GeForce RTX 2080 Ti
[2020-12-14 15:11:35,401][__main__][INFO] - 
exp_id: ptb_bert_graph_seed3
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: bert-large-uncased
use_tags: false
use_words: false
d_kqv: 64
d_ff: 2048
word_emb_dropout: 0.3
tag_emb_dropout: 0
relu_dropout: 0.2
residual_dropout: 0
attention_dropout: 0
num_attn_layers: 4
num_attn_heads: 8
decoder: graph
num_gcn_layers: 4
learning_rate: 3.0e-05
weight_decay: 0
subbatch_max_tokens: 700

[2020-12-14 15:11:35,403][dataloader][INFO] - Loading constituency trees from /n/fs/pvl-mathqa/attach-juxtapose-parser/data/02-21.10way.clean
[2020-12-14 15:12:46,881][dataloader][INFO] - Loading constituency trees from /n/fs/pvl-mathqa/attach-juxtapose-parser/data/22.auto.clean
[2020-12-14 15:13:34,715][__main__][INFO] - 
Parser(
  (encoder): Encoder(
    (word_embedding): TransformerEmbedding(
      (contextual_embedding): BertModel(
        (embeddings): BertEmbeddings(
          (word_embeddings): Embedding(30522, 1024, padding_idx=0)
          (position_embeddings): Embedding(512, 1024)
          (token_type_embeddings): Embedding(2, 1024)
          (LayerNorm): LayerNorm((1024,), eps=1e-12, elementwise_affine=True)
          (dropout): Dropout(p=0.1, inplace=False)
        )
        (encoder): BertEncoder(
          (layer): ModuleList(
            (0): BertLayer(
              (attention): BertAttention(
                (self): BertSelfAttention(
                  (query): Linear(in_features=1024, out_features=1024, bias=True)
                  (key): Linear(in_features=1024, out_features=1024, bias=True)
                  (value): Linear(in_features=1024, out_features=1024, bias=True)
                  (dropout): Dropout(p=0.1, inplace=False)
                )
                (output): BertSelfOutput(
                  (dense): Linear(in_features=1024, out_features=1024, bias=True)
                  (LayerNorm): LayerNorm((1024,), eps=1e-12, elementwise_affine=True)
                  (dropout): Dropout(p=0.1, inplace=False)
                )
              )
              (intermediate): BertIntermediate(
                (dense): Linear(in_features=1024, out_features=4096, bias=True)
              )
              (output): BertOutput(
                (dense): Linear(in_features=4096, out_features=1024, bias=True)
                (LayerNorm): LayerNorm((1024,), eps=1e-12, elementwise_affine=True)
                (dropout): Dropout(p=0.1, inplace=False)
              )
            )
            (1): BertLayer(
              (attention): BertAttention(
                (self): BertSelfAttention(
                  (query): Linear(in_features=1024, out_features=1024, bias=True)
                  (key): Linear(in_features=1024, out_features=1024, bias=True)
                  (value): Linear(in_features=1024, out_features=1024, bias=True)
                  (dropout): Dropout(p=0.1, inplace=False)
                )
                (output): BertSelfOutput(
                  (dense): Linear(in_features=1024, out_features=1024, bias=True)
                  (LayerNorm): LayerNorm((1024,), eps=1e-12, elementwise_affine=True)
                  (dropout): Dropout(p=0.1, inplace=False)
                )
              )
              (intermediate): BertIntermediate(
                (dense): Linear(in_features=1024, out_features=4096, bias=True)
              )
              (output): BertOutput(
                (dense): Linear(in_features=4096, out_features=1024, bias=True)
                (LayerNorm): LayerNorm((1024,), eps=1e-12, elementwise_affine=True)
                (dropout): Dropout(p=0.1, inplace=False)
              )
            )
            (2): BertLayer(
              (attention): BertAttention(
                (self): BertSelfAttention(
                  (query): Linear(in_features=1024, out_features=1024, bias=True)
                  (key): Linear(in_features=1024, out_features=1024, bias=True)
                  (value): Linear(in_features=1024, out_features=1024, bias=True)
                  (dropout): Dropout(p=0.1, inplace=False)
                )
                (output): BertSelfOutput(
                  (dense): Linear(in_features=1024, out_features=1024, bias=True)
                  (LayerNorm): LayerNorm((1024,), eps=1e-12, elementwise_affine=True)
                  (dropout): Dropout(p=0.1, inplace=False)
                )
              )
              (intermediate): BertIntermediate(
                (dense): Linear(in_features=1024, out_features=4096, bias=True)
              )
              (output): BertOutput(
                (dense): Linear(in_features=4096, out_features=1024, bias=True)
                (LayerNorm): LayerNorm((1024,), eps=1e-12, elementwise_affine=True)
                (dropout): Dropout(p=0.1, inplace=False)
              )
            )
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                (self): BertSelfAttention(
                  (query): Linear(in_features=1024, out_features=1024, bias=True)
                  (key): Linear(in_features=1024, out_features=1024, bias=True)
                  (value): Linear(in_features=1024, out_features=1024, bias=True)
                  (dropout): Dropout(p=0.1, inplace=False)
                )
                (output): BertSelfOutput(
                  (dense): Linear(in_features=1024, out_features=1024, bias=True)
                  (LayerNorm): LayerNorm((1024,), eps=1e-12, elementwise_affine=True)
                  (dropout): Dropout(p=0.1, inplace=False)
                )
              )
              (intermediate): BertIntermediate(
                (dense): Linear(in_features=1024, out_features=4096, bias=True)
              )
              (output): BertOutput(
                (dense): Linear(in_features=4096, out_features=1024, bias=True)
                (LayerNorm): LayerNorm((1024,), eps=1e-12, elementwise_affine=True)
                (dropout): Dropout(p=0.1, inplace=False)
              )
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                  (value): Linear(in_features=1024, out_features=1024, bias=True)
                  (dropout): Dropout(p=0.1, inplace=False)
                )
                (output): BertSelfOutput(
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                  (LayerNorm): LayerNorm((1024,), eps=1e-12, elementwise_affine=True)
                  (dropout): Dropout(p=0.1, inplace=False)
                )
              )
              (intermediate): BertIntermediate(
                (dense): Linear(in_features=1024, out_features=4096, bias=True)
              )
              (output): BertOutput(
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                (LayerNorm): LayerNorm((1024,), eps=1e-12, elementwise_affine=True)
                (dropout): Dropout(p=0.1, inplace=False)
              )
            )
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              (attention): BertAttention(
                (self): BertSelfAttention(
                  (query): Linear(in_features=1024, out_features=1024, bias=True)
                  (key): Linear(in_features=1024, out_features=1024, bias=True)
                  (value): Linear(in_features=1024, out_features=1024, bias=True)
                  (dropout): Dropout(p=0.1, inplace=False)
                )
                (output): BertSelfOutput(
                  (dense): Linear(in_features=1024, out_features=1024, bias=True)
                  (LayerNorm): LayerNorm((1024,), eps=1e-12, elementwise_affine=True)
                  (dropout): Dropout(p=0.1, inplace=False)
                )
              )
              (intermediate): BertIntermediate(
                (dense): Linear(in_features=1024, out_features=4096, bias=True)
              )
              (output): BertOutput(
                (dense): Linear(in_features=4096, out_features=1024, bias=True)
                (LayerNorm): LayerNorm((1024,), eps=1e-12, elementwise_affine=True)
                (dropout): Dropout(p=0.1, inplace=False)
              )
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                  (key): Linear(in_features=1024, out_features=1024, bias=True)
                  (value): Linear(in_features=1024, out_features=1024, bias=True)
                  (dropout): Dropout(p=0.1, inplace=False)
                )
                (output): BertSelfOutput(
                  (dense): Linear(in_features=1024, out_features=1024, bias=True)
                  (LayerNorm): LayerNorm((1024,), eps=1e-12, elementwise_affine=True)
                  (dropout): Dropout(p=0.1, inplace=False)
                )
              )
              (intermediate): BertIntermediate(
                (dense): Linear(in_features=1024, out_features=4096, bias=True)
              )
              (output): BertOutput(
                (dense): Linear(in_features=4096, out_features=1024, bias=True)
                (LayerNorm): LayerNorm((1024,), eps=1e-12, elementwise_affine=True)
                (dropout): Dropout(p=0.1, inplace=False)
              )
            )
            (7): BertLayer(
              (attention): BertAttention(
                (self): BertSelfAttention(
                  (query): Linear(in_features=1024, out_features=1024, bias=True)
                  (key): Linear(in_features=1024, out_features=1024, bias=True)
                  (value): Linear(in_features=1024, out_features=1024, bias=True)
                  (dropout): Dropout(p=0.1, inplace=False)
                )
                (output): BertSelfOutput(
                  (dense): Linear(in_features=1024, out_features=1024, bias=True)
                  (LayerNorm): LayerNorm((1024,), eps=1e-12, elementwise_affine=True)
                  (dropout): Dropout(p=0.1, inplace=False)
                )
              )
              (intermediate): BertIntermediate(
                (dense): Linear(in_features=1024, out_features=4096, bias=True)
              )
              (output): BertOutput(
                (dense): Linear(in_features=4096, out_features=1024, bias=True)
                (LayerNorm): LayerNorm((1024,), eps=1e-12, elementwise_affine=True)
                (dropout): Dropout(p=0.1, inplace=False)
              )
            )
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              (attention): BertAttention(
                (self): BertSelfAttention(
                  (query): Linear(in_features=1024, out_features=1024, bias=True)
                  (key): Linear(in_features=1024, out_features=1024, bias=True)
                  (value): Linear(in_features=1024, out_features=1024, bias=True)
                  (dropout): Dropout(p=0.1, inplace=False)
                )
                (output): BertSelfOutput(
                  (dense): Linear(in_features=1024, out_features=1024, bias=True)
                  (LayerNorm): LayerNorm((1024,), eps=1e-12, elementwise_affine=True)
                  (dropout): Dropout(p=0.1, inplace=False)
                )
              )
              (intermediate): BertIntermediate(
                (dense): Linear(in_features=1024, out_features=4096, bias=True)
              )
              (output): BertOutput(
                (dense): Linear(in_features=4096, out_features=1024, bias=True)
                (LayerNorm): LayerNorm((1024,), eps=1e-12, elementwise_affine=True)
                (dropout): Dropout(p=0.1, inplace=False)
              )
            )
            (9): BertLayer(
              (attention): BertAttention(
                (self): BertSelfAttention(
                  (query): Linear(in_features=1024, out_features=1024, bias=True)
                  (key): Linear(in_features=1024, out_features=1024, bias=True)
                  (value): Linear(in_features=1024, out_features=1024, bias=True)
                  (dropout): Dropout(p=0.1, inplace=False)
                )
                (output): BertSelfOutput(
                  (dense): Linear(in_features=1024, out_features=1024, bias=True)
                  (LayerNorm): LayerNorm((1024,), eps=1e-12, elementwise_affine=True)
                  (dropout): Dropout(p=0.1, inplace=False)
                )
              )
              (intermediate): BertIntermediate(
                (dense): Linear(in_features=1024, out_features=4096, bias=True)
              )
              (output): BertOutput(
                (dense): Linear(in_features=4096, out_features=1024, bias=True)
                (LayerNorm): LayerNorm((1024,), eps=1e-12, elementwise_affine=True)
                (dropout): Dropout(p=0.1, inplace=False)
              )
            )
            (10): BertLayer(
              (attention): BertAttention(
                (self): BertSelfAttention(
                  (query): Linear(in_features=1024, out_features=1024, bias=True)
                  (key): Linear(in_features=1024, out_features=1024, bias=True)
                  (value): Linear(in_features=1024, out_features=1024, bias=True)
                  (dropout): Dropout(p=0.1, inplace=False)
                )
                (output): BertSelfOutput(
                  (dense): Linear(in_features=1024, out_features=1024, bias=True)
                  (LayerNorm): LayerNorm((1024,), eps=1e-12, elementwise_affine=True)
                  (dropout): Dropout(p=0.1, inplace=False)
                )
              )
              (intermediate): BertIntermediate(
                (dense): Linear(in_features=1024, out_features=4096, bias=True)
              )
              (output): BertOutput(
                (dense): Linear(in_features=4096, out_features=1024, bias=True)
                (LayerNorm): LayerNorm((1024,), eps=1e-12, elementwise_affine=True)
                (dropout): Dropout(p=0.1, inplace=False)
              )
            )
            (11): BertLayer(
              (attention): BertAttention(
                (self): BertSelfAttention(
                  (query): Linear(in_features=1024, out_features=1024, bias=True)
                  (key): Linear(in_features=1024, out_features=1024, bias=True)
                  (value): Linear(in_features=1024, out_features=1024, bias=True)
                  (dropout): Dropout(p=0.1, inplace=False)
                )
                (output): BertSelfOutput(
                  (dense): Linear(in_features=1024, out_features=1024, bias=True)
                  (LayerNorm): LayerNorm((1024,), eps=1e-12, elementwise_affine=True)
                  (dropout): Dropout(p=0.1, inplace=False)
                )
              )
              (intermediate): BertIntermediate(
                (dense): Linear(in_features=1024, out_features=4096, bias=True)
              )
              (output): BertOutput(
                (dense): Linear(in_features=4096, out_features=1024, bias=True)
                (LayerNorm): LayerNorm((1024,), eps=1e-12, elementwise_affine=True)
                (dropout): Dropout(p=0.1, inplace=False)
              )
            )
            (12): BertLayer(
              (attention): BertAttention(
                (self): BertSelfAttention(
                  (query): Linear(in_features=1024, out_features=1024, bias=True)
                  (key): Linear(in_features=1024, out_features=1024, bias=True)
                  (value): Linear(in_features=1024, out_features=1024, bias=True)
                  (dropout): Dropout(p=0.1, inplace=False)
                )
                (output): BertSelfOutput(
                  (dense): Linear(in_features=1024, out_features=1024, bias=True)
                  (LayerNorm): LayerNorm((1024,), eps=1e-12, elementwise_affine=True)
                  (dropout): Dropout(p=0.1, inplace=False)
                )
              )
              (intermediate): BertIntermediate(
                (dense): Linear(in_features=1024, out_features=4096, bias=True)
              )
              (output): BertOutput(
                (dense): Linear(in_features=4096, out_features=1024, bias=True)
                (LayerNorm): LayerNorm((1024,), eps=1e-12, elementwise_affine=True)
                (dropout): Dropout(p=0.1, inplace=False)
              )
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            (13): BertLayer(
              (attention): BertAttention(
                (self): BertSelfAttention(
                  (query): Linear(in_features=1024, out_features=1024, bias=True)
                  (key): Linear(in_features=1024, out_features=1024, bias=True)
                  (value): Linear(in_features=1024, out_features=1024, bias=True)
                  (dropout): Dropout(p=0.1, inplace=False)
                )
                (output): BertSelfOutput(
                  (dense): Linear(in_features=1024, out_features=1024, bias=True)
                  (LayerNorm): LayerNorm((1024,), eps=1e-12, elementwise_affine=True)
                  (dropout): Dropout(p=0.1, inplace=False)
                )
              )
              (intermediate): BertIntermediate(
                (dense): Linear(in_features=1024, out_features=4096, bias=True)
              )
              (output): BertOutput(
                (dense): Linear(in_features=4096, out_features=1024, bias=True)
                (LayerNorm): LayerNorm((1024,), eps=1e-12, elementwise_affine=True)
                (dropout): Dropout(p=0.1, inplace=False)
              )
            )
            (14): BertLayer(
              (attention): BertAttention(
                (self): BertSelfAttention(
                  (query): Linear(in_features=1024, out_features=1024, bias=True)
                  (key): Linear(in_features=1024, out_features=1024, bias=True)
                  (value): Linear(in_features=1024, out_features=1024, bias=True)
                  (dropout): Dropout(p=0.1, inplace=False)
                )
                (output): BertSelfOutput(
                  (dense): Linear(in_features=1024, out_features=1024, bias=True)
                  (LayerNorm): LayerNorm((1024,), eps=1e-12, elementwise_affine=True)
                  (dropout): Dropout(p=0.1, inplace=False)
                )
              )
              (intermediate): BertIntermediate(
                (dense): Linear(in_features=1024, out_features=4096, bias=True)
              )
              (output): BertOutput(
                (dense): Linear(in_features=4096, out_features=1024, bias=True)
                (LayerNorm): LayerNorm((1024,), eps=1e-12, elementwise_affine=True)
                (dropout): Dropout(p=0.1, inplace=False)
              )
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            (15): BertLayer(
              (attention): BertAttention(
                (self): BertSelfAttention(
                  (query): Linear(in_features=1024, out_features=1024, bias=True)
                  (key): Linear(in_features=1024, out_features=1024, bias=True)
                  (value): Linear(in_features=1024, out_features=1024, bias=True)
                  (dropout): Dropout(p=0.1, inplace=False)
                )
                (output): BertSelfOutput(
                  (dense): Linear(in_features=1024, out_features=1024, bias=True)
                  (LayerNorm): LayerNorm((1024,), eps=1e-12, elementwise_affine=True)
                  (dropout): Dropout(p=0.1, inplace=False)
                )
              )
              (intermediate): BertIntermediate(
                (dense): Linear(in_features=1024, out_features=4096, bias=True)
              )
              (output): BertOutput(
                (dense): Linear(in_features=4096, out_features=1024, bias=True)
                (LayerNorm): LayerNorm((1024,), eps=1e-12, elementwise_affine=True)
                (dropout): Dropout(p=0.1, inplace=False)
              )
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            (16): BertLayer(
              (attention): BertAttention(
                (self): BertSelfAttention(
                  (query): Linear(in_features=1024, out_features=1024, bias=True)
                  (key): Linear(in_features=1024, out_features=1024, bias=True)
                  (value): Linear(in_features=1024, out_features=1024, bias=True)
                  (dropout): Dropout(p=0.1, inplace=False)
                )
                (output): BertSelfOutput(
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                  (LayerNorm): LayerNorm((1024,), eps=1e-12, elementwise_affine=True)
                  (dropout): Dropout(p=0.1, inplace=False)
                )
              )
              (intermediate): BertIntermediate(
                (dense): Linear(in_features=1024, out_features=4096, bias=True)
              )
              (output): BertOutput(
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                (LayerNorm): LayerNorm((1024,), eps=1e-12, elementwise_affine=True)
                (dropout): Dropout(p=0.1, inplace=False)
              )
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              (attention): BertAttention(
                (self): BertSelfAttention(
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                  (key): Linear(in_features=1024, out_features=1024, bias=True)
                  (value): Linear(in_features=1024, out_features=1024, bias=True)
                  (dropout): Dropout(p=0.1, inplace=False)
                )
                (output): BertSelfOutput(
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                  (LayerNorm): LayerNorm((1024,), eps=1e-12, elementwise_affine=True)
                  (dropout): Dropout(p=0.1, inplace=False)
                )
              )
              (intermediate): BertIntermediate(
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              )
              (output): BertOutput(
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                (LayerNorm): LayerNorm((1024,), eps=1e-12, elementwise_affine=True)
                (dropout): Dropout(p=0.1, inplace=False)
              )
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              (attention): BertAttention(
                (self): BertSelfAttention(
                  (query): Linear(in_features=1024, out_features=1024, bias=True)
                  (key): Linear(in_features=1024, out_features=1024, bias=True)
                  (value): Linear(in_features=1024, out_features=1024, bias=True)
                  (dropout): Dropout(p=0.1, inplace=False)
                )
                (output): BertSelfOutput(
                  (dense): Linear(in_features=1024, out_features=1024, bias=True)
                  (LayerNorm): LayerNorm((1024,), eps=1e-12, elementwise_affine=True)
                  (dropout): Dropout(p=0.1, inplace=False)
                )
              )
              (intermediate): BertIntermediate(
                (dense): Linear(in_features=1024, out_features=4096, bias=True)
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              (output): BertOutput(
                (dense): Linear(in_features=4096, out_features=1024, bias=True)
                (LayerNorm): LayerNorm((1024,), eps=1e-12, elementwise_affine=True)
                (dropout): Dropout(p=0.1, inplace=False)
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              (attention): BertAttention(
                (self): BertSelfAttention(
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                  (key): Linear(in_features=1024, out_features=1024, bias=True)
                  (value): Linear(in_features=1024, out_features=1024, bias=True)
                  (dropout): Dropout(p=0.1, inplace=False)
                )
                (output): BertSelfOutput(
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              (output): BertOutput(
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                (LayerNorm): LayerNorm((1024,), eps=1e-12, elementwise_affine=True)
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                (self): BertSelfAttention(
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                  (value): Linear(in_features=1024, out_features=1024, bias=True)
                  (dropout): Dropout(p=0.1, inplace=False)
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                (output): BertSelfOutput(
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                (LayerNorm): LayerNorm((1024,), eps=1e-12, elementwise_affine=True)
                (dropout): Dropout(p=0.1, inplace=False)
              )
            )
            (21): BertLayer(
              (attention): BertAttention(
                (self): BertSelfAttention(
                  (query): Linear(in_features=1024, out_features=1024, bias=True)
                  (key): Linear(in_features=1024, out_features=1024, bias=True)
                  (value): Linear(in_features=1024, out_features=1024, bias=True)
                  (dropout): Dropout(p=0.1, inplace=False)
                )
                (output): BertSelfOutput(
                  (dense): Linear(in_features=1024, out_features=1024, bias=True)
                  (LayerNorm): LayerNorm((1024,), eps=1e-12, elementwise_affine=True)
                  (dropout): Dropout(p=0.1, inplace=False)
                )
              )
              (intermediate): BertIntermediate(
                (dense): Linear(in_features=1024, out_features=4096, bias=True)
              )
              (output): BertOutput(
                (dense): Linear(in_features=4096, out_features=1024, bias=True)
                (LayerNorm): LayerNorm((1024,), eps=1e-12, elementwise_affine=True)
                (dropout): Dropout(p=0.1, inplace=False)
              )
            )
            (22): BertLayer(
              (attention): BertAttention(
                (self): BertSelfAttention(
                  (query): Linear(in_features=1024, out_features=1024, bias=True)
                  (key): Linear(in_features=1024, out_features=1024, bias=True)
                  (value): Linear(in_features=1024, out_features=1024, bias=True)
                  (dropout): Dropout(p=0.1, inplace=False)
                )
                (output): BertSelfOutput(
                  (dense): Linear(in_features=1024, out_features=1024, bias=True)
                  (LayerNorm): LayerNorm((1024,), eps=1e-12, elementwise_affine=True)
                  (dropout): Dropout(p=0.1, inplace=False)
                )
              )
              (intermediate): BertIntermediate(
                (dense): Linear(in_features=1024, out_features=4096, bias=True)
              )
              (output): BertOutput(
                (dense): Linear(in_features=4096, out_features=1024, bias=True)
                (LayerNorm): LayerNorm((1024,), eps=1e-12, elementwise_affine=True)
                (dropout): Dropout(p=0.1, inplace=False)
              )
            )
            (23): BertLayer(
              (attention): BertAttention(
                (self): BertSelfAttention(
                  (query): Linear(in_features=1024, out_features=1024, bias=True)
                  (key): Linear(in_features=1024, out_features=1024, bias=True)
                  (value): Linear(in_features=1024, out_features=1024, bias=True)
                  (dropout): Dropout(p=0.1, inplace=False)
                )
                (output): BertSelfOutput(
                  (dense): Linear(in_features=1024, out_features=1024, bias=True)
                  (LayerNorm): LayerNorm((1024,), eps=1e-12, elementwise_affine=True)
                  (dropout): Dropout(p=0.1, inplace=False)
                )
              )
              (intermediate): BertIntermediate(
                (dense): Linear(in_features=1024, out_features=4096, bias=True)
              )
              (output): BertOutput(
                (dense): Linear(in_features=4096, out_features=1024, bias=True)
                (LayerNorm): LayerNorm((1024,), eps=1e-12, elementwise_affine=True)
                (dropout): Dropout(p=0.1, inplace=False)
              )
            )
          )
        )
        (pooler): BertPooler(
          (dense): Linear(in_features=1024, out_features=1024, bias=True)
          (activation): Tanh()
        )
      )
      (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, 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, 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, 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()
    )
    (attn_3): MultiHeadAttention(
      (attention): ScaledDotProductAttention(
        (dropout): Dropout(p=0, 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_3): 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)
      (conv_2): SeparateGCNConv(
        (linear_c): Linear(in_features=1024, out_features=1024, bias=True)
        (linear_p): Linear(in_features=1024, out_features=1024, bias=True)
      )
      (layernorm_2): LayerNorm((2048,), eps=1e-05, elementwise_affine=True)
      (conv_3): SeparateGCNConv(
        (linear_c): Linear(in_features=1024, out_features=1024, bias=True)
        (linear_p): Linear(in_features=1024, out_features=1024, bias=True)
      )
      (layernorm_3): 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 15:13:34,722][__main__][INFO] - #parameters = 376726756
[2020-12-14 15:13:34,729][__main__][INFO] - Epoch #0
[2020-12-14 15:13:34,730][__main__][INFO] - Training..
[2020-12-14 15:13:44,092][__main__][INFO] - Learning rate adjusted to 0.00000000
[2020-12-14 15:13:46,503][__main__][INFO] - Learning rate adjusted to 0.00000020
[2020-12-14 15:13:48,871][__main__][INFO] - Learning rate adjusted to 0.00000040
[2020-12-14 15:13:50,967][__main__][INFO] - Learning rate adjusted to 0.00000060
[2020-12-14 15:13:53,237][__main__][INFO] - Learning rate adjusted to 0.00000080
[2020-12-14 15:13:55,681][__main__][INFO] - Learning rate adjusted to 0.00000100
[2020-12-14 15:13:58,228][__main__][INFO] - Learning rate adjusted to 0.00000120
[2020-12-14 15:14:01,645][__main__][INFO] - Learning rate adjusted to 0.00000140
[2020-12-14 15:14:03,999][__main__][INFO] - Learning rate adjusted to 0.00000160
[2020-12-14 15:14:06,460][__main__][INFO] - Learning rate adjusted to 0.00000180
[2020-12-14 15:14:06,629][__main__][INFO] - [320] Loss: 202.450, Running accuracy: 0.564, Time: 25.24
[2020-12-14 15:14:08,983][__main__][INFO] - Learning rate adjusted to 0.00000200
[2020-12-14 15:14:11,559][__main__][INFO] - Learning rate adjusted to 0.00000220
[2020-12-14 15:14:14,008][__main__][INFO] - Learning rate adjusted to 0.00000240
[2020-12-14 15:14:16,288][__main__][INFO] - Learning rate adjusted to 0.00000260
[2020-12-14 15:14:18,458][__main__][INFO] - Learning rate adjusted to 0.00000280
[2020-12-14 15:14:20,439][__main__][INFO] - Learning rate adjusted to 0.00000300
[2020-12-14 15:14:22,739][__main__][INFO] - Learning rate adjusted to 0.00000320
[2020-12-14 15:14:25,431][__main__][INFO] - Learning rate adjusted to 0.00000340
[2020-12-14 15:14:27,833][__main__][INFO] - Learning rate adjusted to 0.00000360
[2020-12-14 15:14:30,399][__main__][INFO] - Learning rate adjusted to 0.00000380
[2020-12-14 15:14:30,575][__main__][INFO] - [640] Loss: 147.547, Running accuracy: 11.839, Time: 23.95
[2020-12-14 15:14:32,898][__main__][INFO] - Learning rate adjusted to 0.00000400
[2020-12-14 15:14:35,085][__main__][INFO] - Learning rate adjusted to 0.00000420
[2020-12-14 15:14:37,591][__main__][INFO] - Learning rate adjusted to 0.00000440
[2020-12-14 15:14:40,337][__main__][INFO] - Learning rate adjusted to 0.00000460
[2020-12-14 15:14:42,759][__main__][INFO] - Learning rate adjusted to 0.00000480
[2020-12-14 15:14:45,428][__main__][INFO] - Learning rate adjusted to 0.00000500
[2020-12-14 15:14:47,782][__main__][INFO] - Learning rate adjusted to 0.00000520
[2020-12-14 15:14:50,177][__main__][INFO] - Learning rate adjusted to 0.00000540
[2020-12-14 15:14:52,283][__main__][INFO] - Learning rate adjusted to 0.00000560
[2020-12-14 15:14:54,572][__main__][INFO] - Learning rate adjusted to 0.00000580
[2020-12-14 15:14:54,741][__main__][INFO] - [960] Loss: 91.726, Running accuracy: 19.609, Time: 24.17
[2020-12-14 15:14:57,133][__main__][INFO] - Learning rate adjusted to 0.00000600
[2020-12-14 15:14:59,717][__main__][INFO] - Learning rate adjusted to 0.00000620
[2020-12-14 15:15:02,223][__main__][INFO] - Learning rate adjusted to 0.00000640
[2020-12-14 15:15:04,429][__main__][INFO] - Learning rate adjusted to 0.00000660
[2020-12-14 15:15:06,959][__main__][INFO] - Learning rate adjusted to 0.00000680
[2020-12-14 15:15:09,216][__main__][INFO] - Learning rate adjusted to 0.00000700
[2020-12-14 15:15:11,348][__main__][INFO] - Learning rate adjusted to 0.00000720
[2020-12-14 15:15:13,504][__main__][INFO] - Learning rate adjusted to 0.00000740
[2020-12-14 15:15:15,635][__main__][INFO] - Learning rate adjusted to 0.00000760
[2020-12-14 15:15:18,000][__main__][INFO] - Learning rate adjusted to 0.00000780
[2020-12-14 15:15:18,172][__main__][INFO] - [1280] Loss: 75.910, Running accuracy: 24.379, Time: 23.43
[2020-12-14 15:15:20,566][__main__][INFO] - Learning rate adjusted to 0.00000800
[2020-12-14 15:15:23,257][__main__][INFO] - Learning rate adjusted to 0.00000820
[2020-12-14 15:15:25,466][__main__][INFO] - Learning rate adjusted to 0.00000840
[2020-12-14 15:15:27,690][__main__][INFO] - Learning rate adjusted to 0.00000860
[2020-12-14 15:15:29,904][__main__][INFO] - Learning rate adjusted to 0.00000880
[2020-12-14 15:15:32,156][__main__][INFO] - Learning rate adjusted to 0.00000900
[2020-12-14 15:15:34,440][__main__][INFO] - Learning rate adjusted to 0.00000920
[2020-12-14 15:15:36,571][__main__][INFO] - Learning rate adjusted to 0.00000940
[2020-12-14 15:15:38,757][__main__][INFO] - Learning rate adjusted to 0.00000960
[2020-12-14 15:15:40,942][__main__][INFO] - Learning rate adjusted to 0.00000980
[2020-12-14 15:15:41,123][__main__][INFO] - [1600] Loss: 69.659, Running accuracy: 27.501, Time: 22.95
[2020-12-14 15:15:42,913][__main__][INFO] - Learning rate adjusted to 0.00001000
[2020-12-14 15:15:45,618][__main__][INFO] - Learning rate adjusted to 0.00001020
[2020-12-14 15:15:48,328][__main__][INFO] - Learning rate adjusted to 0.00001040
[2020-12-14 15:15:50,645][__main__][INFO] - Learning rate adjusted to 0.00001060
[2020-12-14 15:15:53,237][__main__][INFO] - Learning rate adjusted to 0.00001080
[2020-12-14 15:15:55,522][__main__][INFO] - Learning rate adjusted to 0.00001100
[2020-12-14 15:15:57,638][__main__][INFO] - Learning rate adjusted to 0.00001120
[2020-12-14 15:16:01,085][__main__][INFO] - Learning rate adjusted to 0.00001140
[2020-12-14 15:16:02,565][__main__][INFO] - Learning rate adjusted to 0.00001160
[2020-12-14 15:16:04,774][__main__][INFO] - Learning rate adjusted to 0.00001180
[2020-12-14 15:16:04,951][__main__][INFO] - [1920] Loss: 68.627, Running accuracy: 29.957, Time: 23.83
[2020-12-14 15:16:07,160][__main__][INFO] - Learning rate adjusted to 0.00001200
[2020-12-14 15:16:09,657][__main__][INFO] - Learning rate adjusted to 0.00001220
[2020-12-14 15:16:11,914][__main__][INFO] - Learning rate adjusted to 0.00001240
[2020-12-14 15:16:14,002][__main__][INFO] - Learning rate adjusted to 0.00001260
[2020-12-14 15:16:16,539][__main__][INFO] - Learning rate adjusted to 0.00001280
[2020-12-14 15:16:18,752][__main__][INFO] - Learning rate adjusted to 0.00001300
[2020-12-14 15:16:21,304][__main__][INFO] - Learning rate adjusted to 0.00001320
[2020-12-14 15:16:24,168][__main__][INFO] - Learning rate adjusted to 0.00001340
[2020-12-14 15:16:26,289][__main__][INFO] - Learning rate adjusted to 0.00001360
[2020-12-14 15:16:28,552][__main__][INFO] - Learning rate adjusted to 0.00001380
[2020-12-14 15:16:28,727][__main__][INFO] - [2240] Loss: 63.688, Running accuracy: 32.085, Time: 23.78
[2020-12-14 15:16:30,881][__main__][INFO] - Learning rate adjusted to 0.00001400
[2020-12-14 15:16:33,209][__main__][INFO] - Learning rate adjusted to 0.00001420
[2020-12-14 15:16:35,609][__main__][INFO] - Learning rate adjusted to 0.00001440
[2020-12-14 15:16:37,532][__main__][INFO] - Learning rate adjusted to 0.00001460
[2020-12-14 15:16:39,808][__main__][INFO] - Learning rate adjusted to 0.00001480
[2020-12-14 15:16:42,331][__main__][INFO] - Learning rate adjusted to 0.00001500
[2020-12-14 15:16:44,399][__main__][INFO] - Learning rate adjusted to 0.00001520
[2020-12-14 15:16:46,850][__main__][INFO] - Learning rate adjusted to 0.00001540
[2020-12-14 15:16:49,312][__main__][INFO] - Learning rate adjusted to 0.00001560
[2020-12-14 15:16:51,572][__main__][INFO] - Learning rate adjusted to 0.00001580
[2020-12-14 15:16:51,740][__main__][INFO] - [2560] Loss: 61.497, Running accuracy: 33.912, Time: 23.01
[2020-12-14 15:16:54,194][__main__][INFO] - Learning rate adjusted to 0.00001600
[2020-12-14 15:16:56,718][__main__][INFO] - Learning rate adjusted to 0.00001620
[2020-12-14 15:16:58,818][__main__][INFO] - Learning rate adjusted to 0.00001640
[2020-12-14 15:17:01,026][__main__][INFO] - Learning rate adjusted to 0.00001660
[2020-12-14 15:17:03,511][__main__][INFO] - Learning rate adjusted to 0.00001680
[2020-12-14 15:17:05,739][__main__][INFO] - Learning rate adjusted to 0.00001700
[2020-12-14 15:17:07,892][__main__][INFO] - Learning rate adjusted to 0.00001720
[2020-12-14 15:17:10,139][__main__][INFO] - Learning rate adjusted to 0.00001740
[2020-12-14 15:17:12,360][__main__][INFO] - Learning rate adjusted to 0.00001760
[2020-12-14 15:17:14,698][__main__][INFO] - Learning rate adjusted to 0.00001780
[2020-12-14 15:17:14,870][__main__][INFO] - [2880] Loss: 54.918, Running accuracy: 35.832, Time: 23.13
[2020-12-14 15:17:17,245][__main__][INFO] - Learning rate adjusted to 0.00001800
[2020-12-14 15:17:19,530][__main__][INFO] - Learning rate adjusted to 0.00001820
[2020-12-14 15:17:22,073][__main__][INFO] - Learning rate adjusted to 0.00001840
[2020-12-14 15:17:24,988][__main__][INFO] - Learning rate adjusted to 0.00001860
[2020-12-14 15:17:27,358][__main__][INFO] - Learning rate adjusted to 0.00001880
[2020-12-14 15:17:30,086][__main__][INFO] - Learning rate adjusted to 0.00001900
[2020-12-14 15:17:32,584][__main__][INFO] - Learning rate adjusted to 0.00001920
[2020-12-14 15:17:34,801][__main__][INFO] - Learning rate adjusted to 0.00001940
[2020-12-14 15:17:37,220][__main__][INFO] - Learning rate adjusted to 0.00001960
[2020-12-14 15:17:39,395][__main__][INFO] - Learning rate adjusted to 0.00001980
[2020-12-14 15:17:39,571][__main__][INFO] - [3200] Loss: 46.020, Running accuracy: 38.112, Time: 24.70
[2020-12-14 15:17:41,603][__main__][INFO] - Learning rate adjusted to 0.00002000
[2020-12-14 15:17:44,900][__main__][INFO] - Learning rate adjusted to 0.00002020
[2020-12-14 15:17:47,323][__main__][INFO] - Learning rate adjusted to 0.00002040
[2020-12-14 15:17:49,940][__main__][INFO] - Learning rate adjusted to 0.00002060
[2020-12-14 15:17:52,431][__main__][INFO] - Learning rate adjusted to 0.00002080
[2020-12-14 15:17:54,713][__main__][INFO] - Learning rate adjusted to 0.00002100
[2020-12-14 15:17:57,068][__main__][INFO] - Learning rate adjusted to 0.00002120
[2020-12-14 15:17:59,710][__main__][INFO] - Learning rate adjusted to 0.00002140
[2020-12-14 15:18:01,958][__main__][INFO] - Learning rate adjusted to 0.00002160
[2020-12-14 15:18:04,186][__main__][INFO] - Learning rate adjusted to 0.00002180
[2020-12-14 15:18:04,358][__main__][INFO] - [3520] Loss: 37.867, Running accuracy: 40.717, Time: 24.79
[2020-12-14 15:18:06,593][__main__][INFO] - Learning rate adjusted to 0.00002200
[2020-12-14 15:18:09,205][__main__][INFO] - Learning rate adjusted to 0.00002220
[2020-12-14 15:18:11,355][__main__][INFO] - Learning rate adjusted to 0.00002240
[2020-12-14 15:18:13,357][__main__][INFO] - Learning rate adjusted to 0.00002260
[2020-12-14 15:18:15,686][__main__][INFO] - Learning rate adjusted to 0.00002280
[2020-12-14 15:18:17,947][__main__][INFO] - Learning rate adjusted to 0.00002300
[2020-12-14 15:18:20,127][__main__][INFO] - Learning rate adjusted to 0.00002320
[2020-12-14 15:18:22,544][__main__][INFO] - Learning rate adjusted to 0.00002340
[2020-12-14 15:18:24,987][__main__][INFO] - Learning rate adjusted to 0.00002360
[2020-12-14 15:18:27,358][__main__][INFO] - Learning rate adjusted to 0.00002380
[2020-12-14 15:18:27,531][__main__][INFO] - [3840] Loss: 32.619, Running accuracy: 42.928, Time: 23.17
[2020-12-14 15:18:29,557][__main__][INFO] - Learning rate adjusted to 0.00002400
[2020-12-14 15:18:32,099][__main__][INFO] - Learning rate adjusted to 0.00002420
[2020-12-14 15:18:35,588][__main__][INFO] - Learning rate adjusted to 0.00002440
[2020-12-14 15:18:37,702][__main__][INFO] - Learning rate adjusted to 0.00002460
[2020-12-14 15:18:40,211][__main__][INFO] - Learning rate adjusted to 0.00002480
[2020-12-14 15:18:42,514][__main__][INFO] - Learning rate adjusted to 0.00002500
[2020-12-14 15:18:44,965][__main__][INFO] - Learning rate adjusted to 0.00002520
[2020-12-14 15:18:47,607][__main__][INFO] - Learning rate adjusted to 0.00002540
[2020-12-14 15:18:55,346][__main__][INFO] - Learning rate adjusted to 0.00002560
[2020-12-14 15:18:57,837][__main__][INFO] - Learning rate adjusted to 0.00002580
[2020-12-14 15:18:58,018][__main__][INFO] - [4160] Loss: 30.839, Running accuracy: 45.221, Time: 30.49
[2020-12-14 15:19:00,213][__main__][INFO] - Learning rate adjusted to 0.00002600
[2020-12-14 15:19:02,792][__main__][INFO] - Learning rate adjusted to 0.00002620
[2020-12-14 15:19:05,372][__main__][INFO] - Learning rate adjusted to 0.00002640
[2020-12-14 15:19:07,301][__main__][INFO] - Learning rate adjusted to 0.00002660
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[2020-12-14 15:19:22,096][__main__][INFO] - Learning rate adjusted to 0.00002780
[2020-12-14 15:19:22,265][__main__][INFO] - [4480] Loss: 26.889, Running accuracy: 47.357, Time: 24.25
[2020-12-14 15:19:24,923][__main__][INFO] - Learning rate adjusted to 0.00002800
[2020-12-14 15:19:26,529][__main__][INFO] - Learning rate adjusted to 0.00002820
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[2020-12-14 15:19:49,103][__main__][INFO] - Learning rate adjusted to 0.00003000
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[2020-12-14 16:00:19,739][__main__][INFO] - [36480] Loss: 4.315, Running accuracy: 87.332, Time: 26.07
[2020-12-14 16:00:45,665][__main__][INFO] - [36800] Loss: 4.448, Running accuracy: 87.401, Time: 25.93
[2020-12-14 16:01:09,351][__main__][INFO] - [37120] Loss: 4.432, Running accuracy: 87.467, Time: 23.69
[2020-12-14 16:01:33,345][__main__][INFO] - [37440] Loss: 4.778, Running accuracy: 87.531, Time: 23.99
[2020-12-14 16:01:59,078][__main__][INFO] - [37760] Loss: 4.520, Running accuracy: 87.596, Time: 25.73
[2020-12-14 16:02:25,910][__main__][INFO] - [38080] Loss: 4.436, Running accuracy: 87.665, Time: 26.83
[2020-12-14 16:02:50,815][__main__][INFO] - [38400] Loss: 4.086, Running accuracy: 87.734, Time: 24.90
[2020-12-14 16:03:13,629][__main__][INFO] - [38720] Loss: 4.749, Running accuracy: 87.790, Time: 22.81
[2020-12-14 16:03:36,686][__main__][INFO] - [39040] Loss: 4.489, Running accuracy: 87.853, Time: 23.06
[2020-12-14 16:04:05,450][__main__][INFO] - [39360] Loss: 4.267, Running accuracy: 87.913, Time: 28.76
[2020-12-14 16:04:30,130][__main__][INFO] - [39680] Loss: 4.219, Running accuracy: 87.973, Time: 24.68
[2020-12-14 16:04:40,583][__main__][INFO] - Action accuracy: 87.996, Loss: 15.864
[2020-12-14 16:04:40,585][__main__][INFO] - Validating..
[2020-12-14 16:05:06,527][test][INFO] - Time elapsed: 24.050741
[2020-12-14 16:05:06,532][__main__][INFO] - Validation F1 score: 94.110, Exact match: 48.290, Precision: 94.480, Recall: 93.740
[2020-12-14 16:05:06,532][__main__][INFO] - F1 score has improved
[2020-12-14 16:05:41,157][__main__][INFO] - Checkpoint saved to checkpoints/model_latest.pth
[2020-12-14 16:05:42,123][__main__][INFO] - Epoch #1
[2020-12-14 16:05:42,123][__main__][INFO] - Training..
[2020-12-14 16:06:06,833][__main__][INFO] - [320] Loss: 3.540, Running accuracy: 96.165, Time: 23.29
[2020-12-14 16:06:31,503][__main__][INFO] - [640] Loss: 3.897, Running accuracy: 96.064, Time: 24.57
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[2020-12-14 16:25:57,424][__main__][INFO] - [15680] Loss: 3.457, Running accuracy: 96.187, Time: 25.04
[2020-12-14 16:26:21,985][__main__][INFO] - [16000] Loss: 3.383, Running accuracy: 96.192, Time: 24.56
[2020-12-14 16:26:45,668][__main__][INFO] - [16320] Loss: 3.001, Running accuracy: 96.199, Time: 23.68
[2020-12-14 16:27:09,119][__main__][INFO] - [16640] Loss: 3.041, Running accuracy: 96.211, Time: 23.45
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[2020-12-14 16:27:58,299][__main__][INFO] - [17280] Loss: 3.434, Running accuracy: 96.223, Time: 25.40
[2020-12-14 16:28:22,779][__main__][INFO] - [17600] Loss: 3.349, Running accuracy: 96.228, Time: 24.48
[2020-12-14 16:28:47,957][__main__][INFO] - [17920] Loss: 3.525, Running accuracy: 96.224, Time: 25.18
[2020-12-14 16:29:12,397][__main__][INFO] - [18240] Loss: 3.268, Running accuracy: 96.225, Time: 24.44
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[2020-12-14 16:30:30,278][__main__][INFO] - [19200] Loss: 3.778, Running accuracy: 96.234, Time: 29.31
[2020-12-14 16:30:53,836][__main__][INFO] - [19520] Loss: 3.077, Running accuracy: 96.237, Time: 23.56
[2020-12-14 16:31:18,377][__main__][INFO] - [19840] Loss: 3.364, Running accuracy: 96.242, Time: 24.54
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[2020-12-14 16:32:06,353][__main__][INFO] - [20480] Loss: 3.184, Running accuracy: 96.252, Time: 24.52
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[2020-12-14 16:53:23,856][__main__][INFO] - [37120] Loss: 2.873, Running accuracy: 96.320, Time: 23.71
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[2020-12-14 16:54:11,135][__main__][INFO] - [37760] Loss: 3.350, Running accuracy: 96.321, Time: 23.44
[2020-12-14 16:54:35,602][__main__][INFO] - [38080] Loss: 3.299, Running accuracy: 96.320, Time: 24.47
[2020-12-14 16:55:00,096][__main__][INFO] - [38400] Loss: 3.319, Running accuracy: 96.320, Time: 24.49
[2020-12-14 16:55:24,543][__main__][INFO] - [38720] Loss: 3.303, Running accuracy: 96.321, Time: 24.45
[2020-12-14 16:55:48,161][__main__][INFO] - [39040] Loss: 2.835, Running accuracy: 96.322, Time: 23.62
[2020-12-14 16:56:13,015][__main__][INFO] - [39360] Loss: 3.065, Running accuracy: 96.322, Time: 24.85
[2020-12-14 16:56:37,331][__main__][INFO] - [39680] Loss: 3.254, Running accuracy: 96.323, Time: 24.32
[2020-12-14 16:56:47,856][__main__][INFO] - Action accuracy: 96.322, Loss: 3.710
[2020-12-14 16:56:47,856][__main__][INFO] - Validating..
[2020-12-14 16:57:18,314][test][INFO] - Time elapsed: 28.470848
[2020-12-14 16:57:18,319][__main__][INFO] - Validation F1 score: 94.660, Exact match: 51.240, Precision: 94.810, Recall: 94.500
[2020-12-14 16:57:18,319][__main__][INFO] - F1 score has improved
[2020-12-14 16:57:54,353][__main__][INFO] - Checkpoint saved to checkpoints/model_latest.pth
[2020-12-14 16:57:55,306][__main__][INFO] - Epoch #2
[2020-12-14 16:57:55,306][__main__][INFO] - Training..
[2020-12-14 16:58:20,964][__main__][INFO] - [320] Loss: 2.659, Running accuracy: 96.925, Time: 24.65
[2020-12-14 16:58:45,078][__main__][INFO] - [640] Loss: 2.613, Running accuracy: 97.073, Time: 24.11
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[2020-12-14 17:08:18,882][__main__][INFO] - [8000] Loss: 2.452, Running accuracy: 97.253, Time: 29.41
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[2020-12-14 17:09:32,099][__main__][INFO] - [8960] Loss: 2.115, Running accuracy: 97.276, Time: 24.56
[2020-12-14 17:09:57,939][__main__][INFO] - [9280] Loss: 2.327, Running accuracy: 97.285, Time: 25.84
[2020-12-14 17:10:23,424][__main__][INFO] - [9600] Loss: 2.385, Running accuracy: 97.291, Time: 25.48
[2020-12-14 17:10:49,630][__main__][INFO] - [9920] Loss: 2.226, Running accuracy: 97.304, Time: 26.20
[2020-12-14 17:11:13,877][__main__][INFO] - [10240] Loss: 2.333, Running accuracy: 97.308, Time: 24.25
[2020-12-14 17:11:36,075][__main__][INFO] - [10560] Loss: 2.037, Running accuracy: 97.310, Time: 22.20
[2020-12-14 17:12:00,444][__main__][INFO] - [10880] Loss: 2.413, Running accuracy: 97.310, Time: 24.37
[2020-12-14 17:12:23,585][__main__][INFO] - [11200] Loss: 2.666, Running accuracy: 97.299, Time: 23.14
[2020-12-14 17:12:47,774][__main__][INFO] - [11520] Loss: 2.311, Running accuracy: 97.293, Time: 24.19
[2020-12-14 17:13:12,288][__main__][INFO] - [11840] Loss: 2.350, Running accuracy: 97.291, Time: 24.51
[2020-12-14 17:13:41,949][__main__][INFO] - [12160] Loss: 2.334, Running accuracy: 97.294, Time: 29.66
[2020-12-14 17:14:06,225][__main__][INFO] - [12480] Loss: 2.242, Running accuracy: 97.294, Time: 24.28
[2020-12-14 17:14:31,205][__main__][INFO] - [12800] Loss: 2.106, Running accuracy: 97.300, Time: 24.98
[2020-12-14 17:14:53,914][__main__][INFO] - [13120] Loss: 2.226, Running accuracy: 97.305, Time: 22.71
[2020-12-14 17:15:19,478][__main__][INFO] - [13440] Loss: 2.366, Running accuracy: 97.303, Time: 25.56
[2020-12-14 17:15:42,119][__main__][INFO] - [13760] Loss: 2.831, Running accuracy: 97.295, Time: 22.64
[2020-12-14 17:16:07,741][__main__][INFO] - [14080] Loss: 2.438, Running accuracy: 97.299, Time: 25.62
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[2020-12-14 17:19:29,285][__main__][INFO] - [16640] Loss: 2.278, Running accuracy: 97.299, Time: 29.76
[2020-12-14 17:19:52,566][__main__][INFO] - [16960] Loss: 2.465, Running accuracy: 97.297, Time: 23.28
[2020-12-14 17:20:17,541][__main__][INFO] - [17280] Loss: 2.542, Running accuracy: 97.297, Time: 24.97
[2020-12-14 17:20:42,672][__main__][INFO] - [17600] Loss: 2.141, Running accuracy: 97.301, Time: 25.13
[2020-12-14 17:21:06,204][__main__][INFO] - [17920] Loss: 2.750, Running accuracy: 97.295, Time: 23.53
[2020-12-14 17:21:31,569][__main__][INFO] - [18240] Loss: 2.287, Running accuracy: 97.296, Time: 25.36
[2020-12-14 17:21:55,443][__main__][INFO] - [18560] Loss: 2.317, Running accuracy: 97.293, Time: 23.87
[2020-12-14 17:22:21,292][__main__][INFO] - [18880] Loss: 2.374, Running accuracy: 97.289, Time: 25.85
[2020-12-14 17:22:46,632][__main__][INFO] - [19200] Loss: 2.269, Running accuracy: 97.293, Time: 25.34
[2020-12-14 17:23:09,638][__main__][INFO] - [19520] Loss: 2.415, Running accuracy: 97.292, Time: 23.00
[2020-12-14 17:23:33,315][__main__][INFO] - [19840] Loss: 2.016, Running accuracy: 97.292, Time: 23.68
[2020-12-14 17:23:57,409][__main__][INFO] - [20160] Loss: 2.004, Running accuracy: 97.294, Time: 24.09
[2020-12-14 17:24:21,326][__main__][INFO] - [20480] Loss: 2.200, Running accuracy: 97.292, Time: 23.92
[2020-12-14 17:24:45,727][__main__][INFO] - [20800] Loss: 2.379, Running accuracy: 97.288, Time: 24.40
[2020-12-14 17:25:16,243][__main__][INFO] - [21120] Loss: 2.241, Running accuracy: 97.292, Time: 30.51
[2020-12-14 17:25:43,239][__main__][INFO] - [21440] Loss: 2.558, Running accuracy: 97.291, Time: 26.99
[2020-12-14 17:26:05,560][__main__][INFO] - [21760] Loss: 2.319, Running accuracy: 97.291, Time: 22.32
[2020-12-14 17:26:28,645][__main__][INFO] - [22080] Loss: 2.125, Running accuracy: 97.294, Time: 23.08
[2020-12-14 17:26:52,596][__main__][INFO] - [22400] Loss: 2.439, Running accuracy: 97.293, Time: 23.95
[2020-12-14 17:27:15,046][__main__][INFO] - [22720] Loss: 2.084, Running accuracy: 97.294, Time: 22.45
[2020-12-14 17:27:39,405][__main__][INFO] - [23040] Loss: 2.275, Running accuracy: 97.296, Time: 24.36
[2020-12-14 17:28:02,779][__main__][INFO] - [23360] Loss: 2.729, Running accuracy: 97.294, Time: 23.37
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[2020-12-14 17:28:48,751][__main__][INFO] - [24000] Loss: 2.145, Running accuracy: 97.299, Time: 21.62
[2020-12-14 17:29:11,063][__main__][INFO] - [24320] Loss: 2.416, Running accuracy: 97.299, Time: 22.31
[2020-12-14 17:29:34,871][__main__][INFO] - [24640] Loss: 2.118, Running accuracy: 97.301, Time: 23.81
[2020-12-14 17:29:59,889][__main__][INFO] - [24960] Loss: 2.554, Running accuracy: 97.297, Time: 25.02
[2020-12-14 17:30:24,337][__main__][INFO] - [25280] Loss: 2.647, Running accuracy: 97.295, Time: 24.45
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[2020-12-14 17:31:20,274][__main__][INFO] - [25920] Loss: 2.466, Running accuracy: 97.288, Time: 25.55
[2020-12-14 17:31:44,986][__main__][INFO] - [26240] Loss: 2.660, Running accuracy: 97.285, Time: 24.71
[2020-12-14 17:32:09,404][__main__][INFO] - [26560] Loss: 2.407, Running accuracy: 97.284, Time: 24.42
[2020-12-14 17:32:32,931][__main__][INFO] - [26880] Loss: 2.109, Running accuracy: 97.287, Time: 23.53
[2020-12-14 17:32:58,507][__main__][INFO] - [27200] Loss: 2.431, Running accuracy: 97.286, Time: 25.57
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[2020-12-14 17:34:58,531][__main__][INFO] - [28800] Loss: 2.421, Running accuracy: 97.285, Time: 22.80
[2020-12-14 17:35:23,223][__main__][INFO] - [29120] Loss: 2.450, Running accuracy: 97.285, Time: 24.69
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[2020-12-14 17:36:17,570][__main__][INFO] - [29760] Loss: 2.260, Running accuracy: 97.287, Time: 30.46
[2020-12-14 17:36:40,190][__main__][INFO] - [30080] Loss: 2.096, Running accuracy: 97.288, Time: 22.62
[2020-12-14 17:37:05,116][__main__][INFO] - [30400] Loss: 2.279, Running accuracy: 97.291, Time: 24.93
[2020-12-14 17:37:31,268][__main__][INFO] - [30720] Loss: 2.451, Running accuracy: 97.291, Time: 26.15
[2020-12-14 17:37:53,073][__main__][INFO] - [31040] Loss: 1.813, Running accuracy: 97.295, Time: 21.80
[2020-12-14 17:38:17,054][__main__][INFO] - [31360] Loss: 2.281, Running accuracy: 97.296, Time: 23.98
[2020-12-14 17:38:41,404][__main__][INFO] - [31680] Loss: 2.307, Running accuracy: 97.297, Time: 24.35
[2020-12-14 17:39:05,642][__main__][INFO] - [32000] Loss: 2.227, Running accuracy: 97.298, Time: 24.24
[2020-12-14 17:39:30,708][__main__][INFO] - [32320] Loss: 2.508, Running accuracy: 97.295, Time: 25.07
[2020-12-14 17:39:57,703][__main__][INFO] - [32640] Loss: 2.625, Running accuracy: 97.293, Time: 26.99
[2020-12-14 17:40:21,915][__main__][INFO] - [32960] Loss: 2.357, Running accuracy: 97.294, Time: 24.21
[2020-12-14 17:40:46,594][__main__][INFO] - [33280] Loss: 2.125, Running accuracy: 97.295, Time: 24.68
[2020-12-14 17:41:12,557][__main__][INFO] - [33600] Loss: 2.462, Running accuracy: 97.293, Time: 25.96
[2020-12-14 17:41:35,391][__main__][INFO] - [33920] Loss: 2.365, Running accuracy: 97.293, Time: 22.83
[2020-12-14 17:42:05,035][__main__][INFO] - [34240] Loss: 2.444, Running accuracy: 97.294, Time: 29.64
[2020-12-14 17:42:28,110][__main__][INFO] - [34560] Loss: 2.288, Running accuracy: 97.295, Time: 23.07
[2020-12-14 17:42:52,759][__main__][INFO] - [34880] Loss: 2.126, Running accuracy: 97.298, Time: 24.65
[2020-12-14 17:43:16,546][__main__][INFO] - [35200] Loss: 2.153, Running accuracy: 97.300, Time: 23.79
[2020-12-14 17:43:41,850][__main__][INFO] - [35520] Loss: 2.734, Running accuracy: 97.297, Time: 25.30
[2020-12-14 17:44:07,192][__main__][INFO] - [35840] Loss: 2.550, Running accuracy: 97.297, Time: 25.34
[2020-12-14 17:44:31,814][__main__][INFO] - [36160] Loss: 2.309, Running accuracy: 97.297, Time: 24.62
[2020-12-14 17:44:57,424][__main__][INFO] - [36480] Loss: 2.451, Running accuracy: 97.299, Time: 25.61
[2020-12-14 17:45:22,874][__main__][INFO] - [36800] Loss: 2.408, Running accuracy: 97.298, Time: 25.45
[2020-12-14 17:45:46,502][__main__][INFO] - [37120] Loss: 2.301, Running accuracy: 97.297, Time: 23.63
[2020-12-14 17:46:11,344][__main__][INFO] - [37440] Loss: 2.426, Running accuracy: 97.295, Time: 24.84
[2020-12-14 17:46:35,813][__main__][INFO] - [37760] Loss: 2.488, Running accuracy: 97.294, Time: 24.47
[2020-12-14 17:47:00,594][__main__][INFO] - [38080] Loss: 2.410, Running accuracy: 97.293, Time: 24.78
[2020-12-14 17:47:25,113][__main__][INFO] - [38400] Loss: 2.098, Running accuracy: 97.297, Time: 24.52
[2020-12-14 17:47:53,971][__main__][INFO] - [38720] Loss: 2.780, Running accuracy: 97.293, Time: 28.86
[2020-12-14 17:48:17,438][__main__][INFO] - [39040] Loss: 2.220, Running accuracy: 97.294, Time: 23.47
[2020-12-14 17:48:40,455][__main__][INFO] - [39360] Loss: 2.499, Running accuracy: 97.293, Time: 23.02
[2020-12-14 17:49:05,257][__main__][INFO] - [39680] Loss: 2.311, Running accuracy: 97.294, Time: 24.80
[2020-12-14 17:49:16,407][__main__][INFO] - Action accuracy: 97.294, Loss: 2.615
[2020-12-14 17:49:16,409][__main__][INFO] - Validating..
[2020-12-14 17:49:42,839][test][INFO] - Time elapsed: 24.949488
[2020-12-14 17:49:42,844][__main__][INFO] - Validation F1 score: 94.830, Exact match: 52.530, Precision: 94.930, Recall: 94.740
[2020-12-14 17:49:42,844][__main__][INFO] - F1 score has improved
[2020-12-14 17:50:17,710][__main__][INFO] - Checkpoint saved to checkpoints/model_latest.pth
[2020-12-14 17:50:18,511][__main__][INFO] - Epoch #3
[2020-12-14 17:50:18,511][__main__][INFO] - Training..
[2020-12-14 17:50:44,845][__main__][INFO] - [320] Loss: 1.627, Running accuracy: 98.102, Time: 25.03
[2020-12-14 17:51:12,708][__main__][INFO] - [640] Loss: 1.870, Running accuracy: 97.988, Time: 27.86
[2020-12-14 17:51:43,985][__main__][INFO] - [960] Loss: 1.798, Running accuracy: 97.958, Time: 31.28
[2020-12-14 17:52:08,977][__main__][INFO] - [1280] Loss: 1.614, Running accuracy: 97.964, Time: 24.90
[2020-12-14 17:52:33,013][__main__][INFO] - [1600] Loss: 1.588, Running accuracy: 97.952, Time: 24.04
[2020-12-14 17:52:59,222][__main__][INFO] - [1920] Loss: 1.608, Running accuracy: 98.000, Time: 26.21
[2020-12-14 17:53:22,409][__main__][INFO] - [2240] Loss: 1.630, Running accuracy: 98.007, Time: 23.19
[2020-12-14 17:53:48,783][__main__][INFO] - [2560] Loss: 1.875, Running accuracy: 98.012, Time: 26.37
[2020-12-14 17:54:15,287][__main__][INFO] - [2880] Loss: 2.191, Running accuracy: 97.965, Time: 26.50
[2020-12-14 17:54:38,629][__main__][INFO] - [3200] Loss: 1.550, Running accuracy: 97.952, Time: 23.34
[2020-12-14 17:55:01,287][__main__][INFO] - [3520] Loss: 1.727, Running accuracy: 97.970, Time: 22.66
[2020-12-14 17:55:24,847][__main__][INFO] - [3840] Loss: 1.645, Running accuracy: 97.957, Time: 23.56
[2020-12-14 17:55:50,236][__main__][INFO] - [4160] Loss: 1.628, Running accuracy: 97.970, Time: 25.39
[2020-12-14 17:56:15,237][__main__][INFO] - [4480] Loss: 1.764, Running accuracy: 97.984, Time: 25.00
[2020-12-14 17:56:38,823][__main__][INFO] - [4800] Loss: 1.601, Running accuracy: 97.993, Time: 23.58
[2020-12-14 17:57:03,904][__main__][INFO] - [5120] Loss: 1.685, Running accuracy: 98.011, Time: 25.08
[2020-12-14 17:57:35,433][__main__][INFO] - [5440] Loss: 1.955, Running accuracy: 98.005, Time: 31.53
[2020-12-14 17:57:57,066][__main__][INFO] - [5760] Loss: 1.815, Running accuracy: 97.997, Time: 21.63
[2020-12-14 17:58:19,806][__main__][INFO] - [6080] Loss: 1.714, Running accuracy: 97.986, Time: 22.74
[2020-12-14 17:58:42,484][__main__][INFO] - [6400] Loss: 1.503, Running accuracy: 97.991, Time: 22.68
[2020-12-14 17:59:08,393][__main__][INFO] - [6720] Loss: 1.791, Running accuracy: 97.976, Time: 25.91
[2020-12-14 17:59:33,360][__main__][INFO] - [7040] Loss: 1.602, Running accuracy: 97.981, Time: 24.97
[2020-12-14 17:59:57,773][__main__][INFO] - [7360] Loss: 1.595, Running accuracy: 97.976, Time: 24.41
[2020-12-14 18:00:22,125][__main__][INFO] - [7680] Loss: 1.519, Running accuracy: 97.984, Time: 24.35
[2020-12-14 18:00:46,264][__main__][INFO] - [8000] Loss: 1.579, Running accuracy: 97.987, Time: 24.14
[2020-12-14 18:01:10,505][__main__][INFO] - [8320] Loss: 1.687, Running accuracy: 97.988, Time: 24.24
[2020-12-14 18:01:34,621][__main__][INFO] - [8640] Loss: 1.709, Running accuracy: 97.992, Time: 24.11
[2020-12-14 18:01:58,943][__main__][INFO] - [8960] Loss: 1.645, Running accuracy: 97.985, Time: 24.32
[2020-12-14 18:02:24,470][__main__][INFO] - [9280] Loss: 1.573, Running accuracy: 97.992, Time: 25.53
[2020-12-14 18:02:47,642][__main__][INFO] - [9600] Loss: 1.617, Running accuracy: 97.993, Time: 23.17
[2020-12-14 18:03:17,369][__main__][INFO] - [9920] Loss: 1.681, Running accuracy: 98.001, Time: 29.73
[2020-12-14 18:03:44,610][__main__][INFO] - [10240] Loss: 1.510, Running accuracy: 98.009, Time: 27.24
[2020-12-14 18:04:07,265][__main__][INFO] - [10560] Loss: 1.563, Running accuracy: 98.011, Time: 22.65
[2020-12-14 18:04:31,902][__main__][INFO] - [10880] Loss: 1.872, Running accuracy: 98.017, Time: 24.64
[2020-12-14 18:04:56,910][__main__][INFO] - [11200] Loss: 2.106, Running accuracy: 98.014, Time: 25.01
[2020-12-14 18:05:20,972][__main__][INFO] - [11520] Loss: 1.742, Running accuracy: 98.010, Time: 24.06
[2020-12-14 18:05:45,799][__main__][INFO] - [11840] Loss: 2.014, Running accuracy: 98.006, Time: 24.83
[2020-12-14 18:06:10,414][__main__][INFO] - [12160] Loss: 2.333, Running accuracy: 97.989, Time: 24.61
[2020-12-14 18:06:35,180][__main__][INFO] - [12480] Loss: 1.249, Running accuracy: 98.003, Time: 24.76
[2020-12-14 18:07:00,051][__main__][INFO] - [12800] Loss: 1.792, Running accuracy: 97.993, Time: 24.87
[2020-12-14 18:07:24,979][__main__][INFO] - [13120] Loss: 1.910, Running accuracy: 97.996, Time: 24.93
[2020-12-14 18:07:50,163][__main__][INFO] - [13440] Loss: 1.740, Running accuracy: 97.994, Time: 25.18
[2020-12-14 18:08:15,882][__main__][INFO] - [13760] Loss: 2.101, Running accuracy: 97.978, Time: 25.72
[2020-12-14 18:08:45,540][__main__][INFO] - [14080] Loss: 1.681, Running accuracy: 97.981, Time: 29.66
[2020-12-14 18:09:11,972][__main__][INFO] - [14400] Loss: 1.667, Running accuracy: 97.981, Time: 26.43
[2020-12-14 18:09:36,910][__main__][INFO] - [14720] Loss: 1.681, Running accuracy: 97.982, Time: 24.94
[2020-12-14 18:10:02,313][__main__][INFO] - [15040] Loss: 1.739, Running accuracy: 97.978, Time: 25.40
[2020-12-14 18:10:27,799][__main__][INFO] - [15360] Loss: 2.213, Running accuracy: 97.971, Time: 25.48
[2020-12-14 18:10:51,485][__main__][INFO] - [15680] Loss: 2.164, Running accuracy: 97.965, Time: 23.69
[2020-12-14 18:11:15,262][__main__][INFO] - [16000] Loss: 1.636, Running accuracy: 97.962, Time: 23.78
[2020-12-14 18:11:40,243][__main__][INFO] - [16320] Loss: 2.190, Running accuracy: 97.959, Time: 24.98
[2020-12-14 18:12:03,138][__main__][INFO] - [16640] Loss: 2.123, Running accuracy: 97.953, Time: 22.89
[2020-12-14 18:12:28,418][__main__][INFO] - [16960] Loss: 1.649, Running accuracy: 97.958, Time: 25.28
[2020-12-14 18:12:52,361][__main__][INFO] - [17280] Loss: 1.744, Running accuracy: 97.958, Time: 23.94
[2020-12-14 18:13:17,000][__main__][INFO] - [17600] Loss: 1.922, Running accuracy: 97.959, Time: 24.64
[2020-12-14 18:13:42,061][__main__][INFO] - [17920] Loss: 1.532, Running accuracy: 97.962, Time: 25.06
[2020-12-14 18:14:08,224][__main__][INFO] - [18240] Loss: 2.152, Running accuracy: 97.953, Time: 26.16
[2020-12-14 18:14:37,218][__main__][INFO] - [18560] Loss: 1.707, Running accuracy: 97.958, Time: 28.99
[2020-12-14 18:15:02,669][__main__][INFO] - [18880] Loss: 1.781, Running accuracy: 97.955, Time: 25.45
[2020-12-14 18:15:27,817][__main__][INFO] - [19200] Loss: 1.583, Running accuracy: 97.957, Time: 25.15
[2020-12-14 18:15:53,703][__main__][INFO] - [19520] Loss: 1.893, Running accuracy: 97.958, Time: 25.89
[2020-12-14 18:16:16,589][__main__][INFO] - [19840] Loss: 1.670, Running accuracy: 97.958, Time: 22.88
[2020-12-14 18:16:41,294][__main__][INFO] - [20160] Loss: 1.926, Running accuracy: 97.954, Time: 24.70
[2020-12-14 18:17:07,831][__main__][INFO] - [20480] Loss: 1.853, Running accuracy: 97.952, Time: 26.54
[2020-12-14 18:17:32,259][__main__][INFO] - [20800] Loss: 1.866, Running accuracy: 97.947, Time: 24.43
[2020-12-14 18:17:55,955][__main__][INFO] - [21120] Loss: 1.397, Running accuracy: 97.950, Time: 23.69
[2020-12-14 18:18:20,163][__main__][INFO] - [21440] Loss: 1.548, Running accuracy: 97.950, Time: 24.21
[2020-12-14 18:18:44,212][__main__][INFO] - [21760] Loss: 2.066, Running accuracy: 97.946, Time: 24.05
[2020-12-14 18:19:10,830][__main__][INFO] - [22080] Loss: 1.956, Running accuracy: 97.945, Time: 26.62
[2020-12-14 18:19:35,091][__main__][INFO] - [22400] Loss: 1.703, Running accuracy: 97.942, Time: 24.26
[2020-12-14 18:20:00,172][__main__][INFO] - [22720] Loss: 1.906, Running accuracy: 97.939, Time: 25.08
[2020-12-14 18:20:31,074][__main__][INFO] - [23040] Loss: 2.341, Running accuracy: 97.932, Time: 30.90
[2020-12-14 18:20:55,805][__main__][INFO] - [23360] Loss: 2.118, Running accuracy: 97.926, Time: 24.73
[2020-12-14 18:21:20,726][__main__][INFO] - [23680] Loss: 2.067, Running accuracy: 97.923, Time: 24.92
[2020-12-14 18:21:42,670][__main__][INFO] - [24000] Loss: 1.454, Running accuracy: 97.925, Time: 21.94
[2020-12-14 18:22:07,471][__main__][INFO] - [24320] Loss: 1.901, Running accuracy: 97.926, Time: 24.80
[2020-12-14 18:22:30,654][__main__][INFO] - [24640] Loss: 1.416, Running accuracy: 97.925, Time: 23.18
[2020-12-14 18:22:55,471][__main__][INFO] - [24960] Loss: 2.015, Running accuracy: 97.921, Time: 24.82
[2020-12-14 18:23:20,584][__main__][INFO] - [25280] Loss: 1.913, Running accuracy: 97.918, Time: 25.11
[2020-12-14 18:23:43,930][__main__][INFO] - [25600] Loss: 1.619, Running accuracy: 97.919, Time: 23.34
[2020-12-14 18:24:08,788][__main__][INFO] - [25920] Loss: 1.684, Running accuracy: 97.920, Time: 24.86
[2020-12-14 18:24:33,679][__main__][INFO] - [26240] Loss: 1.741, Running accuracy: 97.921, Time: 24.89
[2020-12-14 18:24:56,376][__main__][INFO] - [26560] Loss: 1.782, Running accuracy: 97.918, Time: 22.70
[2020-12-14 18:25:20,788][__main__][INFO] - [26880] Loss: 1.734, Running accuracy: 97.917, Time: 24.41
[2020-12-14 18:25:49,461][__main__][INFO] - [27200] Loss: 1.411, Running accuracy: 97.920, Time: 28.67
[2020-12-14 18:26:13,015][__main__][INFO] - [27520] Loss: 1.759, Running accuracy: 97.922, Time: 23.55
[2020-12-14 18:26:37,477][__main__][INFO] - [27840] Loss: 1.881, Running accuracy: 97.918, Time: 24.46
[2020-12-14 18:27:02,941][__main__][INFO] - [28160] Loss: 1.972, Running accuracy: 97.915, Time: 25.46
[2020-12-14 18:27:26,795][__main__][INFO] - [28480] Loss: 1.640, Running accuracy: 97.914, Time: 23.85
[2020-12-14 18:27:51,025][__main__][INFO] - [28800] Loss: 2.149, Running accuracy: 97.910, Time: 24.23
[2020-12-14 18:28:14,723][__main__][INFO] - [29120] Loss: 1.773, Running accuracy: 97.911, Time: 23.70
[2020-12-14 18:28:38,245][__main__][INFO] - [29440] Loss: 1.952, Running accuracy: 97.911, Time: 23.52
[2020-12-14 18:29:01,331][__main__][INFO] - [29760] Loss: 1.824, Running accuracy: 97.908, Time: 23.08
[2020-12-14 18:29:25,772][__main__][INFO] - [30080] Loss: 1.728, Running accuracy: 97.907, Time: 24.44
[2020-12-14 18:29:49,609][__main__][INFO] - [30400] Loss: 1.607, Running accuracy: 97.906, Time: 23.84
[2020-12-14 18:30:14,788][__main__][INFO] - [30720] Loss: 1.722, Running accuracy: 97.905, Time: 25.18
[2020-12-14 18:30:37,515][__main__][INFO] - [31040] Loss: 1.605, Running accuracy: 97.905, Time: 22.73
[2020-12-14 18:31:03,273][__main__][INFO] - [31360] Loss: 1.762, Running accuracy: 97.906, Time: 25.76
[2020-12-14 18:31:33,338][__main__][INFO] - [31680] Loss: 1.881, Running accuracy: 97.905, Time: 30.06
[2020-12-14 18:31:56,833][__main__][INFO] - [32000] Loss: 1.564, Running accuracy: 97.908, Time: 23.49
[2020-12-14 18:32:21,156][__main__][INFO] - [32320] Loss: 2.060, Running accuracy: 97.904, Time: 24.32
[2020-12-14 18:32:45,272][__main__][INFO] - [32640] Loss: 1.892, Running accuracy: 97.904, Time: 24.12
[2020-12-14 18:33:08,849][__main__][INFO] - [32960] Loss: 1.624, Running accuracy: 97.906, Time: 23.58
[2020-12-14 18:33:32,811][__main__][INFO] - [33280] Loss: 2.093, Running accuracy: 97.902, Time: 23.96
[2020-12-14 18:33:59,064][__main__][INFO] - [33600] Loss: 1.751, Running accuracy: 97.903, Time: 26.25
[2020-12-14 18:34:22,647][__main__][INFO] - [33920] Loss: 1.821, Running accuracy: 97.900, Time: 23.58
[2020-12-14 18:34:48,694][__main__][INFO] - [34240] Loss: 1.973, Running accuracy: 97.898, Time: 26.05
[2020-12-14 18:35:14,306][__main__][INFO] - [34560] Loss: 1.560, Running accuracy: 97.901, Time: 25.61
[2020-12-14 18:35:39,921][__main__][INFO] - [34880] Loss: 1.746, Running accuracy: 97.903, Time: 25.61
[2020-12-14 18:36:05,852][__main__][INFO] - [35200] Loss: 2.260, Running accuracy: 97.899, Time: 25.93
[2020-12-14 18:36:31,735][__main__][INFO] - [35520] Loss: 1.950, Running accuracy: 97.898, Time: 25.88
[2020-12-14 18:36:56,209][__main__][INFO] - [35840] Loss: 1.714, Running accuracy: 97.898, Time: 24.47
[2020-12-14 18:37:26,681][__main__][INFO] - [36160] Loss: 1.766, Running accuracy: 97.897, Time: 30.47
[2020-12-14 18:37:50,710][__main__][INFO] - [36480] Loss: 1.840, Running accuracy: 97.897, Time: 24.03
[2020-12-14 18:38:12,973][__main__][INFO] - [36800] Loss: 1.552, Running accuracy: 97.898, Time: 22.26
[2020-12-14 18:38:34,870][__main__][INFO] - [37120] Loss: 1.405, Running accuracy: 97.899, Time: 21.90
[2020-12-14 18:39:01,132][__main__][INFO] - [37440] Loss: 2.060, Running accuracy: 97.896, Time: 26.26
[2020-12-14 18:39:25,545][__main__][INFO] - [37760] Loss: 1.746, Running accuracy: 97.897, Time: 24.41
[2020-12-14 18:39:49,358][__main__][INFO] - [38080] Loss: 1.618, Running accuracy: 97.897, Time: 23.81
[2020-12-14 18:40:13,295][__main__][INFO] - [38400] Loss: 1.986, Running accuracy: 97.895, Time: 23.94
[2020-12-14 18:40:37,218][__main__][INFO] - [38720] Loss: 1.858, Running accuracy: 97.894, Time: 23.92
[2020-12-14 18:41:01,827][__main__][INFO] - [39040] Loss: 1.599, Running accuracy: 97.895, Time: 24.61
[2020-12-14 18:41:24,233][__main__][INFO] - [39360] Loss: 1.894, Running accuracy: 97.894, Time: 22.40
[2020-12-14 18:41:47,560][__main__][INFO] - [39680] Loss: 1.838, Running accuracy: 97.893, Time: 23.33
[2020-12-14 18:41:57,161][__main__][INFO] - Action accuracy: 97.893, Loss: 1.965
[2020-12-14 18:41:57,161][__main__][INFO] - Validating..
[2020-12-14 18:42:29,582][test][INFO] - Time elapsed: 30.492511
[2020-12-14 18:42:29,586][__main__][INFO] - Validation F1 score: 95.050, Exact match: 53.240, Precision: 95.050, Recall: 95.060
[2020-12-14 18:42:29,586][__main__][INFO] - F1 score has improved
[2020-12-14 18:43:02,104][__main__][INFO] - Checkpoint saved to checkpoints/model_latest.pth
[2020-12-14 18:43:03,116][__main__][INFO] - Epoch #4
[2020-12-14 18:43:03,116][__main__][INFO] - Training..
[2020-12-14 18:43:28,654][__main__][INFO] - [320] Loss: 1.361, Running accuracy: 98.296, Time: 24.15
[2020-12-14 18:43:51,167][__main__][INFO] - [640] Loss: 1.408, Running accuracy: 98.295, Time: 22.51
[2020-12-14 18:44:17,541][__main__][INFO] - [960] Loss: 1.131, Running accuracy: 98.417, Time: 26.37
[2020-12-14 18:44:42,910][__main__][INFO] - [1280] Loss: 1.353, Running accuracy: 98.410, Time: 25.37
[2020-12-14 18:45:06,640][__main__][INFO] - [1600] Loss: 1.286, Running accuracy: 98.433, Time: 23.65
[2020-12-14 18:45:31,056][__main__][INFO] - [1920] Loss: 1.336, Running accuracy: 98.455, Time: 24.41
[2020-12-14 18:45:52,579][__main__][INFO] - [2240] Loss: 1.160, Running accuracy: 98.467, Time: 21.52
[2020-12-14 18:46:16,501][__main__][INFO] - [2560] Loss: 1.306, Running accuracy: 98.433, Time: 23.92
[2020-12-14 18:46:46,955][__main__][INFO] - [2880] Loss: 1.462, Running accuracy: 98.384, Time: 30.45
[2020-12-14 18:47:10,547][__main__][INFO] - [3200] Loss: 1.224, Running accuracy: 98.398, Time: 23.59
[2020-12-14 18:47:35,769][__main__][INFO] - [3520] Loss: 1.275, Running accuracy: 98.405, Time: 25.22
[2020-12-14 18:48:00,588][__main__][INFO] - [3840] Loss: 1.103, Running accuracy: 98.446, Time: 24.82
[2020-12-14 18:48:24,804][__main__][INFO] - [4160] Loss: 1.188, Running accuracy: 98.456, Time: 24.21
[2020-12-14 18:48:47,734][__main__][INFO] - [4480] Loss: 1.338, Running accuracy: 98.447, Time: 22.93
[2020-12-14 18:49:13,771][__main__][INFO] - [4800] Loss: 1.713, Running accuracy: 98.426, Time: 26.04
[2020-12-14 18:49:38,373][__main__][INFO] - [5120] Loss: 1.270, Running accuracy: 98.422, Time: 24.60
[2020-12-14 18:50:02,701][__main__][INFO] - [5440] Loss: 1.633, Running accuracy: 98.401, Time: 24.33
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[2020-12-14 18:51:15,967][__main__][INFO] - [6400] Loss: 1.314, Running accuracy: 98.399, Time: 25.46
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[2020-12-14 18:53:23,009][__main__][INFO] - [8000] Loss: 1.215, Running accuracy: 98.435, Time: 24.25
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[2020-12-14 18:55:26,091][__main__][INFO] - [9600] Loss: 1.406, Running accuracy: 98.434, Time: 24.39
[2020-12-14 18:55:50,197][__main__][INFO] - [9920] Loss: 1.458, Running accuracy: 98.427, Time: 24.11
[2020-12-14 18:56:12,484][__main__][INFO] - [10240] Loss: 1.314, Running accuracy: 98.427, Time: 22.29
[2020-12-14 18:56:37,127][__main__][INFO] - [10560] Loss: 1.077, Running accuracy: 98.437, Time: 24.64
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[2020-12-14 18:57:24,417][__main__][INFO] - [11200] Loss: 1.524, Running accuracy: 98.428, Time: 24.36
[2020-12-14 18:57:53,659][__main__][INFO] - [11520] Loss: 1.558, Running accuracy: 98.426, Time: 29.24
[2020-12-14 18:58:16,824][__main__][INFO] - [11840] Loss: 1.390, Running accuracy: 98.419, Time: 23.16
[2020-12-14 18:58:40,702][__main__][INFO] - [12160] Loss: 1.173, Running accuracy: 98.419, Time: 23.88
[2020-12-14 18:59:03,591][__main__][INFO] - [12480] Loss: 1.295, Running accuracy: 98.416, Time: 22.89
[2020-12-14 18:59:26,723][__main__][INFO] - [12800] Loss: 1.421, Running accuracy: 98.409, Time: 23.13
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[2020-12-14 19:31:42,383][__main__][INFO] - [37760] Loss: 1.527, Running accuracy: 98.344, Time: 24.16
[2020-12-14 19:32:13,840][__main__][INFO] - [38080] Loss: 1.557, Running accuracy: 98.341, Time: 31.46
[2020-12-14 19:32:38,104][__main__][INFO] - [38400] Loss: 1.248, Running accuracy: 98.341, Time: 24.26
[2020-12-14 19:33:01,447][__main__][INFO] - [38720] Loss: 1.537, Running accuracy: 98.340, Time: 23.34
[2020-12-14 19:33:25,103][__main__][INFO] - [39040] Loss: 1.150, Running accuracy: 98.341, Time: 23.66
[2020-12-14 19:33:49,667][__main__][INFO] - [39360] Loss: 1.532, Running accuracy: 98.340, Time: 24.56
[2020-12-14 19:34:13,620][__main__][INFO] - [39680] Loss: 1.573, Running accuracy: 98.338, Time: 23.95
[2020-12-14 19:34:22,714][__main__][INFO] - Action accuracy: 98.339, Loss: 1.533
[2020-12-14 19:34:22,716][__main__][INFO] - Validating..
[2020-12-14 19:34:49,289][test][INFO] - Time elapsed: 25.124761
[2020-12-14 19:34:49,293][__main__][INFO] - Validation F1 score: 95.080, Exact match: 54.470, Precision: 94.910, Recall: 95.260
[2020-12-14 19:34:49,293][__main__][INFO] - F1 score has improved
[2020-12-14 19:35:22,954][__main__][INFO] - Checkpoint saved to checkpoints/model_latest.pth
[2020-12-14 19:35:23,826][__main__][INFO] - Epoch #5
[2020-12-14 19:35:23,826][__main__][INFO] - Training..
[2020-12-14 19:35:55,272][__main__][INFO] - [320] Loss: 0.909, Running accuracy: 98.847, Time: 30.23
[2020-12-14 19:36:18,576][__main__][INFO] - [640] Loss: 1.028, Running accuracy: 98.795, Time: 23.30
[2020-12-14 19:36:42,389][__main__][INFO] - [960] Loss: 0.700, Running accuracy: 98.914, Time: 23.81
[2020-12-14 19:37:07,598][__main__][INFO] - [1280] Loss: 1.183, Running accuracy: 98.862, Time: 25.21
[2020-12-14 19:37:31,539][__main__][INFO] - [1600] Loss: 1.146, Running accuracy: 98.828, Time: 23.94
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[2020-12-14 19:38:21,044][__main__][INFO] - [2240] Loss: 0.951, Running accuracy: 98.782, Time: 24.88
[2020-12-14 19:38:45,226][__main__][INFO] - [2560] Loss: 0.968, Running accuracy: 98.788, Time: 24.18
[2020-12-14 19:39:12,682][__main__][INFO] - [2880] Loss: 0.940, Running accuracy: 98.773, Time: 27.45
[2020-12-14 19:39:35,943][__main__][INFO] - [3200] Loss: 1.266, Running accuracy: 98.750, Time: 23.26
[2020-12-14 19:40:00,285][__main__][INFO] - [3520] Loss: 0.864, Running accuracy: 98.760, Time: 24.34
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[2020-12-14 19:41:10,191][__main__][INFO] - [4480] Loss: 0.915, Running accuracy: 98.759, Time: 22.97
[2020-12-14 19:41:36,831][__main__][INFO] - [4800] Loss: 1.085, Running accuracy: 98.756, Time: 26.64
[2020-12-14 19:42:01,533][__main__][INFO] - [5120] Loss: 1.076, Running accuracy: 98.762, Time: 24.70
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[2020-12-14 19:43:14,451][__main__][INFO] - [6080] Loss: 1.224, Running accuracy: 98.733, Time: 25.33
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[2020-12-14 19:44:52,633][__main__][INFO] - [7360] Loss: 1.010, Running accuracy: 98.721, Time: 22.57
[2020-12-14 19:45:15,688][__main__][INFO] - [7680] Loss: 1.019, Running accuracy: 98.721, Time: 23.05
[2020-12-14 19:45:40,069][__main__][INFO] - [8000] Loss: 1.067, Running accuracy: 98.718, Time: 24.38
[2020-12-14 19:46:05,279][__main__][INFO] - [8320] Loss: 1.029, Running accuracy: 98.716, Time: 25.21
[2020-12-14 19:46:27,606][__main__][INFO] - [8640] Loss: 1.151, Running accuracy: 98.712, Time: 22.33
[2020-12-14 19:46:51,955][__main__][INFO] - [8960] Loss: 1.163, Running accuracy: 98.701, Time: 24.35
[2020-12-14 19:47:19,618][__main__][INFO] - [9280] Loss: 1.339, Running accuracy: 98.697, Time: 27.66
[2020-12-14 19:47:44,134][__main__][INFO] - [9600] Loss: 1.094, Running accuracy: 98.697, Time: 24.51
[2020-12-14 19:48:07,869][__main__][INFO] - [9920] Loss: 0.895, Running accuracy: 98.696, Time: 23.73
[2020-12-14 19:48:32,012][__main__][INFO] - [10240] Loss: 1.109, Running accuracy: 98.693, Time: 24.14
[2020-12-14 19:48:53,968][__main__][INFO] - [10560] Loss: 0.964, Running accuracy: 98.697, Time: 21.96
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[2020-12-14 19:50:04,351][__main__][INFO] - [11520] Loss: 0.879, Running accuracy: 98.696, Time: 23.96
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[2020-12-14 19:50:51,345][__main__][INFO] - [12160] Loss: 0.970, Running accuracy: 98.699, Time: 23.04
[2020-12-14 19:51:15,508][__main__][INFO] - [12480] Loss: 1.289, Running accuracy: 98.694, Time: 24.16
[2020-12-14 19:51:40,831][__main__][INFO] - [12800] Loss: 0.889, Running accuracy: 98.699, Time: 25.32
[2020-12-14 19:52:06,050][__main__][INFO] - [13120] Loss: 1.108, Running accuracy: 98.699, Time: 25.22
[2020-12-14 19:52:33,854][__main__][INFO] - [13440] Loss: 1.248, Running accuracy: 98.695, Time: 27.80
[2020-12-14 19:52:58,244][__main__][INFO] - [13760] Loss: 1.231, Running accuracy: 98.689, Time: 24.39
[2020-12-14 19:53:22,594][__main__][INFO] - [14080] Loss: 1.225, Running accuracy: 98.687, Time: 24.35
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[2020-12-14 19:57:25,271][__main__][INFO] - [17280] Loss: 1.120, Running accuracy: 98.677, Time: 26.17
[2020-12-14 19:57:49,048][__main__][INFO] - [17600] Loss: 1.011, Running accuracy: 98.676, Time: 23.78
[2020-12-14 19:58:16,760][__main__][INFO] - [17920] Loss: 1.138, Running accuracy: 98.676, Time: 27.71
[2020-12-14 19:58:40,614][__main__][INFO] - [18240] Loss: 1.142, Running accuracy: 98.673, Time: 23.85
[2020-12-14 19:59:05,435][__main__][INFO] - [18560] Loss: 0.995, Running accuracy: 98.673, Time: 24.82
[2020-12-14 19:59:29,124][__main__][INFO] - [18880] Loss: 1.093, Running accuracy: 98.673, Time: 23.69
[2020-12-14 19:59:53,670][__main__][INFO] - [19200] Loss: 1.093, Running accuracy: 98.673, Time: 24.54
[2020-12-14 20:00:18,957][__main__][INFO] - [19520] Loss: 1.264, Running accuracy: 98.671, Time: 25.29
[2020-12-14 20:00:43,127][__main__][INFO] - [19840] Loss: 1.034, Running accuracy: 98.669, Time: 24.17
[2020-12-14 20:01:08,680][__main__][INFO] - [20160] Loss: 1.109, Running accuracy: 98.667, Time: 25.55
[2020-12-14 20:01:33,143][__main__][INFO] - [20480] Loss: 1.133, Running accuracy: 98.668, Time: 24.46
[2020-12-14 20:01:57,861][__main__][INFO] - [20800] Loss: 0.913, Running accuracy: 98.669, Time: 24.72
[2020-12-14 20:02:21,469][__main__][INFO] - [21120] Loss: 0.995, Running accuracy: 98.671, Time: 23.61
[2020-12-14 20:02:49,437][__main__][INFO] - [21440] Loss: 0.940, Running accuracy: 98.673, Time: 27.97
[2020-12-14 20:03:14,227][__main__][INFO] - [21760] Loss: 1.153, Running accuracy: 98.671, Time: 24.79
[2020-12-14 20:03:36,653][__main__][INFO] - [22080] Loss: 1.295, Running accuracy: 98.667, Time: 22.43
[2020-12-14 20:04:04,790][__main__][INFO] - [22400] Loss: 1.099, Running accuracy: 98.667, Time: 28.14
[2020-12-14 20:04:28,943][__main__][INFO] - [22720] Loss: 1.283, Running accuracy: 98.666, Time: 24.15
[2020-12-14 20:04:53,718][__main__][INFO] - [23040] Loss: 1.325, Running accuracy: 98.664, Time: 24.77
[2020-12-14 20:05:17,699][__main__][INFO] - [23360] Loss: 1.096, Running accuracy: 98.664, Time: 23.98
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[2020-12-14 20:07:19,964][__main__][INFO] - [24960] Loss: 0.873, Running accuracy: 98.668, Time: 25.95
[2020-12-14 20:07:44,788][__main__][INFO] - [25280] Loss: 1.095, Running accuracy: 98.670, Time: 24.82
[2020-12-14 20:08:08,945][__main__][INFO] - [25600] Loss: 1.389, Running accuracy: 98.665, Time: 24.16
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[2020-12-14 20:08:57,451][__main__][INFO] - [26240] Loss: 1.146, Running accuracy: 98.665, Time: 23.27
[2020-12-14 20:09:25,311][__main__][INFO] - [26560] Loss: 1.260, Running accuracy: 98.663, Time: 27.86
[2020-12-14 20:09:49,142][__main__][INFO] - [26880] Loss: 1.212, Running accuracy: 98.663, Time: 23.83
[2020-12-14 20:10:11,992][__main__][INFO] - [27200] Loss: 1.188, Running accuracy: 98.661, Time: 22.85
[2020-12-14 20:10:36,549][__main__][INFO] - [27520] Loss: 1.094, Running accuracy: 98.661, Time: 24.56
[2020-12-14 20:10:59,823][__main__][INFO] - [27840] Loss: 0.931, Running accuracy: 98.663, Time: 23.27
[2020-12-14 20:11:24,329][__main__][INFO] - [28160] Loss: 1.112, Running accuracy: 98.664, Time: 24.50
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[2020-12-14 20:13:03,823][__main__][INFO] - [29440] Loss: 1.288, Running accuracy: 98.655, Time: 23.85
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[2020-12-14 20:21:20,293][__main__][INFO] - [35840] Loss: 0.951, Running accuracy: 98.638, Time: 21.58
[2020-12-14 20:21:45,334][__main__][INFO] - [36160] Loss: 0.842, Running accuracy: 98.640, Time: 25.04
[2020-12-14 20:22:09,160][__main__][INFO] - [36480] Loss: 1.136, Running accuracy: 98.638, Time: 23.83
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[2020-12-14 20:22:57,195][__main__][INFO] - [37120] Loss: 1.395, Running accuracy: 98.633, Time: 24.57
[2020-12-14 20:23:22,200][__main__][INFO] - [37440] Loss: 1.094, Running accuracy: 98.634, Time: 25.00
[2020-12-14 20:23:46,204][__main__][INFO] - [37760] Loss: 1.110, Running accuracy: 98.634, Time: 24.00
[2020-12-14 20:24:11,210][__main__][INFO] - [38080] Loss: 1.277, Running accuracy: 98.632, Time: 25.00
[2020-12-14 20:24:35,228][__main__][INFO] - [38400] Loss: 1.195, Running accuracy: 98.630, Time: 24.02
[2020-12-14 20:24:58,805][__main__][INFO] - [38720] Loss: 0.842, Running accuracy: 98.633, Time: 23.58
[2020-12-14 20:25:22,593][__main__][INFO] - [39040] Loss: 1.065, Running accuracy: 98.634, Time: 23.79
[2020-12-14 20:25:46,862][__main__][INFO] - [39360] Loss: 1.244, Running accuracy: 98.633, Time: 24.27
[2020-12-14 20:26:12,814][__main__][INFO] - [39680] Loss: 1.106, Running accuracy: 98.633, Time: 25.95
[2020-12-14 20:26:26,394][__main__][INFO] - Action accuracy: 98.633, Loss: 1.238
[2020-12-14 20:26:26,395][__main__][INFO] - Validating..
[2020-12-14 20:26:52,686][test][INFO] - Time elapsed: 24.161359
[2020-12-14 20:26:52,690][__main__][INFO] - Validation F1 score: 94.920, Exact match: 52.820, Precision: 94.890, Recall: 94.940
[2020-12-14 20:27:27,102][__main__][INFO] - Checkpoint saved to checkpoints/model_latest.pth
[2020-12-14 20:27:27,917][__main__][INFO] - Epoch #6
[2020-12-14 20:27:27,918][__main__][INFO] - Training..
[2020-12-14 20:27:55,483][__main__][INFO] - [320] Loss: 0.896, Running accuracy: 98.864, Time: 26.55
[2020-12-14 20:28:19,054][__main__][INFO] - [640] Loss: 0.737, Running accuracy: 98.953, Time: 23.57
[2020-12-14 20:28:43,285][__main__][INFO] - [960] Loss: 0.674, Running accuracy: 99.045, Time: 24.23
[2020-12-14 20:29:07,156][__main__][INFO] - [1280] Loss: 0.815, Running accuracy: 99.020, Time: 23.87
[2020-12-14 20:29:29,759][__main__][INFO] - [1600] Loss: 0.826, Running accuracy: 99.041, Time: 22.60
[2020-12-14 20:29:53,733][__main__][INFO] - [1920] Loss: 0.669, Running accuracy: 99.075, Time: 23.97
[2020-12-14 20:30:21,741][__main__][INFO] - [2240] Loss: 0.714, Running accuracy: 99.088, Time: 28.01
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[2020-12-14 20:31:11,703][__main__][INFO] - [2880] Loss: 0.845, Running accuracy: 99.049, Time: 24.70
[2020-12-14 20:31:35,860][__main__][INFO] - [3200] Loss: 0.774, Running accuracy: 99.055, Time: 24.16
[2020-12-14 20:31:59,626][__main__][INFO] - [3520] Loss: 0.885, Running accuracy: 99.047, Time: 23.77
[2020-12-14 20:32:21,903][__main__][INFO] - [3840] Loss: 0.874, Running accuracy: 99.047, Time: 22.28
[2020-12-14 20:32:48,370][__main__][INFO] - [4160] Loss: 1.144, Running accuracy: 99.031, Time: 26.47
[2020-12-14 20:33:13,255][__main__][INFO] - [4480] Loss: 0.733, Running accuracy: 99.044, Time: 24.88
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[2020-12-14 20:34:01,376][__main__][INFO] - [5120] Loss: 0.768, Running accuracy: 99.045, Time: 24.92
[2020-12-14 20:34:26,961][__main__][INFO] - [5440] Loss: 0.952, Running accuracy: 99.040, Time: 25.58
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[2020-12-14 20:35:15,826][__main__][INFO] - [6080] Loss: 1.100, Running accuracy: 99.023, Time: 24.67
[2020-12-14 20:35:43,439][__main__][INFO] - [6400] Loss: 0.866, Running accuracy: 99.019, Time: 27.61
[2020-12-14 20:36:06,913][__main__][INFO] - [6720] Loss: 0.703, Running accuracy: 99.020, Time: 23.47
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[2020-12-14 20:37:17,931][__main__][INFO] - [7680] Loss: 0.759, Running accuracy: 98.992, Time: 22.09
[2020-12-14 20:37:41,374][__main__][INFO] - [8000] Loss: 0.698, Running accuracy: 98.990, Time: 23.44
[2020-12-14 20:38:04,885][__main__][INFO] - [8320] Loss: 0.916, Running accuracy: 98.989, Time: 23.51
[2020-12-14 20:38:30,487][__main__][INFO] - [8640] Loss: 0.787, Running accuracy: 98.992, Time: 25.60
[2020-12-14 20:38:54,392][__main__][INFO] - [8960] Loss: 0.852, Running accuracy: 98.987, Time: 23.90
[2020-12-14 20:39:16,912][__main__][INFO] - [9280] Loss: 0.894, Running accuracy: 98.988, Time: 22.52
[2020-12-14 20:39:40,665][__main__][INFO] - [9600] Loss: 0.882, Running accuracy: 98.987, Time: 23.75
[2020-12-14 20:40:04,437][__main__][INFO] - [9920] Loss: 0.822, Running accuracy: 98.988, Time: 23.77
[2020-12-14 20:40:27,888][__main__][INFO] - [10240] Loss: 0.856, Running accuracy: 98.981, Time: 23.45
[2020-12-14 20:40:52,433][__main__][INFO] - [10560] Loss: 0.857, Running accuracy: 98.981, Time: 24.54
[2020-12-14 20:41:20,858][__main__][INFO] - [10880] Loss: 0.886, Running accuracy: 98.983, Time: 28.42
[2020-12-14 20:41:45,645][__main__][INFO] - [11200] Loss: 1.025, Running accuracy: 98.979, Time: 24.79
[2020-12-14 20:42:09,117][__main__][INFO] - [11520] Loss: 0.841, Running accuracy: 98.979, Time: 23.47
[2020-12-14 20:42:33,935][__main__][INFO] - [11840] Loss: 0.668, Running accuracy: 98.983, Time: 24.82
[2020-12-14 20:42:57,887][__main__][INFO] - [12160] Loss: 0.916, Running accuracy: 98.978, Time: 23.95
[2020-12-14 20:43:23,188][__main__][INFO] - [12480] Loss: 1.158, Running accuracy: 98.971, Time: 25.30
[2020-12-14 20:43:46,212][__main__][INFO] - [12800] Loss: 0.649, Running accuracy: 98.973, Time: 23.02
[2020-12-14 20:44:10,872][__main__][INFO] - [13120] Loss: 1.061, Running accuracy: 98.967, Time: 24.66
[2020-12-14 20:44:36,736][__main__][INFO] - [13440] Loss: 0.810, Running accuracy: 98.970, Time: 25.86
[2020-12-14 20:45:01,042][__main__][INFO] - [13760] Loss: 0.896, Running accuracy: 98.968, Time: 24.30
[2020-12-14 20:45:25,363][__main__][INFO] - [14080] Loss: 1.047, Running accuracy: 98.963, Time: 24.32
[2020-12-14 20:45:49,093][__main__][INFO] - [14400] Loss: 0.929, Running accuracy: 98.964, Time: 23.73
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[2020-12-14 20:47:32,782][__main__][INFO] - [15680] Loss: 1.083, Running accuracy: 98.958, Time: 25.86
[2020-12-14 20:47:57,574][__main__][INFO] - [16000] Loss: 1.111, Running accuracy: 98.952, Time: 24.79
[2020-12-14 20:48:21,302][__main__][INFO] - [16320] Loss: 0.711, Running accuracy: 98.950, Time: 23.73
[2020-12-14 20:48:45,248][__main__][INFO] - [16640] Loss: 0.922, Running accuracy: 98.949, Time: 23.94
[2020-12-14 20:49:08,890][__main__][INFO] - [16960] Loss: 1.034, Running accuracy: 98.949, Time: 23.64
[2020-12-14 20:49:32,882][__main__][INFO] - [17280] Loss: 0.693, Running accuracy: 98.952, Time: 23.99
[2020-12-14 20:49:57,830][__main__][INFO] - [17600] Loss: 0.784, Running accuracy: 98.953, Time: 24.95
[2020-12-14 20:50:23,395][__main__][INFO] - [17920] Loss: 1.003, Running accuracy: 98.951, Time: 25.56
[2020-12-14 20:50:47,500][__main__][INFO] - [18240] Loss: 1.064, Running accuracy: 98.947, Time: 24.10
[2020-12-14 20:51:11,584][__main__][INFO] - [18560] Loss: 0.895, Running accuracy: 98.946, Time: 24.08
[2020-12-14 20:51:35,250][__main__][INFO] - [18880] Loss: 0.695, Running accuracy: 98.947, Time: 23.66
[2020-12-14 20:51:58,882][__main__][INFO] - [19200] Loss: 0.949, Running accuracy: 98.945, Time: 23.63
[2020-12-14 20:52:26,969][__main__][INFO] - [19520] Loss: 0.942, Running accuracy: 98.940, Time: 28.09
[2020-12-14 20:52:50,977][__main__][INFO] - [19840] Loss: 0.933, Running accuracy: 98.939, Time: 24.01
[2020-12-14 20:53:17,171][__main__][INFO] - [20160] Loss: 1.009, Running accuracy: 98.937, Time: 26.19
[2020-12-14 20:53:43,318][__main__][INFO] - [20480] Loss: 1.096, Running accuracy: 98.934, Time: 26.15
[2020-12-14 20:54:07,257][__main__][INFO] - [20800] Loss: 0.925, Running accuracy: 98.934, Time: 23.94
[2020-12-14 20:54:33,694][__main__][INFO] - [21120] Loss: 1.023, Running accuracy: 98.932, Time: 26.44
[2020-12-14 20:54:57,810][__main__][INFO] - [21440] Loss: 1.021, Running accuracy: 98.928, Time: 24.11
[2020-12-14 20:55:20,477][__main__][INFO] - [21760] Loss: 0.978, Running accuracy: 98.927, Time: 22.67
[2020-12-14 20:55:43,159][__main__][INFO] - [22080] Loss: 1.116, Running accuracy: 98.923, Time: 22.68
[2020-12-14 20:56:07,395][__main__][INFO] - [22400] Loss: 0.939, Running accuracy: 98.921, Time: 24.23
[2020-12-14 20:56:32,250][__main__][INFO] - [22720] Loss: 0.782, Running accuracy: 98.921, Time: 24.85
[2020-12-14 20:56:55,108][__main__][INFO] - [23040] Loss: 0.878, Running accuracy: 98.922, Time: 22.86
[2020-12-14 20:57:19,066][__main__][INFO] - [23360] Loss: 0.958, Running accuracy: 98.920, Time: 23.96
[2020-12-14 20:57:41,432][__main__][INFO] - [23680] Loss: 0.859, Running accuracy: 98.917, Time: 22.37
[2020-12-14 20:58:07,804][__main__][INFO] - [24000] Loss: 0.692, Running accuracy: 98.918, Time: 26.37
[2020-12-14 20:58:31,614][__main__][INFO] - [24320] Loss: 0.805, Running accuracy: 98.918, Time: 23.81
[2020-12-14 20:58:56,328][__main__][INFO] - [24640] Loss: 0.955, Running accuracy: 98.917, Time: 24.71
[2020-12-14 20:59:22,284][__main__][INFO] - [24960] Loss: 0.859, Running accuracy: 98.919, Time: 25.96
[2020-12-14 20:59:45,745][__main__][INFO] - [25280] Loss: 0.985, Running accuracy: 98.917, Time: 23.46
[2020-12-14 21:00:08,912][__main__][INFO] - [25600] Loss: 0.798, Running accuracy: 98.918, Time: 23.17
[2020-12-14 21:00:34,079][__main__][INFO] - [25920] Loss: 0.821, Running accuracy: 98.918, Time: 25.17
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[2020-12-14 21:01:21,617][__main__][INFO] - [26560] Loss: 1.048, Running accuracy: 98.916, Time: 24.19
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[2020-12-14 21:03:29,668][__main__][INFO] - [28160] Loss: 0.897, Running accuracy: 98.911, Time: 28.79
[2020-12-14 21:03:53,560][__main__][INFO] - [28480] Loss: 0.894, Running accuracy: 98.910, Time: 23.89
[2020-12-14 21:04:16,800][__main__][INFO] - [28800] Loss: 0.762, Running accuracy: 98.911, Time: 23.24
[2020-12-14 21:04:40,313][__main__][INFO] - [29120] Loss: 0.903, Running accuracy: 98.910, Time: 23.51
[2020-12-14 21:05:04,239][__main__][INFO] - [29440] Loss: 0.927, Running accuracy: 98.909, Time: 23.93
[2020-12-14 21:05:28,972][__main__][INFO] - [29760] Loss: 1.052, Running accuracy: 98.908, Time: 24.73
[2020-12-14 21:05:53,055][__main__][INFO] - [30080] Loss: 0.949, Running accuracy: 98.906, Time: 24.08
[2020-12-14 21:06:17,922][__main__][INFO] - [30400] Loss: 0.716, Running accuracy: 98.909, Time: 24.87
[2020-12-14 21:06:40,880][__main__][INFO] - [30720] Loss: 0.928, Running accuracy: 98.907, Time: 22.96
[2020-12-14 21:07:02,216][__main__][INFO] - [31040] Loss: 1.007, Running accuracy: 98.906, Time: 21.33
[2020-12-14 21:07:26,317][__main__][INFO] - [31360] Loss: 0.852, Running accuracy: 98.906, Time: 24.10
[2020-12-14 21:07:51,231][__main__][INFO] - [31680] Loss: 0.955, Running accuracy: 98.906, Time: 24.91
[2020-12-14 21:08:18,013][__main__][INFO] - [32000] Loss: 0.951, Running accuracy: 98.905, Time: 26.78
[2020-12-14 21:08:40,713][__main__][INFO] - [32320] Loss: 0.844, Running accuracy: 98.904, Time: 22.70
[2020-12-14 21:09:08,301][__main__][INFO] - [32640] Loss: 0.824, Running accuracy: 98.904, Time: 27.59
[2020-12-14 21:09:31,391][__main__][INFO] - [32960] Loss: 1.007, Running accuracy: 98.903, Time: 23.09
[2020-12-14 21:09:56,524][__main__][INFO] - [33280] Loss: 0.947, Running accuracy: 98.902, Time: 25.13
[2020-12-14 21:10:22,476][__main__][INFO] - [33600] Loss: 1.190, Running accuracy: 98.901, Time: 25.95
[2020-12-14 21:10:46,349][__main__][INFO] - [33920] Loss: 0.833, Running accuracy: 98.900, Time: 23.87
[2020-12-14 21:11:09,884][__main__][INFO] - [34240] Loss: 0.808, Running accuracy: 98.902, Time: 23.53
[2020-12-14 21:11:36,383][__main__][INFO] - [34560] Loss: 0.958, Running accuracy: 98.900, Time: 26.50
[2020-12-14 21:11:59,608][__main__][INFO] - [34880] Loss: 0.950, Running accuracy: 98.900, Time: 23.22
[2020-12-14 21:12:23,541][__main__][INFO] - [35200] Loss: 1.055, Running accuracy: 98.899, Time: 23.93
[2020-12-14 21:12:48,178][__main__][INFO] - [35520] Loss: 0.918, Running accuracy: 98.899, Time: 24.64
[2020-12-14 21:13:13,642][__main__][INFO] - [35840] Loss: 1.015, Running accuracy: 98.897, Time: 25.46
[2020-12-14 21:13:39,251][__main__][INFO] - [36160] Loss: 1.090, Running accuracy: 98.896, Time: 25.61
[2020-12-14 21:14:03,545][__main__][INFO] - [36480] Loss: 0.814, Running accuracy: 98.896, Time: 24.29
[2020-12-14 21:14:26,808][__main__][INFO] - [36800] Loss: 0.900, Running accuracy: 98.895, Time: 23.26
[2020-12-14 21:14:57,306][__main__][INFO] - [37120] Loss: 1.232, Running accuracy: 98.893, Time: 30.50
[2020-12-14 21:15:20,523][__main__][INFO] - [37440] Loss: 0.788, Running accuracy: 98.893, Time: 23.22
[2020-12-14 21:15:44,850][__main__][INFO] - [37760] Loss: 1.095, Running accuracy: 98.892, Time: 24.33
[2020-12-14 21:16:10,084][__main__][INFO] - [38080] Loss: 1.035, Running accuracy: 98.890, Time: 25.23
[2020-12-14 21:16:33,360][__main__][INFO] - [38400] Loss: 0.846, Running accuracy: 98.889, Time: 23.28
[2020-12-14 21:16:57,743][__main__][INFO] - [38720] Loss: 0.954, Running accuracy: 98.890, Time: 24.38
[2020-12-14 21:17:22,475][__main__][INFO] - [39040] Loss: 0.681, Running accuracy: 98.891, Time: 24.73
[2020-12-14 21:17:48,803][__main__][INFO] - [39360] Loss: 1.037, Running accuracy: 98.890, Time: 26.33
[2020-12-14 21:18:11,967][__main__][INFO] - [39680] Loss: 0.734, Running accuracy: 98.891, Time: 23.16
[2020-12-14 21:18:21,779][__main__][INFO] - Action accuracy: 98.891, Loss: 0.998
[2020-12-14 21:18:21,780][__main__][INFO] - Validating..
[2020-12-14 21:18:52,374][test][INFO] - Time elapsed: 28.298649
[2020-12-14 21:18:52,379][__main__][INFO] - Validation F1 score: 95.140, Exact match: 51.710, Precision: 95.190, Recall: 95.080
[2020-12-14 21:18:52,379][__main__][INFO] - F1 score has improved
[2020-12-14 21:19:26,777][__main__][INFO] - Checkpoint saved to checkpoints/model_latest.pth
[2020-12-14 21:19:27,603][__main__][INFO] - Epoch #7
[2020-12-14 21:19:27,603][__main__][INFO] - Training..
[2020-12-14 21:19:52,942][__main__][INFO] - [320] Loss: 0.732, Running accuracy: 99.172, Time: 24.17
[2020-12-14 21:20:18,988][__main__][INFO] - [640] Loss: 0.706, Running accuracy: 99.197, Time: 26.04
[2020-12-14 21:20:45,542][__main__][INFO] - [960] Loss: 0.803, Running accuracy: 99.175, Time: 26.55
[2020-12-14 21:21:08,581][__main__][INFO] - [1280] Loss: 0.707, Running accuracy: 99.174, Time: 23.04
[2020-12-14 21:21:32,113][__main__][INFO] - [1600] Loss: 0.662, Running accuracy: 99.172, Time: 23.53
[2020-12-14 21:21:55,812][__main__][INFO] - [1920] Loss: 0.641, Running accuracy: 99.165, Time: 23.70
[2020-12-14 21:22:20,576][__main__][INFO] - [2240] Loss: 0.859, Running accuracy: 99.144, Time: 24.76
[2020-12-14 21:22:45,277][__main__][INFO] - [2560] Loss: 0.691, Running accuracy: 99.143, Time: 24.70
[2020-12-14 21:23:10,180][__main__][INFO] - [2880] Loss: 0.633, Running accuracy: 99.144, Time: 24.90
[2020-12-14 21:23:34,016][__main__][INFO] - [3200] Loss: 0.639, Running accuracy: 99.148, Time: 23.75
[2020-12-14 21:23:57,863][__main__][INFO] - [3520] Loss: 0.592, Running accuracy: 99.157, Time: 23.85
[2020-12-14 21:24:21,499][__main__][INFO] - [3840] Loss: 0.580, Running accuracy: 99.161, Time: 23.64
[2020-12-14 21:24:52,119][__main__][INFO] - [4160] Loss: 0.626, Running accuracy: 99.166, Time: 30.62
[2020-12-14 21:25:14,658][__main__][INFO] - [4480] Loss: 0.520, Running accuracy: 99.177, Time: 22.54
[2020-12-14 21:25:38,442][__main__][INFO] - [4800] Loss: 0.592, Running accuracy: 99.179, Time: 23.78
[2020-12-14 21:26:01,878][__main__][INFO] - [5120] Loss: 0.880, Running accuracy: 99.162, Time: 23.44
[2020-12-14 21:26:24,424][__main__][INFO] - [5440] Loss: 0.532, Running accuracy: 99.176, Time: 22.55
[2020-12-14 21:26:48,788][__main__][INFO] - [5760] Loss: 0.847, Running accuracy: 99.167, Time: 24.36
[2020-12-14 21:27:15,643][__main__][INFO] - [6080] Loss: 0.617, Running accuracy: 99.170, Time: 26.85
[2020-12-14 21:27:40,337][__main__][INFO] - [6400] Loss: 0.738, Running accuracy: 99.166, Time: 24.69
[2020-12-14 21:28:04,252][__main__][INFO] - [6720] Loss: 0.816, Running accuracy: 99.160, Time: 23.91
[2020-12-14 21:28:27,018][__main__][INFO] - [7040] Loss: 0.754, Running accuracy: 99.154, Time: 22.77
[2020-12-14 21:28:51,889][__main__][INFO] - [7360] Loss: 0.635, Running accuracy: 99.157, Time: 24.87
[2020-12-14 21:29:15,930][__main__][INFO] - [7680] Loss: 0.831, Running accuracy: 99.153, Time: 24.04
[2020-12-14 21:29:40,417][__main__][INFO] - [8000] Loss: 0.789, Running accuracy: 99.156, Time: 24.49
[2020-12-14 21:30:08,609][__main__][INFO] - [8320] Loss: 0.618, Running accuracy: 99.157, Time: 28.19
[2020-12-14 21:30:32,836][__main__][INFO] - [8640] Loss: 0.803, Running accuracy: 99.152, Time: 24.23
[2020-12-14 21:30:58,267][__main__][INFO] - [8960] Loss: 0.820, Running accuracy: 99.148, Time: 25.43
[2020-12-14 21:31:23,165][__main__][INFO] - [9280] Loss: 0.328, Running accuracy: 99.160, Time: 24.90
[2020-12-14 21:31:48,339][__main__][INFO] - [9600] Loss: 0.877, Running accuracy: 99.149, Time: 25.17
[2020-12-14 21:32:13,706][__main__][INFO] - [9920] Loss: 0.784, Running accuracy: 99.146, Time: 25.37
[2020-12-14 21:32:37,287][__main__][INFO] - [10240] Loss: 0.741, Running accuracy: 99.143, Time: 23.58
[2020-12-14 21:33:01,334][__main__][INFO] - [10560] Loss: 0.730, Running accuracy: 99.140, Time: 24.05
[2020-12-14 21:33:25,099][__main__][INFO] - [10880] Loss: 0.703, Running accuracy: 99.139, Time: 23.76
[2020-12-14 21:33:49,446][__main__][INFO] - [11200] Loss: 0.628, Running accuracy: 99.139, Time: 24.35
[2020-12-14 21:34:13,778][__main__][INFO] - [11520] Loss: 0.603, Running accuracy: 99.143, Time: 24.33
[2020-12-14 21:34:40,868][__main__][INFO] - [11840] Loss: 0.836, Running accuracy: 99.140, Time: 27.09
[2020-12-14 21:35:05,498][__main__][INFO] - [12160] Loss: 0.942, Running accuracy: 99.133, Time: 24.63
[2020-12-14 21:35:29,483][__main__][INFO] - [12480] Loss: 0.939, Running accuracy: 99.128, Time: 23.98
[2020-12-14 21:35:57,122][__main__][INFO] - [12800] Loss: 0.873, Running accuracy: 99.120, Time: 27.64
[2020-12-14 21:36:19,132][__main__][INFO] - [13120] Loss: 0.790, Running accuracy: 99.117, Time: 22.01
[2020-12-14 21:36:43,173][__main__][INFO] - [13440] Loss: 0.601, Running accuracy: 99.119, Time: 24.04
[2020-12-14 21:37:06,828][__main__][INFO] - [13760] Loss: 0.775, Running accuracy: 99.120, Time: 23.65
[2020-12-14 21:37:30,579][__main__][INFO] - [14080] Loss: 0.740, Running accuracy: 99.119, Time: 23.75
[2020-12-14 21:37:54,904][__main__][INFO] - [14400] Loss: 0.804, Running accuracy: 99.115, Time: 24.32
[2020-12-14 21:38:19,086][__main__][INFO] - [14720] Loss: 0.743, Running accuracy: 99.115, Time: 24.18
[2020-12-14 21:38:43,403][__main__][INFO] - [15040] Loss: 0.724, Running accuracy: 99.113, Time: 24.32
[2020-12-14 21:39:06,261][__main__][INFO] - [15360] Loss: 0.608, Running accuracy: 99.115, Time: 22.86
[2020-12-14 21:39:30,908][__main__][INFO] - [15680] Loss: 0.878, Running accuracy: 99.111, Time: 24.65
[2020-12-14 21:39:54,545][__main__][INFO] - [16000] Loss: 0.697, Running accuracy: 99.109, Time: 23.64
[2020-12-14 21:40:19,491][__main__][INFO] - [16320] Loss: 0.773, Running accuracy: 99.111, Time: 24.95
[2020-12-14 21:40:44,815][__main__][INFO] - [16640] Loss: 0.824, Running accuracy: 99.105, Time: 25.32
[2020-12-14 21:41:10,656][__main__][INFO] - [16960] Loss: 0.795, Running accuracy: 99.104, Time: 25.84
[2020-12-14 21:41:38,725][__main__][INFO] - [17280] Loss: 0.765, Running accuracy: 99.103, Time: 28.07
[2020-12-14 21:42:04,459][__main__][INFO] - [17600] Loss: 0.766, Running accuracy: 99.103, Time: 25.73
[2020-12-14 21:42:27,301][__main__][INFO] - [17920] Loss: 0.802, Running accuracy: 99.101, Time: 22.84
[2020-12-14 21:42:52,918][__main__][INFO] - [18240] Loss: 0.705, Running accuracy: 99.103, Time: 25.62
[2020-12-14 21:43:16,345][__main__][INFO] - [18560] Loss: 0.722, Running accuracy: 99.103, Time: 23.43
[2020-12-14 21:43:39,096][__main__][INFO] - [18880] Loss: 0.647, Running accuracy: 99.104, Time: 22.75
[2020-12-14 21:44:03,454][__main__][INFO] - [19200] Loss: 0.653, Running accuracy: 99.104, Time: 24.36
[2020-12-14 21:44:27,751][__main__][INFO] - [19520] Loss: 0.570, Running accuracy: 99.107, Time: 24.30
[2020-12-14 21:44:52,101][__main__][INFO] - [19840] Loss: 0.765, Running accuracy: 99.103, Time: 24.35
[2020-12-14 21:45:15,626][__main__][INFO] - [20160] Loss: 0.590, Running accuracy: 99.105, Time: 23.52
[2020-12-14 21:45:38,147][__main__][INFO] - [20480] Loss: 0.729, Running accuracy: 99.100, Time: 22.52
[2020-12-14 21:46:04,640][__main__][INFO] - [20800] Loss: 0.946, Running accuracy: 99.094, Time: 26.49
[2020-12-14 21:46:27,396][__main__][INFO] - [21120] Loss: 0.678, Running accuracy: 99.094, Time: 22.76
[2020-12-14 21:46:52,768][__main__][INFO] - [21440] Loss: 0.914, Running accuracy: 99.093, Time: 25.37
[2020-12-14 21:47:24,207][__main__][INFO] - [21760] Loss: 0.919, Running accuracy: 99.091, Time: 31.44
[2020-12-14 21:47:47,475][__main__][INFO] - [22080] Loss: 0.881, Running accuracy: 99.090, Time: 23.27
[2020-12-14 21:48:09,850][__main__][INFO] - [22400] Loss: 0.973, Running accuracy: 99.086, Time: 22.37
[2020-12-14 21:48:34,475][__main__][INFO] - [22720] Loss: 0.782, Running accuracy: 99.087, Time: 24.62
[2020-12-14 21:48:58,297][__main__][INFO] - [23040] Loss: 0.788, Running accuracy: 99.087, Time: 23.82
[2020-12-14 21:49:22,795][__main__][INFO] - [23360] Loss: 0.674, Running accuracy: 99.087, Time: 24.50
[2020-12-14 21:49:46,572][__main__][INFO] - [23680] Loss: 0.810, Running accuracy: 99.085, Time: 23.78
[2020-12-14 21:50:11,404][__main__][INFO] - [24000] Loss: 0.841, Running accuracy: 99.082, Time: 24.83
[2020-12-14 21:50:35,717][__main__][INFO] - [24320] Loss: 0.827, Running accuracy: 99.081, Time: 24.31
[2020-12-14 21:50:58,136][__main__][INFO] - [24640] Loss: 0.660, Running accuracy: 99.080, Time: 22.42
[2020-12-14 21:51:22,055][__main__][INFO] - [24960] Loss: 0.812, Running accuracy: 99.079, Time: 23.92
[2020-12-14 21:51:46,696][__main__][INFO] - [25280] Loss: 0.652, Running accuracy: 99.079, Time: 24.64
[2020-12-14 21:52:11,860][__main__][INFO] - [25600] Loss: 0.663, Running accuracy: 99.079, Time: 25.16
[2020-12-14 21:52:41,682][__main__][INFO] - [25920] Loss: 0.653, Running accuracy: 99.081, Time: 29.82
[2020-12-14 21:53:06,083][__main__][INFO] - [26240] Loss: 0.769, Running accuracy: 99.081, Time: 24.40
[2020-12-14 21:53:32,773][__main__][INFO] - [26560] Loss: 0.740, Running accuracy: 99.080, Time: 26.69
[2020-12-14 21:53:57,823][__main__][INFO] - [26880] Loss: 0.839, Running accuracy: 99.077, Time: 25.05
[2020-12-14 21:54:21,853][__main__][INFO] - [27200] Loss: 0.691, Running accuracy: 99.077, Time: 24.03
[2020-12-14 21:54:47,316][__main__][INFO] - [27520] Loss: 0.711, Running accuracy: 99.078, Time: 25.46
[2020-12-14 21:55:12,416][__main__][INFO] - [27840] Loss: 0.774, Running accuracy: 99.078, Time: 25.10
[2020-12-14 21:55:38,795][__main__][INFO] - [28160] Loss: 0.698, Running accuracy: 99.078, Time: 26.38
[2020-12-14 21:56:02,574][__main__][INFO] - [28480] Loss: 0.934, Running accuracy: 99.076, Time: 23.78
[2020-12-14 21:56:28,026][__main__][INFO] - [28800] Loss: 0.802, Running accuracy: 99.075, Time: 25.45
[2020-12-14 21:56:55,795][__main__][INFO] - [29120] Loss: 0.894, Running accuracy: 99.072, Time: 27.77
[2020-12-14 21:57:19,048][__main__][INFO] - [29440] Loss: 0.874, Running accuracy: 99.072, Time: 23.25
[2020-12-14 21:57:42,909][__main__][INFO] - [29760] Loss: 0.938, Running accuracy: 99.070, Time: 23.86
[2020-12-14 21:58:08,112][__main__][INFO] - [30080] Loss: 0.855, Running accuracy: 99.068, Time: 25.20
[2020-12-14 21:58:37,147][__main__][INFO] - [30400] Loss: 0.789, Running accuracy: 99.068, Time: 29.03
[2020-12-14 21:59:00,628][__main__][INFO] - [30720] Loss: 0.608, Running accuracy: 99.069, Time: 23.48
[2020-12-14 21:59:26,061][__main__][INFO] - [31040] Loss: 0.810, Running accuracy: 99.068, Time: 25.43
[2020-12-14 21:59:50,474][__main__][INFO] - [31360] Loss: 0.944, Running accuracy: 99.066, Time: 24.41
[2020-12-14 22:00:15,664][__main__][INFO] - [31680] Loss: 0.931, Running accuracy: 99.064, Time: 25.19
[2020-12-14 22:00:40,360][__main__][INFO] - [32000] Loss: 0.684, Running accuracy: 99.064, Time: 24.70
[2020-12-14 22:01:05,975][__main__][INFO] - [32320] Loss: 1.081, Running accuracy: 99.060, Time: 25.61
[2020-12-14 22:01:30,193][__main__][INFO] - [32640] Loss: 0.847, Running accuracy: 99.060, Time: 24.22
[2020-12-14 22:01:56,886][__main__][INFO] - [32960] Loss: 0.735, Running accuracy: 99.060, Time: 26.69
[2020-12-14 22:02:22,549][__main__][INFO] - [33280] Loss: 1.060, Running accuracy: 99.058, Time: 25.66
[2020-12-14 22:02:46,184][__main__][INFO] - [33600] Loss: 1.000, Running accuracy: 99.057, Time: 23.63
[2020-12-14 22:03:11,655][__main__][INFO] - [33920] Loss: 0.726, Running accuracy: 99.058, Time: 25.47
[2020-12-14 22:03:34,745][__main__][INFO] - [34240] Loss: 0.730, Running accuracy: 99.058, Time: 23.09
[2020-12-14 22:03:58,199][__main__][INFO] - [34560] Loss: 0.990, Running accuracy: 99.055, Time: 23.45
[2020-12-14 22:04:25,415][__main__][INFO] - [34880] Loss: 0.765, Running accuracy: 99.054, Time: 27.22
[2020-12-14 22:04:49,866][__main__][INFO] - [35200] Loss: 0.884, Running accuracy: 99.053, Time: 24.45
[2020-12-14 22:05:15,304][__main__][INFO] - [35520] Loss: 0.724, Running accuracy: 99.053, Time: 25.44
[2020-12-14 22:05:38,560][__main__][INFO] - [35840] Loss: 0.809, Running accuracy: 99.054, Time: 23.26
[2020-12-14 22:06:02,782][__main__][INFO] - [36160] Loss: 0.924, Running accuracy: 99.053, Time: 24.22
[2020-12-14 22:06:27,880][__main__][INFO] - [36480] Loss: 0.766, Running accuracy: 99.054, Time: 25.10
[2020-12-14 22:06:50,506][__main__][INFO] - [36800] Loss: 0.653, Running accuracy: 99.056, Time: 22.62
[2020-12-14 22:07:13,242][__main__][INFO] - [37120] Loss: 0.736, Running accuracy: 99.056, Time: 22.74
[2020-12-14 22:07:37,776][__main__][INFO] - [37440] Loss: 0.593, Running accuracy: 99.056, Time: 24.53
[2020-12-14 22:08:03,350][__main__][INFO] - [37760] Loss: 0.803, Running accuracy: 99.056, Time: 25.57
[2020-12-14 22:08:28,083][__main__][INFO] - [38080] Loss: 0.913, Running accuracy: 99.055, Time: 24.73
[2020-12-14 22:08:53,649][__main__][INFO] - [38400] Loss: 1.021, Running accuracy: 99.052, Time: 25.56
[2020-12-14 22:09:18,417][__main__][INFO] - [38720] Loss: 0.695, Running accuracy: 99.052, Time: 24.77
[2020-12-14 22:09:46,051][__main__][INFO] - [39040] Loss: 0.844, Running accuracy: 99.050, Time: 27.63
[2020-12-14 22:10:10,621][__main__][INFO] - [39360] Loss: 0.755, Running accuracy: 99.050, Time: 24.57
[2020-12-14 22:10:34,865][__main__][INFO] - [39680] Loss: 0.762, Running accuracy: 99.051, Time: 24.24
[2020-12-14 22:10:44,635][__main__][INFO] - Action accuracy: 99.051, Loss: 0.851
[2020-12-14 22:10:44,636][__main__][INFO] - Validating..
[2020-12-14 22:11:10,843][test][INFO] - Time elapsed: 24.056993
[2020-12-14 22:11:10,847][__main__][INFO] - Validation F1 score: 95.200, Exact match: 53.180, Precision: 95.120, Recall: 95.280
[2020-12-14 22:11:10,847][__main__][INFO] - F1 score has improved
[2020-12-14 22:11:45,394][__main__][INFO] - Checkpoint saved to checkpoints/model_latest.pth
[2020-12-14 22:11:46,206][__main__][INFO] - Epoch #8
[2020-12-14 22:11:46,207][__main__][INFO] - Training..
[2020-12-14 22:12:11,927][__main__][INFO] - [320] Loss: 0.534, Running accuracy: 99.220, Time: 24.30
[2020-12-14 22:12:35,998][__main__][INFO] - [640] Loss: 0.815, Running accuracy: 99.124, Time: 24.07
[2020-12-14 22:12:59,132][__main__][INFO] - [960] Loss: 0.657, Running accuracy: 99.179, Time: 23.13
[2020-12-14 22:13:23,784][__main__][INFO] - [1280] Loss: 0.422, Running accuracy: 99.248, Time: 24.65
[2020-12-14 22:13:52,816][__main__][INFO] - [1600] Loss: 0.740, Running accuracy: 99.241, Time: 29.03
[2020-12-14 22:14:16,897][__main__][INFO] - [1920] Loss: 0.574, Running accuracy: 99.259, Time: 24.08
[2020-12-14 22:14:41,374][__main__][INFO] - [2240] Loss: 0.610, Running accuracy: 99.257, Time: 24.48
[2020-12-14 22:15:06,094][__main__][INFO] - [2560] Loss: 0.572, Running accuracy: 99.259, Time: 24.72
[2020-12-14 22:15:29,330][__main__][INFO] - [2880] Loss: 0.419, Running accuracy: 99.257, Time: 23.23
[2020-12-14 22:15:52,928][__main__][INFO] - [3200] Loss: 0.677, Running accuracy: 99.254, Time: 23.60
[2020-12-14 22:16:16,294][__main__][INFO] - [3520] Loss: 0.664, Running accuracy: 99.246, Time: 23.36
[2020-12-14 22:16:38,997][__main__][INFO] - [3840] Loss: 0.478, Running accuracy: 99.257, Time: 22.70
[2020-12-14 22:17:04,793][__main__][INFO] - [4160] Loss: 0.531, Running accuracy: 99.265, Time: 25.79
[2020-12-14 22:17:28,401][__main__][INFO] - [4480] Loss: 0.703, Running accuracy: 99.251, Time: 23.61
[2020-12-14 22:17:51,394][__main__][INFO] - [4800] Loss: 0.685, Running accuracy: 99.251, Time: 22.99
[2020-12-14 22:18:16,852][__main__][INFO] - [5120] Loss: 0.529, Running accuracy: 99.261, Time: 25.46
[2020-12-14 22:18:42,722][__main__][INFO] - [5440] Loss: 0.722, Running accuracy: 99.246, Time: 25.87
[2020-12-14 22:19:09,503][__main__][INFO] - [5760] Loss: 0.673, Running accuracy: 99.233, Time: 26.78
[2020-12-14 22:19:32,953][__main__][INFO] - [6080] Loss: 0.465, Running accuracy: 99.238, Time: 23.45
[2020-12-14 22:19:57,897][__main__][INFO] - [6400] Loss: 0.551, Running accuracy: 99.238, Time: 24.94
[2020-12-14 22:20:21,721][__main__][INFO] - [6720] Loss: 0.579, Running accuracy: 99.240, Time: 23.82
[2020-12-14 22:20:43,535][__main__][INFO] - [7040] Loss: 0.669, Running accuracy: 99.231, Time: 21.81
[2020-12-14 22:21:07,518][__main__][INFO] - [7360] Loss: 0.710, Running accuracy: 99.231, Time: 23.98
[2020-12-14 22:21:32,637][__main__][INFO] - [7680] Loss: 0.665, Running accuracy: 99.232, Time: 25.12
[2020-12-14 22:21:56,164][__main__][INFO] - [8000] Loss: 0.641, Running accuracy: 99.234, Time: 23.53
[2020-12-14 22:22:23,147][__main__][INFO] - [8320] Loss: 0.658, Running accuracy: 99.233, Time: 26.98
[2020-12-14 22:22:48,231][__main__][INFO] - [8640] Loss: 0.525, Running accuracy: 99.233, Time: 25.08
[2020-12-14 22:23:11,476][__main__][INFO] - [8960] Loss: 0.433, Running accuracy: 99.236, Time: 23.25
[2020-12-14 22:23:35,782][__main__][INFO] - [9280] Loss: 0.687, Running accuracy: 99.234, Time: 24.30
[2020-12-14 22:23:59,951][__main__][INFO] - [9600] Loss: 0.628, Running accuracy: 99.230, Time: 24.17
[2020-12-14 22:24:25,735][__main__][INFO] - [9920] Loss: 0.653, Running accuracy: 99.231, Time: 25.78
[2020-12-14 22:24:53,463][__main__][INFO] - [10240] Loss: 0.812, Running accuracy: 99.226, Time: 27.73
[2020-12-14 22:25:17,467][__main__][INFO] - [10560] Loss: 0.561, Running accuracy: 99.226, Time: 24.00
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[2020-12-14 23:01:27,957][__main__][INFO] - [38720] Loss: 0.881, Running accuracy: 99.181, Time: 25.29
[2020-12-14 23:01:50,541][__main__][INFO] - [39040] Loss: 0.540, Running accuracy: 99.182, Time: 22.58
[2020-12-14 23:02:15,398][__main__][INFO] - [39360] Loss: 0.719, Running accuracy: 99.182, Time: 24.86
[2020-12-14 23:02:38,897][__main__][INFO] - [39680] Loss: 0.815, Running accuracy: 99.181, Time: 23.50
[2020-12-14 23:02:49,283][__main__][INFO] - Action accuracy: 99.181, Loss: 0.730
[2020-12-14 23:02:49,284][__main__][INFO] - Validating..
[2020-12-14 23:03:19,725][test][INFO] - Time elapsed: 28.346287
[2020-12-14 23:03:19,730][__main__][INFO] - Validation F1 score: 95.210, Exact match: 52.760, Precision: 95.110, Recall: 95.320
[2020-12-14 23:03:19,730][__main__][INFO] - F1 score has improved
[2020-12-14 23:03:54,104][__main__][INFO] - Checkpoint saved to checkpoints/model_latest.pth
[2020-12-14 23:03:55,138][__main__][INFO] - Epoch #9
[2020-12-14 23:03:55,138][__main__][INFO] - Training..
[2020-12-14 23:04:22,333][__main__][INFO] - [320] Loss: 0.425, Running accuracy: 99.437, Time: 25.73
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[2020-12-14 23:37:42,358][__main__][INFO] - [26240] Loss: 0.674, Running accuracy: 99.293, Time: 25.07
[2020-12-14 23:38:08,139][__main__][INFO] - [26560] Loss: 0.678, Running accuracy: 99.293, Time: 25.78
[2020-12-14 23:38:31,908][__main__][INFO] - [26880] Loss: 0.733, Running accuracy: 99.291, Time: 23.77
[2020-12-14 23:38:55,786][__main__][INFO] - [27200] Loss: 0.456, Running accuracy: 99.292, Time: 23.88
[2020-12-14 23:39:18,761][__main__][INFO] - [27520] Loss: 0.605, Running accuracy: 99.292, Time: 22.97
[2020-12-14 23:39:41,046][__main__][INFO] - [27840] Loss: 0.645, Running accuracy: 99.292, Time: 22.29
[2020-12-14 23:40:05,984][__main__][INFO] - [28160] Loss: 0.516, Running accuracy: 99.293, Time: 24.94
[2020-12-14 23:40:29,307][__main__][INFO] - [28480] Loss: 0.688, Running accuracy: 99.291, Time: 23.32
[2020-12-14 23:40:53,128][__main__][INFO] - [28800] Loss: 0.716, Running accuracy: 99.290, Time: 23.82
[2020-12-14 23:41:16,924][__main__][INFO] - [29120] Loss: 0.491, Running accuracy: 99.291, Time: 23.80
[2020-12-14 23:41:42,630][__main__][INFO] - [29440] Loss: 0.524, Running accuracy: 99.293, Time: 25.70
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[2020-12-14 23:43:02,237][__main__][INFO] - [30400] Loss: 0.643, Running accuracy: 99.292, Time: 24.60
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[2020-12-14 23:43:51,608][__main__][INFO] - [31040] Loss: 0.495, Running accuracy: 99.292, Time: 25.15
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[2020-12-14 23:51:16,355][__main__][INFO] - [36800] Loss: 0.577, Running accuracy: 99.279, Time: 26.76
[2020-12-14 23:51:40,469][__main__][INFO] - [37120] Loss: 0.619, Running accuracy: 99.278, Time: 24.11
[2020-12-14 23:52:04,509][__main__][INFO] - [37440] Loss: 0.668, Running accuracy: 99.277, Time: 24.04
[2020-12-14 23:52:28,351][__main__][INFO] - [37760] Loss: 0.689, Running accuracy: 99.276, Time: 23.84
[2020-12-14 23:52:51,798][__main__][INFO] - [38080] Loss: 0.557, Running accuracy: 99.277, Time: 23.45
[2020-12-14 23:53:22,879][__main__][INFO] - [38400] Loss: 0.636, Running accuracy: 99.277, Time: 31.08
[2020-12-14 23:53:46,937][__main__][INFO] - [38720] Loss: 0.795, Running accuracy: 99.275, Time: 24.06
[2020-12-14 23:54:11,878][__main__][INFO] - [39040] Loss: 0.520, Running accuracy: 99.276, Time: 24.94
[2020-12-14 23:54:36,301][__main__][INFO] - [39360] Loss: 0.746, Running accuracy: 99.275, Time: 24.42
[2020-12-14 23:54:58,234][__main__][INFO] - [39680] Loss: 0.570, Running accuracy: 99.274, Time: 21.93
[2020-12-14 23:55:09,763][__main__][INFO] - Action accuracy: 99.273, Loss: 0.652
[2020-12-14 23:55:09,764][__main__][INFO] - Validating..
[2020-12-14 23:55:36,106][test][INFO] - Time elapsed: 24.819372
[2020-12-14 23:55:36,110][__main__][INFO] - Validation F1 score: 83.390, Exact match: 39.940, Precision: 83.780, Recall: 83.000
[2020-12-14 23:56:10,216][__main__][INFO] - Checkpoint saved to checkpoints/model_latest.pth
[2020-12-14 23:56:11,045][__main__][INFO] - Epoch #10
[2020-12-14 23:56:11,045][__main__][INFO] - Training..
[2020-12-14 23:56:35,100][__main__][INFO] - [320] Loss: 1.373, Running accuracy: 98.333, Time: 22.68
[2020-12-14 23:57:00,467][__main__][INFO] - [640] Loss: 0.452, Running accuracy: 98.820, Time: 25.37
[2020-12-14 23:57:29,326][__main__][INFO] - [960] Loss: 0.540, Running accuracy: 99.036, Time: 28.86
[2020-12-14 23:57:54,031][__main__][INFO] - [1280] Loss: 0.372, Running accuracy: 99.151, Time: 24.70
[2020-12-14 23:58:16,837][__main__][INFO] - [1600] Loss: 0.460, Running accuracy: 99.207, Time: 22.81
[2020-12-14 23:58:39,885][__main__][INFO] - [1920] Loss: 0.492, Running accuracy: 99.236, Time: 23.05
[2020-12-14 23:59:03,217][__main__][INFO] - [2240] Loss: 0.418, Running accuracy: 99.274, Time: 23.33
[2020-12-14 23:59:27,548][__main__][INFO] - [2560] Loss: 0.503, Running accuracy: 99.285, Time: 24.33
[2020-12-14 23:59:50,913][__main__][INFO] - [2880] Loss: 0.378, Running accuracy: 99.311, Time: 23.36
[2020-12-15 00:00:15,597][__main__][INFO] - [3200] Loss: 0.431, Running accuracy: 99.321, Time: 24.68
[2020-12-15 00:00:39,643][__main__][INFO] - [3520] Loss: 0.458, Running accuracy: 99.329, Time: 24.05
[2020-12-15 00:01:02,803][__main__][INFO] - [3840] Loss: 0.471, Running accuracy: 99.337, Time: 23.16
[2020-12-15 00:01:27,517][__main__][INFO] - [4160] Loss: 0.682, Running accuracy: 99.332, Time: 24.71
[2020-12-15 00:01:52,871][__main__][INFO] - [4480] Loss: 0.476, Running accuracy: 99.337, Time: 25.35
[2020-12-15 00:02:15,917][__main__][INFO] - [4800] Loss: 0.494, Running accuracy: 99.339, Time: 23.05
[2020-12-15 00:02:44,847][__main__][INFO] - [5120] Loss: 0.473, Running accuracy: 99.345, Time: 28.93
[2020-12-15 00:03:07,967][__main__][INFO] - [5440] Loss: 0.589, Running accuracy: 99.346, Time: 23.12
[2020-12-15 00:03:33,621][__main__][INFO] - [5760] Loss: 0.477, Running accuracy: 99.350, Time: 25.65
[2020-12-15 00:03:59,664][__main__][INFO] - [6080] Loss: 0.496, Running accuracy: 99.352, Time: 26.04
[2020-12-15 00:04:23,325][__main__][INFO] - [6400] Loss: 0.559, Running accuracy: 99.351, Time: 23.66
[2020-12-15 00:04:48,872][__main__][INFO] - [6720] Loss: 0.409, Running accuracy: 99.363, Time: 25.55
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[2020-12-15 00:06:29,070][__main__][INFO] - [8000] Loss: 0.588, Running accuracy: 99.371, Time: 26.22
[2020-12-15 00:06:53,175][__main__][INFO] - [8320] Loss: 0.677, Running accuracy: 99.361, Time: 24.10
[2020-12-15 00:07:17,817][__main__][INFO] - [8640] Loss: 0.613, Running accuracy: 99.358, Time: 24.64
[2020-12-15 00:07:42,214][__main__][INFO] - [8960] Loss: 0.671, Running accuracy: 99.357, Time: 24.40
[2020-12-15 00:08:09,206][__main__][INFO] - [9280] Loss: 0.770, Running accuracy: 99.350, Time: 26.99
[2020-12-15 00:08:38,877][__main__][INFO] - [9600] Loss: 0.641, Running accuracy: 99.346, Time: 29.67
[2020-12-15 00:09:04,081][__main__][INFO] - [9920] Loss: 0.541, Running accuracy: 99.344, Time: 25.20
[2020-12-15 00:09:26,937][__main__][INFO] - [10240] Loss: 0.564, Running accuracy: 99.344, Time: 22.86
[2020-12-15 00:09:53,674][__main__][INFO] - [10560] Loss: 0.504, Running accuracy: 99.348, Time: 26.74
[2020-12-15 00:10:17,160][__main__][INFO] - [10880] Loss: 0.664, Running accuracy: 99.345, Time: 23.49
[2020-12-15 00:10:41,461][__main__][INFO] - [11200] Loss: 0.537, Running accuracy: 99.346, Time: 24.30
[2020-12-15 00:11:05,393][__main__][INFO] - [11520] Loss: 0.499, Running accuracy: 99.347, Time: 23.93
[2020-12-15 00:11:28,984][__main__][INFO] - [11840] Loss: 0.515, Running accuracy: 99.346, Time: 23.59
[2020-12-15 00:11:53,779][__main__][INFO] - [12160] Loss: 0.560, Running accuracy: 99.348, Time: 24.79
[2020-12-15 00:12:17,835][__main__][INFO] - [12480] Loss: 0.689, Running accuracy: 99.343, Time: 24.06
[2020-12-15 00:12:43,563][__main__][INFO] - [12800] Loss: 0.513, Running accuracy: 99.343, Time: 25.73
[2020-12-15 00:13:05,701][__main__][INFO] - [13120] Loss: 0.350, Running accuracy: 99.348, Time: 22.14
[2020-12-15 00:13:32,542][__main__][INFO] - [13440] Loss: 0.408, Running accuracy: 99.354, Time: 26.84
[2020-12-15 00:13:56,514][__main__][INFO] - [13760] Loss: 0.750, Running accuracy: 99.350, Time: 23.97
[2020-12-15 00:14:26,423][__main__][INFO] - [14080] Loss: 0.573, Running accuracy: 99.348, Time: 29.91
[2020-12-15 00:14:51,156][__main__][INFO] - [14400] Loss: 0.478, Running accuracy: 99.351, Time: 24.73
[2020-12-15 00:15:15,473][__main__][INFO] - [14720] Loss: 0.612, Running accuracy: 99.352, Time: 24.32
[2020-12-15 00:15:39,370][__main__][INFO] - [15040] Loss: 0.518, Running accuracy: 99.354, Time: 23.90
[2020-12-15 00:16:04,148][__main__][INFO] - [15360] Loss: 0.505, Running accuracy: 99.356, Time: 24.78
[2020-12-15 00:16:27,496][__main__][INFO] - [15680] Loss: 0.456, Running accuracy: 99.357, Time: 23.35
[2020-12-15 00:16:52,190][__main__][INFO] - [16000] Loss: 0.456, Running accuracy: 99.357, Time: 24.69
[2020-12-15 00:17:15,900][__main__][INFO] - [16320] Loss: 0.599, Running accuracy: 99.357, Time: 23.71
[2020-12-15 00:17:39,395][__main__][INFO] - [16640] Loss: 0.583, Running accuracy: 99.356, Time: 23.49
[2020-12-15 00:18:03,458][__main__][INFO] - [16960] Loss: 0.726, Running accuracy: 99.356, Time: 24.06
[2020-12-15 00:18:28,850][__main__][INFO] - [17280] Loss: 0.541, Running accuracy: 99.357, Time: 25.39
[2020-12-15 00:18:53,724][__main__][INFO] - [17600] Loss: 0.543, Running accuracy: 99.356, Time: 24.87
[2020-12-15 00:19:17,400][__main__][INFO] - [17920] Loss: 0.497, Running accuracy: 99.355, Time: 23.68
[2020-12-15 00:19:46,047][__main__][INFO] - [18240] Loss: 0.444, Running accuracy: 99.354, Time: 28.65
[2020-12-15 00:20:10,148][__main__][INFO] - [18560] Loss: 0.600, Running accuracy: 99.355, Time: 24.10
[2020-12-15 00:20:34,601][__main__][INFO] - [18880] Loss: 0.464, Running accuracy: 99.356, Time: 24.45
[2020-12-15 00:20:59,025][__main__][INFO] - [19200] Loss: 0.447, Running accuracy: 99.357, Time: 24.42
[2020-12-15 00:21:23,213][__main__][INFO] - [19520] Loss: 0.487, Running accuracy: 99.358, Time: 24.19
[2020-12-15 00:21:47,206][__main__][INFO] - [19840] Loss: 0.498, Running accuracy: 99.357, Time: 23.99
[2020-12-15 00:22:12,932][__main__][INFO] - [20160] Loss: 0.660, Running accuracy: 99.353, Time: 25.73
[2020-12-15 00:22:34,671][__main__][INFO] - [20480] Loss: 0.512, Running accuracy: 99.353, Time: 21.74
[2020-12-15 00:22:59,017][__main__][INFO] - [20800] Loss: 0.562, Running accuracy: 99.353, Time: 24.34
[2020-12-15 00:23:22,752][__main__][INFO] - [21120] Loss: 0.745, Running accuracy: 99.351, Time: 23.73
[2020-12-15 00:23:47,074][__main__][INFO] - [21440] Loss: 0.393, Running accuracy: 99.351, Time: 24.32
[2020-12-15 00:24:10,504][__main__][INFO] - [21760] Loss: 0.548, Running accuracy: 99.351, Time: 23.43
[2020-12-15 00:24:33,952][__main__][INFO] - [22080] Loss: 0.576, Running accuracy: 99.350, Time: 23.45
[2020-12-15 00:24:57,166][__main__][INFO] - [22400] Loss: 0.446, Running accuracy: 99.351, Time: 23.21
[2020-12-15 00:25:23,918][__main__][INFO] - [22720] Loss: 0.538, Running accuracy: 99.350, Time: 26.75
[2020-12-15 00:25:47,552][__main__][INFO] - [23040] Loss: 0.572, Running accuracy: 99.349, Time: 23.63
[2020-12-15 00:26:11,728][__main__][INFO] - [23360] Loss: 0.598, Running accuracy: 99.348, Time: 24.17
[2020-12-15 00:26:36,254][__main__][INFO] - [23680] Loss: 0.520, Running accuracy: 99.348, Time: 24.53
[2020-12-15 00:27:00,909][__main__][INFO] - [24000] Loss: 0.407, Running accuracy: 99.351, Time: 24.65
[2020-12-15 00:27:24,160][__main__][INFO] - [24320] Loss: 0.422, Running accuracy: 99.352, Time: 23.25
[2020-12-15 00:27:47,468][__main__][INFO] - [24640] Loss: 0.554, Running accuracy: 99.351, Time: 23.31
[2020-12-15 00:28:11,122][__main__][INFO] - [24960] Loss: 0.576, Running accuracy: 99.352, Time: 23.65
[2020-12-15 00:28:34,886][__main__][INFO] - [25280] Loss: 0.542, Running accuracy: 99.350, Time: 23.76
[2020-12-15 00:28:58,653][__main__][INFO] - [25600] Loss: 0.613, Running accuracy: 99.350, Time: 23.77
[2020-12-15 00:29:23,592][__main__][INFO] - [25920] Loss: 0.938, Running accuracy: 99.347, Time: 24.94
[2020-12-15 00:29:49,128][__main__][INFO] - [26240] Loss: 0.534, Running accuracy: 99.348, Time: 25.54
[2020-12-15 00:30:14,899][__main__][INFO] - [26560] Loss: 0.673, Running accuracy: 99.347, Time: 25.77
[2020-12-15 00:30:40,129][__main__][INFO] - [26880] Loss: 0.698, Running accuracy: 99.345, Time: 25.23
[2020-12-15 00:31:07,641][__main__][INFO] - [27200] Loss: 0.650, Running accuracy: 99.345, Time: 27.51
[2020-12-15 00:31:30,649][__main__][INFO] - [27520] Loss: 1.962, Running accuracy: 99.336, Time: 23.01
[2020-12-15 00:31:56,306][__main__][INFO] - [27840] Loss: 0.810, Running accuracy: 99.335, Time: 25.66
[2020-12-15 00:32:20,069][__main__][INFO] - [28160] Loss: 0.642, Running accuracy: 99.334, Time: 23.76
[2020-12-15 00:32:43,732][__main__][INFO] - [28480] Loss: 0.607, Running accuracy: 99.334, Time: 23.66
[2020-12-15 00:33:08,907][__main__][INFO] - [28800] Loss: 0.704, Running accuracy: 99.333, Time: 25.17
[2020-12-15 00:33:32,309][__main__][INFO] - [29120] Loss: 0.911, Running accuracy: 99.329, Time: 23.40
[2020-12-15 00:33:56,110][__main__][INFO] - [29440] Loss: 0.624, Running accuracy: 99.329, Time: 23.80
[2020-12-15 00:34:18,041][__main__][INFO] - [29760] Loss: 0.504, Running accuracy: 99.330, Time: 21.93
[2020-12-15 00:34:39,929][__main__][INFO] - [30080] Loss: 0.568, Running accuracy: 99.329, Time: 21.89
[2020-12-15 00:35:04,928][__main__][INFO] - [30400] Loss: 0.662, Running accuracy: 99.327, Time: 25.00
[2020-12-15 00:35:28,869][__main__][INFO] - [30720] Loss: 0.562, Running accuracy: 99.329, Time: 23.94
[2020-12-15 00:35:51,750][__main__][INFO] - [31040] Loss: 0.574, Running accuracy: 99.330, Time: 22.88
[2020-12-15 00:36:18,226][__main__][INFO] - [31360] Loss: 0.488, Running accuracy: 99.329, Time: 26.47
[2020-12-15 00:36:43,449][__main__][INFO] - [31680] Loss: 0.630, Running accuracy: 99.328, Time: 25.22
[2020-12-15 00:37:07,958][__main__][INFO] - [32000] Loss: 0.703, Running accuracy: 99.326, Time: 24.51
[2020-12-15 00:37:33,317][__main__][INFO] - [32320] Loss: 0.671, Running accuracy: 99.325, Time: 25.36
[2020-12-15 00:37:57,074][__main__][INFO] - [32640] Loss: 0.545, Running accuracy: 99.325, Time: 23.76
[2020-12-15 00:38:22,844][__main__][INFO] - [32960] Loss: 0.673, Running accuracy: 99.324, Time: 25.77
[2020-12-15 00:38:47,991][__main__][INFO] - [33280] Loss: 0.524, Running accuracy: 99.323, Time: 25.15
[2020-12-15 00:39:13,409][__main__][INFO] - [33600] Loss: 0.672, Running accuracy: 99.322, Time: 25.42
[2020-12-15 00:39:38,002][__main__][INFO] - [33920] Loss: 0.646, Running accuracy: 99.322, Time: 24.59
[2020-12-15 00:40:01,473][__main__][INFO] - [34240] Loss: 0.730, Running accuracy: 99.321, Time: 23.47
[2020-12-15 00:40:26,087][__main__][INFO] - [34560] Loss: 0.594, Running accuracy: 99.321, Time: 24.61
[2020-12-15 00:40:49,713][__main__][INFO] - [34880] Loss: 0.682, Running accuracy: 99.321, Time: 23.63
[2020-12-15 00:41:13,333][__main__][INFO] - [35200] Loss: 0.368, Running accuracy: 99.323, Time: 23.62
[2020-12-15 00:41:39,382][__main__][INFO] - [35520] Loss: 0.573, Running accuracy: 99.322, Time: 26.05
[2020-12-15 00:42:05,840][__main__][INFO] - [35840] Loss: 0.602, Running accuracy: 99.322, Time: 26.46
[2020-12-15 00:42:30,291][__main__][INFO] - [36160] Loss: 0.664, Running accuracy: 99.322, Time: 24.45
[2020-12-15 00:42:54,771][__main__][INFO] - [36480] Loss: 0.543, Running accuracy: 99.323, Time: 24.48
[2020-12-15 00:43:16,436][__main__][INFO] - [36800] Loss: 0.576, Running accuracy: 99.322, Time: 21.66
[2020-12-15 00:43:41,685][__main__][INFO] - [37120] Loss: 0.540, Running accuracy: 99.322, Time: 25.25
[2020-12-15 00:44:05,203][__main__][INFO] - [37440] Loss: 0.679, Running accuracy: 99.321, Time: 23.52
[2020-12-15 00:44:28,816][__main__][INFO] - [37760] Loss: 0.648, Running accuracy: 99.321, Time: 23.61
[2020-12-15 00:44:55,577][__main__][INFO] - [38080] Loss: 0.500, Running accuracy: 99.321, Time: 26.76
[2020-12-15 00:45:20,330][__main__][INFO] - [38400] Loss: 0.354, Running accuracy: 99.323, Time: 24.75
[2020-12-15 00:45:46,279][__main__][INFO] - [38720] Loss: 0.399, Running accuracy: 99.325, Time: 25.95
[2020-12-15 00:46:11,422][__main__][INFO] - [39040] Loss: 0.595, Running accuracy: 99.324, Time: 25.14
[2020-12-15 00:46:34,976][__main__][INFO] - [39360] Loss: 0.680, Running accuracy: 99.323, Time: 23.55
[2020-12-15 00:46:59,146][__main__][INFO] - [39680] Loss: 0.442, Running accuracy: 99.325, Time: 24.17
[2020-12-15 00:47:09,117][__main__][INFO] - Action accuracy: 99.324, Loss: 0.643
[2020-12-15 00:47:09,118][__main__][INFO] - Validating..
[2020-12-15 00:47:39,667][test][INFO] - Time elapsed: 28.374082
[2020-12-15 00:47:39,671][__main__][INFO] - Validation F1 score: 94.930, Exact match: 52.290, Precision: 94.890, Recall: 94.980
[2020-12-15 00:48:13,958][__main__][INFO] - Checkpoint saved to checkpoints/model_latest.pth
[2020-12-15 00:48:14,977][__main__][INFO] - Epoch #11
[2020-12-15 00:48:14,977][__main__][INFO] - Training..
[2020-12-15 00:48:40,177][__main__][INFO] - [320] Loss: 0.416, Running accuracy: 99.562, Time: 23.80
[2020-12-15 00:49:06,433][__main__][INFO] - [640] Loss: 0.369, Running accuracy: 99.555, Time: 26.25
[2020-12-15 00:49:31,997][__main__][INFO] - [960] Loss: 0.423, Running accuracy: 99.546, Time: 25.56
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[2020-12-15 01:37:18,656][__main__][INFO] - [38080] Loss: 0.450, Running accuracy: 99.404, Time: 23.65
[2020-12-15 01:37:43,743][__main__][INFO] - [38400] Loss: 0.600, Running accuracy: 99.403, Time: 25.09
[2020-12-15 01:38:09,429][__main__][INFO] - [38720] Loss: 0.443, Running accuracy: 99.403, Time: 25.68
[2020-12-15 01:38:33,536][__main__][INFO] - [39040] Loss: 0.461, Running accuracy: 99.405, Time: 24.11
[2020-12-15 01:38:57,073][__main__][INFO] - [39360] Loss: 0.550, Running accuracy: 99.405, Time: 23.54
[2020-12-15 01:39:22,809][__main__][INFO] - [39680] Loss: 0.773, Running accuracy: 99.402, Time: 25.74
[2020-12-15 01:39:32,385][__main__][INFO] - Action accuracy: 99.402, Loss: 0.540
[2020-12-15 01:39:32,385][__main__][INFO] - Validating..
[2020-12-15 01:39:58,727][test][INFO] - Time elapsed: 24.121530
[2020-12-15 01:39:58,731][__main__][INFO] - Validation F1 score: 95.210, Exact match: 52.710, Precision: 95.260, Recall: 95.150
[2020-12-15 01:40:32,982][__main__][INFO] - Checkpoint saved to checkpoints/model_latest.pth
[2020-12-15 01:40:33,826][__main__][INFO] - Epoch #12
[2020-12-15 01:40:33,827][__main__][INFO] - Training..
[2020-12-15 01:41:06,239][__main__][INFO] - [320] Loss: 0.322, Running accuracy: 99.557, Time: 31.40
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[2020-12-15 02:31:58,268][__main__][INFO] - [39680] Loss: 0.373, Running accuracy: 99.474, Time: 25.71
[2020-12-15 02:32:08,818][__main__][INFO] - Action accuracy: 99.474, Loss: 0.478
[2020-12-15 02:32:08,819][__main__][INFO] - Validating..
[2020-12-15 02:32:35,348][test][INFO] - Time elapsed: 24.549118
[2020-12-15 02:32:35,352][__main__][INFO] - Validation F1 score: 95.020, Exact match: 53.650, Precision: 95.040, Recall: 95.010
[2020-12-15 02:33:07,495][__main__][INFO] - Checkpoint saved to checkpoints/model_latest.pth
[2020-12-15 02:33:08,319][__main__][INFO] - Epoch #13
[2020-12-15 02:33:08,320][__main__][INFO] - Training..
[2020-12-15 02:33:34,027][__main__][INFO] - [320] Loss: 0.262, Running accuracy: 99.664, Time: 24.42
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[2020-12-15 03:15:56,968][__main__][INFO] - [33280] Loss: 0.421, Running accuracy: 99.522, Time: 24.81
[2020-12-15 03:16:21,326][__main__][INFO] - [33600] Loss: 0.414, Running accuracy: 99.522, Time: 24.36
[2020-12-15 03:16:46,293][__main__][INFO] - [33920] Loss: 0.428, Running accuracy: 99.521, Time: 24.97
[2020-12-15 03:17:11,748][__main__][INFO] - [34240] Loss: 0.434, Running accuracy: 99.520, Time: 25.45
[2020-12-15 03:17:35,441][__main__][INFO] - [34560] Loss: 0.382, Running accuracy: 99.521, Time: 23.69
[2020-12-15 03:17:58,623][__main__][INFO] - [34880] Loss: 0.626, Running accuracy: 99.519, Time: 23.18
[2020-12-15 03:18:23,843][__main__][INFO] - [35200] Loss: 0.465, Running accuracy: 99.518, Time: 25.22
[2020-12-15 03:18:51,630][__main__][INFO] - [35520] Loss: 0.621, Running accuracy: 99.516, Time: 27.79
[2020-12-15 03:19:15,839][__main__][INFO] - [35840] Loss: 0.542, Running accuracy: 99.515, Time: 24.21
[2020-12-15 03:19:38,486][__main__][INFO] - [36160] Loss: 0.358, Running accuracy: 99.515, Time: 22.65
[2020-12-15 03:20:01,586][__main__][INFO] - [36480] Loss: 0.507, Running accuracy: 99.515, Time: 23.10
[2020-12-15 03:20:29,763][__main__][INFO] - [36800] Loss: 0.524, Running accuracy: 99.515, Time: 28.18
[2020-12-15 03:20:54,257][__main__][INFO] - [37120] Loss: 0.395, Running accuracy: 99.515, Time: 24.49
[2020-12-15 03:21:19,508][__main__][INFO] - [37440] Loss: 0.452, Running accuracy: 99.516, Time: 25.25
[2020-12-15 03:21:43,761][__main__][INFO] - [37760] Loss: 0.316, Running accuracy: 99.516, Time: 24.25
[2020-12-15 03:22:08,399][__main__][INFO] - [38080] Loss: 0.474, Running accuracy: 99.516, Time: 24.64
[2020-12-15 03:22:33,198][__main__][INFO] - [38400] Loss: 0.363, Running accuracy: 99.517, Time: 24.80
[2020-12-15 03:22:56,729][__main__][INFO] - [38720] Loss: 0.329, Running accuracy: 99.516, Time: 23.53
[2020-12-15 03:23:19,703][__main__][INFO] - [39040] Loss: 0.336, Running accuracy: 99.516, Time: 22.97
[2020-12-15 03:23:42,899][__main__][INFO] - [39360] Loss: 0.347, Running accuracy: 99.516, Time: 23.20
[2020-12-15 03:24:07,570][__main__][INFO] - [39680] Loss: 0.276, Running accuracy: 99.517, Time: 24.67
[2020-12-15 03:24:18,060][__main__][INFO] - Action accuracy: 99.516, Loss: 0.442
[2020-12-15 03:24:18,061][__main__][INFO] - Validating..
[2020-12-15 03:24:48,326][test][INFO] - Time elapsed: 28.843741
[2020-12-15 03:24:48,330][__main__][INFO] - Validation F1 score: 95.090, Exact match: 52.880, Precision: 95.150, Recall: 95.040
[2020-12-15 03:25:22,566][__main__][INFO] - Checkpoint saved to checkpoints/model_latest.pth
[2020-12-15 03:25:23,448][__main__][INFO] - Epoch #14
[2020-12-15 03:25:23,448][__main__][INFO] - Training..
[2020-12-15 03:25:48,878][__main__][INFO] - [320] Loss: 0.308, Running accuracy: 99.565, Time: 24.32
[2020-12-15 03:26:12,715][__main__][INFO] - [640] Loss: 0.360, Running accuracy: 99.577, Time: 23.84
[2020-12-15 03:26:37,828][__main__][INFO] - [960] Loss: 0.247, Running accuracy: 99.611, Time: 25.11
[2020-12-15 03:27:01,590][__main__][INFO] - [1280] Loss: 0.387, Running accuracy: 99.573, Time: 23.76
[2020-12-15 03:27:27,208][__main__][INFO] - [1600] Loss: 0.398, Running accuracy: 99.572, Time: 25.62
[2020-12-15 03:27:51,752][__main__][INFO] - [1920] Loss: 0.342, Running accuracy: 99.567, Time: 24.54
[2020-12-15 03:28:14,681][__main__][INFO] - [2240] Loss: 0.273, Running accuracy: 99.580, Time: 22.93
[2020-12-15 03:28:39,408][__main__][INFO] - [2560] Loss: 0.303, Running accuracy: 99.571, Time: 24.73
[2020-12-15 03:29:06,549][__main__][INFO] - [2880] Loss: 0.386, Running accuracy: 99.580, Time: 27.14
[2020-12-15 03:29:31,058][__main__][INFO] - [3200] Loss: 0.312, Running accuracy: 99.582, Time: 24.51
[2020-12-15 03:29:54,178][__main__][INFO] - [3520] Loss: 0.404, Running accuracy: 99.576, Time: 23.12
[2020-12-15 03:30:23,303][__main__][INFO] - [3840] Loss: 0.268, Running accuracy: 99.582, Time: 29.12
[2020-12-15 03:30:46,420][__main__][INFO] - [4160] Loss: 0.346, Running accuracy: 99.577, Time: 23.12
[2020-12-15 03:31:12,418][__main__][INFO] - [4480] Loss: 0.371, Running accuracy: 99.570, Time: 26.00
[2020-12-15 03:31:36,291][__main__][INFO] - [4800] Loss: 0.393, Running accuracy: 99.563, Time: 23.87
[2020-12-15 03:31:59,752][__main__][INFO] - [5120] Loss: 0.290, Running accuracy: 99.567, Time: 23.45
[2020-12-15 03:32:23,222][__main__][INFO] - [5440] Loss: 0.302, Running accuracy: 99.568, Time: 23.47
[2020-12-15 03:32:49,483][__main__][INFO] - [5760] Loss: 0.346, Running accuracy: 99.572, Time: 26.26
[2020-12-15 03:33:12,070][__main__][INFO] - [6080] Loss: 0.283, Running accuracy: 99.575, Time: 22.49
[2020-12-15 03:33:36,890][__main__][INFO] - [6400] Loss: 0.318, Running accuracy: 99.574, Time: 24.82
[2020-12-15 03:34:01,349][__main__][INFO] - [6720] Loss: 0.221, Running accuracy: 99.581, Time: 24.46
[2020-12-15 03:34:24,962][__main__][INFO] - [7040] Loss: 0.270, Running accuracy: 99.584, Time: 23.61
[2020-12-15 03:34:49,803][__main__][INFO] - [7360] Loss: 0.283, Running accuracy: 99.582, Time: 24.84
[2020-12-15 03:35:13,051][__main__][INFO] - [7680] Loss: 0.233, Running accuracy: 99.587, Time: 23.25
[2020-12-15 03:35:38,911][__main__][INFO] - [8000] Loss: 0.459, Running accuracy: 99.576, Time: 25.86
[2020-12-15 03:36:06,986][__main__][INFO] - [8320] Loss: 0.361, Running accuracy: 99.576, Time: 28.07
[2020-12-15 03:36:31,444][__main__][INFO] - [8640] Loss: 0.306, Running accuracy: 99.575, Time: 24.46
[2020-12-15 03:36:54,181][__main__][INFO] - [8960] Loss: 0.451, Running accuracy: 99.575, Time: 22.74
[2020-12-15 03:37:18,722][__main__][INFO] - [9280] Loss: 0.306, Running accuracy: 99.573, Time: 24.54
[2020-12-15 03:37:42,101][__main__][INFO] - [9600] Loss: 0.256, Running accuracy: 99.575, Time: 23.38
[2020-12-15 03:38:06,108][__main__][INFO] - [9920] Loss: 0.425, Running accuracy: 99.577, Time: 24.01
[2020-12-15 03:38:31,390][__main__][INFO] - [10240] Loss: 0.347, Running accuracy: 99.576, Time: 25.28
[2020-12-15 03:38:56,432][__main__][INFO] - [10560] Loss: 0.444, Running accuracy: 99.570, Time: 25.04
[2020-12-15 03:39:22,551][__main__][INFO] - [10880] Loss: 0.317, Running accuracy: 99.571, Time: 26.12
[2020-12-15 03:39:47,296][__main__][INFO] - [11200] Loss: 0.378, Running accuracy: 99.574, Time: 24.74
[2020-12-15 03:40:10,675][__main__][INFO] - [11520] Loss: 0.369, Running accuracy: 99.571, Time: 23.38
[2020-12-15 03:40:33,097][__main__][INFO] - [11840] Loss: 0.365, Running accuracy: 99.571, Time: 22.42
[2020-12-15 03:40:57,383][__main__][INFO] - [12160] Loss: 0.360, Running accuracy: 99.569, Time: 24.28
[2020-12-15 03:41:22,258][__main__][INFO] - [12480] Loss: 0.415, Running accuracy: 99.567, Time: 24.87
[2020-12-15 03:41:48,797][__main__][INFO] - [12800] Loss: 0.414, Running accuracy: 99.564, Time: 26.54
[2020-12-15 03:42:12,976][__main__][INFO] - [13120] Loss: 0.368, Running accuracy: 99.562, Time: 24.18
[2020-12-15 03:42:37,749][__main__][INFO] - [13440] Loss: 0.350, Running accuracy: 99.563, Time: 24.77
[2020-12-15 03:43:01,398][__main__][INFO] - [13760] Loss: 0.257, Running accuracy: 99.566, Time: 23.65
[2020-12-15 03:43:24,342][__main__][INFO] - [14080] Loss: 0.409, Running accuracy: 99.568, Time: 22.94
[2020-12-15 03:43:48,524][__main__][INFO] - [14400] Loss: 0.299, Running accuracy: 99.569, Time: 24.18
[2020-12-15 03:44:12,656][__main__][INFO] - [14720] Loss: 0.346, Running accuracy: 99.568, Time: 24.13
[2020-12-15 03:44:36,857][__main__][INFO] - [15040] Loss: 0.414, Running accuracy: 99.568, Time: 24.20
[2020-12-15 03:45:03,066][__main__][INFO] - [15360] Loss: 0.396, Running accuracy: 99.568, Time: 26.21
[2020-12-15 03:45:29,134][__main__][INFO] - [15680] Loss: 0.279, Running accuracy: 99.569, Time: 26.07
[2020-12-15 03:45:53,789][__main__][INFO] - [16000] Loss: 0.339, Running accuracy: 99.568, Time: 24.66
[2020-12-15 03:46:17,397][__main__][INFO] - [16320] Loss: 0.310, Running accuracy: 99.570, Time: 23.61
[2020-12-15 03:46:41,828][__main__][INFO] - [16640] Loss: 0.299, Running accuracy: 99.571, Time: 24.43
[2020-12-15 03:47:09,307][__main__][INFO] - [16960] Loss: 0.362, Running accuracy: 99.571, Time: 27.48
[2020-12-15 03:47:32,668][__main__][INFO] - [17280] Loss: 0.414, Running accuracy: 99.571, Time: 23.36
[2020-12-15 03:47:56,835][__main__][INFO] - [17600] Loss: 0.265, Running accuracy: 99.573, Time: 24.17
[2020-12-15 03:48:20,596][__main__][INFO] - [17920] Loss: 0.356, Running accuracy: 99.573, Time: 23.76
[2020-12-15 03:48:43,733][__main__][INFO] - [18240] Loss: 0.744, Running accuracy: 99.567, Time: 23.14
[2020-12-15 03:49:06,978][__main__][INFO] - [18560] Loss: 0.355, Running accuracy: 99.567, Time: 23.24
[2020-12-15 03:49:31,488][__main__][INFO] - [18880] Loss: 0.357, Running accuracy: 99.568, Time: 24.51
[2020-12-15 03:49:57,819][__main__][INFO] - [19200] Loss: 0.559, Running accuracy: 99.564, Time: 26.33
[2020-12-15 03:50:21,846][__main__][INFO] - [19520] Loss: 0.465, Running accuracy: 99.561, Time: 24.03
[2020-12-15 03:50:45,979][__main__][INFO] - [19840] Loss: 0.350, Running accuracy: 99.560, Time: 24.13
[2020-12-15 03:51:11,867][__main__][INFO] - [20160] Loss: 0.365, Running accuracy: 99.561, Time: 25.89
[2020-12-15 03:51:35,202][__main__][INFO] - [20480] Loss: 0.404, Running accuracy: 99.562, Time: 23.33
[2020-12-15 03:51:59,356][__main__][INFO] - [20800] Loss: 0.356, Running accuracy: 99.563, Time: 24.15
[2020-12-15 03:52:22,848][__main__][INFO] - [21120] Loss: 0.332, Running accuracy: 99.565, Time: 23.49
[2020-12-15 03:52:52,028][__main__][INFO] - [21440] Loss: 0.376, Running accuracy: 99.566, Time: 29.18
[2020-12-15 03:53:15,715][__main__][INFO] - [21760] Loss: 0.405, Running accuracy: 99.565, Time: 23.69
[2020-12-15 03:53:40,895][__main__][INFO] - [22080] Loss: 0.449, Running accuracy: 99.563, Time: 25.18
[2020-12-15 03:54:05,202][__main__][INFO] - [22400] Loss: 0.529, Running accuracy: 99.561, Time: 24.31
[2020-12-15 03:54:30,410][__main__][INFO] - [22720] Loss: 0.344, Running accuracy: 99.561, Time: 25.21
[2020-12-15 03:54:55,515][__main__][INFO] - [23040] Loss: 0.325, Running accuracy: 99.562, Time: 25.10
[2020-12-15 03:55:18,333][__main__][INFO] - [23360] Loss: 0.271, Running accuracy: 99.562, Time: 22.82
[2020-12-15 03:55:42,303][__main__][INFO] - [23680] Loss: 0.361, Running accuracy: 99.562, Time: 23.97
[2020-12-15 03:56:06,755][__main__][INFO] - [24000] Loss: 0.372, Running accuracy: 99.561, Time: 24.45
[2020-12-15 03:56:30,158][__main__][INFO] - [24320] Loss: 0.369, Running accuracy: 99.561, Time: 23.40
[2020-12-15 03:56:54,760][__main__][INFO] - [24640] Loss: 0.411, Running accuracy: 99.561, Time: 24.60
[2020-12-15 03:57:18,354][__main__][INFO] - [24960] Loss: 0.335, Running accuracy: 99.561, Time: 23.59
[2020-12-15 03:57:43,162][__main__][INFO] - [25280] Loss: 0.353, Running accuracy: 99.561, Time: 24.81
[2020-12-15 03:58:10,007][__main__][INFO] - [25600] Loss: 0.232, Running accuracy: 99.563, Time: 26.84
[2020-12-15 03:58:34,007][__main__][INFO] - [25920] Loss: 0.435, Running accuracy: 99.563, Time: 24.00
[2020-12-15 03:59:00,151][__main__][INFO] - [26240] Loss: 0.416, Running accuracy: 99.563, Time: 26.14
[2020-12-15 03:59:25,830][__main__][INFO] - [26560] Loss: 0.298, Running accuracy: 99.564, Time: 25.68
[2020-12-15 03:59:49,535][__main__][INFO] - [26880] Loss: 0.263, Running accuracy: 99.564, Time: 23.70
[2020-12-15 04:00:13,046][__main__][INFO] - [27200] Loss: 0.369, Running accuracy: 99.565, Time: 23.51
[2020-12-15 04:00:36,557][__main__][INFO] - [27520] Loss: 0.365, Running accuracy: 99.564, Time: 23.51
[2020-12-15 04:00:59,382][__main__][INFO] - [27840] Loss: 0.366, Running accuracy: 99.564, Time: 22.82
[2020-12-15 04:01:23,926][__main__][INFO] - [28160] Loss: 0.550, Running accuracy: 99.562, Time: 24.54
[2020-12-15 04:01:49,713][__main__][INFO] - [28480] Loss: 0.276, Running accuracy: 99.563, Time: 25.79
[2020-12-15 04:02:13,163][__main__][INFO] - [28800] Loss: 0.304, Running accuracy: 99.562, Time: 23.45
[2020-12-15 04:02:36,530][__main__][INFO] - [29120] Loss: 0.414, Running accuracy: 99.562, Time: 23.37
[2020-12-15 04:03:01,284][__main__][INFO] - [29440] Loss: 0.326, Running accuracy: 99.563, Time: 24.75
[2020-12-15 04:03:26,458][__main__][INFO] - [29760] Loss: 0.562, Running accuracy: 99.561, Time: 25.17
[2020-12-15 04:03:53,914][__main__][INFO] - [30080] Loss: 0.476, Running accuracy: 99.561, Time: 27.45
[2020-12-15 04:04:17,714][__main__][INFO] - [30400] Loss: 0.368, Running accuracy: 99.562, Time: 23.80
[2020-12-15 04:04:41,381][__main__][INFO] - [30720] Loss: 0.302, Running accuracy: 99.562, Time: 23.67
[2020-12-15 04:05:05,432][__main__][INFO] - [31040] Loss: 0.330, Running accuracy: 99.563, Time: 24.05
[2020-12-15 04:05:27,400][__main__][INFO] - [31360] Loss: 0.512, Running accuracy: 99.562, Time: 21.97
[2020-12-15 04:05:52,138][__main__][INFO] - [31680] Loss: 0.259, Running accuracy: 99.563, Time: 24.74
[2020-12-15 04:06:17,032][__main__][INFO] - [32000] Loss: 0.368, Running accuracy: 99.563, Time: 24.89
[2020-12-15 04:06:41,154][__main__][INFO] - [32320] Loss: 0.290, Running accuracy: 99.563, Time: 24.12
[2020-12-15 04:07:05,379][__main__][INFO] - [32640] Loss: 0.369, Running accuracy: 99.563, Time: 24.22
[2020-12-15 04:07:28,849][__main__][INFO] - [32960] Loss: 0.275, Running accuracy: 99.563, Time: 23.47
[2020-12-15 04:07:52,795][__main__][INFO] - [33280] Loss: 0.374, Running accuracy: 99.563, Time: 23.95
[2020-12-15 04:08:18,364][__main__][INFO] - [33600] Loss: 0.497, Running accuracy: 99.563, Time: 25.57
[2020-12-15 04:08:42,115][__main__][INFO] - [33920] Loss: 0.381, Running accuracy: 99.563, Time: 23.75
[2020-12-15 04:09:05,199][__main__][INFO] - [34240] Loss: 0.329, Running accuracy: 99.563, Time: 23.08
[2020-12-15 04:09:32,305][__main__][INFO] - [34560] Loss: 0.522, Running accuracy: 99.561, Time: 27.11
[2020-12-15 04:09:57,944][__main__][INFO] - [34880] Loss: 0.357, Running accuracy: 99.562, Time: 25.64
[2020-12-15 04:10:22,768][__main__][INFO] - [35200] Loss: 0.528, Running accuracy: 99.559, Time: 24.82
[2020-12-15 04:10:44,074][__main__][INFO] - [35520] Loss: 0.224, Running accuracy: 99.560, Time: 21.30
[2020-12-15 04:11:09,642][__main__][INFO] - [35840] Loss: 0.360, Running accuracy: 99.561, Time: 25.57
[2020-12-15 04:11:33,742][__main__][INFO] - [36160] Loss: 0.373, Running accuracy: 99.560, Time: 24.10
[2020-12-15 04:11:58,841][__main__][INFO] - [36480] Loss: 0.381, Running accuracy: 99.560, Time: 25.10
[2020-12-15 04:12:24,317][__main__][INFO] - [36800] Loss: 0.603, Running accuracy: 99.557, Time: 25.48
[2020-12-15 04:12:45,624][__main__][INFO] - [37120] Loss: 0.287, Running accuracy: 99.558, Time: 21.31
[2020-12-15 04:13:10,151][__main__][INFO] - [37440] Loss: 0.345, Running accuracy: 99.558, Time: 24.53
[2020-12-15 04:13:35,886][__main__][INFO] - [37760] Loss: 0.286, Running accuracy: 99.558, Time: 25.73
[2020-12-15 04:13:59,999][__main__][INFO] - [38080] Loss: 0.455, Running accuracy: 99.557, Time: 24.11
[2020-12-15 04:14:23,275][__main__][INFO] - [38400] Loss: 0.345, Running accuracy: 99.557, Time: 23.28
[2020-12-15 04:14:51,204][__main__][INFO] - [38720] Loss: 0.510, Running accuracy: 99.555, Time: 27.93
[2020-12-15 04:15:13,771][__main__][INFO] - [39040] Loss: 0.315, Running accuracy: 99.556, Time: 22.57
[2020-12-15 04:15:36,384][__main__][INFO] - [39360] Loss: 0.323, Running accuracy: 99.556, Time: 22.61
[2020-12-15 04:15:58,134][__main__][INFO] - [39680] Loss: 0.327, Running accuracy: 99.555, Time: 21.75
[2020-12-15 04:16:09,452][__main__][INFO] - Action accuracy: 99.555, Loss: 0.403
[2020-12-15 04:16:09,453][__main__][INFO] - Validating..
[2020-12-15 04:16:35,757][test][INFO] - Time elapsed: 24.902223
[2020-12-15 04:16:35,761][__main__][INFO] - Validation F1 score: 95.200, Exact match: 52.290, Precision: 95.270, Recall: 95.140
Epoch    15: reducing learning rate of group 0 to 1.5000e-05.
[2020-12-15 04:17:08,521][__main__][INFO] - Checkpoint saved to checkpoints/model_latest.pth
[2020-12-15 04:17:09,447][__main__][INFO] - Epoch #15
[2020-12-15 04:17:09,448][__main__][INFO] - Training..
[2020-12-15 04:17:34,352][__main__][INFO] - [320] Loss: 0.287, Running accuracy: 99.695, Time: 23.59
[2020-12-15 04:17:58,876][__main__][INFO] - [640] Loss: 0.246, Running accuracy: 99.674, Time: 24.52
[2020-12-15 04:18:24,934][__main__][INFO] - [960] Loss: 0.234, Running accuracy: 99.677, Time: 26.06
[2020-12-15 04:18:53,973][__main__][INFO] - [1280] Loss: 0.277, Running accuracy: 99.676, Time: 29.04
[2020-12-15 04:19:16,846][__main__][INFO] - [1600] Loss: 0.289, Running accuracy: 99.662, Time: 22.87
[2020-12-15 04:19:42,825][__main__][INFO] - [1920] Loss: 0.271, Running accuracy: 99.682, Time: 25.98
[2020-12-15 04:20:07,523][__main__][INFO] - [2240] Loss: 0.195, Running accuracy: 99.696, Time: 24.70
[2020-12-15 04:20:30,663][__main__][INFO] - [2560] Loss: 0.286, Running accuracy: 99.684, Time: 23.14
[2020-12-15 04:20:55,517][__main__][INFO] - [2880] Loss: 0.298, Running accuracy: 99.680, Time: 24.85
[2020-12-15 04:21:19,178][__main__][INFO] - [3200] Loss: 0.223, Running accuracy: 99.681, Time: 23.66
[2020-12-15 04:21:43,404][__main__][INFO] - [3520] Loss: 0.282, Running accuracy: 99.684, Time: 24.22
[2020-12-15 04:22:08,077][__main__][INFO] - [3840] Loss: 0.214, Running accuracy: 99.688, Time: 24.67
[2020-12-15 04:22:32,174][__main__][INFO] - [4160] Loss: 0.343, Running accuracy: 99.686, Time: 24.10
[2020-12-15 04:22:56,928][__main__][INFO] - [4480] Loss: 0.175, Running accuracy: 99.697, Time: 24.75
[2020-12-15 04:23:21,164][__main__][INFO] - [4800] Loss: 0.278, Running accuracy: 99.693, Time: 24.23
[2020-12-15 04:23:45,145][__main__][INFO] - [5120] Loss: 0.158, Running accuracy: 99.704, Time: 23.98
[2020-12-15 04:24:09,260][__main__][INFO] - [5440] Loss: 0.288, Running accuracy: 99.706, Time: 24.11
[2020-12-15 04:24:38,220][__main__][INFO] - [5760] Loss: 0.178, Running accuracy: 99.711, Time: 28.96
[2020-12-15 04:25:04,023][__main__][INFO] - [6080] Loss: 0.245, Running accuracy: 99.712, Time: 25.80
[2020-12-15 04:25:26,855][__main__][INFO] - [6400] Loss: 0.185, Running accuracy: 99.717, Time: 22.83
[2020-12-15 04:25:54,495][__main__][INFO] - [6720] Loss: 0.225, Running accuracy: 99.719, Time: 27.56
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[2020-12-15 05:07:11,553][__main__][INFO] - [39040] Loss: 0.174, Running accuracy: 99.727, Time: 23.39
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[2020-12-15 05:07:58,276][__main__][INFO] - [39680] Loss: 0.202, Running accuracy: 99.726, Time: 22.69
[2020-12-15 05:08:09,145][__main__][INFO] - Action accuracy: 99.727, Loss: 0.250
[2020-12-15 05:08:09,146][__main__][INFO] - Validating..
[2020-12-15 05:08:40,300][test][INFO] - Time elapsed: 29.699447
[2020-12-15 05:08:40,304][__main__][INFO] - Validation F1 score: 95.300, Exact match: 53.410, Precision: 95.340, Recall: 95.250
[2020-12-15 05:08:40,304][__main__][INFO] - F1 score has improved
[2020-12-15 05:09:14,569][__main__][INFO] - Checkpoint saved to checkpoints/model_latest.pth
[2020-12-15 05:09:15,441][__main__][INFO] - Epoch #16
[2020-12-15 05:09:15,441][__main__][INFO] - Training..
[2020-12-15 05:09:41,213][__main__][INFO] - [320] Loss: 0.120, Running accuracy: 99.806, Time: 24.76
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[2020-12-15 05:58:46,708][__main__][INFO] - [38720] Loss: 0.150, Running accuracy: 99.808, Time: 22.76
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[2020-12-15 05:59:56,608][__main__][INFO] - [39680] Loss: 0.097, Running accuracy: 99.810, Time: 22.27
[2020-12-15 06:00:06,205][__main__][INFO] - Action accuracy: 99.810, Loss: 0.174
[2020-12-15 06:00:06,206][__main__][INFO] - Validating..
[2020-12-15 06:00:32,482][test][INFO] - Time elapsed: 24.813251
[2020-12-15 06:00:32,486][__main__][INFO] - Validation F1 score: 95.410, Exact match: 54.120, Precision: 95.410, Recall: 95.400
[2020-12-15 06:00:32,486][__main__][INFO] - F1 score has improved
[2020-12-15 06:01:04,667][__main__][INFO] - Checkpoint saved to checkpoints/model_latest.pth
[2020-12-15 06:01:05,669][__main__][INFO] - Epoch #17
[2020-12-15 06:01:05,669][__main__][INFO] - Training..
[2020-12-15 06:01:30,037][__main__][INFO] - [320] Loss: 0.111, Running accuracy: 99.844, Time: 23.28
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[2020-12-15 06:27:01,132][__main__][INFO] - [20160] Loss: 0.103, Running accuracy: 99.830, Time: 24.06
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[2020-12-15 06:27:48,973][__main__][INFO] - [20800] Loss: 0.168, Running accuracy: 99.831, Time: 24.60
[2020-12-15 06:28:11,563][__main__][INFO] - [21120] Loss: 0.128, Running accuracy: 99.831, Time: 22.59
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[2020-12-15 06:49:19,660][__main__][INFO] - [37760] Loss: 0.198, Running accuracy: 99.835, Time: 24.25
[2020-12-15 06:49:44,375][__main__][INFO] - [38080] Loss: 0.317, Running accuracy: 99.833, Time: 24.71
[2020-12-15 06:50:08,441][__main__][INFO] - [38400] Loss: 0.177, Running accuracy: 99.833, Time: 24.07
[2020-12-15 06:50:31,530][__main__][INFO] - [38720] Loss: 0.078, Running accuracy: 99.833, Time: 23.09
[2020-12-15 06:50:54,870][__main__][INFO] - [39040] Loss: 0.229, Running accuracy: 99.833, Time: 23.34
[2020-12-15 06:51:19,445][__main__][INFO] - [39360] Loss: 0.163, Running accuracy: 99.833, Time: 24.57
[2020-12-15 06:51:44,388][__main__][INFO] - [39680] Loss: 0.110, Running accuracy: 99.833, Time: 24.94
[2020-12-15 06:51:53,348][__main__][INFO] - Action accuracy: 99.833, Loss: 0.161
[2020-12-15 06:51:53,349][__main__][INFO] - Validating..
[2020-12-15 06:52:23,793][test][INFO] - Time elapsed: 28.551181
[2020-12-15 06:52:23,797][__main__][INFO] - Validation F1 score: 95.400, Exact match: 54.410, Precision: 95.430, Recall: 95.360
[2020-12-15 06:52:58,065][__main__][INFO] - Checkpoint saved to checkpoints/model_latest.pth
[2020-12-15 06:52:59,064][__main__][INFO] - Epoch #18
[2020-12-15 06:52:59,064][__main__][INFO] - Training..
[2020-12-15 06:53:26,434][__main__][INFO] - [320] Loss: 0.074, Running accuracy: 99.886, Time: 26.33
[2020-12-15 06:53:49,212][__main__][INFO] - [640] Loss: 0.115, Running accuracy: 99.877, Time: 22.78
[2020-12-15 06:54:12,972][__main__][INFO] - [960] Loss: 0.128, Running accuracy: 99.862, Time: 23.76
[2020-12-15 06:54:37,265][__main__][INFO] - [1280] Loss: 0.171, Running accuracy: 99.864, Time: 24.29
[2020-12-15 06:54:59,825][__main__][INFO] - [1600] Loss: 0.146, Running accuracy: 99.854, Time: 22.56
[2020-12-15 06:55:23,662][__main__][INFO] - [1920] Loss: 0.152, Running accuracy: 99.856, Time: 23.84
[2020-12-15 06:55:47,394][__main__][INFO] - [2240] Loss: 0.131, Running accuracy: 99.856, Time: 23.73
[2020-12-15 06:56:18,104][__main__][INFO] - [2560] Loss: 0.133, Running accuracy: 99.852, Time: 30.71
[2020-12-15 06:56:41,593][__main__][INFO] - [2880] Loss: 0.156, Running accuracy: 99.842, Time: 23.49
[2020-12-15 06:57:05,616][__main__][INFO] - [3200] Loss: 0.123, Running accuracy: 99.844, Time: 24.02
[2020-12-15 06:57:29,897][__main__][INFO] - [3520] Loss: 0.169, Running accuracy: 99.843, Time: 24.28
[2020-12-15 06:57:53,528][__main__][INFO] - [3840] Loss: 0.126, Running accuracy: 99.845, Time: 23.63
[2020-12-15 06:58:18,532][__main__][INFO] - [4160] Loss: 0.044, Running accuracy: 99.851, Time: 25.00
[2020-12-15 06:58:43,217][__main__][INFO] - [4480] Loss: 0.061, Running accuracy: 99.856, Time: 24.68
[2020-12-15 06:59:07,825][__main__][INFO] - [4800] Loss: 0.148, Running accuracy: 99.853, Time: 24.61
[2020-12-15 06:59:31,079][__main__][INFO] - [5120] Loss: 0.071, Running accuracy: 99.859, Time: 23.25
[2020-12-15 06:59:56,446][__main__][INFO] - [5440] Loss: 0.066, Running accuracy: 99.861, Time: 25.37
[2020-12-15 07:00:19,770][__main__][INFO] - [5760] Loss: 0.074, Running accuracy: 99.865, Time: 23.32
[2020-12-15 07:00:43,763][__main__][INFO] - [6080] Loss: 0.068, Running accuracy: 99.867, Time: 23.99
[2020-12-15 07:01:10,651][__main__][INFO] - [6400] Loss: 0.127, Running accuracy: 99.866, Time: 26.89
[2020-12-15 07:01:39,082][__main__][INFO] - [6720] Loss: 0.050, Running accuracy: 99.870, Time: 28.43
[2020-12-15 07:02:04,147][__main__][INFO] - [7040] Loss: 0.110, Running accuracy: 99.871, Time: 25.06
[2020-12-15 07:02:27,837][__main__][INFO] - [7360] Loss: 0.066, Running accuracy: 99.873, Time: 23.69
[2020-12-15 07:02:52,606][__main__][INFO] - [7680] Loss: 0.101, Running accuracy: 99.875, Time: 24.77
[2020-12-15 07:03:16,243][__main__][INFO] - [8000] Loss: 0.109, Running accuracy: 99.877, Time: 23.64
[2020-12-15 07:03:42,125][__main__][INFO] - [8320] Loss: 0.054, Running accuracy: 99.877, Time: 25.88
[2020-12-15 07:04:05,909][__main__][INFO] - [8640] Loss: 0.115, Running accuracy: 99.875, Time: 23.70
[2020-12-15 07:04:28,851][__main__][INFO] - [8960] Loss: 0.170, Running accuracy: 99.871, Time: 22.94
[2020-12-15 07:04:53,730][__main__][INFO] - [9280] Loss: 0.104, Running accuracy: 99.872, Time: 24.88
[2020-12-15 07:05:18,317][__main__][INFO] - [9600] Loss: 0.118, Running accuracy: 99.872, Time: 24.59
[2020-12-15 07:05:41,604][__main__][INFO] - [9920] Loss: 0.074, Running accuracy: 99.873, Time: 23.29
[2020-12-15 07:06:06,309][__main__][INFO] - [10240] Loss: 0.172, Running accuracy: 99.872, Time: 24.70
[2020-12-15 07:06:31,293][__main__][INFO] - [10560] Loss: 0.162, Running accuracy: 99.870, Time: 24.98
[2020-12-15 07:06:56,177][__main__][INFO] - [10880] Loss: 0.160, Running accuracy: 99.870, Time: 24.88
[2020-12-15 07:07:25,167][__main__][INFO] - [11200] Loss: 0.130, Running accuracy: 99.867, Time: 28.99
[2020-12-15 07:07:49,403][__main__][INFO] - [11520] Loss: 0.088, Running accuracy: 99.867, Time: 24.23
[2020-12-15 07:08:13,783][__main__][INFO] - [11840] Loss: 0.143, Running accuracy: 99.867, Time: 24.38
[2020-12-15 07:08:38,422][__main__][INFO] - [12160] Loss: 0.070, Running accuracy: 99.867, Time: 24.64
[2020-12-15 07:09:03,942][__main__][INFO] - [12480] Loss: 0.121, Running accuracy: 99.866, Time: 25.52
[2020-12-15 07:09:27,515][__main__][INFO] - [12800] Loss: 0.179, Running accuracy: 99.866, Time: 23.57
[2020-12-15 07:09:50,107][__main__][INFO] - [13120] Loss: 0.251, Running accuracy: 99.864, Time: 22.59
[2020-12-15 07:10:14,803][__main__][INFO] - [13440] Loss: 0.161, Running accuracy: 99.864, Time: 24.69
[2020-12-15 07:10:39,234][__main__][INFO] - [13760] Loss: 0.105, Running accuracy: 99.862, Time: 24.43
[2020-12-15 07:11:02,718][__main__][INFO] - [14080] Loss: 0.103, Running accuracy: 99.862, Time: 23.48
[2020-12-15 07:11:27,508][__main__][INFO] - [14400] Loss: 0.104, Running accuracy: 99.862, Time: 24.79
[2020-12-15 07:11:53,399][__main__][INFO] - [14720] Loss: 0.053, Running accuracy: 99.863, Time: 25.89
[2020-12-15 07:12:18,920][__main__][INFO] - [15040] Loss: 0.134, Running accuracy: 99.862, Time: 25.52
[2020-12-15 07:12:42,980][__main__][INFO] - [15360] Loss: 0.049, Running accuracy: 99.863, Time: 24.06
[2020-12-15 07:13:09,621][__main__][INFO] - [15680] Loss: 0.115, Running accuracy: 99.863, Time: 26.64
[2020-12-15 07:13:34,560][__main__][INFO] - [16000] Loss: 0.176, Running accuracy: 99.863, Time: 24.94
[2020-12-15 07:13:58,676][__main__][INFO] - [16320] Loss: 0.199, Running accuracy: 99.861, Time: 24.11
[2020-12-15 07:14:25,722][__main__][INFO] - [16640] Loss: 0.190, Running accuracy: 99.858, Time: 27.04
[2020-12-15 07:14:52,965][__main__][INFO] - [16960] Loss: 0.123, Running accuracy: 99.857, Time: 27.24
[2020-12-15 07:15:17,273][__main__][INFO] - [17280] Loss: 0.175, Running accuracy: 99.856, Time: 24.31
[2020-12-15 07:15:40,672][__main__][INFO] - [17600] Loss: 0.103, Running accuracy: 99.856, Time: 23.40
[2020-12-15 07:16:04,350][__main__][INFO] - [17920] Loss: 0.081, Running accuracy: 99.855, Time: 23.68
[2020-12-15 07:16:27,846][__main__][INFO] - [18240] Loss: 0.202, Running accuracy: 99.855, Time: 23.49
[2020-12-15 07:16:51,466][__main__][INFO] - [18560] Loss: 0.131, Running accuracy: 99.855, Time: 23.62
[2020-12-15 07:17:15,342][__main__][INFO] - [18880] Loss: 0.254, Running accuracy: 99.852, Time: 23.87
[2020-12-15 07:17:42,142][__main__][INFO] - [19200] Loss: 0.116, Running accuracy: 99.852, Time: 26.80
[2020-12-15 07:18:05,787][__main__][INFO] - [19520] Loss: 0.136, Running accuracy: 99.852, Time: 23.64
[2020-12-15 07:18:34,834][__main__][INFO] - [19840] Loss: 0.202, Running accuracy: 99.851, Time: 29.05
[2020-12-15 07:18:59,830][__main__][INFO] - [20160] Loss: 0.121, Running accuracy: 99.851, Time: 25.00
[2020-12-15 07:19:24,133][__main__][INFO] - [20480] Loss: 0.134, Running accuracy: 99.850, Time: 24.30
[2020-12-15 07:19:47,901][__main__][INFO] - [20800] Loss: 0.068, Running accuracy: 99.851, Time: 23.77
[2020-12-15 07:20:12,251][__main__][INFO] - [21120] Loss: 0.165, Running accuracy: 99.850, Time: 24.35
[2020-12-15 07:20:35,908][__main__][INFO] - [21440] Loss: 0.075, Running accuracy: 99.851, Time: 23.66
[2020-12-15 07:20:58,849][__main__][INFO] - [21760] Loss: 0.190, Running accuracy: 99.850, Time: 22.94
[2020-12-15 07:21:21,898][__main__][INFO] - [22080] Loss: 0.077, Running accuracy: 99.851, Time: 23.05
[2020-12-15 07:21:45,609][__main__][INFO] - [22400] Loss: 0.111, Running accuracy: 99.852, Time: 23.71
[2020-12-15 07:22:10,116][__main__][INFO] - [22720] Loss: 0.097, Running accuracy: 99.852, Time: 24.51
[2020-12-15 07:22:32,629][__main__][INFO] - [23040] Loss: 0.106, Running accuracy: 99.851, Time: 22.51
[2020-12-15 07:22:56,408][__main__][INFO] - [23360] Loss: 0.095, Running accuracy: 99.852, Time: 23.78
[2020-12-15 07:23:20,334][__main__][INFO] - [23680] Loss: 0.087, Running accuracy: 99.853, Time: 23.93
[2020-12-15 07:23:46,411][__main__][INFO] - [24000] Loss: 0.176, Running accuracy: 99.852, Time: 26.08
[2020-12-15 07:24:14,972][__main__][INFO] - [24320] Loss: 0.157, Running accuracy: 99.853, Time: 28.56
[2020-12-15 07:24:39,065][__main__][INFO] - [24640] Loss: 0.086, Running accuracy: 99.853, Time: 24.09
[2020-12-15 07:25:02,172][__main__][INFO] - [24960] Loss: 0.137, Running accuracy: 99.854, Time: 23.11
[2020-12-15 07:25:26,021][__main__][INFO] - [25280] Loss: 0.205, Running accuracy: 99.853, Time: 23.85
[2020-12-15 07:25:52,457][__main__][INFO] - [25600] Loss: 0.105, Running accuracy: 99.853, Time: 26.43
[2020-12-15 07:26:15,850][__main__][INFO] - [25920] Loss: 0.125, Running accuracy: 99.853, Time: 23.39
[2020-12-15 07:26:38,465][__main__][INFO] - [26240] Loss: 0.128, Running accuracy: 99.854, Time: 22.61
[2020-12-15 07:27:02,797][__main__][INFO] - [26560] Loss: 0.152, Running accuracy: 99.853, Time: 24.33
[2020-12-15 07:27:27,315][__main__][INFO] - [26880] Loss: 0.124, Running accuracy: 99.853, Time: 24.52
[2020-12-15 07:27:50,959][__main__][INFO] - [27200] Loss: 0.098, Running accuracy: 99.853, Time: 23.64
[2020-12-15 07:28:13,245][__main__][INFO] - [27520] Loss: 0.131, Running accuracy: 99.852, Time: 22.28
[2020-12-15 07:28:37,157][__main__][INFO] - [27840] Loss: 0.171, Running accuracy: 99.852, Time: 23.91
[2020-12-15 07:29:03,554][__main__][INFO] - [28160] Loss: 0.133, Running accuracy: 99.852, Time: 26.40
[2020-12-15 07:29:27,438][__main__][INFO] - [28480] Loss: 0.097, Running accuracy: 99.852, Time: 23.88
[2020-12-15 07:29:55,438][__main__][INFO] - [28800] Loss: 0.047, Running accuracy: 99.853, Time: 28.00
[2020-12-15 07:30:19,789][__main__][INFO] - [29120] Loss: 0.084, Running accuracy: 99.853, Time: 24.35
[2020-12-15 07:30:44,544][__main__][INFO] - [29440] Loss: 0.101, Running accuracy: 99.853, Time: 24.75
[2020-12-15 07:31:10,358][__main__][INFO] - [29760] Loss: 0.078, Running accuracy: 99.853, Time: 25.81
[2020-12-15 07:31:37,040][__main__][INFO] - [30080] Loss: 0.118, Running accuracy: 99.853, Time: 26.68
[2020-12-15 07:32:01,375][__main__][INFO] - [30400] Loss: 0.107, Running accuracy: 99.854, Time: 24.33
[2020-12-15 07:32:27,166][__main__][INFO] - [30720] Loss: 0.124, Running accuracy: 99.854, Time: 25.79
[2020-12-15 07:32:51,861][__main__][INFO] - [31040] Loss: 0.189, Running accuracy: 99.854, Time: 24.69
[2020-12-15 07:33:15,442][__main__][INFO] - [31360] Loss: 0.129, Running accuracy: 99.854, Time: 23.58
[2020-12-15 07:33:39,064][__main__][INFO] - [31680] Loss: 0.083, Running accuracy: 99.855, Time: 23.62
[2020-12-15 07:34:03,597][__main__][INFO] - [32000] Loss: 0.203, Running accuracy: 99.854, Time: 24.53
[2020-12-15 07:34:29,886][__main__][INFO] - [32320] Loss: 0.244, Running accuracy: 99.852, Time: 26.29
[2020-12-15 07:34:53,493][__main__][INFO] - [32640] Loss: 0.081, Running accuracy: 99.853, Time: 23.61
[2020-12-15 07:35:21,993][__main__][INFO] - [32960] Loss: 0.118, Running accuracy: 99.853, Time: 28.50
[2020-12-15 07:35:47,206][__main__][INFO] - [33280] Loss: 0.116, Running accuracy: 99.853, Time: 25.21
[2020-12-15 07:36:11,066][__main__][INFO] - [33600] Loss: 0.080, Running accuracy: 99.854, Time: 23.86
[2020-12-15 07:36:35,568][__main__][INFO] - [33920] Loss: 0.190, Running accuracy: 99.852, Time: 24.50
[2020-12-15 07:37:00,870][__main__][INFO] - [34240] Loss: 0.192, Running accuracy: 99.851, Time: 25.30
[2020-12-15 07:37:24,842][__main__][INFO] - [34560] Loss: 0.126, Running accuracy: 99.852, Time: 23.97
[2020-12-15 07:37:49,139][__main__][INFO] - [34880] Loss: 0.178, Running accuracy: 99.851, Time: 24.30
[2020-12-15 07:38:12,646][__main__][INFO] - [35200] Loss: 0.084, Running accuracy: 99.850, Time: 23.51
[2020-12-15 07:38:35,993][__main__][INFO] - [35520] Loss: 0.115, Running accuracy: 99.850, Time: 23.35
[2020-12-15 07:39:00,405][__main__][INFO] - [35840] Loss: 0.172, Running accuracy: 99.849, Time: 24.41
[2020-12-15 07:39:24,619][__main__][INFO] - [36160] Loss: 0.112, Running accuracy: 99.849, Time: 24.21
[2020-12-15 07:39:47,801][__main__][INFO] - [36480] Loss: 0.079, Running accuracy: 99.850, Time: 23.18
[2020-12-15 07:40:14,326][__main__][INFO] - [36800] Loss: 0.092, Running accuracy: 99.851, Time: 26.52
[2020-12-15 07:40:38,031][__main__][INFO] - [37120] Loss: 0.183, Running accuracy: 99.850, Time: 23.70
[2020-12-15 07:41:05,785][__main__][INFO] - [37440] Loss: 0.091, Running accuracy: 99.850, Time: 27.75
[2020-12-15 07:41:31,136][__main__][INFO] - [37760] Loss: 0.089, Running accuracy: 99.850, Time: 25.35
[2020-12-15 07:41:54,537][__main__][INFO] - [38080] Loss: 0.149, Running accuracy: 99.850, Time: 23.40
[2020-12-15 07:42:17,594][__main__][INFO] - [38400] Loss: 0.076, Running accuracy: 99.850, Time: 23.06
[2020-12-15 07:42:40,719][__main__][INFO] - [38720] Loss: 0.132, Running accuracy: 99.850, Time: 23.12
[2020-12-15 07:43:04,739][__main__][INFO] - [39040] Loss: 0.103, Running accuracy: 99.851, Time: 24.02
[2020-12-15 07:43:28,864][__main__][INFO] - [39360] Loss: 0.160, Running accuracy: 99.850, Time: 24.12
[2020-12-15 07:43:50,744][__main__][INFO] - [39680] Loss: 0.173, Running accuracy: 99.850, Time: 21.88
[2020-12-15 07:44:00,937][__main__][INFO] - Action accuracy: 99.850, Loss: 0.138
[2020-12-15 07:44:00,939][__main__][INFO] - Validating..
[2020-12-15 07:44:31,191][test][INFO] - Time elapsed: 28.427697
[2020-12-15 07:44:31,195][__main__][INFO] - Validation F1 score: 95.440, Exact match: 54.590, Precision: 95.410, Recall: 95.470
[2020-12-15 07:44:31,195][__main__][INFO] - F1 score has improved
[2020-12-15 07:45:05,444][__main__][INFO] - Checkpoint saved to checkpoints/model_latest.pth
[2020-12-15 07:45:06,245][__main__][INFO] - Epoch #19
[2020-12-15 07:45:06,246][__main__][INFO] - Training..
[2020-12-15 07:45:32,505][__main__][INFO] - [320] Loss: 0.060, Running accuracy: 99.924, Time: 25.11
[2020-12-15 07:45:56,421][__main__][INFO] - [640] Loss: 0.111, Running accuracy: 99.869, Time: 23.91
[2020-12-15 07:46:20,864][__main__][INFO] - [960] Loss: 0.144, Running accuracy: 99.862, Time: 24.44
[2020-12-15 07:46:45,892][__main__][INFO] - [1280] Loss: 0.046, Running accuracy: 99.877, Time: 25.03
[2020-12-15 07:47:10,253][__main__][INFO] - [1600] Loss: 0.053, Running accuracy: 99.894, Time: 24.36
[2020-12-15 07:47:34,161][__main__][INFO] - [1920] Loss: 0.117, Running accuracy: 99.886, Time: 23.91
[2020-12-15 07:47:56,575][__main__][INFO] - [2240] Loss: 0.151, Running accuracy: 99.875, Time: 22.41
[2020-12-15 07:48:20,532][__main__][INFO] - [2560] Loss: 0.078, Running accuracy: 99.878, Time: 23.96
[2020-12-15 07:48:46,665][__main__][INFO] - [2880] Loss: 0.150, Running accuracy: 99.867, Time: 26.13
[2020-12-15 07:49:11,715][__main__][INFO] - [3200] Loss: 0.107, Running accuracy: 99.865, Time: 25.05
[2020-12-15 07:49:35,320][__main__][INFO] - [3520] Loss: 0.144, Running accuracy: 99.860, Time: 23.60
[2020-12-15 07:50:01,849][__main__][INFO] - [3840] Loss: 0.153, Running accuracy: 99.855, Time: 26.53
[2020-12-15 07:50:25,953][__main__][INFO] - [4160] Loss: 0.067, Running accuracy: 99.855, Time: 24.10
[2020-12-15 07:50:56,808][__main__][INFO] - [4480] Loss: 0.078, Running accuracy: 99.858, Time: 30.85
[2020-12-15 07:51:20,291][__main__][INFO] - [4800] Loss: 0.099, Running accuracy: 99.862, Time: 23.48
[2020-12-15 07:51:42,670][__main__][INFO] - [5120] Loss: 0.103, Running accuracy: 99.861, Time: 22.38
[2020-12-15 07:52:05,168][__main__][INFO] - [5440] Loss: 0.074, Running accuracy: 99.862, Time: 22.50
[2020-12-15 07:52:28,024][__main__][INFO] - [5760] Loss: 0.156, Running accuracy: 99.857, Time: 22.85
[2020-12-15 07:52:50,642][__main__][INFO] - [6080] Loss: 0.057, Running accuracy: 99.862, Time: 22.62
[2020-12-15 07:53:13,795][__main__][INFO] - [6400] Loss: 0.076, Running accuracy: 99.865, Time: 23.15
[2020-12-15 07:53:36,155][__main__][INFO] - [6720] Loss: 0.029, Running accuracy: 99.869, Time: 22.36
[2020-12-15 07:54:00,813][__main__][INFO] - [7040] Loss: 0.083, Running accuracy: 99.871, Time: 24.66
[2020-12-15 07:54:24,406][__main__][INFO] - [7360] Loss: 0.108, Running accuracy: 99.871, Time: 23.59
[2020-12-15 07:54:46,794][__main__][INFO] - [7680] Loss: 0.120, Running accuracy: 99.869, Time: 22.39
[2020-12-15 07:55:11,877][__main__][INFO] - [8000] Loss: 0.113, Running accuracy: 99.868, Time: 25.08
[2020-12-15 07:55:37,220][__main__][INFO] - [8320] Loss: 0.114, Running accuracy: 99.869, Time: 25.34
[2020-12-15 07:56:01,046][__main__][INFO] - [8640] Loss: 0.156, Running accuracy: 99.865, Time: 23.83
[2020-12-15 07:56:29,472][__main__][INFO] - [8960] Loss: 0.138, Running accuracy: 99.864, Time: 28.42
[2020-12-15 07:56:52,452][__main__][INFO] - [9280] Loss: 0.138, Running accuracy: 99.864, Time: 22.89
[2020-12-15 07:57:16,788][__main__][INFO] - [9600] Loss: 0.054, Running accuracy: 99.865, Time: 24.34
[2020-12-15 07:57:42,048][__main__][INFO] - [9920] Loss: 0.105, Running accuracy: 99.865, Time: 25.26
[2020-12-15 07:58:06,184][__main__][INFO] - [10240] Loss: 0.160, Running accuracy: 99.865, Time: 24.13
[2020-12-15 07:58:30,505][__main__][INFO] - [10560] Loss: 0.092, Running accuracy: 99.866, Time: 24.32
[2020-12-15 07:58:54,172][__main__][INFO] - [10880] Loss: 0.041, Running accuracy: 99.868, Time: 23.67
[2020-12-15 07:59:16,984][__main__][INFO] - [11200] Loss: 0.072, Running accuracy: 99.868, Time: 22.81
[2020-12-15 07:59:40,025][__main__][INFO] - [11520] Loss: 0.085, Running accuracy: 99.869, Time: 23.04
[2020-12-15 08:00:03,137][__main__][INFO] - [11840] Loss: 0.124, Running accuracy: 99.869, Time: 23.11
[2020-12-15 08:00:26,586][__main__][INFO] - [12160] Loss: 0.159, Running accuracy: 99.870, Time: 23.45
[2020-12-15 08:00:53,622][__main__][INFO] - [12480] Loss: 0.084, Running accuracy: 99.869, Time: 27.03
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[2020-12-15 08:33:34,526][__main__][INFO] - [38080] Loss: 0.116, Running accuracy: 99.863, Time: 22.88
[2020-12-15 08:33:58,209][__main__][INFO] - [38400] Loss: 0.206, Running accuracy: 99.862, Time: 23.68
[2020-12-15 08:34:23,902][__main__][INFO] - [38720] Loss: 0.095, Running accuracy: 99.862, Time: 25.69
[2020-12-15 08:34:47,322][__main__][INFO] - [39040] Loss: 0.100, Running accuracy: 99.862, Time: 23.42
[2020-12-15 08:35:16,724][__main__][INFO] - [39360] Loss: 0.070, Running accuracy: 99.863, Time: 29.40
[2020-12-15 08:35:40,276][__main__][INFO] - [39680] Loss: 0.135, Running accuracy: 99.863, Time: 23.55
[2020-12-15 08:35:50,256][__main__][INFO] - Action accuracy: 99.863, Loss: 0.126
[2020-12-15 08:35:50,256][__main__][INFO] - Validating..
[2020-12-15 08:36:16,590][test][INFO] - Time elapsed: 24.295272
[2020-12-15 08:36:16,596][__main__][INFO] - Validation F1 score: 95.390, Exact match: 53.880, Precision: 95.390, Recall: 95.380
[2020-12-15 08:36:50,764][__main__][INFO] - Checkpoint saved to checkpoints/model_latest.pth
[2020-12-15 08:36:51,602][__main__][INFO] - Epoch #20
[2020-12-15 08:36:51,602][__main__][INFO] - Training..
[2020-12-15 08:37:15,437][__main__][INFO] - [320] Loss: 0.146, Running accuracy: 99.839, Time: 22.44
[2020-12-15 08:37:39,275][__main__][INFO] - [640] Loss: 0.060, Running accuracy: 99.894, Time: 23.84
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[2020-12-15 09:12:24,301][__main__][INFO] - [27840] Loss: 0.053, Running accuracy: 99.866, Time: 23.42
[2020-12-15 09:12:54,215][__main__][INFO] - [28160] Loss: 0.139, Running accuracy: 99.865, Time: 29.91
[2020-12-15 09:13:18,157][__main__][INFO] - [28480] Loss: 0.184, Running accuracy: 99.864, Time: 23.94
[2020-12-15 09:13:42,356][__main__][INFO] - [28800] Loss: 0.152, Running accuracy: 99.863, Time: 24.20
[2020-12-15 09:14:04,930][__main__][INFO] - [29120] Loss: 0.123, Running accuracy: 99.863, Time: 22.57
[2020-12-15 09:14:29,973][__main__][INFO] - [29440] Loss: 0.143, Running accuracy: 99.863, Time: 25.04
[2020-12-15 09:14:54,979][__main__][INFO] - [29760] Loss: 0.122, Running accuracy: 99.863, Time: 25.00
[2020-12-15 09:15:21,646][__main__][INFO] - [30080] Loss: 0.151, Running accuracy: 99.862, Time: 26.67
[2020-12-15 09:15:45,549][__main__][INFO] - [30400] Loss: 0.094, Running accuracy: 99.862, Time: 23.90
[2020-12-15 09:16:09,167][__main__][INFO] - [30720] Loss: 0.143, Running accuracy: 99.862, Time: 23.62
[2020-12-15 09:16:33,177][__main__][INFO] - [31040] Loss: 0.056, Running accuracy: 99.863, Time: 24.01
[2020-12-15 09:16:57,370][__main__][INFO] - [31360] Loss: 0.068, Running accuracy: 99.863, Time: 24.19
[2020-12-15 09:17:22,681][__main__][INFO] - [31680] Loss: 0.111, Running accuracy: 99.863, Time: 25.31
[2020-12-15 09:17:47,220][__main__][INFO] - [32000] Loss: 0.096, Running accuracy: 99.863, Time: 24.54
[2020-12-15 09:18:14,075][__main__][INFO] - [32320] Loss: 0.107, Running accuracy: 99.863, Time: 26.85
[2020-12-15 09:18:38,616][__main__][INFO] - [32640] Loss: 0.060, Running accuracy: 99.863, Time: 24.54
[2020-12-15 09:19:00,998][__main__][INFO] - [32960] Loss: 0.127, Running accuracy: 99.863, Time: 22.38
[2020-12-15 09:19:24,060][__main__][INFO] - [33280] Loss: 0.096, Running accuracy: 99.863, Time: 23.06
[2020-12-15 09:19:48,520][__main__][INFO] - [33600] Loss: 0.081, Running accuracy: 99.863, Time: 24.46
[2020-12-15 09:20:12,991][__main__][INFO] - [33920] Loss: 0.133, Running accuracy: 99.863, Time: 24.47
[2020-12-15 09:20:36,763][__main__][INFO] - [34240] Loss: 0.058, Running accuracy: 99.864, Time: 23.77
[2020-12-15 09:21:01,414][__main__][INFO] - [34560] Loss: 0.039, Running accuracy: 99.865, Time: 24.65
[2020-12-15 09:21:28,035][__main__][INFO] - [34880] Loss: 0.067, Running accuracy: 99.865, Time: 26.62
[2020-12-15 09:21:52,396][__main__][INFO] - [35200] Loss: 0.105, Running accuracy: 99.865, Time: 24.36
[2020-12-15 09:22:16,816][__main__][INFO] - [35520] Loss: 0.145, Running accuracy: 99.865, Time: 24.42
[2020-12-15 09:22:41,309][__main__][INFO] - [35840] Loss: 0.117, Running accuracy: 99.865, Time: 24.49
[2020-12-15 09:23:05,248][__main__][INFO] - [36160] Loss: 0.107, Running accuracy: 99.865, Time: 23.94
[2020-12-15 09:23:29,986][__main__][INFO] - [36480] Loss: 0.160, Running accuracy: 99.864, Time: 24.74
[2020-12-15 09:23:57,325][__main__][INFO] - [36800] Loss: 0.095, Running accuracy: 99.864, Time: 27.34
[2020-12-15 09:24:20,248][__main__][INFO] - [37120] Loss: 0.193, Running accuracy: 99.863, Time: 22.92
[2020-12-15 09:24:44,698][__main__][INFO] - [37440] Loss: 0.041, Running accuracy: 99.864, Time: 24.45
[2020-12-15 09:25:08,465][__main__][INFO] - [37760] Loss: 0.160, Running accuracy: 99.864, Time: 23.77
[2020-12-15 09:25:35,566][__main__][INFO] - [38080] Loss: 0.148, Running accuracy: 99.863, Time: 27.10
[2020-12-15 09:26:00,608][__main__][INFO] - [38400] Loss: 0.091, Running accuracy: 99.863, Time: 25.04
[2020-12-15 09:26:24,639][__main__][INFO] - [38720] Loss: 0.121, Running accuracy: 99.863, Time: 24.03
[2020-12-15 09:26:49,634][__main__][INFO] - [39040] Loss: 0.081, Running accuracy: 99.864, Time: 24.99
[2020-12-15 09:27:15,155][__main__][INFO] - [39360] Loss: 0.085, Running accuracy: 99.864, Time: 25.52
[2020-12-15 09:27:38,606][__main__][INFO] - [39680] Loss: 0.095, Running accuracy: 99.864, Time: 23.45
[2020-12-15 09:27:49,401][__main__][INFO] - Action accuracy: 99.864, Loss: 0.128
[2020-12-15 09:27:49,402][__main__][INFO] - Validating..
[2020-12-15 09:28:19,624][test][INFO] - Time elapsed: 27.943334
[2020-12-15 09:28:19,628][__main__][INFO] - Validation F1 score: 95.290, Exact match: 53.940, Precision: 95.300, Recall: 95.280
[2020-12-15 09:28:53,873][__main__][INFO] - Checkpoint saved to checkpoints/model_latest.pth
[2020-12-15 09:28:54,925][__main__][INFO] - Epoch #21
[2020-12-15 09:28:54,925][__main__][INFO] - Training..
[2020-12-15 09:29:19,948][__main__][INFO] - [320] Loss: 0.156, Running accuracy: 99.906, Time: 23.79
[2020-12-15 09:29:43,159][__main__][INFO] - [640] Loss: 0.075, Running accuracy: 99.893, Time: 23.21
[2020-12-15 09:30:08,120][__main__][INFO] - [960] Loss: 0.065, Running accuracy: 99.903, Time: 24.96
[2020-12-15 09:30:32,926][__main__][INFO] - [1280] Loss: 0.062, Running accuracy: 99.908, Time: 24.80
[2020-12-15 09:30:57,248][__main__][INFO] - [1600] Loss: 0.117, Running accuracy: 99.897, Time: 24.32
[2020-12-15 09:31:19,958][__main__][INFO] - [1920] Loss: 0.060, Running accuracy: 99.901, Time: 22.71
[2020-12-15 09:31:41,789][__main__][INFO] - [2240] Loss: 0.059, Running accuracy: 99.909, Time: 21.83
[2020-12-15 09:32:04,253][__main__][INFO] - [2560] Loss: 0.073, Running accuracy: 99.911, Time: 22.46
[2020-12-15 09:32:29,321][__main__][INFO] - [2880] Loss: 0.055, Running accuracy: 99.916, Time: 25.07
[2020-12-15 09:32:53,268][__main__][INFO] - [3200] Loss: 0.080, Running accuracy: 99.917, Time: 23.95
[2020-12-15 09:33:16,905][__main__][INFO] - [3520] Loss: 0.083, Running accuracy: 99.910, Time: 23.64
[2020-12-15 09:33:48,972][__main__][INFO] - [3840] Loss: 0.077, Running accuracy: 99.909, Time: 32.07
[2020-12-15 09:34:13,946][__main__][INFO] - [4160] Loss: 0.040, Running accuracy: 99.909, Time: 24.97
[2020-12-15 09:34:36,680][__main__][INFO] - [4480] Loss: 0.055, Running accuracy: 99.908, Time: 22.73
[2020-12-15 09:35:00,115][__main__][INFO] - [4800] Loss: 0.100, Running accuracy: 99.905, Time: 23.43
[2020-12-15 09:35:24,610][__main__][INFO] - [5120] Loss: 0.100, Running accuracy: 99.901, Time: 24.49
[2020-12-15 09:35:48,714][__main__][INFO] - [5440] Loss: 0.031, Running accuracy: 99.904, Time: 24.10
[2020-12-15 09:36:12,459][__main__][INFO] - [5760] Loss: 0.164, Running accuracy: 99.899, Time: 23.74
[2020-12-15 09:36:36,603][__main__][INFO] - [6080] Loss: 0.134, Running accuracy: 99.894, Time: 24.14
[2020-12-15 09:37:00,689][__main__][INFO] - [6400] Loss: 0.070, Running accuracy: 99.893, Time: 24.09
[2020-12-15 09:37:23,693][__main__][INFO] - [6720] Loss: 0.091, Running accuracy: 99.891, Time: 23.00
[2020-12-15 09:37:48,992][__main__][INFO] - [7040] Loss: 0.113, Running accuracy: 99.888, Time: 25.30
[2020-12-15 09:38:13,347][__main__][INFO] - [7360] Loss: 0.091, Running accuracy: 99.886, Time: 24.35
[2020-12-15 09:38:37,450][__main__][INFO] - [7680] Loss: 0.091, Running accuracy: 99.887, Time: 24.10
[2020-12-15 09:39:07,776][__main__][INFO] - [8000] Loss: 0.121, Running accuracy: 99.884, Time: 30.33
[2020-12-15 09:39:33,187][__main__][INFO] - [8320] Loss: 0.053, Running accuracy: 99.887, Time: 25.41
[2020-12-15 09:39:56,972][__main__][INFO] - [8640] Loss: 0.138, Running accuracy: 99.888, Time: 23.78
[2020-12-15 09:40:20,517][__main__][INFO] - [8960] Loss: 0.063, Running accuracy: 99.890, Time: 23.54
[2020-12-15 09:40:44,981][__main__][INFO] - [9280] Loss: 0.087, Running accuracy: 99.889, Time: 24.46
[2020-12-15 09:41:07,889][__main__][INFO] - [9600] Loss: 0.166, Running accuracy: 99.889, Time: 22.91
[2020-12-15 09:41:30,924][__main__][INFO] - [9920] Loss: 0.090, Running accuracy: 99.888, Time: 23.03
[2020-12-15 09:41:53,928][__main__][INFO] - [10240] Loss: 0.131, Running accuracy: 99.887, Time: 23.00
[2020-12-15 09:42:18,165][__main__][INFO] - [10560] Loss: 0.133, Running accuracy: 99.885, Time: 24.15
[2020-12-15 09:42:42,468][__main__][INFO] - [10880] Loss: 0.045, Running accuracy: 99.887, Time: 24.30
[2020-12-15 09:43:06,136][__main__][INFO] - [11200] Loss: 0.089, Running accuracy: 99.887, Time: 23.67
[2020-12-15 09:43:30,366][__main__][INFO] - [11520] Loss: 0.286, Running accuracy: 99.881, Time: 24.23
[2020-12-15 09:43:55,526][__main__][INFO] - [11840] Loss: 0.084, Running accuracy: 99.882, Time: 25.16
[2020-12-15 09:44:21,802][__main__][INFO] - [12160] Loss: 0.073, Running accuracy: 99.884, Time: 26.28
[2020-12-15 09:44:52,887][__main__][INFO] - [12480] Loss: 0.061, Running accuracy: 99.885, Time: 31.08
[2020-12-15 09:45:17,014][__main__][INFO] - [12800] Loss: 0.068, Running accuracy: 99.885, Time: 24.13
[2020-12-15 09:45:41,266][__main__][INFO] - [13120] Loss: 0.081, Running accuracy: 99.885, Time: 24.25
[2020-12-15 09:46:08,826][__main__][INFO] - [13440] Loss: 0.211, Running accuracy: 99.885, Time: 27.56
[2020-12-15 09:46:33,263][__main__][INFO] - [13760] Loss: 0.094, Running accuracy: 99.885, Time: 24.44
[2020-12-15 09:46:58,529][__main__][INFO] - [14080] Loss: 0.153, Running accuracy: 99.882, Time: 25.27
[2020-12-15 09:47:21,787][__main__][INFO] - [14400] Loss: 0.052, Running accuracy: 99.883, Time: 23.26
[2020-12-15 09:47:45,951][__main__][INFO] - [14720] Loss: 0.178, Running accuracy: 99.883, Time: 24.16
[2020-12-15 09:48:10,274][__main__][INFO] - [15040] Loss: 0.099, Running accuracy: 99.882, Time: 24.32
[2020-12-15 09:48:35,788][__main__][INFO] - [15360] Loss: 0.075, Running accuracy: 99.883, Time: 25.51
[2020-12-15 09:48:59,855][__main__][INFO] - [15680] Loss: 0.107, Running accuracy: 99.882, Time: 24.07
[2020-12-15 09:49:24,207][__main__][INFO] - [16000] Loss: 0.094, Running accuracy: 99.882, Time: 24.35
[2020-12-15 09:49:50,348][__main__][INFO] - [16320] Loss: 0.250, Running accuracy: 99.878, Time: 26.14
[2020-12-15 09:50:16,335][__main__][INFO] - [16640] Loss: 0.173, Running accuracy: 99.877, Time: 25.99
[2020-12-15 09:50:44,220][__main__][INFO] - [16960] Loss: 0.101, Running accuracy: 99.876, Time: 27.88
[2020-12-15 09:51:08,640][__main__][INFO] - [17280] Loss: 0.032, Running accuracy: 99.878, Time: 24.42
[2020-12-15 09:51:31,708][__main__][INFO] - [17600] Loss: 0.056, Running accuracy: 99.878, Time: 23.07
[2020-12-15 09:51:56,479][__main__][INFO] - [17920] Loss: 0.075, Running accuracy: 99.878, Time: 24.77
[2020-12-15 09:52:19,610][__main__][INFO] - [18240] Loss: 0.044, Running accuracy: 99.880, Time: 23.13
[2020-12-15 09:52:43,853][__main__][INFO] - [18560] Loss: 0.099, Running accuracy: 99.880, Time: 24.24
[2020-12-15 09:53:05,562][__main__][INFO] - [18880] Loss: 0.067, Running accuracy: 99.880, Time: 21.71
[2020-12-15 09:53:29,487][__main__][INFO] - [19200] Loss: 0.057, Running accuracy: 99.881, Time: 23.93
[2020-12-15 09:53:53,728][__main__][INFO] - [19520] Loss: 0.147, Running accuracy: 99.880, Time: 24.24
[2020-12-15 09:54:17,938][__main__][INFO] - [19840] Loss: 0.084, Running accuracy: 99.880, Time: 24.21
[2020-12-15 09:54:40,198][__main__][INFO] - [20160] Loss: 0.090, Running accuracy: 99.881, Time: 22.26
[2020-12-15 09:55:03,262][__main__][INFO] - [20480] Loss: 0.095, Running accuracy: 99.881, Time: 23.06
[2020-12-15 09:55:27,912][__main__][INFO] - [20800] Loss: 0.044, Running accuracy: 99.882, Time: 24.65
[2020-12-15 09:55:51,036][__main__][INFO] - [21120] Loss: 0.090, Running accuracy: 99.882, Time: 23.12
[2020-12-15 09:56:20,348][__main__][INFO] - [21440] Loss: 0.084, Running accuracy: 99.882, Time: 29.31
[2020-12-15 09:56:44,523][__main__][INFO] - [21760] Loss: 0.036, Running accuracy: 99.884, Time: 24.17
[2020-12-15 09:57:09,383][__main__][INFO] - [22080] Loss: 0.086, Running accuracy: 99.885, Time: 24.86
[2020-12-15 09:57:33,801][__main__][INFO] - [22400] Loss: 0.100, Running accuracy: 99.885, Time: 24.42
[2020-12-15 09:57:59,719][__main__][INFO] - [22720] Loss: 0.110, Running accuracy: 99.884, Time: 25.92
[2020-12-15 09:58:24,131][__main__][INFO] - [23040] Loss: 0.091, Running accuracy: 99.885, Time: 24.41
[2020-12-15 09:58:48,731][__main__][INFO] - [23360] Loss: 0.158, Running accuracy: 99.883, Time: 24.60
[2020-12-15 09:59:13,269][__main__][INFO] - [23680] Loss: 0.082, Running accuracy: 99.883, Time: 24.54
[2020-12-15 09:59:37,046][__main__][INFO] - [24000] Loss: 0.103, Running accuracy: 99.882, Time: 23.77
[2020-12-15 10:00:02,173][__main__][INFO] - [24320] Loss: 0.150, Running accuracy: 99.883, Time: 25.13
[2020-12-15 10:00:28,359][__main__][INFO] - [24640] Loss: 0.148, Running accuracy: 99.882, Time: 26.19
[2020-12-15 10:00:52,947][__main__][INFO] - [24960] Loss: 0.100, Running accuracy: 99.882, Time: 24.59
[2020-12-15 10:01:17,561][__main__][INFO] - [25280] Loss: 0.076, Running accuracy: 99.882, Time: 24.61
[2020-12-15 10:01:46,793][__main__][INFO] - [25600] Loss: 0.035, Running accuracy: 99.883, Time: 29.23
[2020-12-15 10:02:11,419][__main__][INFO] - [25920] Loss: 0.072, Running accuracy: 99.883, Time: 24.63
[2020-12-15 10:02:36,068][__main__][INFO] - [26240] Loss: 0.161, Running accuracy: 99.881, Time: 24.65
[2020-12-15 10:03:00,227][__main__][INFO] - [26560] Loss: 0.081, Running accuracy: 99.881, Time: 24.16
[2020-12-15 10:03:23,594][__main__][INFO] - [26880] Loss: 0.075, Running accuracy: 99.881, Time: 23.37
[2020-12-15 10:03:46,932][__main__][INFO] - [27200] Loss: 0.161, Running accuracy: 99.880, Time: 23.34
[2020-12-15 10:04:11,100][__main__][INFO] - [27520] Loss: 0.061, Running accuracy: 99.880, Time: 24.17
[2020-12-15 10:04:36,090][__main__][INFO] - [27840] Loss: 0.056, Running accuracy: 99.881, Time: 24.99
[2020-12-15 10:04:59,761][__main__][INFO] - [28160] Loss: 0.144, Running accuracy: 99.880, Time: 23.67
[2020-12-15 10:05:23,077][__main__][INFO] - [28480] Loss: 0.085, Running accuracy: 99.880, Time: 23.32
[2020-12-15 10:05:46,788][__main__][INFO] - [28800] Loss: 0.201, Running accuracy: 99.879, Time: 23.71
[2020-12-15 10:06:10,238][__main__][INFO] - [29120] Loss: 0.061, Running accuracy: 99.879, Time: 23.45
[2020-12-15 10:06:33,825][__main__][INFO] - [29440] Loss: 0.075, Running accuracy: 99.878, Time: 23.59
[2020-12-15 10:06:57,506][__main__][INFO] - [29760] Loss: 0.045, Running accuracy: 99.879, Time: 23.68
[2020-12-15 10:07:27,083][__main__][INFO] - [30080] Loss: 0.156, Running accuracy: 99.879, Time: 29.58
[2020-12-15 10:07:51,847][__main__][INFO] - [30400] Loss: 0.085, Running accuracy: 99.879, Time: 24.76
[2020-12-15 10:08:15,724][__main__][INFO] - [30720] Loss: 0.162, Running accuracy: 99.878, Time: 23.88
[2020-12-15 10:08:40,374][__main__][INFO] - [31040] Loss: 0.156, Running accuracy: 99.878, Time: 24.65
[2020-12-15 10:09:05,122][__main__][INFO] - [31360] Loss: 0.146, Running accuracy: 99.878, Time: 24.75
[2020-12-15 10:09:28,189][__main__][INFO] - [31680] Loss: 0.092, Running accuracy: 99.878, Time: 23.07
[2020-12-15 10:09:50,108][__main__][INFO] - [32000] Loss: 0.088, Running accuracy: 99.878, Time: 21.92
[2020-12-15 10:10:13,867][__main__][INFO] - [32320] Loss: 0.097, Running accuracy: 99.878, Time: 23.76
[2020-12-15 10:10:37,356][__main__][INFO] - [32640] Loss: 0.101, Running accuracy: 99.877, Time: 23.49
[2020-12-15 10:11:02,429][__main__][INFO] - [32960] Loss: 0.042, Running accuracy: 99.878, Time: 25.07
[2020-12-15 10:11:25,738][__main__][INFO] - [33280] Loss: 0.147, Running accuracy: 99.878, Time: 23.31
[2020-12-15 10:11:51,192][__main__][INFO] - [33600] Loss: 0.108, Running accuracy: 99.878, Time: 25.45
[2020-12-15 10:12:14,454][__main__][INFO] - [33920] Loss: 0.254, Running accuracy: 99.877, Time: 23.26
[2020-12-15 10:12:38,253][__main__][INFO] - [34240] Loss: 0.076, Running accuracy: 99.878, Time: 23.80
[2020-12-15 10:13:06,792][__main__][INFO] - [34560] Loss: 0.154, Running accuracy: 99.878, Time: 28.54
[2020-12-15 10:13:30,030][__main__][INFO] - [34880] Loss: 0.106, Running accuracy: 99.878, Time: 23.24
[2020-12-15 10:13:56,861][__main__][INFO] - [35200] Loss: 0.051, Running accuracy: 99.879, Time: 26.83
[2020-12-15 10:14:20,331][__main__][INFO] - [35520] Loss: 0.074, Running accuracy: 99.879, Time: 23.47
[2020-12-15 10:14:45,166][__main__][INFO] - [35840] Loss: 0.074, Running accuracy: 99.879, Time: 24.83
[2020-12-15 10:15:09,757][__main__][INFO] - [36160] Loss: 0.142, Running accuracy: 99.878, Time: 24.59
[2020-12-15 10:15:33,858][__main__][INFO] - [36480] Loss: 0.080, Running accuracy: 99.878, Time: 24.10
[2020-12-15 10:15:58,594][__main__][INFO] - [36800] Loss: 0.128, Running accuracy: 99.878, Time: 24.74
[2020-12-15 10:16:22,029][__main__][INFO] - [37120] Loss: 0.119, Running accuracy: 99.878, Time: 23.43
[2020-12-15 10:16:47,633][__main__][INFO] - [37440] Loss: 0.064, Running accuracy: 99.878, Time: 25.60
[2020-12-15 10:17:11,598][__main__][INFO] - [37760] Loss: 0.053, Running accuracy: 99.879, Time: 23.96
[2020-12-15 10:17:37,578][__main__][INFO] - [38080] Loss: 0.199, Running accuracy: 99.878, Time: 25.98
[2020-12-15 10:18:01,842][__main__][INFO] - [38400] Loss: 0.228, Running accuracy: 99.877, Time: 24.26
[2020-12-15 10:18:31,507][__main__][INFO] - [38720] Loss: 0.116, Running accuracy: 99.877, Time: 29.66
[2020-12-15 10:18:55,085][__main__][INFO] - [39040] Loss: 0.127, Running accuracy: 99.877, Time: 23.58
[2020-12-15 10:19:18,861][__main__][INFO] - [39360] Loss: 0.185, Running accuracy: 99.876, Time: 23.78
[2020-12-15 10:19:45,917][__main__][INFO] - [39680] Loss: 0.065, Running accuracy: 99.877, Time: 27.05
[2020-12-15 10:19:54,866][__main__][INFO] - Action accuracy: 99.877, Loss: 0.114
[2020-12-15 10:19:54,867][__main__][INFO] - Validating..
[2020-12-15 10:20:21,270][test][INFO] - Time elapsed: 24.304122
[2020-12-15 10:20:21,274][__main__][INFO] - Validation F1 score: 95.380, Exact match: 54.820, Precision: 95.350, Recall: 95.400
[2020-12-15 10:20:55,554][__main__][INFO] - Checkpoint saved to checkpoints/model_latest.pth
[2020-12-15 10:20:56,596][__main__][INFO] - Epoch #22
[2020-12-15 10:20:56,597][__main__][INFO] - Training..
[2020-12-15 10:21:21,028][__main__][INFO] - [320] Loss: 0.153, Running accuracy: 99.878, Time: 23.09
[2020-12-15 10:21:46,022][__main__][INFO] - [640] Loss: 0.061, Running accuracy: 99.888, Time: 24.99
[2020-12-15 10:22:10,981][__main__][INFO] - [960] Loss: 0.158, Running accuracy: 99.878, Time: 24.96
[2020-12-15 10:22:39,559][__main__][INFO] - [1280] Loss: 0.204, Running accuracy: 99.866, Time: 28.58
[2020-12-15 10:23:02,928][__main__][INFO] - [1600] Loss: 0.041, Running accuracy: 99.882, Time: 23.37
[2020-12-15 10:23:26,931][__main__][INFO] - [1920] Loss: 0.050, Running accuracy: 99.888, Time: 24.00
[2020-12-15 10:23:51,176][__main__][INFO] - [2240] Loss: 0.035, Running accuracy: 99.896, Time: 24.24
[2020-12-15 10:24:13,693][__main__][INFO] - [2560] Loss: 0.169, Running accuracy: 99.876, Time: 22.52
[2020-12-15 10:24:38,188][__main__][INFO] - [2880] Loss: 0.052, Running accuracy: 99.882, Time: 24.49
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[2020-12-15 11:10:08,879][__main__][INFO] - [38400] Loss: 0.239, Running accuracy: 99.878, Time: 24.67
[2020-12-15 11:10:33,815][__main__][INFO] - [38720] Loss: 0.109, Running accuracy: 99.878, Time: 24.94
[2020-12-15 11:10:57,248][__main__][INFO] - [39040] Loss: 0.057, Running accuracy: 99.878, Time: 23.43
[2020-12-15 11:11:24,287][__main__][INFO] - [39360] Loss: 0.100, Running accuracy: 99.878, Time: 27.04
[2020-12-15 11:11:47,731][__main__][INFO] - [39680] Loss: 0.102, Running accuracy: 99.879, Time: 23.44
[2020-12-15 11:11:57,706][__main__][INFO] - Action accuracy: 99.878, Loss: 0.112
[2020-12-15 11:11:57,707][__main__][INFO] - Validating..
[2020-12-15 11:12:29,469][test][INFO] - Time elapsed: 29.797234
[2020-12-15 11:12:29,474][__main__][INFO] - Validation F1 score: 95.260, Exact match: 53.880, Precision: 95.340, Recall: 95.180
[2020-12-15 11:13:02,681][__main__][INFO] - Checkpoint saved to checkpoints/model_latest.pth
[2020-12-15 11:13:03,706][__main__][INFO] - Epoch #23
[2020-12-15 11:13:03,707][__main__][INFO] - Training..
[2020-12-15 11:13:28,741][__main__][INFO] - [320] Loss: 0.087, Running accuracy: 99.866, Time: 24.01
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[2020-12-15 12:03:41,876][__main__][INFO] - [39680] Loss: 0.147, Running accuracy: 99.885, Time: 26.52
[2020-12-15 12:03:52,720][__main__][INFO] - Action accuracy: 99.884, Loss: 0.104
[2020-12-15 12:03:52,721][__main__][INFO] - Validating..
[2020-12-15 12:04:19,189][test][INFO] - Time elapsed: 24.477018
[2020-12-15 12:04:19,194][__main__][INFO] - Validation F1 score: 95.340, Exact match: 54.410, Precision: 95.400, Recall: 95.290
[2020-12-15 12:04:53,810][__main__][INFO] - Checkpoint saved to checkpoints/model_latest.pth
[2020-12-15 12:04:54,864][__main__][INFO] - Epoch #24
[2020-12-15 12:04:54,865][__main__][INFO] - Training..
[2020-12-15 12:05:24,631][__main__][INFO] - [320] Loss: 0.080, Running accuracy: 99.923, Time: 28.36
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[2020-12-15 12:49:21,339][__main__][INFO] - [34560] Loss: 0.046, Running accuracy: 99.889, Time: 24.11
[2020-12-15 12:49:44,270][__main__][INFO] - [34880] Loss: 0.099, Running accuracy: 99.889, Time: 22.93
[2020-12-15 12:50:11,882][__main__][INFO] - [35200] Loss: 0.063, Running accuracy: 99.889, Time: 27.61
[2020-12-15 12:50:34,796][__main__][INFO] - [35520] Loss: 0.045, Running accuracy: 99.890, Time: 22.91
[2020-12-15 12:50:58,386][__main__][INFO] - [35840] Loss: 0.043, Running accuracy: 99.890, Time: 23.59
[2020-12-15 12:51:22,911][__main__][INFO] - [36160] Loss: 0.086, Running accuracy: 99.890, Time: 24.52
[2020-12-15 12:51:47,414][__main__][INFO] - [36480] Loss: 0.031, Running accuracy: 99.891, Time: 24.50
[2020-12-15 12:52:11,724][__main__][INFO] - [36800] Loss: 0.156, Running accuracy: 99.890, Time: 24.31
[2020-12-15 12:52:35,010][__main__][INFO] - [37120] Loss: 0.118, Running accuracy: 99.890, Time: 23.28
[2020-12-15 12:52:59,891][__main__][INFO] - [37440] Loss: 0.107, Running accuracy: 99.890, Time: 24.88
[2020-12-15 12:53:25,750][__main__][INFO] - [37760] Loss: 0.092, Running accuracy: 99.890, Time: 25.86
[2020-12-15 12:53:51,605][__main__][INFO] - [38080] Loss: 0.097, Running accuracy: 99.889, Time: 25.85
[2020-12-15 12:54:17,322][__main__][INFO] - [38400] Loss: 0.128, Running accuracy: 99.889, Time: 25.72
[2020-12-15 12:54:41,923][__main__][INFO] - [38720] Loss: 0.083, Running accuracy: 99.890, Time: 24.60
[2020-12-15 12:55:07,366][__main__][INFO] - [39040] Loss: 0.150, Running accuracy: 99.889, Time: 25.44
[2020-12-15 12:55:31,249][__main__][INFO] - [39360] Loss: 0.060, Running accuracy: 99.889, Time: 23.88
[2020-12-15 12:55:57,822][__main__][INFO] - [39680] Loss: 0.101, Running accuracy: 99.889, Time: 26.57
[2020-12-15 12:56:07,935][__main__][INFO] - Action accuracy: 99.889, Loss: 0.098
[2020-12-15 12:56:07,936][__main__][INFO] - Validating..
[2020-12-15 12:56:34,324][test][INFO] - Time elapsed: 23.929510
[2020-12-15 12:56:34,328][__main__][INFO] - Validation F1 score: 95.270, Exact match: 54.650, Precision: 95.240, Recall: 95.310
Epoch    25: reducing learning rate of group 0 to 7.5000e-06.
[2020-12-15 12:57:08,943][__main__][INFO] - Checkpoint saved to checkpoints/model_latest.pth
[2020-12-15 12:57:09,752][__main__][INFO] - Epoch #25
[2020-12-15 12:57:09,753][__main__][INFO] - Training..
[2020-12-15 12:57:33,821][__main__][INFO] - [320] Loss: 0.023, Running accuracy: 99.972, Time: 22.92
[2020-12-15 12:57:59,048][__main__][INFO] - [640] Loss: 0.022, Running accuracy: 99.966, Time: 25.23
[2020-12-15 12:58:23,385][__main__][INFO] - [960] Loss: 0.050, Running accuracy: 99.955, Time: 24.34
[2020-12-15 12:58:47,673][__main__][INFO] - [1280] Loss: 0.069, Running accuracy: 99.944, Time: 24.29
[2020-12-15 12:59:11,513][__main__][INFO] - [1600] Loss: 0.045, Running accuracy: 99.939, Time: 23.84
[2020-12-15 12:59:41,147][__main__][INFO] - [1920] Loss: 0.175, Running accuracy: 99.917, Time: 29.63
[2020-12-15 13:00:05,770][__main__][INFO] - [2240] Loss: 0.103, Running accuracy: 99.912, Time: 24.62
[2020-12-15 13:00:31,091][__main__][INFO] - [2560] Loss: 0.023, Running accuracy: 99.917, Time: 25.32
[2020-12-15 13:00:56,382][__main__][INFO] - [2880] Loss: 0.042, Running accuracy: 99.911, Time: 25.29
[2020-12-15 13:01:19,842][__main__][INFO] - [3200] Loss: 0.065, Running accuracy: 99.913, Time: 23.46
[2020-12-15 13:01:43,197][__main__][INFO] - [3520] Loss: 0.034, Running accuracy: 99.915, Time: 23.35
[2020-12-15 13:02:06,559][__main__][INFO] - [3840] Loss: 0.056, Running accuracy: 99.918, Time: 23.36
[2020-12-15 13:02:32,221][__main__][INFO] - [4160] Loss: 0.115, Running accuracy: 99.916, Time: 25.66
[2020-12-15 13:02:56,143][__main__][INFO] - [4480] Loss: 0.083, Running accuracy: 99.912, Time: 23.92
[2020-12-15 13:03:19,242][__main__][INFO] - [4800] Loss: 0.050, Running accuracy: 99.913, Time: 23.10
[2020-12-15 13:03:43,117][__main__][INFO] - [5120] Loss: 0.067, Running accuracy: 99.915, Time: 23.87
[2020-12-15 13:04:07,369][__main__][INFO] - [5440] Loss: 0.030, Running accuracy: 99.919, Time: 24.25
[2020-12-15 13:04:31,033][__main__][INFO] - [5760] Loss: 0.059, Running accuracy: 99.917, Time: 23.66
[2020-12-15 13:04:55,585][__main__][INFO] - [6080] Loss: 0.058, Running accuracy: 99.919, Time: 24.55
[2020-12-15 13:05:25,683][__main__][INFO] - [6400] Loss: 0.054, Running accuracy: 99.920, Time: 30.10
[2020-12-15 13:05:49,859][__main__][INFO] - [6720] Loss: 0.061, Running accuracy: 99.918, Time: 24.17
[2020-12-15 13:06:11,629][__main__][INFO] - [7040] Loss: 0.055, Running accuracy: 99.919, Time: 21.77
[2020-12-15 13:06:36,843][__main__][INFO] - [7360] Loss: 0.094, Running accuracy: 99.916, Time: 25.21
[2020-12-15 13:07:02,210][__main__][INFO] - [7680] Loss: 0.032, Running accuracy: 99.918, Time: 25.37
[2020-12-15 13:07:24,728][__main__][INFO] - [8000] Loss: 0.062, Running accuracy: 99.916, Time: 22.52
[2020-12-15 13:07:49,378][__main__][INFO] - [8320] Loss: 0.038, Running accuracy: 99.918, Time: 24.65
[2020-12-15 13:08:14,426][__main__][INFO] - [8640] Loss: 0.042, Running accuracy: 99.918, Time: 25.05
[2020-12-15 13:08:36,577][__main__][INFO] - [8960] Loss: 0.042, Running accuracy: 99.919, Time: 22.15
[2020-12-15 13:09:01,212][__main__][INFO] - [9280] Loss: 0.061, Running accuracy: 99.919, Time: 24.63
[2020-12-15 13:09:26,550][__main__][INFO] - [9600] Loss: 0.041, Running accuracy: 99.919, Time: 25.34
[2020-12-15 13:09:50,755][__main__][INFO] - [9920] Loss: 0.112, Running accuracy: 99.916, Time: 24.20
[2020-12-15 13:10:16,605][__main__][INFO] - [10240] Loss: 0.041, Running accuracy: 99.918, Time: 25.85
[2020-12-15 13:10:41,332][__main__][INFO] - [10560] Loss: 0.043, Running accuracy: 99.919, Time: 24.73
[2020-12-15 13:11:09,725][__main__][INFO] - [10880] Loss: 0.013, Running accuracy: 99.921, Time: 28.39
[2020-12-15 13:11:33,258][__main__][INFO] - [11200] Loss: 0.034, Running accuracy: 99.923, Time: 23.53
[2020-12-15 13:11:57,516][__main__][INFO] - [11520] Loss: 0.061, Running accuracy: 99.924, Time: 24.26
[2020-12-15 13:12:24,231][__main__][INFO] - [11840] Loss: 0.028, Running accuracy: 99.924, Time: 26.71
[2020-12-15 13:12:48,855][__main__][INFO] - [12160] Loss: 0.066, Running accuracy: 99.924, Time: 24.54
[2020-12-15 13:13:13,761][__main__][INFO] - [12480] Loss: 0.074, Running accuracy: 99.924, Time: 24.90
[2020-12-15 13:13:37,801][__main__][INFO] - [12800] Loss: 0.055, Running accuracy: 99.923, Time: 24.04
[2020-12-15 13:14:01,526][__main__][INFO] - [13120] Loss: 0.092, Running accuracy: 99.922, Time: 23.72
[2020-12-15 13:14:26,505][__main__][INFO] - [13440] Loss: 0.103, Running accuracy: 99.921, Time: 24.98
[2020-12-15 13:14:53,186][__main__][INFO] - [13760] Loss: 0.112, Running accuracy: 99.919, Time: 26.68
[2020-12-15 13:15:18,618][__main__][INFO] - [14080] Loss: 0.065, Running accuracy: 99.918, Time: 25.43
[2020-12-15 13:15:41,110][__main__][INFO] - [14400] Loss: 0.038, Running accuracy: 99.918, Time: 22.49
[2020-12-15 13:16:05,453][__main__][INFO] - [14720] Loss: 0.033, Running accuracy: 99.919, Time: 24.34
[2020-12-15 13:16:33,920][__main__][INFO] - [15040] Loss: 0.077, Running accuracy: 99.919, Time: 28.47
[2020-12-15 13:16:58,201][__main__][INFO] - [15360] Loss: 0.041, Running accuracy: 99.919, Time: 24.28
[2020-12-15 13:17:22,622][__main__][INFO] - [15680] Loss: 0.056, Running accuracy: 99.920, Time: 24.42
[2020-12-15 13:17:47,704][__main__][INFO] - [16000] Loss: 0.048, Running accuracy: 99.921, Time: 25.08
[2020-12-15 13:18:12,885][__main__][INFO] - [16320] Loss: 0.036, Running accuracy: 99.921, Time: 25.18
[2020-12-15 13:18:39,111][__main__][INFO] - [16640] Loss: 0.049, Running accuracy: 99.921, Time: 26.22
[2020-12-15 13:19:02,262][__main__][INFO] - [16960] Loss: 0.076, Running accuracy: 99.920, Time: 23.15
[2020-12-15 13:19:26,210][__main__][INFO] - [17280] Loss: 0.082, Running accuracy: 99.919, Time: 23.95
[2020-12-15 13:19:49,024][__main__][INFO] - [17600] Loss: 0.047, Running accuracy: 99.919, Time: 22.81
[2020-12-15 13:20:14,782][__main__][INFO] - [17920] Loss: 0.061, Running accuracy: 99.920, Time: 25.76
[2020-12-15 13:20:37,227][__main__][INFO] - [18240] Loss: 0.046, Running accuracy: 99.920, Time: 22.44
[2020-12-15 13:21:02,241][__main__][INFO] - [18560] Loss: 0.072, Running accuracy: 99.920, Time: 25.01
[2020-12-15 13:21:25,422][__main__][INFO] - [18880] Loss: 0.055, Running accuracy: 99.920, Time: 23.18
[2020-12-15 13:21:49,621][__main__][INFO] - [19200] Loss: 0.036, Running accuracy: 99.920, Time: 24.20
[2020-12-15 13:22:17,690][__main__][INFO] - [19520] Loss: 0.045, Running accuracy: 99.920, Time: 28.07
[2020-12-15 13:22:40,984][__main__][INFO] - [19840] Loss: 0.094, Running accuracy: 99.920, Time: 23.29
[2020-12-15 13:23:04,954][__main__][INFO] - [20160] Loss: 0.088, Running accuracy: 99.919, Time: 23.97
[2020-12-15 13:23:29,014][__main__][INFO] - [20480] Loss: 0.073, Running accuracy: 99.919, Time: 24.06
[2020-12-15 13:23:51,800][__main__][INFO] - [20800] Loss: 0.040, Running accuracy: 99.919, Time: 22.79
[2020-12-15 13:24:16,598][__main__][INFO] - [21120] Loss: 0.071, Running accuracy: 99.918, Time: 24.80
[2020-12-15 13:24:41,438][__main__][INFO] - [21440] Loss: 0.115, Running accuracy: 99.918, Time: 24.84
[2020-12-15 13:25:04,735][__main__][INFO] - [21760] Loss: 0.047, Running accuracy: 99.919, Time: 23.30
[2020-12-15 13:25:28,843][__main__][INFO] - [22080] Loss: 0.050, Running accuracy: 99.919, Time: 24.11
[2020-12-15 13:25:53,035][__main__][INFO] - [22400] Loss: 0.035, Running accuracy: 99.919, Time: 24.19
[2020-12-15 13:26:19,455][__main__][INFO] - [22720] Loss: 0.046, Running accuracy: 99.920, Time: 26.42
[2020-12-15 13:26:43,936][__main__][INFO] - [23040] Loss: 0.050, Running accuracy: 99.920, Time: 24.48
[2020-12-15 13:27:08,541][__main__][INFO] - [23360] Loss: 0.035, Running accuracy: 99.920, Time: 24.60
[2020-12-15 13:27:32,250][__main__][INFO] - [23680] Loss: 0.049, Running accuracy: 99.919, Time: 23.71
[2020-12-15 13:28:00,772][__main__][INFO] - [24000] Loss: 0.081, Running accuracy: 99.919, Time: 28.52
[2020-12-15 13:28:26,046][__main__][INFO] - [24320] Loss: 0.038, Running accuracy: 99.920, Time: 25.27
[2020-12-15 13:28:50,673][__main__][INFO] - [24640] Loss: 0.020, Running accuracy: 99.920, Time: 24.63
[2020-12-15 13:29:15,611][__main__][INFO] - [24960] Loss: 0.044, Running accuracy: 99.920, Time: 24.94
[2020-12-15 13:29:38,738][__main__][INFO] - [25280] Loss: 0.055, Running accuracy: 99.920, Time: 23.13
[2020-12-15 13:30:02,659][__main__][INFO] - [25600] Loss: 0.036, Running accuracy: 99.921, Time: 23.92
[2020-12-15 13:30:27,657][__main__][INFO] - [25920] Loss: 0.037, Running accuracy: 99.921, Time: 25.00
[2020-12-15 13:30:52,276][__main__][INFO] - [26240] Loss: 0.105, Running accuracy: 99.920, Time: 24.62
[2020-12-15 13:31:17,556][__main__][INFO] - [26560] Loss: 0.050, Running accuracy: 99.920, Time: 25.28
[2020-12-15 13:31:41,922][__main__][INFO] - [26880] Loss: 0.068, Running accuracy: 99.920, Time: 24.37
[2020-12-15 13:32:07,653][__main__][INFO] - [27200] Loss: 0.098, Running accuracy: 99.920, Time: 25.73
[2020-12-15 13:32:32,413][__main__][INFO] - [27520] Loss: 0.042, Running accuracy: 99.921, Time: 24.76
[2020-12-15 13:32:57,468][__main__][INFO] - [27840] Loss: 0.039, Running accuracy: 99.921, Time: 25.05
[2020-12-15 13:33:27,410][__main__][INFO] - [28160] Loss: 0.078, Running accuracy: 99.921, Time: 29.94
[2020-12-15 13:33:50,556][__main__][INFO] - [28480] Loss: 0.054, Running accuracy: 99.922, Time: 23.14
[2020-12-15 13:34:15,851][__main__][INFO] - [28800] Loss: 0.074, Running accuracy: 99.921, Time: 25.29
[2020-12-15 13:34:40,821][__main__][INFO] - [29120] Loss: 0.010, Running accuracy: 99.922, Time: 24.97
[2020-12-15 13:35:03,831][__main__][INFO] - [29440] Loss: 0.014, Running accuracy: 99.923, Time: 23.01
[2020-12-15 13:35:27,971][__main__][INFO] - [29760] Loss: 0.053, Running accuracy: 99.923, Time: 24.14
[2020-12-15 13:35:50,520][__main__][INFO] - [30080] Loss: 0.114, Running accuracy: 99.923, Time: 22.55
[2020-12-15 13:36:16,417][__main__][INFO] - [30400] Loss: 0.056, Running accuracy: 99.923, Time: 25.90
[2020-12-15 13:36:41,576][__main__][INFO] - [30720] Loss: 0.045, Running accuracy: 99.923, Time: 25.16
[2020-12-15 13:37:05,766][__main__][INFO] - [31040] Loss: 0.033, Running accuracy: 99.923, Time: 24.19
[2020-12-15 13:37:31,208][__main__][INFO] - [31360] Loss: 0.051, Running accuracy: 99.923, Time: 25.44
[2020-12-15 13:37:55,124][__main__][INFO] - [31680] Loss: 0.045, Running accuracy: 99.924, Time: 23.92
[2020-12-15 13:38:19,712][__main__][INFO] - [32000] Loss: 0.083, Running accuracy: 99.923, Time: 24.59
[2020-12-15 13:38:43,572][__main__][INFO] - [32320] Loss: 0.063, Running accuracy: 99.923, Time: 23.86
[2020-12-15 13:39:13,340][__main__][INFO] - [32640] Loss: 0.090, Running accuracy: 99.923, Time: 29.77
[2020-12-15 13:39:36,953][__main__][INFO] - [32960] Loss: 0.063, Running accuracy: 99.923, Time: 23.61
[2020-12-15 13:40:00,741][__main__][INFO] - [33280] Loss: 0.067, Running accuracy: 99.922, Time: 23.79
[2020-12-15 13:40:23,818][__main__][INFO] - [33600] Loss: 0.073, Running accuracy: 99.922, Time: 23.08
[2020-12-15 13:40:48,256][__main__][INFO] - [33920] Loss: 0.145, Running accuracy: 99.922, Time: 24.44
[2020-12-15 13:41:13,797][__main__][INFO] - [34240] Loss: 0.029, Running accuracy: 99.922, Time: 25.54
[2020-12-15 13:41:37,935][__main__][INFO] - [34560] Loss: 0.050, Running accuracy: 99.922, Time: 24.14
[2020-12-15 13:42:01,018][__main__][INFO] - [34880] Loss: 0.049, Running accuracy: 99.922, Time: 23.08
[2020-12-15 13:42:27,749][__main__][INFO] - [35200] Loss: 0.077, Running accuracy: 99.922, Time: 26.73
[2020-12-15 13:42:51,443][__main__][INFO] - [35520] Loss: 0.023, Running accuracy: 99.922, Time: 23.69
[2020-12-15 13:43:15,900][__main__][INFO] - [35840] Loss: 0.130, Running accuracy: 99.921, Time: 24.46
[2020-12-15 13:43:40,089][__main__][INFO] - [36160] Loss: 0.029, Running accuracy: 99.922, Time: 24.19
[2020-12-15 13:44:03,886][__main__][INFO] - [36480] Loss: 0.035, Running accuracy: 99.922, Time: 23.80
[2020-12-15 13:44:31,687][__main__][INFO] - [36800] Loss: 0.027, Running accuracy: 99.922, Time: 27.80
[2020-12-15 13:44:55,210][__main__][INFO] - [37120] Loss: 0.107, Running accuracy: 99.922, Time: 23.52
[2020-12-15 13:45:19,386][__main__][INFO] - [37440] Loss: 0.090, Running accuracy: 99.921, Time: 24.17
[2020-12-15 13:45:42,101][__main__][INFO] - [37760] Loss: 0.144, Running accuracy: 99.920, Time: 22.71
[2020-12-15 13:46:04,895][__main__][INFO] - [38080] Loss: 0.053, Running accuracy: 99.920, Time: 22.79
[2020-12-15 13:46:27,735][__main__][INFO] - [38400] Loss: 0.130, Running accuracy: 99.920, Time: 22.84
[2020-12-15 13:46:51,833][__main__][INFO] - [38720] Loss: 0.040, Running accuracy: 99.920, Time: 24.10
[2020-12-15 13:47:15,461][__main__][INFO] - [39040] Loss: 0.022, Running accuracy: 99.920, Time: 23.63
[2020-12-15 13:47:40,709][__main__][INFO] - [39360] Loss: 0.053, Running accuracy: 99.920, Time: 25.25
[2020-12-15 13:48:04,666][__main__][INFO] - [39680] Loss: 0.055, Running accuracy: 99.920, Time: 23.96
[2020-12-15 13:48:16,570][__main__][INFO] - Action accuracy: 99.920, Loss: 0.069
[2020-12-15 13:48:16,571][__main__][INFO] - Validating..
[2020-12-15 13:48:47,301][test][INFO] - Time elapsed: 29.288307
[2020-12-15 13:48:47,306][__main__][INFO] - Validation F1 score: 95.450, Exact match: 55.880, Precision: 95.430, Recall: 95.460
[2020-12-15 13:48:47,306][__main__][INFO] - F1 score has improved
[2020-12-15 13:49:21,578][__main__][INFO] - Checkpoint saved to checkpoints/model_latest.pth
[2020-12-15 13:49:22,394][__main__][INFO] - Epoch #26
[2020-12-15 13:49:22,395][__main__][INFO] - Training..
[2020-12-15 13:49:48,605][__main__][INFO] - [320] Loss: 0.044, Running accuracy: 99.935, Time: 24.72
[2020-12-15 13:50:13,548][__main__][INFO] - [640] Loss: 0.052, Running accuracy: 99.928, Time: 24.94
[2020-12-15 13:50:36,766][__main__][INFO] - [960] Loss: 0.042, Running accuracy: 99.942, Time: 23.22
[2020-12-15 13:51:03,197][__main__][INFO] - [1280] Loss: 0.043, Running accuracy: 99.934, Time: 26.43
[2020-12-15 13:51:25,987][__main__][INFO] - [1600] Loss: 0.093, Running accuracy: 99.915, Time: 22.79
[2020-12-15 13:51:50,061][__main__][INFO] - [1920] Loss: 0.043, Running accuracy: 99.925, Time: 24.07
[2020-12-15 13:52:15,238][__main__][INFO] - [2240] Loss: 0.022, Running accuracy: 99.932, Time: 25.18
[2020-12-15 13:52:39,833][__main__][INFO] - [2560] Loss: 0.051, Running accuracy: 99.932, Time: 24.59
[2020-12-15 13:53:06,560][__main__][INFO] - [2880] Loss: 0.034, Running accuracy: 99.934, Time: 26.73
[2020-12-15 13:53:30,502][__main__][INFO] - [3200] Loss: 0.033, Running accuracy: 99.935, Time: 23.94
[2020-12-15 13:53:54,143][__main__][INFO] - [3520] Loss: 0.061, Running accuracy: 99.935, Time: 23.64
[2020-12-15 13:54:18,924][__main__][INFO] - [3840] Loss: 0.074, Running accuracy: 99.936, Time: 24.78
[2020-12-15 13:54:51,330][__main__][INFO] - [4160] Loss: 0.054, Running accuracy: 99.937, Time: 32.40
[2020-12-15 13:55:15,958][__main__][INFO] - [4480] Loss: 0.034, Running accuracy: 99.937, Time: 24.63
[2020-12-15 13:55:39,959][__main__][INFO] - [4800] Loss: 0.053, Running accuracy: 99.936, Time: 24.00
[2020-12-15 13:56:04,630][__main__][INFO] - [5120] Loss: 0.045, Running accuracy: 99.934, Time: 24.67
[2020-12-15 13:56:28,062][__main__][INFO] - [5440] Loss: 0.029, Running accuracy: 99.934, Time: 23.43
[2020-12-15 13:56:54,069][__main__][INFO] - [5760] Loss: 0.056, Running accuracy: 99.934, Time: 26.01
[2020-12-15 13:57:17,193][__main__][INFO] - [6080] Loss: 0.067, Running accuracy: 99.933, Time: 23.12
[2020-12-15 13:57:42,292][__main__][INFO] - [6400] Loss: 0.033, Running accuracy: 99.934, Time: 25.10
[2020-12-15 13:58:06,524][__main__][INFO] - [6720] Loss: 0.064, Running accuracy: 99.933, Time: 24.23
[2020-12-15 13:58:30,600][__main__][INFO] - [7040] Loss: 0.017, Running accuracy: 99.934, Time: 24.08
[2020-12-15 13:58:52,772][__main__][INFO] - [7360] Loss: 0.040, Running accuracy: 99.935, Time: 22.17
[2020-12-15 13:59:15,156][__main__][INFO] - [7680] Loss: 0.015, Running accuracy: 99.938, Time: 22.38
[2020-12-15 13:59:39,973][__main__][INFO] - [8000] Loss: 0.070, Running accuracy: 99.936, Time: 24.82
[2020-12-15 14:00:09,592][__main__][INFO] - [8320] Loss: 0.076, Running accuracy: 99.932, Time: 29.62
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[2020-12-15 14:39:25,258][__main__][INFO] - [39040] Loss: 0.019, Running accuracy: 99.940, Time: 29.71
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[2020-12-15 14:40:12,301][__main__][INFO] - [39680] Loss: 0.061, Running accuracy: 99.939, Time: 24.29
[2020-12-15 14:40:23,161][__main__][INFO] - Action accuracy: 99.939, Loss: 0.053
[2020-12-15 14:40:23,162][__main__][INFO] - Validating..
[2020-12-15 14:40:49,524][test][INFO] - Time elapsed: 24.573635
[2020-12-15 14:40:49,528][__main__][INFO] - Validation F1 score: 95.460, Exact match: 55.350, Precision: 95.450, Recall: 95.470
[2020-12-15 14:40:49,528][__main__][INFO] - F1 score has improved
[2020-12-15 14:41:24,523][__main__][INFO] - Checkpoint saved to checkpoints/model_latest.pth
[2020-12-15 14:41:25,375][__main__][INFO] - Epoch #27
[2020-12-15 14:41:25,376][__main__][INFO] - Training..
[2020-12-15 14:41:51,148][__main__][INFO] - [320] Loss: 0.030, Running accuracy: 99.962, Time: 24.29
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[2020-12-15 15:31:00,187][__main__][INFO] - [38720] Loss: 0.028, Running accuracy: 99.941, Time: 23.38
[2020-12-15 15:31:25,842][__main__][INFO] - [39040] Loss: 0.046, Running accuracy: 99.941, Time: 25.65
[2020-12-15 15:31:49,776][__main__][INFO] - [39360] Loss: 0.132, Running accuracy: 99.941, Time: 23.93
[2020-12-15 15:32:13,346][__main__][INFO] - [39680] Loss: 0.053, Running accuracy: 99.941, Time: 23.57
[2020-12-15 15:32:22,730][__main__][INFO] - Action accuracy: 99.940, Loss: 0.053
[2020-12-15 15:32:22,731][__main__][INFO] - Validating..
[2020-12-15 15:32:53,489][test][INFO] - Time elapsed: 28.184789
[2020-12-15 15:32:53,494][__main__][INFO] - Validation F1 score: 95.410, Exact match: 54.650, Precision: 95.390, Recall: 95.440
[2020-12-15 15:33:28,250][__main__][INFO] - Checkpoint saved to checkpoints/model_latest.pth
[2020-12-15 15:33:29,081][__main__][INFO] - Epoch #28
[2020-12-15 15:33:29,081][__main__][INFO] - Training..
[2020-12-15 15:33:54,779][__main__][INFO] - [320] Loss: 0.032, Running accuracy: 99.961, Time: 24.60
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[2020-12-15 16:21:58,353][__main__][INFO] - [37760] Loss: 0.025, Running accuracy: 99.945, Time: 23.82
[2020-12-15 16:22:22,875][__main__][INFO] - [38080] Loss: 0.039, Running accuracy: 99.945, Time: 24.52
[2020-12-15 16:22:52,621][__main__][INFO] - [38400] Loss: 0.136, Running accuracy: 99.944, Time: 29.75
[2020-12-15 16:23:16,186][__main__][INFO] - [38720] Loss: 0.066, Running accuracy: 99.944, Time: 23.56
[2020-12-15 16:23:42,851][__main__][INFO] - [39040] Loss: 0.027, Running accuracy: 99.944, Time: 26.66
[2020-12-15 16:24:08,117][__main__][INFO] - [39360] Loss: 0.046, Running accuracy: 99.944, Time: 25.26
[2020-12-15 16:24:31,715][__main__][INFO] - [39680] Loss: 0.029, Running accuracy: 99.944, Time: 23.60
[2020-12-15 16:24:44,001][__main__][INFO] - Action accuracy: 99.944, Loss: 0.048
[2020-12-15 16:24:44,003][__main__][INFO] - Validating..
[2020-12-15 16:25:10,467][test][INFO] - Time elapsed: 24.618173
[2020-12-15 16:25:10,471][__main__][INFO] - Validation F1 score: 95.320, Exact match: 54.760, Precision: 95.330, Recall: 95.310
[2020-12-15 16:25:44,621][__main__][INFO] - Checkpoint saved to checkpoints/model_latest.pth
[2020-12-15 16:25:45,584][__main__][INFO] - Epoch #29
[2020-12-15 16:25:45,584][__main__][INFO] - Training..
[2020-12-15 16:26:11,611][__main__][INFO] - [320] Loss: 0.050, Running accuracy: 99.904, Time: 24.58
[2020-12-15 16:26:33,263][__main__][INFO] - [640] Loss: 0.014, Running accuracy: 99.937, Time: 21.65
[2020-12-15 16:27:02,210][__main__][INFO] - [960] Loss: 0.034, Running accuracy: 99.931, Time: 28.95
[2020-12-15 16:27:25,175][__main__][INFO] - [1280] Loss: 0.060, Running accuracy: 99.942, Time: 22.96
[2020-12-15 16:27:48,967][__main__][INFO] - [1600] Loss: 0.045, Running accuracy: 99.935, Time: 23.79
[2020-12-15 16:28:14,091][__main__][INFO] - [1920] Loss: 0.037, Running accuracy: 99.939, Time: 25.12
[2020-12-15 16:28:37,999][__main__][INFO] - [2240] Loss: 0.059, Running accuracy: 99.941, Time: 23.91
[2020-12-15 16:29:02,105][__main__][INFO] - [2560] Loss: 0.031, Running accuracy: 99.940, Time: 24.10
[2020-12-15 16:29:28,015][__main__][INFO] - [2880] Loss: 0.044, Running accuracy: 99.941, Time: 25.91
[2020-12-15 16:29:53,976][__main__][INFO] - [3200] Loss: 0.029, Running accuracy: 99.942, Time: 25.96
[2020-12-15 16:30:18,151][__main__][INFO] - [3520] Loss: 0.038, Running accuracy: 99.942, Time: 24.17
[2020-12-15 16:30:40,976][__main__][INFO] - [3840] Loss: 0.027, Running accuracy: 99.944, Time: 22.82
[2020-12-15 16:31:06,465][__main__][INFO] - [4160] Loss: 0.019, Running accuracy: 99.946, Time: 25.49
[2020-12-15 16:31:28,847][__main__][INFO] - [4480] Loss: 0.068, Running accuracy: 99.944, Time: 22.38
[2020-12-15 16:31:51,900][__main__][INFO] - [4800] Loss: 0.014, Running accuracy: 99.947, Time: 23.05
[2020-12-15 16:32:16,896][__main__][INFO] - [5120] Loss: 0.059, Running accuracy: 99.946, Time: 25.00
[2020-12-15 16:32:46,765][__main__][INFO] - [5440] Loss: 0.086, Running accuracy: 99.942, Time: 29.87
[2020-12-15 16:33:12,551][__main__][INFO] - [5760] Loss: 0.047, Running accuracy: 99.942, Time: 25.79
[2020-12-15 16:33:35,932][__main__][INFO] - [6080] Loss: 0.034, Running accuracy: 99.944, Time: 23.38
[2020-12-15 16:33:59,416][__main__][INFO] - [6400] Loss: 0.020, Running accuracy: 99.944, Time: 23.48
[2020-12-15 16:34:26,199][__main__][INFO] - [6720] Loss: 0.016, Running accuracy: 99.947, Time: 26.78
[2020-12-15 16:34:52,403][__main__][INFO] - [7040] Loss: 0.028, Running accuracy: 99.947, Time: 26.20
[2020-12-15 16:35:18,683][__main__][INFO] - [7360] Loss: 0.040, Running accuracy: 99.947, Time: 26.28
[2020-12-15 16:35:43,967][__main__][INFO] - [7680] Loss: 0.052, Running accuracy: 99.946, Time: 25.28
[2020-12-15 16:36:08,725][__main__][INFO] - [8000] Loss: 0.059, Running accuracy: 99.946, Time: 24.76
[2020-12-15 16:36:32,833][__main__][INFO] - [8320] Loss: 0.032, Running accuracy: 99.946, Time: 24.11
[2020-12-15 16:36:57,835][__main__][INFO] - [8640] Loss: 0.039, Running accuracy: 99.946, Time: 25.00
[2020-12-15 16:37:24,009][__main__][INFO] - [8960] Loss: 0.024, Running accuracy: 99.945, Time: 26.17
[2020-12-15 16:37:47,747][__main__][INFO] - [9280] Loss: 0.007, Running accuracy: 99.947, Time: 23.74
[2020-12-15 16:38:17,089][__main__][INFO] - [9600] Loss: 0.031, Running accuracy: 99.947, Time: 29.34
[2020-12-15 16:38:40,275][__main__][INFO] - [9920] Loss: 0.057, Running accuracy: 99.947, Time: 23.18
[2020-12-15 16:39:04,166][__main__][INFO] - [10240] Loss: 0.050, Running accuracy: 99.948, Time: 23.89
[2020-12-15 16:39:29,393][__main__][INFO] - [10560] Loss: 0.032, Running accuracy: 99.947, Time: 25.23
[2020-12-15 16:39:53,136][__main__][INFO] - [10880] Loss: 0.016, Running accuracy: 99.947, Time: 23.74
[2020-12-15 16:40:18,580][__main__][INFO] - [11200] Loss: 0.018, Running accuracy: 99.948, Time: 25.44
[2020-12-15 16:40:45,129][__main__][INFO] - [11520] Loss: 0.014, Running accuracy: 99.949, Time: 26.55
[2020-12-15 16:41:08,022][__main__][INFO] - [11840] Loss: 0.044, Running accuracy: 99.949, Time: 22.89
[2020-12-15 16:41:30,585][__main__][INFO] - [12160] Loss: 0.053, Running accuracy: 99.949, Time: 22.56
[2020-12-15 16:41:52,900][__main__][INFO] - [12480] Loss: 0.038, Running accuracy: 99.949, Time: 22.31
[2020-12-15 16:42:16,766][__main__][INFO] - [12800] Loss: 0.055, Running accuracy: 99.948, Time: 23.87
[2020-12-15 16:42:39,199][__main__][INFO] - [13120] Loss: 0.042, Running accuracy: 99.948, Time: 22.43
[2020-12-15 16:43:02,743][__main__][INFO] - [13440] Loss: 0.054, Running accuracy: 99.949, Time: 23.54
[2020-12-15 16:43:25,757][__main__][INFO] - [13760] Loss: 0.017, Running accuracy: 99.949, Time: 23.01
[2020-12-15 16:43:54,718][__main__][INFO] - [14080] Loss: 0.041, Running accuracy: 99.949, Time: 28.87
[2020-12-15 16:44:20,109][__main__][INFO] - [14400] Loss: 0.069, Running accuracy: 99.948, Time: 25.39
[2020-12-15 16:44:43,719][__main__][INFO] - [14720] Loss: 0.022, Running accuracy: 99.949, Time: 23.61
[2020-12-15 16:45:08,427][__main__][INFO] - [15040] Loss: 0.031, Running accuracy: 99.948, Time: 24.71
[2020-12-15 16:45:32,760][__main__][INFO] - [15360] Loss: 0.062, Running accuracy: 99.947, Time: 24.33
[2020-12-15 16:45:57,566][__main__][INFO] - [15680] Loss: 0.065, Running accuracy: 99.947, Time: 24.80
[2020-12-15 16:46:21,622][__main__][INFO] - [16000] Loss: 0.070, Running accuracy: 99.947, Time: 24.05
[2020-12-15 16:46:45,188][__main__][INFO] - [16320] Loss: 0.074, Running accuracy: 99.947, Time: 23.57
[2020-12-15 16:47:10,873][__main__][INFO] - [16640] Loss: 0.049, Running accuracy: 99.947, Time: 25.68
[2020-12-15 16:47:35,318][__main__][INFO] - [16960] Loss: 0.073, Running accuracy: 99.947, Time: 24.44
[2020-12-15 16:47:58,889][__main__][INFO] - [17280] Loss: 0.019, Running accuracy: 99.947, Time: 23.57
[2020-12-15 16:48:23,778][__main__][INFO] - [17600] Loss: 0.113, Running accuracy: 99.947, Time: 24.89
[2020-12-15 16:48:47,619][__main__][INFO] - [17920] Loss: 0.006, Running accuracy: 99.948, Time: 23.84
[2020-12-15 16:49:09,194][__main__][INFO] - [18240] Loss: 0.034, Running accuracy: 99.947, Time: 21.57
[2020-12-15 16:49:35,125][__main__][INFO] - [18560] Loss: 0.089, Running accuracy: 99.946, Time: 25.93
[2020-12-15 16:49:57,989][__main__][INFO] - [18880] Loss: 0.022, Running accuracy: 99.947, Time: 22.86
[2020-12-15 16:50:21,708][__main__][INFO] - [19200] Loss: 0.029, Running accuracy: 99.947, Time: 23.72
[2020-12-15 16:50:43,004][__main__][INFO] - [19520] Loss: 0.048, Running accuracy: 99.947, Time: 21.30
[2020-12-15 16:51:06,731][__main__][INFO] - [19840] Loss: 0.024, Running accuracy: 99.947, Time: 23.73
[2020-12-15 16:51:29,606][__main__][INFO] - [20160] Loss: 0.048, Running accuracy: 99.946, Time: 22.87
[2020-12-15 16:51:54,390][__main__][INFO] - [20480] Loss: 0.027, Running accuracy: 99.947, Time: 24.78
[2020-12-15 16:52:18,570][__main__][INFO] - [20800] Loss: 0.019, Running accuracy: 99.947, Time: 24.18
[2020-12-15 16:52:43,990][__main__][INFO] - [21120] Loss: 0.020, Running accuracy: 99.948, Time: 25.42
[2020-12-15 16:53:08,647][__main__][INFO] - [21440] Loss: 0.106, Running accuracy: 99.947, Time: 24.65
[2020-12-15 16:53:34,918][__main__][INFO] - [21760] Loss: 0.041, Running accuracy: 99.946, Time: 26.27
[2020-12-15 16:53:59,891][__main__][INFO] - [22080] Loss: 0.030, Running accuracy: 99.946, Time: 24.97
[2020-12-15 16:54:26,588][__main__][INFO] - [22400] Loss: 0.022, Running accuracy: 99.947, Time: 26.69
[2020-12-15 16:54:54,603][__main__][INFO] - [22720] Loss: 0.043, Running accuracy: 99.946, Time: 28.01
[2020-12-15 16:55:18,429][__main__][INFO] - [23040] Loss: 0.039, Running accuracy: 99.946, Time: 23.83
[2020-12-15 16:55:43,160][__main__][INFO] - [23360] Loss: 0.057, Running accuracy: 99.946, Time: 24.73
[2020-12-15 16:56:07,290][__main__][INFO] - [23680] Loss: 0.037, Running accuracy: 99.947, Time: 24.13
[2020-12-15 16:56:34,594][__main__][INFO] - [24000] Loss: 0.038, Running accuracy: 99.947, Time: 27.30
[2020-12-15 16:56:57,131][__main__][INFO] - [24320] Loss: 0.042, Running accuracy: 99.947, Time: 22.54
[2020-12-15 16:57:21,891][__main__][INFO] - [24640] Loss: 0.013, Running accuracy: 99.948, Time: 24.76
[2020-12-15 16:57:45,635][__main__][INFO] - [24960] Loss: 0.009, Running accuracy: 99.948, Time: 23.74
[2020-12-15 16:58:12,910][__main__][INFO] - [25280] Loss: 0.073, Running accuracy: 99.947, Time: 27.27
[2020-12-15 16:58:36,065][__main__][INFO] - [25600] Loss: 0.067, Running accuracy: 99.947, Time: 23.15
[2020-12-15 16:58:59,082][__main__][INFO] - [25920] Loss: 0.028, Running accuracy: 99.947, Time: 23.02
[2020-12-15 16:59:25,359][__main__][INFO] - [26240] Loss: 0.051, Running accuracy: 99.947, Time: 26.28
[2020-12-15 16:59:48,596][__main__][INFO] - [26560] Loss: 0.071, Running accuracy: 99.946, Time: 23.24
[2020-12-15 17:00:12,784][__main__][INFO] - [26880] Loss: 0.044, Running accuracy: 99.946, Time: 24.19
[2020-12-15 17:00:40,885][__main__][INFO] - [27200] Loss: 0.074, Running accuracy: 99.945, Time: 28.10
[2020-12-15 17:01:05,515][__main__][INFO] - [27520] Loss: 0.046, Running accuracy: 99.945, Time: 24.63
[2020-12-15 17:01:30,669][__main__][INFO] - [27840] Loss: 0.031, Running accuracy: 99.945, Time: 25.15
[2020-12-15 17:01:55,096][__main__][INFO] - [28160] Loss: 0.022, Running accuracy: 99.945, Time: 24.43
[2020-12-15 17:02:21,333][__main__][INFO] - [28480] Loss: 0.051, Running accuracy: 99.945, Time: 26.24
[2020-12-15 17:02:45,828][__main__][INFO] - [28800] Loss: 0.029, Running accuracy: 99.945, Time: 24.49
[2020-12-15 17:03:09,774][__main__][INFO] - [29120] Loss: 0.020, Running accuracy: 99.946, Time: 23.95
[2020-12-15 17:03:33,765][__main__][INFO] - [29440] Loss: 0.016, Running accuracy: 99.946, Time: 23.99
[2020-12-15 17:03:58,169][__main__][INFO] - [29760] Loss: 0.078, Running accuracy: 99.946, Time: 24.40
[2020-12-15 17:04:24,092][__main__][INFO] - [30080] Loss: 0.066, Running accuracy: 99.945, Time: 25.92
[2020-12-15 17:04:47,510][__main__][INFO] - [30400] Loss: 0.070, Running accuracy: 99.945, Time: 23.42
[2020-12-15 17:05:12,028][__main__][INFO] - [30720] Loss: 0.034, Running accuracy: 99.945, Time: 24.52
[2020-12-15 17:05:35,365][__main__][INFO] - [31040] Loss: 0.044, Running accuracy: 99.945, Time: 23.34
[2020-12-15 17:06:03,758][__main__][INFO] - [31360] Loss: 0.051, Running accuracy: 99.944, Time: 28.39
[2020-12-15 17:06:27,325][__main__][INFO] - [31680] Loss: 0.028, Running accuracy: 99.945, Time: 23.57
[2020-12-15 17:06:49,659][__main__][INFO] - [32000] Loss: 0.053, Running accuracy: 99.945, Time: 22.33
[2020-12-15 17:07:17,163][__main__][INFO] - [32320] Loss: 0.012, Running accuracy: 99.945, Time: 27.50
[2020-12-15 17:07:41,320][__main__][INFO] - [32640] Loss: 0.027, Running accuracy: 99.945, Time: 24.16
[2020-12-15 17:08:08,073][__main__][INFO] - [32960] Loss: 0.042, Running accuracy: 99.946, Time: 26.75
[2020-12-15 17:08:31,934][__main__][INFO] - [33280] Loss: 0.042, Running accuracy: 99.945, Time: 23.86
[2020-12-15 17:08:56,129][__main__][INFO] - [33600] Loss: 0.098, Running accuracy: 99.945, Time: 24.19
[2020-12-15 17:09:20,369][__main__][INFO] - [33920] Loss: 0.013, Running accuracy: 99.945, Time: 24.24
[2020-12-15 17:09:44,534][__main__][INFO] - [34240] Loss: 0.100, Running accuracy: 99.945, Time: 24.16
[2020-12-15 17:10:08,640][__main__][INFO] - [34560] Loss: 0.007, Running accuracy: 99.946, Time: 24.11
[2020-12-15 17:10:32,866][__main__][INFO] - [34880] Loss: 0.031, Running accuracy: 99.946, Time: 24.23
[2020-12-15 17:10:59,229][__main__][INFO] - [35200] Loss: 0.015, Running accuracy: 99.946, Time: 26.36
[2020-12-15 17:11:23,106][__main__][INFO] - [35520] Loss: 0.028, Running accuracy: 99.946, Time: 23.88
[2020-12-15 17:11:49,656][__main__][INFO] - [35840] Loss: 0.016, Running accuracy: 99.947, Time: 26.55
[2020-12-15 17:12:13,986][__main__][INFO] - [36160] Loss: 0.033, Running accuracy: 99.947, Time: 24.33
[2020-12-15 17:12:37,791][__main__][INFO] - [36480] Loss: 0.027, Running accuracy: 99.947, Time: 23.80
[2020-12-15 17:13:03,032][__main__][INFO] - [36800] Loss: 0.059, Running accuracy: 99.947, Time: 25.24
[2020-12-15 17:13:26,380][__main__][INFO] - [37120] Loss: 0.038, Running accuracy: 99.947, Time: 23.35
[2020-12-15 17:13:49,817][__main__][INFO] - [37440] Loss: 0.044, Running accuracy: 99.947, Time: 23.44
[2020-12-15 17:14:14,206][__main__][INFO] - [37760] Loss: 0.032, Running accuracy: 99.947, Time: 24.39
[2020-12-15 17:14:37,828][__main__][INFO] - [38080] Loss: 0.026, Running accuracy: 99.947, Time: 23.62
[2020-12-15 17:15:02,171][__main__][INFO] - [38400] Loss: 0.046, Running accuracy: 99.947, Time: 24.34
[2020-12-15 17:15:25,160][__main__][INFO] - [38720] Loss: 0.022, Running accuracy: 99.948, Time: 22.99
[2020-12-15 17:15:50,045][__main__][INFO] - [39040] Loss: 0.023, Running accuracy: 99.948, Time: 24.88
[2020-12-15 17:16:15,151][__main__][INFO] - [39360] Loss: 0.012, Running accuracy: 99.948, Time: 25.10
[2020-12-15 17:16:39,999][__main__][INFO] - [39680] Loss: 0.032, Running accuracy: 99.949, Time: 24.85
[2020-12-15 17:16:49,353][__main__][INFO] - Action accuracy: 99.949, Loss: 0.045
[2020-12-15 17:16:49,353][__main__][INFO] - Validating..
[2020-12-15 17:17:19,764][test][INFO] - Time elapsed: 28.082753
[2020-12-15 17:17:19,768][__main__][INFO] - Validation F1 score: 95.480, Exact match: 54.710, Precision: 95.450, Recall: 95.510
[2020-12-15 17:17:19,768][__main__][INFO] - F1 score has improved
[2020-12-15 17:17:54,194][__main__][INFO] - Checkpoint saved to checkpoints/model_latest.pth
[2020-12-15 17:17:55,072][__main__][INFO] - Epoch #30
[2020-12-15 17:17:55,073][__main__][INFO] - Training..
[2020-12-15 17:18:21,628][__main__][INFO] - [320] Loss: 0.008, Running accuracy: 99.974, Time: 25.18
[2020-12-15 17:18:45,779][__main__][INFO] - [640] Loss: 0.046, Running accuracy: 99.954, Time: 24.15
[2020-12-15 17:19:10,330][__main__][INFO] - [960] Loss: 0.007, Running accuracy: 99.970, Time: 24.55
[2020-12-15 17:19:33,214][__main__][INFO] - [1280] Loss: 0.052, Running accuracy: 99.951, Time: 22.88
[2020-12-15 17:19:58,282][__main__][INFO] - [1600] Loss: 0.004, Running accuracy: 99.961, Time: 25.07
[2020-12-15 17:20:22,097][__main__][INFO] - [1920] Loss: 0.046, Running accuracy: 99.961, Time: 23.81
[2020-12-15 17:20:46,279][__main__][INFO] - [2240] Loss: 0.038, Running accuracy: 99.953, Time: 24.18
[2020-12-15 17:21:11,370][__main__][INFO] - [2560] Loss: 0.059, Running accuracy: 99.948, Time: 25.09
[2020-12-15 17:21:39,573][__main__][INFO] - [2880] Loss: 0.026, Running accuracy: 99.948, Time: 28.20
[2020-12-15 17:22:03,495][__main__][INFO] - [3200] Loss: 0.005, Running accuracy: 99.953, Time: 23.92
[2020-12-15 17:22:27,438][__main__][INFO] - [3520] Loss: 0.041, Running accuracy: 99.951, Time: 23.94
[2020-12-15 17:22:52,408][__main__][INFO] - [3840] Loss: 0.010, Running accuracy: 99.955, Time: 24.97
[2020-12-15 17:23:14,975][__main__][INFO] - [4160] Loss: 0.013, Running accuracy: 99.958, Time: 22.57
[2020-12-15 17:23:39,339][__main__][INFO] - [4480] Loss: 0.038, Running accuracy: 99.957, Time: 24.36
[2020-12-15 17:24:03,073][__main__][INFO] - [4800] Loss: 0.022, Running accuracy: 99.958, Time: 23.73
[2020-12-15 17:24:27,669][__main__][INFO] - [5120] Loss: 0.051, Running accuracy: 99.955, Time: 24.59
[2020-12-15 17:24:51,642][__main__][INFO] - [5440] Loss: 0.093, Running accuracy: 99.954, Time: 23.97
[2020-12-15 17:25:13,705][__main__][INFO] - [5760] Loss: 0.073, Running accuracy: 99.951, Time: 22.06
[2020-12-15 17:25:38,264][__main__][INFO] - [6080] Loss: 0.045, Running accuracy: 99.950, Time: 24.56
[2020-12-15 17:26:05,247][__main__][INFO] - [6400] Loss: 0.008, Running accuracy: 99.953, Time: 26.98
[2020-12-15 17:26:28,268][__main__][INFO] - [6720] Loss: 0.023, Running accuracy: 99.954, Time: 23.02
[2020-12-15 17:26:55,887][__main__][INFO] - [7040] Loss: 0.006, Running accuracy: 99.955, Time: 27.62
[2020-12-15 17:27:19,786][__main__][INFO] - [7360] Loss: 0.019, Running accuracy: 99.956, Time: 23.90
[2020-12-15 17:27:43,805][__main__][INFO] - [7680] Loss: 0.059, Running accuracy: 99.956, Time: 24.02
[2020-12-15 17:28:06,069][__main__][INFO] - [8000] Loss: 0.031, Running accuracy: 99.957, Time: 22.26
[2020-12-15 17:28:29,189][__main__][INFO] - [8320] Loss: 0.019, Running accuracy: 99.958, Time: 23.12
[2020-12-15 17:28:53,963][__main__][INFO] - [8640] Loss: 0.054, Running accuracy: 99.957, Time: 24.77
[2020-12-15 17:29:17,258][__main__][INFO] - [8960] Loss: 0.032, Running accuracy: 99.956, Time: 23.29
[2020-12-15 17:29:41,590][__main__][INFO] - [9280] Loss: 0.019, Running accuracy: 99.956, Time: 24.33
[2020-12-15 17:30:05,302][__main__][INFO] - [9600] Loss: 0.008, Running accuracy: 99.957, Time: 23.71
[2020-12-15 17:30:28,804][__main__][INFO] - [9920] Loss: 0.019, Running accuracy: 99.958, Time: 23.50
[2020-12-15 17:30:53,195][__main__][INFO] - [10240] Loss: 0.015, Running accuracy: 99.959, Time: 24.39
[2020-12-15 17:31:17,100][__main__][INFO] - [10560] Loss: 0.076, Running accuracy: 99.957, Time: 23.90
[2020-12-15 17:31:42,989][__main__][INFO] - [10880] Loss: 0.008, Running accuracy: 99.958, Time: 25.89
[2020-12-15 17:32:07,610][__main__][INFO] - [11200] Loss: 0.028, Running accuracy: 99.958, Time: 24.62
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[2020-12-15 18:08:41,975][__main__][INFO] - Action accuracy: 99.947, Loss: 0.043
[2020-12-15 18:08:41,976][__main__][INFO] - Validating..
[2020-12-15 18:09:08,205][test][INFO] - Time elapsed: 24.162203
[2020-12-15 18:09:08,209][__main__][INFO] - Validation F1 score: 95.450, Exact match: 55.350, Precision: 95.400, Recall: 95.490
[2020-12-15 18:09:42,822][__main__][INFO] - Checkpoint saved to checkpoints/model_latest.pth
[2020-12-15 18:09:43,630][__main__][INFO] - Epoch #31
[2020-12-15 18:09:43,630][__main__][INFO] - Training..
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[2020-12-15 18:45:14,059][__main__][INFO] - [27520] Loss: 0.035, Running accuracy: 99.948, Time: 25.26
[2020-12-15 18:45:36,523][__main__][INFO] - [27840] Loss: 0.022, Running accuracy: 99.948, Time: 22.46
[2020-12-15 18:46:00,863][__main__][INFO] - [28160] Loss: 0.010, Running accuracy: 99.949, Time: 24.34
[2020-12-15 18:46:26,458][__main__][INFO] - [28480] Loss: 0.080, Running accuracy: 99.947, Time: 25.59
[2020-12-15 18:46:51,050][__main__][INFO] - [28800] Loss: 0.020, Running accuracy: 99.947, Time: 24.59
[2020-12-15 18:47:15,455][__main__][INFO] - [29120] Loss: 0.022, Running accuracy: 99.948, Time: 24.40
[2020-12-15 18:47:39,566][__main__][INFO] - [29440] Loss: 0.053, Running accuracy: 99.947, Time: 24.11
[2020-12-15 18:48:03,812][__main__][INFO] - [29760] Loss: 0.031, Running accuracy: 99.948, Time: 24.25
[2020-12-15 18:48:27,753][__main__][INFO] - [30080] Loss: 0.029, Running accuracy: 99.948, Time: 23.94
[2020-12-15 18:48:51,663][__main__][INFO] - [30400] Loss: 0.035, Running accuracy: 99.948, Time: 23.91
[2020-12-15 18:49:21,115][__main__][INFO] - [30720] Loss: 0.033, Running accuracy: 99.948, Time: 29.45
[2020-12-15 18:49:46,518][__main__][INFO] - [31040] Loss: 0.010, Running accuracy: 99.949, Time: 25.40
[2020-12-15 18:50:12,790][__main__][INFO] - [31360] Loss: 0.058, Running accuracy: 99.948, Time: 26.27
[2020-12-15 18:50:37,512][__main__][INFO] - [31680] Loss: 0.060, Running accuracy: 99.948, Time: 24.72
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[2020-12-15 18:51:25,223][__main__][INFO] - [32320] Loss: 0.055, Running accuracy: 99.946, Time: 24.36
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[2020-12-15 18:52:14,316][__main__][INFO] - [32960] Loss: 0.032, Running accuracy: 99.946, Time: 23.85
[2020-12-15 18:52:38,569][__main__][INFO] - [33280] Loss: 0.032, Running accuracy: 99.946, Time: 24.25
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[2020-12-15 18:54:15,902][__main__][INFO] - [34560] Loss: 0.015, Running accuracy: 99.946, Time: 25.33
[2020-12-15 18:54:39,584][__main__][INFO] - [34880] Loss: 0.006, Running accuracy: 99.947, Time: 23.68
[2020-12-15 18:55:06,791][__main__][INFO] - [35200] Loss: 0.020, Running accuracy: 99.947, Time: 27.21
[2020-12-15 18:55:31,017][__main__][INFO] - [35520] Loss: 0.022, Running accuracy: 99.947, Time: 24.23
[2020-12-15 18:55:55,833][__main__][INFO] - [35840] Loss: 0.033, Running accuracy: 99.947, Time: 24.82
[2020-12-15 18:56:20,219][__main__][INFO] - [36160] Loss: 0.032, Running accuracy: 99.947, Time: 24.38
[2020-12-15 18:56:43,766][__main__][INFO] - [36480] Loss: 0.061, Running accuracy: 99.947, Time: 23.55
[2020-12-15 18:57:07,494][__main__][INFO] - [36800] Loss: 0.045, Running accuracy: 99.947, Time: 23.73
[2020-12-15 18:57:30,625][__main__][INFO] - [37120] Loss: 0.031, Running accuracy: 99.947, Time: 23.13
[2020-12-15 18:57:54,414][__main__][INFO] - [37440] Loss: 0.069, Running accuracy: 99.946, Time: 23.79
[2020-12-15 18:58:18,363][__main__][INFO] - [37760] Loss: 0.037, Running accuracy: 99.946, Time: 23.95
[2020-12-15 18:58:41,240][__main__][INFO] - [38080] Loss: 0.157, Running accuracy: 99.947, Time: 22.88
[2020-12-15 18:59:05,129][__main__][INFO] - [38400] Loss: 0.026, Running accuracy: 99.947, Time: 23.89
[2020-12-15 18:59:29,814][__main__][INFO] - [38720] Loss: 0.052, Running accuracy: 99.947, Time: 24.68
[2020-12-15 18:59:51,974][__main__][INFO] - [39040] Loss: 0.030, Running accuracy: 99.947, Time: 22.16
[2020-12-15 19:00:16,454][__main__][INFO] - [39360] Loss: 0.100, Running accuracy: 99.947, Time: 24.48
[2020-12-15 19:00:45,540][__main__][INFO] - [39680] Loss: 0.009, Running accuracy: 99.947, Time: 29.09
[2020-12-15 19:00:55,433][__main__][INFO] - Action accuracy: 99.947, Loss: 0.045
[2020-12-15 19:00:55,434][__main__][INFO] - Validating..
[2020-12-15 19:01:21,787][test][INFO] - Time elapsed: 24.265983
[2020-12-15 19:01:21,791][__main__][INFO] - Validation F1 score: 95.310, Exact match: 54.650, Precision: 95.260, Recall: 95.360
[2020-12-15 19:01:56,171][__main__][INFO] - Checkpoint saved to checkpoints/model_latest.pth
[2020-12-15 19:01:57,025][__main__][INFO] - Epoch #32
[2020-12-15 19:01:57,025][__main__][INFO] - Training..
[2020-12-15 19:02:21,934][__main__][INFO] - [320] Loss: 0.089, Running accuracy: 99.947, Time: 23.52
[2020-12-15 19:02:46,678][__main__][INFO] - [640] Loss: 0.029, Running accuracy: 99.961, Time: 24.74
[2020-12-15 19:03:11,473][__main__][INFO] - [960] Loss: 0.013, Running accuracy: 99.970, Time: 24.79
[2020-12-15 19:03:34,838][__main__][INFO] - [1280] Loss: 0.013, Running accuracy: 99.974, Time: 23.36
[2020-12-15 19:03:58,674][__main__][INFO] - [1600] Loss: 0.051, Running accuracy: 99.971, Time: 23.83
[2020-12-15 19:04:29,250][__main__][INFO] - [1920] Loss: 0.022, Running accuracy: 99.963, Time: 30.58
[2020-12-15 19:04:54,590][__main__][INFO] - [2240] Loss: 0.069, Running accuracy: 99.954, Time: 25.34
[2020-12-15 19:05:18,696][__main__][INFO] - [2560] Loss: 0.075, Running accuracy: 99.950, Time: 24.10
[2020-12-15 19:05:43,501][__main__][INFO] - [2880] Loss: 0.028, Running accuracy: 99.952, Time: 24.80
[2020-12-15 19:06:07,678][__main__][INFO] - [3200] Loss: 0.031, Running accuracy: 99.952, Time: 24.18
[2020-12-15 19:06:31,546][__main__][INFO] - [3520] Loss: 0.071, Running accuracy: 99.946, Time: 23.87
[2020-12-15 19:06:56,959][__main__][INFO] - [3840] Loss: 0.059, Running accuracy: 99.944, Time: 25.41
[2020-12-15 19:07:23,219][__main__][INFO] - [4160] Loss: 0.009, Running accuracy: 99.948, Time: 26.26
[2020-12-15 19:07:47,575][__main__][INFO] - [4480] Loss: 0.047, Running accuracy: 99.948, Time: 24.35
[2020-12-15 19:08:14,341][__main__][INFO] - [4800] Loss: 0.010, Running accuracy: 99.951, Time: 26.76
[2020-12-15 19:08:37,763][__main__][INFO] - [5120] Loss: 0.040, Running accuracy: 99.952, Time: 23.42
[2020-12-15 19:09:03,032][__main__][INFO] - [5440] Loss: 0.068, Running accuracy: 99.950, Time: 25.27
[2020-12-15 19:09:25,990][__main__][INFO] - [5760] Loss: 0.025, Running accuracy: 99.951, Time: 22.96
[2020-12-15 19:09:49,266][__main__][INFO] - [6080] Loss: 0.018, Running accuracy: 99.952, Time: 23.28
[2020-12-15 19:10:18,560][__main__][INFO] - [6400] Loss: 0.042, Running accuracy: 99.952, Time: 29.29
[2020-12-15 19:10:41,819][__main__][INFO] - [6720] Loss: 0.046, Running accuracy: 99.952, Time: 23.26
[2020-12-15 19:11:06,600][__main__][INFO] - [7040] Loss: 0.147, Running accuracy: 99.953, Time: 24.78
[2020-12-15 19:11:31,944][__main__][INFO] - [7360] Loss: 0.013, Running accuracy: 99.953, Time: 25.34
[2020-12-15 19:11:55,763][__main__][INFO] - [7680] Loss: 0.026, Running accuracy: 99.954, Time: 23.82
[2020-12-15 19:12:19,569][__main__][INFO] - [8000] Loss: 0.029, Running accuracy: 99.954, Time: 23.81
[2020-12-15 19:12:43,904][__main__][INFO] - [8320] Loss: 0.049, Running accuracy: 99.953, Time: 24.33
[2020-12-15 19:13:06,607][__main__][INFO] - [8640] Loss: 0.015, Running accuracy: 99.954, Time: 22.70
[2020-12-15 19:13:29,999][__main__][INFO] - [8960] Loss: 0.045, Running accuracy: 99.954, Time: 23.39
[2020-12-15 19:13:53,169][__main__][INFO] - [9280] Loss: 0.019, Running accuracy: 99.955, Time: 23.17
[2020-12-15 19:14:19,295][__main__][INFO] - [9600] Loss: 0.040, Running accuracy: 99.955, Time: 26.12
[2020-12-15 19:14:44,981][__main__][INFO] - [9920] Loss: 0.033, Running accuracy: 99.955, Time: 25.69
[2020-12-15 19:15:10,216][__main__][INFO] - [10240] Loss: 0.050, Running accuracy: 99.955, Time: 25.23
[2020-12-15 19:15:40,827][__main__][INFO] - [10560] Loss: 0.046, Running accuracy: 99.954, Time: 30.61
[2020-12-15 19:16:05,121][__main__][INFO] - [10880] Loss: 0.030, Running accuracy: 99.955, Time: 24.29
[2020-12-15 19:16:28,544][__main__][INFO] - [11200] Loss: 0.044, Running accuracy: 99.954, Time: 23.42
[2020-12-15 19:16:51,056][__main__][INFO] - [11520] Loss: 0.030, Running accuracy: 99.955, Time: 22.51
[2020-12-15 19:17:14,575][__main__][INFO] - [11840] Loss: 0.051, Running accuracy: 99.955, Time: 23.52
[2020-12-15 19:17:37,967][__main__][INFO] - [12160] Loss: 0.037, Running accuracy: 99.954, Time: 23.39
[2020-12-15 19:18:02,868][__main__][INFO] - [12480] Loss: 0.006, Running accuracy: 99.955, Time: 24.90
[2020-12-15 19:18:27,840][__main__][INFO] - [12800] Loss: 0.034, Running accuracy: 99.955, Time: 24.97
[2020-12-15 19:18:52,665][__main__][INFO] - [13120] Loss: 0.018, Running accuracy: 99.955, Time: 24.82
[2020-12-15 19:19:19,995][__main__][INFO] - [13440] Loss: 0.037, Running accuracy: 99.956, Time: 27.33
[2020-12-15 19:19:43,486][__main__][INFO] - [13760] Loss: 0.063, Running accuracy: 99.955, Time: 23.49
[2020-12-15 19:20:08,568][__main__][INFO] - [14080] Loss: 0.036, Running accuracy: 99.955, Time: 25.08
[2020-12-15 19:20:34,583][__main__][INFO] - [14400] Loss: 0.020, Running accuracy: 99.955, Time: 26.01
[2020-12-15 19:20:59,367][__main__][INFO] - [14720] Loss: 0.141, Running accuracy: 99.954, Time: 24.78
[2020-12-15 19:21:28,126][__main__][INFO] - [15040] Loss: 0.015, Running accuracy: 99.954, Time: 28.64
[2020-12-15 19:21:51,971][__main__][INFO] - [15360] Loss: 0.028, Running accuracy: 99.954, Time: 23.84
[2020-12-15 19:22:17,341][__main__][INFO] - [15680] Loss: 0.032, Running accuracy: 99.954, Time: 25.37
[2020-12-15 19:22:40,901][__main__][INFO] - [16000] Loss: 0.025, Running accuracy: 99.955, Time: 23.56
[2020-12-15 19:23:06,662][__main__][INFO] - [16320] Loss: 0.028, Running accuracy: 99.955, Time: 25.76
[2020-12-15 19:23:28,747][__main__][INFO] - [16640] Loss: 0.016, Running accuracy: 99.955, Time: 22.08
[2020-12-15 19:23:54,888][__main__][INFO] - [16960] Loss: 0.025, Running accuracy: 99.956, Time: 26.14
[2020-12-15 19:24:20,062][__main__][INFO] - [17280] Loss: 0.023, Running accuracy: 99.956, Time: 25.17
[2020-12-15 19:24:44,229][__main__][INFO] - [17600] Loss: 0.022, Running accuracy: 99.957, Time: 24.17
[2020-12-15 19:25:10,884][__main__][INFO] - [17920] Loss: 0.033, Running accuracy: 99.956, Time: 26.65
[2020-12-15 19:25:34,818][__main__][INFO] - [18240] Loss: 0.077, Running accuracy: 99.956, Time: 23.93
[2020-12-15 19:26:00,210][__main__][INFO] - [18560] Loss: 0.020, Running accuracy: 99.956, Time: 25.39
[2020-12-15 19:26:27,547][__main__][INFO] - [18880] Loss: 0.012, Running accuracy: 99.956, Time: 27.34
[2020-12-15 19:26:51,395][__main__][INFO] - [19200] Loss: 0.020, Running accuracy: 99.957, Time: 23.85
[2020-12-15 19:27:20,453][__main__][INFO] - [19520] Loss: 0.040, Running accuracy: 99.957, Time: 29.06
[2020-12-15 19:27:45,680][__main__][INFO] - [19840] Loss: 0.051, Running accuracy: 99.957, Time: 25.23
[2020-12-15 19:28:09,067][__main__][INFO] - [20160] Loss: 0.035, Running accuracy: 99.957, Time: 23.39
[2020-12-15 19:28:32,189][__main__][INFO] - [20480] Loss: 0.045, Running accuracy: 99.956, Time: 23.12
[2020-12-15 19:28:54,980][__main__][INFO] - [20800] Loss: 0.017, Running accuracy: 99.957, Time: 22.79
[2020-12-15 19:29:17,911][__main__][INFO] - [21120] Loss: 0.007, Running accuracy: 99.957, Time: 22.93
[2020-12-15 19:29:41,576][__main__][INFO] - [21440] Loss: 0.012, Running accuracy: 99.957, Time: 23.66
[2020-12-15 19:30:04,589][__main__][INFO] - [21760] Loss: 0.026, Running accuracy: 99.957, Time: 23.01
[2020-12-15 19:30:27,967][__main__][INFO] - [22080] Loss: 0.024, Running accuracy: 99.958, Time: 23.38
[2020-12-15 19:30:52,697][__main__][INFO] - [22400] Loss: 0.041, Running accuracy: 99.958, Time: 24.73
[2020-12-15 19:31:16,795][__main__][INFO] - [22720] Loss: 0.041, Running accuracy: 99.958, Time: 24.10
[2020-12-15 19:31:40,945][__main__][INFO] - [23040] Loss: 0.033, Running accuracy: 99.957, Time: 24.15
[2020-12-15 19:32:04,722][__main__][INFO] - [23360] Loss: 0.072, Running accuracy: 99.956, Time: 23.78
[2020-12-15 19:32:33,113][__main__][INFO] - [23680] Loss: 0.069, Running accuracy: 99.956, Time: 28.39
[2020-12-15 19:32:56,908][__main__][INFO] - [24000] Loss: 0.042, Running accuracy: 99.956, Time: 23.79
[2020-12-15 19:33:20,829][__main__][INFO] - [24320] Loss: 0.033, Running accuracy: 99.956, Time: 23.92
[2020-12-15 19:33:45,602][__main__][INFO] - [24640] Loss: 0.018, Running accuracy: 99.956, Time: 24.77
[2020-12-15 19:34:11,660][__main__][INFO] - [24960] Loss: 0.087, Running accuracy: 99.955, Time: 26.06
[2020-12-15 19:34:34,337][__main__][INFO] - [25280] Loss: 0.013, Running accuracy: 99.955, Time: 22.68
[2020-12-15 19:34:58,202][__main__][INFO] - [25600] Loss: 0.023, Running accuracy: 99.955, Time: 23.86
[2020-12-15 19:35:21,467][__main__][INFO] - [25920] Loss: 0.025, Running accuracy: 99.955, Time: 23.26
[2020-12-15 19:35:45,134][__main__][INFO] - [26240] Loss: 0.067, Running accuracy: 99.954, Time: 23.67
[2020-12-15 19:36:09,402][__main__][INFO] - [26560] Loss: 0.057, Running accuracy: 99.954, Time: 24.27
[2020-12-15 19:36:33,306][__main__][INFO] - [26880] Loss: 0.027, Running accuracy: 99.954, Time: 23.90
[2020-12-15 19:36:56,362][__main__][INFO] - [27200] Loss: 0.054, Running accuracy: 99.954, Time: 23.05
[2020-12-15 19:37:19,049][__main__][INFO] - [27520] Loss: 0.060, Running accuracy: 99.953, Time: 22.69
[2020-12-15 19:37:41,375][__main__][INFO] - [27840] Loss: 0.027, Running accuracy: 99.953, Time: 22.32
[2020-12-15 19:38:10,380][__main__][INFO] - [28160] Loss: 0.035, Running accuracy: 99.953, Time: 29.00
[2020-12-15 19:38:33,428][__main__][INFO] - [28480] Loss: 0.028, Running accuracy: 99.953, Time: 23.05
[2020-12-15 19:38:57,998][__main__][INFO] - [28800] Loss: 0.071, Running accuracy: 99.952, Time: 24.57
[2020-12-15 19:39:21,867][__main__][INFO] - [29120] Loss: 0.034, Running accuracy: 99.952, Time: 23.87
[2020-12-15 19:39:45,108][__main__][INFO] - [29440] Loss: 0.027, Running accuracy: 99.952, Time: 23.24
[2020-12-15 19:40:08,322][__main__][INFO] - [29760] Loss: 0.022, Running accuracy: 99.952, Time: 23.21
[2020-12-15 19:40:35,056][__main__][INFO] - [30080] Loss: 0.016, Running accuracy: 99.953, Time: 26.73
[2020-12-15 19:40:58,817][__main__][INFO] - [30400] Loss: 0.046, Running accuracy: 99.952, Time: 23.76
[2020-12-15 19:41:21,109][__main__][INFO] - [30720] Loss: 0.046, Running accuracy: 99.952, Time: 22.29
[2020-12-15 19:41:44,658][__main__][INFO] - [31040] Loss: 0.019, Running accuracy: 99.952, Time: 23.55
[2020-12-15 19:42:08,925][__main__][INFO] - [31360] Loss: 0.026, Running accuracy: 99.952, Time: 24.27
[2020-12-15 19:42:32,775][__main__][INFO] - [31680] Loss: 0.021, Running accuracy: 99.952, Time: 23.85
[2020-12-15 19:42:57,820][__main__][INFO] - [32000] Loss: 0.054, Running accuracy: 99.952, Time: 25.04
[2020-12-15 19:43:22,401][__main__][INFO] - [32320] Loss: 0.048, Running accuracy: 99.952, Time: 24.58
[2020-12-15 19:43:49,231][__main__][INFO] - [32640] Loss: 0.090, Running accuracy: 99.952, Time: 26.83
[2020-12-15 19:44:13,949][__main__][INFO] - [32960] Loss: 0.061, Running accuracy: 99.952, Time: 24.72
[2020-12-15 19:44:37,362][__main__][INFO] - [33280] Loss: 0.045, Running accuracy: 99.952, Time: 23.41
[2020-12-15 19:44:59,037][__main__][INFO] - [33600] Loss: 0.021, Running accuracy: 99.952, Time: 21.67
[2020-12-15 19:45:23,081][__main__][INFO] - [33920] Loss: 0.050, Running accuracy: 99.952, Time: 24.04
[2020-12-15 19:45:50,293][__main__][INFO] - [34240] Loss: 0.057, Running accuracy: 99.952, Time: 27.21
[2020-12-15 19:46:12,749][__main__][INFO] - [34560] Loss: 0.047, Running accuracy: 99.952, Time: 22.45
[2020-12-15 19:46:36,663][__main__][INFO] - [34880] Loss: 0.041, Running accuracy: 99.951, Time: 23.91
[2020-12-15 19:46:59,955][__main__][INFO] - [35200] Loss: 0.032, Running accuracy: 99.952, Time: 23.29
[2020-12-15 19:47:23,214][__main__][INFO] - [35520] Loss: 0.084, Running accuracy: 99.951, Time: 23.26
[2020-12-15 19:47:46,697][__main__][INFO] - [35840] Loss: 0.068, Running accuracy: 99.950, Time: 23.48
[2020-12-15 19:48:10,218][__main__][INFO] - [36160] Loss: 0.052, Running accuracy: 99.949, Time: 23.52
[2020-12-15 19:48:34,320][__main__][INFO] - [36480] Loss: 0.012, Running accuracy: 99.949, Time: 24.10
[2020-12-15 19:48:57,167][__main__][INFO] - [36800] Loss: 0.068, Running accuracy: 99.949, Time: 22.85
[2020-12-15 19:49:26,068][__main__][INFO] - [37120] Loss: 0.041, Running accuracy: 99.949, Time: 28.90
[2020-12-15 19:49:50,772][__main__][INFO] - [37440] Loss: 0.040, Running accuracy: 99.949, Time: 24.70
[2020-12-15 19:50:14,373][__main__][INFO] - [37760] Loss: 0.052, Running accuracy: 99.950, Time: 23.60
[2020-12-15 19:50:38,993][__main__][INFO] - [38080] Loss: 0.049, Running accuracy: 99.949, Time: 24.62
[2020-12-15 19:51:02,287][__main__][INFO] - [38400] Loss: 0.050, Running accuracy: 99.950, Time: 23.29
[2020-12-15 19:51:26,586][__main__][INFO] - [38720] Loss: 0.015, Running accuracy: 99.950, Time: 24.30
[2020-12-15 19:51:50,345][__main__][INFO] - [39040] Loss: 0.076, Running accuracy: 99.949, Time: 23.76
[2020-12-15 19:52:13,965][__main__][INFO] - [39360] Loss: 0.012, Running accuracy: 99.950, Time: 23.62
[2020-12-15 19:52:37,977][__main__][INFO] - [39680] Loss: 0.041, Running accuracy: 99.950, Time: 24.01
[2020-12-15 19:52:48,263][__main__][INFO] - Action accuracy: 99.950, Loss: 0.045
[2020-12-15 19:52:48,264][__main__][INFO] - Validating..
[2020-12-15 19:53:18,778][test][INFO] - Time elapsed: 28.260998
[2020-12-15 19:53:18,782][__main__][INFO] - Validation F1 score: 95.370, Exact match: 55.120, Precision: 95.350, Recall: 95.390
[2020-12-15 19:53:53,107][__main__][INFO] - Checkpoint saved to checkpoints/model_latest.pth
[2020-12-15 19:53:54,047][__main__][INFO] - Epoch #33
[2020-12-15 19:53:54,047][__main__][INFO] - Training..
[2020-12-15 19:54:19,833][__main__][INFO] - [320] Loss: 0.012, Running accuracy: 100.000, Time: 24.82
[2020-12-15 19:54:44,735][__main__][INFO] - [640] Loss: 0.007, Running accuracy: 99.993, Time: 24.90
[2020-12-15 19:55:08,058][__main__][INFO] - [960] Loss: 0.047, Running accuracy: 99.982, Time: 23.32
[2020-12-15 19:55:32,077][__main__][INFO] - [1280] Loss: 0.029, Running accuracy: 99.973, Time: 24.02
[2020-12-15 19:55:58,167][__main__][INFO] - [1600] Loss: 0.025, Running accuracy: 99.974, Time: 26.09
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[2020-12-15 20:44:27,805][__main__][INFO] - [39360] Loss: 0.059, Running accuracy: 99.954, Time: 24.61
[2020-12-15 20:44:50,446][__main__][INFO] - [39680] Loss: 0.018, Running accuracy: 99.955, Time: 22.64
[2020-12-15 20:45:01,135][__main__][INFO] - Action accuracy: 99.955, Loss: 0.039
[2020-12-15 20:45:01,136][__main__][INFO] - Validating..
[2020-12-15 20:45:27,503][test][INFO] - Time elapsed: 24.122652
[2020-12-15 20:45:27,507][__main__][INFO] - Validation F1 score: 95.490, Exact match: 55.530, Precision: 95.490, Recall: 95.500
[2020-12-15 20:45:27,508][__main__][INFO] - F1 score has improved
[2020-12-15 20:46:01,725][__main__][INFO] - Checkpoint saved to checkpoints/model_latest.pth
[2020-12-15 20:46:02,770][__main__][INFO] - Epoch #34
[2020-12-15 20:46:02,770][__main__][INFO] - Training..
[2020-12-15 20:46:28,658][__main__][INFO] - [320] Loss: 0.043, Running accuracy: 99.933, Time: 24.44
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[2020-12-15 21:35:08,050][__main__][INFO] - [38400] Loss: 0.065, Running accuracy: 99.956, Time: 24.57
[2020-12-15 21:35:35,121][__main__][INFO] - [38720] Loss: 0.034, Running accuracy: 99.956, Time: 27.07
[2020-12-15 21:35:59,523][__main__][INFO] - [39040] Loss: 0.062, Running accuracy: 99.956, Time: 24.40
[2020-12-15 21:36:26,181][__main__][INFO] - [39360] Loss: 0.029, Running accuracy: 99.956, Time: 26.66
[2020-12-15 21:36:51,462][__main__][INFO] - [39680] Loss: 0.019, Running accuracy: 99.956, Time: 25.28
[2020-12-15 21:37:02,866][__main__][INFO] - Action accuracy: 99.956, Loss: 0.039
[2020-12-15 21:37:02,868][__main__][INFO] - Validating..
[2020-12-15 21:37:33,194][test][INFO] - Time elapsed: 28.271800
[2020-12-15 21:37:33,198][__main__][INFO] - Validation F1 score: 95.330, Exact match: 54.290, Precision: 95.400, Recall: 95.270
[2020-12-15 21:38:07,576][__main__][INFO] - Checkpoint saved to checkpoints/model_latest.pth
[2020-12-15 21:38:08,446][__main__][INFO] - Epoch #35
[2020-12-15 21:38:08,446][__main__][INFO] - Training..
[2020-12-15 21:38:33,449][__main__][INFO] - [320] Loss: 0.031, Running accuracy: 99.960, Time: 23.63
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[2020-12-15 22:25:13,106][__main__][INFO] - [36800] Loss: 0.027, Running accuracy: 99.956, Time: 22.60
[2020-12-15 22:25:37,350][__main__][INFO] - [37120] Loss: 0.020, Running accuracy: 99.956, Time: 24.24
[2020-12-15 22:26:01,903][__main__][INFO] - [37440] Loss: 0.030, Running accuracy: 99.956, Time: 24.55
[2020-12-15 22:26:25,953][__main__][INFO] - [37760] Loss: 0.031, Running accuracy: 99.956, Time: 24.05
[2020-12-15 22:26:50,757][__main__][INFO] - [38080] Loss: 0.042, Running accuracy: 99.956, Time: 24.80
[2020-12-15 22:27:18,580][__main__][INFO] - [38400] Loss: 0.032, Running accuracy: 99.956, Time: 27.82
[2020-12-15 22:27:42,932][__main__][INFO] - [38720] Loss: 0.012, Running accuracy: 99.956, Time: 24.35
[2020-12-15 22:28:07,937][__main__][INFO] - [39040] Loss: 0.019, Running accuracy: 99.956, Time: 25.00
[2020-12-15 22:28:33,442][__main__][INFO] - [39360] Loss: 0.075, Running accuracy: 99.956, Time: 25.50
[2020-12-15 22:28:57,370][__main__][INFO] - [39680] Loss: 0.011, Running accuracy: 99.956, Time: 23.93
[2020-12-15 22:29:07,518][__main__][INFO] - Action accuracy: 99.956, Loss: 0.035
[2020-12-15 22:29:07,519][__main__][INFO] - Validating..
[2020-12-15 22:29:34,098][test][INFO] - Time elapsed: 24.672113
[2020-12-15 22:29:34,103][__main__][INFO] - Validation F1 score: 95.320, Exact match: 54.410, Precision: 95.310, Recall: 95.330
[2020-12-15 22:30:08,999][__main__][INFO] - Checkpoint saved to checkpoints/model_latest.pth
[2020-12-15 22:30:09,834][__main__][INFO] - Epoch #36
[2020-12-15 22:30:09,834][__main__][INFO] - Training..
[2020-12-15 22:30:37,801][__main__][INFO] - [320] Loss: 0.041, Running accuracy: 99.949, Time: 26.90
[2020-12-15 22:31:06,441][__main__][INFO] - [640] Loss: 0.013, Running accuracy: 99.961, Time: 28.64
[2020-12-15 22:31:30,551][__main__][INFO] - [960] Loss: 0.040, Running accuracy: 99.965, Time: 24.11
[2020-12-15 22:31:57,237][__main__][INFO] - [1280] Loss: 0.015, Running accuracy: 99.971, Time: 26.68
[2020-12-15 22:32:20,430][__main__][INFO] - [1600] Loss: 0.033, Running accuracy: 99.966, Time: 23.19
[2020-12-15 22:32:46,951][__main__][INFO] - [1920] Loss: 0.043, Running accuracy: 99.963, Time: 26.52
[2020-12-15 22:33:10,612][__main__][INFO] - [2240] Loss: 0.016, Running accuracy: 99.966, Time: 23.66
[2020-12-15 22:33:36,426][__main__][INFO] - [2560] Loss: 0.038, Running accuracy: 99.966, Time: 25.81
[2020-12-15 22:34:01,808][__main__][INFO] - [2880] Loss: 0.019, Running accuracy: 99.968, Time: 25.38
[2020-12-15 22:34:26,374][__main__][INFO] - [3200] Loss: 0.025, Running accuracy: 99.967, Time: 24.57
[2020-12-15 22:34:49,983][__main__][INFO] - [3520] Loss: 0.024, Running accuracy: 99.967, Time: 23.60
[2020-12-15 22:35:12,287][__main__][INFO] - [3840] Loss: 0.065, Running accuracy: 99.960, Time: 22.30
[2020-12-15 22:35:37,209][__main__][INFO] - [4160] Loss: 0.009, Running accuracy: 99.962, Time: 24.92
[2020-12-15 22:36:02,780][__main__][INFO] - [4480] Loss: 0.019, Running accuracy: 99.963, Time: 25.57
[2020-12-15 22:36:27,286][__main__][INFO] - [4800] Loss: 0.022, Running accuracy: 99.963, Time: 24.51
[2020-12-15 22:36:53,667][__main__][INFO] - [5120] Loss: 0.007, Running accuracy: 99.965, Time: 26.38
[2020-12-15 22:37:17,379][__main__][INFO] - [5440] Loss: 0.021, Running accuracy: 99.967, Time: 23.71
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[2020-12-15 22:38:53,830][__main__][INFO] - [6720] Loss: 0.003, Running accuracy: 99.966, Time: 23.07
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[2020-12-15 22:40:28,411][__main__][INFO] - [8000] Loss: 0.039, Running accuracy: 99.968, Time: 23.18
[2020-12-15 22:40:52,689][__main__][INFO] - [8320] Loss: 0.010, Running accuracy: 99.969, Time: 24.28
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[2020-12-15 22:41:43,462][__main__][INFO] - [8960] Loss: 0.031, Running accuracy: 99.969, Time: 25.16
[2020-12-15 22:42:08,399][__main__][INFO] - [9280] Loss: 0.050, Running accuracy: 99.967, Time: 24.94
[2020-12-15 22:42:35,500][__main__][INFO] - [9600] Loss: 0.054, Running accuracy: 99.966, Time: 27.10
[2020-12-15 22:43:00,996][__main__][INFO] - [9920] Loss: 0.030, Running accuracy: 99.966, Time: 25.49
[2020-12-15 22:43:25,498][__main__][INFO] - [10240] Loss: 0.044, Running accuracy: 99.966, Time: 24.50
[2020-12-15 22:43:50,328][__main__][INFO] - [10560] Loss: 0.042, Running accuracy: 99.965, Time: 24.83
[2020-12-15 22:44:14,962][__main__][INFO] - [10880] Loss: 0.070, Running accuracy: 99.963, Time: 24.63
[2020-12-15 22:44:39,657][__main__][INFO] - [11200] Loss: 0.006, Running accuracy: 99.962, Time: 24.69
[2020-12-15 22:45:06,068][__main__][INFO] - [11520] Loss: 0.073, Running accuracy: 99.960, Time: 26.41
[2020-12-15 22:45:27,818][__main__][INFO] - [11840] Loss: 0.011, Running accuracy: 99.960, Time: 21.75
[2020-12-15 22:45:51,316][__main__][INFO] - [12160] Loss: 0.033, Running accuracy: 99.960, Time: 23.50
[2020-12-15 22:46:16,467][__main__][INFO] - [12480] Loss: 0.005, Running accuracy: 99.960, Time: 25.15
[2020-12-15 22:46:42,856][__main__][INFO] - [12800] Loss: 0.010, Running accuracy: 99.961, Time: 26.39
[2020-12-15 22:47:06,976][__main__][INFO] - [13120] Loss: 0.045, Running accuracy: 99.960, Time: 24.12
[2020-12-15 22:47:31,662][__main__][INFO] - [13440] Loss: 0.024, Running accuracy: 99.961, Time: 24.69
[2020-12-15 22:47:59,950][__main__][INFO] - [13760] Loss: 0.010, Running accuracy: 99.961, Time: 28.29
[2020-12-15 22:48:24,630][__main__][INFO] - [14080] Loss: 0.009, Running accuracy: 99.962, Time: 24.68
[2020-12-15 22:48:50,945][__main__][INFO] - [14400] Loss: 0.025, Running accuracy: 99.962, Time: 26.31
[2020-12-15 22:49:14,237][__main__][INFO] - [14720] Loss: 0.026, Running accuracy: 99.963, Time: 23.29
[2020-12-15 22:49:38,055][__main__][INFO] - [15040] Loss: 0.013, Running accuracy: 99.963, Time: 23.82
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[2020-12-15 22:50:28,176][__main__][INFO] - [15680] Loss: 0.056, Running accuracy: 99.962, Time: 24.88
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[2020-12-15 22:51:17,904][__main__][INFO] - [16320] Loss: 0.021, Running accuracy: 99.961, Time: 25.32
[2020-12-15 22:51:41,960][__main__][INFO] - [16640] Loss: 0.009, Running accuracy: 99.962, Time: 23.97
[2020-12-15 22:52:05,297][__main__][INFO] - [16960] Loss: 0.019, Running accuracy: 99.962, Time: 23.34
[2020-12-15 22:52:28,720][__main__][INFO] - [17280] Loss: 0.014, Running accuracy: 99.963, Time: 23.42
[2020-12-15 22:52:52,822][__main__][INFO] - [17600] Loss: 0.055, Running accuracy: 99.962, Time: 24.10
[2020-12-15 22:53:16,020][__main__][INFO] - [17920] Loss: 0.083, Running accuracy: 99.961, Time: 23.20
[2020-12-15 22:53:43,157][__main__][INFO] - [18240] Loss: 0.013, Running accuracy: 99.961, Time: 27.14
[2020-12-15 22:54:05,869][__main__][INFO] - [18560] Loss: 0.128, Running accuracy: 99.961, Time: 22.71
[2020-12-15 22:54:30,457][__main__][INFO] - [18880] Loss: 0.021, Running accuracy: 99.961, Time: 24.59
[2020-12-15 22:54:55,076][__main__][INFO] - [19200] Loss: 0.011, Running accuracy: 99.961, Time: 24.62
[2020-12-15 22:55:19,291][__main__][INFO] - [19520] Loss: 0.060, Running accuracy: 99.961, Time: 24.21
[2020-12-15 22:55:43,947][__main__][INFO] - [19840] Loss: 0.013, Running accuracy: 99.961, Time: 24.66
[2020-12-15 22:56:07,421][__main__][INFO] - [20160] Loss: 0.034, Running accuracy: 99.961, Time: 23.47
[2020-12-15 22:56:31,247][__main__][INFO] - [20480] Loss: 0.006, Running accuracy: 99.962, Time: 23.82
[2020-12-15 22:56:55,921][__main__][INFO] - [20800] Loss: 0.039, Running accuracy: 99.961, Time: 24.67
[2020-12-15 22:57:20,594][__main__][INFO] - [21120] Loss: 0.026, Running accuracy: 99.961, Time: 24.67
[2020-12-15 22:57:44,362][__main__][INFO] - [21440] Loss: 0.013, Running accuracy: 99.962, Time: 23.77
[2020-12-15 22:58:08,331][__main__][INFO] - [21760] Loss: 0.031, Running accuracy: 99.962, Time: 23.97
[2020-12-15 22:58:30,969][__main__][INFO] - [22080] Loss: 0.020, Running accuracy: 99.962, Time: 22.64
[2020-12-15 22:58:58,498][__main__][INFO] - [22400] Loss: 0.023, Running accuracy: 99.962, Time: 27.53
[2020-12-15 22:59:21,777][__main__][INFO] - [22720] Loss: 0.025, Running accuracy: 99.962, Time: 23.28
[2020-12-15 22:59:43,590][__main__][INFO] - [23040] Loss: 0.027, Running accuracy: 99.961, Time: 21.81
[2020-12-15 23:00:08,185][__main__][INFO] - [23360] Loss: 0.027, Running accuracy: 99.961, Time: 24.59
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[2020-12-15 23:00:55,640][__main__][INFO] - [24000] Loss: 0.042, Running accuracy: 99.960, Time: 23.32
[2020-12-15 23:01:19,145][__main__][INFO] - [24320] Loss: 0.018, Running accuracy: 99.960, Time: 23.50
[2020-12-15 23:01:43,870][__main__][INFO] - [24640] Loss: 0.013, Running accuracy: 99.961, Time: 24.72
[2020-12-15 23:02:09,083][__main__][INFO] - [24960] Loss: 0.133, Running accuracy: 99.960, Time: 25.21
[2020-12-15 23:02:32,748][__main__][INFO] - [25280] Loss: 0.120, Running accuracy: 99.960, Time: 23.66
[2020-12-15 23:02:57,892][__main__][INFO] - [25600] Loss: 0.032, Running accuracy: 99.959, Time: 25.14
[2020-12-15 23:03:24,148][__main__][INFO] - [25920] Loss: 0.023, Running accuracy: 99.959, Time: 26.25
[2020-12-15 23:03:47,720][__main__][INFO] - [26240] Loss: 0.037, Running accuracy: 99.960, Time: 23.57
[2020-12-15 23:04:10,816][__main__][INFO] - [26560] Loss: 0.073, Running accuracy: 99.959, Time: 23.10
[2020-12-15 23:04:38,026][__main__][INFO] - [26880] Loss: 0.041, Running accuracy: 99.959, Time: 27.21
[2020-12-15 23:05:03,792][__main__][INFO] - [27200] Loss: 0.024, Running accuracy: 99.959, Time: 25.76
[2020-12-15 23:05:25,875][__main__][INFO] - [27520] Loss: 0.028, Running accuracy: 99.959, Time: 22.08
[2020-12-15 23:05:50,396][__main__][INFO] - [27840] Loss: 0.012, Running accuracy: 99.959, Time: 24.52
[2020-12-15 23:06:15,360][__main__][INFO] - [28160] Loss: 0.056, Running accuracy: 99.958, Time: 24.96
[2020-12-15 23:06:38,698][__main__][INFO] - [28480] Loss: 0.015, Running accuracy: 99.958, Time: 23.34
[2020-12-15 23:07:03,654][__main__][INFO] - [28800] Loss: 0.057, Running accuracy: 99.958, Time: 24.95
[2020-12-15 23:07:26,588][__main__][INFO] - [29120] Loss: 0.039, Running accuracy: 99.957, Time: 22.93
[2020-12-15 23:07:49,820][__main__][INFO] - [29440] Loss: 0.035, Running accuracy: 99.957, Time: 23.23
[2020-12-15 23:08:12,224][__main__][INFO] - [29760] Loss: 0.129, Running accuracy: 99.956, Time: 22.40
[2020-12-15 23:08:35,582][__main__][INFO] - [30080] Loss: 0.020, Running accuracy: 99.956, Time: 23.36
[2020-12-15 23:08:58,846][__main__][INFO] - [30400] Loss: 0.028, Running accuracy: 99.956, Time: 23.26
[2020-12-15 23:09:22,239][__main__][INFO] - [30720] Loss: 0.008, Running accuracy: 99.957, Time: 23.39
[2020-12-15 23:09:44,748][__main__][INFO] - [31040] Loss: 0.044, Running accuracy: 99.956, Time: 22.51
[2020-12-15 23:10:11,996][__main__][INFO] - [31360] Loss: 0.064, Running accuracy: 99.956, Time: 27.25
[2020-12-15 23:10:33,812][__main__][INFO] - [31680] Loss: 0.032, Running accuracy: 99.956, Time: 21.82
[2020-12-15 23:10:58,335][__main__][INFO] - [32000] Loss: 0.041, Running accuracy: 99.956, Time: 24.52
[2020-12-15 23:11:21,672][__main__][INFO] - [32320] Loss: 0.067, Running accuracy: 99.955, Time: 23.34
[2020-12-15 23:11:46,694][__main__][INFO] - [32640] Loss: 0.088, Running accuracy: 99.955, Time: 25.02
[2020-12-15 23:12:11,380][__main__][INFO] - [32960] Loss: 0.048, Running accuracy: 99.955, Time: 24.69
[2020-12-15 23:12:35,490][__main__][INFO] - [33280] Loss: 0.028, Running accuracy: 99.955, Time: 24.11
[2020-12-15 23:12:59,692][__main__][INFO] - [33600] Loss: 0.064, Running accuracy: 99.954, Time: 24.20
[2020-12-15 23:13:26,545][__main__][INFO] - [33920] Loss: 0.033, Running accuracy: 99.954, Time: 26.85
[2020-12-15 23:13:51,108][__main__][INFO] - [34240] Loss: 0.032, Running accuracy: 99.954, Time: 24.56
[2020-12-15 23:14:14,637][__main__][INFO] - [34560] Loss: 0.048, Running accuracy: 99.954, Time: 23.53
[2020-12-15 23:14:38,442][__main__][INFO] - [34880] Loss: 0.015, Running accuracy: 99.954, Time: 23.80
[2020-12-15 23:15:03,064][__main__][INFO] - [35200] Loss: 0.012, Running accuracy: 99.955, Time: 24.62
[2020-12-15 23:15:31,563][__main__][INFO] - [35520] Loss: 0.035, Running accuracy: 99.954, Time: 28.50
[2020-12-15 23:15:54,618][__main__][INFO] - [35840] Loss: 0.047, Running accuracy: 99.954, Time: 23.05
[2020-12-15 23:16:19,010][__main__][INFO] - [36160] Loss: 0.004, Running accuracy: 99.955, Time: 24.39
[2020-12-15 23:16:43,788][__main__][INFO] - [36480] Loss: 0.015, Running accuracy: 99.955, Time: 24.78
[2020-12-15 23:17:07,376][__main__][INFO] - [36800] Loss: 0.022, Running accuracy: 99.955, Time: 23.59
[2020-12-15 23:17:30,919][__main__][INFO] - [37120] Loss: 0.036, Running accuracy: 99.955, Time: 23.54
[2020-12-15 23:17:55,757][__main__][INFO] - [37440] Loss: 0.080, Running accuracy: 99.955, Time: 24.84
[2020-12-15 23:18:20,864][__main__][INFO] - [37760] Loss: 0.032, Running accuracy: 99.955, Time: 25.11
[2020-12-15 23:18:45,975][__main__][INFO] - [38080] Loss: 0.017, Running accuracy: 99.955, Time: 25.11
[2020-12-15 23:19:12,022][__main__][INFO] - [38400] Loss: 0.019, Running accuracy: 99.955, Time: 26.05
[2020-12-15 23:19:34,779][__main__][INFO] - [38720] Loss: 0.044, Running accuracy: 99.955, Time: 22.76
[2020-12-15 23:19:57,985][__main__][INFO] - [39040] Loss: 0.042, Running accuracy: 99.955, Time: 23.21
[2020-12-15 23:20:22,243][__main__][INFO] - [39360] Loss: 0.038, Running accuracy: 99.955, Time: 24.26
[2020-12-15 23:20:46,269][__main__][INFO] - [39680] Loss: 0.028, Running accuracy: 99.955, Time: 24.02
[2020-12-15 23:20:55,717][__main__][INFO] - Action accuracy: 99.955, Loss: 0.038
[2020-12-15 23:20:55,719][__main__][INFO] - Validating..
[2020-12-15 23:21:25,975][test][INFO] - Time elapsed: 28.205249
[2020-12-15 23:21:25,980][__main__][INFO] - Validation F1 score: 95.440, Exact match: 55.240, Precision: 95.390, Recall: 95.500
[2020-12-15 23:22:00,077][__main__][INFO] - Checkpoint saved to checkpoints/model_latest.pth
[2020-12-15 23:22:00,966][__main__][INFO] - Epoch #37
[2020-12-15 23:22:00,967][__main__][INFO] - Training..
[2020-12-15 23:22:28,168][__main__][INFO] - [320] Loss: 0.048, Running accuracy: 99.949, Time: 25.86
[2020-12-15 23:22:52,504][__main__][INFO] - [640] Loss: 0.061, Running accuracy: 99.941, Time: 24.33
[2020-12-15 23:23:15,588][__main__][INFO] - [960] Loss: 0.008, Running accuracy: 99.960, Time: 23.08
[2020-12-15 23:23:40,536][__main__][INFO] - [1280] Loss: 0.007, Running accuracy: 99.967, Time: 24.95
[2020-12-15 23:24:04,479][__main__][INFO] - [1600] Loss: 0.003, Running accuracy: 99.974, Time: 23.94
[2020-12-15 23:24:27,552][__main__][INFO] - [1920] Loss: 0.004, Running accuracy: 99.978, Time: 23.07
[2020-12-15 23:24:51,684][__main__][INFO] - [2240] Loss: 0.050, Running accuracy: 99.976, Time: 24.13
[2020-12-15 23:25:22,964][__main__][INFO] - [2560] Loss: 0.068, Running accuracy: 99.962, Time: 31.28
[2020-12-15 23:25:47,082][__main__][INFO] - [2880] Loss: 0.025, Running accuracy: 99.965, Time: 24.12
[2020-12-15 23:26:11,386][__main__][INFO] - [3200] Loss: 0.013, Running accuracy: 99.967, Time: 24.30
[2020-12-15 23:26:35,961][__main__][INFO] - [3520] Loss: 0.064, Running accuracy: 99.964, Time: 24.57
[2020-12-15 23:27:00,764][__main__][INFO] - [3840] Loss: 0.016, Running accuracy: 99.965, Time: 24.80
[2020-12-15 23:27:24,131][__main__][INFO] - [4160] Loss: 0.050, Running accuracy: 99.962, Time: 23.36
[2020-12-15 23:27:49,153][__main__][INFO] - [4480] Loss: 0.045, Running accuracy: 99.961, Time: 25.02
[2020-12-15 23:28:12,514][__main__][INFO] - [4800] Loss: 0.026, Running accuracy: 99.962, Time: 23.36
[2020-12-15 23:28:35,189][__main__][INFO] - [5120] Loss: 0.055, Running accuracy: 99.961, Time: 22.67
[2020-12-15 23:28:58,685][__main__][INFO] - [5440] Loss: 0.022, Running accuracy: 99.961, Time: 23.49
[2020-12-15 23:29:20,841][__main__][INFO] - [5760] Loss: 0.015, Running accuracy: 99.962, Time: 22.15
[2020-12-15 23:29:44,135][__main__][INFO] - [6080] Loss: 0.010, Running accuracy: 99.963, Time: 23.28
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[2020-12-16 00:12:52,574][__main__][INFO] - Action accuracy: 99.958, Loss: 0.039
[2020-12-16 00:12:52,576][__main__][INFO] - Validating..
[2020-12-16 00:13:22,668][test][INFO] - Time elapsed: 27.845860
[2020-12-16 00:13:22,674][__main__][INFO] - Validation F1 score: 95.370, Exact match: 55.350, Precision: 95.330, Recall: 95.420
[2020-12-16 00:13:56,265][__main__][INFO] - Checkpoint saved to checkpoints/model_latest.pth
[2020-12-16 00:13:57,252][__main__][INFO] - Epoch #38
[2020-12-16 00:13:57,252][__main__][INFO] - Training..
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[2020-12-16 01:04:36,693][__main__][INFO] - [39680] Loss: 0.025, Running accuracy: 99.955, Time: 23.76
[2020-12-16 01:04:46,393][__main__][INFO] - Action accuracy: 99.955, Loss: 0.039
[2020-12-16 01:04:46,394][__main__][INFO] - Validating..
[2020-12-16 01:05:12,781][test][INFO] - Time elapsed: 24.032436
[2020-12-16 01:05:12,785][__main__][INFO] - Validation F1 score: 95.340, Exact match: 54.760, Precision: 95.290, Recall: 95.390
[2020-12-16 01:05:47,136][__main__][INFO] - Checkpoint saved to checkpoints/model_latest.pth
[2020-12-16 01:05:47,949][__main__][INFO] - Epoch #39
[2020-12-16 01:05:47,950][__main__][INFO] - Training..
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[2020-12-16 01:54:27,225][__main__][INFO] - [38080] Loss: 0.047, Running accuracy: 99.957, Time: 23.12
[2020-12-16 01:54:50,267][__main__][INFO] - [38400] Loss: 0.002, Running accuracy: 99.957, Time: 23.04
[2020-12-16 01:55:14,678][__main__][INFO] - [38720] Loss: 0.049, Running accuracy: 99.957, Time: 24.41
[2020-12-16 01:55:38,589][__main__][INFO] - [39040] Loss: 0.038, Running accuracy: 99.956, Time: 23.91
[2020-12-16 01:56:04,668][__main__][INFO] - [39360] Loss: 0.030, Running accuracy: 99.956, Time: 26.08
[2020-12-16 01:56:26,976][__main__][INFO] - [39680] Loss: 0.036, Running accuracy: 99.956, Time: 22.31
[2020-12-16 01:56:36,933][__main__][INFO] - Action accuracy: 99.956, Loss: 0.037
[2020-12-16 01:56:36,935][__main__][INFO] - Validating..
[2020-12-16 01:57:07,283][test][INFO] - Time elapsed: 28.891884
[2020-12-16 01:57:07,288][__main__][INFO] - Validation F1 score: 95.340, Exact match: 54.760, Precision: 95.320, Recall: 95.370
Epoch    40: reducing learning rate of group 0 to 3.7500e-06.
[2020-12-16 01:57:41,974][__main__][INFO] - Checkpoint saved to checkpoints/model_latest.pth
[2020-12-16 01:57:42,940][__main__][INFO] - Epoch #40
[2020-12-16 01:57:42,941][__main__][INFO] - Training..
[2020-12-16 01:58:08,879][__main__][INFO] - [320] Loss: 0.034, Running accuracy: 99.960, Time: 24.50
[2020-12-16 01:58:34,935][__main__][INFO] - [640] Loss: 0.132, Running accuracy: 99.910, Time: 26.05
[2020-12-16 01:58:58,925][__main__][INFO] - [960] Loss: 0.013, Running accuracy: 99.935, Time: 23.99
[2020-12-16 01:59:22,748][__main__][INFO] - [1280] Loss: 0.024, Running accuracy: 99.941, Time: 23.82
[2020-12-16 01:59:47,070][__main__][INFO] - [1600] Loss: 0.016, Running accuracy: 99.948, Time: 24.32
[2020-12-16 02:00:11,315][__main__][INFO] - [1920] Loss: 0.054, Running accuracy: 99.952, Time: 24.24
[2020-12-16 02:00:34,296][__main__][INFO] - [2240] Loss: 0.015, Running accuracy: 99.955, Time: 22.98
[2020-12-16 02:01:02,110][__main__][INFO] - [2560] Loss: 0.013, Running accuracy: 99.959, Time: 27.81
[2020-12-16 02:01:26,796][__main__][INFO] - [2880] Loss: 0.075, Running accuracy: 99.961, Time: 24.68
[2020-12-16 02:01:51,473][__main__][INFO] - [3200] Loss: 0.009, Running accuracy: 99.965, Time: 24.68
[2020-12-16 02:02:15,448][__main__][INFO] - [3520] Loss: 0.014, Running accuracy: 99.967, Time: 23.97
[2020-12-16 02:02:47,736][__main__][INFO] - [3840] Loss: 0.040, Running accuracy: 99.964, Time: 32.29
[2020-12-16 02:03:13,414][__main__][INFO] - [4160] Loss: 0.039, Running accuracy: 99.962, Time: 25.68
[2020-12-16 02:03:38,039][__main__][INFO] - [4480] Loss: 0.024, Running accuracy: 99.962, Time: 24.62
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[2020-12-16 02:10:57,261][__main__][INFO] - [10240] Loss: 0.047, Running accuracy: 99.963, Time: 24.51
[2020-12-16 02:11:22,858][__main__][INFO] - [10560] Loss: 0.008, Running accuracy: 99.965, Time: 25.60
[2020-12-16 02:11:46,244][__main__][INFO] - [10880] Loss: 0.002, Running accuracy: 99.966, Time: 23.39
[2020-12-16 02:12:10,206][__main__][INFO] - [11200] Loss: 0.068, Running accuracy: 99.962, Time: 23.96
[2020-12-16 02:12:36,859][__main__][INFO] - [11520] Loss: 0.012, Running accuracy: 99.962, Time: 26.65
[2020-12-16 02:13:02,382][__main__][INFO] - [11840] Loss: 0.020, Running accuracy: 99.963, Time: 25.52
[2020-12-16 02:13:25,612][__main__][INFO] - [12160] Loss: 0.020, Running accuracy: 99.963, Time: 23.23
[2020-12-16 02:13:49,349][__main__][INFO] - [12480] Loss: 0.028, Running accuracy: 99.963, Time: 23.74
[2020-12-16 02:14:19,323][__main__][INFO] - [12800] Loss: 0.010, Running accuracy: 99.964, Time: 29.97
[2020-12-16 02:14:42,254][__main__][INFO] - [13120] Loss: 0.008, Running accuracy: 99.964, Time: 22.93
[2020-12-16 02:15:07,214][__main__][INFO] - [13440] Loss: 0.025, Running accuracy: 99.964, Time: 24.96
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[2020-12-16 02:16:20,722][__main__][INFO] - [14400] Loss: 0.027, Running accuracy: 99.965, Time: 23.47
[2020-12-16 02:16:43,650][__main__][INFO] - [14720] Loss: 0.039, Running accuracy: 99.965, Time: 22.93
[2020-12-16 02:17:10,155][__main__][INFO] - [15040] Loss: 0.009, Running accuracy: 99.966, Time: 26.50
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[2020-12-16 02:20:55,893][__main__][INFO] - [17920] Loss: 0.009, Running accuracy: 99.966, Time: 24.33
[2020-12-16 02:21:18,238][__main__][INFO] - [18240] Loss: 0.044, Running accuracy: 99.965, Time: 22.26
[2020-12-16 02:21:43,672][__main__][INFO] - [18560] Loss: 0.018, Running accuracy: 99.966, Time: 25.43
[2020-12-16 02:22:10,040][__main__][INFO] - [18880] Loss: 0.011, Running accuracy: 99.966, Time: 26.37
[2020-12-16 02:22:33,325][__main__][INFO] - [19200] Loss: 0.010, Running accuracy: 99.966, Time: 23.28
[2020-12-16 02:22:57,511][__main__][INFO] - [19520] Loss: 0.049, Running accuracy: 99.966, Time: 24.19
[2020-12-16 02:23:23,495][__main__][INFO] - [19840] Loss: 0.037, Running accuracy: 99.966, Time: 25.98
[2020-12-16 02:23:46,582][__main__][INFO] - [20160] Loss: 0.016, Running accuracy: 99.966, Time: 23.09
[2020-12-16 02:24:11,330][__main__][INFO] - [20480] Loss: 0.017, Running accuracy: 99.966, Time: 24.75
[2020-12-16 02:24:35,455][__main__][INFO] - [20800] Loss: 0.004, Running accuracy: 99.967, Time: 24.12
[2020-12-16 02:24:58,501][__main__][INFO] - [21120] Loss: 0.033, Running accuracy: 99.966, Time: 23.04
[2020-12-16 02:25:28,706][__main__][INFO] - [21440] Loss: 0.039, Running accuracy: 99.966, Time: 30.20
[2020-12-16 02:25:52,788][__main__][INFO] - [21760] Loss: 0.019, Running accuracy: 99.965, Time: 24.08
[2020-12-16 02:26:18,622][__main__][INFO] - [22080] Loss: 0.003, Running accuracy: 99.966, Time: 25.83
[2020-12-16 02:26:43,291][__main__][INFO] - [22400] Loss: 0.013, Running accuracy: 99.966, Time: 24.67
[2020-12-16 02:27:07,435][__main__][INFO] - [22720] Loss: 0.003, Running accuracy: 99.967, Time: 24.14
[2020-12-16 02:27:33,353][__main__][INFO] - [23040] Loss: 0.020, Running accuracy: 99.967, Time: 25.92
[2020-12-16 02:27:58,926][__main__][INFO] - [23360] Loss: 0.010, Running accuracy: 99.967, Time: 25.57
[2020-12-16 02:28:22,940][__main__][INFO] - [23680] Loss: 0.028, Running accuracy: 99.966, Time: 24.01
[2020-12-16 02:28:47,035][__main__][INFO] - [24000] Loss: 0.024, Running accuracy: 99.967, Time: 24.09
[2020-12-16 02:29:11,265][__main__][INFO] - [24320] Loss: 0.031, Running accuracy: 99.966, Time: 24.23
[2020-12-16 02:29:36,683][__main__][INFO] - [24640] Loss: 0.053, Running accuracy: 99.966, Time: 25.42
[2020-12-16 02:30:01,550][__main__][INFO] - [24960] Loss: 0.045, Running accuracy: 99.966, Time: 24.87
[2020-12-16 02:30:28,629][__main__][INFO] - [25280] Loss: 0.011, Running accuracy: 99.966, Time: 27.08
[2020-12-16 02:30:53,674][__main__][INFO] - [25600] Loss: 0.042, Running accuracy: 99.966, Time: 25.04
[2020-12-16 02:31:21,716][__main__][INFO] - [25920] Loss: 0.045, Running accuracy: 99.965, Time: 28.04
[2020-12-16 02:31:46,165][__main__][INFO] - [26240] Loss: 0.058, Running accuracy: 99.965, Time: 24.45
[2020-12-16 02:32:11,152][__main__][INFO] - [26560] Loss: 0.018, Running accuracy: 99.965, Time: 24.99
[2020-12-16 02:32:34,835][__main__][INFO] - [26880] Loss: 0.034, Running accuracy: 99.965, Time: 23.68
[2020-12-16 02:33:00,521][__main__][INFO] - [27200] Loss: 0.033, Running accuracy: 99.964, Time: 25.69
[2020-12-16 02:33:25,512][__main__][INFO] - [27520] Loss: 0.030, Running accuracy: 99.965, Time: 24.99
[2020-12-16 02:33:50,916][__main__][INFO] - [27840] Loss: 0.043, Running accuracy: 99.965, Time: 25.40
[2020-12-16 02:34:15,110][__main__][INFO] - [28160] Loss: 0.031, Running accuracy: 99.964, Time: 24.19
[2020-12-16 02:34:40,719][__main__][INFO] - [28480] Loss: 0.023, Running accuracy: 99.964, Time: 25.61
[2020-12-16 02:35:05,475][__main__][INFO] - [28800] Loss: 0.010, Running accuracy: 99.965, Time: 24.76
[2020-12-16 02:35:28,374][__main__][INFO] - [29120] Loss: 0.034, Running accuracy: 99.965, Time: 22.90
[2020-12-16 02:35:52,039][__main__][INFO] - [29440] Loss: 0.004, Running accuracy: 99.965, Time: 23.66
[2020-12-16 02:36:15,132][__main__][INFO] - [29760] Loss: 0.019, Running accuracy: 99.965, Time: 23.09
[2020-12-16 02:36:44,545][__main__][INFO] - [30080] Loss: 0.035, Running accuracy: 99.965, Time: 29.41
[2020-12-16 02:37:09,846][__main__][INFO] - [30400] Loss: 0.050, Running accuracy: 99.965, Time: 25.30
[2020-12-16 02:37:33,789][__main__][INFO] - [30720] Loss: 0.026, Running accuracy: 99.965, Time: 23.94
[2020-12-16 02:37:58,258][__main__][INFO] - [31040] Loss: 0.069, Running accuracy: 99.964, Time: 24.47
[2020-12-16 02:38:22,863][__main__][INFO] - [31360] Loss: 0.003, Running accuracy: 99.964, Time: 24.60
[2020-12-16 02:38:46,916][__main__][INFO] - [31680] Loss: 0.009, Running accuracy: 99.964, Time: 24.05
[2020-12-16 02:39:12,823][__main__][INFO] - [32000] Loss: 0.035, Running accuracy: 99.964, Time: 25.91
[2020-12-16 02:39:36,235][__main__][INFO] - [32320] Loss: 0.034, Running accuracy: 99.964, Time: 23.41
[2020-12-16 02:39:59,824][__main__][INFO] - [32640] Loss: 0.011, Running accuracy: 99.964, Time: 23.59
[2020-12-16 02:40:24,365][__main__][INFO] - [32960] Loss: 0.037, Running accuracy: 99.964, Time: 24.54
[2020-12-16 02:40:48,434][__main__][INFO] - [33280] Loss: 0.025, Running accuracy: 99.964, Time: 24.07
[2020-12-16 02:41:12,095][__main__][INFO] - [33600] Loss: 0.032, Running accuracy: 99.964, Time: 23.66
[2020-12-16 02:41:35,671][__main__][INFO] - [33920] Loss: 0.014, Running accuracy: 99.964, Time: 23.58
[2020-12-16 02:42:02,037][__main__][INFO] - [34240] Loss: 0.011, Running accuracy: 99.964, Time: 26.36
[2020-12-16 02:42:32,294][__main__][INFO] - [34560] Loss: 0.005, Running accuracy: 99.964, Time: 30.26
[2020-12-16 02:42:55,998][__main__][INFO] - [34880] Loss: 0.015, Running accuracy: 99.964, Time: 23.70
[2020-12-16 02:43:19,838][__main__][INFO] - [35200] Loss: 0.040, Running accuracy: 99.964, Time: 23.84
[2020-12-16 02:43:43,816][__main__][INFO] - [35520] Loss: 0.013, Running accuracy: 99.964, Time: 23.98
[2020-12-16 02:44:07,201][__main__][INFO] - [35840] Loss: 0.004, Running accuracy: 99.965, Time: 23.38
[2020-12-16 02:44:29,912][__main__][INFO] - [36160] Loss: 0.011, Running accuracy: 99.965, Time: 22.71
[2020-12-16 02:44:54,454][__main__][INFO] - [36480] Loss: 0.043, Running accuracy: 99.965, Time: 24.54
[2020-12-16 02:45:20,595][__main__][INFO] - [36800] Loss: 0.016, Running accuracy: 99.965, Time: 26.14
[2020-12-16 02:45:43,273][__main__][INFO] - [37120] Loss: 0.012, Running accuracy: 99.965, Time: 22.68
[2020-12-16 02:46:08,136][__main__][INFO] - [37440] Loss: 0.028, Running accuracy: 99.965, Time: 24.86
[2020-12-16 02:46:32,334][__main__][INFO] - [37760] Loss: 0.045, Running accuracy: 99.964, Time: 24.20
[2020-12-16 02:46:57,122][__main__][INFO] - [38080] Loss: 0.067, Running accuracy: 99.964, Time: 24.79
[2020-12-16 02:47:21,464][__main__][INFO] - [38400] Loss: 0.021, Running accuracy: 99.964, Time: 24.34
[2020-12-16 02:47:53,276][__main__][INFO] - [38720] Loss: 0.047, Running accuracy: 99.964, Time: 31.81
[2020-12-16 02:48:17,519][__main__][INFO] - [39040] Loss: 0.028, Running accuracy: 99.964, Time: 24.24
[2020-12-16 02:48:41,110][__main__][INFO] - [39360] Loss: 0.003, Running accuracy: 99.965, Time: 23.59
[2020-12-16 02:49:04,995][__main__][INFO] - [39680] Loss: 0.022, Running accuracy: 99.965, Time: 23.88
[2020-12-16 02:49:13,841][__main__][INFO] - Action accuracy: 99.965, Loss: 0.028
[2020-12-16 02:49:13,841][__main__][INFO] - Validating..
[2020-12-16 02:49:40,189][test][INFO] - Time elapsed: 24.874303
[2020-12-16 02:49:40,193][__main__][INFO] - Validation F1 score: 95.370, Exact match: 54.710, Precision: 95.310, Recall: 95.430
[2020-12-16 02:50:14,185][__main__][INFO] - Checkpoint saved to checkpoints/model_latest.pth
[2020-12-16 02:50:14,996][__main__][INFO] - Epoch #41
[2020-12-16 02:50:14,996][__main__][INFO] - Training..
[2020-12-16 02:50:41,651][__main__][INFO] - [320] Loss: 0.023, Running accuracy: 99.974, Time: 25.62
[2020-12-16 02:51:07,562][__main__][INFO] - [640] Loss: 0.040, Running accuracy: 99.968, Time: 25.91
[2020-12-16 02:51:32,903][__main__][INFO] - [960] Loss: 0.010, Running accuracy: 99.970, Time: 25.34
[2020-12-16 02:52:02,928][__main__][INFO] - [1280] Loss: 0.026, Running accuracy: 99.965, Time: 30.02
[2020-12-16 02:52:26,978][__main__][INFO] - [1600] Loss: 0.012, Running accuracy: 99.969, Time: 24.05
[2020-12-16 02:52:50,379][__main__][INFO] - [1920] Loss: 0.019, Running accuracy: 99.972, Time: 23.40
[2020-12-16 02:53:15,103][__main__][INFO] - [2240] Loss: 0.021, Running accuracy: 99.970, Time: 24.72
[2020-12-16 02:53:38,989][__main__][INFO] - [2560] Loss: 0.012, Running accuracy: 99.972, Time: 23.89
[2020-12-16 02:54:02,904][__main__][INFO] - [2880] Loss: 0.018, Running accuracy: 99.971, Time: 23.91
[2020-12-16 02:54:26,636][__main__][INFO] - [3200] Loss: 0.014, Running accuracy: 99.973, Time: 23.73
[2020-12-16 02:54:51,822][__main__][INFO] - [3520] Loss: 0.099, Running accuracy: 99.967, Time: 25.18
[2020-12-16 02:55:15,593][__main__][INFO] - [3840] Loss: 0.017, Running accuracy: 99.967, Time: 23.77
[2020-12-16 02:55:39,260][__main__][INFO] - [4160] Loss: 0.007, Running accuracy: 99.970, Time: 23.67
[2020-12-16 02:56:03,743][__main__][INFO] - [4480] Loss: 0.033, Running accuracy: 99.967, Time: 24.48
[2020-12-16 02:56:28,160][__main__][INFO] - [4800] Loss: 0.004, Running accuracy: 99.970, Time: 24.42
[2020-12-16 02:56:53,308][__main__][INFO] - [5120] Loss: 0.010, Running accuracy: 99.970, Time: 25.15
[2020-12-16 02:57:19,326][__main__][INFO] - [5440] Loss: 0.006, Running accuracy: 99.970, Time: 26.02
[2020-12-16 02:57:50,069][__main__][INFO] - [5760] Loss: 0.042, Running accuracy: 99.970, Time: 30.74
[2020-12-16 02:58:15,953][__main__][INFO] - [6080] Loss: 0.003, Running accuracy: 99.971, Time: 25.88
[2020-12-16 02:58:39,115][__main__][INFO] - [6400] Loss: 0.002, Running accuracy: 99.973, Time: 23.16
[2020-12-16 02:59:03,979][__main__][INFO] - [6720] Loss: 0.002, Running accuracy: 99.974, Time: 24.86
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[2020-12-16 02:59:54,489][__main__][INFO] - [7360] Loss: 0.009, Running accuracy: 99.975, Time: 24.48
[2020-12-16 03:00:17,737][__main__][INFO] - [7680] Loss: 0.014, Running accuracy: 99.975, Time: 23.25
[2020-12-16 03:00:43,836][__main__][INFO] - [8000] Loss: 0.006, Running accuracy: 99.976, Time: 26.10
[2020-12-16 03:01:09,492][__main__][INFO] - [8320] Loss: 0.020, Running accuracy: 99.976, Time: 25.65
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[2020-12-16 03:01:58,072][__main__][INFO] - [8960] Loss: 0.013, Running accuracy: 99.975, Time: 23.81
[2020-12-16 03:02:21,580][__main__][INFO] - [9280] Loss: 0.003, Running accuracy: 99.976, Time: 23.51
[2020-12-16 03:02:46,392][__main__][INFO] - [9600] Loss: 0.015, Running accuracy: 99.976, Time: 24.81
[2020-12-16 03:03:15,577][__main__][INFO] - [9920] Loss: 0.016, Running accuracy: 99.976, Time: 29.18
[2020-12-16 03:03:38,623][__main__][INFO] - [10240] Loss: 0.018, Running accuracy: 99.976, Time: 23.05
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[2020-12-16 03:04:25,465][__main__][INFO] - [10880] Loss: 0.014, Running accuracy: 99.976, Time: 24.23
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[2020-12-16 03:40:15,271][__main__][INFO] - [39040] Loss: 0.015, Running accuracy: 99.970, Time: 23.40
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[2020-12-16 03:41:03,462][__main__][INFO] - [39680] Loss: 0.025, Running accuracy: 99.970, Time: 25.41
[2020-12-16 03:41:12,580][__main__][INFO] - Action accuracy: 99.970, Loss: 0.023
[2020-12-16 03:41:12,581][__main__][INFO] - Validating..
[2020-12-16 03:41:42,940][test][INFO] - Time elapsed: 28.168176
[2020-12-16 03:41:42,944][__main__][INFO] - Validation F1 score: 95.380, Exact match: 54.820, Precision: 95.350, Recall: 95.420
[2020-12-16 03:42:16,464][__main__][INFO] - Checkpoint saved to checkpoints/model_latest.pth
[2020-12-16 03:42:17,479][__main__][INFO] - Epoch #42
[2020-12-16 03:42:17,480][__main__][INFO] - Training..
[2020-12-16 03:42:44,525][__main__][INFO] - [320] Loss: 0.004, Running accuracy: 100.000, Time: 25.78
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[2020-12-16 04:17:19,158][__main__][INFO] - [27520] Loss: 0.006, Running accuracy: 99.971, Time: 24.81
[2020-12-16 04:17:44,695][__main__][INFO] - [27840] Loss: 0.003, Running accuracy: 99.971, Time: 25.54
[2020-12-16 04:18:08,482][__main__][INFO] - [28160] Loss: 0.003, Running accuracy: 99.971, Time: 23.79
[2020-12-16 04:18:31,878][__main__][INFO] - [28480] Loss: 0.055, Running accuracy: 99.971, Time: 23.39
[2020-12-16 04:18:56,011][__main__][INFO] - [28800] Loss: 0.003, Running accuracy: 99.972, Time: 24.13
[2020-12-16 04:19:19,996][__main__][INFO] - [29120] Loss: 0.052, Running accuracy: 99.972, Time: 23.98
[2020-12-16 04:19:44,316][__main__][INFO] - [29440] Loss: 0.030, Running accuracy: 99.972, Time: 24.32
[2020-12-16 04:20:14,242][__main__][INFO] - [29760] Loss: 0.005, Running accuracy: 99.972, Time: 29.93
[2020-12-16 04:20:38,036][__main__][INFO] - [30080] Loss: 0.078, Running accuracy: 99.971, Time: 23.79
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[2020-12-16 04:29:21,357][__main__][INFO] - [36800] Loss: 0.018, Running accuracy: 99.971, Time: 24.83
[2020-12-16 04:29:46,372][__main__][INFO] - [37120] Loss: 0.022, Running accuracy: 99.970, Time: 25.01
[2020-12-16 04:30:10,288][__main__][INFO] - [37440] Loss: 0.005, Running accuracy: 99.971, Time: 23.92
[2020-12-16 04:30:33,481][__main__][INFO] - [37760] Loss: 0.011, Running accuracy: 99.971, Time: 23.19
[2020-12-16 04:30:56,206][__main__][INFO] - [38080] Loss: 0.056, Running accuracy: 99.971, Time: 22.72
[2020-12-16 04:31:25,422][__main__][INFO] - [38400] Loss: 0.028, Running accuracy: 99.970, Time: 29.21
[2020-12-16 04:31:50,077][__main__][INFO] - [38720] Loss: 0.005, Running accuracy: 99.971, Time: 24.65
[2020-12-16 04:32:14,233][__main__][INFO] - [39040] Loss: 0.030, Running accuracy: 99.971, Time: 24.15
[2020-12-16 04:32:38,725][__main__][INFO] - [39360] Loss: 0.045, Running accuracy: 99.970, Time: 24.49
[2020-12-16 04:33:01,930][__main__][INFO] - [39680] Loss: 0.013, Running accuracy: 99.970, Time: 23.20
[2020-12-16 04:33:11,585][__main__][INFO] - Action accuracy: 99.971, Loss: 0.026
[2020-12-16 04:33:11,586][__main__][INFO] - Validating..
[2020-12-16 04:33:38,000][test][INFO] - Time elapsed: 24.949998
[2020-12-16 04:33:38,004][__main__][INFO] - Validation F1 score: 95.440, Exact match: 54.590, Precision: 95.420, Recall: 95.450
[2020-12-16 04:34:11,838][__main__][INFO] - Checkpoint saved to checkpoints/model_latest.pth
[2020-12-16 04:34:12,647][__main__][INFO] - Epoch #43
[2020-12-16 04:34:12,647][__main__][INFO] - Training..
[2020-12-16 04:34:39,519][__main__][INFO] - [320] Loss: 0.026, Running accuracy: 99.951, Time: 25.39
[2020-12-16 04:35:09,870][__main__][INFO] - [640] Loss: 0.006, Running accuracy: 99.968, Time: 30.35
[2020-12-16 04:35:34,279][__main__][INFO] - [960] Loss: 0.005, Running accuracy: 99.970, Time: 24.41
[2020-12-16 04:35:57,606][__main__][INFO] - [1280] Loss: 0.006, Running accuracy: 99.974, Time: 23.33
[2020-12-16 04:36:23,178][__main__][INFO] - [1600] Loss: 0.020, Running accuracy: 99.974, Time: 25.57
[2020-12-16 04:36:46,575][__main__][INFO] - [1920] Loss: 0.004, Running accuracy: 99.976, Time: 23.40
[2020-12-16 04:37:11,913][__main__][INFO] - [2240] Loss: 0.019, Running accuracy: 99.974, Time: 25.34
[2020-12-16 04:37:34,986][__main__][INFO] - [2560] Loss: 0.036, Running accuracy: 99.971, Time: 23.07
[2020-12-16 04:37:58,562][__main__][INFO] - [2880] Loss: 0.012, Running accuracy: 99.972, Time: 23.58
[2020-12-16 04:38:23,065][__main__][INFO] - [3200] Loss: 0.014, Running accuracy: 99.973, Time: 24.50
[2020-12-16 04:38:47,632][__main__][INFO] - [3520] Loss: 0.032, Running accuracy: 99.971, Time: 24.57
[2020-12-16 04:39:12,748][__main__][INFO] - [3840] Loss: 0.022, Running accuracy: 99.969, Time: 25.11
[2020-12-16 04:39:37,543][__main__][INFO] - [4160] Loss: 0.018, Running accuracy: 99.969, Time: 24.79
[2020-12-16 04:40:01,053][__main__][INFO] - [4480] Loss: 0.008, Running accuracy: 99.969, Time: 23.51
[2020-12-16 04:40:26,447][__main__][INFO] - [4800] Loss: 0.018, Running accuracy: 99.969, Time: 25.39
[2020-12-16 04:40:56,002][__main__][INFO] - [5120] Loss: 0.029, Running accuracy: 99.970, Time: 29.55
[2020-12-16 04:41:21,309][__main__][INFO] - [5440] Loss: 0.005, Running accuracy: 99.972, Time: 25.31
[2020-12-16 04:41:43,978][__main__][INFO] - [5760] Loss: 0.004, Running accuracy: 99.973, Time: 22.67
[2020-12-16 04:42:07,418][__main__][INFO] - [6080] Loss: 0.013, Running accuracy: 99.974, Time: 23.44
[2020-12-16 04:42:31,615][__main__][INFO] - [6400] Loss: 0.023, Running accuracy: 99.974, Time: 24.20
[2020-12-16 04:42:57,694][__main__][INFO] - [6720] Loss: 0.020, Running accuracy: 99.974, Time: 26.08
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[2020-12-16 04:43:45,433][__main__][INFO] - [7360] Loss: 0.037, Running accuracy: 99.972, Time: 24.72
[2020-12-16 04:44:10,366][__main__][INFO] - [7680] Loss: 0.024, Running accuracy: 99.971, Time: 24.93
[2020-12-16 04:44:36,105][__main__][INFO] - [8000] Loss: 0.009, Running accuracy: 99.972, Time: 25.74
[2020-12-16 04:44:57,653][__main__][INFO] - [8320] Loss: 0.054, Running accuracy: 99.971, Time: 21.55
[2020-12-16 04:45:22,398][__main__][INFO] - [8640] Loss: 0.010, Running accuracy: 99.971, Time: 24.74
[2020-12-16 04:45:47,705][__main__][INFO] - [8960] Loss: 0.029, Running accuracy: 99.971, Time: 25.29
[2020-12-16 04:46:17,121][__main__][INFO] - [9280] Loss: 0.022, Running accuracy: 99.971, Time: 29.41
[2020-12-16 04:46:41,153][__main__][INFO] - [9600] Loss: 0.024, Running accuracy: 99.971, Time: 24.03
[2020-12-16 04:47:05,477][__main__][INFO] - [9920] Loss: 0.041, Running accuracy: 99.970, Time: 24.32
[2020-12-16 04:47:31,289][__main__][INFO] - [10240] Loss: 0.025, Running accuracy: 99.969, Time: 25.81
[2020-12-16 04:47:55,544][__main__][INFO] - [10560] Loss: 0.022, Running accuracy: 99.969, Time: 24.25
[2020-12-16 04:48:18,767][__main__][INFO] - [10880] Loss: 0.040, Running accuracy: 99.969, Time: 23.22
[2020-12-16 04:48:42,575][__main__][INFO] - [11200] Loss: 0.045, Running accuracy: 99.968, Time: 23.81
[2020-12-16 04:49:06,560][__main__][INFO] - [11520] Loss: 0.008, Running accuracy: 99.969, Time: 23.98
[2020-12-16 04:49:32,197][__main__][INFO] - [11840] Loss: 0.008, Running accuracy: 99.969, Time: 25.64
[2020-12-16 04:49:56,555][__main__][INFO] - [12160] Loss: 0.047, Running accuracy: 99.969, Time: 24.36
[2020-12-16 04:50:20,630][__main__][INFO] - [12480] Loss: 0.035, Running accuracy: 99.969, Time: 24.07
[2020-12-16 04:50:43,652][__main__][INFO] - [12800] Loss: 0.061, Running accuracy: 99.969, Time: 23.02
[2020-12-16 04:51:07,058][__main__][INFO] - [13120] Loss: 0.017, Running accuracy: 99.969, Time: 23.40
[2020-12-16 04:51:33,621][__main__][INFO] - [13440] Loss: 0.013, Running accuracy: 99.970, Time: 26.56
[2020-12-16 04:52:05,024][__main__][INFO] - [13760] Loss: 0.012, Running accuracy: 99.970, Time: 31.40
[2020-12-16 04:52:31,093][__main__][INFO] - [14080] Loss: 0.045, Running accuracy: 99.970, Time: 26.07
[2020-12-16 04:52:55,091][__main__][INFO] - [14400] Loss: 0.007, Running accuracy: 99.970, Time: 24.00
[2020-12-16 04:53:19,680][__main__][INFO] - [14720] Loss: 0.029, Running accuracy: 99.970, Time: 24.59
[2020-12-16 04:53:43,111][__main__][INFO] - [15040] Loss: 0.020, Running accuracy: 99.969, Time: 23.43
[2020-12-16 04:54:06,950][__main__][INFO] - [15360] Loss: 0.026, Running accuracy: 99.969, Time: 23.84
[2020-12-16 04:54:30,406][__main__][INFO] - [15680] Loss: 0.011, Running accuracy: 99.970, Time: 23.45
[2020-12-16 04:54:55,887][__main__][INFO] - [16000] Loss: 0.003, Running accuracy: 99.970, Time: 25.48
[2020-12-16 04:55:19,192][__main__][INFO] - [16320] Loss: 0.024, Running accuracy: 99.970, Time: 23.30
[2020-12-16 04:55:43,603][__main__][INFO] - [16640] Loss: 0.044, Running accuracy: 99.969, Time: 24.41
[2020-12-16 04:56:08,660][__main__][INFO] - [16960] Loss: 0.005, Running accuracy: 99.970, Time: 25.06
[2020-12-16 04:56:31,607][__main__][INFO] - [17280] Loss: 0.021, Running accuracy: 99.970, Time: 22.95
[2020-12-16 04:56:56,804][__main__][INFO] - [17600] Loss: 0.012, Running accuracy: 99.970, Time: 25.20
[2020-12-16 04:57:21,196][__main__][INFO] - [17920] Loss: 0.023, Running accuracy: 99.970, Time: 24.39
[2020-12-16 04:57:52,082][__main__][INFO] - [18240] Loss: 0.010, Running accuracy: 99.970, Time: 30.89
[2020-12-16 04:58:14,980][__main__][INFO] - [18560] Loss: 0.019, Running accuracy: 99.970, Time: 22.90
[2020-12-16 04:58:37,674][__main__][INFO] - [18880] Loss: 0.021, Running accuracy: 99.970, Time: 22.69
[2020-12-16 04:59:01,635][__main__][INFO] - [19200] Loss: 0.063, Running accuracy: 99.970, Time: 23.87
[2020-12-16 04:59:27,889][__main__][INFO] - [19520] Loss: 0.008, Running accuracy: 99.971, Time: 26.25
[2020-12-16 04:59:51,760][__main__][INFO] - [19840] Loss: 0.014, Running accuracy: 99.971, Time: 23.87
[2020-12-16 05:00:16,671][__main__][INFO] - [20160] Loss: 0.023, Running accuracy: 99.971, Time: 24.91
[2020-12-16 05:00:40,182][__main__][INFO] - [20480] Loss: 0.013, Running accuracy: 99.971, Time: 23.51
[2020-12-16 05:01:04,072][__main__][INFO] - [20800] Loss: 0.027, Running accuracy: 99.971, Time: 23.89
[2020-12-16 05:01:29,767][__main__][INFO] - [21120] Loss: 0.020, Running accuracy: 99.971, Time: 25.69
[2020-12-16 05:01:53,450][__main__][INFO] - [21440] Loss: 0.020, Running accuracy: 99.971, Time: 23.68
[2020-12-16 05:02:16,595][__main__][INFO] - [21760] Loss: 0.006, Running accuracy: 99.971, Time: 23.14
[2020-12-16 05:02:42,265][__main__][INFO] - [22080] Loss: 0.006, Running accuracy: 99.971, Time: 25.67
[2020-12-16 05:03:06,458][__main__][INFO] - [22400] Loss: 0.012, Running accuracy: 99.971, Time: 24.19
[2020-12-16 05:03:35,425][__main__][INFO] - [22720] Loss: 0.022, Running accuracy: 99.971, Time: 28.97
[2020-12-16 05:04:00,622][__main__][INFO] - [23040] Loss: 0.009, Running accuracy: 99.971, Time: 25.20
[2020-12-16 05:04:24,827][__main__][INFO] - [23360] Loss: 0.033, Running accuracy: 99.971, Time: 24.20
[2020-12-16 05:04:49,591][__main__][INFO] - [23680] Loss: 0.026, Running accuracy: 99.971, Time: 24.76
[2020-12-16 05:05:14,224][__main__][INFO] - [24000] Loss: 0.065, Running accuracy: 99.970, Time: 24.63
[2020-12-16 05:05:37,539][__main__][INFO] - [24320] Loss: 0.022, Running accuracy: 99.970, Time: 23.31
[2020-12-16 05:06:00,538][__main__][INFO] - [24640] Loss: 0.010, Running accuracy: 99.970, Time: 23.00
[2020-12-16 05:06:25,299][__main__][INFO] - [24960] Loss: 0.022, Running accuracy: 99.970, Time: 24.76
[2020-12-16 05:06:49,346][__main__][INFO] - [25280] Loss: 0.006, Running accuracy: 99.970, Time: 24.05
[2020-12-16 05:07:14,481][__main__][INFO] - [25600] Loss: 0.016, Running accuracy: 99.971, Time: 25.13
[2020-12-16 05:07:40,356][__main__][INFO] - [25920] Loss: 0.012, Running accuracy: 99.971, Time: 25.88
[2020-12-16 05:08:03,831][__main__][INFO] - [26240] Loss: 0.012, Running accuracy: 99.971, Time: 23.47
[2020-12-16 05:08:26,717][__main__][INFO] - [26560] Loss: 0.020, Running accuracy: 99.971, Time: 22.89
[2020-12-16 05:08:57,099][__main__][INFO] - [26880] Loss: 0.004, Running accuracy: 99.971, Time: 30.38
[2020-12-16 05:09:21,693][__main__][INFO] - [27200] Loss: 0.011, Running accuracy: 99.971, Time: 24.59
[2020-12-16 05:09:44,579][__main__][INFO] - [27520] Loss: 0.025, Running accuracy: 99.971, Time: 22.88
[2020-12-16 05:10:09,164][__main__][INFO] - [27840] Loss: 0.016, Running accuracy: 99.971, Time: 24.58
[2020-12-16 05:10:32,837][__main__][INFO] - [28160] Loss: 0.011, Running accuracy: 99.971, Time: 23.67
[2020-12-16 05:10:57,235][__main__][INFO] - [28480] Loss: 0.043, Running accuracy: 99.970, Time: 24.40
[2020-12-16 05:11:21,120][__main__][INFO] - [28800] Loss: 0.010, Running accuracy: 99.971, Time: 23.88
[2020-12-16 05:11:43,597][__main__][INFO] - [29120] Loss: 0.042, Running accuracy: 99.970, Time: 22.48
[2020-12-16 05:12:08,551][__main__][INFO] - [29440] Loss: 0.003, Running accuracy: 99.971, Time: 24.95
[2020-12-16 05:12:32,460][__main__][INFO] - [29760] Loss: 0.012, Running accuracy: 99.971, Time: 23.91
[2020-12-16 05:12:55,569][__main__][INFO] - [30080] Loss: 0.020, Running accuracy: 99.971, Time: 23.11
[2020-12-16 05:13:20,340][__main__][INFO] - [30400] Loss: 0.009, Running accuracy: 99.971, Time: 24.77
[2020-12-16 05:13:45,384][__main__][INFO] - [30720] Loss: 0.008, Running accuracy: 99.971, Time: 25.04
[2020-12-16 05:14:11,408][__main__][INFO] - [31040] Loss: 0.008, Running accuracy: 99.971, Time: 26.02
[2020-12-16 05:14:43,506][__main__][INFO] - [31360] Loss: 0.019, Running accuracy: 99.971, Time: 32.10
[2020-12-16 05:15:08,791][__main__][INFO] - [31680] Loss: 0.021, Running accuracy: 99.971, Time: 25.28
[2020-12-16 05:15:32,504][__main__][INFO] - [32000] Loss: 0.004, Running accuracy: 99.970, Time: 23.71
[2020-12-16 05:15:57,672][__main__][INFO] - [32320] Loss: 0.018, Running accuracy: 99.971, Time: 25.17
[2020-12-16 05:16:22,366][__main__][INFO] - [32640] Loss: 0.044, Running accuracy: 99.970, Time: 24.69
[2020-12-16 05:16:46,465][__main__][INFO] - [32960] Loss: 0.003, Running accuracy: 99.971, Time: 24.10
[2020-12-16 05:17:09,809][__main__][INFO] - [33280] Loss: 0.059, Running accuracy: 99.970, Time: 23.34
[2020-12-16 05:17:33,489][__main__][INFO] - [33600] Loss: 0.002, Running accuracy: 99.970, Time: 23.68
[2020-12-16 05:17:58,269][__main__][INFO] - [33920] Loss: 0.052, Running accuracy: 99.970, Time: 24.78
[2020-12-16 05:18:21,768][__main__][INFO] - [34240] Loss: 0.019, Running accuracy: 99.970, Time: 23.50
[2020-12-16 05:18:45,600][__main__][INFO] - [34560] Loss: 0.010, Running accuracy: 99.970, Time: 23.83
[2020-12-16 05:19:08,942][__main__][INFO] - [34880] Loss: 0.009, Running accuracy: 99.970, Time: 23.34
[2020-12-16 05:19:31,841][__main__][INFO] - [35200] Loss: 0.009, Running accuracy: 99.970, Time: 22.90
[2020-12-16 05:19:57,662][__main__][INFO] - [35520] Loss: 0.030, Running accuracy: 99.970, Time: 25.82
[2020-12-16 05:20:26,178][__main__][INFO] - [35840] Loss: 0.072, Running accuracy: 99.970, Time: 28.52
[2020-12-16 05:20:50,456][__main__][INFO] - [36160] Loss: 0.026, Running accuracy: 99.970, Time: 24.28
[2020-12-16 05:21:14,141][__main__][INFO] - [36480] Loss: 0.019, Running accuracy: 99.970, Time: 23.68
[2020-12-16 05:21:37,472][__main__][INFO] - [36800] Loss: 0.031, Running accuracy: 99.970, Time: 23.33
[2020-12-16 05:22:01,263][__main__][INFO] - [37120] Loss: 0.024, Running accuracy: 99.969, Time: 23.79
[2020-12-16 05:22:23,899][__main__][INFO] - [37440] Loss: 0.020, Running accuracy: 99.969, Time: 22.63
[2020-12-16 05:22:48,349][__main__][INFO] - [37760] Loss: 0.011, Running accuracy: 99.970, Time: 24.45
[2020-12-16 05:23:12,032][__main__][INFO] - [38080] Loss: 0.021, Running accuracy: 99.970, Time: 23.68
[2020-12-16 05:23:35,762][__main__][INFO] - [38400] Loss: 0.005, Running accuracy: 99.970, Time: 23.73
[2020-12-16 05:24:00,199][__main__][INFO] - [38720] Loss: 0.012, Running accuracy: 99.970, Time: 24.44
[2020-12-16 05:24:24,449][__main__][INFO] - [39040] Loss: 0.008, Running accuracy: 99.970, Time: 24.25
[2020-12-16 05:24:47,677][__main__][INFO] - [39360] Loss: 0.022, Running accuracy: 99.970, Time: 23.23
[2020-12-16 05:25:13,610][__main__][INFO] - [39680] Loss: 0.047, Running accuracy: 99.970, Time: 25.93
[2020-12-16 05:25:23,831][__main__][INFO] - Action accuracy: 99.970, Loss: 0.023
[2020-12-16 05:25:23,832][__main__][INFO] - Validating..
[2020-12-16 05:25:56,374][test][INFO] - Time elapsed: 31.007731
[2020-12-16 05:25:56,379][__main__][INFO] - Validation F1 score: 95.490, Exact match: 55.290, Precision: 95.480, Recall: 95.500
[2020-12-16 05:26:30,794][__main__][INFO] - Checkpoint saved to checkpoints/model_latest.pth
[2020-12-16 05:26:31,612][__main__][INFO] - Epoch #44
[2020-12-16 05:26:31,613][__main__][INFO] - Training..
[2020-12-16 05:26:58,006][__main__][INFO] - [320] Loss: 0.019, Running accuracy: 99.974, Time: 25.01
[2020-12-16 05:27:22,923][__main__][INFO] - [640] Loss: 0.045, Running accuracy: 99.955, Time: 24.92
[2020-12-16 05:27:46,682][__main__][INFO] - [960] Loss: 0.011, Running accuracy: 99.965, Time: 23.75
[2020-12-16 05:28:10,441][__main__][INFO] - [1280] Loss: 0.040, Running accuracy: 99.957, Time: 23.76
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[2020-12-16 06:18:49,209][__main__][INFO] - [39680] Loss: 0.009, Running accuracy: 99.974, Time: 24.78
[2020-12-16 06:18:58,911][__main__][INFO] - Action accuracy: 99.974, Loss: 0.021
[2020-12-16 06:18:58,913][__main__][INFO] - Validating..
[2020-12-16 06:19:26,108][test][INFO] - Time elapsed: 25.713093
[2020-12-16 06:19:26,113][__main__][INFO] - Validation F1 score: 95.480, Exact match: 55.120, Precision: 95.480, Recall: 95.470
[2020-12-16 06:19:58,732][__main__][INFO] - Checkpoint saved to checkpoints/model_latest.pth
[2020-12-16 06:19:59,794][__main__][INFO] - Epoch #45
[2020-12-16 06:19:59,794][__main__][INFO] - Training..
[2020-12-16 06:20:32,931][__main__][INFO] - [320] Loss: 0.027, Running accuracy: 99.987, Time: 31.66
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[2020-12-16 07:12:55,552][__main__][INFO] - Action accuracy: 99.973, Loss: 0.022
[2020-12-16 07:12:55,553][__main__][INFO] - Validating..
[2020-12-16 07:13:22,820][test][INFO] - Time elapsed: 25.068027
[2020-12-16 07:13:22,824][__main__][INFO] - Validation F1 score: 95.420, Exact match: 54.710, Precision: 95.400, Recall: 95.450
Epoch    46: reducing learning rate of group 0 to 1.8750e-06.
[2020-12-16 07:13:57,854][__main__][INFO] - Checkpoint saved to checkpoints/model_latest.pth
[2020-12-16 07:13:58,731][__main__][INFO] - Epoch #46
[2020-12-16 07:13:58,732][__main__][INFO] - Training..
[2020-12-16 07:14:23,317][__main__][INFO] - [320] Loss: 0.040, Running accuracy: 99.945, Time: 23.32
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[2020-12-16 07:54:38,787][__main__][INFO] - [30720] Loss: 0.023, Running accuracy: 99.974, Time: 23.64
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[2020-12-16 07:55:26,729][__main__][INFO] - [31360] Loss: 0.026, Running accuracy: 99.975, Time: 23.74
[2020-12-16 07:55:51,157][__main__][INFO] - [31680] Loss: 0.002, Running accuracy: 99.975, Time: 24.43
[2020-12-16 07:56:18,201][__main__][INFO] - [32000] Loss: 0.007, Running accuracy: 99.975, Time: 27.04
[2020-12-16 07:56:47,302][__main__][INFO] - [32320] Loss: 0.004, Running accuracy: 99.975, Time: 29.10
[2020-12-16 07:57:12,466][__main__][INFO] - [32640] Loss: 0.003, Running accuracy: 99.975, Time: 25.16
[2020-12-16 07:57:36,803][__main__][INFO] - [32960] Loss: 0.006, Running accuracy: 99.976, Time: 24.34
[2020-12-16 07:58:04,600][__main__][INFO] - [33280] Loss: 0.034, Running accuracy: 99.975, Time: 27.80
[2020-12-16 07:58:28,801][__main__][INFO] - [33600] Loss: 0.003, Running accuracy: 99.976, Time: 24.20
[2020-12-16 07:58:56,127][__main__][INFO] - [33920] Loss: 0.025, Running accuracy: 99.975, Time: 27.32
[2020-12-16 07:59:23,212][__main__][INFO] - [34240] Loss: 0.021, Running accuracy: 99.975, Time: 27.08
[2020-12-16 07:59:48,355][__main__][INFO] - [34560] Loss: 0.013, Running accuracy: 99.975, Time: 25.14
[2020-12-16 08:00:10,933][__main__][INFO] - [34880] Loss: 0.001, Running accuracy: 99.975, Time: 22.57
[2020-12-16 08:00:34,246][__main__][INFO] - [35200] Loss: 0.017, Running accuracy: 99.975, Time: 23.31
[2020-12-16 08:01:00,113][__main__][INFO] - [35520] Loss: 0.050, Running accuracy: 99.975, Time: 25.87
[2020-12-16 08:01:24,480][__main__][INFO] - [35840] Loss: 0.029, Running accuracy: 99.975, Time: 24.37
[2020-12-16 08:01:49,880][__main__][INFO] - [36160] Loss: 0.027, Running accuracy: 99.975, Time: 25.40
[2020-12-16 08:02:13,977][__main__][INFO] - [36480] Loss: 0.008, Running accuracy: 99.975, Time: 24.10
[2020-12-16 08:02:41,154][__main__][INFO] - [36800] Loss: 0.019, Running accuracy: 99.975, Time: 27.18
[2020-12-16 08:03:06,659][__main__][INFO] - [37120] Loss: 0.007, Running accuracy: 99.975, Time: 25.50
[2020-12-16 08:03:30,699][__main__][INFO] - [37440] Loss: 0.018, Running accuracy: 99.975, Time: 24.04
[2020-12-16 08:03:54,456][__main__][INFO] - [37760] Loss: 0.024, Running accuracy: 99.974, Time: 23.76
[2020-12-16 08:04:19,959][__main__][INFO] - [38080] Loss: 0.015, Running accuracy: 99.974, Time: 25.50
[2020-12-16 08:04:45,416][__main__][INFO] - [38400] Loss: 0.003, Running accuracy: 99.975, Time: 25.46
[2020-12-16 08:05:10,986][__main__][INFO] - [38720] Loss: 0.006, Running accuracy: 99.975, Time: 25.57
[2020-12-16 08:05:35,937][__main__][INFO] - [39040] Loss: 0.051, Running accuracy: 99.975, Time: 24.95
[2020-12-16 08:06:01,189][__main__][INFO] - [39360] Loss: 0.018, Running accuracy: 99.974, Time: 25.25
[2020-12-16 08:06:24,235][__main__][INFO] - [39680] Loss: 0.030, Running accuracy: 99.974, Time: 23.04
[2020-12-16 08:06:34,212][__main__][INFO] - Action accuracy: 99.975, Loss: 0.019
[2020-12-16 08:06:34,213][__main__][INFO] - Validating..
[2020-12-16 08:07:06,931][test][INFO] - Time elapsed: 30.624721
[2020-12-16 08:07:06,936][__main__][INFO] - Validation F1 score: 95.480, Exact match: 55.000, Precision: 95.430, Recall: 95.530
[2020-12-16 08:07:41,829][__main__][INFO] - Checkpoint saved to checkpoints/model_latest.pth
[2020-12-16 08:07:42,651][__main__][INFO] - Epoch #47
[2020-12-16 08:07:42,651][__main__][INFO] - Training..
[2020-12-16 08:08:10,679][__main__][INFO] - [320] Loss: 0.006, Running accuracy: 100.000, Time: 26.90
[2020-12-16 08:08:38,272][__main__][INFO] - [640] Loss: 0.007, Running accuracy: 99.994, Time: 27.59
[2020-12-16 08:09:07,120][__main__][INFO] - [960] Loss: 0.018, Running accuracy: 99.979, Time: 28.85
[2020-12-16 08:09:32,439][__main__][INFO] - [1280] Loss: 0.010, Running accuracy: 99.981, Time: 25.32
[2020-12-16 08:09:59,003][__main__][INFO] - [1600] Loss: 0.031, Running accuracy: 99.974, Time: 26.56
[2020-12-16 08:10:25,968][__main__][INFO] - [1920] Loss: 0.003, Running accuracy: 99.979, Time: 26.96
[2020-12-16 08:10:49,528][__main__][INFO] - [2240] Loss: 0.007, Running accuracy: 99.978, Time: 23.56
[2020-12-16 08:11:14,785][__main__][INFO] - [2560] Loss: 0.005, Running accuracy: 99.980, Time: 25.26
[2020-12-16 08:11:40,224][__main__][INFO] - [2880] Loss: 0.010, Running accuracy: 99.981, Time: 25.44
[2020-12-16 08:12:05,785][__main__][INFO] - [3200] Loss: 0.011, Running accuracy: 99.982, Time: 25.56
[2020-12-16 08:12:33,794][__main__][INFO] - [3520] Loss: 0.004, Running accuracy: 99.982, Time: 28.01
[2020-12-16 08:13:07,403][__main__][INFO] - [3840] Loss: 0.010, Running accuracy: 99.982, Time: 33.61
[2020-12-16 08:13:33,304][__main__][INFO] - [4160] Loss: 0.064, Running accuracy: 99.975, Time: 25.90
[2020-12-16 08:13:57,871][__main__][INFO] - [4480] Loss: 0.008, Running accuracy: 99.976, Time: 24.57
[2020-12-16 08:14:24,248][__main__][INFO] - [4800] Loss: 0.012, Running accuracy: 99.977, Time: 26.38
[2020-12-16 08:14:51,323][__main__][INFO] - [5120] Loss: 0.017, Running accuracy: 99.977, Time: 27.07
[2020-12-16 08:15:16,777][__main__][INFO] - [5440] Loss: 0.003, Running accuracy: 99.979, Time: 25.45
[2020-12-16 08:15:43,206][__main__][INFO] - [5760] Loss: 0.005, Running accuracy: 99.980, Time: 26.43
[2020-12-16 08:16:08,919][__main__][INFO] - [6080] Loss: 0.033, Running accuracy: 99.979, Time: 25.71
[2020-12-16 08:16:35,544][__main__][INFO] - [6400] Loss: 0.001, Running accuracy: 99.980, Time: 26.62
[2020-12-16 08:17:01,936][__main__][INFO] - [6720] Loss: 0.032, Running accuracy: 99.978, Time: 26.39
[2020-12-16 08:17:30,192][__main__][INFO] - [7040] Loss: 0.010, Running accuracy: 99.979, Time: 28.25
[2020-12-16 08:17:55,847][__main__][INFO] - [7360] Loss: 0.005, Running accuracy: 99.979, Time: 25.65
[2020-12-16 08:18:20,409][__main__][INFO] - [7680] Loss: 0.024, Running accuracy: 99.978, Time: 24.56
[2020-12-16 08:18:51,964][__main__][INFO] - [8000] Loss: 0.002, Running accuracy: 99.979, Time: 31.55
[2020-12-16 08:19:16,501][__main__][INFO] - [8320] Loss: 0.012, Running accuracy: 99.979, Time: 24.54
[2020-12-16 08:19:44,416][__main__][INFO] - [8640] Loss: 0.025, Running accuracy: 99.979, Time: 27.92
[2020-12-16 08:20:12,419][__main__][INFO] - [8960] Loss: 0.013, Running accuracy: 99.980, Time: 28.00
[2020-12-16 08:20:39,738][__main__][INFO] - [9280] Loss: 0.007, Running accuracy: 99.980, Time: 27.32
[2020-12-16 08:21:06,810][__main__][INFO] - [9600] Loss: 0.025, Running accuracy: 99.978, Time: 27.07
[2020-12-16 08:21:32,102][__main__][INFO] - [9920] Loss: 0.036, Running accuracy: 99.977, Time: 25.29
[2020-12-16 08:21:54,414][__main__][INFO] - [10240] Loss: 0.023, Running accuracy: 99.976, Time: 22.31
[2020-12-16 08:22:19,854][__main__][INFO] - [10560] Loss: 0.007, Running accuracy: 99.977, Time: 25.44
[2020-12-16 08:22:46,188][__main__][INFO] - [10880] Loss: 0.019, Running accuracy: 99.977, Time: 26.33
[2020-12-16 08:23:11,656][__main__][INFO] - [11200] Loss: 0.009, Running accuracy: 99.977, Time: 25.47
[2020-12-16 08:23:37,573][__main__][INFO] - [11520] Loss: 0.004, Running accuracy: 99.978, Time: 25.92
[2020-12-16 08:24:05,791][__main__][INFO] - [11840] Loss: 0.027, Running accuracy: 99.978, Time: 28.21
[2020-12-16 08:24:32,853][__main__][INFO] - [12160] Loss: 0.009, Running accuracy: 99.977, Time: 27.06
[2020-12-16 08:25:03,883][__main__][INFO] - [12480] Loss: 0.006, Running accuracy: 99.977, Time: 31.03
[2020-12-16 08:25:29,244][__main__][INFO] - [12800] Loss: 0.003, Running accuracy: 99.978, Time: 25.36
[2020-12-16 08:25:55,598][__main__][INFO] - [13120] Loss: 0.020, Running accuracy: 99.977, Time: 26.35
[2020-12-16 08:26:19,523][__main__][INFO] - [13440] Loss: 0.041, Running accuracy: 99.977, Time: 23.92
[2020-12-16 08:26:45,039][__main__][INFO] - [13760] Loss: 0.008, Running accuracy: 99.977, Time: 25.52
[2020-12-16 08:27:10,407][__main__][INFO] - [14080] Loss: 0.029, Running accuracy: 99.976, Time: 25.37
[2020-12-16 08:27:35,173][__main__][INFO] - [14400] Loss: 0.014, Running accuracy: 99.976, Time: 24.77
[2020-12-16 08:28:00,412][__main__][INFO] - [14720] Loss: 0.006, Running accuracy: 99.976, Time: 25.24
[2020-12-16 08:28:28,400][__main__][INFO] - [15040] Loss: 0.006, Running accuracy: 99.977, Time: 27.99
[2020-12-16 08:28:54,844][__main__][INFO] - [15360] Loss: 0.019, Running accuracy: 99.977, Time: 26.44
[2020-12-16 08:29:21,015][__main__][INFO] - [15680] Loss: 0.010, Running accuracy: 99.977, Time: 26.17
[2020-12-16 08:29:46,352][__main__][INFO] - [16000] Loss: 0.003, Running accuracy: 99.977, Time: 25.34
[2020-12-16 08:30:11,494][__main__][INFO] - [16320] Loss: 0.002, Running accuracy: 99.978, Time: 25.14
[2020-12-16 08:30:36,924][__main__][INFO] - [16640] Loss: 0.004, Running accuracy: 99.978, Time: 25.43
[2020-12-16 08:31:09,664][__main__][INFO] - [16960] Loss: 0.026, Running accuracy: 99.977, Time: 32.74
[2020-12-16 08:31:34,473][__main__][INFO] - [17280] Loss: 0.014, Running accuracy: 99.977, Time: 24.81
[2020-12-16 08:31:59,238][__main__][INFO] - [17600] Loss: 0.009, Running accuracy: 99.977, Time: 24.76
[2020-12-16 08:32:26,446][__main__][INFO] - [17920] Loss: 0.015, Running accuracy: 99.977, Time: 27.21
[2020-12-16 08:32:53,269][__main__][INFO] - [18240] Loss: 0.014, Running accuracy: 99.977, Time: 26.82
[2020-12-16 08:33:19,721][__main__][INFO] - [18560] Loss: 0.006, Running accuracy: 99.977, Time: 26.45
[2020-12-16 08:33:45,614][__main__][INFO] - [18880] Loss: 0.032, Running accuracy: 99.976, Time: 25.89
[2020-12-16 08:34:10,544][__main__][INFO] - [19200] Loss: 0.012, Running accuracy: 99.976, Time: 24.93
[2020-12-16 08:34:35,188][__main__][INFO] - [19520] Loss: 0.013, Running accuracy: 99.977, Time: 24.64
[2020-12-16 08:35:01,297][__main__][INFO] - [19840] Loss: 0.012, Running accuracy: 99.977, Time: 26.11
[2020-12-16 08:35:27,625][__main__][INFO] - [20160] Loss: 0.013, Running accuracy: 99.977, Time: 26.33
[2020-12-16 08:35:52,802][__main__][INFO] - [20480] Loss: 0.007, Running accuracy: 99.977, Time: 25.18
[2020-12-16 08:36:17,982][__main__][INFO] - [20800] Loss: 0.004, Running accuracy: 99.977, Time: 25.09
[2020-12-16 08:36:47,292][__main__][INFO] - [21120] Loss: 0.003, Running accuracy: 99.978, Time: 29.31
[2020-12-16 08:37:14,171][__main__][INFO] - [21440] Loss: 0.012, Running accuracy: 99.978, Time: 26.88
[2020-12-16 08:37:40,038][__main__][INFO] - [21760] Loss: 0.022, Running accuracy: 99.977, Time: 25.87
[2020-12-16 08:38:05,947][__main__][INFO] - [22080] Loss: 0.032, Running accuracy: 99.977, Time: 25.91
[2020-12-16 08:38:32,230][__main__][INFO] - [22400] Loss: 0.021, Running accuracy: 99.977, Time: 26.28
[2020-12-16 08:38:56,929][__main__][INFO] - [22720] Loss: 0.005, Running accuracy: 99.977, Time: 24.70
[2020-12-16 08:39:22,809][__main__][INFO] - [23040] Loss: 0.012, Running accuracy: 99.977, Time: 25.88
[2020-12-16 08:39:47,132][__main__][INFO] - [23360] Loss: 0.001, Running accuracy: 99.977, Time: 24.32
[2020-12-16 08:40:11,258][__main__][INFO] - [23680] Loss: 0.006, Running accuracy: 99.977, Time: 24.12
[2020-12-16 08:40:36,824][__main__][INFO] - [24000] Loss: 0.013, Running accuracy: 99.977, Time: 25.57
[2020-12-16 08:41:03,036][__main__][INFO] - [24320] Loss: 0.005, Running accuracy: 99.977, Time: 26.21
[2020-12-16 08:41:30,223][__main__][INFO] - [24640] Loss: 0.003, Running accuracy: 99.978, Time: 27.19
[2020-12-16 08:41:55,084][__main__][INFO] - [24960] Loss: 0.015, Running accuracy: 99.977, Time: 24.86
[2020-12-16 08:42:19,722][__main__][INFO] - [25280] Loss: 0.017, Running accuracy: 99.977, Time: 24.64
[2020-12-16 08:42:52,667][__main__][INFO] - [25600] Loss: 0.012, Running accuracy: 99.977, Time: 32.94
[2020-12-16 08:43:18,041][__main__][INFO] - [25920] Loss: 0.014, Running accuracy: 99.977, Time: 25.37
[2020-12-16 08:43:43,027][__main__][INFO] - [26240] Loss: 0.003, Running accuracy: 99.978, Time: 24.98
[2020-12-16 08:44:12,086][__main__][INFO] - [26560] Loss: 0.003, Running accuracy: 99.978, Time: 29.06
[2020-12-16 08:44:37,422][__main__][INFO] - [26880] Loss: 0.003, Running accuracy: 99.978, Time: 25.33
[2020-12-16 08:45:01,703][__main__][INFO] - [27200] Loss: 0.030, Running accuracy: 99.978, Time: 24.28
[2020-12-16 08:45:25,903][__main__][INFO] - [27520] Loss: 0.023, Running accuracy: 99.978, Time: 24.20
[2020-12-16 08:45:51,491][__main__][INFO] - [27840] Loss: 0.022, Running accuracy: 99.978, Time: 25.59
[2020-12-16 08:46:14,895][__main__][INFO] - [28160] Loss: 0.019, Running accuracy: 99.978, Time: 23.40
[2020-12-16 08:46:41,475][__main__][INFO] - [28480] Loss: 0.009, Running accuracy: 99.978, Time: 26.58
[2020-12-16 08:47:08,194][__main__][INFO] - [28800] Loss: 0.027, Running accuracy: 99.978, Time: 26.72
[2020-12-16 08:47:33,407][__main__][INFO] - [29120] Loss: 0.009, Running accuracy: 99.978, Time: 25.21
[2020-12-16 08:47:57,540][__main__][INFO] - [29440] Loss: 0.007, Running accuracy: 99.979, Time: 24.13
[2020-12-16 08:48:23,321][__main__][INFO] - [29760] Loss: 0.019, Running accuracy: 99.978, Time: 25.78
[2020-12-16 08:48:55,790][__main__][INFO] - [30080] Loss: 0.027, Running accuracy: 99.978, Time: 32.47
[2020-12-16 08:49:20,673][__main__][INFO] - [30400] Loss: 0.010, Running accuracy: 99.978, Time: 24.88
[2020-12-16 08:49:47,389][__main__][INFO] - [30720] Loss: 0.034, Running accuracy: 99.978, Time: 26.72
[2020-12-16 08:50:10,493][__main__][INFO] - [31040] Loss: 0.038, Running accuracy: 99.977, Time: 23.10
[2020-12-16 08:50:35,148][__main__][INFO] - [31360] Loss: 0.002, Running accuracy: 99.977, Time: 24.65
[2020-12-16 08:50:59,485][__main__][INFO] - [31680] Loss: 0.017, Running accuracy: 99.977, Time: 24.34
[2020-12-16 08:51:25,607][__main__][INFO] - [32000] Loss: 0.007, Running accuracy: 99.977, Time: 26.12
[2020-12-16 08:51:50,153][__main__][INFO] - [32320] Loss: 0.004, Running accuracy: 99.977, Time: 24.54
[2020-12-16 08:52:13,640][__main__][INFO] - [32640] Loss: 0.040, Running accuracy: 99.977, Time: 23.49
[2020-12-16 08:52:38,346][__main__][INFO] - [32960] Loss: 0.033, Running accuracy: 99.977, Time: 24.70
[2020-12-16 08:53:03,775][__main__][INFO] - [33280] Loss: 0.009, Running accuracy: 99.977, Time: 25.43
[2020-12-16 08:53:27,772][__main__][INFO] - [33600] Loss: 0.022, Running accuracy: 99.976, Time: 24.00
[2020-12-16 08:53:52,152][__main__][INFO] - [33920] Loss: 0.024, Running accuracy: 99.976, Time: 24.38
[2020-12-16 08:54:21,223][__main__][INFO] - [34240] Loss: 0.034, Running accuracy: 99.976, Time: 29.07
[2020-12-16 08:54:45,999][__main__][INFO] - [34560] Loss: 0.017, Running accuracy: 99.976, Time: 24.78
[2020-12-16 08:55:09,478][__main__][INFO] - [34880] Loss: 0.014, Running accuracy: 99.976, Time: 23.48
[2020-12-16 08:55:32,004][__main__][INFO] - [35200] Loss: 0.021, Running accuracy: 99.976, Time: 22.52
[2020-12-16 08:55:58,305][__main__][INFO] - [35520] Loss: 0.009, Running accuracy: 99.976, Time: 26.30
[2020-12-16 08:56:23,656][__main__][INFO] - [35840] Loss: 0.006, Running accuracy: 99.976, Time: 25.35
[2020-12-16 08:56:48,119][__main__][INFO] - [36160] Loss: 0.014, Running accuracy: 99.976, Time: 24.46
[2020-12-16 08:57:12,113][__main__][INFO] - [36480] Loss: 0.009, Running accuracy: 99.976, Time: 23.99
[2020-12-16 08:57:36,460][__main__][INFO] - [36800] Loss: 0.055, Running accuracy: 99.975, Time: 24.35
[2020-12-16 08:58:00,895][__main__][INFO] - [37120] Loss: 0.038, Running accuracy: 99.975, Time: 24.43
[2020-12-16 08:58:25,106][__main__][INFO] - [37440] Loss: 0.006, Running accuracy: 99.975, Time: 24.21
[2020-12-16 08:58:49,183][__main__][INFO] - [37760] Loss: 0.031, Running accuracy: 99.975, Time: 24.08
[2020-12-16 08:59:15,343][__main__][INFO] - [38080] Loss: 0.011, Running accuracy: 99.975, Time: 26.16
[2020-12-16 08:59:40,631][__main__][INFO] - [38400] Loss: 0.015, Running accuracy: 99.975, Time: 25.29
[2020-12-16 09:00:10,153][__main__][INFO] - [38720] Loss: 0.009, Running accuracy: 99.975, Time: 29.52
[2020-12-16 09:00:35,293][__main__][INFO] - [39040] Loss: 0.020, Running accuracy: 99.975, Time: 25.14
[2020-12-16 09:01:00,490][__main__][INFO] - [39360] Loss: 0.034, Running accuracy: 99.975, Time: 25.20
[2020-12-16 09:01:25,096][__main__][INFO] - [39680] Loss: 0.008, Running accuracy: 99.975, Time: 24.60
[2020-12-16 09:01:35,277][__main__][INFO] - Action accuracy: 99.975, Loss: 0.017
[2020-12-16 09:01:35,278][__main__][INFO] - Validating..
[2020-12-16 09:02:02,575][test][INFO] - Time elapsed: 25.024985
[2020-12-16 09:02:02,579][__main__][INFO] - Validation F1 score: 95.490, Exact match: 54.880, Precision: 95.470, Recall: 95.510
[2020-12-16 09:02:36,820][__main__][INFO] - Checkpoint saved to checkpoints/model_latest.pth
[2020-12-16 09:02:37,821][__main__][INFO] - Epoch #48
[2020-12-16 09:02:37,822][__main__][INFO] - Training..
[2020-12-16 09:03:05,565][__main__][INFO] - [320] Loss: 0.008, Running accuracy: 99.975, Time: 26.65
[2020-12-16 09:03:29,795][__main__][INFO] - [640] Loss: 0.020, Running accuracy: 99.961, Time: 24.23
[2020-12-16 09:03:57,663][__main__][INFO] - [960] Loss: 0.036, Running accuracy: 99.962, Time: 27.87
[2020-12-16 09:04:31,152][__main__][INFO] - [1280] Loss: 0.028, Running accuracy: 99.965, Time: 33.49
[2020-12-16 09:04:53,861][__main__][INFO] - [1600] Loss: 0.006, Running accuracy: 99.971, Time: 22.71
[2020-12-16 09:05:21,136][__main__][INFO] - [1920] Loss: 0.023, Running accuracy: 99.972, Time: 27.27
[2020-12-16 09:05:45,855][__main__][INFO] - [2240] Loss: 0.003, Running accuracy: 99.976, Time: 24.72
[2020-12-16 09:06:10,170][__main__][INFO] - [2560] Loss: 0.025, Running accuracy: 99.974, Time: 24.31
[2020-12-16 09:06:34,616][__main__][INFO] - [2880] Loss: 0.007, Running accuracy: 99.977, Time: 24.44
[2020-12-16 09:07:02,777][__main__][INFO] - [3200] Loss: 0.042, Running accuracy: 99.975, Time: 28.16
[2020-12-16 09:07:26,375][__main__][INFO] - [3520] Loss: 0.007, Running accuracy: 99.976, Time: 23.60
[2020-12-16 09:07:52,916][__main__][INFO] - [3840] Loss: 0.023, Running accuracy: 99.976, Time: 26.54
[2020-12-16 09:08:18,602][__main__][INFO] - [4160] Loss: 0.009, Running accuracy: 99.975, Time: 25.68
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[2020-12-16 09:55:30,782][__main__][INFO] - [39680] Loss: 0.011, Running accuracy: 99.977, Time: 26.21
[2020-12-16 09:55:41,729][__main__][INFO] - Action accuracy: 99.977, Loss: 0.018
[2020-12-16 09:55:41,730][__main__][INFO] - Validating..
[2020-12-16 09:56:16,181][test][INFO] - Time elapsed: 32.227820
[2020-12-16 09:56:16,186][__main__][INFO] - Validation F1 score: 95.480, Exact match: 54.940, Precision: 95.450, Recall: 95.520
[2020-12-16 09:56:48,897][__main__][INFO] - Checkpoint saved to checkpoints/model_latest.pth
[2020-12-16 09:56:49,964][__main__][INFO] - Epoch #49
[2020-12-16 09:56:49,964][__main__][INFO] - Training..
[2020-12-16 09:57:17,600][__main__][INFO] - [320] Loss: 0.010, Running accuracy: 99.974, Time: 26.27
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[2020-12-16 10:48:56,384][__main__][INFO] - [39040] Loss: 0.026, Running accuracy: 99.975, Time: 24.86
[2020-12-16 10:49:20,307][__main__][INFO] - [39360] Loss: 0.004, Running accuracy: 99.975, Time: 23.92
[2020-12-16 10:49:47,305][__main__][INFO] - [39680] Loss: 0.001, Running accuracy: 99.975, Time: 27.00
[2020-12-16 10:49:57,460][__main__][INFO] - Action accuracy: 99.975, Loss: 0.019
[2020-12-16 10:49:57,461][__main__][INFO] - Validating..
[2020-12-16 10:50:24,803][test][INFO] - Time elapsed: 25.841096
[2020-12-16 10:50:24,807][__main__][INFO] - Validation F1 score: 95.460, Exact match: 54.820, Precision: 95.440, Recall: 95.490
[2020-12-16 10:50:57,320][__main__][INFO] - Checkpoint saved to checkpoints/model_latest.pth
[2020-12-16 10:50:58,186][__main__][INFO] - Epoch #50
[2020-12-16 10:50:58,186][__main__][INFO] - Training..
[2020-12-16 10:51:32,399][__main__][INFO] - [320] Loss: 0.002, Running accuracy: 99.987, Time: 33.14
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[2020-12-16 11:38:56,601][__main__][INFO] - [35840] Loss: 0.015, Running accuracy: 99.976, Time: 26.10
[2020-12-16 11:39:22,350][__main__][INFO] - [36160] Loss: 0.018, Running accuracy: 99.976, Time: 25.75
[2020-12-16 11:39:47,718][__main__][INFO] - [36480] Loss: 0.008, Running accuracy: 99.976, Time: 25.37
[2020-12-16 11:40:10,585][__main__][INFO] - [36800] Loss: 0.022, Running accuracy: 99.976, Time: 22.87
[2020-12-16 11:40:36,705][__main__][INFO] - [37120] Loss: 0.025, Running accuracy: 99.976, Time: 26.12
[2020-12-16 11:41:01,860][__main__][INFO] - [37440] Loss: 0.025, Running accuracy: 99.976, Time: 25.15
[2020-12-16 11:41:26,402][__main__][INFO] - [37760] Loss: 0.017, Running accuracy: 99.976, Time: 24.54
[2020-12-16 11:41:50,959][__main__][INFO] - [38080] Loss: 0.033, Running accuracy: 99.976, Time: 24.56
[2020-12-16 11:42:14,974][__main__][INFO] - [38400] Loss: 0.037, Running accuracy: 99.976, Time: 24.01
[2020-12-16 11:42:38,692][__main__][INFO] - [38720] Loss: 0.011, Running accuracy: 99.976, Time: 23.71
[2020-12-16 11:43:03,659][__main__][INFO] - [39040] Loss: 0.001, Running accuracy: 99.976, Time: 24.97
[2020-12-16 11:43:29,511][__main__][INFO] - [39360] Loss: 0.007, Running accuracy: 99.976, Time: 25.85
[2020-12-16 11:44:00,170][__main__][INFO] - [39680] Loss: 0.019, Running accuracy: 99.976, Time: 30.66
[2020-12-16 11:44:10,370][__main__][INFO] - Action accuracy: 99.976, Loss: 0.018
[2020-12-16 11:44:10,371][__main__][INFO] - Validating..
[2020-12-16 11:44:37,723][test][INFO] - Time elapsed: 24.882233
[2020-12-16 11:44:37,727][__main__][INFO] - Validation F1 score: 95.520, Exact match: 54.820, Precision: 95.500, Recall: 95.530
[2020-12-16 11:44:37,727][__main__][INFO] - F1 score has improved
[2020-12-16 11:45:12,244][__main__][INFO] - Checkpoint saved to checkpoints/model_latest.pth
[2020-12-16 11:45:13,298][__main__][INFO] - Epoch #51
[2020-12-16 11:45:13,298][__main__][INFO] - Training..
[2020-12-16 11:45:43,589][__main__][INFO] - [320] Loss: 0.015, Running accuracy: 99.962, Time: 28.91
[2020-12-16 11:46:08,045][__main__][INFO] - [640] Loss: 0.014, Running accuracy: 99.968, Time: 24.45
[2020-12-16 11:46:33,372][__main__][INFO] - [960] Loss: 0.002, Running accuracy: 99.978, Time: 25.33
[2020-12-16 11:46:59,912][__main__][INFO] - [1280] Loss: 0.013, Running accuracy: 99.980, Time: 26.54
[2020-12-16 11:47:23,834][__main__][INFO] - [1600] Loss: 0.056, Running accuracy: 99.963, Time: 23.92
[2020-12-16 11:47:47,365][__main__][INFO] - [1920] Loss: 0.012, Running accuracy: 99.967, Time: 23.53
[2020-12-16 11:48:18,352][__main__][INFO] - [2240] Loss: 0.001, Running accuracy: 99.972, Time: 30.99
[2020-12-16 11:48:43,488][__main__][INFO] - [2560] Loss: 0.007, Running accuracy: 99.976, Time: 25.13
[2020-12-16 11:49:09,463][__main__][INFO] - [2880] Loss: 0.016, Running accuracy: 99.975, Time: 25.97
[2020-12-16 11:49:33,530][__main__][INFO] - [3200] Loss: 0.001, Running accuracy: 99.977, Time: 24.07
[2020-12-16 11:49:58,077][__main__][INFO] - [3520] Loss: 0.012, Running accuracy: 99.977, Time: 24.55
[2020-12-16 11:50:21,364][__main__][INFO] - [3840] Loss: 0.007, Running accuracy: 99.978, Time: 23.29
[2020-12-16 11:50:48,038][__main__][INFO] - [4160] Loss: 0.008, Running accuracy: 99.978, Time: 26.67
[2020-12-16 11:51:12,923][__main__][INFO] - [4480] Loss: 0.002, Running accuracy: 99.979, Time: 24.88
[2020-12-16 11:51:37,994][__main__][INFO] - [4800] Loss: 0.006, Running accuracy: 99.981, Time: 25.07
[2020-12-16 11:52:02,153][__main__][INFO] - [5120] Loss: 0.014, Running accuracy: 99.980, Time: 24.16
[2020-12-16 11:52:25,171][__main__][INFO] - [5440] Loss: 0.014, Running accuracy: 99.981, Time: 23.02
[2020-12-16 11:52:50,289][__main__][INFO] - [5760] Loss: 0.041, Running accuracy: 99.980, Time: 25.12
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[2020-12-16 11:53:48,436][__main__][INFO] - [6400] Loss: 0.028, Running accuracy: 99.980, Time: 32.06
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[2020-12-16 11:55:57,091][__main__][INFO] - [8000] Loss: 0.020, Running accuracy: 99.981, Time: 27.50
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[2020-12-16 11:57:14,622][__main__][INFO] - [8960] Loss: 0.013, Running accuracy: 99.981, Time: 24.55
[2020-12-16 11:57:39,514][__main__][INFO] - [9280] Loss: 0.013, Running accuracy: 99.981, Time: 24.89
[2020-12-16 11:58:02,546][__main__][INFO] - [9600] Loss: 0.001, Running accuracy: 99.981, Time: 23.03
[2020-12-16 11:58:26,625][__main__][INFO] - [9920] Loss: 0.026, Running accuracy: 99.981, Time: 24.08
[2020-12-16 11:58:53,179][__main__][INFO] - [10240] Loss: 0.013, Running accuracy: 99.982, Time: 26.55
[2020-12-16 11:59:17,586][__main__][INFO] - [10560] Loss: 0.004, Running accuracy: 99.982, Time: 24.40
[2020-12-16 11:59:48,272][__main__][INFO] - [10880] Loss: 0.002, Running accuracy: 99.982, Time: 30.68
[2020-12-16 12:00:11,985][__main__][INFO] - [11200] Loss: 0.006, Running accuracy: 99.982, Time: 23.71
[2020-12-16 12:00:35,187][__main__][INFO] - [11520] Loss: 0.006, Running accuracy: 99.983, Time: 23.19
[2020-12-16 12:01:01,154][__main__][INFO] - [11840] Loss: 0.042, Running accuracy: 99.982, Time: 25.97
[2020-12-16 12:01:27,007][__main__][INFO] - [12160] Loss: 0.043, Running accuracy: 99.981, Time: 25.85
[2020-12-16 12:01:52,324][__main__][INFO] - [12480] Loss: 0.023, Running accuracy: 99.981, Time: 25.31
[2020-12-16 12:02:17,540][__main__][INFO] - [12800] Loss: 0.022, Running accuracy: 99.981, Time: 25.21
[2020-12-16 12:02:42,636][__main__][INFO] - [13120] Loss: 0.011, Running accuracy: 99.981, Time: 25.09
[2020-12-16 12:03:07,964][__main__][INFO] - [13440] Loss: 0.016, Running accuracy: 99.981, Time: 25.33
[2020-12-16 12:03:33,477][__main__][INFO] - [13760] Loss: 0.006, Running accuracy: 99.981, Time: 25.51
[2020-12-16 12:03:56,704][__main__][INFO] - [14080] Loss: 0.014, Running accuracy: 99.981, Time: 23.23
[2020-12-16 12:04:22,069][__main__][INFO] - [14400] Loss: 0.009, Running accuracy: 99.980, Time: 25.36
[2020-12-16 12:04:46,105][__main__][INFO] - [14720] Loss: 0.008, Running accuracy: 99.981, Time: 24.03
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[2020-12-16 12:05:42,568][__main__][INFO] - [15360] Loss: 0.028, Running accuracy: 99.981, Time: 31.10
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[2020-12-16 12:07:47,777][__main__][INFO] - [16960] Loss: 0.035, Running accuracy: 99.980, Time: 23.05
[2020-12-16 12:08:14,935][__main__][INFO] - [17280] Loss: 0.018, Running accuracy: 99.980, Time: 27.16
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[2020-12-16 12:09:04,480][__main__][INFO] - [17920] Loss: 0.003, Running accuracy: 99.980, Time: 24.63
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[2020-12-16 12:10:20,530][__main__][INFO] - [18880] Loss: 0.027, Running accuracy: 99.980, Time: 24.51
[2020-12-16 12:10:44,008][__main__][INFO] - [19200] Loss: 0.003, Running accuracy: 99.981, Time: 23.48
[2020-12-16 12:11:15,463][__main__][INFO] - [19520] Loss: 0.007, Running accuracy: 99.981, Time: 31.45
[2020-12-16 12:11:41,027][__main__][INFO] - [19840] Loss: 0.011, Running accuracy: 99.981, Time: 25.56
[2020-12-16 12:12:04,341][__main__][INFO] - [20160] Loss: 0.013, Running accuracy: 99.981, Time: 23.31
[2020-12-16 12:12:31,005][__main__][INFO] - [20480] Loss: 0.047, Running accuracy: 99.980, Time: 26.66
[2020-12-16 12:12:56,517][__main__][INFO] - [20800] Loss: 0.010, Running accuracy: 99.980, Time: 25.51
[2020-12-16 12:13:21,134][__main__][INFO] - [21120] Loss: 0.015, Running accuracy: 99.980, Time: 24.62
[2020-12-16 12:13:45,814][__main__][INFO] - [21440] Loss: 0.017, Running accuracy: 99.980, Time: 24.68
[2020-12-16 12:14:09,706][__main__][INFO] - [21760] Loss: 0.019, Running accuracy: 99.980, Time: 23.89
[2020-12-16 12:14:34,151][__main__][INFO] - [22080] Loss: 0.011, Running accuracy: 99.980, Time: 24.35
[2020-12-16 12:14:58,575][__main__][INFO] - [22400] Loss: 0.013, Running accuracy: 99.980, Time: 24.42
[2020-12-16 12:15:23,317][__main__][INFO] - [22720] Loss: 0.026, Running accuracy: 99.980, Time: 24.74
[2020-12-16 12:15:47,096][__main__][INFO] - [23040] Loss: 0.022, Running accuracy: 99.979, Time: 23.78
[2020-12-16 12:16:12,014][__main__][INFO] - [23360] Loss: 0.020, Running accuracy: 99.979, Time: 24.92
[2020-12-16 12:16:36,358][__main__][INFO] - [23680] Loss: 0.020, Running accuracy: 99.979, Time: 24.34
[2020-12-16 12:17:07,733][__main__][INFO] - [24000] Loss: 0.023, Running accuracy: 99.978, Time: 31.37
[2020-12-16 12:17:33,491][__main__][INFO] - [24320] Loss: 0.014, Running accuracy: 99.978, Time: 25.76
[2020-12-16 12:17:59,050][__main__][INFO] - [24640] Loss: 0.004, Running accuracy: 99.978, Time: 25.56
[2020-12-16 12:18:24,777][__main__][INFO] - [24960] Loss: 0.019, Running accuracy: 99.978, Time: 25.73
[2020-12-16 12:18:50,931][__main__][INFO] - [25280] Loss: 0.047, Running accuracy: 99.978, Time: 26.15
[2020-12-16 12:19:16,419][__main__][INFO] - [25600] Loss: 0.010, Running accuracy: 99.978, Time: 25.49
[2020-12-16 12:19:41,422][__main__][INFO] - [25920] Loss: 0.019, Running accuracy: 99.978, Time: 25.00
[2020-12-16 12:20:04,786][__main__][INFO] - [26240] Loss: 0.014, Running accuracy: 99.978, Time: 23.36
[2020-12-16 12:20:31,032][__main__][INFO] - [26560] Loss: 0.028, Running accuracy: 99.977, Time: 26.25
[2020-12-16 12:20:57,753][__main__][INFO] - [26880] Loss: 0.023, Running accuracy: 99.977, Time: 26.72
[2020-12-16 12:21:24,257][__main__][INFO] - [27200] Loss: 0.003, Running accuracy: 99.977, Time: 26.50
[2020-12-16 12:21:49,438][__main__][INFO] - [27520] Loss: 0.003, Running accuracy: 99.978, Time: 25.18
[2020-12-16 12:22:14,357][__main__][INFO] - [27840] Loss: 0.016, Running accuracy: 99.978, Time: 24.92
[2020-12-16 12:22:39,936][__main__][INFO] - [28160] Loss: 0.015, Running accuracy: 99.977, Time: 25.58
[2020-12-16 12:23:10,424][__main__][INFO] - [28480] Loss: 0.014, Running accuracy: 99.978, Time: 30.49
[2020-12-16 12:23:33,822][__main__][INFO] - [28800] Loss: 0.043, Running accuracy: 99.977, Time: 23.40
[2020-12-16 12:23:58,454][__main__][INFO] - [29120] Loss: 0.031, Running accuracy: 99.977, Time: 24.63
[2020-12-16 12:24:24,463][__main__][INFO] - [29440] Loss: 0.007, Running accuracy: 99.977, Time: 26.01
[2020-12-16 12:24:49,191][__main__][INFO] - [29760] Loss: 0.003, Running accuracy: 99.977, Time: 24.72
[2020-12-16 12:25:12,329][__main__][INFO] - [30080] Loss: 0.024, Running accuracy: 99.977, Time: 23.14
[2020-12-16 12:25:36,113][__main__][INFO] - [30400] Loss: 0.011, Running accuracy: 99.977, Time: 23.78
[2020-12-16 12:26:01,635][__main__][INFO] - [30720] Loss: 0.011, Running accuracy: 99.978, Time: 25.52
[2020-12-16 12:26:27,328][__main__][INFO] - [31040] Loss: 0.033, Running accuracy: 99.977, Time: 25.69
[2020-12-16 12:26:54,832][__main__][INFO] - [31360] Loss: 0.023, Running accuracy: 99.977, Time: 27.50
[2020-12-16 12:27:19,252][__main__][INFO] - [31680] Loss: 0.011, Running accuracy: 99.977, Time: 24.42
[2020-12-16 12:27:43,693][__main__][INFO] - [32000] Loss: 0.016, Running accuracy: 99.977, Time: 24.44
[2020-12-16 12:28:10,752][__main__][INFO] - [32320] Loss: 0.016, Running accuracy: 99.977, Time: 27.06
[2020-12-16 12:28:39,264][__main__][INFO] - [32640] Loss: 0.006, Running accuracy: 99.977, Time: 28.51
[2020-12-16 12:29:03,375][__main__][INFO] - [32960] Loss: 0.007, Running accuracy: 99.977, Time: 24.11
[2020-12-16 12:29:29,833][__main__][INFO] - [33280] Loss: 0.011, Running accuracy: 99.977, Time: 26.46
[2020-12-16 12:29:55,490][__main__][INFO] - [33600] Loss: 0.012, Running accuracy: 99.977, Time: 25.66
[2020-12-16 12:30:20,303][__main__][INFO] - [33920] Loss: 0.009, Running accuracy: 99.977, Time: 24.81
[2020-12-16 12:30:46,643][__main__][INFO] - [34240] Loss: 0.008, Running accuracy: 99.977, Time: 26.34
[2020-12-16 12:31:11,801][__main__][INFO] - [34560] Loss: 0.002, Running accuracy: 99.978, Time: 25.16
[2020-12-16 12:31:36,737][__main__][INFO] - [34880] Loss: 0.003, Running accuracy: 99.978, Time: 24.94
[2020-12-16 12:31:59,953][__main__][INFO] - [35200] Loss: 0.016, Running accuracy: 99.978, Time: 23.21
[2020-12-16 12:32:27,636][__main__][INFO] - [35520] Loss: 0.006, Running accuracy: 99.978, Time: 27.68
[2020-12-16 12:32:51,948][__main__][INFO] - [35840] Loss: 0.003, Running accuracy: 99.978, Time: 24.31
[2020-12-16 12:33:15,247][__main__][INFO] - [36160] Loss: 0.011, Running accuracy: 99.978, Time: 23.30
[2020-12-16 12:33:40,543][__main__][INFO] - [36480] Loss: 0.026, Running accuracy: 99.978, Time: 25.30
[2020-12-16 12:34:03,619][__main__][INFO] - [36800] Loss: 0.002, Running accuracy: 99.978, Time: 23.08
[2020-12-16 12:34:33,986][__main__][INFO] - [37120] Loss: 0.001, Running accuracy: 99.978, Time: 30.37
[2020-12-16 12:34:59,639][__main__][INFO] - [37440] Loss: 0.002, Running accuracy: 99.978, Time: 25.65
[2020-12-16 12:35:24,573][__main__][INFO] - [37760] Loss: 0.002, Running accuracy: 99.978, Time: 24.93
[2020-12-16 12:35:50,255][__main__][INFO] - [38080] Loss: 0.025, Running accuracy: 99.978, Time: 25.68
[2020-12-16 12:36:16,005][__main__][INFO] - [38400] Loss: 0.011, Running accuracy: 99.978, Time: 25.75
[2020-12-16 12:36:40,567][__main__][INFO] - [38720] Loss: 0.018, Running accuracy: 99.978, Time: 24.56
[2020-12-16 12:37:06,223][__main__][INFO] - [39040] Loss: 0.054, Running accuracy: 99.978, Time: 25.66
[2020-12-16 12:37:29,923][__main__][INFO] - [39360] Loss: 0.010, Running accuracy: 99.978, Time: 23.70
[2020-12-16 12:37:58,195][__main__][INFO] - [39680] Loss: 0.004, Running accuracy: 99.978, Time: 28.27
[2020-12-16 12:38:08,744][__main__][INFO] - Action accuracy: 99.978, Loss: 0.016
[2020-12-16 12:38:08,745][__main__][INFO] - Validating..
[2020-12-16 12:38:41,914][test][INFO] - Time elapsed: 30.955453
[2020-12-16 12:38:41,918][__main__][INFO] - Validation F1 score: 95.490, Exact match: 54.760, Precision: 95.470, Recall: 95.500
[2020-12-16 12:39:16,535][__main__][INFO] - Checkpoint saved to checkpoints/model_latest.pth
[2020-12-16 12:39:17,357][__main__][INFO] - Epoch #52
[2020-12-16 12:39:17,357][__main__][INFO] - Training..
[2020-12-16 12:39:45,731][__main__][INFO] - [320] Loss: 0.072, Running accuracy: 99.975, Time: 26.79
[2020-12-16 12:40:10,664][__main__][INFO] - [640] Loss: 0.003, Running accuracy: 99.981, Time: 24.93
[2020-12-16 12:40:35,847][__main__][INFO] - [960] Loss: 0.022, Running accuracy: 99.983, Time: 25.18
[2020-12-16 12:41:01,531][__main__][INFO] - [1280] Loss: 0.021, Running accuracy: 99.984, Time: 25.68
[2020-12-16 12:41:31,725][__main__][INFO] - [1600] Loss: 0.003, Running accuracy: 99.987, Time: 30.19
[2020-12-16 12:41:57,802][__main__][INFO] - [1920] Loss: 0.015, Running accuracy: 99.983, Time: 26.08
[2020-12-16 12:42:23,897][__main__][INFO] - [2240] Loss: 0.001, Running accuracy: 99.985, Time: 26.09
[2020-12-16 12:42:50,631][__main__][INFO] - [2560] Loss: 0.013, Running accuracy: 99.986, Time: 26.73
[2020-12-16 12:43:15,879][__main__][INFO] - [2880] Loss: 0.003, Running accuracy: 99.987, Time: 25.25
[2020-12-16 12:43:41,657][__main__][INFO] - [3200] Loss: 0.017, Running accuracy: 99.986, Time: 25.78
[2020-12-16 12:44:07,998][__main__][INFO] - [3520] Loss: 0.007, Running accuracy: 99.985, Time: 26.34
[2020-12-16 12:44:33,780][__main__][INFO] - [3840] Loss: 0.001, Running accuracy: 99.986, Time: 25.78
[2020-12-16 12:45:05,225][__main__][INFO] - [4160] Loss: 0.004, Running accuracy: 99.985, Time: 31.44
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[2020-12-16 12:50:08,864][__main__][INFO] - [8000] Loss: 0.012, Running accuracy: 99.983, Time: 25.22
[2020-12-16 12:50:42,975][__main__][INFO] - [8320] Loss: 0.008, Running accuracy: 99.983, Time: 34.11
[2020-12-16 12:51:07,572][__main__][INFO] - [8640] Loss: 0.014, Running accuracy: 99.983, Time: 24.60
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[2020-12-16 13:32:08,138][__main__][INFO] - Action accuracy: 99.977, Loss: 0.018
[2020-12-16 13:32:08,139][__main__][INFO] - Validating..
[2020-12-16 13:32:35,311][test][INFO] - Time elapsed: 25.721597
[2020-12-16 13:32:35,315][__main__][INFO] - Validation F1 score: 95.500, Exact match: 54.940, Precision: 95.470, Recall: 95.520
[2020-12-16 13:33:07,479][__main__][INFO] - Checkpoint saved to checkpoints/model_latest.pth
[2020-12-16 13:33:08,320][__main__][INFO] - Epoch #53
[2020-12-16 13:33:08,320][__main__][INFO] - Training..
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[2020-12-16 14:25:54,514][__main__][INFO] - [39680] Loss: 0.016, Running accuracy: 99.976, Time: 24.02
[2020-12-16 14:26:04,859][__main__][INFO] - Action accuracy: 99.976, Loss: 0.016
[2020-12-16 14:26:04,861][__main__][INFO] - Validating..
[2020-12-16 14:26:38,554][test][INFO] - Time elapsed: 31.362755
[2020-12-16 14:26:38,559][__main__][INFO] - Validation F1 score: 95.520, Exact match: 55.530, Precision: 95.460, Recall: 95.580
[2020-12-16 14:27:12,850][__main__][INFO] - Checkpoint saved to checkpoints/model_latest.pth
[2020-12-16 14:27:13,669][__main__][INFO] - Epoch #54
[2020-12-16 14:27:13,669][__main__][INFO] - Training..
[2020-12-16 14:27:40,053][__main__][INFO] - [320] Loss: 0.013, Running accuracy: 99.987, Time: 24.83
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[2020-12-16 15:19:28,360][__main__][INFO] - [39040] Loss: 0.036, Running accuracy: 99.976, Time: 22.60
[2020-12-16 15:19:53,295][__main__][INFO] - [39360] Loss: 0.004, Running accuracy: 99.977, Time: 24.93
[2020-12-16 15:20:16,812][__main__][INFO] - [39680] Loss: 0.004, Running accuracy: 99.977, Time: 23.52
[2020-12-16 15:20:27,267][__main__][INFO] - Action accuracy: 99.977, Loss: 0.017
[2020-12-16 15:20:27,268][__main__][INFO] - Validating..
[2020-12-16 15:20:54,860][test][INFO] - Time elapsed: 25.356895
[2020-12-16 15:20:54,864][__main__][INFO] - Validation F1 score: 95.560, Exact match: 55.180, Precision: 95.490, Recall: 95.620
[2020-12-16 15:20:54,864][__main__][INFO] - F1 score has improved
[2020-12-16 15:21:29,149][__main__][INFO] - Checkpoint saved to checkpoints/model_latest.pth
[2020-12-16 15:21:29,976][__main__][INFO] - Epoch #55
[2020-12-16 15:21:29,976][__main__][INFO] - Training..
[2020-12-16 15:21:57,722][__main__][INFO] - [320] Loss: 0.010, Running accuracy: 99.961, Time: 26.33
[2020-12-16 15:22:22,624][__main__][INFO] - [640] Loss: 0.004, Running accuracy: 99.981, Time: 24.90
[2020-12-16 15:22:54,045][__main__][INFO] - [960] Loss: 0.030, Running accuracy: 99.966, Time: 31.42
[2020-12-16 15:23:19,725][__main__][INFO] - [1280] Loss: 0.018, Running accuracy: 99.965, Time: 25.68
[2020-12-16 15:23:44,597][__main__][INFO] - [1600] Loss: 0.030, Running accuracy: 99.967, Time: 24.87
[2020-12-16 15:24:08,935][__main__][INFO] - [1920] Loss: 0.003, Running accuracy: 99.972, Time: 24.34
[2020-12-16 15:24:32,515][__main__][INFO] - [2240] Loss: 0.022, Running accuracy: 99.974, Time: 23.58
[2020-12-16 15:24:56,691][__main__][INFO] - [2560] Loss: 0.016, Running accuracy: 99.972, Time: 24.18
[2020-12-16 15:25:21,207][__main__][INFO] - [2880] Loss: 0.008, Running accuracy: 99.972, Time: 24.52
[2020-12-16 15:25:48,220][__main__][INFO] - [3200] Loss: 0.019, Running accuracy: 99.971, Time: 27.01
[2020-12-16 15:26:13,070][__main__][INFO] - [3520] Loss: 0.008, Running accuracy: 99.971, Time: 24.85
[2020-12-16 15:26:37,079][__main__][INFO] - [3840] Loss: 0.020, Running accuracy: 99.973, Time: 24.01
[2020-12-16 15:27:01,486][__main__][INFO] - [4160] Loss: 0.014, Running accuracy: 99.972, Time: 24.41
[2020-12-16 15:27:27,055][__main__][INFO] - [4480] Loss: 0.003, Running accuracy: 99.974, Time: 25.57
[2020-12-16 15:27:52,425][__main__][INFO] - [4800] Loss: 0.002, Running accuracy: 99.976, Time: 25.37
[2020-12-16 15:28:18,419][__main__][INFO] - [5120] Loss: 0.001, Running accuracy: 99.977, Time: 25.99
[2020-12-16 15:28:49,250][__main__][INFO] - [5440] Loss: 0.035, Running accuracy: 99.975, Time: 30.83
[2020-12-16 15:29:15,464][__main__][INFO] - [5760] Loss: 0.009, Running accuracy: 99.975, Time: 26.21
[2020-12-16 15:29:40,905][__main__][INFO] - [6080] Loss: 0.025, Running accuracy: 99.974, Time: 25.44
[2020-12-16 15:30:08,346][__main__][INFO] - [6400] Loss: 0.025, Running accuracy: 99.974, Time: 27.44
[2020-12-16 15:30:33,381][__main__][INFO] - [6720] Loss: 0.001, Running accuracy: 99.976, Time: 25.03
[2020-12-16 15:30:59,734][__main__][INFO] - [7040] Loss: 0.028, Running accuracy: 99.974, Time: 26.35
[2020-12-16 15:31:25,917][__main__][INFO] - [7360] Loss: 0.072, Running accuracy: 99.973, Time: 26.18
[2020-12-16 15:31:51,277][__main__][INFO] - [7680] Loss: 0.002, Running accuracy: 99.974, Time: 25.36
[2020-12-16 15:32:15,380][__main__][INFO] - [8000] Loss: 0.019, Running accuracy: 99.974, Time: 24.10
[2020-12-16 15:32:38,354][__main__][INFO] - [8320] Loss: 0.024, Running accuracy: 99.973, Time: 22.97
[2020-12-16 15:33:03,894][__main__][INFO] - [8640] Loss: 0.014, Running accuracy: 99.974, Time: 25.54
[2020-12-16 15:33:27,603][__main__][INFO] - [8960] Loss: 0.013, Running accuracy: 99.974, Time: 23.71
[2020-12-16 15:33:52,084][__main__][INFO] - [9280] Loss: 0.002, Running accuracy: 99.975, Time: 24.48
[2020-12-16 15:34:21,133][__main__][INFO] - [9600] Loss: 0.007, Running accuracy: 99.975, Time: 29.05
[2020-12-16 15:34:45,754][__main__][INFO] - [9920] Loss: 0.001, Running accuracy: 99.976, Time: 24.62
[2020-12-16 15:35:10,549][__main__][INFO] - [10240] Loss: 0.011, Running accuracy: 99.976, Time: 24.79
[2020-12-16 15:35:36,544][__main__][INFO] - [10560] Loss: 0.008, Running accuracy: 99.976, Time: 25.99
[2020-12-16 15:36:01,541][__main__][INFO] - [10880] Loss: 0.028, Running accuracy: 99.976, Time: 25.00
[2020-12-16 15:36:26,248][__main__][INFO] - [11200] Loss: 0.004, Running accuracy: 99.977, Time: 24.71
[2020-12-16 15:36:50,466][__main__][INFO] - [11520] Loss: 0.009, Running accuracy: 99.977, Time: 24.22
[2020-12-16 15:37:16,223][__main__][INFO] - [11840] Loss: 0.012, Running accuracy: 99.977, Time: 25.76
[2020-12-16 15:37:41,164][__main__][INFO] - [12160] Loss: 0.003, Running accuracy: 99.977, Time: 24.94
[2020-12-16 15:38:07,520][__main__][INFO] - [12480] Loss: 0.008, Running accuracy: 99.978, Time: 26.35
[2020-12-16 15:38:33,458][__main__][INFO] - [12800] Loss: 0.003, Running accuracy: 99.978, Time: 25.94
[2020-12-16 15:38:57,944][__main__][INFO] - [13120] Loss: 0.014, Running accuracy: 99.978, Time: 24.49
[2020-12-16 15:39:22,008][__main__][INFO] - [13440] Loss: 0.003, Running accuracy: 99.978, Time: 24.06
[2020-12-16 15:39:45,749][__main__][INFO] - [13760] Loss: 0.023, Running accuracy: 99.978, Time: 23.74
[2020-12-16 15:40:17,677][__main__][INFO] - [14080] Loss: 0.011, Running accuracy: 99.978, Time: 31.93
[2020-12-16 15:40:42,928][__main__][INFO] - [14400] Loss: 0.005, Running accuracy: 99.979, Time: 25.25
[2020-12-16 15:41:08,255][__main__][INFO] - [14720] Loss: 0.014, Running accuracy: 99.979, Time: 25.33
[2020-12-16 15:41:34,408][__main__][INFO] - [15040] Loss: 0.021, Running accuracy: 99.979, Time: 26.15
[2020-12-16 15:41:59,126][__main__][INFO] - [15360] Loss: 0.038, Running accuracy: 99.978, Time: 24.72
[2020-12-16 15:42:24,440][__main__][INFO] - [15680] Loss: 0.011, Running accuracy: 99.978, Time: 25.31
[2020-12-16 15:42:49,414][__main__][INFO] - [16000] Loss: 0.060, Running accuracy: 99.977, Time: 24.97
[2020-12-16 15:43:13,242][__main__][INFO] - [16320] Loss: 0.001, Running accuracy: 99.977, Time: 23.83
[2020-12-16 15:43:37,496][__main__][INFO] - [16640] Loss: 0.009, Running accuracy: 99.977, Time: 24.25
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[2020-12-16 15:44:27,693][__main__][INFO] - [17280] Loss: 0.017, Running accuracy: 99.977, Time: 24.67
[2020-12-16 15:44:52,245][__main__][INFO] - [17600] Loss: 0.006, Running accuracy: 99.977, Time: 24.55
[2020-12-16 15:45:18,436][__main__][INFO] - [17920] Loss: 0.008, Running accuracy: 99.977, Time: 26.19
[2020-12-16 15:45:44,543][__main__][INFO] - [18240] Loss: 0.016, Running accuracy: 99.977, Time: 26.11
[2020-12-16 15:46:13,730][__main__][INFO] - [18560] Loss: 0.102, Running accuracy: 99.977, Time: 29.19
[2020-12-16 15:46:41,290][__main__][INFO] - [18880] Loss: 0.025, Running accuracy: 99.977, Time: 27.56
[2020-12-16 15:47:05,977][__main__][INFO] - [19200] Loss: 0.010, Running accuracy: 99.977, Time: 24.69
[2020-12-16 15:47:30,369][__main__][INFO] - [19520] Loss: 0.007, Running accuracy: 99.978, Time: 24.39
[2020-12-16 15:47:54,176][__main__][INFO] - [19840] Loss: 0.006, Running accuracy: 99.978, Time: 23.81
[2020-12-16 15:48:21,307][__main__][INFO] - [20160] Loss: 0.030, Running accuracy: 99.977, Time: 27.13
[2020-12-16 15:48:45,506][__main__][INFO] - [20480] Loss: 0.029, Running accuracy: 99.977, Time: 24.20
[2020-12-16 15:49:09,586][__main__][INFO] - [20800] Loss: 0.017, Running accuracy: 99.977, Time: 24.08
[2020-12-16 15:49:34,336][__main__][INFO] - [21120] Loss: 0.009, Running accuracy: 99.977, Time: 24.75
[2020-12-16 15:50:00,455][__main__][INFO] - [21440] Loss: 0.018, Running accuracy: 99.977, Time: 26.12
[2020-12-16 15:50:25,030][__main__][INFO] - [21760] Loss: 0.005, Running accuracy: 99.977, Time: 24.57
[2020-12-16 15:50:50,357][__main__][INFO] - [22080] Loss: 0.030, Running accuracy: 99.977, Time: 25.33
[2020-12-16 15:51:15,042][__main__][INFO] - [22400] Loss: 0.050, Running accuracy: 99.976, Time: 24.68
[2020-12-16 15:51:43,835][__main__][INFO] - [22720] Loss: 0.007, Running accuracy: 99.977, Time: 28.79
[2020-12-16 15:52:09,677][__main__][INFO] - [23040] Loss: 0.042, Running accuracy: 99.976, Time: 25.84
[2020-12-16 15:52:34,191][__main__][INFO] - [23360] Loss: 0.010, Running accuracy: 99.976, Time: 24.14
[2020-12-16 15:53:00,183][__main__][INFO] - [23680] Loss: 0.023, Running accuracy: 99.976, Time: 25.89
[2020-12-16 15:53:25,833][__main__][INFO] - [24000] Loss: 0.014, Running accuracy: 99.976, Time: 25.65
[2020-12-16 15:53:50,502][__main__][INFO] - [24320] Loss: 0.003, Running accuracy: 99.976, Time: 24.67
[2020-12-16 15:54:15,835][__main__][INFO] - [24640] Loss: 0.011, Running accuracy: 99.977, Time: 25.33
[2020-12-16 15:54:42,883][__main__][INFO] - [24960] Loss: 0.005, Running accuracy: 99.977, Time: 27.05
[2020-12-16 15:55:08,468][__main__][INFO] - [25280] Loss: 0.006, Running accuracy: 99.977, Time: 25.58
[2020-12-16 15:55:34,379][__main__][INFO] - [25600] Loss: 0.041, Running accuracy: 99.976, Time: 25.91
[2020-12-16 15:55:59,581][__main__][INFO] - [25920] Loss: 0.016, Running accuracy: 99.976, Time: 25.20
[2020-12-16 15:56:22,349][__main__][INFO] - [26240] Loss: 0.010, Running accuracy: 99.976, Time: 22.77
[2020-12-16 15:56:47,511][__main__][INFO] - [26560] Loss: 0.003, Running accuracy: 99.977, Time: 25.16
[2020-12-16 15:57:13,885][__main__][INFO] - [26880] Loss: 0.011, Running accuracy: 99.976, Time: 26.37
[2020-12-16 15:57:44,562][__main__][INFO] - [27200] Loss: 0.006, Running accuracy: 99.977, Time: 30.68
[2020-12-16 15:58:09,674][__main__][INFO] - [27520] Loss: 0.001, Running accuracy: 99.977, Time: 25.11
[2020-12-16 15:58:37,589][__main__][INFO] - [27840] Loss: 0.008, Running accuracy: 99.977, Time: 27.91
[2020-12-16 15:59:01,958][__main__][INFO] - [28160] Loss: 0.041, Running accuracy: 99.977, Time: 24.37
[2020-12-16 15:59:26,781][__main__][INFO] - [28480] Loss: 0.005, Running accuracy: 99.977, Time: 24.82
[2020-12-16 15:59:52,812][__main__][INFO] - [28800] Loss: 0.049, Running accuracy: 99.976, Time: 26.03
[2020-12-16 16:00:17,595][__main__][INFO] - [29120] Loss: 0.038, Running accuracy: 99.976, Time: 24.78
[2020-12-16 16:00:42,953][__main__][INFO] - [29440] Loss: 0.005, Running accuracy: 99.976, Time: 25.36
[2020-12-16 16:01:07,833][__main__][INFO] - [29760] Loss: 0.009, Running accuracy: 99.976, Time: 24.88
[2020-12-16 16:01:34,990][__main__][INFO] - [30080] Loss: 0.021, Running accuracy: 99.976, Time: 27.16
[2020-12-16 16:02:00,016][__main__][INFO] - [30400] Loss: 0.040, Running accuracy: 99.976, Time: 25.02
[2020-12-16 16:02:26,991][__main__][INFO] - [30720] Loss: 0.006, Running accuracy: 99.976, Time: 26.97
[2020-12-16 16:02:51,921][__main__][INFO] - [31040] Loss: 0.005, Running accuracy: 99.976, Time: 24.93
[2020-12-16 16:03:17,478][__main__][INFO] - [31360] Loss: 0.042, Running accuracy: 99.976, Time: 25.56
[2020-12-16 16:03:47,348][__main__][INFO] - [31680] Loss: 0.001, Running accuracy: 99.976, Time: 29.87
[2020-12-16 16:04:14,616][__main__][INFO] - [32000] Loss: 0.014, Running accuracy: 99.976, Time: 27.27
[2020-12-16 16:04:38,419][__main__][INFO] - [32320] Loss: 0.013, Running accuracy: 99.976, Time: 23.80
[2020-12-16 16:05:04,884][__main__][INFO] - [32640] Loss: 0.014, Running accuracy: 99.976, Time: 26.46
[2020-12-16 16:05:28,841][__main__][INFO] - [32960] Loss: 0.014, Running accuracy: 99.976, Time: 23.96
[2020-12-16 16:05:54,760][__main__][INFO] - [33280] Loss: 0.024, Running accuracy: 99.976, Time: 25.92
[2020-12-16 16:06:19,861][__main__][INFO] - [33600] Loss: 0.001, Running accuracy: 99.976, Time: 25.10
[2020-12-16 16:06:44,598][__main__][INFO] - [33920] Loss: 0.006, Running accuracy: 99.976, Time: 24.74
[2020-12-16 16:07:09,466][__main__][INFO] - [34240] Loss: 0.004, Running accuracy: 99.977, Time: 24.87
[2020-12-16 16:07:34,157][__main__][INFO] - [34560] Loss: 0.014, Running accuracy: 99.977, Time: 24.69
[2020-12-16 16:07:58,631][__main__][INFO] - [34880] Loss: 0.016, Running accuracy: 99.977, Time: 24.47
[2020-12-16 16:08:24,009][__main__][INFO] - [35200] Loss: 0.005, Running accuracy: 99.977, Time: 25.38
[2020-12-16 16:08:51,030][__main__][INFO] - [35520] Loss: 0.002, Running accuracy: 99.977, Time: 27.02
[2020-12-16 16:09:19,588][__main__][INFO] - [35840] Loss: 0.016, Running accuracy: 99.977, Time: 28.56
[2020-12-16 16:09:43,874][__main__][INFO] - [36160] Loss: 0.019, Running accuracy: 99.977, Time: 24.29
[2020-12-16 16:10:06,923][__main__][INFO] - [36480] Loss: 0.025, Running accuracy: 99.977, Time: 23.05
[2020-12-16 16:10:32,502][__main__][INFO] - [36800] Loss: 0.005, Running accuracy: 99.977, Time: 25.58
[2020-12-16 16:10:57,165][__main__][INFO] - [37120] Loss: 0.016, Running accuracy: 99.977, Time: 24.66
[2020-12-16 16:11:21,793][__main__][INFO] - [37440] Loss: 0.003, Running accuracy: 99.977, Time: 24.63
[2020-12-16 16:11:46,435][__main__][INFO] - [37760] Loss: 0.008, Running accuracy: 99.977, Time: 24.64
[2020-12-16 16:12:13,058][__main__][INFO] - [38080] Loss: 0.053, Running accuracy: 99.977, Time: 26.62
[2020-12-16 16:12:37,692][__main__][INFO] - [38400] Loss: 0.004, Running accuracy: 99.977, Time: 24.63
[2020-12-16 16:13:02,648][__main__][INFO] - [38720] Loss: 0.001, Running accuracy: 99.977, Time: 24.96
[2020-12-16 16:13:27,899][__main__][INFO] - [39040] Loss: 0.013, Running accuracy: 99.977, Time: 25.25
[2020-12-16 16:13:52,389][__main__][INFO] - [39360] Loss: 0.012, Running accuracy: 99.977, Time: 24.49
[2020-12-16 16:14:15,700][__main__][INFO] - [39680] Loss: 0.013, Running accuracy: 99.977, Time: 23.31
[2020-12-16 16:14:26,493][__main__][INFO] - Action accuracy: 99.977, Loss: 0.017
[2020-12-16 16:14:26,494][__main__][INFO] - Validating..
[2020-12-16 16:15:00,475][test][INFO] - Time elapsed: 31.738928
[2020-12-16 16:15:00,480][__main__][INFO] - Validation F1 score: 95.550, Exact match: 55.240, Precision: 95.490, Recall: 95.610
[2020-12-16 16:15:34,826][__main__][INFO] - Checkpoint saved to checkpoints/model_latest.pth
[2020-12-16 16:15:35,782][__main__][INFO] - Epoch #56
[2020-12-16 16:15:35,782][__main__][INFO] - Training..
[2020-12-16 16:16:01,385][__main__][INFO] - [320] Loss: 0.008, Running accuracy: 99.987, Time: 24.60
[2020-12-16 16:16:26,606][__main__][INFO] - [640] Loss: 0.005, Running accuracy: 99.987, Time: 25.22
[2020-12-16 16:16:52,130][__main__][INFO] - [960] Loss: 0.020, Running accuracy: 99.983, Time: 25.52
[2020-12-16 16:17:17,545][__main__][INFO] - [1280] Loss: 0.008, Running accuracy: 99.980, Time: 25.41
[2020-12-16 16:17:40,655][__main__][INFO] - [1600] Loss: 0.004, Running accuracy: 99.982, Time: 23.11
[2020-12-16 16:18:04,026][__main__][INFO] - [1920] Loss: 0.012, Running accuracy: 99.978, Time: 23.37
[2020-12-16 16:18:29,559][__main__][INFO] - [2240] Loss: 0.002, Running accuracy: 99.981, Time: 25.53
[2020-12-16 16:18:52,881][__main__][INFO] - [2560] Loss: 0.028, Running accuracy: 99.979, Time: 23.32
[2020-12-16 16:19:23,050][__main__][INFO] - [2880] Loss: 0.008, Running accuracy: 99.978, Time: 30.17
[2020-12-16 16:19:48,747][__main__][INFO] - [3200] Loss: 0.025, Running accuracy: 99.976, Time: 25.70
[2020-12-16 16:20:12,717][__main__][INFO] - [3520] Loss: 0.008, Running accuracy: 99.977, Time: 23.97
[2020-12-16 16:20:37,113][__main__][INFO] - [3840] Loss: 0.003, Running accuracy: 99.978, Time: 24.39
[2020-12-16 16:21:00,012][__main__][INFO] - [4160] Loss: 0.014, Running accuracy: 99.978, Time: 22.90
[2020-12-16 16:21:27,182][__main__][INFO] - [4480] Loss: 0.007, Running accuracy: 99.977, Time: 27.17
[2020-12-16 16:21:52,352][__main__][INFO] - [4800] Loss: 0.017, Running accuracy: 99.976, Time: 25.17
[2020-12-16 16:22:17,174][__main__][INFO] - [5120] Loss: 0.015, Running accuracy: 99.976, Time: 24.82
[2020-12-16 16:22:40,410][__main__][INFO] - [5440] Loss: 0.003, Running accuracy: 99.977, Time: 23.23
[2020-12-16 16:23:06,367][__main__][INFO] - [5760] Loss: 0.006, Running accuracy: 99.979, Time: 25.96
[2020-12-16 16:23:31,232][__main__][INFO] - [6080] Loss: 0.013, Running accuracy: 99.979, Time: 24.86
[2020-12-16 16:23:55,560][__main__][INFO] - [6400] Loss: 0.035, Running accuracy: 99.976, Time: 24.33
[2020-12-16 16:24:21,426][__main__][INFO] - [6720] Loss: 0.003, Running accuracy: 99.977, Time: 25.87
[2020-12-16 16:24:52,447][__main__][INFO] - [7040] Loss: 0.028, Running accuracy: 99.975, Time: 31.02
[2020-12-16 16:25:17,192][__main__][INFO] - [7360] Loss: 0.002, Running accuracy: 99.977, Time: 24.74
[2020-12-16 16:25:42,486][__main__][INFO] - [7680] Loss: 0.015, Running accuracy: 99.977, Time: 25.29
[2020-12-16 16:26:08,453][__main__][INFO] - [8000] Loss: 0.004, Running accuracy: 99.977, Time: 25.97
[2020-12-16 16:26:35,372][__main__][INFO] - [8320] Loss: 0.001, Running accuracy: 99.978, Time: 26.92
[2020-12-16 16:27:00,886][__main__][INFO] - [8640] Loss: 0.012, Running accuracy: 99.979, Time: 25.51
[2020-12-16 16:27:24,404][__main__][INFO] - [8960] Loss: 0.039, Running accuracy: 99.978, Time: 23.52
[2020-12-16 16:27:51,319][__main__][INFO] - [9280] Loss: 0.006, Running accuracy: 99.978, Time: 26.91
[2020-12-16 16:28:14,580][__main__][INFO] - [9600] Loss: 0.023, Running accuracy: 99.978, Time: 23.26
[2020-12-16 16:28:38,505][__main__][INFO] - [9920] Loss: 0.008, Running accuracy: 99.978, Time: 23.92
[2020-12-16 16:29:03,965][__main__][INFO] - [10240] Loss: 0.025, Running accuracy: 99.977, Time: 25.46
[2020-12-16 16:29:28,957][__main__][INFO] - [10560] Loss: 0.050, Running accuracy: 99.976, Time: 24.99
[2020-12-16 16:29:53,411][__main__][INFO] - [10880] Loss: 0.004, Running accuracy: 99.976, Time: 24.45
[2020-12-16 16:30:17,918][__main__][INFO] - [11200] Loss: 0.003, Running accuracy: 99.977, Time: 24.51
[2020-12-16 16:30:47,326][__main__][INFO] - [11520] Loss: 0.003, Running accuracy: 99.977, Time: 29.41
[2020-12-16 16:31:12,070][__main__][INFO] - [11840] Loss: 0.007, Running accuracy: 99.977, Time: 24.74
[2020-12-16 16:31:36,997][__main__][INFO] - [12160] Loss: 0.003, Running accuracy: 99.978, Time: 24.93
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[2020-12-16 17:08:22,231][__main__][INFO] - Action accuracy: 99.978, Loss: 0.016
[2020-12-16 17:08:22,232][__main__][INFO] - Validating..
[2020-12-16 17:08:49,479][test][INFO] - Time elapsed: 25.820182
[2020-12-16 17:08:49,483][__main__][INFO] - Validation F1 score: 95.550, Exact match: 55.240, Precision: 95.510, Recall: 95.590
[2020-12-16 17:09:22,020][__main__][INFO] - Checkpoint saved to checkpoints/model_latest.pth
[2020-12-16 17:09:23,192][__main__][INFO] - Epoch #57
[2020-12-16 17:09:23,192][__main__][INFO] - Training..
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[2020-12-16 17:47:23,026][__main__][INFO] - [28480] Loss: 0.008, Running accuracy: 99.977, Time: 23.94
[2020-12-16 17:47:47,965][__main__][INFO] - [28800] Loss: 0.080, Running accuracy: 99.977, Time: 24.94
[2020-12-16 17:48:15,281][__main__][INFO] - [29120] Loss: 0.015, Running accuracy: 99.977, Time: 27.32
[2020-12-16 17:48:38,702][__main__][INFO] - [29440] Loss: 0.001, Running accuracy: 99.977, Time: 23.42
[2020-12-16 17:49:03,041][__main__][INFO] - [29760] Loss: 0.001, Running accuracy: 99.977, Time: 24.34
[2020-12-16 17:49:28,380][__main__][INFO] - [30080] Loss: 0.017, Running accuracy: 99.977, Time: 25.34
[2020-12-16 17:49:54,012][__main__][INFO] - [30400] Loss: 0.049, Running accuracy: 99.977, Time: 25.63
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[2020-12-16 17:50:49,438][__main__][INFO] - [31040] Loss: 0.014, Running accuracy: 99.977, Time: 30.80
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[2020-12-16 17:51:38,080][__main__][INFO] - [31680] Loss: 0.010, Running accuracy: 99.977, Time: 25.29
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[2020-12-16 17:57:34,677][__main__][INFO] - [36160] Loss: 0.014, Running accuracy: 99.976, Time: 23.74
[2020-12-16 17:57:59,789][__main__][INFO] - [36480] Loss: 0.023, Running accuracy: 99.976, Time: 25.11
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[2020-12-16 17:58:48,511][__main__][INFO] - [37120] Loss: 0.005, Running accuracy: 99.976, Time: 23.99
[2020-12-16 17:59:13,731][__main__][INFO] - [37440] Loss: 0.002, Running accuracy: 99.976, Time: 25.22
[2020-12-16 17:59:38,351][__main__][INFO] - [37760] Loss: 0.039, Running accuracy: 99.976, Time: 24.62
[2020-12-16 18:00:02,089][__main__][INFO] - [38080] Loss: 0.004, Running accuracy: 99.976, Time: 23.74
[2020-12-16 18:00:27,216][__main__][INFO] - [38400] Loss: 0.026, Running accuracy: 99.976, Time: 25.13
[2020-12-16 18:00:51,746][__main__][INFO] - [38720] Loss: 0.004, Running accuracy: 99.977, Time: 24.53
[2020-12-16 18:01:18,448][__main__][INFO] - [39040] Loss: 0.003, Running accuracy: 99.977, Time: 26.70
[2020-12-16 18:01:44,087][__main__][INFO] - [39360] Loss: 0.008, Running accuracy: 99.977, Time: 25.64
[2020-12-16 18:02:13,587][__main__][INFO] - [39680] Loss: 0.007, Running accuracy: 99.977, Time: 29.50
[2020-12-16 18:02:23,324][__main__][INFO] - Action accuracy: 99.977, Loss: 0.016
[2020-12-16 18:02:23,326][__main__][INFO] - Validating..
[2020-12-16 18:02:50,429][test][INFO] - Time elapsed: 25.082375
[2020-12-16 18:02:50,434][__main__][INFO] - Validation F1 score: 95.560, Exact match: 55.350, Precision: 95.520, Recall: 95.590
[2020-12-16 18:03:24,718][__main__][INFO] - Checkpoint saved to checkpoints/model_latest.pth
[2020-12-16 18:03:25,519][__main__][INFO] - Epoch #58
[2020-12-16 18:03:25,520][__main__][INFO] - Training..
[2020-12-16 18:03:53,411][__main__][INFO] - [320] Loss: 0.005, Running accuracy: 100.000, Time: 26.84
[2020-12-16 18:04:17,556][__main__][INFO] - [640] Loss: 0.001, Running accuracy: 100.000, Time: 24.14
[2020-12-16 18:04:42,394][__main__][INFO] - [960] Loss: 0.013, Running accuracy: 99.991, Time: 24.84
[2020-12-16 18:05:07,254][__main__][INFO] - [1280] Loss: 0.036, Running accuracy: 99.983, Time: 24.86
[2020-12-16 18:05:31,666][__main__][INFO] - [1600] Loss: 0.006, Running accuracy: 99.987, Time: 24.41
[2020-12-16 18:06:03,780][__main__][INFO] - [1920] Loss: 0.008, Running accuracy: 99.987, Time: 32.11
[2020-12-16 18:06:28,140][__main__][INFO] - [2240] Loss: 0.013, Running accuracy: 99.987, Time: 24.36
[2020-12-16 18:06:52,656][__main__][INFO] - [2560] Loss: 0.011, Running accuracy: 99.985, Time: 24.52
[2020-12-16 18:07:19,045][__main__][INFO] - [2880] Loss: 0.007, Running accuracy: 99.985, Time: 26.39
[2020-12-16 18:07:43,885][__main__][INFO] - [3200] Loss: 0.020, Running accuracy: 99.983, Time: 24.84
[2020-12-16 18:08:08,504][__main__][INFO] - [3520] Loss: 0.024, Running accuracy: 99.983, Time: 24.62
[2020-12-16 18:08:35,222][__main__][INFO] - [3840] Loss: 0.005, Running accuracy: 99.984, Time: 26.72
[2020-12-16 18:09:02,084][__main__][INFO] - [4160] Loss: 0.002, Running accuracy: 99.985, Time: 26.86
[2020-12-16 18:09:26,295][__main__][INFO] - [4480] Loss: 0.014, Running accuracy: 99.982, Time: 24.21
[2020-12-16 18:09:50,457][__main__][INFO] - [4800] Loss: 0.007, Running accuracy: 99.981, Time: 24.16
[2020-12-16 18:10:15,998][__main__][INFO] - [5120] Loss: 0.003, Running accuracy: 99.982, Time: 25.54
[2020-12-16 18:10:41,407][__main__][INFO] - [5440] Loss: 0.029, Running accuracy: 99.980, Time: 25.41
[2020-12-16 18:11:06,848][__main__][INFO] - [5760] Loss: 0.002, Running accuracy: 99.981, Time: 25.44
[2020-12-16 18:11:32,802][__main__][INFO] - [6080] Loss: 0.084, Running accuracy: 99.980, Time: 25.95
[2020-12-16 18:12:04,139][__main__][INFO] - [6400] Loss: 0.003, Running accuracy: 99.981, Time: 31.34
[2020-12-16 18:12:30,141][__main__][INFO] - [6720] Loss: 0.009, Running accuracy: 99.981, Time: 26.00
[2020-12-16 18:12:57,098][__main__][INFO] - [7040] Loss: 0.006, Running accuracy: 99.982, Time: 26.96
[2020-12-16 18:13:22,509][__main__][INFO] - [7360] Loss: 0.003, Running accuracy: 99.982, Time: 25.41
[2020-12-16 18:13:50,304][__main__][INFO] - [7680] Loss: 0.010, Running accuracy: 99.982, Time: 27.79
[2020-12-16 18:14:13,911][__main__][INFO] - [8000] Loss: 0.009, Running accuracy: 99.982, Time: 23.61
[2020-12-16 18:14:39,089][__main__][INFO] - [8320] Loss: 0.014, Running accuracy: 99.981, Time: 25.18
[2020-12-16 18:15:05,051][__main__][INFO] - [8640] Loss: 0.033, Running accuracy: 99.981, Time: 25.96
[2020-12-16 18:15:30,384][__main__][INFO] - [8960] Loss: 0.014, Running accuracy: 99.980, Time: 25.33
[2020-12-16 18:15:57,872][__main__][INFO] - [9280] Loss: 0.029, Running accuracy: 99.979, Time: 27.49
[2020-12-16 18:16:22,262][__main__][INFO] - [9600] Loss: 0.007, Running accuracy: 99.979, Time: 24.39
[2020-12-16 18:16:47,293][__main__][INFO] - [9920] Loss: 0.033, Running accuracy: 99.979, Time: 25.03
[2020-12-16 18:17:12,606][__main__][INFO] - [10240] Loss: 0.004, Running accuracy: 99.979, Time: 25.31
[2020-12-16 18:17:43,975][__main__][INFO] - [10560] Loss: 0.021, Running accuracy: 99.979, Time: 31.37
[2020-12-16 18:18:08,066][__main__][INFO] - [10880] Loss: 0.010, Running accuracy: 99.979, Time: 24.09
[2020-12-16 18:18:31,523][__main__][INFO] - [11200] Loss: 0.024, Running accuracy: 99.979, Time: 23.46
[2020-12-16 18:18:57,019][__main__][INFO] - [11520] Loss: 0.017, Running accuracy: 99.978, Time: 25.50
[2020-12-16 18:19:21,572][__main__][INFO] - [11840] Loss: 0.006, Running accuracy: 99.978, Time: 24.55
[2020-12-16 18:19:45,719][__main__][INFO] - [12160] Loss: 0.019, Running accuracy: 99.978, Time: 24.15
[2020-12-16 18:20:09,790][__main__][INFO] - [12480] Loss: 0.010, Running accuracy: 99.978, Time: 24.07
[2020-12-16 18:20:35,572][__main__][INFO] - [12800] Loss: 0.016, Running accuracy: 99.978, Time: 25.78
[2020-12-16 18:21:00,046][__main__][INFO] - [13120] Loss: 0.014, Running accuracy: 99.979, Time: 24.47
[2020-12-16 18:21:24,737][__main__][INFO] - [13440] Loss: 0.025, Running accuracy: 99.979, Time: 24.69
[2020-12-16 18:21:50,351][__main__][INFO] - [13760] Loss: 0.003, Running accuracy: 99.979, Time: 25.61
[2020-12-16 18:22:17,831][__main__][INFO] - [14080] Loss: 0.012, Running accuracy: 99.979, Time: 27.48
[2020-12-16 18:22:43,053][__main__][INFO] - [14400] Loss: 0.002, Running accuracy: 99.979, Time: 25.21
[2020-12-16 18:23:06,884][__main__][INFO] - [14720] Loss: 0.005, Running accuracy: 99.980, Time: 23.83
[2020-12-16 18:23:37,229][__main__][INFO] - [15040] Loss: 0.023, Running accuracy: 99.979, Time: 30.34
[2020-12-16 18:24:01,603][__main__][INFO] - [15360] Loss: 0.011, Running accuracy: 99.979, Time: 24.37
[2020-12-16 18:24:28,083][__main__][INFO] - [15680] Loss: 0.008, Running accuracy: 99.979, Time: 26.48
[2020-12-16 18:24:52,253][__main__][INFO] - [16000] Loss: 0.025, Running accuracy: 99.979, Time: 24.17
[2020-12-16 18:25:15,783][__main__][INFO] - [16320] Loss: 0.007, Running accuracy: 99.979, Time: 23.53
[2020-12-16 18:25:41,809][__main__][INFO] - [16640] Loss: 0.010, Running accuracy: 99.979, Time: 26.02
[2020-12-16 18:26:06,295][__main__][INFO] - [16960] Loss: 0.015, Running accuracy: 99.979, Time: 24.49
[2020-12-16 18:26:31,371][__main__][INFO] - [17280] Loss: 0.004, Running accuracy: 99.979, Time: 25.07
[2020-12-16 18:26:55,695][__main__][INFO] - [17600] Loss: 0.005, Running accuracy: 99.980, Time: 24.32
[2020-12-16 18:27:19,346][__main__][INFO] - [17920] Loss: 0.023, Running accuracy: 99.979, Time: 23.65
[2020-12-16 18:27:42,854][__main__][INFO] - [18240] Loss: 0.021, Running accuracy: 99.978, Time: 23.51
[2020-12-16 18:28:06,935][__main__][INFO] - [18560] Loss: 0.054, Running accuracy: 99.978, Time: 24.08
[2020-12-16 18:28:32,403][__main__][INFO] - [18880] Loss: 0.018, Running accuracy: 99.978, Time: 25.47
[2020-12-16 18:28:57,284][__main__][INFO] - [19200] Loss: 0.029, Running accuracy: 99.978, Time: 24.88
[2020-12-16 18:29:27,549][__main__][INFO] - [19520] Loss: 0.004, Running accuracy: 99.978, Time: 30.26
[2020-12-16 18:29:54,288][__main__][INFO] - [19840] Loss: 0.010, Running accuracy: 99.978, Time: 26.74
[2020-12-16 18:30:18,939][__main__][INFO] - [20160] Loss: 0.001, Running accuracy: 99.979, Time: 24.65
[2020-12-16 18:30:42,578][__main__][INFO] - [20480] Loss: 0.018, Running accuracy: 99.979, Time: 23.64
[2020-12-16 18:31:05,522][__main__][INFO] - [20800] Loss: 0.005, Running accuracy: 99.979, Time: 22.94
[2020-12-16 18:31:32,047][__main__][INFO] - [21120] Loss: 0.007, Running accuracy: 99.979, Time: 26.52
[2020-12-16 18:31:56,817][__main__][INFO] - [21440] Loss: 0.007, Running accuracy: 99.979, Time: 24.77
[2020-12-16 18:32:19,344][__main__][INFO] - [21760] Loss: 0.011, Running accuracy: 99.979, Time: 22.53
[2020-12-16 18:32:45,210][__main__][INFO] - [22080] Loss: 0.023, Running accuracy: 99.979, Time: 25.87
[2020-12-16 18:33:10,575][__main__][INFO] - [22400] Loss: 0.041, Running accuracy: 99.979, Time: 25.36
[2020-12-16 18:33:34,478][__main__][INFO] - [22720] Loss: 0.011, Running accuracy: 99.979, Time: 23.90
[2020-12-16 18:33:59,648][__main__][INFO] - [23040] Loss: 0.007, Running accuracy: 99.979, Time: 25.17
[2020-12-16 18:34:25,111][__main__][INFO] - [23360] Loss: 0.008, Running accuracy: 99.979, Time: 25.46
[2020-12-16 18:34:50,451][__main__][INFO] - [23680] Loss: 0.014, Running accuracy: 99.979, Time: 25.34
[2020-12-16 18:35:22,159][__main__][INFO] - [24000] Loss: 0.007, Running accuracy: 99.979, Time: 31.71
[2020-12-16 18:35:46,936][__main__][INFO] - [24320] Loss: 0.011, Running accuracy: 99.979, Time: 24.78
[2020-12-16 18:36:14,028][__main__][INFO] - [24640] Loss: 0.033, Running accuracy: 99.979, Time: 27.09
[2020-12-16 18:36:37,567][__main__][INFO] - [24960] Loss: 0.005, Running accuracy: 99.979, Time: 23.45
[2020-12-16 18:37:03,813][__main__][INFO] - [25280] Loss: 0.013, Running accuracy: 99.979, Time: 26.24
[2020-12-16 18:37:28,572][__main__][INFO] - [25600] Loss: 0.014, Running accuracy: 99.980, Time: 24.76
[2020-12-16 18:37:53,069][__main__][INFO] - [25920] Loss: 0.020, Running accuracy: 99.979, Time: 24.50
[2020-12-16 18:38:19,440][__main__][INFO] - [26240] Loss: 0.003, Running accuracy: 99.980, Time: 26.37
[2020-12-16 18:38:43,584][__main__][INFO] - [26560] Loss: 0.006, Running accuracy: 99.980, Time: 24.14
[2020-12-16 18:39:07,895][__main__][INFO] - [26880] Loss: 0.012, Running accuracy: 99.980, Time: 24.31
[2020-12-16 18:39:33,823][__main__][INFO] - [27200] Loss: 0.003, Running accuracy: 99.980, Time: 25.93
[2020-12-16 18:39:58,105][__main__][INFO] - [27520] Loss: 0.015, Running accuracy: 99.980, Time: 24.27
[2020-12-16 18:40:21,893][__main__][INFO] - [27840] Loss: 0.039, Running accuracy: 99.980, Time: 23.79
[2020-12-16 18:40:53,373][__main__][INFO] - [28160] Loss: 0.030, Running accuracy: 99.979, Time: 31.48
[2020-12-16 18:41:18,830][__main__][INFO] - [28480] Loss: 0.006, Running accuracy: 99.979, Time: 25.46
[2020-12-16 18:41:45,404][__main__][INFO] - [28800] Loss: 0.025, Running accuracy: 99.979, Time: 26.57
[2020-12-16 18:42:11,237][__main__][INFO] - [29120] Loss: 0.014, Running accuracy: 99.979, Time: 25.83
[2020-12-16 18:42:35,666][__main__][INFO] - [29440] Loss: 0.002, Running accuracy: 99.979, Time: 24.43
[2020-12-16 18:43:00,577][__main__][INFO] - [29760] Loss: 0.008, Running accuracy: 99.979, Time: 24.91
[2020-12-16 18:43:24,192][__main__][INFO] - [30080] Loss: 0.025, Running accuracy: 99.979, Time: 23.61
[2020-12-16 18:43:48,878][__main__][INFO] - [30400] Loss: 0.038, Running accuracy: 99.979, Time: 24.68
[2020-12-16 18:44:13,874][__main__][INFO] - [30720] Loss: 0.022, Running accuracy: 99.979, Time: 24.99
[2020-12-16 18:44:39,242][__main__][INFO] - [31040] Loss: 0.010, Running accuracy: 99.979, Time: 25.37
[2020-12-16 18:45:04,614][__main__][INFO] - [31360] Loss: 0.022, Running accuracy: 99.978, Time: 25.37
[2020-12-16 18:45:29,524][__main__][INFO] - [31680] Loss: 0.030, Running accuracy: 99.978, Time: 24.91
[2020-12-16 18:45:54,616][__main__][INFO] - [32000] Loss: 0.003, Running accuracy: 99.978, Time: 25.09
[2020-12-16 18:46:21,683][__main__][INFO] - [32320] Loss: 0.015, Running accuracy: 99.978, Time: 27.07
[2020-12-16 18:46:52,530][__main__][INFO] - [32640] Loss: 0.017, Running accuracy: 99.978, Time: 30.85
[2020-12-16 18:47:16,803][__main__][INFO] - [32960] Loss: 0.013, Running accuracy: 99.978, Time: 24.27
[2020-12-16 18:47:42,669][__main__][INFO] - [33280] Loss: 0.014, Running accuracy: 99.978, Time: 25.86
[2020-12-16 18:48:07,660][__main__][INFO] - [33600] Loss: 0.002, Running accuracy: 99.979, Time: 24.99
[2020-12-16 18:48:31,996][__main__][INFO] - [33920] Loss: 0.012, Running accuracy: 99.979, Time: 24.34
[2020-12-16 18:48:57,688][__main__][INFO] - [34240] Loss: 0.002, Running accuracy: 99.979, Time: 25.69
[2020-12-16 18:49:22,422][__main__][INFO] - [34560] Loss: 0.006, Running accuracy: 99.979, Time: 24.73
[2020-12-16 18:49:46,993][__main__][INFO] - [34880] Loss: 0.022, Running accuracy: 99.979, Time: 24.57
[2020-12-16 18:50:12,261][__main__][INFO] - [35200] Loss: 0.007, Running accuracy: 99.979, Time: 25.27
[2020-12-16 18:50:37,310][__main__][INFO] - [35520] Loss: 0.015, Running accuracy: 99.979, Time: 25.05
[2020-12-16 18:51:02,557][__main__][INFO] - [35840] Loss: 0.016, Running accuracy: 99.979, Time: 25.25
[2020-12-16 18:51:28,293][__main__][INFO] - [36160] Loss: 0.001, Running accuracy: 99.979, Time: 25.74
[2020-12-16 18:51:53,369][__main__][INFO] - [36480] Loss: 0.023, Running accuracy: 99.979, Time: 25.08
[2020-12-16 18:52:17,144][__main__][INFO] - [36800] Loss: 0.053, Running accuracy: 99.978, Time: 23.77
[2020-12-16 18:52:46,309][__main__][INFO] - [37120] Loss: 0.021, Running accuracy: 99.978, Time: 29.16
[2020-12-16 18:53:11,100][__main__][INFO] - [37440] Loss: 0.030, Running accuracy: 99.978, Time: 24.79
[2020-12-16 18:53:34,535][__main__][INFO] - [37760] Loss: 0.012, Running accuracy: 99.978, Time: 23.43
[2020-12-16 18:54:00,247][__main__][INFO] - [38080] Loss: 0.002, Running accuracy: 99.978, Time: 25.71
[2020-12-16 18:54:25,247][__main__][INFO] - [38400] Loss: 0.036, Running accuracy: 99.978, Time: 25.00
[2020-12-16 18:54:48,405][__main__][INFO] - [38720] Loss: 0.065, Running accuracy: 99.977, Time: 23.16
[2020-12-16 18:55:13,932][__main__][INFO] - [39040] Loss: 0.043, Running accuracy: 99.977, Time: 25.53
[2020-12-16 18:55:38,996][__main__][INFO] - [39360] Loss: 0.016, Running accuracy: 99.977, Time: 25.06
[2020-12-16 18:56:04,644][__main__][INFO] - [39680] Loss: 0.020, Running accuracy: 99.977, Time: 25.65
[2020-12-16 18:56:14,772][__main__][INFO] - Action accuracy: 99.977, Loss: 0.017
[2020-12-16 18:56:14,773][__main__][INFO] - Validating..
[2020-12-16 18:56:48,333][test][INFO] - Time elapsed: 31.264519
[2020-12-16 18:56:48,338][__main__][INFO] - Validation F1 score: 95.550, Exact match: 55.590, Precision: 95.520, Recall: 95.580
[2020-12-16 18:57:22,695][__main__][INFO] - Checkpoint saved to checkpoints/model_latest.pth
[2020-12-16 18:57:23,703][__main__][INFO] - Epoch #59
[2020-12-16 18:57:23,704][__main__][INFO] - Training..
[2020-12-16 18:57:49,339][__main__][INFO] - [320] Loss: 0.019, Running accuracy: 99.987, Time: 24.55
[2020-12-16 18:58:15,947][__main__][INFO] - [640] Loss: 0.001, Running accuracy: 99.994, Time: 26.61
[2020-12-16 18:58:43,146][__main__][INFO] - [960] Loss: 0.005, Running accuracy: 99.996, Time: 27.20
[2020-12-16 18:59:10,532][__main__][INFO] - [1280] Loss: 0.013, Running accuracy: 99.990, Time: 27.39
[2020-12-16 18:59:35,308][__main__][INFO] - [1600] Loss: 0.012, Running accuracy: 99.990, Time: 24.77
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[2020-12-16 19:50:12,391][__main__][INFO] - Action accuracy: 99.977, Loss: 0.017
[2020-12-16 19:50:12,392][__main__][INFO] - Validating..
[2020-12-16 19:50:39,938][test][INFO] - Time elapsed: 25.288291
[2020-12-16 19:50:39,943][__main__][INFO] - Validation F1 score: 95.510, Exact match: 55.410, Precision: 95.470, Recall: 95.560
[2020-12-16 19:51:14,260][__main__][INFO] - Checkpoint saved to checkpoints/model_latest.pth
[2020-12-16 19:51:15,425][__main__][INFO] - Training completed. Launching the testing script..
[2020-12-16 19:51:22,060][__main__][INFO] - python test.py model_path=runs/ptb_bert_graph_seed3/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 19:51:40,487][__main__][INFO] - 
exp_id: test
model_path: runs/ptb_bert_graph_seed3/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 19:51:40,487][__main__][INFO] - Loading the model from /n/fs/pvl-mathqa/attach-juxtapose-parser/runs/ptb_bert_graph_seed3/checkpoints/model_latest.pth
[2020-12-16 19:51:47,698][__main__][INFO] - Restoring hyperparameters from the saved model checkpoint..
[2020-12-16 19:51:47,699][__main__][INFO] - d_model: 2048
[2020-12-16 19:51:47,699][__main__][INFO] - encoder: bert-large-uncased
[2020-12-16 19:51:47,700][__main__][INFO] - use_words: False
[2020-12-16 19:51:47,700][__main__][INFO] - use_tags: False
[2020-12-16 19:51:47,700][__main__][INFO] - d_kqv: 64
[2020-12-16 19:51:47,700][__main__][INFO] - d_ff: 2048
[2020-12-16 19:51:47,701][__main__][INFO] - word_emb_dropout: 0.3
[2020-12-16 19:51:47,701][__main__][INFO] - tag_emb_dropout: 0
[2020-12-16 19:51:47,701][__main__][INFO] - relu_dropout: 0.2
[2020-12-16 19:51:47,701][__main__][INFO] - residual_dropout: 0
[2020-12-16 19:51:47,702][__main__][INFO] - attention_dropout: 0
[2020-12-16 19:51:47,702][__main__][INFO] - num_attn_layers: 4
[2020-12-16 19:51:47,702][__main__][INFO] - num_attn_heads: 8
[2020-12-16 19:51:47,702][__main__][INFO] - decoder: graph
[2020-12-16 19:51:47,703][__main__][INFO] - num_gcn_layers: 4
[2020-12-16 19:51:47,703][__main__][WARNING] - Missing: d_decoder
[2020-12-16 19:51:47,703][__main__][INFO] - max_sentence_len: 250
[2020-12-16 19:51:47,704][dataloader][INFO] - Loading constituency trees from /n/fs/pvl-mathqa/attach-juxtapose-parser/data/22.auto.clean
[2020-12-16 19:51:51,116][dataloader][INFO] - Loading constituency trees from /n/fs/pvl-mathqa/attach-juxtapose-parser/data/23.auto.clean
[2020-12-16 19:52:08,187][__main__][INFO] - 
Parser(
  (encoder): Encoder(
    (word_embedding): TransformerEmbedding(
      (contextual_embedding): BertModel(
        (embeddings): BertEmbeddings(
          (word_embeddings): Embedding(30522, 1024, padding_idx=0)
          (position_embeddings): Embedding(512, 1024)
          (token_type_embeddings): Embedding(2, 1024)
          (LayerNorm): LayerNorm((1024,), eps=1e-12, elementwise_affine=True)
          (dropout): Dropout(p=0.1, inplace=False)
        )
        (encoder): BertEncoder(
          (layer): ModuleList(
            (0): BertLayer(
              (attention): BertAttention(
                (self): BertSelfAttention(
                  (query): Linear(in_features=1024, out_features=1024, bias=True)
                  (key): Linear(in_features=1024, out_features=1024, bias=True)
                  (value): Linear(in_features=1024, out_features=1024, bias=True)
                  (dropout): Dropout(p=0.1, inplace=False)
                )
                (output): BertSelfOutput(
                  (dense): Linear(in_features=1024, out_features=1024, bias=True)
                  (LayerNorm): LayerNorm((1024,), eps=1e-12, elementwise_affine=True)
                  (dropout): Dropout(p=0.1, inplace=False)
                )
              )
              (intermediate): BertIntermediate(
                (dense): Linear(in_features=1024, out_features=4096, bias=True)
              )
              (output): BertOutput(
                (dense): Linear(in_features=4096, out_features=1024, bias=True)
                (LayerNorm): LayerNorm((1024,), eps=1e-12, elementwise_affine=True)
                (dropout): Dropout(p=0.1, inplace=False)
              )
            )
            (1): BertLayer(
              (attention): BertAttention(
                (self): BertSelfAttention(
                  (query): Linear(in_features=1024, out_features=1024, bias=True)
                  (key): Linear(in_features=1024, out_features=1024, bias=True)
                  (value): Linear(in_features=1024, out_features=1024, bias=True)
                  (dropout): Dropout(p=0.1, inplace=False)
                )
                (output): BertSelfOutput(
                  (dense): Linear(in_features=1024, out_features=1024, bias=True)
                  (LayerNorm): LayerNorm((1024,), eps=1e-12, elementwise_affine=True)
                  (dropout): Dropout(p=0.1, inplace=False)
                )
              )
              (intermediate): BertIntermediate(
                (dense): Linear(in_features=1024, out_features=4096, bias=True)
              )
              (output): BertOutput(
                (dense): Linear(in_features=4096, out_features=1024, bias=True)
                (LayerNorm): LayerNorm((1024,), eps=1e-12, elementwise_affine=True)
                (dropout): Dropout(p=0.1, inplace=False)
              )
            )
            (2): BertLayer(
              (attention): BertAttention(
                (self): BertSelfAttention(
                  (query): Linear(in_features=1024, out_features=1024, bias=True)
                  (key): Linear(in_features=1024, out_features=1024, bias=True)
                  (value): Linear(in_features=1024, out_features=1024, bias=True)
                  (dropout): Dropout(p=0.1, inplace=False)
                )
                (output): BertSelfOutput(
                  (dense): Linear(in_features=1024, out_features=1024, bias=True)
                  (LayerNorm): LayerNorm((1024,), eps=1e-12, elementwise_affine=True)
                  (dropout): Dropout(p=0.1, inplace=False)
                )
              )
              (intermediate): BertIntermediate(
                (dense): Linear(in_features=1024, out_features=4096, bias=True)
              )
              (output): BertOutput(
                (dense): Linear(in_features=4096, out_features=1024, bias=True)
                (LayerNorm): LayerNorm((1024,), eps=1e-12, elementwise_affine=True)
                (dropout): Dropout(p=0.1, inplace=False)
              )
            )
            (3): BertLayer(
              (attention): BertAttention(
                (self): BertSelfAttention(
                  (query): Linear(in_features=1024, out_features=1024, bias=True)
                  (key): Linear(in_features=1024, out_features=1024, bias=True)
                  (value): Linear(in_features=1024, out_features=1024, bias=True)
                  (dropout): Dropout(p=0.1, inplace=False)
                )
                (output): BertSelfOutput(
                  (dense): Linear(in_features=1024, out_features=1024, bias=True)
                  (LayerNorm): LayerNorm((1024,), eps=1e-12, elementwise_affine=True)
                  (dropout): Dropout(p=0.1, inplace=False)
                )
              )
              (intermediate): BertIntermediate(
                (dense): Linear(in_features=1024, out_features=4096, bias=True)
              )
              (output): BertOutput(
                (dense): Linear(in_features=4096, out_features=1024, bias=True)
                (LayerNorm): LayerNorm((1024,), eps=1e-12, elementwise_affine=True)
                (dropout): Dropout(p=0.1, inplace=False)
              )
            )
            (4): BertLayer(
              (attention): BertAttention(
                (self): BertSelfAttention(
                  (query): Linear(in_features=1024, out_features=1024, bias=True)
                  (key): Linear(in_features=1024, out_features=1024, bias=True)
                  (value): Linear(in_features=1024, out_features=1024, bias=True)
                  (dropout): Dropout(p=0.1, inplace=False)
                )
                (output): BertSelfOutput(
                  (dense): Linear(in_features=1024, out_features=1024, bias=True)
                  (LayerNorm): LayerNorm((1024,), eps=1e-12, elementwise_affine=True)
                  (dropout): Dropout(p=0.1, inplace=False)
                )
              )
              (intermediate): BertIntermediate(
                (dense): Linear(in_features=1024, out_features=4096, bias=True)
              )
              (output): BertOutput(
                (dense): Linear(in_features=4096, out_features=1024, bias=True)
                (LayerNorm): LayerNorm((1024,), eps=1e-12, elementwise_affine=True)
                (dropout): Dropout(p=0.1, inplace=False)
              )
            )
            (5): BertLayer(
              (attention): BertAttention(
                (self): BertSelfAttention(
                  (query): Linear(in_features=1024, out_features=1024, bias=True)
                  (key): Linear(in_features=1024, out_features=1024, bias=True)
                  (value): Linear(in_features=1024, out_features=1024, bias=True)
                  (dropout): Dropout(p=0.1, inplace=False)
                )
                (output): BertSelfOutput(
                  (dense): Linear(in_features=1024, out_features=1024, bias=True)
                  (LayerNorm): LayerNorm((1024,), eps=1e-12, elementwise_affine=True)
                  (dropout): Dropout(p=0.1, inplace=False)
                )
              )
              (intermediate): BertIntermediate(
                (dense): Linear(in_features=1024, out_features=4096, bias=True)
              )
              (output): BertOutput(
                (dense): Linear(in_features=4096, out_features=1024, bias=True)
                (LayerNorm): LayerNorm((1024,), eps=1e-12, elementwise_affine=True)
                (dropout): Dropout(p=0.1, inplace=False)
              )
            )
            (6): BertLayer(
              (attention): BertAttention(
                (self): BertSelfAttention(
                  (query): Linear(in_features=1024, out_features=1024, bias=True)
                  (key): Linear(in_features=1024, out_features=1024, bias=True)
                  (value): Linear(in_features=1024, out_features=1024, bias=True)
                  (dropout): Dropout(p=0.1, inplace=False)
                )
                (output): BertSelfOutput(
                  (dense): Linear(in_features=1024, out_features=1024, bias=True)
                  (LayerNorm): LayerNorm((1024,), eps=1e-12, elementwise_affine=True)
                  (dropout): Dropout(p=0.1, inplace=False)
                )
              )
              (intermediate): BertIntermediate(
                (dense): Linear(in_features=1024, out_features=4096, bias=True)
              )
              (output): BertOutput(
                (dense): Linear(in_features=4096, out_features=1024, bias=True)
                (LayerNorm): LayerNorm((1024,), eps=1e-12, elementwise_affine=True)
                (dropout): Dropout(p=0.1, inplace=False)
              )
            )
            (7): BertLayer(
              (attention): BertAttention(
                (self): BertSelfAttention(
                  (query): Linear(in_features=1024, out_features=1024, bias=True)
                  (key): Linear(in_features=1024, out_features=1024, bias=True)
                  (value): Linear(in_features=1024, out_features=1024, bias=True)
                  (dropout): Dropout(p=0.1, inplace=False)
                )
                (output): BertSelfOutput(
                  (dense): Linear(in_features=1024, out_features=1024, bias=True)
                  (LayerNorm): LayerNorm((1024,), eps=1e-12, elementwise_affine=True)
                  (dropout): Dropout(p=0.1, inplace=False)
                )
              )
              (intermediate): BertIntermediate(
                (dense): Linear(in_features=1024, out_features=4096, bias=True)
              )
              (output): BertOutput(
                (dense): Linear(in_features=4096, out_features=1024, bias=True)
                (LayerNorm): LayerNorm((1024,), eps=1e-12, elementwise_affine=True)
                (dropout): Dropout(p=0.1, inplace=False)
              )
            )
            (8): BertLayer(
              (attention): BertAttention(
                (self): BertSelfAttention(
                  (query): Linear(in_features=1024, out_features=1024, bias=True)
                  (key): Linear(in_features=1024, out_features=1024, bias=True)
                  (value): Linear(in_features=1024, out_features=1024, bias=True)
                  (dropout): Dropout(p=0.1, inplace=False)
                )
                (output): BertSelfOutput(
                  (dense): Linear(in_features=1024, out_features=1024, bias=True)
                  (LayerNorm): LayerNorm((1024,), eps=1e-12, elementwise_affine=True)
                  (dropout): Dropout(p=0.1, inplace=False)
                )
              )
              (intermediate): BertIntermediate(
                (dense): Linear(in_features=1024, out_features=4096, bias=True)
              )
              (output): BertOutput(
                (dense): Linear(in_features=4096, out_features=1024, bias=True)
                (LayerNorm): LayerNorm((1024,), eps=1e-12, elementwise_affine=True)
                (dropout): Dropout(p=0.1, inplace=False)
              )
            )
            (9): BertLayer(
              (attention): BertAttention(
                (self): BertSelfAttention(
                  (query): Linear(in_features=1024, out_features=1024, bias=True)
                  (key): Linear(in_features=1024, out_features=1024, bias=True)
                  (value): Linear(in_features=1024, out_features=1024, bias=True)
                  (dropout): Dropout(p=0.1, inplace=False)
                )
                (output): BertSelfOutput(
                  (dense): Linear(in_features=1024, out_features=1024, bias=True)
                  (LayerNorm): LayerNorm((1024,), eps=1e-12, elementwise_affine=True)
                  (dropout): Dropout(p=0.1, inplace=False)
                )
              )
              (intermediate): BertIntermediate(
                (dense): Linear(in_features=1024, out_features=4096, bias=True)
              )
              (output): BertOutput(
                (dense): Linear(in_features=4096, out_features=1024, bias=True)
                (LayerNorm): LayerNorm((1024,), eps=1e-12, elementwise_affine=True)
                (dropout): Dropout(p=0.1, inplace=False)
              )
            )
            (10): BertLayer(
              (attention): BertAttention(
                (self): BertSelfAttention(
                  (query): Linear(in_features=1024, out_features=1024, bias=True)
                  (key): Linear(in_features=1024, out_features=1024, bias=True)
                  (value): Linear(in_features=1024, out_features=1024, bias=True)
                  (dropout): Dropout(p=0.1, inplace=False)
                )
                (output): BertSelfOutput(
                  (dense): Linear(in_features=1024, out_features=1024, bias=True)
                  (LayerNorm): LayerNorm((1024,), eps=1e-12, elementwise_affine=True)
                  (dropout): Dropout(p=0.1, inplace=False)
                )
              )
              (intermediate): BertIntermediate(
                (dense): Linear(in_features=1024, out_features=4096, bias=True)
              )
              (output): BertOutput(
                (dense): Linear(in_features=4096, out_features=1024, bias=True)
                (LayerNorm): LayerNorm((1024,), eps=1e-12, elementwise_affine=True)
                (dropout): Dropout(p=0.1, inplace=False)
              )
            )
            (11): BertLayer(
              (attention): BertAttention(
                (self): BertSelfAttention(
                  (query): Linear(in_features=1024, out_features=1024, bias=True)
                  (key): Linear(in_features=1024, out_features=1024, bias=True)
                  (value): Linear(in_features=1024, out_features=1024, bias=True)
                  (dropout): Dropout(p=0.1, inplace=False)
                )
                (output): BertSelfOutput(
                  (dense): Linear(in_features=1024, out_features=1024, bias=True)
                  (LayerNorm): LayerNorm((1024,), eps=1e-12, elementwise_affine=True)
                  (dropout): Dropout(p=0.1, inplace=False)
                )
              )
              (intermediate): BertIntermediate(
                (dense): Linear(in_features=1024, out_features=4096, bias=True)
              )
              (output): BertOutput(
                (dense): Linear(in_features=4096, out_features=1024, bias=True)
                (LayerNorm): LayerNorm((1024,), eps=1e-12, elementwise_affine=True)
                (dropout): Dropout(p=0.1, inplace=False)
              )
            )
            (12): BertLayer(
              (attention): BertAttention(
                (self): BertSelfAttention(
                  (query): Linear(in_features=1024, out_features=1024, bias=True)
                  (key): Linear(in_features=1024, out_features=1024, bias=True)
                  (value): Linear(in_features=1024, out_features=1024, bias=True)
                  (dropout): Dropout(p=0.1, inplace=False)
                )
                (output): BertSelfOutput(
                  (dense): Linear(in_features=1024, out_features=1024, bias=True)
                  (LayerNorm): LayerNorm((1024,), eps=1e-12, elementwise_affine=True)
                  (dropout): Dropout(p=0.1, inplace=False)
                )
              )
              (intermediate): BertIntermediate(
                (dense): Linear(in_features=1024, out_features=4096, bias=True)
              )
              (output): BertOutput(
                (dense): Linear(in_features=4096, out_features=1024, bias=True)
                (LayerNorm): LayerNorm((1024,), eps=1e-12, elementwise_affine=True)
                (dropout): Dropout(p=0.1, inplace=False)
              )
            )
            (13): BertLayer(
              (attention): BertAttention(
                (self): BertSelfAttention(
                  (query): Linear(in_features=1024, out_features=1024, bias=True)
                  (key): Linear(in_features=1024, out_features=1024, bias=True)
                  (value): Linear(in_features=1024, out_features=1024, bias=True)
                  (dropout): Dropout(p=0.1, inplace=False)
                )
                (output): BertSelfOutput(
                  (dense): Linear(in_features=1024, out_features=1024, bias=True)
                  (LayerNorm): LayerNorm((1024,), eps=1e-12, elementwise_affine=True)
                  (dropout): Dropout(p=0.1, inplace=False)
                )
              )
              (intermediate): BertIntermediate(
                (dense): Linear(in_features=1024, out_features=4096, bias=True)
              )
              (output): BertOutput(
                (dense): Linear(in_features=4096, out_features=1024, bias=True)
                (LayerNorm): LayerNorm((1024,), eps=1e-12, elementwise_affine=True)
                (dropout): Dropout(p=0.1, inplace=False)
              )
            )
            (14): BertLayer(
              (attention): BertAttention(
                (self): BertSelfAttention(
                  (query): Linear(in_features=1024, out_features=1024, bias=True)
                  (key): Linear(in_features=1024, out_features=1024, bias=True)
                  (value): Linear(in_features=1024, out_features=1024, bias=True)
                  (dropout): Dropout(p=0.1, inplace=False)
                )
                (output): BertSelfOutput(
                  (dense): Linear(in_features=1024, out_features=1024, bias=True)
                  (LayerNorm): LayerNorm((1024,), eps=1e-12, elementwise_affine=True)
                  (dropout): Dropout(p=0.1, inplace=False)
                )
              )
              (intermediate): BertIntermediate(
                (dense): Linear(in_features=1024, out_features=4096, bias=True)
              )
              (output): BertOutput(
                (dense): Linear(in_features=4096, out_features=1024, bias=True)
                (LayerNorm): LayerNorm((1024,), eps=1e-12, elementwise_affine=True)
                (dropout): Dropout(p=0.1, inplace=False)
              )
            )
            (15): BertLayer(
              (attention): BertAttention(
                (self): BertSelfAttention(
                  (query): Linear(in_features=1024, out_features=1024, bias=True)
                  (key): Linear(in_features=1024, out_features=1024, bias=True)
                  (value): Linear(in_features=1024, out_features=1024, bias=True)
                  (dropout): Dropout(p=0.1, inplace=False)
                )
                (output): BertSelfOutput(
                  (dense): Linear(in_features=1024, out_features=1024, bias=True)
                  (LayerNorm): LayerNorm((1024,), eps=1e-12, elementwise_affine=True)
                  (dropout): Dropout(p=0.1, inplace=False)
                )
              )
              (intermediate): BertIntermediate(
                (dense): Linear(in_features=1024, out_features=4096, bias=True)
              )
              (output): BertOutput(
                (dense): Linear(in_features=4096, out_features=1024, bias=True)
                (LayerNorm): LayerNorm((1024,), eps=1e-12, elementwise_affine=True)
                (dropout): Dropout(p=0.1, inplace=False)
              )
            )
            (16): BertLayer(
              (attention): BertAttention(
                (self): BertSelfAttention(
                  (query): Linear(in_features=1024, out_features=1024, bias=True)
                  (key): Linear(in_features=1024, out_features=1024, bias=True)
                  (value): Linear(in_features=1024, out_features=1024, bias=True)
                  (dropout): Dropout(p=0.1, inplace=False)
                )
                (output): BertSelfOutput(
                  (dense): Linear(in_features=1024, out_features=1024, bias=True)
                  (LayerNorm): LayerNorm((1024,), eps=1e-12, elementwise_affine=True)
                  (dropout): Dropout(p=0.1, inplace=False)
                )
              )
              (intermediate): BertIntermediate(
                (dense): Linear(in_features=1024, out_features=4096, bias=True)
              )
              (output): BertOutput(
                (dense): Linear(in_features=4096, out_features=1024, bias=True)
                (LayerNorm): LayerNorm((1024,), eps=1e-12, elementwise_affine=True)
                (dropout): Dropout(p=0.1, inplace=False)
              )
            )
            (17): BertLayer(
              (attention): BertAttention(
                (self): BertSelfAttention(
                  (query): Linear(in_features=1024, out_features=1024, bias=True)
                  (key): Linear(in_features=1024, out_features=1024, bias=True)
                  (value): Linear(in_features=1024, out_features=1024, bias=True)
                  (dropout): Dropout(p=0.1, inplace=False)
                )
                (output): BertSelfOutput(
                  (dense): Linear(in_features=1024, out_features=1024, bias=True)
                  (LayerNorm): LayerNorm((1024,), eps=1e-12, elementwise_affine=True)
                  (dropout): Dropout(p=0.1, inplace=False)
                )
              )
              (intermediate): BertIntermediate(
                (dense): Linear(in_features=1024, out_features=4096, bias=True)
              )
              (output): BertOutput(
                (dense): Linear(in_features=4096, out_features=1024, bias=True)
                (LayerNorm): LayerNorm((1024,), eps=1e-12, elementwise_affine=True)
                (dropout): Dropout(p=0.1, inplace=False)
              )
            )
            (18): BertLayer(
              (attention): BertAttention(
                (self): BertSelfAttention(
                  (query): Linear(in_features=1024, out_features=1024, bias=True)
                  (key): Linear(in_features=1024, out_features=1024, bias=True)
                  (value): Linear(in_features=1024, out_features=1024, bias=True)
                  (dropout): Dropout(p=0.1, inplace=False)
                )
                (output): BertSelfOutput(
                  (dense): Linear(in_features=1024, out_features=1024, bias=True)
                  (LayerNorm): LayerNorm((1024,), eps=1e-12, elementwise_affine=True)
                  (dropout): Dropout(p=0.1, inplace=False)
                )
              )
              (intermediate): BertIntermediate(
                (dense): Linear(in_features=1024, out_features=4096, bias=True)
              )
              (output): BertOutput(
                (dense): Linear(in_features=4096, out_features=1024, bias=True)
                (LayerNorm): LayerNorm((1024,), eps=1e-12, elementwise_affine=True)
                (dropout): Dropout(p=0.1, inplace=False)
              )
            )
            (19): BertLayer(
              (attention): BertAttention(
                (self): BertSelfAttention(
                  (query): Linear(in_features=1024, out_features=1024, bias=True)
                  (key): Linear(in_features=1024, out_features=1024, bias=True)
                  (value): Linear(in_features=1024, out_features=1024, bias=True)
                  (dropout): Dropout(p=0.1, inplace=False)
                )
                (output): BertSelfOutput(
                  (dense): Linear(in_features=1024, out_features=1024, bias=True)
                  (LayerNorm): LayerNorm((1024,), eps=1e-12, elementwise_affine=True)
                  (dropout): Dropout(p=0.1, inplace=False)
                )
              )
              (intermediate): BertIntermediate(
                (dense): Linear(in_features=1024, out_features=4096, bias=True)
              )
              (output): BertOutput(
                (dense): Linear(in_features=4096, out_features=1024, bias=True)
                (LayerNorm): LayerNorm((1024,), eps=1e-12, elementwise_affine=True)
                (dropout): Dropout(p=0.1, inplace=False)
              )
            )
            (20): BertLayer(
              (attention): BertAttention(
                (self): BertSelfAttention(
                  (query): Linear(in_features=1024, out_features=1024, bias=True)
                  (key): Linear(in_features=1024, out_features=1024, bias=True)
                  (value): Linear(in_features=1024, out_features=1024, bias=True)
                  (dropout): Dropout(p=0.1, inplace=False)
                )
                (output): BertSelfOutput(
                  (dense): Linear(in_features=1024, out_features=1024, bias=True)
                  (LayerNorm): LayerNorm((1024,), eps=1e-12, elementwise_affine=True)
                  (dropout): Dropout(p=0.1, inplace=False)
                )
              )
              (intermediate): BertIntermediate(
                (dense): Linear(in_features=1024, out_features=4096, bias=True)
              )
              (output): BertOutput(
                (dense): Linear(in_features=4096, out_features=1024, bias=True)
                (LayerNorm): LayerNorm((1024,), eps=1e-12, elementwise_affine=True)
                (dropout): Dropout(p=0.1, inplace=False)
              )
            )
            (21): BertLayer(
              (attention): BertAttention(
                (self): BertSelfAttention(
                  (query): Linear(in_features=1024, out_features=1024, bias=True)
                  (key): Linear(in_features=1024, out_features=1024, bias=True)
                  (value): Linear(in_features=1024, out_features=1024, bias=True)
                  (dropout): Dropout(p=0.1, inplace=False)
                )
                (output): BertSelfOutput(
                  (dense): Linear(in_features=1024, out_features=1024, bias=True)
                  (LayerNorm): LayerNorm((1024,), eps=1e-12, elementwise_affine=True)
                  (dropout): Dropout(p=0.1, inplace=False)
                )
              )
              (intermediate): BertIntermediate(
                (dense): Linear(in_features=1024, out_features=4096, bias=True)
              )
              (output): BertOutput(
                (dense): Linear(in_features=4096, out_features=1024, bias=True)
                (LayerNorm): LayerNorm((1024,), eps=1e-12, elementwise_affine=True)
                (dropout): Dropout(p=0.1, inplace=False)
              )
            )
            (22): BertLayer(
              (attention): BertAttention(
                (self): BertSelfAttention(
                  (query): Linear(in_features=1024, out_features=1024, bias=True)
                  (key): Linear(in_features=1024, out_features=1024, bias=True)
                  (value): Linear(in_features=1024, out_features=1024, bias=True)
                  (dropout): Dropout(p=0.1, inplace=False)
                )
                (output): BertSelfOutput(
                  (dense): Linear(in_features=1024, out_features=1024, bias=True)
                  (LayerNorm): LayerNorm((1024,), eps=1e-12, elementwise_affine=True)
                  (dropout): Dropout(p=0.1, inplace=False)
                )
              )
              (intermediate): BertIntermediate(
                (dense): Linear(in_features=1024, out_features=4096, bias=True)
              )
              (output): BertOutput(
                (dense): Linear(in_features=4096, out_features=1024, bias=True)
                (LayerNorm): LayerNorm((1024,), eps=1e-12, elementwise_affine=True)
                (dropout): Dropout(p=0.1, inplace=False)
              )
            )
            (23): BertLayer(
              (attention): BertAttention(
                (self): BertSelfAttention(
                  (query): Linear(in_features=1024, out_features=1024, bias=True)
                  (key): Linear(in_features=1024, out_features=1024, bias=True)
                  (value): Linear(in_features=1024, out_features=1024, bias=True)
                  (dropout): Dropout(p=0.1, inplace=False)
                )
                (output): BertSelfOutput(
                  (dense): Linear(in_features=1024, out_features=1024, bias=True)
                  (LayerNorm): LayerNorm((1024,), eps=1e-12, elementwise_affine=True)
                  (dropout): Dropout(p=0.1, inplace=False)
                )
              )
              (intermediate): BertIntermediate(
                (dense): Linear(in_features=1024, out_features=4096, bias=True)
              )
              (output): BertOutput(
                (dense): Linear(in_features=4096, out_features=1024, bias=True)
                (LayerNorm): LayerNorm((1024,), eps=1e-12, elementwise_affine=True)
                (dropout): Dropout(p=0.1, inplace=False)
              )
            )
          )
        )
        (pooler): BertPooler(
          (dense): Linear(in_features=1024, out_features=1024, bias=True)
          (activation): Tanh()
        )
      )
      (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, 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, 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, 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()
    )
    (attn_3): MultiHeadAttention(
      (attention): ScaledDotProductAttention(
        (dropout): Dropout(p=0, 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_3): 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)
      (conv_2): SeparateGCNConv(
        (linear_c): Linear(in_features=1024, out_features=1024, bias=True)
        (linear_p): Linear(in_features=1024, out_features=1024, bias=True)
      )
      (layernorm_2): LayerNorm((2048,), eps=1e-05, elementwise_affine=True)
      (conv_3): SeparateGCNConv(
        (linear_c): Linear(in_features=1024, out_features=1024, bias=True)
        (linear_p): Linear(in_features=1024, out_features=1024, bias=True)
      )
      (layernorm_3): 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 19:52:08,193][__main__][INFO] - #parameters = 376726756
[2020-12-16 19:52:08,193][__main__][INFO] - Validating..
[2020-12-16 19:52:34,814][__main__][INFO] - Time elapsed: 19.569163
[2020-12-16 19:52:34,819][__main__][INFO] - Validation F1 score: 95.520, Exact match: 55.470, Precision: 95.480, Recall: 95.560
[2020-12-16 19:52:34,819][__main__][INFO] - Testing..
[2020-12-16 19:52:34,819][__main__][INFO] - Running without beam search..
[2020-12-16 19:53:09,439][__main__][INFO] - Time elapsed: 30.376287
[2020-12-16 19:53:09,444][__main__][INFO] - Testing F1 score: 95.800, Exact match: 57.410, Precision: 96.010, Recall: 95.590