2019-08-19 16:50:00,801 ----------------------------------------------------------------------------------------------------
2019-08-19 16:50:00,801 Model: "SequenceTagger(
  (embeddings): StackedEmbeddings(
    (list_embedding_0): BytePairEmbeddings(model=bpe-en-100000-50)
    (list_embedding_1): FlairEmbeddings(
      (lm): LanguageModel(
        (drop): Dropout(p=0.25)
        (encoder): Embedding(275, 100)
        (rnn): LSTM(100, 1024)
        (decoder): Linear(in_features=1024, out_features=275, bias=True)
      )
    )
    (list_embedding_2): FlairEmbeddings(
      (lm): LanguageModel(
        (drop): Dropout(p=0.25)
        (encoder): Embedding(275, 100)
        (rnn): LSTM(100, 1024)
        (decoder): Linear(in_features=1024, out_features=275, bias=True)
      )
    )
  )
  (word_dropout): WordDropout(p=0.05)
  (locked_dropout): LockedDropout(p=0.5)
  (embedding2nn): Linear(in_features=2148, out_features=2148, bias=True)
  (rnn): LSTM(2148, 256, bidirectional=True)
  (linear): Linear(in_features=512, out_features=5196, bias=True)
)"
2019-08-19 16:50:00,801 ----------------------------------------------------------------------------------------------------
2019-08-19 16:50:00,801 Corpus: "Corpus: 75187 train + 9603 dev + 9479 test sentences"
2019-08-19 16:50:00,801 ----------------------------------------------------------------------------------------------------
2019-08-19 16:50:00,801 Parameters:
2019-08-19 16:50:00,801  - learning_rate: "0.1"
2019-08-19 16:50:00,801  - mini_batch_size: "32"
2019-08-19 16:50:00,801  - patience: "3"
2019-08-19 16:50:00,801  - anneal_factor: "0.5"
2019-08-19 16:50:00,801  - max_epochs: "150"
2019-08-19 16:50:00,801  - shuffle: "True"
2019-08-19 16:50:00,801  - train_with_dev: "True"
2019-08-19 16:50:00,801 ----------------------------------------------------------------------------------------------------
2019-08-19 16:50:00,802 Model training base path: "resources/taggers/release-frame-fast-0"
2019-08-19 16:50:00,802 ----------------------------------------------------------------------------------------------------
2019-08-19 16:50:00,802 Device: cuda:0
2019-08-19 16:50:00,802 ----------------------------------------------------------------------------------------------------
2019-08-19 16:50:00,802 Embeddings storage mode: cpu
2019-08-19 16:50:00,804 ----------------------------------------------------------------------------------------------------
2019-08-19 16:50:00,962 epoch 1 - iter 0/2650 - loss 8.52185535 throughput (samples/sec): 55006.67
2019-08-19 16:50:45,973 epoch 1 - iter 265/2650 - loss 1.68050930 throughput (samples/sec): 188.81
2019-08-19 16:51:28,202 epoch 1 - iter 530/2650 - loss 1.43546006 throughput (samples/sec): 201.38
2019-08-19 16:52:12,565 epoch 1 - iter 795/2650 - loss 1.32177486 throughput (samples/sec): 191.62
2019-08-19 16:52:53,502 epoch 1 - iter 1060/2650 - loss 1.24814261 throughput (samples/sec): 207.67
2019-08-19 16:53:35,571 epoch 1 - iter 1325/2650 - loss 1.19266758 throughput (samples/sec): 202.11
2019-08-19 16:54:17,545 epoch 1 - iter 1590/2650 - loss 1.14994578 throughput (samples/sec): 202.57
2019-08-19 16:55:04,341 epoch 1 - iter 1855/2650 - loss 1.11177424 throughput (samples/sec): 181.59
2019-08-19 16:55:47,008 epoch 1 - iter 2120/2650 - loss 1.08253310 throughput (samples/sec): 199.25
2019-08-19 16:56:31,390 epoch 1 - iter 2385/2650 - loss 1.05545362 throughput (samples/sec): 191.55
2019-08-19 16:57:14,178 ----------------------------------------------------------------------------------------------------
2019-08-19 16:57:14,178 EPOCH 1 done: loss 1.0332 - lr 0.1000
2019-08-19 16:57:14,179 BAD EPOCHS (no improvement): 0
2019-08-19 16:57:14,179 ----------------------------------------------------------------------------------------------------
2019-08-19 16:57:14,233 epoch 2 - iter 0/2650 - loss 0.86784875 throughput (samples/sec): 171516.40
2019-08-19 16:57:25,980 epoch 2 - iter 265/2650 - loss 0.81080838 throughput (samples/sec): 728.89
2019-08-19 16:57:38,525 epoch 2 - iter 530/2650 - loss 0.80621785 throughput (samples/sec): 682.22
2019-08-19 16:57:50,191 epoch 2 - iter 795/2650 - loss 0.79707456 throughput (samples/sec): 733.70
2019-08-19 16:58:01,793 epoch 2 - iter 1060/2650 - loss 0.79285750 throughput (samples/sec): 737.86
2019-08-19 16:58:13,734 epoch 2 - iter 1325/2650 - loss 0.78786233 throughput (samples/sec): 716.33
2019-08-19 16:58:25,684 epoch 2 - iter 1590/2650 - loss 0.77946093 throughput (samples/sec): 716.39
2019-08-19 16:58:37,498 epoch 2 - iter 1855/2650 - loss 0.77242163 throughput (samples/sec): 724.56
2019-08-19 16:58:49,390 epoch 2 - iter 2120/2650 - loss 0.76609618 throughput (samples/sec): 719.11
2019-08-19 16:59:00,543 epoch 2 - iter 2385/2650 - loss 0.75965044 throughput (samples/sec): 767.31
2019-08-19 16:59:12,317 ----------------------------------------------------------------------------------------------------
2019-08-19 16:59:12,318 EPOCH 2 done: loss 0.7534 - lr 0.1000
2019-08-19 16:59:12,318 BAD EPOCHS (no improvement): 0
2019-08-19 16:59:12,318 ----------------------------------------------------------------------------------------------------
2019-08-19 16:59:12,367 epoch 3 - iter 0/2650 - loss 0.60228997 throughput (samples/sec): 188379.25
2019-08-19 16:59:23,750 epoch 3 - iter 265/2650 - loss 0.68359405 throughput (samples/sec): 751.72
2019-08-19 16:59:35,567 epoch 3 - iter 530/2650 - loss 0.67901548 throughput (samples/sec): 723.84
2019-08-19 16:59:47,871 epoch 3 - iter 795/2650 - loss 0.67506286 throughput (samples/sec): 695.78
2019-08-19 17:00:00,283 epoch 3 - iter 1060/2650 - loss 0.67232615 throughput (samples/sec): 689.68
2019-08-19 17:00:12,723 epoch 3 - iter 1325/2650 - loss 0.66832767 throughput (samples/sec): 687.41
2019-08-19 17:00:25,054 epoch 3 - iter 1590/2650 - loss 0.66422820 throughput (samples/sec): 694.03
2019-08-19 17:00:37,380 epoch 3 - iter 1855/2650 - loss 0.66097289 throughput (samples/sec): 694.32
2019-08-19 17:00:49,653 epoch 3 - iter 2120/2650 - loss 0.65781732 throughput (samples/sec): 697.09
2019-08-19 17:01:02,036 epoch 3 - iter 2385/2650 - loss 0.65379953 throughput (samples/sec): 691.48
2019-08-19 17:01:13,999 ----------------------------------------------------------------------------------------------------
2019-08-19 17:01:13,999 EPOCH 3 done: loss 0.6513 - lr 0.1000
2019-08-19 17:01:13,999 BAD EPOCHS (no improvement): 0
2019-08-19 17:01:13,999 ----------------------------------------------------------------------------------------------------
2019-08-19 17:01:14,048 epoch 4 - iter 0/2650 - loss 0.52035195 throughput (samples/sec): 196651.12
2019-08-19 17:01:25,830 epoch 4 - iter 265/2650 - loss 0.59877114 throughput (samples/sec): 726.23
2019-08-19 17:01:38,316 epoch 4 - iter 530/2650 - loss 0.59589098 throughput (samples/sec): 685.53
2019-08-19 17:01:50,370 epoch 4 - iter 795/2650 - loss 0.59561691 throughput (samples/sec): 709.96
2019-08-19 17:02:02,668 epoch 4 - iter 1060/2650 - loss 0.59466463 throughput (samples/sec): 696.08
2019-08-19 17:02:14,496 epoch 4 - iter 1325/2650 - loss 0.59254019 throughput (samples/sec): 724.03
2019-08-19 17:02:26,228 epoch 4 - iter 1590/2650 - loss 0.59082262 throughput (samples/sec): 729.74
2019-08-19 17:02:38,243 epoch 4 - iter 1855/2650 - loss 0.58917507 throughput (samples/sec): 712.62
2019-08-19 17:02:50,457 epoch 4 - iter 2120/2650 - loss 0.58643451 throughput (samples/sec): 700.72
2019-08-19 17:03:02,336 epoch 4 - iter 2385/2650 - loss 0.58356540 throughput (samples/sec): 720.56
2019-08-19 17:03:14,200 ----------------------------------------------------------------------------------------------------
2019-08-19 17:03:14,200 EPOCH 4 done: loss 0.5802 - lr 0.1000
2019-08-19 17:03:14,200 BAD EPOCHS (no improvement): 0
2019-08-19 17:03:14,201 ----------------------------------------------------------------------------------------------------
2019-08-19 17:03:14,247 epoch 5 - iter 0/2650 - loss 0.58826411 throughput (samples/sec): 210478.43
2019-08-19 17:03:26,409 epoch 5 - iter 265/2650 - loss 0.55661753 throughput (samples/sec): 704.05
2019-08-19 17:03:38,671 epoch 5 - iter 530/2650 - loss 0.55266627 throughput (samples/sec): 698.35
2019-08-19 17:03:49,994 epoch 5 - iter 795/2650 - loss 0.54958417 throughput (samples/sec): 755.35
2019-08-19 17:04:01,412 epoch 5 - iter 1060/2650 - loss 0.54773470 throughput (samples/sec): 749.01
2019-08-19 17:04:13,748 epoch 5 - iter 1325/2650 - loss 0.54470934 throughput (samples/sec): 693.65
2019-08-19 17:04:25,341 epoch 5 - iter 1590/2650 - loss 0.54130804 throughput (samples/sec): 737.75
2019-08-19 17:04:36,845 epoch 5 - iter 1855/2650 - loss 0.53863266 throughput (samples/sec): 743.14
2019-08-19 17:04:48,570 epoch 5 - iter 2120/2650 - loss 0.53578216 throughput (samples/sec): 729.38
2019-08-19 17:05:00,695 epoch 5 - iter 2385/2650 - loss 0.53435228 throughput (samples/sec): 706.26
2019-08-19 17:05:13,144 ----------------------------------------------------------------------------------------------------
2019-08-19 17:05:13,144 EPOCH 5 done: loss 0.5319 - lr 0.1000
2019-08-19 17:05:13,144 BAD EPOCHS (no improvement): 0
2019-08-19 17:05:13,145 ----------------------------------------------------------------------------------------------------
2019-08-19 17:05:13,189 epoch 6 - iter 0/2650 - loss 0.48283404 throughput (samples/sec): 210148.88
2019-08-19 17:05:25,256 epoch 6 - iter 265/2650 - loss 0.50797523 throughput (samples/sec): 708.65
2019-08-19 17:05:36,833 epoch 6 - iter 530/2650 - loss 0.50590742 throughput (samples/sec): 738.39
2019-08-19 17:05:49,131 epoch 6 - iter 795/2650 - loss 0.50613159 throughput (samples/sec): 695.75
2019-08-19 17:06:01,381 epoch 6 - iter 1060/2650 - loss 0.50356570 throughput (samples/sec): 697.92
2019-08-19 17:06:13,257 epoch 6 - iter 1325/2650 - loss 0.50008888 throughput (samples/sec): 721.16
2019-08-19 17:06:25,418 epoch 6 - iter 1590/2650 - loss 0.49793645 throughput (samples/sec): 704.09
2019-08-19 17:06:37,923 epoch 6 - iter 1855/2650 - loss 0.49700106 throughput (samples/sec): 684.06
2019-08-19 17:06:50,372 epoch 6 - iter 2120/2650 - loss 0.49534308 throughput (samples/sec): 687.65
2019-08-19 17:07:02,110 epoch 6 - iter 2385/2650 - loss 0.49350542 throughput (samples/sec): 728.29
2019-08-19 17:07:13,857 ----------------------------------------------------------------------------------------------------
2019-08-19 17:07:13,858 EPOCH 6 done: loss 0.4918 - lr 0.1000
2019-08-19 17:07:13,858 BAD EPOCHS (no improvement): 0
2019-08-19 17:07:13,858 ----------------------------------------------------------------------------------------------------
2019-08-19 17:07:13,917 epoch 7 - iter 0/2650 - loss 0.39869389 throughput (samples/sec): 158981.67
2019-08-19 17:07:26,673 epoch 7 - iter 265/2650 - loss 0.47553680 throughput (samples/sec): 670.68
2019-08-19 17:07:38,421 epoch 7 - iter 530/2650 - loss 0.47453629 throughput (samples/sec): 728.95
2019-08-19 17:07:50,515 epoch 7 - iter 795/2650 - loss 0.47281299 throughput (samples/sec): 707.94
2019-08-19 17:08:01,728 epoch 7 - iter 1060/2650 - loss 0.47325321 throughput (samples/sec): 762.77
2019-08-19 17:08:13,843 epoch 7 - iter 1325/2650 - loss 0.47227016 throughput (samples/sec): 706.63
2019-08-19 17:08:26,703 epoch 7 - iter 1590/2650 - loss 0.46949911 throughput (samples/sec): 665.52
2019-08-19 17:08:38,984 epoch 7 - iter 1855/2650 - loss 0.46827366 throughput (samples/sec): 696.98
2019-08-19 17:08:51,274 epoch 7 - iter 2120/2650 - loss 0.46760394 throughput (samples/sec): 696.72
2019-08-19 17:09:02,284 epoch 7 - iter 2385/2650 - loss 0.46553373 throughput (samples/sec): 778.29
2019-08-19 17:09:14,083 ----------------------------------------------------------------------------------------------------
2019-08-19 17:09:14,083 EPOCH 7 done: loss 0.4637 - lr 0.1000
2019-08-19 17:09:14,083 BAD EPOCHS (no improvement): 0
2019-08-19 17:09:14,084 ----------------------------------------------------------------------------------------------------
2019-08-19 17:09:14,125 epoch 8 - iter 0/2650 - loss 0.44571328 throughput (samples/sec): 225676.20
2019-08-19 17:09:25,810 epoch 8 - iter 265/2650 - loss 0.45079209 throughput (samples/sec): 732.02
2019-08-19 17:09:37,881 epoch 8 - iter 530/2650 - loss 0.45168513 throughput (samples/sec): 708.96
2019-08-19 17:09:50,088 epoch 8 - iter 795/2650 - loss 0.44754631 throughput (samples/sec): 700.93
2019-08-19 17:10:02,280 epoch 8 - iter 1060/2650 - loss 0.44667370 throughput (samples/sec): 701.94
2019-08-19 17:10:14,441 epoch 8 - iter 1325/2650 - loss 0.44593658 throughput (samples/sec): 703.62
2019-08-19 17:10:26,396 epoch 8 - iter 1590/2650 - loss 0.44462336 throughput (samples/sec): 716.28
2019-08-19 17:10:38,293 epoch 8 - iter 1855/2650 - loss 0.44353853 throughput (samples/sec): 719.09
2019-08-19 17:10:50,449 epoch 8 - iter 2120/2650 - loss 0.44264028 throughput (samples/sec): 704.25
2019-08-19 17:11:01,886 epoch 8 - iter 2385/2650 - loss 0.44067033 throughput (samples/sec): 748.20
2019-08-19 17:11:13,640 ----------------------------------------------------------------------------------------------------
2019-08-19 17:11:13,641 EPOCH 8 done: loss 0.4397 - lr 0.1000
2019-08-19 17:11:13,641 BAD EPOCHS (no improvement): 0
2019-08-19 17:11:13,642 ----------------------------------------------------------------------------------------------------
2019-08-19 17:11:13,686 epoch 9 - iter 0/2650 - loss 0.41412807 throughput (samples/sec): 219388.47
2019-08-19 17:11:26,465 epoch 9 - iter 265/2650 - loss 0.42319575 throughput (samples/sec): 670.11
2019-08-19 17:11:38,970 epoch 9 - iter 530/2650 - loss 0.41656691 throughput (samples/sec): 684.73
2019-08-19 17:11:50,640 epoch 9 - iter 795/2650 - loss 0.41643758 throughput (samples/sec): 733.01
2019-08-19 17:12:02,568 epoch 9 - iter 1060/2650 - loss 0.41638424 throughput (samples/sec): 717.66
2019-08-19 17:12:15,177 epoch 9 - iter 1325/2650 - loss 0.41622613 throughput (samples/sec): 679.00
2019-08-19 17:12:27,335 epoch 9 - iter 1590/2650 - loss 0.41593619 throughput (samples/sec): 703.83
2019-08-19 17:12:39,417 epoch 9 - iter 1855/2650 - loss 0.41470165 throughput (samples/sec): 707.65
2019-08-19 17:12:51,394 epoch 9 - iter 2120/2650 - loss 0.41488146 throughput (samples/sec): 714.68
2019-08-19 17:13:02,834 epoch 9 - iter 2385/2650 - loss 0.41466347 throughput (samples/sec): 747.80
2019-08-19 17:13:15,441 ----------------------------------------------------------------------------------------------------
2019-08-19 17:13:15,441 EPOCH 9 done: loss 0.4139 - lr 0.1000
2019-08-19 17:13:15,442 BAD EPOCHS (no improvement): 0
2019-08-19 17:13:15,442 ----------------------------------------------------------------------------------------------------
2019-08-19 17:13:15,494 epoch 10 - iter 0/2650 - loss 0.24614160 throughput (samples/sec): 186196.87
2019-08-19 17:13:27,820 epoch 10 - iter 265/2650 - loss 0.40739879 throughput (samples/sec): 694.52
2019-08-19 17:13:39,583 epoch 10 - iter 530/2650 - loss 0.40752169 throughput (samples/sec): 726.85
2019-08-19 17:13:51,297 epoch 10 - iter 795/2650 - loss 0.40655733 throughput (samples/sec): 730.51
2019-08-19 17:14:02,938 epoch 10 - iter 1060/2650 - loss 0.40421046 throughput (samples/sec): 734.46
2019-08-19 17:14:15,132 epoch 10 - iter 1325/2650 - loss 0.40131641 throughput (samples/sec): 702.06
2019-08-19 17:14:27,790 epoch 10 - iter 1590/2650 - loss 0.40171354 throughput (samples/sec): 676.18
2019-08-19 17:14:40,386 epoch 10 - iter 1855/2650 - loss 0.40042234 throughput (samples/sec): 679.71
2019-08-19 17:14:52,285 epoch 10 - iter 2120/2650 - loss 0.39947138 throughput (samples/sec): 719.57
2019-08-19 17:15:04,154 epoch 10 - iter 2385/2650 - loss 0.39884618 throughput (samples/sec): 721.22
2019-08-19 17:15:16,137 ----------------------------------------------------------------------------------------------------
2019-08-19 17:15:16,138 EPOCH 10 done: loss 0.3974 - lr 0.1000
2019-08-19 17:15:16,138 BAD EPOCHS (no improvement): 0
2019-08-19 17:15:16,139 ----------------------------------------------------------------------------------------------------
2019-08-19 17:15:16,188 epoch 11 - iter 0/2650 - loss 0.34269258 throughput (samples/sec): 192469.02
2019-08-19 17:15:27,710 epoch 11 - iter 265/2650 - loss 0.38532157 throughput (samples/sec): 742.80
2019-08-19 17:15:40,223 epoch 11 - iter 530/2650 - loss 0.38616615 throughput (samples/sec): 684.39
2019-08-19 17:15:52,151 epoch 11 - iter 795/2650 - loss 0.39052318 throughput (samples/sec): 717.67
2019-08-19 17:16:03,532 epoch 11 - iter 1060/2650 - loss 0.38945858 throughput (samples/sec): 751.45
2019-08-19 17:16:15,763 epoch 11 - iter 1325/2650 - loss 0.38784701 throughput (samples/sec): 699.77
2019-08-19 17:16:28,093 epoch 11 - iter 1590/2650 - loss 0.38613798 throughput (samples/sec): 694.01
2019-08-19 17:16:39,990 epoch 11 - iter 1855/2650 - loss 0.38616650 throughput (samples/sec): 719.25
2019-08-19 17:16:51,535 epoch 11 - iter 2120/2650 - loss 0.38541819 throughput (samples/sec): 740.55
2019-08-19 17:17:03,330 epoch 11 - iter 2385/2650 - loss 0.38418612 throughput (samples/sec): 725.31
2019-08-19 17:17:15,691 ----------------------------------------------------------------------------------------------------
2019-08-19 17:17:15,691 EPOCH 11 done: loss 0.3830 - lr 0.1000
2019-08-19 17:17:15,691 BAD EPOCHS (no improvement): 0
2019-08-19 17:17:15,691 ----------------------------------------------------------------------------------------------------
2019-08-19 17:17:15,731 epoch 12 - iter 0/2650 - loss 0.25702477 throughput (samples/sec): 236275.27
2019-08-19 17:17:27,634 epoch 12 - iter 265/2650 - loss 0.37468654 throughput (samples/sec): 718.98
2019-08-19 17:17:39,424 epoch 12 - iter 530/2650 - loss 0.37370148 throughput (samples/sec): 726.09
2019-08-19 17:17:51,470 epoch 12 - iter 795/2650 - loss 0.37370290 throughput (samples/sec): 710.40
2019-08-19 17:18:03,899 epoch 12 - iter 1060/2650 - loss 0.37292476 throughput (samples/sec): 688.44
2019-08-19 17:18:16,238 epoch 12 - iter 1325/2650 - loss 0.37354859 throughput (samples/sec): 693.79
2019-08-19 17:18:27,780 epoch 12 - iter 1590/2650 - loss 0.37189044 throughput (samples/sec): 742.02
2019-08-19 17:18:39,048 epoch 12 - iter 1855/2650 - loss 0.37119002 throughput (samples/sec): 759.18
2019-08-19 17:18:50,901 epoch 12 - iter 2120/2650 - loss 0.36974923 throughput (samples/sec): 722.11
2019-08-19 17:19:02,798 epoch 12 - iter 2385/2650 - loss 0.36932040 throughput (samples/sec): 719.58
2019-08-19 17:19:14,503 ----------------------------------------------------------------------------------------------------
2019-08-19 17:19:14,503 EPOCH 12 done: loss 0.3686 - lr 0.1000
2019-08-19 17:19:14,503 BAD EPOCHS (no improvement): 0
2019-08-19 17:19:14,504 ----------------------------------------------------------------------------------------------------
2019-08-19 17:19:14,547 epoch 13 - iter 0/2650 - loss 0.28977445 throughput (samples/sec): 215794.60
2019-08-19 17:19:26,328 epoch 13 - iter 265/2650 - loss 0.36155234 throughput (samples/sec): 725.69
2019-08-19 17:19:38,334 epoch 13 - iter 530/2650 - loss 0.36350951 throughput (samples/sec): 713.03
2019-08-19 17:19:50,605 epoch 13 - iter 795/2650 - loss 0.36303956 throughput (samples/sec): 697.91
2019-08-19 17:20:02,644 epoch 13 - iter 1060/2650 - loss 0.36256523 throughput (samples/sec): 711.25
2019-08-19 17:20:14,681 epoch 13 - iter 1325/2650 - loss 0.36092867 throughput (samples/sec): 711.18
2019-08-19 17:20:26,605 epoch 13 - iter 1590/2650 - loss 0.36076537 throughput (samples/sec): 717.26
2019-08-19 17:20:39,312 epoch 13 - iter 1855/2650 - loss 0.36007293 throughput (samples/sec): 673.28
2019-08-19 17:20:51,492 epoch 13 - iter 2120/2650 - loss 0.35735381 throughput (samples/sec): 702.63
2019-08-19 17:21:03,649 epoch 13 - iter 2385/2650 - loss 0.35681198 throughput (samples/sec): 704.41
2019-08-19 17:21:15,975 ----------------------------------------------------------------------------------------------------
2019-08-19 17:21:15,975 EPOCH 13 done: loss 0.3569 - lr 0.1000
2019-08-19 17:21:15,975 BAD EPOCHS (no improvement): 0
2019-08-19 17:21:15,976 ----------------------------------------------------------------------------------------------------
2019-08-19 17:21:16,033 epoch 14 - iter 0/2650 - loss 0.24969481 throughput (samples/sec): 172442.76
2019-08-19 17:21:28,225 epoch 14 - iter 265/2650 - loss 0.35151556 throughput (samples/sec): 701.93
2019-08-19 17:21:40,725 epoch 14 - iter 530/2650 - loss 0.35110632 throughput (samples/sec): 684.17
2019-08-19 17:21:52,733 epoch 14 - iter 795/2650 - loss 0.34757870 throughput (samples/sec): 712.54
2019-08-19 17:22:04,817 epoch 14 - iter 1060/2650 - loss 0.34495460 throughput (samples/sec): 708.09
2019-08-19 17:22:16,874 epoch 14 - iter 1325/2650 - loss 0.34688014 throughput (samples/sec): 709.95
2019-08-19 17:22:29,156 epoch 14 - iter 1590/2650 - loss 0.34634196 throughput (samples/sec): 697.18
2019-08-19 17:22:40,574 epoch 14 - iter 1855/2650 - loss 0.34744844 throughput (samples/sec): 749.84
2019-08-19 17:22:52,483 epoch 14 - iter 2120/2650 - loss 0.34660645 throughput (samples/sec): 718.78
2019-08-19 17:23:04,496 epoch 14 - iter 2385/2650 - loss 0.34564608 throughput (samples/sec): 712.48
2019-08-19 17:23:16,770 ----------------------------------------------------------------------------------------------------
2019-08-19 17:23:16,771 EPOCH 14 done: loss 0.3454 - lr 0.1000
2019-08-19 17:23:16,771 BAD EPOCHS (no improvement): 0
2019-08-19 17:23:16,771 ----------------------------------------------------------------------------------------------------
2019-08-19 17:23:16,828 epoch 15 - iter 0/2650 - loss 0.36004269 throughput (samples/sec): 168653.61
2019-08-19 17:23:29,664 epoch 15 - iter 265/2650 - loss 0.34297051 throughput (samples/sec): 666.36
2019-08-19 17:23:41,532 epoch 15 - iter 530/2650 - loss 0.33917273 throughput (samples/sec): 721.43
2019-08-19 17:23:53,669 epoch 15 - iter 795/2650 - loss 0.33847494 throughput (samples/sec): 705.08
2019-08-19 17:24:05,278 epoch 15 - iter 1060/2650 - loss 0.33885443 throughput (samples/sec): 736.58
2019-08-19 17:24:17,174 epoch 15 - iter 1325/2650 - loss 0.33977828 throughput (samples/sec): 719.55
2019-08-19 17:24:29,204 epoch 15 - iter 1590/2650 - loss 0.33989441 throughput (samples/sec): 711.47
2019-08-19 17:24:41,317 epoch 15 - iter 1855/2650 - loss 0.33972829 throughput (samples/sec): 705.96
2019-08-19 17:24:53,855 epoch 15 - iter 2120/2650 - loss 0.33989556 throughput (samples/sec): 682.67
2019-08-19 17:25:06,498 epoch 15 - iter 2385/2650 - loss 0.33936120 throughput (samples/sec): 677.03
2019-08-19 17:25:19,178 ----------------------------------------------------------------------------------------------------
2019-08-19 17:25:19,178 EPOCH 15 done: loss 0.3376 - lr 0.1000
2019-08-19 17:25:19,178 BAD EPOCHS (no improvement): 0
2019-08-19 17:25:19,179 ----------------------------------------------------------------------------------------------------
2019-08-19 17:25:19,243 epoch 16 - iter 0/2650 - loss 0.42160085 throughput (samples/sec): 146179.03
2019-08-19 17:25:31,639 epoch 16 - iter 265/2650 - loss 0.32798696 throughput (samples/sec): 690.33
2019-08-19 17:25:44,233 epoch 16 - iter 530/2650 - loss 0.32446545 throughput (samples/sec): 679.54
2019-08-19 17:25:56,310 epoch 16 - iter 795/2650 - loss 0.32511691 throughput (samples/sec): 708.48
2019-08-19 17:26:07,931 epoch 16 - iter 1060/2650 - loss 0.32424429 throughput (samples/sec): 735.69
2019-08-19 17:26:20,173 epoch 16 - iter 1325/2650 - loss 0.32617015 throughput (samples/sec): 703.36
2019-08-19 17:26:32,140 epoch 16 - iter 1590/2650 - loss 0.32741602 throughput (samples/sec): 715.50
2019-08-19 17:26:44,011 epoch 16 - iter 1855/2650 - loss 0.32689739 throughput (samples/sec): 721.23
2019-08-19 17:26:55,963 epoch 16 - iter 2120/2650 - loss 0.32770748 throughput (samples/sec): 716.12
2019-08-19 17:27:08,122 epoch 16 - iter 2385/2650 - loss 0.32699749 throughput (samples/sec): 703.93
2019-08-19 17:27:19,581 ----------------------------------------------------------------------------------------------------
2019-08-19 17:27:19,581 EPOCH 16 done: loss 0.3270 - lr 0.1000
2019-08-19 17:27:19,581 BAD EPOCHS (no improvement): 0
2019-08-19 17:27:19,582 ----------------------------------------------------------------------------------------------------
2019-08-19 17:27:19,639 epoch 17 - iter 0/2650 - loss 0.26352662 throughput (samples/sec): 159768.65
2019-08-19 17:27:31,720 epoch 17 - iter 265/2650 - loss 0.31845402 throughput (samples/sec): 707.84
2019-08-19 17:27:43,479 epoch 17 - iter 530/2650 - loss 0.32214636 throughput (samples/sec): 728.04
2019-08-19 17:27:55,361 epoch 17 - iter 795/2650 - loss 0.32286564 throughput (samples/sec): 720.60
2019-08-19 17:28:07,219 epoch 17 - iter 1060/2650 - loss 0.32359117 throughput (samples/sec): 721.93
2019-08-19 17:28:19,427 epoch 17 - iter 1325/2650 - loss 0.32410646 throughput (samples/sec): 701.08
2019-08-19 17:28:32,079 epoch 17 - iter 1590/2650 - loss 0.32329230 throughput (samples/sec): 676.17
2019-08-19 17:28:44,855 epoch 17 - iter 1855/2650 - loss 0.32305005 throughput (samples/sec): 669.32
2019-08-19 17:28:56,714 epoch 17 - iter 2120/2650 - loss 0.32272627 throughput (samples/sec): 721.70
2019-08-19 17:29:08,341 epoch 17 - iter 2385/2650 - loss 0.32169968 throughput (samples/sec): 735.84
2019-08-19 17:29:20,174 ----------------------------------------------------------------------------------------------------
2019-08-19 17:29:20,174 EPOCH 17 done: loss 0.3214 - lr 0.1000
2019-08-19 17:29:20,174 BAD EPOCHS (no improvement): 0
2019-08-19 17:29:20,175 ----------------------------------------------------------------------------------------------------
2019-08-19 17:29:20,221 epoch 18 - iter 0/2650 - loss 0.33711746 throughput (samples/sec): 197981.09
2019-08-19 17:29:31,489 epoch 18 - iter 265/2650 - loss 0.31196002 throughput (samples/sec): 758.86
2019-08-19 17:29:44,022 epoch 18 - iter 530/2650 - loss 0.31058160 throughput (samples/sec): 682.12
2019-08-19 17:29:56,468 epoch 18 - iter 795/2650 - loss 0.31064741 throughput (samples/sec): 687.60
2019-08-19 17:30:08,590 epoch 18 - iter 1060/2650 - loss 0.31178084 throughput (samples/sec): 705.95
2019-08-19 17:30:20,215 epoch 18 - iter 1325/2650 - loss 0.31154156 throughput (samples/sec): 735.83
2019-08-19 17:30:31,925 epoch 18 - iter 1590/2650 - loss 0.31394671 throughput (samples/sec): 730.42
2019-08-19 17:30:43,568 epoch 18 - iter 1855/2650 - loss 0.31398752 throughput (samples/sec): 735.20
2019-08-19 17:30:55,527 epoch 18 - iter 2120/2650 - loss 0.31201187 throughput (samples/sec): 715.74
2019-08-19 17:31:07,451 epoch 18 - iter 2385/2650 - loss 0.31247279 throughput (samples/sec): 717.82
2019-08-19 17:31:19,915 ----------------------------------------------------------------------------------------------------
2019-08-19 17:31:19,916 EPOCH 18 done: loss 0.3122 - lr 0.1000
2019-08-19 17:31:19,916 BAD EPOCHS (no improvement): 0
2019-08-19 17:31:19,916 ----------------------------------------------------------------------------------------------------
2019-08-19 17:31:19,951 epoch 19 - iter 0/2650 - loss 0.40095913 throughput (samples/sec): 306832.34
2019-08-19 17:31:32,539 epoch 19 - iter 265/2650 - loss 0.29803475 throughput (samples/sec): 679.53
2019-08-19 17:31:45,271 epoch 19 - iter 530/2650 - loss 0.30243255 throughput (samples/sec): 672.41
2019-08-19 17:31:57,833 epoch 19 - iter 795/2650 - loss 0.30425244 throughput (samples/sec): 681.41
2019-08-19 17:32:10,171 epoch 19 - iter 1060/2650 - loss 0.30450708 throughput (samples/sec): 693.62
2019-08-19 17:32:21,332 epoch 19 - iter 1325/2650 - loss 0.30595828 throughput (samples/sec): 766.68
2019-08-19 17:32:32,169 epoch 19 - iter 1590/2650 - loss 0.30600795 throughput (samples/sec): 789.49
2019-08-19 17:32:43,148 epoch 19 - iter 1855/2650 - loss 0.30534687 throughput (samples/sec): 779.72
2019-08-19 17:32:54,907 epoch 19 - iter 2120/2650 - loss 0.30580846 throughput (samples/sec): 727.63
2019-08-19 17:33:06,814 epoch 19 - iter 2385/2650 - loss 0.30544611 throughput (samples/sec): 719.23
2019-08-19 17:33:18,876 ----------------------------------------------------------------------------------------------------
2019-08-19 17:33:18,876 EPOCH 19 done: loss 0.3062 - lr 0.1000
2019-08-19 17:33:18,876 BAD EPOCHS (no improvement): 0
2019-08-19 17:33:18,877 ----------------------------------------------------------------------------------------------------
2019-08-19 17:33:18,918 epoch 20 - iter 0/2650 - loss 0.31434426 throughput (samples/sec): 232670.87
2019-08-19 17:33:31,449 epoch 20 - iter 265/2650 - loss 0.30245214 throughput (samples/sec): 683.15
2019-08-19 17:33:43,327 epoch 20 - iter 530/2650 - loss 0.30462512 throughput (samples/sec): 720.38
2019-08-19 17:33:54,999 epoch 20 - iter 795/2650 - loss 0.30298397 throughput (samples/sec): 733.11
2019-08-19 17:34:06,107 epoch 20 - iter 1060/2650 - loss 0.30124792 throughput (samples/sec): 769.92
2019-08-19 17:34:17,160 epoch 20 - iter 1325/2650 - loss 0.30191037 throughput (samples/sec): 774.42
2019-08-19 17:34:28,820 epoch 20 - iter 1590/2650 - loss 0.30159840 throughput (samples/sec): 733.83
2019-08-19 17:34:40,342 epoch 20 - iter 1855/2650 - loss 0.30137755 throughput (samples/sec): 742.35
2019-08-19 17:34:51,949 epoch 20 - iter 2120/2650 - loss 0.30051020 throughput (samples/sec): 736.97
2019-08-19 17:35:03,524 epoch 20 - iter 2385/2650 - loss 0.30045963 throughput (samples/sec): 739.35
2019-08-19 17:35:15,038 ----------------------------------------------------------------------------------------------------
2019-08-19 17:35:15,038 EPOCH 20 done: loss 0.2996 - lr 0.1000
2019-08-19 17:35:15,038 BAD EPOCHS (no improvement): 0
2019-08-19 17:35:15,039 ----------------------------------------------------------------------------------------------------
2019-08-19 17:35:15,089 epoch 21 - iter 0/2650 - loss 0.34020928 throughput (samples/sec): 181756.25
2019-08-19 17:35:26,881 epoch 21 - iter 265/2650 - loss 0.29417952 throughput (samples/sec): 725.13
2019-08-19 17:35:38,719 epoch 21 - iter 530/2650 - loss 0.29426581 throughput (samples/sec): 722.46
2019-08-19 17:35:50,809 epoch 21 - iter 795/2650 - loss 0.29470998 throughput (samples/sec): 708.47
2019-08-19 17:36:03,059 epoch 21 - iter 1060/2650 - loss 0.29478367 throughput (samples/sec): 698.86
2019-08-19 17:36:15,038 epoch 21 - iter 1325/2650 - loss 0.29281134 throughput (samples/sec): 714.31
2019-08-19 17:36:26,983 epoch 21 - iter 1590/2650 - loss 0.29386612 throughput (samples/sec): 716.55
2019-08-19 17:36:38,952 epoch 21 - iter 1855/2650 - loss 0.29440855 throughput (samples/sec): 714.97
2019-08-19 17:36:51,167 epoch 21 - iter 2120/2650 - loss 0.29417590 throughput (samples/sec): 700.72
2019-08-19 17:37:03,098 epoch 21 - iter 2385/2650 - loss 0.29471446 throughput (samples/sec): 717.34
2019-08-19 17:37:14,610 ----------------------------------------------------------------------------------------------------
2019-08-19 17:37:14,610 EPOCH 21 done: loss 0.2948 - lr 0.1000
2019-08-19 17:37:14,610 BAD EPOCHS (no improvement): 0
2019-08-19 17:37:14,611 ----------------------------------------------------------------------------------------------------
2019-08-19 17:37:14,658 epoch 22 - iter 0/2650 - loss 0.27656740 throughput (samples/sec): 198904.46
2019-08-19 17:37:25,928 epoch 22 - iter 265/2650 - loss 0.28668403 throughput (samples/sec): 758.61
2019-08-19 17:37:38,076 epoch 22 - iter 530/2650 - loss 0.28572542 throughput (samples/sec): 703.69
2019-08-19 17:37:50,672 epoch 22 - iter 795/2650 - loss 0.28814578 throughput (samples/sec): 679.33
2019-08-19 17:38:03,411 epoch 22 - iter 1060/2650 - loss 0.28880269 throughput (samples/sec): 671.43
2019-08-19 17:38:16,135 epoch 22 - iter 1325/2650 - loss 0.29091185 throughput (samples/sec): 672.64
2019-08-19 17:38:28,864 epoch 22 - iter 1590/2650 - loss 0.29037967 throughput (samples/sec): 672.43
2019-08-19 17:38:40,901 epoch 22 - iter 1855/2650 - loss 0.28922229 throughput (samples/sec): 711.04
2019-08-19 17:38:52,998 epoch 22 - iter 2120/2650 - loss 0.28895215 throughput (samples/sec): 707.62
2019-08-19 17:39:04,925 epoch 22 - iter 2385/2650 - loss 0.28897117 throughput (samples/sec): 717.31
2019-08-19 17:39:17,567 ----------------------------------------------------------------------------------------------------
2019-08-19 17:39:17,567 EPOCH 22 done: loss 0.2890 - lr 0.1000
2019-08-19 17:39:17,568 BAD EPOCHS (no improvement): 0
2019-08-19 17:39:17,568 ----------------------------------------------------------------------------------------------------
2019-08-19 17:39:17,618 epoch 23 - iter 0/2650 - loss 0.30959150 throughput (samples/sec): 202609.53
2019-08-19 17:39:29,380 epoch 23 - iter 265/2650 - loss 0.29118833 throughput (samples/sec): 727.42
2019-08-19 17:39:41,005 epoch 23 - iter 530/2650 - loss 0.28948907 throughput (samples/sec): 736.66
2019-08-19 17:39:52,825 epoch 23 - iter 795/2650 - loss 0.28457829 throughput (samples/sec): 723.76
2019-08-19 17:40:04,766 epoch 23 - iter 1060/2650 - loss 0.28456577 throughput (samples/sec): 716.62
2019-08-19 17:40:16,626 epoch 23 - iter 1325/2650 - loss 0.28505536 throughput (samples/sec): 721.86
2019-08-19 17:40:28,698 epoch 23 - iter 1590/2650 - loss 0.28409117 throughput (samples/sec): 708.93
2019-08-19 17:40:40,753 epoch 23 - iter 1855/2650 - loss 0.28389174 throughput (samples/sec): 709.86
2019-08-19 17:40:53,660 epoch 23 - iter 2120/2650 - loss 0.28355753 throughput (samples/sec): 662.90
2019-08-19 17:41:06,310 epoch 23 - iter 2385/2650 - loss 0.28348386 throughput (samples/sec): 676.70
2019-08-19 17:41:18,311 ----------------------------------------------------------------------------------------------------
2019-08-19 17:41:18,312 EPOCH 23 done: loss 0.2831 - lr 0.1000
2019-08-19 17:41:18,312 BAD EPOCHS (no improvement): 0
2019-08-19 17:41:18,313 ----------------------------------------------------------------------------------------------------
2019-08-19 17:41:18,357 epoch 24 - iter 0/2650 - loss 0.26445863 throughput (samples/sec): 208521.37
2019-08-19 17:41:29,938 epoch 24 - iter 265/2650 - loss 0.28018502 throughput (samples/sec): 738.52
2019-08-19 17:41:41,752 epoch 24 - iter 530/2650 - loss 0.27962314 throughput (samples/sec): 724.13
2019-08-19 17:41:53,716 epoch 24 - iter 795/2650 - loss 0.27864952 throughput (samples/sec): 714.67
2019-08-19 17:42:04,892 epoch 24 - iter 1060/2650 - loss 0.27923338 throughput (samples/sec): 766.17
2019-08-19 17:42:16,675 epoch 24 - iter 1325/2650 - loss 0.27997684 throughput (samples/sec): 726.43
2019-08-19 17:42:28,359 epoch 24 - iter 1590/2650 - loss 0.27967577 throughput (samples/sec): 733.18
2019-08-19 17:42:40,594 epoch 24 - iter 1855/2650 - loss 0.27896390 throughput (samples/sec): 699.50
2019-08-19 17:42:52,538 epoch 24 - iter 2120/2650 - loss 0.27938061 throughput (samples/sec): 716.67
2019-08-19 17:43:04,084 epoch 24 - iter 2385/2650 - loss 0.27911248 throughput (samples/sec): 740.53
2019-08-19 17:43:16,799 ----------------------------------------------------------------------------------------------------
2019-08-19 17:43:16,799 EPOCH 24 done: loss 0.2788 - lr 0.1000
2019-08-19 17:43:16,800 BAD EPOCHS (no improvement): 0
2019-08-19 17:43:16,800 ----------------------------------------------------------------------------------------------------
2019-08-19 17:43:16,855 epoch 25 - iter 0/2650 - loss 0.26982605 throughput (samples/sec): 175070.62
2019-08-19 17:43:28,900 epoch 25 - iter 265/2650 - loss 0.27963520 throughput (samples/sec): 710.44
2019-08-19 17:43:40,771 epoch 25 - iter 530/2650 - loss 0.27612192 throughput (samples/sec): 720.77
2019-08-19 17:43:53,139 epoch 25 - iter 795/2650 - loss 0.27343484 throughput (samples/sec): 692.39
2019-08-19 17:44:05,210 epoch 25 - iter 1060/2650 - loss 0.27229193 throughput (samples/sec): 709.24
2019-08-19 17:44:17,788 epoch 25 - iter 1325/2650 - loss 0.27258959 throughput (samples/sec): 680.44
2019-08-19 17:44:29,745 epoch 25 - iter 1590/2650 - loss 0.27404190 throughput (samples/sec): 715.58
2019-08-19 17:44:41,660 epoch 25 - iter 1855/2650 - loss 0.27434866 throughput (samples/sec): 718.18
2019-08-19 17:44:53,999 epoch 25 - iter 2120/2650 - loss 0.27463850 throughput (samples/sec): 693.00
2019-08-19 17:45:06,110 epoch 25 - iter 2385/2650 - loss 0.27438252 throughput (samples/sec): 706.99
2019-08-19 17:45:18,431 ----------------------------------------------------------------------------------------------------
2019-08-19 17:45:18,431 EPOCH 25 done: loss 0.2749 - lr 0.1000
2019-08-19 17:45:18,432 BAD EPOCHS (no improvement): 0
2019-08-19 17:45:18,432 ----------------------------------------------------------------------------------------------------
2019-08-19 17:45:18,480 epoch 26 - iter 0/2650 - loss 0.25150743 throughput (samples/sec): 206275.65
2019-08-19 17:45:31,047 epoch 26 - iter 265/2650 - loss 0.26523599 throughput (samples/sec): 680.92
2019-08-19 17:45:42,831 epoch 26 - iter 530/2650 - loss 0.26942665 throughput (samples/sec): 726.37
2019-08-19 17:45:55,191 epoch 26 - iter 795/2650 - loss 0.26920713 throughput (samples/sec): 692.25
2019-08-19 17:46:06,718 epoch 26 - iter 1060/2650 - loss 0.27041395 throughput (samples/sec): 742.11
2019-08-19 17:46:19,024 epoch 26 - iter 1325/2650 - loss 0.27016622 throughput (samples/sec): 694.99
2019-08-19 17:46:31,649 epoch 26 - iter 1590/2650 - loss 0.27031997 throughput (samples/sec): 678.18
2019-08-19 17:46:43,795 epoch 26 - iter 1855/2650 - loss 0.26944139 throughput (samples/sec): 704.76
2019-08-19 17:46:55,662 epoch 26 - iter 2120/2650 - loss 0.26917912 throughput (samples/sec): 720.59
2019-08-19 17:47:07,610 epoch 26 - iter 2385/2650 - loss 0.26962445 throughput (samples/sec): 716.39
2019-08-19 17:47:19,216 ----------------------------------------------------------------------------------------------------
2019-08-19 17:47:19,217 EPOCH 26 done: loss 0.2696 - lr 0.1000
2019-08-19 17:47:19,217 BAD EPOCHS (no improvement): 0
2019-08-19 17:47:19,217 ----------------------------------------------------------------------------------------------------
2019-08-19 17:47:19,264 epoch 27 - iter 0/2650 - loss 0.35072535 throughput (samples/sec): 204879.51
2019-08-19 17:47:31,753 epoch 27 - iter 265/2650 - loss 0.25849895 throughput (samples/sec): 684.88
2019-08-19 17:47:44,553 epoch 27 - iter 530/2650 - loss 0.26153284 throughput (samples/sec): 668.78
2019-08-19 17:47:57,166 epoch 27 - iter 795/2650 - loss 0.26182819 throughput (samples/sec): 678.68
2019-08-19 17:48:08,495 epoch 27 - iter 1060/2650 - loss 0.26176390 throughput (samples/sec): 755.57
2019-08-19 17:48:20,305 epoch 27 - iter 1325/2650 - loss 0.26254683 throughput (samples/sec): 724.05
2019-08-19 17:48:31,978 epoch 27 - iter 1590/2650 - loss 0.26236275 throughput (samples/sec): 732.39
2019-08-19 17:48:44,211 epoch 27 - iter 1855/2650 - loss 0.26295311 throughput (samples/sec): 699.40
2019-08-19 17:48:56,383 epoch 27 - iter 2120/2650 - loss 0.26303618 throughput (samples/sec): 702.96
2019-08-19 17:49:08,446 epoch 27 - iter 2385/2650 - loss 0.26322511 throughput (samples/sec): 709.84
2019-08-19 17:49:20,783 ----------------------------------------------------------------------------------------------------
2019-08-19 17:49:20,783 EPOCH 27 done: loss 0.2637 - lr 0.1000
2019-08-19 17:49:20,783 BAD EPOCHS (no improvement): 0
2019-08-19 17:49:20,784 ----------------------------------------------------------------------------------------------------
2019-08-19 17:49:20,831 epoch 28 - iter 0/2650 - loss 0.30805418 throughput (samples/sec): 212371.09
2019-08-19 17:49:32,647 epoch 28 - iter 265/2650 - loss 0.26732861 throughput (samples/sec): 724.20
2019-08-19 17:49:44,940 epoch 28 - iter 530/2650 - loss 0.26756367 throughput (samples/sec): 696.18
2019-08-19 17:49:56,974 epoch 28 - iter 795/2650 - loss 0.26691218 throughput (samples/sec): 711.21
2019-08-19 17:50:08,827 epoch 28 - iter 1060/2650 - loss 0.26722838 throughput (samples/sec): 721.26
2019-08-19 17:50:20,700 epoch 28 - iter 1325/2650 - loss 0.26780321 throughput (samples/sec): 720.05
2019-08-19 17:50:32,718 epoch 28 - iter 1590/2650 - loss 0.26613635 throughput (samples/sec): 712.55
2019-08-19 17:50:44,777 epoch 28 - iter 1855/2650 - loss 0.26563533 throughput (samples/sec): 709.99
2019-08-19 17:50:56,572 epoch 28 - iter 2120/2650 - loss 0.26489422 throughput (samples/sec): 725.20
2019-08-19 17:51:08,774 epoch 28 - iter 2385/2650 - loss 0.26476272 throughput (samples/sec): 701.54
2019-08-19 17:51:21,234 ----------------------------------------------------------------------------------------------------
2019-08-19 17:51:21,234 EPOCH 28 done: loss 0.2644 - lr 0.1000
2019-08-19 17:51:21,234 BAD EPOCHS (no improvement): 1
2019-08-19 17:51:21,235 ----------------------------------------------------------------------------------------------------
2019-08-19 17:51:21,290 epoch 29 - iter 0/2650 - loss 0.18793395 throughput (samples/sec): 172931.56
2019-08-19 17:51:33,784 epoch 29 - iter 265/2650 - loss 0.25911771 throughput (samples/sec): 684.78
2019-08-19 17:51:45,877 epoch 29 - iter 530/2650 - loss 0.25816021 throughput (samples/sec): 707.77
2019-08-19 17:51:57,854 epoch 29 - iter 795/2650 - loss 0.25894733 throughput (samples/sec): 714.48
2019-08-19 17:52:09,643 epoch 29 - iter 1060/2650 - loss 0.25871637 throughput (samples/sec): 725.87
2019-08-19 17:52:21,589 epoch 29 - iter 1325/2650 - loss 0.25888054 throughput (samples/sec): 716.64
2019-08-19 17:52:33,556 epoch 29 - iter 1590/2650 - loss 0.25844501 throughput (samples/sec): 715.28
2019-08-19 17:52:45,616 epoch 29 - iter 1855/2650 - loss 0.25870154 throughput (samples/sec): 709.62
2019-08-19 17:52:58,096 epoch 29 - iter 2120/2650 - loss 0.25891712 throughput (samples/sec): 685.85
2019-08-19 17:53:09,899 epoch 29 - iter 2385/2650 - loss 0.25830874 throughput (samples/sec): 724.69
2019-08-19 17:53:21,778 ----------------------------------------------------------------------------------------------------
2019-08-19 17:53:21,778 EPOCH 29 done: loss 0.2583 - lr 0.1000
2019-08-19 17:53:21,778 BAD EPOCHS (no improvement): 0
2019-08-19 17:53:21,779 ----------------------------------------------------------------------------------------------------
2019-08-19 17:53:21,830 epoch 30 - iter 0/2650 - loss 0.30124110 throughput (samples/sec): 183016.01
2019-08-19 17:53:34,422 epoch 30 - iter 265/2650 - loss 0.26034614 throughput (samples/sec): 679.77
2019-08-19 17:53:47,143 epoch 30 - iter 530/2650 - loss 0.25636060 throughput (samples/sec): 672.45
2019-08-19 17:54:00,024 epoch 30 - iter 795/2650 - loss 0.25621297 throughput (samples/sec): 664.02
2019-08-19 17:54:11,692 epoch 30 - iter 1060/2650 - loss 0.25658918 throughput (samples/sec): 733.14
2019-08-19 17:54:23,617 epoch 30 - iter 1325/2650 - loss 0.25599529 throughput (samples/sec): 717.75
2019-08-19 17:54:35,991 epoch 30 - iter 1590/2650 - loss 0.25664372 throughput (samples/sec): 692.12
2019-08-19 17:54:48,462 epoch 30 - iter 1855/2650 - loss 0.25676785 throughput (samples/sec): 686.35
2019-08-19 17:55:00,357 epoch 30 - iter 2120/2650 - loss 0.25677979 throughput (samples/sec): 719.64
2019-08-19 17:55:12,563 epoch 30 - iter 2385/2650 - loss 0.25711299 throughput (samples/sec): 701.32
2019-08-19 17:55:25,066 ----------------------------------------------------------------------------------------------------
2019-08-19 17:55:25,066 EPOCH 30 done: loss 0.2567 - lr 0.1000
2019-08-19 17:55:25,066 BAD EPOCHS (no improvement): 0
2019-08-19 17:55:25,067 ----------------------------------------------------------------------------------------------------
2019-08-19 17:55:25,111 epoch 31 - iter 0/2650 - loss 0.27951309 throughput (samples/sec): 226988.26
2019-08-19 17:55:36,718 epoch 31 - iter 265/2650 - loss 0.25702580 throughput (samples/sec): 737.21
2019-08-19 17:55:48,654 epoch 31 - iter 530/2650 - loss 0.25963426 throughput (samples/sec): 717.31
2019-08-19 17:56:00,173 epoch 31 - iter 795/2650 - loss 0.25535843 throughput (samples/sec): 743.22
2019-08-19 17:56:12,036 epoch 31 - iter 1060/2650 - loss 0.25311134 throughput (samples/sec): 721.06
2019-08-19 17:56:23,770 epoch 31 - iter 1325/2650 - loss 0.25304331 throughput (samples/sec): 729.44
2019-08-19 17:56:35,061 epoch 31 - iter 1590/2650 - loss 0.25301085 throughput (samples/sec): 757.59
2019-08-19 17:56:46,935 epoch 31 - iter 1855/2650 - loss 0.25316826 throughput (samples/sec): 720.39
2019-08-19 17:56:58,800 epoch 31 - iter 2120/2650 - loss 0.25300463 throughput (samples/sec): 721.15
2019-08-19 17:57:10,392 epoch 31 - iter 2385/2650 - loss 0.25247141 throughput (samples/sec): 737.59
2019-08-19 17:57:22,012 ----------------------------------------------------------------------------------------------------
2019-08-19 17:57:22,013 EPOCH 31 done: loss 0.2524 - lr 0.1000
2019-08-19 17:57:22,013 BAD EPOCHS (no improvement): 0
2019-08-19 17:57:22,014 ----------------------------------------------------------------------------------------------------
2019-08-19 17:57:22,066 epoch 32 - iter 0/2650 - loss 0.31662893 throughput (samples/sec): 182560.41
2019-08-19 17:57:33,676 epoch 32 - iter 265/2650 - loss 0.24924151 throughput (samples/sec): 737.62
2019-08-19 17:57:45,899 epoch 32 - iter 530/2650 - loss 0.25385967 throughput (samples/sec): 700.24
2019-08-19 17:57:58,668 epoch 32 - iter 795/2650 - loss 0.25522494 throughput (samples/sec): 670.21
2019-08-19 17:58:11,017 epoch 32 - iter 1060/2650 - loss 0.25238760 throughput (samples/sec): 693.13
2019-08-19 17:58:23,122 epoch 32 - iter 1325/2650 - loss 0.25100075 throughput (samples/sec): 706.79
2019-08-19 17:58:34,989 epoch 32 - iter 1590/2650 - loss 0.25000941 throughput (samples/sec): 721.50
2019-08-19 17:58:47,487 epoch 32 - iter 1855/2650 - loss 0.24922892 throughput (samples/sec): 685.03
2019-08-19 17:59:00,155 epoch 32 - iter 2120/2650 - loss 0.24806292 throughput (samples/sec): 675.73
2019-08-19 17:59:12,799 epoch 32 - iter 2385/2650 - loss 0.24844811 throughput (samples/sec): 677.20
2019-08-19 17:59:25,455 ----------------------------------------------------------------------------------------------------
2019-08-19 17:59:25,455 EPOCH 32 done: loss 0.2491 - lr 0.1000
2019-08-19 17:59:25,455 BAD EPOCHS (no improvement): 0
2019-08-19 17:59:25,456 ----------------------------------------------------------------------------------------------------
2019-08-19 17:59:25,503 epoch 33 - iter 0/2650 - loss 0.22995272 throughput (samples/sec): 204676.73
2019-08-19 17:59:37,226 epoch 33 - iter 265/2650 - loss 0.24476457 throughput (samples/sec): 729.74
2019-08-19 17:59:49,404 epoch 33 - iter 530/2650 - loss 0.24656088 throughput (samples/sec): 702.25
2019-08-19 18:00:00,436 epoch 33 - iter 795/2650 - loss 0.24662153 throughput (samples/sec): 776.17
2019-08-19 18:00:11,476 epoch 33 - iter 1060/2650 - loss 0.24579152 throughput (samples/sec): 775.30
2019-08-19 18:00:22,456 epoch 33 - iter 1325/2650 - loss 0.24659248 throughput (samples/sec): 779.38
2019-08-19 18:00:33,580 epoch 33 - iter 1590/2650 - loss 0.24662819 throughput (samples/sec): 769.23
2019-08-19 18:00:44,577 epoch 33 - iter 1855/2650 - loss 0.24619211 throughput (samples/sec): 778.10
2019-08-19 18:00:55,699 epoch 33 - iter 2120/2650 - loss 0.24682064 throughput (samples/sec): 769.27
2019-08-19 18:01:06,903 epoch 33 - iter 2385/2650 - loss 0.24674135 throughput (samples/sec): 763.90
2019-08-19 18:01:17,714 ----------------------------------------------------------------------------------------------------
2019-08-19 18:01:17,715 EPOCH 33 done: loss 0.2477 - lr 0.1000
2019-08-19 18:01:17,715 BAD EPOCHS (no improvement): 0
2019-08-19 18:01:17,715 ----------------------------------------------------------------------------------------------------
2019-08-19 18:01:17,756 epoch 34 - iter 0/2650 - loss 0.23535991 throughput (samples/sec): 225617.51
2019-08-19 18:01:28,586 epoch 34 - iter 265/2650 - loss 0.24708416 throughput (samples/sec): 790.29
2019-08-19 18:01:40,415 epoch 34 - iter 530/2650 - loss 0.24356153 throughput (samples/sec): 723.51
2019-08-19 18:01:53,059 epoch 34 - iter 795/2650 - loss 0.24428704 throughput (samples/sec): 676.73
2019-08-19 18:02:04,719 epoch 34 - iter 1060/2650 - loss 0.24485880 throughput (samples/sec): 733.64
2019-08-19 18:02:16,278 epoch 34 - iter 1325/2650 - loss 0.24397159 throughput (samples/sec): 739.63
2019-08-19 18:02:28,064 epoch 34 - iter 1590/2650 - loss 0.24472768 throughput (samples/sec): 725.75
2019-08-19 18:02:40,679 epoch 34 - iter 1855/2650 - loss 0.24353609 throughput (samples/sec): 678.62
2019-08-19 18:02:52,688 epoch 34 - iter 2120/2650 - loss 0.24355824 throughput (samples/sec): 712.91
2019-08-19 18:03:04,526 epoch 34 - iter 2385/2650 - loss 0.24400710 throughput (samples/sec): 722.62
2019-08-19 18:03:16,198 ----------------------------------------------------------------------------------------------------
2019-08-19 18:03:16,198 EPOCH 34 done: loss 0.2439 - lr 0.1000
2019-08-19 18:03:16,198 BAD EPOCHS (no improvement): 0
2019-08-19 18:03:16,199 ----------------------------------------------------------------------------------------------------
2019-08-19 18:03:16,242 epoch 35 - iter 0/2650 - loss 0.18991825 throughput (samples/sec): 218347.39
2019-08-19 18:03:27,653 epoch 35 - iter 265/2650 - loss 0.24001168 throughput (samples/sec): 749.23
2019-08-19 18:03:39,649 epoch 35 - iter 530/2650 - loss 0.24201679 throughput (samples/sec): 712.87
2019-08-19 18:03:52,414 epoch 35 - iter 795/2650 - loss 0.24072888 throughput (samples/sec): 670.83
2019-08-19 18:04:04,720 epoch 35 - iter 1060/2650 - loss 0.24104915 throughput (samples/sec): 696.02
2019-08-19 18:04:17,087 epoch 35 - iter 1325/2650 - loss 0.24166496 throughput (samples/sec): 691.59
2019-08-19 18:04:29,070 epoch 35 - iter 1590/2650 - loss 0.24178493 throughput (samples/sec): 714.17
2019-08-19 18:04:40,859 epoch 35 - iter 1855/2650 - loss 0.24116544 throughput (samples/sec): 725.43
2019-08-19 18:04:52,329 epoch 35 - iter 2120/2650 - loss 0.24099458 throughput (samples/sec): 745.84
2019-08-19 18:05:03,513 epoch 35 - iter 2385/2650 - loss 0.24088479 throughput (samples/sec): 765.02
2019-08-19 18:05:14,799 ----------------------------------------------------------------------------------------------------
2019-08-19 18:05:14,799 EPOCH 35 done: loss 0.2412 - lr 0.1000
2019-08-19 18:05:14,799 BAD EPOCHS (no improvement): 0
2019-08-19 18:05:14,800 ----------------------------------------------------------------------------------------------------
2019-08-19 18:05:14,848 epoch 36 - iter 0/2650 - loss 0.18916214 throughput (samples/sec): 192238.08
2019-08-19 18:05:26,104 epoch 36 - iter 265/2650 - loss 0.23710373 throughput (samples/sec): 759.71
2019-08-19 18:05:37,909 epoch 36 - iter 530/2650 - loss 0.23822211 throughput (samples/sec): 724.48
2019-08-19 18:05:50,312 epoch 36 - iter 795/2650 - loss 0.23883372 throughput (samples/sec): 690.13
2019-08-19 18:06:02,429 epoch 36 - iter 1060/2650 - loss 0.23969475 throughput (samples/sec): 706.34
2019-08-19 18:06:14,648 epoch 36 - iter 1325/2650 - loss 0.23856615 throughput (samples/sec): 700.50
2019-08-19 18:06:27,544 epoch 36 - iter 1590/2650 - loss 0.23883440 throughput (samples/sec): 663.70
2019-08-19 18:06:39,970 epoch 36 - iter 1855/2650 - loss 0.23939186 throughput (samples/sec): 689.33
2019-08-19 18:06:51,545 epoch 36 - iter 2120/2650 - loss 0.23900033 throughput (samples/sec): 739.22
2019-08-19 18:07:03,343 epoch 36 - iter 2385/2650 - loss 0.23831161 throughput (samples/sec): 725.69
2019-08-19 18:07:15,855 ----------------------------------------------------------------------------------------------------
2019-08-19 18:07:15,855 EPOCH 36 done: loss 0.2386 - lr 0.1000
2019-08-19 18:07:15,855 BAD EPOCHS (no improvement): 0
2019-08-19 18:07:15,857 ----------------------------------------------------------------------------------------------------
2019-08-19 18:07:15,912 epoch 37 - iter 0/2650 - loss 0.13170101 throughput (samples/sec): 171806.37
2019-08-19 18:07:27,969 epoch 37 - iter 265/2650 - loss 0.23015323 throughput (samples/sec): 709.68
2019-08-19 18:07:40,623 epoch 37 - iter 530/2650 - loss 0.23732401 throughput (samples/sec): 675.82
2019-08-19 18:07:53,384 epoch 37 - iter 795/2650 - loss 0.23630463 throughput (samples/sec): 670.57
2019-08-19 18:08:05,575 epoch 37 - iter 1060/2650 - loss 0.23669973 throughput (samples/sec): 702.04
2019-08-19 18:08:18,201 epoch 37 - iter 1325/2650 - loss 0.23713529 throughput (samples/sec): 677.87
2019-08-19 18:08:29,640 epoch 37 - iter 1590/2650 - loss 0.23703804 throughput (samples/sec): 747.79
2019-08-19 18:08:41,631 epoch 37 - iter 1855/2650 - loss 0.23656060 throughput (samples/sec): 713.46
2019-08-19 18:08:53,687 epoch 37 - iter 2120/2650 - loss 0.23666445 throughput (samples/sec): 709.92
2019-08-19 18:09:05,269 epoch 37 - iter 2385/2650 - loss 0.23604210 throughput (samples/sec): 738.51
2019-08-19 18:09:17,123 ----------------------------------------------------------------------------------------------------
2019-08-19 18:09:17,123 EPOCH 37 done: loss 0.2357 - lr 0.1000
2019-08-19 18:09:17,123 BAD EPOCHS (no improvement): 0
2019-08-19 18:09:17,124 ----------------------------------------------------------------------------------------------------
2019-08-19 18:09:17,176 epoch 38 - iter 0/2650 - loss 0.17838463 throughput (samples/sec): 184033.99
2019-08-19 18:09:29,400 epoch 38 - iter 265/2650 - loss 0.23129194 throughput (samples/sec): 700.35
2019-08-19 18:09:42,090 epoch 38 - iter 530/2650 - loss 0.23363122 throughput (samples/sec): 674.72
2019-08-19 18:09:53,745 epoch 38 - iter 795/2650 - loss 0.23334409 throughput (samples/sec): 733.90
2019-08-19 18:10:05,803 epoch 38 - iter 1060/2650 - loss 0.23297286 throughput (samples/sec): 709.86
2019-08-19 18:10:18,616 epoch 38 - iter 1325/2650 - loss 0.23224357 throughput (samples/sec): 667.79
2019-08-19 18:10:31,446 epoch 38 - iter 1590/2650 - loss 0.23289326 throughput (samples/sec): 666.90
2019-08-19 18:10:43,298 epoch 38 - iter 1855/2650 - loss 0.23407575 throughput (samples/sec): 722.55
2019-08-19 18:10:55,231 epoch 38 - iter 2120/2650 - loss 0.23428072 throughput (samples/sec): 717.06
2019-08-19 18:11:07,652 epoch 38 - iter 2385/2650 - loss 0.23506197 throughput (samples/sec): 689.30
2019-08-19 18:11:19,980 ----------------------------------------------------------------------------------------------------
2019-08-19 18:11:19,980 EPOCH 38 done: loss 0.2352 - lr 0.1000
2019-08-19 18:11:19,981 BAD EPOCHS (no improvement): 0
2019-08-19 18:11:19,981 ----------------------------------------------------------------------------------------------------
2019-08-19 18:11:20,023 epoch 39 - iter 0/2650 - loss 0.16383472 throughput (samples/sec): 224919.83
2019-08-19 18:11:31,907 epoch 39 - iter 265/2650 - loss 0.23503086 throughput (samples/sec): 719.61
2019-08-19 18:11:44,414 epoch 39 - iter 530/2650 - loss 0.23189886 throughput (samples/sec): 683.76
2019-08-19 18:11:56,704 epoch 39 - iter 795/2650 - loss 0.23417262 throughput (samples/sec): 696.39
2019-08-19 18:12:09,302 epoch 39 - iter 1060/2650 - loss 0.23368633 throughput (samples/sec): 679.72
2019-08-19 18:12:21,673 epoch 39 - iter 1325/2650 - loss 0.23294201 throughput (samples/sec): 692.17
2019-08-19 18:12:33,833 epoch 39 - iter 1590/2650 - loss 0.23290420 throughput (samples/sec): 703.91
2019-08-19 18:12:45,789 epoch 39 - iter 1855/2650 - loss 0.23319804 throughput (samples/sec): 715.82
2019-08-19 18:12:57,644 epoch 39 - iter 2120/2650 - loss 0.23319470 throughput (samples/sec): 721.59
2019-08-19 18:13:09,699 epoch 39 - iter 2385/2650 - loss 0.23437341 throughput (samples/sec): 709.52
2019-08-19 18:13:21,894 ----------------------------------------------------------------------------------------------------
2019-08-19 18:13:21,895 EPOCH 39 done: loss 0.2339 - lr 0.1000
2019-08-19 18:13:21,895 BAD EPOCHS (no improvement): 0
2019-08-19 18:13:21,896 ----------------------------------------------------------------------------------------------------
2019-08-19 18:13:21,946 epoch 40 - iter 0/2650 - loss 0.32228515 throughput (samples/sec): 189017.96
2019-08-19 18:13:33,815 epoch 40 - iter 265/2650 - loss 0.23088217 throughput (samples/sec): 721.61
2019-08-19 18:13:45,140 epoch 40 - iter 530/2650 - loss 0.23228915 throughput (samples/sec): 755.42
2019-08-19 18:13:57,098 epoch 40 - iter 795/2650 - loss 0.23136555 throughput (samples/sec): 715.50
2019-08-19 18:14:09,387 epoch 40 - iter 1060/2650 - loss 0.22935241 throughput (samples/sec): 696.60
2019-08-19 18:14:22,028 epoch 40 - iter 1325/2650 - loss 0.22878725 throughput (samples/sec): 676.64
2019-08-19 18:14:34,635 epoch 40 - iter 1590/2650 - loss 0.22944231 throughput (samples/sec): 678.78
2019-08-19 18:14:46,774 epoch 40 - iter 1855/2650 - loss 0.22948178 throughput (samples/sec): 705.20
2019-08-19 18:14:58,657 epoch 40 - iter 2120/2650 - loss 0.22966042 throughput (samples/sec): 720.09
2019-08-19 18:15:10,313 epoch 40 - iter 2385/2650 - loss 0.22986314 throughput (samples/sec): 733.73
2019-08-19 18:15:22,036 ----------------------------------------------------------------------------------------------------
2019-08-19 18:15:22,036 EPOCH 40 done: loss 0.2307 - lr 0.1000
2019-08-19 18:15:22,036 BAD EPOCHS (no improvement): 0
2019-08-19 18:15:22,037 ----------------------------------------------------------------------------------------------------
2019-08-19 18:15:22,090 epoch 41 - iter 0/2650 - loss 0.19588199 throughput (samples/sec): 180149.81
2019-08-19 18:15:34,672 epoch 41 - iter 265/2650 - loss 0.23180060 throughput (samples/sec): 679.78
2019-08-19 18:15:46,083 epoch 41 - iter 530/2650 - loss 0.22975771 throughput (samples/sec): 749.62
2019-08-19 18:15:58,324 epoch 41 - iter 795/2650 - loss 0.22835552 throughput (samples/sec): 698.49
2019-08-19 18:16:10,469 epoch 41 - iter 1060/2650 - loss 0.22914443 throughput (samples/sec): 704.83
2019-08-19 18:16:22,367 epoch 41 - iter 1325/2650 - loss 0.22957292 throughput (samples/sec): 719.84
2019-08-19 18:16:33,577 epoch 41 - iter 1590/2650 - loss 0.22844529 throughput (samples/sec): 763.07
2019-08-19 18:16:44,916 epoch 41 - iter 1855/2650 - loss 0.22839340 throughput (samples/sec): 754.48
2019-08-19 18:16:57,164 epoch 41 - iter 2120/2650 - loss 0.22809158 throughput (samples/sec): 698.14
2019-08-19 18:17:09,200 epoch 41 - iter 2385/2650 - loss 0.22764974 throughput (samples/sec): 710.67
2019-08-19 18:17:22,041 ----------------------------------------------------------------------------------------------------
2019-08-19 18:17:22,041 EPOCH 41 done: loss 0.2272 - lr 0.1000
2019-08-19 18:17:22,041 BAD EPOCHS (no improvement): 0
2019-08-19 18:17:22,042 ----------------------------------------------------------------------------------------------------
2019-08-19 18:17:22,091 epoch 42 - iter 0/2650 - loss 0.28260866 throughput (samples/sec): 200610.83
2019-08-19 18:17:34,126 epoch 42 - iter 265/2650 - loss 0.22866965 throughput (samples/sec): 711.65
2019-08-19 18:17:45,887 epoch 42 - iter 530/2650 - loss 0.22841674 throughput (samples/sec): 727.42
2019-08-19 18:17:57,673 epoch 42 - iter 795/2650 - loss 0.22727976 throughput (samples/sec): 725.85
2019-08-19 18:18:09,048 epoch 42 - iter 1060/2650 - loss 0.22724507 throughput (samples/sec): 752.76
2019-08-19 18:18:21,097 epoch 42 - iter 1325/2650 - loss 0.22594073 throughput (samples/sec): 709.70
2019-08-19 18:18:33,956 epoch 42 - iter 1590/2650 - loss 0.22607386 throughput (samples/sec): 665.43
2019-08-19 18:18:46,758 epoch 42 - iter 1855/2650 - loss 0.22535064 throughput (samples/sec): 668.43
2019-08-19 18:18:58,998 epoch 42 - iter 2120/2650 - loss 0.22438726 throughput (samples/sec): 699.84
2019-08-19 18:19:10,829 epoch 42 - iter 2385/2650 - loss 0.22395761 throughput (samples/sec): 723.48
2019-08-19 18:19:22,587 ----------------------------------------------------------------------------------------------------
2019-08-19 18:19:22,587 EPOCH 42 done: loss 0.2242 - lr 0.1000
2019-08-19 18:19:22,587 BAD EPOCHS (no improvement): 0
2019-08-19 18:19:22,588 ----------------------------------------------------------------------------------------------------
2019-08-19 18:19:22,634 epoch 43 - iter 0/2650 - loss 0.23945720 throughput (samples/sec): 210691.64
2019-08-19 18:19:34,085 epoch 43 - iter 265/2650 - loss 0.21876241 throughput (samples/sec): 746.90
2019-08-19 18:19:46,508 epoch 43 - iter 530/2650 - loss 0.22003612 throughput (samples/sec): 688.13
2019-08-19 18:19:58,570 epoch 43 - iter 795/2650 - loss 0.21792837 throughput (samples/sec): 709.44
2019-08-19 18:20:11,039 epoch 43 - iter 1060/2650 - loss 0.21886064 throughput (samples/sec): 686.11
2019-08-19 18:20:22,775 epoch 43 - iter 1325/2650 - loss 0.22019047 throughput (samples/sec): 729.22
2019-08-19 18:20:34,707 epoch 43 - iter 1590/2650 - loss 0.22071249 throughput (samples/sec): 716.99
2019-08-19 18:20:46,299 epoch 43 - iter 1855/2650 - loss 0.22154296 throughput (samples/sec): 739.03
2019-08-19 18:20:58,132 epoch 43 - iter 2120/2650 - loss 0.22099066 throughput (samples/sec): 722.83
2019-08-19 18:21:10,091 epoch 43 - iter 2385/2650 - loss 0.22119737 throughput (samples/sec): 715.75
2019-08-19 18:21:22,090 ----------------------------------------------------------------------------------------------------
2019-08-19 18:21:22,091 EPOCH 43 done: loss 0.2224 - lr 0.1000
2019-08-19 18:21:22,091 BAD EPOCHS (no improvement): 0
2019-08-19 18:21:22,091 ----------------------------------------------------------------------------------------------------
2019-08-19 18:21:22,152 epoch 44 - iter 0/2650 - loss 0.18005544 throughput (samples/sec): 159915.19
2019-08-19 18:21:34,469 epoch 44 - iter 265/2650 - loss 0.22845506 throughput (samples/sec): 695.00
2019-08-19 18:21:46,310 epoch 44 - iter 530/2650 - loss 0.22558393 throughput (samples/sec): 723.42
2019-08-19 18:21:58,137 epoch 44 - iter 795/2650 - loss 0.22447855 throughput (samples/sec): 723.95
2019-08-19 18:22:10,257 epoch 44 - iter 1060/2650 - loss 0.22184268 throughput (samples/sec): 706.30
2019-08-19 18:22:22,453 epoch 44 - iter 1325/2650 - loss 0.22174383 throughput (samples/sec): 701.69
2019-08-19 18:22:34,420 epoch 44 - iter 1590/2650 - loss 0.22120803 throughput (samples/sec): 715.45
2019-08-19 18:22:46,720 epoch 44 - iter 1855/2650 - loss 0.22053542 throughput (samples/sec): 695.24
2019-08-19 18:22:57,933 epoch 44 - iter 2120/2650 - loss 0.22076209 throughput (samples/sec): 763.00
2019-08-19 18:23:09,710 epoch 44 - iter 2385/2650 - loss 0.22124650 throughput (samples/sec): 727.38
2019-08-19 18:23:21,363 ----------------------------------------------------------------------------------------------------
2019-08-19 18:23:21,364 EPOCH 44 done: loss 0.2213 - lr 0.1000
2019-08-19 18:23:21,364 BAD EPOCHS (no improvement): 0
2019-08-19 18:23:21,364 ----------------------------------------------------------------------------------------------------
2019-08-19 18:23:21,412 epoch 45 - iter 0/2650 - loss 0.15182313 throughput (samples/sec): 199078.14
2019-08-19 18:23:33,133 epoch 45 - iter 265/2650 - loss 0.20910663 throughput (samples/sec): 730.81
2019-08-19 18:23:45,185 epoch 45 - iter 530/2650 - loss 0.21616020 throughput (samples/sec): 710.18
2019-08-19 18:23:57,915 epoch 45 - iter 795/2650 - loss 0.21701019 throughput (samples/sec): 672.10
2019-08-19 18:24:10,094 epoch 45 - iter 1060/2650 - loss 0.21913785 throughput (samples/sec): 703.11
2019-08-19 18:24:22,030 epoch 45 - iter 1325/2650 - loss 0.21810261 throughput (samples/sec): 717.58
2019-08-19 18:24:33,826 epoch 45 - iter 1590/2650 - loss 0.21877979 throughput (samples/sec): 725.96
2019-08-19 18:24:45,828 epoch 45 - iter 1855/2650 - loss 0.21910723 throughput (samples/sec): 713.19
2019-08-19 18:24:57,329 epoch 45 - iter 2120/2650 - loss 0.21997163 throughput (samples/sec): 743.27
2019-08-19 18:25:09,435 epoch 45 - iter 2385/2650 - loss 0.22051872 throughput (samples/sec): 706.14
2019-08-19 18:25:21,676 ----------------------------------------------------------------------------------------------------
2019-08-19 18:25:21,677 EPOCH 45 done: loss 0.2200 - lr 0.1000
2019-08-19 18:25:21,677 BAD EPOCHS (no improvement): 0
2019-08-19 18:25:21,677 ----------------------------------------------------------------------------------------------------
2019-08-19 18:25:21,722 epoch 46 - iter 0/2650 - loss 0.20673397 throughput (samples/sec): 206348.65
2019-08-19 18:25:33,201 epoch 46 - iter 265/2650 - loss 0.22114012 throughput (samples/sec): 744.93
2019-08-19 18:25:44,967 epoch 46 - iter 530/2650 - loss 0.22036788 throughput (samples/sec): 728.03
2019-08-19 18:25:57,390 epoch 46 - iter 795/2650 - loss 0.21926090 throughput (samples/sec): 689.16
2019-08-19 18:26:08,961 epoch 46 - iter 1060/2650 - loss 0.21816503 throughput (samples/sec): 738.87
2019-08-19 18:26:20,748 epoch 46 - iter 1325/2650 - loss 0.21665141 throughput (samples/sec): 725.28
2019-08-19 18:26:32,319 epoch 46 - iter 1590/2650 - loss 0.21671101 throughput (samples/sec): 738.69
2019-08-19 18:26:44,210 epoch 46 - iter 1855/2650 - loss 0.21690559 throughput (samples/sec): 719.88
2019-08-19 18:26:56,089 epoch 46 - iter 2120/2650 - loss 0.21684390 throughput (samples/sec): 720.64
2019-08-19 18:27:07,154 epoch 46 - iter 2385/2650 - loss 0.21700182 throughput (samples/sec): 773.51
2019-08-19 18:27:18,099 ----------------------------------------------------------------------------------------------------
2019-08-19 18:27:18,099 EPOCH 46 done: loss 0.2182 - lr 0.1000
2019-08-19 18:27:18,099 BAD EPOCHS (no improvement): 0
2019-08-19 18:27:18,100 ----------------------------------------------------------------------------------------------------
2019-08-19 18:27:18,135 epoch 47 - iter 0/2650 - loss 0.18777919 throughput (samples/sec): 266898.52
2019-08-19 18:27:29,160 epoch 47 - iter 265/2650 - loss 0.20961298 throughput (samples/sec): 776.29
2019-08-19 18:27:41,053 epoch 47 - iter 530/2650 - loss 0.21297218 throughput (samples/sec): 719.38
2019-08-19 18:27:52,537 epoch 47 - iter 795/2650 - loss 0.21251210 throughput (samples/sec): 745.18
2019-08-19 18:28:03,769 epoch 47 - iter 1060/2650 - loss 0.21353662 throughput (samples/sec): 761.86
2019-08-19 18:28:16,391 epoch 47 - iter 1325/2650 - loss 0.21407095 throughput (samples/sec): 677.67
2019-08-19 18:28:28,316 epoch 47 - iter 1590/2650 - loss 0.21524045 throughput (samples/sec): 718.02
2019-08-19 18:28:41,044 epoch 47 - iter 1855/2650 - loss 0.21568782 throughput (samples/sec): 672.60
2019-08-19 18:28:53,604 epoch 47 - iter 2120/2650 - loss 0.21656365 throughput (samples/sec): 681.40
2019-08-19 18:29:06,327 epoch 47 - iter 2385/2650 - loss 0.21591261 throughput (samples/sec): 672.61
2019-08-19 18:29:18,601 ----------------------------------------------------------------------------------------------------
2019-08-19 18:29:18,602 EPOCH 47 done: loss 0.2162 - lr 0.1000
2019-08-19 18:29:18,602 BAD EPOCHS (no improvement): 0
2019-08-19 18:29:18,602 ----------------------------------------------------------------------------------------------------
2019-08-19 18:29:18,647 epoch 48 - iter 0/2650 - loss 0.22103284 throughput (samples/sec): 207524.93
2019-08-19 18:29:30,857 epoch 48 - iter 265/2650 - loss 0.21303874 throughput (samples/sec): 700.46
2019-08-19 18:29:43,560 epoch 48 - iter 530/2650 - loss 0.21128606 throughput (samples/sec): 673.88
2019-08-19 18:29:55,946 epoch 48 - iter 795/2650 - loss 0.21253301 throughput (samples/sec): 690.99
2019-08-19 18:30:08,072 epoch 48 - iter 1060/2650 - loss 0.21257674 throughput (samples/sec): 705.92
2019-08-19 18:30:20,701 epoch 48 - iter 1325/2650 - loss 0.21312349 throughput (samples/sec): 677.74
2019-08-19 18:30:32,573 epoch 48 - iter 1590/2650 - loss 0.21296783 throughput (samples/sec): 720.93
2019-08-19 18:30:44,488 epoch 48 - iter 1855/2650 - loss 0.21192271 throughput (samples/sec): 717.56
2019-08-19 18:30:56,784 epoch 48 - iter 2120/2650 - loss 0.21261960 throughput (samples/sec): 695.53
2019-08-19 18:31:09,117 epoch 48 - iter 2385/2650 - loss 0.21294187 throughput (samples/sec): 694.28
2019-08-19 18:31:21,291 ----------------------------------------------------------------------------------------------------
2019-08-19 18:31:21,291 EPOCH 48 done: loss 0.2129 - lr 0.1000
2019-08-19 18:31:21,291 BAD EPOCHS (no improvement): 0
2019-08-19 18:31:21,292 ----------------------------------------------------------------------------------------------------
2019-08-19 18:31:21,351 epoch 49 - iter 0/2650 - loss 0.19509727 throughput (samples/sec): 162011.58
2019-08-19 18:31:33,742 epoch 49 - iter 265/2650 - loss 0.21922868 throughput (samples/sec): 690.56
2019-08-19 18:31:46,338 epoch 49 - iter 530/2650 - loss 0.21257377 throughput (samples/sec): 679.32
2019-08-19 18:31:58,431 epoch 49 - iter 795/2650 - loss 0.21500071 throughput (samples/sec): 706.84
2019-08-19 18:32:10,566 epoch 49 - iter 1060/2650 - loss 0.21601772 throughput (samples/sec): 704.58
2019-08-19 18:32:23,123 epoch 49 - iter 1325/2650 - loss 0.21503636 throughput (samples/sec): 681.81
2019-08-19 18:32:34,862 epoch 49 - iter 1590/2650 - loss 0.21411202 throughput (samples/sec): 729.27
2019-08-19 18:32:46,294 epoch 49 - iter 1855/2650 - loss 0.21418414 throughput (samples/sec): 747.88
2019-08-19 18:32:58,038 epoch 49 - iter 2120/2650 - loss 0.21371725 throughput (samples/sec): 728.75
2019-08-19 18:33:10,566 epoch 49 - iter 2385/2650 - loss 0.21403420 throughput (samples/sec): 682.38
2019-08-19 18:33:22,648 ----------------------------------------------------------------------------------------------------
2019-08-19 18:33:22,648 EPOCH 49 done: loss 0.2142 - lr 0.1000
2019-08-19 18:33:22,648 BAD EPOCHS (no improvement): 1
2019-08-19 18:33:22,649 ----------------------------------------------------------------------------------------------------
2019-08-19 18:33:22,699 epoch 50 - iter 0/2650 - loss 0.20827709 throughput (samples/sec): 184908.46
2019-08-19 18:33:35,153 epoch 50 - iter 265/2650 - loss 0.20957705 throughput (samples/sec): 686.85
2019-08-19 18:33:47,992 epoch 50 - iter 530/2650 - loss 0.20883966 throughput (samples/sec): 666.82
2019-08-19 18:34:00,037 epoch 50 - iter 795/2650 - loss 0.21018067 throughput (samples/sec): 710.39
2019-08-19 18:34:11,888 epoch 50 - iter 1060/2650 - loss 0.20885101 throughput (samples/sec): 722.37
2019-08-19 18:34:24,063 epoch 50 - iter 1325/2650 - loss 0.20934102 throughput (samples/sec): 702.92
2019-08-19 18:34:36,831 epoch 50 - iter 1590/2650 - loss 0.20908498 throughput (samples/sec): 670.16
2019-08-19 18:34:48,867 epoch 50 - iter 1855/2650 - loss 0.20992419 throughput (samples/sec): 710.95
2019-08-19 18:35:01,062 epoch 50 - iter 2120/2650 - loss 0.20933167 throughput (samples/sec): 700.84
2019-08-19 18:35:13,768 epoch 50 - iter 2385/2650 - loss 0.21033254 throughput (samples/sec): 673.89
2019-08-19 18:35:25,349 ----------------------------------------------------------------------------------------------------
2019-08-19 18:35:25,350 EPOCH 50 done: loss 0.2102 - lr 0.1000
2019-08-19 18:35:25,350 BAD EPOCHS (no improvement): 0
2019-08-19 18:35:25,351 ----------------------------------------------------------------------------------------------------
2019-08-19 18:35:25,400 epoch 51 - iter 0/2650 - loss 0.28950837 throughput (samples/sec): 194692.00
2019-08-19 18:35:37,295 epoch 51 - iter 265/2650 - loss 0.20814597 throughput (samples/sec): 719.70
2019-08-19 18:35:49,285 epoch 51 - iter 530/2650 - loss 0.21006627 throughput (samples/sec): 713.92
2019-08-19 18:36:00,790 epoch 51 - iter 795/2650 - loss 0.21075226 throughput (samples/sec): 742.99
2019-08-19 18:36:12,721 epoch 51 - iter 1060/2650 - loss 0.21146521 throughput (samples/sec): 716.20
2019-08-19 18:36:24,797 epoch 51 - iter 1325/2650 - loss 0.21188530 throughput (samples/sec): 708.95
2019-08-19 18:36:36,722 epoch 51 - iter 1590/2650 - loss 0.21130509 throughput (samples/sec): 718.20
2019-08-19 18:36:48,918 epoch 51 - iter 1855/2650 - loss 0.21084528 throughput (samples/sec): 701.98
2019-08-19 18:37:00,667 epoch 51 - iter 2120/2650 - loss 0.21099218 throughput (samples/sec): 727.95
2019-08-19 18:37:13,375 epoch 51 - iter 2385/2650 - loss 0.21060916 throughput (samples/sec): 673.46
2019-08-19 18:37:24,793 ----------------------------------------------------------------------------------------------------
2019-08-19 18:37:24,793 EPOCH 51 done: loss 0.2112 - lr 0.1000
2019-08-19 18:37:24,794 BAD EPOCHS (no improvement): 1
2019-08-19 18:37:24,794 ----------------------------------------------------------------------------------------------------
2019-08-19 18:37:24,840 epoch 52 - iter 0/2650 - loss 0.20036638 throughput (samples/sec): 201698.40
2019-08-19 18:37:36,303 epoch 52 - iter 265/2650 - loss 0.20819991 throughput (samples/sec): 745.78
2019-08-19 18:37:47,532 epoch 52 - iter 530/2650 - loss 0.21069450 throughput (samples/sec): 761.73
2019-08-19 18:37:58,950 epoch 52 - iter 795/2650 - loss 0.21173091 throughput (samples/sec): 749.06
2019-08-19 18:38:10,538 epoch 52 - iter 1060/2650 - loss 0.21049659 throughput (samples/sec): 738.16
2019-08-19 18:38:23,029 epoch 52 - iter 1325/2650 - loss 0.20959214 throughput (samples/sec): 685.44
2019-08-19 18:38:35,156 epoch 52 - iter 1590/2650 - loss 0.20911563 throughput (samples/sec): 705.75
2019-08-19 18:38:47,421 epoch 52 - iter 1855/2650 - loss 0.20850643 throughput (samples/sec): 697.48
2019-08-19 18:38:59,186 epoch 52 - iter 2120/2650 - loss 0.20874014 throughput (samples/sec): 727.19
2019-08-19 18:39:11,401 epoch 52 - iter 2385/2650 - loss 0.20869472 throughput (samples/sec): 700.89
2019-08-19 18:39:24,153 ----------------------------------------------------------------------------------------------------
2019-08-19 18:39:24,160 EPOCH 52 done: loss 0.2086 - lr 0.1000
2019-08-19 18:39:24,161 BAD EPOCHS (no improvement): 0
2019-08-19 18:39:24,161 ----------------------------------------------------------------------------------------------------
2019-08-19 18:39:24,213 epoch 53 - iter 0/2650 - loss 0.21933979 throughput (samples/sec): 180669.58
2019-08-19 18:39:36,501 epoch 53 - iter 265/2650 - loss 0.20497846 throughput (samples/sec): 696.62
2019-08-19 18:39:48,361 epoch 53 - iter 530/2650 - loss 0.20459961 throughput (samples/sec): 720.93
2019-08-19 18:39:59,852 epoch 53 - iter 795/2650 - loss 0.20506467 throughput (samples/sec): 744.83
2019-08-19 18:40:11,813 epoch 53 - iter 1060/2650 - loss 0.20620522 throughput (samples/sec): 715.73
2019-08-19 18:40:23,944 epoch 53 - iter 1325/2650 - loss 0.20759674 throughput (samples/sec): 710.36
2019-08-19 18:40:36,033 epoch 53 - iter 1590/2650 - loss 0.20699551 throughput (samples/sec): 708.35
2019-08-19 18:40:47,970 epoch 53 - iter 1855/2650 - loss 0.20685398 throughput (samples/sec): 717.28
2019-08-19 18:41:00,307 epoch 53 - iter 2120/2650 - loss 0.20778145 throughput (samples/sec): 693.20
2019-08-19 18:41:12,748 epoch 53 - iter 2385/2650 - loss 0.20744359 throughput (samples/sec): 687.96
2019-08-19 18:41:25,340 ----------------------------------------------------------------------------------------------------
2019-08-19 18:41:25,341 EPOCH 53 done: loss 0.2070 - lr 0.1000
2019-08-19 18:41:25,341 BAD EPOCHS (no improvement): 0
2019-08-19 18:41:25,342 ----------------------------------------------------------------------------------------------------
2019-08-19 18:41:25,402 epoch 54 - iter 0/2650 - loss 0.14670137 throughput (samples/sec): 161903.17
2019-08-19 18:41:37,529 epoch 54 - iter 265/2650 - loss 0.20541256 throughput (samples/sec): 705.60
2019-08-19 18:41:49,349 epoch 54 - iter 530/2650 - loss 0.20404290 throughput (samples/sec): 723.54
2019-08-19 18:42:01,434 epoch 54 - iter 795/2650 - loss 0.20454517 throughput (samples/sec): 707.99
2019-08-19 18:42:14,017 epoch 54 - iter 1060/2650 - loss 0.20353741 throughput (samples/sec): 680.37
2019-08-19 18:42:26,286 epoch 54 - iter 1325/2650 - loss 0.20360301 throughput (samples/sec): 697.81
2019-08-19 18:42:37,455 epoch 54 - iter 1590/2650 - loss 0.20482166 throughput (samples/sec): 766.25
2019-08-19 18:42:48,558 epoch 54 - iter 1855/2650 - loss 0.20425539 throughput (samples/sec): 770.75
2019-08-19 18:43:00,674 epoch 54 - iter 2120/2650 - loss 0.20408707 throughput (samples/sec): 706.72
2019-08-19 18:43:12,625 epoch 54 - iter 2385/2650 - loss 0.20369267 throughput (samples/sec): 716.67
2019-08-19 18:43:23,894 ----------------------------------------------------------------------------------------------------
2019-08-19 18:43:23,894 EPOCH 54 done: loss 0.2035 - lr 0.1000
2019-08-19 18:43:23,894 BAD EPOCHS (no improvement): 0
2019-08-19 18:43:23,895 ----------------------------------------------------------------------------------------------------
2019-08-19 18:43:23,956 epoch 55 - iter 0/2650 - loss 0.37222201 throughput (samples/sec): 163525.89
2019-08-19 18:43:35,563 epoch 55 - iter 265/2650 - loss 0.20693792 throughput (samples/sec): 737.57
2019-08-19 18:43:48,041 epoch 55 - iter 530/2650 - loss 0.20401078 throughput (samples/sec): 686.00
2019-08-19 18:43:59,518 epoch 55 - iter 795/2650 - loss 0.20144302 throughput (samples/sec): 745.73
2019-08-19 18:44:11,347 epoch 55 - iter 1060/2650 - loss 0.20119961 throughput (samples/sec): 722.87
2019-08-19 18:44:23,766 epoch 55 - iter 1325/2650 - loss 0.20228157 throughput (samples/sec): 689.32
2019-08-19 18:44:36,316 epoch 55 - iter 1590/2650 - loss 0.20304199 throughput (samples/sec): 682.46
2019-08-19 18:44:49,068 epoch 55 - iter 1855/2650 - loss 0.20295972 throughput (samples/sec): 671.41
2019-08-19 18:45:01,206 epoch 55 - iter 2120/2650 - loss 0.20382886 throughput (samples/sec): 705.15
2019-08-19 18:45:13,342 epoch 55 - iter 2385/2650 - loss 0.20391944 throughput (samples/sec): 705.34
2019-08-19 18:45:25,427 ----------------------------------------------------------------------------------------------------
2019-08-19 18:45:25,428 EPOCH 55 done: loss 0.2036 - lr 0.1000
2019-08-19 18:45:25,428 BAD EPOCHS (no improvement): 1
2019-08-19 18:45:25,428 ----------------------------------------------------------------------------------------------------
2019-08-19 18:45:25,482 epoch 56 - iter 0/2650 - loss 0.34415540 throughput (samples/sec): 180910.35
2019-08-19 18:45:37,635 epoch 56 - iter 265/2650 - loss 0.20016689 throughput (samples/sec): 704.27
2019-08-19 18:45:49,517 epoch 56 - iter 530/2650 - loss 0.20292604 throughput (samples/sec): 720.73
2019-08-19 18:46:01,954 epoch 56 - iter 795/2650 - loss 0.20167064 throughput (samples/sec): 688.58
2019-08-19 18:46:13,517 epoch 56 - iter 1060/2650 - loss 0.20123600 throughput (samples/sec): 740.47
2019-08-19 18:46:25,640 epoch 56 - iter 1325/2650 - loss 0.20153249 throughput (samples/sec): 706.13
2019-08-19 18:46:37,516 epoch 56 - iter 1590/2650 - loss 0.20209210 throughput (samples/sec): 720.56
2019-08-19 18:46:49,411 epoch 56 - iter 1855/2650 - loss 0.20256154 throughput (samples/sec): 719.54
2019-08-19 18:47:01,528 epoch 56 - iter 2120/2650 - loss 0.20271688 throughput (samples/sec): 706.49
2019-08-19 18:47:13,393 epoch 56 - iter 2385/2650 - loss 0.20284491 throughput (samples/sec): 721.60
2019-08-19 18:47:26,089 ----------------------------------------------------------------------------------------------------
2019-08-19 18:47:26,089 EPOCH 56 done: loss 0.2035 - lr 0.1000
2019-08-19 18:47:26,089 BAD EPOCHS (no improvement): 2
2019-08-19 18:47:26,090 ----------------------------------------------------------------------------------------------------
2019-08-19 18:47:26,143 epoch 57 - iter 0/2650 - loss 0.12266492 throughput (samples/sec): 184008.29
2019-08-19 18:47:38,247 epoch 57 - iter 265/2650 - loss 0.20367886 throughput (samples/sec): 707.33
2019-08-19 18:47:50,323 epoch 57 - iter 530/2650 - loss 0.20469891 throughput (samples/sec): 708.70
2019-08-19 18:48:02,468 epoch 57 - iter 795/2650 - loss 0.20110042 throughput (samples/sec): 704.37
2019-08-19 18:48:14,699 epoch 57 - iter 1060/2650 - loss 0.20117809 throughput (samples/sec): 699.79
2019-08-19 18:48:26,799 epoch 57 - iter 1325/2650 - loss 0.20212097 throughput (samples/sec): 707.84
2019-08-19 18:48:38,303 epoch 57 - iter 1590/2650 - loss 0.20235443 throughput (samples/sec): 744.19
2019-08-19 18:48:50,088 epoch 57 - iter 1855/2650 - loss 0.20204161 throughput (samples/sec): 725.68
2019-08-19 18:49:02,210 epoch 57 - iter 2120/2650 - loss 0.20238083 throughput (samples/sec): 706.14
2019-08-19 18:49:13,908 epoch 57 - iter 2385/2650 - loss 0.20212645 throughput (samples/sec): 732.42
2019-08-19 18:49:25,554 ----------------------------------------------------------------------------------------------------
2019-08-19 18:49:25,554 EPOCH 57 done: loss 0.2018 - lr 0.1000
2019-08-19 18:49:25,555 BAD EPOCHS (no improvement): 0
2019-08-19 18:49:25,555 ----------------------------------------------------------------------------------------------------
2019-08-19 18:49:25,603 epoch 58 - iter 0/2650 - loss 0.21354020 throughput (samples/sec): 193057.19
2019-08-19 18:49:37,385 epoch 58 - iter 265/2650 - loss 0.19763093 throughput (samples/sec): 725.59
2019-08-19 18:49:49,333 epoch 58 - iter 530/2650 - loss 0.19961085 throughput (samples/sec): 716.74
2019-08-19 18:50:00,854 epoch 58 - iter 795/2650 - loss 0.19848858 throughput (samples/sec): 743.26
2019-08-19 18:50:12,888 epoch 58 - iter 1060/2650 - loss 0.19869247 throughput (samples/sec): 710.63
2019-08-19 18:50:24,553 epoch 58 - iter 1325/2650 - loss 0.20009362 throughput (samples/sec): 733.08
2019-08-19 18:50:36,695 epoch 58 - iter 1590/2650 - loss 0.19956512 throughput (samples/sec): 704.86
2019-08-19 18:50:48,749 epoch 58 - iter 1855/2650 - loss 0.19923077 throughput (samples/sec): 710.20
2019-08-19 18:51:00,906 epoch 58 - iter 2120/2650 - loss 0.19878348 throughput (samples/sec): 704.28
2019-08-19 18:51:12,708 epoch 58 - iter 2385/2650 - loss 0.19877101 throughput (samples/sec): 724.66
2019-08-19 18:51:24,879 ----------------------------------------------------------------------------------------------------
2019-08-19 18:51:24,879 EPOCH 58 done: loss 0.1994 - lr 0.1000
2019-08-19 18:51:24,879 BAD EPOCHS (no improvement): 0
2019-08-19 18:51:24,880 ----------------------------------------------------------------------------------------------------
2019-08-19 18:51:24,925 epoch 59 - iter 0/2650 - loss 0.32030973 throughput (samples/sec): 209683.06
2019-08-19 18:51:36,597 epoch 59 - iter 265/2650 - loss 0.20031985 throughput (samples/sec): 732.51
2019-08-19 18:51:48,624 epoch 59 - iter 530/2650 - loss 0.19944971 throughput (samples/sec): 711.73
2019-08-19 18:52:00,937 epoch 59 - iter 795/2650 - loss 0.20034641 throughput (samples/sec): 694.86
2019-08-19 18:52:13,106 epoch 59 - iter 1060/2650 - loss 0.19999116 throughput (samples/sec): 703.51
2019-08-19 18:52:25,240 epoch 59 - iter 1325/2650 - loss 0.20005021 throughput (samples/sec): 706.00
2019-08-19 18:52:37,205 epoch 59 - iter 1590/2650 - loss 0.20048842 throughput (samples/sec): 715.37
2019-08-19 18:52:49,213 epoch 59 - iter 1855/2650 - loss 0.20046760 throughput (samples/sec): 713.04
2019-08-19 18:53:00,995 epoch 59 - iter 2120/2650 - loss 0.19981869 throughput (samples/sec): 725.84
2019-08-19 18:53:13,262 epoch 59 - iter 2385/2650 - loss 0.20063166 throughput (samples/sec): 697.71
2019-08-19 18:53:25,510 ----------------------------------------------------------------------------------------------------
2019-08-19 18:53:25,511 EPOCH 59 done: loss 0.2000 - lr 0.1000
2019-08-19 18:53:25,511 BAD EPOCHS (no improvement): 1
2019-08-19 18:53:25,512 ----------------------------------------------------------------------------------------------------
2019-08-19 18:53:25,556 epoch 60 - iter 0/2650 - loss 0.24555789 throughput (samples/sec): 210941.55
2019-08-19 18:53:37,577 epoch 60 - iter 265/2650 - loss 0.20075484 throughput (samples/sec): 711.40
2019-08-19 18:53:49,836 epoch 60 - iter 530/2650 - loss 0.19947166 throughput (samples/sec): 698.61
2019-08-19 18:54:01,104 epoch 60 - iter 795/2650 - loss 0.19806083 throughput (samples/sec): 759.04
2019-08-19 18:54:12,374 epoch 60 - iter 1060/2650 - loss 0.19942932 throughput (samples/sec): 758.90
2019-08-19 18:54:24,432 epoch 60 - iter 1325/2650 - loss 0.19932675 throughput (samples/sec): 709.78
2019-08-19 18:54:36,485 epoch 60 - iter 1590/2650 - loss 0.19841676 throughput (samples/sec): 710.93
2019-08-19 18:54:48,876 epoch 60 - iter 1855/2650 - loss 0.19841709 throughput (samples/sec): 690.63
2019-08-19 18:55:00,783 epoch 60 - iter 2120/2650 - loss 0.19798058 throughput (samples/sec): 719.01
2019-08-19 18:55:12,835 epoch 60 - iter 2385/2650 - loss 0.19826451 throughput (samples/sec): 710.51
2019-08-19 18:55:24,172 ----------------------------------------------------------------------------------------------------
2019-08-19 18:55:24,173 EPOCH 60 done: loss 0.1987 - lr 0.1000
2019-08-19 18:55:24,173 BAD EPOCHS (no improvement): 0
2019-08-19 18:55:24,173 ----------------------------------------------------------------------------------------------------
2019-08-19 18:55:24,210 epoch 61 - iter 0/2650 - loss 0.18495460 throughput (samples/sec): 280721.52
2019-08-19 18:55:36,578 epoch 61 - iter 265/2650 - loss 0.19114779 throughput (samples/sec): 692.03
2019-08-19 18:55:48,852 epoch 61 - iter 530/2650 - loss 0.19324950 throughput (samples/sec): 697.30
2019-08-19 18:56:00,511 epoch 61 - iter 795/2650 - loss 0.19517120 throughput (samples/sec): 733.22
2019-08-19 18:56:12,571 epoch 61 - iter 1060/2650 - loss 0.19583475 throughput (samples/sec): 709.67
2019-08-19 18:56:24,560 epoch 61 - iter 1325/2650 - loss 0.19538346 throughput (samples/sec): 714.04
2019-08-19 18:56:36,241 epoch 61 - iter 1590/2650 - loss 0.19610793 throughput (samples/sec): 732.37
2019-08-19 18:56:48,170 epoch 61 - iter 1855/2650 - loss 0.19595215 throughput (samples/sec): 717.49
2019-08-19 18:57:00,842 epoch 61 - iter 2120/2650 - loss 0.19602211 throughput (samples/sec): 675.46
2019-08-19 18:57:12,897 epoch 61 - iter 2385/2650 - loss 0.19628401 throughput (samples/sec): 710.10
2019-08-19 18:57:25,625 ----------------------------------------------------------------------------------------------------
2019-08-19 18:57:25,625 EPOCH 61 done: loss 0.1963 - lr 0.1000
2019-08-19 18:57:25,625 BAD EPOCHS (no improvement): 0
2019-08-19 18:57:25,630 ----------------------------------------------------------------------------------------------------
2019-08-19 18:57:25,678 epoch 62 - iter 0/2650 - loss 0.18443511 throughput (samples/sec): 200907.72
2019-08-19 18:57:37,951 epoch 62 - iter 265/2650 - loss 0.19597270 throughput (samples/sec): 697.12
2019-08-19 18:57:50,576 epoch 62 - iter 530/2650 - loss 0.19368978 throughput (samples/sec): 678.39
2019-08-19 18:58:02,422 epoch 62 - iter 795/2650 - loss 0.19486832 throughput (samples/sec): 722.63
2019-08-19 18:58:15,160 epoch 62 - iter 1060/2650 - loss 0.19561142 throughput (samples/sec): 671.58
2019-08-19 18:58:27,475 epoch 62 - iter 1325/2650 - loss 0.19485139 throughput (samples/sec): 694.53
2019-08-19 18:58:39,991 epoch 62 - iter 1590/2650 - loss 0.19443839 throughput (samples/sec): 683.33
2019-08-19 18:58:52,889 epoch 62 - iter 1855/2650 - loss 0.19394291 throughput (samples/sec): 663.74
2019-08-19 18:59:05,433 epoch 62 - iter 2120/2650 - loss 0.19406690 throughput (samples/sec): 682.76
2019-08-19 18:59:18,140 epoch 62 - iter 2385/2650 - loss 0.19477005 throughput (samples/sec): 673.82
2019-08-19 18:59:29,963 ----------------------------------------------------------------------------------------------------
2019-08-19 18:59:29,963 EPOCH 62 done: loss 0.1947 - lr 0.1000
2019-08-19 18:59:29,963 BAD EPOCHS (no improvement): 0
2019-08-19 18:59:29,964 ----------------------------------------------------------------------------------------------------
2019-08-19 18:59:30,006 epoch 63 - iter 0/2650 - loss 0.14907822 throughput (samples/sec): 233056.59
2019-08-19 18:59:41,781 epoch 63 - iter 265/2650 - loss 0.19683802 throughput (samples/sec): 727.20
2019-08-19 18:59:54,166 epoch 63 - iter 530/2650 - loss 0.19690317 throughput (samples/sec): 691.04
2019-08-19 19:00:05,894 epoch 63 - iter 795/2650 - loss 0.19685997 throughput (samples/sec): 729.77
2019-08-19 19:00:18,361 epoch 63 - iter 1060/2650 - loss 0.19628518 throughput (samples/sec): 685.98
2019-08-19 19:00:29,758 epoch 63 - iter 1325/2650 - loss 0.19433318 throughput (samples/sec): 750.54
2019-08-19 19:00:41,280 epoch 63 - iter 1590/2650 - loss 0.19406074 throughput (samples/sec): 742.39
2019-08-19 19:00:53,464 epoch 63 - iter 1855/2650 - loss 0.19378401 throughput (samples/sec): 702.56
2019-08-19 19:01:05,405 epoch 63 - iter 2120/2650 - loss 0.19423155 throughput (samples/sec): 716.92
2019-08-19 19:01:17,451 epoch 63 - iter 2385/2650 - loss 0.19453265 throughput (samples/sec): 710.47
2019-08-19 19:01:29,909 ----------------------------------------------------------------------------------------------------
2019-08-19 19:01:29,909 EPOCH 63 done: loss 0.1952 - lr 0.1000
2019-08-19 19:01:29,909 BAD EPOCHS (no improvement): 1
2019-08-19 19:01:29,910 ----------------------------------------------------------------------------------------------------
2019-08-19 19:01:29,961 epoch 64 - iter 0/2650 - loss 0.12812115 throughput (samples/sec): 181739.53
2019-08-19 19:01:41,316 epoch 64 - iter 265/2650 - loss 0.19419354 throughput (samples/sec): 752.89
2019-08-19 19:01:53,630 epoch 64 - iter 530/2650 - loss 0.19495883 throughput (samples/sec): 694.73
2019-08-19 19:02:04,679 epoch 64 - iter 795/2650 - loss 0.19492281 throughput (samples/sec): 775.17
2019-08-19 19:02:15,647 epoch 64 - iter 1060/2650 - loss 0.19322992 throughput (samples/sec): 780.83
2019-08-19 19:02:26,442 epoch 64 - iter 1325/2650 - loss 0.19360449 throughput (samples/sec): 793.55
2019-08-19 19:02:38,547 epoch 64 - iter 1590/2650 - loss 0.19426006 throughput (samples/sec): 706.84
2019-08-19 19:02:51,184 epoch 64 - iter 1855/2650 - loss 0.19391419 throughput (samples/sec): 677.26
2019-08-19 19:03:03,733 epoch 64 - iter 2120/2650 - loss 0.19424093 throughput (samples/sec): 682.10
2019-08-19 19:03:16,132 epoch 64 - iter 2385/2650 - loss 0.19354817 throughput (samples/sec): 690.59
2019-08-19 19:03:28,335 ----------------------------------------------------------------------------------------------------
2019-08-19 19:03:28,335 EPOCH 64 done: loss 0.1933 - lr 0.1000
2019-08-19 19:03:28,335 BAD EPOCHS (no improvement): 0
2019-08-19 19:03:28,336 ----------------------------------------------------------------------------------------------------
2019-08-19 19:03:28,390 epoch 65 - iter 0/2650 - loss 0.17987567 throughput (samples/sec): 174447.24
2019-08-19 19:03:40,191 epoch 65 - iter 265/2650 - loss 0.18777868 throughput (samples/sec): 725.42
2019-08-19 19:03:52,052 epoch 65 - iter 530/2650 - loss 0.18911607 throughput (samples/sec): 721.67
2019-08-19 19:04:03,679 epoch 65 - iter 795/2650 - loss 0.18851398 throughput (samples/sec): 735.46
2019-08-19 19:04:15,547 epoch 65 - iter 1060/2650 - loss 0.18944649 throughput (samples/sec): 721.12
2019-08-19 19:04:27,651 epoch 65 - iter 1325/2650 - loss 0.19010325 throughput (samples/sec): 706.70
2019-08-19 19:04:39,580 epoch 65 - iter 1590/2650 - loss 0.19132769 throughput (samples/sec): 717.87
2019-08-19 19:04:51,333 epoch 65 - iter 1855/2650 - loss 0.19186513 throughput (samples/sec): 728.81
2019-08-19 19:05:03,813 epoch 65 - iter 2120/2650 - loss 0.19082707 throughput (samples/sec): 686.00
2019-08-19 19:05:16,297 epoch 65 - iter 2385/2650 - loss 0.19093551 throughput (samples/sec): 685.06
2019-08-19 19:05:28,507 ----------------------------------------------------------------------------------------------------
2019-08-19 19:05:28,508 EPOCH 65 done: loss 0.1909 - lr 0.1000
2019-08-19 19:05:28,508 BAD EPOCHS (no improvement): 0
2019-08-19 19:05:28,509 ----------------------------------------------------------------------------------------------------
2019-08-19 19:05:28,554 epoch 66 - iter 0/2650 - loss 0.24350353 throughput (samples/sec): 214997.60
2019-08-19 19:05:40,811 epoch 66 - iter 265/2650 - loss 0.19723650 throughput (samples/sec): 698.28
2019-08-19 19:05:53,263 epoch 66 - iter 530/2650 - loss 0.19533571 throughput (samples/sec): 687.79
2019-08-19 19:06:05,831 epoch 66 - iter 795/2650 - loss 0.19482201 throughput (samples/sec): 681.31
2019-08-19 19:06:17,905 epoch 66 - iter 1060/2650 - loss 0.19372108 throughput (samples/sec): 708.98
2019-08-19 19:06:30,751 epoch 66 - iter 1325/2650 - loss 0.19289518 throughput (samples/sec): 666.40
2019-08-19 19:06:42,968 epoch 66 - iter 1590/2650 - loss 0.19212196 throughput (samples/sec): 700.62
2019-08-19 19:06:55,117 epoch 66 - iter 1855/2650 - loss 0.19229489 throughput (samples/sec): 704.38
2019-08-19 19:07:07,243 epoch 66 - iter 2120/2650 - loss 0.19218069 throughput (samples/sec): 706.12
2019-08-19 19:07:18,898 epoch 66 - iter 2385/2650 - loss 0.19211061 throughput (samples/sec): 734.77
2019-08-19 19:07:31,697 ----------------------------------------------------------------------------------------------------
2019-08-19 19:07:31,697 EPOCH 66 done: loss 0.1918 - lr 0.1000
2019-08-19 19:07:31,698 BAD EPOCHS (no improvement): 1
2019-08-19 19:07:31,698 ----------------------------------------------------------------------------------------------------
2019-08-19 19:07:31,749 epoch 67 - iter 0/2650 - loss 0.14718726 throughput (samples/sec): 195633.29
2019-08-19 19:07:43,425 epoch 67 - iter 265/2650 - loss 0.19158348 throughput (samples/sec): 733.23
2019-08-19 19:07:55,100 epoch 67 - iter 530/2650 - loss 0.19330669 throughput (samples/sec): 732.52
2019-08-19 19:08:07,301 epoch 67 - iter 795/2650 - loss 0.19311941 throughput (samples/sec): 701.33
2019-08-19 19:08:19,892 epoch 67 - iter 1060/2650 - loss 0.19218358 throughput (samples/sec): 679.55
2019-08-19 19:08:32,218 epoch 67 - iter 1325/2650 - loss 0.19189392 throughput (samples/sec): 694.05
2019-08-19 19:08:44,090 epoch 67 - iter 1590/2650 - loss 0.19164855 throughput (samples/sec): 720.62
2019-08-19 19:08:56,762 epoch 67 - iter 1855/2650 - loss 0.19152571 throughput (samples/sec): 675.62
2019-08-19 19:09:09,449 epoch 67 - iter 2120/2650 - loss 0.19110973 throughput (samples/sec): 674.71
2019-08-19 19:09:22,083 epoch 67 - iter 2385/2650 - loss 0.19090827 throughput (samples/sec): 681.88
2019-08-19 19:09:33,872 ----------------------------------------------------------------------------------------------------
2019-08-19 19:09:33,872 EPOCH 67 done: loss 0.1905 - lr 0.1000
2019-08-19 19:09:33,873 BAD EPOCHS (no improvement): 0
2019-08-19 19:09:33,873 ----------------------------------------------------------------------------------------------------
2019-08-19 19:09:33,917 epoch 68 - iter 0/2650 - loss 0.23632422 throughput (samples/sec): 210750.31
2019-08-19 19:09:45,391 epoch 68 - iter 265/2650 - loss 0.18491981 throughput (samples/sec): 745.48
2019-08-19 19:09:57,277 epoch 68 - iter 530/2650 - loss 0.18722522 throughput (samples/sec): 720.35
2019-08-19 19:10:09,812 epoch 68 - iter 795/2650 - loss 0.18929192 throughput (samples/sec): 682.99
2019-08-19 19:10:22,067 epoch 68 - iter 1060/2650 - loss 0.18924186 throughput (samples/sec): 698.65
2019-08-19 19:10:34,176 epoch 68 - iter 1325/2650 - loss 0.18942614 throughput (samples/sec): 706.87
2019-08-19 19:10:45,679 epoch 68 - iter 1590/2650 - loss 0.18926293 throughput (samples/sec): 744.25
2019-08-19 19:10:57,613 epoch 68 - iter 1855/2650 - loss 0.18926344 throughput (samples/sec): 716.56
2019-08-19 19:11:09,984 epoch 68 - iter 2120/2650 - loss 0.18953323 throughput (samples/sec): 691.74
2019-08-19 19:11:21,613 epoch 68 - iter 2385/2650 - loss 0.18940125 throughput (samples/sec): 735.87
2019-08-19 19:11:33,014 ----------------------------------------------------------------------------------------------------
2019-08-19 19:11:33,014 EPOCH 68 done: loss 0.1891 - lr 0.1000
2019-08-19 19:11:33,014 BAD EPOCHS (no improvement): 0
2019-08-19 19:11:33,015 ----------------------------------------------------------------------------------------------------
2019-08-19 19:11:33,057 epoch 69 - iter 0/2650 - loss 0.10609931 throughput (samples/sec): 232420.00
2019-08-19 19:11:44,493 epoch 69 - iter 265/2650 - loss 0.18658787 throughput (samples/sec): 748.58
2019-08-19 19:11:56,846 epoch 69 - iter 530/2650 - loss 0.18531782 throughput (samples/sec): 692.23
2019-08-19 19:12:09,152 epoch 69 - iter 795/2650 - loss 0.18621385 throughput (samples/sec): 695.58
2019-08-19 19:12:21,149 epoch 69 - iter 1060/2650 - loss 0.18602366 throughput (samples/sec): 712.79
2019-08-19 19:12:33,405 epoch 69 - iter 1325/2650 - loss 0.18631890 throughput (samples/sec): 698.55
2019-08-19 19:12:45,235 epoch 69 - iter 1590/2650 - loss 0.18647409 throughput (samples/sec): 724.00
2019-08-19 19:12:57,667 epoch 69 - iter 1855/2650 - loss 0.18708492 throughput (samples/sec): 688.60
2019-08-19 19:13:10,279 epoch 69 - iter 2120/2650 - loss 0.18748053 throughput (samples/sec): 678.87
2019-08-19 19:13:21,976 epoch 69 - iter 2385/2650 - loss 0.18841168 throughput (samples/sec): 732.02
2019-08-19 19:13:33,609 ----------------------------------------------------------------------------------------------------
2019-08-19 19:13:33,609 EPOCH 69 done: loss 0.1882 - lr 0.1000
2019-08-19 19:13:33,609 BAD EPOCHS (no improvement): 0
2019-08-19 19:13:33,610 ----------------------------------------------------------------------------------------------------
2019-08-19 19:13:33,653 epoch 70 - iter 0/2650 - loss 0.17041592 throughput (samples/sec): 216989.99
2019-08-19 19:13:45,043 epoch 70 - iter 265/2650 - loss 0.18509268 throughput (samples/sec): 750.75
2019-08-19 19:13:56,967 epoch 70 - iter 530/2650 - loss 0.18446010 throughput (samples/sec): 717.08
2019-08-19 19:14:09,156 epoch 70 - iter 795/2650 - loss 0.18547596 throughput (samples/sec): 702.63
2019-08-19 19:14:21,717 epoch 70 - iter 1060/2650 - loss 0.18699378 throughput (samples/sec): 681.69
2019-08-19 19:14:33,675 epoch 70 - iter 1325/2650 - loss 0.18715753 throughput (samples/sec): 715.84
2019-08-19 19:14:45,618 epoch 70 - iter 1590/2650 - loss 0.18650919 throughput (samples/sec): 716.67
2019-08-19 19:14:57,713 epoch 70 - iter 1855/2650 - loss 0.18667284 throughput (samples/sec): 707.58
2019-08-19 19:15:10,076 epoch 70 - iter 2120/2650 - loss 0.18661953 throughput (samples/sec): 691.71
2019-08-19 19:15:22,863 epoch 70 - iter 2385/2650 - loss 0.18646635 throughput (samples/sec): 669.63
2019-08-19 19:15:34,376 ----------------------------------------------------------------------------------------------------
2019-08-19 19:15:34,377 EPOCH 70 done: loss 0.1863 - lr 0.1000
2019-08-19 19:15:34,377 BAD EPOCHS (no improvement): 0
2019-08-19 19:15:34,378 ----------------------------------------------------------------------------------------------------
2019-08-19 19:15:34,427 epoch 71 - iter 0/2650 - loss 0.15237895 throughput (samples/sec): 194410.00
2019-08-19 19:15:46,239 epoch 71 - iter 265/2650 - loss 0.18815795 throughput (samples/sec): 724.97
2019-08-19 19:15:58,667 epoch 71 - iter 530/2650 - loss 0.18575488 throughput (samples/sec): 688.52
2019-08-19 19:16:10,768 epoch 71 - iter 795/2650 - loss 0.18746004 throughput (samples/sec): 707.52
2019-08-19 19:16:22,992 epoch 71 - iter 1060/2650 - loss 0.18571760 throughput (samples/sec): 700.01
2019-08-19 19:16:34,809 epoch 71 - iter 1325/2650 - loss 0.18544371 throughput (samples/sec): 723.68
2019-08-19 19:16:47,191 epoch 71 - iter 1590/2650 - loss 0.18529058 throughput (samples/sec): 691.54
2019-08-19 19:16:59,763 epoch 71 - iter 1855/2650 - loss 0.18562375 throughput (samples/sec): 680.95
2019-08-19 19:17:11,620 epoch 71 - iter 2120/2650 - loss 0.18512468 throughput (samples/sec): 722.10
2019-08-19 19:17:23,381 epoch 71 - iter 2385/2650 - loss 0.18565835 throughput (samples/sec): 727.23
2019-08-19 19:17:36,185 ----------------------------------------------------------------------------------------------------
2019-08-19 19:17:36,186 EPOCH 71 done: loss 0.1862 - lr 0.1000
2019-08-19 19:17:36,186 BAD EPOCHS (no improvement): 0
2019-08-19 19:17:36,187 ----------------------------------------------------------------------------------------------------
2019-08-19 19:17:36,235 epoch 72 - iter 0/2650 - loss 0.27685264 throughput (samples/sec): 203738.78
2019-08-19 19:17:49,000 epoch 72 - iter 265/2650 - loss 0.18715402 throughput (samples/sec): 670.11
2019-08-19 19:18:01,440 epoch 72 - iter 530/2650 - loss 0.18496944 throughput (samples/sec): 688.11
2019-08-19 19:18:13,198 epoch 72 - iter 795/2650 - loss 0.18439924 throughput (samples/sec): 728.47
2019-08-19 19:18:25,084 epoch 72 - iter 1060/2650 - loss 0.18400282 throughput (samples/sec): 720.27
2019-08-19 19:18:37,703 epoch 72 - iter 1325/2650 - loss 0.18319829 throughput (samples/sec): 678.34
2019-08-19 19:18:49,589 epoch 72 - iter 1590/2650 - loss 0.18291022 throughput (samples/sec): 720.08
2019-08-19 19:19:01,500 epoch 72 - iter 1855/2650 - loss 0.18329472 throughput (samples/sec): 718.52
2019-08-19 19:19:14,429 epoch 72 - iter 2120/2650 - loss 0.18335461 throughput (samples/sec): 661.72
2019-08-19 19:19:26,884 epoch 72 - iter 2385/2650 - loss 0.18385290 throughput (samples/sec): 687.54
2019-08-19 19:19:38,403 ----------------------------------------------------------------------------------------------------
2019-08-19 19:19:38,403 EPOCH 72 done: loss 0.1840 - lr 0.1000
2019-08-19 19:19:38,403 BAD EPOCHS (no improvement): 0
2019-08-19 19:19:38,404 ----------------------------------------------------------------------------------------------------
2019-08-19 19:19:38,446 epoch 73 - iter 0/2650 - loss 0.29946369 throughput (samples/sec): 219841.38
2019-08-19 19:19:50,323 epoch 73 - iter 265/2650 - loss 0.18552752 throughput (samples/sec): 720.18
2019-08-19 19:20:02,386 epoch 73 - iter 530/2650 - loss 0.18389852 throughput (samples/sec): 709.62
2019-08-19 19:20:14,379 epoch 73 - iter 795/2650 - loss 0.18421486 throughput (samples/sec): 713.66
2019-08-19 19:20:26,015 epoch 73 - iter 1060/2650 - loss 0.18541042 throughput (samples/sec): 734.67
2019-08-19 19:20:37,953 epoch 73 - iter 1325/2650 - loss 0.18508942 throughput (samples/sec): 716.37
2019-08-19 19:20:50,602 epoch 73 - iter 1590/2650 - loss 0.18482756 throughput (samples/sec): 677.11
2019-08-19 19:21:03,128 epoch 73 - iter 1855/2650 - loss 0.18478673 throughput (samples/sec): 683.60
2019-08-19 19:21:14,625 epoch 73 - iter 2120/2650 - loss 0.18419981 throughput (samples/sec): 744.16
2019-08-19 19:21:26,891 epoch 73 - iter 2385/2650 - loss 0.18464742 throughput (samples/sec): 697.74
2019-08-19 19:21:38,904 ----------------------------------------------------------------------------------------------------
2019-08-19 19:21:38,905 EPOCH 73 done: loss 0.1848 - lr 0.1000
2019-08-19 19:21:38,905 BAD EPOCHS (no improvement): 1
2019-08-19 19:21:38,905 ----------------------------------------------------------------------------------------------------
2019-08-19 19:21:38,953 epoch 74 - iter 0/2650 - loss 0.21666758 throughput (samples/sec): 202548.38
2019-08-19 19:21:51,011 epoch 74 - iter 265/2650 - loss 0.18308531 throughput (samples/sec): 709.69
2019-08-19 19:22:03,333 epoch 74 - iter 530/2650 - loss 0.18336271 throughput (samples/sec): 694.70
2019-08-19 19:22:15,893 epoch 74 - iter 795/2650 - loss 0.18382814 throughput (samples/sec): 681.91
2019-08-19 19:22:27,993 epoch 74 - iter 1060/2650 - loss 0.18505800 throughput (samples/sec): 707.81
2019-08-19 19:22:39,882 epoch 74 - iter 1325/2650 - loss 0.18502191 throughput (samples/sec): 720.06
2019-08-19 19:22:52,021 epoch 74 - iter 1590/2650 - loss 0.18377374 throughput (samples/sec): 704.49
2019-08-19 19:23:04,396 epoch 74 - iter 1855/2650 - loss 0.18318749 throughput (samples/sec): 691.01
2019-08-19 19:23:16,656 epoch 74 - iter 2120/2650 - loss 0.18295040 throughput (samples/sec): 698.24
2019-08-19 19:23:28,862 epoch 74 - iter 2385/2650 - loss 0.18305738 throughput (samples/sec): 701.52
2019-08-19 19:23:40,739 ----------------------------------------------------------------------------------------------------
2019-08-19 19:23:40,739 EPOCH 74 done: loss 0.1827 - lr 0.1000
2019-08-19 19:23:40,739 BAD EPOCHS (no improvement): 0
2019-08-19 19:23:40,740 ----------------------------------------------------------------------------------------------------
2019-08-19 19:23:40,788 epoch 75 - iter 0/2650 - loss 0.17495307 throughput (samples/sec): 197909.48
2019-08-19 19:23:52,835 epoch 75 - iter 265/2650 - loss 0.18296709 throughput (samples/sec): 710.61
2019-08-19 19:24:04,947 epoch 75 - iter 530/2650 - loss 0.17957318 throughput (samples/sec): 707.36
2019-08-19 19:24:17,430 epoch 75 - iter 795/2650 - loss 0.17995702 throughput (samples/sec): 685.01
2019-08-19 19:24:29,612 epoch 75 - iter 1060/2650 - loss 0.17936774 throughput (samples/sec): 702.40
2019-08-19 19:24:41,973 epoch 75 - iter 1325/2650 - loss 0.17950019 throughput (samples/sec): 692.51
2019-08-19 19:24:53,789 epoch 75 - iter 1590/2650 - loss 0.18031028 throughput (samples/sec): 724.82
2019-08-19 19:25:05,653 epoch 75 - iter 1855/2650 - loss 0.18095168 throughput (samples/sec): 721.90
2019-08-19 19:25:17,688 epoch 75 - iter 2120/2650 - loss 0.18157447 throughput (samples/sec): 711.22
2019-08-19 19:25:30,016 epoch 75 - iter 2385/2650 - loss 0.18172431 throughput (samples/sec): 694.24
2019-08-19 19:25:42,584 ----------------------------------------------------------------------------------------------------
2019-08-19 19:25:42,584 EPOCH 75 done: loss 0.1818 - lr 0.1000
2019-08-19 19:25:42,584 BAD EPOCHS (no improvement): 0
2019-08-19 19:25:42,585 ----------------------------------------------------------------------------------------------------
2019-08-19 19:25:42,636 epoch 76 - iter 0/2650 - loss 0.22108111 throughput (samples/sec): 178541.05
2019-08-19 19:25:54,087 epoch 76 - iter 265/2650 - loss 0.18157251 throughput (samples/sec): 746.84
2019-08-19 19:26:06,770 epoch 76 - iter 530/2650 - loss 0.18088631 throughput (samples/sec): 675.04
2019-08-19 19:26:18,413 epoch 76 - iter 795/2650 - loss 0.17996049 throughput (samples/sec): 735.66
2019-08-19 19:26:30,294 epoch 76 - iter 1060/2650 - loss 0.18072793 throughput (samples/sec): 720.54
2019-08-19 19:26:42,211 epoch 76 - iter 1325/2650 - loss 0.18050110 throughput (samples/sec): 718.23
2019-08-19 19:26:55,104 epoch 76 - iter 1590/2650 - loss 0.18041908 throughput (samples/sec): 663.54
2019-08-19 19:27:07,025 epoch 76 - iter 1855/2650 - loss 0.18106524 throughput (samples/sec): 718.17
2019-08-19 19:27:19,921 epoch 76 - iter 2120/2650 - loss 0.18123168 throughput (samples/sec): 663.89
2019-08-19 19:27:31,557 epoch 76 - iter 2385/2650 - loss 0.18178530 throughput (samples/sec): 736.33
2019-08-19 19:27:43,511 ----------------------------------------------------------------------------------------------------
2019-08-19 19:27:43,512 EPOCH 76 done: loss 0.1818 - lr 0.1000
2019-08-19 19:27:43,512 BAD EPOCHS (no improvement): 1
2019-08-19 19:27:43,512 ----------------------------------------------------------------------------------------------------
2019-08-19 19:27:43,561 epoch 77 - iter 0/2650 - loss 0.24987443 throughput (samples/sec): 202427.34
2019-08-19 19:27:55,398 epoch 77 - iter 265/2650 - loss 0.18059275 throughput (samples/sec): 723.31
2019-08-19 19:28:07,052 epoch 77 - iter 530/2650 - loss 0.18118678 throughput (samples/sec): 733.72
2019-08-19 19:28:19,770 epoch 77 - iter 795/2650 - loss 0.18051289 throughput (samples/sec): 672.74
2019-08-19 19:28:32,529 epoch 77 - iter 1060/2650 - loss 0.17928455 throughput (samples/sec): 670.81
2019-08-19 19:28:45,406 epoch 77 - iter 1325/2650 - loss 0.17963129 throughput (samples/sec): 664.89
2019-08-19 19:28:57,562 epoch 77 - iter 1590/2650 - loss 0.18036885 throughput (samples/sec): 703.73
2019-08-19 19:29:10,076 epoch 77 - iter 1855/2650 - loss 0.18016299 throughput (samples/sec): 684.22
2019-08-19 19:29:22,548 epoch 77 - iter 2120/2650 - loss 0.17901938 throughput (samples/sec): 686.29
2019-08-19 19:29:34,252 epoch 77 - iter 2385/2650 - loss 0.17902186 throughput (samples/sec): 730.68
2019-08-19 19:29:46,662 ----------------------------------------------------------------------------------------------------
2019-08-19 19:29:46,663 EPOCH 77 done: loss 0.1792 - lr 0.1000
2019-08-19 19:29:46,663 BAD EPOCHS (no improvement): 0
2019-08-19 19:29:46,664 ----------------------------------------------------------------------------------------------------
2019-08-19 19:29:46,723 epoch 78 - iter 0/2650 - loss 0.17673168 throughput (samples/sec): 164934.81
2019-08-19 19:29:57,929 epoch 78 - iter 265/2650 - loss 0.17799551 throughput (samples/sec): 763.81
2019-08-19 19:30:09,054 epoch 78 - iter 530/2650 - loss 0.18060750 throughput (samples/sec): 769.76
2019-08-19 19:30:20,141 epoch 78 - iter 795/2650 - loss 0.18064432 throughput (samples/sec): 772.29
2019-08-19 19:30:31,070 epoch 78 - iter 1060/2650 - loss 0.18060532 throughput (samples/sec): 783.48
2019-08-19 19:30:42,036 epoch 78 - iter 1325/2650 - loss 0.18009627 throughput (samples/sec): 780.65
2019-08-19 19:30:53,175 epoch 78 - iter 1590/2650 - loss 0.17979629 throughput (samples/sec): 768.52
2019-08-19 19:31:04,609 epoch 78 - iter 1855/2650 - loss 0.17980104 throughput (samples/sec): 748.87
2019-08-19 19:31:17,571 epoch 78 - iter 2120/2650 - loss 0.17964082 throughput (samples/sec): 660.27
2019-08-19 19:31:30,187 epoch 78 - iter 2385/2650 - loss 0.17998349 throughput (samples/sec): 678.92
2019-08-19 19:31:41,044 ----------------------------------------------------------------------------------------------------
2019-08-19 19:31:41,044 EPOCH 78 done: loss 0.1793 - lr 0.1000
2019-08-19 19:31:41,044 BAD EPOCHS (no improvement): 1
2019-08-19 19:31:41,045 ----------------------------------------------------------------------------------------------------
2019-08-19 19:31:41,087 epoch 79 - iter 0/2650 - loss 0.25371000 throughput (samples/sec): 217257.73
2019-08-19 19:31:52,486 epoch 79 - iter 265/2650 - loss 0.17874388 throughput (samples/sec): 751.04
2019-08-19 19:32:04,546 epoch 79 - iter 530/2650 - loss 0.17595349 throughput (samples/sec): 709.87
2019-08-19 19:32:16,888 epoch 79 - iter 795/2650 - loss 0.17773985 throughput (samples/sec): 693.28
2019-08-19 19:32:28,516 epoch 79 - iter 1060/2650 - loss 0.17891372 throughput (samples/sec): 736.03
2019-08-19 19:32:40,770 epoch 79 - iter 1325/2650 - loss 0.17790471 throughput (samples/sec): 697.85
2019-08-19 19:32:53,322 epoch 79 - iter 1590/2650 - loss 0.17814964 throughput (samples/sec): 682.36
2019-08-19 19:33:05,263 epoch 79 - iter 1855/2650 - loss 0.17825161 throughput (samples/sec): 717.17
2019-08-19 19:33:17,201 epoch 79 - iter 2120/2650 - loss 0.17841201 throughput (samples/sec): 717.04
2019-08-19 19:33:30,024 epoch 79 - iter 2385/2650 - loss 0.17815153 throughput (samples/sec): 667.55
2019-08-19 19:33:42,756 ----------------------------------------------------------------------------------------------------
2019-08-19 19:33:42,756 EPOCH 79 done: loss 0.1787 - lr 0.1000
2019-08-19 19:33:42,756 BAD EPOCHS (no improvement): 0
2019-08-19 19:33:42,757 ----------------------------------------------------------------------------------------------------
2019-08-19 19:33:42,807 epoch 80 - iter 0/2650 - loss 0.17532077 throughput (samples/sec): 195750.65
2019-08-19 19:33:55,476 epoch 80 - iter 265/2650 - loss 0.18374315 throughput (samples/sec): 675.55
2019-08-19 19:34:08,033 epoch 80 - iter 530/2650 - loss 0.18212054 throughput (samples/sec): 682.06
2019-08-19 19:34:19,913 epoch 80 - iter 795/2650 - loss 0.18037175 throughput (samples/sec): 720.87
2019-08-19 19:34:31,963 epoch 80 - iter 1060/2650 - loss 0.18002429 throughput (samples/sec): 710.59
2019-08-19 19:34:44,280 epoch 80 - iter 1325/2650 - loss 0.17954910 throughput (samples/sec): 695.01
2019-08-19 19:34:55,866 epoch 80 - iter 1590/2650 - loss 0.17911845 throughput (samples/sec): 738.92
2019-08-19 19:35:07,521 epoch 80 - iter 1855/2650 - loss 0.17917816 throughput (samples/sec): 733.45
2019-08-19 19:35:19,583 epoch 80 - iter 2120/2650 - loss 0.17883593 throughput (samples/sec): 709.35
2019-08-19 19:35:31,642 epoch 80 - iter 2385/2650 - loss 0.17938943 throughput (samples/sec): 710.03
2019-08-19 19:35:43,893 ----------------------------------------------------------------------------------------------------
2019-08-19 19:35:43,893 EPOCH 80 done: loss 0.1789 - lr 0.1000
2019-08-19 19:35:43,893 BAD EPOCHS (no improvement): 1
2019-08-19 19:35:43,894 ----------------------------------------------------------------------------------------------------
2019-08-19 19:35:43,955 epoch 81 - iter 0/2650 - loss 0.15943146 throughput (samples/sec): 158761.69
2019-08-19 19:35:55,810 epoch 81 - iter 265/2650 - loss 0.18409231 throughput (samples/sec): 722.15
2019-08-19 19:36:07,741 epoch 81 - iter 530/2650 - loss 0.18365822 throughput (samples/sec): 716.75
2019-08-19 19:36:20,420 epoch 81 - iter 795/2650 - loss 0.18283703 throughput (samples/sec): 675.05
2019-08-19 19:36:33,147 epoch 81 - iter 1060/2650 - loss 0.18107938 throughput (samples/sec): 672.72
2019-08-19 19:36:45,828 epoch 81 - iter 1325/2650 - loss 0.18014028 throughput (samples/sec): 674.95
2019-08-19 19:36:58,004 epoch 81 - iter 1590/2650 - loss 0.17843649 throughput (samples/sec): 703.78
2019-08-19 19:37:09,875 epoch 81 - iter 1855/2650 - loss 0.17823576 throughput (samples/sec): 721.23
2019-08-19 19:37:21,921 epoch 81 - iter 2120/2650 - loss 0.17831707 throughput (samples/sec): 710.73
2019-08-19 19:37:33,924 epoch 81 - iter 2385/2650 - loss 0.17825944 throughput (samples/sec): 712.91
2019-08-19 19:37:45,000 ----------------------------------------------------------------------------------------------------
2019-08-19 19:37:45,001 EPOCH 81 done: loss 0.1783 - lr 0.1000
2019-08-19 19:37:45,001 BAD EPOCHS (no improvement): 0
2019-08-19 19:37:45,001 ----------------------------------------------------------------------------------------------------
2019-08-19 19:37:45,056 epoch 82 - iter 0/2650 - loss 0.17682041 throughput (samples/sec): 168793.68
2019-08-19 19:37:56,014 epoch 82 - iter 265/2650 - loss 0.17840995 throughput (samples/sec): 780.13
2019-08-19 19:38:07,125 epoch 82 - iter 530/2650 - loss 0.17315180 throughput (samples/sec): 770.68
2019-08-19 19:38:18,082 epoch 82 - iter 795/2650 - loss 0.17396574 throughput (samples/sec): 781.60
2019-08-19 19:38:28,929 epoch 82 - iter 1060/2650 - loss 0.17440261 throughput (samples/sec): 789.54
2019-08-19 19:38:41,258 epoch 82 - iter 1325/2650 - loss 0.17512262 throughput (samples/sec): 693.48
2019-08-19 19:38:53,637 epoch 82 - iter 1590/2650 - loss 0.17601378 throughput (samples/sec): 691.23
2019-08-19 19:39:05,505 epoch 82 - iter 1855/2650 - loss 0.17618394 throughput (samples/sec): 721.67
2019-08-19 19:39:17,839 epoch 82 - iter 2120/2650 - loss 0.17546515 throughput (samples/sec): 693.76
2019-08-19 19:39:30,518 epoch 82 - iter 2385/2650 - loss 0.17550116 throughput (samples/sec): 675.57
2019-08-19 19:39:42,277 ----------------------------------------------------------------------------------------------------
2019-08-19 19:39:42,278 EPOCH 82 done: loss 0.1758 - lr 0.1000
2019-08-19 19:39:42,278 BAD EPOCHS (no improvement): 0
2019-08-19 19:39:42,278 ----------------------------------------------------------------------------------------------------
2019-08-19 19:39:42,329 epoch 83 - iter 0/2650 - loss 0.27860749 throughput (samples/sec): 180993.20
2019-08-19 19:39:54,146 epoch 83 - iter 265/2650 - loss 0.18224216 throughput (samples/sec): 723.65
2019-08-19 19:40:05,838 epoch 83 - iter 530/2650 - loss 0.17949911 throughput (samples/sec): 732.17
2019-08-19 19:40:17,977 epoch 83 - iter 795/2650 - loss 0.17879768 throughput (samples/sec): 704.82
2019-08-19 19:40:30,388 epoch 83 - iter 1060/2650 - loss 0.17911476 throughput (samples/sec): 689.52
2019-08-19 19:40:42,587 epoch 83 - iter 1325/2650 - loss 0.17921562 throughput (samples/sec): 701.87
2019-08-19 19:40:54,565 epoch 83 - iter 1590/2650 - loss 0.17819341 throughput (samples/sec): 715.06
2019-08-19 19:41:06,671 epoch 83 - iter 1855/2650 - loss 0.17726331 throughput (samples/sec): 706.99
2019-08-19 19:41:19,333 epoch 83 - iter 2120/2650 - loss 0.17713002 throughput (samples/sec): 675.98
2019-08-19 19:41:31,465 epoch 83 - iter 2385/2650 - loss 0.17682080 throughput (samples/sec): 705.62
2019-08-19 19:41:43,478 ----------------------------------------------------------------------------------------------------
2019-08-19 19:41:43,479 EPOCH 83 done: loss 0.1762 - lr 0.1000
2019-08-19 19:41:43,479 BAD EPOCHS (no improvement): 1
2019-08-19 19:41:43,480 ----------------------------------------------------------------------------------------------------
2019-08-19 19:41:43,528 epoch 84 - iter 0/2650 - loss 0.16266789 throughput (samples/sec): 201434.53
2019-08-19 19:41:55,686 epoch 84 - iter 265/2650 - loss 0.17566446 throughput (samples/sec): 703.69
2019-08-19 19:42:08,121 epoch 84 - iter 530/2650 - loss 0.17590408 throughput (samples/sec): 688.76
2019-08-19 19:42:19,358 epoch 84 - iter 795/2650 - loss 0.17504105 throughput (samples/sec): 761.11
2019-08-19 19:42:31,092 epoch 84 - iter 1060/2650 - loss 0.17599895 throughput (samples/sec): 728.91
2019-08-19 19:42:43,215 epoch 84 - iter 1325/2650 - loss 0.17617940 throughput (samples/sec): 706.62
2019-08-19 19:42:55,537 epoch 84 - iter 1590/2650 - loss 0.17629382 throughput (samples/sec): 694.46
2019-08-19 19:43:07,328 epoch 84 - iter 1855/2650 - loss 0.17610219 throughput (samples/sec): 725.77
2019-08-19 19:43:18,858 epoch 84 - iter 2120/2650 - loss 0.17666071 throughput (samples/sec): 741.66
2019-08-19 19:43:31,261 epoch 84 - iter 2385/2650 - loss 0.17714232 throughput (samples/sec): 690.13
2019-08-19 19:43:43,662 ----------------------------------------------------------------------------------------------------
2019-08-19 19:43:43,662 EPOCH 84 done: loss 0.1773 - lr 0.1000
2019-08-19 19:43:43,662 BAD EPOCHS (no improvement): 2
2019-08-19 19:43:43,662 ----------------------------------------------------------------------------------------------------
2019-08-19 19:43:43,711 epoch 85 - iter 0/2650 - loss 0.10375286 throughput (samples/sec): 199345.92
2019-08-19 19:43:55,063 epoch 85 - iter 265/2650 - loss 0.17341691 throughput (samples/sec): 754.21
2019-08-19 19:44:06,357 epoch 85 - iter 530/2650 - loss 0.17511261 throughput (samples/sec): 757.24
2019-08-19 19:44:17,736 epoch 85 - iter 795/2650 - loss 0.17412913 throughput (samples/sec): 751.28
2019-08-19 19:44:29,271 epoch 85 - iter 1060/2650 - loss 0.17454462 throughput (samples/sec): 740.95
2019-08-19 19:44:41,367 epoch 85 - iter 1325/2650 - loss 0.17358561 throughput (samples/sec): 706.70
2019-08-19 19:44:54,093 epoch 85 - iter 1590/2650 - loss 0.17276206 throughput (samples/sec): 672.97
2019-08-19 19:45:05,897 epoch 85 - iter 1855/2650 - loss 0.17305109 throughput (samples/sec): 725.59
2019-08-19 19:45:17,399 epoch 85 - iter 2120/2650 - loss 0.17334936 throughput (samples/sec): 743.43
2019-08-19 19:45:29,029 epoch 85 - iter 2385/2650 - loss 0.17379398 throughput (samples/sec): 735.23
2019-08-19 19:45:41,567 ----------------------------------------------------------------------------------------------------
2019-08-19 19:45:41,567 EPOCH 85 done: loss 0.1741 - lr 0.1000
2019-08-19 19:45:41,568 BAD EPOCHS (no improvement): 0
2019-08-19 19:45:41,568 ----------------------------------------------------------------------------------------------------
2019-08-19 19:45:41,621 epoch 86 - iter 0/2650 - loss 0.19798380 throughput (samples/sec): 184120.69
2019-08-19 19:45:54,021 epoch 86 - iter 265/2650 - loss 0.17135954 throughput (samples/sec): 690.19
2019-08-19 19:46:06,638 epoch 86 - iter 530/2650 - loss 0.17526271 throughput (samples/sec): 678.33
2019-08-19 19:46:19,284 epoch 86 - iter 795/2650 - loss 0.17570056 throughput (samples/sec): 677.74
2019-08-19 19:46:31,659 epoch 86 - iter 1060/2650 - loss 0.17410691 throughput (samples/sec): 692.06
2019-08-19 19:46:43,637 epoch 86 - iter 1325/2650 - loss 0.17446382 throughput (samples/sec): 713.90
2019-08-19 19:46:55,554 epoch 86 - iter 1590/2650 - loss 0.17416979 throughput (samples/sec): 718.43
2019-08-19 19:47:07,543 epoch 86 - iter 1855/2650 - loss 0.17407627 throughput (samples/sec): 713.76
2019-08-19 19:47:20,286 epoch 86 - iter 2120/2650 - loss 0.17444596 throughput (samples/sec): 671.32
2019-08-19 19:47:33,076 epoch 86 - iter 2385/2650 - loss 0.17479590 throughput (samples/sec): 669.26
2019-08-19 19:47:44,793 ----------------------------------------------------------------------------------------------------
2019-08-19 19:47:44,793 EPOCH 86 done: loss 0.1746 - lr 0.1000
2019-08-19 19:47:44,794 BAD EPOCHS (no improvement): 1
2019-08-19 19:47:44,794 ----------------------------------------------------------------------------------------------------
2019-08-19 19:47:44,844 epoch 87 - iter 0/2650 - loss 0.18595459 throughput (samples/sec): 185607.07
2019-08-19 19:47:56,247 epoch 87 - iter 265/2650 - loss 0.17588782 throughput (samples/sec): 749.86
2019-08-19 19:48:07,529 epoch 87 - iter 530/2650 - loss 0.17354693 throughput (samples/sec): 757.79
2019-08-19 19:48:19,331 epoch 87 - iter 795/2650 - loss 0.17285897 throughput (samples/sec): 724.59
2019-08-19 19:48:31,297 epoch 87 - iter 1060/2650 - loss 0.17233875 throughput (samples/sec): 715.22
2019-08-19 19:48:43,464 epoch 87 - iter 1325/2650 - loss 0.17338322 throughput (samples/sec): 703.21
2019-08-19 19:48:55,137 epoch 87 - iter 1590/2650 - loss 0.17315607 throughput (samples/sec): 733.07
2019-08-19 19:49:06,543 epoch 87 - iter 1855/2650 - loss 0.17348679 throughput (samples/sec): 750.15
2019-08-19 19:49:19,213 epoch 87 - iter 2120/2650 - loss 0.17334554 throughput (samples/sec): 675.91
2019-08-19 19:49:31,573 epoch 87 - iter 2385/2650 - loss 0.17293648 throughput (samples/sec): 692.85
2019-08-19 19:49:43,927 ----------------------------------------------------------------------------------------------------
2019-08-19 19:49:43,927 EPOCH 87 done: loss 0.1728 - lr 0.1000
2019-08-19 19:49:43,927 BAD EPOCHS (no improvement): 0
2019-08-19 19:49:43,928 ----------------------------------------------------------------------------------------------------
2019-08-19 19:49:43,981 epoch 88 - iter 0/2650 - loss 0.09771585 throughput (samples/sec): 177906.98
2019-08-19 19:49:54,778 epoch 88 - iter 265/2650 - loss 0.17296549 throughput (samples/sec): 792.94
2019-08-19 19:50:05,897 epoch 88 - iter 530/2650 - loss 0.17491983 throughput (samples/sec): 769.75
2019-08-19 19:50:17,062 epoch 88 - iter 795/2650 - loss 0.17295652 throughput (samples/sec): 766.92
2019-08-19 19:50:27,890 epoch 88 - iter 1060/2650 - loss 0.17234961 throughput (samples/sec): 790.98
2019-08-19 19:50:38,982 epoch 88 - iter 1325/2650 - loss 0.17305396 throughput (samples/sec): 771.95
2019-08-19 19:50:50,920 epoch 88 - iter 1590/2650 - loss 0.17288129 throughput (samples/sec): 716.17
2019-08-19 19:51:03,348 epoch 88 - iter 1855/2650 - loss 0.17208992 throughput (samples/sec): 688.67
2019-08-19 19:51:15,398 epoch 88 - iter 2120/2650 - loss 0.17155974 throughput (samples/sec): 710.28
2019-08-19 19:51:27,552 epoch 88 - iter 2385/2650 - loss 0.17189758 throughput (samples/sec): 703.43
2019-08-19 19:51:38,941 ----------------------------------------------------------------------------------------------------
2019-08-19 19:51:38,941 EPOCH 88 done: loss 0.1718 - lr 0.1000
2019-08-19 19:51:38,941 BAD EPOCHS (no improvement): 0
2019-08-19 19:51:38,942 ----------------------------------------------------------------------------------------------------
2019-08-19 19:51:38,988 epoch 89 - iter 0/2650 - loss 0.10814421 throughput (samples/sec): 198025.18
2019-08-19 19:51:50,865 epoch 89 - iter 265/2650 - loss 0.17452179 throughput (samples/sec): 720.11
2019-08-19 19:52:03,466 epoch 89 - iter 530/2650 - loss 0.17642360 throughput (samples/sec): 679.46
2019-08-19 19:52:15,990 epoch 89 - iter 795/2650 - loss 0.17750373 throughput (samples/sec): 683.55
2019-08-19 19:52:28,700 epoch 89 - iter 1060/2650 - loss 0.17682034 throughput (samples/sec): 673.30
2019-08-19 19:52:40,865 epoch 89 - iter 1325/2650 - loss 0.17602721 throughput (samples/sec): 703.28
2019-08-19 19:52:53,128 epoch 89 - iter 1590/2650 - loss 0.17506888 throughput (samples/sec): 698.07
2019-08-19 19:53:05,138 epoch 89 - iter 1855/2650 - loss 0.17348710 throughput (samples/sec): 712.12
2019-08-19 19:53:17,056 epoch 89 - iter 2120/2650 - loss 0.17275395 throughput (samples/sec): 718.10
2019-08-19 19:53:29,641 epoch 89 - iter 2385/2650 - loss 0.17251704 throughput (samples/sec): 680.30
2019-08-19 19:53:41,417 ----------------------------------------------------------------------------------------------------
2019-08-19 19:53:41,417 EPOCH 89 done: loss 0.1723 - lr 0.1000
2019-08-19 19:53:41,417 BAD EPOCHS (no improvement): 1
2019-08-19 19:53:41,418 ----------------------------------------------------------------------------------------------------
2019-08-19 19:53:41,463 epoch 90 - iter 0/2650 - loss 0.16301513 throughput (samples/sec): 206166.84
2019-08-19 19:53:53,882 epoch 90 - iter 265/2650 - loss 0.16684494 throughput (samples/sec): 688.92
2019-08-19 19:54:05,940 epoch 90 - iter 530/2650 - loss 0.16792274 throughput (samples/sec): 708.99
2019-08-19 19:54:18,065 epoch 90 - iter 795/2650 - loss 0.17041069 throughput (samples/sec): 706.19
2019-08-19 19:54:29,847 epoch 90 - iter 1060/2650 - loss 0.17048773 throughput (samples/sec): 726.20
2019-08-19 19:54:41,865 epoch 90 - iter 1325/2650 - loss 0.17081313 throughput (samples/sec): 712.19
2019-08-19 19:54:53,626 epoch 90 - iter 1590/2650 - loss 0.17081720 throughput (samples/sec): 727.20
2019-08-19 19:55:05,431 epoch 90 - iter 1855/2650 - loss 0.17072524 throughput (samples/sec): 724.16
2019-08-19 19:55:17,705 epoch 90 - iter 2120/2650 - loss 0.17075701 throughput (samples/sec): 696.57
2019-08-19 19:55:29,900 epoch 90 - iter 2385/2650 - loss 0.17121634 throughput (samples/sec): 701.80
2019-08-19 19:55:41,514 ----------------------------------------------------------------------------------------------------
2019-08-19 19:55:41,515 EPOCH 90 done: loss 0.1714 - lr 0.1000
2019-08-19 19:55:41,515 BAD EPOCHS (no improvement): 0
2019-08-19 19:55:41,515 ----------------------------------------------------------------------------------------------------
2019-08-19 19:55:41,569 epoch 91 - iter 0/2650 - loss 0.21594846 throughput (samples/sec): 170062.39
2019-08-19 19:55:53,099 epoch 91 - iter 265/2650 - loss 0.16356024 throughput (samples/sec): 742.71
2019-08-19 19:56:04,937 epoch 91 - iter 530/2650 - loss 0.16370762 throughput (samples/sec): 723.26
2019-08-19 19:56:16,529 epoch 91 - iter 795/2650 - loss 0.16534800 throughput (samples/sec): 737.65
2019-08-19 19:56:28,477 epoch 91 - iter 1060/2650 - loss 0.17018372 throughput (samples/sec): 716.29
2019-08-19 19:56:40,493 epoch 91 - iter 1325/2650 - loss 0.17181288 throughput (samples/sec): 711.37
2019-08-19 19:56:52,749 epoch 91 - iter 1590/2650 - loss 0.17142684 throughput (samples/sec): 697.68
2019-08-19 19:57:04,351 epoch 91 - iter 1855/2650 - loss 0.17104404 throughput (samples/sec): 738.15
2019-08-19 19:57:15,743 epoch 91 - iter 2120/2650 - loss 0.17220674 throughput (samples/sec): 750.64
2019-08-19 19:57:27,303 epoch 91 - iter 2385/2650 - loss 0.17229899 throughput (samples/sec): 739.72
2019-08-19 19:57:39,781 ----------------------------------------------------------------------------------------------------
2019-08-19 19:57:39,782 EPOCH 91 done: loss 0.1724 - lr 0.1000
2019-08-19 19:57:39,782 BAD EPOCHS (no improvement): 1
2019-08-19 19:57:39,783 ----------------------------------------------------------------------------------------------------
2019-08-19 19:57:39,832 epoch 92 - iter 0/2650 - loss 0.15791188 throughput (samples/sec): 204315.77
2019-08-19 19:57:52,459 epoch 92 - iter 265/2650 - loss 0.16362281 throughput (samples/sec): 677.66
2019-08-19 19:58:05,083 epoch 92 - iter 530/2650 - loss 0.16432890 throughput (samples/sec): 677.77
2019-08-19 19:58:17,320 epoch 92 - iter 795/2650 - loss 0.16590037 throughput (samples/sec): 699.47
2019-08-19 19:58:28,790 epoch 92 - iter 1060/2650 - loss 0.16832541 throughput (samples/sec): 745.67
2019-08-19 19:58:39,891 epoch 92 - iter 1325/2650 - loss 0.16873110 throughput (samples/sec): 770.49
2019-08-19 19:58:51,610 epoch 92 - iter 1590/2650 - loss 0.16962828 throughput (samples/sec): 729.76
2019-08-19 19:59:03,715 epoch 92 - iter 1855/2650 - loss 0.17002119 throughput (samples/sec): 707.00
2019-08-19 19:59:16,707 epoch 92 - iter 2120/2650 - loss 0.16949235 throughput (samples/sec): 658.62
2019-08-19 19:59:28,640 epoch 92 - iter 2385/2650 - loss 0.16895254 throughput (samples/sec): 717.06
2019-08-19 19:59:39,605 ----------------------------------------------------------------------------------------------------
2019-08-19 19:59:39,605 EPOCH 92 done: loss 0.1692 - lr 0.1000
2019-08-19 19:59:39,605 BAD EPOCHS (no improvement): 0
2019-08-19 19:59:39,606 ----------------------------------------------------------------------------------------------------
2019-08-19 19:59:39,650 epoch 93 - iter 0/2650 - loss 0.20217675 throughput (samples/sec): 208560.49
2019-08-19 19:59:50,775 epoch 93 - iter 265/2650 - loss 0.17467967 throughput (samples/sec): 769.46
2019-08-19 20:00:01,568 epoch 93 - iter 530/2650 - loss 0.17308881 throughput (samples/sec): 793.30
2019-08-19 20:00:12,734 epoch 93 - iter 795/2650 - loss 0.17184024 throughput (samples/sec): 766.46
2019-08-19 20:00:23,732 epoch 93 - iter 1060/2650 - loss 0.17035317 throughput (samples/sec): 778.27
2019-08-19 20:00:34,811 epoch 93 - iter 1325/2650 - loss 0.16941108 throughput (samples/sec): 772.43
2019-08-19 20:00:45,893 epoch 93 - iter 1590/2650 - loss 0.16927729 throughput (samples/sec): 772.18
2019-08-19 20:00:56,890 epoch 93 - iter 1855/2650 - loss 0.16930488 throughput (samples/sec): 778.44
2019-08-19 20:01:08,663 epoch 93 - iter 2120/2650 - loss 0.16972730 throughput (samples/sec): 727.17
2019-08-19 20:01:20,623 epoch 93 - iter 2385/2650 - loss 0.16956696 throughput (samples/sec): 715.73
2019-08-19 20:01:33,147 ----------------------------------------------------------------------------------------------------
2019-08-19 20:01:33,147 EPOCH 93 done: loss 0.1691 - lr 0.1000
2019-08-19 20:01:33,147 BAD EPOCHS (no improvement): 1
2019-08-19 20:01:33,148 ----------------------------------------------------------------------------------------------------
2019-08-19 20:01:33,199 epoch 94 - iter 0/2650 - loss 0.08595832 throughput (samples/sec): 196089.52
2019-08-19 20:01:44,983 epoch 94 - iter 265/2650 - loss 0.16663371 throughput (samples/sec): 726.18
2019-08-19 20:01:56,761 epoch 94 - iter 530/2650 - loss 0.16642118 throughput (samples/sec): 726.53
2019-08-19 20:02:08,944 epoch 94 - iter 795/2650 - loss 0.16461541 throughput (samples/sec): 701.72
2019-08-19 20:02:20,937 epoch 94 - iter 1060/2650 - loss 0.16552050 throughput (samples/sec): 713.91
2019-08-19 20:02:33,278 epoch 94 - iter 1325/2650 - loss 0.16798765 throughput (samples/sec): 693.87
2019-08-19 20:02:45,256 epoch 94 - iter 1590/2650 - loss 0.16926717 throughput (samples/sec): 714.56
2019-08-19 20:02:57,828 epoch 94 - iter 1855/2650 - loss 0.16918768 throughput (samples/sec): 680.63
2019-08-19 20:03:09,633 epoch 94 - iter 2120/2650 - loss 0.16847439 throughput (samples/sec): 725.02
2019-08-19 20:03:21,658 epoch 94 - iter 2385/2650 - loss 0.16940105 throughput (samples/sec): 710.93
2019-08-19 20:03:33,433 ----------------------------------------------------------------------------------------------------
2019-08-19 20:03:33,434 EPOCH 94 done: loss 0.1695 - lr 0.1000
2019-08-19 20:03:33,434 BAD EPOCHS (no improvement): 2
2019-08-19 20:03:33,434 ----------------------------------------------------------------------------------------------------
2019-08-19 20:03:33,479 epoch 95 - iter 0/2650 - loss 0.14835800 throughput (samples/sec): 206911.64
2019-08-19 20:03:45,225 epoch 95 - iter 265/2650 - loss 0.16790029 throughput (samples/sec): 728.39
2019-08-19 20:03:57,091 epoch 95 - iter 530/2650 - loss 0.16819706 throughput (samples/sec): 721.22
2019-08-19 20:04:08,357 epoch 95 - iter 795/2650 - loss 0.17039658 throughput (samples/sec): 759.07
2019-08-19 20:04:19,618 epoch 95 - iter 1060/2650 - loss 0.16901487 throughput (samples/sec): 759.41
2019-08-19 20:04:31,419 epoch 95 - iter 1325/2650 - loss 0.16834445 throughput (samples/sec): 724.57
2019-08-19 20:04:43,611 epoch 95 - iter 1590/2650 - loss 0.16771219 throughput (samples/sec): 701.73
2019-08-19 20:04:56,116 epoch 95 - iter 1855/2650 - loss 0.16788662 throughput (samples/sec): 684.38
2019-08-19 20:05:08,762 epoch 95 - iter 2120/2650 - loss 0.16791180 throughput (samples/sec): 677.25
2019-08-19 20:05:21,377 epoch 95 - iter 2385/2650 - loss 0.16748109 throughput (samples/sec): 678.70
2019-08-19 20:05:32,645 ----------------------------------------------------------------------------------------------------
2019-08-19 20:05:32,645 EPOCH 95 done: loss 0.1671 - lr 0.1000
2019-08-19 20:05:32,645 BAD EPOCHS (no improvement): 0
2019-08-19 20:05:32,646 ----------------------------------------------------------------------------------------------------
2019-08-19 20:05:32,687 epoch 96 - iter 0/2650 - loss 0.12533721 throughput (samples/sec): 226649.79
2019-08-19 20:05:44,225 epoch 96 - iter 265/2650 - loss 0.17381836 throughput (samples/sec): 741.01
2019-08-19 20:05:56,193 epoch 96 - iter 530/2650 - loss 0.17106547 throughput (samples/sec): 713.81
2019-08-19 20:06:07,612 epoch 96 - iter 795/2650 - loss 0.16983310 throughput (samples/sec): 748.81
2019-08-19 20:06:19,387 epoch 96 - iter 1060/2650 - loss 0.16846697 throughput (samples/sec): 725.99
2019-08-19 20:06:31,010 epoch 96 - iter 1325/2650 - loss 0.16798828 throughput (samples/sec): 735.88
2019-08-19 20:06:43,378 epoch 96 - iter 1590/2650 - loss 0.16818472 throughput (samples/sec): 692.38
2019-08-19 20:06:55,734 epoch 96 - iter 1855/2650 - loss 0.16753489 throughput (samples/sec): 692.71
2019-08-19 20:07:08,284 epoch 96 - iter 2120/2650 - loss 0.16721694 throughput (samples/sec): 682.05
2019-08-19 20:07:20,173 epoch 96 - iter 2385/2650 - loss 0.16769297 throughput (samples/sec): 719.50
2019-08-19 20:07:32,463 ----------------------------------------------------------------------------------------------------
2019-08-19 20:07:32,463 EPOCH 96 done: loss 0.1674 - lr 0.1000
2019-08-19 20:07:32,463 BAD EPOCHS (no improvement): 1
2019-08-19 20:07:32,466 ----------------------------------------------------------------------------------------------------
2019-08-19 20:07:32,516 epoch 97 - iter 0/2650 - loss 0.19989762 throughput (samples/sec): 189853.36
2019-08-19 20:07:45,267 epoch 97 - iter 265/2650 - loss 0.16779470 throughput (samples/sec): 671.07
2019-08-19 20:07:57,374 epoch 97 - iter 530/2650 - loss 0.16785977 throughput (samples/sec): 707.31
2019-08-19 20:08:09,832 epoch 97 - iter 795/2650 - loss 0.16707595 throughput (samples/sec): 687.11
2019-08-19 20:08:21,333 epoch 97 - iter 1060/2650 - loss 0.16781321 throughput (samples/sec): 744.23
2019-08-19 20:08:33,976 epoch 97 - iter 1325/2650 - loss 0.16848816 throughput (samples/sec): 676.86
2019-08-19 20:08:46,468 epoch 97 - iter 1590/2650 - loss 0.16728740 throughput (samples/sec): 685.11
2019-08-19 20:08:58,520 epoch 97 - iter 1855/2650 - loss 0.16745628 throughput (samples/sec): 709.90
2019-08-19 20:09:11,166 epoch 97 - iter 2120/2650 - loss 0.16680069 throughput (samples/sec): 676.80
2019-08-19 20:09:23,229 epoch 97 - iter 2385/2650 - loss 0.16698704 throughput (samples/sec): 709.78
2019-08-19 20:09:34,414 ----------------------------------------------------------------------------------------------------
2019-08-19 20:09:34,414 EPOCH 97 done: loss 0.1670 - lr 0.1000
2019-08-19 20:09:34,414 BAD EPOCHS (no improvement): 0
2019-08-19 20:09:34,415 ----------------------------------------------------------------------------------------------------
2019-08-19 20:09:34,464 epoch 98 - iter 0/2650 - loss 0.12436760 throughput (samples/sec): 187158.02
2019-08-19 20:09:45,927 epoch 98 - iter 265/2650 - loss 0.16390006 throughput (samples/sec): 746.00
2019-08-19 20:09:57,590 epoch 98 - iter 530/2650 - loss 0.16604853 throughput (samples/sec): 733.59
2019-08-19 20:10:09,327 epoch 98 - iter 795/2650 - loss 0.16689452 throughput (samples/sec): 728.46
2019-08-19 20:10:21,298 epoch 98 - iter 1060/2650 - loss 0.16635816 throughput (samples/sec): 714.75
2019-08-19 20:10:33,836 epoch 98 - iter 1325/2650 - loss 0.16687253 throughput (samples/sec): 682.78
2019-08-19 20:10:45,495 epoch 98 - iter 1590/2650 - loss 0.16590759 throughput (samples/sec): 733.67
2019-08-19 20:10:58,190 epoch 98 - iter 1855/2650 - loss 0.16621856 throughput (samples/sec): 674.41
2019-08-19 20:11:10,263 epoch 98 - iter 2120/2650 - loss 0.16629098 throughput (samples/sec): 708.81
2019-08-19 20:11:22,419 epoch 98 - iter 2385/2650 - loss 0.16532679 throughput (samples/sec): 704.01
2019-08-19 20:11:34,803 ----------------------------------------------------------------------------------------------------
2019-08-19 20:11:34,803 EPOCH 98 done: loss 0.1649 - lr 0.1000
2019-08-19 20:11:34,804 BAD EPOCHS (no improvement): 0
2019-08-19 20:11:34,804 ----------------------------------------------------------------------------------------------------
2019-08-19 20:11:34,845 epoch 99 - iter 0/2650 - loss 0.07505674 throughput (samples/sec): 226273.45
2019-08-19 20:11:45,890 epoch 99 - iter 265/2650 - loss 0.16653020 throughput (samples/sec): 774.15
2019-08-19 20:11:56,880 epoch 99 - iter 530/2650 - loss 0.16377515 throughput (samples/sec): 779.12
2019-08-19 20:12:07,842 epoch 99 - iter 795/2650 - loss 0.16506428 throughput (samples/sec): 780.81
2019-08-19 20:12:18,773 epoch 99 - iter 1060/2650 - loss 0.16457448 throughput (samples/sec): 782.99
2019-08-19 20:12:29,895 epoch 99 - iter 1325/2650 - loss 0.16618976 throughput (samples/sec): 769.58
2019-08-19 20:12:41,113 epoch 99 - iter 1590/2650 - loss 0.16641705 throughput (samples/sec): 762.64
2019-08-19 20:12:52,284 epoch 99 - iter 1855/2650 - loss 0.16572980 throughput (samples/sec): 766.14
2019-08-19 20:13:04,365 epoch 99 - iter 2120/2650 - loss 0.16611090 throughput (samples/sec): 707.77
2019-08-19 20:13:16,883 epoch 99 - iter 2385/2650 - loss 0.16627123 throughput (samples/sec): 683.92
2019-08-19 20:13:28,631 ----------------------------------------------------------------------------------------------------
2019-08-19 20:13:28,632 EPOCH 99 done: loss 0.1665 - lr 0.1000
2019-08-19 20:13:28,632 BAD EPOCHS (no improvement): 1
2019-08-19 20:13:28,633 ----------------------------------------------------------------------------------------------------
2019-08-19 20:13:28,684 epoch 100 - iter 0/2650 - loss 0.16899247 throughput (samples/sec): 185054.70
2019-08-19 20:13:41,188 epoch 100 - iter 265/2650 - loss 0.16150488 throughput (samples/sec): 684.35
2019-08-19 20:13:53,479 epoch 100 - iter 530/2650 - loss 0.16515796 throughput (samples/sec): 696.38
2019-08-19 20:14:05,485 epoch 100 - iter 795/2650 - loss 0.16561438 throughput (samples/sec): 712.81
2019-08-19 20:14:17,815 epoch 100 - iter 1060/2650 - loss 0.16552003 throughput (samples/sec): 693.75
2019-08-19 20:14:30,018 epoch 100 - iter 1325/2650 - loss 0.16502126 throughput (samples/sec): 701.69
2019-08-19 20:14:41,670 epoch 100 - iter 1590/2650 - loss 0.16549337 throughput (samples/sec): 733.99
2019-08-19 20:14:53,907 epoch 100 - iter 1855/2650 - loss 0.16527689 throughput (samples/sec): 699.64
2019-08-19 20:15:05,710 epoch 100 - iter 2120/2650 - loss 0.16565579 throughput (samples/sec): 725.05
2019-08-19 20:15:17,300 epoch 100 - iter 2385/2650 - loss 0.16494885 throughput (samples/sec): 738.07
2019-08-19 20:15:29,383 ----------------------------------------------------------------------------------------------------
2019-08-19 20:15:29,383 EPOCH 100 done: loss 0.1652 - lr 0.1000
2019-08-19 20:15:29,383 BAD EPOCHS (no improvement): 2
2019-08-19 20:15:29,384 ----------------------------------------------------------------------------------------------------
2019-08-19 20:15:29,429 epoch 101 - iter 0/2650 - loss 0.07735409 throughput (samples/sec): 212130.44
2019-08-19 20:15:41,972 epoch 101 - iter 265/2650 - loss 0.16524570 throughput (samples/sec): 682.17
2019-08-19 20:15:54,242 epoch 101 - iter 530/2650 - loss 0.16556856 throughput (samples/sec): 698.04
2019-08-19 20:16:06,868 epoch 101 - iter 795/2650 - loss 0.16524954 throughput (samples/sec): 678.07
2019-08-19 20:16:19,229 epoch 101 - iter 1060/2650 - loss 0.16459493 throughput (samples/sec): 692.53
2019-08-19 20:16:31,073 epoch 101 - iter 1325/2650 - loss 0.16509847 throughput (samples/sec): 722.69
2019-08-19 20:16:43,449 epoch 101 - iter 1590/2650 - loss 0.16457427 throughput (samples/sec): 691.38
2019-08-19 20:16:55,450 epoch 101 - iter 1855/2650 - loss 0.16468865 throughput (samples/sec): 712.54
2019-08-19 20:17:07,486 epoch 101 - iter 2120/2650 - loss 0.16446490 throughput (samples/sec): 711.26
2019-08-19 20:17:19,224 epoch 101 - iter 2385/2650 - loss 0.16420534 throughput (samples/sec): 729.60
2019-08-19 20:17:31,115 ----------------------------------------------------------------------------------------------------
2019-08-19 20:17:31,115 EPOCH 101 done: loss 0.1640 - lr 0.1000
2019-08-19 20:17:31,115 BAD EPOCHS (no improvement): 0
2019-08-19 20:17:31,116 ----------------------------------------------------------------------------------------------------
2019-08-19 20:17:31,160 epoch 102 - iter 0/2650 - loss 0.12855580 throughput (samples/sec): 210447.30
2019-08-19 20:17:42,754 epoch 102 - iter 265/2650 - loss 0.16220313 throughput (samples/sec): 737.52
2019-08-19 20:17:55,091 epoch 102 - iter 530/2650 - loss 0.16233169 throughput (samples/sec): 693.55
2019-08-19 20:18:07,465 epoch 102 - iter 795/2650 - loss 0.16203927 throughput (samples/sec): 691.52
2019-08-19 20:18:19,439 epoch 102 - iter 1060/2650 - loss 0.16314379 throughput (samples/sec): 714.63
2019-08-19 20:18:31,683 epoch 102 - iter 1325/2650 - loss 0.16331201 throughput (samples/sec): 699.35
2019-08-19 20:18:43,822 epoch 102 - iter 1590/2650 - loss 0.16261113 throughput (samples/sec): 705.29
2019-08-19 20:18:55,632 epoch 102 - iter 1855/2650 - loss 0.16302241 throughput (samples/sec): 724.69
2019-08-19 20:19:07,536 epoch 102 - iter 2120/2650 - loss 0.16311109 throughput (samples/sec): 719.02
2019-08-19 20:19:19,455 epoch 102 - iter 2385/2650 - loss 0.16326419 throughput (samples/sec): 718.10
2019-08-19 20:19:31,607 ----------------------------------------------------------------------------------------------------
2019-08-19 20:19:31,607 EPOCH 102 done: loss 0.1630 - lr 0.1000
2019-08-19 20:19:31,607 BAD EPOCHS (no improvement): 0
2019-08-19 20:19:31,608 ----------------------------------------------------------------------------------------------------
2019-08-19 20:19:31,657 epoch 103 - iter 0/2650 - loss 0.17208301 throughput (samples/sec): 193386.79
2019-08-19 20:19:43,753 epoch 103 - iter 265/2650 - loss 0.16276731 throughput (samples/sec): 707.76
2019-08-19 20:19:55,817 epoch 103 - iter 530/2650 - loss 0.16292639 throughput (samples/sec): 709.89
2019-08-19 20:20:07,712 epoch 103 - iter 795/2650 - loss 0.16275833 throughput (samples/sec): 719.57
2019-08-19 20:20:19,633 epoch 103 - iter 1060/2650 - loss 0.16325383 throughput (samples/sec): 718.19
2019-08-19 20:20:32,581 epoch 103 - iter 1325/2650 - loss 0.16256229 throughput (samples/sec): 660.95
2019-08-19 20:20:45,133 epoch 103 - iter 1590/2650 - loss 0.16292699 throughput (samples/sec): 681.77
2019-08-19 20:20:56,905 epoch 103 - iter 1855/2650 - loss 0.16240701 throughput (samples/sec): 726.95
2019-08-19 20:21:07,827 epoch 103 - iter 2120/2650 - loss 0.16240669 throughput (samples/sec): 784.02
2019-08-19 20:21:19,479 epoch 103 - iter 2385/2650 - loss 0.16228362 throughput (samples/sec): 734.20
2019-08-19 20:21:31,851 ----------------------------------------------------------------------------------------------------
2019-08-19 20:21:31,851 EPOCH 103 done: loss 0.1626 - lr 0.1000
2019-08-19 20:21:31,851 BAD EPOCHS (no improvement): 0
2019-08-19 20:21:31,852 ----------------------------------------------------------------------------------------------------
2019-08-19 20:21:31,905 epoch 104 - iter 0/2650 - loss 0.22760251 throughput (samples/sec): 175070.62
2019-08-19 20:21:44,399 epoch 104 - iter 265/2650 - loss 0.16292406 throughput (samples/sec): 685.07
2019-08-19 20:21:56,500 epoch 104 - iter 530/2650 - loss 0.16418370 throughput (samples/sec): 707.33
2019-08-19 20:22:08,648 epoch 104 - iter 795/2650 - loss 0.16290460 throughput (samples/sec): 704.31
2019-08-19 20:22:20,875 epoch 104 - iter 1060/2650 - loss 0.16318196 throughput (samples/sec): 700.04
2019-08-19 20:22:33,452 epoch 104 - iter 1325/2650 - loss 0.16412677 throughput (samples/sec): 680.84
2019-08-19 20:22:44,967 epoch 104 - iter 1590/2650 - loss 0.16341500 throughput (samples/sec): 742.86
2019-08-19 20:22:56,902 epoch 104 - iter 1855/2650 - loss 0.16311824 throughput (samples/sec): 717.19
2019-08-19 20:23:08,801 epoch 104 - iter 2120/2650 - loss 0.16303285 throughput (samples/sec): 719.38
2019-08-19 20:23:20,699 epoch 104 - iter 2385/2650 - loss 0.16257778 throughput (samples/sec): 719.39
2019-08-19 20:23:33,007 ----------------------------------------------------------------------------------------------------
2019-08-19 20:23:33,007 EPOCH 104 done: loss 0.1625 - lr 0.1000
2019-08-19 20:23:33,008 BAD EPOCHS (no improvement): 0
2019-08-19 20:23:33,008 ----------------------------------------------------------------------------------------------------
2019-08-19 20:23:33,064 epoch 105 - iter 0/2650 - loss 0.12166908 throughput (samples/sec): 162332.49
2019-08-19 20:23:45,253 epoch 105 - iter 265/2650 - loss 0.15640555 throughput (samples/sec): 701.97
2019-08-19 20:23:57,790 epoch 105 - iter 530/2650 - loss 0.15785606 throughput (samples/sec): 682.80
2019-08-19 20:24:09,956 epoch 105 - iter 795/2650 - loss 0.15838603 throughput (samples/sec): 703.61
2019-08-19 20:24:21,991 epoch 105 - iter 1060/2650 - loss 0.15964751 throughput (samples/sec): 711.50
2019-08-19 20:24:33,947 epoch 105 - iter 1325/2650 - loss 0.16113380 throughput (samples/sec): 715.47
2019-08-19 20:24:46,581 epoch 105 - iter 1590/2650 - loss 0.16187094 throughput (samples/sec): 677.34
2019-08-19 20:24:58,762 epoch 105 - iter 1855/2650 - loss 0.16178262 throughput (samples/sec): 702.69
2019-08-19 20:25:10,659 epoch 105 - iter 2120/2650 - loss 0.16170841 throughput (samples/sec): 719.73
2019-08-19 20:25:22,129 epoch 105 - iter 2385/2650 - loss 0.16159184 throughput (samples/sec): 746.60
2019-08-19 20:25:33,959 ----------------------------------------------------------------------------------------------------
2019-08-19 20:25:33,959 EPOCH 105 done: loss 0.1622 - lr 0.1000
2019-08-19 20:25:33,959 BAD EPOCHS (no improvement): 0
2019-08-19 20:25:33,960 ----------------------------------------------------------------------------------------------------
2019-08-19 20:25:34,012 epoch 106 - iter 0/2650 - loss 0.13024408 throughput (samples/sec): 177511.87
2019-08-19 20:25:46,446 epoch 106 - iter 265/2650 - loss 0.16622634 throughput (samples/sec): 687.40
2019-08-19 20:25:58,752 epoch 106 - iter 530/2650 - loss 0.16189437 throughput (samples/sec): 695.40
2019-08-19 20:26:11,183 epoch 106 - iter 795/2650 - loss 0.16048438 throughput (samples/sec): 688.26
2019-08-19 20:26:23,671 epoch 106 - iter 1060/2650 - loss 0.16027267 throughput (samples/sec): 685.57
2019-08-19 20:26:36,257 epoch 106 - iter 1325/2650 - loss 0.16135861 throughput (samples/sec): 680.33
2019-08-19 20:26:47,961 epoch 106 - iter 1590/2650 - loss 0.16179408 throughput (samples/sec): 731.43
2019-08-19 20:26:59,448 epoch 106 - iter 1855/2650 - loss 0.16209889 throughput (samples/sec): 744.74
2019-08-19 20:27:11,293 epoch 106 - iter 2120/2650 - loss 0.16163401 throughput (samples/sec): 722.69
2019-08-19 20:27:23,334 epoch 106 - iter 2385/2650 - loss 0.16233916 throughput (samples/sec): 710.64
2019-08-19 20:27:35,238 ----------------------------------------------------------------------------------------------------
2019-08-19 20:27:35,239 EPOCH 106 done: loss 0.1620 - lr 0.1000
2019-08-19 20:27:35,239 BAD EPOCHS (no improvement): 0
2019-08-19 20:27:35,240 ----------------------------------------------------------------------------------------------------
2019-08-19 20:27:35,292 epoch 107 - iter 0/2650 - loss 0.12224628 throughput (samples/sec): 187305.86
2019-08-19 20:27:47,028 epoch 107 - iter 265/2650 - loss 0.16432142 throughput (samples/sec): 729.54
2019-08-19 20:27:59,218 epoch 107 - iter 530/2650 - loss 0.16094321 throughput (samples/sec): 702.29
2019-08-19 20:28:11,446 epoch 107 - iter 795/2650 - loss 0.16237067 throughput (samples/sec): 700.17
2019-08-19 20:28:22,362 epoch 107 - iter 1060/2650 - loss 0.16174976 throughput (samples/sec): 784.27
2019-08-19 20:28:33,474 epoch 107 - iter 1325/2650 - loss 0.16184540 throughput (samples/sec): 770.10
2019-08-19 20:28:44,551 epoch 107 - iter 1590/2650 - loss 0.16250472 throughput (samples/sec): 772.42
2019-08-19 20:28:56,143 epoch 107 - iter 1855/2650 - loss 0.16232327 throughput (samples/sec): 738.12
2019-08-19 20:29:07,612 epoch 107 - iter 2120/2650 - loss 0.16104360 throughput (samples/sec): 746.59
2019-08-19 20:29:19,402 epoch 107 - iter 2385/2650 - loss 0.16118024 throughput (samples/sec): 726.27
2019-08-19 20:29:31,216 ----------------------------------------------------------------------------------------------------
2019-08-19 20:29:31,217 EPOCH 107 done: loss 0.1613 - lr 0.1000
2019-08-19 20:29:31,217 BAD EPOCHS (no improvement): 0
2019-08-19 20:29:31,217 ----------------------------------------------------------------------------------------------------
2019-08-19 20:29:31,266 epoch 108 - iter 0/2650 - loss 0.14027414 throughput (samples/sec): 194343.08
2019-08-19 20:29:43,127 epoch 108 - iter 265/2650 - loss 0.15793461 throughput (samples/sec): 721.41
2019-08-19 20:29:55,566 epoch 108 - iter 530/2650 - loss 0.15604168 throughput (samples/sec): 687.69
2019-08-19 20:30:07,526 epoch 108 - iter 795/2650 - loss 0.15586407 throughput (samples/sec): 715.29
2019-08-19 20:30:19,342 epoch 108 - iter 1060/2650 - loss 0.15730694 throughput (samples/sec): 723.94
2019-08-19 20:30:31,376 epoch 108 - iter 1325/2650 - loss 0.15816089 throughput (samples/sec): 711.40
2019-08-19 20:30:43,769 epoch 108 - iter 1590/2650 - loss 0.15915199 throughput (samples/sec): 691.01
2019-08-19 20:30:56,244 epoch 108 - iter 1855/2650 - loss 0.15990373 throughput (samples/sec): 686.20
2019-08-19 20:31:08,713 epoch 108 - iter 2120/2650 - loss 0.16004035 throughput (samples/sec): 686.37
2019-08-19 20:31:21,563 epoch 108 - iter 2385/2650 - loss 0.15982747 throughput (samples/sec): 665.75
2019-08-19 20:31:34,364 ----------------------------------------------------------------------------------------------------
2019-08-19 20:31:34,365 EPOCH 108 done: loss 0.1600 - lr 0.1000
2019-08-19 20:31:34,365 BAD EPOCHS (no improvement): 0
2019-08-19 20:31:34,366 ----------------------------------------------------------------------------------------------------
2019-08-19 20:31:34,415 epoch 109 - iter 0/2650 - loss 0.10511114 throughput (samples/sec): 202988.80
2019-08-19 20:31:47,051 epoch 109 - iter 265/2650 - loss 0.15987492 throughput (samples/sec): 677.72
2019-08-19 20:31:58,156 epoch 109 - iter 530/2650 - loss 0.16138321 throughput (samples/sec): 770.86
2019-08-19 20:32:08,941 epoch 109 - iter 795/2650 - loss 0.16079386 throughput (samples/sec): 793.80
2019-08-19 20:32:19,866 epoch 109 - iter 1060/2650 - loss 0.16079145 throughput (samples/sec): 783.49
2019-08-19 20:32:31,218 epoch 109 - iter 1325/2650 - loss 0.16060571 throughput (samples/sec): 753.74
2019-08-19 20:32:42,244 epoch 109 - iter 1590/2650 - loss 0.16046653 throughput (samples/sec): 776.14
2019-08-19 20:32:53,247 epoch 109 - iter 1855/2650 - loss 0.16125404 throughput (samples/sec): 777.75
2019-08-19 20:33:04,422 epoch 109 - iter 2120/2650 - loss 0.16082723 throughput (samples/sec): 766.13
2019-08-19 20:33:15,496 epoch 109 - iter 2385/2650 - loss 0.16112259 throughput (samples/sec): 772.91
2019-08-19 20:33:26,537 ----------------------------------------------------------------------------------------------------
2019-08-19 20:33:26,538 EPOCH 109 done: loss 0.1607 - lr 0.1000
2019-08-19 20:33:26,538 BAD EPOCHS (no improvement): 1
2019-08-19 20:33:26,538 ----------------------------------------------------------------------------------------------------
2019-08-19 20:33:26,581 epoch 110 - iter 0/2650 - loss 0.11161678 throughput (samples/sec): 215338.64
2019-08-19 20:33:37,565 epoch 110 - iter 265/2650 - loss 0.15795344 throughput (samples/sec): 779.00
2019-08-19 20:33:48,733 epoch 110 - iter 530/2650 - loss 0.15928802 throughput (samples/sec): 766.43
2019-08-19 20:34:00,727 epoch 110 - iter 795/2650 - loss 0.15984920 throughput (samples/sec): 713.34
2019-08-19 20:34:13,122 epoch 110 - iter 1060/2650 - loss 0.15962818 throughput (samples/sec): 690.63
2019-08-19 20:34:24,842 epoch 110 - iter 1325/2650 - loss 0.15818117 throughput (samples/sec): 730.07
2019-08-19 20:34:37,024 epoch 110 - iter 1590/2650 - loss 0.15829749 throughput (samples/sec): 702.20
2019-08-19 20:34:49,378 epoch 110 - iter 1855/2650 - loss 0.15877638 throughput (samples/sec): 692.62
2019-08-19 20:35:01,071 epoch 110 - iter 2120/2650 - loss 0.15839214 throughput (samples/sec): 731.64
2019-08-19 20:35:12,872 epoch 110 - iter 2385/2650 - loss 0.15841422 throughput (samples/sec): 724.73
2019-08-19 20:35:25,117 ----------------------------------------------------------------------------------------------------
2019-08-19 20:35:25,118 EPOCH 110 done: loss 0.1589 - lr 0.1000
2019-08-19 20:35:25,118 BAD EPOCHS (no improvement): 0
2019-08-19 20:35:25,118 ----------------------------------------------------------------------------------------------------
2019-08-19 20:35:25,167 epoch 111 - iter 0/2650 - loss 0.17376798 throughput (samples/sec): 190360.40
2019-08-19 20:35:36,541 epoch 111 - iter 265/2650 - loss 0.15837294 throughput (samples/sec): 751.87
2019-08-19 20:35:47,939 epoch 111 - iter 530/2650 - loss 0.16046645 throughput (samples/sec): 750.62
2019-08-19 20:36:00,080 epoch 111 - iter 795/2650 - loss 0.15946585 throughput (samples/sec): 705.13
2019-08-19 20:36:12,561 epoch 111 - iter 1060/2650 - loss 0.15890948 throughput (samples/sec): 685.75
2019-08-19 20:36:25,291 epoch 111 - iter 1325/2650 - loss 0.15789383 throughput (samples/sec): 672.32
2019-08-19 20:36:37,732 epoch 111 - iter 1590/2650 - loss 0.15858002 throughput (samples/sec): 687.86
2019-08-19 20:36:49,205 epoch 111 - iter 1855/2650 - loss 0.15901275 throughput (samples/sec): 745.26
2019-08-19 20:37:01,963 epoch 111 - iter 2120/2650 - loss 0.15920456 throughput (samples/sec): 671.18
2019-08-19 20:37:13,621 epoch 111 - iter 2385/2650 - loss 0.15893904 throughput (samples/sec): 734.58
2019-08-19 20:37:26,167 ----------------------------------------------------------------------------------------------------
2019-08-19 20:37:26,167 EPOCH 111 done: loss 0.1585 - lr 0.1000
2019-08-19 20:37:26,167 BAD EPOCHS (no improvement): 0
2019-08-19 20:37:26,168 ----------------------------------------------------------------------------------------------------
2019-08-19 20:37:26,216 epoch 112 - iter 0/2650 - loss 0.17082362 throughput (samples/sec): 203421.84
2019-08-19 20:37:38,709 epoch 112 - iter 265/2650 - loss 0.15535394 throughput (samples/sec): 685.02
2019-08-19 20:37:51,248 epoch 112 - iter 530/2650 - loss 0.15814109 throughput (samples/sec): 682.45
2019-08-19 20:38:03,157 epoch 112 - iter 795/2650 - loss 0.15886062 throughput (samples/sec): 718.64
2019-08-19 20:38:15,895 epoch 112 - iter 1060/2650 - loss 0.15873541 throughput (samples/sec): 671.74
2019-08-19 20:38:27,763 epoch 112 - iter 1325/2650 - loss 0.15883457 throughput (samples/sec): 721.51
2019-08-19 20:38:40,386 epoch 112 - iter 1590/2650 - loss 0.15905407 throughput (samples/sec): 678.20
2019-08-19 20:38:52,256 epoch 112 - iter 1855/2650 - loss 0.15918328 throughput (samples/sec): 721.17
2019-08-19 20:39:04,248 epoch 112 - iter 2120/2650 - loss 0.15907630 throughput (samples/sec): 713.61
2019-08-19 20:39:16,124 epoch 112 - iter 2385/2650 - loss 0.15882706 throughput (samples/sec): 720.67
2019-08-19 20:39:28,549 ----------------------------------------------------------------------------------------------------
2019-08-19 20:39:28,550 EPOCH 112 done: loss 0.1592 - lr 0.1000
2019-08-19 20:39:28,550 BAD EPOCHS (no improvement): 1
2019-08-19 20:39:28,550 ----------------------------------------------------------------------------------------------------
2019-08-19 20:39:28,597 epoch 113 - iter 0/2650 - loss 0.18553202 throughput (samples/sec): 196464.29
2019-08-19 20:39:39,769 epoch 113 - iter 265/2650 - loss 0.15274214 throughput (samples/sec): 766.07
2019-08-19 20:39:50,811 epoch 113 - iter 530/2650 - loss 0.15534816 throughput (samples/sec): 775.35
2019-08-19 20:40:01,558 epoch 113 - iter 795/2650 - loss 0.15533643 throughput (samples/sec): 796.44
2019-08-19 20:40:12,471 epoch 113 - iter 1060/2650 - loss 0.15564168 throughput (samples/sec): 784.37
2019-08-19 20:40:23,563 epoch 113 - iter 1325/2650 - loss 0.15622384 throughput (samples/sec): 771.45
2019-08-19 20:40:34,677 epoch 113 - iter 1590/2650 - loss 0.15742784 throughput (samples/sec): 769.99
2019-08-19 20:40:45,602 epoch 113 - iter 1855/2650 - loss 0.15803942 throughput (samples/sec): 783.34
2019-08-19 20:40:56,690 epoch 113 - iter 2120/2650 - loss 0.15734512 throughput (samples/sec): 771.82
2019-08-19 20:41:08,237 epoch 113 - iter 2385/2650 - loss 0.15690311 throughput (samples/sec): 741.45
2019-08-19 20:41:20,244 ----------------------------------------------------------------------------------------------------
2019-08-19 20:41:20,245 EPOCH 113 done: loss 0.1570 - lr 0.1000
2019-08-19 20:41:20,245 BAD EPOCHS (no improvement): 0
2019-08-19 20:41:20,245 ----------------------------------------------------------------------------------------------------
2019-08-19 20:41:20,291 epoch 114 - iter 0/2650 - loss 0.09519857 throughput (samples/sec): 204080.13
2019-08-19 20:41:31,583 epoch 114 - iter 265/2650 - loss 0.15075066 throughput (samples/sec): 757.25
2019-08-19 20:41:44,272 epoch 114 - iter 530/2650 - loss 0.15468531 throughput (samples/sec): 674.70
2019-08-19 20:41:57,017 epoch 114 - iter 795/2650 - loss 0.15436017 throughput (samples/sec): 671.36
2019-08-19 20:42:09,666 epoch 114 - iter 1060/2650 - loss 0.15476866 throughput (samples/sec): 676.52
2019-08-19 20:42:22,139 epoch 114 - iter 1325/2650 - loss 0.15572735 throughput (samples/sec): 686.43
2019-08-19 20:42:33,489 epoch 114 - iter 1590/2650 - loss 0.15545223 throughput (samples/sec): 753.58
2019-08-19 20:42:45,531 epoch 114 - iter 1855/2650 - loss 0.15526752 throughput (samples/sec): 710.36
2019-08-19 20:42:57,668 epoch 114 - iter 2120/2650 - loss 0.15608828 throughput (samples/sec): 706.06
2019-08-19 20:43:09,963 epoch 114 - iter 2385/2650 - loss 0.15611181 throughput (samples/sec): 696.45
2019-08-19 20:43:21,965 ----------------------------------------------------------------------------------------------------
2019-08-19 20:43:21,966 EPOCH 114 done: loss 0.1564 - lr 0.1000
2019-08-19 20:43:21,966 BAD EPOCHS (no improvement): 0
2019-08-19 20:43:21,966 ----------------------------------------------------------------------------------------------------
2019-08-19 20:43:22,015 epoch 115 - iter 0/2650 - loss 0.10394065 throughput (samples/sec): 195055.02
2019-08-19 20:43:34,413 epoch 115 - iter 265/2650 - loss 0.15763292 throughput (samples/sec): 690.09
2019-08-19 20:43:46,455 epoch 115 - iter 530/2650 - loss 0.15458913 throughput (samples/sec): 711.12
2019-08-19 20:43:58,614 epoch 115 - iter 795/2650 - loss 0.15329353 throughput (samples/sec): 704.22
2019-08-19 20:44:10,091 epoch 115 - iter 1060/2650 - loss 0.15478810 throughput (samples/sec): 745.87
2019-08-19 20:44:22,078 epoch 115 - iter 1325/2650 - loss 0.15467256 throughput (samples/sec): 714.05
2019-08-19 20:44:34,064 epoch 115 - iter 1590/2650 - loss 0.15464598 throughput (samples/sec): 713.84
2019-08-19 20:44:46,223 epoch 115 - iter 1855/2650 - loss 0.15487008 throughput (samples/sec): 703.69
2019-08-19 20:44:58,414 epoch 115 - iter 2120/2650 - loss 0.15485694 throughput (samples/sec): 702.33
2019-08-19 20:45:10,418 epoch 115 - iter 2385/2650 - loss 0.15491920 throughput (samples/sec): 712.92
2019-08-19 20:45:22,673 ----------------------------------------------------------------------------------------------------
2019-08-19 20:45:22,674 EPOCH 115 done: loss 0.1556 - lr 0.1000
2019-08-19 20:45:22,674 BAD EPOCHS (no improvement): 0
2019-08-19 20:45:22,675 ----------------------------------------------------------------------------------------------------
2019-08-19 20:45:22,724 epoch 116 - iter 0/2650 - loss 0.25861630 throughput (samples/sec): 185656.48
2019-08-19 20:45:34,631 epoch 116 - iter 265/2650 - loss 0.15638262 throughput (samples/sec): 718.79
2019-08-19 20:45:46,344 epoch 116 - iter 530/2650 - loss 0.15693425 throughput (samples/sec): 730.79
2019-08-19 20:45:57,856 epoch 116 - iter 795/2650 - loss 0.15579196 throughput (samples/sec): 743.36
2019-08-19 20:46:10,393 epoch 116 - iter 1060/2650 - loss 0.15644801 throughput (samples/sec): 682.17
2019-08-19 20:46:22,369 epoch 116 - iter 1325/2650 - loss 0.15567938 throughput (samples/sec): 714.47
2019-08-19 20:46:34,001 epoch 116 - iter 1590/2650 - loss 0.15577232 throughput (samples/sec): 736.27
2019-08-19 20:46:45,432 epoch 116 - iter 1855/2650 - loss 0.15639633 throughput (samples/sec): 748.29
2019-08-19 20:46:57,002 epoch 116 - iter 2120/2650 - loss 0.15611052 throughput (samples/sec): 740.03
2019-08-19 20:47:08,832 epoch 116 - iter 2385/2650 - loss 0.15642372 throughput (samples/sec): 722.77
2019-08-19 20:47:20,321 ----------------------------------------------------------------------------------------------------
2019-08-19 20:47:20,321 EPOCH 116 done: loss 0.1569 - lr 0.1000
2019-08-19 20:47:20,322 BAD EPOCHS (no improvement): 1
2019-08-19 20:47:20,322 ----------------------------------------------------------------------------------------------------
2019-08-19 20:47:20,365 epoch 117 - iter 0/2650 - loss 0.18426244 throughput (samples/sec): 218672.25
2019-08-19 20:47:31,788 epoch 117 - iter 265/2650 - loss 0.15518151 throughput (samples/sec): 748.58
2019-08-19 20:47:43,899 epoch 117 - iter 530/2650 - loss 0.15495991 throughput (samples/sec): 706.78
2019-08-19 20:47:55,577 epoch 117 - iter 795/2650 - loss 0.15611650 throughput (samples/sec): 732.71
2019-08-19 20:48:07,536 epoch 117 - iter 1060/2650 - loss 0.15476577 throughput (samples/sec): 715.88
2019-08-19 20:48:19,477 epoch 117 - iter 1325/2650 - loss 0.15466840 throughput (samples/sec): 716.79
2019-08-19 20:48:32,114 epoch 117 - iter 1590/2650 - loss 0.15517605 throughput (samples/sec): 677.32
2019-08-19 20:48:43,697 epoch 117 - iter 1855/2650 - loss 0.15508222 throughput (samples/sec): 738.83
2019-08-19 20:48:55,425 epoch 117 - iter 2120/2650 - loss 0.15547553 throughput (samples/sec): 729.12
2019-08-19 20:49:07,066 epoch 117 - iter 2385/2650 - loss 0.15573870 throughput (samples/sec): 734.77
2019-08-19 20:49:19,117 ----------------------------------------------------------------------------------------------------
2019-08-19 20:49:19,117 EPOCH 117 done: loss 0.1554 - lr 0.1000
2019-08-19 20:49:19,117 BAD EPOCHS (no improvement): 0
2019-08-19 20:49:19,118 ----------------------------------------------------------------------------------------------------
2019-08-19 20:49:19,169 epoch 118 - iter 0/2650 - loss 0.11025486 throughput (samples/sec): 177366.69
2019-08-19 20:49:31,365 epoch 118 - iter 265/2650 - loss 0.15564000 throughput (samples/sec): 701.87
2019-08-19 20:49:42,859 epoch 118 - iter 530/2650 - loss 0.15776694 throughput (samples/sec): 744.21
2019-08-19 20:49:54,996 epoch 118 - iter 795/2650 - loss 0.15749238 throughput (samples/sec): 705.44
2019-08-19 20:50:07,822 epoch 118 - iter 1060/2650 - loss 0.15755272 throughput (samples/sec): 666.98
2019-08-19 20:50:20,587 epoch 118 - iter 1325/2650 - loss 0.15718812 throughput (samples/sec): 670.34
2019-08-19 20:50:33,356 epoch 118 - iter 1590/2650 - loss 0.15654168 throughput (samples/sec): 670.62
2019-08-19 20:50:45,765 epoch 118 - iter 1855/2650 - loss 0.15598825 throughput (samples/sec): 689.97
2019-08-19 20:50:57,524 epoch 118 - iter 2120/2650 - loss 0.15656523 throughput (samples/sec): 728.08
2019-08-19 20:51:10,038 epoch 118 - iter 2385/2650 - loss 0.15657336 throughput (samples/sec): 684.07
2019-08-19 20:51:21,988 ----------------------------------------------------------------------------------------------------
2019-08-19 20:51:21,988 EPOCH 118 done: loss 0.1563 - lr 0.1000
2019-08-19 20:51:21,988 BAD EPOCHS (no improvement): 1
2019-08-19 20:51:21,989 ----------------------------------------------------------------------------------------------------
2019-08-19 20:51:22,049 epoch 119 - iter 0/2650 - loss 0.11639366 throughput (samples/sec): 155181.25
2019-08-19 20:51:34,228 epoch 119 - iter 265/2650 - loss 0.15332765 throughput (samples/sec): 702.48
2019-08-19 20:51:46,294 epoch 119 - iter 530/2650 - loss 0.15198538 throughput (samples/sec): 709.33
2019-08-19 20:51:58,310 epoch 119 - iter 795/2650 - loss 0.15525462 throughput (samples/sec): 712.76
2019-08-19 20:52:10,520 epoch 119 - iter 1060/2650 - loss 0.15523987 throughput (samples/sec): 700.96
2019-08-19 20:52:22,651 epoch 119 - iter 1325/2650 - loss 0.15546336 throughput (samples/sec): 705.07
2019-08-19 20:52:35,245 epoch 119 - iter 1590/2650 - loss 0.15597206 throughput (samples/sec): 679.73
2019-08-19 20:52:46,710 epoch 119 - iter 1855/2650 - loss 0.15600805 throughput (samples/sec): 745.67
2019-08-19 20:52:59,443 epoch 119 - iter 2120/2650 - loss 0.15609848 throughput (samples/sec): 672.02
2019-08-19 20:53:11,969 epoch 119 - iter 2385/2650 - loss 0.15634151 throughput (samples/sec): 683.64
2019-08-19 20:53:23,592 ----------------------------------------------------------------------------------------------------
2019-08-19 20:53:23,593 EPOCH 119 done: loss 0.1562 - lr 0.1000
2019-08-19 20:53:23,593 BAD EPOCHS (no improvement): 2
2019-08-19 20:53:23,593 ----------------------------------------------------------------------------------------------------
2019-08-19 20:53:23,643 epoch 120 - iter 0/2650 - loss 0.16097212 throughput (samples/sec): 186651.23
2019-08-19 20:53:35,867 epoch 120 - iter 265/2650 - loss 0.15466361 throughput (samples/sec): 699.68
2019-08-19 20:53:47,887 epoch 120 - iter 530/2650 - loss 0.15591566 throughput (samples/sec): 712.11
2019-08-19 20:54:00,198 epoch 120 - iter 795/2650 - loss 0.15517003 throughput (samples/sec): 694.56
2019-08-19 20:54:11,947 epoch 120 - iter 1060/2650 - loss 0.15318986 throughput (samples/sec): 728.54
2019-08-19 20:54:24,120 epoch 120 - iter 1325/2650 - loss 0.15506670 throughput (samples/sec): 703.24
2019-08-19 20:54:36,240 epoch 120 - iter 1590/2650 - loss 0.15410019 throughput (samples/sec): 706.50
2019-08-19 20:54:47,743 epoch 120 - iter 1855/2650 - loss 0.15421551 throughput (samples/sec): 743.44
2019-08-19 20:54:59,924 epoch 120 - iter 2120/2650 - loss 0.15429928 throughput (samples/sec): 702.78
2019-08-19 20:55:11,975 epoch 120 - iter 2385/2650 - loss 0.15415468 throughput (samples/sec): 709.72
2019-08-19 20:55:23,745 ----------------------------------------------------------------------------------------------------
2019-08-19 20:55:23,745 EPOCH 120 done: loss 0.1540 - lr 0.1000
2019-08-19 20:55:23,745 BAD EPOCHS (no improvement): 0
2019-08-19 20:55:23,746 ----------------------------------------------------------------------------------------------------
2019-08-19 20:55:23,790 epoch 121 - iter 0/2650 - loss 0.07567585 throughput (samples/sec): 208406.52
2019-08-19 20:55:35,343 epoch 121 - iter 265/2650 - loss 0.15924115 throughput (samples/sec): 740.77
2019-08-19 20:55:47,220 epoch 121 - iter 530/2650 - loss 0.15659951 throughput (samples/sec): 719.96
2019-08-19 20:55:59,279 epoch 121 - iter 795/2650 - loss 0.15596520 throughput (samples/sec): 710.17
2019-08-19 20:56:11,034 epoch 121 - iter 1060/2650 - loss 0.15572199 throughput (samples/sec): 728.51
2019-08-19 20:56:22,872 epoch 121 - iter 1325/2650 - loss 0.15578506 throughput (samples/sec): 722.57
2019-08-19 20:56:35,257 epoch 121 - iter 1590/2650 - loss 0.15580625 throughput (samples/sec): 690.04
2019-08-19 20:56:47,485 epoch 121 - iter 1855/2650 - loss 0.15523354 throughput (samples/sec): 699.80
2019-08-19 20:56:59,637 epoch 121 - iter 2120/2650 - loss 0.15544012 throughput (samples/sec): 703.62
2019-08-19 20:57:11,815 epoch 121 - iter 2385/2650 - loss 0.15543467 throughput (samples/sec): 702.83
2019-08-19 20:57:23,738 ----------------------------------------------------------------------------------------------------
2019-08-19 20:57:23,739 EPOCH 121 done: loss 0.1557 - lr 0.1000
2019-08-19 20:57:23,739 BAD EPOCHS (no improvement): 1
2019-08-19 20:57:23,740 ----------------------------------------------------------------------------------------------------
2019-08-19 20:57:23,797 epoch 122 - iter 0/2650 - loss 0.31354469 throughput (samples/sec): 165389.61
2019-08-19 20:57:35,416 epoch 122 - iter 265/2650 - loss 0.15157980 throughput (samples/sec): 736.86
2019-08-19 20:57:48,277 epoch 122 - iter 530/2650 - loss 0.15105469 throughput (samples/sec): 665.50
2019-08-19 20:58:00,727 epoch 122 - iter 795/2650 - loss 0.15230884 throughput (samples/sec): 686.77
2019-08-19 20:58:12,536 epoch 122 - iter 1060/2650 - loss 0.15084883 throughput (samples/sec): 724.09
2019-08-19 20:58:24,758 epoch 122 - iter 1325/2650 - loss 0.15061427 throughput (samples/sec): 700.15
2019-08-19 20:58:37,371 epoch 122 - iter 1590/2650 - loss 0.15075298 throughput (samples/sec): 678.74
2019-08-19 20:58:49,670 epoch 122 - iter 1855/2650 - loss 0.15191707 throughput (samples/sec): 696.13
2019-08-19 20:59:02,271 epoch 122 - iter 2120/2650 - loss 0.15215379 throughput (samples/sec): 679.42
2019-08-19 20:59:14,404 epoch 122 - iter 2385/2650 - loss 0.15287728 throughput (samples/sec): 705.67
2019-08-19 20:59:26,002 ----------------------------------------------------------------------------------------------------
2019-08-19 20:59:26,003 EPOCH 122 done: loss 0.1529 - lr 0.1000
2019-08-19 20:59:26,003 BAD EPOCHS (no improvement): 0
2019-08-19 20:59:26,003 ----------------------------------------------------------------------------------------------------
2019-08-19 20:59:26,056 epoch 123 - iter 0/2650 - loss 0.17122467 throughput (samples/sec): 180204.58
2019-08-19 20:59:38,908 epoch 123 - iter 265/2650 - loss 0.16012806 throughput (samples/sec): 665.90
2019-08-19 20:59:51,307 epoch 123 - iter 530/2650 - loss 0.15886899 throughput (samples/sec): 690.08
2019-08-19 21:00:03,997 epoch 123 - iter 795/2650 - loss 0.15806896 throughput (samples/sec): 674.72
2019-08-19 21:00:15,612 epoch 123 - iter 1060/2650 - loss 0.15758300 throughput (samples/sec): 736.53
2019-08-19 21:00:27,410 epoch 123 - iter 1325/2650 - loss 0.15709992 throughput (samples/sec): 725.79
2019-08-19 21:00:39,764 epoch 123 - iter 1590/2650 - loss 0.15707669 throughput (samples/sec): 692.76
2019-08-19 21:00:52,226 epoch 123 - iter 1855/2650 - loss 0.15710511 throughput (samples/sec): 686.80
2019-08-19 21:01:04,245 epoch 123 - iter 2120/2650 - loss 0.15684220 throughput (samples/sec): 712.04
2019-08-19 21:01:16,327 epoch 123 - iter 2385/2650 - loss 0.15642773 throughput (samples/sec): 708.27
2019-08-19 21:01:27,798 ----------------------------------------------------------------------------------------------------
2019-08-19 21:01:27,799 EPOCH 123 done: loss 0.1564 - lr 0.1000
2019-08-19 21:01:27,799 BAD EPOCHS (no improvement): 1
2019-08-19 21:01:27,800 ----------------------------------------------------------------------------------------------------
2019-08-19 21:01:27,844 epoch 124 - iter 0/2650 - loss 0.40992337 throughput (samples/sec): 209107.36
2019-08-19 21:01:39,572 epoch 124 - iter 265/2650 - loss 0.15188281 throughput (samples/sec): 729.22
2019-08-19 21:01:51,520 epoch 124 - iter 530/2650 - loss 0.15109932 throughput (samples/sec): 716.18
2019-08-19 21:02:02,920 epoch 124 - iter 795/2650 - loss 0.15171877 throughput (samples/sec): 750.79
2019-08-19 21:02:15,337 epoch 124 - iter 1060/2650 - loss 0.15276821 throughput (samples/sec): 689.12
2019-08-19 21:02:27,209 epoch 124 - iter 1325/2650 - loss 0.15144696 throughput (samples/sec): 720.83
2019-08-19 21:02:38,772 epoch 124 - iter 1590/2650 - loss 0.15166805 throughput (samples/sec): 740.17
2019-08-19 21:02:50,661 epoch 124 - iter 1855/2650 - loss 0.15163261 throughput (samples/sec): 719.62
2019-08-19 21:03:02,922 epoch 124 - iter 2120/2650 - loss 0.15228070 throughput (samples/sec): 698.42
2019-08-19 21:03:14,815 epoch 124 - iter 2385/2650 - loss 0.15302176 throughput (samples/sec): 719.52
2019-08-19 21:03:26,263 ----------------------------------------------------------------------------------------------------
2019-08-19 21:03:26,263 EPOCH 124 done: loss 0.1527 - lr 0.1000
2019-08-19 21:03:26,263 BAD EPOCHS (no improvement): 0
2019-08-19 21:03:26,264 ----------------------------------------------------------------------------------------------------
2019-08-19 21:03:26,306 epoch 125 - iter 0/2650 - loss 0.60782695 throughput (samples/sec): 234818.33
2019-08-19 21:03:38,979 epoch 125 - iter 265/2650 - loss 0.14930228 throughput (samples/sec): 675.48
2019-08-19 21:03:51,477 epoch 125 - iter 530/2650 - loss 0.15193233 throughput (samples/sec): 684.69
2019-08-19 21:04:03,761 epoch 125 - iter 795/2650 - loss 0.15259593 throughput (samples/sec): 696.92
2019-08-19 21:04:15,223 epoch 125 - iter 1060/2650 - loss 0.15140841 throughput (samples/sec): 746.29
2019-08-19 21:04:27,927 epoch 125 - iter 1325/2650 - loss 0.15181632 throughput (samples/sec): 673.99
2019-08-19 21:04:40,388 epoch 125 - iter 1590/2650 - loss 0.15203169 throughput (samples/sec): 686.89
2019-08-19 21:04:52,473 epoch 125 - iter 1855/2650 - loss 0.15131166 throughput (samples/sec): 707.69
2019-08-19 21:05:04,563 epoch 125 - iter 2120/2650 - loss 0.15154128 throughput (samples/sec): 707.84
2019-08-19 21:05:16,526 epoch 125 - iter 2385/2650 - loss 0.15169261 throughput (samples/sec): 714.61
2019-08-19 21:05:28,314 ----------------------------------------------------------------------------------------------------
2019-08-19 21:05:28,315 EPOCH 125 done: loss 0.1526 - lr 0.1000
2019-08-19 21:05:28,315 BAD EPOCHS (no improvement): 0
2019-08-19 21:05:28,316 ----------------------------------------------------------------------------------------------------
2019-08-19 21:05:28,360 epoch 126 - iter 0/2650 - loss 0.06399079 throughput (samples/sec): 211988.83
2019-08-19 21:05:40,373 epoch 126 - iter 265/2650 - loss 0.15448322 throughput (samples/sec): 712.87
2019-08-19 21:05:53,079 epoch 126 - iter 530/2650 - loss 0.15592356 throughput (samples/sec): 673.75
2019-08-19 21:06:05,062 epoch 126 - iter 795/2650 - loss 0.15318315 throughput (samples/sec): 714.32
2019-08-19 21:06:17,019 epoch 126 - iter 1060/2650 - loss 0.15225556 throughput (samples/sec): 716.01
2019-08-19 21:06:28,581 epoch 126 - iter 1325/2650 - loss 0.15283526 throughput (samples/sec): 739.57
2019-08-19 21:06:40,627 epoch 126 - iter 1590/2650 - loss 0.15255187 throughput (samples/sec): 710.51
2019-08-19 21:06:52,842 epoch 126 - iter 1855/2650 - loss 0.15353634 throughput (samples/sec): 700.97
2019-08-19 21:07:04,919 epoch 126 - iter 2120/2650 - loss 0.15308802 throughput (samples/sec): 708.89
2019-08-19 21:07:17,639 epoch 126 - iter 2385/2650 - loss 0.15255991 throughput (samples/sec): 672.82
2019-08-19 21:07:30,176 ----------------------------------------------------------------------------------------------------
2019-08-19 21:07:30,176 EPOCH 126 done: loss 0.1525 - lr 0.1000
2019-08-19 21:07:30,176 BAD EPOCHS (no improvement): 0
2019-08-19 21:07:30,177 ----------------------------------------------------------------------------------------------------
2019-08-19 21:07:30,218 epoch 127 - iter 0/2650 - loss 0.12046520 throughput (samples/sec): 255040.53
2019-08-19 21:07:42,126 epoch 127 - iter 265/2650 - loss 0.15323241 throughput (samples/sec): 718.54
2019-08-19 21:07:54,366 epoch 127 - iter 530/2650 - loss 0.15291379 throughput (samples/sec): 699.25
2019-08-19 21:08:06,707 epoch 127 - iter 795/2650 - loss 0.15288953 throughput (samples/sec): 693.76
2019-08-19 21:08:18,197 epoch 127 - iter 1060/2650 - loss 0.15188041 throughput (samples/sec): 744.46
2019-08-19 21:08:29,837 epoch 127 - iter 1325/2650 - loss 0.15235724 throughput (samples/sec): 734.96
2019-08-19 21:08:42,467 epoch 127 - iter 1590/2650 - loss 0.15224826 throughput (samples/sec): 677.68
2019-08-19 21:08:54,459 epoch 127 - iter 1855/2650 - loss 0.15217029 throughput (samples/sec): 713.80
2019-08-19 21:09:06,803 epoch 127 - iter 2120/2650 - loss 0.15266175 throughput (samples/sec): 693.40
2019-08-19 21:09:18,955 epoch 127 - iter 2385/2650 - loss 0.15231002 throughput (samples/sec): 704.49
2019-08-19 21:09:31,321 ----------------------------------------------------------------------------------------------------
2019-08-19 21:09:31,322 EPOCH 127 done: loss 0.1518 - lr 0.1000
2019-08-19 21:09:31,322 BAD EPOCHS (no improvement): 0
2019-08-19 21:09:31,323 ----------------------------------------------------------------------------------------------------
2019-08-19 21:09:31,369 epoch 128 - iter 0/2650 - loss 0.17846347 throughput (samples/sec): 200836.25
2019-08-19 21:09:43,026 epoch 128 - iter 265/2650 - loss 0.15304842 throughput (samples/sec): 733.82
2019-08-19 21:09:55,057 epoch 128 - iter 530/2650 - loss 0.15029060 throughput (samples/sec): 711.42
2019-08-19 21:10:06,522 epoch 128 - iter 795/2650 - loss 0.15093131 throughput (samples/sec): 746.14
2019-08-19 21:10:18,974 epoch 128 - iter 1060/2650 - loss 0.15196061 throughput (samples/sec): 687.30
2019-08-19 21:10:31,768 epoch 128 - iter 1325/2650 - loss 0.15266905 throughput (samples/sec): 668.93
2019-08-19 21:10:44,017 epoch 128 - iter 1590/2650 - loss 0.15205650 throughput (samples/sec): 698.72
2019-08-19 21:10:56,215 epoch 128 - iter 1855/2650 - loss 0.15107123 throughput (samples/sec): 702.01
2019-08-19 21:11:08,157 epoch 128 - iter 2120/2650 - loss 0.15205129 throughput (samples/sec): 717.23
2019-08-19 21:11:20,427 epoch 128 - iter 2385/2650 - loss 0.15212570 throughput (samples/sec): 697.66
2019-08-19 21:11:32,122 ----------------------------------------------------------------------------------------------------
2019-08-19 21:11:32,122 EPOCH 128 done: loss 0.1516 - lr 0.1000
2019-08-19 21:11:32,122 BAD EPOCHS (no improvement): 0
2019-08-19 21:11:32,123 ----------------------------------------------------------------------------------------------------
2019-08-19 21:11:32,175 epoch 129 - iter 0/2650 - loss 0.10275340 throughput (samples/sec): 180747.62
2019-08-19 21:11:44,239 epoch 129 - iter 265/2650 - loss 0.15086532 throughput (samples/sec): 709.38
2019-08-19 21:11:56,747 epoch 129 - iter 530/2650 - loss 0.15093756 throughput (samples/sec): 683.42
2019-08-19 21:12:08,963 epoch 129 - iter 795/2650 - loss 0.15126994 throughput (samples/sec): 700.54
2019-08-19 21:12:21,493 epoch 129 - iter 1060/2650 - loss 0.15038539 throughput (samples/sec): 683.41
2019-08-19 21:12:33,840 epoch 129 - iter 1325/2650 - loss 0.15071660 throughput (samples/sec): 693.50
2019-08-19 21:12:45,584 epoch 129 - iter 1590/2650 - loss 0.15022671 throughput (samples/sec): 728.30
2019-08-19 21:12:58,453 epoch 129 - iter 1855/2650 - loss 0.14983873 throughput (samples/sec): 665.03
2019-08-19 21:13:11,429 epoch 129 - iter 2120/2650 - loss 0.14985678 throughput (samples/sec): 659.45
2019-08-19 21:13:23,709 epoch 129 - iter 2385/2650 - loss 0.14974865 throughput (samples/sec): 696.79
2019-08-19 21:13:35,595 ----------------------------------------------------------------------------------------------------
2019-08-19 21:13:35,596 EPOCH 129 done: loss 0.1494 - lr 0.1000
2019-08-19 21:13:35,596 BAD EPOCHS (no improvement): 0
2019-08-19 21:13:35,596 ----------------------------------------------------------------------------------------------------
2019-08-19 21:13:35,664 epoch 130 - iter 0/2650 - loss 0.17236404 throughput (samples/sec): 136467.12
2019-08-19 21:13:47,626 epoch 130 - iter 265/2650 - loss 0.15214208 throughput (samples/sec): 715.76
2019-08-19 21:13:58,890 epoch 130 - iter 530/2650 - loss 0.15224246 throughput (samples/sec): 760.16
2019-08-19 21:14:10,825 epoch 130 - iter 795/2650 - loss 0.15372126 throughput (samples/sec): 717.23
2019-08-19 21:14:23,205 epoch 130 - iter 1060/2650 - loss 0.15223700 throughput (samples/sec): 691.32
2019-08-19 21:14:35,917 epoch 130 - iter 1325/2650 - loss 0.15285141 throughput (samples/sec): 673.26
2019-08-19 21:14:48,074 epoch 130 - iter 1590/2650 - loss 0.15296676 throughput (samples/sec): 704.14
2019-08-19 21:15:00,116 epoch 130 - iter 1855/2650 - loss 0.15270690 throughput (samples/sec): 710.84
2019-08-19 21:15:12,084 epoch 130 - iter 2120/2650 - loss 0.15176418 throughput (samples/sec): 715.42
2019-08-19 21:15:24,699 epoch 130 - iter 2385/2650 - loss 0.15150326 throughput (samples/sec): 678.54
2019-08-19 21:15:36,686 ----------------------------------------------------------------------------------------------------
2019-08-19 21:15:36,686 EPOCH 130 done: loss 0.1511 - lr 0.1000
2019-08-19 21:15:36,686 BAD EPOCHS (no improvement): 1
2019-08-19 21:15:36,687 ----------------------------------------------------------------------------------------------------
2019-08-19 21:15:36,762 epoch 131 - iter 0/2650 - loss 0.12510037 throughput (samples/sec): 121947.93
2019-08-19 21:15:49,417 epoch 131 - iter 265/2650 - loss 0.14597526 throughput (samples/sec): 676.01
2019-08-19 21:16:01,699 epoch 131 - iter 530/2650 - loss 0.14402551 throughput (samples/sec): 696.58
2019-08-19 21:16:14,486 epoch 131 - iter 795/2650 - loss 0.14641341 throughput (samples/sec): 669.15
2019-08-19 21:16:26,679 epoch 131 - iter 1060/2650 - loss 0.14757437 throughput (samples/sec): 702.14
2019-08-19 21:16:39,276 epoch 131 - iter 1325/2650 - loss 0.14819115 throughput (samples/sec): 679.49
2019-08-19 21:16:51,947 epoch 131 - iter 1590/2650 - loss 0.14768044 throughput (samples/sec): 675.26
2019-08-19 21:17:04,097 epoch 131 - iter 1855/2650 - loss 0.14837959 throughput (samples/sec): 704.36
2019-08-19 21:17:16,791 epoch 131 - iter 2120/2650 - loss 0.14894337 throughput (samples/sec): 674.12
2019-08-19 21:17:29,635 epoch 131 - iter 2385/2650 - loss 0.14916202 throughput (samples/sec): 666.08
2019-08-19 21:17:41,573 ----------------------------------------------------------------------------------------------------
2019-08-19 21:17:41,574 EPOCH 131 done: loss 0.1499 - lr 0.1000
2019-08-19 21:17:41,574 BAD EPOCHS (no improvement): 2
2019-08-19 21:17:41,575 ----------------------------------------------------------------------------------------------------
2019-08-19 21:17:41,615 epoch 132 - iter 0/2650 - loss 0.08958975 throughput (samples/sec): 232508.13
2019-08-19 21:17:53,562 epoch 132 - iter 265/2650 - loss 0.14504561 throughput (samples/sec): 716.80
2019-08-19 21:18:05,732 epoch 132 - iter 530/2650 - loss 0.14775463 throughput (samples/sec): 703.39
2019-08-19 21:18:18,148 epoch 132 - iter 795/2650 - loss 0.14762622 throughput (samples/sec): 689.46
2019-08-19 21:18:30,280 epoch 132 - iter 1060/2650 - loss 0.14972764 throughput (samples/sec): 705.44
2019-08-19 21:18:42,272 epoch 132 - iter 1325/2650 - loss 0.14963105 throughput (samples/sec): 713.88
2019-08-19 21:18:54,184 epoch 132 - iter 1590/2650 - loss 0.14846162 throughput (samples/sec): 718.55
2019-08-19 21:19:06,224 epoch 132 - iter 1855/2650 - loss 0.14872974 throughput (samples/sec): 711.22
2019-08-19 21:19:17,820 epoch 132 - iter 2120/2650 - loss 0.14913118 throughput (samples/sec): 737.72
2019-08-19 21:19:29,734 epoch 132 - iter 2385/2650 - loss 0.14931789 throughput (samples/sec): 718.58
2019-08-19 21:19:41,848 ----------------------------------------------------------------------------------------------------
2019-08-19 21:19:41,848 EPOCH 132 done: loss 0.1500 - lr 0.1000
2019-08-19 21:19:41,848 BAD EPOCHS (no improvement): 3
2019-08-19 21:19:41,849 ----------------------------------------------------------------------------------------------------
2019-08-19 21:19:41,905 epoch 133 - iter 0/2650 - loss 0.09084061 throughput (samples/sec): 184990.21
2019-08-19 21:19:54,264 epoch 133 - iter 265/2650 - loss 0.15317668 throughput (samples/sec): 692.30
2019-08-19 21:20:06,725 epoch 133 - iter 530/2650 - loss 0.15080797 throughput (samples/sec): 686.73
2019-08-19 21:20:18,546 epoch 133 - iter 795/2650 - loss 0.15196702 throughput (samples/sec): 723.78
2019-08-19 21:20:30,410 epoch 133 - iter 1060/2650 - loss 0.15082621 throughput (samples/sec): 721.83
2019-08-19 21:20:42,513 epoch 133 - iter 1325/2650 - loss 0.15038042 throughput (samples/sec): 707.25
2019-08-19 21:20:54,749 epoch 133 - iter 1590/2650 - loss 0.14969984 throughput (samples/sec): 699.41
2019-08-19 21:21:07,413 epoch 133 - iter 1855/2650 - loss 0.15029185 throughput (samples/sec): 675.71
2019-08-19 21:21:20,012 epoch 133 - iter 2120/2650 - loss 0.14993458 throughput (samples/sec): 679.02
2019-08-19 21:21:31,574 epoch 133 - iter 2385/2650 - loss 0.14968470 throughput (samples/sec): 739.54
2019-08-19 21:21:43,663 ----------------------------------------------------------------------------------------------------
2019-08-19 21:21:43,664 EPOCH 133 done: loss 0.1497 - lr 0.1000
2019-08-19 21:21:43,664 BAD EPOCHS (no improvement): 4
2019-08-19 21:21:43,665 ----------------------------------------------------------------------------------------------------
2019-08-19 21:21:43,717 epoch 134 - iter 0/2650 - loss 0.06456186 throughput (samples/sec): 189103.38
2019-08-19 21:21:55,792 epoch 134 - iter 265/2650 - loss 0.14976298 throughput (samples/sec): 708.91
2019-08-19 21:22:07,956 epoch 134 - iter 530/2650 - loss 0.14896148 throughput (samples/sec): 703.91
2019-08-19 21:22:19,825 epoch 134 - iter 795/2650 - loss 0.15018284 throughput (samples/sec): 720.88
2019-08-19 21:22:31,447 epoch 134 - iter 1060/2650 - loss 0.15137764 throughput (samples/sec): 735.70
2019-08-19 21:22:43,307 epoch 134 - iter 1325/2650 - loss 0.14975091 throughput (samples/sec): 720.92
2019-08-19 21:22:55,833 epoch 134 - iter 1590/2650 - loss 0.15027392 throughput (samples/sec): 683.20
2019-08-19 21:23:07,560 epoch 134 - iter 1855/2650 - loss 0.14933187 throughput (samples/sec): 730.08
2019-08-19 21:23:18,988 epoch 134 - iter 2120/2650 - loss 0.14858683 throughput (samples/sec): 748.37
2019-08-19 21:23:30,262 epoch 134 - iter 2385/2650 - loss 0.14866186 throughput (samples/sec): 758.78
2019-08-19 21:23:41,695 ----------------------------------------------------------------------------------------------------
2019-08-19 21:23:41,696 EPOCH 134 done: loss 0.1478 - lr 0.0500
2019-08-19 21:23:41,696 BAD EPOCHS (no improvement): 0
2019-08-19 21:23:41,696 ----------------------------------------------------------------------------------------------------
2019-08-19 21:23:41,748 epoch 135 - iter 0/2650 - loss 0.15422532 throughput (samples/sec): 177252.67
2019-08-19 21:23:53,621 epoch 135 - iter 265/2650 - loss 0.14475537 throughput (samples/sec): 720.10
2019-08-19 21:24:06,138 epoch 135 - iter 530/2650 - loss 0.14277589 throughput (samples/sec): 683.52
2019-08-19 21:24:17,980 epoch 135 - iter 795/2650 - loss 0.14448171 throughput (samples/sec): 722.49
2019-08-19 21:24:30,359 epoch 135 - iter 1060/2650 - loss 0.14458382 throughput (samples/sec): 691.23
2019-08-19 21:24:41,938 epoch 135 - iter 1325/2650 - loss 0.14417413 throughput (samples/sec): 738.82
2019-08-19 21:24:54,539 epoch 135 - iter 1590/2650 - loss 0.14545263 throughput (samples/sec): 679.34
2019-08-19 21:25:07,232 epoch 135 - iter 1855/2650 - loss 0.14652581 throughput (samples/sec): 674.11
2019-08-19 21:25:19,169 epoch 135 - iter 2120/2650 - loss 0.14630023 throughput (samples/sec): 717.25
2019-08-19 21:25:31,072 epoch 135 - iter 2385/2650 - loss 0.14686364 throughput (samples/sec): 719.08
2019-08-19 21:25:42,887 ----------------------------------------------------------------------------------------------------
2019-08-19 21:25:42,888 EPOCH 135 done: loss 0.1473 - lr 0.0500
2019-08-19 21:25:42,888 BAD EPOCHS (no improvement): 0
2019-08-19 21:25:42,888 ----------------------------------------------------------------------------------------------------
2019-08-19 21:25:42,934 epoch 136 - iter 0/2650 - loss 0.10102805 throughput (samples/sec): 200864.60
2019-08-19 21:25:54,825 epoch 136 - iter 265/2650 - loss 0.14935222 throughput (samples/sec): 719.80
2019-08-19 21:26:06,414 epoch 136 - iter 530/2650 - loss 0.14669831 throughput (samples/sec): 738.61
2019-08-19 21:26:18,615 epoch 136 - iter 795/2650 - loss 0.14645962 throughput (samples/sec): 701.69
2019-08-19 21:26:30,399 epoch 136 - iter 1060/2650 - loss 0.14682925 throughput (samples/sec): 726.50
2019-08-19 21:26:42,467 epoch 136 - iter 1325/2650 - loss 0.14764070 throughput (samples/sec): 709.18
2019-08-19 21:26:54,810 epoch 136 - iter 1590/2650 - loss 0.14824996 throughput (samples/sec): 692.73
2019-08-19 21:27:06,915 epoch 136 - iter 1855/2650 - loss 0.14803025 throughput (samples/sec): 707.13
2019-08-19 21:27:18,892 epoch 136 - iter 2120/2650 - loss 0.14752515 throughput (samples/sec): 714.44
2019-08-19 21:27:31,070 epoch 136 - iter 2385/2650 - loss 0.14742225 throughput (samples/sec): 702.93
2019-08-19 21:27:42,661 ----------------------------------------------------------------------------------------------------
2019-08-19 21:27:42,662 EPOCH 136 done: loss 0.1468 - lr 0.0500
2019-08-19 21:27:42,662 BAD EPOCHS (no improvement): 0
2019-08-19 21:27:42,663 ----------------------------------------------------------------------------------------------------
2019-08-19 21:27:42,715 epoch 137 - iter 0/2650 - loss 0.10385603 throughput (samples/sec): 183180.96
2019-08-19 21:27:54,800 epoch 137 - iter 265/2650 - loss 0.14797075 throughput (samples/sec): 708.14
2019-08-19 21:28:07,011 epoch 137 - iter 530/2650 - loss 0.14838092 throughput (samples/sec): 700.62
2019-08-19 21:28:19,476 epoch 137 - iter 795/2650 - loss 0.14697822 throughput (samples/sec): 686.39
2019-08-19 21:28:31,926 epoch 137 - iter 1060/2650 - loss 0.14575715 throughput (samples/sec): 687.83
2019-08-19 21:28:44,215 epoch 137 - iter 1325/2650 - loss 0.14578760 throughput (samples/sec): 696.64
2019-08-19 21:28:55,822 epoch 137 - iter 1590/2650 - loss 0.14588339 throughput (samples/sec): 737.01
2019-08-19 21:29:07,241 epoch 137 - iter 1855/2650 - loss 0.14593737 throughput (samples/sec): 748.84
2019-08-19 21:29:18,665 epoch 137 - iter 2120/2650 - loss 0.14664858 throughput (samples/sec): 748.30
2019-08-19 21:29:30,352 epoch 137 - iter 2385/2650 - loss 0.14744709 throughput (samples/sec): 731.57
2019-08-19 21:29:42,765 ----------------------------------------------------------------------------------------------------
2019-08-19 21:29:42,765 EPOCH 137 done: loss 0.1477 - lr 0.0500
2019-08-19 21:29:42,765 BAD EPOCHS (no improvement): 1
2019-08-19 21:29:42,766 ----------------------------------------------------------------------------------------------------
2019-08-19 21:29:42,819 epoch 138 - iter 0/2650 - loss 0.19973886 throughput (samples/sec): 186113.08
2019-08-19 21:29:55,515 epoch 138 - iter 265/2650 - loss 0.14798241 throughput (samples/sec): 674.33
2019-08-19 21:30:06,689 epoch 138 - iter 530/2650 - loss 0.14589746 throughput (samples/sec): 765.62
2019-08-19 21:30:18,447 epoch 138 - iter 795/2650 - loss 0.14563504 throughput (samples/sec): 727.60
2019-08-19 21:30:30,736 epoch 138 - iter 1060/2650 - loss 0.14728166 throughput (samples/sec): 696.36
2019-08-19 21:30:42,895 epoch 138 - iter 1325/2650 - loss 0.14870437 throughput (samples/sec): 703.38
2019-08-19 21:30:54,246 epoch 138 - iter 1590/2650 - loss 0.14721783 throughput (samples/sec): 754.16
2019-08-19 21:31:05,359 epoch 138 - iter 1855/2650 - loss 0.14718789 throughput (samples/sec): 769.88
2019-08-19 21:31:16,485 epoch 138 - iter 2120/2650 - loss 0.14714337 throughput (samples/sec): 769.29
2019-08-19 21:31:28,251 epoch 138 - iter 2385/2650 - loss 0.14691464 throughput (samples/sec): 727.60
2019-08-19 21:31:40,987 ----------------------------------------------------------------------------------------------------
2019-08-19 21:31:40,987 EPOCH 138 done: loss 0.1472 - lr 0.0500
2019-08-19 21:31:40,988 BAD EPOCHS (no improvement): 2
2019-08-19 21:31:40,988 ----------------------------------------------------------------------------------------------------
2019-08-19 21:31:41,037 epoch 139 - iter 0/2650 - loss 0.10552792 throughput (samples/sec): 194057.84
2019-08-19 21:31:52,983 epoch 139 - iter 265/2650 - loss 0.14136131 throughput (samples/sec): 715.84
2019-08-19 21:32:04,746 epoch 139 - iter 530/2650 - loss 0.14254131 throughput (samples/sec): 726.92
2019-08-19 21:32:17,398 epoch 139 - iter 795/2650 - loss 0.14347761 throughput (samples/sec): 676.10
2019-08-19 21:32:29,560 epoch 139 - iter 1060/2650 - loss 0.14385035 throughput (samples/sec): 703.74
2019-08-19 21:32:42,297 epoch 139 - iter 1325/2650 - loss 0.14417817 throughput (samples/sec): 672.37
2019-08-19 21:32:54,537 epoch 139 - iter 1590/2650 - loss 0.14515712 throughput (samples/sec): 699.41
2019-08-19 21:33:06,068 epoch 139 - iter 1855/2650 - loss 0.14556143 throughput (samples/sec): 741.60
2019-08-19 21:33:18,327 epoch 139 - iter 2120/2650 - loss 0.14580895 throughput (samples/sec): 698.21
2019-08-19 21:33:30,983 epoch 139 - iter 2385/2650 - loss 0.14548948 throughput (samples/sec): 676.19
2019-08-19 21:33:43,450 ----------------------------------------------------------------------------------------------------
2019-08-19 21:33:43,450 EPOCH 139 done: loss 0.1453 - lr 0.0500
2019-08-19 21:33:43,450 BAD EPOCHS (no improvement): 0
2019-08-19 21:33:43,452 ----------------------------------------------------------------------------------------------------
2019-08-19 21:33:43,501 epoch 140 - iter 0/2650 - loss 0.21929766 throughput (samples/sec): 186042.99
2019-08-19 21:33:55,457 epoch 140 - iter 265/2650 - loss 0.14440290 throughput (samples/sec): 715.29
2019-08-19 21:34:07,930 epoch 140 - iter 530/2650 - loss 0.14288549 throughput (samples/sec): 686.50
2019-08-19 21:34:19,225 epoch 140 - iter 795/2650 - loss 0.14560689 throughput (samples/sec): 757.37
2019-08-19 21:34:31,004 epoch 140 - iter 1060/2650 - loss 0.14649058 throughput (samples/sec): 726.16
2019-08-19 21:34:43,136 epoch 140 - iter 1325/2650 - loss 0.14704833 throughput (samples/sec): 705.33
2019-08-19 21:34:55,276 epoch 140 - iter 1590/2650 - loss 0.14729987 throughput (samples/sec): 704.82
2019-08-19 21:35:07,363 epoch 140 - iter 1855/2650 - loss 0.14672036 throughput (samples/sec): 707.69
2019-08-19 21:35:19,514 epoch 140 - iter 2120/2650 - loss 0.14695336 throughput (samples/sec): 704.46
2019-08-19 21:35:32,218 epoch 140 - iter 2385/2650 - loss 0.14663410 throughput (samples/sec): 673.85
2019-08-19 21:35:43,833 ----------------------------------------------------------------------------------------------------
2019-08-19 21:35:43,833 EPOCH 140 done: loss 0.1462 - lr 0.0500
2019-08-19 21:35:43,833 BAD EPOCHS (no improvement): 1
2019-08-19 21:35:43,834 ----------------------------------------------------------------------------------------------------
2019-08-19 21:35:43,879 epoch 141 - iter 0/2650 - loss 0.09471304 throughput (samples/sec): 204171.51
2019-08-19 21:35:56,046 epoch 141 - iter 265/2650 - loss 0.14850177 throughput (samples/sec): 703.03
2019-08-19 21:36:08,405 epoch 141 - iter 530/2650 - loss 0.14955585 throughput (samples/sec): 692.18
2019-08-19 21:36:21,103 epoch 141 - iter 795/2650 - loss 0.14789642 throughput (samples/sec): 673.68
2019-08-19 21:36:33,347 epoch 141 - iter 1060/2650 - loss 0.14677939 throughput (samples/sec): 698.91
2019-08-19 21:36:45,639 epoch 141 - iter 1325/2650 - loss 0.14631216 throughput (samples/sec): 696.52
2019-08-19 21:36:57,889 epoch 141 - iter 1590/2650 - loss 0.14632113 throughput (samples/sec): 698.89
2019-08-19 21:37:10,556 epoch 141 - iter 1855/2650 - loss 0.14486085 throughput (samples/sec): 675.75
2019-08-19 21:37:22,586 epoch 141 - iter 2120/2650 - loss 0.14492007 throughput (samples/sec): 711.38
2019-08-19 21:37:35,122 epoch 141 - iter 2385/2650 - loss 0.14500903 throughput (samples/sec): 682.59
2019-08-19 21:37:47,081 ----------------------------------------------------------------------------------------------------
2019-08-19 21:37:47,081 EPOCH 141 done: loss 0.1454 - lr 0.0500
2019-08-19 21:37:47,082 BAD EPOCHS (no improvement): 2
2019-08-19 21:37:47,082 ----------------------------------------------------------------------------------------------------
2019-08-19 21:37:47,139 epoch 142 - iter 0/2650 - loss 0.14256661 throughput (samples/sec): 166824.72
2019-08-19 21:37:59,461 epoch 142 - iter 265/2650 - loss 0.15041484 throughput (samples/sec): 694.49
2019-08-19 21:38:11,223 epoch 142 - iter 530/2650 - loss 0.14615409 throughput (samples/sec): 727.40
2019-08-19 21:38:22,981 epoch 142 - iter 795/2650 - loss 0.14496129 throughput (samples/sec): 727.87
2019-08-19 21:38:35,294 epoch 142 - iter 1060/2650 - loss 0.14573798 throughput (samples/sec): 695.18
2019-08-19 21:38:47,808 epoch 142 - iter 1325/2650 - loss 0.14636349 throughput (samples/sec): 684.08
2019-08-19 21:39:00,333 epoch 142 - iter 1590/2650 - loss 0.14619545 throughput (samples/sec): 683.36
2019-08-19 21:39:12,017 epoch 142 - iter 1855/2650 - loss 0.14613222 throughput (samples/sec): 731.87
2019-08-19 21:39:24,537 epoch 142 - iter 2120/2650 - loss 0.14588512 throughput (samples/sec): 683.73
2019-08-19 21:39:36,034 epoch 142 - iter 2385/2650 - loss 0.14664347 throughput (samples/sec): 745.35
2019-08-19 21:39:47,810 ----------------------------------------------------------------------------------------------------
2019-08-19 21:39:47,811 EPOCH 142 done: loss 0.1467 - lr 0.0500
2019-08-19 21:39:47,811 BAD EPOCHS (no improvement): 3
2019-08-19 21:39:47,811 ----------------------------------------------------------------------------------------------------
2019-08-19 21:39:47,857 epoch 143 - iter 0/2650 - loss 0.16868088 throughput (samples/sec): 209336.27
2019-08-19 21:40:00,522 epoch 143 - iter 265/2650 - loss 0.15188645 throughput (samples/sec): 675.70
2019-08-19 21:40:13,253 epoch 143 - iter 530/2650 - loss 0.14961777 throughput (samples/sec): 672.44
2019-08-19 21:40:25,976 epoch 143 - iter 795/2650 - loss 0.14702663 throughput (samples/sec): 672.64
2019-08-19 21:40:38,484 epoch 143 - iter 1060/2650 - loss 0.14654193 throughput (samples/sec): 684.77
2019-08-19 21:40:50,671 epoch 143 - iter 1325/2650 - loss 0.14639855 throughput (samples/sec): 702.91
2019-08-19 21:41:02,681 epoch 143 - iter 1590/2650 - loss 0.14635661 throughput (samples/sec): 712.82
2019-08-19 21:41:14,554 epoch 143 - iter 1855/2650 - loss 0.14634126 throughput (samples/sec): 721.09
2019-08-19 21:41:26,501 epoch 143 - iter 2120/2650 - loss 0.14604410 throughput (samples/sec): 716.43
2019-08-19 21:41:39,162 epoch 143 - iter 2385/2650 - loss 0.14629707 throughput (samples/sec): 676.04
2019-08-19 21:41:51,459 ----------------------------------------------------------------------------------------------------
2019-08-19 21:41:51,459 EPOCH 143 done: loss 0.1463 - lr 0.0500
2019-08-19 21:41:51,459 BAD EPOCHS (no improvement): 4
2019-08-19 21:41:51,460 ----------------------------------------------------------------------------------------------------
2019-08-19 21:41:51,496 epoch 144 - iter 0/2650 - loss 0.11901164 throughput (samples/sec): 260302.24
2019-08-19 21:42:03,584 epoch 144 - iter 265/2650 - loss 0.14357652 throughput (samples/sec): 707.61
2019-08-19 21:42:15,145 epoch 144 - iter 530/2650 - loss 0.14273012 throughput (samples/sec): 740.10
2019-08-19 21:42:27,754 epoch 144 - iter 795/2650 - loss 0.14210383 throughput (samples/sec): 679.11
2019-08-19 21:42:39,975 epoch 144 - iter 1060/2650 - loss 0.14259210 throughput (samples/sec): 700.47
2019-08-19 21:42:51,534 epoch 144 - iter 1325/2650 - loss 0.14362608 throughput (samples/sec): 739.95
2019-08-19 21:43:03,820 epoch 144 - iter 1590/2650 - loss 0.14493209 throughput (samples/sec): 696.69
2019-08-19 21:43:15,295 epoch 144 - iter 1855/2650 - loss 0.14482704 throughput (samples/sec): 744.90
2019-08-19 21:43:26,922 epoch 144 - iter 2120/2650 - loss 0.14537472 throughput (samples/sec): 735.40
2019-08-19 21:43:38,900 epoch 144 - iter 2385/2650 - loss 0.14536512 throughput (samples/sec): 714.89
2019-08-19 21:43:51,214 ----------------------------------------------------------------------------------------------------
2019-08-19 21:43:51,215 EPOCH 144 done: loss 0.1452 - lr 0.0250
2019-08-19 21:43:51,215 BAD EPOCHS (no improvement): 0
2019-08-19 21:43:51,216 ----------------------------------------------------------------------------------------------------
2019-08-19 21:43:51,275 epoch 145 - iter 0/2650 - loss 0.17150716 throughput (samples/sec): 158515.46
2019-08-19 21:44:03,567 epoch 145 - iter 265/2650 - loss 0.14408051 throughput (samples/sec): 696.13
2019-08-19 21:44:15,217 epoch 145 - iter 530/2650 - loss 0.14527135 throughput (samples/sec): 733.92
2019-08-19 21:44:27,312 epoch 145 - iter 795/2650 - loss 0.14425343 throughput (samples/sec): 707.47
2019-08-19 21:44:39,859 epoch 145 - iter 1060/2650 - loss 0.14347769 throughput (samples/sec): 682.03
2019-08-19 21:44:51,615 epoch 145 - iter 1325/2650 - loss 0.14394280 throughput (samples/sec): 727.69
2019-08-19 21:45:04,456 epoch 145 - iter 1590/2650 - loss 0.14452889 throughput (samples/sec): 666.92
2019-08-19 21:45:16,610 epoch 145 - iter 1855/2650 - loss 0.14409251 throughput (samples/sec): 704.67
2019-08-19 21:45:28,704 epoch 145 - iter 2120/2650 - loss 0.14433454 throughput (samples/sec): 713.74
2019-08-19 21:45:40,950 epoch 145 - iter 2385/2650 - loss 0.14474077 throughput (samples/sec): 698.82
2019-08-19 21:45:52,996 ----------------------------------------------------------------------------------------------------
2019-08-19 21:45:52,997 EPOCH 145 done: loss 0.1454 - lr 0.0250
2019-08-19 21:45:52,997 BAD EPOCHS (no improvement): 1
2019-08-19 21:45:52,997 ----------------------------------------------------------------------------------------------------
2019-08-19 21:45:53,040 epoch 146 - iter 0/2650 - loss 0.22471941 throughput (samples/sec): 216630.52
2019-08-19 21:46:04,362 epoch 146 - iter 265/2650 - loss 0.13985584 throughput (samples/sec): 755.93
2019-08-19 21:46:16,821 epoch 146 - iter 530/2650 - loss 0.14263551 throughput (samples/sec): 687.35
2019-08-19 21:46:29,025 epoch 146 - iter 795/2650 - loss 0.14262331 throughput (samples/sec): 701.64
2019-08-19 21:46:40,624 epoch 146 - iter 1060/2650 - loss 0.14407508 throughput (samples/sec): 737.85
2019-08-19 21:46:53,032 epoch 146 - iter 1325/2650 - loss 0.14467938 throughput (samples/sec): 689.28
2019-08-19 21:47:05,804 epoch 146 - iter 1590/2650 - loss 0.14546377 throughput (samples/sec): 670.01
2019-08-19 21:47:18,664 epoch 146 - iter 1855/2650 - loss 0.14477937 throughput (samples/sec): 665.31
2019-08-19 21:47:30,897 epoch 146 - iter 2120/2650 - loss 0.14444699 throughput (samples/sec): 699.73
2019-08-19 21:47:43,273 epoch 146 - iter 2385/2650 - loss 0.14495938 throughput (samples/sec): 691.48
2019-08-19 21:47:55,038 ----------------------------------------------------------------------------------------------------
2019-08-19 21:47:55,038 EPOCH 146 done: loss 0.1447 - lr 0.0250
2019-08-19 21:47:55,039 BAD EPOCHS (no improvement): 0
2019-08-19 21:47:55,039 ----------------------------------------------------------------------------------------------------
2019-08-19 21:47:55,087 epoch 147 - iter 0/2650 - loss 0.12938000 throughput (samples/sec): 193659.50
2019-08-19 21:48:06,199 epoch 147 - iter 265/2650 - loss 0.14726862 throughput (samples/sec): 769.90
2019-08-19 21:48:17,962 epoch 147 - iter 530/2650 - loss 0.14560647 throughput (samples/sec): 727.08
2019-08-19 21:48:30,418 epoch 147 - iter 795/2650 - loss 0.14635695 throughput (samples/sec): 687.58
2019-08-19 21:48:43,129 epoch 147 - iter 1060/2650 - loss 0.14679666 throughput (samples/sec): 673.17
2019-08-19 21:48:55,000 epoch 147 - iter 1325/2650 - loss 0.14614944 throughput (samples/sec): 720.46
2019-08-19 21:49:06,918 epoch 147 - iter 1590/2650 - loss 0.14589227 throughput (samples/sec): 718.68
2019-08-19 21:49:18,367 epoch 147 - iter 1855/2650 - loss 0.14490752 throughput (samples/sec): 747.29
2019-08-19 21:49:30,637 epoch 147 - iter 2120/2650 - loss 0.14454018 throughput (samples/sec): 697.80
2019-08-19 21:49:43,139 epoch 147 - iter 2385/2650 - loss 0.14487669 throughput (samples/sec): 684.60
2019-08-19 21:49:55,153 ----------------------------------------------------------------------------------------------------
2019-08-19 21:49:55,153 EPOCH 147 done: loss 0.1446 - lr 0.0250
2019-08-19 21:49:55,154 BAD EPOCHS (no improvement): 0
2019-08-19 21:49:55,154 ----------------------------------------------------------------------------------------------------
2019-08-19 21:49:55,196 epoch 148 - iter 0/2650 - loss 0.10625295 throughput (samples/sec): 231880.58
2019-08-19 21:50:07,171 epoch 148 - iter 265/2650 - loss 0.14030612 throughput (samples/sec): 714.70
2019-08-19 21:50:19,695 epoch 148 - iter 530/2650 - loss 0.14230193 throughput (samples/sec): 683.67
2019-08-19 21:50:31,783 epoch 148 - iter 795/2650 - loss 0.14301540 throughput (samples/sec): 708.47
2019-08-19 21:50:43,795 epoch 148 - iter 1060/2650 - loss 0.14506234 throughput (samples/sec): 712.18
2019-08-19 21:50:56,054 epoch 148 - iter 1325/2650 - loss 0.14460309 throughput (samples/sec): 698.25
2019-08-19 21:51:07,694 epoch 148 - iter 1590/2650 - loss 0.14407021 throughput (samples/sec): 735.32
2019-08-19 21:51:19,121 epoch 148 - iter 1855/2650 - loss 0.14476563 throughput (samples/sec): 748.20
2019-08-19 21:51:30,860 epoch 148 - iter 2120/2650 - loss 0.14466534 throughput (samples/sec): 728.17
2019-08-19 21:51:43,158 epoch 148 - iter 2385/2650 - loss 0.14423366 throughput (samples/sec): 695.62
2019-08-19 21:51:55,123 ----------------------------------------------------------------------------------------------------
2019-08-19 21:51:55,123 EPOCH 148 done: loss 0.1441 - lr 0.0250
2019-08-19 21:51:55,123 BAD EPOCHS (no improvement): 0
2019-08-19 21:51:55,124 ----------------------------------------------------------------------------------------------------
2019-08-19 21:51:55,170 epoch 149 - iter 0/2650 - loss 0.10099556 throughput (samples/sec): 201914.81
2019-08-19 21:52:07,416 epoch 149 - iter 265/2650 - loss 0.15033938 throughput (samples/sec): 698.51
2019-08-19 21:52:19,989 epoch 149 - iter 530/2650 - loss 0.14584964 throughput (samples/sec): 680.95
2019-08-19 21:52:32,456 epoch 149 - iter 795/2650 - loss 0.14457668 throughput (samples/sec): 686.52
2019-08-19 21:52:44,320 epoch 149 - iter 1060/2650 - loss 0.14672254 throughput (samples/sec): 720.75
2019-08-19 21:52:56,380 epoch 149 - iter 1325/2650 - loss 0.14639197 throughput (samples/sec): 709.59
2019-08-19 21:53:08,724 epoch 149 - iter 1590/2650 - loss 0.14602114 throughput (samples/sec): 693.68
2019-08-19 21:53:21,390 epoch 149 - iter 1855/2650 - loss 0.14574314 throughput (samples/sec): 675.99
2019-08-19 21:53:33,161 epoch 149 - iter 2120/2650 - loss 0.14557587 throughput (samples/sec): 727.30
2019-08-19 21:53:45,233 epoch 149 - iter 2385/2650 - loss 0.14526130 throughput (samples/sec): 708.45
2019-08-19 21:53:57,479 ----------------------------------------------------------------------------------------------------
2019-08-19 21:53:57,480 EPOCH 149 done: loss 0.1451 - lr 0.0250
2019-08-19 21:53:57,480 BAD EPOCHS (no improvement): 1
2019-08-19 21:53:57,481 ----------------------------------------------------------------------------------------------------
2019-08-19 21:53:57,536 epoch 150 - iter 0/2650 - loss 0.17845322 throughput (samples/sec): 176604.90
2019-08-19 21:54:10,276 epoch 150 - iter 265/2650 - loss 0.13988595 throughput (samples/sec): 671.71
2019-08-19 21:54:23,035 epoch 150 - iter 530/2650 - loss 0.14458330 throughput (samples/sec): 670.68
2019-08-19 21:54:35,601 epoch 150 - iter 795/2650 - loss 0.14217215 throughput (samples/sec): 681.56
2019-08-19 21:54:47,307 epoch 150 - iter 1060/2650 - loss 0.14289340 throughput (samples/sec): 731.52
2019-08-19 21:54:58,783 epoch 150 - iter 1325/2650 - loss 0.14339658 throughput (samples/sec): 746.03
2019-08-19 21:55:10,676 epoch 150 - iter 1590/2650 - loss 0.14347641 throughput (samples/sec): 719.16
2019-08-19 21:55:22,325 epoch 150 - iter 1855/2650 - loss 0.14394641 throughput (samples/sec): 733.98
2019-08-19 21:55:34,718 epoch 150 - iter 2120/2650 - loss 0.14391863 throughput (samples/sec): 690.42
2019-08-19 21:55:46,826 epoch 150 - iter 2385/2650 - loss 0.14464286 throughput (samples/sec): 707.04
2019-08-19 21:55:58,902 ----------------------------------------------------------------------------------------------------
2019-08-19 21:55:58,902 EPOCH 150 done: loss 0.1447 - lr 0.0250
2019-08-19 21:55:58,902 BAD EPOCHS (no improvement): 2
2019-08-19 21:56:02,509 ----------------------------------------------------------------------------------------------------
2019-08-19 21:56:02,510 Testing using best model ...
2019-08-19 22:35:39,649 0.9731	0.9731	0.9731
2019-08-19 22:35:39,649 
MICRO_AVG: acc 0.9475 - f1-score 0.9731
MACRO_AVG: acc 0.4379 - f1-score 0.4845036363636343
_          tp: 141154 - fp: 841 - fn: 1657 - tn: 141154 - precision: 0.9941 - recall: 0.9884 - accuracy: 0.9826 - f1-score: 0.9912
abandon.01 tp: 8 - fp: 0 - fn: 0 - tn: 8 - precision: 1.0000 - recall: 1.0000 - accuracy: 1.0000 - f1-score: 1.0000
abate.01   tp: 0 - fp: 0 - fn: 1 - tn: 0 - precision: 0.0000 - recall: 0.0000 - accuracy: 0.0000 - f1-score: 0.0000
abduct.01  tp: 1 - fp: 0 - fn: 1 - tn: 1 - precision: 1.0000 - recall: 0.5000 - accuracy: 0.5000 - f1-score: 0.6667
abide.01   tp: 1 - fp: 0 - fn: 1 - tn: 1 - precision: 1.0000 - recall: 0.5000 - accuracy: 0.5000 - f1-score: 0.6667
abort.01   tp: 5 - fp: 0 - fn: 0 - tn: 5 - precision: 1.0000 - recall: 1.0000 - accuracy: 1.0000 - f1-score: 1.0000
abound.01  tp: 0 - fp: 0 - fn: 2 - tn: 0 - precision: 0.0000 - recall: 0.0000 - accuracy: 0.0000 - f1-score: 0.0000
absent.01  tp: 3 - fp: 0 - fn: 0 - tn: 3 - precision: 1.0000 - recall: 1.0000 - accuracy: 1.0000 - f1-score: 1.0000
absorb.01  tp: 1 - fp: 2 - fn: 0 - tn: 1 - precision: 0.3333 - recall: 1.0000 - accuracy: 0.3333 - f1-score: 0.5000
abuse.01   tp: 5 - fp: 16 - fn: 1 - tn: 5 - precision: 0.2381 - recall: 0.8333 - accuracy: 0.2273 - f1-score: 0.3704
abuse.02   tp: 0 - fp: 1 - fn: 1 - tn: 0 - precision: 0.0000 - recall: 0.0000 - accuracy: 0.0000 - f1-score: 0.0000
abut.01    tp: 0 - fp: 0 - fn: 1 - tn: 0 - precision: 0.0000 - recall: 0.0000 - accuracy: 0.0000 - f1-score: 0.0000
accelerate.01 tp: 8 - fp: 0 - fn: 0 - tn: 8 - precision: 1.0000 - recall: 1.0000 - accuracy: 1.0000 - f1-score: 1.0000
accept.01  tp: 34 - fp: 2 - fn: 1 - tn: 34 - precision: 0.9444 - recall: 0.9714 - accuracy: 0.9189 - f1-score: 0.9577
access.01  tp: 4 - fp: 0 - fn: 1 - tn: 4 - precision: 1.0000 - recall: 0.8000 - accuracy: 0.8000 - f1-score: 0.8889
accession.01 tp: 0 - fp: 0 - fn: 1 - tn: 0 - precision: 0.0000 - recall: 0.0000 - accuracy: 0.0000 - f1-score: 0.0000
accommodate.01 tp: 1 - fp: 1 - fn: 0 - tn: 1 - precision: 0.5000 - recall: 1.0000 - accuracy: 0.5000 - f1-score: 0.6667
accompany.01 tp: 1 - fp: 0 - fn: 1 - tn: 1 - precision: 1.0000 - recall: 0.5000 - accuracy: 0.5000 - f1-score: 0.6667
accomplish.01 tp: 6 - fp: 0 - fn: 0 - tn: 6 - precision: 1.0000 - recall: 1.0000 - accuracy: 1.0000 - f1-score: 1.0000
accord.02  tp: 6 - fp: 0 - fn: 0 - tn: 6 - precision: 1.0000 - recall: 1.0000 - accuracy: 1.0000 - f1-score: 1.0000
account.01 tp: 30 - fp: 1 - fn: 7 - tn: 30 - precision: 0.9677 - recall: 0.8108 - accuracy: 0.7895 - f1-score: 0.8823
accumulate.01 tp: 7 - fp: 0 - fn: 2 - tn: 7 - precision: 1.0000 - recall: 0.7778 - accuracy: 0.7778 - f1-score: 0.8750
accuse.01  tp: 7 - fp: 0 - fn: 0 - tn: 7 - precision: 1.0000 - recall: 1.0000 - accuracy: 1.0000 - f1-score: 1.0000
achieve.01 tp: 18 - fp: 0 - fn: 2 - tn: 18 - precision: 1.0000 - recall: 0.9000 - accuracy: 0.9000 - f1-score: 0.9474
acknowledge.01 tp: 11 - fp: 4 - fn: 0 - tn: 11 - precision: 0.7333 - recall: 1.0000 - accuracy: 0.7333 - f1-score: 0.8461
acquiesce.01 tp: 0 - fp: 0 - fn: 1 - tn: 0 - precision: 0.0000 - recall: 0.0000 - accuracy: 0.0000 - f1-score: 0.0000
acquire.01 tp: 14 - fp: 1 - fn: 0 - tn: 14 - precision: 0.9333 - recall: 1.0000 - accuracy: 0.9333 - f1-score: 0.9655
act.01     tp: 3 - fp: 3 - fn: 1 - tn: 3 - precision: 0.5000 - recall: 0.7500 - accuracy: 0.4286 - f1-score: 0.6000
act.02     tp: 18 - fp: 14 - fn: 2 - tn: 18 - precision: 0.5625 - recall: 0.9000 - accuracy: 0.5294 - f1-score: 0.6923
activate.01 tp: 0 - fp: 0 - fn: 8 - tn: 0 - precision: 0.0000 - recall: 0.0000 - accuracy: 0.0000 - f1-score: 0.0000
adapt.01   tp: 1 - fp: 0 - fn: 0 - tn: 1 - precision: 1.0000 - recall: 1.0000 - accuracy: 1.0000 - f1-score: 1.0000
add.01     tp: 19 - fp: 0 - fn: 0 - tn: 19 - precision: 1.0000 - recall: 1.0000 - accuracy: 1.0000 - f1-score: 1.0000
add.02     tp: 36 - fp: 12 - fn: 0 - tn: 36 - precision: 0.7500 - recall: 1.0000 - accuracy: 0.7500 - f1-score: 0.8571
add.03     tp: 0 - fp: 1 - fn: 4 - tn: 0 - precision: 0.0000 - recall: 0.0000 - accuracy: 0.0000 - f1-score: 0.0000
add_up.04  tp: 0 - fp: 0 - fn: 1 - tn: 0 - precision: 0.0000 - recall: 0.0000 - accuracy: 0.0000 - f1-score: 0.0000
address.01 tp: 0 - fp: 0 - fn: 2 - tn: 0 - precision: 0.0000 - recall: 0.0000 - accuracy: 0.0000 - f1-score: 0.0000
address.02 tp: 6 - fp: 2 - fn: 0 - tn: 6 - precision: 0.7500 - recall: 1.0000 - accuracy: 0.7500 - f1-score: 0.8571
address.03 tp: 2 - fp: 0 - fn: 0 - tn: 2 - precision: 1.0000 - recall: 1.0000 - accuracy: 1.0000 - f1-score: 1.0000
adhere.02  tp: 1 - fp: 0 - fn: 0 - tn: 1 - precision: 1.0000 - recall: 1.0000 - accuracy: 1.0000 - f1-score: 1.0000
adjust.01  tp: 11 - fp: 2 - fn: 0 - tn: 11 - precision: 0.8462 - recall: 1.0000 - accuracy: 0.8462 - f1-score: 0.9167
administer.01 tp: 0 - fp: 0 - fn: 1 - tn: 0 - precision: 0.0000 - recall: 0.0000 - accuracy: 0.0000 - f1-score: 0.0000
admire.01  tp: 1 - fp: 0 - fn: 0 - tn: 1 - precision: 1.0000 - recall: 1.0000 - accuracy: 1.0000 - f1-score: 1.0000
admit.01   tp: 4 - fp: 1 - fn: 0 - tn: 4 - precision: 0.8000 - recall: 1.0000 - accuracy: 0.8000 - f1-score: 0.8889
admit.02   tp: 2 - fp: 0 - fn: 1 - tn: 2 - precision: 1.0000 - recall: 0.6667 - accuracy: 0.6667 - f1-score: 0.8000
admonish.02 tp: 0 - fp: 0 - fn: 1 - tn: 0 - precision: 0.0000 - recall: 0.0000 - accuracy: 0.0000 - f1-score: 0.0000
adopt.01   tp: 8 - fp: 0 - fn: 0 - tn: 8 - precision: 1.0000 - recall: 1.0000 - accuracy: 1.0000 - f1-score: 1.0000
advance.01 tp: 3 - fp: 6 - fn: 1 - tn: 3 - precision: 0.3333 - recall: 0.7500 - accuracy: 0.3000 - f1-score: 0.4615
advertise.01 tp: 2 - fp: 4 - fn: 0 - tn: 2 - precision: 0.3333 - recall: 1.0000 - accuracy: 0.3333 - f1-score: 0.5000
advise.01  tp: 2 - fp: 0 - fn: 0 - tn: 2 - precision: 1.0000 - recall: 1.0000 - accuracy: 1.0000 - f1-score: 1.0000
advocate.01 tp: 2 - fp: 0 - fn: 0 - tn: 2 - precision: 1.0000 - recall: 1.0000 - accuracy: 1.0000 - f1-score: 1.0000
affect.01  tp: 16 - fp: 1 - fn: 0 - tn: 16 - precision: 0.9412 - recall: 1.0000 - accuracy: 0.9412 - f1-score: 0.9697
affiliate.01 tp: 0 - fp: 0 - fn: 4 - tn: 0 - precision: 0.0000 - recall: 0.0000 - accuracy: 0.0000 - f1-score: 0.0000
affirm.01  tp: 0 - fp: 0 - fn: 2 - tn: 0 - precision: 0.0000 - recall: 0.0000 - accuracy: 0.0000 - f1-score: 0.0000
afford.01  tp: 5 - fp: 0 - fn: 0 - tn: 5 - precision: 1.0000 - recall: 1.0000 - accuracy: 1.0000 - f1-score: 1.0000
age.01     tp: 5 - fp: 1 - fn: 2 - tn: 5 - precision: 0.8333 - recall: 0.7143 - accuracy: 0.6250 - f1-score: 0.7692
agitate.01 tp: 0 - fp: 0 - fn: 1 - tn: 0 - precision: 0.0000 - recall: 0.0000 - accuracy: 0.0000 - f1-score: 0.0000
agree.01   tp: 50 - fp: 0 - fn: 3 - tn: 50 - precision: 1.0000 - recall: 0.9434 - accuracy: 0.9434 - f1-score: 0.9709
aid.01     tp: 3 - fp: 1 - fn: 0 - tn: 3 - precision: 0.7500 - recall: 1.0000 - accuracy: 0.7500 - f1-score: 0.8571
aim.01     tp: 1 - fp: 3 - fn: 0 - tn: 1 - precision: 0.2500 - recall: 1.0000 - accuracy: 0.2500 - f1-score: 0.4000
aim.02     tp: 10 - fp: 0 - fn: 1 - tn: 10 - precision: 1.0000 - recall: 0.9091 - accuracy: 0.9091 - f1-score: 0.9524
air.01     tp: 5 - fp: 1 - fn: 1 - tn: 5 - precision: 0.8333 - recall: 0.8333 - accuracy: 0.7143 - f1-score: 0.8333
alert.01   tp: 0 - fp: 0 - fn: 1 - tn: 0 - precision: 0.0000 - recall: 0.0000 - accuracy: 0.0000 - f1-score: 0.0000
align.01   tp: 1 - fp: 0 - fn: 0 - tn: 1 - precision: 1.0000 - recall: 1.0000 - accuracy: 1.0000 - f1-score: 1.0000
allege.01  tp: 11 - fp: 3 - fn: 1 - tn: 11 - precision: 0.7857 - recall: 0.9167 - accuracy: 0.7333 - f1-score: 0.8462
alleviate.01 tp: 0 - fp: 0 - fn: 2 - tn: 0 - precision: 0.0000 - recall: 0.0000 - accuracy: 0.0000 - f1-score: 0.0000
allocate.01 tp: 3 - fp: 0 - fn: 0 - tn: 3 - precision: 1.0000 - recall: 1.0000 - accuracy: 1.0000 - f1-score: 1.0000
allot.01   tp: 0 - fp: 0 - fn: 1 - tn: 0 - precision: 0.0000 - recall: 0.0000 - accuracy: 0.0000 - f1-score: 0.0000
allow.01   tp: 27 - fp: 2 - fn: 0 - tn: 27 - precision: 0.9310 - recall: 1.0000 - accuracy: 0.9310 - f1-score: 0.9643
allow.02   tp: 0 - fp: 0 - fn: 2 - tn: 0 - precision: 0.0000 - recall: 0.0000 - accuracy: 0.0000 - f1-score: 0.0000
ally.01    tp: 0 - fp: 0 - fn: 1 - tn: 0 - precision: 0.0000 - recall: 0.0000 - accuracy: 0.0000 - f1-score: 0.0000
amaze.01   tp: 0 - fp: 2 - fn: 0 - tn: 0 - precision: 0.0000 - recall: 0.0000 - accuracy: 0.0000 - f1-score: 0.0000
ambush.01  tp: 1 - fp: 0 - fn: 0 - tn: 1 - precision: 1.0000 - recall: 1.0000 - accuracy: 1.0000 - f1-score: 1.0000
amend.01   tp: 5 - fp: 4 - fn: 0 - tn: 5 - precision: 0.5556 - recall: 1.0000 - accuracy: 0.5556 - f1-score: 0.7143
amount.01  tp: 3 - fp: 0 - fn: 0 - tn: 3 - precision: 1.0000 - recall: 1.0000 - accuracy: 1.0000 - f1-score: 1.0000
amplify.01 tp: 0 - fp: 0 - fn: 1 - tn: 0 - precision: 0.0000 - recall: 0.0000 - accuracy: 0.0000 - f1-score: 0.0000
analyze.01 tp: 18 - fp: 4 - fn: 0 - tn: 18 - precision: 0.8182 - recall: 1.0000 - accuracy: 0.8182 - f1-score: 0.9000
anchor.01  tp: 0 - fp: 0 - fn: 1 - tn: 0 - precision: 0.0000 - recall: 0.0000 - accuracy: 0.0000 - f1-score: 0.0000
anger.01   tp: 1 - fp: 0 - fn: 0 - tn: 1 - precision: 1.0000 - recall: 1.0000 - accuracy: 1.0000 - f1-score: 1.0000
animate.01 tp: 0 - fp: 0 - fn: 1 - tn: 0 - precision: 0.0000 - recall: 0.0000 - accuracy: 0.0000 - f1-score: 0.0000
annihilate.01 tp: 0 - fp: 0 - fn: 1 - tn: 0 - precision: 0.0000 - recall: 0.0000 - accuracy: 0.0000 - f1-score: 0.0000
anniversary.01 tp: 3 - fp: 0 - fn: 1 - tn: 3 - precision: 1.0000 - recall: 0.7500 - accuracy: 0.7500 - f1-score: 0.8571
announce.01 tp: 31 - fp: 7 - fn: 1 - tn: 31 - precision: 0.8158 - recall: 0.9688 - accuracy: 0.7949 - f1-score: 0.8857
annoy.01   tp: 0 - fp: 1 - fn: 0 - tn: 0 - precision: 0.0000 - recall: 0.0000 - accuracy: 0.0000 - f1-score: 0.0000
answer.01  tp: 55 - fp: 5 - fn: 0 - tn: 55 - precision: 0.9167 - recall: 1.0000 - accuracy: 0.9167 - f1-score: 0.9565
anticipate.01 tp: 5 - fp: 0 - fn: 0 - tn: 5 - precision: 1.0000 - recall: 1.0000 - accuracy: 1.0000 - f1-score: 1.0000
apologize.01 tp: 7 - fp: 1 - fn: 0 - tn: 7 - precision: 0.8750 - recall: 1.0000 - accuracy: 0.8750 - f1-score: 0.9333
appeal.01  tp: 1 - fp: 0 - fn: 0 - tn: 1 - precision: 1.0000 - recall: 1.0000 - accuracy: 1.0000 - f1-score: 1.0000
appeal.02  tp: 1 - fp: 1 - fn: 0 - tn: 1 - precision: 0.5000 - recall: 1.0000 - accuracy: 0.5000 - f1-score: 0.6667
appear.01  tp: 12 - fp: 5 - fn: 1 - tn: 12 - precision: 0.7059 - recall: 0.9231 - accuracy: 0.6667 - f1-score: 0.8000
appear.02  tp: 20 - fp: 1 - fn: 4 - tn: 20 - precision: 0.9524 - recall: 0.8333 - accuracy: 0.8000 - f1-score: 0.8889
appease.01 tp: 0 - fp: 0 - fn: 1 - tn: 0 - precision: 0.0000 - recall: 0.0000 - accuracy: 0.0000 - f1-score: 0.0000
apply.01   tp: 3 - fp: 0 - fn: 1 - tn: 3 - precision: 1.0000 - recall: 0.7500 - accuracy: 0.7500 - f1-score: 0.8571
apply.02   tp: 9 - fp: 1 - fn: 1 - tn: 9 - precision: 0.9000 - recall: 0.9000 - accuracy: 0.8182 - f1-score: 0.9000
appoint.01 tp: 2 - fp: 0 - fn: 0 - tn: 2 - precision: 1.0000 - recall: 1.0000 - accuracy: 1.0000 - f1-score: 1.0000
appraise.01 tp: 0 - fp: 0 - fn: 4 - tn: 0 - precision: 0.0000 - recall: 0.0000 - accuracy: 0.0000 - f1-score: 0.0000
appreciate.02 tp: 6 - fp: 0 - fn: 0 - tn: 6 - precision: 1.0000 - recall: 1.0000 - accuracy: 1.0000 - f1-score: 1.0000
approach.01 tp: 6 - fp: 1 - fn: 0 - tn: 6 - precision: 0.8571 - recall: 1.0000 - accuracy: 0.8571 - f1-score: 0.9231
approach.02 tp: 3 - fp: 1 - fn: 1 - tn: 3 - precision: 0.7500 - recall: 0.7500 - accuracy: 0.6000 - f1-score: 0.7500
appropriate.01 tp: 0 - fp: 0 - fn: 2 - tn: 0 - precision: 0.0000 - recall: 0.0000 - accuracy: 0.0000 - f1-score: 0.0000
approve.01 tp: 26 - fp: 4 - fn: 0 - tn: 26 - precision: 0.8667 - recall: 1.0000 - accuracy: 0.8667 - f1-score: 0.9286
argue.01   tp: 13 - fp: 2 - fn: 1 - tn: 13 - precision: 0.8667 - recall: 0.9286 - accuracy: 0.8125 - f1-score: 0.8966
argue.02   tp: 8 - fp: 1 - fn: 2 - tn: 8 - precision: 0.8889 - recall: 0.8000 - accuracy: 0.7273 - f1-score: 0.8421
arise.02   tp: 4 - fp: 0 - fn: 0 - tn: 4 - precision: 1.0000 - recall: 1.0000 - accuracy: 1.0000 - f1-score: 1.0000
arm.01     tp: 1 - fp: 1 - fn: 1 - tn: 1 - precision: 0.5000 - recall: 0.5000 - accuracy: 0.3333 - f1-score: 0.5000
arrange.01 tp: 4 - fp: 1 - fn: 0 - tn: 4 - precision: 0.8000 - recall: 1.0000 - accuracy: 0.8000 - f1-score: 0.8889
arrest.01  tp: 7 - fp: 1 - fn: 0 - tn: 7 - precision: 0.8750 - recall: 1.0000 - accuracy: 0.8750 - f1-score: 0.9333
arrive.01  tp: 24 - fp: 1 - fn: 0 - tn: 24 - precision: 0.9600 - recall: 1.0000 - accuracy: 0.9600 - f1-score: 0.9796
articulate.01 tp: 0 - fp: 0 - fn: 1 - tn: 0 - precision: 0.0000 - recall: 0.0000 - accuracy: 0.0000 - f1-score: 0.0000
ascend.01  tp: 1 - fp: 0 - fn: 0 - tn: 1 - precision: 1.0000 - recall: 1.0000 - accuracy: 1.0000 - f1-score: 1.0000
ask.01     tp: 59 - fp: 11 - fn: 1 - tn: 59 - precision: 0.8429 - recall: 0.9833 - accuracy: 0.8310 - f1-score: 0.9077
ask.02     tp: 58 - fp: 0 - fn: 11 - tn: 58 - precision: 1.0000 - recall: 0.8406 - accuracy: 0.8406 - f1-score: 0.9134
assail.01  tp: 0 - fp: 0 - fn: 2 - tn: 0 - precision: 0.0000 - recall: 0.0000 - accuracy: 0.0000 - f1-score: 0.0000
assassinate.01 tp: 2 - fp: 0 - fn: 0 - tn: 2 - precision: 1.0000 - recall: 1.0000 - accuracy: 1.0000 - f1-score: 1.0000
assault.01 tp: 2 - fp: 1 - fn: 0 - tn: 2 - precision: 0.6667 - recall: 1.0000 - accuracy: 0.6667 - f1-score: 0.8000
assemble.01 tp: 0 - fp: 0 - fn: 2 - tn: 0 - precision: 0.0000 - recall: 0.0000 - accuracy: 0.0000 - f1-score: 0.0000
assemble.02 tp: 1 - fp: 2 - fn: 0 - tn: 1 - precision: 0.3333 - recall: 1.0000 - accuracy: 0.3333 - f1-score: 0.5000
assert.03  tp: 0 - fp: 0 - fn: 3 - tn: 0 - precision: 0.0000 - recall: 0.0000 - accuracy: 0.0000 - f1-score: 0.0000
assess.01  tp: 5 - fp: 2 - fn: 0 - tn: 5 - precision: 0.7143 - recall: 1.0000 - accuracy: 0.7143 - f1-score: 0.8333
assign.01  tp: 1 - fp: 0 - fn: 0 - tn: 1 - precision: 1.0000 - recall: 1.0000 - accuracy: 1.0000 - f1-score: 1.0000
assimilate.01 tp: 0 - fp: 0 - fn: 1 - tn: 0 - precision: 0.0000 - recall: 0.0000 - accuracy: 0.0000 - f1-score: 0.0000
assist.01  tp: 13 - fp: 3 - fn: 0 - tn: 13 - precision: 0.8125 - recall: 1.0000 - accuracy: 0.8125 - f1-score: 0.8966
associate.01 tp: 3 - fp: 0 - fn: 2 - tn: 3 - precision: 1.0000 - recall: 0.6000 - accuracy: 0.6000 - f1-score: 0.7500
assume.01  tp: 3 - fp: 0 - fn: 3 - tn: 3 - precision: 1.0000 - recall: 0.5000 - accuracy: 0.5000 - f1-score: 0.6667
assume.02  tp: 4 - fp: 4 - fn: 0 - tn: 4 - precision: 0.5000 - recall: 1.0000 - accuracy: 0.5000 - f1-score: 0.6667
assure.01  tp: 8 - fp: 0 - fn: 2 - tn: 8 - precision: 1.0000 - recall: 0.8000 - accuracy: 0.8000 - f1-score: 0.8889
attach.01  tp: 2 - fp: 2 - fn: 0 - tn: 2 - precision: 0.5000 - recall: 1.0000 - accuracy: 0.5000 - f1-score: 0.6667
attack.01  tp: 24 - fp: 0 - fn: 0 - tn: 24 - precision: 1.0000 - recall: 1.0000 - accuracy: 1.0000 - f1-score: 1.0000
attain.01  tp: 3 - fp: 0 - fn: 0 - tn: 3 - precision: 1.0000 - recall: 1.0000 - accuracy: 1.0000 - f1-score: 1.0000
attempt.01 tp: 7 - fp: 2 - fn: 0 - tn: 7 - precision: 0.7778 - recall: 1.0000 - accuracy: 0.7778 - f1-score: 0.8750
attend.01  tp: 8 - fp: 1 - fn: 0 - tn: 8 - precision: 0.8889 - recall: 1.0000 - accuracy: 0.8889 - f1-score: 0.9412
attract.01 tp: 22 - fp: 0 - fn: 1 - tn: 22 - precision: 1.0000 - recall: 0.9565 - accuracy: 0.9565 - f1-score: 0.9778
attribute.01 tp: 3 - fp: 0 - fn: 0 - tn: 3 - precision: 1.0000 - recall: 1.0000 - accuracy: 1.0000 - f1-score: 1.0000
auction_off.01 tp: 2 - fp: 0 - fn: 1 - tn: 2 - precision: 1.0000 - recall: 0.6667 - accuracy: 0.6667 - f1-score: 0.8000
audit.01   tp: 1 - fp: 1 - fn: 0 - tn: 1 - precision: 0.5000 - recall: 1.0000 - accuracy: 0.5000 - f1-score: 0.6667
authorize.01 tp: 4 - fp: 0 - fn: 1 - tn: 4 - precision: 1.0000 - recall: 0.8000 - accuracy: 0.8000 - f1-score: 0.8889
automate.01 tp: 0 - fp: 0 - fn: 1 - tn: 0 - precision: 0.0000 - recall: 0.0000 - accuracy: 0.0000 - f1-score: 0.0000
average.01 tp: 4 - fp: 0 - fn: 0 - tn: 4 - precision: 1.0000 - recall: 1.0000 - accuracy: 1.0000 - f1-score: 1.0000
avoid.01   tp: 5 - fp: 0 - fn: 0 - tn: 5 - precision: 1.0000 - recall: 1.0000 - accuracy: 1.0000 - f1-score: 1.0000
await.01   tp: 1 - fp: 1 - fn: 1 - tn: 1 - precision: 0.5000 - recall: 0.5000 - accuracy: 0.3333 - f1-score: 0.5000
award.01   tp: 10 - fp: 2 - fn: 0 - tn: 10 - precision: 0.8333 - recall: 1.0000 - accuracy: 0.8333 - f1-score: 0.9091
back.01    tp: 6 - fp: 1 - fn: 1 - tn: 6 - precision: 0.8571 - recall: 0.8571 - accuracy: 0.7500 - f1-score: 0.8571
back.02    tp: 3 - fp: 1 - fn: 1 - tn: 3 - precision: 0.7500 - recall: 0.7500 - accuracy: 0.6000 - f1-score: 0.7500
back_off.11 tp: 0 - fp: 0 - fn: 1 - tn: 0 - precision: 0.0000 - recall: 0.0000 - accuracy: 0.0000 - f1-score: 0.0000
bail_out.01 tp: 0 - fp: 0 - fn: 2 - tn: 0 - precision: 0.0000 - recall: 0.0000 - accuracy: 0.0000 - f1-score: 0.0000
ban.01     tp: 3 - fp: 1 - fn: 0 - tn: 3 - precision: 0.7500 - recall: 1.0000 - accuracy: 0.7500 - f1-score: 0.8571
bank.01    tp: 1 - fp: 0 - fn: 0 - tn: 1 - precision: 1.0000 - recall: 1.0000 - accuracy: 1.0000 - f1-score: 1.0000
bankrupt.01 tp: 4 - fp: 2 - fn: 0 - tn: 4 - precision: 0.6667 - recall: 1.0000 - accuracy: 0.6667 - f1-score: 0.8000
baptize.02 tp: 3 - fp: 4 - fn: 0 - tn: 3 - precision: 0.4286 - recall: 1.0000 - accuracy: 0.4286 - f1-score: 0.6000
bar.01     tp: 2 - fp: 1 - fn: 0 - tn: 2 - precision: 0.6667 - recall: 1.0000 - accuracy: 0.6667 - f1-score: 0.8000
bargain.01 tp: 0 - fp: 0 - fn: 1 - tn: 0 - precision: 0.0000 - recall: 0.0000 - accuracy: 0.0000 - f1-score: 0.0000
barrage.01 tp: 0 - fp: 0 - fn: 1 - tn: 0 - precision: 0.0000 - recall: 0.0000 - accuracy: 0.0000 - f1-score: 0.0000
barricade.01 tp: 0 - fp: 0 - fn: 1 - tn: 0 - precision: 0.0000 - recall: 0.0000 - accuracy: 0.0000 - f1-score: 0.0000
base.01    tp: 7 - fp: 7 - fn: 1 - tn: 7 - precision: 0.5000 - recall: 0.8750 - accuracy: 0.4667 - f1-score: 0.6364
base.02    tp: 11 - fp: 11 - fn: 3 - tn: 11 - precision: 0.5000 - recall: 0.7857 - accuracy: 0.4400 - f1-score: 0.6111
bathe.01   tp: 0 - fp: 0 - fn: 1 - tn: 0 - precision: 0.0000 - recall: 0.0000 - accuracy: 0.0000 - f1-score: 0.0000
battle.01  tp: 8 - fp: 6 - fn: 1 - tn: 8 - precision: 0.5714 - recall: 0.8889 - accuracy: 0.5333 - f1-score: 0.6956
be.01      tp: 3481 - fp: 135 - fn: 81 - tn: 3481 - precision: 0.9627 - recall: 0.9773 - accuracy: 0.9416 - f1-score: 0.9699
be.02      tp: 296 - fp: 21 - fn: 14 - tn: 296 - precision: 0.9338 - recall: 0.9548 - accuracy: 0.8943 - f1-score: 0.9442
be.03      tp: 1865 - fp: 121 - fn: 97 - tn: 1865 - precision: 0.9391 - recall: 0.9506 - accuracy: 0.8953 - f1-score: 0.9448
be_like.04 tp: 3 - fp: 0 - fn: 2 - tn: 3 - precision: 1.0000 - recall: 0.6000 - accuracy: 0.6000 - f1-score: 0.7500
beam.02    tp: 0 - fp: 0 - fn: 1 - tn: 0 - precision: 0.0000 - recall: 0.0000 - accuracy: 0.0000 - f1-score: 0.0000
bear.01    tp: 9 - fp: 3 - fn: 1 - tn: 9 - precision: 0.7500 - recall: 0.9000 - accuracy: 0.6923 - f1-score: 0.8182
bear.02    tp: 24 - fp: 0 - fn: 0 - tn: 24 - precision: 1.0000 - recall: 1.0000 - accuracy: 1.0000 - f1-score: 1.0000
bear_up.04 tp: 0 - fp: 0 - fn: 1 - tn: 0 - precision: 0.0000 - recall: 0.0000 - accuracy: 0.0000 - f1-score: 0.0000
beat.01    tp: 2 - fp: 3 - fn: 0 - tn: 2 - precision: 0.4000 - recall: 1.0000 - accuracy: 0.4000 - f1-score: 0.5714
beat.02    tp: 0 - fp: 0 - fn: 1 - tn: 0 - precision: 0.0000 - recall: 0.0000 - accuracy: 0.0000 - f1-score: 0.0000
beat.03    tp: 1 - fp: 3 - fn: 0 - tn: 1 - precision: 0.2500 - recall: 1.0000 - accuracy: 0.2500 - f1-score: 0.4000
beat_out.04 tp: 0 - fp: 0 - fn: 1 - tn: 0 - precision: 0.0000 - recall: 0.0000 - accuracy: 0.0000 - f1-score: 0.0000
beat_up.05 tp: 0 - fp: 0 - fn: 1 - tn: 0 - precision: 0.0000 - recall: 0.0000 - accuracy: 0.0000 - f1-score: 0.0000
become.01  tp: 106 - fp: 2 - fn: 0 - tn: 106 - precision: 0.9815 - recall: 1.0000 - accuracy: 0.9815 - f1-score: 0.9907
beg.01     tp: 4 - fp: 1 - fn: 1 - tn: 4 - precision: 0.8000 - recall: 0.8000 - accuracy: 0.6667 - f1-score: 0.8000
begin.01   tp: 76 - fp: 5 - fn: 1 - tn: 76 - precision: 0.9383 - recall: 0.9870 - accuracy: 0.9268 - f1-score: 0.9620
believe.01 tp: 113 - fp: 0 - fn: 0 - tn: 113 - precision: 1.0000 - recall: 1.0000 - accuracy: 1.0000 - f1-score: 1.0000
belong.01  tp: 14 - fp: 0 - fn: 0 - tn: 14 - precision: 1.0000 - recall: 1.0000 - accuracy: 1.0000 - f1-score: 1.0000
bemoan.01  tp: 0 - fp: 0 - fn: 1 - tn: 0 - precision: 0.0000 - recall: 0.0000 - accuracy: 0.0000 - f1-score: 0.0000
benefit.01 tp: 7 - fp: 0 - fn: 0 - tn: 7 - precision: 1.0000 - recall: 1.0000 - accuracy: 1.0000 - f1-score: 1.0000
besiege.01 tp: 0 - fp: 0 - fn: 1 - tn: 0 - precision: 0.0000 - recall: 0.0000 - accuracy: 0.0000 - f1-score: 0.0000
besmirch.01 tp: 0 - fp: 0 - fn: 1 - tn: 0 - precision: 0.0000 - recall: 0.0000 - accuracy: 0.0000 - f1-score: 0.0000
bet.01     tp: 5 - fp: 1 - fn: 0 - tn: 5 - precision: 0.8333 - recall: 1.0000 - accuracy: 0.8333 - f1-score: 0.9091
bid.01     tp: 14 - fp: 2 - fn: 0 - tn: 14 - precision: 0.8750 - recall: 1.0000 - accuracy: 0.8750 - f1-score: 0.9333
bid.03     tp: 0 - fp: 0 - fn: 2 - tn: 0 - precision: 0.0000 - recall: 0.0000 - accuracy: 0.0000 - f1-score: 0.0000
bill.01    tp: 1 - fp: 0 - fn: 0 - tn: 1 - precision: 1.0000 - recall: 1.0000 - accuracy: 1.0000 - f1-score: 1.0000
bill.02    tp: 0 - fp: 0 - fn: 1 - tn: 0 - precision: 0.0000 - recall: 0.0000 - accuracy: 0.0000 - f1-score: 0.0000
bind.01    tp: 4 - fp: 1 - fn: 1 - tn: 4 - precision: 0.8000 - recall: 0.8000 - accuracy: 0.6667 - f1-score: 0.8000
bind.02    tp: 3 - fp: 2 - fn: 0 - tn: 3 - precision: 0.6000 - recall: 1.0000 - accuracy: 0.6000 - f1-score: 0.7500
bite.01    tp: 1 - fp: 1 - fn: 0 - tn: 1 - precision: 0.5000 - recall: 1.0000 - accuracy: 0.5000 - f1-score: 0.6667
blame.01   tp: 9 - fp: 1 - fn: 2 - tn: 9 - precision: 0.9000 - recall: 0.8182 - accuracy: 0.7500 - f1-score: 0.8572
blare.01   tp: 0 - fp: 0 - fn: 1 - tn: 0 - precision: 0.0000 - recall: 0.0000 - accuracy: 0.0000 - f1-score: 0.0000
blast.02   tp: 0 - fp: 0 - fn: 1 - tn: 0 - precision: 0.0000 - recall: 0.0000 - accuracy: 0.0000 - f1-score: 0.0000
blast.05   tp: 5 - fp: 1 - fn: 0 - tn: 5 - precision: 0.8333 - recall: 1.0000 - accuracy: 0.8333 - f1-score: 0.9091
bleed.01   tp: 1 - fp: 0 - fn: 1 - tn: 1 - precision: 1.0000 - recall: 0.5000 - accuracy: 0.5000 - f1-score: 0.6667
bless.01   tp: 15 - fp: 1 - fn: 0 - tn: 15 - precision: 0.9375 - recall: 1.0000 - accuracy: 0.9375 - f1-score: 0.9677
blind.01   tp: 0 - fp: 0 - fn: 1 - tn: 0 - precision: 0.0000 - recall: 0.0000 - accuracy: 0.0000 - f1-score: 0.0000
blink.01   tp: 1 - fp: 0 - fn: 0 - tn: 1 - precision: 1.0000 - recall: 1.0000 - accuracy: 1.0000 - f1-score: 1.0000
block.01   tp: 4 - fp: 3 - fn: 2 - tn: 4 - precision: 0.5714 - recall: 0.6667 - accuracy: 0.4444 - f1-score: 0.6154
block_up.02 tp: 0 - fp: 0 - fn: 1 - tn: 0 - precision: 0.0000 - recall: 0.0000 - accuracy: 0.0000 - f1-score: 0.0000
blossom.01 tp: 0 - fp: 0 - fn: 2 - tn: 0 - precision: 0.0000 - recall: 0.0000 - accuracy: 0.0000 - f1-score: 0.0000
blow.01    tp: 2 - fp: 0 - fn: 1 - tn: 2 - precision: 1.0000 - recall: 0.6667 - accuracy: 0.6667 - f1-score: 0.8000
blow.08    tp: 0 - fp: 0 - fn: 1 - tn: 0 - precision: 0.0000 - recall: 0.0000 - accuracy: 0.0000 - f1-score: 0.0000
blow.09    tp: 2 - fp: 2 - fn: 0 - tn: 2 - precision: 0.5000 - recall: 1.0000 - accuracy: 0.5000 - f1-score: 0.6667
blow.14    tp: 2 - fp: 0 - fn: 0 - tn: 2 - precision: 1.0000 - recall: 1.0000 - accuracy: 1.0000 - f1-score: 1.0000
blow_up.06 tp: 1 - fp: 1 - fn: 0 - tn: 1 - precision: 0.5000 - recall: 1.0000 - accuracy: 0.5000 - f1-score: 0.6667
blunt.01   tp: 0 - fp: 0 - fn: 1 - tn: 0 - precision: 0.0000 - recall: 0.0000 - accuracy: 0.0000 - f1-score: 0.0000
board.01   tp: 1 - fp: 0 - fn: 0 - tn: 1 - precision: 1.0000 - recall: 1.0000 - accuracy: 1.0000 - f1-score: 1.0000
boast.01   tp: 6 - fp: 0 - fn: 0 - tn: 6 - precision: 1.0000 - recall: 1.0000 - accuracy: 1.0000 - f1-score: 1.0000
bob.01     tp: 0 - fp: 0 - fn: 1 - tn: 0 - precision: 0.0000 - recall: 0.0000 - accuracy: 0.0000 - f1-score: 0.0000
bog_down.01 tp: 0 - fp: 0 - fn: 1 - tn: 0 - precision: 0.0000 - recall: 0.0000 - accuracy: 0.0000 - f1-score: 0.0000
boil.01    tp: 1 - fp: 2 - fn: 0 - tn: 1 - precision: 0.3333 - recall: 1.0000 - accuracy: 0.3333 - f1-score: 0.5000
boil_over.02 tp: 0 - fp: 0 - fn: 2 - tn: 0 - precision: 0.0000 - recall: 0.0000 - accuracy: 0.0000 - f1-score: 0.0000
bolster.01 tp: 0 - fp: 1 - fn: 0 - tn: 0 - precision: 0.0000 - recall: 0.0000 - accuracy: 0.0000 - f1-score: 0.0000
bomb.01    tp: 23 - fp: 1 - fn: 0 - tn: 23 - precision: 0.9583 - recall: 1.0000 - accuracy: 0.9583 - f1-score: 0.9787
bomb.02    tp: 0 - fp: 0 - fn: 1 - tn: 0 - precision: 0.0000 - recall: 0.0000 - accuracy: 0.0000 - f1-score: 0.0000
bond.02    tp: 16 - fp: 0 - fn: 1 - tn: 16 - precision: 1.0000 - recall: 0.9412 - accuracy: 0.9412 - f1-score: 0.9697
boost.01   tp: 4 - fp: 1 - fn: 0 - tn: 4 - precision: 0.8000 - recall: 1.0000 - accuracy: 0.8000 - f1-score: 0.8889
borrow.01  tp: 11 - fp: 3 - fn: 0 - tn: 11 - precision: 0.7857 - recall: 1.0000 - accuracy: 0.7857 - f1-score: 0.8800
bother.01  tp: 2 - fp: 1 - fn: 0 - tn: 2 - precision: 0.6667 - recall: 1.0000 - accuracy: 0.6667 - f1-score: 0.8000
bother.02  tp: 0 - fp: 0 - fn: 1 - tn: 0 - precision: 0.0000 - recall: 0.0000 - accuracy: 0.0000 - f1-score: 0.0000
bounce.01  tp: 1 - fp: 1 - fn: 0 - tn: 1 - precision: 0.5000 - recall: 1.0000 - accuracy: 0.5000 - f1-score: 0.6667
bound.05   tp: 0 - fp: 0 - fn: 1 - tn: 0 - precision: 0.0000 - recall: 0.0000 - accuracy: 0.0000 - f1-score: 0.0000
bow.01     tp: 1 - fp: 1 - fn: 0 - tn: 1 - precision: 0.5000 - recall: 1.0000 - accuracy: 0.5000 - f1-score: 0.6667
bow.02     tp: 0 - fp: 0 - fn: 2 - tn: 0 - precision: 0.0000 - recall: 0.0000 - accuracy: 0.0000 - f1-score: 0.0000
bow_down.04 tp: 3 - fp: 1 - fn: 0 - tn: 3 - precision: 0.7500 - recall: 1.0000 - accuracy: 0.7500 - f1-score: 0.8571
bowl.01    tp: 0 - fp: 0 - fn: 1 - tn: 0 - precision: 0.0000 - recall: 0.0000 - accuracy: 0.0000 - f1-score: 0.0000
box.02     tp: 0 - fp: 0 - fn: 1 - tn: 0 - precision: 0.0000 - recall: 0.0000 - accuracy: 0.0000 - f1-score: 0.0000
boycott.01 tp: 1 - fp: 0 - fn: 0 - tn: 1 - precision: 1.0000 - recall: 1.0000 - accuracy: 1.0000 - f1-score: 1.0000
brag.01    tp: 1 - fp: 0 - fn: 1 - tn: 1 - precision: 1.0000 - recall: 0.5000 - accuracy: 0.5000 - f1-score: 0.6667
brainwash.01 tp: 1 - fp: 0 - fn: 1 - tn: 1 - precision: 1.0000 - recall: 0.5000 - accuracy: 0.5000 - f1-score: 0.6667
braise.01  tp: 0 - fp: 0 - fn: 1 - tn: 0 - precision: 0.0000 - recall: 0.0000 - accuracy: 0.0000 - f1-score: 0.0000
brand.01   tp: 0 - fp: 0 - fn: 1 - tn: 0 - precision: 0.0000 - recall: 0.0000 - accuracy: 0.0000 - f1-score: 0.0000
brandish.01 tp: 0 - fp: 0 - fn: 1 - tn: 0 - precision: 0.0000 - recall: 0.0000 - accuracy: 0.0000 - f1-score: 0.0000
break.01   tp: 12 - fp: 13 - fn: 2 - tn: 12 - precision: 0.4800 - recall: 0.8571 - accuracy: 0.4444 - f1-score: 0.6154
break.02   tp: 0 - fp: 0 - fn: 5 - tn: 0 - precision: 0.0000 - recall: 0.0000 - accuracy: 0.0000 - f1-score: 0.0000
break.13   tp: 0 - fp: 0 - fn: 4 - tn: 0 - precision: 0.0000 - recall: 0.0000 - accuracy: 0.0000 - f1-score: 0.0000
break.16   tp: 0 - fp: 0 - fn: 3 - tn: 0 - precision: 0.0000 - recall: 0.0000 - accuracy: 0.0000 - f1-score: 0.0000
break.18   tp: 2 - fp: 4 - fn: 2 - tn: 2 - precision: 0.3333 - recall: 0.5000 - accuracy: 0.2500 - f1-score: 0.4000
break.19   tp: 2 - fp: 2 - fn: 0 - tn: 2 - precision: 0.5000 - recall: 1.0000 - accuracy: 0.5000 - f1-score: 0.6667
break_down.11 tp: 0 - fp: 0 - fn: 1 - tn: 0 - precision: 0.0000 - recall: 0.0000 - accuracy: 0.0000 - f1-score: 0.0000
break_down.12 tp: 0 - fp: 4 - fn: 0 - tn: 0 - precision: 0.0000 - recall: 0.0000 - accuracy: 0.0000 - f1-score: 0.0000
break_off.04 tp: 0 - fp: 1 - fn: 0 - tn: 0 - precision: 0.0000 - recall: 0.0000 - accuracy: 0.0000 - f1-score: 0.0000
break_out.06 tp: 0 - fp: 1 - fn: 4 - tn: 0 - precision: 0.0000 - recall: 0.0000 - accuracy: 0.0000 - f1-score: 0.0000
break_through.22 tp: 0 - fp: 0 - fn: 3 - tn: 0 - precision: 0.0000 - recall: 0.0000 - accuracy: 0.0000 - f1-score: 0.0000
break_up.08 tp: 1 - fp: 0 - fn: 0 - tn: 1 - precision: 1.0000 - recall: 1.0000 - accuracy: 1.0000 - f1-score: 1.0000
breathe.01 tp: 2 - fp: 0 - fn: 0 - tn: 2 - precision: 1.0000 - recall: 1.0000 - accuracy: 1.0000 - f1-score: 1.0000
breed.01   tp: 4 - fp: 0 - fn: 1 - tn: 4 - precision: 1.0000 - recall: 0.8000 - accuracy: 0.8000 - f1-score: 0.8889
bribe.01   tp: 0 - fp: 0 - fn: 1 - tn: 0 - precision: 0.0000 - recall: 0.0000 - accuracy: 0.0000 - f1-score: 0.0000
bridge.01  tp: 1 - fp: 0 - fn: 0 - tn: 1 - precision: 1.0000 - recall: 1.0000 - accuracy: 1.0000 - f1-score: 1.0000
brief.01   tp: 0 - fp: 0 - fn: 1 - tn: 0 - precision: 0.0000 - recall: 0.0000 - accuracy: 0.0000 - f1-score: 0.0000
bring.01   tp: 74 - fp: 6 - fn: 0 - tn: 74 - precision: 0.9250 - recall: 1.0000 - accuracy: 0.9250 - f1-score: 0.9610
bring_about.05 tp: 0 - fp: 0 - fn: 3 - tn: 0 - precision: 0.0000 - recall: 0.0000 - accuracy: 0.0000 - f1-score: 0.0000
bring_down.03 tp: 0 - fp: 0 - fn: 3 - tn: 0 - precision: 0.0000 - recall: 0.0000 - accuracy: 0.0000 - f1-score: 0.0000
broadcast.01 tp: 11 - fp: 2 - fn: 0 - tn: 11 - precision: 0.8462 - recall: 1.0000 - accuracy: 0.8462 - f1-score: 0.9167
broker.01  tp: 0 - fp: 0 - fn: 1 - tn: 0 - precision: 0.0000 - recall: 0.0000 - accuracy: 0.0000 - f1-score: 0.0000
buck.01    tp: 0 - fp: 0 - fn: 1 - tn: 0 - precision: 0.0000 - recall: 0.0000 - accuracy: 0.0000 - f1-score: 0.0000
bud.01     tp: 0 - fp: 0 - fn: 1 - tn: 0 - precision: 0.0000 - recall: 0.0000 - accuracy: 0.0000 - f1-score: 0.0000
build.01   tp: 29 - fp: 6 - fn: 0 - tn: 29 - precision: 0.8286 - recall: 1.0000 - accuracy: 0.8286 - f1-score: 0.9063
build.02   tp: 0 - fp: 0 - fn: 4 - tn: 0 - precision: 0.0000 - recall: 0.0000 - accuracy: 0.0000 - f1-score: 0.0000
build_up.05 tp: 7 - fp: 1 - fn: 1 - tn: 7 - precision: 0.8750 - recall: 0.8750 - accuracy: 0.7778 - f1-score: 0.8750
bulldoze.01 tp: 0 - fp: 0 - fn: 1 - tn: 0 - precision: 0.0000 - recall: 0.0000 - accuracy: 0.0000 - f1-score: 0.0000
bump.01    tp: 0 - fp: 0 - fn: 1 - tn: 0 - precision: 0.0000 - recall: 0.0000 - accuracy: 0.0000 - f1-score: 0.0000
burden.01  tp: 3 - fp: 3 - fn: 0 - tn: 3 - precision: 0.5000 - recall: 1.0000 - accuracy: 0.5000 - f1-score: 0.6667
burn.01    tp: 9 - fp: 2 - fn: 0 - tn: 9 - precision: 0.8182 - recall: 1.0000 - accuracy: 0.8182 - f1-score: 0.9000
burn_up.02 tp: 0 - fp: 0 - fn: 1 - tn: 0 - precision: 0.0000 - recall: 0.0000 - accuracy: 0.0000 - f1-score: 0.0000
burst.01   tp: 0 - fp: 1 - fn: 0 - tn: 0 - precision: 0.0000 - recall: 0.0000 - accuracy: 0.0000 - f1-score: 0.0000
burst.02   tp: 0 - fp: 0 - fn: 1 - tn: 0 - precision: 0.0000 - recall: 0.0000 - accuracy: 0.0000 - f1-score: 0.0000
bury.01    tp: 6 - fp: 2 - fn: 0 - tn: 6 - precision: 0.7500 - recall: 1.0000 - accuracy: 0.7500 - f1-score: 0.8571
buy.01     tp: 47 - fp: 2 - fn: 0 - tn: 47 - precision: 0.9592 - recall: 1.0000 - accuracy: 0.9592 - f1-score: 0.9792
buy_out.03 tp: 0 - fp: 0 - fn: 1 - tn: 0 - precision: 0.0000 - recall: 0.0000 - accuracy: 0.0000 - f1-score: 0.0000
bypass.01  tp: 0 - fp: 0 - fn: 2 - tn: 0 - precision: 0.0000 - recall: 0.0000 - accuracy: 0.0000 - f1-score: 0.0000
calculate.01 tp: 2 - fp: 1 - fn: 0 - tn: 2 - precision: 0.6667 - recall: 1.0000 - accuracy: 0.6667 - f1-score: 0.8000
call.01    tp: 74 - fp: 16 - fn: 1 - tn: 74 - precision: 0.8222 - recall: 0.9867 - accuracy: 0.8132 - f1-score: 0.8970
call.02    tp: 35 - fp: 5 - fn: 17 - tn: 35 - precision: 0.8750 - recall: 0.6731 - accuracy: 0.6140 - f1-score: 0.7609
call.03    tp: 12 - fp: 4 - fn: 1 - tn: 12 - precision: 0.7500 - recall: 0.9231 - accuracy: 0.7059 - f1-score: 0.8276
call.07    tp: 0 - fp: 0 - fn: 2 - tn: 0 - precision: 0.0000 - recall: 0.0000 - accuracy: 0.0000 - f1-score: 0.0000
call_off.06 tp: 1 - fp: 0 - fn: 0 - tn: 1 - precision: 1.0000 - recall: 1.0000 - accuracy: 1.0000 - f1-score: 1.0000
call_on.05 tp: 3 - fp: 1 - fn: 1 - tn: 3 - precision: 0.7500 - recall: 0.7500 - accuracy: 0.6000 - f1-score: 0.7500
call_up.08 tp: 0 - fp: 0 - fn: 1 - tn: 0 - precision: 0.0000 - recall: 0.0000 - accuracy: 0.0000 - f1-score: 0.0000
calm_down.02 tp: 0 - fp: 0 - fn: 1 - tn: 0 - precision: 0.0000 - recall: 0.0000 - accuracy: 0.0000 - f1-score: 0.0000
campaign.01 tp: 12 - fp: 0 - fn: 0 - tn: 12 - precision: 1.0000 - recall: 1.0000 - accuracy: 1.0000 - f1-score: 1.0000
cancel.01  tp: 8 - fp: 4 - fn: 0 - tn: 8 - precision: 0.6667 - recall: 1.0000 - accuracy: 0.6667 - f1-score: 0.8000
cap.01     tp: 0 - fp: 0 - fn: 7 - tn: 0 - precision: 0.0000 - recall: 0.0000 - accuracy: 0.0000 - f1-score: 0.0000
cap.02     tp: 0 - fp: 0 - fn: 3 - tn: 0 - precision: 0.0000 - recall: 0.0000 - accuracy: 0.0000 - f1-score: 0.0000
caption.01 tp: 0 - fp: 0 - fn: 1 - tn: 0 - precision: 0.0000 - recall: 0.0000 - accuracy: 0.0000 - f1-score: 0.0000
captivate.01 tp: 0 - fp: 0 - fn: 1 - tn: 0 - precision: 0.0000 - recall: 0.0000 - accuracy: 0.0000 - f1-score: 0.0000
capture.01 tp: 7 - fp: 0 - fn: 0 - tn: 7 - precision: 1.0000 - recall: 1.0000 - accuracy: 1.0000 - f1-score: 1.0000
care.01    tp: 14 - fp: 1 - fn: 1 - tn: 14 - precision: 0.9333 - recall: 0.9333 - accuracy: 0.8750 - f1-score: 0.9333
care.02    tp: 0 - fp: 0 - fn: 1 - tn: 0 - precision: 0.0000 - recall: 0.0000 - accuracy: 0.0000 - f1-score: 0.0000
care.03    tp: 5 - fp: 10 - fn: 0 - tn: 5 - precision: 0.3333 - recall: 1.0000 - accuracy: 0.3333 - f1-score: 0.5000
carouse.01 tp: 0 - fp: 0 - fn: 1 - tn: 0 - precision: 0.0000 - recall: 0.0000 - accuracy: 0.0000 - f1-score: 0.0000
carry.01   tp: 23 - fp: 5 - fn: 1 - tn: 23 - precision: 0.8214 - recall: 0.9583 - accuracy: 0.7931 - f1-score: 0.8846
carry_on.02 tp: 0 - fp: 0 - fn: 3 - tn: 0 - precision: 0.0000 - recall: 0.0000 - accuracy: 0.0000 - f1-score: 0.0000
carry_out.03 tp: 25 - fp: 2 - fn: 0 - tn: 25 - precision: 0.9259 - recall: 1.0000 - accuracy: 0.9259 - f1-score: 0.9615
carry_over.04 tp: 0 - fp: 0 - fn: 1 - tn: 0 - precision: 0.0000 - recall: 0.0000 - accuracy: 0.0000 - f1-score: 0.0000
carve.01   tp: 0 - fp: 0 - fn: 1 - tn: 0 - precision: 0.0000 - recall: 0.0000 - accuracy: 0.0000 - f1-score: 0.0000
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cause.01   tp: 30 - fp: 2 - fn: 0 - tn: 30 - precision: 0.9375 - recall: 1.0000 - accuracy: 0.9375 - f1-score: 0.9677
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celebrate.02 tp: 4 - fp: 4 - fn: 0 - tn: 4 - precision: 0.5000 - recall: 1.0000 - accuracy: 0.5000 - f1-score: 0.6667
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ceremony.01 tp: 5 - fp: 0 - fn: 0 - tn: 5 - precision: 1.0000 - recall: 1.0000 - accuracy: 1.0000 - f1-score: 1.0000
certify.01 tp: 3 - fp: 1 - fn: 0 - tn: 3 - precision: 0.7500 - recall: 1.0000 - accuracy: 0.7500 - f1-score: 0.8571
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chair.01   tp: 2 - fp: 1 - fn: 0 - tn: 2 - precision: 0.6667 - recall: 1.0000 - accuracy: 0.6667 - f1-score: 0.8000
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championship.02 tp: 1 - fp: 0 - fn: 0 - tn: 1 - precision: 1.0000 - recall: 1.0000 - accuracy: 1.0000 - f1-score: 1.0000
change.01  tp: 55 - fp: 1 - fn: 0 - tn: 55 - precision: 0.9821 - recall: 1.0000 - accuracy: 0.9821 - f1-score: 0.9910
channel.01 tp: 0 - fp: 0 - fn: 1 - tn: 0 - precision: 0.0000 - recall: 0.0000 - accuracy: 0.0000 - f1-score: 0.0000
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charge.06  tp: 1 - fp: 0 - fn: 2 - tn: 1 - precision: 1.0000 - recall: 0.3333 - accuracy: 0.3333 - f1-score: 0.5000
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chat.01    tp: 2 - fp: 0 - fn: 0 - tn: 2 - precision: 1.0000 - recall: 1.0000 - accuracy: 1.0000 - f1-score: 1.0000
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cheat.02   tp: 5 - fp: 2 - fn: 1 - tn: 5 - precision: 0.7143 - recall: 0.8333 - accuracy: 0.6250 - f1-score: 0.7692
check.01   tp: 5 - fp: 1 - fn: 2 - tn: 5 - precision: 0.8333 - recall: 0.7143 - accuracy: 0.6250 - f1-score: 0.7692
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christen.01 tp: 0 - fp: 0 - fn: 1 - tn: 0 - precision: 0.0000 - recall: 0.0000 - accuracy: 0.0000 - f1-score: 0.0000
chronicle.01 tp: 0 - fp: 0 - fn: 2 - tn: 0 - precision: 0.0000 - recall: 0.0000 - accuracy: 0.0000 - f1-score: 0.0000
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circuit.01 tp: 0 - fp: 0 - fn: 1 - tn: 0 - precision: 0.0000 - recall: 0.0000 - accuracy: 0.0000 - f1-score: 0.0000
circulate.01 tp: 2 - fp: 5 - fn: 0 - tn: 2 - precision: 0.2857 - recall: 1.0000 - accuracy: 0.2857 - f1-score: 0.4444
cite.01    tp: 12 - fp: 0 - fn: 0 - tn: 12 - precision: 1.0000 - recall: 1.0000 - accuracy: 1.0000 - f1-score: 1.0000
claim.01   tp: 8 - fp: 2 - fn: 1 - tn: 8 - precision: 0.8000 - recall: 0.8889 - accuracy: 0.7273 - f1-score: 0.8421
claim.02   tp: 0 - fp: 0 - fn: 1 - tn: 0 - precision: 0.0000 - recall: 0.0000 - accuracy: 0.0000 - f1-score: 0.0000
clamber.01 tp: 0 - fp: 0 - fn: 1 - tn: 0 - precision: 0.0000 - recall: 0.0000 - accuracy: 0.0000 - f1-score: 0.0000
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clap.01    tp: 0 - fp: 0 - fn: 1 - tn: 0 - precision: 0.0000 - recall: 0.0000 - accuracy: 0.0000 - f1-score: 0.0000
clarify.10 tp: 2 - fp: 1 - fn: 0 - tn: 2 - precision: 0.6667 - recall: 1.0000 - accuracy: 0.6667 - f1-score: 0.8000
clash.01   tp: 6 - fp: 0 - fn: 0 - tn: 6 - precision: 1.0000 - recall: 1.0000 - accuracy: 1.0000 - f1-score: 1.0000
classify.01 tp: 0 - fp: 0 - fn: 1 - tn: 0 - precision: 0.0000 - recall: 0.0000 - accuracy: 0.0000 - f1-score: 0.0000
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clean_up.02 tp: 1 - fp: 0 - fn: 0 - tn: 1 - precision: 1.0000 - recall: 1.0000 - accuracy: 1.0000 - f1-score: 1.0000
cleanse.01 tp: 0 - fp: 0 - fn: 1 - tn: 0 - precision: 0.0000 - recall: 0.0000 - accuracy: 0.0000 - f1-score: 0.0000
clear.01   tp: 4 - fp: 3 - fn: 1 - tn: 4 - precision: 0.5714 - recall: 0.8000 - accuracy: 0.5000 - f1-score: 0.6666
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clear.03   tp: 0 - fp: 0 - fn: 1 - tn: 0 - precision: 0.0000 - recall: 0.0000 - accuracy: 0.0000 - f1-score: 0.0000
clench.01  tp: 0 - fp: 0 - fn: 1 - tn: 0 - precision: 0.0000 - recall: 0.0000 - accuracy: 0.0000 - f1-score: 0.0000
click.01   tp: 1 - fp: 0 - fn: 0 - tn: 1 - precision: 1.0000 - recall: 1.0000 - accuracy: 1.0000 - f1-score: 1.0000
climax.01  tp: 0 - fp: 0 - fn: 1 - tn: 0 - precision: 0.0000 - recall: 0.0000 - accuracy: 0.0000 - f1-score: 0.0000
climb.01   tp: 7 - fp: 1 - fn: 0 - tn: 7 - precision: 0.8750 - recall: 1.0000 - accuracy: 0.8750 - f1-score: 0.9333
climb.02   tp: 8 - fp: 1 - fn: 0 - tn: 8 - precision: 0.8889 - recall: 1.0000 - accuracy: 0.8889 - f1-score: 0.9412
cling.01   tp: 0 - fp: 0 - fn: 1 - tn: 0 - precision: 0.0000 - recall: 0.0000 - accuracy: 0.0000 - f1-score: 0.0000
clobber.01 tp: 0 - fp: 0 - fn: 1 - tn: 0 - precision: 0.0000 - recall: 0.0000 - accuracy: 0.0000 - f1-score: 0.0000
clock.01   tp: 0 - fp: 0 - fn: 1 - tn: 0 - precision: 0.0000 - recall: 0.0000 - accuracy: 0.0000 - f1-score: 0.0000
clock.02   tp: 0 - fp: 0 - fn: 1 - tn: 0 - precision: 0.0000 - recall: 0.0000 - accuracy: 0.0000 - f1-score: 0.0000
close.01   tp: 15 - fp: 6 - fn: 1 - tn: 15 - precision: 0.7143 - recall: 0.9375 - accuracy: 0.6818 - f1-score: 0.8108
close.03   tp: 0 - fp: 0 - fn: 1 - tn: 0 - precision: 0.0000 - recall: 0.0000 - accuracy: 0.0000 - f1-score: 0.0000
close_down.04 tp: 0 - fp: 0 - fn: 1 - tn: 0 - precision: 0.0000 - recall: 0.0000 - accuracy: 0.0000 - f1-score: 0.0000
close_off.05 tp: 0 - fp: 0 - fn: 1 - tn: 0 - precision: 0.0000 - recall: 0.0000 - accuracy: 0.0000 - f1-score: 0.0000
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coat.01    tp: 0 - fp: 0 - fn: 1 - tn: 0 - precision: 0.0000 - recall: 0.0000 - accuracy: 0.0000 - f1-score: 0.0000
coexist.01 tp: 0 - fp: 1 - fn: 0 - tn: 0 - precision: 0.0000 - recall: 0.0000 - accuracy: 0.0000 - f1-score: 0.0000
coincide.01 tp: 0 - fp: 0 - fn: 3 - tn: 0 - precision: 0.0000 - recall: 0.0000 - accuracy: 0.0000 - f1-score: 0.0000
collaborate.01 tp: 2 - fp: 1 - fn: 0 - tn: 2 - precision: 0.6667 - recall: 1.0000 - accuracy: 0.6667 - f1-score: 0.8000
collapse.01 tp: 4 - fp: 0 - fn: 0 - tn: 4 - precision: 1.0000 - recall: 1.0000 - accuracy: 1.0000 - f1-score: 1.0000
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collect.01 tp: 9 - fp: 0 - fn: 0 - tn: 9 - precision: 1.0000 - recall: 1.0000 - accuracy: 1.0000 - f1-score: 1.0000
collide.01 tp: 1 - fp: 0 - fn: 0 - tn: 1 - precision: 1.0000 - recall: 1.0000 - accuracy: 1.0000 - f1-score: 1.0000
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color.01   tp: 1 - fp: 0 - fn: 0 - tn: 1 - precision: 1.0000 - recall: 1.0000 - accuracy: 1.0000 - f1-score: 1.0000
combat.01  tp: 2 - fp: 0 - fn: 0 - tn: 2 - precision: 1.0000 - recall: 1.0000 - accuracy: 1.0000 - f1-score: 1.0000
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combust.01 tp: 0 - fp: 0 - fn: 1 - tn: 0 - precision: 0.0000 - recall: 0.0000 - accuracy: 0.0000 - f1-score: 0.0000
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come.04    tp: 5 - fp: 2 - fn: 14 - tn: 5 - precision: 0.7143 - recall: 0.2632 - accuracy: 0.2381 - f1-score: 0.3847
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come_across.21 tp: 0 - fp: 0 - fn: 1 - tn: 0 - precision: 0.0000 - recall: 0.0000 - accuracy: 0.0000 - f1-score: 0.0000
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come_out.09 tp: 5 - fp: 1 - fn: 2 - tn: 5 - precision: 0.8333 - recall: 0.7143 - accuracy: 0.6250 - f1-score: 0.7692
come_up.11 tp: 8 - fp: 3 - fn: 0 - tn: 8 - precision: 0.7273 - recall: 1.0000 - accuracy: 0.7273 - f1-score: 0.8421
come_up.13 tp: 0 - fp: 0 - fn: 3 - tn: 0 - precision: 0.0000 - recall: 0.0000 - accuracy: 0.0000 - f1-score: 0.0000
comeback.01 tp: 0 - fp: 1 - fn: 0 - tn: 0 - precision: 0.0000 - recall: 0.0000 - accuracy: 0.0000 - f1-score: 0.0000
comfort.01 tp: 2 - fp: 4 - fn: 0 - tn: 2 - precision: 0.3333 - recall: 1.0000 - accuracy: 0.3333 - f1-score: 0.5000
command.01 tp: 0 - fp: 0 - fn: 1 - tn: 0 - precision: 0.0000 - recall: 0.0000 - accuracy: 0.0000 - f1-score: 0.0000
command.02 tp: 4 - fp: 1 - fn: 0 - tn: 4 - precision: 0.8000 - recall: 1.0000 - accuracy: 0.8000 - f1-score: 0.8889
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commiserate.01 tp: 0 - fp: 0 - fn: 1 - tn: 0 - precision: 0.0000 - recall: 0.0000 - accuracy: 0.0000 - f1-score: 0.0000
commission.01 tp: 1 - fp: 0 - fn: 0 - tn: 1 - precision: 1.0000 - recall: 1.0000 - accuracy: 1.0000 - f1-score: 1.0000
commit.01  tp: 1 - fp: 1 - fn: 3 - tn: 1 - precision: 0.5000 - recall: 0.2500 - accuracy: 0.2000 - f1-score: 0.3333
commit.02  tp: 10 - fp: 2 - fn: 0 - tn: 10 - precision: 0.8333 - recall: 1.0000 - accuracy: 0.8333 - f1-score: 0.9091
communicate.01 tp: 5 - fp: 4 - fn: 0 - tn: 5 - precision: 0.5556 - recall: 1.0000 - accuracy: 0.5556 - f1-score: 0.7143
compare.01 tp: 15 - fp: 1 - fn: 1 - tn: 15 - precision: 0.9375 - recall: 0.9375 - accuracy: 0.8824 - f1-score: 0.9375
compel.01  tp: 2 - fp: 0 - fn: 1 - tn: 2 - precision: 1.0000 - recall: 0.6667 - accuracy: 0.6667 - f1-score: 0.8000
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compete.01 tp: 13 - fp: 4 - fn: 0 - tn: 13 - precision: 0.7647 - recall: 1.0000 - accuracy: 0.7647 - f1-score: 0.8667
compile.01 tp: 2 - fp: 0 - fn: 1 - tn: 2 - precision: 1.0000 - recall: 0.6667 - accuracy: 0.6667 - f1-score: 0.8000
complain.01 tp: 3 - fp: 0 - fn: 1 - tn: 3 - precision: 1.0000 - recall: 0.7500 - accuracy: 0.7500 - f1-score: 0.8571
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complete.01 tp: 13 - fp: 1 - fn: 0 - tn: 13 - precision: 0.9286 - recall: 1.0000 - accuracy: 0.9286 - f1-score: 0.9630
complicate.01 tp: 2 - fp: 0 - fn: 2 - tn: 2 - precision: 1.0000 - recall: 0.5000 - accuracy: 0.5000 - f1-score: 0.6667
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compose.02 tp: 0 - fp: 0 - fn: 1 - tn: 0 - precision: 0.0000 - recall: 0.0000 - accuracy: 0.0000 - f1-score: 0.0000
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concern.02 tp: 3 - fp: 5 - fn: 5 - tn: 3 - precision: 0.3750 - recall: 0.3750 - accuracy: 0.2308 - f1-score: 0.3750
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conduct.01 tp: 13 - fp: 0 - fn: 0 - tn: 13 - precision: 1.0000 - recall: 1.0000 - accuracy: 1.0000 - f1-score: 1.0000
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confess.01 tp: 1 - fp: 0 - fn: 0 - tn: 1 - precision: 1.0000 - recall: 1.0000 - accuracy: 1.0000 - f1-score: 1.0000
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credit.01  tp: 2 - fp: 0 - fn: 2 - tn: 2 - precision: 1.0000 - recall: 0.5000 - accuracy: 0.5000 - f1-score: 0.6667
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cut.02     tp: 25 - fp: 15 - fn: 2 - tn: 25 - precision: 0.6250 - recall: 0.9259 - accuracy: 0.5952 - f1-score: 0.7463
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date.01    tp: 1 - fp: 0 - fn: 0 - tn: 1 - precision: 1.0000 - recall: 1.0000 - accuracy: 1.0000 - f1-score: 1.0000
date.02    tp: 1 - fp: 0 - fn: 0 - tn: 1 - precision: 1.0000 - recall: 1.0000 - accuracy: 1.0000 - f1-score: 1.0000
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declare.01 tp: 9 - fp: 1 - fn: 0 - tn: 9 - precision: 0.9000 - recall: 1.0000 - accuracy: 0.9000 - f1-score: 0.9474
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dedicate.01 tp: 2 - fp: 0 - fn: 0 - tn: 2 - precision: 1.0000 - recall: 1.0000 - accuracy: 1.0000 - f1-score: 1.0000
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delay.01   tp: 5 - fp: 5 - fn: 0 - tn: 5 - precision: 0.5000 - recall: 1.0000 - accuracy: 0.5000 - f1-score: 0.6667
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demand.01  tp: 14 - fp: 3 - fn: 0 - tn: 14 - precision: 0.8235 - recall: 1.0000 - accuracy: 0.8235 - f1-score: 0.9032
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govern.01  tp: 5 - fp: 0 - fn: 0 - tn: 5 - precision: 1.0000 - recall: 1.0000 - accuracy: 1.0000 - f1-score: 1.0000
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grant.01   tp: 10 - fp: 1 - fn: 0 - tn: 10 - precision: 0.9091 - recall: 1.0000 - accuracy: 0.9091 - f1-score: 0.9524
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greet.01   tp: 5 - fp: 3 - fn: 0 - tn: 5 - precision: 0.6250 - recall: 1.0000 - accuracy: 0.6250 - f1-score: 0.7692
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grip.01    tp: 1 - fp: 0 - fn: 0 - tn: 1 - precision: 1.0000 - recall: 1.0000 - accuracy: 1.0000 - f1-score: 1.0000
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grow.03    tp: 7 - fp: 5 - fn: 4 - tn: 7 - precision: 0.5833 - recall: 0.6364 - accuracy: 0.4375 - f1-score: 0.6087
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handle.01  tp: 21 - fp: 1 - fn: 0 - tn: 21 - precision: 0.9545 - recall: 1.0000 - accuracy: 0.9545 - f1-score: 0.9767
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have.01    tp: 1202 - fp: 43 - fn: 23 - tn: 1202 - precision: 0.9655 - recall: 0.9812 - accuracy: 0.9479 - f1-score: 0.9733
have.02    tp: 128 - fp: 8 - fn: 2 - tn: 128 - precision: 0.9412 - recall: 0.9846 - accuracy: 0.9275 - f1-score: 0.9624
have.03    tp: 541 - fp: 69 - fn: 11 - tn: 541 - precision: 0.8869 - recall: 0.9801 - accuracy: 0.8712 - f1-score: 0.9312
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help.01    tp: 65 - fp: 7 - fn: 0 - tn: 65 - precision: 0.9028 - recall: 1.0000 - accuracy: 0.9028 - f1-score: 0.9489
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hit.02     tp: 2 - fp: 0 - fn: 6 - tn: 2 - precision: 1.0000 - recall: 0.2500 - accuracy: 0.2500 - f1-score: 0.4000
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hold.01    tp: 27 - fp: 15 - fn: 4 - tn: 27 - precision: 0.6429 - recall: 0.8710 - accuracy: 0.5870 - f1-score: 0.7398
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hook.01    tp: 0 - fp: 0 - fn: 1 - tn: 0 - precision: 0.0000 - recall: 0.0000 - accuracy: 0.0000 - f1-score: 0.0000
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hurry.01   tp: 5 - fp: 1 - fn: 1 - tn: 5 - precision: 0.8333 - recall: 0.8333 - accuracy: 0.7143 - f1-score: 0.8333
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ignore.01  tp: 4 - fp: 3 - fn: 0 - tn: 4 - precision: 0.5714 - recall: 1.0000 - accuracy: 0.5714 - f1-score: 0.7272
illusion.01 tp: 0 - fp: 0 - fn: 1 - tn: 0 - precision: 0.0000 - recall: 0.0000 - accuracy: 0.0000 - f1-score: 0.0000
illustrate.01 tp: 2 - fp: 4 - fn: 0 - tn: 2 - precision: 0.3333 - recall: 1.0000 - accuracy: 0.3333 - f1-score: 0.5000
imagine.01 tp: 17 - fp: 3 - fn: 1 - tn: 17 - precision: 0.8500 - recall: 0.9444 - accuracy: 0.8095 - f1-score: 0.8947
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impact.01  tp: 9 - fp: 14 - fn: 1 - tn: 9 - precision: 0.3913 - recall: 0.9000 - accuracy: 0.3750 - f1-score: 0.5455
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inherit.01 tp: 1 - fp: 0 - fn: 2 - tn: 1 - precision: 1.0000 - recall: 0.3333 - accuracy: 0.3333 - f1-score: 0.5000
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initiate.01 tp: 3 - fp: 0 - fn: 0 - tn: 3 - precision: 1.0000 - recall: 1.0000 - accuracy: 1.0000 - f1-score: 1.0000
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insist.01  tp: 13 - fp: 0 - fn: 0 - tn: 13 - precision: 1.0000 - recall: 1.0000 - accuracy: 1.0000 - f1-score: 1.0000
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inspire.01 tp: 2 - fp: 0 - fn: 2 - tn: 2 - precision: 1.0000 - recall: 0.5000 - accuracy: 0.5000 - f1-score: 0.6667
install.01 tp: 1 - fp: 0 - fn: 0 - tn: 1 - precision: 1.0000 - recall: 1.0000 - accuracy: 1.0000 - f1-score: 1.0000
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insult.01  tp: 1 - fp: 5 - fn: 2 - tn: 1 - precision: 0.1667 - recall: 0.3333 - accuracy: 0.1250 - f1-score: 0.2222
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intake.01  tp: 0 - fp: 0 - fn: 1 - tn: 0 - precision: 0.0000 - recall: 0.0000 - accuracy: 0.0000 - f1-score: 0.0000
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intend.01  tp: 12 - fp: 3 - fn: 0 - tn: 12 - precision: 0.8000 - recall: 1.0000 - accuracy: 0.8000 - f1-score: 0.8889
intensify.01 tp: 2 - fp: 0 - fn: 0 - tn: 2 - precision: 1.0000 - recall: 1.0000 - accuracy: 1.0000 - f1-score: 1.0000
interact.01 tp: 2 - fp: 2 - fn: 1 - tn: 2 - precision: 0.5000 - recall: 0.6667 - accuracy: 0.4000 - f1-score: 0.5714
interest.01 tp: 0 - fp: 0 - fn: 1 - tn: 0 - precision: 0.0000 - recall: 0.0000 - accuracy: 0.0000 - f1-score: 0.0000
interfere.01 tp: 4 - fp: 1 - fn: 0 - tn: 4 - precision: 0.8000 - recall: 1.0000 - accuracy: 0.8000 - f1-score: 0.8889
intermarry.01 tp: 0 - fp: 0 - fn: 1 - tn: 0 - precision: 0.0000 - recall: 0.0000 - accuracy: 0.0000 - f1-score: 0.0000
internalize.01 tp: 0 - fp: 0 - fn: 1 - tn: 0 - precision: 0.0000 - recall: 0.0000 - accuracy: 0.0000 - f1-score: 0.0000
interpret.01 tp: 3 - fp: 1 - fn: 0 - tn: 3 - precision: 0.7500 - recall: 1.0000 - accuracy: 0.7500 - f1-score: 0.8571
interrogate.01 tp: 2 - fp: 1 - fn: 0 - tn: 2 - precision: 0.6667 - recall: 1.0000 - accuracy: 0.6667 - f1-score: 0.8000
interrupt.01 tp: 2 - fp: 0 - fn: 0 - tn: 2 - precision: 1.0000 - recall: 1.0000 - accuracy: 1.0000 - f1-score: 1.0000
intersect.01 tp: 1 - fp: 0 - fn: 0 - tn: 1 - precision: 1.0000 - recall: 1.0000 - accuracy: 1.0000 - f1-score: 1.0000
intervene.01 tp: 0 - fp: 0 - fn: 1 - tn: 0 - precision: 0.0000 - recall: 0.0000 - accuracy: 0.0000 - f1-score: 0.0000
interview.01 tp: 19 - fp: 11 - fn: 0 - tn: 19 - precision: 0.6333 - recall: 1.0000 - accuracy: 0.6333 - f1-score: 0.7755
intimidate.01 tp: 0 - fp: 1 - fn: 0 - tn: 0 - precision: 0.0000 - recall: 0.0000 - accuracy: 0.0000 - f1-score: 0.0000
introduce.01 tp: 0 - fp: 0 - fn: 2 - tn: 0 - precision: 0.0000 - recall: 0.0000 - accuracy: 0.0000 - f1-score: 0.0000
introduce.02 tp: 18 - fp: 2 - fn: 1 - tn: 18 - precision: 0.9000 - recall: 0.9474 - accuracy: 0.8571 - f1-score: 0.9231
invade.01  tp: 5 - fp: 1 - fn: 0 - tn: 5 - precision: 0.8333 - recall: 1.0000 - accuracy: 0.8333 - f1-score: 0.9091
invent.01  tp: 1 - fp: 0 - fn: 0 - tn: 1 - precision: 1.0000 - recall: 1.0000 - accuracy: 1.0000 - f1-score: 1.0000
invest.01  tp: 79 - fp: 15 - fn: 2 - tn: 79 - precision: 0.8404 - recall: 0.9753 - accuracy: 0.8229 - f1-score: 0.9028
investigate.01 tp: 6 - fp: 0 - fn: 1 - tn: 6 - precision: 1.0000 - recall: 0.8571 - accuracy: 0.8571 - f1-score: 0.9231
invite.01  tp: 18 - fp: 1 - fn: 1 - tn: 18 - precision: 0.9474 - recall: 0.9474 - accuracy: 0.9000 - f1-score: 0.9474
invoke.01  tp: 0 - fp: 0 - fn: 3 - tn: 0 - precision: 0.0000 - recall: 0.0000 - accuracy: 0.0000 - f1-score: 0.0000
involve.01 tp: 29 - fp: 4 - fn: 0 - tn: 29 - precision: 0.8788 - recall: 1.0000 - accuracy: 0.8788 - f1-score: 0.9355
iron_out.01 tp: 0 - fp: 0 - fn: 1 - tn: 0 - precision: 0.0000 - recall: 0.0000 - accuracy: 0.0000 - f1-score: 0.0000
irrigate.01 tp: 0 - fp: 0 - fn: 1 - tn: 0 - precision: 0.0000 - recall: 0.0000 - accuracy: 0.0000 - f1-score: 0.0000
irritate.01 tp: 0 - fp: 0 - fn: 1 - tn: 0 - precision: 0.0000 - recall: 0.0000 - accuracy: 0.0000 - f1-score: 0.0000
isolate.01 tp: 2 - fp: 3 - fn: 0 - tn: 2 - precision: 0.4000 - recall: 1.0000 - accuracy: 0.4000 - f1-score: 0.5714
issue.01   tp: 23 - fp: 0 - fn: 1 - tn: 23 - precision: 1.0000 - recall: 0.9583 - accuracy: 0.9583 - f1-score: 0.9787
jettison.01 tp: 0 - fp: 0 - fn: 1 - tn: 0 - precision: 0.0000 - recall: 0.0000 - accuracy: 0.0000 - f1-score: 0.0000
jilt.01    tp: 0 - fp: 0 - fn: 1 - tn: 0 - precision: 0.0000 - recall: 0.0000 - accuracy: 0.0000 - f1-score: 0.0000
jinx.01    tp: 0 - fp: 0 - fn: 1 - tn: 0 - precision: 0.0000 - recall: 0.0000 - accuracy: 0.0000 - f1-score: 0.0000
join.01    tp: 18 - fp: 3 - fn: 0 - tn: 18 - precision: 0.8571 - recall: 1.0000 - accuracy: 0.8571 - f1-score: 0.9231
join.04    tp: 8 - fp: 1 - fn: 0 - tn: 8 - precision: 0.8889 - recall: 1.0000 - accuracy: 0.8889 - f1-score: 0.9412
join_in.05 tp: 0 - fp: 0 - fn: 2 - tn: 0 - precision: 0.0000 - recall: 0.0000 - accuracy: 0.0000 - f1-score: 0.0000
join_up.02 tp: 0 - fp: 0 - fn: 1 - tn: 0 - precision: 0.0000 - recall: 0.0000 - accuracy: 0.0000 - f1-score: 0.0000
joke.01    tp: 3 - fp: 0 - fn: 1 - tn: 3 - precision: 1.0000 - recall: 0.7500 - accuracy: 0.7500 - f1-score: 0.8571
judge.01   tp: 10 - fp: 0 - fn: 4 - tn: 10 - precision: 1.0000 - recall: 0.7143 - accuracy: 0.7143 - f1-score: 0.8333
jump.01    tp: 6 - fp: 2 - fn: 0 - tn: 6 - precision: 0.7500 - recall: 1.0000 - accuracy: 0.7500 - f1-score: 0.8571
jump.03    tp: 4 - fp: 1 - fn: 0 - tn: 4 - precision: 0.8000 - recall: 1.0000 - accuracy: 0.8000 - f1-score: 0.8889
jump_in.04 tp: 0 - fp: 0 - fn: 1 - tn: 0 - precision: 0.0000 - recall: 0.0000 - accuracy: 0.0000 - f1-score: 0.0000
jump_up.05 tp: 0 - fp: 0 - fn: 1 - tn: 0 - precision: 0.0000 - recall: 0.0000 - accuracy: 0.0000 - f1-score: 0.0000
justify.01 tp: 7 - fp: 0 - fn: 0 - tn: 7 - precision: 1.0000 - recall: 1.0000 - accuracy: 1.0000 - f1-score: 1.0000
keep.01    tp: 12 - fp: 8 - fn: 7 - tn: 12 - precision: 0.6000 - recall: 0.6316 - accuracy: 0.4444 - f1-score: 0.6154
keep.02    tp: 29 - fp: 6 - fn: 6 - tn: 29 - precision: 0.8286 - recall: 0.8286 - accuracy: 0.7073 - f1-score: 0.8286
keep.04    tp: 9 - fp: 3 - fn: 7 - tn: 9 - precision: 0.7500 - recall: 0.5625 - accuracy: 0.4737 - f1-score: 0.6429
keep_on.08 tp: 1 - fp: 0 - fn: 0 - tn: 1 - precision: 1.0000 - recall: 1.0000 - accuracy: 1.0000 - f1-score: 1.0000
keep_up.05 tp: 1 - fp: 2 - fn: 0 - tn: 1 - precision: 0.3333 - recall: 1.0000 - accuracy: 0.3333 - f1-score: 0.5000
kick.01    tp: 1 - fp: 3 - fn: 0 - tn: 1 - precision: 0.2500 - recall: 1.0000 - accuracy: 0.2500 - f1-score: 0.4000
kick_in.02 tp: 0 - fp: 0 - fn: 1 - tn: 0 - precision: 0.0000 - recall: 0.0000 - accuracy: 0.0000 - f1-score: 0.0000
kick_off.04 tp: 0 - fp: 0 - fn: 2 - tn: 0 - precision: 0.0000 - recall: 0.0000 - accuracy: 0.0000 - f1-score: 0.0000
kid.01     tp: 2 - fp: 0 - fn: 0 - tn: 2 - precision: 1.0000 - recall: 1.0000 - accuracy: 1.0000 - f1-score: 1.0000
kidnap.01  tp: 0 - fp: 1 - fn: 1 - tn: 0 - precision: 0.0000 - recall: 0.0000 - accuracy: 0.0000 - f1-score: 0.0000
kill.01    tp: 83 - fp: 5 - fn: 0 - tn: 83 - precision: 0.9432 - recall: 1.0000 - accuracy: 0.9432 - f1-score: 0.9708
kill.03    tp: 0 - fp: 0 - fn: 1 - tn: 0 - precision: 0.0000 - recall: 0.0000 - accuracy: 0.0000 - f1-score: 0.0000
kneel.01   tp: 1 - fp: 0 - fn: 0 - tn: 1 - precision: 1.0000 - recall: 1.0000 - accuracy: 1.0000 - f1-score: 1.0000
knit.01    tp: 0 - fp: 0 - fn: 1 - tn: 0 - precision: 0.0000 - recall: 0.0000 - accuracy: 0.0000 - f1-score: 0.0000
knock.01   tp: 0 - fp: 2 - fn: 0 - tn: 0 - precision: 0.0000 - recall: 0.0000 - accuracy: 0.0000 - f1-score: 0.0000
know.01    tp: 201 - fp: 16 - fn: 44 - tn: 201 - precision: 0.9263 - recall: 0.8204 - accuracy: 0.7701 - f1-score: 0.8701
know.02    tp: 17 - fp: 13 - fn: 9 - tn: 17 - precision: 0.5667 - recall: 0.6538 - accuracy: 0.4359 - f1-score: 0.6071
know.03    tp: 2 - fp: 0 - fn: 1 - tn: 2 - precision: 1.0000 - recall: 0.6667 - accuracy: 0.6667 - f1-score: 0.8000
know.04    tp: 0 - fp: 0 - fn: 1 - tn: 0 - precision: 0.0000 - recall: 0.0000 - accuracy: 0.0000 - f1-score: 0.0000
know.06    tp: 91 - fp: 40 - fn: 6 - tn: 91 - precision: 0.6947 - recall: 0.9381 - accuracy: 0.6642 - f1-score: 0.7983
label.01   tp: 0 - fp: 0 - fn: 1 - tn: 0 - precision: 0.0000 - recall: 0.0000 - accuracy: 0.0000 - f1-score: 0.0000
labor.01   tp: 0 - fp: 0 - fn: 2 - tn: 0 - precision: 0.0000 - recall: 0.0000 - accuracy: 0.0000 - f1-score: 0.0000
lack.01    tp: 9 - fp: 0 - fn: 0 - tn: 9 - precision: 1.0000 - recall: 1.0000 - accuracy: 1.0000 - f1-score: 1.0000
lag.01     tp: 0 - fp: 0 - fn: 1 - tn: 0 - precision: 0.0000 - recall: 0.0000 - accuracy: 0.0000 - f1-score: 0.0000
land.01    tp: 2 - fp: 1 - fn: 1 - tn: 2 - precision: 0.6667 - recall: 0.6667 - accuracy: 0.5000 - f1-score: 0.6667
last.01    tp: 10 - fp: 1 - fn: 1 - tn: 10 - precision: 0.9091 - recall: 0.9091 - accuracy: 0.8333 - f1-score: 0.9091
laugh.01   tp: 4 - fp: 0 - fn: 0 - tn: 4 - precision: 1.0000 - recall: 1.0000 - accuracy: 1.0000 - f1-score: 1.0000
launch.01  tp: 16 - fp: 3 - fn: 0 - tn: 16 - precision: 0.8421 - recall: 1.0000 - accuracy: 0.8421 - f1-score: 0.9143
lay.01     tp: 8 - fp: 4 - fn: 0 - tn: 8 - precision: 0.6667 - recall: 1.0000 - accuracy: 0.6667 - f1-score: 0.8000
lay_off.02 tp: 2 - fp: 0 - fn: 0 - tn: 2 - precision: 1.0000 - recall: 1.0000 - accuracy: 1.0000 - f1-score: 1.0000
lead.01    tp: 11 - fp: 5 - fn: 6 - tn: 11 - precision: 0.6875 - recall: 0.6471 - accuracy: 0.5000 - f1-score: 0.6667
lead.02    tp: 27 - fp: 16 - fn: 3 - tn: 27 - precision: 0.6279 - recall: 0.9000 - accuracy: 0.5870 - f1-score: 0.7397
lead.03    tp: 10 - fp: 1 - fn: 4 - tn: 10 - precision: 0.9091 - recall: 0.7143 - accuracy: 0.6667 - f1-score: 0.8000
lead_up.05 tp: 0 - fp: 0 - fn: 1 - tn: 0 - precision: 0.0000 - recall: 0.0000 - accuracy: 0.0000 - f1-score: 0.0000
leak.01    tp: 5 - fp: 2 - fn: 0 - tn: 5 - precision: 0.7143 - recall: 1.0000 - accuracy: 0.7143 - f1-score: 0.8333
lean.01    tp: 1 - fp: 1 - fn: 0 - tn: 1 - precision: 0.5000 - recall: 1.0000 - accuracy: 0.5000 - f1-score: 0.6667
learn.01   tp: 47 - fp: 0 - fn: 0 - tn: 47 - precision: 1.0000 - recall: 1.0000 - accuracy: 1.0000 - f1-score: 1.0000
leave.02   tp: 0 - fp: 0 - fn: 2 - tn: 0 - precision: 0.0000 - recall: 0.0000 - accuracy: 0.0000 - f1-score: 0.0000
leave.05   tp: 0 - fp: 0 - fn: 1 - tn: 0 - precision: 0.0000 - recall: 0.0000 - accuracy: 0.0000 - f1-score: 0.0000
leave.11   tp: 34 - fp: 11 - fn: 1 - tn: 34 - precision: 0.7556 - recall: 0.9714 - accuracy: 0.7391 - f1-score: 0.8500
leave.12   tp: 7 - fp: 11 - fn: 10 - tn: 7 - precision: 0.3889 - recall: 0.4118 - accuracy: 0.2500 - f1-score: 0.4000
leave.13   tp: 13 - fp: 3 - fn: 3 - tn: 13 - precision: 0.8125 - recall: 0.8125 - accuracy: 0.6842 - f1-score: 0.8125
leave.14   tp: 0 - fp: 1 - fn: 1 - tn: 0 - precision: 0.0000 - recall: 0.0000 - accuracy: 0.0000 - f1-score: 0.0000
leave.15   tp: 6 - fp: 5 - fn: 7 - tn: 6 - precision: 0.5455 - recall: 0.4615 - accuracy: 0.3333 - f1-score: 0.5000
leave.17   tp: 2 - fp: 11 - fn: 2 - tn: 2 - precision: 0.1538 - recall: 0.5000 - accuracy: 0.1333 - f1-score: 0.2352
leave_behind.18 tp: 0 - fp: 0 - fn: 1 - tn: 0 - precision: 0.0000 - recall: 0.0000 - accuracy: 0.0000 - f1-score: 0.0000
legalize.01 tp: 0 - fp: 0 - fn: 2 - tn: 0 - precision: 0.0000 - recall: 0.0000 - accuracy: 0.0000 - f1-score: 0.0000
lend.01    tp: 5 - fp: 0 - fn: 0 - tn: 5 - precision: 1.0000 - recall: 1.0000 - accuracy: 1.0000 - f1-score: 1.0000
let.01     tp: 75 - fp: 1 - fn: 0 - tn: 75 - precision: 0.9868 - recall: 1.0000 - accuracy: 0.9868 - f1-score: 0.9934
level.03   tp: 0 - fp: 0 - fn: 1 - tn: 0 - precision: 0.0000 - recall: 0.0000 - accuracy: 0.0000 - f1-score: 0.0000
leverage.01 tp: 0 - fp: 0 - fn: 5 - tn: 0 - precision: 0.0000 - recall: 0.0000 - accuracy: 0.0000 - f1-score: 0.0000
levy.01    tp: 0 - fp: 0 - fn: 2 - tn: 0 - precision: 0.0000 - recall: 0.0000 - accuracy: 0.0000 - f1-score: 0.0000
liberalize.01 tp: 0 - fp: 0 - fn: 5 - tn: 0 - precision: 0.0000 - recall: 0.0000 - accuracy: 0.0000 - f1-score: 0.0000
liberate.01 tp: 2 - fp: 0 - fn: 0 - tn: 2 - precision: 1.0000 - recall: 1.0000 - accuracy: 1.0000 - f1-score: 1.0000
license.01 tp: 1 - fp: 0 - fn: 2 - tn: 1 - precision: 1.0000 - recall: 0.3333 - accuracy: 0.3333 - f1-score: 0.5000
lie.07     tp: 3 - fp: 3 - fn: 2 - tn: 3 - precision: 0.5000 - recall: 0.6000 - accuracy: 0.3750 - f1-score: 0.5455
lie.08     tp: 3 - fp: 2 - fn: 2 - tn: 3 - precision: 0.6000 - recall: 0.6000 - accuracy: 0.4286 - f1-score: 0.6000
lie_down.09 tp: 0 - fp: 0 - fn: 1 - tn: 0 - precision: 0.0000 - recall: 0.0000 - accuracy: 0.0000 - f1-score: 0.0000
lift.01    tp: 9 - fp: 2 - fn: 0 - tn: 9 - precision: 0.8182 - recall: 1.0000 - accuracy: 0.8182 - f1-score: 0.9000
lift.02    tp: 2 - fp: 1 - fn: 1 - tn: 2 - precision: 0.6667 - recall: 0.6667 - accuracy: 0.5000 - f1-score: 0.6667
light.04   tp: 1 - fp: 1 - fn: 2 - tn: 1 - precision: 0.5000 - recall: 0.3333 - accuracy: 0.2500 - f1-score: 0.4000
light_up.08 tp: 0 - fp: 0 - fn: 1 - tn: 0 - precision: 0.0000 - recall: 0.0000 - accuracy: 0.0000 - f1-score: 0.0000
lighten.01 tp: 0 - fp: 0 - fn: 2 - tn: 0 - precision: 0.0000 - recall: 0.0000 - accuracy: 0.0000 - f1-score: 0.0000
like.02    tp: 25 - fp: 5 - fn: 3 - tn: 25 - precision: 0.8333 - recall: 0.8929 - accuracy: 0.7576 - f1-score: 0.8621
limit.01   tp: 17 - fp: 4 - fn: 2 - tn: 17 - precision: 0.8095 - recall: 0.8947 - accuracy: 0.7391 - f1-score: 0.8500
line.01    tp: 1 - fp: 1 - fn: 1 - tn: 1 - precision: 0.5000 - recall: 0.5000 - accuracy: 0.3333 - f1-score: 0.5000
line_up.02 tp: 2 - fp: 1 - fn: 0 - tn: 2 - precision: 0.6667 - recall: 1.0000 - accuracy: 0.6667 - f1-score: 0.8000
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miss.02    tp: 4 - fp: 4 - fn: 2 - tn: 4 - precision: 0.5000 - recall: 0.6667 - accuracy: 0.4000 - f1-score: 0.5714
mission.01 tp: 6 - fp: 2 - fn: 0 - tn: 6 - precision: 0.7500 - recall: 1.0000 - accuracy: 0.7500 - f1-score: 0.8571
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near.01    tp: 1 - fp: 0 - fn: 2 - tn: 1 - precision: 1.0000 - recall: 0.3333 - accuracy: 0.3333 - f1-score: 0.5000
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patent.01  tp: 0 - fp: 0 - fn: 1 - tn: 0 - precision: 0.0000 - recall: 0.0000 - accuracy: 0.0000 - f1-score: 0.0000
patrol.01  tp: 0 - fp: 0 - fn: 1 - tn: 0 - precision: 0.0000 - recall: 0.0000 - accuracy: 0.0000 - f1-score: 0.0000
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peddle.01  tp: 1 - fp: 0 - fn: 0 - tn: 1 - precision: 1.0000 - recall: 1.0000 - accuracy: 1.0000 - f1-score: 1.0000
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perfect.01 tp: 2 - fp: 0 - fn: 0 - tn: 2 - precision: 1.0000 - recall: 1.0000 - accuracy: 1.0000 - f1-score: 1.0000
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permit.01  tp: 11 - fp: 0 - fn: 1 - tn: 11 - precision: 1.0000 - recall: 0.9167 - accuracy: 0.9167 - f1-score: 0.9565
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persecute.01 tp: 2 - fp: 0 - fn: 0 - tn: 2 - precision: 1.0000 - recall: 1.0000 - accuracy: 1.0000 - f1-score: 1.0000
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persist.01 tp: 3 - fp: 1 - fn: 0 - tn: 3 - precision: 0.7500 - recall: 1.0000 - accuracy: 0.7500 - f1-score: 0.8571
perspire.01 tp: 0 - fp: 0 - fn: 1 - tn: 0 - precision: 0.0000 - recall: 0.0000 - accuracy: 0.0000 - f1-score: 0.0000
persuade.01 tp: 5 - fp: 2 - fn: 0 - tn: 5 - precision: 0.7143 - recall: 1.0000 - accuracy: 0.7143 - f1-score: 0.8333
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photograph.01 tp: 7 - fp: 4 - fn: 0 - tn: 7 - precision: 0.6364 - recall: 1.0000 - accuracy: 0.6364 - f1-score: 0.7778
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pivot.01   tp: 0 - fp: 0 - fn: 1 - tn: 0 - precision: 0.0000 - recall: 0.0000 - accuracy: 0.0000 - f1-score: 0.0000
place.01   tp: 9 - fp: 1 - fn: 0 - tn: 9 - precision: 0.9000 - recall: 1.0000 - accuracy: 0.9000 - f1-score: 0.9474
plague.01  tp: 1 - fp: 3 - fn: 1 - tn: 1 - precision: 0.2500 - recall: 0.5000 - accuracy: 0.2000 - f1-score: 0.3333
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plant.01   tp: 12 - fp: 1 - fn: 0 - tn: 12 - precision: 0.9231 - recall: 1.0000 - accuracy: 0.9231 - f1-score: 0.9600
play.01    tp: 13 - fp: 9 - fn: 1 - tn: 13 - precision: 0.5909 - recall: 0.9286 - accuracy: 0.5652 - f1-score: 0.7222
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plot.01    tp: 4 - fp: 0 - fn: 1 - tn: 4 - precision: 1.0000 - recall: 0.8000 - accuracy: 0.8000 - f1-score: 0.8889
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postpone.01 tp: 4 - fp: 3 - fn: 0 - tn: 4 - precision: 0.5714 - recall: 1.0000 - accuracy: 0.5714 - f1-score: 0.7272
pound.02   tp: 2 - fp: 0 - fn: 1 - tn: 2 - precision: 1.0000 - recall: 0.6667 - accuracy: 0.6667 - f1-score: 0.8000
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sedate.01  tp: 0 - fp: 0 - fn: 1 - tn: 0 - precision: 0.0000 - recall: 0.0000 - accuracy: 0.0000 - f1-score: 0.0000
see.01     tp: 283 - fp: 4 - fn: 1 - tn: 283 - precision: 0.9861 - recall: 0.9965 - accuracy: 0.9826 - f1-score: 0.9913
see.03     tp: 0 - fp: 0 - fn: 4 - tn: 0 - precision: 0.0000 - recall: 0.0000 - accuracy: 0.0000 - f1-score: 0.0000
seek.01    tp: 15 - fp: 1 - fn: 0 - tn: 15 - precision: 0.9375 - recall: 1.0000 - accuracy: 0.9375 - f1-score: 0.9677
seek_out.02 tp: 1 - fp: 0 - fn: 0 - tn: 1 - precision: 1.0000 - recall: 1.0000 - accuracy: 1.0000 - f1-score: 1.0000
seem.01    tp: 56 - fp: 0 - fn: 0 - tn: 56 - precision: 1.0000 - recall: 1.0000 - accuracy: 1.0000 - f1-score: 1.0000
seize.01   tp: 2 - fp: 2 - fn: 0 - tn: 2 - precision: 0.5000 - recall: 1.0000 - accuracy: 0.5000 - f1-score: 0.6667
select.01  tp: 2 - fp: 1 - fn: 0 - tn: 2 - precision: 0.6667 - recall: 1.0000 - accuracy: 0.6667 - f1-score: 0.8000
sell.01    tp: 75 - fp: 15 - fn: 1 - tn: 75 - precision: 0.8333 - recall: 0.9868 - accuracy: 0.8242 - f1-score: 0.9036
sell_out.03 tp: 0 - fp: 0 - fn: 3 - tn: 0 - precision: 0.0000 - recall: 0.0000 - accuracy: 0.0000 - f1-score: 0.0000
send.01    tp: 20 - fp: 11 - fn: 3 - tn: 20 - precision: 0.6452 - recall: 0.8696 - accuracy: 0.5882 - f1-score: 0.7408
send.02    tp: 4 - fp: 3 - fn: 11 - tn: 4 - precision: 0.5714 - recall: 0.2667 - accuracy: 0.2222 - f1-score: 0.3637
send.03    tp: 9 - fp: 5 - fn: 5 - tn: 9 - precision: 0.6429 - recall: 0.6429 - accuracy: 0.4737 - f1-score: 0.6429
send_out.04 tp: 0 - fp: 0 - fn: 1 - tn: 0 - precision: 0.0000 - recall: 0.0000 - accuracy: 0.0000 - f1-score: 0.0000
sense.01   tp: 1 - fp: 0 - fn: 1 - tn: 1 - precision: 1.0000 - recall: 0.5000 - accuracy: 0.5000 - f1-score: 0.6667
sentence.01 tp: 6 - fp: 3 - fn: 0 - tn: 6 - precision: 0.6667 - recall: 1.0000 - accuracy: 0.6667 - f1-score: 0.8000
separate.01 tp: 8 - fp: 1 - fn: 0 - tn: 8 - precision: 0.8889 - recall: 1.0000 - accuracy: 0.8889 - f1-score: 0.9412
serve.01   tp: 31 - fp: 18 - fn: 2 - tn: 31 - precision: 0.6327 - recall: 0.9394 - accuracy: 0.6078 - f1-score: 0.7561
serve.02   tp: 2 - fp: 3 - fn: 2 - tn: 2 - precision: 0.4000 - recall: 0.5000 - accuracy: 0.2857 - f1-score: 0.4444
serve.04   tp: 0 - fp: 0 - fn: 1 - tn: 0 - precision: 0.0000 - recall: 0.0000 - accuracy: 0.0000 - f1-score: 0.0000
serve_up.03 tp: 0 - fp: 0 - fn: 1 - tn: 0 - precision: 0.0000 - recall: 0.0000 - accuracy: 0.0000 - f1-score: 0.0000
session.01 tp: 3 - fp: 2 - fn: 0 - tn: 3 - precision: 0.6000 - recall: 1.0000 - accuracy: 0.6000 - f1-score: 0.7500
set.01     tp: 11 - fp: 12 - fn: 4 - tn: 11 - precision: 0.4783 - recall: 0.7333 - accuracy: 0.4074 - f1-score: 0.5790
set.02     tp: 7 - fp: 6 - fn: 8 - tn: 7 - precision: 0.5385 - recall: 0.4667 - accuracy: 0.3333 - f1-score: 0.5000
set.08     tp: 1 - fp: 0 - fn: 2 - tn: 1 - precision: 1.0000 - recall: 0.3333 - accuracy: 0.3333 - f1-score: 0.5000
set.11     tp: 0 - fp: 0 - fn: 1 - tn: 0 - precision: 0.0000 - recall: 0.0000 - accuracy: 0.0000 - f1-score: 0.0000
set_about.12 tp: 0 - fp: 0 - fn: 2 - tn: 0 - precision: 0.0000 - recall: 0.0000 - accuracy: 0.0000 - f1-score: 0.0000
set_out.06 tp: 0 - fp: 0 - fn: 1 - tn: 0 - precision: 0.0000 - recall: 0.0000 - accuracy: 0.0000 - f1-score: 0.0000
set_out.07 tp: 0 - fp: 0 - fn: 3 - tn: 0 - precision: 0.0000 - recall: 0.0000 - accuracy: 0.0000 - f1-score: 0.0000
set_up.03  tp: 23 - fp: 4 - fn: 0 - tn: 23 - precision: 0.8519 - recall: 1.0000 - accuracy: 0.8519 - f1-score: 0.9200
settle.02  tp: 5 - fp: 6 - fn: 0 - tn: 5 - precision: 0.4545 - recall: 1.0000 - accuracy: 0.4545 - f1-score: 0.6250
settle.03  tp: 4 - fp: 2 - fn: 5 - tn: 4 - precision: 0.6667 - recall: 0.4444 - accuracy: 0.3636 - f1-score: 0.5333
settle_down.04 tp: 0 - fp: 0 - fn: 2 - tn: 0 - precision: 0.0000 - recall: 0.0000 - accuracy: 0.0000 - f1-score: 0.0000
sew.01     tp: 0 - fp: 0 - fn: 1 - tn: 0 - precision: 0.0000 - recall: 0.0000 - accuracy: 0.0000 - f1-score: 0.0000
shackle.01 tp: 0 - fp: 0 - fn: 1 - tn: 0 - precision: 0.0000 - recall: 0.0000 - accuracy: 0.0000 - f1-score: 0.0000
shake.01   tp: 3 - fp: 0 - fn: 0 - tn: 3 - precision: 1.0000 - recall: 1.0000 - accuracy: 1.0000 - f1-score: 1.0000
shape.01   tp: 0 - fp: 1 - fn: 1 - tn: 0 - precision: 0.0000 - recall: 0.0000 - accuracy: 0.0000 - f1-score: 0.0000
share.01   tp: 29 - fp: 8 - fn: 0 - tn: 29 - precision: 0.7838 - recall: 1.0000 - accuracy: 0.7838 - f1-score: 0.8788
sharpen.01 tp: 0 - fp: 0 - fn: 1 - tn: 0 - precision: 0.0000 - recall: 0.0000 - accuracy: 0.0000 - f1-score: 0.0000
shatter.01 tp: 1 - fp: 0 - fn: 0 - tn: 1 - precision: 1.0000 - recall: 1.0000 - accuracy: 1.0000 - f1-score: 1.0000
shave.01   tp: 1 - fp: 0 - fn: 0 - tn: 1 - precision: 1.0000 - recall: 1.0000 - accuracy: 1.0000 - f1-score: 1.0000
shed.01    tp: 1 - fp: 0 - fn: 0 - tn: 1 - precision: 1.0000 - recall: 1.0000 - accuracy: 1.0000 - f1-score: 1.0000
shelter.01 tp: 0 - fp: 0 - fn: 1 - tn: 0 - precision: 0.0000 - recall: 0.0000 - accuracy: 0.0000 - f1-score: 0.0000
shelve.01  tp: 0 - fp: 0 - fn: 1 - tn: 0 - precision: 0.0000 - recall: 0.0000 - accuracy: 0.0000 - f1-score: 0.0000
shift.01   tp: 6 - fp: 0 - fn: 0 - tn: 6 - precision: 1.0000 - recall: 1.0000 - accuracy: 1.0000 - f1-score: 1.0000
shine.01   tp: 2 - fp: 2 - fn: 0 - tn: 2 - precision: 0.5000 - recall: 1.0000 - accuracy: 0.5000 - f1-score: 0.6667
ship.01    tp: 2 - fp: 4 - fn: 0 - tn: 2 - precision: 0.3333 - recall: 1.0000 - accuracy: 0.3333 - f1-score: 0.5000
shock.01   tp: 1 - fp: 1 - fn: 0 - tn: 1 - precision: 0.5000 - recall: 1.0000 - accuracy: 0.5000 - f1-score: 0.6667
shoo.01    tp: 0 - fp: 0 - fn: 1 - tn: 0 - precision: 0.0000 - recall: 0.0000 - accuracy: 0.0000 - f1-score: 0.0000
shoot.01   tp: 2 - fp: 0 - fn: 2 - tn: 2 - precision: 1.0000 - recall: 0.5000 - accuracy: 0.5000 - f1-score: 0.6667
shoot.02   tp: 10 - fp: 3 - fn: 0 - tn: 10 - precision: 0.7692 - recall: 1.0000 - accuracy: 0.7692 - f1-score: 0.8695
shoot.03   tp: 0 - fp: 3 - fn: 1 - tn: 0 - precision: 0.0000 - recall: 0.0000 - accuracy: 0.0000 - f1-score: 0.0000
shoot.06   tp: 0 - fp: 0 - fn: 2 - tn: 0 - precision: 0.0000 - recall: 0.0000 - accuracy: 0.0000 - f1-score: 0.0000
shoot_down.05 tp: 0 - fp: 0 - fn: 1 - tn: 0 - precision: 0.0000 - recall: 0.0000 - accuracy: 0.0000 - f1-score: 0.0000
shoot_off.04 tp: 0 - fp: 0 - fn: 1 - tn: 0 - precision: 0.0000 - recall: 0.0000 - accuracy: 0.0000 - f1-score: 0.0000
shop.01    tp: 1 - fp: 0 - fn: 0 - tn: 1 - precision: 1.0000 - recall: 1.0000 - accuracy: 1.0000 - f1-score: 1.0000
shore_up.01 tp: 0 - fp: 2 - fn: 0 - tn: 0 - precision: 0.0000 - recall: 0.0000 - accuracy: 0.0000 - f1-score: 0.0000
shoulder.01 tp: 0 - fp: 0 - fn: 1 - tn: 0 - precision: 0.0000 - recall: 0.0000 - accuracy: 0.0000 - f1-score: 0.0000
shout.01   tp: 15 - fp: 0 - fn: 0 - tn: 15 - precision: 1.0000 - recall: 1.0000 - accuracy: 1.0000 - f1-score: 1.0000
shove.01   tp: 0 - fp: 0 - fn: 1 - tn: 0 - precision: 0.0000 - recall: 0.0000 - accuracy: 0.0000 - f1-score: 0.0000
show.01    tp: 81 - fp: 3 - fn: 3 - tn: 81 - precision: 0.9643 - recall: 0.9643 - accuracy: 0.9310 - f1-score: 0.9643
show.04    tp: 43 - fp: 10 - fn: 2 - tn: 43 - precision: 0.8113 - recall: 0.9556 - accuracy: 0.7818 - f1-score: 0.8776
show_up.02 tp: 4 - fp: 0 - fn: 0 - tn: 4 - precision: 1.0000 - recall: 1.0000 - accuracy: 1.0000 - f1-score: 1.0000
shrink.01  tp: 5 - fp: 1 - fn: 0 - tn: 5 - precision: 0.8333 - recall: 1.0000 - accuracy: 0.8333 - f1-score: 0.9091
shuffle.02 tp: 0 - fp: 0 - fn: 1 - tn: 0 - precision: 0.0000 - recall: 0.0000 - accuracy: 0.0000 - f1-score: 0.0000
shun.01    tp: 0 - fp: 0 - fn: 2 - tn: 0 - precision: 0.0000 - recall: 0.0000 - accuracy: 0.0000 - f1-score: 0.0000
shut.01    tp: 0 - fp: 0 - fn: 1 - tn: 0 - precision: 0.0000 - recall: 0.0000 - accuracy: 0.0000 - f1-score: 0.0000
shut_down.05 tp: 1 - fp: 0 - fn: 0 - tn: 1 - precision: 1.0000 - recall: 1.0000 - accuracy: 1.0000 - f1-score: 1.0000
shut_up.06 tp: 1 - fp: 0 - fn: 0 - tn: 1 - precision: 1.0000 - recall: 1.0000 - accuracy: 1.0000 - f1-score: 1.0000
side.01    tp: 0 - fp: 0 - fn: 4 - tn: 0 - precision: 0.0000 - recall: 0.0000 - accuracy: 0.0000 - f1-score: 0.0000
sightsee.01 tp: 0 - fp: 0 - fn: 1 - tn: 0 - precision: 0.0000 - recall: 0.0000 - accuracy: 0.0000 - f1-score: 0.0000
sign.01    tp: 1 - fp: 2 - fn: 5 - tn: 1 - precision: 0.3333 - recall: 0.1667 - accuracy: 0.1250 - f1-score: 0.2222
sign.02    tp: 11 - fp: 8 - fn: 2 - tn: 11 - precision: 0.5789 - recall: 0.8462 - accuracy: 0.5238 - f1-score: 0.6875
sign_up.03 tp: 1 - fp: 0 - fn: 0 - tn: 1 - precision: 1.0000 - recall: 1.0000 - accuracy: 1.0000 - f1-score: 1.0000
signal.07  tp: 11 - fp: 4 - fn: 1 - tn: 11 - precision: 0.7333 - recall: 0.9167 - accuracy: 0.6875 - f1-score: 0.8148
signify.01 tp: 1 - fp: 0 - fn: 0 - tn: 1 - precision: 1.0000 - recall: 1.0000 - accuracy: 1.0000 - f1-score: 1.0000
silence.01 tp: 0 - fp: 0 - fn: 1 - tn: 0 - precision: 0.0000 - recall: 0.0000 - accuracy: 0.0000 - f1-score: 0.0000
simmer.01  tp: 0 - fp: 0 - fn: 6 - tn: 0 - precision: 0.0000 - recall: 0.0000 - accuracy: 0.0000 - f1-score: 0.0000
sin.01     tp: 5 - fp: 3 - fn: 0 - tn: 5 - precision: 0.6250 - recall: 1.0000 - accuracy: 0.6250 - f1-score: 0.7692
sing.01    tp: 7 - fp: 0 - fn: 0 - tn: 7 - precision: 1.0000 - recall: 1.0000 - accuracy: 1.0000 - f1-score: 1.0000
sink.01    tp: 6 - fp: 2 - fn: 0 - tn: 6 - precision: 0.7500 - recall: 1.0000 - accuracy: 0.7500 - f1-score: 0.8571
sit.01     tp: 24 - fp: 0 - fn: 2 - tn: 24 - precision: 1.0000 - recall: 0.9231 - accuracy: 0.9231 - f1-score: 0.9600
sit_down.02 tp: 15 - fp: 4 - fn: 0 - tn: 15 - precision: 0.7895 - recall: 1.0000 - accuracy: 0.7895 - f1-score: 0.8824
sit_out.04 tp: 0 - fp: 0 - fn: 1 - tn: 0 - precision: 0.0000 - recall: 0.0000 - accuracy: 0.0000 - f1-score: 0.0000
sit_up.03  tp: 0 - fp: 0 - fn: 1 - tn: 0 - precision: 0.0000 - recall: 0.0000 - accuracy: 0.0000 - f1-score: 0.0000
situate.01 tp: 1 - fp: 0 - fn: 0 - tn: 1 - precision: 1.0000 - recall: 1.0000 - accuracy: 1.0000 - f1-score: 1.0000
size_up.02 tp: 0 - fp: 0 - fn: 1 - tn: 0 - precision: 0.0000 - recall: 0.0000 - accuracy: 0.0000 - f1-score: 0.0000
skew.01    tp: 0 - fp: 0 - fn: 1 - tn: 0 - precision: 0.0000 - recall: 0.0000 - accuracy: 0.0000 - f1-score: 0.0000
skid.01    tp: 0 - fp: 0 - fn: 1 - tn: 0 - precision: 0.0000 - recall: 0.0000 - accuracy: 0.0000 - f1-score: 0.0000
skimp.01   tp: 0 - fp: 0 - fn: 1 - tn: 0 - precision: 0.0000 - recall: 0.0000 - accuracy: 0.0000 - f1-score: 0.0000
skip.01    tp: 1 - fp: 0 - fn: 0 - tn: 1 - precision: 1.0000 - recall: 1.0000 - accuracy: 1.0000 - f1-score: 1.0000
slack_off.01 tp: 0 - fp: 0 - fn: 1 - tn: 0 - precision: 0.0000 - recall: 0.0000 - accuracy: 0.0000 - f1-score: 0.0000
slam.02    tp: 0 - fp: 0 - fn: 3 - tn: 0 - precision: 0.0000 - recall: 0.0000 - accuracy: 0.0000 - f1-score: 0.0000
slap.01    tp: 1 - fp: 0 - fn: 0 - tn: 1 - precision: 1.0000 - recall: 1.0000 - accuracy: 1.0000 - f1-score: 1.0000
slate.01   tp: 1 - fp: 0 - fn: 0 - tn: 1 - precision: 1.0000 - recall: 1.0000 - accuracy: 1.0000 - f1-score: 1.0000
sleep.01   tp: 7 - fp: 2 - fn: 0 - tn: 7 - precision: 0.7778 - recall: 1.0000 - accuracy: 0.7778 - f1-score: 0.8750
slide.01   tp: 0 - fp: 0 - fn: 1 - tn: 0 - precision: 0.0000 - recall: 0.0000 - accuracy: 0.0000 - f1-score: 0.0000
slide.02   tp: 0 - fp: 3 - fn: 0 - tn: 0 - precision: 0.0000 - recall: 0.0000 - accuracy: 0.0000 - f1-score: 0.0000
slip.01    tp: 1 - fp: 1 - fn: 0 - tn: 1 - precision: 0.5000 - recall: 1.0000 - accuracy: 0.5000 - f1-score: 0.6667
slip.02    tp: 0 - fp: 0 - fn: 1 - tn: 0 - precision: 0.0000 - recall: 0.0000 - accuracy: 0.0000 - f1-score: 0.0000
slow_down.03 tp: 1 - fp: 0 - fn: 0 - tn: 1 - precision: 1.0000 - recall: 1.0000 - accuracy: 1.0000 - f1-score: 1.0000
slump.01   tp: 1 - fp: 0 - fn: 0 - tn: 1 - precision: 1.0000 - recall: 1.0000 - accuracy: 1.0000 - f1-score: 1.0000
smear.02   tp: 0 - fp: 0 - fn: 1 - tn: 0 - precision: 0.0000 - recall: 0.0000 - accuracy: 0.0000 - f1-score: 0.0000
smell.01   tp: 0 - fp: 0 - fn: 2 - tn: 0 - precision: 0.0000 - recall: 0.0000 - accuracy: 0.0000 - f1-score: 0.0000
smell.02   tp: 0 - fp: 2 - fn: 0 - tn: 0 - precision: 0.0000 - recall: 0.0000 - accuracy: 0.0000 - f1-score: 0.0000
smile.01   tp: 0 - fp: 1 - fn: 0 - tn: 0 - precision: 0.0000 - recall: 0.0000 - accuracy: 0.0000 - f1-score: 0.0000
smoke.01   tp: 0 - fp: 0 - fn: 1 - tn: 0 - precision: 0.0000 - recall: 0.0000 - accuracy: 0.0000 - f1-score: 0.0000
smoke.02   tp: 0 - fp: 1 - fn: 0 - tn: 0 - precision: 0.0000 - recall: 0.0000 - accuracy: 0.0000 - f1-score: 0.0000
smuggle.01 tp: 0 - fp: 0 - fn: 2 - tn: 0 - precision: 0.0000 - recall: 0.0000 - accuracy: 0.0000 - f1-score: 0.0000
snap.07    tp: 0 - fp: 0 - fn: 1 - tn: 0 - precision: 0.0000 - recall: 0.0000 - accuracy: 0.0000 - f1-score: 0.0000
snatch.01  tp: 0 - fp: 0 - fn: 1 - tn: 0 - precision: 0.0000 - recall: 0.0000 - accuracy: 0.0000 - f1-score: 0.0000
snow.01    tp: 0 - fp: 0 - fn: 1 - tn: 0 - precision: 0.0000 - recall: 0.0000 - accuracy: 0.0000 - f1-score: 0.0000
soak.01    tp: 0 - fp: 1 - fn: 1 - tn: 0 - precision: 0.0000 - recall: 0.0000 - accuracy: 0.0000 - f1-score: 0.0000
soar.01    tp: 0 - fp: 2 - fn: 0 - tn: 0 - precision: 0.0000 - recall: 0.0000 - accuracy: 0.0000 - f1-score: 0.0000
socialize.01 tp: 0 - fp: 0 - fn: 1 - tn: 0 - precision: 0.0000 - recall: 0.0000 - accuracy: 0.0000 - f1-score: 0.0000
soften.01  tp: 1 - fp: 0 - fn: 3 - tn: 1 - precision: 1.0000 - recall: 0.2500 - accuracy: 0.2500 - f1-score: 0.4000
solicit.01 tp: 0 - fp: 0 - fn: 1 - tn: 0 - precision: 0.0000 - recall: 0.0000 - accuracy: 0.0000 - f1-score: 0.0000
solidify.01 tp: 0 - fp: 0 - fn: 1 - tn: 0 - precision: 0.0000 - recall: 0.0000 - accuracy: 0.0000 - f1-score: 0.0000
solve.01   tp: 15 - fp: 0 - fn: 0 - tn: 15 - precision: 1.0000 - recall: 1.0000 - accuracy: 1.0000 - f1-score: 1.0000
sort_out.02 tp: 0 - fp: 0 - fn: 1 - tn: 0 - precision: 0.0000 - recall: 0.0000 - accuracy: 0.0000 - f1-score: 0.0000
sound.01   tp: 14 - fp: 0 - fn: 0 - tn: 14 - precision: 1.0000 - recall: 1.0000 - accuracy: 1.0000 - f1-score: 1.0000
sound.02   tp: 5 - fp: 5 - fn: 0 - tn: 5 - precision: 0.5000 - recall: 1.0000 - accuracy: 0.5000 - f1-score: 0.6667
sound_off.03 tp: 0 - fp: 0 - fn: 1 - tn: 0 - precision: 0.0000 - recall: 0.0000 - accuracy: 0.0000 - f1-score: 0.0000
source.02  tp: 0 - fp: 0 - fn: 1 - tn: 0 - precision: 0.0000 - recall: 0.0000 - accuracy: 0.0000 - f1-score: 0.0000
sow.01     tp: 0 - fp: 0 - fn: 1 - tn: 0 - precision: 0.0000 - recall: 0.0000 - accuracy: 0.0000 - f1-score: 0.0000
space.03   tp: 0 - fp: 0 - fn: 1 - tn: 0 - precision: 0.0000 - recall: 0.0000 - accuracy: 0.0000 - f1-score: 0.0000
span.01    tp: 2 - fp: 0 - fn: 0 - tn: 2 - precision: 1.0000 - recall: 1.0000 - accuracy: 1.0000 - f1-score: 1.0000
spar.01    tp: 0 - fp: 0 - fn: 1 - tn: 0 - precision: 0.0000 - recall: 0.0000 - accuracy: 0.0000 - f1-score: 0.0000
spark.01   tp: 2 - fp: 0 - fn: 0 - tn: 2 - precision: 1.0000 - recall: 1.0000 - accuracy: 1.0000 - f1-score: 1.0000
spawn.01   tp: 1 - fp: 1 - fn: 0 - tn: 1 - precision: 0.5000 - recall: 1.0000 - accuracy: 0.5000 - f1-score: 0.6667
speak.01   tp: 64 - fp: 1 - fn: 0 - tn: 64 - precision: 0.9846 - recall: 1.0000 - accuracy: 0.9846 - f1-score: 0.9922
speak_out.03 tp: 0 - fp: 0 - fn: 2 - tn: 0 - precision: 0.0000 - recall: 0.0000 - accuracy: 0.0000 - f1-score: 0.0000
speak_up.02 tp: 0 - fp: 1 - fn: 0 - tn: 0 - precision: 0.0000 - recall: 0.0000 - accuracy: 0.0000 - f1-score: 0.0000
specialize.01 tp: 1 - fp: 0 - fn: 0 - tn: 1 - precision: 1.0000 - recall: 1.0000 - accuracy: 1.0000 - f1-score: 1.0000
specify.01 tp: 3 - fp: 0 - fn: 0 - tn: 3 - precision: 1.0000 - recall: 1.0000 - accuracy: 1.0000 - f1-score: 1.0000
speculate.01 tp: 17 - fp: 3 - fn: 0 - tn: 17 - precision: 0.8500 - recall: 1.0000 - accuracy: 0.8500 - f1-score: 0.9189
speed.01   tp: 1 - fp: 1 - fn: 1 - tn: 1 - precision: 0.5000 - recall: 0.5000 - accuracy: 0.3333 - f1-score: 0.5000
speed_up.02 tp: 0 - fp: 0 - fn: 2 - tn: 0 - precision: 0.0000 - recall: 0.0000 - accuracy: 0.0000 - f1-score: 0.0000
spell.01   tp: 0 - fp: 0 - fn: 1 - tn: 0 - precision: 0.0000 - recall: 0.0000 - accuracy: 0.0000 - f1-score: 0.0000
spend.01   tp: 13 - fp: 10 - fn: 2 - tn: 13 - precision: 0.5652 - recall: 0.8667 - accuracy: 0.5200 - f1-score: 0.6842
spend.02   tp: 13 - fp: 1 - fn: 6 - tn: 13 - precision: 0.9286 - recall: 0.6842 - accuracy: 0.6500 - f1-score: 0.7879
spew.01    tp: 0 - fp: 0 - fn: 1 - tn: 0 - precision: 0.0000 - recall: 0.0000 - accuracy: 0.0000 - f1-score: 0.0000
spill.01   tp: 1 - fp: 2 - fn: 0 - tn: 1 - precision: 0.3333 - recall: 1.0000 - accuracy: 0.3333 - f1-score: 0.5000
spill_out.03 tp: 0 - fp: 0 - fn: 1 - tn: 0 - precision: 0.0000 - recall: 0.0000 - accuracy: 0.0000 - f1-score: 0.0000
spit.01    tp: 1 - fp: 6 - fn: 0 - tn: 1 - precision: 0.1429 - recall: 1.0000 - accuracy: 0.1429 - f1-score: 0.2501
split.01   tp: 1 - fp: 2 - fn: 0 - tn: 1 - precision: 0.3333 - recall: 1.0000 - accuracy: 0.3333 - f1-score: 0.5000
sponsor.01 tp: 2 - fp: 0 - fn: 0 - tn: 2 - precision: 1.0000 - recall: 1.0000 - accuracy: 1.0000 - f1-score: 1.0000
spook.01   tp: 0 - fp: 0 - fn: 3 - tn: 0 - precision: 0.0000 - recall: 0.0000 - accuracy: 0.0000 - f1-score: 0.0000
sport.01   tp: 0 - fp: 0 - fn: 1 - tn: 0 - precision: 0.0000 - recall: 0.0000 - accuracy: 0.0000 - f1-score: 0.0000
spot.01    tp: 0 - fp: 0 - fn: 1 - tn: 0 - precision: 0.0000 - recall: 0.0000 - accuracy: 0.0000 - f1-score: 0.0000
spotlight.01 tp: 2 - fp: 0 - fn: 1 - tn: 2 - precision: 1.0000 - recall: 0.6667 - accuracy: 0.6667 - f1-score: 0.8000
sprawl.01  tp: 0 - fp: 0 - fn: 1 - tn: 0 - precision: 0.0000 - recall: 0.0000 - accuracy: 0.0000 - f1-score: 0.0000
spray.01   tp: 0 - fp: 0 - fn: 1 - tn: 0 - precision: 0.0000 - recall: 0.0000 - accuracy: 0.0000 - f1-score: 0.0000
spread.01  tp: 0 - fp: 0 - fn: 2 - tn: 0 - precision: 0.0000 - recall: 0.0000 - accuracy: 0.0000 - f1-score: 0.0000
spread.02  tp: 4 - fp: 3 - fn: 2 - tn: 4 - precision: 0.5714 - recall: 0.6667 - accuracy: 0.4444 - f1-score: 0.6154
spread.03  tp: 3 - fp: 3 - fn: 0 - tn: 3 - precision: 0.5000 - recall: 1.0000 - accuracy: 0.5000 - f1-score: 0.6667
sprinkle.01 tp: 0 - fp: 0 - fn: 4 - tn: 0 - precision: 0.0000 - recall: 0.0000 - accuracy: 0.0000 - f1-score: 0.0000
spur.01    tp: 2 - fp: 0 - fn: 0 - tn: 2 - precision: 1.0000 - recall: 1.0000 - accuracy: 1.0000 - f1-score: 1.0000
squeeze.01 tp: 0 - fp: 1 - fn: 2 - tn: 0 - precision: 0.0000 - recall: 0.0000 - accuracy: 0.0000 - f1-score: 0.0000
squeeze.02 tp: 0 - fp: 0 - fn: 1 - tn: 0 - precision: 0.0000 - recall: 0.0000 - accuracy: 0.0000 - f1-score: 0.0000
stab.01    tp: 1 - fp: 1 - fn: 0 - tn: 1 - precision: 0.5000 - recall: 1.0000 - accuracy: 0.5000 - f1-score: 0.6667
stabilize.01 tp: 2 - fp: 3 - fn: 0 - tn: 2 - precision: 0.4000 - recall: 1.0000 - accuracy: 0.4000 - f1-score: 0.5714
stage.01   tp: 2 - fp: 0 - fn: 1 - tn: 2 - precision: 1.0000 - recall: 0.6667 - accuracy: 0.6667 - f1-score: 0.8000
stagger.01 tp: 0 - fp: 0 - fn: 1 - tn: 0 - precision: 0.0000 - recall: 0.0000 - accuracy: 0.0000 - f1-score: 0.0000
stain.01   tp: 0 - fp: 0 - fn: 1 - tn: 0 - precision: 0.0000 - recall: 0.0000 - accuracy: 0.0000 - f1-score: 0.0000
stand.01   tp: 18 - fp: 6 - fn: 0 - tn: 18 - precision: 0.7500 - recall: 1.0000 - accuracy: 0.7500 - f1-score: 0.8571
stand.03   tp: 0 - fp: 0 - fn: 1 - tn: 0 - precision: 0.0000 - recall: 0.0000 - accuracy: 0.0000 - f1-score: 0.0000
stand.04   tp: 3 - fp: 1 - fn: 4 - tn: 3 - precision: 0.7500 - recall: 0.4286 - accuracy: 0.3750 - f1-score: 0.5455
stand.11   tp: 3 - fp: 0 - fn: 1 - tn: 3 - precision: 1.0000 - recall: 0.7500 - accuracy: 0.7500 - f1-score: 0.8571
stand_by.05 tp: 0 - fp: 0 - fn: 2 - tn: 0 - precision: 0.0000 - recall: 0.0000 - accuracy: 0.0000 - f1-score: 0.0000
stand_up.07 tp: 9 - fp: 1 - fn: 0 - tn: 9 - precision: 0.9000 - recall: 1.0000 - accuracy: 0.9000 - f1-score: 0.9474
stare.01   tp: 2 - fp: 3 - fn: 0 - tn: 2 - precision: 0.4000 - recall: 1.0000 - accuracy: 0.4000 - f1-score: 0.5714
start.01   tp: 58 - fp: 11 - fn: 1 - tn: 58 - precision: 0.8406 - recall: 0.9831 - accuracy: 0.8286 - f1-score: 0.9063
start_off.02 tp: 0 - fp: 1 - fn: 2 - tn: 0 - precision: 0.0000 - recall: 0.0000 - accuracy: 0.0000 - f1-score: 0.0000
start_up.04 tp: 0 - fp: 0 - fn: 1 - tn: 0 - precision: 0.0000 - recall: 0.0000 - accuracy: 0.0000 - f1-score: 0.0000
starve.01  tp: 3 - fp: 0 - fn: 0 - tn: 3 - precision: 1.0000 - recall: 1.0000 - accuracy: 1.0000 - f1-score: 1.0000
state.01   tp: 38 - fp: 6 - fn: 0 - tn: 38 - precision: 0.8636 - recall: 1.0000 - accuracy: 0.8636 - f1-score: 0.9268
station.01 tp: 2 - fp: 0 - fn: 0 - tn: 2 - precision: 1.0000 - recall: 1.0000 - accuracy: 1.0000 - f1-score: 1.0000
stay.01    tp: 42 - fp: 3 - fn: 0 - tn: 42 - precision: 0.9333 - recall: 1.0000 - accuracy: 0.9333 - f1-score: 0.9655
steal.01   tp: 11 - fp: 2 - fn: 1 - tn: 11 - precision: 0.8462 - recall: 0.9167 - accuracy: 0.7857 - f1-score: 0.8800
stem.01    tp: 1 - fp: 0 - fn: 0 - tn: 1 - precision: 1.0000 - recall: 1.0000 - accuracy: 1.0000 - f1-score: 1.0000
step.01    tp: 7 - fp: 5 - fn: 2 - tn: 7 - precision: 0.5833 - recall: 0.7778 - accuracy: 0.5000 - f1-score: 0.6667
step_down.04 tp: 2 - fp: 1 - fn: 3 - tn: 2 - precision: 0.6667 - recall: 0.4000 - accuracy: 0.3333 - f1-score: 0.5000
step_up.03 tp: 1 - fp: 0 - fn: 2 - tn: 1 - precision: 1.0000 - recall: 0.3333 - accuracy: 0.3333 - f1-score: 0.5000
stick.01   tp: 3 - fp: 4 - fn: 0 - tn: 3 - precision: 0.4286 - recall: 1.0000 - accuracy: 0.4286 - f1-score: 0.6000
stick_up.04 tp: 0 - fp: 0 - fn: 1 - tn: 0 - precision: 0.0000 - recall: 0.0000 - accuracy: 0.0000 - f1-score: 0.0000
stimulate.01 tp: 2 - fp: 0 - fn: 0 - tn: 2 - precision: 1.0000 - recall: 1.0000 - accuracy: 1.0000 - f1-score: 1.0000
sting.01   tp: 0 - fp: 0 - fn: 2 - tn: 0 - precision: 0.0000 - recall: 0.0000 - accuracy: 0.0000 - f1-score: 0.0000
stipulate.01 tp: 5 - fp: 1 - fn: 0 - tn: 5 - precision: 0.8333 - recall: 1.0000 - accuracy: 0.8333 - f1-score: 0.9091
stir.02    tp: 0 - fp: 0 - fn: 1 - tn: 0 - precision: 0.0000 - recall: 0.0000 - accuracy: 0.0000 - f1-score: 0.0000
stir_up.04 tp: 1 - fp: 2 - fn: 0 - tn: 1 - precision: 0.3333 - recall: 1.0000 - accuracy: 0.3333 - f1-score: 0.5000
stop.01    tp: 39 - fp: 4 - fn: 0 - tn: 39 - precision: 0.9070 - recall: 1.0000 - accuracy: 0.9070 - f1-score: 0.9512
stop.03    tp: 3 - fp: 0 - fn: 2 - tn: 3 - precision: 1.0000 - recall: 0.6000 - accuracy: 0.6000 - f1-score: 0.7500
store.01   tp: 3 - fp: 0 - fn: 1 - tn: 3 - precision: 1.0000 - recall: 0.7500 - accuracy: 0.7500 - f1-score: 0.8571
storm.02   tp: 2 - fp: 0 - fn: 0 - tn: 2 - precision: 1.0000 - recall: 1.0000 - accuracy: 1.0000 - f1-score: 1.0000
straighten_out.02 tp: 0 - fp: 4 - fn: 0 - tn: 0 - precision: 0.0000 - recall: 0.0000 - accuracy: 0.0000 - f1-score: 0.0000
strangle.01 tp: 0 - fp: 1 - fn: 0 - tn: 0 - precision: 0.0000 - recall: 0.0000 - accuracy: 0.0000 - f1-score: 0.0000
strategize.01 tp: 0 - fp: 0 - fn: 1 - tn: 0 - precision: 0.0000 - recall: 0.0000 - accuracy: 0.0000 - f1-score: 0.0000
streamline.01 tp: 0 - fp: 0 - fn: 1 - tn: 0 - precision: 0.0000 - recall: 0.0000 - accuracy: 0.0000 - f1-score: 0.0000
strengthen.01 tp: 14 - fp: 1 - fn: 0 - tn: 14 - precision: 0.9333 - recall: 1.0000 - accuracy: 0.9333 - f1-score: 0.9655
stress.01  tp: 5 - fp: 0 - fn: 0 - tn: 5 - precision: 1.0000 - recall: 1.0000 - accuracy: 1.0000 - f1-score: 1.0000
stretch.01 tp: 3 - fp: 0 - fn: 0 - tn: 3 - precision: 1.0000 - recall: 1.0000 - accuracy: 1.0000 - f1-score: 1.0000
strew.01   tp: 0 - fp: 0 - fn: 1 - tn: 0 - precision: 0.0000 - recall: 0.0000 - accuracy: 0.0000 - f1-score: 0.0000
strike.01  tp: 5 - fp: 3 - fn: 0 - tn: 5 - precision: 0.6250 - recall: 1.0000 - accuracy: 0.6250 - f1-score: 0.7692
strike.02  tp: 0 - fp: 0 - fn: 1 - tn: 0 - precision: 0.0000 - recall: 0.0000 - accuracy: 0.0000 - f1-score: 0.0000
strike.03  tp: 0 - fp: 0 - fn: 3 - tn: 0 - precision: 0.0000 - recall: 0.0000 - accuracy: 0.0000 - f1-score: 0.0000
strike.04  tp: 0 - fp: 0 - fn: 1 - tn: 0 - precision: 0.0000 - recall: 0.0000 - accuracy: 0.0000 - f1-score: 0.0000
strike.05  tp: 0 - fp: 0 - fn: 1 - tn: 0 - precision: 0.0000 - recall: 0.0000 - accuracy: 0.0000 - f1-score: 0.0000
string.01  tp: 0 - fp: 0 - fn: 1 - tn: 0 - precision: 0.0000 - recall: 0.0000 - accuracy: 0.0000 - f1-score: 0.0000
strip.01   tp: 6 - fp: 1 - fn: 0 - tn: 6 - precision: 0.8571 - recall: 1.0000 - accuracy: 0.8571 - f1-score: 0.9231
strip_away.02 tp: 0 - fp: 0 - fn: 1 - tn: 0 - precision: 0.0000 - recall: 0.0000 - accuracy: 0.0000 - f1-score: 0.0000
strive.01  tp: 6 - fp: 1 - fn: 0 - tn: 6 - precision: 0.8571 - recall: 1.0000 - accuracy: 0.8571 - f1-score: 0.9231
stroll.01  tp: 2 - fp: 0 - fn: 1 - tn: 2 - precision: 1.0000 - recall: 0.6667 - accuracy: 0.6667 - f1-score: 0.8000
struggle.01 tp: 0 - fp: 1 - fn: 0 - tn: 0 - precision: 0.0000 - recall: 0.0000 - accuracy: 0.0000 - f1-score: 0.0000
struggle.02 tp: 3 - fp: 0 - fn: 2 - tn: 3 - precision: 1.0000 - recall: 0.6000 - accuracy: 0.6000 - f1-score: 0.7500
study.01   tp: 21 - fp: 10 - fn: 0 - tn: 21 - precision: 0.6774 - recall: 1.0000 - accuracy: 0.6774 - f1-score: 0.8077
stumble.01 tp: 1 - fp: 1 - fn: 0 - tn: 1 - precision: 0.5000 - recall: 1.0000 - accuracy: 0.5000 - f1-score: 0.6667
stunt.02   tp: 0 - fp: 0 - fn: 3 - tn: 0 - precision: 0.0000 - recall: 0.0000 - accuracy: 0.0000 - f1-score: 0.0000
stymie.01  tp: 0 - fp: 0 - fn: 2 - tn: 0 - precision: 0.0000 - recall: 0.0000 - accuracy: 0.0000 - f1-score: 0.0000
subject.01 tp: 1 - fp: 0 - fn: 1 - tn: 1 - precision: 1.0000 - recall: 0.5000 - accuracy: 0.5000 - f1-score: 0.6667
submit.01  tp: 4 - fp: 0 - fn: 0 - tn: 4 - precision: 1.0000 - recall: 1.0000 - accuracy: 1.0000 - f1-score: 1.0000
subscribe.01 tp: 1 - fp: 0 - fn: 1 - tn: 1 - precision: 1.0000 - recall: 0.5000 - accuracy: 0.5000 - f1-score: 0.6667
subside.01 tp: 0 - fp: 0 - fn: 1 - tn: 0 - precision: 0.0000 - recall: 0.0000 - accuracy: 0.0000 - f1-score: 0.0000
subside.02 tp: 0 - fp: 0 - fn: 3 - tn: 0 - precision: 0.0000 - recall: 0.0000 - accuracy: 0.0000 - f1-score: 0.0000
substantiate.01 tp: 0 - fp: 0 - fn: 1 - tn: 0 - precision: 0.0000 - recall: 0.0000 - accuracy: 0.0000 - f1-score: 0.0000
substitute.01 tp: 0 - fp: 0 - fn: 3 - tn: 0 - precision: 0.0000 - recall: 0.0000 - accuracy: 0.0000 - f1-score: 0.0000
succeed.01 tp: 23 - fp: 5 - fn: 1 - tn: 23 - precision: 0.8214 - recall: 0.9583 - accuracy: 0.7931 - f1-score: 0.8846
succeed.02 tp: 1 - fp: 0 - fn: 0 - tn: 1 - precision: 1.0000 - recall: 1.0000 - accuracy: 1.0000 - f1-score: 1.0000
succeed.03 tp: 0 - fp: 1 - fn: 2 - tn: 0 - precision: 0.0000 - recall: 0.0000 - accuracy: 0.0000 - f1-score: 0.0000
suck.01    tp: 1 - fp: 1 - fn: 0 - tn: 1 - precision: 0.5000 - recall: 1.0000 - accuracy: 0.5000 - f1-score: 0.6667
sue.02     tp: 5 - fp: 1 - fn: 0 - tn: 5 - precision: 0.8333 - recall: 1.0000 - accuracy: 0.8333 - f1-score: 0.9091
suffer.01  tp: 17 - fp: 1 - fn: 0 - tn: 17 - precision: 0.9444 - recall: 1.0000 - accuracy: 0.9444 - f1-score: 0.9714
suffocate.01 tp: 0 - fp: 0 - fn: 1 - tn: 0 - precision: 0.0000 - recall: 0.0000 - accuracy: 0.0000 - f1-score: 0.0000
suggest.01 tp: 28 - fp: 0 - fn: 3 - tn: 28 - precision: 1.0000 - recall: 0.9032 - accuracy: 0.9032 - f1-score: 0.9491
sum_up.01  tp: 3 - fp: 0 - fn: 0 - tn: 3 - precision: 1.0000 - recall: 1.0000 - accuracy: 1.0000 - f1-score: 1.0000
summarize.01 tp: 0 - fp: 0 - fn: 1 - tn: 0 - precision: 0.0000 - recall: 0.0000 - accuracy: 0.0000 - f1-score: 0.0000
summit.01  tp: 10 - fp: 2 - fn: 0 - tn: 10 - precision: 0.8333 - recall: 1.0000 - accuracy: 0.8333 - f1-score: 0.9091
supervise.01 tp: 7 - fp: 0 - fn: 0 - tn: 7 - precision: 1.0000 - recall: 1.0000 - accuracy: 1.0000 - f1-score: 1.0000
supply.01  tp: 4 - fp: 4 - fn: 4 - tn: 4 - precision: 0.5000 - recall: 0.5000 - accuracy: 0.3333 - f1-score: 0.5000
support.01 tp: 61 - fp: 5 - fn: 0 - tn: 61 - precision: 0.9242 - recall: 1.0000 - accuracy: 0.9242 - f1-score: 0.9606
suppose.01 tp: 3 - fp: 0 - fn: 0 - tn: 3 - precision: 1.0000 - recall: 1.0000 - accuracy: 1.0000 - f1-score: 1.0000
suppose.02 tp: 7 - fp: 0 - fn: 0 - tn: 7 - precision: 1.0000 - recall: 1.0000 - accuracy: 1.0000 - f1-score: 1.0000
suppress.01 tp: 3 - fp: 0 - fn: 0 - tn: 3 - precision: 1.0000 - recall: 1.0000 - accuracy: 1.0000 - f1-score: 1.0000
surge.01   tp: 4 - fp: 1 - fn: 0 - tn: 4 - precision: 0.8000 - recall: 1.0000 - accuracy: 0.8000 - f1-score: 0.8889
surpass.01 tp: 3 - fp: 0 - fn: 0 - tn: 3 - precision: 1.0000 - recall: 1.0000 - accuracy: 1.0000 - f1-score: 1.0000
surprise.01 tp: 3 - fp: 4 - fn: 3 - tn: 3 - precision: 0.4286 - recall: 0.5000 - accuracy: 0.3000 - f1-score: 0.4616
surrender.01 tp: 1 - fp: 0 - fn: 0 - tn: 1 - precision: 1.0000 - recall: 1.0000 - accuracy: 1.0000 - f1-score: 1.0000
surround.01 tp: 4 - fp: 1 - fn: 0 - tn: 4 - precision: 0.8000 - recall: 1.0000 - accuracy: 0.8000 - f1-score: 0.8889
survey.01  tp: 7 - fp: 4 - fn: 0 - tn: 7 - precision: 0.6364 - recall: 1.0000 - accuracy: 0.6364 - f1-score: 0.7778
survive.01 tp: 12 - fp: 1 - fn: 0 - tn: 12 - precision: 0.9231 - recall: 1.0000 - accuracy: 0.9231 - f1-score: 0.9600
survive.02 tp: 0 - fp: 0 - fn: 1 - tn: 0 - precision: 0.0000 - recall: 0.0000 - accuracy: 0.0000 - f1-score: 0.0000
suspect.01 tp: 5 - fp: 0 - fn: 3 - tn: 5 - precision: 1.0000 - recall: 0.6250 - accuracy: 0.6250 - f1-score: 0.7692
suspend.01 tp: 5 - fp: 0 - fn: 2 - tn: 5 - precision: 1.0000 - recall: 0.7143 - accuracy: 0.7143 - f1-score: 0.8333
suspension.03 tp: 0 - fp: 2 - fn: 0 - tn: 0 - precision: 0.0000 - recall: 0.0000 - accuracy: 0.0000 - f1-score: 0.0000
sustain.01 tp: 2 - fp: 0 - fn: 0 - tn: 2 - precision: 1.0000 - recall: 1.0000 - accuracy: 1.0000 - f1-score: 1.0000
swallow.01 tp: 1 - fp: 1 - fn: 0 - tn: 1 - precision: 0.5000 - recall: 1.0000 - accuracy: 0.5000 - f1-score: 0.6667
swamp.01   tp: 0 - fp: 0 - fn: 1 - tn: 0 - precision: 0.0000 - recall: 0.0000 - accuracy: 0.0000 - f1-score: 0.0000
swap.01    tp: 1 - fp: 0 - fn: 0 - tn: 1 - precision: 1.0000 - recall: 1.0000 - accuracy: 1.0000 - f1-score: 1.0000
swear.01   tp: 1 - fp: 0 - fn: 0 - tn: 1 - precision: 1.0000 - recall: 1.0000 - accuracy: 1.0000 - f1-score: 1.0000
sweat.01   tp: 1 - fp: 0 - fn: 0 - tn: 1 - precision: 1.0000 - recall: 1.0000 - accuracy: 1.0000 - f1-score: 1.0000
sweep.01   tp: 0 - fp: 0 - fn: 3 - tn: 0 - precision: 0.0000 - recall: 0.0000 - accuracy: 0.0000 - f1-score: 0.0000
sweep.02   tp: 0 - fp: 2 - fn: 0 - tn: 0 - precision: 0.0000 - recall: 0.0000 - accuracy: 0.0000 - f1-score: 0.0000
swell.01   tp: 1 - fp: 0 - fn: 1 - tn: 1 - precision: 1.0000 - recall: 0.5000 - accuracy: 0.5000 - f1-score: 0.6667
swing.02   tp: 0 - fp: 1 - fn: 0 - tn: 0 - precision: 0.0000 - recall: 0.0000 - accuracy: 0.0000 - f1-score: 0.0000
swing.03   tp: 0 - fp: 0 - fn: 1 - tn: 0 - precision: 0.0000 - recall: 0.0000 - accuracy: 0.0000 - f1-score: 0.0000
swirl.01   tp: 0 - fp: 0 - fn: 1 - tn: 0 - precision: 0.0000 - recall: 0.0000 - accuracy: 0.0000 - f1-score: 0.0000
switch.01  tp: 2 - fp: 1 - fn: 0 - tn: 2 - precision: 0.6667 - recall: 1.0000 - accuracy: 0.6667 - f1-score: 0.8000
switch_over.02 tp: 0 - fp: 0 - fn: 1 - tn: 0 - precision: 0.0000 - recall: 0.0000 - accuracy: 0.0000 - f1-score: 0.0000
swoop_up.02 tp: 0 - fp: 0 - fn: 1 - tn: 0 - precision: 0.0000 - recall: 0.0000 - accuracy: 0.0000 - f1-score: 0.0000
sympathize.01 tp: 0 - fp: 1 - fn: 0 - tn: 0 - precision: 0.0000 - recall: 0.0000 - accuracy: 0.0000 - f1-score: 0.0000
syncopate.01 tp: 0 - fp: 0 - fn: 1 - tn: 0 - precision: 0.0000 - recall: 0.0000 - accuracy: 0.0000 - f1-score: 0.0000
syndicate.01 tp: 0 - fp: 0 - fn: 3 - tn: 0 - precision: 0.0000 - recall: 0.0000 - accuracy: 0.0000 - f1-score: 0.0000
taboo.01   tp: 0 - fp: 0 - fn: 1 - tn: 0 - precision: 0.0000 - recall: 0.0000 - accuracy: 0.0000 - f1-score: 0.0000
tackle.01  tp: 1 - fp: 0 - fn: 0 - tn: 1 - precision: 1.0000 - recall: 1.0000 - accuracy: 1.0000 - f1-score: 1.0000
tag.01     tp: 0 - fp: 0 - fn: 1 - tn: 0 - precision: 0.0000 - recall: 0.0000 - accuracy: 0.0000 - f1-score: 0.0000
taint.01   tp: 0 - fp: 0 - fn: 1 - tn: 0 - precision: 0.0000 - recall: 0.0000 - accuracy: 0.0000 - f1-score: 0.0000
take.01    tp: 145 - fp: 45 - fn: 15 - tn: 145 - precision: 0.7632 - recall: 0.9062 - accuracy: 0.7073 - f1-score: 0.8286
take.02    tp: 0 - fp: 0 - fn: 1 - tn: 0 - precision: 0.0000 - recall: 0.0000 - accuracy: 0.0000 - f1-score: 0.0000
take.03    tp: 0 - fp: 0 - fn: 1 - tn: 0 - precision: 0.0000 - recall: 0.0000 - accuracy: 0.0000 - f1-score: 0.0000
take.04    tp: 0 - fp: 0 - fn: 4 - tn: 0 - precision: 0.0000 - recall: 0.0000 - accuracy: 0.0000 - f1-score: 0.0000
take.10    tp: 18 - fp: 2 - fn: 2 - tn: 18 - precision: 0.9000 - recall: 0.9000 - accuracy: 0.8182 - f1-score: 0.9000
take.14    tp: 12 - fp: 0 - fn: 0 - tn: 12 - precision: 1.0000 - recall: 1.0000 - accuracy: 1.0000 - f1-score: 1.0000
take.17    tp: 0 - fp: 0 - fn: 2 - tn: 0 - precision: 0.0000 - recall: 0.0000 - accuracy: 0.0000 - f1-score: 0.0000
take.LV    tp: 11 - fp: 14 - fn: 20 - tn: 11 - precision: 0.4400 - recall: 0.3548 - accuracy: 0.2444 - f1-score: 0.3928
take_away.05 tp: 0 - fp: 1 - fn: 6 - tn: 0 - precision: 0.0000 - recall: 0.0000 - accuracy: 0.0000 - f1-score: 0.0000
take_down.22 tp: 0 - fp: 0 - fn: 3 - tn: 0 - precision: 0.0000 - recall: 0.0000 - accuracy: 0.0000 - f1-score: 0.0000
take_off.07 tp: 0 - fp: 6 - fn: 0 - tn: 0 - precision: 0.0000 - recall: 0.0000 - accuracy: 0.0000 - f1-score: 0.0000
take_off.18 tp: 0 - fp: 0 - fn: 2 - tn: 0 - precision: 0.0000 - recall: 0.0000 - accuracy: 0.0000 - f1-score: 0.0000
take_off.19 tp: 0 - fp: 0 - fn: 4 - tn: 0 - precision: 0.0000 - recall: 0.0000 - accuracy: 0.0000 - f1-score: 0.0000
take_on.09 tp: 1 - fp: 0 - fn: 0 - tn: 1 - precision: 1.0000 - recall: 1.0000 - accuracy: 1.0000 - f1-score: 1.0000
take_on.21 tp: 0 - fp: 0 - fn: 1 - tn: 0 - precision: 0.0000 - recall: 0.0000 - accuracy: 0.0000 - f1-score: 0.0000
take_out.11 tp: 3 - fp: 1 - fn: 2 - tn: 3 - precision: 0.7500 - recall: 0.6000 - accuracy: 0.5000 - f1-score: 0.6667
take_over.12 tp: 7 - fp: 3 - fn: 8 - tn: 7 - precision: 0.7000 - recall: 0.4667 - accuracy: 0.3889 - f1-score: 0.5600
take_up.13 tp: 0 - fp: 0 - fn: 2 - tn: 0 - precision: 0.0000 - recall: 0.0000 - accuracy: 0.0000 - f1-score: 0.0000
take_up.30 tp: 0 - fp: 0 - fn: 1 - tn: 0 - precision: 0.0000 - recall: 0.0000 - accuracy: 0.0000 - f1-score: 0.0000
take_up.31 tp: 0 - fp: 0 - fn: 1 - tn: 0 - precision: 0.0000 - recall: 0.0000 - accuracy: 0.0000 - f1-score: 0.0000
talk.01    tp: 156 - fp: 4 - fn: 3 - tn: 156 - precision: 0.9750 - recall: 0.9811 - accuracy: 0.9571 - f1-score: 0.9780
tally.01   tp: 0 - fp: 0 - fn: 1 - tn: 0 - precision: 0.0000 - recall: 0.0000 - accuracy: 0.0000 - f1-score: 0.0000
tame.01    tp: 0 - fp: 0 - fn: 1 - tn: 0 - precision: 0.0000 - recall: 0.0000 - accuracy: 0.0000 - f1-score: 0.0000
tamper.01  tp: 0 - fp: 1 - fn: 0 - tn: 0 - precision: 0.0000 - recall: 0.0000 - accuracy: 0.0000 - f1-score: 0.0000
tangle.03  tp: 0 - fp: 0 - fn: 1 - tn: 0 - precision: 0.0000 - recall: 0.0000 - accuracy: 0.0000 - f1-score: 0.0000
tank.01    tp: 0 - fp: 0 - fn: 1 - tn: 0 - precision: 0.0000 - recall: 0.0000 - accuracy: 0.0000 - f1-score: 0.0000
target.01  tp: 5 - fp: 0 - fn: 0 - tn: 5 - precision: 1.0000 - recall: 1.0000 - accuracy: 1.0000 - f1-score: 1.0000
task.01    tp: 8 - fp: 0 - fn: 0 - tn: 8 - precision: 1.0000 - recall: 1.0000 - accuracy: 1.0000 - f1-score: 1.0000
taste.01   tp: 3 - fp: 0 - fn: 1 - tn: 3 - precision: 1.0000 - recall: 0.7500 - accuracy: 0.7500 - f1-score: 0.8571
tax.01     tp: 4 - fp: 2 - fn: 0 - tn: 4 - precision: 0.6667 - recall: 1.0000 - accuracy: 0.6667 - f1-score: 0.8000
teach.01   tp: 10 - fp: 1 - fn: 0 - tn: 10 - precision: 0.9091 - recall: 1.0000 - accuracy: 0.9091 - f1-score: 0.9524
tear.01    tp: 4 - fp: 3 - fn: 1 - tn: 4 - precision: 0.5714 - recall: 0.8000 - accuracy: 0.5000 - f1-score: 0.6666
tear_down.05 tp: 0 - fp: 1 - fn: 0 - tn: 0 - precision: 0.0000 - recall: 0.0000 - accuracy: 0.0000 - f1-score: 0.0000
telecommunicate.01 tp: 0 - fp: 0 - fn: 2 - tn: 0 - precision: 0.0000 - recall: 0.0000 - accuracy: 0.0000 - f1-score: 0.0000
televise.01 tp: 1 - fp: 0 - fn: 0 - tn: 1 - precision: 1.0000 - recall: 1.0000 - accuracy: 1.0000 - f1-score: 1.0000
tell.01    tp: 176 - fp: 5 - fn: 0 - tn: 176 - precision: 0.9724 - recall: 1.0000 - accuracy: 0.9724 - f1-score: 0.9860
tell.02    tp: 1 - fp: 0 - fn: 3 - tn: 1 - precision: 1.0000 - recall: 0.2500 - accuracy: 0.2500 - f1-score: 0.4000
tempt.01   tp: 1 - fp: 1 - fn: 0 - tn: 1 - precision: 0.5000 - recall: 1.0000 - accuracy: 0.5000 - f1-score: 0.6667
tend.01    tp: 0 - fp: 0 - fn: 2 - tn: 0 - precision: 0.0000 - recall: 0.0000 - accuracy: 0.0000 - f1-score: 0.0000
tend.02    tp: 6 - fp: 1 - fn: 0 - tn: 6 - precision: 0.8571 - recall: 1.0000 - accuracy: 0.8571 - f1-score: 0.9231
term.01    tp: 0 - fp: 0 - fn: 1 - tn: 0 - precision: 0.0000 - recall: 0.0000 - accuracy: 0.0000 - f1-score: 0.0000
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turn.02    tp: 5 - fp: 5 - fn: 11 - tn: 5 - precision: 0.5000 - recall: 0.3125 - accuracy: 0.2381 - f1-score: 0.3846
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update.01  tp: 6 - fp: 2 - fn: 0 - tn: 6 - precision: 0.7500 - recall: 1.0000 - accuracy: 0.7500 - f1-score: 0.8571
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violent.01 tp: 14 - fp: 3 - fn: 0 - tn: 14 - precision: 0.8235 - recall: 1.0000 - accuracy: 0.8235 - f1-score: 0.9032
visit.01   tp: 25 - fp: 1 - fn: 1 - tn: 25 - precision: 0.9615 - recall: 0.9615 - accuracy: 0.9259 - f1-score: 0.9615
voice.01   tp: 26 - fp: 2 - fn: 0 - tn: 26 - precision: 0.9286 - recall: 1.0000 - accuracy: 0.9286 - f1-score: 0.9630
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warn.01    tp: 17 - fp: 6 - fn: 0 - tn: 17 - precision: 0.7391 - recall: 1.0000 - accuracy: 0.7391 - f1-score: 0.8500
wash.01    tp: 8 - fp: 4 - fn: 2 - tn: 8 - precision: 0.6667 - recall: 0.8000 - accuracy: 0.5714 - f1-score: 0.7273
wash.02    tp: 0 - fp: 0 - fn: 1 - tn: 0 - precision: 0.0000 - recall: 0.0000 - accuracy: 0.0000 - f1-score: 0.0000
waste.01   tp: 2 - fp: 1 - fn: 2 - tn: 2 - precision: 0.6667 - recall: 0.5000 - accuracy: 0.4000 - f1-score: 0.5714
watch.01   tp: 33 - fp: 3 - fn: 0 - tn: 33 - precision: 0.9167 - recall: 1.0000 - accuracy: 0.9167 - f1-score: 0.9565
watch_out.02 tp: 1 - fp: 0 - fn: 0 - tn: 1 - precision: 1.0000 - recall: 1.0000 - accuracy: 1.0000 - f1-score: 1.0000
wave.01    tp: 2 - fp: 0 - fn: 0 - tn: 2 - precision: 1.0000 - recall: 1.0000 - accuracy: 1.0000 - f1-score: 1.0000
wave.04    tp: 2 - fp: 1 - fn: 0 - tn: 2 - precision: 0.6667 - recall: 1.0000 - accuracy: 0.6667 - f1-score: 0.8000
weaken.01  tp: 2 - fp: 1 - fn: 0 - tn: 2 - precision: 0.6667 - recall: 1.0000 - accuracy: 0.6667 - f1-score: 0.8000
wean.01    tp: 0 - fp: 0 - fn: 1 - tn: 0 - precision: 0.0000 - recall: 0.0000 - accuracy: 0.0000 - f1-score: 0.0000
wear.01    tp: 14 - fp: 0 - fn: 0 - tn: 14 - precision: 1.0000 - recall: 1.0000 - accuracy: 1.0000 - f1-score: 1.0000
wear_out.03 tp: 1 - fp: 0 - fn: 0 - tn: 1 - precision: 1.0000 - recall: 1.0000 - accuracy: 1.0000 - f1-score: 1.0000
weave.01   tp: 1 - fp: 1 - fn: 0 - tn: 1 - precision: 0.5000 - recall: 1.0000 - accuracy: 0.5000 - f1-score: 0.6667
wedge.01   tp: 0 - fp: 0 - fn: 1 - tn: 0 - precision: 0.0000 - recall: 0.0000 - accuracy: 0.0000 - f1-score: 0.0000
weep.01    tp: 0 - fp: 0 - fn: 3 - tn: 0 - precision: 0.0000 - recall: 0.0000 - accuracy: 0.0000 - f1-score: 0.0000
weigh.02   tp: 1 - fp: 0 - fn: 0 - tn: 1 - precision: 1.0000 - recall: 1.0000 - accuracy: 1.0000 - f1-score: 1.0000
weigh_in.03 tp: 1 - fp: 0 - fn: 0 - tn: 1 - precision: 1.0000 - recall: 1.0000 - accuracy: 1.0000 - f1-score: 1.0000
welcome.01 tp: 20 - fp: 4 - fn: 1 - tn: 20 - precision: 0.8333 - recall: 0.9524 - accuracy: 0.8000 - f1-score: 0.8889
wheel.01   tp: 0 - fp: 0 - fn: 1 - tn: 0 - precision: 0.0000 - recall: 0.0000 - accuracy: 0.0000 - f1-score: 0.0000
whip.01    tp: 0 - fp: 0 - fn: 2 - tn: 0 - precision: 0.0000 - recall: 0.0000 - accuracy: 0.0000 - f1-score: 0.0000
whip_out.02 tp: 0 - fp: 0 - fn: 1 - tn: 0 - precision: 0.0000 - recall: 0.0000 - accuracy: 0.0000 - f1-score: 0.0000
widen.01   tp: 2 - fp: 2 - fn: 0 - tn: 2 - precision: 0.5000 - recall: 1.0000 - accuracy: 0.5000 - f1-score: 0.6667
wield.01   tp: 2 - fp: 1 - fn: 0 - tn: 2 - precision: 0.6667 - recall: 1.0000 - accuracy: 0.6667 - f1-score: 0.8000
will.02    tp: 0 - fp: 0 - fn: 1 - tn: 0 - precision: 0.0000 - recall: 0.0000 - accuracy: 0.0000 - f1-score: 0.0000
win.01     tp: 23 - fp: 3 - fn: 0 - tn: 23 - precision: 0.8846 - recall: 1.0000 - accuracy: 0.8846 - f1-score: 0.9388
win_over.02 tp: 0 - fp: 0 - fn: 1 - tn: 0 - precision: 0.0000 - recall: 0.0000 - accuracy: 0.0000 - f1-score: 0.0000
wince.01   tp: 0 - fp: 0 - fn: 1 - tn: 0 - precision: 0.0000 - recall: 0.0000 - accuracy: 0.0000 - f1-score: 0.0000
wind_down.04 tp: 0 - fp: 0 - fn: 2 - tn: 0 - precision: 0.0000 - recall: 0.0000 - accuracy: 0.0000 - f1-score: 0.0000
wind_up.02 tp: 0 - fp: 0 - fn: 1 - tn: 0 - precision: 0.0000 - recall: 0.0000 - accuracy: 0.0000 - f1-score: 0.0000
wipe_out.02 tp: 0 - fp: 2 - fn: 0 - tn: 0 - precision: 0.0000 - recall: 0.0000 - accuracy: 0.0000 - f1-score: 0.0000
wire.01    tp: 0 - fp: 0 - fn: 1 - tn: 0 - precision: 0.0000 - recall: 0.0000 - accuracy: 0.0000 - f1-score: 0.0000
wish.01    tp: 15 - fp: 3 - fn: 0 - tn: 15 - precision: 0.8333 - recall: 1.0000 - accuracy: 0.8333 - f1-score: 0.9091
withdraw.01 tp: 8 - fp: 4 - fn: 0 - tn: 8 - precision: 0.6667 - recall: 1.0000 - accuracy: 0.6667 - f1-score: 0.8000
withdrawal.02 tp: 0 - fp: 0 - fn: 1 - tn: 0 - precision: 0.0000 - recall: 0.0000 - accuracy: 0.0000 - f1-score: 0.0000
withhold.01 tp: 4 - fp: 0 - fn: 1 - tn: 4 - precision: 1.0000 - recall: 0.8000 - accuracy: 0.8000 - f1-score: 0.8889
withstand.01 tp: 1 - fp: 1 - fn: 0 - tn: 1 - precision: 0.5000 - recall: 1.0000 - accuracy: 0.5000 - f1-score: 0.6667
witness.01 tp: 1 - fp: 0 - fn: 0 - tn: 1 - precision: 1.0000 - recall: 1.0000 - accuracy: 1.0000 - f1-score: 1.0000
wonder.01  tp: 10 - fp: 0 - fn: 0 - tn: 10 - precision: 1.0000 - recall: 1.0000 - accuracy: 1.0000 - f1-score: 1.0000
woo.01     tp: 0 - fp: 0 - fn: 1 - tn: 0 - precision: 0.0000 - recall: 0.0000 - accuracy: 0.0000 - f1-score: 0.0000
work.01    tp: 104 - fp: 24 - fn: 9 - tn: 104 - precision: 0.8125 - recall: 0.9204 - accuracy: 0.7591 - f1-score: 0.8631
work.06    tp: 0 - fp: 0 - fn: 1 - tn: 0 - precision: 0.0000 - recall: 0.0000 - accuracy: 0.0000 - f1-score: 0.0000
work.07    tp: 0 - fp: 0 - fn: 1 - tn: 0 - precision: 0.0000 - recall: 0.0000 - accuracy: 0.0000 - f1-score: 0.0000
work.09    tp: 6 - fp: 1 - fn: 9 - tn: 6 - precision: 0.8571 - recall: 0.4000 - accuracy: 0.3750 - f1-score: 0.5454
work.13    tp: 0 - fp: 0 - fn: 1 - tn: 0 - precision: 0.0000 - recall: 0.0000 - accuracy: 0.0000 - f1-score: 0.0000
work_out.02 tp: 3 - fp: 1 - fn: 0 - tn: 3 - precision: 0.7500 - recall: 1.0000 - accuracy: 0.7500 - f1-score: 0.8571
work_out.03 tp: 1 - fp: 0 - fn: 0 - tn: 1 - precision: 1.0000 - recall: 1.0000 - accuracy: 1.0000 - f1-score: 1.0000
worry.01   tp: 2 - fp: 5 - fn: 3 - tn: 2 - precision: 0.2857 - recall: 0.4000 - accuracy: 0.2000 - f1-score: 0.3333
worry.02   tp: 5 - fp: 4 - fn: 1 - tn: 5 - precision: 0.5556 - recall: 0.8333 - accuracy: 0.5000 - f1-score: 0.6667
worsen.01  tp: 2 - fp: 0 - fn: 0 - tn: 2 - precision: 1.0000 - recall: 1.0000 - accuracy: 1.0000 - f1-score: 1.0000
worship.01 tp: 11 - fp: 3 - fn: 0 - tn: 11 - precision: 0.7857 - recall: 1.0000 - accuracy: 0.7857 - f1-score: 0.8800
wound.01   tp: 6 - fp: 0 - fn: 0 - tn: 6 - precision: 1.0000 - recall: 1.0000 - accuracy: 1.0000 - f1-score: 1.0000
wrap.01    tp: 2 - fp: 2 - fn: 1 - tn: 2 - precision: 0.5000 - recall: 0.6667 - accuracy: 0.4000 - f1-score: 0.5714
wrap_up.02 tp: 2 - fp: 3 - fn: 0 - tn: 2 - precision: 0.4000 - recall: 1.0000 - accuracy: 0.4000 - f1-score: 0.5714
wreak.01   tp: 0 - fp: 0 - fn: 1 - tn: 0 - precision: 0.0000 - recall: 0.0000 - accuracy: 0.0000 - f1-score: 0.0000
wreck.01   tp: 1 - fp: 1 - fn: 0 - tn: 1 - precision: 0.5000 - recall: 1.0000 - accuracy: 0.5000 - f1-score: 0.6667
wrestle.01 tp: 0 - fp: 0 - fn: 2 - tn: 0 - precision: 0.0000 - recall: 0.0000 - accuracy: 0.0000 - f1-score: 0.0000
write.01   tp: 62 - fp: 9 - fn: 0 - tn: 62 - precision: 0.8732 - recall: 1.0000 - accuracy: 0.8732 - f1-score: 0.9323
write_down.03 tp: 1 - fp: 1 - fn: 0 - tn: 1 - precision: 0.5000 - recall: 1.0000 - accuracy: 0.5000 - f1-score: 0.6667
write_up.07 tp: 0 - fp: 0 - fn: 1 - tn: 0 - precision: 0.0000 - recall: 0.0000 - accuracy: 0.0000 - f1-score: 0.0000
yearn.01   tp: 0 - fp: 0 - fn: 1 - tn: 0 - precision: 0.0000 - recall: 0.0000 - accuracy: 0.0000 - f1-score: 0.0000
yield.01   tp: 3 - fp: 1 - fn: 0 - tn: 3 - precision: 0.7500 - recall: 1.0000 - accuracy: 0.7500 - f1-score: 0.8571
yield.02   tp: 0 - fp: 0 - fn: 1 - tn: 0 - precision: 0.0000 - recall: 0.0000 - accuracy: 0.0000 - f1-score: 0.0000
zap.01     tp: 0 - fp: 0 - fn: 1 - tn: 0 - precision: 0.0000 - recall: 0.0000 - accuracy: 0.0000 - f1-score: 0.0000
zip.01     tp: 0 - fp: 0 - fn: 1 - tn: 0 - precision: 0.0000 - recall: 0.0000 - accuracy: 0.0000 - f1-score: 0.0000
zoom.01    tp: 0 - fp: 0 - fn: 1 - tn: 0 - precision: 0.0000 - recall: 0.0000 - accuracy: 0.0000 - f1-score: 0.0000
2019-08-19 22:35:39,659 ----------------------------------------------------------------------------------------------------