17:28:19,630 epoch 107 - iter 54/274 - loss 0.01616813 - samples/sec: 75.46 - lr: 0.006250 -2022-11-01 17:28:32,113 epoch 107 - iter 81/274 - loss 0.01536312 - samples/sec: 69.23 - lr: 0.006250 -2022-11-01 17:28:44,519 epoch 107 - iter 108/274 - loss 0.01581316 - samples/sec: 69.67 - lr: 0.006250 -2022-11-01 17:28:57,543 epoch 107 - iter 135/274 - loss 0.01573153 - samples/sec: 66.35 - lr: 0.006250 -2022-11-01 17:29:09,596 epoch 107 - iter 162/274 - loss 0.01590482 - samples/sec: 71.71 - lr: 0.006250 -2022-11-01 17:29:21,223 epoch 107 - iter 189/274 - loss 0.01568356 - samples/sec: 74.32 - lr: 0.006250 -2022-11-01 17:29:35,337 epoch 107 - iter 216/274 - loss 0.01557378 - samples/sec: 61.23 - lr: 0.006250 -2022-11-01 17:29:47,568 epoch 107 - iter 243/274 - loss 0.01547147 - samples/sec: 70.66 - lr: 0.006250 -2022-11-01 17:30:00,199 epoch 107 - iter 270/274 - loss 0.01556995 - samples/sec: 68.42 - lr: 0.006250 -2022-11-01 17:30:02,023 ---------------------------------------------------------------------------------------------------- -2022-11-01 17:30:02,024 EPOCH 107 done: loss 0.0156 - lr 0.006250 -2022-11-01 17:30:26,714 Evaluating as a multi-label problem: False -2022-11-01 17:30:26,729 TEST : loss 0.031312357634305954 - f1-score (micro avg) 0.8558 -2022-11-01 17:30:26,780 BAD EPOCHS (no improvement): 2 -2022-11-01 17:30:26,872 ---------------------------------------------------------------------------------------------------- -2022-11-01 17:30:39,718 epoch 108 - iter 27/274 - loss 0.01390738 - samples/sec: 67.28 - lr: 0.006250 -2022-11-01 17:30:52,052 epoch 108 - iter 54/274 - loss 0.01439151 - samples/sec: 70.07 - lr: 0.006250 -2022-11-01 17:31:05,111 epoch 108 - iter 81/274 - loss 0.01442134 - samples/sec: 66.17 - lr: 0.006250 -2022-11-01 17:31:16,827 epoch 108 - iter 108/274 - loss 0.01435974 - samples/sec: 73.77 - lr: 0.006250 -2022-11-01 17:31:29,153 epoch 108 - iter 135/274 - loss 0.01387339 - samples/sec: 70.11 - lr: 0.006250 -2022-11-01 17:31:43,801 epoch 108 - iter 162/274 - loss 0.01425564 - samples/sec: 59.00 - lr: 0.006250 -2022-11-01 17:31:55,727 epoch 108 - iter 189/274 - loss 0.01421163 - samples/sec: 72.46 - lr: 0.006250 -2022-11-01 17:32:07,607 epoch 108 - iter 216/274 - loss 0.01410484 - samples/sec: 72.75 - lr: 0.006250 -2022-11-01 17:32:20,751 epoch 108 - iter 243/274 - loss 0.01457425 - samples/sec: 65.75 - lr: 0.006250 -2022-11-01 17:32:32,581 epoch 108 - iter 270/274 - loss 0.01465172 - samples/sec: 73.05 - lr: 0.006250 -2022-11-01 17:32:34,099 ---------------------------------------------------------------------------------------------------- -2022-11-01 17:32:34,100 EPOCH 108 done: loss 0.0148 - lr 0.006250 -2022-11-01 17:32:58,961 Evaluating as a multi-label problem: False -2022-11-01 17:32:58,977 TEST : loss 0.031135080382227898 - f1-score (micro avg) 0.8554 -2022-11-01 17:32:59,030 BAD EPOCHS (no improvement): 3 -2022-11-01 17:32:59,122 ---------------------------------------------------------------------------------------------------- -2022-11-01 17:33:12,240 epoch 109 - iter 27/274 - loss 0.01912379 - samples/sec: 65.88 - lr: 0.006250 -2022-11-01 17:33:24,444 epoch 109 - iter 54/274 - loss 0.01804758 - samples/sec: 70.82 - lr: 0.006250 -2022-11-01 17:33:37,289 epoch 109 - iter 81/274 - loss 0.01646409 - samples/sec: 67.28 - lr: 0.006250 -2022-11-01 17:33:49,388 epoch 109 - iter 108/274 - loss 0.01560171 - samples/sec: 71.43 - lr: 0.006250 -2022-11-01 17:34:01,172 epoch 109 - iter 135/274 - loss 0.01511687 - samples/sec: 73.34 - lr: 0.006250 -2022-11-01 17:34:12,746 epoch 109 - iter 162/274 - loss 0.01504958 - samples/sec: 74.68 - lr: 0.006250 -2022-11-01 17:34:25,860 epoch 109 - iter 189/274 - loss 0.01522205 - samples/sec: 65.90 - lr: 0.006250 -2022-11-01 17:34:38,292 epoch 109 - iter 216/274 - loss 0.01524269 - samples/sec: 69.52 - lr: 0.006250 -2022-11-01 17:34:51,412 epoch 109 - iter 243/274 - loss 0.01492633 - samples/sec: 65.87 - lr: 0.006250 -2022-11-01 17:35:04,115 epoch 109 - iter 270/274 - loss 0.01465676 - samples/sec: 68.04 - lr: 0.006250 -2022-11-01 17:35:05,583 ---------------------------------------------------------------------------------------------------- -2022-11-01 17:35:05,583 EPOCH 109 done: loss 0.0147 - lr 0.006250 -2022-11-01 17:35:30,502 Evaluating as a multi-label problem: False -2022-11-01 17:35:30,517 TEST : loss 0.031781088560819626 - f1-score (micro avg) 0.8552 -2022-11-01 17:35:30,570 Epoch 109: reducing learning rate of group 0 to 3.1250e-03. -2022-11-01 17:35:30,571 BAD EPOCHS (no improvement): 4 -2022-11-01 17:35:30,662 ---------------------------------------------------------------------------------------------------- -2022-11-01 17:35:43,277 epoch 110 - iter 27/274 - loss 0.01618152 - samples/sec: 68.51 - lr: 0.003125 -2022-11-01 17:35:57,703 epoch 110 - iter 54/274 - loss 0.01398249 - samples/sec: 59.91 - lr: 0.003125 -2022-11-01 17:36:11,539 epoch 110 - iter 81/274 - loss 0.01436806 - samples/sec: 62.46 - lr: 0.003125 -2022-11-01 17:36:23,687 epoch 110 - iter 108/274 - loss 0.01402755 - samples/sec: 71.14 - lr: 0.003125 -2022-11-01 17:36:35,831 epoch 110 - iter 135/274 - loss 0.01429670 - samples/sec: 71.17 - lr: 0.003125 -2022-11-01 17:36:47,885 epoch 110 - iter 162/274 - loss 0.01429064 - samples/sec: 71.69 - lr: 0.003125 -2022-11-01 17:36:59,675 epoch 110 - iter 189/274 - loss 0.01471822 - samples/sec: 73.30 - lr: 0.003125 -2022-11-01 17:37:12,360 epoch 110 - iter 216/274 - loss 0.01430368 - samples/sec: 68.13 - lr: 0.003125 -2022-11-01 17:37:23,509 epoch 110 - iter 243/274 - loss 0.01448710 - samples/sec: 77.52 - lr: 0.003125 -2022-11-01 17:37:35,507 epoch 110 - iter 270/274 - loss 0.01446019 - samples/sec: 72.03 - lr: 0.003125 -2022-11-01 17:37:37,495 ---------------------------------------------------------------------------------------------------- -2022-11-01 17:37:37,495 EPOCH 110 done: loss 0.0144 - lr 0.003125 -2022-11-01 17:38:02,941 Evaluating as a multi-label problem: False -2022-11-01 17:38:02,956 TEST : loss 0.031605690717697144 - f1-score (micro avg) 0.8551 -2022-11-01 17:38:03,011 BAD EPOCHS (no improvement): 0 -2022-11-01 17:38:03,085 ---------------------------------------------------------------------------------------------------- -2022-11-01 17:38:15,036 epoch 111 - iter 27/274 - loss 0.01596760 - samples/sec: 72.32 - lr: 0.003125 -2022-11-01 17:38:28,050 epoch 111 - iter 54/274 - loss 0.01611072 - samples/sec: 66.40 - lr: 0.003125 -2022-11-01 17:38:41,281 epoch 111 - iter 81/274 - loss 0.01586174 - samples/sec: 65.32 - lr: 0.003125 -2022-11-01 17:38:54,263 epoch 111 - iter 108/274 - loss 0.01557685 - samples/sec: 66.57 - lr: 0.003125 -2022-11-01 17:39:08,244 epoch 111 - iter 135/274 - loss 0.01580835 - samples/sec: 61.81 - lr: 0.003125 -2022-11-01 17:39:19,514 epoch 111 - iter 162/274 - loss 0.01606974 - samples/sec: 76.68 - lr: 0.003125 -2022-11-01 17:39:31,520 epoch 111 - iter 189/274 - loss 0.01547722 - samples/sec: 71.98 - lr: 0.003125 -2022-11-01 17:39:42,815 epoch 111 - iter 216/274 - loss 0.01515104 - samples/sec: 76.52 - lr: 0.003125 -2022-11-01 17:39:54,557 epoch 111 - iter 243/274 - loss 0.01496053 - samples/sec: 73.60 - lr: 0.003125 -2022-11-01 17:40:07,087 epoch 111 - iter 270/274 - loss 0.01483213 - samples/sec: 68.97 - lr: 0.003125 -2022-11-01 17:40:08,469 ---------------------------------------------------------------------------------------------------- -2022-11-01 17:40:08,469 EPOCH 111 done: loss 0.0149 - lr 0.003125 -2022-11-01 17:40:33,329 Evaluating as a multi-label problem: False -2022-11-01 17:40:33,344 TEST : loss 0.031536173075437546 - f1-score (micro avg) 0.8541 -2022-11-01 17:40:33,396 BAD EPOCHS (no improvement): 1 -2022-11-01 17:40:33,470 ---------------------------------------------------------------------------------------------------- -2022-11-01 17:40:46,575 epoch 112 - iter 27/274 - loss 0.01439567 - samples/sec: 65.95 - lr: 0.003125 -2022-11-01 17:40:58,836 epoch 112 - iter 54/274 - loss 0.01354366 - samples/sec: 70.48 - lr: 0.003125 -2022-11-01 17:41:10,562 epoch 112 - iter 81/274 - loss 0.01444438 - samples/sec: 73.70 - lr: 0.003125 -2022-11-01 17:41:24,112 epoch 112 - iter 108/274 - loss 0.01493261 - samples/sec: 63.78 - lr: 0.003125 -2022-11-01 17:41:35,909 epoch 112 - iter 135/274 - loss 0.01562564 - samples/sec: 73.26 - lr: 0.003125 -2022-11-01 17:41:48,092 epoch 112 - iter 162/274 - loss 0.01544606 - samples/sec: 70.94 - lr: 0.003125 -2022-11-01 17:42:00,153 epoch 112 - iter 189/274 - loss 0.01543938 - samples/sec: 71.65 - lr: 0.003125 -2022-11-01 17:42:12,466 epoch 112 - iter 216/274 - loss 0.01503371 - samples/sec: 70.19 - lr: 0.003125 -2022-11-01 17:42:24,873 epoch 112 - iter 243/274 - loss 0.01527409 - samples/sec: 69.66 - lr: 0.003125 -2022-11-01 17:42:37,180 epoch 112 - iter 270/274 - loss 0.01508578 - samples/sec: 70.22 - lr: 0.003125 -2022-11-01 17:42:38,763 ---------------------------------------------------------------------------------------------------- -2022-11-01 17:42:38,763 EPOCH 112 done: loss 0.0151 - lr 0.003125 -2022-11-01 17:43:04,124 Evaluating as a multi-label problem: False -2022-11-01 17:43:04,139 TEST : loss 0.031474340707063675 - f1-score (micro avg) 0.855 -2022-11-01 17:43:04,191 BAD EPOCHS (no improvement): 2 -2022-11-01 17:43:04,280 ---------------------------------------------------------------------------------------------------- -2022-11-01 17:43:16,911 epoch 113 - iter 27/274 - loss 0.01609706 - samples/sec: 68.43 - lr: 0.003125 -2022-11-01 17:43:29,310 epoch 113 - iter 54/274 - loss 0.01433556 - samples/sec: 69.70 - lr: 0.003125 -2022-11-01 17:43:42,128 epoch 113 - iter 81/274 - loss 0.01434582 - samples/sec: 67.42 - lr: 0.003125 -2022-11-01 17:43:55,192 epoch 113 - iter 108/274 - loss 0.01474083 - samples/sec: 66.16 - lr: 0.003125 -2022-11-01 17:44:07,374 epoch 113 - iter 135/274 - loss 0.01445667 - samples/sec: 70.94 - lr: 0.003125 -2022-11-01 17:44:19,671 epoch 113 - iter 162/274 - loss 0.01481096 - samples/sec: 70.28 - lr: 0.003125 -2022-11-01 17:44:31,736 epoch 113 - iter 189/274 - loss 0.01481574 - samples/sec: 71.64 - lr: 0.003125 -2022-11-01 17:44:45,034 epoch 113 - iter 216/274 - loss 0.01524675 - samples/sec: 64.99 - lr: 0.003125 -2022-11-01 17:44:58,586 epoch 113 - iter 243/274 - loss 0.01492049 - samples/sec: 63.77 - lr: 0.003125 -2022-11-01 17:45:09,603 epoch 113 - iter 270/274 - loss 0.01468932 - samples/sec: 78.45 - lr: 0.003125 -2022-11-01 17:45:11,032 ---------------------------------------------------------------------------------------------------- -2022-11-01 17:45:11,032 EPOCH 113 done: loss 0.0147 - lr 0.003125 -2022-11-01 17:45:36,455 Evaluating as a multi-label problem: False -2022-11-01 17:45:36,471 TEST : loss 0.031519342213869095 - f1-score (micro avg) 0.8545 -2022-11-01 17:45:36,523 BAD EPOCHS (no improvement): 3 -2022-11-01 17:45:36,616 ---------------------------------------------------------------------------------------------------- -2022-11-01 17:45:49,215 epoch 114 - iter 27/274 - loss 0.01487123 - samples/sec: 68.60 - lr: 0.003125 -2022-11-01 17:46:00,350 epoch 114 - iter 54/274 - loss 0.01485395 - samples/sec: 77.61 - lr: 0.003125 -2022-11-01 17:46:13,743 epoch 114 - iter 81/274 - loss 0.01571063 - samples/sec: 64.53 - lr: 0.003125 -2022-11-01 17:46:25,662 epoch 114 - iter 108/274 - loss 0.01471453 - samples/sec: 72.51 - lr: 0.003125 -2022-11-01 17:46:38,373 epoch 114 - iter 135/274 - loss 0.01512168 - samples/sec: 67.99 - lr: 0.003125 -2022-11-01 17:46:50,844 epoch 114 - iter 162/274 - loss 0.01516243 - samples/sec: 69.30 - lr: 0.003125 -2022-11-01 17:47:04,008 epoch 114 - iter 189/274 - loss 0.01502249 - samples/sec: 65.65 - lr: 0.003125 -2022-11-01 17:47:15,595 epoch 114 - iter 216/274 - loss 0.01471831 - samples/sec: 74.58 - lr: 0.003125 -2022-11-01 17:47:29,477 epoch 114 - iter 243/274 - loss 0.01445627 - samples/sec: 62.25 - lr: 0.003125 -2022-11-01 17:47:40,993 epoch 114 - iter 270/274 - loss 0.01491303 - samples/sec: 75.05 - lr: 0.003125 -2022-11-01 17:47:43,070 ---------------------------------------------------------------------------------------------------- -2022-11-01 17:47:43,070 EPOCH 114 done: loss 0.0150 - lr 0.003125 -2022-11-01 17:48:08,286 Evaluating as a multi-label problem: False -2022-11-01 17:48:08,302 TEST : loss 0.031727951020002365 - f1-score (micro avg) 0.8533 -2022-11-01 17:48:08,354 Epoch 114: reducing learning rate of group 0 to 1.5625e-03. -2022-11-01 17:48:08,354 BAD EPOCHS (no improvement): 4 -2022-11-01 17:48:08,429 ---------------------------------------------------------------------------------------------------- -2022-11-01 17:48:21,757 epoch 115 - iter 27/274 - loss 0.01438955 - samples/sec: 64.85 - lr: 0.001563 -2022-11-01 17:48:35,019 epoch 115 - iter 54/274 - loss 0.01465102 - samples/sec: 65.16 - lr: 0.001563 -2022-11-01 17:48:48,715 epoch 115 - iter 81/274 - loss 0.01452675 - samples/sec: 63.10 - lr: 0.001563 -2022-11-01 17:49:00,439 epoch 115 - iter 108/274 - loss 0.01446702 - samples/sec: 73.71 - lr: 0.001563 -2022-11-01 17:49:13,470 epoch 115 - iter 135/274 - loss 0.01355820 - samples/sec: 66.32 - lr: 0.001563 -2022-11-01 17:49:26,293 epoch 115 - iter 162/274 - loss 0.01370481 - samples/sec: 67.40 - lr: 0.001563 -2022-11-01 17:49:38,891 epoch 115 - iter 189/274 - loss 0.01345347 - samples/sec: 68.60 - lr: 0.001563 -2022-11-01 17:49:50,147 epoch 115 - iter 216/274 - loss 0.01344039 - samples/sec: 76.78 - lr: 0.001563 -2022-11-01 17:50:01,283 epoch 115 - iter 243/274 - loss 0.01399875 - samples/sec: 77.61 - lr: 0.001563 -2022-11-01 17:50:14,106 epoch 115 - iter 270/274 - loss 0.01443076 - samples/sec: 67.40 - lr: 0.001563 -2022-11-01 17:50:15,622 ---------------------------------------------------------------------------------------------------- -2022-11-01 17:50:15,622 EPOCH 115 done: loss 0.0146 - lr 0.001563 -2022-11-01 17:50:40,571 Evaluating as a multi-label problem: False -2022-11-01 17:50:40,587 TEST : loss 0.03165162727236748 - f1-score (micro avg) 0.8539 -2022-11-01 17:50:40,639 BAD EPOCHS (no improvement): 1 -2022-11-01 17:50:40,731 ---------------------------------------------------------------------------------------------------- -2022-11-01 17:50:54,027 epoch 116 - iter 27/274 - loss 0.01425236 - samples/sec: 65.00 - lr: 0.001563 -2022-11-01 17:51:06,232 epoch 116 - iter 54/274 - loss 0.01383829 - samples/sec: 70.81 - lr: 0.001563 -2022-11-01 17:51:18,685 epoch 116 - iter 81/274 - loss 0.01359765 - samples/sec: 69.40 - lr: 0.001563 -2022-11-01 17:51:29,472 epoch 116 - iter 108/274 - loss 0.01347194 - samples/sec: 80.12 - lr: 0.001563 -2022-11-01 17:51:42,835 epoch 116 - iter 135/274 - loss 0.01379473 - samples/sec: 64.67 - lr: 0.001563 -2022-11-01 17:51:55,231 epoch 116 - iter 162/274 - loss 0.01379061 - samples/sec: 69.72 - lr: 0.001563 -2022-11-01 17:52:08,602 epoch 116 - iter 189/274 - loss 0.01405799 - samples/sec: 64.63 - lr: 0.001563 -2022-11-01 17:52:19,838 epoch 116 - iter 216/274 - loss 0.01425247 - samples/sec: 76.92 - lr: 0.001563 -2022-11-01 17:52:33,205 epoch 116 - iter 243/274 - loss 0.01416433 - samples/sec: 64.65 - lr: 0.001563 -2022-11-01 17:52:45,518 epoch 116 - iter 270/274 - loss 0.01401386 - samples/sec: 70.19 - lr: 0.001563 -2022-11-01 17:52:46,821 ---------------------------------------------------------------------------------------------------- -2022-11-01 17:52:46,821 EPOCH 116 done: loss 0.0141 - lr 0.001563 -2022-11-01 17:53:11,731 Evaluating as a multi-label problem: False -2022-11-01 17:53:11,746 TEST : loss 0.03173290938138962 - f1-score (micro avg) 0.8549 -2022-11-01 17:53:11,799 BAD EPOCHS (no improvement): 0 -2022-11-01 17:53:11,891 ---------------------------------------------------------------------------------------------------- -2022-11-01 17:53:24,606 epoch 117 - iter 27/274 - loss 0.01300331 - samples/sec: 67.97 - lr: 0.001563 -2022-11-01 17:53:36,989 epoch 117 - iter 54/274 - loss 0.01595312 - samples/sec: 69.79 - lr: 0.001563 -2022-11-01 17:53:48,545 epoch 117 - iter 81/274 - loss 0.01527940 - samples/sec: 74.79 - lr: 0.001563 -2022-11-01 17:54:00,263 epoch 117 - iter 108/274 - loss 0.01460286 - samples/sec: 73.76 - lr: 0.001563 -2022-11-01 17:54:12,584 epoch 117 - iter 135/274 - loss 0.01422859 - samples/sec: 70.14 - lr: 0.001563 -2022-11-01 17:54:25,742 epoch 117 - iter 162/274 - loss 0.01434427 - samples/sec: 65.68 - lr: 0.001563 -2022-11-01 17:54:38,379 epoch 117 - iter 189/274 - loss 0.01401961 - samples/sec: 68.39 - lr: 0.001563 -2022-11-01 17:54:51,636 epoch 117 - iter 216/274 - loss 0.01447595 - samples/sec: 65.19 - lr: 0.001563 -2022-11-01 17:55:04,763 epoch 117 - iter 243/274 - loss 0.01405447 - samples/sec: 65.84 - lr: 0.001563 -2022-11-01 17:55:17,004 epoch 117 - iter 270/274 - loss 0.01409952 - samples/sec: 70.60 - lr: 0.001563 -2022-11-01 17:55:18,621 ---------------------------------------------------------------------------------------------------- -2022-11-01 17:55:18,621 EPOCH 117 done: loss 0.0141 - lr 0.001563 -2022-11-01 17:55:43,762 Evaluating as a multi-label problem: False -2022-11-01 17:55:43,777 TEST : loss 0.03180387616157532 - f1-score (micro avg) 0.8554 -2022-11-01 17:55:43,830 BAD EPOCHS (no improvement): 0 -2022-11-01 17:55:43,922 ---------------------------------------------------------------------------------------------------- -2022-11-01 17:55:55,269 epoch 118 - iter 27/274 - loss 0.01405596 - samples/sec: 76.17 - lr: 0.001563 -2022-11-01 17:56:06,720 epoch 118 - iter 54/274 - loss 0.01339267 - samples/sec: 75.48 - lr: 0.001563 -2022-11-01 17:56:20,308 epoch 118 - iter 81/274 - loss 0.01339778 - samples/sec: 63.60 - lr: 0.001563 -2022-11-01 17:56:32,577 epoch 118 - iter 108/274 - loss 0.01319099 - samples/sec: 70.44 - lr: 0.001563 -2022-11-01 17:56:44,998 epoch 118 - iter 135/274 - loss 0.01301660 - samples/sec: 69.58 - lr: 0.001563 -2022-11-01 17:56:56,910 epoch 118 - iter 162/274 - loss 0.01359261 - samples/sec: 72.55 - lr: 0.001563 -2022-11-01 17:57:08,695 epoch 118 - iter 189/274 - loss 0.01402305 - samples/sec: 73.33 - lr: 0.001563 -2022-11-01 17:57:20,558 epoch 118 - iter 216/274 - loss 0.01374466 - samples/sec: 72.85 - lr: 0.001563 -2022-11-01 17:57:33,907 epoch 118 - iter 243/274 - loss 0.01374517 - samples/sec: 64.74 - lr: 0.001563 -2022-11-01 17:57:46,542 epoch 118 - iter 270/274 - loss 0.01403553 - samples/sec: 68.40 - lr: 0.001563 -2022-11-01 17:57:47,973 ---------------------------------------------------------------------------------------------------- -2022-11-01 17:57:47,973 EPOCH 118 done: loss 0.0140 - lr 0.001563 -2022-11-01 17:58:13,100 Evaluating as a multi-label problem: False -2022-11-01 17:58:13,116 TEST : loss 0.03177444264292717 - f1-score (micro avg) 0.8549 -2022-11-01 17:58:13,167 BAD EPOCHS (no improvement): 0 -2022-11-01 17:58:13,258 ---------------------------------------------------------------------------------------------------- -2022-11-01 17:58:25,375 epoch 119 - iter 27/274 - loss 0.01538861 - samples/sec: 71.33 - lr: 0.001563 -2022-11-01 17:58:38,665 epoch 119 - iter 54/274 - loss 0.01428429 - samples/sec: 65.03 - lr: 0.001563 -2022-11-01 17:58:50,968 epoch 119 - iter 81/274 - loss 0.01545027 - samples/sec: 70.24 - lr: 0.001563 -2022-11-01 17:59:03,236 epoch 119 - iter 108/274 - loss 0.01576570 - samples/sec: 70.45 - lr: 0.001563 -2022-11-01 17:59:15,846 epoch 119 - iter 135/274 - loss 0.01500808 - samples/sec: 68.53 - lr: 0.001563 -2022-11-01 17:59:28,587 epoch 119 - iter 162/274 - loss 0.01529803 - samples/sec: 67.83 - lr: 0.001563 -2022-11-01 17:59:40,199 epoch 119 - iter 189/274 - loss 0.01492457 - samples/sec: 74.43 - lr: 0.001563 -2022-11-01 17:59:53,221 epoch 119 - iter 216/274 - loss 0.01480307 - samples/sec: 66.37 - lr: 0.001563 -2022-11-01 18:00:05,989 epoch 119 - iter 243/274 - loss 0.01461764 - samples/sec: 67.69 - lr: 0.001563 -2022-11-01 18:00:18,288 epoch 119 - iter 270/274 - loss 0.01486278 - samples/sec: 70.27 - lr: 0.001563 -2022-11-01 18:00:19,889 ---------------------------------------------------------------------------------------------------- -2022-11-01 18:00:19,889 EPOCH 119 done: loss 0.0150 - lr 0.001563 -2022-11-01 18:00:45,262 Evaluating as a multi-label problem: False -2022-11-01 18:00:45,278 TEST : loss 0.03172260522842407 - f1-score (micro avg) 0.8543 -2022-11-01 18:00:45,330 BAD EPOCHS (no improvement): 1 -2022-11-01 18:00:45,422 ---------------------------------------------------------------------------------------------------- -2022-11-01 18:00:57,044 epoch 120 - iter 27/274 - loss 0.00899055 - samples/sec: 74.37 - lr: 0.001563 -2022-11-01 18:01:09,522 epoch 120 - iter 54/274 - loss 0.01259727 - samples/sec: 69.26 - lr: 0.001563 -2022-11-01 18:01:22,694 epoch 120 - iter 81/274 - loss 0.01366828 - samples/sec: 65.61 - lr: 0.001563 -2022-11-01 18:01:34,264 epoch 120 - iter 108/274 - loss 0.01391092 - samples/sec: 74.69 - lr: 0.001563 -2022-11-01 18:01:45,942 epoch 120 - iter 135/274 - loss 0.01388514 - samples/sec: 74.00 - lr: 0.001563 -2022-11-01 18:01:58,326 epoch 120 - iter 162/274 - loss 0.01440931 - samples/sec: 69.79 - lr: 0.001563 -2022-11-01 18:02:11,386 epoch 120 - iter 189/274 - loss 0.01447825 - samples/sec: 66.17 - lr: 0.001563 -2022-11-01 18:02:24,050 epoch 120 - iter 216/274 - loss 0.01423748 - samples/sec: 68.24 - lr: 0.001563 -2022-11-01 18:02:35,638 epoch 120 - iter 243/274 - loss 0.01403297 - samples/sec: 74.58 - lr: 0.001563 -2022-11-01 18:02:49,124 epoch 120 - iter 270/274 - loss 0.01421427 - samples/sec: 64.08 - lr: 0.001563 -2022-11-01 18:02:51,039 ---------------------------------------------------------------------------------------------------- -2022-11-01 18:02:51,039 EPOCH 120 done: loss 0.0142 - lr 0.001563 -2022-11-01 18:03:16,381 Evaluating as a multi-label problem: False -2022-11-01 18:03:16,397 TEST : loss 0.031805865466594696 - f1-score (micro avg) 0.8541 -2022-11-01 18:03:16,449 BAD EPOCHS (no improvement): 2 -2022-11-01 18:03:16,534 ---------------------------------------------------------------------------------------------------- -2022-11-01 18:03:28,361 epoch 121 - iter 27/274 - loss 0.01605437 - samples/sec: 73.07 - lr: 0.001563 -2022-11-01 18:03:40,389 epoch 121 - iter 54/274 - loss 0.01495214 - samples/sec: 71.86 - lr: 0.001563 -2022-11-01 18:03:52,570 epoch 121 - iter 81/274 - loss 0.01478860 - samples/sec: 70.95 - lr: 0.001563 -2022-11-01 18:04:05,835 epoch 121 - iter 108/274 - loss 0.01482221 - samples/sec: 65.15 - lr: 0.001563 -2022-11-01 18:04:19,311 epoch 121 - iter 135/274 - loss 0.01481010 - samples/sec: 64.13 - lr: 0.001563 -2022-11-01 18:04:31,812 epoch 121 - iter 162/274 - loss 0.01420799 - samples/sec: 69.13 - lr: 0.001563 -2022-11-01 18:04:45,297 epoch 121 - iter 189/274 - loss 0.01428585 - samples/sec: 64.09 - lr: 0.001563 -2022-11-01 18:04:56,845 epoch 121 - iter 216/274 - loss 0.01414983 - samples/sec: 74.84 - lr: 0.001563 -2022-11-01 18:05:10,343 epoch 121 - iter 243/274 - loss 0.01463416 - samples/sec: 64.02 - lr: 0.001563 -2022-11-01 18:05:22,196 epoch 121 - iter 270/274 - loss 0.01435355 - samples/sec: 72.92 - lr: 0.001563 -2022-11-01 18:05:23,959 ---------------------------------------------------------------------------------------------------- -2022-11-01 18:05:23,959 EPOCH 121 done: loss 0.0143 - lr 0.001563 -2022-11-01 18:05:49,338 Evaluating as a multi-label problem: False -2022-11-01 18:05:49,354 TEST : loss 0.03179682791233063 - f1-score (micro avg) 0.8553 -2022-11-01 18:05:49,405 BAD EPOCHS (no improvement): 3 -2022-11-01 18:05:49,496 ---------------------------------------------------------------------------------------------------- -2022-11-01 18:06:02,504 epoch 122 - iter 27/274 - loss 0.01348554 - samples/sec: 66.44 - lr: 0.001563 -2022-11-01 18:06:15,580 epoch 122 - iter 54/274 - loss 0.01432921 - samples/sec: 66.10 - lr: 0.001563 -2022-11-01 18:06:28,964 epoch 122 - iter 81/274 - loss 0.01316887 - samples/sec: 64.57 - lr: 0.001563 -2022-11-01 18:06:40,994 epoch 122 - iter 108/274 - loss 0.01422191 - samples/sec: 71.84 - lr: 0.001563 -2022-11-01 18:06:52,690 epoch 122 - iter 135/274 - loss 0.01408750 - samples/sec: 73.89 - lr: 0.001563 -2022-11-01 18:07:04,944 epoch 122 - iter 162/274 - loss 0.01386067 - samples/sec: 70.53 - lr: 0.001563 -2022-11-01 18:07:17,208 epoch 122 - iter 189/274 - loss 0.01357114 - samples/sec: 70.47 - lr: 0.001563 -2022-11-01 18:07:29,672 epoch 122 - iter 216/274 - loss 0.01412466 - samples/sec: 69.33 - lr: 0.001563 -2022-11-01 18:07:42,807 epoch 122 - iter 243/274 - loss 0.01395056 - samples/sec: 65.79 - lr: 0.001563 -2022-11-01 18:07:55,077 epoch 122 - iter 270/274 - loss 0.01399248 - samples/sec: 70.44 - lr: 0.001563 -2022-11-01 18:07:57,015 ---------------------------------------------------------------------------------------------------- -2022-11-01 18:07:57,015 EPOCH 122 done: loss 0.0140 - lr 0.001563 -2022-11-01 18:08:22,422 Evaluating as a multi-label problem: False -2022-11-01 18:08:22,438 TEST : loss 0.03200670704245567 - f1-score (micro avg) 0.8546 -2022-11-01 18:08:22,489 BAD EPOCHS (no improvement): 0 -2022-11-01 18:08:22,581 ---------------------------------------------------------------------------------------------------- -2022-11-01 18:08:35,223 epoch 123 - iter 27/274 - loss 0.01535170 - samples/sec: 68.37 - lr: 0.001563 -2022-11-01 18:08:46,897 epoch 123 - iter 54/274 - loss 0.01369532 - samples/sec: 74.03 - lr: 0.001563 -2022-11-01 18:08:59,453 epoch 123 - iter 81/274 - loss 0.01398782 - samples/sec: 68.83 - lr: 0.001563 -2022-11-01 18:09:11,954 epoch 123 - iter 108/274 - loss 0.01383519 - samples/sec: 69.13 - lr: 0.001563 -2022-11-01 18:09:26,337 epoch 123 - iter 135/274 - loss 0.01394853 - samples/sec: 60.08 - lr: 0.001563 -2022-11-01 18:09:37,371 epoch 123 - iter 162/274 - loss 0.01390282 - samples/sec: 78.33 - lr: 0.001563 -2022-11-01 18:09:49,682 epoch 123 - iter 189/274 - loss 0.01422790 - samples/sec: 70.20 - lr: 0.001563 -2022-11-01 18:10:02,333 epoch 123 - iter 216/274 - loss 0.01462927 - samples/sec: 68.31 - lr: 0.001563 -2022-11-01 18:10:13,694 epoch 123 - iter 243/274 - loss 0.01470027 - samples/sec: 76.07 - lr: 0.001563 -2022-11-01 18:10:26,453 epoch 123 - iter 270/274 - loss 0.01468566 - samples/sec: 67.73 - lr: 0.001563 -2022-11-01 18:10:28,284 ---------------------------------------------------------------------------------------------------- -2022-11-01 18:10:28,284 EPOCH 123 done: loss 0.0147 - lr 0.001563 -2022-11-01 18:10:53,622 Evaluating as a multi-label problem: False -2022-11-01 18:10:53,638 TEST : loss 0.03187215328216553 - f1-score (micro avg) 0.8541 -2022-11-01 18:10:53,691 BAD EPOCHS (no improvement): 1 -2022-11-01 18:10:53,786 ---------------------------------------------------------------------------------------------------- -2022-11-01 18:11:07,362 epoch 124 - iter 27/274 - loss 0.01179336 - samples/sec: 63.66 - lr: 0.001563 -2022-11-01 18:11:19,048 epoch 124 - iter 54/274 - loss 0.01307793 - samples/sec: 73.95 - lr: 0.001563 -2022-11-01 18:11:31,062 epoch 124 - iter 81/274 - loss 0.01339808 - samples/sec: 71.94 - lr: 0.001563 -2022-11-01 18:11:42,429 epoch 124 - iter 108/274 - loss 0.01306621 - samples/sec: 76.03 - lr: 0.001563 -2022-11-01 18:11:54,785 epoch 124 - iter 135/274 - loss 0.01296568 - samples/sec: 69.94 - lr: 0.001563 -2022-11-01 18:12:06,411 epoch 124 - iter 162/274 - loss 0.01327152 - samples/sec: 74.34 - lr: 0.001563 -2022-11-01 18:12:18,715 epoch 124 - iter 189/274 - loss 0.01373990 - samples/sec: 70.24 - lr: 0.001563 -2022-11-01 18:12:31,980 epoch 124 - iter 216/274 - loss 0.01394854 - samples/sec: 65.15 - lr: 0.001563 -2022-11-01 18:12:44,200 epoch 124 - iter 243/274 - loss 0.01405277 - samples/sec: 70.73 - lr: 0.001563 -2022-11-01 18:12:57,416 epoch 124 - iter 270/274 - loss 0.01405971 - samples/sec: 65.39 - lr: 0.001563 -2022-11-01 18:12:59,384 ---------------------------------------------------------------------------------------------------- -2022-11-01 18:12:59,384 EPOCH 124 done: loss 0.0141 - lr 0.001563 -2022-11-01 18:13:24,782 Evaluating as a multi-label problem: False -2022-11-01 18:13:24,798 TEST : loss 0.03188449889421463 - f1-score (micro avg) 0.8543 -2022-11-01 18:13:24,850 BAD EPOCHS (no improvement): 2 -2022-11-01 18:13:24,945 ---------------------------------------------------------------------------------------------------- -2022-11-01 18:13:37,316 epoch 125 - iter 27/274 - loss 0.01168586 - samples/sec: 69.86 - lr: 0.001563 -2022-11-01 18:13:51,327 epoch 125 - iter 54/274 - loss 0.01431803 - samples/sec: 61.68 - lr: 0.001563 -2022-11-01 18:14:03,919 epoch 125 - iter 81/274 - loss 0.01415967 - samples/sec: 68.63 - lr: 0.001563 -2022-11-01 18:14:16,961 epoch 125 - iter 108/274 - loss 0.01380225 - samples/sec: 66.26 - lr: 0.001563 -2022-11-01 18:14:28,811 epoch 125 - iter 135/274 - loss 0.01381803 - samples/sec: 72.93 - lr: 0.001563 -2022-11-01 18:14:41,675 epoch 125 - iter 162/274 - loss 0.01389812 - samples/sec: 67.18 - lr: 0.001563 -2022-11-01 18:14:53,191 epoch 125 - iter 189/274 - loss 0.01408517 - samples/sec: 75.05 - lr: 0.001563 -2022-11-01 18:15:05,054 epoch 125 - iter 216/274 - loss 0.01435856 - samples/sec: 72.85 - lr: 0.001563 -2022-11-01 18:15:17,052 epoch 125 - iter 243/274 - loss 0.01424790 - samples/sec: 72.04 - lr: 0.001563 -2022-11-01 18:15:29,655 epoch 125 - iter 270/274 - loss 0.01424784 - samples/sec: 68.57 - lr: 0.001563 -2022-11-01 18:15:31,485 ---------------------------------------------------------------------------------------------------- -2022-11-01 18:15:31,485 EPOCH 125 done: loss 0.0142 - lr 0.001563 -2022-11-01 18:15:56,846 Evaluating as a multi-label problem: False -2022-11-01 18:15:56,861 TEST : loss 0.03190324455499649 - f1-score (micro avg) 0.8544 -2022-11-01 18:15:56,915 BAD EPOCHS (no improvement): 3 -2022-11-01 18:15:57,007 ---------------------------------------------------------------------------------------------------- -2022-11-01 18:16:10,448 epoch 126 - iter 27/274 - loss 0.01233038 - samples/sec: 64.30 - lr: 0.001563 -2022-11-01 18:16:22,275 epoch 126 - iter 54/274 - loss 0.01381905 - samples/sec: 73.07 - lr: 0.001563 -2022-11-01 18:16:34,930 epoch 126 - iter 81/274 - loss 0.01419482 - samples/sec: 68.30 - lr: 0.001563 -2022-11-01 18:16:46,563 epoch 126 - iter 108/274 - loss 0.01483839 - samples/sec: 74.29 - lr: 0.001563 -2022-11-01 18:16:58,287 epoch 126 - iter 135/274 - loss 0.01521833 - samples/sec: 73.72 - lr: 0.001563 -2022-11-01 18:17:09,874 epoch 126 - iter 162/274 - loss 0.01466584 - samples/sec: 74.59 - lr: 0.001563 -2022-11-01 18:17:24,081 epoch 126 - iter 189/274 - loss 0.01470213 - samples/sec: 60.83 - lr: 0.001563 -2022-11-01 18:17:36,797 epoch 126 - iter 216/274 - loss 0.01444898 - samples/sec: 67.96 - lr: 0.001563 -2022-11-01 18:17:48,690 epoch 126 - iter 243/274 - loss 0.01437726 - samples/sec: 72.67 - lr: 0.001563 -2022-11-01 18:18:00,074 epoch 126 - iter 270/274 - loss 0.01456913 - samples/sec: 75.92 - lr: 0.001563 -2022-11-01 18:18:01,780 ---------------------------------------------------------------------------------------------------- -2022-11-01 18:18:01,780 EPOCH 126 done: loss 0.0145 - lr 0.001563 -2022-11-01 18:18:27,195 Evaluating as a multi-label problem: False -2022-11-01 18:18:27,211 TEST : loss 0.031878840178251266 - f1-score (micro avg) 0.8544 -2022-11-01 18:18:27,263 Epoch 126: reducing learning rate of group 0 to 7.8125e-04. -2022-11-01 18:18:27,263 BAD EPOCHS (no improvement): 4 -2022-11-01 18:18:27,356 ---------------------------------------------------------------------------------------------------- -2022-11-01 18:18:38,405 epoch 127 - iter 27/274 - loss 0.01214851 - samples/sec: 78.22 - lr: 0.000781 -2022-11-01 18:18:51,529 epoch 127 - iter 54/274 - loss 0.01322803 - samples/sec: 65.85 - lr: 0.000781 -2022-11-01 18:19:03,957 epoch 127 - iter 81/274 - loss 0.01371365 - samples/sec: 69.54 - lr: 0.000781 -2022-11-01 18:19:17,792 epoch 127 - iter 108/274 - loss 0.01357113 - samples/sec: 62.46 - lr: 0.000781 -2022-11-01 18:19:30,305 epoch 127 - iter 135/274 - loss 0.01397682 - samples/sec: 69.07 - lr: 0.000781 -2022-11-01 18:19:44,368 epoch 127 - iter 162/274 - loss 0.01396536 - samples/sec: 61.45 - lr: 0.000781 -2022-11-01 18:19:55,789 epoch 127 - iter 189/274 - loss 0.01376907 - samples/sec: 75.68 - lr: 0.000781 -2022-11-01 18:20:09,810 epoch 127 - iter 216/274 - loss 0.01406742 - samples/sec: 61.64 - lr: 0.000781 -2022-11-01 18:20:20,817 epoch 127 - iter 243/274 - loss 0.01420367 - samples/sec: 78.52 - lr: 0.000781 -2022-11-01 18:20:32,276 epoch 127 - iter 270/274 - loss 0.01427245 - samples/sec: 75.42 - lr: 0.000781 -2022-11-01 18:20:33,974 ---------------------------------------------------------------------------------------------------- -2022-11-01 18:20:33,975 EPOCH 127 done: loss 0.0141 - lr 0.000781 -2022-11-01 18:20:59,316 Evaluating as a multi-label problem: False -2022-11-01 18:20:59,332 TEST : loss 0.031853314489126205 - f1-score (micro avg) 0.8541 -2022-11-01 18:20:59,384 BAD EPOCHS (no improvement): 1 -2022-11-01 18:20:59,476 ---------------------------------------------------------------------------------------------------- -2022-11-01 18:21:12,554 epoch 128 - iter 27/274 - loss 0.01442018 - samples/sec: 66.08 - lr: 0.000781 -2022-11-01 18:21:25,402 epoch 128 - iter 54/274 - loss 0.01455128 - samples/sec: 67.26 - lr: 0.000781 -2022-11-01 18:21:37,344 epoch 128 - iter 81/274 - loss 0.01443058 - samples/sec: 72.37 - lr: 0.000781 -2022-11-01 18:21:49,031 epoch 128 - iter 108/274 - loss 0.01426873 - samples/sec: 73.95 - lr: 0.000781 -2022-11-01 18:22:01,548 epoch 128 - iter 135/274 - loss 0.01495891 - samples/sec: 69.04 - lr: 0.000781 -2022-11-01 18:22:13,136 epoch 128 - iter 162/274 - loss 0.01507623 - samples/sec: 74.58 - lr: 0.000781 -2022-11-01 18:22:25,508 epoch 128 - iter 189/274 - loss 0.01498890 - samples/sec: 69.85 - lr: 0.000781 -2022-11-01 18:22:38,731 epoch 128 - iter 216/274 - loss 0.01477757 - samples/sec: 65.36 - lr: 0.000781 -2022-11-01 18:22:52,156 epoch 128 - iter 243/274 - loss 0.01465474 - samples/sec: 64.37 - lr: 0.000781 -2022-11-01 18:23:04,893 epoch 128 - iter 270/274 - loss 0.01489369 - samples/sec: 67.85 - lr: 0.000781 -2022-11-01 18:23:06,594 ---------------------------------------------------------------------------------------------------- -2022-11-01 18:23:06,594 EPOCH 128 done: loss 0.0149 - lr 0.000781 -2022-11-01 18:23:31,895 Evaluating as a multi-label problem: False -2022-11-01 18:23:31,911 TEST : loss 0.0317995622754097 - f1-score (micro avg) 0.8545 -2022-11-01 18:23:31,962 BAD EPOCHS (no improvement): 2 -2022-11-01 18:23:32,056 ---------------------------------------------------------------------------------------------------- -2022-11-01 18:23:44,890 epoch 129 - iter 27/274 - loss 0.01701574 - samples/sec: 67.34 - lr: 0.000781 -2022-11-01 18:23:56,118 epoch 129 - iter 54/274 - loss 0.01559604 - samples/sec: 76.98 - lr: 0.000781 -2022-11-01 18:24:08,335 epoch 129 - iter 81/274 - loss 0.01464102 - samples/sec: 70.74 - lr: 0.000781 -2022-11-01 18:24:21,755 epoch 129 - iter 108/274 - loss 0.01516544 - samples/sec: 64.39 - lr: 0.000781 -2022-11-01 18:24:34,920 epoch 129 - iter 135/274 - loss 0.01553544 - samples/sec: 65.65 - lr: 0.000781 -2022-11-01 18:24:46,816 epoch 129 - iter 162/274 - loss 0.01577649 - samples/sec: 72.65 - lr: 0.000781 -2022-11-01 18:24:59,344 epoch 129 - iter 189/274 - loss 0.01573528 - samples/sec: 68.98 - lr: 0.000781 -2022-11-01 18:25:12,318 epoch 129 - iter 216/274 - loss 0.01545947 - samples/sec: 66.61 - lr: 0.000781 -2022-11-01 18:25:23,959 epoch 129 - iter 243/274 - loss 0.01549187 - samples/sec: 74.24 - lr: 0.000781 -2022-11-01 18:25:36,740 epoch 129 - iter 270/274 - loss 0.01594032 - samples/sec: 67.62 - lr: 0.000781 -2022-11-01 18:25:38,294 ---------------------------------------------------------------------------------------------------- -2022-11-01 18:25:38,295 EPOCH 129 done: loss 0.0159 - lr 0.000781 -2022-11-01 18:26:03,654 Evaluating as a multi-label problem: False -2022-11-01 18:26:03,669 TEST : loss 0.031778719276189804 - f1-score (micro avg) 0.8547 -2022-11-01 18:26:03,722 BAD EPOCHS (no improvement): 3 -2022-11-01 18:26:03,813 ---------------------------------------------------------------------------------------------------- -2022-11-01 18:26:15,664 epoch 130 - iter 27/274 - loss 0.01545049 - samples/sec: 72.93 - lr: 0.000781 -2022-11-01 18:26:28,299 epoch 130 - iter 54/274 - loss 0.01380701 - samples/sec: 68.40 - lr: 0.000781 -2022-11-01 18:26:41,697 epoch 130 - iter 81/274 - loss 0.01339731 - samples/sec: 64.50 - lr: 0.000781 -2022-11-01 18:26:54,110 epoch 130 - iter 108/274 - loss 0.01311691 - samples/sec: 69.62 - lr: 0.000781 -2022-11-01 18:27:06,825 epoch 130 - iter 135/274 - loss 0.01372726 - samples/sec: 67.97 - lr: 0.000781 -2022-11-01 18:27:18,546 epoch 130 - iter 162/274 - loss 0.01377890 - samples/sec: 73.74 - lr: 0.000781 -2022-11-01 18:27:29,845 epoch 130 - iter 189/274 - loss 0.01383286 - samples/sec: 76.49 - lr: 0.000781 -2022-11-01 18:27:43,698 epoch 130 - iter 216/274 - loss 0.01401131 - samples/sec: 62.39 - lr: 0.000781 -2022-11-01 18:27:55,900 epoch 130 - iter 243/274 - loss 0.01402296 - samples/sec: 70.83 - lr: 0.000781 -2022-11-01 18:28:07,241 epoch 130 - iter 270/274 - loss 0.01396332 - samples/sec: 76.21 - lr: 0.000781 -2022-11-01 18:28:08,598 ---------------------------------------------------------------------------------------------------- -2022-11-01 18:28:08,598 EPOCH 130 done: loss 0.0140 - lr 0.000781 -2022-11-01 18:28:33,883 Evaluating as a multi-label problem: False -2022-11-01 18:28:33,898 TEST : loss 0.031718671321868896 - f1-score (micro avg) 0.8547 -2022-11-01 18:28:33,952 BAD EPOCHS (no improvement): 0 -2022-11-01 18:28:34,043 ---------------------------------------------------------------------------------------------------- -2022-11-01 18:28:45,979 epoch 131 - iter 27/274 - loss 0.01307243 - samples/sec: 72.41 - lr: 0.000781 -2022-11-01 18:28:58,110 epoch 131 - iter 54/274 - loss 0.01315823 - samples/sec: 71.24 - lr: 0.000781 -2022-11-01 18:29:11,717 epoch 131 - iter 81/274 - loss 0.01382737 - samples/sec: 63.51 - lr: 0.000781 -2022-11-01 18:29:24,334 epoch 131 - iter 108/274 - loss 0.01367923 - samples/sec: 68.50 - lr: 0.000781 -2022-11-01 18:29:36,846 epoch 131 - iter 135/274 - loss 0.01293479 - samples/sec: 69.07 - lr: 0.000781 -2022-11-01 18:29:49,418 epoch 131 - iter 162/274 - loss 0.01360035 - samples/sec: 68.74 - lr: 0.000781 -2022-11-01 18:30:00,934 epoch 131 - iter 189/274 - loss 0.01349732 - samples/sec: 75.05 - lr: 0.000781 -2022-11-01 18:30:13,375 epoch 131 - iter 216/274 - loss 0.01383598 - samples/sec: 69.47 - lr: 0.000781 -2022-11-01 18:30:25,302 epoch 131 - iter 243/274 - loss 0.01377255 - samples/sec: 72.46 - lr: 0.000781 -2022-11-01 18:30:37,001 epoch 131 - iter 270/274 - loss 0.01388772 - samples/sec: 73.88 - lr: 0.000781 -2022-11-01 18:30:38,646 ---------------------------------------------------------------------------------------------------- -2022-11-01 18:30:38,646 EPOCH 131 done: loss 0.0138 - lr 0.000781 -2022-11-01 18:31:04,103 Evaluating as a multi-label problem: False -2022-11-01 18:31:04,119 TEST : loss 0.03182306885719299 - f1-score (micro avg) 0.8546 -2022-11-01 18:31:04,173 BAD EPOCHS (no improvement): 0 -2022-11-01 18:31:04,247 ---------------------------------------------------------------------------------------------------- -2022-11-01 18:31:16,478 epoch 132 - iter 27/274 - loss 0.01766349 - samples/sec: 70.66 - lr: 0.000781 -2022-11-01 18:31:28,684 epoch 132 - iter 54/274 - loss 0.01453588 - samples/sec: 70.80 - lr: 0.000781 -2022-11-01 18:31:40,661 epoch 132 - iter 81/274 - loss 0.01396542 - samples/sec: 72.16 - lr: 0.000781 -2022-11-01 18:31:53,393 epoch 132 - iter 108/274 - loss 0.01430618 - samples/sec: 67.88 - lr: 0.000781 -2022-11-01 18:32:06,520 epoch 132 - iter 135/274 - loss 0.01439286 - samples/sec: 65.83 - lr: 0.000781 -2022-11-01 18:32:19,528 epoch 132 - iter 162/274 - loss 0.01473819 - samples/sec: 66.43 - lr: 0.000781 -2022-11-01 18:32:31,572 epoch 132 - iter 189/274 - loss 0.01429222 - samples/sec: 71.76 - lr: 0.000781 -2022-11-01 18:32:43,127 epoch 132 - iter 216/274 - loss 0.01431037 - samples/sec: 74.79 - lr: 0.000781 -2022-11-01 18:32:57,838 epoch 132 - iter 243/274 - loss 0.01464379 - samples/sec: 58.75 - lr: 0.000781 -2022-11-01 18:33:10,135 epoch 132 - iter 270/274 - loss 0.01477125 - samples/sec: 70.28 - lr: 0.000781 -2022-11-01 18:33:12,056 ---------------------------------------------------------------------------------------------------- -2022-11-01 18:33:12,056 EPOCH 132 done: loss 0.0148 - lr 0.000781 -2022-11-01 18:33:37,535 Evaluating as a multi-label problem: False -2022-11-01 18:33:37,551 TEST : loss 0.03184701129794121 - f1-score (micro avg) 0.8546 -2022-11-01 18:33:37,601 BAD EPOCHS (no improvement): 1 -2022-11-01 18:33:37,697 ---------------------------------------------------------------------------------------------------- -2022-11-01 18:33:50,117 epoch 133 - iter 27/274 - loss 0.01314103 - samples/sec: 69.59 - lr: 0.000781 -2022-11-01 18:34:02,137 epoch 133 - iter 54/274 - loss 0.01319767 - samples/sec: 71.90 - lr: 0.000781 -2022-11-01 18:34:15,737 epoch 133 - iter 81/274 - loss 0.01357056 - samples/sec: 63.55 - lr: 0.000781 -2022-11-01 18:34:27,341 epoch 133 - iter 108/274 - loss 0.01377149 - samples/sec: 74.48 - lr: 0.000781 -2022-11-01 18:34:39,606 epoch 133 - iter 135/274 - loss 0.01401248 - samples/sec: 70.46 - lr: 0.000781 -2022-11-01 18:34:53,225 epoch 133 - iter 162/274 - loss 0.01435865 - samples/sec: 63.46 - lr: 0.000781 -2022-11-01 18:35:05,127 epoch 133 - iter 189/274 - loss 0.01460999 - samples/sec: 72.61 - lr: 0.000781 -2022-11-01 18:35:17,438 epoch 133 - iter 216/274 - loss 0.01523430 - samples/sec: 70.20 - lr: 0.000781 -2022-11-01 18:35:30,196 epoch 133 - iter 243/274 - loss 0.01523406 - samples/sec: 67.74 - lr: 0.000781 -2022-11-01 18:35:42,029 epoch 133 - iter 270/274 - loss 0.01521377 - samples/sec: 73.03 - lr: 0.000781 -2022-11-01 18:35:43,607 ---------------------------------------------------------------------------------------------------- -2022-11-01 18:35:43,607 EPOCH 133 done: loss 0.0151 - lr 0.000781 -2022-11-01 18:36:09,113 Evaluating as a multi-label problem: False -2022-11-01 18:36:09,129 TEST : loss 0.03188365697860718 - f1-score (micro avg) 0.8546 -2022-11-01 18:36:09,182 BAD EPOCHS (no improvement): 2 -2022-11-01 18:36:09,268 ---------------------------------------------------------------------------------------------------- -2022-11-01 18:36:21,391 epoch 134 - iter 27/274 - loss 0.01570005 - samples/sec: 71.29 - lr: 0.000781 -2022-11-01 18:36:34,709 epoch 134 - iter 54/274 - loss 0.01353171 - samples/sec: 64.89 - lr: 0.000781 -2022-11-01 18:36:47,667 epoch 134 - iter 81/274 - loss 0.01290484 - samples/sec: 66.70 - lr: 0.000781 -2022-11-01 18:36:59,759 epoch 134 - iter 108/274 - loss 0.01333661 - samples/sec: 71.47 - lr: 0.000781 -2022-11-01 18:37:12,839 epoch 134 - iter 135/274 - loss 0.01340435 - samples/sec: 66.07 - lr: 0.000781 -2022-11-01 18:37:25,282 epoch 134 - iter 162/274 - loss 0.01310120 - samples/sec: 69.45 - lr: 0.000781 -2022-11-01 18:37:37,095 epoch 134 - iter 189/274 - loss 0.01362252 - samples/sec: 73.16 - lr: 0.000781 -2022-11-01 18:37:49,281 epoch 134 - iter 216/274 - loss 0.01378086 - samples/sec: 70.92 - lr: 0.000781 -2022-11-01 18:38:01,142 epoch 134 - iter 243/274 - loss 0.01385806 - samples/sec: 72.87 - lr: 0.000781 -2022-11-01 18:38:14,124 epoch 134 - iter 270/274 - loss 0.01414799 - samples/sec: 66.57 - lr: 0.000781 -2022-11-01 18:38:15,844 ---------------------------------------------------------------------------------------------------- -2022-11-01 18:38:15,844 EPOCH 134 done: loss 0.0142 - lr 0.000781 -2022-11-01 18:38:41,212 Evaluating as a multi-label problem: False -2022-11-01 18:38:41,227 TEST : loss 0.03192955255508423 - f1-score (micro avg) 0.8538 -2022-11-01 18:38:41,279 BAD EPOCHS (no improvement): 3 -2022-11-01 18:38:41,364 ---------------------------------------------------------------------------------------------------- -2022-11-01 18:38:54,723 epoch 135 - iter 27/274 - loss 0.01327526 - samples/sec: 64.69 - lr: 0.000781 -2022-11-01 18:39:07,129 epoch 135 - iter 54/274 - loss 0.01409919 - samples/sec: 69.67 - lr: 0.000781 -2022-11-01 18:39:19,915 epoch 135 - iter 81/274 - loss 0.01531283 - samples/sec: 67.59 - lr: 0.000781 -2022-11-01 18:39:32,995 epoch 135 - iter 108/274 - loss 0.01543934 - samples/sec: 66.07 - lr: 0.000781 -2022-11-01 18:39:45,099 epoch 135 - iter 135/274 - loss 0.01553804 - samples/sec: 71.41 - lr: 0.000781 -2022-11-01 18:39:58,722 epoch 135 - iter 162/274 - loss 0.01537747 - samples/sec: 63.44 - lr: 0.000781 -2022-11-01 18:40:10,924 epoch 135 - iter 189/274 - loss 0.01505536 - samples/sec: 70.82 - lr: 0.000781 -2022-11-01 18:40:22,920 epoch 135 - iter 216/274 - loss 0.01518738 - samples/sec: 72.05 - lr: 0.000781 -2022-11-01 18:40:34,740 epoch 135 - iter 243/274 - loss 0.01497708 - samples/sec: 73.12 - lr: 0.000781 -2022-11-01 18:40:47,071 epoch 135 - iter 270/274 - loss 0.01503585 - samples/sec: 70.08 - lr: 0.000781 -2022-11-01 18:40:48,750 ---------------------------------------------------------------------------------------------------- -2022-11-01 18:40:48,750 EPOCH 135 done: loss 0.0152 - lr 0.000781 -2022-11-01 18:41:13,738 Evaluating as a multi-label problem: False -2022-11-01 18:41:13,753 TEST : loss 0.031969308853149414 - f1-score (micro avg) 0.8544 -2022-11-01 18:41:13,805 Epoch 135: reducing learning rate of group 0 to 3.9063e-04. -2022-11-01 18:41:13,805 BAD EPOCHS (no improvement): 4 -2022-11-01 18:41:13,900 ---------------------------------------------------------------------------------------------------- -2022-11-01 18:41:26,650 epoch 136 - iter 27/274 - loss 0.01290276 - samples/sec: 67.79 - lr: 0.000391 -2022-11-01 18:41:39,459 epoch 136 - iter 54/274 - loss 0.01299663 - samples/sec: 67.47 - lr: 0.000391 -2022-11-01 18:41:51,985 epoch 136 - iter 81/274 - loss 0.01282157 - samples/sec: 68.99 - lr: 0.000391 -2022-11-01 18:42:06,560 epoch 136 - iter 108/274 - loss 0.01339859 - samples/sec: 59.30 - lr: 0.000391 -2022-11-01 18:42:18,770 epoch 136 - iter 135/274 - loss 0.01366404 - samples/sec: 70.78 - lr: 0.000391 -2022-11-01 18:42:32,008 epoch 136 - iter 162/274 - loss 0.01492463 - samples/sec: 65.28 - lr: 0.000391 -2022-11-01 18:42:42,929 epoch 136 - iter 189/274 - loss 0.01462456 - samples/sec: 79.14 - lr: 0.000391 -2022-11-01 18:42:55,591 epoch 136 - iter 216/274 - loss 0.01414258 - samples/sec: 68.26 - lr: 0.000391 -2022-11-01 18:43:07,774 epoch 136 - iter 243/274 - loss 0.01382027 - samples/sec: 70.93 - lr: 0.000391 -2022-11-01 18:43:19,311 epoch 136 - iter 270/274 - loss 0.01399665 - samples/sec: 74.91 - lr: 0.000391 -2022-11-01 18:43:20,917 ---------------------------------------------------------------------------------------------------- -2022-11-01 18:43:20,917 EPOCH 136 done: loss 0.0140 - lr 0.000391 -2022-11-01 18:43:46,411 Evaluating as a multi-label problem: False -2022-11-01 18:43:46,426 TEST : loss 0.0319695845246315 - f1-score (micro avg) 0.855 -2022-11-01 18:43:46,478 BAD EPOCHS (no improvement): 1 -2022-11-01 18:43:46,571 ---------------------------------------------------------------------------------------------------- -2022-11-01 18:43:59,477 epoch 137 - iter 27/274 - loss 0.01202838 - samples/sec: 66.96 - lr: 0.000391 -2022-11-01 18:44:12,382 epoch 137 - iter 54/274 - loss 0.01280634 - samples/sec: 66.97 - lr: 0.000391 -2022-11-01 18:44:23,487 epoch 137 - iter 81/274 - loss 0.01300539 - samples/sec: 77.82 - lr: 0.000391 -2022-11-01 18:44:35,621 epoch 137 - iter 108/274 - loss 0.01354442 - samples/sec: 71.22 - lr: 0.000391 -2022-11-01 18:44:47,256 epoch 137 - iter 135/274 - loss 0.01373392 - samples/sec: 74.28 - lr: 0.000391 -2022-11-01 18:44:59,898 epoch 137 - iter 162/274 - loss 0.01416349 - samples/sec: 68.36 - lr: 0.000391 -2022-11-01 18:45:13,470 epoch 137 - iter 189/274 - loss 0.01416036 - samples/sec: 63.67 - lr: 0.000391 -2022-11-01 18:45:27,207 epoch 137 - iter 216/274 - loss 0.01452940 - samples/sec: 62.91 - lr: 0.000391 -2022-11-01 18:45:39,752 epoch 137 - iter 243/274 - loss 0.01448564 - samples/sec: 68.89 - lr: 0.000391 -2022-11-01 18:45:51,493 epoch 137 - iter 270/274 - loss 0.01453954 - samples/sec: 73.61 - lr: 0.000391 -2022-11-01 18:45:53,154 ---------------------------------------------------------------------------------------------------- -2022-11-01 18:45:53,154 EPOCH 137 done: loss 0.0145 - lr 0.000391 -2022-11-01 18:46:18,540 Evaluating as a multi-label problem: False -2022-11-01 18:46:18,556 TEST : loss 0.03197849541902542 - f1-score (micro avg) 0.8548 -2022-11-01 18:46:18,609 BAD EPOCHS (no improvement): 2 -2022-11-01 18:46:18,701 ---------------------------------------------------------------------------------------------------- -2022-11-01 18:46:32,629 epoch 138 - iter 27/274 - loss 0.01287313 - samples/sec: 62.05 - lr: 0.000391 -2022-11-01 18:46:44,889 epoch 138 - iter 54/274 - loss 0.01411019 - samples/sec: 70.49 - lr: 0.000391 -2022-11-01 18:46:57,085 epoch 138 - iter 81/274 - loss 0.01373632 - samples/sec: 70.86 - lr: 0.000391 -2022-11-01 18:47:10,008 epoch 138 - iter 108/274 - loss 0.01368310 - samples/sec: 66.88 - lr: 0.000391 -2022-11-01 18:47:22,002 epoch 138 - iter 135/274 - loss 0.01385494 - samples/sec: 72.05 - lr: 0.000391 -2022-11-01 18:47:34,426 epoch 138 - iter 162/274 - loss 0.01378742 - samples/sec: 69.56 - lr: 0.000391 -2022-11-01 18:47:46,756 epoch 138 - iter 189/274 - loss 0.01386216 - samples/sec: 70.09 - lr: 0.000391 -2022-11-01 18:47:58,425 epoch 138 - iter 216/274 - loss 0.01407536 - samples/sec: 74.06 - lr: 0.000391 -2022-11-01 18:48:10,418 epoch 138 - iter 243/274 - loss 0.01377845 - samples/sec: 72.06 - lr: 0.000391 -2022-11-01 18:48:22,138 epoch 138 - iter 270/274 - loss 0.01386241 - samples/sec: 73.74 - lr: 0.000391 -2022-11-01 18:48:24,823 ---------------------------------------------------------------------------------------------------- -2022-11-01 18:48:24,823 EPOCH 138 done: loss 0.0137 - lr 0.000391 -2022-11-01 18:48:50,296 Evaluating as a multi-label problem: False -2022-11-01 18:48:50,312 TEST : loss 0.03200221434235573 - f1-score (micro avg) 0.8548 -2022-11-01 18:48:50,364 BAD EPOCHS (no improvement): 0 -2022-11-01 18:48:50,457 ---------------------------------------------------------------------------------------------------- -2022-11-01 18:49:02,999 epoch 139 - iter 27/274 - loss 0.01582375 - samples/sec: 68.91 - lr: 0.000391 -2022-11-01 18:49:15,457 epoch 139 - iter 54/274 - loss 0.01495674 - samples/sec: 69.37 - lr: 0.000391 -2022-11-01 18:49:28,117 epoch 139 - iter 81/274 - loss 0.01450503 - samples/sec: 68.26 - lr: 0.000391 -2022-11-01 18:49:40,212 epoch 139 - iter 108/274 - loss 0.01445288 - samples/sec: 71.45 - lr: 0.000391 -2022-11-01 18:49:53,995 epoch 139 - iter 135/274 - loss 0.01467858 - samples/sec: 62.70 - lr: 0.000391 -2022-11-01 18:50:06,504 epoch 139 - iter 162/274 - loss 0.01471299 - samples/sec: 69.08 - lr: 0.000391 -2022-11-01 18:50:19,341 epoch 139 - iter 189/274 - loss 0.01431085 - samples/sec: 67.32 - lr: 0.000391 -2022-11-01 18:50:30,753 epoch 139 - iter 216/274 - loss 0.01414775 - samples/sec: 75.73 - lr: 0.000391 -2022-11-01 18:50:43,649 epoch 139 - iter 243/274 - loss 0.01416123 - samples/sec: 67.01 - lr: 0.000391 -2022-11-01 18:50:56,349 epoch 139 - iter 270/274 - loss 0.01429129 - samples/sec: 68.05 - lr: 0.000391 -2022-11-01 18:50:57,846 ---------------------------------------------------------------------------------------------------- -2022-11-01 18:50:57,846 EPOCH 139 done: loss 0.0142 - lr 0.000391 -2022-11-01 18:51:23,229 Evaluating as a multi-label problem: False -2022-11-01 18:51:23,244 TEST : loss 0.03198350593447685 - f1-score (micro avg) 0.8544 -2022-11-01 18:51:23,296 BAD EPOCHS (no improvement): 1 -2022-11-01 18:51:23,387 ---------------------------------------------------------------------------------------------------- -2022-11-01 18:51:36,170 epoch 140 - iter 27/274 - loss 0.01491385 - samples/sec: 67.61 - lr: 0.000391 -2022-11-01 18:51:48,603 epoch 140 - iter 54/274 - loss 0.01542907 - samples/sec: 69.51 - lr: 0.000391 -2022-11-01 18:52:02,681 epoch 140 - iter 81/274 - loss 0.01483332 - samples/sec: 61.39 - lr: 0.000391 -2022-11-01 18:52:14,616 epoch 140 - iter 108/274 - loss 0.01461136 - samples/sec: 72.41 - lr: 0.000391 -2022-11-01 18:52:26,886 epoch 140 - iter 135/274 - loss 0.01441321 - samples/sec: 70.44 - lr: 0.000391 -2022-11-01 18:52:38,892 epoch 140 - iter 162/274 - loss 0.01482984 - samples/sec: 71.98 - lr: 0.000391 -2022-11-01 18:52:51,513 epoch 140 - iter 189/274 - loss 0.01465169 - samples/sec: 68.48 - lr: 0.000391 -2022-11-01 18:53:03,540 epoch 140 - iter 216/274 - loss 0.01454677 - samples/sec: 71.86 - lr: 0.000391 -2022-11-01 18:53:17,309 epoch 140 - iter 243/274 - loss 0.01465699 - samples/sec: 62.77 - lr: 0.000391 -2022-11-01 18:53:29,003 epoch 140 - iter 270/274 - loss 0.01447624 - samples/sec: 73.91 - lr: 0.000391 -2022-11-01 18:53:30,881 ---------------------------------------------------------------------------------------------------- -2022-11-01 18:53:30,881 EPOCH 140 done: loss 0.0146 - lr 0.000391 -2022-11-01 18:53:56,246 Evaluating as a multi-label problem: False -2022-11-01 18:53:56,262 TEST : loss 0.03198152035474777 - f1-score (micro avg) 0.8548 -2022-11-01 18:53:56,314 BAD EPOCHS (no improvement): 2 -2022-11-01 18:53:56,401 ---------------------------------------------------------------------------------------------------- -2022-11-01 18:54:07,635 epoch 141 - iter 27/274 - loss 0.01380211 - samples/sec: 76.94 - lr: 0.000391 -2022-11-01 18:54:20,777 epoch 141 - iter 54/274 - loss 0.01299416 - samples/sec: 65.76 - lr: 0.000391 -2022-11-01 18:54:32,989 epoch 141 - iter 81/274 - loss 0.01288500 - samples/sec: 70.77 - lr: 0.000391 -2022-11-01 18:54:44,408 epoch 141 - iter 108/274 - loss 0.01280394 - samples/sec: 75.69 - lr: 0.000391 -2022-11-01 18:54:56,270 epoch 141 - iter 135/274 - loss 0.01294786 - samples/sec: 72.86 - lr: 0.000391 -2022-11-01 18:55:09,407 epoch 141 - iter 162/274 - loss 0.01290578 - samples/sec: 65.78 - lr: 0.000391 -2022-11-01 18:55:21,882 epoch 141 - iter 189/274 - loss 0.01312051 - samples/sec: 69.28 - lr: 0.000391 -2022-11-01 18:55:33,330 epoch 141 - iter 216/274 - loss 0.01338580 - samples/sec: 75.50 - lr: 0.000391 -2022-11-01 18:55:47,281 epoch 141 - iter 243/274 - loss 0.01371702 - samples/sec: 61.94 - lr: 0.000391 -2022-11-01 18:55:59,370 epoch 141 - iter 270/274 - loss 0.01411856 - samples/sec: 71.49 - lr: 0.000391 -2022-11-01 18:56:01,282 ---------------------------------------------------------------------------------------------------- -2022-11-01 18:56:01,282 EPOCH 141 done: loss 0.0141 - lr 0.000391 -2022-11-01 18:56:26,636 Evaluating as a multi-label problem: False -2022-11-01 18:56:26,651 TEST : loss 0.032018985599279404 - f1-score (micro avg) 0.8544 -2022-11-01 18:56:26,705 BAD EPOCHS (no improvement): 3 -2022-11-01 18:56:26,796 ---------------------------------------------------------------------------------------------------- -2022-11-01 18:56:38,798 epoch 142 - iter 27/274 - loss 0.01461087 - samples/sec: 72.01 - lr: 0.000391 -2022-11-01 18:56:50,263 epoch 142 - iter 54/274 - loss 0.01406950 - samples/sec: 75.38 - lr: 0.000391 -2022-11-01 18:57:01,513 epoch 142 - iter 81/274 - loss 0.01326957 - samples/sec: 76.82 - lr: 0.000391 -2022-11-01 18:57:13,833 epoch 142 - iter 108/274 - loss 0.01459213 - samples/sec: 70.15 - lr: 0.000391 -2022-11-01 18:57:25,595 epoch 142 - iter 135/274 - loss 0.01422328 - samples/sec: 73.48 - lr: 0.000391 -2022-11-01 18:57:39,117 epoch 142 - iter 162/274 - loss 0.01387491 - samples/sec: 63.91 - lr: 0.000391 -2022-11-01 18:57:53,314 epoch 142 - iter 189/274 - loss 0.01399542 - samples/sec: 60.87 - lr: 0.000391 -2022-11-01 18:58:06,060 epoch 142 - iter 216/274 - loss 0.01406924 - samples/sec: 67.80 - lr: 0.000391 -2022-11-01 18:58:18,871 epoch 142 - iter 243/274 - loss 0.01429993 - samples/sec: 67.46 - lr: 0.000391 -2022-11-01 18:58:32,342 epoch 142 - iter 270/274 - loss 0.01424921 - samples/sec: 64.15 - lr: 0.000391 -2022-11-01 18:58:34,122 ---------------------------------------------------------------------------------------------------- -2022-11-01 18:58:34,122 EPOCH 142 done: loss 0.0142 - lr 0.000391 -2022-11-01 18:58:59,595 Evaluating as a multi-label problem: False -2022-11-01 18:58:59,611 TEST : loss 0.03204723075032234 - f1-score (micro avg) 0.8541 -2022-11-01 18:58:59,664 Epoch 142: reducing learning rate of group 0 to 1.9531e-04. -2022-11-01 18:58:59,664 BAD EPOCHS (no improvement): 4 -2022-11-01 18:58:59,757 ---------------------------------------------------------------------------------------------------- -2022-11-01 18:59:12,947 epoch 143 - iter 27/274 - loss 0.01607101 - samples/sec: 65.52 - lr: 0.000195 -2022-11-01 18:59:25,410 epoch 143 - iter 54/274 - loss 0.01551964 - samples/sec: 69.34 - lr: 0.000195 -2022-11-01 18:59:37,705 epoch 143 - iter 81/274 - loss 0.01508179 - samples/sec: 70.29 - lr: 0.000195 -2022-11-01 18:59:50,633 epoch 143 - iter 108/274 - loss 0.01503203 - samples/sec: 66.85 - lr: 0.000195 -2022-11-01 19:00:02,333 epoch 143 - iter 135/274 - loss 0.01500703 - samples/sec: 73.87 - lr: 0.000195 -2022-11-01 19:00:15,575 epoch 143 - iter 162/274 - loss 0.01451339 - samples/sec: 65.26 - lr: 0.000195 -2022-11-01 19:00:29,024 epoch 143 - iter 189/274 - loss 0.01453377 - samples/sec: 64.26 - lr: 0.000195 -2022-11-01 19:00:40,418 epoch 143 - iter 216/274 - loss 0.01460606 - samples/sec: 75.85 - lr: 0.000195 -2022-11-01 19:00:53,208 epoch 143 - iter 243/274 - loss 0.01465891 - samples/sec: 67.57 - lr: 0.000195 -2022-11-01 19:01:05,277 epoch 143 - iter 270/274 - loss 0.01452026 - samples/sec: 71.61 - lr: 0.000195 -2022-11-01 19:01:06,801 ---------------------------------------------------------------------------------------------------- -2022-11-01 19:01:06,801 EPOCH 143 done: loss 0.0145 - lr 0.000195 -2022-11-01 19:01:32,286 Evaluating as a multi-label problem: False -2022-11-01 19:01:32,301 TEST : loss 0.03204527124762535 - f1-score (micro avg) 0.8544 -2022-11-01 19:01:32,353 BAD EPOCHS (no improvement): 1 -2022-11-01 19:01:32,446 ---------------------------------------------------------------------------------------------------- -2022-11-01 19:01:43,483 epoch 144 - iter 27/274 - loss 0.01275661 - samples/sec: 78.31 - lr: 0.000195 -2022-11-01 19:01:55,891 epoch 144 - iter 54/274 - loss 0.01255751 - samples/sec: 69.65 - lr: 0.000195 -2022-11-01 19:02:08,138 epoch 144 - iter 81/274 - loss 0.01352715 - samples/sec: 70.57 - lr: 0.000195 -2022-11-01 19:02:20,657 epoch 144 - iter 108/274 - loss 0.01424597 - samples/sec: 69.03 - lr: 0.000195 -2022-11-01 19:02:32,689 epoch 144 - iter 135/274 - loss 0.01388532 - samples/sec: 71.83 - lr: 0.000195 -2022-11-01 19:02:46,254 epoch 144 - iter 162/274 - loss 0.01418345 - samples/sec: 63.71 - lr: 0.000195 -2022-11-01 19:02:58,873 epoch 144 - iter 189/274 - loss 0.01401984 - samples/sec: 68.49 - lr: 0.000195 -2022-11-01 19:03:10,582 epoch 144 - iter 216/274 - loss 0.01378628 - samples/sec: 73.81 - lr: 0.000195 -2022-11-01 19:03:23,555 epoch 144 - iter 243/274 - loss 0.01386198 - samples/sec: 66.62 - lr: 0.000195 -2022-11-01 19:03:37,102 epoch 144 - iter 270/274 - loss 0.01403064 - samples/sec: 63.79 - lr: 0.000195 -2022-11-01 19:03:38,807 ---------------------------------------------------------------------------------------------------- -2022-11-01 19:03:38,807 EPOCH 144 done: loss 0.0141 - lr 0.000195 -2022-11-01 19:04:04,183 Evaluating as a multi-label problem: False -2022-11-01 19:04:04,199 TEST : loss 0.03205322101712227 - f1-score (micro avg) 0.8544 -2022-11-01 19:04:04,251 BAD EPOCHS (no improvement): 2 -2022-11-01 19:04:04,337 ---------------------------------------------------------------------------------------------------- -2022-11-01 19:04:15,584 epoch 145 - iter 27/274 - loss 0.01196807 - samples/sec: 76.84 - lr: 0.000195 -2022-11-01 19:04:27,733 epoch 145 - iter 54/274 - loss 0.01428555 - samples/sec: 71.14 - lr: 0.000195 -2022-11-01 19:04:39,618 epoch 145 - iter 81/274 - loss 0.01458192 - samples/sec: 72.72 - lr: 0.000195 -2022-11-01 19:04:52,925 epoch 145 - iter 108/274 - loss 0.01460566 - samples/sec: 64.95 - lr: 0.000195 -2022-11-01 19:05:04,454 epoch 145 - iter 135/274 - loss 0.01511987 - samples/sec: 74.96 - lr: 0.000195 -2022-11-01 19:05:16,725 epoch 145 - iter 162/274 - loss 0.01514862 - samples/sec: 70.43 - lr: 0.000195 -2022-11-01 19:05:29,601 epoch 145 - iter 189/274 - loss 0.01476270 - samples/sec: 67.12 - lr: 0.000195 -2022-11-01 19:05:42,378 epoch 145 - iter 216/274 - loss 0.01491464 - samples/sec: 67.64 - lr: 0.000195 -2022-11-01 19:05:54,791 epoch 145 - iter 243/274 - loss 0.01472325 - samples/sec: 69.62 - lr: 0.000195 -2022-11-01 19:06:08,380 epoch 145 - iter 270/274 - loss 0.01450260 - samples/sec: 63.59 - lr: 0.000195 -2022-11-01 19:06:10,102 ---------------------------------------------------------------------------------------------------- -2022-11-01 19:06:10,103 EPOCH 145 done: loss 0.0145 - lr 0.000195 -2022-11-01 19:06:35,540 Evaluating as a multi-label problem: False -2022-11-01 19:06:35,556 TEST : loss 0.03206166997551918 - f1-score (micro avg) 0.8544 -2022-11-01 19:06:35,609 BAD EPOCHS (no improvement): 3 -2022-11-01 19:06:35,703 ---------------------------------------------------------------------------------------------------- -2022-11-01 19:06:48,592 epoch 146 - iter 27/274 - loss 0.01138730 - samples/sec: 67.06 - lr: 0.000195 -2022-11-01 19:07:00,468 epoch 146 - iter 54/274 - loss 0.01167755 - samples/sec: 72.77 - lr: 0.000195 -2022-11-01 19:07:12,555 epoch 146 - iter 81/274 - loss 0.01291096 - samples/sec: 71.50 - lr: 0.000195 -2022-11-01 19:07:25,188 epoch 146 - iter 108/274 - loss 0.01365870 - samples/sec: 68.41 - lr: 0.000195 -2022-11-01 19:07:39,012 epoch 146 - iter 135/274 - loss 0.01360006 - samples/sec: 62.51 - lr: 0.000195 -2022-11-01 19:07:50,620 epoch 146 - iter 162/274 - loss 0.01400603 - samples/sec: 74.45 - lr: 0.000195 -2022-11-01 19:08:02,972 epoch 146 - iter 189/274 - loss 0.01390354 - samples/sec: 69.97 - lr: 0.000195 -2022-11-01 19:08:15,150 epoch 146 - iter 216/274 - loss 0.01413519 - samples/sec: 70.96 - lr: 0.000195 -2022-11-01 19:08:27,039 epoch 146 - iter 243/274 - loss 0.01425737 - samples/sec: 72.69 - lr: 0.000195 -2022-11-01 19:08:38,896 epoch 146 - iter 270/274 - loss 0.01407049 - samples/sec: 72.89 - lr: 0.000195 -2022-11-01 19:08:41,119 ---------------------------------------------------------------------------------------------------- -2022-11-01 19:08:41,119 EPOCH 146 done: loss 0.0140 - lr 0.000195 -2022-11-01 19:09:06,382 Evaluating as a multi-label problem: False -2022-11-01 19:09:06,397 TEST : loss 0.032056890428066254 - f1-score (micro avg) 0.8544 -2022-11-01 19:09:06,450 Epoch 146: reducing learning rate of group 0 to 9.7656e-05. -2022-11-01 19:09:06,451 BAD EPOCHS (no improvement): 4 -2022-11-01 19:09:06,524 ---------------------------------------------------------------------------------------------------- -2022-11-01 19:09:06,524 ---------------------------------------------------------------------------------------------------- -2022-11-01 19:09:06,524 learning rate too small - quitting training! -2022-11-01 19:09:06,524 ---------------------------------------------------------------------------------------------------- -2022-11-01 19:09:06,599 ---------------------------------------------------------------------------------------------------- -2022-11-01 19:09:06,599 Testing using last state of model ... -2022-11-01 19:09:31,767 Evaluating as a multi-label problem: False -2022-11-01 19:09:31,783 0.8572 0.8516 0.8544 0.798 -2022-11-01 19:09:31,783 +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) 0.8544 -- F-score (macro) 0.7406 -- Accuracy 0.798 +- F-score (micro) 0.8605 +- F-score (macro) 0.7472 +- Accuracy 0.8033 By class: precision recall f1-score support - PERS 0.9231 0.9374 0.9302 1678 - LOC 0.8204 0.8429 0.8315 401 - ORG 0.6708 0.6245 0.6468 261 - MISC 0.6029 0.5125 0.5541 240 + 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 0.8572 0.8516 0.8544 2580 - macro avg 0.7543 0.7293 0.7406 2580 -weighted avg 0.8518 0.8516 0.8512 2580 + 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 -2022-11-01 19:09:31,783 ---------------------------------------------------------------------------------------------------- +2022-11-06 18:48:16,693 ----------------------------------------------------------------------------------------------------