GeForce GTX 1080 Ti
[2020-12-14 11:03:38,274][__main__][INFO] - 
exp_id: ptb_xlnet_graph_seed5
random_seed: null
num_workers: 4
batch_size: 32
eval_batch_size: 64
resume: null
amp: true
num_epochs: 60
learning_rate_warmup_steps: 150
learning_rate_patience: 5
learning_rate_cooldown: 0
max_grad_norm: 10
skip_training: false
log_freq: 10
model_spec:
- d_model
- encoder
- use_words
- use_tags
- d_kqv
- d_ff
- word_emb_dropout
- tag_emb_dropout
- relu_dropout
- residual_dropout
- attention_dropout
- num_attn_layers
- num_attn_heads
- decoder
- num_gcn_layers
- d_decoder
- max_sentence_len
path_train: data/02-21.10way.clean
path_val: data/22.auto.clean
path_test: data/23.auto.clean
max_sentence_len: 250
d_model: 2048
encoder: xlnet-large-cased
use_tags: false
use_words: false
d_kqv: 64
d_ff: 2048
word_emb_dropout: 0.2
tag_emb_dropout: 0
relu_dropout: 0.2
residual_dropout: 0
attention_dropout: 0.2
num_attn_layers: 3
num_attn_heads: 8
decoder: graph
num_gcn_layers: 2
learning_rate: 1.0e-05
weight_decay: 0
subbatch_max_tokens: 700

[2020-12-14 11:03:38,275][dataloader][INFO] - Loading constituency trees from /n/fs/pvl-mathqa/attach-juxtapose-parser/data/02-21.10way.clean
[2020-12-14 11:04:49,270][dataloader][INFO] - Loading constituency trees from /n/fs/pvl-mathqa/attach-juxtapose-parser/data/22.auto.clean
[2020-12-14 11:05:38,186][__main__][INFO] - 
Parser(
  (encoder): Encoder(
    (word_embedding): TransformerEmbedding(
      (contextual_embedding): XLNetModel(
        (word_embedding): Embedding(32000, 1024)
        (layer): ModuleList(
          (0): XLNetLayer(
            (rel_attn): XLNetRelativeAttention(
              (layer_norm): LayerNorm((1024,), eps=1e-12, elementwise_affine=True)
              (dropout): Dropout(p=0.1, inplace=False)
            )
            (ff): XLNetFeedForward(
              (layer_norm): LayerNorm((1024,), eps=1e-12, elementwise_affine=True)
              (layer_1): Linear(in_features=1024, out_features=4096, bias=True)
              (layer_2): Linear(in_features=4096, out_features=1024, bias=True)
              (dropout): Dropout(p=0.1, inplace=False)
            )
            (dropout): Dropout(p=0.1, inplace=False)
          )
          (1): XLNetLayer(
            (rel_attn): XLNetRelativeAttention(
              (layer_norm): LayerNorm((1024,), eps=1e-12, elementwise_affine=True)
              (dropout): Dropout(p=0.1, inplace=False)
            )
            (ff): XLNetFeedForward(
              (layer_norm): LayerNorm((1024,), eps=1e-12, elementwise_affine=True)
              (layer_1): Linear(in_features=1024, out_features=4096, bias=True)
              (layer_2): Linear(in_features=4096, out_features=1024, bias=True)
              (dropout): Dropout(p=0.1, inplace=False)
            )
            (dropout): Dropout(p=0.1, inplace=False)
          )
          (2): XLNetLayer(
            (rel_attn): XLNetRelativeAttention(
              (layer_norm): LayerNorm((1024,), eps=1e-12, elementwise_affine=True)
              (dropout): Dropout(p=0.1, inplace=False)
            )
            (ff): XLNetFeedForward(
              (layer_norm): LayerNorm((1024,), eps=1e-12, elementwise_affine=True)
              (layer_1): Linear(in_features=1024, out_features=4096, bias=True)
              (layer_2): Linear(in_features=4096, out_features=1024, bias=True)
              (dropout): Dropout(p=0.1, inplace=False)
            )
            (dropout): Dropout(p=0.1, inplace=False)
          )
          (3): XLNetLayer(
            (rel_attn): XLNetRelativeAttention(
              (layer_norm): LayerNorm((1024,), eps=1e-12, elementwise_affine=True)
              (dropout): Dropout(p=0.1, inplace=False)
            )
            (ff): XLNetFeedForward(
              (layer_norm): LayerNorm((1024,), eps=1e-12, elementwise_affine=True)
              (layer_1): Linear(in_features=1024, out_features=4096, bias=True)
              (layer_2): Linear(in_features=4096, out_features=1024, bias=True)
              (dropout): Dropout(p=0.1, inplace=False)
            )
            (dropout): Dropout(p=0.1, inplace=False)
          )
          (4): XLNetLayer(
            (rel_attn): XLNetRelativeAttention(
              (layer_norm): LayerNorm((1024,), eps=1e-12, elementwise_affine=True)
              (dropout): Dropout(p=0.1, inplace=False)
            )
            (ff): XLNetFeedForward(
              (layer_norm): LayerNorm((1024,), eps=1e-12, elementwise_affine=True)
              (layer_1): Linear(in_features=1024, out_features=4096, bias=True)
              (layer_2): Linear(in_features=4096, out_features=1024, bias=True)
              (dropout): Dropout(p=0.1, inplace=False)
            )
            (dropout): Dropout(p=0.1, inplace=False)
          )
          (5): XLNetLayer(
            (rel_attn): XLNetRelativeAttention(
              (layer_norm): LayerNorm((1024,), eps=1e-12, elementwise_affine=True)
              (dropout): Dropout(p=0.1, inplace=False)
            )
            (ff): XLNetFeedForward(
              (layer_norm): LayerNorm((1024,), eps=1e-12, elementwise_affine=True)
              (layer_1): Linear(in_features=1024, out_features=4096, bias=True)
              (layer_2): Linear(in_features=4096, out_features=1024, bias=True)
              (dropout): Dropout(p=0.1, inplace=False)
            )
            (dropout): Dropout(p=0.1, inplace=False)
          )
          (6): XLNetLayer(
            (rel_attn): XLNetRelativeAttention(
              (layer_norm): LayerNorm((1024,), eps=1e-12, elementwise_affine=True)
              (dropout): Dropout(p=0.1, inplace=False)
            )
            (ff): XLNetFeedForward(
              (layer_norm): LayerNorm((1024,), eps=1e-12, elementwise_affine=True)
              (layer_1): Linear(in_features=1024, out_features=4096, bias=True)
              (layer_2): Linear(in_features=4096, out_features=1024, bias=True)
              (dropout): Dropout(p=0.1, inplace=False)
            )
            (dropout): Dropout(p=0.1, inplace=False)
          )
          (7): XLNetLayer(
            (rel_attn): XLNetRelativeAttention(
              (layer_norm): LayerNorm((1024,), eps=1e-12, elementwise_affine=True)
              (dropout): Dropout(p=0.1, inplace=False)
            )
            (ff): XLNetFeedForward(
              (layer_norm): LayerNorm((1024,), eps=1e-12, elementwise_affine=True)
              (layer_1): Linear(in_features=1024, out_features=4096, bias=True)
              (layer_2): Linear(in_features=4096, out_features=1024, bias=True)
              (dropout): Dropout(p=0.1, inplace=False)
            )
            (dropout): Dropout(p=0.1, inplace=False)
          )
          (8): XLNetLayer(
            (rel_attn): XLNetRelativeAttention(
              (layer_norm): LayerNorm((1024,), eps=1e-12, elementwise_affine=True)
              (dropout): Dropout(p=0.1, inplace=False)
            )
            (ff): XLNetFeedForward(
              (layer_norm): LayerNorm((1024,), eps=1e-12, elementwise_affine=True)
              (layer_1): Linear(in_features=1024, out_features=4096, bias=True)
              (layer_2): Linear(in_features=4096, out_features=1024, bias=True)
              (dropout): Dropout(p=0.1, inplace=False)
            )
            (dropout): Dropout(p=0.1, inplace=False)
          )
          (9): XLNetLayer(
            (rel_attn): XLNetRelativeAttention(
              (layer_norm): LayerNorm((1024,), eps=1e-12, elementwise_affine=True)
              (dropout): Dropout(p=0.1, inplace=False)
            )
            (ff): XLNetFeedForward(
              (layer_norm): LayerNorm((1024,), eps=1e-12, elementwise_affine=True)
              (layer_1): Linear(in_features=1024, out_features=4096, bias=True)
              (layer_2): Linear(in_features=4096, out_features=1024, bias=True)
              (dropout): Dropout(p=0.1, inplace=False)
            )
            (dropout): Dropout(p=0.1, inplace=False)
          )
          (10): XLNetLayer(
            (rel_attn): XLNetRelativeAttention(
              (layer_norm): LayerNorm((1024,), eps=1e-12, elementwise_affine=True)
              (dropout): Dropout(p=0.1, inplace=False)
            )
            (ff): XLNetFeedForward(
              (layer_norm): LayerNorm((1024,), eps=1e-12, elementwise_affine=True)
              (layer_1): Linear(in_features=1024, out_features=4096, bias=True)
              (layer_2): Linear(in_features=4096, out_features=1024, bias=True)
              (dropout): Dropout(p=0.1, inplace=False)
            )
            (dropout): Dropout(p=0.1, inplace=False)
          )
          (11): XLNetLayer(
            (rel_attn): XLNetRelativeAttention(
              (layer_norm): LayerNorm((1024,), eps=1e-12, elementwise_affine=True)
              (dropout): Dropout(p=0.1, inplace=False)
            )
            (ff): XLNetFeedForward(
              (layer_norm): LayerNorm((1024,), eps=1e-12, elementwise_affine=True)
              (layer_1): Linear(in_features=1024, out_features=4096, bias=True)
              (layer_2): Linear(in_features=4096, out_features=1024, bias=True)
              (dropout): Dropout(p=0.1, inplace=False)
            )
            (dropout): Dropout(p=0.1, inplace=False)
          )
          (12): XLNetLayer(
            (rel_attn): XLNetRelativeAttention(
              (layer_norm): LayerNorm((1024,), eps=1e-12, elementwise_affine=True)
              (dropout): Dropout(p=0.1, inplace=False)
            )
            (ff): XLNetFeedForward(
              (layer_norm): LayerNorm((1024,), eps=1e-12, elementwise_affine=True)
              (layer_1): Linear(in_features=1024, out_features=4096, bias=True)
              (layer_2): Linear(in_features=4096, out_features=1024, bias=True)
              (dropout): Dropout(p=0.1, inplace=False)
            )
            (dropout): Dropout(p=0.1, inplace=False)
          )
          (13): XLNetLayer(
            (rel_attn): XLNetRelativeAttention(
              (layer_norm): LayerNorm((1024,), eps=1e-12, elementwise_affine=True)
              (dropout): Dropout(p=0.1, inplace=False)
            )
            (ff): XLNetFeedForward(
              (layer_norm): LayerNorm((1024,), eps=1e-12, elementwise_affine=True)
              (layer_1): Linear(in_features=1024, out_features=4096, bias=True)
              (layer_2): Linear(in_features=4096, out_features=1024, bias=True)
              (dropout): Dropout(p=0.1, inplace=False)
            )
            (dropout): Dropout(p=0.1, inplace=False)
          )
          (14): XLNetLayer(
            (rel_attn): XLNetRelativeAttention(
              (layer_norm): LayerNorm((1024,), eps=1e-12, elementwise_affine=True)
              (dropout): Dropout(p=0.1, inplace=False)
            )
            (ff): XLNetFeedForward(
              (layer_norm): LayerNorm((1024,), eps=1e-12, elementwise_affine=True)
              (layer_1): Linear(in_features=1024, out_features=4096, bias=True)
              (layer_2): Linear(in_features=4096, out_features=1024, bias=True)
              (dropout): Dropout(p=0.1, inplace=False)
            )
            (dropout): Dropout(p=0.1, inplace=False)
          )
          (15): XLNetLayer(
            (rel_attn): XLNetRelativeAttention(
              (layer_norm): LayerNorm((1024,), eps=1e-12, elementwise_affine=True)
              (dropout): Dropout(p=0.1, inplace=False)
            )
            (ff): XLNetFeedForward(
              (layer_norm): LayerNorm((1024,), eps=1e-12, elementwise_affine=True)
              (layer_1): Linear(in_features=1024, out_features=4096, bias=True)
              (layer_2): Linear(in_features=4096, out_features=1024, bias=True)
              (dropout): Dropout(p=0.1, inplace=False)
            )
            (dropout): Dropout(p=0.1, inplace=False)
          )
          (16): XLNetLayer(
            (rel_attn): XLNetRelativeAttention(
              (layer_norm): LayerNorm((1024,), eps=1e-12, elementwise_affine=True)
              (dropout): Dropout(p=0.1, inplace=False)
            )
            (ff): XLNetFeedForward(
              (layer_norm): LayerNorm((1024,), eps=1e-12, elementwise_affine=True)
              (layer_1): Linear(in_features=1024, out_features=4096, bias=True)
              (layer_2): Linear(in_features=4096, out_features=1024, bias=True)
              (dropout): Dropout(p=0.1, inplace=False)
            )
            (dropout): Dropout(p=0.1, inplace=False)
          )
          (17): XLNetLayer(
            (rel_attn): XLNetRelativeAttention(
              (layer_norm): LayerNorm((1024,), eps=1e-12, elementwise_affine=True)
              (dropout): Dropout(p=0.1, inplace=False)
            )
            (ff): XLNetFeedForward(
              (layer_norm): LayerNorm((1024,), eps=1e-12, elementwise_affine=True)
              (layer_1): Linear(in_features=1024, out_features=4096, bias=True)
              (layer_2): Linear(in_features=4096, out_features=1024, bias=True)
              (dropout): Dropout(p=0.1, inplace=False)
            )
            (dropout): Dropout(p=0.1, inplace=False)
          )
          (18): XLNetLayer(
            (rel_attn): XLNetRelativeAttention(
              (layer_norm): LayerNorm((1024,), eps=1e-12, elementwise_affine=True)
              (dropout): Dropout(p=0.1, inplace=False)
            )
            (ff): XLNetFeedForward(
              (layer_norm): LayerNorm((1024,), eps=1e-12, elementwise_affine=True)
              (layer_1): Linear(in_features=1024, out_features=4096, bias=True)
              (layer_2): Linear(in_features=4096, out_features=1024, bias=True)
              (dropout): Dropout(p=0.1, inplace=False)
            )
            (dropout): Dropout(p=0.1, inplace=False)
          )
          (19): XLNetLayer(
            (rel_attn): XLNetRelativeAttention(
              (layer_norm): LayerNorm((1024,), eps=1e-12, elementwise_affine=True)
              (dropout): Dropout(p=0.1, inplace=False)
            )
            (ff): XLNetFeedForward(
              (layer_norm): LayerNorm((1024,), eps=1e-12, elementwise_affine=True)
              (layer_1): Linear(in_features=1024, out_features=4096, bias=True)
              (layer_2): Linear(in_features=4096, out_features=1024, bias=True)
              (dropout): Dropout(p=0.1, inplace=False)
            )
            (dropout): Dropout(p=0.1, inplace=False)
          )
          (20): XLNetLayer(
            (rel_attn): XLNetRelativeAttention(
              (layer_norm): LayerNorm((1024,), eps=1e-12, elementwise_affine=True)
              (dropout): Dropout(p=0.1, inplace=False)
            )
            (ff): XLNetFeedForward(
              (layer_norm): LayerNorm((1024,), eps=1e-12, elementwise_affine=True)
              (layer_1): Linear(in_features=1024, out_features=4096, bias=True)
              (layer_2): Linear(in_features=4096, out_features=1024, bias=True)
              (dropout): Dropout(p=0.1, inplace=False)
            )
            (dropout): Dropout(p=0.1, inplace=False)
          )
          (21): XLNetLayer(
            (rel_attn): XLNetRelativeAttention(
              (layer_norm): LayerNorm((1024,), eps=1e-12, elementwise_affine=True)
              (dropout): Dropout(p=0.1, inplace=False)
            )
            (ff): XLNetFeedForward(
              (layer_norm): LayerNorm((1024,), eps=1e-12, elementwise_affine=True)
              (layer_1): Linear(in_features=1024, out_features=4096, bias=True)
              (layer_2): Linear(in_features=4096, out_features=1024, bias=True)
              (dropout): Dropout(p=0.1, inplace=False)
            )
            (dropout): Dropout(p=0.1, inplace=False)
          )
          (22): XLNetLayer(
            (rel_attn): XLNetRelativeAttention(
              (layer_norm): LayerNorm((1024,), eps=1e-12, elementwise_affine=True)
              (dropout): Dropout(p=0.1, inplace=False)
            )
            (ff): XLNetFeedForward(
              (layer_norm): LayerNorm((1024,), eps=1e-12, elementwise_affine=True)
              (layer_1): Linear(in_features=1024, out_features=4096, bias=True)
              (layer_2): Linear(in_features=4096, out_features=1024, bias=True)
              (dropout): Dropout(p=0.1, inplace=False)
            )
            (dropout): Dropout(p=0.1, inplace=False)
          )
          (23): XLNetLayer(
            (rel_attn): XLNetRelativeAttention(
              (layer_norm): LayerNorm((1024,), eps=1e-12, elementwise_affine=True)
              (dropout): Dropout(p=0.1, inplace=False)
            )
            (ff): XLNetFeedForward(
              (layer_norm): LayerNorm((1024,), eps=1e-12, elementwise_affine=True)
              (layer_1): Linear(in_features=1024, out_features=4096, bias=True)
              (layer_2): Linear(in_features=4096, out_features=1024, bias=True)
              (dropout): Dropout(p=0.1, inplace=False)
            )
            (dropout): Dropout(p=0.1, inplace=False)
          )
        )
        (dropout): Dropout(p=0.1, inplace=False)
      )
      (linear): Linear(in_features=1024, out_features=1024, bias=False)
    )
    (word_dropout): FeatureDropout()
    (layer_norm): LayerNorm((2048,), eps=1e-05, elementwise_affine=True)
    (attn_0): MultiHeadAttention(
      (attention): ScaledDotProductAttention(
        (dropout): Dropout(p=0.2, inplace=False)
        (softmax): Softmax(dim=-1)
      )
      (layer_norm): LayerNormalization()
      (proj1): Linear(in_features=256, out_features=1024, bias=False)
      (proj2): Linear(in_features=256, out_features=1024, bias=False)
      (residual_dropout): FeatureDropout()
    )
    (feedforward_0): PartitionedPositionwiseFeedForward(
      (w_1c): Linear(in_features=1024, out_features=1024, bias=True)
      (w_1p): Linear(in_features=1024, out_features=1024, bias=True)
      (w_2c): Linear(in_features=1024, out_features=1024, bias=True)
      (w_2p): Linear(in_features=1024, out_features=1024, bias=True)
      (layer_norm): LayerNormalization()
      (relu_dropout): FeatureDropout()
      (residual_dropout): FeatureDropout()
      (relu): ReLU()
    )
    (attn_1): MultiHeadAttention(
      (attention): ScaledDotProductAttention(
        (dropout): Dropout(p=0.2, inplace=False)
        (softmax): Softmax(dim=-1)
      )
      (layer_norm): LayerNormalization()
      (proj1): Linear(in_features=256, out_features=1024, bias=False)
      (proj2): Linear(in_features=256, out_features=1024, bias=False)
      (residual_dropout): FeatureDropout()
    )
    (feedforward_1): PartitionedPositionwiseFeedForward(
      (w_1c): Linear(in_features=1024, out_features=1024, bias=True)
      (w_1p): Linear(in_features=1024, out_features=1024, bias=True)
      (w_2c): Linear(in_features=1024, out_features=1024, bias=True)
      (w_2p): Linear(in_features=1024, out_features=1024, bias=True)
      (layer_norm): LayerNormalization()
      (relu_dropout): FeatureDropout()
      (residual_dropout): FeatureDropout()
      (relu): ReLU()
    )
    (attn_2): MultiHeadAttention(
      (attention): ScaledDotProductAttention(
        (dropout): Dropout(p=0.2, inplace=False)
        (softmax): Softmax(dim=-1)
      )
      (layer_norm): LayerNormalization()
      (proj1): Linear(in_features=256, out_features=1024, bias=False)
      (proj2): Linear(in_features=256, out_features=1024, bias=False)
      (residual_dropout): FeatureDropout()
    )
    (feedforward_2): PartitionedPositionwiseFeedForward(
      (w_1c): Linear(in_features=1024, out_features=1024, bias=True)
      (w_1p): Linear(in_features=1024, out_features=1024, bias=True)
      (w_2c): Linear(in_features=1024, out_features=1024, bias=True)
      (w_2p): Linear(in_features=1024, out_features=1024, bias=True)
      (layer_norm): LayerNormalization()
      (relu_dropout): FeatureDropout()
      (residual_dropout): FeatureDropout()
      (relu): ReLU()
    )
  )
  (decoder): GraphDecoder(
    (label_embedding): Embedding(113, 1024)
    (graph_embedding): GraphNeuralNetwork(
      (conv_0): SeparateGCNConv(
        (linear_c): Linear(in_features=1024, out_features=1024, bias=True)
        (linear_p): Linear(in_features=1024, out_features=1024, bias=True)
      )
      (layernorm_0): LayerNorm((2048,), eps=1e-05, elementwise_affine=True)
      (conv_1): SeparateGCNConv(
        (linear_c): Linear(in_features=1024, out_features=1024, bias=True)
        (linear_p): Linear(in_features=1024, out_features=1024, bias=True)
      )
      (layernorm_1): LayerNorm((2048,), eps=1e-05, elementwise_affine=True)
    )
    (action_decoder): ActionDecoder(
      (attn_layers_c): Sequential(
        (0): Linear(in_features=2048, out_features=512, bias=True)
        (1): LayerNorm((512,), eps=1e-05, elementwise_affine=True)
        (2): ReLU()
        (3): Linear(in_features=512, out_features=1, bias=True)
      )
      (attn_layers_p): Sequential(
        (0): Linear(in_features=2048, out_features=512, bias=True)
        (1): LayerNorm((512,), eps=1e-05, elementwise_affine=True)
        (2): ReLU()
        (3): Linear(in_features=512, out_features=1, bias=True)
      )
      (labels_layers): Sequential(
        (0): Linear(in_features=4096, out_features=1024, bias=True)
        (1): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)
        (2): ReLU()
        (3): Linear(in_features=1024, out_features=226, bias=True)
      )
    )
  )
)
[2020-12-14 11:05:38,191][__main__][INFO] - #parameters = 391343332
[2020-12-14 11:05:38,197][__main__][INFO] - Epoch #0
[2020-12-14 11:05:38,197][__main__][INFO] - Training..
[2020-12-14 11:05:45,681][__main__][INFO] - Learning rate adjusted to 0.00000000
[2020-12-14 11:05:47,918][__main__][INFO] - Learning rate adjusted to 0.00000007
[2020-12-14 11:05:50,095][__main__][INFO] - Learning rate adjusted to 0.00000013
[2020-12-14 11:05:53,374][__main__][INFO] - Learning rate adjusted to 0.00000020
[2020-12-14 11:05:55,646][__main__][INFO] - Learning rate adjusted to 0.00000027
[2020-12-14 11:05:58,931][__main__][INFO] - Learning rate adjusted to 0.00000033
[2020-12-14 11:06:01,796][__main__][INFO] - Learning rate adjusted to 0.00000040
[2020-12-14 11:06:03,723][__main__][INFO] - Learning rate adjusted to 0.00000047
[2020-12-14 11:06:05,949][__main__][INFO] - Learning rate adjusted to 0.00000053
[2020-12-14 11:06:08,652][__main__][INFO] - Learning rate adjusted to 0.00000060
[2020-12-14 11:06:08,850][__main__][INFO] - [320] Loss: 210.858, Running accuracy: 0.080, Time: 25.88
[2020-12-14 11:06:10,945][__main__][INFO] - Learning rate adjusted to 0.00000067
[2020-12-14 11:06:12,957][__main__][INFO] - Learning rate adjusted to 0.00000073
[2020-12-14 11:06:15,805][__main__][INFO] - Learning rate adjusted to 0.00000080
[2020-12-14 11:06:17,884][__main__][INFO] - Learning rate adjusted to 0.00000087
[2020-12-14 11:06:20,035][__main__][INFO] - Learning rate adjusted to 0.00000093
[2020-12-14 11:06:22,403][__main__][INFO] - Learning rate adjusted to 0.00000100
[2020-12-14 11:06:24,602][__main__][INFO] - Learning rate adjusted to 0.00000107
[2020-12-14 11:06:26,492][__main__][INFO] - Learning rate adjusted to 0.00000113
[2020-12-14 11:06:29,479][__main__][INFO] - Learning rate adjusted to 0.00000120
[2020-12-14 11:06:31,339][__main__][INFO] - Learning rate adjusted to 0.00000127
[2020-12-14 11:06:31,541][__main__][INFO] - [640] Loss: 192.139, Running accuracy: 2.948, Time: 22.69
[2020-12-14 11:06:33,413][__main__][INFO] - Learning rate adjusted to 0.00000133
[2020-12-14 11:06:35,639][__main__][INFO] - Learning rate adjusted to 0.00000140
[2020-12-14 11:06:37,986][__main__][INFO] - Learning rate adjusted to 0.00000147
[2020-12-14 11:06:40,163][__main__][INFO] - Learning rate adjusted to 0.00000153
[2020-12-14 11:06:42,296][__main__][INFO] - Learning rate adjusted to 0.00000160
[2020-12-14 11:06:44,404][__main__][INFO] - Learning rate adjusted to 0.00000167
[2020-12-14 11:06:46,653][__main__][INFO] - Learning rate adjusted to 0.00000173
[2020-12-14 11:06:48,677][__main__][INFO] - Learning rate adjusted to 0.00000180
[2020-12-14 11:06:51,421][__main__][INFO] - Learning rate adjusted to 0.00000187
[2020-12-14 11:06:53,478][__main__][INFO] - Learning rate adjusted to 0.00000193
[2020-12-14 11:06:53,732][__main__][INFO] - [960] Loss: 160.157, Running accuracy: 10.826, Time: 22.19
[2020-12-14 11:06:55,672][__main__][INFO] - Learning rate adjusted to 0.00000200
[2020-12-14 11:06:58,278][__main__][INFO] - Learning rate adjusted to 0.00000207
[2020-12-14 11:07:01,289][__main__][INFO] - Learning rate adjusted to 0.00000213
[2020-12-14 11:07:03,806][__main__][INFO] - Learning rate adjusted to 0.00000220
[2020-12-14 11:07:06,124][__main__][INFO] - Learning rate adjusted to 0.00000227
[2020-12-14 11:07:08,294][__main__][INFO] - Learning rate adjusted to 0.00000233
[2020-12-14 11:07:10,268][__main__][INFO] - Learning rate adjusted to 0.00000240
[2020-12-14 11:07:12,986][__main__][INFO] - Learning rate adjusted to 0.00000247
[2020-12-14 11:07:16,003][__main__][INFO] - Learning rate adjusted to 0.00000253
[2020-12-14 11:07:18,130][__main__][INFO] - Learning rate adjusted to 0.00000260
[2020-12-14 11:07:18,387][__main__][INFO] - [1280] Loss: 135.499, Running accuracy: 16.562, Time: 24.65
[2020-12-14 11:07:20,207][__main__][INFO] - Learning rate adjusted to 0.00000267
[2020-12-14 11:07:22,269][__main__][INFO] - Learning rate adjusted to 0.00000273
[2020-12-14 11:07:24,548][__main__][INFO] - Learning rate adjusted to 0.00000280
[2020-12-14 11:07:26,881][__main__][INFO] - Learning rate adjusted to 0.00000287
[2020-12-14 11:07:30,201][__main__][INFO] - Learning rate adjusted to 0.00000293
[2020-12-14 11:07:32,275][__main__][INFO] - Learning rate adjusted to 0.00000300
[2020-12-14 11:07:34,398][__main__][INFO] - Learning rate adjusted to 0.00000307
[2020-12-14 11:07:36,500][__main__][INFO] - Learning rate adjusted to 0.00000313
[2020-12-14 11:07:38,426][__main__][INFO] - Learning rate adjusted to 0.00000320
[2020-12-14 11:07:41,132][__main__][INFO] - Learning rate adjusted to 0.00000327
[2020-12-14 11:07:41,340][__main__][INFO] - [1600] Loss: 106.214, Running accuracy: 19.859, Time: 22.95
[2020-12-14 11:07:43,427][__main__][INFO] - Learning rate adjusted to 0.00000333
[2020-12-14 11:07:45,728][__main__][INFO] - Learning rate adjusted to 0.00000340
[2020-12-14 11:07:50,206][__main__][INFO] - Learning rate adjusted to 0.00000347
[2020-12-14 11:07:52,356][__main__][INFO] - Learning rate adjusted to 0.00000353
[2020-12-14 11:07:54,495][__main__][INFO] - Learning rate adjusted to 0.00000360
[2020-12-14 11:07:57,345][__main__][INFO] - Learning rate adjusted to 0.00000367
[2020-12-14 11:07:59,412][__main__][INFO] - Learning rate adjusted to 0.00000373
[2020-12-14 11:08:01,665][__main__][INFO] - Learning rate adjusted to 0.00000380
[2020-12-14 11:08:03,990][__main__][INFO] - Learning rate adjusted to 0.00000387
[2020-12-14 11:08:06,753][__main__][INFO] - Learning rate adjusted to 0.00000393
[2020-12-14 11:08:06,964][__main__][INFO] - [1920] Loss: 91.985, Running accuracy: 22.343, Time: 25.62
[2020-12-14 11:08:08,980][__main__][INFO] - Learning rate adjusted to 0.00000400
[2020-12-14 11:08:11,021][__main__][INFO] - Learning rate adjusted to 0.00000407
[2020-12-14 11:08:13,848][__main__][INFO] - Learning rate adjusted to 0.00000413
[2020-12-14 11:08:15,859][__main__][INFO] - Learning rate adjusted to 0.00000420
[2020-12-14 11:08:18,511][__main__][INFO] - Learning rate adjusted to 0.00000427
[2020-12-14 11:08:20,809][__main__][INFO] - Learning rate adjusted to 0.00000433
[2020-12-14 11:08:22,740][__main__][INFO] - Learning rate adjusted to 0.00000440
[2020-12-14 11:08:24,979][__main__][INFO] - Learning rate adjusted to 0.00000447
[2020-12-14 11:08:27,495][__main__][INFO] - Learning rate adjusted to 0.00000453
[2020-12-14 11:08:30,626][__main__][INFO] - Learning rate adjusted to 0.00000460
[2020-12-14 11:08:30,846][__main__][INFO] - [2240] Loss: 86.218, Running accuracy: 24.730, Time: 23.88
[2020-12-14 11:08:33,639][__main__][INFO] - Learning rate adjusted to 0.00000467
[2020-12-14 11:08:35,738][__main__][INFO] - Learning rate adjusted to 0.00000473
[2020-12-14 11:08:37,752][__main__][INFO] - Learning rate adjusted to 0.00000480
[2020-12-14 11:08:40,551][__main__][INFO] - Learning rate adjusted to 0.00000487
[2020-12-14 11:08:43,427][__main__][INFO] - Learning rate adjusted to 0.00000493
[2020-12-14 11:08:45,700][__main__][INFO] - Learning rate adjusted to 0.00000500
[2020-12-14 11:08:47,856][__main__][INFO] - Learning rate adjusted to 0.00000507
[2020-12-14 11:08:50,332][__main__][INFO] - Learning rate adjusted to 0.00000513
[2020-12-14 11:08:52,361][__main__][INFO] - Learning rate adjusted to 0.00000520
[2020-12-14 11:08:55,200][__main__][INFO] - Learning rate adjusted to 0.00000527
[2020-12-14 11:08:55,436][__main__][INFO] - [2560] Loss: 76.387, Running accuracy: 26.720, Time: 24.59
[2020-12-14 11:08:56,895][__main__][INFO] - Learning rate adjusted to 0.00000533
[2020-12-14 11:08:59,246][__main__][INFO] - Learning rate adjusted to 0.00000540
[2020-12-14 11:09:01,843][__main__][INFO] - Learning rate adjusted to 0.00000547
[2020-12-14 11:09:04,144][__main__][INFO] - Learning rate adjusted to 0.00000553
[2020-12-14 11:09:06,385][__main__][INFO] - Learning rate adjusted to 0.00000560
[2020-12-14 11:09:08,557][__main__][INFO] - Learning rate adjusted to 0.00000567
[2020-12-14 11:09:10,874][__main__][INFO] - Learning rate adjusted to 0.00000573
[2020-12-14 11:09:12,996][__main__][INFO] - Learning rate adjusted to 0.00000580
[2020-12-14 11:09:15,061][__main__][INFO] - Learning rate adjusted to 0.00000587
[2020-12-14 11:09:17,840][__main__][INFO] - Learning rate adjusted to 0.00000593
[2020-12-14 11:09:18,028][__main__][INFO] - [2880] Loss: 73.701, Running accuracy: 28.383, Time: 22.59
[2020-12-14 11:09:20,534][__main__][INFO] - Learning rate adjusted to 0.00000600
[2020-12-14 11:09:22,514][__main__][INFO] - Learning rate adjusted to 0.00000607
[2020-12-14 11:09:25,384][__main__][INFO] - Learning rate adjusted to 0.00000613
[2020-12-14 11:09:28,623][__main__][INFO] - Learning rate adjusted to 0.00000620
[2020-12-14 11:09:30,703][__main__][INFO] - Learning rate adjusted to 0.00000627
[2020-12-14 11:09:33,549][__main__][INFO] - Learning rate adjusted to 0.00000633
[2020-12-14 11:09:35,592][__main__][INFO] - Learning rate adjusted to 0.00000640
[2020-12-14 11:09:38,439][__main__][INFO] - Learning rate adjusted to 0.00000647
[2020-12-14 11:09:40,760][__main__][INFO] - Learning rate adjusted to 0.00000653
[2020-12-14 11:09:43,798][__main__][INFO] - Learning rate adjusted to 0.00000660
[2020-12-14 11:09:43,990][__main__][INFO] - [3200] Loss: 73.499, Running accuracy: 29.950, Time: 25.96
[2020-12-14 11:09:46,402][__main__][INFO] - Learning rate adjusted to 0.00000667
[2020-12-14 11:09:48,590][__main__][INFO] - Learning rate adjusted to 0.00000673
[2020-12-14 11:09:50,436][__main__][INFO] - Learning rate adjusted to 0.00000680
[2020-12-14 11:09:52,480][__main__][INFO] - Learning rate adjusted to 0.00000687
[2020-12-14 11:09:54,852][__main__][INFO] - Learning rate adjusted to 0.00000693
[2020-12-14 11:09:56,849][__main__][INFO] - Learning rate adjusted to 0.00000700
[2020-12-14 11:09:59,139][__main__][INFO] - Learning rate adjusted to 0.00000707
[2020-12-14 11:10:02,113][__main__][INFO] - Learning rate adjusted to 0.00000713
[2020-12-14 11:10:04,888][__main__][INFO] - Learning rate adjusted to 0.00000720
[2020-12-14 11:10:07,046][__main__][INFO] - Learning rate adjusted to 0.00000727
[2020-12-14 11:10:07,286][__main__][INFO] - [3520] Loss: 65.629, Running accuracy: 31.137, Time: 23.30
[2020-12-14 11:10:09,219][__main__][INFO] - Learning rate adjusted to 0.00000733
[2020-12-14 11:10:11,403][__main__][INFO] - Learning rate adjusted to 0.00000740
[2020-12-14 11:10:13,617][__main__][INFO] - Learning rate adjusted to 0.00000747
[2020-12-14 11:10:15,829][__main__][INFO] - Learning rate adjusted to 0.00000753
[2020-12-14 11:10:18,270][__main__][INFO] - Learning rate adjusted to 0.00000760
[2020-12-14 11:10:20,378][__main__][INFO] - Learning rate adjusted to 0.00000767
[2020-12-14 11:10:23,154][__main__][INFO] - Learning rate adjusted to 0.00000773
[2020-12-14 11:10:26,208][__main__][INFO] - Learning rate adjusted to 0.00000780
[2020-12-14 11:10:28,159][__main__][INFO] - Learning rate adjusted to 0.00000787
[2020-12-14 11:10:30,258][__main__][INFO] - Learning rate adjusted to 0.00000793
[2020-12-14 11:10:30,467][__main__][INFO] - [3840] Loss: 70.759, Running accuracy: 32.406, Time: 23.18
[2020-12-14 11:10:32,582][__main__][INFO] - Learning rate adjusted to 0.00000800
[2020-12-14 11:10:34,637][__main__][INFO] - Learning rate adjusted to 0.00000807
[2020-12-14 11:10:36,882][__main__][INFO] - Learning rate adjusted to 0.00000813
[2020-12-14 11:10:39,185][__main__][INFO] - Learning rate adjusted to 0.00000820
[2020-12-14 11:10:41,108][__main__][INFO] - Learning rate adjusted to 0.00000827
[2020-12-14 11:10:43,091][__main__][INFO] - Learning rate adjusted to 0.00000833
[2020-12-14 11:10:45,335][__main__][INFO] - Learning rate adjusted to 0.00000840
[2020-12-14 11:10:47,494][__main__][INFO] - Learning rate adjusted to 0.00000847
[2020-12-14 11:10:50,906][__main__][INFO] - Learning rate adjusted to 0.00000853
[2020-12-14 11:10:53,161][__main__][INFO] - Learning rate adjusted to 0.00000860
[2020-12-14 11:10:53,433][__main__][INFO] - [4160] Loss: 69.553, Running accuracy: 33.387, Time: 22.97
[2020-12-14 11:10:56,296][__main__][INFO] - Learning rate adjusted to 0.00000867
[2020-12-14 11:11:03,247][__main__][INFO] - Learning rate adjusted to 0.00000873
[2020-12-14 11:11:05,313][__main__][INFO] - Learning rate adjusted to 0.00000880
[2020-12-14 11:11:08,261][__main__][INFO] - Learning rate adjusted to 0.00000887
[2020-12-14 11:11:10,494][__main__][INFO] - Learning rate adjusted to 0.00000893
[2020-12-14 11:11:12,622][__main__][INFO] - Learning rate adjusted to 0.00000900
[2020-12-14 11:11:14,784][__main__][INFO] - Learning rate adjusted to 0.00000907
[2020-12-14 11:11:16,988][__main__][INFO] - Learning rate adjusted to 0.00000913
[2020-12-14 11:11:19,073][__main__][INFO] - Learning rate adjusted to 0.00000920
[2020-12-14 11:11:21,395][__main__][INFO] - Learning rate adjusted to 0.00000927
[2020-12-14 11:11:21,647][__main__][INFO] - [4480] Loss: 65.625, Running accuracy: 34.337, Time: 28.21
[2020-12-14 11:11:23,671][__main__][INFO] - Learning rate adjusted to 0.00000933
[2020-12-14 11:11:25,668][__main__][INFO] - Learning rate adjusted to 0.00000940
[2020-12-14 11:11:27,774][__main__][INFO] - Learning rate adjusted to 0.00000947
[2020-12-14 11:11:30,589][__main__][INFO] - Learning rate adjusted to 0.00000953
[2020-12-14 11:11:32,660][__main__][INFO] - Learning rate adjusted to 0.00000960
[2020-12-14 11:11:34,887][__main__][INFO] - Learning rate adjusted to 0.00000967
[2020-12-14 11:11:36,992][__main__][INFO] - Learning rate adjusted to 0.00000973
[2020-12-14 11:11:39,040][__main__][INFO] - Learning rate adjusted to 0.00000980
[2020-12-14 11:11:41,126][__main__][INFO] - Learning rate adjusted to 0.00000987
[2020-12-14 11:11:43,076][__main__][INFO] - Learning rate adjusted to 0.00000993
[2020-12-14 11:11:43,313][__main__][INFO] - [4800] Loss: 60.701, Running accuracy: 35.294, Time: 21.67
[2020-12-14 11:11:45,039][__main__][INFO] - Learning rate adjusted to 0.00001000
[2020-12-14 11:12:04,969][__main__][INFO] - [5120] Loss: 56.687, Running accuracy: 36.328, Time: 21.66
[2020-12-14 11:12:28,194][__main__][INFO] - [5440] Loss: 53.132, Running accuracy: 37.439, Time: 23.22
[2020-12-14 11:12:51,225][__main__][INFO] - [5760] Loss: 48.846, Running accuracy: 38.621, Time: 23.03
[2020-12-14 11:13:14,601][__main__][INFO] - [6080] Loss: 43.953, Running accuracy: 39.795, Time: 23.37
[2020-12-14 11:13:38,747][__main__][INFO] - [6400] Loss: 41.140, Running accuracy: 41.045, Time: 24.15
[2020-12-14 11:14:04,545][__main__][INFO] - [6720] Loss: 38.765, Running accuracy: 42.301, Time: 25.80
[2020-12-14 11:14:29,466][__main__][INFO] - [7040] Loss: 35.040, Running accuracy: 43.519, Time: 24.92
[2020-12-14 11:14:52,615][__main__][INFO] - [7360] Loss: 31.947, Running accuracy: 44.658, Time: 23.15
[2020-12-14 11:15:16,898][__main__][INFO] - [7680] Loss: 30.296, Running accuracy: 45.768, Time: 24.28
[2020-12-14 11:15:42,081][__main__][INFO] - [8000] Loss: 31.411, Running accuracy: 46.869, Time: 25.18
[2020-12-14 11:16:04,540][__main__][INFO] - [8320] Loss: 28.486, Running accuracy: 47.952, Time: 22.45
[2020-12-14 11:16:28,052][__main__][INFO] - [8640] Loss: 26.059, Running accuracy: 48.963, Time: 23.51
[2020-12-14 11:16:55,428][__main__][INFO] - [8960] Loss: 24.652, Running accuracy: 49.976, Time: 27.37
[2020-12-14 11:17:16,361][__main__][INFO] - [9280] Loss: 21.881, Running accuracy: 50.917, Time: 20.93
[2020-12-14 11:17:40,679][__main__][INFO] - [9600] Loss: 23.734, Running accuracy: 51.815, Time: 24.32
[2020-12-14 11:18:05,141][__main__][INFO] - [9920] Loss: 23.047, Running accuracy: 52.727, Time: 24.43
[2020-12-14 11:18:28,736][__main__][INFO] - [10240] Loss: 21.818, Running accuracy: 53.597, Time: 23.59
[2020-12-14 11:18:52,040][__main__][INFO] - [10560] Loss: 20.051, Running accuracy: 54.425, Time: 23.30
[2020-12-14 11:19:15,458][__main__][INFO] - [10880] Loss: 19.793, Running accuracy: 55.211, Time: 23.42
[2020-12-14 11:19:37,631][__main__][INFO] - [11200] Loss: 17.741, Running accuracy: 55.982, Time: 22.17
[2020-12-14 11:20:00,190][__main__][INFO] - [11520] Loss: 17.380, Running accuracy: 56.714, Time: 22.56
[2020-12-14 11:20:21,828][__main__][INFO] - [11840] Loss: 17.180, Running accuracy: 57.447, Time: 21.64
[2020-12-14 11:20:45,714][__main__][INFO] - [12160] Loss: 16.238, Running accuracy: 58.166, Time: 23.89
[2020-12-14 11:21:08,377][__main__][INFO] - [12480] Loss: 15.643, Running accuracy: 58.832, Time: 22.66
[2020-12-14 11:21:32,060][__main__][INFO] - [12800] Loss: 17.055, Running accuracy: 59.500, Time: 23.68
[2020-12-14 11:21:56,037][__main__][INFO] - [13120] Loss: 16.681, Running accuracy: 60.131, Time: 23.98
[2020-12-14 11:22:22,010][__main__][INFO] - [13440] Loss: 14.361, Running accuracy: 60.745, Time: 25.97
[2020-12-14 11:22:45,228][__main__][INFO] - [13760] Loss: 16.040, Running accuracy: 61.334, Time: 23.21
[2020-12-14 11:23:10,056][__main__][INFO] - [14080] Loss: 14.662, Running accuracy: 61.928, Time: 24.83
[2020-12-14 11:23:36,428][__main__][INFO] - [14400] Loss: 14.633, Running accuracy: 62.497, Time: 26.37
[2020-12-14 11:24:00,211][__main__][INFO] - [14720] Loss: 13.714, Running accuracy: 63.060, Time: 23.78
[2020-12-14 11:24:21,988][__main__][INFO] - [15040] Loss: 12.265, Running accuracy: 63.606, Time: 21.78
[2020-12-14 11:24:47,569][__main__][INFO] - [15360] Loss: 12.753, Running accuracy: 64.151, Time: 25.58
[2020-12-14 11:25:11,063][__main__][INFO] - [15680] Loss: 12.413, Running accuracy: 64.654, Time: 23.49
[2020-12-14 11:25:34,296][__main__][INFO] - [16000] Loss: 11.674, Running accuracy: 65.129, Time: 23.23
[2020-12-14 11:25:57,648][__main__][INFO] - [16320] Loss: 11.083, Running accuracy: 65.601, Time: 23.35
[2020-12-14 11:26:22,088][__main__][INFO] - [16640] Loss: 12.649, Running accuracy: 66.037, Time: 24.44
[2020-12-14 11:26:45,077][__main__][INFO] - [16960] Loss: 11.223, Running accuracy: 66.487, Time: 22.99
[2020-12-14 11:27:09,233][__main__][INFO] - [17280] Loss: 12.043, Running accuracy: 66.937, Time: 24.15
[2020-12-14 11:27:33,339][__main__][INFO] - [17600] Loss: 11.868, Running accuracy: 67.333, Time: 24.10
[2020-12-14 11:28:01,268][__main__][INFO] - [17920] Loss: 11.324, Running accuracy: 67.737, Time: 27.93
[2020-12-14 11:28:24,343][__main__][INFO] - [18240] Loss: 10.539, Running accuracy: 68.110, Time: 23.07
[2020-12-14 11:28:47,179][__main__][INFO] - [18560] Loss: 10.221, Running accuracy: 68.484, Time: 22.84
[2020-12-14 11:29:10,788][__main__][INFO] - [18880] Loss: 10.913, Running accuracy: 68.845, Time: 23.61
[2020-12-14 11:29:34,101][__main__][INFO] - [19200] Loss: 9.447, Running accuracy: 69.202, Time: 23.31
[2020-12-14 11:29:58,843][__main__][INFO] - [19520] Loss: 10.237, Running accuracy: 69.558, Time: 24.74
[2020-12-14 11:30:23,415][__main__][INFO] - [19840] Loss: 10.243, Running accuracy: 69.906, Time: 24.57
[2020-12-14 11:30:46,567][__main__][INFO] - [20160] Loss: 9.794, Running accuracy: 70.241, Time: 23.15
[2020-12-14 11:31:10,138][__main__][INFO] - [20480] Loss: 10.071, Running accuracy: 70.543, Time: 23.57
[2020-12-14 11:31:32,327][__main__][INFO] - [20800] Loss: 9.377, Running accuracy: 70.854, Time: 21.64
[2020-12-14 11:31:54,890][__main__][INFO] - [21120] Loss: 8.899, Running accuracy: 71.151, Time: 22.56
[2020-12-14 11:32:19,421][__main__][INFO] - [21440] Loss: 9.494, Running accuracy: 71.439, Time: 24.53
[2020-12-14 11:32:42,554][__main__][INFO] - [21760] Loss: 8.951, Running accuracy: 71.734, Time: 23.13
[2020-12-14 11:33:04,835][__main__][INFO] - [22080] Loss: 9.479, Running accuracy: 72.015, Time: 22.28
[2020-12-14 11:33:32,706][__main__][INFO] - [22400] Loss: 9.455, Running accuracy: 72.282, Time: 27.87
[2020-12-14 11:33:56,286][__main__][INFO] - [22720] Loss: 8.953, Running accuracy: 72.551, Time: 23.58
[2020-12-14 11:34:20,740][__main__][INFO] - [23040] Loss: 8.961, Running accuracy: 72.819, Time: 24.45
[2020-12-14 11:34:44,414][__main__][INFO] - [23360] Loss: 9.248, Running accuracy: 73.068, Time: 23.67
[2020-12-14 11:35:06,969][__main__][INFO] - [23680] Loss: 8.254, Running accuracy: 73.313, Time: 22.55
[2020-12-14 11:35:31,045][__main__][INFO] - [24000] Loss: 8.263, Running accuracy: 73.561, Time: 24.07
[2020-12-14 11:35:54,447][__main__][INFO] - [24320] Loss: 8.508, Running accuracy: 73.804, Time: 23.40
[2020-12-14 11:36:18,176][__main__][INFO] - [24640] Loss: 8.371, Running accuracy: 74.042, Time: 23.73
[2020-12-14 11:36:40,783][__main__][INFO] - [24960] Loss: 8.089, Running accuracy: 74.278, Time: 22.61
[2020-12-14 11:37:02,211][__main__][INFO] - [25280] Loss: 8.325, Running accuracy: 74.487, Time: 21.42
[2020-12-14 11:37:25,440][__main__][INFO] - [25600] Loss: 8.189, Running accuracy: 74.700, Time: 23.23
[2020-12-14 11:37:48,058][__main__][INFO] - [25920] Loss: 7.979, Running accuracy: 74.924, Time: 22.62
[2020-12-14 11:38:11,362][__main__][INFO] - [26240] Loss: 7.792, Running accuracy: 75.133, Time: 23.30
[2020-12-14 11:38:36,060][__main__][INFO] - [26560] Loss: 8.177, Running accuracy: 75.343, Time: 24.70
[2020-12-14 11:39:01,782][__main__][INFO] - [26880] Loss: 8.904, Running accuracy: 75.540, Time: 25.72
[2020-12-14 11:39:28,268][__main__][INFO] - [27200] Loss: 7.789, Running accuracy: 75.739, Time: 26.48
[2020-12-14 11:39:51,708][__main__][INFO] - [27520] Loss: 7.786, Running accuracy: 75.933, Time: 23.44
[2020-12-14 11:40:13,940][__main__][INFO] - [27840] Loss: 7.888, Running accuracy: 76.106, Time: 22.23
[2020-12-14 11:40:36,229][__main__][INFO] - [28160] Loss: 7.551, Running accuracy: 76.294, Time: 22.29
[2020-12-14 11:40:59,594][__main__][INFO] - [28480] Loss: 8.414, Running accuracy: 76.472, Time: 23.36
[2020-12-14 11:41:25,024][__main__][INFO] - [28800] Loss: 8.087, Running accuracy: 76.657, Time: 25.43
[2020-12-14 11:41:49,437][__main__][INFO] - [29120] Loss: 7.631, Running accuracy: 76.832, Time: 24.41
[2020-12-14 11:42:12,327][__main__][INFO] - [29440] Loss: 7.205, Running accuracy: 77.000, Time: 22.89
[2020-12-14 11:42:34,605][__main__][INFO] - [29760] Loss: 6.923, Running accuracy: 77.171, Time: 22.28
[2020-12-14 11:42:59,643][__main__][INFO] - [30080] Loss: 7.619, Running accuracy: 77.343, Time: 25.04
[2020-12-14 11:43:22,157][__main__][INFO] - [30400] Loss: 6.650, Running accuracy: 77.509, Time: 22.51
[2020-12-14 11:43:47,900][__main__][INFO] - [30720] Loss: 7.038, Running accuracy: 77.676, Time: 25.74
[2020-12-14 11:44:09,705][__main__][INFO] - [31040] Loss: 6.836, Running accuracy: 77.840, Time: 21.81
[2020-12-14 11:44:34,299][__main__][INFO] - [31360] Loss: 7.363, Running accuracy: 77.988, Time: 24.59
[2020-12-14 11:45:01,222][__main__][INFO] - [31680] Loss: 6.489, Running accuracy: 78.140, Time: 26.92
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[2020-12-14 11:49:29,705][__main__][INFO] - [35200] Loss: 5.869, Running accuracy: 79.633, Time: 25.39
[2020-12-14 11:49:55,291][__main__][INFO] - [35520] Loss: 6.256, Running accuracy: 79.754, Time: 25.58
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[2020-12-14 11:50:48,605][__main__][INFO] - [36160] Loss: 6.761, Running accuracy: 80.003, Time: 29.63
[2020-12-14 11:51:13,088][__main__][INFO] - [36480] Loss: 5.511, Running accuracy: 80.118, Time: 24.48
[2020-12-14 11:51:39,877][__main__][INFO] - [36800] Loss: 6.394, Running accuracy: 80.242, Time: 26.79
[2020-12-14 11:52:05,643][__main__][INFO] - [37120] Loss: 5.814, Running accuracy: 80.362, Time: 25.76
[2020-12-14 11:52:31,057][__main__][INFO] - [37440] Loss: 5.981, Running accuracy: 80.479, Time: 25.41
[2020-12-14 11:52:55,598][__main__][INFO] - [37760] Loss: 5.839, Running accuracy: 80.585, Time: 24.54
[2020-12-14 11:53:20,089][__main__][INFO] - [38080] Loss: 5.954, Running accuracy: 80.694, Time: 24.49
[2020-12-14 11:53:44,524][__main__][INFO] - [38400] Loss: 5.862, Running accuracy: 80.804, Time: 24.44
[2020-12-14 11:54:09,666][__main__][INFO] - [38720] Loss: 6.613, Running accuracy: 80.908, Time: 25.14
[2020-12-14 11:54:33,918][__main__][INFO] - [39040] Loss: 6.315, Running accuracy: 81.014, Time: 24.25
[2020-12-14 11:54:59,378][__main__][INFO] - [39360] Loss: 6.337, Running accuracy: 81.117, Time: 25.46
[2020-12-14 11:55:23,958][__main__][INFO] - [39680] Loss: 6.106, Running accuracy: 81.216, Time: 24.56
[2020-12-14 11:55:33,417][__main__][INFO] - Action accuracy: 81.258, Loss: 26.925
[2020-12-14 11:55:33,418][__main__][INFO] - Validating..
[2020-12-14 11:56:14,746][test][INFO] - Time elapsed: 39.374477
[2020-12-14 11:56:14,750][__main__][INFO] - Validation F1 score: 92.890, Exact match: 42.760, Precision: 93.250, Recall: 92.530
[2020-12-14 11:56:14,750][__main__][INFO] - F1 score has improved
[2020-12-14 11:56:53,857][__main__][INFO] - Checkpoint saved to checkpoints/model_latest.pth
[2020-12-14 11:56:54,806][__main__][INFO] - Epoch #1
[2020-12-14 11:56:54,806][__main__][INFO] - Training..
[2020-12-14 11:57:21,842][__main__][INFO] - [320] Loss: 5.787, Running accuracy: 94.178, Time: 25.76
[2020-12-14 11:57:45,515][__main__][INFO] - [640] Loss: 6.209, Running accuracy: 93.912, Time: 23.67
[2020-12-14 11:58:11,747][__main__][INFO] - [960] Loss: 5.345, Running accuracy: 94.099, Time: 26.23
[2020-12-14 11:58:36,649][__main__][INFO] - [1280] Loss: 5.191, Running accuracy: 94.282, Time: 24.90
[2020-12-14 11:59:01,177][__main__][INFO] - [1600] Loss: 5.106, Running accuracy: 94.438, Time: 24.53
[2020-12-14 11:59:27,236][__main__][INFO] - [1920] Loss: 5.963, Running accuracy: 94.351, Time: 26.06
[2020-12-14 11:59:52,017][__main__][INFO] - [2240] Loss: 5.452, Running accuracy: 94.336, Time: 24.78
[2020-12-14 12:00:15,666][__main__][INFO] - [2560] Loss: 6.070, Running accuracy: 94.245, Time: 23.65
[2020-12-14 12:00:40,158][__main__][INFO] - [2880] Loss: 5.663, Running accuracy: 94.264, Time: 24.49
[2020-12-14 12:01:09,590][__main__][INFO] - [3200] Loss: 5.711, Running accuracy: 94.258, Time: 29.42
[2020-12-14 12:01:35,318][__main__][INFO] - [3520] Loss: 5.042, Running accuracy: 94.280, Time: 25.73
[2020-12-14 12:02:03,101][__main__][INFO] - [3840] Loss: 5.025, Running accuracy: 94.339, Time: 27.78
[2020-12-14 12:02:27,897][__main__][INFO] - [4160] Loss: 5.194, Running accuracy: 94.369, Time: 24.00
[2020-12-14 12:02:51,857][__main__][INFO] - [4480] Loss: 5.568, Running accuracy: 94.372, Time: 23.96
[2020-12-14 12:03:15,201][__main__][INFO] - [4800] Loss: 4.547, Running accuracy: 94.402, Time: 23.34
[2020-12-14 12:03:39,782][__main__][INFO] - [5120] Loss: 5.599, Running accuracy: 94.375, Time: 24.58
[2020-12-14 12:04:05,260][__main__][INFO] - [5440] Loss: 5.270, Running accuracy: 94.383, Time: 25.47
[2020-12-14 12:04:32,309][__main__][INFO] - [5760] Loss: 5.541, Running accuracy: 94.413, Time: 27.05
[2020-12-14 12:04:56,280][__main__][INFO] - [6080] Loss: 5.529, Running accuracy: 94.381, Time: 23.97
[2020-12-14 12:05:21,632][__main__][INFO] - [6400] Loss: 5.518, Running accuracy: 94.396, Time: 25.35
[2020-12-14 12:05:45,721][__main__][INFO] - [6720] Loss: 5.261, Running accuracy: 94.405, Time: 24.09
[2020-12-14 12:06:10,508][__main__][INFO] - [7040] Loss: 5.888, Running accuracy: 94.397, Time: 24.79
[2020-12-14 12:06:34,131][__main__][INFO] - [7360] Loss: 4.588, Running accuracy: 94.433, Time: 23.62
[2020-12-14 12:07:03,427][__main__][INFO] - [7680] Loss: 5.069, Running accuracy: 94.454, Time: 29.29
[2020-12-14 12:07:29,198][__main__][INFO] - [8000] Loss: 4.674, Running accuracy: 94.480, Time: 25.77
[2020-12-14 12:07:54,259][__main__][INFO] - [8320] Loss: 4.667, Running accuracy: 94.498, Time: 25.06
[2020-12-14 12:08:18,723][__main__][INFO] - [8640] Loss: 4.875, Running accuracy: 94.502, Time: 24.46
[2020-12-14 12:08:43,419][__main__][INFO] - [8960] Loss: 5.127, Running accuracy: 94.517, Time: 24.69
[2020-12-14 12:09:09,386][__main__][INFO] - [9280] Loss: 4.798, Running accuracy: 94.518, Time: 25.97
[2020-12-14 12:09:33,848][__main__][INFO] - [9600] Loss: 4.833, Running accuracy: 94.531, Time: 24.46
[2020-12-14 12:10:00,189][__main__][INFO] - [9920] Loss: 5.779, Running accuracy: 94.522, Time: 26.34
[2020-12-14 12:10:24,938][__main__][INFO] - [10240] Loss: 5.055, Running accuracy: 94.528, Time: 24.75
[2020-12-14 12:10:50,635][__main__][INFO] - [10560] Loss: 5.081, Running accuracy: 94.539, Time: 25.70
[2020-12-14 12:11:16,973][__main__][INFO] - [10880] Loss: 5.142, Running accuracy: 94.547, Time: 26.34
[2020-12-14 12:11:43,731][__main__][INFO] - [11200] Loss: 5.172, Running accuracy: 94.552, Time: 26.75
[2020-12-14 12:12:05,871][__main__][INFO] - [11520] Loss: 4.575, Running accuracy: 94.564, Time: 22.14
[2020-12-14 12:12:30,036][__main__][INFO] - [11840] Loss: 4.535, Running accuracy: 94.575, Time: 24.16
[2020-12-14 12:12:59,334][__main__][INFO] - [12160] Loss: 4.714, Running accuracy: 94.584, Time: 29.30
[2020-12-14 12:13:26,325][__main__][INFO] - [12480] Loss: 4.779, Running accuracy: 94.597, Time: 26.99
[2020-12-14 12:13:50,034][__main__][INFO] - [12800] Loss: 4.643, Running accuracy: 94.609, Time: 23.71
[2020-12-14 12:14:15,676][__main__][INFO] - [13120] Loss: 4.970, Running accuracy: 94.624, Time: 25.64
[2020-12-14 12:14:40,589][__main__][INFO] - [13440] Loss: 4.560, Running accuracy: 94.640, Time: 24.91
[2020-12-14 12:15:03,567][__main__][INFO] - [13760] Loss: 4.161, Running accuracy: 94.666, Time: 22.98
[2020-12-14 12:15:28,312][__main__][INFO] - [14080] Loss: 4.603, Running accuracy: 94.674, Time: 24.74
[2020-12-14 12:15:53,253][__main__][INFO] - [14400] Loss: 4.275, Running accuracy: 94.698, Time: 24.94
[2020-12-14 12:16:18,023][__main__][INFO] - [14720] Loss: 4.593, Running accuracy: 94.711, Time: 24.77
[2020-12-14 12:16:41,579][__main__][INFO] - [15040] Loss: 4.722, Running accuracy: 94.715, Time: 23.55
[2020-12-14 12:17:04,587][__main__][INFO] - [15360] Loss: 4.081, Running accuracy: 94.736, Time: 23.01
[2020-12-14 12:17:28,980][__main__][INFO] - [15680] Loss: 4.704, Running accuracy: 94.747, Time: 24.39
[2020-12-14 12:17:52,378][__main__][INFO] - [16000] Loss: 4.635, Running accuracy: 94.757, Time: 23.40
[2020-12-14 12:18:16,136][__main__][INFO] - [16320] Loss: 4.862, Running accuracy: 94.770, Time: 23.76
[2020-12-14 12:18:40,696][__main__][INFO] - [16640] Loss: 4.686, Running accuracy: 94.776, Time: 24.55
[2020-12-14 12:19:07,505][__main__][INFO] - [16960] Loss: 4.165, Running accuracy: 94.786, Time: 26.81
[2020-12-14 12:19:33,341][__main__][INFO] - [17280] Loss: 4.689, Running accuracy: 94.796, Time: 25.84
[2020-12-14 12:19:59,105][__main__][INFO] - [17600] Loss: 4.578, Running accuracy: 94.794, Time: 25.76
[2020-12-14 12:20:24,416][__main__][INFO] - [17920] Loss: 4.746, Running accuracy: 94.801, Time: 25.31
[2020-12-14 12:20:49,553][__main__][INFO] - [18240] Loss: 4.526, Running accuracy: 94.808, Time: 25.14
[2020-12-14 12:21:15,379][__main__][INFO] - [18560] Loss: 5.268, Running accuracy: 94.807, Time: 25.82
[2020-12-14 12:21:40,631][__main__][INFO] - [18880] Loss: 4.651, Running accuracy: 94.808, Time: 25.25
[2020-12-14 12:22:03,702][__main__][INFO] - [19200] Loss: 4.978, Running accuracy: 94.805, Time: 23.07
[2020-12-14 12:22:27,095][__main__][INFO] - [19520] Loss: 3.861, Running accuracy: 94.815, Time: 23.39
[2020-12-14 12:22:54,066][__main__][INFO] - [19840] Loss: 4.462, Running accuracy: 94.824, Time: 26.97
[2020-12-14 12:23:18,571][__main__][INFO] - [20160] Loss: 4.719, Running accuracy: 94.828, Time: 24.50
[2020-12-14 12:23:42,878][__main__][INFO] - [20480] Loss: 4.589, Running accuracy: 94.833, Time: 24.31
[2020-12-14 12:24:06,980][__main__][INFO] - [20800] Loss: 4.327, Running accuracy: 94.843, Time: 24.10
[2020-12-14 12:24:32,161][__main__][INFO] - [21120] Loss: 5.332, Running accuracy: 94.842, Time: 25.18
[2020-12-14 12:24:59,603][__main__][INFO] - [21440] Loss: 4.326, Running accuracy: 94.849, Time: 27.44
[2020-12-14 12:25:23,968][__main__][INFO] - [21760] Loss: 4.642, Running accuracy: 94.854, Time: 24.36
[2020-12-14 12:25:49,095][__main__][INFO] - [22080] Loss: 4.128, Running accuracy: 94.865, Time: 25.13
[2020-12-14 12:26:12,976][__main__][INFO] - [22400] Loss: 3.798, Running accuracy: 94.875, Time: 23.88
[2020-12-14 12:26:36,747][__main__][INFO] - [22720] Loss: 4.554, Running accuracy: 94.881, Time: 23.77
[2020-12-14 12:27:00,805][__main__][INFO] - [23040] Loss: 4.743, Running accuracy: 94.882, Time: 24.06
[2020-12-14 12:27:23,446][__main__][INFO] - [23360] Loss: 4.398, Running accuracy: 94.886, Time: 22.64
[2020-12-14 12:27:48,608][__main__][INFO] - [23680] Loss: 4.936, Running accuracy: 94.887, Time: 25.16
[2020-12-14 12:28:13,377][__main__][INFO] - [24000] Loss: 4.718, Running accuracy: 94.894, Time: 24.77
[2020-12-14 12:28:37,761][__main__][INFO] - [24320] Loss: 4.411, Running accuracy: 94.901, Time: 24.38
[2020-12-14 12:29:02,943][__main__][INFO] - [24640] Loss: 4.281, Running accuracy: 94.905, Time: 25.18
[2020-12-14 12:29:26,906][__main__][INFO] - [24960] Loss: 4.235, Running accuracy: 94.914, Time: 23.96
[2020-12-14 12:29:50,999][__main__][INFO] - [25280] Loss: 4.507, Running accuracy: 94.917, Time: 24.09
[2020-12-14 12:30:16,287][__main__][INFO] - [25600] Loss: 4.422, Running accuracy: 94.923, Time: 25.29
[2020-12-14 12:30:40,775][__main__][INFO] - [25920] Loss: 4.148, Running accuracy: 94.931, Time: 24.49
[2020-12-14 12:31:08,127][__main__][INFO] - [26240] Loss: 4.434, Running accuracy: 94.936, Time: 27.35
[2020-12-14 12:31:34,609][__main__][INFO] - [26560] Loss: 4.130, Running accuracy: 94.945, Time: 26.48
[2020-12-14 12:32:01,721][__main__][INFO] - [26880] Loss: 4.881, Running accuracy: 94.947, Time: 27.11
[2020-12-14 12:32:27,039][__main__][INFO] - [27200] Loss: 4.299, Running accuracy: 94.952, Time: 25.32
[2020-12-14 12:32:52,897][__main__][INFO] - [27520] Loss: 4.709, Running accuracy: 94.950, Time: 25.86
[2020-12-14 12:33:15,964][__main__][INFO] - [27840] Loss: 4.131, Running accuracy: 94.959, Time: 23.06
[2020-12-14 12:33:41,576][__main__][INFO] - [28160] Loss: 4.201, Running accuracy: 94.965, Time: 25.61
[2020-12-14 12:34:05,246][__main__][INFO] - [28480] Loss: 4.184, Running accuracy: 94.971, Time: 23.67
[2020-12-14 12:34:31,462][__main__][INFO] - [28800] Loss: 4.300, Running accuracy: 94.978, Time: 26.21
[2020-12-14 12:34:57,352][__main__][INFO] - [29120] Loss: 4.296, Running accuracy: 94.985, Time: 25.89
[2020-12-14 12:35:21,634][__main__][INFO] - [29440] Loss: 3.950, Running accuracy: 94.990, Time: 24.28
[2020-12-14 12:35:45,594][__main__][INFO] - [29760] Loss: 4.517, Running accuracy: 94.993, Time: 23.96
[2020-12-14 12:36:11,005][__main__][INFO] - [30080] Loss: 4.183, Running accuracy: 95.001, Time: 25.41
[2020-12-14 12:36:36,414][__main__][INFO] - [30400] Loss: 4.094, Running accuracy: 95.006, Time: 25.41
[2020-12-14 12:37:01,911][__main__][INFO] - [30720] Loss: 4.193, Running accuracy: 95.010, Time: 25.50
[2020-12-14 12:37:25,120][__main__][INFO] - [31040] Loss: 4.293, Running accuracy: 95.016, Time: 23.21
[2020-12-14 12:37:50,733][__main__][INFO] - [31360] Loss: 4.171, Running accuracy: 95.023, Time: 25.61
[2020-12-14 12:38:15,343][__main__][INFO] - [31680] Loss: 4.020, Running accuracy: 95.029, Time: 24.61
[2020-12-14 12:38:40,621][__main__][INFO] - [32000] Loss: 4.613, Running accuracy: 95.030, Time: 25.27
[2020-12-14 12:39:04,705][__main__][INFO] - [32320] Loss: 4.164, Running accuracy: 95.037, Time: 24.08
[2020-12-14 12:39:27,863][__main__][INFO] - [32640] Loss: 3.686, Running accuracy: 95.044, Time: 23.16
[2020-12-14 12:39:50,210][__main__][INFO] - [32960] Loss: 3.556, Running accuracy: 95.050, Time: 22.35
[2020-12-14 12:40:16,120][__main__][INFO] - [33280] Loss: 4.202, Running accuracy: 95.059, Time: 25.91
[2020-12-14 12:40:41,119][__main__][INFO] - [33600] Loss: 4.725, Running accuracy: 95.063, Time: 25.00
[2020-12-14 12:41:06,056][__main__][INFO] - [33920] Loss: 4.064, Running accuracy: 95.071, Time: 24.94
[2020-12-14 12:41:33,498][__main__][INFO] - [34240] Loss: 4.875, Running accuracy: 95.072, Time: 27.44
[2020-12-14 12:41:56,620][__main__][INFO] - [34560] Loss: 4.517, Running accuracy: 95.072, Time: 23.12
[2020-12-14 12:42:21,004][__main__][INFO] - [34880] Loss: 4.145, Running accuracy: 95.076, Time: 24.38
[2020-12-14 12:42:48,333][__main__][INFO] - [35200] Loss: 3.945, Running accuracy: 95.079, Time: 27.33
[2020-12-14 12:43:13,161][__main__][INFO] - [35520] Loss: 4.332, Running accuracy: 95.082, Time: 24.83
[2020-12-14 12:43:37,769][__main__][INFO] - [35840] Loss: 3.910, Running accuracy: 95.091, Time: 24.60
[2020-12-14 12:44:00,314][__main__][INFO] - [36160] Loss: 4.050, Running accuracy: 95.094, Time: 22.54
[2020-12-14 12:44:25,578][__main__][INFO] - [36480] Loss: 4.680, Running accuracy: 95.097, Time: 25.26
[2020-12-14 12:44:50,916][__main__][INFO] - [36800] Loss: 4.206, Running accuracy: 95.099, Time: 25.34
[2020-12-14 12:45:14,621][__main__][INFO] - [37120] Loss: 3.811, Running accuracy: 95.108, Time: 23.70
[2020-12-14 12:45:39,655][__main__][INFO] - [37440] Loss: 3.927, Running accuracy: 95.111, Time: 25.03
[2020-12-14 12:46:04,838][__main__][INFO] - [37760] Loss: 4.119, Running accuracy: 95.116, Time: 25.18
[2020-12-14 12:46:27,664][__main__][INFO] - [38080] Loss: 4.120, Running accuracy: 95.119, Time: 22.82
[2020-12-14 12:46:51,640][__main__][INFO] - [38400] Loss: 4.276, Running accuracy: 95.122, Time: 23.97
[2020-12-14 12:47:16,715][__main__][INFO] - [38720] Loss: 4.206, Running accuracy: 95.125, Time: 25.08
[2020-12-14 12:47:42,198][__main__][INFO] - [39040] Loss: 4.446, Running accuracy: 95.126, Time: 25.48
[2020-12-14 12:48:09,889][__main__][INFO] - [39360] Loss: 4.695, Running accuracy: 95.129, Time: 27.68
[2020-12-14 12:48:36,127][__main__][INFO] - [39680] Loss: 3.522, Running accuracy: 95.136, Time: 26.23
[2020-12-14 12:48:45,826][__main__][INFO] - Action accuracy: 95.138, Loss: 5.167
[2020-12-14 12:48:45,827][__main__][INFO] - Validating..
[2020-12-14 12:49:22,555][test][INFO] - Time elapsed: 34.380364
[2020-12-14 12:49:22,559][__main__][INFO] - Validation F1 score: 94.720, Exact match: 50.760, Precision: 94.810, Recall: 94.630
[2020-12-14 12:49:22,559][__main__][INFO] - F1 score has improved
[2020-12-14 12:50:02,186][__main__][INFO] - Checkpoint saved to checkpoints/model_latest.pth
[2020-12-14 12:50:03,266][__main__][INFO] - Epoch #2
[2020-12-14 12:50:03,267][__main__][INFO] - Training..
[2020-12-14 12:50:28,636][__main__][INFO] - [320] Loss: 3.774, Running accuracy: 96.002, Time: 24.05
[2020-12-14 12:50:54,535][__main__][INFO] - [640] Loss: 3.758, Running accuracy: 95.942, Time: 25.90
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[2020-12-14 13:37:06,031][__main__][INFO] - [36160] Loss: 3.217, Running accuracy: 96.323, Time: 26.05
[2020-12-14 13:37:29,941][__main__][INFO] - [36480] Loss: 3.190, Running accuracy: 96.325, Time: 23.91
[2020-12-14 13:37:55,913][__main__][INFO] - [36800] Loss: 3.102, Running accuracy: 96.327, Time: 25.97
[2020-12-14 13:38:21,539][__main__][INFO] - [37120] Loss: 3.025, Running accuracy: 96.329, Time: 25.63
[2020-12-14 13:38:46,818][__main__][INFO] - [37440] Loss: 3.464, Running accuracy: 96.329, Time: 25.28
[2020-12-14 13:39:10,058][__main__][INFO] - [37760] Loss: 3.508, Running accuracy: 96.328, Time: 23.24
[2020-12-14 13:39:35,699][__main__][INFO] - [38080] Loss: 3.189, Running accuracy: 96.328, Time: 25.63
[2020-12-14 13:40:02,821][__main__][INFO] - [38400] Loss: 3.264, Running accuracy: 96.329, Time: 27.12
[2020-12-14 13:40:27,460][__main__][INFO] - [38720] Loss: 3.187, Running accuracy: 96.329, Time: 24.64
[2020-12-14 13:40:50,701][__main__][INFO] - [39040] Loss: 3.214, Running accuracy: 96.328, Time: 23.24
[2020-12-14 13:41:15,034][__main__][INFO] - [39360] Loss: 3.112, Running accuracy: 96.332, Time: 24.33
[2020-12-14 13:41:38,511][__main__][INFO] - [39680] Loss: 3.300, Running accuracy: 96.334, Time: 23.48
[2020-12-14 13:41:49,478][__main__][INFO] - Action accuracy: 96.336, Loss: 3.741
[2020-12-14 13:41:49,480][__main__][INFO] - Validating..
[2020-12-14 13:42:26,642][test][INFO] - Time elapsed: 34.833493
[2020-12-14 13:42:26,647][__main__][INFO] - Validation F1 score: 95.050, Exact match: 52.530, Precision: 95.300, Recall: 94.800
[2020-12-14 13:42:26,647][__main__][INFO] - F1 score has improved
[2020-12-14 13:43:05,056][__main__][INFO] - Checkpoint saved to checkpoints/model_latest.pth
[2020-12-14 13:43:06,139][__main__][INFO] - Epoch #3
[2020-12-14 13:43:06,140][__main__][INFO] - Training..
[2020-12-14 13:43:34,365][__main__][INFO] - [320] Loss: 2.932, Running accuracy: 96.928, Time: 26.90
[2020-12-14 13:44:00,747][__main__][INFO] - [640] Loss: 2.576, Running accuracy: 96.996, Time: 26.38
[2020-12-14 13:44:30,723][__main__][INFO] - [960] Loss: 2.644, Running accuracy: 97.035, Time: 29.97
[2020-12-14 13:44:54,402][__main__][INFO] - [1280] Loss: 2.748, Running accuracy: 97.026, Time: 23.68
[2020-12-14 13:45:19,269][__main__][INFO] - [1600] Loss: 2.887, Running accuracy: 97.006, Time: 24.86
[2020-12-14 13:45:42,984][__main__][INFO] - [1920] Loss: 2.927, Running accuracy: 97.002, Time: 23.71
[2020-12-14 13:46:09,465][__main__][INFO] - [2240] Loss: 3.342, Running accuracy: 96.953, Time: 26.48
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[2020-12-14 13:47:01,030][__main__][INFO] - [2880] Loss: 2.713, Running accuracy: 96.947, Time: 25.29
[2020-12-14 13:47:24,366][__main__][INFO] - [3200] Loss: 2.808, Running accuracy: 96.948, Time: 23.34
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[2020-12-14 13:55:19,765][__main__][INFO] - [9280] Loss: 2.650, Running accuracy: 96.938, Time: 25.36
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[2020-12-14 13:58:17,164][__main__][INFO] - [11520] Loss: 2.598, Running accuracy: 96.950, Time: 25.55
[2020-12-14 13:58:40,836][__main__][INFO] - [11840] Loss: 2.913, Running accuracy: 96.944, Time: 23.67
[2020-12-14 13:59:07,130][__main__][INFO] - [12160] Loss: 2.902, Running accuracy: 96.937, Time: 26.29
[2020-12-14 13:59:29,726][__main__][INFO] - [12480] Loss: 2.569, Running accuracy: 96.940, Time: 22.60
[2020-12-14 13:59:53,417][__main__][INFO] - [12800] Loss: 2.607, Running accuracy: 96.943, Time: 23.69
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[2020-12-14 14:33:28,879][__main__][INFO] - [38720] Loss: 2.536, Running accuracy: 96.975, Time: 25.01
[2020-12-14 14:33:51,633][__main__][INFO] - [39040] Loss: 2.747, Running accuracy: 96.973, Time: 22.75
[2020-12-14 14:34:17,307][__main__][INFO] - [39360] Loss: 2.662, Running accuracy: 96.973, Time: 25.67
[2020-12-14 14:34:41,826][__main__][INFO] - [39680] Loss: 2.650, Running accuracy: 96.974, Time: 24.52
[2020-12-14 14:34:51,203][__main__][INFO] - Action accuracy: 96.973, Loss: 3.022
[2020-12-14 14:34:51,204][__main__][INFO] - Validating..
[2020-12-14 14:35:32,822][test][INFO] - Time elapsed: 39.663405
[2020-12-14 14:35:32,828][__main__][INFO] - Validation F1 score: 95.230, Exact match: 54.350, Precision: 95.140, Recall: 95.330
[2020-12-14 14:35:32,828][__main__][INFO] - F1 score has improved
[2020-12-14 14:36:09,605][__main__][INFO] - Checkpoint saved to checkpoints/model_latest.pth
[2020-12-14 14:36:10,583][__main__][INFO] - Epoch #4
[2020-12-14 14:36:10,583][__main__][INFO] - Training..
[2020-12-14 14:36:38,229][__main__][INFO] - [320] Loss: 1.875, Running accuracy: 97.979, Time: 26.34
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[2020-12-14 15:16:41,701][__main__][INFO] - [31040] Loss: 2.347, Running accuracy: 97.399, Time: 23.51
[2020-12-14 15:17:11,208][__main__][INFO] - [31360] Loss: 2.143, Running accuracy: 97.398, Time: 29.50
[2020-12-14 15:17:37,053][__main__][INFO] - [31680] Loss: 2.269, Running accuracy: 97.398, Time: 25.84
[2020-12-14 15:18:03,097][__main__][INFO] - [32000] Loss: 2.356, Running accuracy: 97.399, Time: 26.04
[2020-12-14 15:18:27,785][__main__][INFO] - [32320] Loss: 2.487, Running accuracy: 97.395, Time: 24.69
[2020-12-14 15:18:52,304][__main__][INFO] - [32640] Loss: 2.165, Running accuracy: 97.395, Time: 24.52
[2020-12-14 15:19:17,381][__main__][INFO] - [32960] Loss: 2.089, Running accuracy: 97.396, Time: 25.07
[2020-12-14 15:19:41,819][__main__][INFO] - [33280] Loss: 2.306, Running accuracy: 97.395, Time: 24.43
[2020-12-14 15:20:05,465][__main__][INFO] - [33600] Loss: 2.378, Running accuracy: 97.394, Time: 23.64
[2020-12-14 15:20:28,134][__main__][INFO] - [33920] Loss: 2.145, Running accuracy: 97.396, Time: 22.67
[2020-12-14 15:20:52,569][__main__][INFO] - [34240] Loss: 2.339, Running accuracy: 97.395, Time: 24.43
[2020-12-14 15:21:16,682][__main__][INFO] - [34560] Loss: 2.021, Running accuracy: 97.397, Time: 24.11
[2020-12-14 15:21:41,093][__main__][INFO] - [34880] Loss: 2.271, Running accuracy: 97.397, Time: 24.41
[2020-12-14 15:22:06,524][__main__][INFO] - [35200] Loss: 2.231, Running accuracy: 97.396, Time: 25.43
[2020-12-14 15:22:30,528][__main__][INFO] - [35520] Loss: 2.292, Running accuracy: 97.396, Time: 24.00
[2020-12-14 15:23:00,141][__main__][INFO] - [35840] Loss: 2.318, Running accuracy: 97.397, Time: 29.61
[2020-12-14 15:23:25,331][__main__][INFO] - [36160] Loss: 2.142, Running accuracy: 97.398, Time: 25.19
[2020-12-14 15:23:50,995][__main__][INFO] - [36480] Loss: 2.200, Running accuracy: 97.398, Time: 25.66
[2020-12-14 15:24:16,037][__main__][INFO] - [36800] Loss: 2.272, Running accuracy: 97.398, Time: 25.04
[2020-12-14 15:24:39,935][__main__][INFO] - [37120] Loss: 2.428, Running accuracy: 97.396, Time: 23.90
[2020-12-14 15:25:04,654][__main__][INFO] - [37440] Loss: 2.246, Running accuracy: 97.397, Time: 24.72
[2020-12-14 15:25:29,034][__main__][INFO] - [37760] Loss: 2.159, Running accuracy: 97.396, Time: 24.38
[2020-12-14 15:25:52,215][__main__][INFO] - [38080] Loss: 2.314, Running accuracy: 97.395, Time: 23.18
[2020-12-14 15:26:14,867][__main__][INFO] - [38400] Loss: 2.002, Running accuracy: 97.397, Time: 22.65
[2020-12-14 15:26:40,432][__main__][INFO] - [38720] Loss: 2.575, Running accuracy: 97.396, Time: 25.56
[2020-12-14 15:27:04,439][__main__][INFO] - [39040] Loss: 2.375, Running accuracy: 97.396, Time: 24.01
[2020-12-14 15:27:29,133][__main__][INFO] - [39360] Loss: 2.294, Running accuracy: 97.395, Time: 24.69
[2020-12-14 15:27:54,041][__main__][INFO] - [39680] Loss: 2.443, Running accuracy: 97.396, Time: 24.91
[2020-12-14 15:28:04,356][__main__][INFO] - Action accuracy: 97.397, Loss: 2.522
[2020-12-14 15:28:04,357][__main__][INFO] - Validating..
[2020-12-14 15:28:46,156][test][INFO] - Time elapsed: 39.558581
[2020-12-14 15:28:46,161][__main__][INFO] - Validation F1 score: 95.290, Exact match: 54.410, Precision: 95.400, Recall: 95.180
[2020-12-14 15:28:46,161][__main__][INFO] - F1 score has improved
[2020-12-14 15:29:23,259][__main__][INFO] - Checkpoint saved to checkpoints/model_latest.pth
[2020-12-14 15:29:24,320][__main__][INFO] - Epoch #5
[2020-12-14 15:29:24,320][__main__][INFO] - Training..
[2020-12-14 15:29:49,077][__main__][INFO] - [320] Loss: 1.767, Running accuracy: 97.641, Time: 23.41
[2020-12-14 15:30:12,672][__main__][INFO] - [640] Loss: 1.475, Running accuracy: 97.907, Time: 23.59
[2020-12-14 15:30:36,591][__main__][INFO] - [960] Loss: 1.716, Running accuracy: 97.912, Time: 23.92
[2020-12-14 15:31:00,359][__main__][INFO] - [1280] Loss: 1.854, Running accuracy: 97.889, Time: 23.77
[2020-12-14 15:31:24,665][__main__][INFO] - [1600] Loss: 1.871, Running accuracy: 97.871, Time: 24.31
[2020-12-14 15:31:48,668][__main__][INFO] - [1920] Loss: 1.756, Running accuracy: 97.855, Time: 24.00
[2020-12-14 15:32:12,989][__main__][INFO] - [2240] Loss: 1.917, Running accuracy: 97.844, Time: 24.32
[2020-12-14 15:32:35,562][__main__][INFO] - [2560] Loss: 1.979, Running accuracy: 97.817, Time: 22.57
[2020-12-14 15:33:01,418][__main__][INFO] - [2880] Loss: 1.861, Running accuracy: 97.825, Time: 25.86
[2020-12-14 15:33:32,544][__main__][INFO] - [3200] Loss: 1.895, Running accuracy: 97.826, Time: 31.12
[2020-12-14 15:33:57,185][__main__][INFO] - [3520] Loss: 1.851, Running accuracy: 97.825, Time: 24.64
[2020-12-14 15:34:23,228][__main__][INFO] - [3840] Loss: 1.539, Running accuracy: 97.835, Time: 26.04
[2020-12-14 15:34:49,151][__main__][INFO] - [4160] Loss: 2.212, Running accuracy: 97.813, Time: 25.92
[2020-12-14 15:35:15,889][__main__][INFO] - [4480] Loss: 2.025, Running accuracy: 97.798, Time: 26.74
[2020-12-14 15:35:40,167][__main__][INFO] - [4800] Loss: 2.319, Running accuracy: 97.783, Time: 24.28
[2020-12-14 15:36:06,943][__main__][INFO] - [5120] Loss: 1.805, Running accuracy: 97.797, Time: 26.77
[2020-12-14 15:36:29,901][__main__][INFO] - [5440] Loss: 2.068, Running accuracy: 97.783, Time: 22.96
[2020-12-14 15:36:55,643][__main__][INFO] - [5760] Loss: 1.967, Running accuracy: 97.786, Time: 25.74
[2020-12-14 15:37:19,995][__main__][INFO] - [6080] Loss: 1.903, Running accuracy: 97.786, Time: 24.35
[2020-12-14 15:37:45,496][__main__][INFO] - [6400] Loss: 1.593, Running accuracy: 97.805, Time: 25.50
[2020-12-14 15:38:11,085][__main__][INFO] - [6720] Loss: 1.685, Running accuracy: 97.811, Time: 25.59
[2020-12-14 15:38:36,095][__main__][INFO] - [7040] Loss: 1.682, Running accuracy: 97.818, Time: 25.01
[2020-12-14 15:39:00,993][__main__][INFO] - [7360] Loss: 1.626, Running accuracy: 97.823, Time: 24.90
[2020-12-14 15:39:29,055][__main__][INFO] - [7680] Loss: 1.778, Running accuracy: 97.824, Time: 28.06
[2020-12-14 15:39:55,620][__main__][INFO] - [8000] Loss: 1.973, Running accuracy: 97.823, Time: 26.56
[2020-12-14 15:40:20,791][__main__][INFO] - [8320] Loss: 1.910, Running accuracy: 97.828, Time: 25.17
[2020-12-14 15:40:44,051][__main__][INFO] - [8640] Loss: 1.728, Running accuracy: 97.824, Time: 23.26
[2020-12-14 15:41:10,239][__main__][INFO] - [8960] Loss: 1.623, Running accuracy: 97.835, Time: 26.19
[2020-12-14 15:41:35,266][__main__][INFO] - [9280] Loss: 1.877, Running accuracy: 97.835, Time: 25.03
[2020-12-14 15:42:00,742][__main__][INFO] - [9600] Loss: 1.952, Running accuracy: 97.832, Time: 25.48
[2020-12-14 15:42:24,173][__main__][INFO] - [9920] Loss: 1.916, Running accuracy: 97.824, Time: 23.43
[2020-12-14 15:42:47,757][__main__][INFO] - [10240] Loss: 1.692, Running accuracy: 97.819, Time: 23.58
[2020-12-14 15:43:13,075][__main__][INFO] - [10560] Loss: 2.083, Running accuracy: 97.820, Time: 25.32
[2020-12-14 15:43:38,628][__main__][INFO] - [10880] Loss: 2.023, Running accuracy: 97.817, Time: 25.55
[2020-12-14 15:44:02,620][__main__][INFO] - [11200] Loss: 1.947, Running accuracy: 97.809, Time: 23.99
[2020-12-14 15:44:26,671][__main__][INFO] - [11520] Loss: 1.988, Running accuracy: 97.810, Time: 24.05
[2020-12-14 15:44:52,338][__main__][INFO] - [11840] Loss: 2.032, Running accuracy: 97.804, Time: 25.67
[2020-12-14 15:45:20,255][__main__][INFO] - [12160] Loss: 1.997, Running accuracy: 97.803, Time: 27.92
[2020-12-14 15:45:44,417][__main__][INFO] - [12480] Loss: 1.858, Running accuracy: 97.800, Time: 24.16
[2020-12-14 15:46:10,591][__main__][INFO] - [12800] Loss: 1.910, Running accuracy: 97.803, Time: 26.17
[2020-12-14 15:46:34,039][__main__][INFO] - [13120] Loss: 1.937, Running accuracy: 97.804, Time: 23.45
[2020-12-14 15:46:58,984][__main__][INFO] - [13440] Loss: 1.624, Running accuracy: 97.809, Time: 24.94
[2020-12-14 15:47:25,527][__main__][INFO] - [13760] Loss: 1.747, Running accuracy: 97.815, Time: 26.54
[2020-12-14 15:47:50,931][__main__][INFO] - [14080] Loss: 1.811, Running accuracy: 97.813, Time: 25.40
[2020-12-14 15:48:15,208][__main__][INFO] - [14400] Loss: 1.982, Running accuracy: 97.806, Time: 24.28
[2020-12-14 15:48:41,299][__main__][INFO] - [14720] Loss: 1.775, Running accuracy: 97.805, Time: 26.09
[2020-12-14 15:49:04,522][__main__][INFO] - [15040] Loss: 1.782, Running accuracy: 97.813, Time: 23.22
[2020-12-14 15:49:30,515][__main__][INFO] - [15360] Loss: 1.857, Running accuracy: 97.814, Time: 25.99
[2020-12-14 15:49:55,079][__main__][INFO] - [15680] Loss: 1.877, Running accuracy: 97.810, Time: 24.56
[2020-12-14 15:50:21,411][__main__][INFO] - [16000] Loss: 1.753, Running accuracy: 97.810, Time: 26.33
[2020-12-14 15:50:45,995][__main__][INFO] - [16320] Loss: 1.969, Running accuracy: 97.811, Time: 24.58
[2020-12-14 15:51:15,197][__main__][INFO] - [16640] Loss: 1.767, Running accuracy: 97.813, Time: 29.20
[2020-12-14 15:51:37,696][__main__][INFO] - [16960] Loss: 1.721, Running accuracy: 97.817, Time: 22.50
[2020-12-14 15:52:02,131][__main__][INFO] - [17280] Loss: 1.807, Running accuracy: 97.819, Time: 24.43
[2020-12-14 15:52:25,986][__main__][INFO] - [17600] Loss: 2.004, Running accuracy: 97.817, Time: 23.85
[2020-12-14 15:52:51,692][__main__][INFO] - [17920] Loss: 2.332, Running accuracy: 97.807, Time: 25.71
[2020-12-14 15:53:16,277][__main__][INFO] - [18240] Loss: 2.239, Running accuracy: 97.803, Time: 24.58
[2020-12-14 15:53:39,668][__main__][INFO] - [18560] Loss: 1.935, Running accuracy: 97.802, Time: 23.39
[2020-12-14 15:54:03,150][__main__][INFO] - [18880] Loss: 2.049, Running accuracy: 97.800, Time: 23.48
[2020-12-14 15:54:27,738][__main__][INFO] - [19200] Loss: 1.900, Running accuracy: 97.799, Time: 24.59
[2020-12-14 15:54:50,102][__main__][INFO] - [19520] Loss: 1.536, Running accuracy: 97.803, Time: 22.36
[2020-12-14 15:55:13,686][__main__][INFO] - [19840] Loss: 1.836, Running accuracy: 97.804, Time: 23.58
[2020-12-14 15:55:37,381][__main__][INFO] - [20160] Loss: 1.886, Running accuracy: 97.804, Time: 23.69
[2020-12-14 15:56:02,070][__main__][INFO] - [20480] Loss: 1.778, Running accuracy: 97.802, Time: 24.69
[2020-12-14 15:56:25,296][__main__][INFO] - [20800] Loss: 2.181, Running accuracy: 97.797, Time: 23.23
[2020-12-14 15:56:52,437][__main__][INFO] - [21120] Loss: 1.799, Running accuracy: 97.797, Time: 27.14
[2020-12-14 15:57:17,227][__main__][INFO] - [21440] Loss: 1.944, Running accuracy: 97.797, Time: 24.79
[2020-12-14 15:57:41,301][__main__][INFO] - [21760] Loss: 1.896, Running accuracy: 97.797, Time: 24.07
[2020-12-14 15:58:04,923][__main__][INFO] - [22080] Loss: 1.902, Running accuracy: 97.797, Time: 23.62
[2020-12-14 15:58:30,900][__main__][INFO] - [22400] Loss: 1.673, Running accuracy: 97.800, Time: 25.98
[2020-12-14 15:58:57,097][__main__][INFO] - [22720] Loss: 2.224, Running accuracy: 97.796, Time: 26.20
[2020-12-14 15:59:21,402][__main__][INFO] - [23040] Loss: 2.381, Running accuracy: 97.792, Time: 24.30
[2020-12-14 15:59:45,778][__main__][INFO] - [23360] Loss: 2.012, Running accuracy: 97.793, Time: 24.38
[2020-12-14 16:00:11,313][__main__][INFO] - [23680] Loss: 1.956, Running accuracy: 97.794, Time: 25.53
[2020-12-14 16:00:36,839][__main__][INFO] - [24000] Loss: 1.651, Running accuracy: 97.798, Time: 25.53
[2020-12-14 16:01:01,699][__main__][INFO] - [24320] Loss: 1.971, Running accuracy: 97.798, Time: 24.86
[2020-12-14 16:01:25,554][__main__][INFO] - [24640] Loss: 1.949, Running accuracy: 97.796, Time: 23.85
[2020-12-14 16:01:50,975][__main__][INFO] - [24960] Loss: 1.922, Running accuracy: 97.794, Time: 25.42
[2020-12-14 16:02:16,294][__main__][INFO] - [25280] Loss: 2.066, Running accuracy: 97.790, Time: 25.32
[2020-12-14 16:02:45,229][__main__][INFO] - [25600] Loss: 1.723, Running accuracy: 97.789, Time: 28.93
[2020-12-14 16:03:08,247][__main__][INFO] - [25920] Loss: 2.171, Running accuracy: 97.786, Time: 23.02
[2020-12-14 16:03:31,206][__main__][INFO] - [26240] Loss: 1.852, Running accuracy: 97.786, Time: 22.96
[2020-12-14 16:03:57,093][__main__][INFO] - [26560] Loss: 1.971, Running accuracy: 97.785, Time: 25.89
[2020-12-14 16:04:21,652][__main__][INFO] - [26880] Loss: 1.970, Running accuracy: 97.785, Time: 24.56
[2020-12-14 16:04:45,955][__main__][INFO] - [27200] Loss: 2.066, Running accuracy: 97.784, Time: 24.30
[2020-12-14 16:05:08,069][__main__][INFO] - [27520] Loss: 1.735, Running accuracy: 97.783, Time: 22.11
[2020-12-14 16:05:32,066][__main__][INFO] - [27840] Loss: 2.001, Running accuracy: 97.780, Time: 24.00
[2020-12-14 16:05:57,843][__main__][INFO] - [28160] Loss: 2.153, Running accuracy: 97.776, Time: 25.78
[2020-12-14 16:06:23,691][__main__][INFO] - [28480] Loss: 1.761, Running accuracy: 97.780, Time: 25.85
[2020-12-14 16:06:50,193][__main__][INFO] - [28800] Loss: 2.077, Running accuracy: 97.780, Time: 26.50
[2020-12-14 16:07:15,707][__main__][INFO] - [29120] Loss: 1.665, Running accuracy: 97.782, Time: 25.51
[2020-12-14 16:07:38,219][__main__][INFO] - [29440] Loss: 1.582, Running accuracy: 97.783, Time: 22.51
[2020-12-14 16:08:02,952][__main__][INFO] - [29760] Loss: 2.123, Running accuracy: 97.781, Time: 24.73
[2020-12-14 16:08:33,349][__main__][INFO] - [30080] Loss: 2.171, Running accuracy: 97.779, Time: 30.40
[2020-12-14 16:08:57,869][__main__][INFO] - [30400] Loss: 2.410, Running accuracy: 97.775, Time: 24.52
[2020-12-14 16:09:23,921][__main__][INFO] - [30720] Loss: 2.079, Running accuracy: 97.772, Time: 26.05
[2020-12-14 16:09:47,576][__main__][INFO] - [31040] Loss: 1.852, Running accuracy: 97.774, Time: 23.65
[2020-12-14 16:10:12,641][__main__][INFO] - [31360] Loss: 1.852, Running accuracy: 97.773, Time: 25.06
[2020-12-14 16:10:37,346][__main__][INFO] - [31680] Loss: 1.653, Running accuracy: 97.774, Time: 24.70
[2020-12-14 16:11:00,010][__main__][INFO] - [32000] Loss: 1.628, Running accuracy: 97.775, Time: 22.66
[2020-12-14 16:11:25,712][__main__][INFO] - [32320] Loss: 2.218, Running accuracy: 97.774, Time: 25.70
[2020-12-14 16:11:52,130][__main__][INFO] - [32640] Loss: 2.173, Running accuracy: 97.771, Time: 26.42
[2020-12-14 16:12:16,897][__main__][INFO] - [32960] Loss: 1.972, Running accuracy: 97.771, Time: 24.77
[2020-12-14 16:12:43,471][__main__][INFO] - [33280] Loss: 2.154, Running accuracy: 97.770, Time: 26.57
[2020-12-14 16:13:07,276][__main__][INFO] - [33600] Loss: 1.970, Running accuracy: 97.767, Time: 23.45
[2020-12-14 16:13:31,964][__main__][INFO] - [33920] Loss: 1.765, Running accuracy: 97.769, Time: 24.69
[2020-12-14 16:13:56,185][__main__][INFO] - [34240] Loss: 1.631, Running accuracy: 97.773, Time: 24.22
[2020-12-14 16:14:25,017][__main__][INFO] - [34560] Loss: 2.211, Running accuracy: 97.773, Time: 28.83
[2020-12-14 16:14:50,734][__main__][INFO] - [34880] Loss: 1.810, Running accuracy: 97.773, Time: 25.71
[2020-12-14 16:15:16,940][__main__][INFO] - [35200] Loss: 1.859, Running accuracy: 97.774, Time: 26.21
[2020-12-14 16:15:43,832][__main__][INFO] - [35520] Loss: 2.022, Running accuracy: 97.774, Time: 26.89
[2020-12-14 16:16:08,393][__main__][INFO] - [35840] Loss: 1.941, Running accuracy: 97.773, Time: 24.56
[2020-12-14 16:16:32,762][__main__][INFO] - [36160] Loss: 2.023, Running accuracy: 97.774, Time: 24.37
[2020-12-14 16:16:59,018][__main__][INFO] - [36480] Loss: 1.894, Running accuracy: 97.775, Time: 26.25
[2020-12-14 16:17:25,115][__main__][INFO] - [36800] Loss: 1.991, Running accuracy: 97.775, Time: 26.10
[2020-12-14 16:17:50,024][__main__][INFO] - [37120] Loss: 1.789, Running accuracy: 97.776, Time: 24.91
[2020-12-14 16:18:16,736][__main__][INFO] - [37440] Loss: 1.909, Running accuracy: 97.776, Time: 26.71
[2020-12-14 16:18:39,792][__main__][INFO] - [37760] Loss: 1.998, Running accuracy: 97.774, Time: 23.06
[2020-12-14 16:19:07,967][__main__][INFO] - [38080] Loss: 1.801, Running accuracy: 97.774, Time: 28.17
[2020-12-14 16:19:34,220][__main__][INFO] - [38400] Loss: 1.875, Running accuracy: 97.776, Time: 26.25
[2020-12-14 16:19:57,860][__main__][INFO] - [38720] Loss: 2.073, Running accuracy: 97.775, Time: 23.64
[2020-12-14 16:20:28,237][__main__][INFO] - [39040] Loss: 1.956, Running accuracy: 97.776, Time: 30.38
[2020-12-14 16:20:53,510][__main__][INFO] - [39360] Loss: 2.115, Running accuracy: 97.776, Time: 25.27
[2020-12-14 16:21:19,024][__main__][INFO] - [39680] Loss: 2.147, Running accuracy: 97.776, Time: 25.51
[2020-12-14 16:21:28,365][__main__][INFO] - Action accuracy: 97.776, Loss: 2.121
[2020-12-14 16:21:28,368][__main__][INFO] - Validating..
[2020-12-14 16:22:06,464][test][INFO] - Time elapsed: 35.784506
[2020-12-14 16:22:06,469][__main__][INFO] - Validation F1 score: 95.600, Exact match: 55.880, Precision: 95.510, Recall: 95.690
[2020-12-14 16:22:06,469][__main__][INFO] - F1 score has improved
[2020-12-14 16:22:45,305][__main__][INFO] - Checkpoint saved to checkpoints/model_latest.pth
[2020-12-14 16:22:46,430][__main__][INFO] - Epoch #6
[2020-12-14 16:22:46,431][__main__][INFO] - Training..
[2020-12-14 16:23:16,408][__main__][INFO] - [320] Loss: 1.804, Running accuracy: 97.926, Time: 28.62
[2020-12-14 16:23:43,280][__main__][INFO] - [640] Loss: 1.788, Running accuracy: 98.030, Time: 26.87
[2020-12-14 16:24:09,159][__main__][INFO] - [960] Loss: 1.547, Running accuracy: 98.142, Time: 25.88
[2020-12-14 16:24:35,725][__main__][INFO] - [1280] Loss: 1.451, Running accuracy: 98.158, Time: 26.57
[2020-12-14 16:25:05,977][__main__][INFO] - [1600] Loss: 1.507, Running accuracy: 98.178, Time: 30.25
[2020-12-14 16:25:31,451][__main__][INFO] - [1920] Loss: 1.482, Running accuracy: 98.203, Time: 25.47
[2020-12-14 16:25:55,528][__main__][INFO] - [2240] Loss: 1.619, Running accuracy: 98.191, Time: 24.08
[2020-12-14 16:26:20,496][__main__][INFO] - [2560] Loss: 1.326, Running accuracy: 98.208, Time: 24.97
[2020-12-14 16:26:44,282][__main__][INFO] - [2880] Loss: 1.499, Running accuracy: 98.221, Time: 23.78
[2020-12-14 16:27:08,498][__main__][INFO] - [3200] Loss: 1.732, Running accuracy: 98.181, Time: 24.22
[2020-12-14 16:27:32,743][__main__][INFO] - [3520] Loss: 1.614, Running accuracy: 98.177, Time: 24.24
[2020-12-14 16:28:01,321][__main__][INFO] - [3840] Loss: 1.824, Running accuracy: 98.176, Time: 28.58
[2020-12-14 16:28:27,058][__main__][INFO] - [4160] Loss: 1.310, Running accuracy: 98.184, Time: 25.74
[2020-12-14 16:28:51,495][__main__][INFO] - [4480] Loss: 1.280, Running accuracy: 98.195, Time: 24.44
[2020-12-14 16:29:17,388][__main__][INFO] - [4800] Loss: 1.505, Running accuracy: 98.190, Time: 25.89
[2020-12-14 16:29:41,507][__main__][INFO] - [5120] Loss: 1.651, Running accuracy: 98.197, Time: 24.12
[2020-12-14 16:30:06,818][__main__][INFO] - [5440] Loss: 1.686, Running accuracy: 98.191, Time: 25.31
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[2020-12-14 17:12:01,775][__main__][INFO] - [38080] Loss: 1.735, Running accuracy: 98.072, Time: 25.03
[2020-12-14 17:12:24,861][__main__][INFO] - [38400] Loss: 1.714, Running accuracy: 98.071, Time: 23.09
[2020-12-14 17:12:47,172][__main__][INFO] - [38720] Loss: 1.541, Running accuracy: 98.070, Time: 22.31
[2020-12-14 17:13:10,841][__main__][INFO] - [39040] Loss: 1.595, Running accuracy: 98.071, Time: 23.67
[2020-12-14 17:13:35,649][__main__][INFO] - [39360] Loss: 1.668, Running accuracy: 98.071, Time: 24.81
[2020-12-14 17:14:00,144][__main__][INFO] - [39680] Loss: 1.541, Running accuracy: 98.073, Time: 24.49
[2020-12-14 17:14:09,141][__main__][INFO] - Action accuracy: 98.073, Loss: 1.816
[2020-12-14 17:14:09,142][__main__][INFO] - Validating..
[2020-12-14 17:14:46,026][test][INFO] - Time elapsed: 34.664408
[2020-12-14 17:14:46,030][__main__][INFO] - Validation F1 score: 95.600, Exact match: 56.940, Precision: 95.590, Recall: 95.620
[2020-12-14 17:15:25,714][__main__][INFO] - Checkpoint saved to checkpoints/model_latest.pth
[2020-12-14 17:15:26,674][__main__][INFO] - Epoch #7
[2020-12-14 17:15:26,674][__main__][INFO] - Training..
[2020-12-14 17:15:56,139][__main__][INFO] - [320] Loss: 1.251, Running accuracy: 98.456, Time: 28.11
[2020-12-14 17:16:21,112][__main__][INFO] - [640] Loss: 1.248, Running accuracy: 98.396, Time: 24.97
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[2020-12-14 17:24:29,359][__main__][INFO] - [7040] Loss: 1.158, Running accuracy: 98.339, Time: 23.04
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[2020-12-14 17:25:40,656][__main__][INFO] - [8000] Loss: 1.401, Running accuracy: 98.327, Time: 25.48
[2020-12-14 17:26:03,931][__main__][INFO] - [8320] Loss: 1.427, Running accuracy: 98.321, Time: 23.27
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[2020-12-14 17:26:51,058][__main__][INFO] - [8960] Loss: 1.477, Running accuracy: 98.326, Time: 24.29
[2020-12-14 17:27:18,799][__main__][INFO] - [9280] Loss: 1.358, Running accuracy: 98.325, Time: 27.74
[2020-12-14 17:27:43,984][__main__][INFO] - [9600] Loss: 1.330, Running accuracy: 98.328, Time: 25.18
[2020-12-14 17:28:07,624][__main__][INFO] - [9920] Loss: 1.331, Running accuracy: 98.334, Time: 23.64
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[2020-12-14 17:42:36,483][__main__][INFO] - [21120] Loss: 1.315, Running accuracy: 98.346, Time: 25.25
[2020-12-14 17:43:00,934][__main__][INFO] - [21440] Loss: 1.131, Running accuracy: 98.352, Time: 24.45
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[2020-12-14 18:05:33,204][__main__][INFO] - [38400] Loss: 1.345, Running accuracy: 98.341, Time: 25.29
[2020-12-14 18:05:57,687][__main__][INFO] - [38720] Loss: 1.499, Running accuracy: 98.341, Time: 24.48
[2020-12-14 18:06:22,120][__main__][INFO] - [39040] Loss: 1.205, Running accuracy: 98.340, Time: 24.43
[2020-12-14 18:06:45,570][__main__][INFO] - [39360] Loss: 1.627, Running accuracy: 98.340, Time: 23.45
[2020-12-14 18:07:09,763][__main__][INFO] - [39680] Loss: 1.221, Running accuracy: 98.339, Time: 24.19
[2020-12-14 18:07:19,982][__main__][INFO] - Action accuracy: 98.338, Loss: 1.553
[2020-12-14 18:07:19,983][__main__][INFO] - Validating..
[2020-12-14 18:08:03,073][test][INFO] - Time elapsed: 41.078812
[2020-12-14 18:08:03,079][__main__][INFO] - Validation F1 score: 95.360, Exact match: 55.650, Precision: 95.260, Recall: 95.460
[2020-12-14 18:08:44,166][__main__][INFO] - Checkpoint saved to checkpoints/model_latest.pth
[2020-12-14 18:08:45,190][__main__][INFO] - Epoch #8
[2020-12-14 18:08:45,190][__main__][INFO] - Training..
[2020-12-14 18:09:11,614][__main__][INFO] - [320] Loss: 1.437, Running accuracy: 98.372, Time: 25.11
[2020-12-14 18:09:36,620][__main__][INFO] - [640] Loss: 1.056, Running accuracy: 98.510, Time: 24.34
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[2020-12-14 18:10:53,289][__main__][INFO] - [1600] Loss: 1.332, Running accuracy: 98.560, Time: 26.65
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[2020-12-14 18:21:12,052][__main__][INFO] - [9600] Loss: 1.279, Running accuracy: 98.573, Time: 26.94
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[2020-12-14 18:24:35,327][__main__][INFO] - [12160] Loss: 1.183, Running accuracy: 98.575, Time: 24.24
[2020-12-14 18:25:04,694][__main__][INFO] - [12480] Loss: 1.176, Running accuracy: 98.574, Time: 29.37
[2020-12-14 18:25:29,550][__main__][INFO] - [12800] Loss: 1.177, Running accuracy: 98.573, Time: 24.86
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[2020-12-14 18:26:44,791][__main__][INFO] - [13760] Loss: 1.327, Running accuracy: 98.569, Time: 24.75
[2020-12-14 18:27:10,291][__main__][INFO] - [14080] Loss: 1.064, Running accuracy: 98.568, Time: 25.50
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[2020-12-14 18:31:08,128][__main__][INFO] - [16960] Loss: 1.240, Running accuracy: 98.576, Time: 29.61
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[2020-12-14 18:31:56,695][__main__][INFO] - [17600] Loss: 1.065, Running accuracy: 98.578, Time: 25.82
[2020-12-14 18:32:21,247][__main__][INFO] - [17920] Loss: 1.282, Running accuracy: 98.575, Time: 24.55
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[2020-12-14 18:33:11,674][__main__][INFO] - [18560] Loss: 1.003, Running accuracy: 98.577, Time: 24.27
[2020-12-14 18:33:37,185][__main__][INFO] - [18880] Loss: 1.197, Running accuracy: 98.578, Time: 25.51
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[2020-12-14 18:34:51,924][__main__][INFO] - [19840] Loss: 1.099, Running accuracy: 98.572, Time: 23.40
[2020-12-14 18:35:15,916][__main__][INFO] - [20160] Loss: 1.189, Running accuracy: 98.573, Time: 23.99
[2020-12-14 18:35:40,120][__main__][INFO] - [20480] Loss: 1.095, Running accuracy: 98.574, Time: 24.20
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[2020-12-14 18:36:29,573][__main__][INFO] - [21120] Loss: 1.386, Running accuracy: 98.576, Time: 23.68
[2020-12-14 18:36:57,944][__main__][INFO] - [21440] Loss: 1.325, Running accuracy: 98.577, Time: 28.37
[2020-12-14 18:37:21,911][__main__][INFO] - [21760] Loss: 1.285, Running accuracy: 98.576, Time: 23.97
[2020-12-14 18:37:46,746][__main__][INFO] - [22080] Loss: 1.292, Running accuracy: 98.572, Time: 24.83
[2020-12-14 18:38:11,203][__main__][INFO] - [22400] Loss: 1.371, Running accuracy: 98.567, Time: 24.46
[2020-12-14 18:38:35,770][__main__][INFO] - [22720] Loss: 1.373, Running accuracy: 98.563, Time: 24.57
[2020-12-14 18:39:01,334][__main__][INFO] - [23040] Loss: 1.367, Running accuracy: 98.562, Time: 25.56
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[2020-12-14 18:39:53,915][__main__][INFO] - [23680] Loss: 1.319, Running accuracy: 98.567, Time: 24.91
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[2020-12-14 18:40:41,314][__main__][INFO] - [24320] Loss: 1.241, Running accuracy: 98.567, Time: 23.59
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[2020-12-14 18:57:45,272][__main__][INFO] - [37120] Loss: 1.029, Running accuracy: 98.537, Time: 26.83
[2020-12-14 18:58:11,555][__main__][INFO] - [37440] Loss: 1.172, Running accuracy: 98.539, Time: 26.28
[2020-12-14 18:58:36,806][__main__][INFO] - [37760] Loss: 1.100, Running accuracy: 98.538, Time: 25.25
[2020-12-14 18:59:00,804][__main__][INFO] - [38080] Loss: 1.112, Running accuracy: 98.539, Time: 24.00
[2020-12-14 18:59:25,155][__main__][INFO] - [38400] Loss: 1.362, Running accuracy: 98.538, Time: 24.35
[2020-12-14 18:59:48,965][__main__][INFO] - [38720] Loss: 1.213, Running accuracy: 98.538, Time: 23.81
[2020-12-14 19:00:13,842][__main__][INFO] - [39040] Loss: 1.310, Running accuracy: 98.537, Time: 24.88
[2020-12-14 19:00:43,754][__main__][INFO] - [39360] Loss: 1.498, Running accuracy: 98.536, Time: 29.91
[2020-12-14 19:01:07,699][__main__][INFO] - [39680] Loss: 1.164, Running accuracy: 98.534, Time: 23.94
[2020-12-14 19:01:18,434][__main__][INFO] - Action accuracy: 98.535, Loss: 1.353
[2020-12-14 19:01:18,435][__main__][INFO] - Validating..
[2020-12-14 19:01:56,676][test][INFO] - Time elapsed: 35.879384
[2020-12-14 19:01:56,680][__main__][INFO] - Validation F1 score: 95.770, Exact match: 56.590, Precision: 95.720, Recall: 95.820
[2020-12-14 19:01:56,680][__main__][INFO] - F1 score has improved
[2020-12-14 19:02:35,459][__main__][INFO] - Checkpoint saved to checkpoints/model_latest.pth
[2020-12-14 19:02:36,364][__main__][INFO] - Epoch #9
[2020-12-14 19:02:36,365][__main__][INFO] - Training..
[2020-12-14 19:03:02,387][__main__][INFO] - [320] Loss: 0.939, Running accuracy: 98.760, Time: 24.61
[2020-12-14 19:03:27,824][__main__][INFO] - [640] Loss: 0.956, Running accuracy: 98.682, Time: 25.44
[2020-12-14 19:03:54,277][__main__][INFO] - [960] Loss: 1.297, Running accuracy: 98.629, Time: 26.45
[2020-12-14 19:04:19,757][__main__][INFO] - [1280] Loss: 0.801, Running accuracy: 98.713, Time: 25.48
[2020-12-14 19:04:46,725][__main__][INFO] - [1600] Loss: 1.253, Running accuracy: 98.690, Time: 26.97
[2020-12-14 19:05:17,649][__main__][INFO] - [1920] Loss: 0.892, Running accuracy: 98.712, Time: 30.92
[2020-12-14 19:05:43,043][__main__][INFO] - [2240] Loss: 1.294, Running accuracy: 98.689, Time: 25.39
[2020-12-14 19:06:07,746][__main__][INFO] - [2560] Loss: 1.225, Running accuracy: 98.681, Time: 24.70
[2020-12-14 19:06:34,993][__main__][INFO] - [2880] Loss: 1.110, Running accuracy: 98.700, Time: 27.25
[2020-12-14 19:06:58,538][__main__][INFO] - [3200] Loss: 1.050, Running accuracy: 98.713, Time: 23.54
[2020-12-14 19:07:22,755][__main__][INFO] - [3520] Loss: 0.984, Running accuracy: 98.731, Time: 24.22
[2020-12-14 19:07:47,678][__main__][INFO] - [3840] Loss: 1.000, Running accuracy: 98.730, Time: 24.92
[2020-12-14 19:08:11,126][__main__][INFO] - [4160] Loss: 0.839, Running accuracy: 98.745, Time: 23.45
[2020-12-14 19:08:36,038][__main__][INFO] - [4480] Loss: 0.834, Running accuracy: 98.749, Time: 24.91
[2020-12-14 19:09:03,937][__main__][INFO] - [4800] Loss: 1.051, Running accuracy: 98.751, Time: 27.90
[2020-12-14 19:09:30,522][__main__][INFO] - [5120] Loss: 0.962, Running accuracy: 98.755, Time: 26.58
[2020-12-14 19:09:57,900][__main__][INFO] - [5440] Loss: 0.951, Running accuracy: 98.759, Time: 27.38
[2020-12-14 19:10:22,955][__main__][INFO] - [5760] Loss: 0.767, Running accuracy: 98.774, Time: 25.05
[2020-12-14 19:10:49,013][__main__][INFO] - [6080] Loss: 1.079, Running accuracy: 98.759, Time: 26.06
[2020-12-14 19:11:19,526][__main__][INFO] - [6400] Loss: 1.011, Running accuracy: 98.756, Time: 30.51
[2020-12-14 19:11:45,393][__main__][INFO] - [6720] Loss: 0.853, Running accuracy: 98.762, Time: 25.87
[2020-12-14 19:12:08,566][__main__][INFO] - [7040] Loss: 1.085, Running accuracy: 98.756, Time: 23.17
[2020-12-14 19:12:32,597][__main__][INFO] - [7360] Loss: 1.227, Running accuracy: 98.746, Time: 24.03
[2020-12-14 19:12:56,128][__main__][INFO] - [7680] Loss: 1.073, Running accuracy: 98.747, Time: 23.53
[2020-12-14 19:13:19,606][__main__][INFO] - [8000] Loss: 0.986, Running accuracy: 98.747, Time: 23.48
[2020-12-14 19:13:43,452][__main__][INFO] - [8320] Loss: 0.934, Running accuracy: 98.751, Time: 23.84
[2020-12-14 19:14:09,165][__main__][INFO] - [8640] Loss: 1.017, Running accuracy: 98.755, Time: 25.71
[2020-12-14 19:14:30,793][__main__][INFO] - [8960] Loss: 1.118, Running accuracy: 98.754, Time: 21.63
[2020-12-14 19:14:53,864][__main__][INFO] - [9280] Loss: 0.861, Running accuracy: 98.765, Time: 23.07
[2020-12-14 19:15:16,976][__main__][INFO] - [9600] Loss: 1.096, Running accuracy: 98.761, Time: 23.11
[2020-12-14 19:15:40,112][__main__][INFO] - [9920] Loss: 0.801, Running accuracy: 98.769, Time: 23.13
[2020-12-14 19:16:04,681][__main__][INFO] - [10240] Loss: 1.200, Running accuracy: 98.763, Time: 24.57
[2020-12-14 19:16:28,193][__main__][INFO] - [10560] Loss: 1.046, Running accuracy: 98.761, Time: 23.51
[2020-12-14 19:16:53,695][__main__][INFO] - [10880] Loss: 0.985, Running accuracy: 98.765, Time: 25.50
[2020-12-14 19:17:17,288][__main__][INFO] - [11200] Loss: 1.002, Running accuracy: 98.769, Time: 23.59
[2020-12-14 19:17:41,723][__main__][INFO] - [11520] Loss: 1.020, Running accuracy: 98.770, Time: 24.43
[2020-12-14 19:18:05,677][__main__][INFO] - [11840] Loss: 0.978, Running accuracy: 98.773, Time: 23.95
[2020-12-14 19:18:32,288][__main__][INFO] - [12160] Loss: 1.106, Running accuracy: 98.774, Time: 26.61
[2020-12-14 19:18:53,928][__main__][INFO] - [12480] Loss: 1.024, Running accuracy: 98.771, Time: 21.64
[2020-12-14 19:19:16,749][__main__][INFO] - [12800] Loss: 1.125, Running accuracy: 98.768, Time: 22.82
[2020-12-14 19:19:39,878][__main__][INFO] - [13120] Loss: 1.308, Running accuracy: 98.763, Time: 23.13
[2020-12-14 19:20:04,546][__main__][INFO] - [13440] Loss: 0.976, Running accuracy: 98.767, Time: 24.67
[2020-12-14 19:20:27,366][__main__][INFO] - [13760] Loss: 1.073, Running accuracy: 98.768, Time: 22.82
[2020-12-14 19:20:51,918][__main__][INFO] - [14080] Loss: 1.184, Running accuracy: 98.765, Time: 24.55
[2020-12-14 19:21:13,973][__main__][INFO] - [14400] Loss: 0.839, Running accuracy: 98.767, Time: 22.05
[2020-12-14 19:21:37,543][__main__][INFO] - [14720] Loss: 1.079, Running accuracy: 98.765, Time: 23.57
[2020-12-14 19:22:00,082][__main__][INFO] - [15040] Loss: 0.867, Running accuracy: 98.769, Time: 22.54
[2020-12-14 19:22:27,045][__main__][INFO] - [15360] Loss: 1.112, Running accuracy: 98.766, Time: 26.96
[2020-12-14 19:22:51,572][__main__][INFO] - [15680] Loss: 0.907, Running accuracy: 98.769, Time: 24.53
[2020-12-14 19:23:14,789][__main__][INFO] - [16000] Loss: 1.196, Running accuracy: 98.765, Time: 23.22
[2020-12-14 19:23:38,171][__main__][INFO] - [16320] Loss: 1.155, Running accuracy: 98.767, Time: 23.38
[2020-12-14 19:24:01,191][__main__][INFO] - [16640] Loss: 0.797, Running accuracy: 98.769, Time: 23.02
[2020-12-14 19:24:25,909][__main__][INFO] - [16960] Loss: 0.987, Running accuracy: 98.772, Time: 24.72
[2020-12-14 19:24:49,988][__main__][INFO] - [17280] Loss: 1.031, Running accuracy: 98.774, Time: 24.08
[2020-12-14 19:25:14,304][__main__][INFO] - [17600] Loss: 0.787, Running accuracy: 98.777, Time: 24.32
[2020-12-14 19:25:36,972][__main__][INFO] - [17920] Loss: 1.145, Running accuracy: 98.772, Time: 22.67
[2020-12-14 19:26:00,699][__main__][INFO] - [18240] Loss: 1.168, Running accuracy: 98.770, Time: 23.73
[2020-12-14 19:26:22,226][__main__][INFO] - [18560] Loss: 1.105, Running accuracy: 98.769, Time: 21.53
[2020-12-14 19:26:44,113][__main__][INFO] - [18880] Loss: 1.062, Running accuracy: 98.769, Time: 21.89
[2020-12-14 19:27:07,388][__main__][INFO] - [19200] Loss: 1.029, Running accuracy: 98.771, Time: 23.27
[2020-12-14 19:27:31,019][__main__][INFO] - [19520] Loss: 1.180, Running accuracy: 98.766, Time: 23.63
[2020-12-14 19:27:57,602][__main__][INFO] - [19840] Loss: 1.051, Running accuracy: 98.765, Time: 26.58
[2020-12-14 19:28:21,311][__main__][INFO] - [20160] Loss: 0.822, Running accuracy: 98.767, Time: 23.71
[2020-12-14 19:28:45,557][__main__][INFO] - [20480] Loss: 1.170, Running accuracy: 98.766, Time: 24.24
[2020-12-14 19:29:08,119][__main__][INFO] - [20800] Loss: 1.097, Running accuracy: 98.766, Time: 22.56
[2020-12-14 19:29:31,035][__main__][INFO] - [21120] Loss: 1.061, Running accuracy: 98.764, Time: 22.92
[2020-12-14 19:29:54,584][__main__][INFO] - [21440] Loss: 1.021, Running accuracy: 98.765, Time: 23.55
[2020-12-14 19:30:19,283][__main__][INFO] - [21760] Loss: 1.374, Running accuracy: 98.762, Time: 24.70
[2020-12-14 19:30:42,670][__main__][INFO] - [22080] Loss: 1.068, Running accuracy: 98.760, Time: 23.39
[2020-12-14 19:31:05,036][__main__][INFO] - [22400] Loss: 0.910, Running accuracy: 98.763, Time: 22.02
[2020-12-14 19:31:27,792][__main__][INFO] - [22720] Loss: 0.890, Running accuracy: 98.764, Time: 22.76
[2020-12-14 19:31:50,570][__main__][INFO] - [23040] Loss: 1.305, Running accuracy: 98.762, Time: 22.78
[2020-12-14 19:32:15,671][__main__][INFO] - [23360] Loss: 1.319, Running accuracy: 98.759, Time: 24.54
[2020-12-14 19:32:38,848][__main__][INFO] - [23680] Loss: 1.087, Running accuracy: 98.759, Time: 23.18
[2020-12-14 19:33:01,162][__main__][INFO] - [24000] Loss: 1.020, Running accuracy: 98.760, Time: 22.31
[2020-12-14 19:33:27,135][__main__][INFO] - [24320] Loss: 1.272, Running accuracy: 98.755, Time: 25.97
[2020-12-14 19:33:52,238][__main__][INFO] - [24640] Loss: 1.063, Running accuracy: 98.756, Time: 25.10
[2020-12-14 19:34:17,367][__main__][INFO] - [24960] Loss: 1.009, Running accuracy: 98.755, Time: 25.13
[2020-12-14 19:34:41,443][__main__][INFO] - [25280] Loss: 0.988, Running accuracy: 98.756, Time: 24.08
[2020-12-14 19:35:05,436][__main__][INFO] - [25600] Loss: 1.159, Running accuracy: 98.755, Time: 23.99
[2020-12-14 19:35:27,314][__main__][INFO] - [25920] Loss: 1.260, Running accuracy: 98.753, Time: 21.88
[2020-12-14 19:35:49,865][__main__][INFO] - [26240] Loss: 0.985, Running accuracy: 98.753, Time: 22.55
[2020-12-14 19:36:17,247][__main__][INFO] - [26560] Loss: 1.000, Running accuracy: 98.750, Time: 27.38
[2020-12-14 19:36:41,231][__main__][INFO] - [26880] Loss: 0.871, Running accuracy: 98.752, Time: 23.98
[2020-12-14 19:37:04,192][__main__][INFO] - [27200] Loss: 0.727, Running accuracy: 98.757, Time: 22.96
[2020-12-14 19:37:26,522][__main__][INFO] - [27520] Loss: 1.111, Running accuracy: 98.758, Time: 22.33
[2020-12-14 19:37:49,864][__main__][INFO] - [27840] Loss: 0.922, Running accuracy: 98.758, Time: 23.34
[2020-12-14 19:38:13,975][__main__][INFO] - [28160] Loss: 1.014, Running accuracy: 98.759, Time: 24.11
[2020-12-14 19:38:38,525][__main__][INFO] - [28480] Loss: 1.386, Running accuracy: 98.753, Time: 24.40
[2020-12-14 19:39:05,174][__main__][INFO] - [28800] Loss: 0.942, Running accuracy: 98.754, Time: 26.65
[2020-12-14 19:39:30,013][__main__][INFO] - [29120] Loss: 1.232, Running accuracy: 98.751, Time: 24.84
[2020-12-14 19:39:54,400][__main__][INFO] - [29440] Loss: 1.315, Running accuracy: 98.749, Time: 24.39
[2020-12-14 19:40:17,315][__main__][INFO] - [29760] Loss: 1.044, Running accuracy: 98.748, Time: 22.92
[2020-12-14 19:40:39,202][__main__][INFO] - [30080] Loss: 0.970, Running accuracy: 98.746, Time: 21.89
[2020-12-14 19:41:01,486][__main__][INFO] - [30400] Loss: 1.162, Running accuracy: 98.745, Time: 22.28
[2020-12-14 19:41:24,427][__main__][INFO] - [30720] Loss: 1.124, Running accuracy: 98.745, Time: 22.94
[2020-12-14 19:41:45,952][__main__][INFO] - [31040] Loss: 1.104, Running accuracy: 98.743, Time: 21.52
[2020-12-14 19:42:09,147][__main__][INFO] - [31360] Loss: 0.908, Running accuracy: 98.744, Time: 23.19
[2020-12-14 19:42:32,848][__main__][INFO] - [31680] Loss: 1.004, Running accuracy: 98.744, Time: 23.70
[2020-12-14 19:42:56,181][__main__][INFO] - [32000] Loss: 1.090, Running accuracy: 98.743, Time: 23.33
[2020-12-14 19:43:21,131][__main__][INFO] - [32320] Loss: 1.239, Running accuracy: 98.744, Time: 24.95
[2020-12-14 19:43:44,421][__main__][INFO] - [32640] Loss: 1.058, Running accuracy: 98.742, Time: 23.29
[2020-12-14 19:44:07,201][__main__][INFO] - [32960] Loss: 0.911, Running accuracy: 98.743, Time: 22.78
[2020-12-14 19:44:35,235][__main__][INFO] - [33280] Loss: 1.258, Running accuracy: 98.742, Time: 28.03
[2020-12-14 19:44:57,408][__main__][INFO] - [33600] Loss: 1.053, Running accuracy: 98.742, Time: 22.17
[2020-12-14 19:45:19,639][__main__][INFO] - [33920] Loss: 0.932, Running accuracy: 98.742, Time: 22.23
[2020-12-14 19:45:41,982][__main__][INFO] - [34240] Loss: 1.147, Running accuracy: 98.741, Time: 22.34
[2020-12-14 19:46:04,546][__main__][INFO] - [34560] Loss: 1.062, Running accuracy: 98.740, Time: 22.56
[2020-12-14 19:46:27,428][__main__][INFO] - [34880] Loss: 1.411, Running accuracy: 98.736, Time: 22.88
[2020-12-14 19:46:51,863][__main__][INFO] - [35200] Loss: 1.230, Running accuracy: 98.734, Time: 24.43
[2020-12-14 19:47:15,855][__main__][INFO] - [35520] Loss: 1.049, Running accuracy: 98.734, Time: 23.99
[2020-12-14 19:47:39,563][__main__][INFO] - [35840] Loss: 0.928, Running accuracy: 98.736, Time: 23.71
[2020-12-14 19:48:02,645][__main__][INFO] - [36160] Loss: 0.876, Running accuracy: 98.737, Time: 23.08
[2020-12-14 19:48:27,583][__main__][INFO] - [36480] Loss: 0.956, Running accuracy: 98.738, Time: 24.94
[2020-12-14 19:48:51,470][__main__][INFO] - [36800] Loss: 1.209, Running accuracy: 98.736, Time: 23.89
[2020-12-14 19:49:14,692][__main__][INFO] - [37120] Loss: 1.115, Running accuracy: 98.736, Time: 23.22
[2020-12-14 19:49:38,320][__main__][INFO] - [37440] Loss: 1.137, Running accuracy: 98.736, Time: 23.62
[2020-12-14 19:50:00,595][__main__][INFO] - [37760] Loss: 1.163, Running accuracy: 98.734, Time: 22.27
[2020-12-14 19:50:26,662][__main__][INFO] - [38080] Loss: 1.170, Running accuracy: 98.732, Time: 26.07
[2020-12-14 19:50:52,223][__main__][INFO] - [38400] Loss: 1.037, Running accuracy: 98.733, Time: 25.56
[2020-12-14 19:51:15,119][__main__][INFO] - [38720] Loss: 1.110, Running accuracy: 98.732, Time: 22.90
[2020-12-14 19:51:37,601][__main__][INFO] - [39040] Loss: 1.089, Running accuracy: 98.732, Time: 22.48
[2020-12-14 19:52:01,631][__main__][INFO] - [39360] Loss: 0.988, Running accuracy: 98.733, Time: 24.03
[2020-12-14 19:52:24,830][__main__][INFO] - [39680] Loss: 1.131, Running accuracy: 98.733, Time: 23.20
[2020-12-14 19:52:33,367][__main__][INFO] - Action accuracy: 98.733, Loss: 1.171
[2020-12-14 19:52:33,368][__main__][INFO] - Validating..
[2020-12-14 19:53:09,324][test][INFO] - Time elapsed: 33.834683
[2020-12-14 19:53:09,328][__main__][INFO] - Validation F1 score: 95.580, Exact match: 55.880, Precision: 95.590, Recall: 95.580
[2020-12-14 19:53:48,078][__main__][INFO] - Checkpoint saved to checkpoints/model_latest.pth
[2020-12-14 19:53:49,140][__main__][INFO] - Epoch #10
[2020-12-14 19:53:49,141][__main__][INFO] - Training..
[2020-12-14 19:54:23,287][__main__][INFO] - [320] Loss: 0.918, Running accuracy: 98.892, Time: 32.92
[2020-12-14 19:54:46,565][__main__][INFO] - [640] Loss: 0.960, Running accuracy: 98.823, Time: 23.28
[2020-12-14 19:55:10,324][__main__][INFO] - [960] Loss: 1.140, Running accuracy: 98.766, Time: 23.76
[2020-12-14 19:55:35,314][__main__][INFO] - [1280] Loss: 1.156, Running accuracy: 98.768, Time: 24.99
[2020-12-14 19:55:58,435][__main__][INFO] - [1600] Loss: 0.985, Running accuracy: 98.765, Time: 23.12
[2020-12-14 19:56:20,573][__main__][INFO] - [1920] Loss: 1.134, Running accuracy: 98.765, Time: 22.14
[2020-12-14 19:56:43,687][__main__][INFO] - [2240] Loss: 0.980, Running accuracy: 98.790, Time: 23.11
[2020-12-14 19:57:07,289][__main__][INFO] - [2560] Loss: 1.009, Running accuracy: 98.805, Time: 23.60
[2020-12-14 19:57:31,046][__main__][INFO] - [2880] Loss: 0.846, Running accuracy: 98.837, Time: 23.76
[2020-12-14 19:57:55,532][__main__][INFO] - [3200] Loss: 0.993, Running accuracy: 98.829, Time: 24.49
[2020-12-14 19:58:18,068][__main__][INFO] - [3520] Loss: 0.889, Running accuracy: 98.840, Time: 22.53
[2020-12-14 19:58:41,286][__main__][INFO] - [3840] Loss: 0.742, Running accuracy: 98.848, Time: 23.22
[2020-12-14 19:59:08,194][__main__][INFO] - [4160] Loss: 1.115, Running accuracy: 98.843, Time: 26.91
[2020-12-14 19:59:32,781][__main__][INFO] - [4480] Loss: 0.894, Running accuracy: 98.862, Time: 24.59
[2020-12-14 20:00:00,387][__main__][INFO] - [4800] Loss: 0.902, Running accuracy: 98.872, Time: 27.60
[2020-12-14 20:00:23,924][__main__][INFO] - [5120] Loss: 0.939, Running accuracy: 98.868, Time: 23.54
[2020-12-14 20:00:46,957][__main__][INFO] - [5440] Loss: 1.061, Running accuracy: 98.862, Time: 23.03
[2020-12-14 20:01:09,813][__main__][INFO] - [5760] Loss: 0.900, Running accuracy: 98.861, Time: 22.85
[2020-12-14 20:01:32,276][__main__][INFO] - [6080] Loss: 0.737, Running accuracy: 98.870, Time: 22.46
[2020-12-14 20:01:54,670][__main__][INFO] - [6400] Loss: 0.842, Running accuracy: 98.876, Time: 22.39
[2020-12-14 20:02:18,677][__main__][INFO] - [6720] Loss: 0.950, Running accuracy: 98.886, Time: 24.01
[2020-12-14 20:02:42,736][__main__][INFO] - [7040] Loss: 0.927, Running accuracy: 98.889, Time: 24.06
[2020-12-14 20:03:06,035][__main__][INFO] - [7360] Loss: 0.835, Running accuracy: 98.889, Time: 23.30
[2020-12-14 20:03:31,296][__main__][INFO] - [7680] Loss: 0.945, Running accuracy: 98.890, Time: 25.26
[2020-12-14 20:03:53,754][__main__][INFO] - [8000] Loss: 0.750, Running accuracy: 98.896, Time: 22.46
[2020-12-14 20:04:16,304][__main__][INFO] - [8320] Loss: 1.012, Running accuracy: 98.887, Time: 22.55
[2020-12-14 20:04:40,966][__main__][INFO] - [8640] Loss: 0.890, Running accuracy: 98.895, Time: 24.66
[2020-12-14 20:05:04,626][__main__][INFO] - [8960] Loss: 0.772, Running accuracy: 98.903, Time: 23.66
[2020-12-14 20:05:33,552][__main__][INFO] - [9280] Loss: 0.979, Running accuracy: 98.901, Time: 28.93
[2020-12-14 20:05:57,211][__main__][INFO] - [9600] Loss: 0.877, Running accuracy: 98.904, Time: 23.66
[2020-12-14 20:06:21,391][__main__][INFO] - [9920] Loss: 0.979, Running accuracy: 98.907, Time: 24.18
[2020-12-14 20:06:45,054][__main__][INFO] - [10240] Loss: 0.894, Running accuracy: 98.903, Time: 23.66
[2020-12-14 20:07:09,163][__main__][INFO] - [10560] Loss: 0.890, Running accuracy: 98.903, Time: 24.11
[2020-12-14 20:07:33,528][__main__][INFO] - [10880] Loss: 0.917, Running accuracy: 98.900, Time: 24.36
[2020-12-14 20:07:55,322][__main__][INFO] - [11200] Loss: 0.728, Running accuracy: 98.906, Time: 21.79
[2020-12-14 20:08:18,049][__main__][INFO] - [11520] Loss: 0.741, Running accuracy: 98.911, Time: 22.73
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[2020-12-14 20:41:27,892][__main__][INFO] - [38400] Loss: 0.846, Running accuracy: 98.898, Time: 21.95
[2020-12-14 20:41:51,430][__main__][INFO] - [38720] Loss: 1.054, Running accuracy: 98.898, Time: 23.54
[2020-12-14 20:42:15,941][__main__][INFO] - [39040] Loss: 0.831, Running accuracy: 98.899, Time: 24.51
[2020-12-14 20:42:40,883][__main__][INFO] - [39360] Loss: 0.875, Running accuracy: 98.899, Time: 24.52
[2020-12-14 20:43:03,513][__main__][INFO] - [39680] Loss: 0.906, Running accuracy: 98.898, Time: 22.63
[2020-12-14 20:43:13,154][__main__][INFO] - Action accuracy: 98.897, Loss: 1.015
[2020-12-14 20:43:13,155][__main__][INFO] - Validating..
[2020-12-14 20:43:54,798][test][INFO] - Time elapsed: 39.733783
[2020-12-14 20:43:54,803][__main__][INFO] - Validation F1 score: 95.410, Exact match: 54.180, Precision: 95.320, Recall: 95.490
[2020-12-14 20:44:34,657][__main__][INFO] - Checkpoint saved to checkpoints/model_latest.pth
[2020-12-14 20:44:35,603][__main__][INFO] - Epoch #11
[2020-12-14 20:44:35,603][__main__][INFO] - Training..
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[2020-12-14 21:17:28,588][__main__][INFO] - [26560] Loss: 0.679, Running accuracy: 99.033, Time: 21.24
[2020-12-14 21:17:52,750][__main__][INFO] - [26880] Loss: 0.931, Running accuracy: 99.032, Time: 24.16
[2020-12-14 21:18:14,691][__main__][INFO] - [27200] Loss: 0.557, Running accuracy: 99.034, Time: 21.94
[2020-12-14 21:18:39,006][__main__][INFO] - [27520] Loss: 0.853, Running accuracy: 99.033, Time: 24.31
[2020-12-14 21:19:02,991][__main__][INFO] - [27840] Loss: 0.699, Running accuracy: 99.034, Time: 23.98
[2020-12-14 21:19:25,708][__main__][INFO] - [28160] Loss: 0.787, Running accuracy: 99.033, Time: 22.72
[2020-12-14 21:19:47,773][__main__][INFO] - [28480] Loss: 0.891, Running accuracy: 99.032, Time: 22.06
[2020-12-14 21:20:10,621][__main__][INFO] - [28800] Loss: 0.825, Running accuracy: 99.034, Time: 22.85
[2020-12-14 21:20:32,620][__main__][INFO] - [29120] Loss: 0.643, Running accuracy: 99.036, Time: 22.00
[2020-12-14 21:20:54,974][__main__][INFO] - [29440] Loss: 0.787, Running accuracy: 99.033, Time: 22.35
[2020-12-14 21:21:18,658][__main__][INFO] - [29760] Loss: 0.836, Running accuracy: 99.033, Time: 23.68
[2020-12-14 21:21:40,721][__main__][INFO] - [30080] Loss: 0.706, Running accuracy: 99.033, Time: 22.06
[2020-12-14 21:22:09,331][__main__][INFO] - [30400] Loss: 0.897, Running accuracy: 99.032, Time: 28.61
[2020-12-14 21:22:32,555][__main__][INFO] - [30720] Loss: 0.613, Running accuracy: 99.033, Time: 23.22
[2020-12-14 21:22:54,506][__main__][INFO] - [31040] Loss: 0.901, Running accuracy: 99.032, Time: 21.95
[2020-12-14 21:23:17,012][__main__][INFO] - [31360] Loss: 1.192, Running accuracy: 99.029, Time: 22.51
[2020-12-14 21:23:41,099][__main__][INFO] - [31680] Loss: 0.794, Running accuracy: 99.028, Time: 24.09
[2020-12-14 21:24:04,116][__main__][INFO] - [32000] Loss: 0.959, Running accuracy: 99.027, Time: 23.02
[2020-12-14 21:24:27,348][__main__][INFO] - [32320] Loss: 0.844, Running accuracy: 99.025, Time: 23.23
[2020-12-14 21:24:52,593][__main__][INFO] - [32640] Loss: 0.803, Running accuracy: 99.025, Time: 25.24
[2020-12-14 21:25:17,028][__main__][INFO] - [32960] Loss: 1.049, Running accuracy: 99.021, Time: 24.43
[2020-12-14 21:25:41,356][__main__][INFO] - [33280] Loss: 0.918, Running accuracy: 99.020, Time: 24.33
[2020-12-14 21:26:04,810][__main__][INFO] - [33600] Loss: 0.761, Running accuracy: 99.022, Time: 23.45
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[2020-12-14 21:26:51,792][__main__][INFO] - [34240] Loss: 0.750, Running accuracy: 99.022, Time: 22.58
[2020-12-14 21:27:13,666][__main__][INFO] - [34560] Loss: 0.976, Running accuracy: 99.021, Time: 21.87
[2020-12-14 21:27:42,296][__main__][INFO] - [34880] Loss: 0.803, Running accuracy: 99.021, Time: 28.63
[2020-12-14 21:28:07,421][__main__][INFO] - [35200] Loss: 0.850, Running accuracy: 99.020, Time: 25.12
[2020-12-14 21:28:32,567][__main__][INFO] - [35520] Loss: 0.704, Running accuracy: 99.020, Time: 25.14
[2020-12-14 21:28:56,852][__main__][INFO] - [35840] Loss: 0.777, Running accuracy: 99.020, Time: 24.28
[2020-12-14 21:29:18,469][__main__][INFO] - [36160] Loss: 0.656, Running accuracy: 99.021, Time: 21.62
[2020-12-14 21:29:41,834][__main__][INFO] - [36480] Loss: 0.684, Running accuracy: 99.022, Time: 23.36
[2020-12-14 21:30:04,449][__main__][INFO] - [36800] Loss: 1.226, Running accuracy: 99.020, Time: 22.61
[2020-12-14 21:30:27,842][__main__][INFO] - [37120] Loss: 0.813, Running accuracy: 99.021, Time: 23.39
[2020-12-14 21:30:51,542][__main__][INFO] - [37440] Loss: 0.800, Running accuracy: 99.019, Time: 23.70
[2020-12-14 21:31:15,139][__main__][INFO] - [37760] Loss: 0.732, Running accuracy: 99.020, Time: 23.15
[2020-12-14 21:31:36,757][__main__][INFO] - [38080] Loss: 0.885, Running accuracy: 99.019, Time: 21.62
[2020-12-14 21:31:58,907][__main__][INFO] - [38400] Loss: 0.713, Running accuracy: 99.019, Time: 22.15
[2020-12-14 21:32:22,916][__main__][INFO] - [38720] Loss: 0.791, Running accuracy: 99.020, Time: 24.01
[2020-12-14 21:32:47,469][__main__][INFO] - [39040] Loss: 0.783, Running accuracy: 99.020, Time: 24.55
[2020-12-14 21:33:15,034][__main__][INFO] - [39360] Loss: 0.873, Running accuracy: 99.019, Time: 27.56
[2020-12-14 21:33:37,809][__main__][INFO] - [39680] Loss: 0.682, Running accuracy: 99.020, Time: 22.77
[2020-12-14 21:33:47,775][__main__][INFO] - Action accuracy: 99.020, Loss: 0.891
[2020-12-14 21:33:47,776][__main__][INFO] - Validating..
[2020-12-14 21:34:23,999][test][INFO] - Time elapsed: 34.056430
[2020-12-14 21:34:24,004][__main__][INFO] - Validation F1 score: 95.550, Exact match: 55.940, Precision: 95.640, Recall: 95.470
[2020-12-14 21:35:03,361][__main__][INFO] - Checkpoint saved to checkpoints/model_latest.pth
[2020-12-14 21:35:04,187][__main__][INFO] - Epoch #12
[2020-12-14 21:35:04,188][__main__][INFO] - Training..
[2020-12-14 21:35:30,689][__main__][INFO] - [320] Loss: 0.817, Running accuracy: 99.101, Time: 25.18
[2020-12-14 21:35:53,433][__main__][INFO] - [640] Loss: 0.701, Running accuracy: 99.146, Time: 22.74
[2020-12-14 21:36:17,909][__main__][INFO] - [960] Loss: 0.746, Running accuracy: 99.108, Time: 24.48
[2020-12-14 21:36:40,431][__main__][INFO] - [1280] Loss: 0.996, Running accuracy: 99.059, Time: 22.52
[2020-12-14 21:37:05,167][__main__][INFO] - [1600] Loss: 0.614, Running accuracy: 99.122, Time: 24.74
[2020-12-14 21:37:36,609][__main__][INFO] - [1920] Loss: 0.672, Running accuracy: 99.118, Time: 31.44
[2020-12-14 21:38:00,804][__main__][INFO] - [2240] Loss: 0.772, Running accuracy: 99.110, Time: 24.19
[2020-12-14 21:38:24,218][__main__][INFO] - [2560] Loss: 0.704, Running accuracy: 99.111, Time: 23.41
[2020-12-14 21:38:48,545][__main__][INFO] - [2880] Loss: 0.581, Running accuracy: 99.140, Time: 24.33
[2020-12-14 21:39:12,493][__main__][INFO] - [3200] Loss: 0.700, Running accuracy: 99.144, Time: 23.95
[2020-12-14 21:39:35,195][__main__][INFO] - [3520] Loss: 0.587, Running accuracy: 99.141, Time: 22.70
[2020-12-14 21:39:58,724][__main__][INFO] - [3840] Loss: 0.705, Running accuracy: 99.140, Time: 23.53
[2020-12-14 21:40:20,137][__main__][INFO] - [4160] Loss: 0.862, Running accuracy: 99.137, Time: 21.41
[2020-12-14 21:40:44,568][__main__][INFO] - [4480] Loss: 0.814, Running accuracy: 99.127, Time: 24.43
[2020-12-14 21:41:07,013][__main__][INFO] - [4800] Loss: 0.763, Running accuracy: 99.128, Time: 22.44
[2020-12-14 21:41:29,489][__main__][INFO] - [5120] Loss: 0.640, Running accuracy: 99.132, Time: 22.47
[2020-12-14 21:41:52,384][__main__][INFO] - [5440] Loss: 0.704, Running accuracy: 99.134, Time: 22.89
[2020-12-14 21:42:16,467][__main__][INFO] - [5760] Loss: 0.869, Running accuracy: 99.130, Time: 24.08
[2020-12-14 21:42:41,722][__main__][INFO] - [6080] Loss: 0.794, Running accuracy: 99.127, Time: 25.25
[2020-12-14 21:43:11,205][__main__][INFO] - [6400] Loss: 0.757, Running accuracy: 99.129, Time: 29.48
[2020-12-14 21:43:33,941][__main__][INFO] - [6720] Loss: 0.728, Running accuracy: 99.130, Time: 22.73
[2020-12-14 21:43:56,797][__main__][INFO] - [7040] Loss: 0.595, Running accuracy: 99.136, Time: 22.86
[2020-12-14 21:44:19,431][__main__][INFO] - [7360] Loss: 0.752, Running accuracy: 99.135, Time: 22.63
[2020-12-14 21:44:41,546][__main__][INFO] - [7680] Loss: 0.571, Running accuracy: 99.140, Time: 22.11
[2020-12-14 21:45:03,044][__main__][INFO] - [8000] Loss: 0.759, Running accuracy: 99.137, Time: 21.50
[2020-12-14 21:45:26,765][__main__][INFO] - [8320] Loss: 0.729, Running accuracy: 99.135, Time: 23.72
[2020-12-14 21:45:50,357][__main__][INFO] - [8640] Loss: 0.598, Running accuracy: 99.140, Time: 23.59
[2020-12-14 21:46:15,282][__main__][INFO] - [8960] Loss: 0.722, Running accuracy: 99.138, Time: 24.92
[2020-12-14 21:46:39,885][__main__][INFO] - [9280] Loss: 0.595, Running accuracy: 99.144, Time: 24.60
[2020-12-14 21:47:03,320][__main__][INFO] - [9600] Loss: 0.734, Running accuracy: 99.144, Time: 23.43
[2020-12-14 21:47:27,244][__main__][INFO] - [9920] Loss: 0.677, Running accuracy: 99.142, Time: 23.92
[2020-12-14 21:47:49,858][__main__][INFO] - [10240] Loss: 0.699, Running accuracy: 99.140, Time: 22.61
[2020-12-14 21:48:14,024][__main__][INFO] - [10560] Loss: 0.716, Running accuracy: 99.143, Time: 24.16
[2020-12-14 21:48:42,939][__main__][INFO] - [10880] Loss: 0.688, Running accuracy: 99.143, Time: 28.91
[2020-12-14 21:49:06,316][__main__][INFO] - [11200] Loss: 0.737, Running accuracy: 99.142, Time: 23.38
[2020-12-14 21:49:28,679][__main__][INFO] - [11520] Loss: 0.558, Running accuracy: 99.148, Time: 22.36
[2020-12-14 21:49:52,793][__main__][INFO] - [11840] Loss: 0.804, Running accuracy: 99.146, Time: 24.11
[2020-12-14 21:50:17,191][__main__][INFO] - [12160] Loss: 0.754, Running accuracy: 99.144, Time: 24.40
[2020-12-14 21:50:40,345][__main__][INFO] - [12480] Loss: 0.716, Running accuracy: 99.147, Time: 23.15
[2020-12-14 21:51:05,432][__main__][INFO] - [12800] Loss: 0.721, Running accuracy: 99.142, Time: 25.09
[2020-12-14 21:51:27,069][__main__][INFO] - [13120] Loss: 0.811, Running accuracy: 99.139, Time: 21.64
[2020-12-14 21:51:49,722][__main__][INFO] - [13440] Loss: 0.684, Running accuracy: 99.141, Time: 22.65
[2020-12-14 21:52:13,749][__main__][INFO] - [13760] Loss: 0.681, Running accuracy: 99.143, Time: 24.03
[2020-12-14 21:52:39,483][__main__][INFO] - [14080] Loss: 1.009, Running accuracy: 99.137, Time: 25.73
[2020-12-14 21:53:01,212][__main__][INFO] - [14400] Loss: 0.579, Running accuracy: 99.140, Time: 21.73
[2020-12-14 21:53:26,714][__main__][INFO] - [14720] Loss: 0.677, Running accuracy: 99.141, Time: 25.50
[2020-12-14 21:53:49,386][__main__][INFO] - [15040] Loss: 0.644, Running accuracy: 99.142, Time: 22.67
[2020-12-14 21:54:16,799][__main__][INFO] - [15360] Loss: 0.523, Running accuracy: 99.146, Time: 27.41
[2020-12-14 21:54:41,811][__main__][INFO] - [15680] Loss: 0.738, Running accuracy: 99.144, Time: 25.01
[2020-12-14 21:55:04,399][__main__][INFO] - [16000] Loss: 0.833, Running accuracy: 99.143, Time: 22.23
[2020-12-14 21:55:28,450][__main__][INFO] - [16320] Loss: 0.574, Running accuracy: 99.146, Time: 24.05
[2020-12-14 21:55:52,656][__main__][INFO] - [16640] Loss: 0.918, Running accuracy: 99.142, Time: 24.21
[2020-12-14 21:56:15,138][__main__][INFO] - [16960] Loss: 0.761, Running accuracy: 99.139, Time: 22.48
[2020-12-14 21:56:38,043][__main__][INFO] - [17280] Loss: 0.580, Running accuracy: 99.140, Time: 22.85
[2020-12-14 21:57:01,184][__main__][INFO] - [17600] Loss: 0.697, Running accuracy: 99.140, Time: 23.14
[2020-12-14 21:57:23,651][__main__][INFO] - [17920] Loss: 0.601, Running accuracy: 99.142, Time: 22.47
[2020-12-14 21:57:45,887][__main__][INFO] - [18240] Loss: 0.935, Running accuracy: 99.140, Time: 22.24
[2020-12-14 21:58:08,599][__main__][INFO] - [18560] Loss: 0.795, Running accuracy: 99.136, Time: 22.71
[2020-12-14 21:58:32,164][__main__][INFO] - [18880] Loss: 0.587, Running accuracy: 99.138, Time: 23.56
[2020-12-14 21:58:56,407][__main__][INFO] - [19200] Loss: 0.665, Running accuracy: 99.138, Time: 24.24
[2020-12-14 21:59:20,709][__main__][INFO] - [19520] Loss: 0.744, Running accuracy: 99.138, Time: 24.30
[2020-12-14 21:59:49,777][__main__][INFO] - [19840] Loss: 0.822, Running accuracy: 99.136, Time: 29.07
[2020-12-14 22:00:12,708][__main__][INFO] - [20160] Loss: 0.654, Running accuracy: 99.136, Time: 22.93
[2020-12-14 22:00:36,261][__main__][INFO] - [20480] Loss: 0.596, Running accuracy: 99.136, Time: 23.55
[2020-12-14 22:00:58,363][__main__][INFO] - [20800] Loss: 0.573, Running accuracy: 99.139, Time: 22.10
[2020-12-14 22:01:22,271][__main__][INFO] - [21120] Loss: 0.685, Running accuracy: 99.140, Time: 23.91
[2020-12-14 22:01:45,326][__main__][INFO] - [21440] Loss: 0.545, Running accuracy: 99.142, Time: 23.05
[2020-12-14 22:02:09,005][__main__][INFO] - [21760] Loss: 0.721, Running accuracy: 99.142, Time: 23.68
[2020-12-14 22:02:31,696][__main__][INFO] - [22080] Loss: 0.747, Running accuracy: 99.143, Time: 22.69
[2020-12-14 22:02:53,983][__main__][INFO] - [22400] Loss: 0.568, Running accuracy: 99.144, Time: 22.29
[2020-12-14 22:03:16,712][__main__][INFO] - [22720] Loss: 0.600, Running accuracy: 99.145, Time: 22.73
[2020-12-14 22:03:41,189][__main__][INFO] - [23040] Loss: 1.062, Running accuracy: 99.139, Time: 24.48
[2020-12-14 22:04:03,823][__main__][INFO] - [23360] Loss: 0.516, Running accuracy: 99.141, Time: 22.63
[2020-12-14 22:04:26,770][__main__][INFO] - [23680] Loss: 0.713, Running accuracy: 99.140, Time: 22.95
[2020-12-14 22:04:49,145][__main__][INFO] - [24000] Loss: 0.685, Running accuracy: 99.140, Time: 22.37
[2020-12-14 22:05:17,555][__main__][INFO] - [24320] Loss: 0.688, Running accuracy: 99.141, Time: 28.41
[2020-12-14 22:05:43,356][__main__][INFO] - [24640] Loss: 0.729, Running accuracy: 99.141, Time: 25.80
[2020-12-14 22:06:07,933][__main__][INFO] - [24960] Loss: 0.930, Running accuracy: 99.139, Time: 24.58
[2020-12-14 22:06:31,592][__main__][INFO] - [25280] Loss: 0.613, Running accuracy: 99.139, Time: 23.66
[2020-12-14 22:06:55,407][__main__][INFO] - [25600] Loss: 0.700, Running accuracy: 99.139, Time: 23.81
[2020-12-14 22:07:17,770][__main__][INFO] - [25920] Loss: 0.894, Running accuracy: 99.136, Time: 22.36
[2020-12-14 22:07:40,505][__main__][INFO] - [26240] Loss: 0.572, Running accuracy: 99.137, Time: 22.73
[2020-12-14 22:08:03,509][__main__][INFO] - [26560] Loss: 0.719, Running accuracy: 99.137, Time: 23.00
[2020-12-14 22:08:27,519][__main__][INFO] - [26880] Loss: 0.685, Running accuracy: 99.138, Time: 24.01
[2020-12-14 22:08:50,899][__main__][INFO] - [27200] Loss: 0.812, Running accuracy: 99.137, Time: 23.38
[2020-12-14 22:09:13,618][__main__][INFO] - [27520] Loss: 0.809, Running accuracy: 99.136, Time: 22.72
[2020-12-14 22:09:35,614][__main__][INFO] - [27840] Loss: 0.629, Running accuracy: 99.134, Time: 22.00
[2020-12-14 22:09:59,905][__main__][INFO] - [28160] Loss: 0.756, Running accuracy: 99.133, Time: 24.29
[2020-12-14 22:10:22,124][__main__][INFO] - [28480] Loss: 0.562, Running accuracy: 99.133, Time: 22.22
[2020-12-14 22:10:49,722][__main__][INFO] - [28800] Loss: 0.531, Running accuracy: 99.136, Time: 27.60
[2020-12-14 22:11:11,096][__main__][INFO] - [29120] Loss: 0.848, Running accuracy: 99.132, Time: 21.37
[2020-12-14 22:11:34,953][__main__][INFO] - [29440] Loss: 0.636, Running accuracy: 99.132, Time: 23.86
[2020-12-14 22:11:57,281][__main__][INFO] - [29760] Loss: 0.500, Running accuracy: 99.133, Time: 22.33
[2020-12-14 22:12:21,551][__main__][INFO] - [30080] Loss: 0.779, Running accuracy: 99.133, Time: 24.27
[2020-12-14 22:12:43,237][__main__][INFO] - [30400] Loss: 0.771, Running accuracy: 99.132, Time: 21.69
[2020-12-14 22:13:06,230][__main__][INFO] - [30720] Loss: 0.695, Running accuracy: 99.132, Time: 22.99
[2020-12-14 22:13:30,876][__main__][INFO] - [31040] Loss: 0.623, Running accuracy: 99.132, Time: 24.64
[2020-12-14 22:13:53,730][__main__][INFO] - [31360] Loss: 0.712, Running accuracy: 99.132, Time: 22.85
[2020-12-14 22:14:16,143][__main__][INFO] - [31680] Loss: 0.633, Running accuracy: 99.132, Time: 22.41
[2020-12-14 22:14:38,790][__main__][INFO] - [32000] Loss: 0.705, Running accuracy: 99.132, Time: 22.65
[2020-12-14 22:15:04,664][__main__][INFO] - [32320] Loss: 0.809, Running accuracy: 99.130, Time: 25.10
[2020-12-14 22:15:28,132][__main__][INFO] - [32640] Loss: 0.740, Running accuracy: 99.130, Time: 23.47
[2020-12-14 22:15:52,456][__main__][INFO] - [32960] Loss: 0.699, Running accuracy: 99.129, Time: 24.32
[2020-12-14 22:16:21,731][__main__][INFO] - [33280] Loss: 0.755, Running accuracy: 99.128, Time: 29.27
[2020-12-14 22:16:43,251][__main__][INFO] - [33600] Loss: 0.601, Running accuracy: 99.129, Time: 21.52
[2020-12-14 22:17:04,843][__main__][INFO] - [33920] Loss: 0.694, Running accuracy: 99.128, Time: 21.59
[2020-12-14 22:17:27,681][__main__][INFO] - [34240] Loss: 0.665, Running accuracy: 99.127, Time: 22.84
[2020-12-14 22:17:52,079][__main__][INFO] - [34560] Loss: 0.731, Running accuracy: 99.128, Time: 24.40
[2020-12-14 22:18:17,537][__main__][INFO] - [34880] Loss: 0.718, Running accuracy: 99.127, Time: 25.46
[2020-12-14 22:18:40,885][__main__][INFO] - [35200] Loss: 0.824, Running accuracy: 99.126, Time: 23.35
[2020-12-14 22:19:03,156][__main__][INFO] - [35520] Loss: 0.631, Running accuracy: 99.128, Time: 22.27
[2020-12-14 22:19:27,952][__main__][INFO] - [35840] Loss: 0.861, Running accuracy: 99.127, Time: 24.79
[2020-12-14 22:19:51,581][__main__][INFO] - [36160] Loss: 0.817, Running accuracy: 99.127, Time: 23.63
[2020-12-14 22:20:14,545][__main__][INFO] - [36480] Loss: 0.694, Running accuracy: 99.127, Time: 22.96
[2020-12-14 22:20:37,378][__main__][INFO] - [36800] Loss: 0.577, Running accuracy: 99.127, Time: 22.83
[2020-12-14 22:21:00,304][__main__][INFO] - [37120] Loss: 0.757, Running accuracy: 99.127, Time: 22.92
[2020-12-14 22:21:22,897][__main__][INFO] - [37440] Loss: 0.575, Running accuracy: 99.129, Time: 22.59
[2020-12-14 22:21:49,615][__main__][INFO] - [37760] Loss: 0.796, Running accuracy: 99.127, Time: 26.72
[2020-12-14 22:22:13,208][__main__][INFO] - [38080] Loss: 0.799, Running accuracy: 99.125, Time: 23.59
[2020-12-14 22:22:35,487][__main__][INFO] - [38400] Loss: 0.731, Running accuracy: 99.126, Time: 22.28
[2020-12-14 22:22:59,452][__main__][INFO] - [38720] Loss: 0.717, Running accuracy: 99.126, Time: 23.96
[2020-12-14 22:23:21,269][__main__][INFO] - [39040] Loss: 0.639, Running accuracy: 99.126, Time: 21.82
[2020-12-14 22:23:47,731][__main__][INFO] - [39360] Loss: 0.673, Running accuracy: 99.126, Time: 25.72
[2020-12-14 22:24:10,247][__main__][INFO] - [39680] Loss: 0.866, Running accuracy: 99.124, Time: 22.52
[2020-12-14 22:24:18,868][__main__][INFO] - Action accuracy: 99.124, Loss: 0.794
[2020-12-14 22:24:18,870][__main__][INFO] - Validating..
[2020-12-14 22:24:55,117][test][INFO] - Time elapsed: 34.359215
[2020-12-14 22:24:55,122][__main__][INFO] - Validation F1 score: 95.590, Exact match: 56.180, Precision: 95.490, Recall: 95.700
[2020-12-14 22:25:36,107][__main__][INFO] - Checkpoint saved to checkpoints/model_latest.pth
[2020-12-14 22:25:37,163][__main__][INFO] - Epoch #13
[2020-12-14 22:25:37,163][__main__][INFO] - Training..
[2020-12-14 22:26:09,841][__main__][INFO] - [320] Loss: 0.545, Running accuracy: 99.407, Time: 31.45
[2020-12-14 22:26:34,336][__main__][INFO] - [640] Loss: 0.768, Running accuracy: 99.255, Time: 24.49
[2020-12-14 22:26:57,568][__main__][INFO] - [960] Loss: 0.510, Running accuracy: 99.246, Time: 23.23
[2020-12-14 22:27:22,680][__main__][INFO] - [1280] Loss: 0.600, Running accuracy: 99.221, Time: 25.11
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[2020-12-14 23:13:14,113][__main__][INFO] - [38400] Loss: 0.841, Running accuracy: 99.238, Time: 25.11
[2020-12-14 23:13:37,071][__main__][INFO] - [38720] Loss: 0.582, Running accuracy: 99.238, Time: 22.96
[2020-12-14 23:14:01,466][__main__][INFO] - [39040] Loss: 0.798, Running accuracy: 99.238, Time: 24.39
[2020-12-14 23:14:26,478][__main__][INFO] - [39360] Loss: 0.540, Running accuracy: 99.237, Time: 25.01
[2020-12-14 23:14:49,905][__main__][INFO] - [39680] Loss: 0.659, Running accuracy: 99.237, Time: 23.43
[2020-12-14 23:15:00,286][__main__][INFO] - Action accuracy: 99.237, Loss: 0.697
[2020-12-14 23:15:00,287][__main__][INFO] - Validating..
[2020-12-14 23:15:43,200][test][INFO] - Time elapsed: 41.006212
[2020-12-14 23:15:43,205][__main__][INFO] - Validation F1 score: 95.400, Exact match: 54.820, Precision: 95.390, Recall: 95.420
[2020-12-14 23:16:23,536][__main__][INFO] - Checkpoint saved to checkpoints/model_latest.pth
[2020-12-14 23:16:24,514][__main__][INFO] - Epoch #14
[2020-12-14 23:16:24,515][__main__][INFO] - Training..
[2020-12-14 23:16:48,286][__main__][INFO] - [320] Loss: 0.519, Running accuracy: 99.272, Time: 22.37
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[2020-12-15 00:05:41,112][__main__][INFO] - Action accuracy: 99.304, Loss: 0.623
[2020-12-15 00:05:41,113][__main__][INFO] - Validating..
[2020-12-15 00:06:17,323][test][INFO] - Time elapsed: 34.018745
[2020-12-15 00:06:17,328][__main__][INFO] - Validation F1 score: 95.690, Exact match: 55.940, Precision: 95.570, Recall: 95.810
Epoch    15: reducing learning rate of group 0 to 5.0000e-06.
[2020-12-15 00:06:57,580][__main__][INFO] - Checkpoint saved to checkpoints/model_latest.pth
[2020-12-15 00:06:58,412][__main__][INFO] - Epoch #15
[2020-12-15 00:06:58,413][__main__][INFO] - Training..
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[2020-12-15 00:48:39,464][__main__][INFO] - [33600] Loss: 0.343, Running accuracy: 99.496, Time: 25.34
[2020-12-15 00:49:03,023][__main__][INFO] - [33920] Loss: 0.503, Running accuracy: 99.495, Time: 23.56
[2020-12-15 00:49:26,165][__main__][INFO] - [34240] Loss: 0.394, Running accuracy: 99.495, Time: 23.14
[2020-12-15 00:49:51,281][__main__][INFO] - [34560] Loss: 0.354, Running accuracy: 99.497, Time: 25.11
[2020-12-15 00:50:15,721][__main__][INFO] - [34880] Loss: 0.521, Running accuracy: 99.496, Time: 24.44
[2020-12-15 00:50:38,061][__main__][INFO] - [35200] Loss: 0.412, Running accuracy: 99.496, Time: 22.34
[2020-12-15 00:51:02,165][__main__][INFO] - [35520] Loss: 0.369, Running accuracy: 99.496, Time: 24.10
[2020-12-15 00:51:26,291][__main__][INFO] - [35840] Loss: 0.479, Running accuracy: 99.495, Time: 24.13
[2020-12-15 00:51:48,908][__main__][INFO] - [36160] Loss: 0.427, Running accuracy: 99.494, Time: 22.62
[2020-12-15 00:52:11,214][__main__][INFO] - [36480] Loss: 0.406, Running accuracy: 99.494, Time: 22.31
[2020-12-15 00:52:33,923][__main__][INFO] - [36800] Loss: 0.480, Running accuracy: 99.494, Time: 22.71
[2020-12-15 00:52:56,579][__main__][INFO] - [37120] Loss: 0.644, Running accuracy: 99.493, Time: 22.65
[2020-12-15 00:53:19,209][__main__][INFO] - [37440] Loss: 0.347, Running accuracy: 99.493, Time: 22.63
[2020-12-15 00:53:45,790][__main__][INFO] - [37760] Loss: 0.371, Running accuracy: 99.494, Time: 26.58
[2020-12-15 00:54:08,333][__main__][INFO] - [38080] Loss: 0.369, Running accuracy: 99.495, Time: 22.54
[2020-12-15 00:54:31,790][__main__][INFO] - [38400] Loss: 0.409, Running accuracy: 99.496, Time: 23.46
[2020-12-15 00:54:56,055][__main__][INFO] - [38720] Loss: 0.351, Running accuracy: 99.496, Time: 24.26
[2020-12-15 00:55:20,271][__main__][INFO] - [39040] Loss: 0.325, Running accuracy: 99.497, Time: 24.22
[2020-12-15 00:55:44,777][__main__][INFO] - [39360] Loss: 0.380, Running accuracy: 99.498, Time: 24.51
[2020-12-15 00:56:09,303][__main__][INFO] - [39680] Loss: 0.449, Running accuracy: 99.498, Time: 24.52
[2020-12-15 00:56:18,132][__main__][INFO] - Action accuracy: 99.497, Loss: 0.470
[2020-12-15 00:56:18,133][__main__][INFO] - Validating..
[2020-12-15 00:56:54,263][test][INFO] - Time elapsed: 34.173954
[2020-12-15 00:56:54,267][__main__][INFO] - Validation F1 score: 95.640, Exact match: 56.060, Precision: 95.680, Recall: 95.600
[2020-12-15 00:57:36,939][__main__][INFO] - Checkpoint saved to checkpoints/model_latest.pth
[2020-12-15 00:57:37,987][__main__][INFO] - Epoch #16
[2020-12-15 00:57:37,987][__main__][INFO] - Training..
[2020-12-15 00:58:09,745][__main__][INFO] - [320] Loss: 0.419, Running accuracy: 99.565, Time: 30.47
[2020-12-15 00:58:33,249][__main__][INFO] - [640] Loss: 0.319, Running accuracy: 99.580, Time: 23.50
[2020-12-15 00:58:56,347][__main__][INFO] - [960] Loss: 0.319, Running accuracy: 99.576, Time: 23.10
[2020-12-15 00:59:19,571][__main__][INFO] - [1280] Loss: 0.320, Running accuracy: 99.586, Time: 23.22
[2020-12-15 00:59:43,285][__main__][INFO] - [1600] Loss: 0.425, Running accuracy: 99.588, Time: 23.71
[2020-12-15 01:00:05,646][__main__][INFO] - [1920] Loss: 0.260, Running accuracy: 99.602, Time: 22.36
[2020-12-15 01:00:30,613][__main__][INFO] - [2240] Loss: 0.351, Running accuracy: 99.591, Time: 24.97
[2020-12-15 01:00:53,003][__main__][INFO] - [2560] Loss: 0.300, Running accuracy: 99.610, Time: 22.38
[2020-12-15 01:01:17,207][__main__][INFO] - [2880] Loss: 0.312, Running accuracy: 99.617, Time: 24.20
[2020-12-15 01:01:42,083][__main__][INFO] - [3200] Loss: 0.320, Running accuracy: 99.618, Time: 24.87
[2020-12-15 01:02:05,939][__main__][INFO] - [3520] Loss: 0.322, Running accuracy: 99.616, Time: 23.85
[2020-12-15 01:02:28,063][__main__][INFO] - [3840] Loss: 0.430, Running accuracy: 99.599, Time: 22.12
[2020-12-15 01:02:51,493][__main__][INFO] - [4160] Loss: 0.309, Running accuracy: 99.605, Time: 23.43
[2020-12-15 01:03:14,680][__main__][INFO] - [4480] Loss: 0.403, Running accuracy: 99.603, Time: 23.19
[2020-12-15 01:03:43,581][__main__][INFO] - [4800] Loss: 0.358, Running accuracy: 99.601, Time: 28.90
[2020-12-15 01:04:05,640][__main__][INFO] - [5120] Loss: 0.296, Running accuracy: 99.603, Time: 22.06
[2020-12-15 01:04:27,197][__main__][INFO] - [5440] Loss: 0.281, Running accuracy: 99.608, Time: 21.56
[2020-12-15 01:04:49,269][__main__][INFO] - [5760] Loss: 0.459, Running accuracy: 99.600, Time: 22.07
[2020-12-15 01:05:11,312][__main__][INFO] - [6080] Loss: 0.268, Running accuracy: 99.604, Time: 22.04
[2020-12-15 01:05:34,195][__main__][INFO] - [6400] Loss: 0.440, Running accuracy: 99.596, Time: 22.88
[2020-12-15 01:05:56,852][__main__][INFO] - [6720] Loss: 0.530, Running accuracy: 99.591, Time: 22.66
[2020-12-15 01:06:19,939][__main__][INFO] - [7040] Loss: 0.269, Running accuracy: 99.597, Time: 23.09
[2020-12-15 01:06:43,564][__main__][INFO] - [7360] Loss: 0.392, Running accuracy: 99.597, Time: 23.62
[2020-12-15 01:07:08,052][__main__][INFO] - [7680] Loss: 0.375, Running accuracy: 99.593, Time: 24.49
[2020-12-15 01:07:30,881][__main__][INFO] - [8000] Loss: 0.277, Running accuracy: 99.595, Time: 22.83
[2020-12-15 01:07:53,030][__main__][INFO] - [8320] Loss: 0.313, Running accuracy: 99.598, Time: 22.15
[2020-12-15 01:08:18,806][__main__][INFO] - [8640] Loss: 0.377, Running accuracy: 99.596, Time: 25.77
[2020-12-15 01:08:42,243][__main__][INFO] - [8960] Loss: 0.372, Running accuracy: 99.592, Time: 23.44
[2020-12-15 01:09:10,055][__main__][INFO] - [9280] Loss: 0.437, Running accuracy: 99.587, Time: 27.81
[2020-12-15 01:09:36,028][__main__][INFO] - [9600] Loss: 0.396, Running accuracy: 99.584, Time: 25.97
[2020-12-15 01:09:58,417][__main__][INFO] - [9920] Loss: 0.418, Running accuracy: 99.582, Time: 22.39
[2020-12-15 01:10:24,276][__main__][INFO] - [10240] Loss: 0.458, Running accuracy: 99.577, Time: 25.86
[2020-12-15 01:10:46,991][__main__][INFO] - [10560] Loss: 0.395, Running accuracy: 99.577, Time: 22.71
[2020-12-15 01:11:09,795][__main__][INFO] - [10880] Loss: 0.287, Running accuracy: 99.580, Time: 22.80
[2020-12-15 01:11:37,125][__main__][INFO] - [11200] Loss: 0.380, Running accuracy: 99.580, Time: 27.33
[2020-12-15 01:12:01,333][__main__][INFO] - [11520] Loss: 0.374, Running accuracy: 99.579, Time: 24.20
[2020-12-15 01:12:24,019][__main__][INFO] - [11840] Loss: 0.355, Running accuracy: 99.578, Time: 22.69
[2020-12-15 01:12:46,549][__main__][INFO] - [12160] Loss: 0.346, Running accuracy: 99.577, Time: 22.53
[2020-12-15 01:13:10,179][__main__][INFO] - [12480] Loss: 0.425, Running accuracy: 99.573, Time: 23.63
[2020-12-15 01:13:33,482][__main__][INFO] - [12800] Loss: 0.392, Running accuracy: 99.573, Time: 23.30
[2020-12-15 01:13:56,543][__main__][INFO] - [13120] Loss: 0.284, Running accuracy: 99.576, Time: 23.06
[2020-12-15 01:14:19,649][__main__][INFO] - [13440] Loss: 0.305, Running accuracy: 99.577, Time: 23.10
[2020-12-15 01:14:47,753][__main__][INFO] - [13760] Loss: 0.267, Running accuracy: 99.578, Time: 28.10
[2020-12-15 01:15:11,607][__main__][INFO] - [14080] Loss: 0.318, Running accuracy: 99.579, Time: 23.85
[2020-12-15 01:15:35,691][__main__][INFO] - [14400] Loss: 0.255, Running accuracy: 99.581, Time: 24.08
[2020-12-15 01:15:59,891][__main__][INFO] - [14720] Loss: 0.426, Running accuracy: 99.579, Time: 24.20
[2020-12-15 01:16:23,034][__main__][INFO] - [15040] Loss: 0.321, Running accuracy: 99.580, Time: 23.14
[2020-12-15 01:16:46,234][__main__][INFO] - [15360] Loss: 0.374, Running accuracy: 99.578, Time: 23.20
[2020-12-15 01:17:08,650][__main__][INFO] - [15680] Loss: 0.355, Running accuracy: 99.577, Time: 22.42
[2020-12-15 01:17:31,096][__main__][INFO] - [16000] Loss: 0.319, Running accuracy: 99.578, Time: 22.44
[2020-12-15 01:17:53,073][__main__][INFO] - [16320] Loss: 0.458, Running accuracy: 99.579, Time: 21.98
[2020-12-15 01:18:17,681][__main__][INFO] - [16640] Loss: 0.274, Running accuracy: 99.581, Time: 24.61
[2020-12-15 01:18:41,335][__main__][INFO] - [16960] Loss: 0.268, Running accuracy: 99.582, Time: 23.65
[2020-12-15 01:19:03,680][__main__][INFO] - [17280] Loss: 0.412, Running accuracy: 99.580, Time: 22.34
[2020-12-15 01:19:25,624][__main__][INFO] - [17600] Loss: 0.385, Running accuracy: 99.578, Time: 21.94
[2020-12-15 01:19:48,619][__main__][INFO] - [17920] Loss: 0.296, Running accuracy: 99.579, Time: 22.99
[2020-12-15 01:20:16,248][__main__][INFO] - [18240] Loss: 0.396, Running accuracy: 99.580, Time: 27.63
[2020-12-15 01:20:39,072][__main__][INFO] - [18560] Loss: 0.333, Running accuracy: 99.581, Time: 22.82
[2020-12-15 01:21:02,486][__main__][INFO] - [18880] Loss: 0.290, Running accuracy: 99.581, Time: 23.41
[2020-12-15 01:21:24,747][__main__][INFO] - [19200] Loss: 0.265, Running accuracy: 99.583, Time: 22.26
[2020-12-15 01:21:50,059][__main__][INFO] - [19520] Loss: 0.364, Running accuracy: 99.584, Time: 25.30
[2020-12-15 01:22:11,131][__main__][INFO] - [19840] Loss: 0.432, Running accuracy: 99.583, Time: 21.07
[2020-12-15 01:22:32,446][__main__][INFO] - [20160] Loss: 0.245, Running accuracy: 99.585, Time: 21.31
[2020-12-15 01:23:01,599][__main__][INFO] - [20480] Loss: 0.486, Running accuracy: 99.582, Time: 29.15
[2020-12-15 01:23:26,104][__main__][INFO] - [20800] Loss: 0.369, Running accuracy: 99.584, Time: 24.50
[2020-12-15 01:23:50,908][__main__][INFO] - [21120] Loss: 0.318, Running accuracy: 99.584, Time: 24.80
[2020-12-15 01:24:12,835][__main__][INFO] - [21440] Loss: 0.405, Running accuracy: 99.584, Time: 21.93
[2020-12-15 01:24:35,250][__main__][INFO] - [21760] Loss: 0.343, Running accuracy: 99.584, Time: 22.41
[2020-12-15 01:25:02,203][__main__][INFO] - [22080] Loss: 0.438, Running accuracy: 99.584, Time: 26.95
[2020-12-15 01:25:27,293][__main__][INFO] - [22400] Loss: 0.543, Running accuracy: 99.582, Time: 25.09
[2020-12-15 01:25:56,022][__main__][INFO] - [22720] Loss: 0.404, Running accuracy: 99.582, Time: 28.73
[2020-12-15 01:26:21,108][__main__][INFO] - [23040] Loss: 0.407, Running accuracy: 99.580, Time: 25.08
[2020-12-15 01:26:42,978][__main__][INFO] - [23360] Loss: 0.196, Running accuracy: 99.582, Time: 21.87
[2020-12-15 01:27:07,523][__main__][INFO] - [23680] Loss: 0.375, Running accuracy: 99.581, Time: 24.54
[2020-12-15 01:27:30,395][__main__][INFO] - [24000] Loss: 0.338, Running accuracy: 99.582, Time: 22.87
[2020-12-15 01:27:52,208][__main__][INFO] - [24320] Loss: 0.259, Running accuracy: 99.583, Time: 21.81
[2020-12-15 01:28:13,899][__main__][INFO] - [24640] Loss: 0.299, Running accuracy: 99.584, Time: 21.69
[2020-12-15 01:28:35,185][__main__][INFO] - [24960] Loss: 0.409, Running accuracy: 99.583, Time: 21.29
[2020-12-15 01:28:57,022][__main__][INFO] - [25280] Loss: 0.269, Running accuracy: 99.584, Time: 21.84
[2020-12-15 01:29:19,146][__main__][INFO] - [25600] Loss: 0.316, Running accuracy: 99.584, Time: 22.12
[2020-12-15 01:29:42,214][__main__][INFO] - [25920] Loss: 0.254, Running accuracy: 99.585, Time: 23.07
[2020-12-15 01:30:05,349][__main__][INFO] - [26240] Loss: 0.348, Running accuracy: 99.586, Time: 23.13
[2020-12-15 01:30:29,915][__main__][INFO] - [26560] Loss: 0.500, Running accuracy: 99.585, Time: 24.57
[2020-12-15 01:30:53,182][__main__][INFO] - [26880] Loss: 0.342, Running accuracy: 99.586, Time: 23.26
[2020-12-15 01:31:20,838][__main__][INFO] - [27200] Loss: 0.431, Running accuracy: 99.584, Time: 27.66
[2020-12-15 01:31:44,706][__main__][INFO] - [27520] Loss: 0.389, Running accuracy: 99.584, Time: 23.87
[2020-12-15 01:32:08,906][__main__][INFO] - [27840] Loss: 0.433, Running accuracy: 99.584, Time: 24.20
[2020-12-15 01:32:31,682][__main__][INFO] - [28160] Loss: 0.452, Running accuracy: 99.584, Time: 22.77
[2020-12-15 01:32:54,190][__main__][INFO] - [28480] Loss: 0.338, Running accuracy: 99.583, Time: 22.51
[2020-12-15 01:33:17,853][__main__][INFO] - [28800] Loss: 0.305, Running accuracy: 99.583, Time: 23.66
[2020-12-15 01:33:41,618][__main__][INFO] - [29120] Loss: 0.308, Running accuracy: 99.583, Time: 23.76
[2020-12-15 01:34:05,041][__main__][INFO] - [29440] Loss: 0.407, Running accuracy: 99.581, Time: 23.42
[2020-12-15 01:34:28,696][__main__][INFO] - [29760] Loss: 0.398, Running accuracy: 99.581, Time: 23.65
[2020-12-15 01:34:54,781][__main__][INFO] - [30080] Loss: 0.379, Running accuracy: 99.581, Time: 26.08
[2020-12-15 01:35:19,042][__main__][INFO] - [30400] Loss: 0.370, Running accuracy: 99.580, Time: 24.26
[2020-12-15 01:35:42,197][__main__][INFO] - [30720] Loss: 0.497, Running accuracy: 99.579, Time: 23.15
[2020-12-15 01:36:06,060][__main__][INFO] - [31040] Loss: 0.458, Running accuracy: 99.576, Time: 23.86
[2020-12-15 01:36:29,768][__main__][INFO] - [31360] Loss: 0.308, Running accuracy: 99.577, Time: 23.71
[2020-12-15 01:36:57,758][__main__][INFO] - [31680] Loss: 0.245, Running accuracy: 99.578, Time: 27.99
[2020-12-15 01:37:22,697][__main__][INFO] - [32000] Loss: 0.324, Running accuracy: 99.577, Time: 24.94
[2020-12-15 01:37:44,890][__main__][INFO] - [32320] Loss: 0.405, Running accuracy: 99.576, Time: 22.19
[2020-12-15 01:38:11,710][__main__][INFO] - [32640] Loss: 0.393, Running accuracy: 99.576, Time: 26.82
[2020-12-15 01:38:34,299][__main__][INFO] - [32960] Loss: 0.381, Running accuracy: 99.575, Time: 22.59
[2020-12-15 01:38:57,143][__main__][INFO] - [33280] Loss: 0.191, Running accuracy: 99.576, Time: 22.84
[2020-12-15 01:39:19,215][__main__][INFO] - [33600] Loss: 0.402, Running accuracy: 99.575, Time: 22.07
[2020-12-15 01:39:42,359][__main__][INFO] - [33920] Loss: 0.493, Running accuracy: 99.575, Time: 23.14
[2020-12-15 01:40:04,182][__main__][INFO] - [34240] Loss: 0.330, Running accuracy: 99.575, Time: 21.82
[2020-12-15 01:40:27,382][__main__][INFO] - [34560] Loss: 0.461, Running accuracy: 99.574, Time: 23.20
[2020-12-15 01:40:53,305][__main__][INFO] - [34880] Loss: 0.588, Running accuracy: 99.573, Time: 25.92
[2020-12-15 01:41:18,182][__main__][INFO] - [35200] Loss: 0.303, Running accuracy: 99.574, Time: 24.88
[2020-12-15 01:41:41,462][__main__][INFO] - [35520] Loss: 0.476, Running accuracy: 99.572, Time: 23.28
[2020-12-15 01:42:04,316][__main__][INFO] - [35840] Loss: 0.370, Running accuracy: 99.572, Time: 22.85
[2020-12-15 01:42:31,856][__main__][INFO] - [36160] Loss: 0.419, Running accuracy: 99.570, Time: 27.54
[2020-12-15 01:42:54,197][__main__][INFO] - [36480] Loss: 0.437, Running accuracy: 99.569, Time: 22.34
[2020-12-15 01:43:17,396][__main__][INFO] - [36800] Loss: 0.311, Running accuracy: 99.570, Time: 23.20
[2020-12-15 01:43:41,291][__main__][INFO] - [37120] Loss: 0.593, Running accuracy: 99.567, Time: 23.89
[2020-12-15 01:44:03,512][__main__][INFO] - [37440] Loss: 0.406, Running accuracy: 99.567, Time: 22.22
[2020-12-15 01:44:25,765][__main__][INFO] - [37760] Loss: 0.289, Running accuracy: 99.568, Time: 22.25
[2020-12-15 01:44:48,336][__main__][INFO] - [38080] Loss: 0.323, Running accuracy: 99.569, Time: 22.57
[2020-12-15 01:45:12,716][__main__][INFO] - [38400] Loss: 0.353, Running accuracy: 99.568, Time: 24.38
[2020-12-15 01:45:37,871][__main__][INFO] - [38720] Loss: 0.381, Running accuracy: 99.568, Time: 25.15
[2020-12-15 01:46:02,505][__main__][INFO] - [39040] Loss: 0.411, Running accuracy: 99.567, Time: 24.63
[2020-12-15 01:46:27,656][__main__][INFO] - [39360] Loss: 0.692, Running accuracy: 99.565, Time: 25.15
[2020-12-15 01:46:51,938][__main__][INFO] - [39680] Loss: 0.295, Running accuracy: 99.566, Time: 24.28
[2020-12-15 01:47:01,917][__main__][INFO] - Action accuracy: 99.566, Loss: 0.406
[2020-12-15 01:47:01,919][__main__][INFO] - Validating..
[2020-12-15 01:47:43,745][test][INFO] - Time elapsed: 39.665424
[2020-12-15 01:47:43,750][__main__][INFO] - Validation F1 score: 95.580, Exact match: 56.060, Precision: 95.590, Recall: 95.580
[2020-12-15 01:48:24,507][__main__][INFO] - Checkpoint saved to checkpoints/model_latest.pth
[2020-12-15 01:48:25,548][__main__][INFO] - Epoch #17
[2020-12-15 01:48:25,549][__main__][INFO] - Training..
[2020-12-15 01:48:50,309][__main__][INFO] - [320] Loss: 0.305, Running accuracy: 99.607, Time: 23.48
[2020-12-15 01:49:13,563][__main__][INFO] - [640] Loss: 0.439, Running accuracy: 99.597, Time: 23.25
[2020-12-15 01:49:37,601][__main__][INFO] - [960] Loss: 0.280, Running accuracy: 99.594, Time: 24.04
[2020-12-15 01:50:02,638][__main__][INFO] - [1280] Loss: 0.270, Running accuracy: 99.626, Time: 25.04
[2020-12-15 01:50:26,267][__main__][INFO] - [1600] Loss: 0.222, Running accuracy: 99.650, Time: 23.63
[2020-12-15 01:50:49,084][__main__][INFO] - [1920] Loss: 0.259, Running accuracy: 99.647, Time: 22.82
[2020-12-15 01:51:14,222][__main__][INFO] - [2240] Loss: 0.359, Running accuracy: 99.631, Time: 25.14
[2020-12-15 01:51:38,069][__main__][INFO] - [2560] Loss: 0.333, Running accuracy: 99.636, Time: 23.85
[2020-12-15 01:52:01,162][__main__][INFO] - [2880] Loss: 0.327, Running accuracy: 99.630, Time: 23.09
[2020-12-15 01:52:23,573][__main__][INFO] - [3200] Loss: 0.239, Running accuracy: 99.633, Time: 22.41
[2020-12-15 01:52:53,916][__main__][INFO] - [3520] Loss: 0.227, Running accuracy: 99.644, Time: 30.34
[2020-12-15 01:53:18,650][__main__][INFO] - [3840] Loss: 0.325, Running accuracy: 99.635, Time: 24.73
[2020-12-15 01:53:40,846][__main__][INFO] - [4160] Loss: 0.391, Running accuracy: 99.636, Time: 22.19
[2020-12-15 01:54:05,219][__main__][INFO] - [4480] Loss: 0.309, Running accuracy: 99.635, Time: 24.37
[2020-12-15 01:54:29,023][__main__][INFO] - [4800] Loss: 0.325, Running accuracy: 99.628, Time: 23.80
[2020-12-15 01:54:52,416][__main__][INFO] - [5120] Loss: 0.306, Running accuracy: 99.629, Time: 23.39
[2020-12-15 01:55:15,857][__main__][INFO] - [5440] Loss: 0.214, Running accuracy: 99.635, Time: 23.44
[2020-12-15 01:55:40,136][__main__][INFO] - [5760] Loss: 0.237, Running accuracy: 99.638, Time: 24.28
[2020-12-15 01:56:03,208][__main__][INFO] - [6080] Loss: 0.241, Running accuracy: 99.642, Time: 23.07
[2020-12-15 01:56:27,245][__main__][INFO] - [6400] Loss: 0.253, Running accuracy: 99.645, Time: 24.04
[2020-12-15 01:56:50,366][__main__][INFO] - [6720] Loss: 0.295, Running accuracy: 99.642, Time: 23.12
[2020-12-15 01:57:13,960][__main__][INFO] - [7040] Loss: 0.258, Running accuracy: 99.641, Time: 23.59
[2020-12-15 01:57:37,813][__main__][INFO] - [7360] Loss: 0.366, Running accuracy: 99.638, Time: 23.85
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[2020-12-15 02:36:09,342][__main__][INFO] - [38400] Loss: 0.470, Running accuracy: 99.617, Time: 23.53
[2020-12-15 02:36:32,872][__main__][INFO] - [38720] Loss: 0.269, Running accuracy: 99.617, Time: 23.53
[2020-12-15 02:36:54,445][__main__][INFO] - [39040] Loss: 0.221, Running accuracy: 99.618, Time: 21.57
[2020-12-15 02:37:22,449][__main__][INFO] - [39360] Loss: 0.333, Running accuracy: 99.618, Time: 28.00
[2020-12-15 02:37:46,084][__main__][INFO] - [39680] Loss: 0.248, Running accuracy: 99.618, Time: 23.63
[2020-12-15 02:37:56,152][__main__][INFO] - Action accuracy: 99.618, Loss: 0.359
[2020-12-15 02:37:56,155][__main__][INFO] - Validating..
[2020-12-15 02:38:32,809][test][INFO] - Time elapsed: 34.369776
[2020-12-15 02:38:32,814][__main__][INFO] - Validation F1 score: 95.660, Exact match: 55.940, Precision: 95.630, Recall: 95.700
[2020-12-15 02:39:12,639][__main__][INFO] - Checkpoint saved to checkpoints/model_latest.pth
[2020-12-15 02:39:13,564][__main__][INFO] - Epoch #18
[2020-12-15 02:39:13,564][__main__][INFO] - Training..
[2020-12-15 02:39:38,446][__main__][INFO] - [320] Loss: 0.205, Running accuracy: 99.800, Time: 23.53
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[2020-12-15 03:28:46,962][__main__][INFO] - [39680] Loss: 0.307, Running accuracy: 99.652, Time: 22.67
[2020-12-15 03:28:56,385][__main__][INFO] - Action accuracy: 99.651, Loss: 0.327
[2020-12-15 03:28:56,386][__main__][INFO] - Validating..
[2020-12-15 03:29:32,891][test][INFO] - Time elapsed: 34.380525
[2020-12-15 03:29:32,896][__main__][INFO] - Validation F1 score: 95.580, Exact match: 56.180, Precision: 95.470, Recall: 95.690
[2020-12-15 03:30:11,877][__main__][INFO] - Checkpoint saved to checkpoints/model_latest.pth
[2020-12-15 03:30:12,823][__main__][INFO] - Epoch #19
[2020-12-15 03:30:12,823][__main__][INFO] - Training..
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[2020-12-15 04:19:23,714][__main__][INFO] - [39360] Loss: 0.236, Running accuracy: 99.673, Time: 24.30
[2020-12-15 04:19:47,038][__main__][INFO] - [39680] Loss: 0.227, Running accuracy: 99.673, Time: 23.32
[2020-12-15 04:19:56,018][__main__][INFO] - Action accuracy: 99.673, Loss: 0.303
[2020-12-15 04:19:56,019][__main__][INFO] - Validating..
[2020-12-15 04:20:38,816][test][INFO] - Time elapsed: 40.698972
[2020-12-15 04:20:38,821][__main__][INFO] - Validation F1 score: 95.720, Exact match: 56.710, Precision: 95.660, Recall: 95.780
[2020-12-15 04:21:18,736][__main__][INFO] - Checkpoint saved to checkpoints/model_latest.pth
[2020-12-15 04:21:19,705][__main__][INFO] - Epoch #20
[2020-12-15 04:21:19,706][__main__][INFO] - Training..
[2020-12-15 04:21:45,038][__main__][INFO] - [320] Loss: 0.218, Running accuracy: 99.765, Time: 23.91
[2020-12-15 04:22:07,963][__main__][INFO] - [640] Loss: 0.264, Running accuracy: 99.702, Time: 22.92
[2020-12-15 04:22:32,975][__main__][INFO] - [960] Loss: 0.248, Running accuracy: 99.708, Time: 25.01
[2020-12-15 04:22:55,553][__main__][INFO] - [1280] Loss: 0.290, Running accuracy: 99.715, Time: 22.58
[2020-12-15 04:23:20,129][__main__][INFO] - [1600] Loss: 0.264, Running accuracy: 99.709, Time: 24.58
[2020-12-15 04:23:43,293][__main__][INFO] - [1920] Loss: 0.224, Running accuracy: 99.708, Time: 23.16
[2020-12-15 04:24:06,439][__main__][INFO] - [2240] Loss: 0.178, Running accuracy: 99.721, Time: 23.14
[2020-12-15 04:24:28,791][__main__][INFO] - [2560] Loss: 0.172, Running accuracy: 99.728, Time: 22.35
[2020-12-15 04:24:53,551][__main__][INFO] - [2880] Loss: 0.253, Running accuracy: 99.733, Time: 24.76
[2020-12-15 04:25:17,779][__main__][INFO] - [3200] Loss: 0.438, Running accuracy: 99.707, Time: 24.23
[2020-12-15 04:25:49,265][__main__][INFO] - [3520] Loss: 0.225, Running accuracy: 99.717, Time: 31.49
[2020-12-15 04:26:12,911][__main__][INFO] - [3840] Loss: 0.206, Running accuracy: 99.719, Time: 23.64
[2020-12-15 04:26:36,573][__main__][INFO] - [4160] Loss: 0.230, Running accuracy: 99.722, Time: 23.66
[2020-12-15 04:26:58,751][__main__][INFO] - [4480] Loss: 0.298, Running accuracy: 99.712, Time: 22.18
[2020-12-15 04:27:24,230][__main__][INFO] - [4800] Loss: 0.259, Running accuracy: 99.709, Time: 25.48
[2020-12-15 04:27:50,048][__main__][INFO] - [5120] Loss: 0.277, Running accuracy: 99.708, Time: 25.82
[2020-12-15 04:28:15,107][__main__][INFO] - [5440] Loss: 0.175, Running accuracy: 99.708, Time: 25.06
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[2020-12-15 04:29:28,538][__main__][INFO] - [6400] Loss: 0.232, Running accuracy: 99.710, Time: 23.88
[2020-12-15 04:29:51,600][__main__][INFO] - [6720] Loss: 0.198, Running accuracy: 99.714, Time: 23.06
[2020-12-15 04:30:13,342][__main__][INFO] - [7040] Loss: 0.283, Running accuracy: 99.715, Time: 21.74
[2020-12-15 04:30:34,921][__main__][INFO] - [7360] Loss: 0.254, Running accuracy: 99.715, Time: 21.58
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[2020-12-15 04:31:26,460][__main__][INFO] - [8000] Loss: 0.289, Running accuracy: 99.718, Time: 28.19
[2020-12-15 04:31:50,722][__main__][INFO] - [8320] Loss: 0.203, Running accuracy: 99.720, Time: 24.26
[2020-12-15 04:32:14,459][__main__][INFO] - [8640] Loss: 0.227, Running accuracy: 99.718, Time: 23.66
[2020-12-15 04:32:38,286][__main__][INFO] - [8960] Loss: 0.220, Running accuracy: 99.719, Time: 23.83
[2020-12-15 04:33:00,821][__main__][INFO] - [9280] Loss: 0.206, Running accuracy: 99.720, Time: 22.53
[2020-12-15 04:33:23,824][__main__][INFO] - [9600] Loss: 0.257, Running accuracy: 99.722, Time: 23.00
[2020-12-15 04:33:46,581][__main__][INFO] - [9920] Loss: 0.243, Running accuracy: 99.721, Time: 22.76
[2020-12-15 04:34:09,780][__main__][INFO] - [10240] Loss: 0.148, Running accuracy: 99.725, Time: 23.20
[2020-12-15 04:34:31,670][__main__][INFO] - [10560] Loss: 0.235, Running accuracy: 99.725, Time: 21.89
[2020-12-15 04:34:54,180][__main__][INFO] - [10880] Loss: 0.220, Running accuracy: 99.725, Time: 22.51
[2020-12-15 04:35:18,150][__main__][INFO] - [11200] Loss: 0.259, Running accuracy: 99.726, Time: 23.97
[2020-12-15 04:35:42,067][__main__][INFO] - [11520] Loss: 0.237, Running accuracy: 99.726, Time: 23.92
[2020-12-15 04:36:05,327][__main__][INFO] - [11840] Loss: 0.226, Running accuracy: 99.725, Time: 23.26
[2020-12-15 04:36:28,774][__main__][INFO] - [12160] Loss: 0.353, Running accuracy: 99.724, Time: 23.44
[2020-12-15 04:36:58,093][__main__][INFO] - [12480] Loss: 0.277, Running accuracy: 99.720, Time: 29.32
[2020-12-15 04:37:22,239][__main__][INFO] - [12800] Loss: 0.219, Running accuracy: 99.722, Time: 24.15
[2020-12-15 04:37:45,104][__main__][INFO] - [13120] Loss: 0.246, Running accuracy: 99.720, Time: 22.86
[2020-12-15 04:38:08,735][__main__][INFO] - [13440] Loss: 0.215, Running accuracy: 99.721, Time: 23.63
[2020-12-15 04:38:32,054][__main__][INFO] - [13760] Loss: 0.213, Running accuracy: 99.721, Time: 23.32
[2020-12-15 04:38:56,576][__main__][INFO] - [14080] Loss: 0.219, Running accuracy: 99.723, Time: 24.52
[2020-12-15 04:39:19,161][__main__][INFO] - [14400] Loss: 0.197, Running accuracy: 99.725, Time: 22.58
[2020-12-15 04:39:42,583][__main__][INFO] - [14720] Loss: 0.258, Running accuracy: 99.725, Time: 23.42
[2020-12-15 04:40:07,551][__main__][INFO] - [15040] Loss: 0.211, Running accuracy: 99.726, Time: 24.97
[2020-12-15 04:40:29,164][__main__][INFO] - [15360] Loss: 0.256, Running accuracy: 99.723, Time: 21.61
[2020-12-15 04:40:51,153][__main__][INFO] - [15680] Loss: 0.231, Running accuracy: 99.724, Time: 21.99
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[2020-12-15 04:41:35,233][__main__][INFO] - [16320] Loss: 0.264, Running accuracy: 99.724, Time: 22.43
[2020-12-15 04:41:58,383][__main__][INFO] - [16640] Loss: 0.215, Running accuracy: 99.725, Time: 23.15
[2020-12-15 04:42:26,325][__main__][INFO] - [16960] Loss: 0.273, Running accuracy: 99.724, Time: 27.94
[2020-12-15 04:42:50,583][__main__][INFO] - [17280] Loss: 0.280, Running accuracy: 99.723, Time: 24.26
[2020-12-15 04:43:13,746][__main__][INFO] - [17600] Loss: 0.284, Running accuracy: 99.722, Time: 23.16
[2020-12-15 04:43:35,625][__main__][INFO] - [17920] Loss: 0.277, Running accuracy: 99.721, Time: 21.88
[2020-12-15 04:43:59,232][__main__][INFO] - [18240] Loss: 0.249, Running accuracy: 99.720, Time: 23.61
[2020-12-15 04:44:25,331][__main__][INFO] - [18560] Loss: 0.309, Running accuracy: 99.718, Time: 26.10
[2020-12-15 04:44:48,082][__main__][INFO] - [18880] Loss: 0.157, Running accuracy: 99.718, Time: 22.75
[2020-12-15 04:45:12,582][__main__][INFO] - [19200] Loss: 0.289, Running accuracy: 99.717, Time: 24.50
[2020-12-15 04:45:36,765][__main__][INFO] - [19520] Loss: 0.272, Running accuracy: 99.716, Time: 24.18
[2020-12-15 04:46:01,184][__main__][INFO] - [19840] Loss: 0.272, Running accuracy: 99.716, Time: 24.42
[2020-12-15 04:46:26,579][__main__][INFO] - [20160] Loss: 0.336, Running accuracy: 99.715, Time: 25.39
[2020-12-15 04:46:50,661][__main__][INFO] - [20480] Loss: 0.255, Running accuracy: 99.715, Time: 24.08
[2020-12-15 04:47:13,605][__main__][INFO] - [20800] Loss: 0.410, Running accuracy: 99.714, Time: 22.94
[2020-12-15 04:47:38,644][__main__][INFO] - [21120] Loss: 0.379, Running accuracy: 99.711, Time: 25.04
[2020-12-15 04:48:08,461][__main__][INFO] - [21440] Loss: 0.166, Running accuracy: 99.711, Time: 29.82
[2020-12-15 04:48:30,306][__main__][INFO] - [21760] Loss: 0.215, Running accuracy: 99.712, Time: 21.84
[2020-12-15 04:48:53,079][__main__][INFO] - [22080] Loss: 0.169, Running accuracy: 99.712, Time: 22.77
[2020-12-15 04:49:15,841][__main__][INFO] - [22400] Loss: 0.165, Running accuracy: 99.715, Time: 22.76
[2020-12-15 04:49:38,425][__main__][INFO] - [22720] Loss: 0.275, Running accuracy: 99.714, Time: 22.58
[2020-12-15 04:50:00,976][__main__][INFO] - [23040] Loss: 0.294, Running accuracy: 99.714, Time: 22.55
[2020-12-15 04:50:25,713][__main__][INFO] - [23360] Loss: 0.157, Running accuracy: 99.715, Time: 24.74
[2020-12-15 04:50:48,654][__main__][INFO] - [23680] Loss: 0.297, Running accuracy: 99.713, Time: 22.94
[2020-12-15 04:51:13,709][__main__][INFO] - [24000] Loss: 0.191, Running accuracy: 99.713, Time: 25.05
[2020-12-15 04:51:35,909][__main__][INFO] - [24320] Loss: 0.219, Running accuracy: 99.713, Time: 22.20
[2020-12-15 04:51:58,287][__main__][INFO] - [24640] Loss: 0.205, Running accuracy: 99.713, Time: 22.38
[2020-12-15 04:52:21,442][__main__][INFO] - [24960] Loss: 0.346, Running accuracy: 99.711, Time: 23.15
[2020-12-15 04:52:43,924][__main__][INFO] - [25280] Loss: 0.258, Running accuracy: 99.711, Time: 22.48
[2020-12-15 04:53:07,606][__main__][INFO] - [25600] Loss: 0.296, Running accuracy: 99.710, Time: 23.68
[2020-12-15 04:53:37,539][__main__][INFO] - [25920] Loss: 0.182, Running accuracy: 99.711, Time: 29.93
[2020-12-15 04:54:00,611][__main__][INFO] - [26240] Loss: 0.273, Running accuracy: 99.710, Time: 23.07
[2020-12-15 04:54:23,257][__main__][INFO] - [26560] Loss: 0.224, Running accuracy: 99.710, Time: 22.65
[2020-12-15 04:54:46,481][__main__][INFO] - [26880] Loss: 0.296, Running accuracy: 99.709, Time: 23.22
[2020-12-15 04:55:08,849][__main__][INFO] - [27200] Loss: 0.229, Running accuracy: 99.709, Time: 22.37
[2020-12-15 04:55:33,398][__main__][INFO] - [27520] Loss: 0.203, Running accuracy: 99.710, Time: 24.50
[2020-12-15 04:55:57,967][__main__][INFO] - [27840] Loss: 0.246, Running accuracy: 99.709, Time: 23.76
[2020-12-15 04:56:21,146][__main__][INFO] - [28160] Loss: 0.273, Running accuracy: 99.708, Time: 23.18
[2020-12-15 04:56:46,986][__main__][INFO] - [28480] Loss: 0.339, Running accuracy: 99.708, Time: 25.84
[2020-12-15 04:57:08,967][__main__][INFO] - [28800] Loss: 0.224, Running accuracy: 99.708, Time: 21.98
[2020-12-15 04:57:31,643][__main__][INFO] - [29120] Loss: 0.268, Running accuracy: 99.707, Time: 22.68
[2020-12-15 04:57:55,014][__main__][INFO] - [29440] Loss: 0.267, Running accuracy: 99.706, Time: 23.37
[2020-12-15 04:58:18,490][__main__][INFO] - [29760] Loss: 0.281, Running accuracy: 99.707, Time: 22.88
[2020-12-15 04:58:43,030][__main__][INFO] - [30080] Loss: 0.158, Running accuracy: 99.708, Time: 24.02
[2020-12-15 04:59:10,450][__main__][INFO] - [30400] Loss: 0.210, Running accuracy: 99.708, Time: 27.42
[2020-12-15 04:59:32,828][__main__][INFO] - [30720] Loss: 0.249, Running accuracy: 99.708, Time: 22.38
[2020-12-15 04:59:54,365][__main__][INFO] - [31040] Loss: 0.272, Running accuracy: 99.708, Time: 21.54
[2020-12-15 05:00:18,262][__main__][INFO] - [31360] Loss: 0.238, Running accuracy: 99.708, Time: 23.90
[2020-12-15 05:00:42,186][__main__][INFO] - [31680] Loss: 0.266, Running accuracy: 99.709, Time: 23.92
[2020-12-15 05:01:06,278][__main__][INFO] - [32000] Loss: 0.264, Running accuracy: 99.708, Time: 24.09
[2020-12-15 05:01:31,467][__main__][INFO] - [32320] Loss: 0.218, Running accuracy: 99.708, Time: 25.19
[2020-12-15 05:01:55,413][__main__][INFO] - [32640] Loss: 0.270, Running accuracy: 99.708, Time: 23.95
[2020-12-15 05:02:19,133][__main__][INFO] - [32960] Loss: 0.249, Running accuracy: 99.708, Time: 23.72
[2020-12-15 05:02:42,265][__main__][INFO] - [33280] Loss: 0.260, Running accuracy: 99.708, Time: 23.13
[2020-12-15 05:03:05,209][__main__][INFO] - [33600] Loss: 0.194, Running accuracy: 99.708, Time: 22.94
[2020-12-15 05:03:27,713][__main__][INFO] - [33920] Loss: 0.194, Running accuracy: 99.709, Time: 22.50
[2020-12-15 05:03:49,466][__main__][INFO] - [34240] Loss: 0.271, Running accuracy: 99.708, Time: 21.75
[2020-12-15 05:04:13,711][__main__][INFO] - [34560] Loss: 0.288, Running accuracy: 99.708, Time: 24.24
[2020-12-15 05:04:41,340][__main__][INFO] - [34880] Loss: 0.159, Running accuracy: 99.709, Time: 27.63
[2020-12-15 05:05:07,840][__main__][INFO] - [35200] Loss: 0.271, Running accuracy: 99.709, Time: 26.50
[2020-12-15 05:05:30,094][__main__][INFO] - [35520] Loss: 0.247, Running accuracy: 99.710, Time: 22.25
[2020-12-15 05:05:52,518][__main__][INFO] - [35840] Loss: 0.295, Running accuracy: 99.709, Time: 22.42
[2020-12-15 05:06:16,197][__main__][INFO] - [36160] Loss: 0.266, Running accuracy: 99.709, Time: 23.68
[2020-12-15 05:06:38,916][__main__][INFO] - [36480] Loss: 0.253, Running accuracy: 99.709, Time: 21.90
[2020-12-15 05:07:02,478][__main__][INFO] - [36800] Loss: 0.345, Running accuracy: 99.708, Time: 23.56
[2020-12-15 05:07:26,020][__main__][INFO] - [37120] Loss: 0.152, Running accuracy: 99.709, Time: 23.54
[2020-12-15 05:07:49,653][__main__][INFO] - [37440] Loss: 0.293, Running accuracy: 99.708, Time: 23.63
[2020-12-15 05:08:13,130][__main__][INFO] - [37760] Loss: 0.306, Running accuracy: 99.707, Time: 23.48
[2020-12-15 05:08:37,920][__main__][INFO] - [38080] Loss: 0.263, Running accuracy: 99.707, Time: 24.79
[2020-12-15 05:09:00,819][__main__][INFO] - [38400] Loss: 0.283, Running accuracy: 99.707, Time: 22.90
[2020-12-15 05:09:25,774][__main__][INFO] - [38720] Loss: 0.296, Running accuracy: 99.706, Time: 24.95
[2020-12-15 05:09:50,002][__main__][INFO] - [39040] Loss: 0.264, Running accuracy: 99.706, Time: 24.23
[2020-12-15 05:10:18,359][__main__][INFO] - [39360] Loss: 0.346, Running accuracy: 99.706, Time: 28.36
[2020-12-15 05:10:44,600][__main__][INFO] - [39680] Loss: 0.273, Running accuracy: 99.706, Time: 26.24
[2020-12-15 05:10:53,884][__main__][INFO] - Action accuracy: 99.706, Loss: 0.275
[2020-12-15 05:10:53,885][__main__][INFO] - Validating..
[2020-12-15 05:11:30,277][test][INFO] - Time elapsed: 34.403762
[2020-12-15 05:11:30,281][__main__][INFO] - Validation F1 score: 95.680, Exact match: 56.470, Precision: 95.670, Recall: 95.690
Epoch    21: reducing learning rate of group 0 to 2.5000e-06.
[2020-12-15 05:12:11,909][__main__][INFO] - Checkpoint saved to checkpoints/model_latest.pth
[2020-12-15 05:12:12,724][__main__][INFO] - Epoch #21
[2020-12-15 05:12:12,724][__main__][INFO] - Training..
[2020-12-15 05:12:37,177][__main__][INFO] - [320] Loss: 0.171, Running accuracy: 99.820, Time: 23.11
[2020-12-15 05:13:02,148][__main__][INFO] - [640] Loss: 0.184, Running accuracy: 99.802, Time: 24.97
[2020-12-15 05:13:24,878][__main__][INFO] - [960] Loss: 0.190, Running accuracy: 99.784, Time: 22.73
[2020-12-15 05:13:47,858][__main__][INFO] - [1280] Loss: 0.124, Running accuracy: 99.804, Time: 22.98
[2020-12-15 05:14:10,243][__main__][INFO] - [1600] Loss: 0.291, Running accuracy: 99.784, Time: 22.38
[2020-12-15 05:14:40,769][__main__][INFO] - [1920] Loss: 0.122, Running accuracy: 99.788, Time: 30.52
[2020-12-15 05:15:03,587][__main__][INFO] - [2240] Loss: 0.163, Running accuracy: 99.792, Time: 22.82
[2020-12-15 05:15:27,485][__main__][INFO] - [2560] Loss: 0.298, Running accuracy: 99.773, Time: 23.90
[2020-12-15 05:15:52,969][__main__][INFO] - [2880] Loss: 0.319, Running accuracy: 99.756, Time: 25.48
[2020-12-15 05:16:16,755][__main__][INFO] - [3200] Loss: 0.178, Running accuracy: 99.762, Time: 23.78
[2020-12-15 05:16:39,902][__main__][INFO] - [3520] Loss: 0.214, Running accuracy: 99.758, Time: 23.15
[2020-12-15 05:17:03,084][__main__][INFO] - [3840] Loss: 0.236, Running accuracy: 99.757, Time: 23.18
[2020-12-15 05:17:25,488][__main__][INFO] - [4160] Loss: 0.119, Running accuracy: 99.759, Time: 22.40
[2020-12-15 05:17:48,948][__main__][INFO] - [4480] Loss: 0.157, Running accuracy: 99.765, Time: 23.46
[2020-12-15 05:18:12,833][__main__][INFO] - [4800] Loss: 0.147, Running accuracy: 99.770, Time: 23.88
[2020-12-15 05:18:35,468][__main__][INFO] - [5120] Loss: 0.148, Running accuracy: 99.772, Time: 22.63
[2020-12-15 05:18:57,504][__main__][INFO] - [5440] Loss: 0.138, Running accuracy: 99.773, Time: 22.04
[2020-12-15 05:19:20,772][__main__][INFO] - [5760] Loss: 0.282, Running accuracy: 99.770, Time: 23.27
[2020-12-15 05:19:44,379][__main__][INFO] - [6080] Loss: 0.130, Running accuracy: 99.775, Time: 23.61
[2020-12-15 05:20:12,584][__main__][INFO] - [6400] Loss: 0.273, Running accuracy: 99.773, Time: 28.20
[2020-12-15 05:20:35,714][__main__][INFO] - [6720] Loss: 0.159, Running accuracy: 99.777, Time: 23.13
[2020-12-15 05:20:58,130][__main__][INFO] - [7040] Loss: 0.158, Running accuracy: 99.781, Time: 22.42
[2020-12-15 05:21:20,993][__main__][INFO] - [7360] Loss: 0.229, Running accuracy: 99.779, Time: 22.86
[2020-12-15 05:21:43,426][__main__][INFO] - [7680] Loss: 0.364, Running accuracy: 99.773, Time: 22.43
[2020-12-15 05:22:08,405][__main__][INFO] - [8000] Loss: 0.274, Running accuracy: 99.768, Time: 24.98
[2020-12-15 05:22:30,420][__main__][INFO] - [8320] Loss: 0.132, Running accuracy: 99.773, Time: 22.01
[2020-12-15 05:22:54,861][__main__][INFO] - [8640] Loss: 0.220, Running accuracy: 99.772, Time: 24.44
[2020-12-15 05:23:17,979][__main__][INFO] - [8960] Loss: 0.139, Running accuracy: 99.776, Time: 23.12
[2020-12-15 05:23:42,686][__main__][INFO] - [9280] Loss: 0.191, Running accuracy: 99.777, Time: 24.71
[2020-12-15 05:24:06,690][__main__][INFO] - [9600] Loss: 0.133, Running accuracy: 99.778, Time: 24.00
[2020-12-15 05:24:30,397][__main__][INFO] - [9920] Loss: 0.198, Running accuracy: 99.778, Time: 23.71
[2020-12-15 05:24:52,790][__main__][INFO] - [10240] Loss: 0.249, Running accuracy: 99.777, Time: 22.39
[2020-12-15 05:25:15,273][__main__][INFO] - [10560] Loss: 0.183, Running accuracy: 99.778, Time: 22.48
[2020-12-15 05:25:42,795][__main__][INFO] - [10880] Loss: 0.273, Running accuracy: 99.777, Time: 27.52
[2020-12-15 05:26:04,485][__main__][INFO] - [11200] Loss: 0.128, Running accuracy: 99.778, Time: 21.69
[2020-12-15 05:26:28,334][__main__][INFO] - [11520] Loss: 0.282, Running accuracy: 99.777, Time: 23.85
[2020-12-15 05:26:52,379][__main__][INFO] - [11840] Loss: 0.182, Running accuracy: 99.777, Time: 24.04
[2020-12-15 05:27:16,556][__main__][INFO] - [12160] Loss: 0.268, Running accuracy: 99.775, Time: 24.18
[2020-12-15 05:27:39,717][__main__][INFO] - [12480] Loss: 0.192, Running accuracy: 99.775, Time: 23.16
[2020-12-15 05:28:04,698][__main__][INFO] - [12800] Loss: 0.232, Running accuracy: 99.774, Time: 24.98
[2020-12-15 05:28:28,681][__main__][INFO] - [13120] Loss: 0.181, Running accuracy: 99.774, Time: 23.98
[2020-12-15 05:28:51,638][__main__][INFO] - [13440] Loss: 0.169, Running accuracy: 99.775, Time: 22.96
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[2020-12-15 05:29:36,768][__main__][INFO] - [14080] Loss: 0.237, Running accuracy: 99.776, Time: 20.98
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[2020-12-15 06:00:52,486][__main__][INFO] - Action accuracy: 99.766, Loss: 0.229
[2020-12-15 06:00:52,488][__main__][INFO] - Validating..
[2020-12-15 06:01:27,855][test][INFO] - Time elapsed: 33.211034
[2020-12-15 06:01:27,859][__main__][INFO] - Validation F1 score: 95.640, Exact match: 56.590, Precision: 95.640, Recall: 95.640
[2020-12-15 06:02:05,961][__main__][INFO] - Checkpoint saved to checkpoints/model_latest.pth
[2020-12-15 06:02:06,790][__main__][INFO] - Epoch #22
[2020-12-15 06:02:06,790][__main__][INFO] - Training..
[2020-12-15 06:02:35,137][__main__][INFO] - [320] Loss: 0.152, Running accuracy: 99.856, Time: 27.11
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[2020-12-15 06:36:36,453][__main__][INFO] - [29120] Loss: 0.155, Running accuracy: 99.790, Time: 23.07
[2020-12-15 06:36:59,687][__main__][INFO] - [29440] Loss: 0.206, Running accuracy: 99.789, Time: 23.23
[2020-12-15 06:37:22,553][__main__][INFO] - [29760] Loss: 0.159, Running accuracy: 99.790, Time: 22.87
[2020-12-15 06:37:45,045][__main__][INFO] - [30080] Loss: 0.128, Running accuracy: 99.790, Time: 22.49
[2020-12-15 06:38:06,243][__main__][INFO] - [30400] Loss: 0.128, Running accuracy: 99.791, Time: 21.20
[2020-12-15 06:38:29,713][__main__][INFO] - [30720] Loss: 0.287, Running accuracy: 99.790, Time: 23.47
[2020-12-15 06:38:52,168][__main__][INFO] - [31040] Loss: 0.190, Running accuracy: 99.790, Time: 22.45
[2020-12-15 06:39:14,066][__main__][INFO] - [31360] Loss: 0.092, Running accuracy: 99.791, Time: 21.90
[2020-12-15 06:39:34,419][__main__][INFO] - [31680] Loss: 0.220, Running accuracy: 99.791, Time: 20.35
[2020-12-15 06:39:59,357][__main__][INFO] - [32000] Loss: 0.136, Running accuracy: 99.791, Time: 24.94
[2020-12-15 06:40:22,148][__main__][INFO] - [32320] Loss: 0.185, Running accuracy: 99.792, Time: 22.79
[2020-12-15 06:40:44,264][__main__][INFO] - [32640] Loss: 0.196, Running accuracy: 99.791, Time: 22.11
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[2020-12-15 06:41:27,562][__main__][INFO] - [33280] Loss: 0.258, Running accuracy: 99.789, Time: 22.17
[2020-12-15 06:41:50,063][__main__][INFO] - [33600] Loss: 0.283, Running accuracy: 99.789, Time: 22.50
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[2020-12-15 06:42:33,596][__main__][INFO] - [34240] Loss: 0.180, Running accuracy: 99.789, Time: 21.41
[2020-12-15 06:42:55,347][__main__][INFO] - [34560] Loss: 0.130, Running accuracy: 99.789, Time: 21.75
[2020-12-15 06:43:16,177][__main__][INFO] - [34880] Loss: 0.097, Running accuracy: 99.790, Time: 20.83
[2020-12-15 06:43:39,068][__main__][INFO] - [35200] Loss: 0.182, Running accuracy: 99.790, Time: 22.89
[2020-12-15 06:44:04,464][__main__][INFO] - [35520] Loss: 0.167, Running accuracy: 99.790, Time: 25.39
[2020-12-15 06:44:24,650][__main__][INFO] - [35840] Loss: 0.111, Running accuracy: 99.791, Time: 20.19
[2020-12-15 06:44:45,975][__main__][INFO] - [36160] Loss: 0.152, Running accuracy: 99.791, Time: 21.32
[2020-12-15 06:45:11,757][__main__][INFO] - [36480] Loss: 0.200, Running accuracy: 99.791, Time: 25.78
[2020-12-15 06:45:35,551][__main__][INFO] - [36800] Loss: 0.176, Running accuracy: 99.791, Time: 23.79
[2020-12-15 06:45:57,877][__main__][INFO] - [37120] Loss: 0.236, Running accuracy: 99.790, Time: 22.32
[2020-12-15 06:46:20,975][__main__][INFO] - [37440] Loss: 0.173, Running accuracy: 99.790, Time: 23.10
[2020-12-15 06:46:42,776][__main__][INFO] - [37760] Loss: 0.183, Running accuracy: 99.790, Time: 21.80
[2020-12-15 06:47:04,001][__main__][INFO] - [38080] Loss: 0.217, Running accuracy: 99.789, Time: 21.22
[2020-12-15 06:47:28,233][__main__][INFO] - [38400] Loss: 0.228, Running accuracy: 99.789, Time: 24.23
[2020-12-15 06:47:52,356][__main__][INFO] - [38720] Loss: 0.209, Running accuracy: 99.789, Time: 24.12
[2020-12-15 06:48:14,517][__main__][INFO] - [39040] Loss: 0.160, Running accuracy: 99.789, Time: 22.16
[2020-12-15 06:48:38,082][__main__][INFO] - [39360] Loss: 0.148, Running accuracy: 99.789, Time: 23.56
[2020-12-15 06:48:58,802][__main__][INFO] - [39680] Loss: 0.237, Running accuracy: 99.789, Time: 20.72
[2020-12-15 06:49:07,448][__main__][INFO] - Action accuracy: 99.789, Loss: 0.209
[2020-12-15 06:49:07,449][__main__][INFO] - Validating..
[2020-12-15 06:49:45,631][test][INFO] - Time elapsed: 36.330022
[2020-12-15 06:49:45,635][__main__][INFO] - Validation F1 score: 95.660, Exact match: 56.180, Precision: 95.650, Recall: 95.680
[2020-12-15 06:50:28,772][__main__][INFO] - Checkpoint saved to checkpoints/model_latest.pth
[2020-12-15 06:50:29,576][__main__][INFO] - Epoch #23
[2020-12-15 06:50:29,577][__main__][INFO] - Training..
[2020-12-15 06:50:53,434][__main__][INFO] - [320] Loss: 0.224, Running accuracy: 99.770, Time: 22.53
[2020-12-15 06:51:14,439][__main__][INFO] - [640] Loss: 0.109, Running accuracy: 99.832, Time: 21.00
[2020-12-15 06:51:39,180][__main__][INFO] - [960] Loss: 0.295, Running accuracy: 99.775, Time: 24.74
[2020-12-15 06:52:01,669][__main__][INFO] - [1280] Loss: 0.175, Running accuracy: 99.782, Time: 22.49
[2020-12-15 06:52:23,900][__main__][INFO] - [1600] Loss: 0.152, Running accuracy: 99.797, Time: 22.23
[2020-12-15 06:52:46,419][__main__][INFO] - [1920] Loss: 0.110, Running accuracy: 99.806, Time: 22.52
[2020-12-15 06:53:07,132][__main__][INFO] - [2240] Loss: 0.126, Running accuracy: 99.813, Time: 20.71
[2020-12-15 06:53:28,772][__main__][INFO] - [2560] Loss: 0.139, Running accuracy: 99.819, Time: 21.64
[2020-12-15 06:53:52,112][__main__][INFO] - [2880] Loss: 0.157, Running accuracy: 99.818, Time: 23.34
[2020-12-15 06:54:13,810][__main__][INFO] - [3200] Loss: 0.126, Running accuracy: 99.822, Time: 21.70
[2020-12-15 06:54:34,974][__main__][INFO] - [3520] Loss: 0.115, Running accuracy: 99.826, Time: 21.16
[2020-12-15 06:55:00,790][__main__][INFO] - [3840] Loss: 0.141, Running accuracy: 99.826, Time: 25.82
[2020-12-15 06:55:21,836][__main__][INFO] - [4160] Loss: 0.192, Running accuracy: 99.821, Time: 21.05
[2020-12-15 06:55:44,218][__main__][INFO] - [4480] Loss: 0.189, Running accuracy: 99.814, Time: 22.38
[2020-12-15 06:56:07,219][__main__][INFO] - [4800] Loss: 0.195, Running accuracy: 99.814, Time: 23.00
[2020-12-15 06:56:28,243][__main__][INFO] - [5120] Loss: 0.250, Running accuracy: 99.808, Time: 21.02
[2020-12-15 06:56:50,367][__main__][INFO] - [5440] Loss: 0.152, Running accuracy: 99.805, Time: 22.12
[2020-12-15 06:57:12,590][__main__][INFO] - [5760] Loss: 0.107, Running accuracy: 99.811, Time: 22.22
[2020-12-15 06:57:34,322][__main__][INFO] - [6080] Loss: 0.175, Running accuracy: 99.815, Time: 21.73
[2020-12-15 06:57:54,944][__main__][INFO] - [6400] Loss: 0.160, Running accuracy: 99.815, Time: 20.62
[2020-12-15 06:58:17,239][__main__][INFO] - [6720] Loss: 0.092, Running accuracy: 99.819, Time: 22.29
[2020-12-15 06:58:37,848][__main__][INFO] - [7040] Loss: 0.132, Running accuracy: 99.817, Time: 20.60
[2020-12-15 06:59:01,518][__main__][INFO] - [7360] Loss: 0.162, Running accuracy: 99.815, Time: 23.67
[2020-12-15 06:59:26,073][__main__][INFO] - [7680] Loss: 0.156, Running accuracy: 99.817, Time: 24.55
[2020-12-15 06:59:48,247][__main__][INFO] - [8000] Loss: 0.211, Running accuracy: 99.812, Time: 22.17
[2020-12-15 07:00:12,042][__main__][INFO] - [8320] Loss: 0.118, Running accuracy: 99.815, Time: 23.79
[2020-12-15 07:00:33,503][__main__][INFO] - [8640] Loss: 0.178, Running accuracy: 99.816, Time: 21.46
[2020-12-15 07:00:56,548][__main__][INFO] - [8960] Loss: 0.196, Running accuracy: 99.812, Time: 23.04
[2020-12-15 07:01:18,995][__main__][INFO] - [9280] Loss: 0.102, Running accuracy: 99.815, Time: 22.45
[2020-12-15 07:01:40,558][__main__][INFO] - [9600] Loss: 0.176, Running accuracy: 99.815, Time: 21.56
[2020-12-15 07:02:02,555][__main__][INFO] - [9920] Loss: 0.197, Running accuracy: 99.814, Time: 22.00
[2020-12-15 07:02:25,377][__main__][INFO] - [10240] Loss: 0.188, Running accuracy: 99.814, Time: 22.82
[2020-12-15 07:02:47,762][__main__][INFO] - [10560] Loss: 0.114, Running accuracy: 99.815, Time: 22.38
[2020-12-15 07:03:10,684][__main__][INFO] - [10880] Loss: 0.202, Running accuracy: 99.814, Time: 22.92
[2020-12-15 07:03:35,771][__main__][INFO] - [11200] Loss: 0.133, Running accuracy: 99.815, Time: 25.09
[2020-12-15 07:03:58,632][__main__][INFO] - [11520] Loss: 0.146, Running accuracy: 99.814, Time: 22.86
[2020-12-15 07:04:22,345][__main__][INFO] - [11840] Loss: 0.135, Running accuracy: 99.815, Time: 23.71
[2020-12-15 07:04:44,450][__main__][INFO] - [12160] Loss: 0.166, Running accuracy: 99.814, Time: 22.10
[2020-12-15 07:05:06,483][__main__][INFO] - [12480] Loss: 0.192, Running accuracy: 99.813, Time: 22.03
[2020-12-15 07:05:33,953][__main__][INFO] - [12800] Loss: 0.188, Running accuracy: 99.811, Time: 27.47
[2020-12-15 07:05:55,903][__main__][INFO] - [13120] Loss: 0.194, Running accuracy: 99.812, Time: 21.95
[2020-12-15 07:06:17,071][__main__][INFO] - [13440] Loss: 0.240, Running accuracy: 99.809, Time: 21.17
[2020-12-15 07:06:41,079][__main__][INFO] - [13760] Loss: 0.133, Running accuracy: 99.810, Time: 24.01
[2020-12-15 07:07:03,452][__main__][INFO] - [14080] Loss: 0.123, Running accuracy: 99.810, Time: 22.37
[2020-12-15 07:07:27,335][__main__][INFO] - [14400] Loss: 0.160, Running accuracy: 99.809, Time: 23.88
[2020-12-15 07:07:50,668][__main__][INFO] - [14720] Loss: 0.146, Running accuracy: 99.810, Time: 23.33
[2020-12-15 07:08:14,539][__main__][INFO] - [15040] Loss: 0.208, Running accuracy: 99.810, Time: 23.87
[2020-12-15 07:08:35,906][__main__][INFO] - [15360] Loss: 0.216, Running accuracy: 99.809, Time: 21.37
[2020-12-15 07:08:57,303][__main__][INFO] - [15680] Loss: 0.196, Running accuracy: 99.810, Time: 21.40
[2020-12-15 07:09:18,694][__main__][INFO] - [16000] Loss: 0.215, Running accuracy: 99.808, Time: 21.39
[2020-12-15 07:09:40,598][__main__][INFO] - [16320] Loss: 0.169, Running accuracy: 99.809, Time: 21.90
[2020-12-15 07:10:03,076][__main__][INFO] - [16640] Loss: 0.185, Running accuracy: 99.809, Time: 22.48
[2020-12-15 07:10:25,964][__main__][INFO] - [16960] Loss: 0.189, Running accuracy: 99.808, Time: 22.89
[2020-12-15 07:10:51,653][__main__][INFO] - [17280] Loss: 0.191, Running accuracy: 99.808, Time: 25.69
[2020-12-15 07:11:14,318][__main__][INFO] - [17600] Loss: 0.172, Running accuracy: 99.808, Time: 22.67
[2020-12-15 07:11:36,533][__main__][INFO] - [17920] Loss: 0.257, Running accuracy: 99.808, Time: 22.21
[2020-12-15 07:11:58,744][__main__][INFO] - [18240] Loss: 0.132, Running accuracy: 99.809, Time: 22.21
[2020-12-15 07:12:22,040][__main__][INFO] - [18560] Loss: 0.201, Running accuracy: 99.810, Time: 23.30
[2020-12-15 07:12:44,282][__main__][INFO] - [18880] Loss: 0.114, Running accuracy: 99.811, Time: 22.24
[2020-12-15 07:13:08,396][__main__][INFO] - [19200] Loss: 0.154, Running accuracy: 99.812, Time: 24.11
[2020-12-15 07:13:31,621][__main__][INFO] - [19520] Loss: 0.225, Running accuracy: 99.812, Time: 23.22
[2020-12-15 07:13:53,580][__main__][INFO] - [19840] Loss: 0.243, Running accuracy: 99.812, Time: 21.96
[2020-12-15 07:14:14,605][__main__][INFO] - [20160] Loss: 0.215, Running accuracy: 99.811, Time: 21.03
[2020-12-15 07:14:36,400][__main__][INFO] - [20480] Loss: 0.203, Running accuracy: 99.811, Time: 21.79
[2020-12-15 07:14:57,083][__main__][INFO] - [20800] Loss: 0.229, Running accuracy: 99.810, Time: 20.68
[2020-12-15 07:15:19,041][__main__][INFO] - [21120] Loss: 0.223, Running accuracy: 99.809, Time: 21.96
[2020-12-15 07:15:42,654][__main__][INFO] - [21440] Loss: 0.134, Running accuracy: 99.809, Time: 23.61
[2020-12-15 07:16:09,035][__main__][INFO] - [21760] Loss: 0.241, Running accuracy: 99.809, Time: 26.38
[2020-12-15 07:16:31,263][__main__][INFO] - [22080] Loss: 0.175, Running accuracy: 99.809, Time: 22.23
[2020-12-15 07:16:53,399][__main__][INFO] - [22400] Loss: 0.159, Running accuracy: 99.808, Time: 22.13
[2020-12-15 07:17:16,171][__main__][INFO] - [22720] Loss: 0.090, Running accuracy: 99.810, Time: 22.77
[2020-12-15 07:17:39,630][__main__][INFO] - [23040] Loss: 0.105, Running accuracy: 99.812, Time: 23.46
[2020-12-15 07:18:03,364][__main__][INFO] - [23360] Loss: 0.182, Running accuracy: 99.811, Time: 23.73
[2020-12-15 07:18:25,167][__main__][INFO] - [23680] Loss: 0.152, Running accuracy: 99.812, Time: 21.80
[2020-12-15 07:18:47,895][__main__][INFO] - [24000] Loss: 0.257, Running accuracy: 99.811, Time: 22.73
[2020-12-15 07:19:11,571][__main__][INFO] - [24320] Loss: 0.101, Running accuracy: 99.812, Time: 23.67
[2020-12-15 07:19:32,826][__main__][INFO] - [24640] Loss: 0.139, Running accuracy: 99.812, Time: 21.25
[2020-12-15 07:19:54,694][__main__][INFO] - [24960] Loss: 0.166, Running accuracy: 99.812, Time: 21.87
[2020-12-15 07:20:16,864][__main__][INFO] - [25280] Loss: 0.169, Running accuracy: 99.812, Time: 22.17
[2020-12-15 07:20:38,065][__main__][INFO] - [25600] Loss: 0.141, Running accuracy: 99.813, Time: 21.20
[2020-12-15 07:21:00,567][__main__][INFO] - [25920] Loss: 0.124, Running accuracy: 99.813, Time: 22.50
[2020-12-15 07:21:24,354][__main__][INFO] - [26240] Loss: 0.149, Running accuracy: 99.814, Time: 23.79
[2020-12-15 07:21:49,550][__main__][INFO] - [26560] Loss: 0.189, Running accuracy: 99.813, Time: 25.20
[2020-12-15 07:22:10,343][__main__][INFO] - [26880] Loss: 0.169, Running accuracy: 99.812, Time: 20.79
[2020-12-15 07:22:32,294][__main__][INFO] - [27200] Loss: 0.183, Running accuracy: 99.811, Time: 21.95
[2020-12-15 07:22:53,535][__main__][INFO] - [27520] Loss: 0.225, Running accuracy: 99.811, Time: 21.24
[2020-12-15 07:23:14,907][__main__][INFO] - [27840] Loss: 0.205, Running accuracy: 99.811, Time: 21.37
[2020-12-15 07:23:36,837][__main__][INFO] - [28160] Loss: 0.115, Running accuracy: 99.812, Time: 21.93
[2020-12-15 07:23:58,274][__main__][INFO] - [28480] Loss: 0.204, Running accuracy: 99.811, Time: 21.44
[2020-12-15 07:24:21,551][__main__][INFO] - [28800] Loss: 0.219, Running accuracy: 99.809, Time: 23.28
[2020-12-15 07:24:43,270][__main__][INFO] - [29120] Loss: 0.260, Running accuracy: 99.808, Time: 21.72
[2020-12-15 07:25:04,084][__main__][INFO] - [29440] Loss: 0.110, Running accuracy: 99.808, Time: 20.81
[2020-12-15 07:25:25,660][__main__][INFO] - [29760] Loss: 0.173, Running accuracy: 99.807, Time: 21.57
[2020-12-15 07:25:48,312][__main__][INFO] - [30080] Loss: 0.177, Running accuracy: 99.807, Time: 22.65
[2020-12-15 07:26:10,550][__main__][INFO] - [30400] Loss: 0.132, Running accuracy: 99.808, Time: 22.24
[2020-12-15 07:26:32,368][__main__][INFO] - [30720] Loss: 0.094, Running accuracy: 99.809, Time: 21.82
[2020-12-15 07:26:59,103][__main__][INFO] - [31040] Loss: 0.159, Running accuracy: 99.809, Time: 26.73
[2020-12-15 07:27:21,708][__main__][INFO] - [31360] Loss: 0.176, Running accuracy: 99.809, Time: 22.60
[2020-12-15 07:27:43,895][__main__][INFO] - [31680] Loss: 0.260, Running accuracy: 99.809, Time: 22.19
[2020-12-15 07:28:04,881][__main__][INFO] - [32000] Loss: 0.139, Running accuracy: 99.810, Time: 20.98
[2020-12-15 07:28:26,799][__main__][INFO] - [32320] Loss: 0.247, Running accuracy: 99.808, Time: 21.92
[2020-12-15 07:28:49,417][__main__][INFO] - [32640] Loss: 0.251, Running accuracy: 99.807, Time: 22.62
[2020-12-15 07:29:11,480][__main__][INFO] - [32960] Loss: 0.136, Running accuracy: 99.808, Time: 22.06
[2020-12-15 07:29:32,518][__main__][INFO] - [33280] Loss: 0.134, Running accuracy: 99.808, Time: 21.04
[2020-12-15 07:29:54,934][__main__][INFO] - [33600] Loss: 0.180, Running accuracy: 99.808, Time: 22.42
[2020-12-15 07:30:18,756][__main__][INFO] - [33920] Loss: 0.197, Running accuracy: 99.807, Time: 23.82
[2020-12-15 07:30:40,793][__main__][INFO] - [34240] Loss: 0.139, Running accuracy: 99.808, Time: 22.04
[2020-12-15 07:31:02,915][__main__][INFO] - [34560] Loss: 0.181, Running accuracy: 99.807, Time: 22.12
[2020-12-15 07:31:26,483][__main__][INFO] - [34880] Loss: 0.202, Running accuracy: 99.807, Time: 23.57
[2020-12-15 07:31:49,023][__main__][INFO] - [35200] Loss: 0.199, Running accuracy: 99.808, Time: 22.54
[2020-12-15 07:32:15,438][__main__][INFO] - [35520] Loss: 0.235, Running accuracy: 99.807, Time: 26.41
[2020-12-15 07:32:37,268][__main__][INFO] - [35840] Loss: 0.142, Running accuracy: 99.807, Time: 21.83
[2020-12-15 07:32:58,101][__main__][INFO] - [36160] Loss: 0.091, Running accuracy: 99.808, Time: 20.83
[2020-12-15 07:33:23,486][__main__][INFO] - [36480] Loss: 0.238, Running accuracy: 99.808, Time: 25.38
[2020-12-15 07:33:48,242][__main__][INFO] - [36800] Loss: 0.200, Running accuracy: 99.807, Time: 24.30
[2020-12-15 07:34:09,917][__main__][INFO] - [37120] Loss: 0.111, Running accuracy: 99.808, Time: 21.67
[2020-12-15 07:34:32,978][__main__][INFO] - [37440] Loss: 0.184, Running accuracy: 99.808, Time: 23.06
[2020-12-15 07:34:55,817][__main__][INFO] - [37760] Loss: 0.136, Running accuracy: 99.808, Time: 22.84
[2020-12-15 07:35:19,360][__main__][INFO] - [38080] Loss: 0.112, Running accuracy: 99.808, Time: 23.54
[2020-12-15 07:35:41,465][__main__][INFO] - [38400] Loss: 0.259, Running accuracy: 99.808, Time: 22.10
[2020-12-15 07:36:02,100][__main__][INFO] - [38720] Loss: 0.174, Running accuracy: 99.808, Time: 20.63
[2020-12-15 07:36:25,489][__main__][INFO] - [39040] Loss: 0.184, Running accuracy: 99.808, Time: 23.39
[2020-12-15 07:36:46,853][__main__][INFO] - [39360] Loss: 0.146, Running accuracy: 99.808, Time: 21.36
[2020-12-15 07:37:10,376][__main__][INFO] - [39680] Loss: 0.289, Running accuracy: 99.807, Time: 23.38
[2020-12-15 07:37:21,198][__main__][INFO] - Action accuracy: 99.807, Loss: 0.191
[2020-12-15 07:37:21,198][__main__][INFO] - Validating..
[2020-12-15 07:37:59,387][test][INFO] - Time elapsed: 32.257698
[2020-12-15 07:37:59,392][__main__][INFO] - Validation F1 score: 95.580, Exact match: 55.410, Precision: 95.520, Recall: 95.630
[2020-12-15 07:38:38,613][__main__][INFO] - Checkpoint saved to checkpoints/model_latest.pth
[2020-12-15 07:38:39,583][__main__][INFO] - Epoch #24
[2020-12-15 07:38:39,584][__main__][INFO] - Training..
[2020-12-15 07:39:01,053][__main__][INFO] - [320] Loss: 0.151, Running accuracy: 99.866, Time: 20.34
[2020-12-15 07:39:22,407][__main__][INFO] - [640] Loss: 0.136, Running accuracy: 99.854, Time: 21.35
[2020-12-15 07:39:45,090][__main__][INFO] - [960] Loss: 0.172, Running accuracy: 99.797, Time: 22.68
[2020-12-15 07:40:08,060][__main__][INFO] - [1280] Loss: 0.115, Running accuracy: 99.809, Time: 22.97
[2020-12-15 07:40:30,945][__main__][INFO] - [1600] Loss: 0.173, Running accuracy: 99.799, Time: 22.88
[2020-12-15 07:40:53,938][__main__][INFO] - [1920] Loss: 0.147, Running accuracy: 99.804, Time: 22.99
[2020-12-15 07:41:15,432][__main__][INFO] - [2240] Loss: 0.148, Running accuracy: 99.809, Time: 21.49
[2020-12-15 07:41:43,953][__main__][INFO] - [2560] Loss: 0.224, Running accuracy: 99.801, Time: 28.52
[2020-12-15 07:42:08,086][__main__][INFO] - [2880] Loss: 0.160, Running accuracy: 99.805, Time: 24.13
[2020-12-15 07:42:30,245][__main__][INFO] - [3200] Loss: 0.227, Running accuracy: 99.798, Time: 22.16
[2020-12-15 07:42:52,901][__main__][INFO] - [3520] Loss: 0.162, Running accuracy: 99.798, Time: 22.66
[2020-12-15 07:43:16,237][__main__][INFO] - [3840] Loss: 0.169, Running accuracy: 99.803, Time: 23.33
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[2020-12-15 08:23:48,906][__main__][INFO] - [38400] Loss: 0.211, Running accuracy: 99.808, Time: 24.49
[2020-12-15 08:24:14,644][__main__][INFO] - [38720] Loss: 0.132, Running accuracy: 99.808, Time: 25.74
[2020-12-15 08:24:37,768][__main__][INFO] - [39040] Loss: 0.234, Running accuracy: 99.808, Time: 23.12
[2020-12-15 08:24:59,868][__main__][INFO] - [39360] Loss: 0.140, Running accuracy: 99.809, Time: 22.10
[2020-12-15 08:25:20,950][__main__][INFO] - [39680] Loss: 0.248, Running accuracy: 99.808, Time: 21.08
[2020-12-15 08:25:29,585][__main__][INFO] - Action accuracy: 99.808, Loss: 0.186
[2020-12-15 08:25:29,588][__main__][INFO] - Validating..
[2020-12-15 08:26:04,249][test][INFO] - Time elapsed: 32.536789
[2020-12-15 08:26:04,253][__main__][INFO] - Validation F1 score: 95.650, Exact match: 56.350, Precision: 95.620, Recall: 95.670
[2020-12-15 08:26:45,202][__main__][INFO] - Checkpoint saved to checkpoints/model_latest.pth
[2020-12-15 08:26:46,168][__main__][INFO] - Epoch #25
[2020-12-15 08:26:46,169][__main__][INFO] - Training..
[2020-12-15 08:27:09,535][__main__][INFO] - [320] Loss: 0.119, Running accuracy: 99.854, Time: 22.10
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[2020-12-15 09:13:25,799][__main__][INFO] - [39680] Loss: 0.149, Running accuracy: 99.819, Time: 21.77
[2020-12-15 09:13:35,048][__main__][INFO] - Action accuracy: 99.819, Loss: 0.177
[2020-12-15 09:13:35,049][__main__][INFO] - Validating..
[2020-12-15 09:14:09,484][test][INFO] - Time elapsed: 32.519217
[2020-12-15 09:14:09,488][__main__][INFO] - Validation F1 score: 95.590, Exact match: 55.650, Precision: 95.540, Recall: 95.640
[2020-12-15 09:14:49,426][__main__][INFO] - Checkpoint saved to checkpoints/model_latest.pth
[2020-12-15 09:14:50,427][__main__][INFO] - Epoch #26
[2020-12-15 09:14:50,428][__main__][INFO] - Training..
[2020-12-15 09:15:17,387][__main__][INFO] - [320] Loss: 0.166, Running accuracy: 99.804, Time: 21.87
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[2020-12-15 09:55:07,046][__main__][INFO] - [34240] Loss: 0.198, Running accuracy: 99.832, Time: 21.71
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[2020-12-15 09:55:52,074][__main__][INFO] - [34880] Loss: 0.087, Running accuracy: 99.833, Time: 22.94
[2020-12-15 09:56:13,884][__main__][INFO] - [35200] Loss: 0.133, Running accuracy: 99.833, Time: 21.81
[2020-12-15 09:56:35,745][__main__][INFO] - [35520] Loss: 0.150, Running accuracy: 99.833, Time: 21.86
[2020-12-15 09:56:59,078][__main__][INFO] - [35840] Loss: 0.168, Running accuracy: 99.833, Time: 23.33
[2020-12-15 09:57:25,444][__main__][INFO] - [36160] Loss: 0.232, Running accuracy: 99.832, Time: 26.36
[2020-12-15 09:57:47,984][__main__][INFO] - [36480] Loss: 0.075, Running accuracy: 99.833, Time: 22.54
[2020-12-15 09:58:11,428][__main__][INFO] - [36800] Loss: 0.100, Running accuracy: 99.834, Time: 23.44
[2020-12-15 09:58:34,729][__main__][INFO] - [37120] Loss: 0.133, Running accuracy: 99.833, Time: 23.30
[2020-12-15 09:58:55,899][__main__][INFO] - [37440] Loss: 0.080, Running accuracy: 99.833, Time: 21.17
[2020-12-15 09:59:17,784][__main__][INFO] - [37760] Loss: 0.224, Running accuracy: 99.833, Time: 21.88
[2020-12-15 09:59:39,405][__main__][INFO] - [38080] Loss: 0.156, Running accuracy: 99.832, Time: 21.62
[2020-12-15 10:00:02,341][__main__][INFO] - [38400] Loss: 0.289, Running accuracy: 99.831, Time: 22.93
[2020-12-15 10:00:24,549][__main__][INFO] - [38720] Loss: 0.103, Running accuracy: 99.831, Time: 22.21
[2020-12-15 10:00:48,557][__main__][INFO] - [39040] Loss: 0.175, Running accuracy: 99.831, Time: 24.01
[2020-12-15 10:01:10,476][__main__][INFO] - [39360] Loss: 0.196, Running accuracy: 99.831, Time: 21.92
[2020-12-15 10:01:31,839][__main__][INFO] - [39680] Loss: 0.081, Running accuracy: 99.831, Time: 21.36
[2020-12-15 10:01:41,197][__main__][INFO] - Action accuracy: 99.831, Loss: 0.169
[2020-12-15 10:01:41,198][__main__][INFO] - Validating..
[2020-12-15 10:02:18,806][test][INFO] - Time elapsed: 35.643887
[2020-12-15 10:02:18,810][__main__][INFO] - Validation F1 score: 95.660, Exact match: 55.940, Precision: 95.610, Recall: 95.710
Epoch    27: reducing learning rate of group 0 to 1.2500e-06.
[2020-12-15 10:02:57,708][__main__][INFO] - Checkpoint saved to checkpoints/model_latest.pth
[2020-12-15 10:02:58,517][__main__][INFO] - Epoch #27
[2020-12-15 10:02:58,517][__main__][INFO] - Training..
[2020-12-15 10:03:21,931][__main__][INFO] - [320] Loss: 0.152, Running accuracy: 99.841, Time: 22.14
[2020-12-15 10:03:46,205][__main__][INFO] - [640] Loss: 0.162, Running accuracy: 99.838, Time: 24.27
[2020-12-15 10:04:07,954][__main__][INFO] - [960] Loss: 0.123, Running accuracy: 99.835, Time: 21.75
[2020-12-15 10:04:32,203][__main__][INFO] - [1280] Loss: 0.178, Running accuracy: 99.835, Time: 24.25
[2020-12-15 10:04:54,540][__main__][INFO] - [1600] Loss: 0.150, Running accuracy: 99.849, Time: 22.34
[2020-12-15 10:05:17,762][__main__][INFO] - [1920] Loss: 0.142, Running accuracy: 99.849, Time: 23.22
[2020-12-15 10:05:42,076][__main__][INFO] - [2240] Loss: 0.142, Running accuracy: 99.846, Time: 24.31
[2020-12-15 10:06:05,646][__main__][INFO] - [2560] Loss: 0.122, Running accuracy: 99.848, Time: 23.57
[2020-12-15 10:06:25,917][__main__][INFO] - [2880] Loss: 0.132, Running accuracy: 99.842, Time: 20.27
[2020-12-15 10:06:48,051][__main__][INFO] - [3200] Loss: 0.128, Running accuracy: 99.843, Time: 22.13
[2020-12-15 10:07:14,081][__main__][INFO] - [3520] Loss: 0.071, Running accuracy: 99.849, Time: 26.03
[2020-12-15 10:07:36,209][__main__][INFO] - [3840] Loss: 0.124, Running accuracy: 99.854, Time: 22.13
[2020-12-15 10:07:59,659][__main__][INFO] - [4160] Loss: 0.155, Running accuracy: 99.857, Time: 23.45
[2020-12-15 10:08:22,355][__main__][INFO] - [4480] Loss: 0.120, Running accuracy: 99.855, Time: 22.69
[2020-12-15 10:08:44,957][__main__][INFO] - [4800] Loss: 0.129, Running accuracy: 99.851, Time: 22.60
[2020-12-15 10:09:08,856][__main__][INFO] - [5120] Loss: 0.213, Running accuracy: 99.846, Time: 23.90
[2020-12-15 10:09:31,770][__main__][INFO] - [5440] Loss: 0.156, Running accuracy: 99.846, Time: 22.91
[2020-12-15 10:09:55,188][__main__][INFO] - [5760] Loss: 0.087, Running accuracy: 99.850, Time: 23.42
[2020-12-15 10:10:16,784][__main__][INFO] - [6080] Loss: 0.120, Running accuracy: 99.849, Time: 21.60
[2020-12-15 10:10:38,809][__main__][INFO] - [6400] Loss: 0.149, Running accuracy: 99.850, Time: 22.02
[2020-12-15 10:11:00,779][__main__][INFO] - [6720] Loss: 0.147, Running accuracy: 99.850, Time: 21.97
[2020-12-15 10:11:21,621][__main__][INFO] - [7040] Loss: 0.161, Running accuracy: 99.848, Time: 20.84
[2020-12-15 10:11:44,074][__main__][INFO] - [7360] Loss: 0.091, Running accuracy: 99.849, Time: 21.91
[2020-12-15 10:12:06,771][__main__][INFO] - [7680] Loss: 0.237, Running accuracy: 99.846, Time: 22.70
[2020-12-15 10:12:33,807][__main__][INFO] - [8000] Loss: 0.105, Running accuracy: 99.848, Time: 27.03
[2020-12-15 10:12:56,416][__main__][INFO] - [8320] Loss: 0.082, Running accuracy: 99.851, Time: 22.61
[2020-12-15 10:13:18,871][__main__][INFO] - [8640] Loss: 0.090, Running accuracy: 99.852, Time: 22.45
[2020-12-15 10:13:42,907][__main__][INFO] - [8960] Loss: 0.114, Running accuracy: 99.853, Time: 24.03
[2020-12-15 10:14:05,708][__main__][INFO] - [9280] Loss: 0.160, Running accuracy: 99.852, Time: 22.80
[2020-12-15 10:14:26,760][__main__][INFO] - [9600] Loss: 0.150, Running accuracy: 99.852, Time: 21.05
[2020-12-15 10:14:48,784][__main__][INFO] - [9920] Loss: 0.097, Running accuracy: 99.853, Time: 22.02
[2020-12-15 10:15:10,160][__main__][INFO] - [10240] Loss: 0.151, Running accuracy: 99.854, Time: 21.37
[2020-12-15 10:15:34,100][__main__][INFO] - [10560] Loss: 0.079, Running accuracy: 99.855, Time: 23.94
[2020-12-15 10:15:56,947][__main__][INFO] - [10880] Loss: 0.134, Running accuracy: 99.854, Time: 22.85
[2020-12-15 10:16:17,508][__main__][INFO] - [11200] Loss: 0.080, Running accuracy: 99.855, Time: 20.56
[2020-12-15 10:16:40,285][__main__][INFO] - [11520] Loss: 0.186, Running accuracy: 99.854, Time: 22.78
[2020-12-15 10:17:03,320][__main__][INFO] - [11840] Loss: 0.143, Running accuracy: 99.852, Time: 23.03
[2020-12-15 10:17:24,203][__main__][INFO] - [12160] Loss: 0.129, Running accuracy: 99.853, Time: 20.88
[2020-12-15 10:17:51,154][__main__][INFO] - [12480] Loss: 0.128, Running accuracy: 99.852, Time: 26.95
[2020-12-15 10:18:12,987][__main__][INFO] - [12800] Loss: 0.101, Running accuracy: 99.854, Time: 21.83
[2020-12-15 10:18:36,453][__main__][INFO] - [13120] Loss: 0.117, Running accuracy: 99.855, Time: 23.46
[2020-12-15 10:18:57,260][__main__][INFO] - [13440] Loss: 0.171, Running accuracy: 99.853, Time: 20.81
[2020-12-15 10:19:20,117][__main__][INFO] - [13760] Loss: 0.170, Running accuracy: 99.851, Time: 22.86
[2020-12-15 10:19:41,557][__main__][INFO] - [14080] Loss: 0.145, Running accuracy: 99.853, Time: 21.44
[2020-12-15 10:20:03,978][__main__][INFO] - [14400] Loss: 0.082, Running accuracy: 99.854, Time: 22.42
[2020-12-15 10:20:25,172][__main__][INFO] - [14720] Loss: 0.122, Running accuracy: 99.854, Time: 21.19
[2020-12-15 10:20:47,257][__main__][INFO] - [15040] Loss: 0.129, Running accuracy: 99.854, Time: 22.08
[2020-12-15 10:21:08,722][__main__][INFO] - [15360] Loss: 0.180, Running accuracy: 99.854, Time: 21.46
[2020-12-15 10:21:30,964][__main__][INFO] - [15680] Loss: 0.097, Running accuracy: 99.854, Time: 22.24
[2020-12-15 10:21:53,113][__main__][INFO] - [16000] Loss: 0.113, Running accuracy: 99.855, Time: 22.15
[2020-12-15 10:22:16,657][__main__][INFO] - [16320] Loss: 0.093, Running accuracy: 99.856, Time: 23.54
[2020-12-15 10:22:39,957][__main__][INFO] - [16640] Loss: 0.201, Running accuracy: 99.856, Time: 23.30
[2020-12-15 10:23:06,906][__main__][INFO] - [16960] Loss: 0.171, Running accuracy: 99.855, Time: 26.95
[2020-12-15 10:23:29,637][__main__][INFO] - [17280] Loss: 0.124, Running accuracy: 99.854, Time: 22.73
[2020-12-15 10:23:51,124][__main__][INFO] - [17600] Loss: 0.148, Running accuracy: 99.853, Time: 21.49
[2020-12-15 10:24:12,644][__main__][INFO] - [17920] Loss: 0.086, Running accuracy: 99.854, Time: 21.52
[2020-12-15 10:24:37,462][__main__][INFO] - [18240] Loss: 0.106, Running accuracy: 99.854, Time: 24.82
[2020-12-15 10:24:59,824][__main__][INFO] - [18560] Loss: 0.108, Running accuracy: 99.855, Time: 22.36
[2020-12-15 10:25:23,308][__main__][INFO] - [18880] Loss: 0.132, Running accuracy: 99.855, Time: 23.48
[2020-12-15 10:25:45,791][__main__][INFO] - [19200] Loss: 0.120, Running accuracy: 99.856, Time: 22.48
[2020-12-15 10:26:08,353][__main__][INFO] - [19520] Loss: 0.103, Running accuracy: 99.857, Time: 21.91
[2020-12-15 10:26:28,612][__main__][INFO] - [19840] Loss: 0.046, Running accuracy: 99.858, Time: 20.15
[2020-12-15 10:26:51,375][__main__][INFO] - [20160] Loss: 0.115, Running accuracy: 99.858, Time: 22.76
[2020-12-15 10:27:16,501][__main__][INFO] - [20480] Loss: 0.097, Running accuracy: 99.859, Time: 25.12
[2020-12-15 10:27:38,148][__main__][INFO] - [20800] Loss: 0.262, Running accuracy: 99.857, Time: 21.65
[2020-12-15 10:28:01,899][__main__][INFO] - [21120] Loss: 0.159, Running accuracy: 99.856, Time: 23.75
[2020-12-15 10:28:27,442][__main__][INFO] - [21440] Loss: 0.177, Running accuracy: 99.855, Time: 25.54
[2020-12-15 10:28:53,465][__main__][INFO] - [21760] Loss: 0.164, Running accuracy: 99.856, Time: 25.29
[2020-12-15 10:29:15,692][__main__][INFO] - [22080] Loss: 0.404, Running accuracy: 99.854, Time: 22.23
[2020-12-15 10:29:36,469][__main__][INFO] - [22400] Loss: 0.143, Running accuracy: 99.854, Time: 20.77
[2020-12-15 10:30:00,319][__main__][INFO] - [22720] Loss: 0.105, Running accuracy: 99.855, Time: 23.85
[2020-12-15 10:30:21,597][__main__][INFO] - [23040] Loss: 0.268, Running accuracy: 99.852, Time: 21.28
[2020-12-15 10:30:43,460][__main__][INFO] - [23360] Loss: 0.210, Running accuracy: 99.852, Time: 21.86
[2020-12-15 10:31:05,201][__main__][INFO] - [23680] Loss: 0.109, Running accuracy: 99.852, Time: 21.74
[2020-12-15 10:31:27,173][__main__][INFO] - [24000] Loss: 0.126, Running accuracy: 99.853, Time: 21.97
[2020-12-15 10:31:51,476][__main__][INFO] - [24320] Loss: 0.126, Running accuracy: 99.853, Time: 24.30
[2020-12-15 10:32:13,718][__main__][INFO] - [24640] Loss: 0.107, Running accuracy: 99.854, Time: 22.24
[2020-12-15 10:32:35,233][__main__][INFO] - [24960] Loss: 0.105, Running accuracy: 99.855, Time: 21.51
[2020-12-15 10:32:57,552][__main__][INFO] - [25280] Loss: 0.150, Running accuracy: 99.855, Time: 22.32
[2020-12-15 10:33:20,362][__main__][INFO] - [25600] Loss: 0.115, Running accuracy: 99.854, Time: 22.81
[2020-12-15 10:33:46,253][__main__][INFO] - [25920] Loss: 0.124, Running accuracy: 99.855, Time: 25.89
[2020-12-15 10:34:09,033][__main__][INFO] - [26240] Loss: 0.148, Running accuracy: 99.855, Time: 22.44
[2020-12-15 10:34:32,257][__main__][INFO] - [26560] Loss: 0.080, Running accuracy: 99.856, Time: 23.22
[2020-12-15 10:34:54,709][__main__][INFO] - [26880] Loss: 0.144, Running accuracy: 99.856, Time: 22.45
[2020-12-15 10:35:16,346][__main__][INFO] - [27200] Loss: 0.181, Running accuracy: 99.855, Time: 21.64
[2020-12-15 10:35:37,597][__main__][INFO] - [27520] Loss: 0.146, Running accuracy: 99.855, Time: 21.25
[2020-12-15 10:36:00,314][__main__][INFO] - [27840] Loss: 0.109, Running accuracy: 99.854, Time: 22.72
[2020-12-15 10:36:21,279][__main__][INFO] - [28160] Loss: 0.137, Running accuracy: 99.854, Time: 20.96
[2020-12-15 10:36:41,608][__main__][INFO] - [28480] Loss: 0.132, Running accuracy: 99.855, Time: 20.33
[2020-12-15 10:37:03,438][__main__][INFO] - [28800] Loss: 0.137, Running accuracy: 99.855, Time: 21.83
[2020-12-15 10:37:26,212][__main__][INFO] - [29120] Loss: 0.111, Running accuracy: 99.855, Time: 22.77
[2020-12-15 10:37:50,364][__main__][INFO] - [29440] Loss: 0.170, Running accuracy: 99.855, Time: 24.15
[2020-12-15 10:38:13,956][__main__][INFO] - [29760] Loss: 0.118, Running accuracy: 99.855, Time: 23.59
[2020-12-15 10:38:37,018][__main__][INFO] - [30080] Loss: 0.138, Running accuracy: 99.855, Time: 23.06
[2020-12-15 10:39:03,715][__main__][INFO] - [30400] Loss: 0.159, Running accuracy: 99.855, Time: 26.70
[2020-12-15 10:39:25,717][__main__][INFO] - [30720] Loss: 0.102, Running accuracy: 99.855, Time: 22.00
[2020-12-15 10:39:46,636][__main__][INFO] - [31040] Loss: 0.150, Running accuracy: 99.855, Time: 20.92
[2020-12-15 10:40:08,709][__main__][INFO] - [31360] Loss: 0.089, Running accuracy: 99.856, Time: 22.07
[2020-12-15 10:40:31,412][__main__][INFO] - [31680] Loss: 0.142, Running accuracy: 99.856, Time: 21.83
[2020-12-15 10:40:53,108][__main__][INFO] - [32000] Loss: 0.086, Running accuracy: 99.856, Time: 21.69
[2020-12-15 10:41:14,380][__main__][INFO] - [32320] Loss: 0.100, Running accuracy: 99.856, Time: 21.27
[2020-12-15 10:41:35,386][__main__][INFO] - [32640] Loss: 0.129, Running accuracy: 99.855, Time: 21.00
[2020-12-15 10:42:00,105][__main__][INFO] - [32960] Loss: 0.163, Running accuracy: 99.855, Time: 24.72
[2020-12-15 10:42:22,318][__main__][INFO] - [33280] Loss: 0.223, Running accuracy: 99.854, Time: 22.21
[2020-12-15 10:42:42,574][__main__][INFO] - [33600] Loss: 0.137, Running accuracy: 99.854, Time: 20.25
[2020-12-15 10:43:04,392][__main__][INFO] - [33920] Loss: 0.133, Running accuracy: 99.852, Time: 21.82
[2020-12-15 10:43:27,452][__main__][INFO] - [34240] Loss: 0.116, Running accuracy: 99.853, Time: 23.06
[2020-12-15 10:43:50,027][__main__][INFO] - [34560] Loss: 0.151, Running accuracy: 99.853, Time: 22.57
[2020-12-15 10:44:13,241][__main__][INFO] - [34880] Loss: 0.123, Running accuracy: 99.853, Time: 23.21
[2020-12-15 10:44:33,819][__main__][INFO] - [35200] Loss: 0.177, Running accuracy: 99.853, Time: 20.58
[2020-12-15 10:44:55,944][__main__][INFO] - [35520] Loss: 0.106, Running accuracy: 99.853, Time: 22.12
[2020-12-15 10:45:18,465][__main__][INFO] - [35840] Loss: 0.166, Running accuracy: 99.853, Time: 22.52
[2020-12-15 10:45:39,218][__main__][INFO] - [36160] Loss: 0.111, Running accuracy: 99.852, Time: 20.62
[2020-12-15 10:46:02,245][__main__][INFO] - [36480] Loss: 0.110, Running accuracy: 99.852, Time: 22.46
[2020-12-15 10:46:25,312][__main__][INFO] - [36800] Loss: 0.171, Running accuracy: 99.852, Time: 23.07
[2020-12-15 10:46:48,692][__main__][INFO] - [37120] Loss: 0.119, Running accuracy: 99.852, Time: 23.38
[2020-12-15 10:47:10,744][__main__][INFO] - [37440] Loss: 0.158, Running accuracy: 99.852, Time: 22.05
[2020-12-15 10:47:32,095][__main__][INFO] - [37760] Loss: 0.062, Running accuracy: 99.852, Time: 21.35
[2020-12-15 10:47:55,162][__main__][INFO] - [38080] Loss: 0.089, Running accuracy: 99.853, Time: 23.07
[2020-12-15 10:48:15,537][__main__][INFO] - [38400] Loss: 0.116, Running accuracy: 99.854, Time: 20.37
[2020-12-15 10:48:36,870][__main__][INFO] - [38720] Loss: 0.201, Running accuracy: 99.853, Time: 21.33
[2020-12-15 10:48:58,976][__main__][INFO] - [39040] Loss: 0.073, Running accuracy: 99.854, Time: 22.11
[2020-12-15 10:49:22,173][__main__][INFO] - [39360] Loss: 0.071, Running accuracy: 99.854, Time: 23.20
[2020-12-15 10:49:43,192][__main__][INFO] - [39680] Loss: 0.142, Running accuracy: 99.854, Time: 21.02
[2020-12-15 10:49:53,026][__main__][INFO] - Action accuracy: 99.854, Loss: 0.149
[2020-12-15 10:49:53,027][__main__][INFO] - Validating..
[2020-12-15 10:50:26,593][test][INFO] - Time elapsed: 31.755794
[2020-12-15 10:50:26,596][__main__][INFO] - Validation F1 score: 95.670, Exact match: 56.290, Precision: 95.580, Recall: 95.760
[2020-12-15 10:51:02,891][__main__][INFO] - Checkpoint saved to checkpoints/model_latest.pth
[2020-12-15 10:51:03,900][__main__][INFO] - Epoch #28
[2020-12-15 10:51:03,900][__main__][INFO] - Training..
[2020-12-15 10:51:27,707][__main__][INFO] - [320] Loss: 0.162, Running accuracy: 99.845, Time: 22.59
[2020-12-15 10:51:49,467][__main__][INFO] - [640] Loss: 0.138, Running accuracy: 99.850, Time: 21.76
[2020-12-15 10:52:11,792][__main__][INFO] - [960] Loss: 0.094, Running accuracy: 99.848, Time: 22.32
[2020-12-15 10:52:33,131][__main__][INFO] - [1280] Loss: 0.127, Running accuracy: 99.846, Time: 21.34
[2020-12-15 10:52:54,938][__main__][INFO] - [1600] Loss: 0.121, Running accuracy: 99.861, Time: 21.81
[2020-12-15 10:53:20,576][__main__][INFO] - [1920] Loss: 0.113, Running accuracy: 99.869, Time: 25.64
[2020-12-15 10:53:43,115][__main__][INFO] - [2240] Loss: 0.168, Running accuracy: 99.853, Time: 22.54
[2020-12-15 10:54:06,178][__main__][INFO] - [2560] Loss: 0.062, Running accuracy: 99.863, Time: 23.06
[2020-12-15 10:54:29,675][__main__][INFO] - [2880] Loss: 0.085, Running accuracy: 99.872, Time: 23.50
[2020-12-15 10:54:51,376][__main__][INFO] - [3200] Loss: 0.178, Running accuracy: 99.863, Time: 21.70
[2020-12-15 10:55:15,858][__main__][INFO] - [3520] Loss: 0.081, Running accuracy: 99.866, Time: 24.48
[2020-12-15 10:55:36,823][__main__][INFO] - [3840] Loss: 0.111, Running accuracy: 99.865, Time: 20.96
[2020-12-15 10:55:58,251][__main__][INFO] - [4160] Loss: 0.115, Running accuracy: 99.858, Time: 21.43
[2020-12-15 10:56:20,589][__main__][INFO] - [4480] Loss: 0.254, Running accuracy: 99.852, Time: 22.34
[2020-12-15 10:56:44,641][__main__][INFO] - [4800] Loss: 0.089, Running accuracy: 99.856, Time: 24.05
[2020-12-15 10:57:06,453][__main__][INFO] - [5120] Loss: 0.121, Running accuracy: 99.856, Time: 21.81
[2020-12-15 10:57:28,917][__main__][INFO] - [5440] Loss: 0.164, Running accuracy: 99.853, Time: 22.46
[2020-12-15 10:57:52,229][__main__][INFO] - [5760] Loss: 0.135, Running accuracy: 99.854, Time: 23.31
[2020-12-15 10:58:12,504][__main__][INFO] - [6080] Loss: 0.119, Running accuracy: 99.853, Time: 20.27
[2020-12-15 10:58:38,076][__main__][INFO] - [6400] Loss: 0.135, Running accuracy: 99.853, Time: 25.57
[2020-12-15 10:58:59,659][__main__][INFO] - [6720] Loss: 0.100, Running accuracy: 99.855, Time: 21.58
[2020-12-15 10:59:23,350][__main__][INFO] - [7040] Loss: 0.141, Running accuracy: 99.854, Time: 23.69
[2020-12-15 10:59:45,680][__main__][INFO] - [7360] Loss: 0.124, Running accuracy: 99.854, Time: 22.33
[2020-12-15 11:00:07,008][__main__][INFO] - [7680] Loss: 0.073, Running accuracy: 99.857, Time: 21.33
[2020-12-15 11:00:27,829][__main__][INFO] - [8000] Loss: 0.181, Running accuracy: 99.859, Time: 20.82
[2020-12-15 11:00:49,264][__main__][INFO] - [8320] Loss: 0.085, Running accuracy: 99.861, Time: 21.43
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[2020-12-15 11:37:11,165][__main__][INFO] - [39680] Loss: 0.095, Running accuracy: 99.861, Time: 21.42
[2020-12-15 11:37:20,827][__main__][INFO] - Action accuracy: 99.860, Loss: 0.143
[2020-12-15 11:37:20,828][__main__][INFO] - Validating..
[2020-12-15 11:37:55,351][test][INFO] - Time elapsed: 32.392134
[2020-12-15 11:37:55,355][__main__][INFO] - Validation F1 score: 95.580, Exact match: 55.820, Precision: 95.490, Recall: 95.670
[2020-12-15 11:38:34,380][__main__][INFO] - Checkpoint saved to checkpoints/model_latest.pth
[2020-12-15 11:38:35,266][__main__][INFO] - Epoch #29
[2020-12-15 11:38:35,267][__main__][INFO] - Training..
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[2020-12-15 12:07:40,411][__main__][INFO] - [24960] Loss: 0.087, Running accuracy: 99.869, Time: 22.30
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[2020-12-15 12:08:28,012][__main__][INFO] - [25600] Loss: 0.123, Running accuracy: 99.869, Time: 23.95
[2020-12-15 12:08:50,797][__main__][INFO] - [25920] Loss: 0.093, Running accuracy: 99.869, Time: 22.78
[2020-12-15 12:09:11,149][__main__][INFO] - [26240] Loss: 0.135, Running accuracy: 99.870, Time: 20.35
[2020-12-15 12:09:35,032][__main__][INFO] - [26560] Loss: 0.102, Running accuracy: 99.870, Time: 23.88
[2020-12-15 12:09:59,717][__main__][INFO] - [26880] Loss: 0.162, Running accuracy: 99.870, Time: 24.68
[2020-12-15 12:10:23,016][__main__][INFO] - [27200] Loss: 0.145, Running accuracy: 99.870, Time: 23.30
[2020-12-15 12:10:44,725][__main__][INFO] - [27520] Loss: 0.117, Running accuracy: 99.870, Time: 21.71
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[2020-12-15 12:11:27,084][__main__][INFO] - [28160] Loss: 0.163, Running accuracy: 99.871, Time: 21.50
[2020-12-15 12:11:49,731][__main__][INFO] - [28480] Loss: 0.172, Running accuracy: 99.870, Time: 22.65
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[2020-12-15 12:21:53,966][__main__][INFO] - [37120] Loss: 0.093, Running accuracy: 99.868, Time: 23.60
[2020-12-15 12:22:18,279][__main__][INFO] - [37440] Loss: 0.222, Running accuracy: 99.867, Time: 24.31
[2020-12-15 12:22:39,861][__main__][INFO] - [37760] Loss: 0.075, Running accuracy: 99.868, Time: 21.58
[2020-12-15 12:23:00,399][__main__][INFO] - [38080] Loss: 0.087, Running accuracy: 99.868, Time: 20.54
[2020-12-15 12:23:22,232][__main__][INFO] - [38400] Loss: 0.102, Running accuracy: 99.868, Time: 21.83
[2020-12-15 12:23:44,694][__main__][INFO] - [38720] Loss: 0.099, Running accuracy: 99.868, Time: 22.46
[2020-12-15 12:24:07,159][__main__][INFO] - [39040] Loss: 0.108, Running accuracy: 99.868, Time: 22.46
[2020-12-15 12:24:27,892][__main__][INFO] - [39360] Loss: 0.094, Running accuracy: 99.869, Time: 20.73
[2020-12-15 12:24:49,351][__main__][INFO] - [39680] Loss: 0.158, Running accuracy: 99.868, Time: 21.46
[2020-12-15 12:24:56,816][__main__][INFO] - Action accuracy: 99.869, Loss: 0.136
[2020-12-15 12:24:56,817][__main__][INFO] - Validating..
[2020-12-15 12:25:34,876][test][INFO] - Time elapsed: 36.074766
[2020-12-15 12:25:34,880][__main__][INFO] - Validation F1 score: 95.630, Exact match: 56.180, Precision: 95.560, Recall: 95.700
[2020-12-15 12:26:17,629][__main__][INFO] - Checkpoint saved to checkpoints/model_latest.pth
[2020-12-15 12:26:18,659][__main__][INFO] - Epoch #30
[2020-12-15 12:26:18,659][__main__][INFO] - Training..
[2020-12-15 12:26:40,909][__main__][INFO] - [320] Loss: 0.148, Running accuracy: 99.864, Time: 21.08
[2020-12-15 12:27:03,822][__main__][INFO] - [640] Loss: 0.191, Running accuracy: 99.854, Time: 22.91
[2020-12-15 12:27:24,942][__main__][INFO] - [960] Loss: 0.102, Running accuracy: 99.854, Time: 21.12
[2020-12-15 12:27:47,192][__main__][INFO] - [1280] Loss: 0.094, Running accuracy: 99.874, Time: 22.25
[2020-12-15 12:28:09,943][__main__][INFO] - [1600] Loss: 0.091, Running accuracy: 99.887, Time: 22.75
[2020-12-15 12:28:32,388][__main__][INFO] - [1920] Loss: 0.165, Running accuracy: 99.882, Time: 22.44
[2020-12-15 12:28:53,545][__main__][INFO] - [2240] Loss: 0.093, Running accuracy: 99.880, Time: 21.16
[2020-12-15 12:29:16,160][__main__][INFO] - [2560] Loss: 0.103, Running accuracy: 99.887, Time: 22.61
[2020-12-15 12:29:37,505][__main__][INFO] - [2880] Loss: 0.115, Running accuracy: 99.885, Time: 21.34
[2020-12-15 12:30:04,450][__main__][INFO] - [3200] Loss: 0.109, Running accuracy: 99.887, Time: 26.94
[2020-12-15 12:30:25,918][__main__][INFO] - [3520] Loss: 0.216, Running accuracy: 99.877, Time: 21.47
[2020-12-15 12:30:47,600][__main__][INFO] - [3840] Loss: 0.082, Running accuracy: 99.879, Time: 21.68
[2020-12-15 12:31:08,863][__main__][INFO] - [4160] Loss: 0.101, Running accuracy: 99.880, Time: 21.26
[2020-12-15 12:31:30,495][__main__][INFO] - [4480] Loss: 0.143, Running accuracy: 99.881, Time: 21.63
[2020-12-15 12:31:51,425][__main__][INFO] - [4800] Loss: 0.139, Running accuracy: 99.883, Time: 20.93
[2020-12-15 12:32:13,698][__main__][INFO] - [5120] Loss: 0.059, Running accuracy: 99.886, Time: 22.27
[2020-12-15 12:32:37,265][__main__][INFO] - [5440] Loss: 0.105, Running accuracy: 99.888, Time: 23.57
[2020-12-15 12:32:59,012][__main__][INFO] - [5760] Loss: 0.132, Running accuracy: 99.886, Time: 21.75
[2020-12-15 12:33:20,344][__main__][INFO] - [6080] Loss: 0.123, Running accuracy: 99.888, Time: 21.33
[2020-12-15 12:33:41,039][__main__][INFO] - [6400] Loss: 0.099, Running accuracy: 99.887, Time: 20.69
[2020-12-15 12:34:03,993][__main__][INFO] - [6720] Loss: 0.115, Running accuracy: 99.888, Time: 22.95
[2020-12-15 12:34:26,481][__main__][INFO] - [7040] Loss: 0.191, Running accuracy: 99.885, Time: 22.49
[2020-12-15 12:34:54,111][__main__][INFO] - [7360] Loss: 0.117, Running accuracy: 99.885, Time: 27.63
[2020-12-15 12:35:15,117][__main__][INFO] - [7680] Loss: 0.177, Running accuracy: 99.884, Time: 21.00
[2020-12-15 12:35:36,472][__main__][INFO] - [8000] Loss: 0.156, Running accuracy: 99.880, Time: 21.35
[2020-12-15 12:35:56,678][__main__][INFO] - [8320] Loss: 0.119, Running accuracy: 99.880, Time: 20.21
[2020-12-15 12:36:18,833][__main__][INFO] - [8640] Loss: 0.084, Running accuracy: 99.880, Time: 22.15
[2020-12-15 12:36:41,479][__main__][INFO] - [8960] Loss: 0.160, Running accuracy: 99.879, Time: 22.65
[2020-12-15 12:37:02,856][__main__][INFO] - [9280] Loss: 0.098, Running accuracy: 99.877, Time: 21.38
[2020-12-15 12:37:25,239][__main__][INFO] - [9600] Loss: 0.078, Running accuracy: 99.880, Time: 22.38
[2020-12-15 12:37:48,134][__main__][INFO] - [9920] Loss: 0.177, Running accuracy: 99.877, Time: 22.89
[2020-12-15 12:38:09,401][__main__][INFO] - [10240] Loss: 0.108, Running accuracy: 99.878, Time: 21.27
[2020-12-15 12:38:30,586][__main__][INFO] - [10560] Loss: 0.095, Running accuracy: 99.877, Time: 21.18
[2020-12-15 12:38:54,453][__main__][INFO] - [10880] Loss: 0.101, Running accuracy: 99.876, Time: 23.87
[2020-12-15 12:39:16,302][__main__][INFO] - [11200] Loss: 0.167, Running accuracy: 99.877, Time: 21.85
[2020-12-15 12:39:38,208][__main__][INFO] - [11520] Loss: 0.133, Running accuracy: 99.876, Time: 21.91
[2020-12-15 12:40:01,660][__main__][INFO] - [11840] Loss: 0.073, Running accuracy: 99.877, Time: 23.45
[2020-12-15 12:40:23,195][__main__][INFO] - [12160] Loss: 0.132, Running accuracy: 99.877, Time: 21.53
[2020-12-15 12:40:47,210][__main__][INFO] - [12480] Loss: 0.128, Running accuracy: 99.877, Time: 24.01
[2020-12-15 12:41:08,934][__main__][INFO] - [12800] Loss: 0.086, Running accuracy: 99.878, Time: 21.72
[2020-12-15 12:41:33,693][__main__][INFO] - [13120] Loss: 0.089, Running accuracy: 99.879, Time: 24.76
[2020-12-15 12:41:55,989][__main__][INFO] - [13440] Loss: 0.090, Running accuracy: 99.880, Time: 21.95
[2020-12-15 12:42:17,053][__main__][INFO] - [13760] Loss: 0.095, Running accuracy: 99.880, Time: 21.06
[2020-12-15 12:42:40,708][__main__][INFO] - [14080] Loss: 0.102, Running accuracy: 99.881, Time: 23.65
[2020-12-15 12:43:02,323][__main__][INFO] - [14400] Loss: 0.112, Running accuracy: 99.881, Time: 21.61
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[2020-12-15 12:43:45,506][__main__][INFO] - [15040] Loss: 0.200, Running accuracy: 99.878, Time: 21.37
[2020-12-15 12:44:07,741][__main__][INFO] - [15360] Loss: 0.097, Running accuracy: 99.879, Time: 21.70
[2020-12-15 12:44:29,878][__main__][INFO] - [15680] Loss: 0.109, Running accuracy: 99.879, Time: 22.14
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[2020-12-15 12:45:18,301][__main__][INFO] - [16320] Loss: 0.096, Running accuracy: 99.878, Time: 26.12
[2020-12-15 12:45:39,435][__main__][INFO] - [16640] Loss: 0.178, Running accuracy: 99.877, Time: 21.13
[2020-12-15 12:46:02,440][__main__][INFO] - [16960] Loss: 0.137, Running accuracy: 99.875, Time: 23.00
[2020-12-15 12:46:24,479][__main__][INFO] - [17280] Loss: 0.094, Running accuracy: 99.876, Time: 21.88
[2020-12-15 12:46:47,816][__main__][INFO] - [17600] Loss: 0.167, Running accuracy: 99.875, Time: 23.34
[2020-12-15 12:47:11,607][__main__][INFO] - [17920] Loss: 0.062, Running accuracy: 99.877, Time: 23.79
[2020-12-15 12:47:34,890][__main__][INFO] - [18240] Loss: 0.082, Running accuracy: 99.877, Time: 23.28
[2020-12-15 12:47:58,148][__main__][INFO] - [18560] Loss: 0.144, Running accuracy: 99.877, Time: 23.26
[2020-12-15 12:48:20,076][__main__][INFO] - [18880] Loss: 0.161, Running accuracy: 99.877, Time: 21.93
[2020-12-15 12:48:44,640][__main__][INFO] - [19200] Loss: 0.131, Running accuracy: 99.877, Time: 24.56
[2020-12-15 12:49:05,545][__main__][INFO] - [19520] Loss: 0.072, Running accuracy: 99.878, Time: 20.90
[2020-12-15 12:49:27,550][__main__][INFO] - [19840] Loss: 0.110, Running accuracy: 99.878, Time: 22.00
[2020-12-15 12:49:48,824][__main__][INFO] - [20160] Loss: 0.089, Running accuracy: 99.878, Time: 21.27
[2020-12-15 12:50:10,764][__main__][INFO] - [20480] Loss: 0.122, Running accuracy: 99.877, Time: 21.94
[2020-12-15 12:50:35,527][__main__][INFO] - [20800] Loss: 0.123, Running accuracy: 99.877, Time: 24.76
[2020-12-15 12:50:57,701][__main__][INFO] - [21120] Loss: 0.120, Running accuracy: 99.877, Time: 22.17
[2020-12-15 12:51:20,386][__main__][INFO] - [21440] Loss: 0.081, Running accuracy: 99.878, Time: 22.68
[2020-12-15 12:51:41,750][__main__][INFO] - [21760] Loss: 0.102, Running accuracy: 99.877, Time: 21.36
[2020-12-15 12:52:03,670][__main__][INFO] - [22080] Loss: 0.097, Running accuracy: 99.877, Time: 21.92
[2020-12-15 12:52:24,199][__main__][INFO] - [22400] Loss: 0.066, Running accuracy: 99.877, Time: 20.53
[2020-12-15 12:52:47,131][__main__][INFO] - [22720] Loss: 0.169, Running accuracy: 99.876, Time: 22.93
[2020-12-15 12:53:10,044][__main__][INFO] - [23040] Loss: 0.127, Running accuracy: 99.876, Time: 22.91
[2020-12-15 12:53:31,590][__main__][INFO] - [23360] Loss: 0.101, Running accuracy: 99.877, Time: 21.54
[2020-12-15 12:53:51,930][__main__][INFO] - [23680] Loss: 0.129, Running accuracy: 99.875, Time: 20.34
[2020-12-15 12:54:15,120][__main__][INFO] - [24000] Loss: 0.044, Running accuracy: 99.876, Time: 23.19
[2020-12-15 12:54:36,137][__main__][INFO] - [24320] Loss: 0.097, Running accuracy: 99.876, Time: 21.02
[2020-12-15 12:54:56,856][__main__][INFO] - [24640] Loss: 0.121, Running accuracy: 99.876, Time: 20.72
[2020-12-15 12:55:17,471][__main__][INFO] - [24960] Loss: 0.146, Running accuracy: 99.875, Time: 20.61
[2020-12-15 12:55:42,602][__main__][INFO] - [25280] Loss: 0.081, Running accuracy: 99.876, Time: 25.13
[2020-12-15 12:56:05,080][__main__][INFO] - [25600] Loss: 0.177, Running accuracy: 99.875, Time: 22.48
[2020-12-15 12:56:27,066][__main__][INFO] - [25920] Loss: 0.133, Running accuracy: 99.875, Time: 21.98
[2020-12-15 12:56:51,000][__main__][INFO] - [26240] Loss: 0.107, Running accuracy: 99.875, Time: 23.93
[2020-12-15 12:57:12,994][__main__][INFO] - [26560] Loss: 0.163, Running accuracy: 99.875, Time: 21.99
[2020-12-15 12:57:36,034][__main__][INFO] - [26880] Loss: 0.065, Running accuracy: 99.876, Time: 23.04
[2020-12-15 12:57:57,723][__main__][INFO] - [27200] Loss: 0.140, Running accuracy: 99.875, Time: 21.69
[2020-12-15 12:58:19,138][__main__][INFO] - [27520] Loss: 0.110, Running accuracy: 99.875, Time: 21.41
[2020-12-15 12:58:39,608][__main__][INFO] - [27840] Loss: 0.084, Running accuracy: 99.876, Time: 20.47
[2020-12-15 12:59:01,470][__main__][INFO] - [28160] Loss: 0.056, Running accuracy: 99.876, Time: 21.86
[2020-12-15 12:59:23,155][__main__][INFO] - [28480] Loss: 0.185, Running accuracy: 99.876, Time: 21.68
[2020-12-15 12:59:45,627][__main__][INFO] - [28800] Loss: 0.072, Running accuracy: 99.877, Time: 22.47
[2020-12-15 13:00:06,553][__main__][INFO] - [29120] Loss: 0.128, Running accuracy: 99.876, Time: 20.93
[2020-12-15 13:00:29,722][__main__][INFO] - [29440] Loss: 0.088, Running accuracy: 99.876, Time: 23.17
[2020-12-15 13:00:55,297][__main__][INFO] - [29760] Loss: 0.070, Running accuracy: 99.876, Time: 25.57
[2020-12-15 13:01:18,533][__main__][INFO] - [30080] Loss: 0.152, Running accuracy: 99.876, Time: 23.23
[2020-12-15 13:01:41,075][__main__][INFO] - [30400] Loss: 0.090, Running accuracy: 99.877, Time: 22.54
[2020-12-15 13:02:04,700][__main__][INFO] - [30720] Loss: 0.123, Running accuracy: 99.877, Time: 23.62
[2020-12-15 13:02:26,345][__main__][INFO] - [31040] Loss: 0.073, Running accuracy: 99.878, Time: 21.64
[2020-12-15 13:02:48,211][__main__][INFO] - [31360] Loss: 0.115, Running accuracy: 99.877, Time: 21.86
[2020-12-15 13:03:10,272][__main__][INFO] - [31680] Loss: 0.095, Running accuracy: 99.877, Time: 22.06
[2020-12-15 13:03:32,803][__main__][INFO] - [32000] Loss: 0.287, Running accuracy: 99.875, Time: 22.53
[2020-12-15 13:03:54,464][__main__][INFO] - [32320] Loss: 0.100, Running accuracy: 99.875, Time: 21.66
[2020-12-15 13:04:16,565][__main__][INFO] - [32640] Loss: 0.104, Running accuracy: 99.876, Time: 22.10
[2020-12-15 13:04:38,648][__main__][INFO] - [32960] Loss: 0.093, Running accuracy: 99.876, Time: 22.08
[2020-12-15 13:05:00,656][__main__][INFO] - [33280] Loss: 0.131, Running accuracy: 99.875, Time: 22.01
[2020-12-15 13:05:22,408][__main__][INFO] - [33600] Loss: 0.148, Running accuracy: 99.875, Time: 21.75
[2020-12-15 13:05:44,991][__main__][INFO] - [33920] Loss: 0.309, Running accuracy: 99.873, Time: 22.58
[2020-12-15 13:06:08,768][__main__][INFO] - [34240] Loss: 0.132, Running accuracy: 99.873, Time: 23.78
[2020-12-15 13:06:32,194][__main__][INFO] - [34560] Loss: 0.133, Running accuracy: 99.873, Time: 23.42
[2020-12-15 13:06:53,970][__main__][INFO] - [34880] Loss: 0.095, Running accuracy: 99.873, Time: 21.77
[2020-12-15 13:07:16,912][__main__][INFO] - [35200] Loss: 0.091, Running accuracy: 99.874, Time: 22.94
[2020-12-15 13:07:37,761][__main__][INFO] - [35520] Loss: 0.094, Running accuracy: 99.874, Time: 20.85
[2020-12-15 13:07:59,387][__main__][INFO] - [35840] Loss: 0.040, Running accuracy: 99.875, Time: 21.62
[2020-12-15 13:08:19,855][__main__][INFO] - [36160] Loss: 0.127, Running accuracy: 99.874, Time: 20.47
[2020-12-15 13:08:42,226][__main__][INFO] - [36480] Loss: 0.070, Running accuracy: 99.875, Time: 22.37
[2020-12-15 13:09:03,807][__main__][INFO] - [36800] Loss: 0.145, Running accuracy: 99.874, Time: 21.58
[2020-12-15 13:09:25,332][__main__][INFO] - [37120] Loss: 0.118, Running accuracy: 99.874, Time: 21.52
[2020-12-15 13:09:47,140][__main__][INFO] - [37440] Loss: 0.140, Running accuracy: 99.874, Time: 21.81
[2020-12-15 13:10:08,295][__main__][INFO] - [37760] Loss: 0.057, Running accuracy: 99.875, Time: 21.15
[2020-12-15 13:10:32,096][__main__][INFO] - [38080] Loss: 0.106, Running accuracy: 99.874, Time: 23.80
[2020-12-15 13:10:55,227][__main__][INFO] - [38400] Loss: 0.132, Running accuracy: 99.874, Time: 23.13
[2020-12-15 13:11:18,995][__main__][INFO] - [38720] Loss: 0.081, Running accuracy: 99.875, Time: 23.77
[2020-12-15 13:11:41,470][__main__][INFO] - [39040] Loss: 0.115, Running accuracy: 99.875, Time: 22.47
[2020-12-15 13:12:02,638][__main__][INFO] - [39360] Loss: 0.073, Running accuracy: 99.875, Time: 21.17
[2020-12-15 13:12:24,978][__main__][INFO] - [39680] Loss: 0.173, Running accuracy: 99.875, Time: 22.34
[2020-12-15 13:12:32,955][__main__][INFO] - Action accuracy: 99.875, Loss: 0.131
[2020-12-15 13:12:32,956][__main__][INFO] - Validating..
[2020-12-15 13:13:07,037][test][INFO] - Time elapsed: 32.343207
[2020-12-15 13:13:07,041][__main__][INFO] - Validation F1 score: 95.670, Exact match: 56.530, Precision: 95.590, Recall: 95.740
[2020-12-15 13:13:43,338][__main__][INFO] - Checkpoint saved to checkpoints/model_latest.pth
[2020-12-15 13:13:44,285][__main__][INFO] - Epoch #31
[2020-12-15 13:13:44,285][__main__][INFO] - Training..
[2020-12-15 13:14:07,638][__main__][INFO] - [320] Loss: 0.109, Running accuracy: 99.856, Time: 22.18
[2020-12-15 13:14:30,559][__main__][INFO] - [640] Loss: 0.121, Running accuracy: 99.855, Time: 22.92
[2020-12-15 13:14:54,248][__main__][INFO] - [960] Loss: 0.121, Running accuracy: 99.862, Time: 23.69
[2020-12-15 13:15:19,883][__main__][INFO] - [1280] Loss: 0.116, Running accuracy: 99.854, Time: 25.63
[2020-12-15 13:15:42,229][__main__][INFO] - [1600] Loss: 0.067, Running accuracy: 99.865, Time: 22.35
[2020-12-15 13:16:05,703][__main__][INFO] - [1920] Loss: 0.064, Running accuracy: 99.868, Time: 23.47
[2020-12-15 13:16:29,611][__main__][INFO] - [2240] Loss: 0.065, Running accuracy: 99.875, Time: 23.91
[2020-12-15 13:16:51,106][__main__][INFO] - [2560] Loss: 0.097, Running accuracy: 99.881, Time: 21.49
[2020-12-15 13:17:12,804][__main__][INFO] - [2880] Loss: 0.185, Running accuracy: 99.872, Time: 21.70
[2020-12-15 13:17:34,422][__main__][INFO] - [3200] Loss: 0.068, Running accuracy: 99.878, Time: 21.62
[2020-12-15 13:17:56,289][__main__][INFO] - [3520] Loss: 0.127, Running accuracy: 99.876, Time: 21.87
[2020-12-15 13:18:18,629][__main__][INFO] - [3840] Loss: 0.091, Running accuracy: 99.878, Time: 22.34
[2020-12-15 13:18:40,900][__main__][INFO] - [4160] Loss: 0.111, Running accuracy: 99.878, Time: 22.27
[2020-12-15 13:19:01,672][__main__][INFO] - [4480] Loss: 0.060, Running accuracy: 99.884, Time: 20.77
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[2020-12-15 13:24:13,397][__main__][INFO] - [8960] Loss: 0.054, Running accuracy: 99.878, Time: 23.40
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[2020-12-15 13:24:58,148][__main__][INFO] - [9600] Loss: 0.117, Running accuracy: 99.878, Time: 21.59
[2020-12-15 13:25:19,379][__main__][INFO] - [9920] Loss: 0.082, Running accuracy: 99.880, Time: 21.23
[2020-12-15 13:25:43,911][__main__][INFO] - [10240] Loss: 0.089, Running accuracy: 99.879, Time: 24.53
[2020-12-15 13:26:07,617][__main__][INFO] - [10560] Loss: 0.197, Running accuracy: 99.878, Time: 23.71
[2020-12-15 13:26:28,464][__main__][INFO] - [10880] Loss: 0.074, Running accuracy: 99.879, Time: 20.85
[2020-12-15 13:26:50,632][__main__][INFO] - [11200] Loss: 0.056, Running accuracy: 99.880, Time: 22.17
[2020-12-15 13:27:12,637][__main__][INFO] - [11520] Loss: 0.070, Running accuracy: 99.882, Time: 22.00
[2020-12-15 13:27:37,871][__main__][INFO] - [11840] Loss: 0.105, Running accuracy: 99.882, Time: 25.23
[2020-12-15 13:27:58,523][__main__][INFO] - [12160] Loss: 0.087, Running accuracy: 99.883, Time: 20.65
[2020-12-15 13:28:19,727][__main__][INFO] - [12480] Loss: 0.094, Running accuracy: 99.884, Time: 21.20
[2020-12-15 13:28:43,670][__main__][INFO] - [12800] Loss: 0.117, Running accuracy: 99.884, Time: 23.94
[2020-12-15 13:29:05,016][__main__][INFO] - [13120] Loss: 0.101, Running accuracy: 99.885, Time: 21.34
[2020-12-15 13:29:27,889][__main__][INFO] - [13440] Loss: 0.093, Running accuracy: 99.886, Time: 22.87
[2020-12-15 13:29:49,555][__main__][INFO] - [13760] Loss: 0.062, Running accuracy: 99.886, Time: 21.67
[2020-12-15 13:30:11,435][__main__][INFO] - [14080] Loss: 0.066, Running accuracy: 99.887, Time: 21.88
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[2020-12-15 13:31:22,008][__main__][INFO] - [15040] Loss: 0.150, Running accuracy: 99.885, Time: 23.58
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[2020-12-15 13:34:40,697][__main__][INFO] - [17920] Loss: 0.113, Running accuracy: 99.880, Time: 23.01
[2020-12-15 13:35:04,494][__main__][INFO] - [18240] Loss: 0.120, Running accuracy: 99.880, Time: 23.80
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[2020-12-15 13:35:48,209][__main__][INFO] - [18880] Loss: 0.117, Running accuracy: 99.879, Time: 22.22
[2020-12-15 13:36:12,890][__main__][INFO] - [19200] Loss: 0.099, Running accuracy: 99.879, Time: 24.68
[2020-12-15 13:36:34,628][__main__][INFO] - [19520] Loss: 0.142, Running accuracy: 99.879, Time: 21.74
[2020-12-15 13:36:55,457][__main__][INFO] - [19840] Loss: 0.081, Running accuracy: 99.879, Time: 20.83
[2020-12-15 13:37:17,941][__main__][INFO] - [20160] Loss: 0.097, Running accuracy: 99.879, Time: 22.48
[2020-12-15 13:37:41,236][__main__][INFO] - [20480] Loss: 0.067, Running accuracy: 99.880, Time: 23.29
[2020-12-15 13:38:01,223][__main__][INFO] - [20800] Loss: 0.127, Running accuracy: 99.880, Time: 19.99
[2020-12-15 13:38:22,822][__main__][INFO] - [21120] Loss: 0.044, Running accuracy: 99.881, Time: 21.60
[2020-12-15 13:38:43,874][__main__][INFO] - [21440] Loss: 0.128, Running accuracy: 99.881, Time: 21.05
[2020-12-15 13:39:05,471][__main__][INFO] - [21760] Loss: 0.050, Running accuracy: 99.883, Time: 21.60
[2020-12-15 13:39:28,586][__main__][INFO] - [22080] Loss: 0.096, Running accuracy: 99.883, Time: 23.11
[2020-12-15 13:39:49,139][__main__][INFO] - [22400] Loss: 0.080, Running accuracy: 99.884, Time: 20.55
[2020-12-15 13:40:12,112][__main__][INFO] - [22720] Loss: 0.072, Running accuracy: 99.885, Time: 22.97
[2020-12-15 13:40:35,114][__main__][INFO] - [23040] Loss: 0.119, Running accuracy: 99.884, Time: 23.00
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[2020-12-15 13:43:12,791][__main__][INFO] - [25280] Loss: 0.144, Running accuracy: 99.886, Time: 20.67
[2020-12-15 13:43:34,526][__main__][INFO] - [25600] Loss: 0.117, Running accuracy: 99.886, Time: 21.73
[2020-12-15 13:43:55,817][__main__][INFO] - [25920] Loss: 0.158, Running accuracy: 99.885, Time: 21.29
[2020-12-15 13:44:18,469][__main__][INFO] - [26240] Loss: 0.070, Running accuracy: 99.885, Time: 22.65
[2020-12-15 13:44:42,005][__main__][INFO] - [26560] Loss: 0.077, Running accuracy: 99.886, Time: 23.54
[2020-12-15 13:45:03,964][__main__][INFO] - [26880] Loss: 0.144, Running accuracy: 99.886, Time: 21.96
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[2020-12-15 13:45:46,540][__main__][INFO] - [27520] Loss: 0.114, Running accuracy: 99.885, Time: 20.01
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[2020-12-15 13:46:32,185][__main__][INFO] - [28160] Loss: 0.130, Running accuracy: 99.886, Time: 24.06
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[2020-12-15 13:47:38,309][__main__][INFO] - [29120] Loss: 0.097, Running accuracy: 99.885, Time: 22.59
[2020-12-15 13:48:00,716][__main__][INFO] - [29440] Loss: 0.254, Running accuracy: 99.885, Time: 22.41
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[2020-12-15 13:48:46,469][__main__][INFO] - [30080] Loss: 0.148, Running accuracy: 99.884, Time: 22.78
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[2020-12-15 13:49:32,167][__main__][INFO] - [30720] Loss: 0.180, Running accuracy: 99.883, Time: 22.20
[2020-12-15 13:49:54,112][__main__][INFO] - [31040] Loss: 0.200, Running accuracy: 99.883, Time: 21.94
[2020-12-15 13:50:17,427][__main__][INFO] - [31360] Loss: 0.112, Running accuracy: 99.883, Time: 23.31
[2020-12-15 13:50:38,707][__main__][INFO] - [31680] Loss: 0.174, Running accuracy: 99.882, Time: 21.28
[2020-12-15 13:51:01,427][__main__][INFO] - [32000] Loss: 0.090, Running accuracy: 99.882, Time: 22.72
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[2020-12-15 13:52:33,833][__main__][INFO] - [33280] Loss: 0.119, Running accuracy: 99.882, Time: 22.20
[2020-12-15 13:52:56,633][__main__][INFO] - [33600] Loss: 0.070, Running accuracy: 99.882, Time: 22.80
[2020-12-15 13:53:19,745][__main__][INFO] - [33920] Loss: 0.062, Running accuracy: 99.883, Time: 23.11
[2020-12-15 13:53:42,665][__main__][INFO] - [34240] Loss: 0.113, Running accuracy: 99.883, Time: 22.92
[2020-12-15 13:54:06,521][__main__][INFO] - [34560] Loss: 0.108, Running accuracy: 99.883, Time: 23.86
[2020-12-15 13:54:28,886][__main__][INFO] - [34880] Loss: 0.084, Running accuracy: 99.883, Time: 21.71
[2020-12-15 13:54:50,203][__main__][INFO] - [35200] Loss: 0.166, Running accuracy: 99.883, Time: 21.32
[2020-12-15 13:55:12,327][__main__][INFO] - [35520] Loss: 0.103, Running accuracy: 99.883, Time: 22.12
[2020-12-15 13:55:33,203][__main__][INFO] - [35840] Loss: 0.148, Running accuracy: 99.883, Time: 20.88
[2020-12-15 13:55:56,428][__main__][INFO] - [36160] Loss: 0.074, Running accuracy: 99.883, Time: 23.22
[2020-12-15 13:56:17,541][__main__][INFO] - [36480] Loss: 0.138, Running accuracy: 99.883, Time: 21.11
[2020-12-15 13:56:40,497][__main__][INFO] - [36800] Loss: 0.111, Running accuracy: 99.882, Time: 22.95
[2020-12-15 13:57:08,568][__main__][INFO] - [37120] Loss: 0.135, Running accuracy: 99.882, Time: 28.07
[2020-12-15 13:57:32,101][__main__][INFO] - [37440] Loss: 0.106, Running accuracy: 99.882, Time: 23.53
[2020-12-15 13:57:53,415][__main__][INFO] - [37760] Loss: 0.145, Running accuracy: 99.882, Time: 21.31
[2020-12-15 13:58:14,655][__main__][INFO] - [38080] Loss: 0.122, Running accuracy: 99.882, Time: 21.24
[2020-12-15 13:58:34,902][__main__][INFO] - [38400] Loss: 0.127, Running accuracy: 99.882, Time: 20.25
[2020-12-15 13:58:55,869][__main__][INFO] - [38720] Loss: 0.093, Running accuracy: 99.882, Time: 20.97
[2020-12-15 13:59:17,029][__main__][INFO] - [39040] Loss: 0.109, Running accuracy: 99.882, Time: 21.16
[2020-12-15 13:59:40,253][__main__][INFO] - [39360] Loss: 0.131, Running accuracy: 99.882, Time: 23.22
[2020-12-15 14:00:00,957][__main__][INFO] - [39680] Loss: 0.096, Running accuracy: 99.882, Time: 20.70
[2020-12-15 14:00:09,723][__main__][INFO] - Action accuracy: 99.883, Loss: 0.126
[2020-12-15 14:00:09,724][__main__][INFO] - Validating..
[2020-12-15 14:00:47,414][test][INFO] - Time elapsed: 35.853166
[2020-12-15 14:00:47,419][__main__][INFO] - Validation F1 score: 95.670, Exact match: 56.590, Precision: 95.580, Recall: 95.750
[2020-12-15 14:01:28,534][__main__][INFO] - Checkpoint saved to checkpoints/model_latest.pth
[2020-12-15 14:01:29,465][__main__][INFO] - Epoch #32
[2020-12-15 14:01:29,466][__main__][INFO] - Training..
[2020-12-15 14:01:51,871][__main__][INFO] - [320] Loss: 0.138, Running accuracy: 99.897, Time: 21.26
[2020-12-15 14:02:13,549][__main__][INFO] - [640] Loss: 0.101, Running accuracy: 99.903, Time: 21.68
[2020-12-15 14:02:35,506][__main__][INFO] - [960] Loss: 0.096, Running accuracy: 99.901, Time: 21.96
[2020-12-15 14:02:57,363][__main__][INFO] - [1280] Loss: 0.100, Running accuracy: 99.899, Time: 21.86
[2020-12-15 14:03:20,149][__main__][INFO] - [1600] Loss: 0.149, Running accuracy: 99.863, Time: 22.78
[2020-12-15 14:03:42,216][__main__][INFO] - [1920] Loss: 0.258, Running accuracy: 99.848, Time: 22.07
[2020-12-15 14:04:05,431][__main__][INFO] - [2240] Loss: 0.096, Running accuracy: 99.861, Time: 23.21
[2020-12-15 14:04:27,375][__main__][INFO] - [2560] Loss: 0.087, Running accuracy: 99.872, Time: 21.94
[2020-12-15 14:04:49,175][__main__][INFO] - [2880] Loss: 0.101, Running accuracy: 99.876, Time: 21.80
[2020-12-15 14:05:10,636][__main__][INFO] - [3200] Loss: 0.083, Running accuracy: 99.878, Time: 21.46
[2020-12-15 14:05:32,494][__main__][INFO] - [3520] Loss: 0.119, Running accuracy: 99.878, Time: 21.86
[2020-12-15 14:05:54,742][__main__][INFO] - [3840] Loss: 0.071, Running accuracy: 99.881, Time: 22.25
[2020-12-15 14:06:16,776][__main__][INFO] - [4160] Loss: 0.170, Running accuracy: 99.882, Time: 22.03
[2020-12-15 14:06:44,143][__main__][INFO] - [4480] Loss: 0.135, Running accuracy: 99.883, Time: 27.37
[2020-12-15 14:07:05,801][__main__][INFO] - [4800] Loss: 0.097, Running accuracy: 99.885, Time: 21.66
[2020-12-15 14:07:28,983][__main__][INFO] - [5120] Loss: 0.128, Running accuracy: 99.886, Time: 23.18
[2020-12-15 14:07:51,630][__main__][INFO] - [5440] Loss: 0.127, Running accuracy: 99.887, Time: 22.09
[2020-12-15 14:08:15,905][__main__][INFO] - [5760] Loss: 0.065, Running accuracy: 99.889, Time: 24.27
[2020-12-15 14:08:40,275][__main__][INFO] - [6080] Loss: 0.097, Running accuracy: 99.887, Time: 24.37
[2020-12-15 14:09:03,764][__main__][INFO] - [6400] Loss: 0.187, Running accuracy: 99.883, Time: 23.49
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[2020-12-15 14:10:33,126][__main__][INFO] - [7680] Loss: 0.120, Running accuracy: 99.880, Time: 22.35
[2020-12-15 14:10:55,896][__main__][INFO] - [8000] Loss: 0.087, Running accuracy: 99.882, Time: 22.77
[2020-12-15 14:11:15,938][__main__][INFO] - [8320] Loss: 0.130, Running accuracy: 99.880, Time: 20.04
[2020-12-15 14:11:37,927][__main__][INFO] - [8640] Loss: 0.093, Running accuracy: 99.880, Time: 21.99
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[2020-12-15 14:12:27,278][__main__][INFO] - [9280] Loss: 0.080, Running accuracy: 99.884, Time: 22.20
[2020-12-15 14:12:49,647][__main__][INFO] - [9600] Loss: 0.126, Running accuracy: 99.883, Time: 22.37
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[2020-12-15 14:13:35,052][__main__][INFO] - [10240] Loss: 0.135, Running accuracy: 99.881, Time: 23.30
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[2020-12-15 14:15:45,398][__main__][INFO] - [12160] Loss: 0.096, Running accuracy: 99.881, Time: 21.23
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[2020-12-15 14:19:05,954][__main__][INFO] - [15040] Loss: 0.193, Running accuracy: 99.881, Time: 21.40
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[2020-12-15 14:47:47,986][__main__][INFO] - Action accuracy: 99.881, Loss: 0.123
[2020-12-15 14:47:47,988][__main__][INFO] - Validating..
[2020-12-15 14:48:25,849][test][INFO] - Time elapsed: 35.741019
[2020-12-15 14:48:25,853][__main__][INFO] - Validation F1 score: 95.600, Exact match: 55.710, Precision: 95.560, Recall: 95.640
Epoch    33: reducing learning rate of group 0 to 6.2500e-07.
[2020-12-15 14:49:02,344][__main__][INFO] - Checkpoint saved to checkpoints/model_latest.pth
[2020-12-15 14:49:03,246][__main__][INFO] - Epoch #33
[2020-12-15 14:49:03,247][__main__][INFO] - Training..
[2020-12-15 14:49:26,478][__main__][INFO] - [320] Loss: 0.103, Running accuracy: 99.897, Time: 22.04
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[2020-12-15 15:24:52,898][__main__][INFO] - [30720] Loss: 0.122, Running accuracy: 99.895, Time: 20.65
[2020-12-15 15:25:15,564][__main__][INFO] - [31040] Loss: 0.062, Running accuracy: 99.896, Time: 22.67
[2020-12-15 15:25:37,797][__main__][INFO] - [31360] Loss: 0.068, Running accuracy: 99.896, Time: 22.23
[2020-12-15 15:25:58,037][__main__][INFO] - [31680] Loss: 0.109, Running accuracy: 99.896, Time: 20.24
[2020-12-15 15:26:21,938][__main__][INFO] - [32000] Loss: 0.138, Running accuracy: 99.895, Time: 23.90
[2020-12-15 15:26:43,033][__main__][INFO] - [32320] Loss: 0.122, Running accuracy: 99.894, Time: 21.09
[2020-12-15 15:27:04,938][__main__][INFO] - [32640] Loss: 0.098, Running accuracy: 99.895, Time: 21.90
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[2020-12-15 15:27:49,952][__main__][INFO] - [33280] Loss: 0.134, Running accuracy: 99.894, Time: 21.97
[2020-12-15 15:28:10,793][__main__][INFO] - [33600] Loss: 0.074, Running accuracy: 99.894, Time: 20.84
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[2020-12-15 15:28:56,706][__main__][INFO] - [34240] Loss: 0.130, Running accuracy: 99.894, Time: 21.14
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[2020-12-15 15:29:38,177][__main__][INFO] - [34880] Loss: 0.108, Running accuracy: 99.893, Time: 20.30
[2020-12-15 15:29:59,704][__main__][INFO] - [35200] Loss: 0.090, Running accuracy: 99.893, Time: 21.53
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[2020-12-15 15:30:42,514][__main__][INFO] - [35840] Loss: 0.071, Running accuracy: 99.894, Time: 21.54
[2020-12-15 15:31:06,217][__main__][INFO] - [36160] Loss: 0.079, Running accuracy: 99.894, Time: 23.70
[2020-12-15 15:31:28,888][__main__][INFO] - [36480] Loss: 0.163, Running accuracy: 99.893, Time: 22.67
[2020-12-15 15:31:52,253][__main__][INFO] - [36800] Loss: 0.132, Running accuracy: 99.893, Time: 23.36
[2020-12-15 15:32:14,479][__main__][INFO] - [37120] Loss: 0.076, Running accuracy: 99.893, Time: 22.22
[2020-12-15 15:32:36,402][__main__][INFO] - [37440] Loss: 0.055, Running accuracy: 99.894, Time: 21.92
[2020-12-15 15:32:59,046][__main__][INFO] - [37760] Loss: 0.129, Running accuracy: 99.893, Time: 22.64
[2020-12-15 15:33:21,904][__main__][INFO] - [38080] Loss: 0.110, Running accuracy: 99.893, Time: 22.86
[2020-12-15 15:33:46,315][__main__][INFO] - [38400] Loss: 0.092, Running accuracy: 99.893, Time: 24.41
[2020-12-15 15:34:08,792][__main__][INFO] - [38720] Loss: 0.116, Running accuracy: 99.894, Time: 22.48
[2020-12-15 15:34:31,146][__main__][INFO] - [39040] Loss: 0.130, Running accuracy: 99.893, Time: 22.35
[2020-12-15 15:34:54,933][__main__][INFO] - [39360] Loss: 0.076, Running accuracy: 99.893, Time: 23.79
[2020-12-15 15:35:16,767][__main__][INFO] - [39680] Loss: 0.082, Running accuracy: 99.893, Time: 21.83
[2020-12-15 15:35:26,347][__main__][INFO] - Action accuracy: 99.893, Loss: 0.114
[2020-12-15 15:35:26,349][__main__][INFO] - Validating..
[2020-12-15 15:36:00,908][test][INFO] - Time elapsed: 32.420944
[2020-12-15 15:36:00,912][__main__][INFO] - Validation F1 score: 95.610, Exact match: 56.000, Precision: 95.570, Recall: 95.660
[2020-12-15 15:36:41,048][__main__][INFO] - Checkpoint saved to checkpoints/model_latest.pth
[2020-12-15 15:36:41,851][__main__][INFO] - Epoch #34
[2020-12-15 15:36:41,852][__main__][INFO] - Training..
[2020-12-15 15:37:04,509][__main__][INFO] - [320] Loss: 0.082, Running accuracy: 99.893, Time: 21.39
[2020-12-15 15:37:26,712][__main__][INFO] - [640] Loss: 0.167, Running accuracy: 99.875, Time: 22.20
[2020-12-15 15:37:53,530][__main__][INFO] - [960] Loss: 0.091, Running accuracy: 99.882, Time: 26.82
[2020-12-15 15:38:16,538][__main__][INFO] - [1280] Loss: 0.076, Running accuracy: 99.895, Time: 23.01
[2020-12-15 15:38:37,560][__main__][INFO] - [1600] Loss: 0.098, Running accuracy: 99.900, Time: 20.43
[2020-12-15 15:38:58,884][__main__][INFO] - [1920] Loss: 0.079, Running accuracy: 99.903, Time: 21.32
[2020-12-15 15:39:19,718][__main__][INFO] - [2240] Loss: 0.150, Running accuracy: 99.886, Time: 20.83
[2020-12-15 15:39:41,470][__main__][INFO] - [2560] Loss: 0.114, Running accuracy: 99.879, Time: 21.75
[2020-12-15 15:40:03,590][__main__][INFO] - [2880] Loss: 0.072, Running accuracy: 99.887, Time: 22.12
[2020-12-15 15:40:24,447][__main__][INFO] - [3200] Loss: 0.100, Running accuracy: 99.888, Time: 20.86
[2020-12-15 15:40:46,795][__main__][INFO] - [3520] Loss: 0.202, Running accuracy: 99.878, Time: 22.35
[2020-12-15 15:41:08,293][__main__][INFO] - [3840] Loss: 0.109, Running accuracy: 99.878, Time: 21.50
[2020-12-15 15:41:31,282][__main__][INFO] - [4160] Loss: 0.084, Running accuracy: 99.876, Time: 22.99
[2020-12-15 15:41:53,535][__main__][INFO] - [4480] Loss: 0.062, Running accuracy: 99.878, Time: 22.25
[2020-12-15 15:42:15,057][__main__][INFO] - [4800] Loss: 0.135, Running accuracy: 99.880, Time: 21.52
[2020-12-15 15:42:35,948][__main__][INFO] - [5120] Loss: 0.087, Running accuracy: 99.882, Time: 20.89
[2020-12-15 15:43:01,836][__main__][INFO] - [5440] Loss: 0.065, Running accuracy: 99.884, Time: 25.89
[2020-12-15 15:43:23,504][__main__][INFO] - [5760] Loss: 0.078, Running accuracy: 99.885, Time: 21.67
[2020-12-15 15:43:45,504][__main__][INFO] - [6080] Loss: 0.065, Running accuracy: 99.888, Time: 22.00
[2020-12-15 15:44:07,830][__main__][INFO] - [6400] Loss: 0.079, Running accuracy: 99.887, Time: 22.33
[2020-12-15 15:44:31,976][__main__][INFO] - [6720] Loss: 0.069, Running accuracy: 99.889, Time: 24.14
[2020-12-15 15:44:52,691][__main__][INFO] - [7040] Loss: 0.142, Running accuracy: 99.888, Time: 20.71
[2020-12-15 15:45:16,313][__main__][INFO] - [7360] Loss: 0.109, Running accuracy: 99.886, Time: 23.62
[2020-12-15 15:45:39,752][__main__][INFO] - [7680] Loss: 0.122, Running accuracy: 99.885, Time: 23.44
[2020-12-15 15:46:01,230][__main__][INFO] - [8000] Loss: 0.033, Running accuracy: 99.888, Time: 21.48
[2020-12-15 15:46:22,855][__main__][INFO] - [8320] Loss: 0.098, Running accuracy: 99.889, Time: 21.62
[2020-12-15 15:46:45,411][__main__][INFO] - [8640] Loss: 0.106, Running accuracy: 99.890, Time: 22.56
[2020-12-15 15:47:08,447][__main__][INFO] - [8960] Loss: 0.092, Running accuracy: 99.890, Time: 23.04
[2020-12-15 15:47:31,406][__main__][INFO] - [9280] Loss: 0.094, Running accuracy: 99.890, Time: 22.96
[2020-12-15 15:47:52,663][__main__][INFO] - [9600] Loss: 0.038, Running accuracy: 99.892, Time: 21.26
[2020-12-15 15:48:18,654][__main__][INFO] - [9920] Loss: 0.101, Running accuracy: 99.891, Time: 25.99
[2020-12-15 15:48:41,283][__main__][INFO] - [10240] Loss: 0.124, Running accuracy: 99.891, Time: 22.63
[2020-12-15 15:49:02,295][__main__][INFO] - [10560] Loss: 0.094, Running accuracy: 99.892, Time: 21.01
[2020-12-15 15:49:23,244][__main__][INFO] - [10880] Loss: 0.125, Running accuracy: 99.891, Time: 20.95
[2020-12-15 15:49:46,317][__main__][INFO] - [11200] Loss: 0.139, Running accuracy: 99.890, Time: 23.07
[2020-12-15 15:50:09,316][__main__][INFO] - [11520] Loss: 0.109, Running accuracy: 99.890, Time: 23.00
[2020-12-15 15:50:30,810][__main__][INFO] - [11840] Loss: 0.132, Running accuracy: 99.890, Time: 21.49
[2020-12-15 15:50:52,304][__main__][INFO] - [12160] Loss: 0.108, Running accuracy: 99.889, Time: 21.49
[2020-12-15 15:51:14,433][__main__][INFO] - [12480] Loss: 0.072, Running accuracy: 99.890, Time: 22.13
[2020-12-15 15:51:38,090][__main__][INFO] - [12800] Loss: 0.055, Running accuracy: 99.892, Time: 23.41
[2020-12-15 15:52:00,371][__main__][INFO] - [13120] Loss: 0.097, Running accuracy: 99.892, Time: 22.28
[2020-12-15 15:52:21,242][__main__][INFO] - [13440] Loss: 0.115, Running accuracy: 99.893, Time: 20.87
[2020-12-15 15:52:44,958][__main__][INFO] - [13760] Loss: 0.134, Running accuracy: 99.891, Time: 23.71
[2020-12-15 15:53:06,800][__main__][INFO] - [14080] Loss: 0.224, Running accuracy: 99.889, Time: 21.84
[2020-12-15 15:53:33,576][__main__][INFO] - [14400] Loss: 0.083, Running accuracy: 99.889, Time: 26.78
[2020-12-15 15:53:55,921][__main__][INFO] - [14720] Loss: 0.066, Running accuracy: 99.890, Time: 22.34
[2020-12-15 15:54:16,845][__main__][INFO] - [15040] Loss: 0.046, Running accuracy: 99.891, Time: 20.92
[2020-12-15 15:54:37,935][__main__][INFO] - [15360] Loss: 0.130, Running accuracy: 99.891, Time: 21.09
[2020-12-15 15:54:59,068][__main__][INFO] - [15680] Loss: 0.062, Running accuracy: 99.892, Time: 21.13
[2020-12-15 15:55:21,017][__main__][INFO] - [16000] Loss: 0.084, Running accuracy: 99.893, Time: 21.95
[2020-12-15 15:55:41,484][__main__][INFO] - [16320] Loss: 0.121, Running accuracy: 99.892, Time: 20.47
[2020-12-15 15:56:01,938][__main__][INFO] - [16640] Loss: 0.057, Running accuracy: 99.893, Time: 20.45
[2020-12-15 15:56:21,902][__main__][INFO] - [16960] Loss: 0.054, Running accuracy: 99.894, Time: 19.96
[2020-12-15 15:56:44,659][__main__][INFO] - [17280] Loss: 0.152, Running accuracy: 99.894, Time: 21.92
[2020-12-15 15:57:07,081][__main__][INFO] - [17600] Loss: 0.069, Running accuracy: 99.894, Time: 22.42
[2020-12-15 15:57:28,298][__main__][INFO] - [17920] Loss: 0.143, Running accuracy: 99.894, Time: 21.22
[2020-12-15 15:57:48,768][__main__][INFO] - [18240] Loss: 0.114, Running accuracy: 99.894, Time: 20.47
[2020-12-15 15:58:12,597][__main__][INFO] - [18560] Loss: 0.093, Running accuracy: 99.895, Time: 23.83
[2020-12-15 15:58:37,699][__main__][INFO] - [18880] Loss: 0.132, Running accuracy: 99.894, Time: 25.10
[2020-12-15 15:58:58,727][__main__][INFO] - [19200] Loss: 0.120, Running accuracy: 99.894, Time: 21.03
[2020-12-15 15:59:20,910][__main__][INFO] - [19520] Loss: 0.179, Running accuracy: 99.891, Time: 22.18
[2020-12-15 15:59:42,560][__main__][INFO] - [19840] Loss: 0.126, Running accuracy: 99.890, Time: 21.65
[2020-12-15 16:00:04,478][__main__][INFO] - [20160] Loss: 0.080, Running accuracy: 99.891, Time: 21.92
[2020-12-15 16:00:25,683][__main__][INFO] - [20480] Loss: 0.095, Running accuracy: 99.890, Time: 21.20
[2020-12-15 16:00:48,265][__main__][INFO] - [20800] Loss: 0.112, Running accuracy: 99.891, Time: 22.58
[2020-12-15 16:01:09,177][__main__][INFO] - [21120] Loss: 0.152, Running accuracy: 99.890, Time: 20.91
[2020-12-15 16:01:31,048][__main__][INFO] - [21440] Loss: 0.142, Running accuracy: 99.889, Time: 21.87
[2020-12-15 16:01:52,951][__main__][INFO] - [21760] Loss: 0.099, Running accuracy: 99.889, Time: 21.90
[2020-12-15 16:02:15,344][__main__][INFO] - [22080] Loss: 0.117, Running accuracy: 99.889, Time: 22.39
[2020-12-15 16:02:36,191][__main__][INFO] - [22400] Loss: 0.089, Running accuracy: 99.890, Time: 20.85
[2020-12-15 16:03:00,912][__main__][INFO] - [22720] Loss: 0.121, Running accuracy: 99.889, Time: 23.97
[2020-12-15 16:03:23,637][__main__][INFO] - [23040] Loss: 0.191, Running accuracy: 99.890, Time: 22.72
[2020-12-15 16:03:49,304][__main__][INFO] - [23360] Loss: 0.069, Running accuracy: 99.890, Time: 25.67
[2020-12-15 16:04:10,365][__main__][INFO] - [23680] Loss: 0.063, Running accuracy: 99.891, Time: 21.06
[2020-12-15 16:04:33,010][__main__][INFO] - [24000] Loss: 0.135, Running accuracy: 99.891, Time: 22.64
[2020-12-15 16:04:55,699][__main__][INFO] - [24320] Loss: 0.102, Running accuracy: 99.891, Time: 22.69
[2020-12-15 16:05:18,585][__main__][INFO] - [24640] Loss: 0.102, Running accuracy: 99.891, Time: 22.89
[2020-12-15 16:05:42,224][__main__][INFO] - [24960] Loss: 0.085, Running accuracy: 99.892, Time: 23.64
[2020-12-15 16:06:05,050][__main__][INFO] - [25280] Loss: 0.192, Running accuracy: 99.890, Time: 22.83
[2020-12-15 16:06:27,998][__main__][INFO] - [25600] Loss: 0.103, Running accuracy: 99.891, Time: 22.95
[2020-12-15 16:06:48,941][__main__][INFO] - [25920] Loss: 0.124, Running accuracy: 99.891, Time: 20.94
[2020-12-15 16:07:10,745][__main__][INFO] - [26240] Loss: 0.053, Running accuracy: 99.891, Time: 21.80
[2020-12-15 16:07:31,806][__main__][INFO] - [26560] Loss: 0.084, Running accuracy: 99.892, Time: 21.06
[2020-12-15 16:07:53,553][__main__][INFO] - [26880] Loss: 0.068, Running accuracy: 99.892, Time: 21.75
[2020-12-15 16:08:15,631][__main__][INFO] - [27200] Loss: 0.035, Running accuracy: 99.893, Time: 22.08
[2020-12-15 16:08:37,131][__main__][INFO] - [27520] Loss: 0.125, Running accuracy: 99.893, Time: 21.50
[2020-12-15 16:09:03,030][__main__][INFO] - [27840] Loss: 0.113, Running accuracy: 99.893, Time: 25.90
[2020-12-15 16:09:25,314][__main__][INFO] - [28160] Loss: 0.062, Running accuracy: 99.893, Time: 22.28
[2020-12-15 16:09:50,034][__main__][INFO] - [28480] Loss: 0.086, Running accuracy: 99.894, Time: 24.72
[2020-12-15 16:10:13,304][__main__][INFO] - [28800] Loss: 0.086, Running accuracy: 99.893, Time: 23.27
[2020-12-15 16:10:36,947][__main__][INFO] - [29120] Loss: 0.136, Running accuracy: 99.893, Time: 23.64
[2020-12-15 16:11:00,375][__main__][INFO] - [29440] Loss: 0.090, Running accuracy: 99.892, Time: 23.43
[2020-12-15 16:11:21,612][__main__][INFO] - [29760] Loss: 0.149, Running accuracy: 99.892, Time: 21.24
[2020-12-15 16:11:42,694][__main__][INFO] - [30080] Loss: 0.063, Running accuracy: 99.892, Time: 21.08
[2020-12-15 16:12:04,608][__main__][INFO] - [30400] Loss: 0.082, Running accuracy: 99.892, Time: 21.91
[2020-12-15 16:12:27,703][__main__][INFO] - [30720] Loss: 0.104, Running accuracy: 99.892, Time: 23.09
[2020-12-15 16:12:51,200][__main__][INFO] - [31040] Loss: 0.083, Running accuracy: 99.893, Time: 23.50
[2020-12-15 16:13:15,059][__main__][INFO] - [31360] Loss: 0.107, Running accuracy: 99.892, Time: 23.86
[2020-12-15 16:13:37,206][__main__][INFO] - [31680] Loss: 0.068, Running accuracy: 99.892, Time: 22.14
[2020-12-15 16:14:00,256][__main__][INFO] - [32000] Loss: 0.135, Running accuracy: 99.892, Time: 23.05
[2020-12-15 16:14:26,093][__main__][INFO] - [32320] Loss: 0.097, Running accuracy: 99.892, Time: 25.84
[2020-12-15 16:14:48,507][__main__][INFO] - [32640] Loss: 0.095, Running accuracy: 99.892, Time: 22.41
[2020-12-15 16:15:10,038][__main__][INFO] - [32960] Loss: 0.120, Running accuracy: 99.892, Time: 21.53
[2020-12-15 16:15:32,219][__main__][INFO] - [33280] Loss: 0.040, Running accuracy: 99.893, Time: 22.18
[2020-12-15 16:15:53,343][__main__][INFO] - [33600] Loss: 0.134, Running accuracy: 99.893, Time: 21.12
[2020-12-15 16:16:14,658][__main__][INFO] - [33920] Loss: 0.083, Running accuracy: 99.893, Time: 20.71
[2020-12-15 16:16:36,698][__main__][INFO] - [34240] Loss: 0.086, Running accuracy: 99.893, Time: 22.04
[2020-12-15 16:16:59,899][__main__][INFO] - [34560] Loss: 0.171, Running accuracy: 99.893, Time: 23.20
[2020-12-15 16:17:21,690][__main__][INFO] - [34880] Loss: 0.048, Running accuracy: 99.893, Time: 21.79
[2020-12-15 16:17:43,958][__main__][INFO] - [35200] Loss: 0.075, Running accuracy: 99.894, Time: 22.27
[2020-12-15 16:18:06,586][__main__][INFO] - [35520] Loss: 0.085, Running accuracy: 99.894, Time: 22.44
[2020-12-15 16:18:28,523][__main__][INFO] - [35840] Loss: 0.137, Running accuracy: 99.893, Time: 21.27
[2020-12-15 16:18:50,793][__main__][INFO] - [36160] Loss: 0.081, Running accuracy: 99.894, Time: 21.52
[2020-12-15 16:19:13,679][__main__][INFO] - [36480] Loss: 0.099, Running accuracy: 99.894, Time: 22.89
[2020-12-15 16:19:39,090][__main__][INFO] - [36800] Loss: 0.060, Running accuracy: 99.894, Time: 25.41
[2020-12-15 16:20:04,234][__main__][INFO] - [37120] Loss: 0.079, Running accuracy: 99.895, Time: 25.14
[2020-12-15 16:20:26,487][__main__][INFO] - [37440] Loss: 0.106, Running accuracy: 99.895, Time: 22.25
[2020-12-15 16:20:50,893][__main__][INFO] - [37760] Loss: 0.135, Running accuracy: 99.895, Time: 24.41
[2020-12-15 16:21:11,996][__main__][INFO] - [38080] Loss: 0.172, Running accuracy: 99.894, Time: 21.10
[2020-12-15 16:21:32,980][__main__][INFO] - [38400] Loss: 0.095, Running accuracy: 99.894, Time: 20.98
[2020-12-15 16:21:54,667][__main__][INFO] - [38720] Loss: 0.064, Running accuracy: 99.894, Time: 21.69
[2020-12-15 16:22:16,524][__main__][INFO] - [39040] Loss: 0.107, Running accuracy: 99.895, Time: 21.86
[2020-12-15 16:22:36,893][__main__][INFO] - [39360] Loss: 0.050, Running accuracy: 99.895, Time: 20.37
[2020-12-15 16:22:59,225][__main__][INFO] - [39680] Loss: 0.152, Running accuracy: 99.895, Time: 22.33
[2020-12-15 16:23:09,985][__main__][INFO] - Action accuracy: 99.894, Loss: 0.114
[2020-12-15 16:23:09,986][__main__][INFO] - Validating..
[2020-12-15 16:23:47,521][test][INFO] - Time elapsed: 35.690325
[2020-12-15 16:23:47,525][__main__][INFO] - Validation F1 score: 95.630, Exact match: 55.940, Precision: 95.570, Recall: 95.690
[2020-12-15 16:24:30,023][__main__][INFO] - Checkpoint saved to checkpoints/model_latest.pth
[2020-12-15 16:24:30,892][__main__][INFO] - Epoch #35
[2020-12-15 16:24:30,892][__main__][INFO] - Training..
[2020-12-15 16:24:53,923][__main__][INFO] - [320] Loss: 0.048, Running accuracy: 99.961, Time: 21.75
[2020-12-15 16:25:14,944][__main__][INFO] - [640] Loss: 0.066, Running accuracy: 99.941, Time: 21.02
[2020-12-15 16:25:37,159][__main__][INFO] - [960] Loss: 0.088, Running accuracy: 99.916, Time: 22.21
[2020-12-15 16:25:59,694][__main__][INFO] - [1280] Loss: 0.090, Running accuracy: 99.918, Time: 22.53
[2020-12-15 16:26:21,769][__main__][INFO] - [1600] Loss: 0.089, Running accuracy: 99.921, Time: 22.07
[2020-12-15 16:26:42,851][__main__][INFO] - [1920] Loss: 0.095, Running accuracy: 99.916, Time: 21.08
[2020-12-15 16:27:04,950][__main__][INFO] - [2240] Loss: 0.138, Running accuracy: 99.910, Time: 22.10
[2020-12-15 16:27:26,984][__main__][INFO] - [2560] Loss: 0.124, Running accuracy: 99.909, Time: 22.03
[2020-12-15 16:27:49,957][__main__][INFO] - [2880] Loss: 0.062, Running accuracy: 99.914, Time: 22.97
[2020-12-15 16:28:11,176][__main__][INFO] - [3200] Loss: 0.099, Running accuracy: 99.908, Time: 21.22
[2020-12-15 16:28:32,576][__main__][INFO] - [3520] Loss: 0.128, Running accuracy: 99.906, Time: 21.40
[2020-12-15 16:28:57,386][__main__][INFO] - [3840] Loss: 0.095, Running accuracy: 99.901, Time: 24.81
[2020-12-15 16:29:20,612][__main__][INFO] - [4160] Loss: 0.100, Running accuracy: 99.903, Time: 23.23
[2020-12-15 16:29:42,499][__main__][INFO] - [4480] Loss: 0.097, Running accuracy: 99.902, Time: 21.89
[2020-12-15 16:30:07,359][__main__][INFO] - [4800] Loss: 0.189, Running accuracy: 99.897, Time: 24.86
[2020-12-15 16:30:30,673][__main__][INFO] - [5120] Loss: 0.094, Running accuracy: 99.901, Time: 23.31
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[2020-12-15 17:10:36,663][__main__][INFO] - [39680] Loss: 0.130, Running accuracy: 99.894, Time: 21.20
[2020-12-15 17:10:45,109][__main__][INFO] - Action accuracy: 99.894, Loss: 0.112
[2020-12-15 17:10:45,112][__main__][INFO] - Validating..
[2020-12-15 17:11:23,432][test][INFO] - Time elapsed: 32.667167
[2020-12-15 17:11:23,437][__main__][INFO] - Validation F1 score: 95.610, Exact match: 55.760, Precision: 95.550, Recall: 95.660
[2020-12-15 17:12:02,349][__main__][INFO] - Checkpoint saved to checkpoints/model_latest.pth
[2020-12-15 17:12:03,202][__main__][INFO] - Epoch #36
[2020-12-15 17:12:03,202][__main__][INFO] - Training..
[2020-12-15 17:12:26,672][__main__][INFO] - [320] Loss: 0.051, Running accuracy: 99.948, Time: 22.28
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[2020-12-15 17:56:38,415][__main__][INFO] - [38400] Loss: 0.091, Running accuracy: 99.900, Time: 22.35
[2020-12-15 17:57:01,490][__main__][INFO] - [38720] Loss: 0.153, Running accuracy: 99.899, Time: 23.08
[2020-12-15 17:57:23,989][__main__][INFO] - [39040] Loss: 0.108, Running accuracy: 99.899, Time: 22.50
[2020-12-15 17:57:46,713][__main__][INFO] - [39360] Loss: 0.080, Running accuracy: 99.899, Time: 22.72
[2020-12-15 17:58:07,776][__main__][INFO] - [39680] Loss: 0.041, Running accuracy: 99.900, Time: 21.06
[2020-12-15 17:58:17,133][__main__][INFO] - Action accuracy: 99.900, Loss: 0.108
[2020-12-15 17:58:17,134][__main__][INFO] - Validating..
[2020-12-15 17:58:51,425][test][INFO] - Time elapsed: 32.240587
[2020-12-15 17:58:51,429][__main__][INFO] - Validation F1 score: 95.670, Exact match: 56.000, Precision: 95.560, Recall: 95.780
[2020-12-15 17:59:30,092][__main__][INFO] - Checkpoint saved to checkpoints/model_latest.pth
[2020-12-15 17:59:30,909][__main__][INFO] - Epoch #37
[2020-12-15 17:59:30,909][__main__][INFO] - Training..
[2020-12-15 17:59:58,102][__main__][INFO] - [320] Loss: 0.087, Running accuracy: 99.961, Time: 25.88
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[2020-12-15 18:41:29,411][__main__][INFO] - [36160] Loss: 0.143, Running accuracy: 99.898, Time: 27.00
[2020-12-15 18:41:51,456][__main__][INFO] - [36480] Loss: 0.058, Running accuracy: 99.898, Time: 22.04
[2020-12-15 18:42:13,474][__main__][INFO] - [36800] Loss: 0.054, Running accuracy: 99.898, Time: 22.02
[2020-12-15 18:42:36,168][__main__][INFO] - [37120] Loss: 0.111, Running accuracy: 99.898, Time: 22.69
[2020-12-15 18:42:59,974][__main__][INFO] - [37440] Loss: 0.102, Running accuracy: 99.898, Time: 23.81
[2020-12-15 18:43:21,133][__main__][INFO] - [37760] Loss: 0.034, Running accuracy: 99.898, Time: 21.16
[2020-12-15 18:43:43,043][__main__][INFO] - [38080] Loss: 0.115, Running accuracy: 99.899, Time: 21.91
[2020-12-15 18:44:06,650][__main__][INFO] - [38400] Loss: 0.119, Running accuracy: 99.898, Time: 23.61
[2020-12-15 18:44:30,035][__main__][INFO] - [38720] Loss: 0.074, Running accuracy: 99.899, Time: 23.38
[2020-12-15 18:44:50,790][__main__][INFO] - [39040] Loss: 0.067, Running accuracy: 99.899, Time: 20.75
[2020-12-15 18:45:12,154][__main__][INFO] - [39360] Loss: 0.202, Running accuracy: 99.899, Time: 21.36
[2020-12-15 18:45:35,046][__main__][INFO] - [39680] Loss: 0.116, Running accuracy: 99.898, Time: 22.89
[2020-12-15 18:45:44,252][__main__][INFO] - Action accuracy: 99.898, Loss: 0.108
[2020-12-15 18:45:44,254][__main__][INFO] - Validating..
[2020-12-15 18:46:21,963][test][INFO] - Time elapsed: 35.760731
[2020-12-15 18:46:21,967][__main__][INFO] - Validation F1 score: 95.640, Exact match: 56.240, Precision: 95.560, Recall: 95.730
[2020-12-15 18:46:58,281][__main__][INFO] - Checkpoint saved to checkpoints/model_latest.pth
[2020-12-15 18:46:59,299][__main__][INFO] - Epoch #38
[2020-12-15 18:46:59,299][__main__][INFO] - Training..
[2020-12-15 18:47:21,372][__main__][INFO] - [320] Loss: 0.100, Running accuracy: 99.897, Time: 20.84
[2020-12-15 18:47:43,736][__main__][INFO] - [640] Loss: 0.047, Running accuracy: 99.928, Time: 22.36
[2020-12-15 18:48:05,905][__main__][INFO] - [960] Loss: 0.098, Running accuracy: 99.917, Time: 22.17
[2020-12-15 18:48:28,037][__main__][INFO] - [1280] Loss: 0.065, Running accuracy: 99.925, Time: 22.13
[2020-12-15 18:48:50,204][__main__][INFO] - [1600] Loss: 0.057, Running accuracy: 99.932, Time: 22.17
[2020-12-15 18:49:12,646][__main__][INFO] - [1920] Loss: 0.099, Running accuracy: 99.928, Time: 22.44
[2020-12-15 18:49:33,692][__main__][INFO] - [2240] Loss: 0.078, Running accuracy: 99.920, Time: 21.04
[2020-12-15 18:49:55,667][__main__][INFO] - [2560] Loss: 0.071, Running accuracy: 99.908, Time: 21.97
[2020-12-15 18:50:18,844][__main__][INFO] - [2880] Loss: 0.118, Running accuracy: 99.912, Time: 23.18
[2020-12-15 18:50:41,693][__main__][INFO] - [3200] Loss: 0.086, Running accuracy: 99.911, Time: 22.85
[2020-12-15 18:51:07,825][__main__][INFO] - [3520] Loss: 0.077, Running accuracy: 99.913, Time: 26.13
[2020-12-15 18:51:28,743][__main__][INFO] - [3840] Loss: 0.193, Running accuracy: 99.902, Time: 20.92
[2020-12-15 18:51:51,036][__main__][INFO] - [4160] Loss: 0.084, Running accuracy: 99.902, Time: 22.29
[2020-12-15 18:52:12,890][__main__][INFO] - [4480] Loss: 0.077, Running accuracy: 99.903, Time: 21.85
[2020-12-15 18:52:34,198][__main__][INFO] - [4800] Loss: 0.091, Running accuracy: 99.902, Time: 21.31
[2020-12-15 18:52:58,206][__main__][INFO] - [5120] Loss: 0.113, Running accuracy: 99.901, Time: 24.01
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[2020-12-15 18:54:46,875][__main__][INFO] - [6720] Loss: 0.099, Running accuracy: 99.902, Time: 20.91
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[2020-12-15 18:55:54,036][__main__][INFO] - [7680] Loss: 0.083, Running accuracy: 99.904, Time: 25.67
[2020-12-15 18:56:15,584][__main__][INFO] - [8000] Loss: 0.058, Running accuracy: 99.904, Time: 20.96
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[2020-12-15 18:57:00,722][__main__][INFO] - [8640] Loss: 0.103, Running accuracy: 99.903, Time: 21.83
[2020-12-15 18:57:22,812][__main__][INFO] - [8960] Loss: 0.067, Running accuracy: 99.902, Time: 22.09
[2020-12-15 18:57:47,278][__main__][INFO] - [9280] Loss: 0.069, Running accuracy: 99.903, Time: 24.46
[2020-12-15 18:58:07,699][__main__][INFO] - [9600] Loss: 0.101, Running accuracy: 99.903, Time: 20.42
[2020-12-15 18:58:31,842][__main__][INFO] - [9920] Loss: 0.055, Running accuracy: 99.905, Time: 24.14
[2020-12-15 18:58:54,008][__main__][INFO] - [10240] Loss: 0.113, Running accuracy: 99.905, Time: 22.17
[2020-12-15 18:59:15,039][__main__][INFO] - [10560] Loss: 0.113, Running accuracy: 99.904, Time: 21.03
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[2020-12-15 18:59:58,352][__main__][INFO] - [11200] Loss: 0.128, Running accuracy: 99.902, Time: 21.94
[2020-12-15 19:00:20,036][__main__][INFO] - [11520] Loss: 0.057, Running accuracy: 99.903, Time: 21.68
[2020-12-15 19:00:42,226][__main__][INFO] - [11840] Loss: 0.070, Running accuracy: 99.904, Time: 22.19
[2020-12-15 19:01:06,954][__main__][INFO] - [12160] Loss: 0.102, Running accuracy: 99.903, Time: 24.73
[2020-12-15 19:01:28,080][__main__][INFO] - [12480] Loss: 0.125, Running accuracy: 99.903, Time: 21.13
[2020-12-15 19:01:48,014][__main__][INFO] - [12800] Loss: 0.090, Running accuracy: 99.903, Time: 19.93
[2020-12-15 19:02:11,740][__main__][INFO] - [13120] Loss: 0.143, Running accuracy: 99.902, Time: 23.72
[2020-12-15 19:02:35,500][__main__][INFO] - [13440] Loss: 0.085, Running accuracy: 99.902, Time: 23.76
[2020-12-15 19:02:56,897][__main__][INFO] - [13760] Loss: 0.096, Running accuracy: 99.902, Time: 21.40
[2020-12-15 19:03:20,879][__main__][INFO] - [14080] Loss: 0.095, Running accuracy: 99.902, Time: 23.98
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[2020-12-15 19:04:49,814][__main__][INFO] - [15360] Loss: 0.083, Running accuracy: 99.903, Time: 21.77
[2020-12-15 19:05:12,368][__main__][INFO] - [15680] Loss: 0.096, Running accuracy: 99.904, Time: 22.33
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[2020-12-15 19:06:23,162][__main__][INFO] - [16640] Loss: 0.111, Running accuracy: 99.904, Time: 24.78
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[2020-12-15 19:07:50,198][__main__][INFO] - [17920] Loss: 0.148, Running accuracy: 99.901, Time: 20.10
[2020-12-15 19:08:12,820][__main__][INFO] - [18240] Loss: 0.123, Running accuracy: 99.900, Time: 22.62
[2020-12-15 19:08:34,608][__main__][INFO] - [18560] Loss: 0.072, Running accuracy: 99.901, Time: 21.79
[2020-12-15 19:08:55,311][__main__][INFO] - [18880] Loss: 0.079, Running accuracy: 99.901, Time: 20.70
[2020-12-15 19:09:16,907][__main__][INFO] - [19200] Loss: 0.084, Running accuracy: 99.901, Time: 21.59
[2020-12-15 19:09:38,309][__main__][INFO] - [19520] Loss: 0.104, Running accuracy: 99.901, Time: 21.40
[2020-12-15 19:10:01,626][__main__][INFO] - [19840] Loss: 0.185, Running accuracy: 99.899, Time: 23.32
[2020-12-15 19:10:25,015][__main__][INFO] - [20160] Loss: 0.118, Running accuracy: 99.899, Time: 23.39
[2020-12-15 19:10:47,848][__main__][INFO] - [20480] Loss: 0.068, Running accuracy: 99.899, Time: 22.83
[2020-12-15 19:11:11,832][__main__][INFO] - [20800] Loss: 0.087, Running accuracy: 99.900, Time: 23.98
[2020-12-15 19:11:36,743][__main__][INFO] - [21120] Loss: 0.059, Running accuracy: 99.901, Time: 24.91
[2020-12-15 19:11:58,261][__main__][INFO] - [21440] Loss: 0.118, Running accuracy: 99.901, Time: 21.52
[2020-12-15 19:12:20,458][__main__][INFO] - [21760] Loss: 0.079, Running accuracy: 99.901, Time: 22.20
[2020-12-15 19:12:42,294][__main__][INFO] - [22080] Loss: 0.098, Running accuracy: 99.900, Time: 21.84
[2020-12-15 19:13:06,785][__main__][INFO] - [22400] Loss: 0.132, Running accuracy: 99.899, Time: 24.49
[2020-12-15 19:13:28,173][__main__][INFO] - [22720] Loss: 0.047, Running accuracy: 99.900, Time: 21.39
[2020-12-15 19:13:51,529][__main__][INFO] - [23040] Loss: 0.132, Running accuracy: 99.899, Time: 23.35
[2020-12-15 19:14:13,500][__main__][INFO] - [23360] Loss: 0.078, Running accuracy: 99.899, Time: 21.97
[2020-12-15 19:14:36,579][__main__][INFO] - [23680] Loss: 0.068, Running accuracy: 99.899, Time: 23.08
[2020-12-15 19:14:58,635][__main__][INFO] - [24000] Loss: 0.122, Running accuracy: 99.899, Time: 22.05
[2020-12-15 19:15:19,762][__main__][INFO] - [24320] Loss: 0.091, Running accuracy: 99.898, Time: 21.13
[2020-12-15 19:15:40,914][__main__][INFO] - [24640] Loss: 0.104, Running accuracy: 99.899, Time: 21.15
[2020-12-15 19:16:02,803][__main__][INFO] - [24960] Loss: 0.083, Running accuracy: 99.899, Time: 21.89
[2020-12-15 19:16:26,184][__main__][INFO] - [25280] Loss: 0.174, Running accuracy: 99.898, Time: 23.38
[2020-12-15 19:16:48,281][__main__][INFO] - [25600] Loss: 0.065, Running accuracy: 99.899, Time: 22.10
[2020-12-15 19:17:15,420][__main__][INFO] - [25920] Loss: 0.108, Running accuracy: 99.898, Time: 27.14
[2020-12-15 19:17:36,996][__main__][INFO] - [26240] Loss: 0.081, Running accuracy: 99.898, Time: 21.58
[2020-12-15 19:17:59,193][__main__][INFO] - [26560] Loss: 0.128, Running accuracy: 99.897, Time: 22.20
[2020-12-15 19:18:21,561][__main__][INFO] - [26880] Loss: 0.083, Running accuracy: 99.897, Time: 22.37
[2020-12-15 19:18:43,689][__main__][INFO] - [27200] Loss: 0.060, Running accuracy: 99.897, Time: 22.13
[2020-12-15 19:19:05,389][__main__][INFO] - [27520] Loss: 0.064, Running accuracy: 99.898, Time: 21.70
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[2020-12-15 19:19:49,467][__main__][INFO] - [28160] Loss: 0.134, Running accuracy: 99.898, Time: 22.42
[2020-12-15 19:20:13,230][__main__][INFO] - [28480] Loss: 0.066, Running accuracy: 99.899, Time: 23.76
[2020-12-15 19:20:34,446][__main__][INFO] - [28800] Loss: 0.084, Running accuracy: 99.898, Time: 21.22
[2020-12-15 19:20:56,025][__main__][INFO] - [29120] Loss: 0.109, Running accuracy: 99.898, Time: 21.58
[2020-12-15 19:21:18,378][__main__][INFO] - [29440] Loss: 0.073, Running accuracy: 99.899, Time: 22.35
[2020-12-15 19:21:39,878][__main__][INFO] - [29760] Loss: 0.049, Running accuracy: 99.899, Time: 21.50
[2020-12-15 19:22:01,054][__main__][INFO] - [30080] Loss: 0.101, Running accuracy: 99.899, Time: 21.18
[2020-12-15 19:22:30,290][__main__][INFO] - [30400] Loss: 0.113, Running accuracy: 99.899, Time: 29.23
[2020-12-15 19:22:52,538][__main__][INFO] - [30720] Loss: 0.097, Running accuracy: 99.899, Time: 22.25
[2020-12-15 19:23:14,666][__main__][INFO] - [31040] Loss: 0.137, Running accuracy: 99.899, Time: 22.13
[2020-12-15 19:23:37,257][__main__][INFO] - [31360] Loss: 0.070, Running accuracy: 99.899, Time: 22.59
[2020-12-15 19:23:59,456][__main__][INFO] - [31680] Loss: 0.095, Running accuracy: 99.899, Time: 22.20
[2020-12-15 19:24:21,372][__main__][INFO] - [32000] Loss: 0.150, Running accuracy: 99.899, Time: 21.91
[2020-12-15 19:24:44,244][__main__][INFO] - [32320] Loss: 0.128, Running accuracy: 99.899, Time: 22.87
[2020-12-15 19:25:06,112][__main__][INFO] - [32640] Loss: 0.067, Running accuracy: 99.899, Time: 21.87
[2020-12-15 19:25:27,420][__main__][INFO] - [32960] Loss: 0.095, Running accuracy: 99.899, Time: 21.31
[2020-12-15 19:25:49,619][__main__][INFO] - [33280] Loss: 0.087, Running accuracy: 99.899, Time: 22.20
[2020-12-15 19:26:10,476][__main__][INFO] - [33600] Loss: 0.052, Running accuracy: 99.899, Time: 20.86
[2020-12-15 19:26:31,358][__main__][INFO] - [33920] Loss: 0.065, Running accuracy: 99.900, Time: 20.88
[2020-12-15 19:26:53,152][__main__][INFO] - [34240] Loss: 0.042, Running accuracy: 99.900, Time: 21.79
[2020-12-15 19:27:14,535][__main__][INFO] - [34560] Loss: 0.072, Running accuracy: 99.900, Time: 21.38
[2020-12-15 19:27:39,101][__main__][INFO] - [34880] Loss: 0.071, Running accuracy: 99.901, Time: 24.57
[2020-12-15 19:28:01,029][__main__][INFO] - [35200] Loss: 0.069, Running accuracy: 99.901, Time: 21.93
[2020-12-15 19:28:22,885][__main__][INFO] - [35520] Loss: 0.058, Running accuracy: 99.901, Time: 21.86
[2020-12-15 19:28:44,817][__main__][INFO] - [35840] Loss: 0.106, Running accuracy: 99.901, Time: 21.93
[2020-12-15 19:29:05,739][__main__][INFO] - [36160] Loss: 0.056, Running accuracy: 99.901, Time: 20.92
[2020-12-15 19:29:28,067][__main__][INFO] - [36480] Loss: 0.070, Running accuracy: 99.902, Time: 22.33
[2020-12-15 19:29:49,152][__main__][INFO] - [36800] Loss: 0.113, Running accuracy: 99.902, Time: 21.08
[2020-12-15 19:30:11,466][__main__][INFO] - [37120] Loss: 0.050, Running accuracy: 99.902, Time: 22.31
[2020-12-15 19:30:33,120][__main__][INFO] - [37440] Loss: 0.107, Running accuracy: 99.902, Time: 21.65
[2020-12-15 19:30:57,227][__main__][INFO] - [37760] Loss: 0.151, Running accuracy: 99.901, Time: 24.11
[2020-12-15 19:31:18,559][__main__][INFO] - [38080] Loss: 0.077, Running accuracy: 99.901, Time: 21.33
[2020-12-15 19:31:39,514][__main__][INFO] - [38400] Loss: 0.122, Running accuracy: 99.901, Time: 20.95
[2020-12-15 19:32:02,598][__main__][INFO] - [38720] Loss: 0.119, Running accuracy: 99.900, Time: 23.08
[2020-12-15 19:32:27,394][__main__][INFO] - [39040] Loss: 0.089, Running accuracy: 99.901, Time: 24.80
[2020-12-15 19:32:50,637][__main__][INFO] - [39360] Loss: 0.054, Running accuracy: 99.901, Time: 23.24
[2020-12-15 19:33:13,829][__main__][INFO] - [39680] Loss: 0.069, Running accuracy: 99.901, Time: 23.19
[2020-12-15 19:33:22,432][__main__][INFO] - Action accuracy: 99.902, Loss: 0.105
[2020-12-15 19:33:22,433][__main__][INFO] - Validating..
[2020-12-15 19:33:57,016][test][INFO] - Time elapsed: 32.552826
[2020-12-15 19:33:57,020][__main__][INFO] - Validation F1 score: 95.620, Exact match: 55.940, Precision: 95.530, Recall: 95.720
Epoch    39: reducing learning rate of group 0 to 3.1250e-07.
[2020-12-15 19:34:39,817][__main__][INFO] - Checkpoint saved to checkpoints/model_latest.pth
[2020-12-15 19:34:40,634][__main__][INFO] - Epoch #39
[2020-12-15 19:34:40,634][__main__][INFO] - Training..
[2020-12-15 19:35:05,421][__main__][INFO] - [320] Loss: 0.063, Running accuracy: 99.937, Time: 23.64
[2020-12-15 19:35:26,879][__main__][INFO] - [640] Loss: 0.072, Running accuracy: 99.916, Time: 21.46
[2020-12-15 19:35:48,775][__main__][INFO] - [960] Loss: 0.084, Running accuracy: 99.910, Time: 21.90
[2020-12-15 19:36:11,662][__main__][INFO] - [1280] Loss: 0.034, Running accuracy: 99.933, Time: 22.89
[2020-12-15 19:36:37,789][__main__][INFO] - [1600] Loss: 0.057, Running accuracy: 99.938, Time: 26.13
[2020-12-15 19:36:59,208][__main__][INFO] - [1920] Loss: 0.090, Running accuracy: 99.935, Time: 21.42
[2020-12-15 19:37:21,467][__main__][INFO] - [2240] Loss: 0.105, Running accuracy: 99.928, Time: 22.26
[2020-12-15 19:37:42,900][__main__][INFO] - [2560] Loss: 0.055, Running accuracy: 99.930, Time: 21.43
[2020-12-15 19:38:04,460][__main__][INFO] - [2880] Loss: 0.092, Running accuracy: 99.932, Time: 21.56
[2020-12-15 19:38:27,509][__main__][INFO] - [3200] Loss: 0.102, Running accuracy: 99.927, Time: 23.05
[2020-12-15 19:38:49,915][__main__][INFO] - [3520] Loss: 0.062, Running accuracy: 99.928, Time: 22.41
[2020-12-15 19:39:11,946][__main__][INFO] - [3840] Loss: 0.143, Running accuracy: 99.923, Time: 22.03
[2020-12-15 19:39:33,921][__main__][INFO] - [4160] Loss: 0.091, Running accuracy: 99.921, Time: 21.97
[2020-12-15 19:39:56,122][__main__][INFO] - [4480] Loss: 0.137, Running accuracy: 99.915, Time: 22.20
[2020-12-15 19:40:17,517][__main__][INFO] - [4800] Loss: 0.107, Running accuracy: 99.917, Time: 21.39
[2020-12-15 19:40:39,560][__main__][INFO] - [5120] Loss: 0.086, Running accuracy: 99.914, Time: 22.04
[2020-12-15 19:41:00,938][__main__][INFO] - [5440] Loss: 0.084, Running accuracy: 99.912, Time: 21.38
[2020-12-15 19:41:23,487][__main__][INFO] - [5760] Loss: 0.118, Running accuracy: 99.910, Time: 22.55
[2020-12-15 19:41:50,277][__main__][INFO] - [6080] Loss: 0.094, Running accuracy: 99.909, Time: 26.79
[2020-12-15 19:42:13,231][__main__][INFO] - [6400] Loss: 0.082, Running accuracy: 99.909, Time: 22.58
[2020-12-15 19:42:34,326][__main__][INFO] - [6720] Loss: 0.112, Running accuracy: 99.910, Time: 21.09
[2020-12-15 19:42:56,394][__main__][INFO] - [7040] Loss: 0.070, Running accuracy: 99.911, Time: 22.07
[2020-12-15 19:43:17,069][__main__][INFO] - [7360] Loss: 0.104, Running accuracy: 99.909, Time: 20.67
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[2020-12-15 19:43:58,270][__main__][INFO] - [8000] Loss: 0.099, Running accuracy: 99.907, Time: 20.59
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[2020-12-15 19:44:41,046][__main__][INFO] - [8640] Loss: 0.091, Running accuracy: 99.905, Time: 22.31
[2020-12-15 19:45:06,063][__main__][INFO] - [8960] Loss: 0.065, Running accuracy: 99.906, Time: 25.02
[2020-12-15 19:45:26,744][__main__][INFO] - [9280] Loss: 0.120, Running accuracy: 99.904, Time: 20.68
[2020-12-15 19:45:50,403][__main__][INFO] - [9600] Loss: 0.065, Running accuracy: 99.905, Time: 23.66
[2020-12-15 19:46:12,189][__main__][INFO] - [9920] Loss: 0.113, Running accuracy: 99.904, Time: 21.79
[2020-12-15 19:46:33,359][__main__][INFO] - [10240] Loss: 0.171, Running accuracy: 99.901, Time: 21.17
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[2020-12-15 20:19:26,451][__main__][INFO] - [38400] Loss: 0.110, Running accuracy: 99.905, Time: 21.74
[2020-12-15 20:19:49,039][__main__][INFO] - [38720] Loss: 0.165, Running accuracy: 99.904, Time: 22.59
[2020-12-15 20:20:10,654][__main__][INFO] - [39040] Loss: 0.101, Running accuracy: 99.904, Time: 21.61
[2020-12-15 20:20:31,494][__main__][INFO] - [39360] Loss: 0.070, Running accuracy: 99.904, Time: 20.84
[2020-12-15 20:20:51,508][__main__][INFO] - [39680] Loss: 0.152, Running accuracy: 99.904, Time: 20.01
[2020-12-15 20:20:59,904][__main__][INFO] - Action accuracy: 99.903, Loss: 0.103
[2020-12-15 20:20:59,907][__main__][INFO] - Validating..
[2020-12-15 20:21:34,241][test][INFO] - Time elapsed: 32.308463
[2020-12-15 20:21:34,245][__main__][INFO] - Validation F1 score: 95.610, Exact match: 55.880, Precision: 95.540, Recall: 95.680
[2020-12-15 20:22:13,393][__main__][INFO] - Checkpoint saved to checkpoints/model_latest.pth
[2020-12-15 20:22:14,386][__main__][INFO] - Epoch #40
[2020-12-15 20:22:14,387][__main__][INFO] - Training..
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[2020-12-15 20:53:11,917][__main__][INFO] - [26560] Loss: 0.075, Running accuracy: 99.905, Time: 22.02
[2020-12-15 20:53:37,420][__main__][INFO] - [26880] Loss: 0.154, Running accuracy: 99.904, Time: 25.50
[2020-12-15 20:53:59,956][__main__][INFO] - [27200] Loss: 0.102, Running accuracy: 99.904, Time: 22.53
[2020-12-15 20:54:21,815][__main__][INFO] - [27520] Loss: 0.062, Running accuracy: 99.905, Time: 21.86
[2020-12-15 20:54:44,584][__main__][INFO] - [27840] Loss: 0.111, Running accuracy: 99.904, Time: 22.77
[2020-12-15 20:55:07,686][__main__][INFO] - [28160] Loss: 0.042, Running accuracy: 99.905, Time: 23.10
[2020-12-15 20:55:28,429][__main__][INFO] - [28480] Loss: 0.137, Running accuracy: 99.905, Time: 20.74
[2020-12-15 20:55:49,273][__main__][INFO] - [28800] Loss: 0.093, Running accuracy: 99.905, Time: 20.84
[2020-12-15 20:56:10,803][__main__][INFO] - [29120] Loss: 0.063, Running accuracy: 99.906, Time: 21.53
[2020-12-15 20:56:32,520][__main__][INFO] - [29440] Loss: 0.067, Running accuracy: 99.906, Time: 21.72
[2020-12-15 20:56:54,609][__main__][INFO] - [29760] Loss: 0.068, Running accuracy: 99.907, Time: 22.09
[2020-12-15 20:57:17,895][__main__][INFO] - [30080] Loss: 0.077, Running accuracy: 99.907, Time: 23.29
[2020-12-15 20:57:40,240][__main__][INFO] - [30400] Loss: 0.062, Running accuracy: 99.907, Time: 22.34
[2020-12-15 20:58:02,249][__main__][INFO] - [30720] Loss: 0.094, Running accuracy: 99.907, Time: 22.01
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[2020-12-15 20:58:46,074][__main__][INFO] - [31360] Loss: 0.103, Running accuracy: 99.907, Time: 23.27
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[2020-12-15 21:03:59,941][__main__][INFO] - [35840] Loss: 0.066, Running accuracy: 99.906, Time: 24.93
[2020-12-15 21:04:21,580][__main__][INFO] - [36160] Loss: 0.073, Running accuracy: 99.906, Time: 21.64
[2020-12-15 21:04:42,779][__main__][INFO] - [36480] Loss: 0.073, Running accuracy: 99.906, Time: 21.20
[2020-12-15 21:05:05,018][__main__][INFO] - [36800] Loss: 0.046, Running accuracy: 99.907, Time: 22.24
[2020-12-15 21:05:25,490][__main__][INFO] - [37120] Loss: 0.090, Running accuracy: 99.907, Time: 20.47
[2020-12-15 21:05:47,723][__main__][INFO] - [37440] Loss: 0.090, Running accuracy: 99.907, Time: 22.23
[2020-12-15 21:06:11,993][__main__][INFO] - [37760] Loss: 0.185, Running accuracy: 99.906, Time: 24.27
[2020-12-15 21:06:33,803][__main__][INFO] - [38080] Loss: 0.099, Running accuracy: 99.907, Time: 21.81
[2020-12-15 21:06:56,289][__main__][INFO] - [38400] Loss: 0.080, Running accuracy: 99.907, Time: 22.49
[2020-12-15 21:07:18,401][__main__][INFO] - [38720] Loss: 0.119, Running accuracy: 99.907, Time: 22.11
[2020-12-15 21:07:40,390][__main__][INFO] - [39040] Loss: 0.067, Running accuracy: 99.907, Time: 21.99
[2020-12-15 21:08:02,023][__main__][INFO] - [39360] Loss: 0.135, Running accuracy: 99.906, Time: 21.63
[2020-12-15 21:08:25,263][__main__][INFO] - [39680] Loss: 0.078, Running accuracy: 99.907, Time: 22.87
[2020-12-15 21:08:34,424][__main__][INFO] - Action accuracy: 99.907, Loss: 0.102
[2020-12-15 21:08:34,426][__main__][INFO] - Validating..
[2020-12-15 21:09:12,088][test][INFO] - Time elapsed: 35.684243
[2020-12-15 21:09:12,092][__main__][INFO] - Validation F1 score: 95.590, Exact match: 55.820, Precision: 95.520, Recall: 95.660
[2020-12-15 21:09:54,141][__main__][INFO] - Checkpoint saved to checkpoints/model_latest.pth
[2020-12-15 21:09:55,114][__main__][INFO] - Epoch #41
[2020-12-15 21:09:55,114][__main__][INFO] - Training..
[2020-12-15 21:10:16,671][__main__][INFO] - [320] Loss: 0.070, Running accuracy: 99.933, Time: 20.31
[2020-12-15 21:10:37,729][__main__][INFO] - [640] Loss: 0.074, Running accuracy: 99.933, Time: 21.06
[2020-12-15 21:11:01,766][__main__][INFO] - [960] Loss: 0.053, Running accuracy: 99.943, Time: 24.04
[2020-12-15 21:11:24,917][__main__][INFO] - [1280] Loss: 0.100, Running accuracy: 99.931, Time: 23.15
[2020-12-15 21:11:46,227][__main__][INFO] - [1600] Loss: 0.135, Running accuracy: 99.916, Time: 21.31
[2020-12-15 21:12:09,426][__main__][INFO] - [1920] Loss: 0.095, Running accuracy: 99.919, Time: 23.20
[2020-12-15 21:12:30,878][__main__][INFO] - [2240] Loss: 0.061, Running accuracy: 99.917, Time: 21.45
[2020-12-15 21:12:52,157][__main__][INFO] - [2560] Loss: 0.080, Running accuracy: 99.919, Time: 21.28
[2020-12-15 21:13:18,342][__main__][INFO] - [2880] Loss: 0.120, Running accuracy: 99.915, Time: 26.18
[2020-12-15 21:13:40,506][__main__][INFO] - [3200] Loss: 0.127, Running accuracy: 99.916, Time: 22.16
[2020-12-15 21:14:03,984][__main__][INFO] - [3520] Loss: 0.068, Running accuracy: 99.920, Time: 23.48
[2020-12-15 21:14:25,737][__main__][INFO] - [3840] Loss: 0.064, Running accuracy: 99.917, Time: 21.75
[2020-12-15 21:14:47,015][__main__][INFO] - [4160] Loss: 0.077, Running accuracy: 99.913, Time: 21.28
[2020-12-15 21:15:08,568][__main__][INFO] - [4480] Loss: 0.055, Running accuracy: 99.916, Time: 21.55
[2020-12-15 21:15:31,255][__main__][INFO] - [4800] Loss: 0.110, Running accuracy: 99.914, Time: 22.69
[2020-12-15 21:15:52,566][__main__][INFO] - [5120] Loss: 0.080, Running accuracy: 99.914, Time: 21.31
[2020-12-15 21:16:13,450][__main__][INFO] - [5440] Loss: 0.070, Running accuracy: 99.916, Time: 20.88
[2020-12-15 21:16:34,629][__main__][INFO] - [5760] Loss: 0.069, Running accuracy: 99.917, Time: 21.18
[2020-12-15 21:16:56,526][__main__][INFO] - [6080] Loss: 0.109, Running accuracy: 99.914, Time: 21.90
[2020-12-15 21:17:18,313][__main__][INFO] - [6400] Loss: 0.063, Running accuracy: 99.915, Time: 21.79
[2020-12-15 21:17:42,378][__main__][INFO] - [6720] Loss: 0.059, Running accuracy: 99.916, Time: 24.06
[2020-12-15 21:18:05,343][__main__][INFO] - [7040] Loss: 0.100, Running accuracy: 99.917, Time: 22.96
[2020-12-15 21:18:32,200][__main__][INFO] - [7360] Loss: 0.081, Running accuracy: 99.918, Time: 26.31
[2020-12-15 21:18:55,625][__main__][INFO] - [7680] Loss: 0.125, Running accuracy: 99.915, Time: 23.42
[2020-12-15 21:19:18,000][__main__][INFO] - [8000] Loss: 0.098, Running accuracy: 99.915, Time: 22.37
[2020-12-15 21:19:39,739][__main__][INFO] - [8320] Loss: 0.075, Running accuracy: 99.915, Time: 21.74
[2020-12-15 21:20:05,179][__main__][INFO] - [8640] Loss: 0.080, Running accuracy: 99.915, Time: 25.44
[2020-12-15 21:20:27,244][__main__][INFO] - [8960] Loss: 0.070, Running accuracy: 99.915, Time: 22.06
[2020-12-15 21:20:49,382][__main__][INFO] - [9280] Loss: 0.067, Running accuracy: 99.916, Time: 22.14
[2020-12-15 21:21:10,622][__main__][INFO] - [9600] Loss: 0.089, Running accuracy: 99.916, Time: 21.24
[2020-12-15 21:21:31,361][__main__][INFO] - [9920] Loss: 0.060, Running accuracy: 99.917, Time: 20.74
[2020-12-15 21:21:53,668][__main__][INFO] - [10240] Loss: 0.097, Running accuracy: 99.918, Time: 22.31
[2020-12-15 21:22:15,418][__main__][INFO] - [10560] Loss: 0.097, Running accuracy: 99.917, Time: 21.75
[2020-12-15 21:22:36,825][__main__][INFO] - [10880] Loss: 0.085, Running accuracy: 99.917, Time: 21.41
[2020-12-15 21:22:59,553][__main__][INFO] - [11200] Loss: 0.056, Running accuracy: 99.918, Time: 22.73
[2020-12-15 21:23:20,587][__main__][INFO] - [11520] Loss: 0.130, Running accuracy: 99.917, Time: 21.03
[2020-12-15 21:23:46,179][__main__][INFO] - [11840] Loss: 0.050, Running accuracy: 99.918, Time: 25.59
[2020-12-15 21:24:07,067][__main__][INFO] - [12160] Loss: 0.061, Running accuracy: 99.918, Time: 20.89
[2020-12-15 21:24:29,213][__main__][INFO] - [12480] Loss: 0.074, Running accuracy: 99.917, Time: 22.15
[2020-12-15 21:24:51,721][__main__][INFO] - [12800] Loss: 0.059, Running accuracy: 99.917, Time: 22.51
[2020-12-15 21:25:13,343][__main__][INFO] - [13120] Loss: 0.259, Running accuracy: 99.914, Time: 21.62
[2020-12-15 21:25:35,277][__main__][INFO] - [13440] Loss: 0.118, Running accuracy: 99.913, Time: 21.93
[2020-12-15 21:25:58,684][__main__][INFO] - [13760] Loss: 0.066, Running accuracy: 99.914, Time: 23.41
[2020-12-15 21:26:21,811][__main__][INFO] - [14080] Loss: 0.055, Running accuracy: 99.914, Time: 23.13
[2020-12-15 21:26:43,840][__main__][INFO] - [14400] Loss: 0.176, Running accuracy: 99.911, Time: 22.03
[2020-12-15 21:27:04,265][__main__][INFO] - [14720] Loss: 0.136, Running accuracy: 99.910, Time: 20.42
[2020-12-15 21:27:26,273][__main__][INFO] - [15040] Loss: 0.056, Running accuracy: 99.910, Time: 22.01
[2020-12-15 21:27:47,573][__main__][INFO] - [15360] Loss: 0.085, Running accuracy: 99.910, Time: 21.30
[2020-12-15 21:28:09,623][__main__][INFO] - [15680] Loss: 0.119, Running accuracy: 99.909, Time: 22.05
[2020-12-15 21:28:36,583][__main__][INFO] - [16000] Loss: 0.075, Running accuracy: 99.910, Time: 26.96
[2020-12-15 21:28:57,795][__main__][INFO] - [16320] Loss: 0.122, Running accuracy: 99.909, Time: 21.21
[2020-12-15 21:29:20,290][__main__][INFO] - [16640] Loss: 0.125, Running accuracy: 99.908, Time: 22.49
[2020-12-15 21:29:42,888][__main__][INFO] - [16960] Loss: 0.095, Running accuracy: 99.907, Time: 22.60
[2020-12-15 21:30:05,718][__main__][INFO] - [17280] Loss: 0.066, Running accuracy: 99.907, Time: 22.83
[2020-12-15 21:30:27,018][__main__][INFO] - [17600] Loss: 0.104, Running accuracy: 99.907, Time: 21.30
[2020-12-15 21:30:48,729][__main__][INFO] - [17920] Loss: 0.077, Running accuracy: 99.907, Time: 21.71
[2020-12-15 21:31:10,795][__main__][INFO] - [18240] Loss: 0.061, Running accuracy: 99.909, Time: 22.07
[2020-12-15 21:31:32,078][__main__][INFO] - [18560] Loss: 0.072, Running accuracy: 99.909, Time: 21.28
[2020-12-15 21:31:54,928][__main__][INFO] - [18880] Loss: 0.146, Running accuracy: 99.908, Time: 22.85
[2020-12-15 21:32:18,911][__main__][INFO] - [19200] Loss: 0.113, Running accuracy: 99.907, Time: 23.98
[2020-12-15 21:32:42,556][__main__][INFO] - [19520] Loss: 0.064, Running accuracy: 99.907, Time: 23.64
[2020-12-15 21:33:04,305][__main__][INFO] - [19840] Loss: 0.062, Running accuracy: 99.907, Time: 21.01
[2020-12-15 21:33:26,124][__main__][INFO] - [20160] Loss: 0.107, Running accuracy: 99.908, Time: 21.82
[2020-12-15 21:33:50,277][__main__][INFO] - [20480] Loss: 0.107, Running accuracy: 99.907, Time: 24.15
[2020-12-15 21:34:12,433][__main__][INFO] - [20800] Loss: 0.063, Running accuracy: 99.908, Time: 22.15
[2020-12-15 21:34:33,055][__main__][INFO] - [21120] Loss: 0.092, Running accuracy: 99.907, Time: 20.62
[2020-12-15 21:34:54,385][__main__][INFO] - [21440] Loss: 0.055, Running accuracy: 99.908, Time: 21.33
[2020-12-15 21:35:17,665][__main__][INFO] - [21760] Loss: 0.131, Running accuracy: 99.907, Time: 23.28
[2020-12-15 21:35:42,579][__main__][INFO] - [22080] Loss: 0.085, Running accuracy: 99.908, Time: 24.49
[2020-12-15 21:36:04,664][__main__][INFO] - [22400] Loss: 0.111, Running accuracy: 99.908, Time: 22.08
[2020-12-15 21:36:27,424][__main__][INFO] - [22720] Loss: 0.104, Running accuracy: 99.908, Time: 22.76
[2020-12-15 21:36:50,692][__main__][INFO] - [23040] Loss: 0.047, Running accuracy: 99.908, Time: 23.27
[2020-12-15 21:37:12,504][__main__][INFO] - [23360] Loss: 0.090, Running accuracy: 99.909, Time: 21.81
[2020-12-15 21:37:32,709][__main__][INFO] - [23680] Loss: 0.081, Running accuracy: 99.909, Time: 20.20
[2020-12-15 21:37:54,535][__main__][INFO] - [24000] Loss: 0.099, Running accuracy: 99.909, Time: 21.83
[2020-12-15 21:38:16,492][__main__][INFO] - [24320] Loss: 0.093, Running accuracy: 99.909, Time: 21.96
[2020-12-15 21:38:37,992][__main__][INFO] - [24640] Loss: 0.104, Running accuracy: 99.910, Time: 21.50
[2020-12-15 21:38:59,805][__main__][INFO] - [24960] Loss: 0.134, Running accuracy: 99.909, Time: 21.81
[2020-12-15 21:39:24,876][__main__][INFO] - [25280] Loss: 0.059, Running accuracy: 99.909, Time: 25.07
[2020-12-15 21:39:46,417][__main__][INFO] - [25600] Loss: 0.097, Running accuracy: 99.909, Time: 21.54
[2020-12-15 21:40:06,849][__main__][INFO] - [25920] Loss: 0.056, Running accuracy: 99.909, Time: 20.43
[2020-12-15 21:40:28,273][__main__][INFO] - [26240] Loss: 0.070, Running accuracy: 99.910, Time: 21.42
[2020-12-15 21:40:49,505][__main__][INFO] - [26560] Loss: 0.064, Running accuracy: 99.910, Time: 21.23
[2020-12-15 21:41:10,981][__main__][INFO] - [26880] Loss: 0.137, Running accuracy: 99.909, Time: 21.47
[2020-12-15 21:41:33,212][__main__][INFO] - [27200] Loss: 0.069, Running accuracy: 99.909, Time: 22.23
[2020-12-15 21:41:53,588][__main__][INFO] - [27520] Loss: 0.064, Running accuracy: 99.909, Time: 20.38
[2020-12-15 21:42:15,917][__main__][INFO] - [27840] Loss: 0.077, Running accuracy: 99.909, Time: 22.33
[2020-12-15 21:42:38,772][__main__][INFO] - [28160] Loss: 0.114, Running accuracy: 99.909, Time: 22.85
[2020-12-15 21:43:01,990][__main__][INFO] - [28480] Loss: 0.096, Running accuracy: 99.908, Time: 23.22
[2020-12-15 21:43:22,538][__main__][INFO] - [28800] Loss: 0.069, Running accuracy: 99.909, Time: 20.55
[2020-12-15 21:43:44,770][__main__][INFO] - [29120] Loss: 0.045, Running accuracy: 99.909, Time: 22.23
[2020-12-15 21:44:07,381][__main__][INFO] - [29440] Loss: 0.126, Running accuracy: 99.909, Time: 22.61
[2020-12-15 21:44:31,937][__main__][INFO] - [29760] Loss: 0.070, Running accuracy: 99.909, Time: 24.55
[2020-12-15 21:44:52,564][__main__][INFO] - [30080] Loss: 0.142, Running accuracy: 99.909, Time: 20.63
[2020-12-15 21:45:14,947][__main__][INFO] - [30400] Loss: 0.058, Running accuracy: 99.910, Time: 22.38
[2020-12-15 21:45:36,975][__main__][INFO] - [30720] Loss: 0.091, Running accuracy: 99.910, Time: 22.03
[2020-12-15 21:45:58,355][__main__][INFO] - [31040] Loss: 0.038, Running accuracy: 99.910, Time: 21.38
[2020-12-15 21:46:21,355][__main__][INFO] - [31360] Loss: 0.088, Running accuracy: 99.910, Time: 23.00
[2020-12-15 21:46:42,124][__main__][INFO] - [31680] Loss: 0.044, Running accuracy: 99.911, Time: 20.77
[2020-12-15 21:47:06,361][__main__][INFO] - [32000] Loss: 0.090, Running accuracy: 99.911, Time: 24.24
[2020-12-15 21:47:27,589][__main__][INFO] - [32320] Loss: 0.106, Running accuracy: 99.911, Time: 21.23
[2020-12-15 21:47:50,029][__main__][INFO] - [32640] Loss: 0.138, Running accuracy: 99.911, Time: 22.44
[2020-12-15 21:48:11,877][__main__][INFO] - [32960] Loss: 0.083, Running accuracy: 99.911, Time: 21.85
[2020-12-15 21:48:34,653][__main__][INFO] - [33280] Loss: 0.064, Running accuracy: 99.911, Time: 22.78
[2020-12-15 21:48:56,240][__main__][INFO] - [33600] Loss: 0.133, Running accuracy: 99.911, Time: 21.59
[2020-12-15 21:49:17,120][__main__][INFO] - [33920] Loss: 0.040, Running accuracy: 99.911, Time: 20.88
[2020-12-15 21:49:40,875][__main__][INFO] - [34240] Loss: 0.059, Running accuracy: 99.911, Time: 23.75
[2020-12-15 21:50:04,506][__main__][INFO] - [34560] Loss: 0.146, Running accuracy: 99.911, Time: 23.63
[2020-12-15 21:50:25,397][__main__][INFO] - [34880] Loss: 0.070, Running accuracy: 99.911, Time: 20.89
[2020-12-15 21:50:46,852][__main__][INFO] - [35200] Loss: 0.175, Running accuracy: 99.911, Time: 21.45
[2020-12-15 21:51:08,622][__main__][INFO] - [35520] Loss: 0.057, Running accuracy: 99.911, Time: 21.77
[2020-12-15 21:51:30,169][__main__][INFO] - [35840] Loss: 0.086, Running accuracy: 99.911, Time: 21.55
[2020-12-15 21:51:51,709][__main__][INFO] - [36160] Loss: 0.111, Running accuracy: 99.911, Time: 21.54
[2020-12-15 21:52:15,583][__main__][INFO] - [36480] Loss: 0.105, Running accuracy: 99.911, Time: 23.87
[2020-12-15 21:52:38,515][__main__][INFO] - [36800] Loss: 0.058, Running accuracy: 99.911, Time: 22.93
[2020-12-15 21:52:59,771][__main__][INFO] - [37120] Loss: 0.050, Running accuracy: 99.912, Time: 21.25
[2020-12-15 21:53:21,897][__main__][INFO] - [37440] Loss: 0.117, Running accuracy: 99.911, Time: 22.13
[2020-12-15 21:53:43,530][__main__][INFO] - [37760] Loss: 0.072, Running accuracy: 99.911, Time: 21.63
[2020-12-15 21:54:07,174][__main__][INFO] - [38080] Loss: 0.114, Running accuracy: 99.911, Time: 23.64
[2020-12-15 21:54:30,202][__main__][INFO] - [38400] Loss: 0.136, Running accuracy: 99.910, Time: 23.03
[2020-12-15 21:54:58,073][__main__][INFO] - [38720] Loss: 0.099, Running accuracy: 99.911, Time: 27.87
[2020-12-15 21:55:18,970][__main__][INFO] - [39040] Loss: 0.110, Running accuracy: 99.910, Time: 20.90
[2020-12-15 21:55:42,205][__main__][INFO] - [39360] Loss: 0.041, Running accuracy: 99.911, Time: 23.23
[2020-12-15 21:56:03,280][__main__][INFO] - [39680] Loss: 0.151, Running accuracy: 99.910, Time: 21.07
[2020-12-15 21:56:11,398][__main__][INFO] - Action accuracy: 99.910, Loss: 0.100
[2020-12-15 21:56:11,399][__main__][INFO] - Validating..
[2020-12-15 21:56:45,695][test][INFO] - Time elapsed: 32.149447
[2020-12-15 21:56:45,698][__main__][INFO] - Validation F1 score: 95.610, Exact match: 56.350, Precision: 95.560, Recall: 95.660
[2020-12-15 21:57:25,774][__main__][INFO] - Checkpoint saved to checkpoints/model_latest.pth
[2020-12-15 21:57:26,827][__main__][INFO] - Epoch #42
[2020-12-15 21:57:26,828][__main__][INFO] - Training..
[2020-12-15 21:57:49,269][__main__][INFO] - [320] Loss: 0.102, Running accuracy: 99.879, Time: 21.25
[2020-12-15 21:58:10,841][__main__][INFO] - [640] Loss: 0.112, Running accuracy: 99.896, Time: 21.57
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[2020-12-15 22:42:05,314][__main__][INFO] - [38400] Loss: 0.113, Running accuracy: 99.905, Time: 22.49
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[2020-12-15 22:42:50,658][__main__][INFO] - [39040] Loss: 0.138, Running accuracy: 99.905, Time: 24.43
[2020-12-15 22:43:16,357][__main__][INFO] - [39360] Loss: 0.103, Running accuracy: 99.906, Time: 25.70
[2020-12-15 22:43:42,526][__main__][INFO] - [39680] Loss: 0.060, Running accuracy: 99.906, Time: 26.17
[2020-12-15 22:43:54,346][__main__][INFO] - Action accuracy: 99.906, Loss: 0.103
[2020-12-15 22:43:54,349][__main__][INFO] - Validating..
[2020-12-15 22:44:37,999][test][INFO] - Time elapsed: 41.238259
[2020-12-15 22:44:38,006][__main__][INFO] - Validation F1 score: 95.650, Exact match: 56.350, Precision: 95.590, Recall: 95.700
[2020-12-15 22:45:16,856][__main__][INFO] - Checkpoint saved to checkpoints/model_latest.pth
[2020-12-15 22:45:17,788][__main__][INFO] - Epoch #43
[2020-12-15 22:45:17,788][__main__][INFO] - Training..
[2020-12-15 22:45:47,518][__main__][INFO] - [320] Loss: 0.084, Running accuracy: 99.886, Time: 28.06
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[2020-12-15 23:36:58,411][__main__][INFO] - [39680] Loss: 0.075, Running accuracy: 99.907, Time: 23.59
[2020-12-15 23:37:07,727][__main__][INFO] - Action accuracy: 99.907, Loss: 0.099
[2020-12-15 23:37:07,728][__main__][INFO] - Validating..
[2020-12-15 23:37:48,922][test][INFO] - Time elapsed: 38.918139
[2020-12-15 23:37:48,927][__main__][INFO] - Validation F1 score: 95.590, Exact match: 55.760, Precision: 95.520, Recall: 95.660
[2020-12-15 23:38:32,400][__main__][INFO] - Checkpoint saved to checkpoints/model_latest.pth
[2020-12-15 23:38:33,303][__main__][INFO] - Epoch #44
[2020-12-15 23:38:33,304][__main__][INFO] - Training..
[2020-12-15 23:38:59,425][__main__][INFO] - [320] Loss: 0.082, Running accuracy: 99.935, Time: 24.86
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[2020-12-16 00:26:45,480][__main__][INFO] - [37120] Loss: 0.090, Running accuracy: 99.906, Time: 25.26
[2020-12-16 00:27:08,325][__main__][INFO] - [37440] Loss: 0.095, Running accuracy: 99.906, Time: 22.84
[2020-12-16 00:27:35,397][__main__][INFO] - [37760] Loss: 0.143, Running accuracy: 99.906, Time: 27.07
[2020-12-16 00:27:59,736][__main__][INFO] - [38080] Loss: 0.088, Running accuracy: 99.906, Time: 24.34
[2020-12-16 00:28:26,112][__main__][INFO] - [38400] Loss: 0.115, Running accuracy: 99.906, Time: 26.37
[2020-12-16 00:28:54,288][__main__][INFO] - [38720] Loss: 0.134, Running accuracy: 99.905, Time: 28.17
[2020-12-16 00:29:20,702][__main__][INFO] - [39040] Loss: 0.086, Running accuracy: 99.906, Time: 26.41
[2020-12-16 00:29:45,155][__main__][INFO] - [39360] Loss: 0.202, Running accuracy: 99.905, Time: 24.45
[2020-12-16 00:30:08,686][__main__][INFO] - [39680] Loss: 0.049, Running accuracy: 99.905, Time: 23.53
[2020-12-16 00:30:17,845][__main__][INFO] - Action accuracy: 99.905, Loss: 0.102
[2020-12-16 00:30:17,846][__main__][INFO] - Validating..
[2020-12-16 00:30:55,690][test][INFO] - Time elapsed: 35.535132
[2020-12-16 00:30:55,695][__main__][INFO] - Validation F1 score: 95.600, Exact match: 56.120, Precision: 95.550, Recall: 95.660
Epoch    45: reducing learning rate of group 0 to 1.5625e-07.
[2020-12-16 00:31:37,473][__main__][INFO] - Checkpoint saved to checkpoints/model_latest.pth
[2020-12-16 00:31:38,580][__main__][INFO] - Epoch #45
[2020-12-16 00:31:38,580][__main__][INFO] - Training..
[2020-12-16 00:32:05,820][__main__][INFO] - [320] Loss: 0.062, Running accuracy: 99.936, Time: 25.89
[2020-12-16 00:32:29,976][__main__][INFO] - [640] Loss: 0.101, Running accuracy: 99.903, Time: 24.15
[2020-12-16 00:32:55,085][__main__][INFO] - [960] Loss: 0.071, Running accuracy: 99.908, Time: 25.11
[2020-12-16 00:33:23,149][__main__][INFO] - [1280] Loss: 0.055, Running accuracy: 99.917, Time: 28.06
[2020-12-16 00:33:46,599][__main__][INFO] - [1600] Loss: 0.061, Running accuracy: 99.921, Time: 23.45
[2020-12-16 00:34:10,673][__main__][INFO] - [1920] Loss: 0.082, Running accuracy: 99.919, Time: 24.07
[2020-12-16 00:34:35,636][__main__][INFO] - [2240] Loss: 0.077, Running accuracy: 99.920, Time: 24.96
[2020-12-16 00:35:00,206][__main__][INFO] - [2560] Loss: 0.045, Running accuracy: 99.921, Time: 24.57
[2020-12-16 00:35:24,140][__main__][INFO] - [2880] Loss: 0.073, Running accuracy: 99.924, Time: 23.93
[2020-12-16 00:35:47,733][__main__][INFO] - [3200] Loss: 0.081, Running accuracy: 99.925, Time: 23.59
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[2020-12-16 00:36:35,441][__main__][INFO] - [3840] Loss: 0.134, Running accuracy: 99.919, Time: 23.43
[2020-12-16 00:37:01,215][__main__][INFO] - [4160] Loss: 0.121, Running accuracy: 99.919, Time: 25.77
[2020-12-16 00:37:23,736][__main__][INFO] - [4480] Loss: 0.097, Running accuracy: 99.914, Time: 22.52
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[2020-12-16 00:45:04,142][__main__][INFO] - [10240] Loss: 0.093, Running accuracy: 99.917, Time: 27.43
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[2020-12-16 00:46:42,779][__main__][INFO] - [11520] Loss: 0.080, Running accuracy: 99.915, Time: 25.63
[2020-12-16 00:47:08,826][__main__][INFO] - [11840] Loss: 0.110, Running accuracy: 99.912, Time: 26.05
[2020-12-16 00:47:33,829][__main__][INFO] - [12160] Loss: 0.081, Running accuracy: 99.913, Time: 25.00
[2020-12-16 00:47:57,009][__main__][INFO] - [12480] Loss: 0.141, Running accuracy: 99.911, Time: 23.18
[2020-12-16 00:48:21,574][__main__][INFO] - [12800] Loss: 0.070, Running accuracy: 99.912, Time: 24.56
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[2020-12-16 00:49:35,935][__main__][INFO] - [13760] Loss: 0.122, Running accuracy: 99.913, Time: 25.36
[2020-12-16 00:49:58,694][__main__][INFO] - [14080] Loss: 0.085, Running accuracy: 99.911, Time: 22.76
[2020-12-16 00:50:24,743][__main__][INFO] - [14400] Loss: 0.087, Running accuracy: 99.912, Time: 26.05
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[2020-12-16 00:52:29,555][__main__][INFO] - [16000] Loss: 0.119, Running accuracy: 99.909, Time: 25.09
[2020-12-16 00:52:53,514][__main__][INFO] - [16320] Loss: 0.161, Running accuracy: 99.908, Time: 23.96
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[2020-12-16 00:54:54,426][__main__][INFO] - [17920] Loss: 0.094, Running accuracy: 99.910, Time: 25.96
[2020-12-16 00:55:17,982][__main__][INFO] - [18240] Loss: 0.066, Running accuracy: 99.911, Time: 23.55
[2020-12-16 00:55:42,941][__main__][INFO] - [18560] Loss: 0.085, Running accuracy: 99.911, Time: 24.96
[2020-12-16 00:56:05,800][__main__][INFO] - [18880] Loss: 0.046, Running accuracy: 99.911, Time: 22.86
[2020-12-16 00:56:35,350][__main__][INFO] - [19200] Loss: 0.057, Running accuracy: 99.912, Time: 29.55
[2020-12-16 00:57:00,685][__main__][INFO] - [19520] Loss: 0.075, Running accuracy: 99.912, Time: 25.33
[2020-12-16 00:57:26,780][__main__][INFO] - [19840] Loss: 0.039, Running accuracy: 99.913, Time: 26.09
[2020-12-16 00:57:55,051][__main__][INFO] - [20160] Loss: 0.060, Running accuracy: 99.913, Time: 28.27
[2020-12-16 00:58:18,397][__main__][INFO] - [20480] Loss: 0.058, Running accuracy: 99.914, Time: 23.34
[2020-12-16 00:58:41,389][__main__][INFO] - [20800] Loss: 0.040, Running accuracy: 99.915, Time: 22.99
[2020-12-16 00:59:06,161][__main__][INFO] - [21120] Loss: 0.119, Running accuracy: 99.915, Time: 24.76
[2020-12-16 00:59:30,843][__main__][INFO] - [21440] Loss: 0.090, Running accuracy: 99.915, Time: 24.68
[2020-12-16 00:59:53,889][__main__][INFO] - [21760] Loss: 0.060, Running accuracy: 99.915, Time: 23.04
[2020-12-16 01:00:18,192][__main__][INFO] - [22080] Loss: 0.080, Running accuracy: 99.915, Time: 24.30
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[2020-12-16 01:01:57,068][__main__][INFO] - [23360] Loss: 0.113, Running accuracy: 99.912, Time: 23.45
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[2020-12-16 01:04:27,124][__main__][INFO] - [25280] Loss: 0.066, Running accuracy: 99.913, Time: 26.05
[2020-12-16 01:04:51,958][__main__][INFO] - [25600] Loss: 0.097, Running accuracy: 99.913, Time: 24.83
[2020-12-16 01:05:16,191][__main__][INFO] - [25920] Loss: 0.106, Running accuracy: 99.913, Time: 24.23
[2020-12-16 01:05:40,541][__main__][INFO] - [26240] Loss: 0.092, Running accuracy: 99.912, Time: 24.35
[2020-12-16 01:06:04,064][__main__][INFO] - [26560] Loss: 0.106, Running accuracy: 99.912, Time: 23.52
[2020-12-16 01:06:31,972][__main__][INFO] - [26880] Loss: 0.064, Running accuracy: 99.912, Time: 27.91
[2020-12-16 01:06:56,835][__main__][INFO] - [27200] Loss: 0.072, Running accuracy: 99.912, Time: 24.86
[2020-12-16 01:07:23,228][__main__][INFO] - [27520] Loss: 0.083, Running accuracy: 99.912, Time: 26.39
[2020-12-16 01:07:47,638][__main__][INFO] - [27840] Loss: 0.093, Running accuracy: 99.912, Time: 24.41
[2020-12-16 01:08:16,837][__main__][INFO] - [28160] Loss: 0.086, Running accuracy: 99.912, Time: 29.20
[2020-12-16 01:08:40,688][__main__][INFO] - [28480] Loss: 0.212, Running accuracy: 99.911, Time: 23.68
[2020-12-16 01:09:04,738][__main__][INFO] - [28800] Loss: 0.114, Running accuracy: 99.911, Time: 24.05
[2020-12-16 01:09:28,955][__main__][INFO] - [29120] Loss: 0.093, Running accuracy: 99.911, Time: 24.22
[2020-12-16 01:09:54,795][__main__][INFO] - [29440] Loss: 0.044, Running accuracy: 99.912, Time: 25.84
[2020-12-16 01:10:21,899][__main__][INFO] - [29760] Loss: 0.130, Running accuracy: 99.911, Time: 27.10
[2020-12-16 01:10:46,546][__main__][INFO] - [30080] Loss: 0.097, Running accuracy: 99.911, Time: 24.64
[2020-12-16 01:11:09,739][__main__][INFO] - [30400] Loss: 0.097, Running accuracy: 99.911, Time: 23.19
[2020-12-16 01:11:33,838][__main__][INFO] - [30720] Loss: 0.044, Running accuracy: 99.912, Time: 24.10
[2020-12-16 01:11:59,439][__main__][INFO] - [31040] Loss: 0.117, Running accuracy: 99.911, Time: 25.60
[2020-12-16 01:12:23,222][__main__][INFO] - [31360] Loss: 0.068, Running accuracy: 99.912, Time: 23.78
[2020-12-16 01:12:47,813][__main__][INFO] - [31680] Loss: 0.053, Running accuracy: 99.912, Time: 24.59
[2020-12-16 01:13:11,954][__main__][INFO] - [32000] Loss: 0.100, Running accuracy: 99.912, Time: 24.14
[2020-12-16 01:13:35,866][__main__][INFO] - [32320] Loss: 0.087, Running accuracy: 99.912, Time: 23.91
[2020-12-16 01:14:04,843][__main__][INFO] - [32640] Loss: 0.069, Running accuracy: 99.913, Time: 28.97
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[2020-12-16 01:14:53,345][__main__][INFO] - [33280] Loss: 0.048, Running accuracy: 99.913, Time: 24.53
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[2020-12-16 01:16:06,262][__main__][INFO] - [34240] Loss: 0.075, Running accuracy: 99.914, Time: 24.08
[2020-12-16 01:16:30,328][__main__][INFO] - [34560] Loss: 0.059, Running accuracy: 99.914, Time: 24.06
[2020-12-16 01:16:54,810][__main__][INFO] - [34880] Loss: 0.087, Running accuracy: 99.914, Time: 24.48
[2020-12-16 01:17:18,316][__main__][INFO] - [35200] Loss: 0.105, Running accuracy: 99.913, Time: 23.51
[2020-12-16 01:17:44,272][__main__][INFO] - [35520] Loss: 0.053, Running accuracy: 99.914, Time: 25.96
[2020-12-16 01:18:09,352][__main__][INFO] - [35840] Loss: 0.105, Running accuracy: 99.914, Time: 25.08
[2020-12-16 01:18:35,908][__main__][INFO] - [36160] Loss: 0.065, Running accuracy: 99.914, Time: 26.56
[2020-12-16 01:19:02,058][__main__][INFO] - [36480] Loss: 0.070, Running accuracy: 99.914, Time: 26.15
[2020-12-16 01:19:26,590][__main__][INFO] - [36800] Loss: 0.084, Running accuracy: 99.914, Time: 24.53
[2020-12-16 01:19:54,956][__main__][INFO] - [37120] Loss: 0.087, Running accuracy: 99.914, Time: 28.37
[2020-12-16 01:20:24,533][__main__][INFO] - [37440] Loss: 0.145, Running accuracy: 99.913, Time: 29.58
[2020-12-16 01:20:48,709][__main__][INFO] - [37760] Loss: 0.055, Running accuracy: 99.914, Time: 24.18
[2020-12-16 01:21:13,227][__main__][INFO] - [38080] Loss: 0.113, Running accuracy: 99.913, Time: 24.52
[2020-12-16 01:21:38,349][__main__][INFO] - [38400] Loss: 0.075, Running accuracy: 99.912, Time: 25.12
[2020-12-16 01:22:00,385][__main__][INFO] - [38720] Loss: 0.113, Running accuracy: 99.912, Time: 22.03
[2020-12-16 01:22:29,170][__main__][INFO] - [39040] Loss: 0.083, Running accuracy: 99.913, Time: 28.78
[2020-12-16 01:22:54,515][__main__][INFO] - [39360] Loss: 0.201, Running accuracy: 99.912, Time: 25.34
[2020-12-16 01:23:20,485][__main__][INFO] - [39680] Loss: 0.070, Running accuracy: 99.912, Time: 25.97
[2020-12-16 01:23:31,043][__main__][INFO] - Action accuracy: 99.912, Loss: 0.097
[2020-12-16 01:23:31,044][__main__][INFO] - Validating..
[2020-12-16 01:24:12,417][test][INFO] - Time elapsed: 39.141340
[2020-12-16 01:24:12,421][__main__][INFO] - Validation F1 score: 95.630, Exact match: 56.120, Precision: 95.570, Recall: 95.690
[2020-12-16 01:24:53,935][__main__][INFO] - Checkpoint saved to checkpoints/model_latest.pth
[2020-12-16 01:24:54,826][__main__][INFO] - Epoch #46
[2020-12-16 01:24:54,827][__main__][INFO] - Training..
[2020-12-16 01:25:19,263][__main__][INFO] - [320] Loss: 0.077, Running accuracy: 99.905, Time: 23.04
[2020-12-16 01:25:43,312][__main__][INFO] - [640] Loss: 0.069, Running accuracy: 99.920, Time: 24.05
[2020-12-16 01:26:06,201][__main__][INFO] - [960] Loss: 0.110, Running accuracy: 99.905, Time: 22.89
[2020-12-16 01:26:29,800][__main__][INFO] - [1280] Loss: 0.124, Running accuracy: 99.892, Time: 23.59
[2020-12-16 01:26:55,104][__main__][INFO] - [1600] Loss: 0.062, Running accuracy: 99.901, Time: 25.30
[2020-12-16 01:27:19,597][__main__][INFO] - [1920] Loss: 0.071, Running accuracy: 99.910, Time: 24.49
[2020-12-16 01:27:43,407][__main__][INFO] - [2240] Loss: 0.074, Running accuracy: 99.911, Time: 23.81
[2020-12-16 01:28:07,957][__main__][INFO] - [2560] Loss: 0.091, Running accuracy: 99.915, Time: 24.55
[2020-12-16 01:28:34,100][__main__][INFO] - [2880] Loss: 0.073, Running accuracy: 99.919, Time: 26.14
[2020-12-16 01:28:57,544][__main__][INFO] - [3200] Loss: 0.093, Running accuracy: 99.919, Time: 23.44
[2020-12-16 01:29:23,037][__main__][INFO] - [3520] Loss: 0.064, Running accuracy: 99.921, Time: 25.49
[2020-12-16 01:29:46,392][__main__][INFO] - [3840] Loss: 0.099, Running accuracy: 99.923, Time: 23.35
[2020-12-16 01:30:12,908][__main__][INFO] - [4160] Loss: 0.045, Running accuracy: 99.925, Time: 26.51
[2020-12-16 01:30:37,143][__main__][INFO] - [4480] Loss: 0.047, Running accuracy: 99.928, Time: 24.23
[2020-12-16 01:31:05,645][__main__][INFO] - [4800] Loss: 0.050, Running accuracy: 99.928, Time: 28.50
[2020-12-16 01:31:30,504][__main__][INFO] - [5120] Loss: 0.105, Running accuracy: 99.924, Time: 24.86
[2020-12-16 01:31:54,291][__main__][INFO] - [5440] Loss: 0.078, Running accuracy: 99.923, Time: 23.79
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[2020-12-16 02:15:36,207][__main__][INFO] - [39040] Loss: 0.137, Running accuracy: 99.916, Time: 24.72
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[2020-12-16 02:16:27,157][__main__][INFO] - [39680] Loss: 0.076, Running accuracy: 99.917, Time: 23.69
[2020-12-16 02:16:36,925][__main__][INFO] - Action accuracy: 99.917, Loss: 0.093
[2020-12-16 02:16:36,926][__main__][INFO] - Validating..
[2020-12-16 02:17:18,446][test][INFO] - Time elapsed: 39.246629
[2020-12-16 02:17:18,451][__main__][INFO] - Validation F1 score: 95.640, Exact match: 56.120, Precision: 95.590, Recall: 95.700
[2020-12-16 02:18:05,118][__main__][INFO] - Checkpoint saved to checkpoints/model_latest.pth
[2020-12-16 02:18:06,074][__main__][INFO] - Epoch #47
[2020-12-16 02:18:06,075][__main__][INFO] - Training..
[2020-12-16 02:18:32,834][__main__][INFO] - [320] Loss: 0.045, Running accuracy: 99.934, Time: 25.42
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[2020-12-16 03:08:20,935][__main__][INFO] - [38720] Loss: 0.068, Running accuracy: 99.913, Time: 23.55
[2020-12-16 03:08:45,863][__main__][INFO] - [39040] Loss: 0.076, Running accuracy: 99.913, Time: 24.93
[2020-12-16 03:09:12,185][__main__][INFO] - [39360] Loss: 0.122, Running accuracy: 99.913, Time: 26.32
[2020-12-16 03:09:34,985][__main__][INFO] - [39680] Loss: 0.076, Running accuracy: 99.913, Time: 22.80
[2020-12-16 03:09:45,256][__main__][INFO] - Action accuracy: 99.913, Loss: 0.095
[2020-12-16 03:09:45,257][__main__][INFO] - Validating..
[2020-12-16 03:10:22,373][test][INFO] - Time elapsed: 35.079256
[2020-12-16 03:10:23,279][__main__][INFO] - Validation F1 score: 95.580, Exact match: 56.060, Precision: 95.510, Recall: 95.650
[2020-12-16 03:11:07,443][__main__][INFO] - Checkpoint saved to checkpoints/model_latest.pth
[2020-12-16 03:11:08,526][__main__][INFO] - Epoch #48
[2020-12-16 03:11:08,526][__main__][INFO] - Training..
[2020-12-16 03:11:33,660][__main__][INFO] - [320] Loss: 0.077, Running accuracy: 99.883, Time: 23.91
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[2020-12-16 03:58:35,187][__main__][INFO] - [36480] Loss: 0.140, Running accuracy: 99.910, Time: 25.74
[2020-12-16 03:59:01,295][__main__][INFO] - [36800] Loss: 0.086, Running accuracy: 99.910, Time: 26.11
[2020-12-16 03:59:24,288][__main__][INFO] - [37120] Loss: 0.147, Running accuracy: 99.909, Time: 22.99
[2020-12-16 03:59:48,886][__main__][INFO] - [37440] Loss: 0.143, Running accuracy: 99.909, Time: 24.60
[2020-12-16 04:00:18,943][__main__][INFO] - [37760] Loss: 0.064, Running accuracy: 99.910, Time: 30.06
[2020-12-16 04:00:43,301][__main__][INFO] - [38080] Loss: 0.076, Running accuracy: 99.910, Time: 24.36
[2020-12-16 04:01:08,167][__main__][INFO] - [38400] Loss: 0.061, Running accuracy: 99.910, Time: 24.86
[2020-12-16 04:01:33,579][__main__][INFO] - [38720] Loss: 0.124, Running accuracy: 99.910, Time: 25.41
[2020-12-16 04:01:56,901][__main__][INFO] - [39040] Loss: 0.066, Running accuracy: 99.910, Time: 23.32
[2020-12-16 04:02:22,334][__main__][INFO] - [39360] Loss: 0.109, Running accuracy: 99.910, Time: 25.43
[2020-12-16 04:02:45,054][__main__][INFO] - [39680] Loss: 0.100, Running accuracy: 99.910, Time: 22.72
[2020-12-16 04:02:55,006][__main__][INFO] - Action accuracy: 99.910, Loss: 0.096
[2020-12-16 04:02:55,009][__main__][INFO] - Validating..
[2020-12-16 04:03:32,624][test][INFO] - Time elapsed: 35.227364
[2020-12-16 04:03:32,629][__main__][INFO] - Validation F1 score: 95.620, Exact match: 55.940, Precision: 95.540, Recall: 95.690
[2020-12-16 04:04:14,378][__main__][INFO] - Checkpoint saved to checkpoints/model_latest.pth
[2020-12-16 04:04:15,478][__main__][INFO] - Epoch #49
[2020-12-16 04:04:15,478][__main__][INFO] - Training..
[2020-12-16 04:04:47,793][__main__][INFO] - [320] Loss: 0.089, Running accuracy: 99.896, Time: 30.92
[2020-12-16 04:05:11,658][__main__][INFO] - [640] Loss: 0.072, Running accuracy: 99.914, Time: 23.86
[2020-12-16 04:05:35,151][__main__][INFO] - [960] Loss: 0.118, Running accuracy: 99.907, Time: 23.49
[2020-12-16 04:05:59,086][__main__][INFO] - [1280] Loss: 0.068, Running accuracy: 99.908, Time: 23.93
[2020-12-16 04:06:23,888][__main__][INFO] - [1600] Loss: 0.054, Running accuracy: 99.920, Time: 24.80
[2020-12-16 04:06:47,874][__main__][INFO] - [1920] Loss: 0.132, Running accuracy: 99.914, Time: 23.99
[2020-12-16 04:07:15,862][__main__][INFO] - [2240] Loss: 0.163, Running accuracy: 99.904, Time: 27.99
[2020-12-16 04:07:41,664][__main__][INFO] - [2560] Loss: 0.066, Running accuracy: 99.908, Time: 25.76
[2020-12-16 04:08:07,014][__main__][INFO] - [2880] Loss: 0.077, Running accuracy: 99.909, Time: 25.35
[2020-12-16 04:08:29,868][__main__][INFO] - [3200] Loss: 0.060, Running accuracy: 99.914, Time: 22.85
[2020-12-16 04:08:57,499][__main__][INFO] - [3520] Loss: 0.085, Running accuracy: 99.914, Time: 27.63
[2020-12-16 04:09:20,758][__main__][INFO] - [3840] Loss: 0.062, Running accuracy: 99.916, Time: 23.26
[2020-12-16 04:09:43,882][__main__][INFO] - [4160] Loss: 0.056, Running accuracy: 99.914, Time: 23.12
[2020-12-16 04:10:08,774][__main__][INFO] - [4480] Loss: 0.045, Running accuracy: 99.916, Time: 24.89
[2020-12-16 04:10:35,355][__main__][INFO] - [4800] Loss: 0.067, Running accuracy: 99.918, Time: 26.06
[2020-12-16 04:10:58,905][__main__][INFO] - [5120] Loss: 0.066, Running accuracy: 99.919, Time: 23.55
[2020-12-16 04:11:24,775][__main__][INFO] - [5440] Loss: 0.077, Running accuracy: 99.917, Time: 25.87
[2020-12-16 04:11:47,847][__main__][INFO] - [5760] Loss: 0.039, Running accuracy: 99.921, Time: 23.07
[2020-12-16 04:12:13,085][__main__][INFO] - [6080] Loss: 0.146, Running accuracy: 99.916, Time: 24.75
[2020-12-16 04:12:38,098][__main__][INFO] - [6400] Loss: 0.099, Running accuracy: 99.915, Time: 25.01
[2020-12-16 04:13:03,011][__main__][INFO] - [6720] Loss: 0.070, Running accuracy: 99.917, Time: 24.91
[2020-12-16 04:13:26,061][__main__][INFO] - [7040] Loss: 0.106, Running accuracy: 99.916, Time: 23.05
[2020-12-16 04:13:51,218][__main__][INFO] - [7360] Loss: 0.113, Running accuracy: 99.914, Time: 25.16
[2020-12-16 04:14:14,702][__main__][INFO] - [7680] Loss: 0.065, Running accuracy: 99.915, Time: 23.48
[2020-12-16 04:14:39,267][__main__][INFO] - [8000] Loss: 0.129, Running accuracy: 99.914, Time: 24.56
[2020-12-16 04:15:03,558][__main__][INFO] - [8320] Loss: 0.096, Running accuracy: 99.913, Time: 24.25
[2020-12-16 04:15:28,266][__main__][INFO] - [8640] Loss: 0.080, Running accuracy: 99.915, Time: 24.71
[2020-12-16 04:15:52,826][__main__][INFO] - [8960] Loss: 0.070, Running accuracy: 99.915, Time: 24.56
[2020-12-16 04:16:20,032][__main__][INFO] - [9280] Loss: 0.109, Running accuracy: 99.914, Time: 27.21
[2020-12-16 04:16:44,445][__main__][INFO] - [9600] Loss: 0.042, Running accuracy: 99.916, Time: 24.41
[2020-12-16 04:17:07,927][__main__][INFO] - [9920] Loss: 0.100, Running accuracy: 99.916, Time: 23.48
[2020-12-16 04:17:32,241][__main__][INFO] - [10240] Loss: 0.061, Running accuracy: 99.916, Time: 24.31
[2020-12-16 04:17:57,717][__main__][INFO] - [10560] Loss: 0.071, Running accuracy: 99.918, Time: 25.48
[2020-12-16 04:18:21,722][__main__][INFO] - [10880] Loss: 0.043, Running accuracy: 99.918, Time: 24.00
[2020-12-16 04:18:44,767][__main__][INFO] - [11200] Loss: 0.113, Running accuracy: 99.917, Time: 23.04
[2020-12-16 04:19:10,660][__main__][INFO] - [11520] Loss: 0.056, Running accuracy: 99.917, Time: 25.89
[2020-12-16 04:19:34,226][__main__][INFO] - [11840] Loss: 0.080, Running accuracy: 99.917, Time: 23.57
[2020-12-16 04:19:58,484][__main__][INFO] - [12160] Loss: 0.040, Running accuracy: 99.918, Time: 24.26
[2020-12-16 04:20:22,865][__main__][INFO] - [12480] Loss: 0.040, Running accuracy: 99.919, Time: 24.38
[2020-12-16 04:20:48,346][__main__][INFO] - [12800] Loss: 0.150, Running accuracy: 99.919, Time: 25.48
[2020-12-16 04:21:13,994][__main__][INFO] - [13120] Loss: 0.060, Running accuracy: 99.920, Time: 25.65
[2020-12-16 04:21:37,146][__main__][INFO] - [13440] Loss: 0.052, Running accuracy: 99.921, Time: 23.15
[2020-12-16 04:22:03,333][__main__][INFO] - [13760] Loss: 0.105, Running accuracy: 99.920, Time: 26.19
[2020-12-16 04:22:28,075][__main__][INFO] - [14080] Loss: 0.081, Running accuracy: 99.920, Time: 24.74
[2020-12-16 04:22:52,477][__main__][INFO] - [14400] Loss: 0.035, Running accuracy: 99.921, Time: 24.40
[2020-12-16 04:23:16,251][__main__][INFO] - [14720] Loss: 0.095, Running accuracy: 99.921, Time: 23.76
[2020-12-16 04:23:42,087][__main__][INFO] - [15040] Loss: 0.072, Running accuracy: 99.921, Time: 25.83
[2020-12-16 04:24:06,544][__main__][INFO] - [15360] Loss: 0.092, Running accuracy: 99.921, Time: 24.46
[2020-12-16 04:24:30,394][__main__][INFO] - [15680] Loss: 0.113, Running accuracy: 99.920, Time: 23.85
[2020-12-16 04:24:54,968][__main__][INFO] - [16000] Loss: 0.096, Running accuracy: 99.920, Time: 24.57
[2020-12-16 04:25:20,166][__main__][INFO] - [16320] Loss: 0.085, Running accuracy: 99.920, Time: 25.20
[2020-12-16 04:25:44,469][__main__][INFO] - [16640] Loss: 0.091, Running accuracy: 99.919, Time: 24.30
[2020-12-16 04:26:13,401][__main__][INFO] - [16960] Loss: 0.130, Running accuracy: 99.918, Time: 28.93
[2020-12-16 04:26:39,279][__main__][INFO] - [17280] Loss: 0.147, Running accuracy: 99.917, Time: 25.88
[2020-12-16 04:27:06,014][__main__][INFO] - [17600] Loss: 0.138, Running accuracy: 99.917, Time: 26.73
[2020-12-16 04:27:28,545][__main__][INFO] - [17920] Loss: 0.067, Running accuracy: 99.917, Time: 22.53
[2020-12-16 04:27:55,957][__main__][INFO] - [18240] Loss: 0.070, Running accuracy: 99.918, Time: 27.41
[2020-12-16 04:28:26,130][__main__][INFO] - [18560] Loss: 0.036, Running accuracy: 99.919, Time: 30.17
[2020-12-16 04:28:51,428][__main__][INFO] - [18880] Loss: 0.083, Running accuracy: 99.918, Time: 25.30
[2020-12-16 04:29:17,191][__main__][INFO] - [19200] Loss: 0.088, Running accuracy: 99.917, Time: 25.76
[2020-12-16 04:29:42,265][__main__][INFO] - [19520] Loss: 0.045, Running accuracy: 99.917, Time: 25.07
[2020-12-16 04:30:09,632][__main__][INFO] - [19840] Loss: 0.058, Running accuracy: 99.917, Time: 27.37
[2020-12-16 04:30:32,686][__main__][INFO] - [20160] Loss: 0.111, Running accuracy: 99.917, Time: 23.05
[2020-12-16 04:30:57,740][__main__][INFO] - [20480] Loss: 0.075, Running accuracy: 99.917, Time: 25.05
[2020-12-16 04:31:21,367][__main__][INFO] - [20800] Loss: 0.040, Running accuracy: 99.917, Time: 23.61
[2020-12-16 04:31:46,358][__main__][INFO] - [21120] Loss: 0.121, Running accuracy: 99.916, Time: 24.99
[2020-12-16 04:32:12,307][__main__][INFO] - [21440] Loss: 0.062, Running accuracy: 99.916, Time: 25.95
[2020-12-16 04:32:35,029][__main__][INFO] - [21760] Loss: 0.116, Running accuracy: 99.916, Time: 22.71
[2020-12-16 04:32:59,650][__main__][INFO] - [22080] Loss: 0.089, Running accuracy: 99.916, Time: 24.62
[2020-12-16 04:33:25,523][__main__][INFO] - [22400] Loss: 0.085, Running accuracy: 99.916, Time: 25.87
[2020-12-16 04:33:54,718][__main__][INFO] - [22720] Loss: 0.064, Running accuracy: 99.916, Time: 29.19
[2020-12-16 04:34:22,023][__main__][INFO] - [23040] Loss: 0.181, Running accuracy: 99.914, Time: 27.30
[2020-12-16 04:34:46,278][__main__][INFO] - [23360] Loss: 0.069, Running accuracy: 99.914, Time: 24.25
[2020-12-16 04:35:10,858][__main__][INFO] - [23680] Loss: 0.051, Running accuracy: 99.914, Time: 24.58
[2020-12-16 04:35:38,274][__main__][INFO] - [24000] Loss: 0.045, Running accuracy: 99.915, Time: 27.42
[2020-12-16 04:36:02,504][__main__][INFO] - [24320] Loss: 0.103, Running accuracy: 99.915, Time: 24.23
[2020-12-16 04:36:27,814][__main__][INFO] - [24640] Loss: 0.107, Running accuracy: 99.914, Time: 24.95
[2020-12-16 04:36:52,018][__main__][INFO] - [24960] Loss: 0.065, Running accuracy: 99.915, Time: 24.20
[2020-12-16 04:37:15,208][__main__][INFO] - [25280] Loss: 0.060, Running accuracy: 99.915, Time: 23.19
[2020-12-16 04:37:39,230][__main__][INFO] - [25600] Loss: 0.074, Running accuracy: 99.916, Time: 24.02
[2020-12-16 04:38:03,853][__main__][INFO] - [25920] Loss: 0.100, Running accuracy: 99.915, Time: 24.62
[2020-12-16 04:38:30,180][__main__][INFO] - [26240] Loss: 0.158, Running accuracy: 99.915, Time: 26.33
[2020-12-16 04:38:56,953][__main__][INFO] - [26560] Loss: 0.075, Running accuracy: 99.915, Time: 26.77
[2020-12-16 04:39:20,000][__main__][INFO] - [26880] Loss: 0.075, Running accuracy: 99.915, Time: 23.05
[2020-12-16 04:39:49,860][__main__][INFO] - [27200] Loss: 0.142, Running accuracy: 99.915, Time: 29.86
[2020-12-16 04:40:14,391][__main__][INFO] - [27520] Loss: 0.105, Running accuracy: 99.914, Time: 24.53
[2020-12-16 04:40:40,840][__main__][INFO] - [27840] Loss: 0.059, Running accuracy: 99.914, Time: 26.45
[2020-12-16 04:41:04,616][__main__][INFO] - [28160] Loss: 0.071, Running accuracy: 99.914, Time: 23.78
[2020-12-16 04:41:30,892][__main__][INFO] - [28480] Loss: 0.046, Running accuracy: 99.915, Time: 26.27
[2020-12-16 04:41:54,663][__main__][INFO] - [28800] Loss: 0.058, Running accuracy: 99.915, Time: 23.77
[2020-12-16 04:42:17,789][__main__][INFO] - [29120] Loss: 0.095, Running accuracy: 99.915, Time: 23.12
[2020-12-16 04:42:40,587][__main__][INFO] - [29440] Loss: 0.084, Running accuracy: 99.915, Time: 22.80
[2020-12-16 04:43:04,576][__main__][INFO] - [29760] Loss: 0.121, Running accuracy: 99.915, Time: 23.99
[2020-12-16 04:43:29,371][__main__][INFO] - [30080] Loss: 0.098, Running accuracy: 99.915, Time: 24.79
[2020-12-16 04:43:52,599][__main__][INFO] - [30400] Loss: 0.149, Running accuracy: 99.914, Time: 23.23
[2020-12-16 04:44:18,142][__main__][INFO] - [30720] Loss: 0.083, Running accuracy: 99.914, Time: 25.54
[2020-12-16 04:44:42,065][__main__][INFO] - [31040] Loss: 0.116, Running accuracy: 99.914, Time: 23.92
[2020-12-16 04:45:08,693][__main__][INFO] - [31360] Loss: 0.071, Running accuracy: 99.914, Time: 26.63
[2020-12-16 04:45:38,598][__main__][INFO] - [31680] Loss: 0.069, Running accuracy: 99.914, Time: 29.90
[2020-12-16 04:46:02,951][__main__][INFO] - [32000] Loss: 0.040, Running accuracy: 99.915, Time: 24.04
[2020-12-16 04:46:28,428][__main__][INFO] - [32320] Loss: 0.039, Running accuracy: 99.915, Time: 25.48
[2020-12-16 04:46:53,572][__main__][INFO] - [32640] Loss: 0.098, Running accuracy: 99.916, Time: 24.86
[2020-12-16 04:47:15,981][__main__][INFO] - [32960] Loss: 0.055, Running accuracy: 99.916, Time: 22.41
[2020-12-16 04:47:40,294][__main__][INFO] - [33280] Loss: 0.062, Running accuracy: 99.916, Time: 24.31
[2020-12-16 04:48:05,934][__main__][INFO] - [33600] Loss: 0.104, Running accuracy: 99.916, Time: 25.64
[2020-12-16 04:48:31,025][__main__][INFO] - [33920] Loss: 0.074, Running accuracy: 99.916, Time: 25.09
[2020-12-16 04:48:56,002][__main__][INFO] - [34240] Loss: 0.054, Running accuracy: 99.916, Time: 24.98
[2020-12-16 04:49:20,113][__main__][INFO] - [34560] Loss: 0.099, Running accuracy: 99.916, Time: 24.11
[2020-12-16 04:49:46,356][__main__][INFO] - [34880] Loss: 0.029, Running accuracy: 99.916, Time: 26.24
[2020-12-16 04:50:12,686][__main__][INFO] - [35200] Loss: 0.068, Running accuracy: 99.916, Time: 26.33
[2020-12-16 04:50:36,154][__main__][INFO] - [35520] Loss: 0.055, Running accuracy: 99.916, Time: 23.47
[2020-12-16 04:51:01,012][__main__][INFO] - [35840] Loss: 0.100, Running accuracy: 99.916, Time: 24.86
[2020-12-16 04:51:29,423][__main__][INFO] - [36160] Loss: 0.072, Running accuracy: 99.916, Time: 28.41
[2020-12-16 04:51:55,245][__main__][INFO] - [36480] Loss: 0.068, Running accuracy: 99.916, Time: 25.82
[2020-12-16 04:52:21,033][__main__][INFO] - [36800] Loss: 0.096, Running accuracy: 99.916, Time: 25.79
[2020-12-16 04:52:47,404][__main__][INFO] - [37120] Loss: 0.089, Running accuracy: 99.916, Time: 26.37
[2020-12-16 04:53:12,887][__main__][INFO] - [37440] Loss: 0.170, Running accuracy: 99.915, Time: 25.48
[2020-12-16 04:53:36,643][__main__][INFO] - [37760] Loss: 0.107, Running accuracy: 99.915, Time: 23.76
[2020-12-16 04:54:01,109][__main__][INFO] - [38080] Loss: 0.082, Running accuracy: 99.915, Time: 24.46
[2020-12-16 04:54:25,278][__main__][INFO] - [38400] Loss: 0.113, Running accuracy: 99.914, Time: 24.17
[2020-12-16 04:54:49,968][__main__][INFO] - [38720] Loss: 0.120, Running accuracy: 99.914, Time: 24.69
[2020-12-16 04:55:14,087][__main__][INFO] - [39040] Loss: 0.144, Running accuracy: 99.914, Time: 24.11
[2020-12-16 04:55:39,121][__main__][INFO] - [39360] Loss: 0.083, Running accuracy: 99.914, Time: 25.03
[2020-12-16 04:56:06,209][__main__][INFO] - [39680] Loss: 0.095, Running accuracy: 99.914, Time: 27.09
[2020-12-16 04:56:17,668][__main__][INFO] - Action accuracy: 99.914, Loss: 0.095
[2020-12-16 04:56:17,669][__main__][INFO] - Validating..
[2020-12-16 04:56:58,743][test][INFO] - Time elapsed: 39.110996
[2020-12-16 04:56:59,533][__main__][INFO] - Validation F1 score: 95.620, Exact match: 56.060, Precision: 95.550, Recall: 95.690
[2020-12-16 04:57:46,338][__main__][INFO] - Checkpoint saved to checkpoints/model_latest.pth
[2020-12-16 04:57:47,193][__main__][INFO] - Epoch #50
[2020-12-16 04:57:47,194][__main__][INFO] - Training..
[2020-12-16 04:58:13,109][__main__][INFO] - [320] Loss: 0.094, Running accuracy: 99.879, Time: 24.58
[2020-12-16 04:58:39,171][__main__][INFO] - [640] Loss: 0.042, Running accuracy: 99.928, Time: 26.04
[2020-12-16 04:59:02,737][__main__][INFO] - [960] Loss: 0.134, Running accuracy: 99.934, Time: 23.56
[2020-12-16 04:59:27,661][__main__][INFO] - [1280] Loss: 0.111, Running accuracy: 99.905, Time: 24.92
[2020-12-16 04:59:52,994][__main__][INFO] - [1600] Loss: 0.200, Running accuracy: 99.898, Time: 25.33
[2020-12-16 05:00:18,096][__main__][INFO] - [1920] Loss: 0.069, Running accuracy: 99.902, Time: 25.10
[2020-12-16 05:00:44,323][__main__][INFO] - [2240] Loss: 0.086, Running accuracy: 99.897, Time: 26.23
[2020-12-16 05:01:07,486][__main__][INFO] - [2560] Loss: 0.131, Running accuracy: 99.890, Time: 23.16
[2020-12-16 05:01:33,119][__main__][INFO] - [2880] Loss: 0.087, Running accuracy: 99.897, Time: 25.63
[2020-12-16 05:02:03,151][__main__][INFO] - [3200] Loss: 0.066, Running accuracy: 99.902, Time: 30.03
[2020-12-16 05:02:30,406][__main__][INFO] - [3520] Loss: 0.111, Running accuracy: 99.899, Time: 27.25
[2020-12-16 05:02:56,542][__main__][INFO] - [3840] Loss: 0.080, Running accuracy: 99.902, Time: 26.13
[2020-12-16 05:03:18,727][__main__][INFO] - [4160] Loss: 0.083, Running accuracy: 99.902, Time: 22.18
[2020-12-16 05:03:43,451][__main__][INFO] - [4480] Loss: 0.111, Running accuracy: 99.903, Time: 24.72
[2020-12-16 05:04:09,770][__main__][INFO] - [4800] Loss: 0.088, Running accuracy: 99.904, Time: 26.31
[2020-12-16 05:04:32,902][__main__][INFO] - [5120] Loss: 0.087, Running accuracy: 99.904, Time: 23.13
[2020-12-16 05:04:56,797][__main__][INFO] - [5440] Loss: 0.049, Running accuracy: 99.907, Time: 23.89
[2020-12-16 05:05:20,079][__main__][INFO] - [5760] Loss: 0.097, Running accuracy: 99.906, Time: 23.28
[2020-12-16 05:05:44,165][__main__][INFO] - [6080] Loss: 0.125, Running accuracy: 99.907, Time: 24.08
[2020-12-16 05:06:08,480][__main__][INFO] - [6400] Loss: 0.096, Running accuracy: 99.908, Time: 23.55
[2020-12-16 05:06:32,905][__main__][INFO] - [6720] Loss: 0.081, Running accuracy: 99.907, Time: 24.42
[2020-12-16 05:06:57,984][__main__][INFO] - [7040] Loss: 0.053, Running accuracy: 99.910, Time: 24.86
[2020-12-16 05:07:21,936][__main__][INFO] - [7360] Loss: 0.130, Running accuracy: 99.907, Time: 23.95
[2020-12-16 05:07:53,107][__main__][INFO] - [7680] Loss: 0.130, Running accuracy: 99.905, Time: 31.17
[2020-12-16 05:08:19,741][__main__][INFO] - [8000] Loss: 0.061, Running accuracy: 99.906, Time: 26.63
[2020-12-16 05:08:44,211][__main__][INFO] - [8320] Loss: 0.047, Running accuracy: 99.908, Time: 24.47
[2020-12-16 05:09:07,870][__main__][INFO] - [8640] Loss: 0.048, Running accuracy: 99.910, Time: 23.66
[2020-12-16 05:09:33,586][__main__][INFO] - [8960] Loss: 0.106, Running accuracy: 99.910, Time: 25.72
[2020-12-16 05:09:56,886][__main__][INFO] - [9280] Loss: 0.089, Running accuracy: 99.910, Time: 23.30
[2020-12-16 05:10:24,027][__main__][INFO] - [9600] Loss: 0.096, Running accuracy: 99.911, Time: 27.14
[2020-12-16 05:10:46,776][__main__][INFO] - [9920] Loss: 0.098, Running accuracy: 99.910, Time: 22.73
[2020-12-16 05:11:10,822][__main__][INFO] - [10240] Loss: 0.080, Running accuracy: 99.909, Time: 24.04
[2020-12-16 05:11:33,584][__main__][INFO] - [10560] Loss: 0.071, Running accuracy: 99.909, Time: 22.76
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[2020-12-16 05:49:43,648][__main__][INFO] - Action accuracy: 99.911, Loss: 0.096
[2020-12-16 05:49:43,649][__main__][INFO] - Validating..
[2020-12-16 05:50:20,615][test][INFO] - Time elapsed: 34.678940
[2020-12-16 05:50:20,619][__main__][INFO] - Validation F1 score: 95.620, Exact match: 56.120, Precision: 95.550, Recall: 95.690
Epoch    51: reducing learning rate of group 0 to 7.8125e-08.
[2020-12-16 05:51:00,618][__main__][INFO] - Checkpoint saved to checkpoints/model_latest.pth
[2020-12-16 05:51:01,489][__main__][INFO] - Epoch #51
[2020-12-16 05:51:01,490][__main__][INFO] - Training..
[2020-12-16 05:51:28,867][__main__][INFO] - [320] Loss: 0.112, Running accuracy: 99.837, Time: 26.00
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[2020-12-16 06:25:07,622][__main__][INFO] - [26240] Loss: 0.068, Running accuracy: 99.916, Time: 23.48
[2020-12-16 06:25:31,692][__main__][INFO] - [26560] Loss: 0.121, Running accuracy: 99.915, Time: 24.07
[2020-12-16 06:25:55,285][__main__][INFO] - [26880] Loss: 0.088, Running accuracy: 99.916, Time: 23.59
[2020-12-16 06:26:19,632][__main__][INFO] - [27200] Loss: 0.078, Running accuracy: 99.915, Time: 24.34
[2020-12-16 06:26:46,960][__main__][INFO] - [27520] Loss: 0.042, Running accuracy: 99.916, Time: 27.33
[2020-12-16 06:27:10,177][__main__][INFO] - [27840] Loss: 0.053, Running accuracy: 99.917, Time: 23.22
[2020-12-16 06:27:36,165][__main__][INFO] - [28160] Loss: 0.079, Running accuracy: 99.917, Time: 25.99
[2020-12-16 06:27:59,153][__main__][INFO] - [28480] Loss: 0.113, Running accuracy: 99.916, Time: 22.99
[2020-12-16 06:28:28,328][__main__][INFO] - [28800] Loss: 0.070, Running accuracy: 99.916, Time: 29.17
[2020-12-16 06:28:53,025][__main__][INFO] - [29120] Loss: 0.064, Running accuracy: 99.917, Time: 24.70
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[2020-12-16 06:39:36,199][__main__][INFO] - [37440] Loss: 0.094, Running accuracy: 99.917, Time: 26.50
[2020-12-16 06:40:07,312][__main__][INFO] - [37760] Loss: 0.083, Running accuracy: 99.917, Time: 31.11
[2020-12-16 06:40:32,003][__main__][INFO] - [38080] Loss: 0.074, Running accuracy: 99.918, Time: 24.69
[2020-12-16 06:40:56,867][__main__][INFO] - [38400] Loss: 0.166, Running accuracy: 99.918, Time: 24.86
[2020-12-16 06:41:21,821][__main__][INFO] - [38720] Loss: 0.076, Running accuracy: 99.918, Time: 24.95
[2020-12-16 06:41:48,724][__main__][INFO] - [39040] Loss: 0.100, Running accuracy: 99.917, Time: 26.90
[2020-12-16 06:42:14,047][__main__][INFO] - [39360] Loss: 0.082, Running accuracy: 99.918, Time: 25.32
[2020-12-16 06:42:38,786][__main__][INFO] - [39680] Loss: 0.087, Running accuracy: 99.918, Time: 24.74
[2020-12-16 06:42:49,253][__main__][INFO] - Action accuracy: 99.918, Loss: 0.095
[2020-12-16 06:42:49,255][__main__][INFO] - Validating..
[2020-12-16 06:43:26,311][test][INFO] - Time elapsed: 35.138450
[2020-12-16 06:43:26,315][__main__][INFO] - Validation F1 score: 95.610, Exact match: 56.120, Precision: 95.530, Recall: 95.690
[2020-12-16 06:44:08,421][__main__][INFO] - Checkpoint saved to checkpoints/model_latest.pth
[2020-12-16 06:44:09,433][__main__][INFO] - Epoch #52
[2020-12-16 06:44:09,433][__main__][INFO] - Training..
[2020-12-16 06:44:41,213][__main__][INFO] - [320] Loss: 0.045, Running accuracy: 99.932, Time: 30.47
[2020-12-16 06:45:04,232][__main__][INFO] - [640] Loss: 0.041, Running accuracy: 99.953, Time: 23.02
[2020-12-16 06:45:28,044][__main__][INFO] - [960] Loss: 0.104, Running accuracy: 99.924, Time: 23.81
[2020-12-16 06:45:52,051][__main__][INFO] - [1280] Loss: 0.087, Running accuracy: 99.927, Time: 24.01
[2020-12-16 06:46:16,687][__main__][INFO] - [1600] Loss: 0.084, Running accuracy: 99.931, Time: 24.64
[2020-12-16 06:46:41,007][__main__][INFO] - [1920] Loss: 0.089, Running accuracy: 99.922, Time: 24.32
[2020-12-16 06:47:06,637][__main__][INFO] - [2240] Loss: 0.067, Running accuracy: 99.924, Time: 25.63
[2020-12-16 06:47:32,925][__main__][INFO] - [2560] Loss: 0.084, Running accuracy: 99.919, Time: 26.29
[2020-12-16 06:47:57,739][__main__][INFO] - [2880] Loss: 0.064, Running accuracy: 99.922, Time: 24.81
[2020-12-16 06:48:21,955][__main__][INFO] - [3200] Loss: 0.149, Running accuracy: 99.911, Time: 24.21
[2020-12-16 06:48:46,434][__main__][INFO] - [3520] Loss: 0.062, Running accuracy: 99.914, Time: 24.48
[2020-12-16 06:49:13,617][__main__][INFO] - [3840] Loss: 0.070, Running accuracy: 99.917, Time: 27.18
[2020-12-16 06:49:37,348][__main__][INFO] - [4160] Loss: 0.063, Running accuracy: 99.918, Time: 23.73
[2020-12-16 06:50:00,857][__main__][INFO] - [4480] Loss: 0.061, Running accuracy: 99.919, Time: 23.51
[2020-12-16 06:50:30,728][__main__][INFO] - [4800] Loss: 0.130, Running accuracy: 99.916, Time: 29.87
[2020-12-16 06:50:54,049][__main__][INFO] - [5120] Loss: 0.100, Running accuracy: 99.914, Time: 23.32
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[2020-12-16 06:51:43,781][__main__][INFO] - [5760] Loss: 0.065, Running accuracy: 99.915, Time: 24.09
[2020-12-16 06:52:08,025][__main__][INFO] - [6080] Loss: 0.096, Running accuracy: 99.917, Time: 24.24
[2020-12-16 06:52:34,531][__main__][INFO] - [6400] Loss: 0.054, Running accuracy: 99.919, Time: 26.51
[2020-12-16 06:52:58,436][__main__][INFO] - [6720] Loss: 0.091, Running accuracy: 99.915, Time: 23.90
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[2020-12-16 06:54:37,035][__main__][INFO] - [8000] Loss: 0.092, Running accuracy: 99.912, Time: 24.22
[2020-12-16 06:55:00,961][__main__][INFO] - [8320] Loss: 0.068, Running accuracy: 99.912, Time: 23.93
[2020-12-16 06:55:26,895][__main__][INFO] - [8640] Loss: 0.085, Running accuracy: 99.914, Time: 25.93
[2020-12-16 06:55:50,595][__main__][INFO] - [8960] Loss: 0.058, Running accuracy: 99.915, Time: 23.70
[2020-12-16 06:56:19,481][__main__][INFO] - [9280] Loss: 0.086, Running accuracy: 99.913, Time: 28.88
[2020-12-16 06:56:42,441][__main__][INFO] - [9600] Loss: 0.079, Running accuracy: 99.914, Time: 22.96
[2020-12-16 06:57:08,750][__main__][INFO] - [9920] Loss: 0.067, Running accuracy: 99.915, Time: 26.31
[2020-12-16 06:57:33,258][__main__][INFO] - [10240] Loss: 0.107, Running accuracy: 99.914, Time: 24.51
[2020-12-16 06:57:59,604][__main__][INFO] - [10560] Loss: 0.053, Running accuracy: 99.915, Time: 26.34
[2020-12-16 06:58:23,859][__main__][INFO] - [10880] Loss: 0.069, Running accuracy: 99.914, Time: 24.25
[2020-12-16 06:58:48,354][__main__][INFO] - [11200] Loss: 0.052, Running accuracy: 99.915, Time: 24.49
[2020-12-16 06:59:11,551][__main__][INFO] - [11520] Loss: 0.117, Running accuracy: 99.914, Time: 23.20
[2020-12-16 06:59:36,540][__main__][INFO] - [11840] Loss: 0.038, Running accuracy: 99.916, Time: 24.99
[2020-12-16 07:00:02,028][__main__][INFO] - [12160] Loss: 0.056, Running accuracy: 99.917, Time: 25.49
[2020-12-16 07:00:26,691][__main__][INFO] - [12480] Loss: 0.035, Running accuracy: 99.918, Time: 24.66
[2020-12-16 07:00:52,787][__main__][INFO] - [12800] Loss: 0.055, Running accuracy: 99.919, Time: 26.09
[2020-12-16 07:01:17,779][__main__][INFO] - [13120] Loss: 0.087, Running accuracy: 99.919, Time: 24.99
[2020-12-16 07:01:41,040][__main__][INFO] - [13440] Loss: 0.070, Running accuracy: 99.920, Time: 23.26
[2020-12-16 07:02:10,113][__main__][INFO] - [13760] Loss: 0.100, Running accuracy: 99.920, Time: 29.07
[2020-12-16 07:02:35,391][__main__][INFO] - [14080] Loss: 0.096, Running accuracy: 99.919, Time: 25.28
[2020-12-16 07:03:00,213][__main__][INFO] - [14400] Loss: 0.041, Running accuracy: 99.920, Time: 24.82
[2020-12-16 07:03:23,367][__main__][INFO] - [14720] Loss: 0.075, Running accuracy: 99.920, Time: 23.15
[2020-12-16 07:03:49,121][__main__][INFO] - [15040] Loss: 0.070, Running accuracy: 99.921, Time: 25.75
[2020-12-16 07:04:11,906][__main__][INFO] - [15360] Loss: 0.068, Running accuracy: 99.921, Time: 22.78
[2020-12-16 07:04:37,591][__main__][INFO] - [15680] Loss: 0.101, Running accuracy: 99.921, Time: 25.68
[2020-12-16 07:05:01,810][__main__][INFO] - [16000] Loss: 0.115, Running accuracy: 99.920, Time: 24.21
[2020-12-16 07:05:25,773][__main__][INFO] - [16320] Loss: 0.036, Running accuracy: 99.921, Time: 23.96
[2020-12-16 07:05:50,828][__main__][INFO] - [16640] Loss: 0.093, Running accuracy: 99.920, Time: 25.05
[2020-12-16 07:06:15,654][__main__][INFO] - [16960] Loss: 0.047, Running accuracy: 99.921, Time: 24.83
[2020-12-16 07:06:41,810][__main__][INFO] - [17280] Loss: 0.076, Running accuracy: 99.921, Time: 26.15
[2020-12-16 07:07:06,558][__main__][INFO] - [17600] Loss: 0.109, Running accuracy: 99.920, Time: 24.73
[2020-12-16 07:07:31,842][__main__][INFO] - [17920] Loss: 0.042, Running accuracy: 99.921, Time: 25.28
[2020-12-16 07:08:00,648][__main__][INFO] - [18240] Loss: 0.117, Running accuracy: 99.921, Time: 28.80
[2020-12-16 07:08:25,114][__main__][INFO] - [18560] Loss: 0.085, Running accuracy: 99.921, Time: 24.46
[2020-12-16 07:08:47,833][__main__][INFO] - [18880] Loss: 0.045, Running accuracy: 99.922, Time: 22.71
[2020-12-16 07:09:13,554][__main__][INFO] - [19200] Loss: 0.113, Running accuracy: 99.921, Time: 25.72
[2020-12-16 07:09:40,158][__main__][INFO] - [19520] Loss: 0.099, Running accuracy: 99.921, Time: 26.60
[2020-12-16 07:10:08,620][__main__][INFO] - [19840] Loss: 0.113, Running accuracy: 99.920, Time: 28.46
[2020-12-16 07:10:35,970][__main__][INFO] - [20160] Loss: 0.164, Running accuracy: 99.920, Time: 27.34
[2020-12-16 07:11:00,493][__main__][INFO] - [20480] Loss: 0.057, Running accuracy: 99.920, Time: 24.52
[2020-12-16 07:11:25,470][__main__][INFO] - [20800] Loss: 0.035, Running accuracy: 99.922, Time: 24.98
[2020-12-16 07:11:50,313][__main__][INFO] - [21120] Loss: 0.081, Running accuracy: 99.921, Time: 24.84
[2020-12-16 07:12:13,478][__main__][INFO] - [21440] Loss: 0.087, Running accuracy: 99.921, Time: 23.16
[2020-12-16 07:12:38,721][__main__][INFO] - [21760] Loss: 0.081, Running accuracy: 99.921, Time: 25.24
[2020-12-16 07:13:05,266][__main__][INFO] - [22080] Loss: 0.144, Running accuracy: 99.920, Time: 26.54
[2020-12-16 07:13:29,677][__main__][INFO] - [22400] Loss: 0.049, Running accuracy: 99.921, Time: 24.41
[2020-12-16 07:14:00,453][__main__][INFO] - [22720] Loss: 0.052, Running accuracy: 99.921, Time: 30.77
[2020-12-16 07:14:25,246][__main__][INFO] - [23040] Loss: 0.092, Running accuracy: 99.921, Time: 24.79
[2020-12-16 07:14:49,402][__main__][INFO] - [23360] Loss: 0.090, Running accuracy: 99.920, Time: 24.16
[2020-12-16 07:15:13,864][__main__][INFO] - [23680] Loss: 0.137, Running accuracy: 99.920, Time: 24.46
[2020-12-16 07:15:40,234][__main__][INFO] - [24000] Loss: 0.101, Running accuracy: 99.920, Time: 26.37
[2020-12-16 07:16:04,921][__main__][INFO] - [24320] Loss: 0.037, Running accuracy: 99.920, Time: 24.69
[2020-12-16 07:16:32,511][__main__][INFO] - [24640] Loss: 0.100, Running accuracy: 99.920, Time: 27.59
[2020-12-16 07:16:56,988][__main__][INFO] - [24960] Loss: 0.094, Running accuracy: 99.921, Time: 24.48
[2020-12-16 07:17:20,603][__main__][INFO] - [25280] Loss: 0.079, Running accuracy: 99.921, Time: 23.61
[2020-12-16 07:17:44,171][__main__][INFO] - [25600] Loss: 0.065, Running accuracy: 99.921, Time: 23.57
[2020-12-16 07:18:08,761][__main__][INFO] - [25920] Loss: 0.077, Running accuracy: 99.921, Time: 24.59
[2020-12-16 07:18:33,247][__main__][INFO] - [26240] Loss: 0.086, Running accuracy: 99.921, Time: 24.48
[2020-12-16 07:18:57,624][__main__][INFO] - [26560] Loss: 0.119, Running accuracy: 99.920, Time: 24.38
[2020-12-16 07:19:22,965][__main__][INFO] - [26880] Loss: 0.065, Running accuracy: 99.919, Time: 25.34
[2020-12-16 07:19:49,918][__main__][INFO] - [27200] Loss: 0.056, Running accuracy: 99.920, Time: 26.95
[2020-12-16 07:20:17,728][__main__][INFO] - [27520] Loss: 0.082, Running accuracy: 99.919, Time: 27.81
[2020-12-16 07:20:41,291][__main__][INFO] - [27840] Loss: 0.091, Running accuracy: 99.919, Time: 23.56
[2020-12-16 07:21:04,779][__main__][INFO] - [28160] Loss: 0.051, Running accuracy: 99.919, Time: 23.49
[2020-12-16 07:21:29,735][__main__][INFO] - [28480] Loss: 0.071, Running accuracy: 99.920, Time: 24.95
[2020-12-16 07:21:55,289][__main__][INFO] - [28800] Loss: 0.031, Running accuracy: 99.920, Time: 25.55
[2020-12-16 07:22:20,082][__main__][INFO] - [29120] Loss: 0.144, Running accuracy: 99.919, Time: 24.79
[2020-12-16 07:22:43,961][__main__][INFO] - [29440] Loss: 0.136, Running accuracy: 99.918, Time: 23.88
[2020-12-16 07:23:07,892][__main__][INFO] - [29760] Loss: 0.121, Running accuracy: 99.918, Time: 23.93
[2020-12-16 07:23:33,197][__main__][INFO] - [30080] Loss: 0.076, Running accuracy: 99.918, Time: 25.30
[2020-12-16 07:23:58,054][__main__][INFO] - [30400] Loss: 0.059, Running accuracy: 99.919, Time: 24.86
[2020-12-16 07:24:22,607][__main__][INFO] - [30720] Loss: 0.043, Running accuracy: 99.919, Time: 24.55
[2020-12-16 07:24:48,261][__main__][INFO] - [31040] Loss: 0.174, Running accuracy: 99.918, Time: 25.65
[2020-12-16 07:25:12,780][__main__][INFO] - [31360] Loss: 0.079, Running accuracy: 99.917, Time: 24.52
[2020-12-16 07:25:41,357][__main__][INFO] - [31680] Loss: 0.095, Running accuracy: 99.917, Time: 28.57
[2020-12-16 07:26:05,495][__main__][INFO] - [32000] Loss: 0.028, Running accuracy: 99.918, Time: 24.14
[2020-12-16 07:26:31,520][__main__][INFO] - [32320] Loss: 0.086, Running accuracy: 99.917, Time: 26.02
[2020-12-16 07:26:54,295][__main__][INFO] - [32640] Loss: 0.071, Running accuracy: 99.918, Time: 22.78
[2020-12-16 07:27:18,471][__main__][INFO] - [32960] Loss: 0.067, Running accuracy: 99.917, Time: 24.17
[2020-12-16 07:27:43,840][__main__][INFO] - [33280] Loss: 0.151, Running accuracy: 99.917, Time: 25.37
[2020-12-16 07:28:11,077][__main__][INFO] - [33600] Loss: 0.093, Running accuracy: 99.917, Time: 27.24
[2020-12-16 07:28:36,252][__main__][INFO] - [33920] Loss: 0.037, Running accuracy: 99.917, Time: 25.17
[2020-12-16 07:28:59,409][__main__][INFO] - [34240] Loss: 0.090, Running accuracy: 99.917, Time: 23.16
[2020-12-16 07:29:23,460][__main__][INFO] - [34560] Loss: 0.090, Running accuracy: 99.917, Time: 24.05
[2020-12-16 07:29:47,311][__main__][INFO] - [34880] Loss: 0.054, Running accuracy: 99.917, Time: 23.85
[2020-12-16 07:30:13,688][__main__][INFO] - [35200] Loss: 0.106, Running accuracy: 99.917, Time: 26.38
[2020-12-16 07:30:38,586][__main__][INFO] - [35520] Loss: 0.176, Running accuracy: 99.917, Time: 24.90
[2020-12-16 07:31:02,008][__main__][INFO] - [35840] Loss: 0.141, Running accuracy: 99.916, Time: 23.42
[2020-12-16 07:31:32,020][__main__][INFO] - [36160] Loss: 0.062, Running accuracy: 99.916, Time: 30.01
[2020-12-16 07:31:57,043][__main__][INFO] - [36480] Loss: 0.125, Running accuracy: 99.916, Time: 25.02
[2020-12-16 07:32:19,774][__main__][INFO] - [36800] Loss: 0.102, Running accuracy: 99.915, Time: 22.73
[2020-12-16 07:32:45,416][__main__][INFO] - [37120] Loss: 0.090, Running accuracy: 99.915, Time: 25.64
[2020-12-16 07:33:10,364][__main__][INFO] - [37440] Loss: 0.133, Running accuracy: 99.915, Time: 24.95
[2020-12-16 07:33:35,953][__main__][INFO] - [37760] Loss: 0.111, Running accuracy: 99.915, Time: 25.59
[2020-12-16 07:34:01,443][__main__][INFO] - [38080] Loss: 0.048, Running accuracy: 99.916, Time: 25.49
[2020-12-16 07:34:26,508][__main__][INFO] - [38400] Loss: 0.111, Running accuracy: 99.915, Time: 25.06
[2020-12-16 07:34:51,565][__main__][INFO] - [38720] Loss: 0.081, Running accuracy: 99.916, Time: 25.06
[2020-12-16 07:35:17,451][__main__][INFO] - [39040] Loss: 0.164, Running accuracy: 99.915, Time: 25.88
[2020-12-16 07:35:41,310][__main__][INFO] - [39360] Loss: 0.102, Running accuracy: 99.915, Time: 23.86
[2020-12-16 07:36:04,477][__main__][INFO] - [39680] Loss: 0.064, Running accuracy: 99.915, Time: 23.16
[2020-12-16 07:36:14,428][__main__][INFO] - Action accuracy: 99.915, Loss: 0.094
[2020-12-16 07:36:14,429][__main__][INFO] - Validating..
[2020-12-16 07:36:55,121][test][INFO] - Time elapsed: 38.419920
[2020-12-16 07:36:55,126][__main__][INFO] - Validation F1 score: 95.630, Exact match: 56.180, Precision: 95.560, Recall: 95.700
[2020-12-16 07:37:37,724][__main__][INFO] - Checkpoint saved to checkpoints/model_latest.pth
[2020-12-16 07:37:38,832][__main__][INFO] - Epoch #53
[2020-12-16 07:37:38,832][__main__][INFO] - Training..
[2020-12-16 07:38:09,411][__main__][INFO] - [320] Loss: 0.092, Running accuracy: 99.910, Time: 29.36
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[2020-12-16 08:25:41,188][__main__][INFO] - [36800] Loss: 0.092, Running accuracy: 99.917, Time: 25.25
[2020-12-16 08:26:07,027][__main__][INFO] - [37120] Loss: 0.059, Running accuracy: 99.917, Time: 25.84
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[2020-12-16 08:26:59,219][__main__][INFO] - [37760] Loss: 0.043, Running accuracy: 99.918, Time: 26.04
[2020-12-16 08:27:21,690][__main__][INFO] - [38080] Loss: 0.061, Running accuracy: 99.918, Time: 22.47
[2020-12-16 08:27:47,269][__main__][INFO] - [38400] Loss: 0.066, Running accuracy: 99.918, Time: 25.58
[2020-12-16 08:28:11,453][__main__][INFO] - [38720] Loss: 0.112, Running accuracy: 99.918, Time: 24.18
[2020-12-16 08:28:41,054][__main__][INFO] - [39040] Loss: 0.091, Running accuracy: 99.918, Time: 29.60
[2020-12-16 08:29:05,718][__main__][INFO] - [39360] Loss: 0.113, Running accuracy: 99.917, Time: 24.66
[2020-12-16 08:29:28,729][__main__][INFO] - [39680] Loss: 0.033, Running accuracy: 99.918, Time: 23.01
[2020-12-16 08:29:38,613][__main__][INFO] - Action accuracy: 99.918, Loss: 0.093
[2020-12-16 08:29:38,615][__main__][INFO] - Validating..
[2020-12-16 08:30:15,922][test][INFO] - Time elapsed: 35.358784
[2020-12-16 08:30:15,927][__main__][INFO] - Validation F1 score: 95.600, Exact match: 56.060, Precision: 95.530, Recall: 95.670
[2020-12-16 08:30:56,170][__main__][INFO] - Checkpoint saved to checkpoints/model_latest.pth
[2020-12-16 08:30:57,246][__main__][INFO] - Epoch #54
[2020-12-16 08:30:57,247][__main__][INFO] - Training..
[2020-12-16 08:31:25,928][__main__][INFO] - [320] Loss: 0.072, Running accuracy: 99.948, Time: 27.36
[2020-12-16 08:31:50,406][__main__][INFO] - [640] Loss: 0.075, Running accuracy: 99.934, Time: 24.47
[2020-12-16 08:32:16,234][__main__][INFO] - [960] Loss: 0.082, Running accuracy: 99.921, Time: 25.83
[2020-12-16 08:32:41,165][__main__][INFO] - [1280] Loss: 0.121, Running accuracy: 99.908, Time: 24.93
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[2020-12-16 09:20:24,857][__main__][INFO] - [38400] Loss: 0.133, Running accuracy: 99.915, Time: 23.70
[2020-12-16 09:20:51,509][__main__][INFO] - [38720] Loss: 0.165, Running accuracy: 99.914, Time: 26.65
[2020-12-16 09:21:17,363][__main__][INFO] - [39040] Loss: 0.099, Running accuracy: 99.914, Time: 25.85
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[2020-12-16 09:22:06,476][__main__][INFO] - [39680] Loss: 0.064, Running accuracy: 99.914, Time: 25.46
[2020-12-16 09:22:15,643][__main__][INFO] - Action accuracy: 99.914, Loss: 0.094
[2020-12-16 09:22:15,644][__main__][INFO] - Validating..
[2020-12-16 09:22:52,501][test][INFO] - Time elapsed: 34.789844
[2020-12-16 09:22:52,506][__main__][INFO] - Validation F1 score: 95.600, Exact match: 55.940, Precision: 95.530, Recall: 95.660
[2020-12-16 09:23:34,559][__main__][INFO] - Checkpoint saved to checkpoints/model_latest.pth
[2020-12-16 09:23:35,663][__main__][INFO] - Epoch #55
[2020-12-16 09:23:35,663][__main__][INFO] - Training..
[2020-12-16 09:24:00,636][__main__][INFO] - [320] Loss: 0.038, Running accuracy: 99.974, Time: 23.84
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[2020-12-16 10:03:53,806][__main__][INFO] - [31040] Loss: 0.066, Running accuracy: 99.913, Time: 28.44
[2020-12-16 10:04:18,401][__main__][INFO] - [31360] Loss: 0.104, Running accuracy: 99.913, Time: 24.59
[2020-12-16 10:04:42,980][__main__][INFO] - [31680] Loss: 0.062, Running accuracy: 99.913, Time: 24.58
[2020-12-16 10:05:07,741][__main__][INFO] - [32000] Loss: 0.162, Running accuracy: 99.912, Time: 24.76
[2020-12-16 10:05:31,584][__main__][INFO] - [32320] Loss: 0.067, Running accuracy: 99.913, Time: 23.84
[2020-12-16 10:05:55,335][__main__][INFO] - [32640] Loss: 0.112, Running accuracy: 99.912, Time: 23.75
[2020-12-16 10:06:19,826][__main__][INFO] - [32960] Loss: 0.075, Running accuracy: 99.912, Time: 24.49
[2020-12-16 10:06:44,410][__main__][INFO] - [33280] Loss: 0.048, Running accuracy: 99.913, Time: 24.58
[2020-12-16 10:07:10,765][__main__][INFO] - [33600] Loss: 0.061, Running accuracy: 99.913, Time: 26.35
[2020-12-16 10:07:34,706][__main__][INFO] - [33920] Loss: 0.058, Running accuracy: 99.913, Time: 23.94
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[2020-12-16 10:08:46,217][__main__][INFO] - [34880] Loss: 0.061, Running accuracy: 99.913, Time: 24.14
[2020-12-16 10:09:11,265][__main__][INFO] - [35200] Loss: 0.081, Running accuracy: 99.913, Time: 25.05
[2020-12-16 10:09:35,114][__main__][INFO] - [35520] Loss: 0.054, Running accuracy: 99.913, Time: 23.85
[2020-12-16 10:10:02,878][__main__][INFO] - [35840] Loss: 0.062, Running accuracy: 99.913, Time: 27.76
[2020-12-16 10:10:26,288][__main__][INFO] - [36160] Loss: 0.055, Running accuracy: 99.914, Time: 23.41
[2020-12-16 10:10:52,468][__main__][INFO] - [36480] Loss: 0.164, Running accuracy: 99.913, Time: 26.18
[2020-12-16 10:11:17,230][__main__][INFO] - [36800] Loss: 0.079, Running accuracy: 99.913, Time: 24.76
[2020-12-16 10:11:41,099][__main__][INFO] - [37120] Loss: 0.047, Running accuracy: 99.914, Time: 23.87
[2020-12-16 10:12:05,119][__main__][INFO] - [37440] Loss: 0.067, Running accuracy: 99.914, Time: 24.02
[2020-12-16 10:12:28,418][__main__][INFO] - [37760] Loss: 0.131, Running accuracy: 99.913, Time: 23.30
[2020-12-16 10:12:52,135][__main__][INFO] - [38080] Loss: 0.098, Running accuracy: 99.913, Time: 23.71
[2020-12-16 10:13:16,425][__main__][INFO] - [38400] Loss: 0.081, Running accuracy: 99.913, Time: 24.29
[2020-12-16 10:13:41,295][__main__][INFO] - [38720] Loss: 0.059, Running accuracy: 99.914, Time: 24.87
[2020-12-16 10:14:05,543][__main__][INFO] - [39040] Loss: 0.045, Running accuracy: 99.914, Time: 24.25
[2020-12-16 10:14:30,783][__main__][INFO] - [39360] Loss: 0.040, Running accuracy: 99.915, Time: 25.24
[2020-12-16 10:14:57,097][__main__][INFO] - [39680] Loss: 0.105, Running accuracy: 99.915, Time: 26.31
[2020-12-16 10:15:07,087][__main__][INFO] - Action accuracy: 99.915, Loss: 0.092
[2020-12-16 10:15:07,088][__main__][INFO] - Validating..
[2020-12-16 10:15:49,619][test][INFO] - Time elapsed: 35.968393
[2020-12-16 10:15:49,624][__main__][INFO] - Validation F1 score: 95.600, Exact match: 55.880, Precision: 95.530, Recall: 95.660
[2020-12-16 10:16:30,904][__main__][INFO] - Checkpoint saved to checkpoints/model_latest.pth
[2020-12-16 10:16:31,808][__main__][INFO] - Epoch #56
[2020-12-16 10:16:31,808][__main__][INFO] - Training..
[2020-12-16 10:16:56,346][__main__][INFO] - [320] Loss: 0.086, Running accuracy: 99.894, Time: 23.22
[2020-12-16 10:17:20,964][__main__][INFO] - [640] Loss: 0.047, Running accuracy: 99.928, Time: 24.62
[2020-12-16 10:17:45,348][__main__][INFO] - [960] Loss: 0.093, Running accuracy: 99.926, Time: 24.38
[2020-12-16 10:18:08,731][__main__][INFO] - [1280] Loss: 0.102, Running accuracy: 99.928, Time: 23.38
[2020-12-16 10:18:33,278][__main__][INFO] - [1600] Loss: 0.137, Running accuracy: 99.924, Time: 24.55
[2020-12-16 10:18:59,766][__main__][INFO] - [1920] Loss: 0.064, Running accuracy: 99.919, Time: 26.49
[2020-12-16 10:19:22,674][__main__][INFO] - [2240] Loss: 0.081, Running accuracy: 99.921, Time: 22.90
[2020-12-16 10:19:46,377][__main__][INFO] - [2560] Loss: 0.085, Running accuracy: 99.924, Time: 23.70
[2020-12-16 10:20:14,130][__main__][INFO] - [2880] Loss: 0.089, Running accuracy: 99.925, Time: 27.65
[2020-12-16 10:20:40,127][__main__][INFO] - [3200] Loss: 0.088, Running accuracy: 99.923, Time: 26.00
[2020-12-16 10:21:04,625][__main__][INFO] - [3520] Loss: 0.070, Running accuracy: 99.923, Time: 24.50
[2020-12-16 10:21:28,454][__main__][INFO] - [3840] Loss: 0.145, Running accuracy: 99.919, Time: 23.82
[2020-12-16 10:21:52,056][__main__][INFO] - [4160] Loss: 0.098, Running accuracy: 99.917, Time: 23.60
[2020-12-16 10:22:19,512][__main__][INFO] - [4480] Loss: 0.080, Running accuracy: 99.915, Time: 27.45
[2020-12-16 10:22:43,113][__main__][INFO] - [4800] Loss: 0.090, Running accuracy: 99.914, Time: 23.60
[2020-12-16 10:23:08,082][__main__][INFO] - [5120] Loss: 0.039, Running accuracy: 99.914, Time: 24.97
[2020-12-16 10:23:33,133][__main__][INFO] - [5440] Loss: 0.101, Running accuracy: 99.916, Time: 25.05
[2020-12-16 10:23:57,973][__main__][INFO] - [5760] Loss: 0.121, Running accuracy: 99.915, Time: 24.84
[2020-12-16 10:24:23,558][__main__][INFO] - [6080] Loss: 0.058, Running accuracy: 99.916, Time: 25.58
[2020-12-16 10:24:48,933][__main__][INFO] - [6400] Loss: 0.083, Running accuracy: 99.914, Time: 25.37
[2020-12-16 10:25:13,519][__main__][INFO] - [6720] Loss: 0.128, Running accuracy: 99.911, Time: 24.59
[2020-12-16 10:25:37,222][__main__][INFO] - [7040] Loss: 0.078, Running accuracy: 99.912, Time: 23.70
[2020-12-16 10:26:03,615][__main__][INFO] - [7360] Loss: 0.058, Running accuracy: 99.914, Time: 26.39
[2020-12-16 10:26:29,395][__main__][INFO] - [7680] Loss: 0.055, Running accuracy: 99.916, Time: 25.78
[2020-12-16 10:26:53,063][__main__][INFO] - [8000] Loss: 0.057, Running accuracy: 99.917, Time: 23.67
[2020-12-16 10:27:16,956][__main__][INFO] - [8320] Loss: 0.143, Running accuracy: 99.915, Time: 23.89
[2020-12-16 10:27:42,292][__main__][INFO] - [8640] Loss: 0.087, Running accuracy: 99.916, Time: 25.34
[2020-12-16 10:28:07,376][__main__][INFO] - [8960] Loss: 0.056, Running accuracy: 99.917, Time: 25.08
[2020-12-16 10:28:34,812][__main__][INFO] - [9280] Loss: 0.055, Running accuracy: 99.918, Time: 27.43
[2020-12-16 10:28:59,935][__main__][INFO] - [9600] Loss: 0.107, Running accuracy: 99.917, Time: 25.12
[2020-12-16 10:29:24,618][__main__][INFO] - [9920] Loss: 0.100, Running accuracy: 99.916, Time: 24.68
[2020-12-16 10:29:49,813][__main__][INFO] - [10240] Loss: 0.073, Running accuracy: 99.917, Time: 25.19
[2020-12-16 10:30:14,055][__main__][INFO] - [10560] Loss: 0.098, Running accuracy: 99.918, Time: 24.24
[2020-12-16 10:30:40,304][__main__][INFO] - [10880] Loss: 0.156, Running accuracy: 99.916, Time: 26.25
[2020-12-16 10:31:04,900][__main__][INFO] - [11200] Loss: 0.061, Running accuracy: 99.916, Time: 24.59
[2020-12-16 10:31:34,706][__main__][INFO] - [11520] Loss: 0.066, Running accuracy: 99.917, Time: 29.81
[2020-12-16 10:32:01,286][__main__][INFO] - [11840] Loss: 0.145, Running accuracy: 99.915, Time: 26.58
[2020-12-16 10:32:24,574][__main__][INFO] - [12160] Loss: 0.122, Running accuracy: 99.914, Time: 23.29
[2020-12-16 10:32:49,491][__main__][INFO] - [12480] Loss: 0.026, Running accuracy: 99.915, Time: 24.68
[2020-12-16 10:33:13,092][__main__][INFO] - [12800] Loss: 0.140, Running accuracy: 99.913, Time: 23.60
[2020-12-16 10:33:36,006][__main__][INFO] - [13120] Loss: 0.030, Running accuracy: 99.914, Time: 22.91
[2020-12-16 10:34:00,730][__main__][INFO] - [13440] Loss: 0.061, Running accuracy: 99.915, Time: 24.72
[2020-12-16 10:34:27,252][__main__][INFO] - [13760] Loss: 0.080, Running accuracy: 99.914, Time: 26.52
[2020-12-16 10:34:49,814][__main__][INFO] - [14080] Loss: 0.092, Running accuracy: 99.915, Time: 22.56
[2020-12-16 10:35:14,140][__main__][INFO] - [14400] Loss: 0.068, Running accuracy: 99.915, Time: 24.32
[2020-12-16 10:35:38,217][__main__][INFO] - [14720] Loss: 0.101, Running accuracy: 99.914, Time: 24.08
[2020-12-16 10:36:04,066][__main__][INFO] - [15040] Loss: 0.158, Running accuracy: 99.913, Time: 25.85
[2020-12-16 10:36:28,517][__main__][INFO] - [15360] Loss: 0.050, Running accuracy: 99.913, Time: 24.45
[2020-12-16 10:36:53,680][__main__][INFO] - [15680] Loss: 0.108, Running accuracy: 99.913, Time: 25.16
[2020-12-16 10:37:22,021][__main__][INFO] - [16000] Loss: 0.100, Running accuracy: 99.913, Time: 28.34
[2020-12-16 10:37:46,139][__main__][INFO] - [16320] Loss: 0.079, Running accuracy: 99.913, Time: 24.12
[2020-12-16 10:38:10,919][__main__][INFO] - [16640] Loss: 0.055, Running accuracy: 99.914, Time: 24.78
[2020-12-16 10:38:34,728][__main__][INFO] - [16960] Loss: 0.073, Running accuracy: 99.914, Time: 23.81
[2020-12-16 10:38:58,505][__main__][INFO] - [17280] Loss: 0.066, Running accuracy: 99.914, Time: 23.78
[2020-12-16 10:39:21,179][__main__][INFO] - [17600] Loss: 0.060, Running accuracy: 99.914, Time: 22.67
[2020-12-16 10:39:47,412][__main__][INFO] - [17920] Loss: 0.044, Running accuracy: 99.914, Time: 26.23
[2020-12-16 10:40:11,851][__main__][INFO] - [18240] Loss: 0.090, Running accuracy: 99.914, Time: 24.44
[2020-12-16 10:40:37,568][__main__][INFO] - [18560] Loss: 0.063, Running accuracy: 99.914, Time: 25.72
[2020-12-16 10:41:02,003][__main__][INFO] - [18880] Loss: 0.104, Running accuracy: 99.914, Time: 24.43
[2020-12-16 10:41:25,967][__main__][INFO] - [19200] Loss: 0.044, Running accuracy: 99.914, Time: 23.96
[2020-12-16 10:41:48,899][__main__][INFO] - [19520] Loss: 0.306, Running accuracy: 99.914, Time: 22.93
[2020-12-16 10:42:15,116][__main__][INFO] - [19840] Loss: 0.092, Running accuracy: 99.912, Time: 26.22
[2020-12-16 10:42:38,606][__main__][INFO] - [20160] Loss: 0.078, Running accuracy: 99.912, Time: 23.49
[2020-12-16 10:43:08,882][__main__][INFO] - [20480] Loss: 0.099, Running accuracy: 99.913, Time: 30.28
[2020-12-16 10:43:34,181][__main__][INFO] - [20800] Loss: 0.097, Running accuracy: 99.912, Time: 25.30
[2020-12-16 10:44:00,437][__main__][INFO] - [21120] Loss: 0.102, Running accuracy: 99.912, Time: 26.25
[2020-12-16 10:44:25,736][__main__][INFO] - [21440] Loss: 0.057, Running accuracy: 99.913, Time: 25.30
[2020-12-16 10:44:48,473][__main__][INFO] - [21760] Loss: 0.056, Running accuracy: 99.913, Time: 22.74
[2020-12-16 10:45:14,278][__main__][INFO] - [22080] Loss: 0.139, Running accuracy: 99.913, Time: 25.80
[2020-12-16 10:45:37,723][__main__][INFO] - [22400] Loss: 0.081, Running accuracy: 99.913, Time: 23.44
[2020-12-16 10:46:01,887][__main__][INFO] - [22720] Loss: 0.084, Running accuracy: 99.912, Time: 24.16
[2020-12-16 10:46:24,850][__main__][INFO] - [23040] Loss: 0.095, Running accuracy: 99.912, Time: 22.96
[2020-12-16 10:46:50,892][__main__][INFO] - [23360] Loss: 0.089, Running accuracy: 99.913, Time: 26.04
[2020-12-16 10:47:14,969][__main__][INFO] - [23680] Loss: 0.144, Running accuracy: 99.913, Time: 24.08
[2020-12-16 10:47:38,991][__main__][INFO] - [24000] Loss: 0.069, Running accuracy: 99.913, Time: 24.02
[2020-12-16 10:48:04,351][__main__][INFO] - [24320] Loss: 0.077, Running accuracy: 99.913, Time: 25.36
[2020-12-16 10:48:30,560][__main__][INFO] - [24640] Loss: 0.054, Running accuracy: 99.913, Time: 26.21
[2020-12-16 10:48:55,163][__main__][INFO] - [24960] Loss: 0.092, Running accuracy: 99.913, Time: 24.60
[2020-12-16 10:49:24,596][__main__][INFO] - [25280] Loss: 0.065, Running accuracy: 99.913, Time: 29.43
[2020-12-16 10:49:52,319][__main__][INFO] - [25600] Loss: 0.072, Running accuracy: 99.914, Time: 27.72
[2020-12-16 10:50:18,328][__main__][INFO] - [25920] Loss: 0.062, Running accuracy: 99.914, Time: 26.01
[2020-12-16 10:50:43,287][__main__][INFO] - [26240] Loss: 0.049, Running accuracy: 99.914, Time: 24.96
[2020-12-16 10:51:11,004][__main__][INFO] - [26560] Loss: 0.117, Running accuracy: 99.914, Time: 27.71
[2020-12-16 10:51:38,266][__main__][INFO] - [26880] Loss: 0.070, Running accuracy: 99.915, Time: 27.26
[2020-12-16 10:52:04,405][__main__][INFO] - [27200] Loss: 0.088, Running accuracy: 99.915, Time: 26.14
[2020-12-16 10:52:28,000][__main__][INFO] - [27520] Loss: 0.126, Running accuracy: 99.914, Time: 23.59
[2020-12-16 10:52:52,072][__main__][INFO] - [27840] Loss: 0.065, Running accuracy: 99.915, Time: 24.07
[2020-12-16 10:53:16,029][__main__][INFO] - [28160] Loss: 0.093, Running accuracy: 99.915, Time: 23.96
[2020-12-16 10:53:40,009][__main__][INFO] - [28480] Loss: 0.041, Running accuracy: 99.915, Time: 23.98
[2020-12-16 10:54:03,864][__main__][INFO] - [28800] Loss: 0.109, Running accuracy: 99.915, Time: 23.85
[2020-12-16 10:54:28,514][__main__][INFO] - [29120] Loss: 0.143, Running accuracy: 99.915, Time: 24.65
[2020-12-16 10:54:51,989][__main__][INFO] - [29440] Loss: 0.042, Running accuracy: 99.915, Time: 23.47
[2020-12-16 10:55:22,597][__main__][INFO] - [29760] Loss: 0.054, Running accuracy: 99.915, Time: 30.61
[2020-12-16 10:55:47,226][__main__][INFO] - [30080] Loss: 0.108, Running accuracy: 99.915, Time: 24.63
[2020-12-16 10:56:10,891][__main__][INFO] - [30400] Loss: 0.077, Running accuracy: 99.915, Time: 23.66
[2020-12-16 10:56:35,310][__main__][INFO] - [30720] Loss: 0.062, Running accuracy: 99.915, Time: 24.42
[2020-12-16 10:57:00,177][__main__][INFO] - [31040] Loss: 0.067, Running accuracy: 99.915, Time: 24.87
[2020-12-16 10:57:24,915][__main__][INFO] - [31360] Loss: 0.118, Running accuracy: 99.915, Time: 24.73
[2020-12-16 10:57:48,326][__main__][INFO] - [31680] Loss: 0.061, Running accuracy: 99.916, Time: 23.41
[2020-12-16 10:58:14,453][__main__][INFO] - [32000] Loss: 0.104, Running accuracy: 99.915, Time: 26.03
[2020-12-16 10:58:36,991][__main__][INFO] - [32320] Loss: 0.065, Running accuracy: 99.916, Time: 22.54
[2020-12-16 10:58:59,811][__main__][INFO] - [32640] Loss: 0.063, Running accuracy: 99.916, Time: 22.74
[2020-12-16 10:59:23,955][__main__][INFO] - [32960] Loss: 0.069, Running accuracy: 99.916, Time: 24.13
[2020-12-16 10:59:49,387][__main__][INFO] - [33280] Loss: 0.034, Running accuracy: 99.916, Time: 25.43
[2020-12-16 11:00:14,834][__main__][INFO] - [33600] Loss: 0.128, Running accuracy: 99.916, Time: 25.45
[2020-12-16 11:00:40,420][__main__][INFO] - [33920] Loss: 0.090, Running accuracy: 99.915, Time: 25.58
[2020-12-16 11:01:07,706][__main__][INFO] - [34240] Loss: 0.096, Running accuracy: 99.915, Time: 27.28
[2020-12-16 11:01:31,711][__main__][INFO] - [34560] Loss: 0.035, Running accuracy: 99.916, Time: 24.00
[2020-12-16 11:01:55,654][__main__][INFO] - [34880] Loss: 0.124, Running accuracy: 99.915, Time: 23.94
[2020-12-16 11:02:21,099][__main__][INFO] - [35200] Loss: 0.048, Running accuracy: 99.915, Time: 25.20
[2020-12-16 11:02:45,455][__main__][INFO] - [35520] Loss: 0.074, Running accuracy: 99.916, Time: 24.35
[2020-12-16 11:03:11,121][__main__][INFO] - [35840] Loss: 0.101, Running accuracy: 99.916, Time: 25.66
[2020-12-16 11:03:35,987][__main__][INFO] - [36160] Loss: 0.051, Running accuracy: 99.916, Time: 24.86
[2020-12-16 11:04:00,499][__main__][INFO] - [36480] Loss: 0.116, Running accuracy: 99.916, Time: 24.51
[2020-12-16 11:04:27,937][__main__][INFO] - [36800] Loss: 0.215, Running accuracy: 99.914, Time: 27.43
[2020-12-16 11:04:52,105][__main__][INFO] - [37120] Loss: 0.069, Running accuracy: 99.915, Time: 24.17
[2020-12-16 11:05:16,618][__main__][INFO] - [37440] Loss: 0.062, Running accuracy: 99.915, Time: 24.51
[2020-12-16 11:05:41,113][__main__][INFO] - [37760] Loss: 0.052, Running accuracy: 99.915, Time: 24.49
[2020-12-16 11:06:08,526][__main__][INFO] - [38080] Loss: 0.041, Running accuracy: 99.916, Time: 27.41
[2020-12-16 11:06:33,153][__main__][INFO] - [38400] Loss: 0.089, Running accuracy: 99.916, Time: 24.63
[2020-12-16 11:07:00,786][__main__][INFO] - [38720] Loss: 0.064, Running accuracy: 99.916, Time: 27.63
[2020-12-16 11:07:27,659][__main__][INFO] - [39040] Loss: 0.075, Running accuracy: 99.916, Time: 26.87
[2020-12-16 11:07:49,807][__main__][INFO] - [39360] Loss: 0.102, Running accuracy: 99.916, Time: 22.15
[2020-12-16 11:08:14,042][__main__][INFO] - [39680] Loss: 0.104, Running accuracy: 99.915, Time: 24.23
[2020-12-16 11:08:23,687][__main__][INFO] - Action accuracy: 99.916, Loss: 0.095
[2020-12-16 11:08:23,688][__main__][INFO] - Validating..
[2020-12-16 11:09:01,183][test][INFO] - Time elapsed: 35.215132
[2020-12-16 11:09:01,187][__main__][INFO] - Validation F1 score: 95.620, Exact match: 55.940, Precision: 95.550, Recall: 95.690
Epoch    57: reducing learning rate of group 0 to 3.9063e-08.
[2020-12-16 11:09:41,736][__main__][INFO] - Checkpoint saved to checkpoints/model_latest.pth
[2020-12-16 11:09:42,758][__main__][INFO] - Epoch #57
[2020-12-16 11:09:42,759][__main__][INFO] - Training..
[2020-12-16 11:10:08,750][__main__][INFO] - [320] Loss: 0.051, Running accuracy: 99.960, Time: 24.59
[2020-12-16 11:10:33,874][__main__][INFO] - [640] Loss: 0.054, Running accuracy: 99.973, Time: 25.12
[2020-12-16 11:11:02,708][__main__][INFO] - [960] Loss: 0.098, Running accuracy: 99.947, Time: 28.83
[2020-12-16 11:11:26,871][__main__][INFO] - [1280] Loss: 0.035, Running accuracy: 99.947, Time: 24.16
[2020-12-16 11:11:52,519][__main__][INFO] - [1600] Loss: 0.133, Running accuracy: 99.934, Time: 25.65
[2020-12-16 11:12:17,183][__main__][INFO] - [1920] Loss: 0.092, Running accuracy: 99.927, Time: 24.43
[2020-12-16 11:12:41,423][__main__][INFO] - [2240] Loss: 0.053, Running accuracy: 99.932, Time: 24.24
[2020-12-16 11:13:05,155][__main__][INFO] - [2560] Loss: 0.059, Running accuracy: 99.932, Time: 23.73
[2020-12-16 11:13:28,232][__main__][INFO] - [2880] Loss: 0.027, Running accuracy: 99.938, Time: 23.08
[2020-12-16 11:13:52,969][__main__][INFO] - [3200] Loss: 0.077, Running accuracy: 99.934, Time: 24.74
[2020-12-16 11:14:18,357][__main__][INFO] - [3520] Loss: 0.066, Running accuracy: 99.934, Time: 25.39
[2020-12-16 11:14:42,396][__main__][INFO] - [3840] Loss: 0.060, Running accuracy: 99.933, Time: 24.04
[2020-12-16 11:15:07,507][__main__][INFO] - [4160] Loss: 0.144, Running accuracy: 99.929, Time: 25.11
[2020-12-16 11:15:31,766][__main__][INFO] - [4480] Loss: 0.105, Running accuracy: 99.924, Time: 24.26
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[2020-12-16 12:01:40,254][__main__][INFO] - Action accuracy: 99.918, Loss: 0.092
[2020-12-16 12:01:40,255][__main__][INFO] - Validating..
[2020-12-16 12:02:21,407][test][INFO] - Time elapsed: 39.004597
[2020-12-16 12:02:21,411][__main__][INFO] - Validation F1 score: 95.610, Exact match: 56.000, Precision: 95.540, Recall: 95.690
[2020-12-16 12:03:09,371][__main__][INFO] - Checkpoint saved to checkpoints/model_latest.pth
[2020-12-16 12:03:10,445][__main__][INFO] - Epoch #58
[2020-12-16 12:03:10,446][__main__][INFO] - Training..
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[2020-12-16 12:52:56,662][__main__][INFO] - [38080] Loss: 0.062, Running accuracy: 99.917, Time: 23.51
[2020-12-16 12:53:20,841][__main__][INFO] - [38400] Loss: 0.061, Running accuracy: 99.918, Time: 24.18
[2020-12-16 12:53:43,715][__main__][INFO] - [38720] Loss: 0.077, Running accuracy: 99.918, Time: 22.87
[2020-12-16 12:54:08,620][__main__][INFO] - [39040] Loss: 0.052, Running accuracy: 99.918, Time: 24.90
[2020-12-16 12:54:31,983][__main__][INFO] - [39360] Loss: 0.086, Running accuracy: 99.918, Time: 23.36
[2020-12-16 12:54:55,858][__main__][INFO] - [39680] Loss: 0.108, Running accuracy: 99.918, Time: 23.87
[2020-12-16 12:55:06,064][__main__][INFO] - Action accuracy: 99.918, Loss: 0.091
[2020-12-16 12:55:06,066][__main__][INFO] - Validating..
[2020-12-16 12:55:47,778][test][INFO] - Time elapsed: 35.301878
[2020-12-16 12:55:47,785][__main__][INFO] - Validation F1 score: 95.580, Exact match: 56.060, Precision: 95.510, Recall: 95.660
[2020-12-16 12:56:23,577][__main__][INFO] - Checkpoint saved to checkpoints/model_latest.pth
[2020-12-16 12:56:24,435][__main__][INFO] - Epoch #59
[2020-12-16 12:56:24,436][__main__][INFO] - Training..
[2020-12-16 12:56:50,543][__main__][INFO] - [320] Loss: 0.118, Running accuracy: 99.896, Time: 24.81
[2020-12-16 12:57:13,620][__main__][INFO] - [640] Loss: 0.051, Running accuracy: 99.915, Time: 23.08
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[2020-12-16 13:41:44,096][__main__][INFO] - [34880] Loss: 0.113, Running accuracy: 99.914, Time: 24.43
[2020-12-16 13:42:08,684][__main__][INFO] - [35200] Loss: 0.036, Running accuracy: 99.915, Time: 24.59
[2020-12-16 13:42:35,413][__main__][INFO] - [35520] Loss: 0.088, Running accuracy: 99.914, Time: 26.73
[2020-12-16 13:43:01,074][__main__][INFO] - [35840] Loss: 0.110, Running accuracy: 99.914, Time: 25.66
[2020-12-16 13:43:27,227][__main__][INFO] - [36160] Loss: 0.073, Running accuracy: 99.914, Time: 26.15
[2020-12-16 13:43:51,720][__main__][INFO] - [36480] Loss: 0.044, Running accuracy: 99.914, Time: 24.49
[2020-12-16 13:44:18,322][__main__][INFO] - [36800] Loss: 0.042, Running accuracy: 99.915, Time: 26.60
[2020-12-16 13:44:42,094][__main__][INFO] - [37120] Loss: 0.068, Running accuracy: 99.915, Time: 23.77
[2020-12-16 13:45:09,036][__main__][INFO] - [37440] Loss: 0.091, Running accuracy: 99.915, Time: 26.94
[2020-12-16 13:45:34,491][__main__][INFO] - [37760] Loss: 0.069, Running accuracy: 99.915, Time: 25.45
[2020-12-16 13:46:00,082][__main__][INFO] - [38080] Loss: 0.093, Running accuracy: 99.915, Time: 25.59
[2020-12-16 13:46:26,761][__main__][INFO] - [38400] Loss: 0.053, Running accuracy: 99.915, Time: 26.68
[2020-12-16 13:46:50,234][__main__][INFO] - [38720] Loss: 0.058, Running accuracy: 99.915, Time: 23.47
[2020-12-16 13:47:18,108][__main__][INFO] - [39040] Loss: 0.038, Running accuracy: 99.916, Time: 27.87
[2020-12-16 13:47:43,092][__main__][INFO] - [39360] Loss: 0.047, Running accuracy: 99.916, Time: 24.98
[2020-12-16 13:48:07,081][__main__][INFO] - [39680] Loss: 0.110, Running accuracy: 99.916, Time: 23.99
[2020-12-16 13:48:17,897][__main__][INFO] - Action accuracy: 99.916, Loss: 0.091
[2020-12-16 13:48:17,899][__main__][INFO] - Validating..
[2020-12-16 13:48:55,654][test][INFO] - Time elapsed: 35.269186
[2020-12-16 13:48:55,659][__main__][INFO] - Validation F1 score: 95.590, Exact match: 56.180, Precision: 95.520, Recall: 95.660
[2020-12-16 13:49:37,017][__main__][INFO] - Checkpoint saved to checkpoints/model_latest.pth
[2020-12-16 13:49:37,954][__main__][INFO] - Training completed. Launching the testing script..
[2020-12-16 13:49:45,148][__main__][INFO] - python test.py model_path=runs/ptb_xlnet_graph_seed5/checkpoints/model_latest.pth dataset=ptb
/u/kaiyuy/projects/miniconda3/envs/parser/lib/python3.6/site-packages/hydra/_internal/hydra.py:71: UserWarning: 
@hydra.main(strict) flag is deprecated and will removed in the next version.
See https://hydra.cc/docs/next/upgrades/0.11_to_1.0/strict_mode_flag_deprecated
  warnings.warn(message=msg, category=UserWarning)
[2020-12-16 13:51:45,953][__main__][INFO] - 
exp_id: test
model_path: runs/ptb_xlnet_graph_seed5/checkpoints/model_latest.pth
num_workers: 4
eval_batch_size: 150
beam_size: 1
amp: true
model_spec:
- d_model
- encoder
- use_words
- use_tags
- d_kqv
- d_ff
- word_emb_dropout
- tag_emb_dropout
- relu_dropout
- residual_dropout
- attention_dropout
- num_attn_layers
- num_attn_heads
- decoder
- num_gcn_layers
- d_decoder
- max_sentence_len
path_train: data/02-21.10way.clean
path_val: data/22.auto.clean
path_test: data/23.auto.clean
max_sentence_len: 250

[2020-12-16 13:51:45,954][__main__][INFO] - Loading the model from /n/fs/pvl-mathqa/attach-juxtapose-parser/runs/ptb_xlnet_graph_seed5/checkpoints/model_latest.pth
[2020-12-16 13:52:11,600][__main__][INFO] - Restoring hyperparameters from the saved model checkpoint..
[2020-12-16 13:52:11,602][__main__][INFO] - d_model: 2048
[2020-12-16 13:52:11,602][__main__][INFO] - encoder: xlnet-large-cased
[2020-12-16 13:52:11,603][__main__][INFO] - use_words: False
[2020-12-16 13:52:11,603][__main__][INFO] - use_tags: False
[2020-12-16 13:52:11,603][__main__][INFO] - d_kqv: 64
[2020-12-16 13:52:11,603][__main__][INFO] - d_ff: 2048
[2020-12-16 13:52:11,604][__main__][INFO] - word_emb_dropout: 0.2
[2020-12-16 13:52:11,604][__main__][INFO] - tag_emb_dropout: 0
[2020-12-16 13:52:11,604][__main__][INFO] - relu_dropout: 0.2
[2020-12-16 13:52:11,604][__main__][INFO] - residual_dropout: 0
[2020-12-16 13:52:11,605][__main__][INFO] - attention_dropout: 0.2
[2020-12-16 13:52:11,605][__main__][INFO] - num_attn_layers: 3
[2020-12-16 13:52:11,605][__main__][INFO] - num_attn_heads: 8
[2020-12-16 13:52:11,606][__main__][INFO] - decoder: graph
[2020-12-16 13:52:11,606][__main__][INFO] - num_gcn_layers: 2
[2020-12-16 13:52:11,606][__main__][WARNING] - Missing: d_decoder
[2020-12-16 13:52:11,606][__main__][INFO] - max_sentence_len: 250
[2020-12-16 13:52:11,607][dataloader][INFO] - Loading constituency trees from /n/fs/pvl-mathqa/attach-juxtapose-parser/data/22.auto.clean
[2020-12-16 13:52:15,606][dataloader][INFO] - Loading constituency trees from /n/fs/pvl-mathqa/attach-juxtapose-parser/data/23.auto.clean
[2020-12-16 13:52:55,538][__main__][INFO] - 
Parser(
  (encoder): Encoder(
    (word_embedding): TransformerEmbedding(
      (contextual_embedding): XLNetModel(
        (word_embedding): Embedding(32000, 1024)
        (layer): ModuleList(
          (0): XLNetLayer(
            (rel_attn): XLNetRelativeAttention(
              (layer_norm): LayerNorm((1024,), eps=1e-12, elementwise_affine=True)
              (dropout): Dropout(p=0.1, inplace=False)
            )
            (ff): XLNetFeedForward(
              (layer_norm): LayerNorm((1024,), eps=1e-12, elementwise_affine=True)
              (layer_1): Linear(in_features=1024, out_features=4096, bias=True)
              (layer_2): Linear(in_features=4096, out_features=1024, bias=True)
              (dropout): Dropout(p=0.1, inplace=False)
            )
            (dropout): Dropout(p=0.1, inplace=False)
          )
          (1): XLNetLayer(
            (rel_attn): XLNetRelativeAttention(
              (layer_norm): LayerNorm((1024,), eps=1e-12, elementwise_affine=True)
              (dropout): Dropout(p=0.1, inplace=False)
            )
            (ff): XLNetFeedForward(
              (layer_norm): LayerNorm((1024,), eps=1e-12, elementwise_affine=True)
              (layer_1): Linear(in_features=1024, out_features=4096, bias=True)
              (layer_2): Linear(in_features=4096, out_features=1024, bias=True)
              (dropout): Dropout(p=0.1, inplace=False)
            )
            (dropout): Dropout(p=0.1, inplace=False)
          )
          (2): XLNetLayer(
            (rel_attn): XLNetRelativeAttention(
              (layer_norm): LayerNorm((1024,), eps=1e-12, elementwise_affine=True)
              (dropout): Dropout(p=0.1, inplace=False)
            )
            (ff): XLNetFeedForward(
              (layer_norm): LayerNorm((1024,), eps=1e-12, elementwise_affine=True)
              (layer_1): Linear(in_features=1024, out_features=4096, bias=True)
              (layer_2): Linear(in_features=4096, out_features=1024, bias=True)
              (dropout): Dropout(p=0.1, inplace=False)
            )
            (dropout): Dropout(p=0.1, inplace=False)
          )
          (3): XLNetLayer(
            (rel_attn): XLNetRelativeAttention(
              (layer_norm): LayerNorm((1024,), eps=1e-12, elementwise_affine=True)
              (dropout): Dropout(p=0.1, inplace=False)
            )
            (ff): XLNetFeedForward(
              (layer_norm): LayerNorm((1024,), eps=1e-12, elementwise_affine=True)
              (layer_1): Linear(in_features=1024, out_features=4096, bias=True)
              (layer_2): Linear(in_features=4096, out_features=1024, bias=True)
              (dropout): Dropout(p=0.1, inplace=False)
            )
            (dropout): Dropout(p=0.1, inplace=False)
          )
          (4): XLNetLayer(
            (rel_attn): XLNetRelativeAttention(
              (layer_norm): LayerNorm((1024,), eps=1e-12, elementwise_affine=True)
              (dropout): Dropout(p=0.1, inplace=False)
            )
            (ff): XLNetFeedForward(
              (layer_norm): LayerNorm((1024,), eps=1e-12, elementwise_affine=True)
              (layer_1): Linear(in_features=1024, out_features=4096, bias=True)
              (layer_2): Linear(in_features=4096, out_features=1024, bias=True)
              (dropout): Dropout(p=0.1, inplace=False)
            )
            (dropout): Dropout(p=0.1, inplace=False)
          )
          (5): XLNetLayer(
            (rel_attn): XLNetRelativeAttention(
              (layer_norm): LayerNorm((1024,), eps=1e-12, elementwise_affine=True)
              (dropout): Dropout(p=0.1, inplace=False)
            )
            (ff): XLNetFeedForward(
              (layer_norm): LayerNorm((1024,), eps=1e-12, elementwise_affine=True)
              (layer_1): Linear(in_features=1024, out_features=4096, bias=True)
              (layer_2): Linear(in_features=4096, out_features=1024, bias=True)
              (dropout): Dropout(p=0.1, inplace=False)
            )
            (dropout): Dropout(p=0.1, inplace=False)
          )
          (6): XLNetLayer(
            (rel_attn): XLNetRelativeAttention(
              (layer_norm): LayerNorm((1024,), eps=1e-12, elementwise_affine=True)
              (dropout): Dropout(p=0.1, inplace=False)
            )
            (ff): XLNetFeedForward(
              (layer_norm): LayerNorm((1024,), eps=1e-12, elementwise_affine=True)
              (layer_1): Linear(in_features=1024, out_features=4096, bias=True)
              (layer_2): Linear(in_features=4096, out_features=1024, bias=True)
              (dropout): Dropout(p=0.1, inplace=False)
            )
            (dropout): Dropout(p=0.1, inplace=False)
          )
          (7): XLNetLayer(
            (rel_attn): XLNetRelativeAttention(
              (layer_norm): LayerNorm((1024,), eps=1e-12, elementwise_affine=True)
              (dropout): Dropout(p=0.1, inplace=False)
            )
            (ff): XLNetFeedForward(
              (layer_norm): LayerNorm((1024,), eps=1e-12, elementwise_affine=True)
              (layer_1): Linear(in_features=1024, out_features=4096, bias=True)
              (layer_2): Linear(in_features=4096, out_features=1024, bias=True)
              (dropout): Dropout(p=0.1, inplace=False)
            )
            (dropout): Dropout(p=0.1, inplace=False)
          )
          (8): XLNetLayer(
            (rel_attn): XLNetRelativeAttention(
              (layer_norm): LayerNorm((1024,), eps=1e-12, elementwise_affine=True)
              (dropout): Dropout(p=0.1, inplace=False)
            )
            (ff): XLNetFeedForward(
              (layer_norm): LayerNorm((1024,), eps=1e-12, elementwise_affine=True)
              (layer_1): Linear(in_features=1024, out_features=4096, bias=True)
              (layer_2): Linear(in_features=4096, out_features=1024, bias=True)
              (dropout): Dropout(p=0.1, inplace=False)
            )
            (dropout): Dropout(p=0.1, inplace=False)
          )
          (9): XLNetLayer(
            (rel_attn): XLNetRelativeAttention(
              (layer_norm): LayerNorm((1024,), eps=1e-12, elementwise_affine=True)
              (dropout): Dropout(p=0.1, inplace=False)
            )
            (ff): XLNetFeedForward(
              (layer_norm): LayerNorm((1024,), eps=1e-12, elementwise_affine=True)
              (layer_1): Linear(in_features=1024, out_features=4096, bias=True)
              (layer_2): Linear(in_features=4096, out_features=1024, bias=True)
              (dropout): Dropout(p=0.1, inplace=False)
            )
            (dropout): Dropout(p=0.1, inplace=False)
          )
          (10): XLNetLayer(
            (rel_attn): XLNetRelativeAttention(
              (layer_norm): LayerNorm((1024,), eps=1e-12, elementwise_affine=True)
              (dropout): Dropout(p=0.1, inplace=False)
            )
            (ff): XLNetFeedForward(
              (layer_norm): LayerNorm((1024,), eps=1e-12, elementwise_affine=True)
              (layer_1): Linear(in_features=1024, out_features=4096, bias=True)
              (layer_2): Linear(in_features=4096, out_features=1024, bias=True)
              (dropout): Dropout(p=0.1, inplace=False)
            )
            (dropout): Dropout(p=0.1, inplace=False)
          )
          (11): XLNetLayer(
            (rel_attn): XLNetRelativeAttention(
              (layer_norm): LayerNorm((1024,), eps=1e-12, elementwise_affine=True)
              (dropout): Dropout(p=0.1, inplace=False)
            )
            (ff): XLNetFeedForward(
              (layer_norm): LayerNorm((1024,), eps=1e-12, elementwise_affine=True)
              (layer_1): Linear(in_features=1024, out_features=4096, bias=True)
              (layer_2): Linear(in_features=4096, out_features=1024, bias=True)
              (dropout): Dropout(p=0.1, inplace=False)
            )
            (dropout): Dropout(p=0.1, inplace=False)
          )
          (12): XLNetLayer(
            (rel_attn): XLNetRelativeAttention(
              (layer_norm): LayerNorm((1024,), eps=1e-12, elementwise_affine=True)
              (dropout): Dropout(p=0.1, inplace=False)
            )
            (ff): XLNetFeedForward(
              (layer_norm): LayerNorm((1024,), eps=1e-12, elementwise_affine=True)
              (layer_1): Linear(in_features=1024, out_features=4096, bias=True)
              (layer_2): Linear(in_features=4096, out_features=1024, bias=True)
              (dropout): Dropout(p=0.1, inplace=False)
            )
            (dropout): Dropout(p=0.1, inplace=False)
          )
          (13): XLNetLayer(
            (rel_attn): XLNetRelativeAttention(
              (layer_norm): LayerNorm((1024,), eps=1e-12, elementwise_affine=True)
              (dropout): Dropout(p=0.1, inplace=False)
            )
            (ff): XLNetFeedForward(
              (layer_norm): LayerNorm((1024,), eps=1e-12, elementwise_affine=True)
              (layer_1): Linear(in_features=1024, out_features=4096, bias=True)
              (layer_2): Linear(in_features=4096, out_features=1024, bias=True)
              (dropout): Dropout(p=0.1, inplace=False)
            )
            (dropout): Dropout(p=0.1, inplace=False)
          )
          (14): XLNetLayer(
            (rel_attn): XLNetRelativeAttention(
              (layer_norm): LayerNorm((1024,), eps=1e-12, elementwise_affine=True)
              (dropout): Dropout(p=0.1, inplace=False)
            )
            (ff): XLNetFeedForward(
              (layer_norm): LayerNorm((1024,), eps=1e-12, elementwise_affine=True)
              (layer_1): Linear(in_features=1024, out_features=4096, bias=True)
              (layer_2): Linear(in_features=4096, out_features=1024, bias=True)
              (dropout): Dropout(p=0.1, inplace=False)
            )
            (dropout): Dropout(p=0.1, inplace=False)
          )
          (15): XLNetLayer(
            (rel_attn): XLNetRelativeAttention(
              (layer_norm): LayerNorm((1024,), eps=1e-12, elementwise_affine=True)
              (dropout): Dropout(p=0.1, inplace=False)
            )
            (ff): XLNetFeedForward(
              (layer_norm): LayerNorm((1024,), eps=1e-12, elementwise_affine=True)
              (layer_1): Linear(in_features=1024, out_features=4096, bias=True)
              (layer_2): Linear(in_features=4096, out_features=1024, bias=True)
              (dropout): Dropout(p=0.1, inplace=False)
            )
            (dropout): Dropout(p=0.1, inplace=False)
          )
          (16): XLNetLayer(
            (rel_attn): XLNetRelativeAttention(
              (layer_norm): LayerNorm((1024,), eps=1e-12, elementwise_affine=True)
              (dropout): Dropout(p=0.1, inplace=False)
            )
            (ff): XLNetFeedForward(
              (layer_norm): LayerNorm((1024,), eps=1e-12, elementwise_affine=True)
              (layer_1): Linear(in_features=1024, out_features=4096, bias=True)
              (layer_2): Linear(in_features=4096, out_features=1024, bias=True)
              (dropout): Dropout(p=0.1, inplace=False)
            )
            (dropout): Dropout(p=0.1, inplace=False)
          )
          (17): XLNetLayer(
            (rel_attn): XLNetRelativeAttention(
              (layer_norm): LayerNorm((1024,), eps=1e-12, elementwise_affine=True)
              (dropout): Dropout(p=0.1, inplace=False)
            )
            (ff): XLNetFeedForward(
              (layer_norm): LayerNorm((1024,), eps=1e-12, elementwise_affine=True)
              (layer_1): Linear(in_features=1024, out_features=4096, bias=True)
              (layer_2): Linear(in_features=4096, out_features=1024, bias=True)
              (dropout): Dropout(p=0.1, inplace=False)
            )
            (dropout): Dropout(p=0.1, inplace=False)
          )
          (18): XLNetLayer(
            (rel_attn): XLNetRelativeAttention(
              (layer_norm): LayerNorm((1024,), eps=1e-12, elementwise_affine=True)
              (dropout): Dropout(p=0.1, inplace=False)
            )
            (ff): XLNetFeedForward(
              (layer_norm): LayerNorm((1024,), eps=1e-12, elementwise_affine=True)
              (layer_1): Linear(in_features=1024, out_features=4096, bias=True)
              (layer_2): Linear(in_features=4096, out_features=1024, bias=True)
              (dropout): Dropout(p=0.1, inplace=False)
            )
            (dropout): Dropout(p=0.1, inplace=False)
          )
          (19): XLNetLayer(
            (rel_attn): XLNetRelativeAttention(
              (layer_norm): LayerNorm((1024,), eps=1e-12, elementwise_affine=True)
              (dropout): Dropout(p=0.1, inplace=False)
            )
            (ff): XLNetFeedForward(
              (layer_norm): LayerNorm((1024,), eps=1e-12, elementwise_affine=True)
              (layer_1): Linear(in_features=1024, out_features=4096, bias=True)
              (layer_2): Linear(in_features=4096, out_features=1024, bias=True)
              (dropout): Dropout(p=0.1, inplace=False)
            )
            (dropout): Dropout(p=0.1, inplace=False)
          )
          (20): XLNetLayer(
            (rel_attn): XLNetRelativeAttention(
              (layer_norm): LayerNorm((1024,), eps=1e-12, elementwise_affine=True)
              (dropout): Dropout(p=0.1, inplace=False)
            )
            (ff): XLNetFeedForward(
              (layer_norm): LayerNorm((1024,), eps=1e-12, elementwise_affine=True)
              (layer_1): Linear(in_features=1024, out_features=4096, bias=True)
              (layer_2): Linear(in_features=4096, out_features=1024, bias=True)
              (dropout): Dropout(p=0.1, inplace=False)
            )
            (dropout): Dropout(p=0.1, inplace=False)
          )
          (21): XLNetLayer(
            (rel_attn): XLNetRelativeAttention(
              (layer_norm): LayerNorm((1024,), eps=1e-12, elementwise_affine=True)
              (dropout): Dropout(p=0.1, inplace=False)
            )
            (ff): XLNetFeedForward(
              (layer_norm): LayerNorm((1024,), eps=1e-12, elementwise_affine=True)
              (layer_1): Linear(in_features=1024, out_features=4096, bias=True)
              (layer_2): Linear(in_features=4096, out_features=1024, bias=True)
              (dropout): Dropout(p=0.1, inplace=False)
            )
            (dropout): Dropout(p=0.1, inplace=False)
          )
          (22): XLNetLayer(
            (rel_attn): XLNetRelativeAttention(
              (layer_norm): LayerNorm((1024,), eps=1e-12, elementwise_affine=True)
              (dropout): Dropout(p=0.1, inplace=False)
            )
            (ff): XLNetFeedForward(
              (layer_norm): LayerNorm((1024,), eps=1e-12, elementwise_affine=True)
              (layer_1): Linear(in_features=1024, out_features=4096, bias=True)
              (layer_2): Linear(in_features=4096, out_features=1024, bias=True)
              (dropout): Dropout(p=0.1, inplace=False)
            )
            (dropout): Dropout(p=0.1, inplace=False)
          )
          (23): XLNetLayer(
            (rel_attn): XLNetRelativeAttention(
              (layer_norm): LayerNorm((1024,), eps=1e-12, elementwise_affine=True)
              (dropout): Dropout(p=0.1, inplace=False)
            )
            (ff): XLNetFeedForward(
              (layer_norm): LayerNorm((1024,), eps=1e-12, elementwise_affine=True)
              (layer_1): Linear(in_features=1024, out_features=4096, bias=True)
              (layer_2): Linear(in_features=4096, out_features=1024, bias=True)
              (dropout): Dropout(p=0.1, inplace=False)
            )
            (dropout): Dropout(p=0.1, inplace=False)
          )
        )
        (dropout): Dropout(p=0.1, inplace=False)
      )
      (linear): Linear(in_features=1024, out_features=1024, bias=False)
    )
    (word_dropout): FeatureDropout()
    (layer_norm): LayerNorm((2048,), eps=1e-05, elementwise_affine=True)
    (attn_0): MultiHeadAttention(
      (attention): ScaledDotProductAttention(
        (dropout): Dropout(p=0.2, inplace=False)
        (softmax): Softmax(dim=-1)
      )
      (layer_norm): LayerNormalization()
      (proj1): Linear(in_features=256, out_features=1024, bias=False)
      (proj2): Linear(in_features=256, out_features=1024, bias=False)
      (residual_dropout): FeatureDropout()
    )
    (feedforward_0): PartitionedPositionwiseFeedForward(
      (w_1c): Linear(in_features=1024, out_features=1024, bias=True)
      (w_1p): Linear(in_features=1024, out_features=1024, bias=True)
      (w_2c): Linear(in_features=1024, out_features=1024, bias=True)
      (w_2p): Linear(in_features=1024, out_features=1024, bias=True)
      (layer_norm): LayerNormalization()
      (relu_dropout): FeatureDropout()
      (residual_dropout): FeatureDropout()
      (relu): ReLU()
    )
    (attn_1): MultiHeadAttention(
      (attention): ScaledDotProductAttention(
        (dropout): Dropout(p=0.2, inplace=False)
        (softmax): Softmax(dim=-1)
      )
      (layer_norm): LayerNormalization()
      (proj1): Linear(in_features=256, out_features=1024, bias=False)
      (proj2): Linear(in_features=256, out_features=1024, bias=False)
      (residual_dropout): FeatureDropout()
    )
    (feedforward_1): PartitionedPositionwiseFeedForward(
      (w_1c): Linear(in_features=1024, out_features=1024, bias=True)
      (w_1p): Linear(in_features=1024, out_features=1024, bias=True)
      (w_2c): Linear(in_features=1024, out_features=1024, bias=True)
      (w_2p): Linear(in_features=1024, out_features=1024, bias=True)
      (layer_norm): LayerNormalization()
      (relu_dropout): FeatureDropout()
      (residual_dropout): FeatureDropout()
      (relu): ReLU()
    )
    (attn_2): MultiHeadAttention(
      (attention): ScaledDotProductAttention(
        (dropout): Dropout(p=0.2, inplace=False)
        (softmax): Softmax(dim=-1)
      )
      (layer_norm): LayerNormalization()
      (proj1): Linear(in_features=256, out_features=1024, bias=False)
      (proj2): Linear(in_features=256, out_features=1024, bias=False)
      (residual_dropout): FeatureDropout()
    )
    (feedforward_2): PartitionedPositionwiseFeedForward(
      (w_1c): Linear(in_features=1024, out_features=1024, bias=True)
      (w_1p): Linear(in_features=1024, out_features=1024, bias=True)
      (w_2c): Linear(in_features=1024, out_features=1024, bias=True)
      (w_2p): Linear(in_features=1024, out_features=1024, bias=True)
      (layer_norm): LayerNormalization()
      (relu_dropout): FeatureDropout()
      (residual_dropout): FeatureDropout()
      (relu): ReLU()
    )
  )
  (decoder): GraphDecoder(
    (label_embedding): Embedding(113, 1024)
    (graph_embedding): GraphNeuralNetwork(
      (conv_0): SeparateGCNConv(
        (linear_c): Linear(in_features=1024, out_features=1024, bias=True)
        (linear_p): Linear(in_features=1024, out_features=1024, bias=True)
      )
      (layernorm_0): LayerNorm((2048,), eps=1e-05, elementwise_affine=True)
      (conv_1): SeparateGCNConv(
        (linear_c): Linear(in_features=1024, out_features=1024, bias=True)
        (linear_p): Linear(in_features=1024, out_features=1024, bias=True)
      )
      (layernorm_1): LayerNorm((2048,), eps=1e-05, elementwise_affine=True)
    )
    (action_decoder): ActionDecoder(
      (attn_layers_c): Sequential(
        (0): Linear(in_features=2048, out_features=512, bias=True)
        (1): LayerNorm((512,), eps=1e-05, elementwise_affine=True)
        (2): ReLU()
        (3): Linear(in_features=512, out_features=1, bias=True)
      )
      (attn_layers_p): Sequential(
        (0): Linear(in_features=2048, out_features=512, bias=True)
        (1): LayerNorm((512,), eps=1e-05, elementwise_affine=True)
        (2): ReLU()
        (3): Linear(in_features=512, out_features=1, bias=True)
      )
      (labels_layers): Sequential(
        (0): Linear(in_features=4096, out_features=1024, bias=True)
        (1): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)
        (2): ReLU()
        (3): Linear(in_features=1024, out_features=226, bias=True)
      )
    )
  )
)
[2020-12-16 13:52:55,550][__main__][INFO] - #parameters = 391343332
[2020-12-16 13:52:55,551][__main__][INFO] - Validating..
[2020-12-16 13:53:37,988][__main__][INFO] - Time elapsed: 33.966348
[2020-12-16 13:53:37,995][__main__][INFO] - Validation F1 score: 95.590, Exact match: 56.120, Precision: 95.520, Recall: 95.660
[2020-12-16 13:53:37,995][__main__][INFO] - Testing..
[2020-12-16 13:53:37,996][__main__][INFO] - Running without beam search..
[2020-12-16 13:54:27,040][__main__][INFO] - Time elapsed: 43.223661
[2020-12-16 13:54:27,049][__main__][INFO] - Testing F1 score: 96.440, Exact match: 59.480, Precision: 96.640, Recall: 96.240