Quadro RTX 6000
[2020-12-15 12:10:27,634][__main__][INFO] - 
exp_id: ctb_bert_graph_seed2
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/ctb_train.txt
path_val: data/ctb_dev.txt
path_test: data/ctb_test.txt
max_sentence_len: 250
d_model: 2048
encoder: bert-base-chinese
use_tags: true
use_words: false
d_kqv: 64
d_ff: 2048
word_emb_dropout: 0
tag_emb_dropout: 0.2
relu_dropout: 0.4
residual_dropout: 0.2
attention_dropout: 0
num_attn_layers: 4
num_attn_heads: 8
decoder: graph
num_gcn_layers: 5
learning_rate: 4.0e-05
weight_decay: 1.0e-06
subbatch_max_tokens: 1000

[2020-12-15 12:10:27,636][dataloader][INFO] - Loading constituency trees from /n/fs/pvl-mathqa/attach-juxtapose-parser/data/ctb_train.txt
[2020-12-15 12:11:14,890][dataloader][INFO] - Loading constituency trees from /n/fs/pvl-mathqa/attach-juxtapose-parser/data/ctb_dev.txt
[2020-12-15 12:11:36,045][__main__][INFO] - 
Parser(
  (encoder): Encoder(
    (word_embedding): TransformerEmbedding(
      (contextual_embedding): BertModel(
        (embeddings): BertEmbeddings(
          (word_embeddings): Embedding(21128, 768, padding_idx=0)
          (position_embeddings): Embedding(512, 768)
          (token_type_embeddings): Embedding(2, 768)
          (LayerNorm): LayerNorm((768,), 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=768, out_features=768, bias=True)
                  (key): Linear(in_features=768, out_features=768, bias=True)
                  (value): Linear(in_features=768, out_features=768, bias=True)
                  (dropout): Dropout(p=0.1, inplace=False)
                )
                (output): BertSelfOutput(
                  (dense): Linear(in_features=768, out_features=768, bias=True)
                  (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)
                  (dropout): Dropout(p=0.1, inplace=False)
                )
              )
              (intermediate): BertIntermediate(
                (dense): Linear(in_features=768, out_features=3072, bias=True)
              )
              (output): BertOutput(
                (dense): Linear(in_features=3072, out_features=768, bias=True)
                (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)
                (dropout): Dropout(p=0.1, inplace=False)
              )
            )
            (1): BertLayer(
              (attention): BertAttention(
                (self): BertSelfAttention(
                  (query): Linear(in_features=768, out_features=768, bias=True)
                  (key): Linear(in_features=768, out_features=768, bias=True)
                  (value): Linear(in_features=768, out_features=768, bias=True)
                  (dropout): Dropout(p=0.1, inplace=False)
                )
                (output): BertSelfOutput(
                  (dense): Linear(in_features=768, out_features=768, bias=True)
                  (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)
                  (dropout): Dropout(p=0.1, inplace=False)
                )
              )
              (intermediate): BertIntermediate(
                (dense): Linear(in_features=768, out_features=3072, bias=True)
              )
              (output): BertOutput(
                (dense): Linear(in_features=3072, out_features=768, bias=True)
                (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)
                (dropout): Dropout(p=0.1, inplace=False)
              )
            )
            (2): BertLayer(
              (attention): BertAttention(
                (self): BertSelfAttention(
                  (query): Linear(in_features=768, out_features=768, bias=True)
                  (key): Linear(in_features=768, out_features=768, bias=True)
                  (value): Linear(in_features=768, out_features=768, bias=True)
                  (dropout): Dropout(p=0.1, inplace=False)
                )
                (output): BertSelfOutput(
                  (dense): Linear(in_features=768, out_features=768, bias=True)
                  (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)
                  (dropout): Dropout(p=0.1, inplace=False)
                )
              )
              (intermediate): BertIntermediate(
                (dense): Linear(in_features=768, out_features=3072, bias=True)
              )
              (output): BertOutput(
                (dense): Linear(in_features=3072, out_features=768, bias=True)
                (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)
                (dropout): Dropout(p=0.1, inplace=False)
              )
            )
            (3): BertLayer(
              (attention): BertAttention(
                (self): BertSelfAttention(
                  (query): Linear(in_features=768, out_features=768, bias=True)
                  (key): Linear(in_features=768, out_features=768, bias=True)
                  (value): Linear(in_features=768, out_features=768, bias=True)
                  (dropout): Dropout(p=0.1, inplace=False)
                )
                (output): BertSelfOutput(
                  (dense): Linear(in_features=768, out_features=768, bias=True)
                  (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)
                  (dropout): Dropout(p=0.1, inplace=False)
                )
              )
              (intermediate): BertIntermediate(
                (dense): Linear(in_features=768, out_features=3072, bias=True)
              )
              (output): BertOutput(
                (dense): Linear(in_features=3072, out_features=768, bias=True)
                (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)
                (dropout): Dropout(p=0.1, inplace=False)
              )
            )
            (4): BertLayer(
              (attention): BertAttention(
                (self): BertSelfAttention(
                  (query): Linear(in_features=768, out_features=768, bias=True)
                  (key): Linear(in_features=768, out_features=768, bias=True)
                  (value): Linear(in_features=768, out_features=768, bias=True)
                  (dropout): Dropout(p=0.1, inplace=False)
                )
                (output): BertSelfOutput(
                  (dense): Linear(in_features=768, out_features=768, bias=True)
                  (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)
                  (dropout): Dropout(p=0.1, inplace=False)
                )
              )
              (intermediate): BertIntermediate(
                (dense): Linear(in_features=768, out_features=3072, bias=True)
              )
              (output): BertOutput(
                (dense): Linear(in_features=3072, out_features=768, bias=True)
                (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)
                (dropout): Dropout(p=0.1, inplace=False)
              )
            )
            (5): BertLayer(
              (attention): BertAttention(
                (self): BertSelfAttention(
                  (query): Linear(in_features=768, out_features=768, bias=True)
                  (key): Linear(in_features=768, out_features=768, bias=True)
                  (value): Linear(in_features=768, out_features=768, bias=True)
                  (dropout): Dropout(p=0.1, inplace=False)
                )
                (output): BertSelfOutput(
                  (dense): Linear(in_features=768, out_features=768, bias=True)
                  (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)
                  (dropout): Dropout(p=0.1, inplace=False)
                )
              )
              (intermediate): BertIntermediate(
                (dense): Linear(in_features=768, out_features=3072, bias=True)
              )
              (output): BertOutput(
                (dense): Linear(in_features=3072, out_features=768, bias=True)
                (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)
                (dropout): Dropout(p=0.1, inplace=False)
              )
            )
            (6): BertLayer(
              (attention): BertAttention(
                (self): BertSelfAttention(
                  (query): Linear(in_features=768, out_features=768, bias=True)
                  (key): Linear(in_features=768, out_features=768, bias=True)
                  (value): Linear(in_features=768, out_features=768, bias=True)
                  (dropout): Dropout(p=0.1, inplace=False)
                )
                (output): BertSelfOutput(
                  (dense): Linear(in_features=768, out_features=768, bias=True)
                  (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)
                  (dropout): Dropout(p=0.1, inplace=False)
                )
              )
              (intermediate): BertIntermediate(
                (dense): Linear(in_features=768, out_features=3072, bias=True)
              )
              (output): BertOutput(
                (dense): Linear(in_features=3072, out_features=768, bias=True)
                (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)
                (dropout): Dropout(p=0.1, inplace=False)
              )
            )
            (7): BertLayer(
              (attention): BertAttention(
                (self): BertSelfAttention(
                  (query): Linear(in_features=768, out_features=768, bias=True)
                  (key): Linear(in_features=768, out_features=768, bias=True)
                  (value): Linear(in_features=768, out_features=768, bias=True)
                  (dropout): Dropout(p=0.1, inplace=False)
                )
                (output): BertSelfOutput(
                  (dense): Linear(in_features=768, out_features=768, bias=True)
                  (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)
                  (dropout): Dropout(p=0.1, inplace=False)
                )
              )
              (intermediate): BertIntermediate(
                (dense): Linear(in_features=768, out_features=3072, bias=True)
              )
              (output): BertOutput(
                (dense): Linear(in_features=3072, out_features=768, bias=True)
                (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)
                (dropout): Dropout(p=0.1, inplace=False)
              )
            )
            (8): BertLayer(
              (attention): BertAttention(
                (self): BertSelfAttention(
                  (query): Linear(in_features=768, out_features=768, bias=True)
                  (key): Linear(in_features=768, out_features=768, bias=True)
                  (value): Linear(in_features=768, out_features=768, bias=True)
                  (dropout): Dropout(p=0.1, inplace=False)
                )
                (output): BertSelfOutput(
                  (dense): Linear(in_features=768, out_features=768, bias=True)
                  (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)
                  (dropout): Dropout(p=0.1, inplace=False)
                )
              )
              (intermediate): BertIntermediate(
                (dense): Linear(in_features=768, out_features=3072, bias=True)
              )
              (output): BertOutput(
                (dense): Linear(in_features=3072, out_features=768, bias=True)
                (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)
                (dropout): Dropout(p=0.1, inplace=False)
              )
            )
            (9): BertLayer(
              (attention): BertAttention(
                (self): BertSelfAttention(
                  (query): Linear(in_features=768, out_features=768, bias=True)
                  (key): Linear(in_features=768, out_features=768, bias=True)
                  (value): Linear(in_features=768, out_features=768, bias=True)
                  (dropout): Dropout(p=0.1, inplace=False)
                )
                (output): BertSelfOutput(
                  (dense): Linear(in_features=768, out_features=768, bias=True)
                  (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)
                  (dropout): Dropout(p=0.1, inplace=False)
                )
              )
              (intermediate): BertIntermediate(
                (dense): Linear(in_features=768, out_features=3072, bias=True)
              )
              (output): BertOutput(
                (dense): Linear(in_features=3072, out_features=768, bias=True)
                (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)
                (dropout): Dropout(p=0.1, inplace=False)
              )
            )
            (10): BertLayer(
              (attention): BertAttention(
                (self): BertSelfAttention(
                  (query): Linear(in_features=768, out_features=768, bias=True)
                  (key): Linear(in_features=768, out_features=768, bias=True)
                  (value): Linear(in_features=768, out_features=768, bias=True)
                  (dropout): Dropout(p=0.1, inplace=False)
                )
                (output): BertSelfOutput(
                  (dense): Linear(in_features=768, out_features=768, bias=True)
                  (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)
                  (dropout): Dropout(p=0.1, inplace=False)
                )
              )
              (intermediate): BertIntermediate(
                (dense): Linear(in_features=768, out_features=3072, bias=True)
              )
              (output): BertOutput(
                (dense): Linear(in_features=3072, out_features=768, bias=True)
                (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)
                (dropout): Dropout(p=0.1, inplace=False)
              )
            )
            (11): BertLayer(
              (attention): BertAttention(
                (self): BertSelfAttention(
                  (query): Linear(in_features=768, out_features=768, bias=True)
                  (key): Linear(in_features=768, out_features=768, bias=True)
                  (value): Linear(in_features=768, out_features=768, bias=True)
                  (dropout): Dropout(p=0.1, inplace=False)
                )
                (output): BertSelfOutput(
                  (dense): Linear(in_features=768, out_features=768, bias=True)
                  (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)
                  (dropout): Dropout(p=0.1, inplace=False)
                )
              )
              (intermediate): BertIntermediate(
                (dense): Linear(in_features=768, out_features=3072, bias=True)
              )
              (output): BertOutput(
                (dense): Linear(in_features=3072, out_features=768, bias=True)
                (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)
                (dropout): Dropout(p=0.1, inplace=False)
              )
            )
          )
        )
        (pooler): BertPooler(
          (dense): Linear(in_features=768, out_features=768, bias=True)
          (activation): Tanh()
        )
      )
      (linear): Linear(in_features=768, out_features=1024, bias=False)
    )
    (word_dropout): FeatureDropout()
    (tag_embedding): OneHotEmbedding(
      (embedding): Embedding(36, 1024)
    )
    (tag_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(709, 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)
      (conv_4): SeparateGCNConv(
        (linear_c): Linear(in_features=1024, out_features=1024, bias=True)
        (linear_p): Linear(in_features=1024, out_features=1024, bias=True)
      )
      (layernorm_4): 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=1418, bias=True)
      )
    )
  )
)
[2020-12-15 12:11:36,049][__main__][INFO] - #parameters = 147562636
[2020-12-15 12:11:36,054][__main__][INFO] - Epoch #0
[2020-12-15 12:11:36,054][__main__][INFO] - Training..
[2020-12-15 12:11:42,692][__main__][INFO] - Learning rate adjusted to 0.00000000
[2020-12-15 12:11:45,456][__main__][INFO] - Learning rate adjusted to 0.00000027
[2020-12-15 12:11:48,108][__main__][INFO] - Learning rate adjusted to 0.00000053
[2020-12-15 12:11:53,776][__main__][INFO] - Learning rate adjusted to 0.00000080
[2020-12-15 12:11:59,451][__main__][INFO] - Learning rate adjusted to 0.00000107
[2020-12-15 12:12:02,013][__main__][INFO] - Learning rate adjusted to 0.00000133
[2020-12-15 12:12:08,033][__main__][INFO] - Learning rate adjusted to 0.00000160
[2020-12-15 12:12:12,018][__main__][INFO] - Learning rate adjusted to 0.00000187
[2020-12-15 12:12:18,180][__main__][INFO] - Learning rate adjusted to 0.00000213
[2020-12-15 12:12:21,789][__main__][INFO] - Learning rate adjusted to 0.00000240
[2020-12-15 12:12:21,905][__main__][INFO] - [320] Loss: 337.167, Running accuracy: 0.168, Time: 42.67
[2020-12-15 12:12:25,972][__main__][INFO] - Learning rate adjusted to 0.00000267
[2020-12-15 12:12:29,450][__main__][INFO] - Learning rate adjusted to 0.00000293
[2020-12-15 12:12:33,206][__main__][INFO] - Learning rate adjusted to 0.00000320
[2020-12-15 12:12:37,085][__main__][INFO] - Learning rate adjusted to 0.00000347
[2020-12-15 12:12:39,833][__main__][INFO] - Learning rate adjusted to 0.00000373
[2020-12-15 12:12:43,224][__main__][INFO] - Learning rate adjusted to 0.00000400
[2020-12-15 12:12:47,133][__main__][INFO] - Learning rate adjusted to 0.00000427
[2020-12-15 12:12:50,808][__main__][INFO] - Learning rate adjusted to 0.00000453
[2020-12-15 12:12:53,053][__main__][INFO] - Learning rate adjusted to 0.00000480
[2020-12-15 12:12:57,426][__main__][INFO] - Learning rate adjusted to 0.00000507
[2020-12-15 12:12:57,548][__main__][INFO] - [640] Loss: 227.518, Running accuracy: 5.839, Time: 35.64
[2020-12-15 12:12:59,754][__main__][INFO] - Learning rate adjusted to 0.00000533
[2020-12-15 12:13:04,575][__main__][INFO] - Learning rate adjusted to 0.00000560
[2020-12-15 12:13:07,581][__main__][INFO] - Learning rate adjusted to 0.00000587
[2020-12-15 12:13:11,133][__main__][INFO] - Learning rate adjusted to 0.00000613
[2020-12-15 12:13:14,186][__main__][INFO] - Learning rate adjusted to 0.00000640
[2020-12-15 12:13:18,228][__main__][INFO] - Learning rate adjusted to 0.00000667
[2020-12-15 12:13:21,735][__main__][INFO] - Learning rate adjusted to 0.00000693
[2020-12-15 12:13:25,799][__main__][INFO] - Learning rate adjusted to 0.00000720
[2020-12-15 12:13:28,683][__main__][INFO] - Learning rate adjusted to 0.00000747
[2020-12-15 12:13:33,247][__main__][INFO] - Learning rate adjusted to 0.00000773
[2020-12-15 12:13:33,372][__main__][INFO] - [960] Loss: 191.097, Running accuracy: 9.177, Time: 35.82
[2020-12-15 12:13:37,070][__main__][INFO] - Learning rate adjusted to 0.00000800
[2020-12-15 12:13:39,776][__main__][INFO] - Learning rate adjusted to 0.00000827
[2020-12-15 12:13:44,267][__main__][INFO] - Learning rate adjusted to 0.00000853
[2020-12-15 12:13:48,556][__main__][INFO] - Learning rate adjusted to 0.00000880
[2020-12-15 12:13:54,564][__main__][INFO] - Learning rate adjusted to 0.00000907
[2020-12-15 12:13:59,295][__main__][INFO] - Learning rate adjusted to 0.00000933
[2020-12-15 12:14:03,596][__main__][INFO] - Learning rate adjusted to 0.00000960
[2020-12-15 12:14:07,319][__main__][INFO] - Learning rate adjusted to 0.00000987
[2020-12-15 12:14:10,562][__main__][INFO] - Learning rate adjusted to 0.00001013
[2020-12-15 12:14:16,192][__main__][INFO] - Learning rate adjusted to 0.00001040
[2020-12-15 12:14:16,311][__main__][INFO] - [1280] Loss: 168.701, Running accuracy: 11.025, Time: 42.94
[2020-12-15 12:14:19,844][__main__][INFO] - Learning rate adjusted to 0.00001067
[2020-12-15 12:14:25,566][__main__][INFO] - Learning rate adjusted to 0.00001093
[2020-12-15 12:14:30,428][__main__][INFO] - Learning rate adjusted to 0.00001120
[2020-12-15 12:14:34,468][__main__][INFO] - Learning rate adjusted to 0.00001147
[2020-12-15 12:14:37,128][__main__][INFO] - Learning rate adjusted to 0.00001173
[2020-12-15 12:14:41,393][__main__][INFO] - Learning rate adjusted to 0.00001200
[2020-12-15 12:14:45,658][__main__][INFO] - Learning rate adjusted to 0.00001227
[2020-12-15 12:14:49,503][__main__][INFO] - Learning rate adjusted to 0.00001253
[2020-12-15 12:14:53,390][__main__][INFO] - Learning rate adjusted to 0.00001280
[2020-12-15 12:14:57,556][__main__][INFO] - Learning rate adjusted to 0.00001307
[2020-12-15 12:14:57,674][__main__][INFO] - [1600] Loss: 144.706, Running accuracy: 12.206, Time: 41.36
[2020-12-15 12:15:00,337][__main__][INFO] - Learning rate adjusted to 0.00001333
[2020-12-15 12:15:04,541][__main__][INFO] - Learning rate adjusted to 0.00001360
[2020-12-15 12:15:08,411][__main__][INFO] - Learning rate adjusted to 0.00001387
[2020-12-15 12:15:12,822][__main__][INFO] - Learning rate adjusted to 0.00001413
[2020-12-15 12:15:16,533][__main__][INFO] - Learning rate adjusted to 0.00001440
[2020-12-15 12:15:20,158][__main__][INFO] - Learning rate adjusted to 0.00001467
[2020-12-15 12:15:23,828][__main__][INFO] - Learning rate adjusted to 0.00001493
[2020-12-15 12:15:26,559][__main__][INFO] - Learning rate adjusted to 0.00001520
[2020-12-15 12:15:31,177][__main__][INFO] - Learning rate adjusted to 0.00001547
[2020-12-15 12:15:35,411][__main__][INFO] - Learning rate adjusted to 0.00001573
[2020-12-15 12:15:35,527][__main__][INFO] - [1920] Loss: 122.876, Running accuracy: 13.732, Time: 37.85
[2020-12-15 12:15:40,663][__main__][INFO] - Learning rate adjusted to 0.00001600
[2020-12-15 12:15:44,809][__main__][INFO] - Learning rate adjusted to 0.00001627
[2020-12-15 12:15:49,058][__main__][INFO] - Learning rate adjusted to 0.00001653
[2020-12-15 12:15:53,359][__main__][INFO] - Learning rate adjusted to 0.00001680
[2020-12-15 12:15:57,000][__main__][INFO] - Learning rate adjusted to 0.00001707
[2020-12-15 12:16:01,070][__main__][INFO] - Learning rate adjusted to 0.00001733
[2020-12-15 12:16:04,546][__main__][INFO] - Learning rate adjusted to 0.00001760
[2020-12-15 12:16:07,745][__main__][INFO] - Learning rate adjusted to 0.00001787
[2020-12-15 12:16:11,325][__main__][INFO] - Learning rate adjusted to 0.00001813
[2020-12-15 12:16:14,123][__main__][INFO] - Learning rate adjusted to 0.00001840
[2020-12-15 12:16:14,242][__main__][INFO] - [2240] Loss: 109.320, Running accuracy: 15.947, Time: 38.71
[2020-12-15 12:16:18,656][__main__][INFO] - Learning rate adjusted to 0.00001867
[2020-12-15 12:16:21,436][__main__][INFO] - Learning rate adjusted to 0.00001893
[2020-12-15 12:16:25,745][__main__][INFO] - Learning rate adjusted to 0.00001920
[2020-12-15 12:16:31,099][__main__][INFO] - Learning rate adjusted to 0.00001947
[2020-12-15 12:16:35,725][__main__][INFO] - Learning rate adjusted to 0.00001973
[2020-12-15 12:16:39,220][__main__][INFO] - Learning rate adjusted to 0.00002000
[2020-12-15 12:16:42,811][__main__][INFO] - Learning rate adjusted to 0.00002027
[2020-12-15 12:16:48,857][__main__][INFO] - Learning rate adjusted to 0.00002053
[2020-12-15 12:16:51,424][__main__][INFO] - Learning rate adjusted to 0.00002080
[2020-12-15 12:16:55,904][__main__][INFO] - Learning rate adjusted to 0.00002107
[2020-12-15 12:16:56,020][__main__][INFO] - [2560] Loss: 90.376, Running accuracy: 18.184, Time: 41.78
[2020-12-15 12:17:00,291][__main__][INFO] - Learning rate adjusted to 0.00002133
[2020-12-15 12:17:04,355][__main__][INFO] - Learning rate adjusted to 0.00002160
[2020-12-15 12:17:06,852][__main__][INFO] - Learning rate adjusted to 0.00002187
[2020-12-15 12:17:09,738][__main__][INFO] - Learning rate adjusted to 0.00002213
[2020-12-15 12:17:14,068][__main__][INFO] - Learning rate adjusted to 0.00002240
[2020-12-15 12:17:18,865][__main__][INFO] - Learning rate adjusted to 0.00002267
[2020-12-15 12:17:22,753][__main__][INFO] - Learning rate adjusted to 0.00002293
[2020-12-15 12:17:26,806][__main__][INFO] - Learning rate adjusted to 0.00002320
[2020-12-15 12:17:30,054][__main__][INFO] - Learning rate adjusted to 0.00002347
[2020-12-15 12:17:32,565][__main__][INFO] - Learning rate adjusted to 0.00002373
[2020-12-15 12:17:32,685][__main__][INFO] - [2880] Loss: 87.660, Running accuracy: 20.494, Time: 36.66
[2020-12-15 12:17:36,013][__main__][INFO] - Learning rate adjusted to 0.00002400
[2020-12-15 12:17:40,574][__main__][INFO] - Learning rate adjusted to 0.00002427
[2020-12-15 12:17:43,468][__main__][INFO] - Learning rate adjusted to 0.00002453
[2020-12-15 12:17:47,430][__main__][INFO] - Learning rate adjusted to 0.00002480
[2020-12-15 12:17:50,983][__main__][INFO] - Learning rate adjusted to 0.00002507
[2020-12-15 12:17:54,985][__main__][INFO] - Learning rate adjusted to 0.00002533
[2020-12-15 12:17:59,093][__main__][INFO] - Learning rate adjusted to 0.00002560
[2020-12-15 12:18:02,867][__main__][INFO] - Learning rate adjusted to 0.00002587
[2020-12-15 12:18:05,526][__main__][INFO] - Learning rate adjusted to 0.00002613
[2020-12-15 12:18:09,058][__main__][INFO] - Learning rate adjusted to 0.00002640
[2020-12-15 12:18:09,175][__main__][INFO] - [3200] Loss: 76.494, Running accuracy: 22.477, Time: 36.49
[2020-12-15 12:18:16,479][__main__][INFO] - Learning rate adjusted to 0.00002667
[2020-12-15 12:18:20,825][__main__][INFO] - Learning rate adjusted to 0.00002693
[2020-12-15 12:18:23,401][__main__][INFO] - Learning rate adjusted to 0.00002720
[2020-12-15 12:18:26,227][__main__][INFO] - Learning rate adjusted to 0.00002747
[2020-12-15 12:18:31,857][__main__][INFO] - Learning rate adjusted to 0.00002773
[2020-12-15 12:18:34,254][__main__][INFO] - Learning rate adjusted to 0.00002800
[2020-12-15 12:18:38,380][__main__][INFO] - Learning rate adjusted to 0.00002827
[2020-12-15 12:18:42,621][__main__][INFO] - Learning rate adjusted to 0.00002853
[2020-12-15 12:18:45,133][__main__][INFO] - Learning rate adjusted to 0.00002880
[2020-12-15 12:18:48,806][__main__][INFO] - Learning rate adjusted to 0.00002907
[2020-12-15 12:18:48,925][__main__][INFO] - [3520] Loss: 68.352, Running accuracy: 24.541, Time: 39.75
[2020-12-15 12:18:54,819][__main__][INFO] - Learning rate adjusted to 0.00002933
[2020-12-15 12:18:58,671][__main__][INFO] - Learning rate adjusted to 0.00002960
[2020-12-15 12:19:04,073][__main__][INFO] - Learning rate adjusted to 0.00002987
[2020-12-15 12:19:07,671][__main__][INFO] - Learning rate adjusted to 0.00003013
[2020-12-15 12:19:10,485][__main__][INFO] - Learning rate adjusted to 0.00003040
[2020-12-15 12:19:14,868][__main__][INFO] - Learning rate adjusted to 0.00003067
[2020-12-15 12:19:19,453][__main__][INFO] - Learning rate adjusted to 0.00003093
[2020-12-15 12:19:23,680][__main__][INFO] - Learning rate adjusted to 0.00003120
[2020-12-15 12:19:28,184][__main__][INFO] - Learning rate adjusted to 0.00003147
[2020-12-15 12:19:32,993][__main__][INFO] - Learning rate adjusted to 0.00003173
[2020-12-15 12:19:33,114][__main__][INFO] - [3840] Loss: 70.029, Running accuracy: 26.390, Time: 44.19
[2020-12-15 12:19:36,760][__main__][INFO] - Learning rate adjusted to 0.00003200
[2020-12-15 12:19:41,051][__main__][INFO] - Learning rate adjusted to 0.00003227
[2020-12-15 12:19:45,224][__main__][INFO] - Learning rate adjusted to 0.00003253
[2020-12-15 12:19:49,595][__main__][INFO] - Learning rate adjusted to 0.00003280
[2020-12-15 12:19:53,816][__main__][INFO] - Learning rate adjusted to 0.00003307
[2020-12-15 12:19:57,831][__main__][INFO] - Learning rate adjusted to 0.00003333
[2020-12-15 12:20:01,392][__main__][INFO] - Learning rate adjusted to 0.00003360
[2020-12-15 12:20:05,785][__main__][INFO] - Learning rate adjusted to 0.00003387
[2020-12-15 12:20:09,802][__main__][INFO] - Learning rate adjusted to 0.00003413
[2020-12-15 12:20:15,655][__main__][INFO] - Learning rate adjusted to 0.00003440
[2020-12-15 12:20:15,772][__main__][INFO] - [4160] Loss: 59.982, Running accuracy: 28.383, Time: 42.66
[2020-12-15 12:20:18,871][__main__][INFO] - Learning rate adjusted to 0.00003467
[2020-12-15 12:20:23,486][__main__][INFO] - Learning rate adjusted to 0.00003493
[2020-12-15 12:20:27,281][__main__][INFO] - Learning rate adjusted to 0.00003520
[2020-12-15 12:20:29,726][__main__][INFO] - Learning rate adjusted to 0.00003547
[2020-12-15 12:20:33,624][__main__][INFO] - Learning rate adjusted to 0.00003573
[2020-12-15 12:20:37,595][__main__][INFO] - Learning rate adjusted to 0.00003600
[2020-12-15 12:20:41,368][__main__][INFO] - Learning rate adjusted to 0.00003627
[2020-12-15 12:20:43,711][__main__][INFO] - Learning rate adjusted to 0.00003653
[2020-12-15 12:20:46,138][__main__][INFO] - Learning rate adjusted to 0.00003680
[2020-12-15 12:20:50,853][__main__][INFO] - Learning rate adjusted to 0.00003707
[2020-12-15 12:20:50,972][__main__][INFO] - [4480] Loss: 53.091, Running accuracy: 30.046, Time: 35.20
[2020-12-15 12:20:54,141][__main__][INFO] - Learning rate adjusted to 0.00003733
[2020-12-15 12:20:58,921][__main__][INFO] - Learning rate adjusted to 0.00003760
[2020-12-15 12:21:03,042][__main__][INFO] - Learning rate adjusted to 0.00003787
[2020-12-15 12:21:05,963][__main__][INFO] - Learning rate adjusted to 0.00003813
[2020-12-15 12:21:08,390][__main__][INFO] - Learning rate adjusted to 0.00003840
[2020-12-15 12:21:13,392][__main__][INFO] - Learning rate adjusted to 0.00003867
[2020-12-15 12:21:17,606][__main__][INFO] - Learning rate adjusted to 0.00003893
[2020-12-15 12:21:20,312][__main__][INFO] - Learning rate adjusted to 0.00003920
[2020-12-15 12:21:23,683][__main__][INFO] - Learning rate adjusted to 0.00003947
[2020-12-15 12:21:27,636][__main__][INFO] - Learning rate adjusted to 0.00003973
[2020-12-15 12:21:27,756][__main__][INFO] - [4800] Loss: 52.716, Running accuracy: 31.799, Time: 36.78
[2020-12-15 12:21:31,491][__main__][INFO] - Learning rate adjusted to 0.00004000
[2020-12-15 12:22:07,210][__main__][INFO] - [5120] Loss: 53.006, Running accuracy: 33.434, Time: 39.45
[2020-12-15 12:22:49,358][__main__][INFO] - [5440] Loss: 49.228, Running accuracy: 35.127, Time: 42.15
[2020-12-15 12:23:24,718][__main__][INFO] - [5760] Loss: 40.628, Running accuracy: 36.644, Time: 35.36
[2020-12-15 12:23:59,150][__main__][INFO] - [6080] Loss: 39.156, Running accuracy: 38.047, Time: 34.43
[2020-12-15 12:24:35,943][__main__][INFO] - [6400] Loss: 37.928, Running accuracy: 39.422, Time: 36.79
[2020-12-15 12:25:15,117][__main__][INFO] - [6720] Loss: 38.611, Running accuracy: 40.720, Time: 39.17
[2020-12-15 12:25:48,883][__main__][INFO] - [7040] Loss: 36.509, Running accuracy: 41.904, Time: 33.76
[2020-12-15 12:26:27,901][__main__][INFO] - [7360] Loss: 35.169, Running accuracy: 43.097, Time: 39.02
[2020-12-15 12:27:04,781][__main__][INFO] - [7680] Loss: 34.477, Running accuracy: 44.239, Time: 36.88
[2020-12-15 12:27:45,884][__main__][INFO] - [8000] Loss: 32.672, Running accuracy: 45.336, Time: 41.10
[2020-12-15 12:28:24,754][__main__][INFO] - [8320] Loss: 35.244, Running accuracy: 46.384, Time: 38.87
[2020-12-15 12:28:58,802][__main__][INFO] - [8640] Loss: 31.275, Running accuracy: 47.297, Time: 34.05
[2020-12-15 12:29:34,860][__main__][INFO] - [8960] Loss: 32.142, Running accuracy: 48.233, Time: 36.06
[2020-12-15 12:30:11,504][__main__][INFO] - [9280] Loss: 30.718, Running accuracy: 49.105, Time: 36.64
[2020-12-15 12:30:50,165][__main__][INFO] - [9600] Loss: 32.799, Running accuracy: 49.988, Time: 38.66
[2020-12-15 12:31:29,381][__main__][INFO] - [9920] Loss: 29.524, Running accuracy: 50.749, Time: 39.21
[2020-12-15 12:32:05,009][__main__][INFO] - [10240] Loss: 28.155, Running accuracy: 51.493, Time: 35.63
[2020-12-15 12:32:37,228][__main__][INFO] - [10560] Loss: 25.475, Running accuracy: 52.213, Time: 32.22
[2020-12-15 12:33:15,913][__main__][INFO] - [10880] Loss: 29.036, Running accuracy: 52.956, Time: 38.68
[2020-12-15 12:33:47,257][__main__][INFO] - [11200] Loss: 23.751, Running accuracy: 53.596, Time: 31.34
[2020-12-15 12:34:27,295][__main__][INFO] - [11520] Loss: 27.095, Running accuracy: 54.247, Time: 40.04
[2020-12-15 12:35:04,220][__main__][INFO] - [11840] Loss: 26.535, Running accuracy: 54.881, Time: 36.92
[2020-12-15 12:35:43,608][__main__][INFO] - [12160] Loss: 25.330, Running accuracy: 55.471, Time: 39.39
[2020-12-15 12:36:17,313][__main__][INFO] - [12480] Loss: 24.890, Running accuracy: 56.031, Time: 33.70
[2020-12-15 12:36:59,239][__main__][INFO] - [12800] Loss: 25.436, Running accuracy: 56.580, Time: 41.93
[2020-12-15 12:37:34,863][__main__][INFO] - [13120] Loss: 25.997, Running accuracy: 57.093, Time: 35.62
[2020-12-15 12:38:12,049][__main__][INFO] - [13440] Loss: 24.203, Running accuracy: 57.588, Time: 37.19
[2020-12-15 12:38:51,461][__main__][INFO] - [13760] Loss: 25.650, Running accuracy: 58.105, Time: 39.41
[2020-12-15 12:39:30,780][__main__][INFO] - [14080] Loss: 24.992, Running accuracy: 58.596, Time: 39.32
[2020-12-15 12:40:10,184][__main__][INFO] - [14400] Loss: 23.385, Running accuracy: 59.034, Time: 39.40
[2020-12-15 12:40:48,132][__main__][INFO] - [14720] Loss: 23.137, Running accuracy: 59.498, Time: 37.95
[2020-12-15 12:41:30,435][__main__][INFO] - [15040] Loss: 23.940, Running accuracy: 59.965, Time: 42.30
[2020-12-15 12:42:13,486][__main__][INFO] - [15360] Loss: 26.466, Running accuracy: 60.374, Time: 43.05
[2020-12-15 12:42:52,333][__main__][INFO] - [15680] Loss: 23.933, Running accuracy: 60.787, Time: 38.85
[2020-12-15 12:43:29,976][__main__][INFO] - [16000] Loss: 22.245, Running accuracy: 61.176, Time: 37.64
[2020-12-15 12:44:07,542][__main__][INFO] - [16320] Loss: 23.339, Running accuracy: 61.577, Time: 37.56
[2020-12-15 12:44:44,836][__main__][INFO] - [16640] Loss: 22.398, Running accuracy: 61.910, Time: 37.29
[2020-12-15 12:45:23,924][__main__][INFO] - [16960] Loss: 22.334, Running accuracy: 62.251, Time: 39.09
[2020-12-15 12:46:03,957][__main__][INFO] - [17280] Loss: 20.116, Running accuracy: 62.626, Time: 40.03
[2020-12-15 12:46:33,376][__main__][INFO] - Action accuracy: 62.886, Loss: 61.583
[2020-12-15 12:46:33,377][__main__][INFO] - Validating..
[2020-12-15 12:46:39,607][test][INFO] - Time elapsed: 5.118920
[2020-12-15 12:46:39,608][__main__][INFO] - Validation F1 score: 90.730, Exact match: 42.900, Precision: 91.250, Recall: 90.210
[2020-12-15 12:46:39,608][__main__][INFO] - F1 score has improved
[2020-12-15 12:46:52,274][__main__][INFO] - Checkpoint saved to checkpoints/model_latest.pth
[2020-12-15 12:46:52,599][__main__][INFO] - Epoch #1
[2020-12-15 12:46:52,600][__main__][INFO] - Training..
[2020-12-15 12:47:33,075][__main__][INFO] - [320] Loss: 19.540, Running accuracy: 83.339, Time: 39.26
[2020-12-15 12:48:14,874][__main__][INFO] - [640] Loss: 18.905, Running accuracy: 83.511, Time: 41.80
[2020-12-15 12:48:48,624][__main__][INFO] - [960] Loss: 19.718, Running accuracy: 83.133, Time: 33.75
[2020-12-15 12:49:24,584][__main__][INFO] - [1280] Loss: 18.050, Running accuracy: 83.266, Time: 35.96
[2020-12-15 12:49:59,980][__main__][INFO] - [1600] Loss: 17.583, Running accuracy: 83.269, Time: 35.40
[2020-12-15 12:50:40,450][__main__][INFO] - [1920] Loss: 19.725, Running accuracy: 83.325, Time: 40.47
[2020-12-15 12:51:19,815][__main__][INFO] - [2240] Loss: 20.769, Running accuracy: 83.186, Time: 39.36
[2020-12-15 12:52:06,905][__main__][INFO] - [2560] Loss: 22.348, Running accuracy: 83.155, Time: 47.09
[2020-12-15 12:52:44,353][__main__][INFO] - [2880] Loss: 16.637, Running accuracy: 83.274, Time: 37.45
[2020-12-15 12:53:22,616][__main__][INFO] - [3200] Loss: 18.363, Running accuracy: 83.352, Time: 38.26
[2020-12-15 12:53:56,738][__main__][INFO] - [3520] Loss: 16.329, Running accuracy: 83.437, Time: 34.12
[2020-12-15 12:54:36,026][__main__][INFO] - [3840] Loss: 18.380, Running accuracy: 83.460, Time: 39.29
[2020-12-15 12:55:13,478][__main__][INFO] - [4160] Loss: 17.841, Running accuracy: 83.455, Time: 37.45
[2020-12-15 12:55:48,828][__main__][INFO] - [4480] Loss: 17.183, Running accuracy: 83.556, Time: 35.35
[2020-12-15 12:56:29,690][__main__][INFO] - [4800] Loss: 16.918, Running accuracy: 83.590, Time: 40.86
[2020-12-15 12:57:08,709][__main__][INFO] - [5120] Loss: 17.399, Running accuracy: 83.641, Time: 39.02
[2020-12-15 12:57:44,208][__main__][INFO] - [5440] Loss: 16.980, Running accuracy: 83.698, Time: 35.50
[2020-12-15 12:58:21,885][__main__][INFO] - [5760] Loss: 16.853, Running accuracy: 83.739, Time: 37.68
[2020-12-15 12:59:03,734][__main__][INFO] - [6080] Loss: 18.172, Running accuracy: 83.786, Time: 41.85
[2020-12-15 12:59:41,050][__main__][INFO] - [6400] Loss: 18.701, Running accuracy: 83.825, Time: 37.31
[2020-12-15 13:00:22,839][__main__][INFO] - [6720] Loss: 19.135, Running accuracy: 83.840, Time: 41.79
[2020-12-15 13:01:07,054][__main__][INFO] - [7040] Loss: 18.318, Running accuracy: 83.860, Time: 44.21
[2020-12-15 13:01:49,974][__main__][INFO] - [7360] Loss: 18.919, Running accuracy: 83.870, Time: 42.92
[2020-12-15 13:02:24,853][__main__][INFO] - [7680] Loss: 16.127, Running accuracy: 83.933, Time: 34.88
[2020-12-15 13:03:04,555][__main__][INFO] - [8000] Loss: 17.982, Running accuracy: 83.971, Time: 39.70
[2020-12-15 13:03:36,086][__main__][INFO] - [8320] Loss: 16.964, Running accuracy: 83.981, Time: 31.53
[2020-12-15 13:04:11,661][__main__][INFO] - [8640] Loss: 16.075, Running accuracy: 84.008, Time: 35.57
[2020-12-15 13:04:54,041][__main__][INFO] - [8960] Loss: 17.578, Running accuracy: 84.023, Time: 42.38
[2020-12-15 13:05:30,533][__main__][INFO] - [9280] Loss: 16.599, Running accuracy: 84.060, Time: 36.49
[2020-12-15 13:06:10,138][__main__][INFO] - [9600] Loss: 16.213, Running accuracy: 84.127, Time: 39.60
[2020-12-15 13:06:44,371][__main__][INFO] - [9920] Loss: 15.695, Running accuracy: 84.177, Time: 34.23
[2020-12-15 13:07:23,514][__main__][INFO] - [10240] Loss: 16.880, Running accuracy: 84.198, Time: 39.14
[2020-12-15 13:08:00,018][__main__][INFO] - [10560] Loss: 16.522, Running accuracy: 84.221, Time: 36.50
[2020-12-15 13:08:42,472][__main__][INFO] - [10880] Loss: 17.880, Running accuracy: 84.224, Time: 42.45
[2020-12-15 13:09:24,309][__main__][INFO] - [11200] Loss: 16.443, Running accuracy: 84.260, Time: 41.84
[2020-12-15 13:09:59,114][__main__][INFO] - [11520] Loss: 14.837, Running accuracy: 84.313, Time: 34.80
[2020-12-15 13:10:37,060][__main__][INFO] - [11840] Loss: 15.633, Running accuracy: 84.360, Time: 37.94
[2020-12-15 13:11:14,880][__main__][INFO] - [12160] Loss: 14.797, Running accuracy: 84.383, Time: 37.82
[2020-12-15 13:11:51,578][__main__][INFO] - [12480] Loss: 15.107, Running accuracy: 84.421, Time: 36.70
[2020-12-15 13:12:32,426][__main__][INFO] - [12800] Loss: 15.010, Running accuracy: 84.462, Time: 40.85
[2020-12-15 13:13:12,991][__main__][INFO] - [13120] Loss: 16.142, Running accuracy: 84.494, Time: 40.56
[2020-12-15 13:13:50,185][__main__][INFO] - [13440] Loss: 15.270, Running accuracy: 84.530, Time: 37.19
[2020-12-15 13:14:27,357][__main__][INFO] - [13760] Loss: 15.667, Running accuracy: 84.566, Time: 37.17
[2020-12-15 13:15:01,989][__main__][INFO] - [14080] Loss: 15.078, Running accuracy: 84.590, Time: 34.63
[2020-12-15 13:15:38,516][__main__][INFO] - [14400] Loss: 15.037, Running accuracy: 84.622, Time: 36.53
[2020-12-15 13:16:16,020][__main__][INFO] - [14720] Loss: 14.783, Running accuracy: 84.660, Time: 37.50
[2020-12-15 13:16:53,681][__main__][INFO] - [15040] Loss: 14.082, Running accuracy: 84.705, Time: 37.66
[2020-12-15 13:17:32,236][__main__][INFO] - [15360] Loss: 14.903, Running accuracy: 84.733, Time: 38.55
[2020-12-15 13:18:11,495][__main__][INFO] - [15680] Loss: 14.611, Running accuracy: 84.758, Time: 39.26
[2020-12-15 13:18:48,922][__main__][INFO] - [16000] Loss: 14.271, Running accuracy: 84.793, Time: 37.43
[2020-12-15 13:19:19,616][__main__][INFO] - [16320] Loss: 11.674, Running accuracy: 84.841, Time: 30.69
[2020-12-15 13:19:55,802][__main__][INFO] - [16640] Loss: 14.845, Running accuracy: 84.874, Time: 36.19
[2020-12-15 13:20:33,722][__main__][INFO] - [16960] Loss: 15.375, Running accuracy: 84.896, Time: 37.92
[2020-12-15 13:21:12,600][__main__][INFO] - [17280] Loss: 15.254, Running accuracy: 84.914, Time: 38.88
[2020-12-15 13:21:41,712][__main__][INFO] - Action accuracy: 84.958, Loss: 18.555
[2020-12-15 13:21:41,712][__main__][INFO] - Validating..
[2020-12-15 13:21:50,701][test][INFO] - Time elapsed: 5.065270
[2020-12-15 13:21:50,702][__main__][INFO] - Validation F1 score: 92.990, Exact match: 48.860, Precision: 93.210, Recall: 92.780
[2020-12-15 13:21:50,703][__main__][INFO] - F1 score has improved
[2020-12-15 13:22:03,366][__main__][INFO] - Checkpoint saved to checkpoints/model_latest.pth
[2020-12-15 13:22:03,749][__main__][INFO] - Epoch #2
[2020-12-15 13:22:03,749][__main__][INFO] - Training..
[2020-12-15 13:22:37,982][__main__][INFO] - [320] Loss: 11.856, Running accuracy: 88.345, Time: 33.28
[2020-12-15 13:23:19,801][__main__][INFO] - [640] Loss: 13.196, Running accuracy: 88.533, Time: 41.82
[2020-12-15 13:23:53,730][__main__][INFO] - [960] Loss: 12.715, Running accuracy: 88.380, Time: 33.93
[2020-12-15 13:24:36,321][__main__][INFO] - [1280] Loss: 12.209, Running accuracy: 88.413, Time: 42.59
[2020-12-15 13:25:11,318][__main__][INFO] - [1600] Loss: 10.789, Running accuracy: 88.481, Time: 35.00
[2020-12-15 13:25:48,853][__main__][INFO] - [1920] Loss: 11.113, Running accuracy: 88.606, Time: 37.53
[2020-12-15 13:26:25,755][__main__][INFO] - [2240] Loss: 11.879, Running accuracy: 88.665, Time: 36.90
[2020-12-15 13:27:03,129][__main__][INFO] - [2560] Loss: 12.641, Running accuracy: 88.658, Time: 37.37
[2020-12-15 13:27:42,717][__main__][INFO] - [2880] Loss: 12.964, Running accuracy: 88.548, Time: 39.59
[2020-12-15 13:28:24,259][__main__][INFO] - [3200] Loss: 12.285, Running accuracy: 88.526, Time: 41.54
[2020-12-15 13:29:03,543][__main__][INFO] - [3520] Loss: 13.975, Running accuracy: 88.452, Time: 39.28
[2020-12-15 13:29:44,583][__main__][INFO] - [3840] Loss: 11.285, Running accuracy: 88.491, Time: 41.04
[2020-12-15 13:30:22,040][__main__][INFO] - [4160] Loss: 12.655, Running accuracy: 88.495, Time: 37.46
[2020-12-15 13:31:00,819][__main__][INFO] - [4480] Loss: 12.841, Running accuracy: 88.523, Time: 38.78
[2020-12-15 13:31:37,381][__main__][INFO] - [4800] Loss: 11.666, Running accuracy: 88.562, Time: 36.56
[2020-12-15 13:32:12,858][__main__][INFO] - [5120] Loss: 11.437, Running accuracy: 88.634, Time: 35.48
[2020-12-15 13:32:52,055][__main__][INFO] - [5440] Loss: 12.794, Running accuracy: 88.626, Time: 39.20
[2020-12-15 13:33:37,343][__main__][INFO] - [5760] Loss: 13.420, Running accuracy: 88.616, Time: 45.29
[2020-12-15 13:34:19,168][__main__][INFO] - [6080] Loss: 11.908, Running accuracy: 88.646, Time: 41.82
[2020-12-15 13:34:56,989][__main__][INFO] - [6400] Loss: 12.114, Running accuracy: 88.636, Time: 37.82
[2020-12-15 13:35:36,935][__main__][INFO] - [6720] Loss: 12.743, Running accuracy: 88.670, Time: 39.95
[2020-12-15 13:36:14,749][__main__][INFO] - [7040] Loss: 11.103, Running accuracy: 88.677, Time: 37.81
[2020-12-15 13:36:51,541][__main__][INFO] - [7360] Loss: 12.054, Running accuracy: 88.729, Time: 36.79
[2020-12-15 13:37:28,976][__main__][INFO] - [7680] Loss: 11.325, Running accuracy: 88.753, Time: 37.43
[2020-12-15 13:38:07,715][__main__][INFO] - [8000] Loss: 12.890, Running accuracy: 88.740, Time: 38.74
[2020-12-15 13:38:48,926][__main__][INFO] - [8320] Loss: 10.965, Running accuracy: 88.770, Time: 41.21
[2020-12-15 13:39:25,296][__main__][INFO] - [8640] Loss: 12.072, Running accuracy: 88.786, Time: 36.37
[2020-12-15 13:40:06,324][__main__][INFO] - [8960] Loss: 12.505, Running accuracy: 88.804, Time: 41.03
[2020-12-15 13:40:41,765][__main__][INFO] - [9280] Loss: 10.258, Running accuracy: 88.849, Time: 35.44
[2020-12-15 13:41:20,326][__main__][INFO] - [9600] Loss: 12.181, Running accuracy: 88.837, Time: 38.56
[2020-12-15 13:41:58,512][__main__][INFO] - [9920] Loss: 12.225, Running accuracy: 88.820, Time: 38.19
[2020-12-15 13:42:38,930][__main__][INFO] - [10240] Loss: 11.915, Running accuracy: 88.823, Time: 40.42
[2020-12-15 13:43:20,932][__main__][INFO] - [10560] Loss: 11.233, Running accuracy: 88.844, Time: 42.00
[2020-12-15 13:44:02,117][__main__][INFO] - [10880] Loss: 12.124, Running accuracy: 88.856, Time: 41.18
[2020-12-15 13:44:40,255][__main__][INFO] - [11200] Loss: 11.842, Running accuracy: 88.885, Time: 38.14
[2020-12-15 13:45:23,870][__main__][INFO] - [11520] Loss: 11.972, Running accuracy: 88.899, Time: 43.61
[2020-12-15 13:46:00,936][__main__][INFO] - [11840] Loss: 10.556, Running accuracy: 88.928, Time: 37.06
[2020-12-15 13:46:40,937][__main__][INFO] - [12160] Loss: 12.473, Running accuracy: 88.920, Time: 40.00
[2020-12-15 13:47:24,134][__main__][INFO] - [12480] Loss: 12.385, Running accuracy: 88.920, Time: 43.20
[2020-12-15 13:48:02,059][__main__][INFO] - [12800] Loss: 11.515, Running accuracy: 88.933, Time: 37.92
[2020-12-15 13:48:39,109][__main__][INFO] - [13120] Loss: 11.113, Running accuracy: 88.921, Time: 37.05
[2020-12-15 13:49:14,840][__main__][INFO] - [13440] Loss: 11.424, Running accuracy: 88.934, Time: 35.73
[2020-12-15 13:49:55,064][__main__][INFO] - [13760] Loss: 11.755, Running accuracy: 88.950, Time: 40.22
[2020-12-15 13:50:28,729][__main__][INFO] - [14080] Loss: 10.347, Running accuracy: 88.958, Time: 33.66
[2020-12-15 13:51:07,501][__main__][INFO] - [14400] Loss: 10.970, Running accuracy: 88.972, Time: 38.77
[2020-12-15 13:51:43,217][__main__][INFO] - [14720] Loss: 11.305, Running accuracy: 88.976, Time: 35.72
[2020-12-15 13:52:21,151][__main__][INFO] - [15040] Loss: 11.271, Running accuracy: 88.978, Time: 37.93
[2020-12-15 13:52:54,974][__main__][INFO] - [15360] Loss: 11.388, Running accuracy: 88.983, Time: 33.82
[2020-12-15 13:53:32,342][__main__][INFO] - [15680] Loss: 12.193, Running accuracy: 88.977, Time: 37.37
[2020-12-15 13:54:14,144][__main__][INFO] - [16000] Loss: 12.739, Running accuracy: 88.987, Time: 41.80
[2020-12-15 13:54:47,744][__main__][INFO] - [16320] Loss: 11.049, Running accuracy: 88.989, Time: 33.60
[2020-12-15 13:55:25,933][__main__][INFO] - [16640] Loss: 12.019, Running accuracy: 88.984, Time: 38.19
[2020-12-15 13:56:01,897][__main__][INFO] - [16960] Loss: 11.377, Running accuracy: 88.993, Time: 35.96
[2020-12-15 13:56:38,846][__main__][INFO] - [17280] Loss: 10.854, Running accuracy: 89.012, Time: 36.95
[2020-12-15 13:57:06,799][__main__][INFO] - Action accuracy: 89.019, Loss: 13.143
[2020-12-15 13:57:06,800][__main__][INFO] - Validating..
[2020-12-15 13:57:13,537][test][INFO] - Time elapsed: 5.513673
[2020-12-15 13:57:13,539][__main__][INFO] - Validation F1 score: 93.080, Exact match: 50.850, Precision: 93.090, Recall: 93.060
[2020-12-15 13:57:13,539][__main__][INFO] - F1 score has improved
[2020-12-15 13:57:25,978][__main__][INFO] - Checkpoint saved to checkpoints/model_latest.pth
[2020-12-15 13:57:26,328][__main__][INFO] - Epoch #3
[2020-12-15 13:57:26,328][__main__][INFO] - Training..
[2020-12-15 13:58:03,903][__main__][INFO] - [320] Loss: 9.513, Running accuracy: 91.407, Time: 36.48
[2020-12-15 13:58:42,289][__main__][INFO] - [640] Loss: 9.353, Running accuracy: 91.267, Time: 38.38
[2020-12-15 13:59:20,476][__main__][INFO] - [960] Loss: 8.620, Running accuracy: 91.440, Time: 38.19
[2020-12-15 13:59:58,929][__main__][INFO] - [1280] Loss: 9.209, Running accuracy: 91.411, Time: 38.45
[2020-12-15 14:00:40,047][__main__][INFO] - [1600] Loss: 9.661, Running accuracy: 91.467, Time: 41.12
[2020-12-15 14:01:15,361][__main__][INFO] - [1920] Loss: 8.960, Running accuracy: 91.441, Time: 35.31
[2020-12-15 14:01:52,552][__main__][INFO] - [2240] Loss: 8.706, Running accuracy: 91.552, Time: 37.19
[2020-12-15 14:02:24,746][__main__][INFO] - [2560] Loss: 8.938, Running accuracy: 91.469, Time: 32.19
[2020-12-15 14:03:05,891][__main__][INFO] - [2880] Loss: 10.077, Running accuracy: 91.455, Time: 41.14
[2020-12-15 14:03:42,600][__main__][INFO] - [3200] Loss: 8.793, Running accuracy: 91.459, Time: 36.71
[2020-12-15 14:04:19,377][__main__][INFO] - [3520] Loss: 9.255, Running accuracy: 91.479, Time: 36.78
[2020-12-15 14:05:01,448][__main__][INFO] - [3840] Loss: 11.385, Running accuracy: 91.373, Time: 42.07
[2020-12-15 14:05:39,376][__main__][INFO] - [4160] Loss: 9.029, Running accuracy: 91.359, Time: 37.93
[2020-12-15 14:06:18,419][__main__][INFO] - [4480] Loss: 9.880, Running accuracy: 91.320, Time: 39.04
[2020-12-15 14:06:51,759][__main__][INFO] - [4800] Loss: 8.528, Running accuracy: 91.318, Time: 33.34
[2020-12-15 14:07:26,734][__main__][INFO] - [5120] Loss: 8.634, Running accuracy: 91.286, Time: 34.97
[2020-12-15 14:08:05,628][__main__][INFO] - [5440] Loss: 8.825, Running accuracy: 91.303, Time: 38.89
[2020-12-15 14:08:41,955][__main__][INFO] - [5760] Loss: 9.153, Running accuracy: 91.306, Time: 36.33
[2020-12-15 14:09:18,922][__main__][INFO] - [6080] Loss: 8.415, Running accuracy: 91.303, Time: 36.97
[2020-12-15 14:09:53,807][__main__][INFO] - [6400] Loss: 8.890, Running accuracy: 91.309, Time: 34.88
[2020-12-15 14:10:34,280][__main__][INFO] - [6720] Loss: 8.878, Running accuracy: 91.333, Time: 40.47
[2020-12-15 14:11:16,467][__main__][INFO] - [7040] Loss: 9.694, Running accuracy: 91.314, Time: 42.19
[2020-12-15 14:11:55,048][__main__][INFO] - [7360] Loss: 9.569, Running accuracy: 91.315, Time: 38.58
[2020-12-15 14:12:33,707][__main__][INFO] - [7680] Loss: 9.521, Running accuracy: 91.318, Time: 38.66
[2020-12-15 14:13:18,252][__main__][INFO] - [8000] Loss: 10.483, Running accuracy: 91.301, Time: 44.54
[2020-12-15 14:13:59,542][__main__][INFO] - [8320] Loss: 9.613, Running accuracy: 91.296, Time: 41.29
[2020-12-15 14:14:40,055][__main__][INFO] - [8640] Loss: 9.449, Running accuracy: 91.303, Time: 40.51
[2020-12-15 14:15:16,463][__main__][INFO] - [8960] Loss: 8.818, Running accuracy: 91.315, Time: 36.41
[2020-12-15 14:15:59,962][__main__][INFO] - [9280] Loss: 9.535, Running accuracy: 91.314, Time: 43.49
[2020-12-15 14:16:42,386][__main__][INFO] - [9600] Loss: 8.972, Running accuracy: 91.331, Time: 42.42
[2020-12-15 14:17:17,071][__main__][INFO] - [9920] Loss: 8.735, Running accuracy: 91.347, Time: 34.68
[2020-12-15 14:17:50,314][__main__][INFO] - [10240] Loss: 9.172, Running accuracy: 91.320, Time: 33.24
[2020-12-15 14:18:26,461][__main__][INFO] - [10560] Loss: 8.946, Running accuracy: 91.337, Time: 36.15
[2020-12-15 14:19:08,958][__main__][INFO] - [10880] Loss: 9.690, Running accuracy: 91.335, Time: 42.50
[2020-12-15 14:19:41,651][__main__][INFO] - [11200] Loss: 9.154, Running accuracy: 91.327, Time: 32.69
[2020-12-15 14:20:24,854][__main__][INFO] - [11520] Loss: 9.728, Running accuracy: 91.336, Time: 43.20
[2020-12-15 14:21:04,291][__main__][INFO] - [11840] Loss: 9.025, Running accuracy: 91.351, Time: 39.44
[2020-12-15 14:21:43,550][__main__][INFO] - [12160] Loss: 9.510, Running accuracy: 91.342, Time: 39.26
[2020-12-15 14:22:23,941][__main__][INFO] - [12480] Loss: 10.129, Running accuracy: 91.330, Time: 40.39
[2020-12-15 14:23:02,279][__main__][INFO] - [12800] Loss: 8.884, Running accuracy: 91.332, Time: 38.34
[2020-12-15 14:23:41,460][__main__][INFO] - [13120] Loss: 8.992, Running accuracy: 91.342, Time: 39.18
[2020-12-15 14:24:21,145][__main__][INFO] - [13440] Loss: 9.046, Running accuracy: 91.344, Time: 39.68
[2020-12-15 14:25:00,117][__main__][INFO] - [13760] Loss: 9.225, Running accuracy: 91.351, Time: 38.97
[2020-12-15 14:25:34,920][__main__][INFO] - [14080] Loss: 9.068, Running accuracy: 91.356, Time: 34.80
[2020-12-15 14:26:08,235][__main__][INFO] - [14400] Loss: 7.930, Running accuracy: 91.362, Time: 33.31
[2020-12-15 14:26:40,803][__main__][INFO] - [14720] Loss: 7.685, Running accuracy: 91.377, Time: 32.57
[2020-12-15 14:27:22,105][__main__][INFO] - [15040] Loss: 10.022, Running accuracy: 91.375, Time: 41.30
[2020-12-15 14:27:59,200][__main__][INFO] - [15360] Loss: 8.775, Running accuracy: 91.374, Time: 37.09
[2020-12-15 14:28:41,170][__main__][INFO] - [15680] Loss: 9.010, Running accuracy: 91.386, Time: 41.97
[2020-12-15 14:29:20,552][__main__][INFO] - [16000] Loss: 9.137, Running accuracy: 91.382, Time: 39.38
[2020-12-15 14:29:56,533][__main__][INFO] - [16320] Loss: 8.757, Running accuracy: 91.387, Time: 35.98
[2020-12-15 14:30:31,556][__main__][INFO] - [16640] Loss: 8.096, Running accuracy: 91.391, Time: 35.02
[2020-12-15 14:31:06,784][__main__][INFO] - [16960] Loss: 8.707, Running accuracy: 91.398, Time: 35.23
[2020-12-15 14:31:46,901][__main__][INFO] - [17280] Loss: 9.259, Running accuracy: 91.402, Time: 40.03
[2020-12-15 14:32:16,137][__main__][INFO] - Action accuracy: 91.406, Loss: 10.216
[2020-12-15 14:32:16,138][__main__][INFO] - Validating..
[2020-12-15 14:32:22,734][test][INFO] - Time elapsed: 5.294953
[2020-12-15 14:32:22,736][__main__][INFO] - Validation F1 score: 93.810, Exact match: 52.840, Precision: 93.880, Recall: 93.730
[2020-12-15 14:32:22,736][__main__][INFO] - F1 score has improved
[2020-12-15 14:32:34,897][__main__][INFO] - Checkpoint saved to checkpoints/model_latest.pth
[2020-12-15 14:32:35,211][__main__][INFO] - Epoch #4
[2020-12-15 14:32:35,211][__main__][INFO] - Training..
[2020-12-15 14:33:14,062][__main__][INFO] - [320] Loss: 6.874, Running accuracy: 92.976, Time: 37.85
[2020-12-15 14:33:57,260][__main__][INFO] - [640] Loss: 7.903, Running accuracy: 93.066, Time: 43.20
[2020-12-15 14:34:40,824][__main__][INFO] - [960] Loss: 8.358, Running accuracy: 92.995, Time: 43.56
[2020-12-15 14:35:15,428][__main__][INFO] - [1280] Loss: 7.151, Running accuracy: 93.029, Time: 34.60
[2020-12-15 14:35:52,817][__main__][INFO] - [1600] Loss: 6.790, Running accuracy: 93.065, Time: 37.39
[2020-12-15 14:36:29,992][__main__][INFO] - [1920] Loss: 7.494, Running accuracy: 93.052, Time: 37.17
[2020-12-15 14:37:10,656][__main__][INFO] - [2240] Loss: 8.253, Running accuracy: 92.994, Time: 40.66
[2020-12-15 14:37:50,143][__main__][INFO] - [2560] Loss: 7.575, Running accuracy: 92.956, Time: 39.49
[2020-12-15 14:38:25,740][__main__][INFO] - [2880] Loss: 6.888, Running accuracy: 92.986, Time: 35.60
[2020-12-15 14:39:06,082][__main__][INFO] - [3200] Loss: 6.901, Running accuracy: 93.047, Time: 40.34
[2020-12-15 14:39:41,160][__main__][INFO] - [3520] Loss: 7.367, Running accuracy: 93.065, Time: 35.08
[2020-12-15 14:40:21,479][__main__][INFO] - [3840] Loss: 7.094, Running accuracy: 93.092, Time: 40.32
[2020-12-15 14:41:01,751][__main__][INFO] - [4160] Loss: 7.663, Running accuracy: 93.106, Time: 40.27
[2020-12-15 14:41:38,360][__main__][INFO] - [4480] Loss: 6.781, Running accuracy: 93.124, Time: 36.61
[2020-12-15 14:42:10,519][__main__][INFO] - [4800] Loss: 6.232, Running accuracy: 93.146, Time: 32.16
[2020-12-15 14:42:44,130][__main__][INFO] - [5120] Loss: 6.276, Running accuracy: 93.166, Time: 33.61
[2020-12-15 14:43:18,304][__main__][INFO] - [5440] Loss: 7.845, Running accuracy: 93.161, Time: 34.17
[2020-12-15 14:43:54,282][__main__][INFO] - [5760] Loss: 7.935, Running accuracy: 93.129, Time: 35.98
[2020-12-15 14:44:32,020][__main__][INFO] - [6080] Loss: 7.842, Running accuracy: 93.120, Time: 37.74
[2020-12-15 14:45:13,289][__main__][INFO] - [6400] Loss: 7.159, Running accuracy: 93.131, Time: 41.27
[2020-12-15 14:45:54,201][__main__][INFO] - [6720] Loss: 7.707, Running accuracy: 93.120, Time: 40.91
[2020-12-15 14:46:26,231][__main__][INFO] - [7040] Loss: 7.302, Running accuracy: 93.109, Time: 32.03
[2020-12-15 14:47:06,013][__main__][INFO] - [7360] Loss: 9.035, Running accuracy: 93.068, Time: 39.78
[2020-12-15 14:47:43,914][__main__][INFO] - [7680] Loss: 7.122, Running accuracy: 93.078, Time: 37.90
[2020-12-15 14:48:20,249][__main__][INFO] - [8000] Loss: 7.661, Running accuracy: 93.070, Time: 36.33
[2020-12-15 14:49:00,678][__main__][INFO] - [8320] Loss: 7.011, Running accuracy: 93.072, Time: 40.43
[2020-12-15 14:49:38,584][__main__][INFO] - [8640] Loss: 7.795, Running accuracy: 93.056, Time: 37.90
[2020-12-15 14:50:16,831][__main__][INFO] - [8960] Loss: 6.615, Running accuracy: 93.063, Time: 38.25
[2020-12-15 14:50:52,652][__main__][INFO] - [9280] Loss: 7.495, Running accuracy: 93.057, Time: 35.82
[2020-12-15 14:51:33,379][__main__][INFO] - [9600] Loss: 7.701, Running accuracy: 93.065, Time: 40.73
[2020-12-15 14:52:07,787][__main__][INFO] - [9920] Loss: 7.775, Running accuracy: 93.053, Time: 34.41
[2020-12-15 14:52:47,696][__main__][INFO] - [10240] Loss: 7.257, Running accuracy: 93.047, Time: 39.91
[2020-12-15 14:53:24,596][__main__][INFO] - [10560] Loss: 7.023, Running accuracy: 93.046, Time: 36.90
[2020-12-15 14:54:03,364][__main__][INFO] - [10880] Loss: 8.137, Running accuracy: 93.024, Time: 38.77
[2020-12-15 14:54:39,699][__main__][INFO] - [11200] Loss: 8.347, Running accuracy: 93.001, Time: 36.33
[2020-12-15 14:55:18,849][__main__][INFO] - [11520] Loss: 7.091, Running accuracy: 93.005, Time: 39.15
[2020-12-15 14:55:52,950][__main__][INFO] - [11840] Loss: 6.818, Running accuracy: 93.013, Time: 34.10
[2020-12-15 14:56:33,298][__main__][INFO] - [12160] Loss: 7.949, Running accuracy: 93.006, Time: 40.35
[2020-12-15 14:57:20,182][__main__][INFO] - [12480] Loss: 8.420, Running accuracy: 92.990, Time: 46.88
[2020-12-15 14:57:59,627][__main__][INFO] - [12800] Loss: 7.842, Running accuracy: 92.995, Time: 39.44
[2020-12-15 14:58:37,972][__main__][INFO] - [13120] Loss: 7.558, Running accuracy: 92.989, Time: 38.34
[2020-12-15 14:59:16,353][__main__][INFO] - [13440] Loss: 7.583, Running accuracy: 92.989, Time: 38.38
[2020-12-15 14:59:54,273][__main__][INFO] - [13760] Loss: 8.009, Running accuracy: 92.981, Time: 37.92
[2020-12-15 15:00:34,350][__main__][INFO] - [14080] Loss: 6.982, Running accuracy: 92.976, Time: 40.08
[2020-12-15 15:01:13,935][__main__][INFO] - [14400] Loss: 7.087, Running accuracy: 92.977, Time: 39.58
[2020-12-15 15:01:53,847][__main__][INFO] - [14720] Loss: 6.479, Running accuracy: 92.988, Time: 39.91
[2020-12-15 15:02:33,644][__main__][INFO] - [15040] Loss: 8.010, Running accuracy: 92.983, Time: 39.80
[2020-12-15 15:03:13,254][__main__][INFO] - [15360] Loss: 7.534, Running accuracy: 92.979, Time: 39.61
[2020-12-15 15:03:47,459][__main__][INFO] - [15680] Loss: 7.277, Running accuracy: 92.984, Time: 34.20
[2020-12-15 15:04:25,751][__main__][INFO] - [16000] Loss: 7.457, Running accuracy: 92.992, Time: 38.29
[2020-12-15 15:05:00,459][__main__][INFO] - [16320] Loss: 6.816, Running accuracy: 92.995, Time: 34.71
[2020-12-15 15:05:37,576][__main__][INFO] - [16640] Loss: 7.164, Running accuracy: 92.988, Time: 37.12
[2020-12-15 15:06:13,367][__main__][INFO] - [16960] Loss: 6.839, Running accuracy: 92.987, Time: 35.79
[2020-12-15 15:06:52,925][__main__][INFO] - [17280] Loss: 7.993, Running accuracy: 92.972, Time: 39.56
[2020-12-15 15:07:24,247][__main__][INFO] - Action accuracy: 92.968, Loss: 8.245
[2020-12-15 15:07:24,248][__main__][INFO] - Validating..
[2020-12-15 15:07:30,893][test][INFO] - Time elapsed: 5.292861
[2020-12-15 15:07:30,895][__main__][INFO] - Validation F1 score: 93.800, Exact match: 52.840, Precision: 94.540, Recall: 93.060
[2020-12-15 15:07:43,621][__main__][INFO] - Checkpoint saved to checkpoints/model_latest.pth
[2020-12-15 15:07:43,933][__main__][INFO] - Epoch #5
[2020-12-15 15:07:43,933][__main__][INFO] - Training..
[2020-12-15 15:08:22,115][__main__][INFO] - [320] Loss: 5.798, Running accuracy: 94.505, Time: 36.76
[2020-12-15 15:09:02,878][__main__][INFO] - [640] Loss: 6.190, Running accuracy: 94.443, Time: 40.76
[2020-12-15 15:09:36,959][__main__][INFO] - [960] Loss: 5.447, Running accuracy: 94.530, Time: 34.08
[2020-12-15 15:10:14,875][__main__][INFO] - [1280] Loss: 6.197, Running accuracy: 94.418, Time: 37.91
[2020-12-15 15:10:54,412][__main__][INFO] - [1600] Loss: 6.822, Running accuracy: 94.311, Time: 39.54
[2020-12-15 15:11:32,508][__main__][INFO] - [1920] Loss: 5.679, Running accuracy: 94.353, Time: 38.10
[2020-12-15 15:12:11,484][__main__][INFO] - [2240] Loss: 6.088, Running accuracy: 94.332, Time: 38.97
[2020-12-15 15:12:49,077][__main__][INFO] - [2560] Loss: 5.729, Running accuracy: 94.388, Time: 37.59
[2020-12-15 15:13:24,155][__main__][INFO] - [2880] Loss: 5.414, Running accuracy: 94.390, Time: 35.08
[2020-12-15 15:14:02,890][__main__][INFO] - [3200] Loss: 5.917, Running accuracy: 94.355, Time: 38.73
[2020-12-15 15:14:43,238][__main__][INFO] - [3520] Loss: 6.621, Running accuracy: 94.311, Time: 40.35
[2020-12-15 15:15:24,808][__main__][INFO] - [3840] Loss: 7.160, Running accuracy: 94.282, Time: 41.57
[2020-12-15 15:16:03,444][__main__][INFO] - [4160] Loss: 6.154, Running accuracy: 94.290, Time: 38.63
[2020-12-15 15:16:41,503][__main__][INFO] - [4480] Loss: 5.890, Running accuracy: 94.283, Time: 38.06
[2020-12-15 15:17:18,623][__main__][INFO] - [4800] Loss: 6.052, Running accuracy: 94.278, Time: 37.12
[2020-12-15 15:17:58,163][__main__][INFO] - [5120] Loss: 6.893, Running accuracy: 94.268, Time: 39.54
[2020-12-15 15:18:35,487][__main__][INFO] - [5440] Loss: 5.341, Running accuracy: 94.288, Time: 37.32
[2020-12-15 15:19:14,620][__main__][INFO] - [5760] Loss: 6.403, Running accuracy: 94.276, Time: 39.13
[2020-12-15 15:19:51,952][__main__][INFO] - [6080] Loss: 6.445, Running accuracy: 94.266, Time: 37.33
[2020-12-15 15:20:34,160][__main__][INFO] - [6400] Loss: 6.580, Running accuracy: 94.257, Time: 42.21
[2020-12-15 15:21:09,704][__main__][INFO] - [6720] Loss: 6.200, Running accuracy: 94.238, Time: 35.54
[2020-12-15 15:21:47,383][__main__][INFO] - [7040] Loss: 6.271, Running accuracy: 94.234, Time: 37.68
[2020-12-15 15:22:27,679][__main__][INFO] - [7360] Loss: 6.167, Running accuracy: 94.233, Time: 40.29
[2020-12-15 15:23:09,415][__main__][INFO] - [7680] Loss: 6.268, Running accuracy: 94.230, Time: 41.74
[2020-12-15 15:23:50,374][__main__][INFO] - [8000] Loss: 6.278, Running accuracy: 94.237, Time: 40.96
[2020-12-15 15:24:26,082][__main__][INFO] - [8320] Loss: 5.315, Running accuracy: 94.243, Time: 35.71
[2020-12-15 15:25:01,393][__main__][INFO] - [8640] Loss: 6.175, Running accuracy: 94.227, Time: 35.31
[2020-12-15 15:25:42,207][__main__][INFO] - [8960] Loss: 6.401, Running accuracy: 94.226, Time: 40.81
[2020-12-15 15:26:18,046][__main__][INFO] - [9280] Loss: 6.249, Running accuracy: 94.226, Time: 35.84
[2020-12-15 15:26:56,731][__main__][INFO] - [9600] Loss: 5.581, Running accuracy: 94.236, Time: 38.68
[2020-12-15 15:27:34,042][__main__][INFO] - [9920] Loss: 5.464, Running accuracy: 94.240, Time: 37.31
[2020-12-15 15:28:10,433][__main__][INFO] - [10240] Loss: 6.178, Running accuracy: 94.236, Time: 36.39
[2020-12-15 15:28:43,549][__main__][INFO] - [10560] Loss: 5.509, Running accuracy: 94.240, Time: 33.12
[2020-12-15 15:29:15,276][__main__][INFO] - [10880] Loss: 5.836, Running accuracy: 94.225, Time: 31.73
[2020-12-15 15:29:57,347][__main__][INFO] - [11200] Loss: 8.149, Running accuracy: 94.203, Time: 42.07
[2020-12-15 15:30:37,082][__main__][INFO] - [11520] Loss: 6.084, Running accuracy: 94.194, Time: 39.73
[2020-12-15 15:31:14,991][__main__][INFO] - [11840] Loss: 6.114, Running accuracy: 94.183, Time: 37.91
[2020-12-15 15:31:55,139][__main__][INFO] - [12160] Loss: 5.862, Running accuracy: 94.194, Time: 40.15
[2020-12-15 15:32:35,880][__main__][INFO] - [12480] Loss: 6.463, Running accuracy: 94.191, Time: 40.74
[2020-12-15 15:33:15,464][__main__][INFO] - [12800] Loss: 6.032, Running accuracy: 94.192, Time: 39.58
[2020-12-15 15:33:50,435][__main__][INFO] - [13120] Loss: 5.296, Running accuracy: 94.193, Time: 34.97
[2020-12-15 15:34:32,253][__main__][INFO] - [13440] Loss: 5.634, Running accuracy: 94.194, Time: 41.82
[2020-12-15 15:35:12,899][__main__][INFO] - [13760] Loss: 6.455, Running accuracy: 94.185, Time: 40.64
[2020-12-15 15:35:54,620][__main__][INFO] - [14080] Loss: 6.641, Running accuracy: 94.177, Time: 41.72
[2020-12-15 15:36:31,432][__main__][INFO] - [14400] Loss: 6.170, Running accuracy: 94.178, Time: 36.81
[2020-12-15 15:37:05,210][__main__][INFO] - [14720] Loss: 5.541, Running accuracy: 94.183, Time: 33.78
[2020-12-15 15:37:45,377][__main__][INFO] - [15040] Loss: 6.393, Running accuracy: 94.182, Time: 40.17
[2020-12-15 15:38:22,477][__main__][INFO] - [15360] Loss: 6.082, Running accuracy: 94.186, Time: 37.10
[2020-12-15 15:39:01,074][__main__][INFO] - [15680] Loss: 6.154, Running accuracy: 94.176, Time: 38.60
[2020-12-15 15:39:42,394][__main__][INFO] - [16000] Loss: 6.488, Running accuracy: 94.171, Time: 41.32
[2020-12-15 15:40:17,051][__main__][INFO] - [16320] Loss: 6.313, Running accuracy: 94.171, Time: 34.66
[2020-12-15 15:40:52,011][__main__][INFO] - [16640] Loss: 6.210, Running accuracy: 94.169, Time: 34.96
[2020-12-15 15:41:29,298][__main__][INFO] - [16960] Loss: 6.052, Running accuracy: 94.163, Time: 37.19
[2020-12-15 15:42:05,164][__main__][INFO] - [17280] Loss: 6.478, Running accuracy: 94.153, Time: 35.87
[2020-12-15 15:42:32,724][__main__][INFO] - Action accuracy: 94.154, Loss: 6.797
[2020-12-15 15:42:32,724][__main__][INFO] - Validating..
[2020-12-15 15:42:39,492][test][INFO] - Time elapsed: 5.256387
[2020-12-15 15:42:39,494][__main__][INFO] - Validation F1 score: 94.600, Exact match: 55.400, Precision: 94.690, Recall: 94.520
[2020-12-15 15:42:39,494][__main__][INFO] - F1 score has improved
[2020-12-15 15:42:52,212][__main__][INFO] - Checkpoint saved to checkpoints/model_latest.pth
[2020-12-15 15:42:52,567][__main__][INFO] - Epoch #6
[2020-12-15 15:42:52,567][__main__][INFO] - Training..
[2020-12-15 15:43:39,030][__main__][INFO] - [320] Loss: 5.224, Running accuracy: 95.280, Time: 45.31
[2020-12-15 15:44:18,328][__main__][INFO] - [640] Loss: 5.179, Running accuracy: 95.211, Time: 39.30
[2020-12-15 15:45:01,314][__main__][INFO] - [960] Loss: 5.340, Running accuracy: 95.246, Time: 42.99
[2020-12-15 15:45:42,253][__main__][INFO] - [1280] Loss: 5.168, Running accuracy: 95.218, Time: 40.94
[2020-12-15 15:46:18,954][__main__][INFO] - [1600] Loss: 5.041, Running accuracy: 95.170, Time: 36.70
[2020-12-15 15:46:58,049][__main__][INFO] - [1920] Loss: 4.834, Running accuracy: 95.123, Time: 39.09
[2020-12-15 15:47:37,590][__main__][INFO] - [2240] Loss: 4.861, Running accuracy: 95.105, Time: 39.54
[2020-12-15 15:48:19,016][__main__][INFO] - [2560] Loss: 5.332, Running accuracy: 95.093, Time: 41.42
[2020-12-15 15:48:55,228][__main__][INFO] - [2880] Loss: 4.902, Running accuracy: 95.125, Time: 36.21
[2020-12-15 15:49:33,668][__main__][INFO] - [3200] Loss: 4.844, Running accuracy: 95.151, Time: 38.44
[2020-12-15 15:50:05,403][__main__][INFO] - [3520] Loss: 4.551, Running accuracy: 95.131, Time: 31.73
[2020-12-15 15:50:45,170][__main__][INFO] - [3840] Loss: 5.394, Running accuracy: 95.142, Time: 39.77
[2020-12-15 15:51:22,607][__main__][INFO] - [4160] Loss: 4.613, Running accuracy: 95.155, Time: 37.44
[2020-12-15 15:52:00,122][__main__][INFO] - [4480] Loss: 5.772, Running accuracy: 95.125, Time: 37.51
[2020-12-15 15:52:40,845][__main__][INFO] - [4800] Loss: 5.212, Running accuracy: 95.122, Time: 40.72
[2020-12-15 15:53:22,400][__main__][INFO] - [5120] Loss: 4.932, Running accuracy: 95.122, Time: 41.55
[2020-12-15 15:53:59,292][__main__][INFO] - [5440] Loss: 4.988, Running accuracy: 95.115, Time: 36.89
[2020-12-15 15:54:38,356][__main__][INFO] - [5760] Loss: 4.719, Running accuracy: 95.124, Time: 39.06
[2020-12-15 15:55:13,340][__main__][INFO] - [6080] Loss: 4.555, Running accuracy: 95.128, Time: 34.98
[2020-12-15 15:55:55,200][__main__][INFO] - [6400] Loss: 5.374, Running accuracy: 95.135, Time: 41.86
[2020-12-15 15:56:35,866][__main__][INFO] - [6720] Loss: 5.015, Running accuracy: 95.147, Time: 40.66
[2020-12-15 15:57:14,996][__main__][INFO] - [7040] Loss: 4.804, Running accuracy: 95.140, Time: 39.13
[2020-12-15 15:57:54,763][__main__][INFO] - [7360] Loss: 5.583, Running accuracy: 95.128, Time: 39.77
[2020-12-15 15:58:33,075][__main__][INFO] - [7680] Loss: 4.892, Running accuracy: 95.126, Time: 38.31
[2020-12-15 15:59:06,900][__main__][INFO] - [8000] Loss: 5.127, Running accuracy: 95.106, Time: 33.82
[2020-12-15 15:59:46,656][__main__][INFO] - [8320] Loss: 5.570, Running accuracy: 95.102, Time: 39.75
[2020-12-15 16:00:23,638][__main__][INFO] - [8640] Loss: 4.950, Running accuracy: 95.110, Time: 36.98
[2020-12-15 16:01:03,764][__main__][INFO] - [8960] Loss: 5.267, Running accuracy: 95.101, Time: 40.12
[2020-12-15 16:01:42,726][__main__][INFO] - [9280] Loss: 4.811, Running accuracy: 95.108, Time: 38.96
[2020-12-15 16:02:20,029][__main__][INFO] - [9600] Loss: 4.950, Running accuracy: 95.110, Time: 37.30
[2020-12-15 16:02:58,434][__main__][INFO] - [9920] Loss: 5.827, Running accuracy: 95.103, Time: 38.40
[2020-12-15 16:03:41,668][__main__][INFO] - [10240] Loss: 6.205, Running accuracy: 95.095, Time: 43.23
[2020-12-15 16:04:17,442][__main__][INFO] - [10560] Loss: 5.171, Running accuracy: 95.097, Time: 35.77
[2020-12-15 16:05:00,810][__main__][INFO] - [10880] Loss: 5.138, Running accuracy: 95.096, Time: 43.37
[2020-12-15 16:05:35,744][__main__][INFO] - [11200] Loss: 5.440, Running accuracy: 95.086, Time: 34.93
[2020-12-15 16:06:10,578][__main__][INFO] - [11520] Loss: 5.142, Running accuracy: 95.080, Time: 34.83
[2020-12-15 16:06:47,865][__main__][INFO] - [11840] Loss: 4.728, Running accuracy: 95.077, Time: 37.29
[2020-12-15 16:07:22,642][__main__][INFO] - [12160] Loss: 4.845, Running accuracy: 95.072, Time: 34.78
[2020-12-15 16:07:59,696][__main__][INFO] - [12480] Loss: 5.368, Running accuracy: 95.065, Time: 37.05
[2020-12-15 16:08:32,232][__main__][INFO] - [12800] Loss: 5.025, Running accuracy: 95.057, Time: 32.54
[2020-12-15 16:09:07,580][__main__][INFO] - [13120] Loss: 4.577, Running accuracy: 95.054, Time: 35.35
[2020-12-15 16:09:45,981][__main__][INFO] - [13440] Loss: 5.783, Running accuracy: 95.034, Time: 38.40
[2020-12-15 16:10:26,005][__main__][INFO] - [13760] Loss: 5.818, Running accuracy: 95.025, Time: 40.02
[2020-12-15 16:11:05,900][__main__][INFO] - [14080] Loss: 4.647, Running accuracy: 95.036, Time: 39.89
[2020-12-15 16:11:44,620][__main__][INFO] - [14400] Loss: 5.082, Running accuracy: 95.033, Time: 38.72
[2020-12-15 16:12:21,435][__main__][INFO] - [14720] Loss: 5.212, Running accuracy: 95.033, Time: 36.81
[2020-12-15 16:13:02,734][__main__][INFO] - [15040] Loss: 5.120, Running accuracy: 95.029, Time: 41.30
[2020-12-15 16:13:42,483][__main__][INFO] - [15360] Loss: 5.732, Running accuracy: 95.022, Time: 39.75
[2020-12-15 16:14:18,414][__main__][INFO] - [15680] Loss: 5.254, Running accuracy: 95.021, Time: 35.93
[2020-12-15 16:14:57,393][__main__][INFO] - [16000] Loss: 5.589, Running accuracy: 95.017, Time: 38.98
[2020-12-15 16:15:29,581][__main__][INFO] - [16320] Loss: 4.946, Running accuracy: 95.018, Time: 32.09
[2020-12-15 16:16:08,976][__main__][INFO] - [16640] Loss: 5.400, Running accuracy: 95.007, Time: 39.39
[2020-12-15 16:16:46,863][__main__][INFO] - [16960] Loss: 4.763, Running accuracy: 95.003, Time: 37.89
[2020-12-15 16:17:23,222][__main__][INFO] - [17280] Loss: 5.282, Running accuracy: 95.003, Time: 36.36
[2020-12-15 16:17:50,469][__main__][INFO] - Action accuracy: 95.001, Loss: 5.734
[2020-12-15 16:17:50,470][__main__][INFO] - Validating..
[2020-12-15 16:17:57,068][test][INFO] - Time elapsed: 5.192736
[2020-12-15 16:17:57,069][__main__][INFO] - Validation F1 score: 94.080, Exact match: 54.550, Precision: 94.480, Recall: 93.680
[2020-12-15 16:18:09,044][__main__][INFO] - Checkpoint saved to checkpoints/model_latest.pth
[2020-12-15 16:18:09,406][__main__][INFO] - Epoch #7
[2020-12-15 16:18:09,406][__main__][INFO] - Training..
[2020-12-15 16:18:57,056][__main__][INFO] - [320] Loss: 5.381, Running accuracy: 95.572, Time: 46.44
[2020-12-15 16:19:33,866][__main__][INFO] - [640] Loss: 3.823, Running accuracy: 95.691, Time: 36.81
[2020-12-15 16:20:08,175][__main__][INFO] - [960] Loss: 3.637, Running accuracy: 95.790, Time: 34.31
[2020-12-15 16:20:43,034][__main__][INFO] - [1280] Loss: 4.253, Running accuracy: 95.768, Time: 34.86
[2020-12-15 16:21:21,390][__main__][INFO] - [1600] Loss: 4.272, Running accuracy: 95.785, Time: 38.36
[2020-12-15 16:21:56,160][__main__][INFO] - [1920] Loss: 4.359, Running accuracy: 95.746, Time: 34.77
[2020-12-15 16:22:29,403][__main__][INFO] - [2240] Loss: 4.195, Running accuracy: 95.763, Time: 33.24
[2020-12-15 16:23:08,110][__main__][INFO] - [2560] Loss: 3.941, Running accuracy: 95.823, Time: 38.71
[2020-12-15 16:23:48,659][__main__][INFO] - [2880] Loss: 4.337, Running accuracy: 95.836, Time: 40.55
[2020-12-15 16:24:23,916][__main__][INFO] - [3200] Loss: 4.876, Running accuracy: 95.781, Time: 35.26
[2020-12-15 16:25:03,657][__main__][INFO] - [3520] Loss: 4.279, Running accuracy: 95.808, Time: 39.74
[2020-12-15 16:25:47,160][__main__][INFO] - [3840] Loss: 4.458, Running accuracy: 95.809, Time: 43.50
[2020-12-15 16:26:28,206][__main__][INFO] - [4160] Loss: 4.072, Running accuracy: 95.831, Time: 41.05
[2020-12-15 16:27:08,095][__main__][INFO] - [4480] Loss: 4.485, Running accuracy: 95.813, Time: 39.89
[2020-12-15 16:27:44,897][__main__][INFO] - [4800] Loss: 4.141, Running accuracy: 95.810, Time: 36.80
[2020-12-15 16:28:21,881][__main__][INFO] - [5120] Loss: 4.397, Running accuracy: 95.818, Time: 36.98
[2020-12-15 16:28:56,390][__main__][INFO] - [5440] Loss: 4.136, Running accuracy: 95.814, Time: 34.51
[2020-12-15 16:29:38,029][__main__][INFO] - [5760] Loss: 4.388, Running accuracy: 95.792, Time: 41.64
[2020-12-15 16:30:18,949][__main__][INFO] - [6080] Loss: 4.250, Running accuracy: 95.807, Time: 40.91
[2020-12-15 16:30:52,713][__main__][INFO] - [6400] Loss: 3.844, Running accuracy: 95.800, Time: 33.76
[2020-12-15 16:31:30,609][__main__][INFO] - [6720] Loss: 4.847, Running accuracy: 95.791, Time: 37.90
[2020-12-15 16:32:10,949][__main__][INFO] - [7040] Loss: 4.307, Running accuracy: 95.808, Time: 40.34
[2020-12-15 16:32:44,143][__main__][INFO] - [7360] Loss: 4.585, Running accuracy: 95.779, Time: 33.19
[2020-12-15 16:33:27,866][__main__][INFO] - [7680] Loss: 4.891, Running accuracy: 95.767, Time: 43.72
[2020-12-15 16:34:08,336][__main__][INFO] - [8000] Loss: 4.119, Running accuracy: 95.764, Time: 40.47
[2020-12-15 16:34:36,088][__main__][INFO] - [8320] Loss: 3.327, Running accuracy: 95.773, Time: 27.75
[2020-12-15 16:35:10,283][__main__][INFO] - [8640] Loss: 4.112, Running accuracy: 95.792, Time: 34.19
[2020-12-15 16:35:44,316][__main__][INFO] - [8960] Loss: 3.740, Running accuracy: 95.802, Time: 34.03
[2020-12-15 16:36:23,852][__main__][INFO] - [9280] Loss: 4.857, Running accuracy: 95.793, Time: 39.53
[2020-12-15 16:37:03,517][__main__][INFO] - [9600] Loss: 4.534, Running accuracy: 95.782, Time: 39.66
[2020-12-15 16:37:46,136][__main__][INFO] - [9920] Loss: 4.715, Running accuracy: 95.780, Time: 42.62
[2020-12-15 16:38:24,552][__main__][INFO] - [10240] Loss: 4.405, Running accuracy: 95.780, Time: 38.41
[2020-12-15 16:39:03,329][__main__][INFO] - [10560] Loss: 3.922, Running accuracy: 95.791, Time: 38.78
[2020-12-15 16:39:39,408][__main__][INFO] - [10880] Loss: 4.591, Running accuracy: 95.790, Time: 36.08
[2020-12-15 16:40:18,516][__main__][INFO] - [11200] Loss: 4.252, Running accuracy: 95.790, Time: 39.11
[2020-12-15 16:40:55,557][__main__][INFO] - [11520] Loss: 4.417, Running accuracy: 95.789, Time: 37.04
[2020-12-15 16:41:35,183][__main__][INFO] - [11840] Loss: 4.911, Running accuracy: 95.780, Time: 39.63
[2020-12-15 16:42:14,715][__main__][INFO] - [12160] Loss: 3.569, Running accuracy: 95.780, Time: 39.53
[2020-12-15 16:42:51,590][__main__][INFO] - [12480] Loss: 4.262, Running accuracy: 95.773, Time: 36.87
[2020-12-15 16:43:30,890][__main__][INFO] - [12800] Loss: 4.426, Running accuracy: 95.765, Time: 39.30
[2020-12-15 16:44:12,808][__main__][INFO] - [13120] Loss: 5.047, Running accuracy: 95.753, Time: 41.92
[2020-12-15 16:44:49,517][__main__][INFO] - [13440] Loss: 4.567, Running accuracy: 95.748, Time: 36.71
[2020-12-15 16:45:29,544][__main__][INFO] - [13760] Loss: 4.568, Running accuracy: 95.745, Time: 40.03
[2020-12-15 16:46:11,859][__main__][INFO] - [14080] Loss: 4.767, Running accuracy: 95.740, Time: 42.31
[2020-12-15 16:46:50,055][__main__][INFO] - [14400] Loss: 3.884, Running accuracy: 95.749, Time: 38.20
[2020-12-15 16:47:26,588][__main__][INFO] - [14720] Loss: 4.867, Running accuracy: 95.744, Time: 36.53
[2020-12-15 16:48:00,961][__main__][INFO] - [15040] Loss: 4.505, Running accuracy: 95.738, Time: 34.37
[2020-12-15 16:48:41,049][__main__][INFO] - [15360] Loss: 4.276, Running accuracy: 95.731, Time: 40.09
[2020-12-15 16:49:23,712][__main__][INFO] - [15680] Loss: 5.474, Running accuracy: 95.713, Time: 42.66
[2020-12-15 16:49:57,591][__main__][INFO] - [16000] Loss: 4.571, Running accuracy: 95.713, Time: 33.88
[2020-12-15 16:50:35,222][__main__][INFO] - [16320] Loss: 4.233, Running accuracy: 95.712, Time: 37.63
[2020-12-15 16:51:13,481][__main__][INFO] - [16640] Loss: 4.736, Running accuracy: 95.707, Time: 38.26
[2020-12-15 16:51:52,824][__main__][INFO] - [16960] Loss: 4.957, Running accuracy: 95.698, Time: 39.34
[2020-12-15 16:52:29,759][__main__][INFO] - [17280] Loss: 4.654, Running accuracy: 95.692, Time: 36.93
[2020-12-15 16:52:58,703][__main__][INFO] - Action accuracy: 95.685, Loss: 4.862
[2020-12-15 16:52:58,704][__main__][INFO] - Validating..
[2020-12-15 16:53:05,336][test][INFO] - Time elapsed: 5.196485
[2020-12-15 16:53:05,338][__main__][INFO] - Validation F1 score: 94.170, Exact match: 55.680, Precision: 94.230, Recall: 94.110
[2020-12-15 16:53:17,142][__main__][INFO] - Checkpoint saved to checkpoints/model_latest.pth
[2020-12-15 16:53:17,451][__main__][INFO] - Epoch #8
[2020-12-15 16:53:17,451][__main__][INFO] - Training..
[2020-12-15 16:53:54,279][__main__][INFO] - [320] Loss: 3.553, Running accuracy: 96.727, Time: 35.56
[2020-12-15 16:54:32,819][__main__][INFO] - [640] Loss: 3.275, Running accuracy: 96.628, Time: 38.54
[2020-12-15 16:55:08,998][__main__][INFO] - [960] Loss: 3.715, Running accuracy: 96.484, Time: 36.18
[2020-12-15 16:55:50,418][__main__][INFO] - [1280] Loss: 3.618, Running accuracy: 96.507, Time: 41.42
[2020-12-15 16:56:23,090][__main__][INFO] - [1600] Loss: 3.358, Running accuracy: 96.533, Time: 32.67
[2020-12-15 16:56:57,602][__main__][INFO] - [1920] Loss: 3.313, Running accuracy: 96.479, Time: 34.51
[2020-12-15 16:57:38,279][__main__][INFO] - [2240] Loss: 3.807, Running accuracy: 96.450, Time: 40.68
[2020-12-15 16:58:14,350][__main__][INFO] - [2560] Loss: 3.423, Running accuracy: 96.486, Time: 36.07
[2020-12-15 16:58:54,639][__main__][INFO] - [2880] Loss: 3.997, Running accuracy: 96.461, Time: 40.29
[2020-12-15 16:59:33,881][__main__][INFO] - [3200] Loss: 3.607, Running accuracy: 96.461, Time: 39.24
[2020-12-15 17:00:09,532][__main__][INFO] - [3520] Loss: 3.768, Running accuracy: 96.455, Time: 35.65
[2020-12-15 17:00:45,312][__main__][INFO] - [3840] Loss: 3.256, Running accuracy: 96.464, Time: 35.78
[2020-12-15 17:01:24,552][__main__][INFO] - [4160] Loss: 3.762, Running accuracy: 96.458, Time: 39.24
[2020-12-15 17:02:06,294][__main__][INFO] - [4480] Loss: 3.516, Running accuracy: 96.447, Time: 41.74
[2020-12-15 17:02:46,407][__main__][INFO] - [4800] Loss: 3.849, Running accuracy: 96.416, Time: 40.11
[2020-12-15 17:03:25,406][__main__][INFO] - [5120] Loss: 3.692, Running accuracy: 96.395, Time: 39.00
[2020-12-15 17:04:02,349][__main__][INFO] - [5440] Loss: 3.504, Running accuracy: 96.401, Time: 36.94
[2020-12-15 17:04:41,512][__main__][INFO] - [5760] Loss: 3.826, Running accuracy: 96.409, Time: 39.16
[2020-12-15 17:05:19,992][__main__][INFO] - [6080] Loss: 3.431, Running accuracy: 96.425, Time: 38.48
[2020-12-15 17:06:01,816][__main__][INFO] - [6400] Loss: 3.831, Running accuracy: 96.424, Time: 41.82
[2020-12-15 17:06:36,626][__main__][INFO] - [6720] Loss: 3.636, Running accuracy: 96.433, Time: 34.81
[2020-12-15 17:07:14,605][__main__][INFO] - [7040] Loss: 3.723, Running accuracy: 96.424, Time: 37.98
[2020-12-15 17:07:49,913][__main__][INFO] - [7360] Loss: 3.468, Running accuracy: 96.423, Time: 35.31
[2020-12-15 17:08:24,047][__main__][INFO] - [7680] Loss: 3.730, Running accuracy: 96.414, Time: 34.13
[2020-12-15 17:08:58,846][__main__][INFO] - [8000] Loss: 3.553, Running accuracy: 96.400, Time: 34.80
[2020-12-15 17:09:34,714][__main__][INFO] - [8320] Loss: 3.858, Running accuracy: 96.401, Time: 35.87
[2020-12-15 17:10:16,532][__main__][INFO] - [8640] Loss: 4.319, Running accuracy: 96.397, Time: 41.82
[2020-12-15 17:10:51,480][__main__][INFO] - [8960] Loss: 3.471, Running accuracy: 96.390, Time: 34.95
[2020-12-15 17:11:32,288][__main__][INFO] - [9280] Loss: 4.145, Running accuracy: 96.374, Time: 40.81
[2020-12-15 17:12:10,861][__main__][INFO] - [9600] Loss: 3.691, Running accuracy: 96.375, Time: 38.57
[2020-12-15 17:12:47,083][__main__][INFO] - [9920] Loss: 3.916, Running accuracy: 96.374, Time: 36.22
[2020-12-15 17:13:26,034][__main__][INFO] - [10240] Loss: 3.795, Running accuracy: 96.376, Time: 38.95
[2020-12-15 17:14:03,537][__main__][INFO] - [10560] Loss: 3.919, Running accuracy: 96.371, Time: 37.50
[2020-12-15 17:14:42,144][__main__][INFO] - [10880] Loss: 3.632, Running accuracy: 96.360, Time: 38.61
[2020-12-15 17:15:22,496][__main__][INFO] - [11200] Loss: 3.992, Running accuracy: 96.353, Time: 40.35
[2020-12-15 17:16:06,032][__main__][INFO] - [11520] Loss: 4.732, Running accuracy: 96.339, Time: 43.53
[2020-12-15 17:16:44,634][__main__][INFO] - [11840] Loss: 3.965, Running accuracy: 96.342, Time: 38.60
[2020-12-15 17:17:24,242][__main__][INFO] - [12160] Loss: 3.803, Running accuracy: 96.345, Time: 39.61
[2020-12-15 17:18:04,335][__main__][INFO] - [12480] Loss: 3.681, Running accuracy: 96.351, Time: 40.09
[2020-12-15 17:18:44,023][__main__][INFO] - [12800] Loss: 3.598, Running accuracy: 96.349, Time: 39.69
[2020-12-15 17:19:23,849][__main__][INFO] - [13120] Loss: 4.190, Running accuracy: 96.346, Time: 39.83
[2020-12-15 17:20:07,245][__main__][INFO] - [13440] Loss: 3.700, Running accuracy: 96.345, Time: 43.40
[2020-12-15 17:20:45,054][__main__][INFO] - [13760] Loss: 4.184, Running accuracy: 96.337, Time: 37.81
[2020-12-15 17:21:23,910][__main__][INFO] - [14080] Loss: 4.042, Running accuracy: 96.328, Time: 38.85
[2020-12-15 17:22:01,897][__main__][INFO] - [14400] Loss: 3.847, Running accuracy: 96.327, Time: 37.99
[2020-12-15 17:22:37,607][__main__][INFO] - [14720] Loss: 3.759, Running accuracy: 96.324, Time: 35.71
[2020-12-15 17:23:14,919][__main__][INFO] - [15040] Loss: 3.819, Running accuracy: 96.319, Time: 37.31
[2020-12-15 17:23:53,458][__main__][INFO] - [15360] Loss: 3.784, Running accuracy: 96.320, Time: 38.54
[2020-12-15 17:24:31,988][__main__][INFO] - [15680] Loss: 3.898, Running accuracy: 96.317, Time: 38.53
[2020-12-15 17:25:07,477][__main__][INFO] - [16000] Loss: 3.884, Running accuracy: 96.318, Time: 35.40
[2020-12-15 17:25:43,795][__main__][INFO] - [16320] Loss: 3.887, Running accuracy: 96.316, Time: 36.32
[2020-12-15 17:26:23,572][__main__][INFO] - [16640] Loss: 3.744, Running accuracy: 96.316, Time: 39.78
[2020-12-15 17:27:04,457][__main__][INFO] - [16960] Loss: 4.276, Running accuracy: 96.305, Time: 40.88
[2020-12-15 17:27:44,434][__main__][INFO] - [17280] Loss: 4.256, Running accuracy: 96.297, Time: 39.98
[2020-12-15 17:28:10,797][__main__][INFO] - Action accuracy: 96.296, Loss: 4.144
[2020-12-15 17:28:10,797][__main__][INFO] - Validating..
[2020-12-15 17:28:19,944][test][INFO] - Time elapsed: 5.324436
[2020-12-15 17:28:19,946][__main__][INFO] - Validation F1 score: 93.930, Exact match: 54.830, Precision: 94.040, Recall: 93.820
[2020-12-15 17:28:31,769][__main__][INFO] - Checkpoint saved to checkpoints/model_latest.pth
[2020-12-15 17:28:32,082][__main__][INFO] - Epoch #9
[2020-12-15 17:28:32,082][__main__][INFO] - Training..
[2020-12-15 17:29:08,048][__main__][INFO] - [320] Loss: 3.105, Running accuracy: 96.599, Time: 34.70
[2020-12-15 17:29:47,968][__main__][INFO] - [640] Loss: 3.433, Running accuracy: 96.752, Time: 39.92
[2020-12-15 17:30:29,330][__main__][INFO] - [960] Loss: 2.895, Running accuracy: 96.831, Time: 41.36
[2020-12-15 17:31:05,892][__main__][INFO] - [1280] Loss: 2.548, Running accuracy: 96.937, Time: 36.56
[2020-12-15 17:31:46,474][__main__][INFO] - [1600] Loss: 3.174, Running accuracy: 96.959, Time: 40.58
[2020-12-15 17:32:24,197][__main__][INFO] - [1920] Loss: 2.805, Running accuracy: 96.980, Time: 37.72
[2020-12-15 17:33:01,631][__main__][INFO] - [2240] Loss: 3.416, Running accuracy: 96.963, Time: 37.43
[2020-12-15 17:33:40,050][__main__][INFO] - [2560] Loss: 2.917, Running accuracy: 96.995, Time: 38.42
[2020-12-15 17:34:19,216][__main__][INFO] - [2880] Loss: 3.451, Running accuracy: 96.973, Time: 39.17
[2020-12-15 17:34:59,831][__main__][INFO] - [3200] Loss: 3.247, Running accuracy: 96.957, Time: 40.61
[2020-12-15 17:35:38,665][__main__][INFO] - [3520] Loss: 3.167, Running accuracy: 96.961, Time: 38.83
[2020-12-15 17:36:20,478][__main__][INFO] - [3840] Loss: 3.190, Running accuracy: 96.962, Time: 41.81
[2020-12-15 17:36:58,255][__main__][INFO] - [4160] Loss: 3.022, Running accuracy: 96.957, Time: 37.78
[2020-12-15 17:37:35,074][__main__][INFO] - [4480] Loss: 3.351, Running accuracy: 96.941, Time: 36.82
[2020-12-15 17:38:15,886][__main__][INFO] - [4800] Loss: 3.265, Running accuracy: 96.934, Time: 40.81
[2020-12-15 17:38:57,032][__main__][INFO] - [5120] Loss: 3.478, Running accuracy: 96.926, Time: 41.14
[2020-12-15 17:39:35,112][__main__][INFO] - [5440] Loss: 3.100, Running accuracy: 96.941, Time: 38.08
[2020-12-15 17:40:16,461][__main__][INFO] - [5760] Loss: 3.924, Running accuracy: 96.911, Time: 41.35
[2020-12-15 17:40:58,243][__main__][INFO] - [6080] Loss: 2.943, Running accuracy: 96.925, Time: 41.78
[2020-12-15 17:41:39,092][__main__][INFO] - [6400] Loss: 3.301, Running accuracy: 96.921, Time: 40.85
[2020-12-15 17:42:17,336][__main__][INFO] - [6720] Loss: 3.026, Running accuracy: 96.925, Time: 38.24
[2020-12-15 17:42:54,471][__main__][INFO] - [7040] Loss: 3.425, Running accuracy: 96.918, Time: 37.13
[2020-12-15 17:43:34,311][__main__][INFO] - [7360] Loss: 2.899, Running accuracy: 96.927, Time: 39.84
[2020-12-15 17:44:16,587][__main__][INFO] - [7680] Loss: 3.573, Running accuracy: 96.915, Time: 42.28
[2020-12-15 17:44:57,398][__main__][INFO] - [8000] Loss: 3.222, Running accuracy: 96.915, Time: 40.81
[2020-12-15 17:45:35,437][__main__][INFO] - [8320] Loss: 3.264, Running accuracy: 96.908, Time: 38.04
[2020-12-15 17:46:15,948][__main__][INFO] - [8640] Loss: 3.571, Running accuracy: 96.894, Time: 40.51
[2020-12-15 17:46:53,069][__main__][INFO] - [8960] Loss: 3.364, Running accuracy: 96.878, Time: 37.12
[2020-12-15 17:47:30,660][__main__][INFO] - [9280] Loss: 3.190, Running accuracy: 96.874, Time: 37.59
[2020-12-15 17:48:15,203][__main__][INFO] - [9600] Loss: 3.646, Running accuracy: 96.868, Time: 44.54
[2020-12-15 17:48:47,715][__main__][INFO] - [9920] Loss: 3.262, Running accuracy: 96.859, Time: 32.51
[2020-12-15 17:49:27,049][__main__][INFO] - [10240] Loss: 3.016, Running accuracy: 96.847, Time: 39.33
[2020-12-15 17:50:04,157][__main__][INFO] - [10560] Loss: 2.582, Running accuracy: 96.846, Time: 37.11
[2020-12-15 17:50:43,229][__main__][INFO] - [10880] Loss: 3.147, Running accuracy: 96.848, Time: 39.07
[2020-12-15 17:51:19,353][__main__][INFO] - [11200] Loss: 3.200, Running accuracy: 96.849, Time: 36.12
[2020-12-15 17:51:55,903][__main__][INFO] - [11520] Loss: 2.981, Running accuracy: 96.851, Time: 36.55
[2020-12-15 17:52:34,862][__main__][INFO] - [11840] Loss: 3.239, Running accuracy: 96.853, Time: 38.96
[2020-12-15 17:53:15,565][__main__][INFO] - [12160] Loss: 3.388, Running accuracy: 96.842, Time: 40.70
[2020-12-15 17:53:49,744][__main__][INFO] - [12480] Loss: 3.292, Running accuracy: 96.833, Time: 34.18
[2020-12-15 17:54:27,038][__main__][INFO] - [12800] Loss: 3.417, Running accuracy: 96.831, Time: 37.29
[2020-12-15 17:55:04,659][__main__][INFO] - [13120] Loss: 3.370, Running accuracy: 96.827, Time: 37.62
[2020-12-15 17:55:39,336][__main__][INFO] - [13440] Loss: 2.985, Running accuracy: 96.823, Time: 34.68
[2020-12-15 17:56:10,988][__main__][INFO] - [13760] Loss: 3.288, Running accuracy: 96.826, Time: 31.65
[2020-12-15 17:56:49,093][__main__][INFO] - [14080] Loss: 3.438, Running accuracy: 96.823, Time: 38.10
[2020-12-15 17:57:31,836][__main__][INFO] - [14400] Loss: 3.120, Running accuracy: 96.819, Time: 42.74
[2020-12-15 17:58:07,202][__main__][INFO] - [14720] Loss: 2.795, Running accuracy: 96.822, Time: 35.37
[2020-12-15 17:58:41,738][__main__][INFO] - [15040] Loss: 2.956, Running accuracy: 96.819, Time: 34.53
[2020-12-15 17:59:19,118][__main__][INFO] - [15360] Loss: 3.209, Running accuracy: 96.819, Time: 37.38
[2020-12-15 17:59:56,591][__main__][INFO] - [15680] Loss: 3.673, Running accuracy: 96.811, Time: 37.47
[2020-12-15 18:00:33,404][__main__][INFO] - [16000] Loss: 3.321, Running accuracy: 96.805, Time: 36.81
[2020-12-15 18:01:06,772][__main__][INFO] - [16320] Loss: 3.210, Running accuracy: 96.800, Time: 33.37
[2020-12-15 18:01:45,889][__main__][INFO] - [16640] Loss: 3.104, Running accuracy: 96.800, Time: 39.12
[2020-12-15 18:02:22,082][__main__][INFO] - [16960] Loss: 3.360, Running accuracy: 96.796, Time: 36.19
[2020-12-15 18:02:58,253][__main__][INFO] - [17280] Loss: 3.803, Running accuracy: 96.784, Time: 36.17
[2020-12-15 18:03:26,700][__main__][INFO] - Action accuracy: 96.778, Loss: 3.578
[2020-12-15 18:03:26,700][__main__][INFO] - Validating..
[2020-12-15 18:03:33,531][test][INFO] - Time elapsed: 5.310327
[2020-12-15 18:03:33,533][__main__][INFO] - Validation F1 score: 94.330, Exact match: 55.400, Precision: 94.710, Recall: 93.950
[2020-12-15 18:03:46,284][__main__][INFO] - Checkpoint saved to checkpoints/model_latest.pth
[2020-12-15 18:03:46,652][__main__][INFO] - Epoch #10
[2020-12-15 18:03:46,653][__main__][INFO] - Training..
[2020-12-15 18:04:25,661][__main__][INFO] - [320] Loss: 3.006, Running accuracy: 96.971, Time: 38.17
[2020-12-15 18:05:04,123][__main__][INFO] - [640] Loss: 2.605, Running accuracy: 97.180, Time: 38.46
[2020-12-15 18:05:42,064][__main__][INFO] - [960] Loss: 2.314, Running accuracy: 97.306, Time: 37.94
[2020-12-15 18:06:15,793][__main__][INFO] - [1280] Loss: 2.511, Running accuracy: 97.287, Time: 33.73
[2020-12-15 18:06:54,689][__main__][INFO] - [1600] Loss: 2.480, Running accuracy: 97.308, Time: 38.90
[2020-12-15 18:07:34,147][__main__][INFO] - [1920] Loss: 2.700, Running accuracy: 97.315, Time: 39.46
[2020-12-15 18:08:12,680][__main__][INFO] - [2240] Loss: 2.623, Running accuracy: 97.327, Time: 38.53
[2020-12-15 18:08:48,410][__main__][INFO] - [2560] Loss: 2.656, Running accuracy: 97.332, Time: 35.73
[2020-12-15 18:09:36,283][__main__][INFO] - [2880] Loss: 2.776, Running accuracy: 97.332, Time: 47.87
[2020-12-15 18:10:16,426][__main__][INFO] - [3200] Loss: 2.919, Running accuracy: 97.346, Time: 40.14
[2020-12-15 18:10:57,677][__main__][INFO] - [3520] Loss: 3.069, Running accuracy: 97.302, Time: 41.25
[2020-12-15 18:11:36,873][__main__][INFO] - [3840] Loss: 3.320, Running accuracy: 97.296, Time: 39.20
[2020-12-15 18:12:13,996][__main__][INFO] - [4160] Loss: 2.817, Running accuracy: 97.269, Time: 37.12
[2020-12-15 18:12:48,779][__main__][INFO] - [4480] Loss: 2.486, Running accuracy: 97.281, Time: 34.78
[2020-12-15 18:13:27,613][__main__][INFO] - [4800] Loss: 2.562, Running accuracy: 97.275, Time: 38.83
[2020-12-15 18:14:07,300][__main__][INFO] - [5120] Loss: 2.938, Running accuracy: 97.251, Time: 39.69
[2020-12-15 18:14:47,378][__main__][INFO] - [5440] Loss: 2.812, Running accuracy: 97.255, Time: 40.08
[2020-12-15 18:15:33,166][__main__][INFO] - [5760] Loss: 3.165, Running accuracy: 97.271, Time: 45.79
[2020-12-15 18:16:11,439][__main__][INFO] - [6080] Loss: 3.099, Running accuracy: 97.277, Time: 38.27
[2020-12-15 18:16:44,420][__main__][INFO] - [6400] Loss: 2.801, Running accuracy: 97.276, Time: 32.98
[2020-12-15 18:17:24,873][__main__][INFO] - [6720] Loss: 2.831, Running accuracy: 97.281, Time: 40.45
[2020-12-15 18:18:02,266][__main__][INFO] - [7040] Loss: 2.577, Running accuracy: 97.279, Time: 37.39
[2020-12-15 18:18:40,020][__main__][INFO] - [7360] Loss: 2.491, Running accuracy: 97.278, Time: 37.75
[2020-12-15 18:19:15,738][__main__][INFO] - [7680] Loss: 2.868, Running accuracy: 97.274, Time: 35.72
[2020-12-15 18:19:51,603][__main__][INFO] - [8000] Loss: 2.695, Running accuracy: 97.276, Time: 35.86
[2020-12-15 18:20:27,246][__main__][INFO] - [8320] Loss: 2.597, Running accuracy: 97.279, Time: 35.64
[2020-12-15 18:20:58,619][__main__][INFO] - [8640] Loss: 2.718, Running accuracy: 97.275, Time: 31.37
[2020-12-15 18:21:31,335][__main__][INFO] - [8960] Loss: 2.522, Running accuracy: 97.273, Time: 32.71
[2020-12-15 18:22:12,293][__main__][INFO] - [9280] Loss: 2.788, Running accuracy: 97.265, Time: 40.96
[2020-12-15 18:22:49,535][__main__][INFO] - [9600] Loss: 2.739, Running accuracy: 97.270, Time: 37.24
[2020-12-15 18:23:22,637][__main__][INFO] - [9920] Loss: 2.949, Running accuracy: 97.262, Time: 33.10
[2020-12-15 18:24:05,152][__main__][INFO] - [10240] Loss: 3.138, Running accuracy: 97.250, Time: 42.51
[2020-12-15 18:24:40,942][__main__][INFO] - [10560] Loss: 2.638, Running accuracy: 97.252, Time: 35.79
[2020-12-15 18:25:20,665][__main__][INFO] - [10880] Loss: 3.026, Running accuracy: 97.252, Time: 39.72
[2020-12-15 18:25:57,448][__main__][INFO] - [11200] Loss: 2.672, Running accuracy: 97.256, Time: 36.78
[2020-12-15 18:26:33,355][__main__][INFO] - [11520] Loss: 2.304, Running accuracy: 97.261, Time: 35.91
[2020-12-15 18:27:15,597][__main__][INFO] - [11840] Loss: 2.850, Running accuracy: 97.259, Time: 42.24
[2020-12-15 18:27:51,195][__main__][INFO] - [12160] Loss: 2.654, Running accuracy: 97.260, Time: 35.60
[2020-12-15 18:28:25,471][__main__][INFO] - [12480] Loss: 2.757, Running accuracy: 97.253, Time: 34.27
[2020-12-15 18:28:59,899][__main__][INFO] - [12800] Loss: 2.483, Running accuracy: 97.247, Time: 34.43
[2020-12-15 18:29:36,443][__main__][INFO] - [13120] Loss: 3.074, Running accuracy: 97.240, Time: 36.54
[2020-12-15 18:30:17,770][__main__][INFO] - [13440] Loss: 3.389, Running accuracy: 97.231, Time: 41.33
[2020-12-15 18:30:54,885][__main__][INFO] - [13760] Loss: 2.746, Running accuracy: 97.234, Time: 37.11
[2020-12-15 18:31:39,747][__main__][INFO] - [14080] Loss: 3.214, Running accuracy: 97.234, Time: 44.86
[2020-12-15 18:32:18,989][__main__][INFO] - [14400] Loss: 3.089, Running accuracy: 97.227, Time: 39.24
[2020-12-15 18:32:56,522][__main__][INFO] - [14720] Loss: 3.040, Running accuracy: 97.217, Time: 37.53
[2020-12-15 18:33:36,759][__main__][INFO] - [15040] Loss: 3.173, Running accuracy: 97.209, Time: 40.24
[2020-12-15 18:34:14,244][__main__][INFO] - [15360] Loss: 2.904, Running accuracy: 97.203, Time: 37.48
[2020-12-15 18:34:57,768][__main__][INFO] - [15680] Loss: 3.104, Running accuracy: 97.198, Time: 43.52
[2020-12-15 18:35:36,346][__main__][INFO] - [16000] Loss: 3.199, Running accuracy: 97.191, Time: 38.58
[2020-12-15 18:36:15,099][__main__][INFO] - [16320] Loss: 3.172, Running accuracy: 97.184, Time: 38.66
[2020-12-15 18:36:57,133][__main__][INFO] - [16640] Loss: 3.254, Running accuracy: 97.180, Time: 42.03
[2020-12-15 18:37:30,318][__main__][INFO] - [16960] Loss: 2.684, Running accuracy: 97.181, Time: 33.18
[2020-12-15 18:38:06,776][__main__][INFO] - [17280] Loss: 2.838, Running accuracy: 97.184, Time: 36.46
[2020-12-15 18:38:37,030][__main__][INFO] - Action accuracy: 97.187, Loss: 3.113
[2020-12-15 18:38:37,031][__main__][INFO] - Validating..
[2020-12-15 18:38:43,609][test][INFO] - Time elapsed: 5.276183
[2020-12-15 18:38:43,610][__main__][INFO] - Validation F1 score: 94.180, Exact match: 55.680, Precision: 94.400, Recall: 93.960
[2020-12-15 18:38:58,506][__main__][INFO] - Checkpoint saved to checkpoints/model_latest.pth
[2020-12-15 18:38:58,853][__main__][INFO] - Epoch #11
[2020-12-15 18:38:58,853][__main__][INFO] - Training..
[2020-12-15 18:39:38,136][__main__][INFO] - [320] Loss: 2.395, Running accuracy: 97.793, Time: 38.20
[2020-12-15 18:40:20,386][__main__][INFO] - [640] Loss: 2.404, Running accuracy: 97.667, Time: 42.25
[2020-12-15 18:40:58,572][__main__][INFO] - [960] Loss: 2.155, Running accuracy: 97.674, Time: 38.18
[2020-12-15 18:41:37,704][__main__][INFO] - [1280] Loss: 2.282, Running accuracy: 97.648, Time: 39.13
[2020-12-15 18:42:12,006][__main__][INFO] - [1600] Loss: 1.952, Running accuracy: 97.719, Time: 34.30
[2020-12-15 18:42:53,573][__main__][INFO] - [1920] Loss: 2.092, Running accuracy: 97.715, Time: 41.57
[2020-12-15 18:43:37,078][__main__][INFO] - [2240] Loss: 2.386, Running accuracy: 97.718, Time: 43.50
[2020-12-15 18:44:13,293][__main__][INFO] - [2560] Loss: 2.428, Running accuracy: 97.711, Time: 36.21
[2020-12-15 18:44:54,378][__main__][INFO] - [2880] Loss: 2.474, Running accuracy: 97.688, Time: 41.08
[2020-12-15 18:45:36,261][__main__][INFO] - [3200] Loss: 2.785, Running accuracy: 97.662, Time: 41.88
[2020-12-15 18:46:12,110][__main__][INFO] - [3520] Loss: 2.273, Running accuracy: 97.642, Time: 35.85
[2020-12-15 18:46:52,189][__main__][INFO] - [3840] Loss: 3.259, Running accuracy: 97.610, Time: 40.08
[2020-12-15 18:47:32,850][__main__][INFO] - [4160] Loss: 2.324, Running accuracy: 97.626, Time: 40.66
[2020-12-15 18:48:13,931][__main__][INFO] - [4480] Loss: 2.840, Running accuracy: 97.602, Time: 41.08
[2020-12-15 18:48:50,503][__main__][INFO] - [4800] Loss: 2.283, Running accuracy: 97.604, Time: 36.57
[2020-12-15 18:49:26,397][__main__][INFO] - [5120] Loss: 2.515, Running accuracy: 97.599, Time: 35.89
[2020-12-15 18:50:04,404][__main__][INFO] - [5440] Loss: 2.256, Running accuracy: 97.608, Time: 38.01
[2020-12-15 18:50:38,282][__main__][INFO] - [5760] Loss: 2.397, Running accuracy: 97.602, Time: 33.88
[2020-12-15 18:51:16,524][__main__][INFO] - [6080] Loss: 2.500, Running accuracy: 97.593, Time: 38.24
[2020-12-15 18:51:59,889][__main__][INFO] - [6400] Loss: 2.528, Running accuracy: 97.585, Time: 43.36
[2020-12-15 18:52:36,501][__main__][INFO] - [6720] Loss: 2.356, Running accuracy: 97.583, Time: 36.61
[2020-12-15 18:53:13,188][__main__][INFO] - [7040] Loss: 2.364, Running accuracy: 97.573, Time: 36.69
[2020-12-15 18:53:52,579][__main__][INFO] - [7360] Loss: 2.607, Running accuracy: 97.557, Time: 39.39
[2020-12-15 18:54:33,374][__main__][INFO] - [7680] Loss: 2.208, Running accuracy: 97.559, Time: 40.79
[2020-12-15 18:55:13,067][__main__][INFO] - [8000] Loss: 2.712, Running accuracy: 97.554, Time: 39.69
[2020-12-15 18:55:46,586][__main__][INFO] - [8320] Loss: 2.067, Running accuracy: 97.555, Time: 33.52
[2020-12-15 18:56:22,554][__main__][INFO] - [8640] Loss: 2.271, Running accuracy: 97.553, Time: 35.97
[2020-12-15 18:57:00,324][__main__][INFO] - [8960] Loss: 2.744, Running accuracy: 97.554, Time: 37.77
[2020-12-15 18:57:35,507][__main__][INFO] - [9280] Loss: 2.367, Running accuracy: 97.552, Time: 35.18
[2020-12-15 18:58:13,233][__main__][INFO] - [9600] Loss: 2.281, Running accuracy: 97.553, Time: 37.73
[2020-12-15 18:58:51,810][__main__][INFO] - [9920] Loss: 2.406, Running accuracy: 97.550, Time: 38.58
[2020-12-15 18:59:28,926][__main__][INFO] - [10240] Loss: 2.593, Running accuracy: 97.545, Time: 37.11
[2020-12-15 19:00:06,339][__main__][INFO] - [10560] Loss: 2.342, Running accuracy: 97.545, Time: 37.41
[2020-12-15 19:00:41,937][__main__][INFO] - [10880] Loss: 2.636, Running accuracy: 97.538, Time: 35.60
[2020-12-15 19:01:22,037][__main__][INFO] - [11200] Loss: 2.678, Running accuracy: 97.534, Time: 40.10
[2020-12-15 19:02:00,060][__main__][INFO] - [11520] Loss: 2.596, Running accuracy: 97.531, Time: 38.02
[2020-12-15 19:02:40,813][__main__][INFO] - [11840] Loss: 2.475, Running accuracy: 97.537, Time: 40.75
[2020-12-15 19:03:18,574][__main__][INFO] - [12160] Loss: 2.570, Running accuracy: 97.536, Time: 37.76
[2020-12-15 19:04:01,049][__main__][INFO] - [12480] Loss: 2.956, Running accuracy: 97.528, Time: 42.47
[2020-12-15 19:04:41,740][__main__][INFO] - [12800] Loss: 2.297, Running accuracy: 97.534, Time: 40.69
[2020-12-15 19:05:17,714][__main__][INFO] - [13120] Loss: 2.442, Running accuracy: 97.533, Time: 35.97
[2020-12-15 19:05:52,921][__main__][INFO] - [13440] Loss: 2.264, Running accuracy: 97.532, Time: 35.21
[2020-12-15 19:06:28,444][__main__][INFO] - [13760] Loss: 2.550, Running accuracy: 97.523, Time: 35.52
[2020-12-15 19:07:05,798][__main__][INFO] - [14080] Loss: 2.461, Running accuracy: 97.517, Time: 37.35
[2020-12-15 19:07:47,344][__main__][INFO] - [14400] Loss: 2.648, Running accuracy: 97.513, Time: 41.55
[2020-12-15 19:08:22,526][__main__][INFO] - [14720] Loss: 2.187, Running accuracy: 97.515, Time: 35.18
[2020-12-15 19:08:58,472][__main__][INFO] - [15040] Loss: 2.532, Running accuracy: 97.516, Time: 35.95
[2020-12-15 19:09:36,743][__main__][INFO] - [15360] Loss: 2.366, Running accuracy: 97.518, Time: 38.27
[2020-12-15 19:10:11,820][__main__][INFO] - [15680] Loss: 2.597, Running accuracy: 97.516, Time: 35.08
[2020-12-15 19:10:50,388][__main__][INFO] - [16000] Loss: 2.316, Running accuracy: 97.517, Time: 38.57
[2020-12-15 19:11:32,532][__main__][INFO] - [16320] Loss: 2.764, Running accuracy: 97.504, Time: 42.14
[2020-12-15 19:12:10,711][__main__][INFO] - [16640] Loss: 2.543, Running accuracy: 97.501, Time: 38.18
[2020-12-15 19:12:50,361][__main__][INFO] - [16960] Loss: 2.603, Running accuracy: 97.495, Time: 39.65
[2020-12-15 19:13:29,208][__main__][INFO] - [17280] Loss: 2.750, Running accuracy: 97.493, Time: 38.85
[2020-12-15 19:13:56,814][__main__][INFO] - Action accuracy: 97.489, Loss: 2.733
[2020-12-15 19:13:56,815][__main__][INFO] - Validating..
[2020-12-15 19:14:03,482][test][INFO] - Time elapsed: 5.225031
[2020-12-15 19:14:03,484][__main__][INFO] - Validation F1 score: 94.270, Exact match: 57.390, Precision: 94.210, Recall: 94.330
Epoch    12: reducing learning rate of group 0 to 2.0000e-05.
[2020-12-15 19:14:15,748][__main__][INFO] - Checkpoint saved to checkpoints/model_latest.pth
[2020-12-15 19:14:16,139][__main__][INFO] - Epoch #12
[2020-12-15 19:14:16,139][__main__][INFO] - Training..
[2020-12-15 19:14:55,255][__main__][INFO] - [320] Loss: 1.771, Running accuracy: 98.083, Time: 38.23
[2020-12-15 19:15:32,103][__main__][INFO] - [640] Loss: 1.607, Running accuracy: 98.160, Time: 36.85
[2020-12-15 19:16:18,304][__main__][INFO] - [960] Loss: 2.148, Running accuracy: 98.120, Time: 46.20
[2020-12-15 19:16:57,571][__main__][INFO] - [1280] Loss: 1.628, Running accuracy: 98.188, Time: 39.27
[2020-12-15 19:17:33,600][__main__][INFO] - [1600] Loss: 1.642, Running accuracy: 98.228, Time: 36.03
[2020-12-15 19:18:17,391][__main__][INFO] - [1920] Loss: 1.794, Running accuracy: 98.240, Time: 43.79
[2020-12-15 19:18:51,875][__main__][INFO] - [2240] Loss: 1.633, Running accuracy: 98.232, Time: 34.48
[2020-12-15 19:19:30,383][__main__][INFO] - [2560] Loss: 1.730, Running accuracy: 98.241, Time: 38.51
[2020-12-15 19:20:05,099][__main__][INFO] - [2880] Loss: 1.490, Running accuracy: 98.243, Time: 34.72
[2020-12-15 19:20:42,394][__main__][INFO] - [3200] Loss: 1.847, Running accuracy: 98.222, Time: 37.29
[2020-12-15 19:21:21,828][__main__][INFO] - [3520] Loss: 1.796, Running accuracy: 98.220, Time: 39.43
[2020-12-15 19:22:02,153][__main__][INFO] - [3840] Loss: 1.890, Running accuracy: 98.221, Time: 40.33
[2020-12-15 19:22:42,185][__main__][INFO] - [4160] Loss: 1.783, Running accuracy: 98.216, Time: 40.03
[2020-12-15 19:23:19,830][__main__][INFO] - [4480] Loss: 2.058, Running accuracy: 98.196, Time: 37.64
[2020-12-15 19:23:57,747][__main__][INFO] - [4800] Loss: 1.917, Running accuracy: 98.195, Time: 37.92
[2020-12-15 19:24:41,216][__main__][INFO] - [5120] Loss: 2.011, Running accuracy: 98.185, Time: 43.47
[2020-12-15 19:25:19,373][__main__][INFO] - [5440] Loss: 1.721, Running accuracy: 98.195, Time: 38.16
[2020-12-15 19:25:58,789][__main__][INFO] - [5760] Loss: 1.634, Running accuracy: 98.216, Time: 39.41
[2020-12-15 19:26:41,022][__main__][INFO] - [6080] Loss: 1.719, Running accuracy: 98.221, Time: 42.23
[2020-12-15 19:27:19,417][__main__][INFO] - [6400] Loss: 1.769, Running accuracy: 98.221, Time: 38.39
[2020-12-15 19:27:56,456][__main__][INFO] - [6720] Loss: 1.619, Running accuracy: 98.232, Time: 37.04
[2020-12-15 19:28:30,984][__main__][INFO] - [7040] Loss: 1.595, Running accuracy: 98.238, Time: 34.53
[2020-12-15 19:29:08,189][__main__][INFO] - [7360] Loss: 1.818, Running accuracy: 98.240, Time: 37.20
[2020-12-15 19:29:45,737][__main__][INFO] - [7680] Loss: 1.744, Running accuracy: 98.232, Time: 37.55
[2020-12-15 19:30:24,224][__main__][INFO] - [8000] Loss: 1.621, Running accuracy: 98.239, Time: 38.49
[2020-12-15 19:30:59,489][__main__][INFO] - [8320] Loss: 1.731, Running accuracy: 98.244, Time: 35.26
[2020-12-15 19:31:39,293][__main__][INFO] - [8640] Loss: 1.682, Running accuracy: 98.248, Time: 39.80
[2020-12-15 19:32:13,617][__main__][INFO] - [8960] Loss: 1.772, Running accuracy: 98.244, Time: 34.32
[2020-12-15 19:32:55,510][__main__][INFO] - [9280] Loss: 1.902, Running accuracy: 98.235, Time: 41.89
[2020-12-15 19:33:31,935][__main__][INFO] - [9600] Loss: 1.584, Running accuracy: 98.231, Time: 36.42
[2020-12-15 19:34:14,954][__main__][INFO] - [9920] Loss: 1.779, Running accuracy: 98.234, Time: 43.02
[2020-12-15 19:34:54,172][__main__][INFO] - [10240] Loss: 1.783, Running accuracy: 98.230, Time: 39.22
[2020-12-15 19:35:28,098][__main__][INFO] - [10560] Loss: 1.742, Running accuracy: 98.234, Time: 33.93
[2020-12-15 19:36:05,723][__main__][INFO] - [10880] Loss: 1.842, Running accuracy: 98.232, Time: 37.62
[2020-12-15 19:36:45,835][__main__][INFO] - [11200] Loss: 2.017, Running accuracy: 98.228, Time: 40.11
[2020-12-15 19:37:27,171][__main__][INFO] - [11520] Loss: 1.834, Running accuracy: 98.226, Time: 41.34
[2020-12-15 19:38:06,641][__main__][INFO] - [11840] Loss: 1.863, Running accuracy: 98.227, Time: 39.47
[2020-12-15 19:38:43,019][__main__][INFO] - [12160] Loss: 1.739, Running accuracy: 98.233, Time: 36.38
[2020-12-15 19:39:18,896][__main__][INFO] - [12480] Loss: 1.820, Running accuracy: 98.229, Time: 35.88
[2020-12-15 19:39:58,549][__main__][INFO] - [12800] Loss: 1.468, Running accuracy: 98.234, Time: 39.65
[2020-12-15 19:40:39,398][__main__][INFO] - [13120] Loss: 1.895, Running accuracy: 98.229, Time: 40.85
[2020-12-15 19:41:16,541][__main__][INFO] - [13440] Loss: 1.498, Running accuracy: 98.238, Time: 37.14
[2020-12-15 19:41:52,034][__main__][INFO] - [13760] Loss: 1.578, Running accuracy: 98.236, Time: 35.49
[2020-12-15 19:42:35,986][__main__][INFO] - [14080] Loss: 1.799, Running accuracy: 98.238, Time: 43.95
[2020-12-15 19:43:14,891][__main__][INFO] - [14400] Loss: 1.971, Running accuracy: 98.238, Time: 38.90
[2020-12-15 19:43:51,170][__main__][INFO] - [14720] Loss: 1.501, Running accuracy: 98.242, Time: 36.28
[2020-12-15 19:44:22,622][__main__][INFO] - [15040] Loss: 1.617, Running accuracy: 98.244, Time: 31.45
[2020-12-15 19:45:04,798][__main__][INFO] - [15360] Loss: 2.101, Running accuracy: 98.241, Time: 42.18
[2020-12-15 19:45:44,921][__main__][INFO] - [15680] Loss: 1.553, Running accuracy: 98.239, Time: 40.12
[2020-12-15 19:46:23,311][__main__][INFO] - [16000] Loss: 1.546, Running accuracy: 98.243, Time: 38.39
[2020-12-15 19:46:56,979][__main__][INFO] - [16320] Loss: 1.682, Running accuracy: 98.242, Time: 33.67
[2020-12-15 19:47:36,738][__main__][INFO] - [16640] Loss: 1.879, Running accuracy: 98.242, Time: 39.76
[2020-12-15 19:48:14,572][__main__][INFO] - [16960] Loss: 1.742, Running accuracy: 98.241, Time: 37.83
[2020-12-15 19:48:47,015][__main__][INFO] - [17280] Loss: 1.633, Running accuracy: 98.241, Time: 32.44
[2020-12-15 19:49:11,095][__main__][INFO] - Action accuracy: 98.245, Loss: 1.936
[2020-12-15 19:49:11,096][__main__][INFO] - Validating..
[2020-12-15 19:49:17,605][test][INFO] - Time elapsed: 5.110817
[2020-12-15 19:49:17,606][__main__][INFO] - Validation F1 score: 94.470, Exact match: 56.530, Precision: 94.550, Recall: 94.380
[2020-12-15 19:49:29,923][__main__][INFO] - Checkpoint saved to checkpoints/model_latest.pth
[2020-12-15 19:49:30,243][__main__][INFO] - Epoch #13
[2020-12-15 19:49:30,244][__main__][INFO] - Training..
[2020-12-15 19:50:10,852][__main__][INFO] - [320] Loss: 1.252, Running accuracy: 98.754, Time: 39.28
[2020-12-15 19:50:45,452][__main__][INFO] - [640] Loss: 1.146, Running accuracy: 98.773, Time: 34.60
[2020-12-15 19:51:22,949][__main__][INFO] - [960] Loss: 1.607, Running accuracy: 98.712, Time: 37.50
[2020-12-15 19:51:59,840][__main__][INFO] - [1280] Loss: 1.241, Running accuracy: 98.713, Time: 36.89
[2020-12-15 19:52:37,874][__main__][INFO] - [1600] Loss: 1.332, Running accuracy: 98.735, Time: 38.03
[2020-12-15 19:53:15,544][__main__][INFO] - [1920] Loss: 1.345, Running accuracy: 98.735, Time: 37.67
[2020-12-15 19:53:59,853][__main__][INFO] - [2240] Loss: 1.541, Running accuracy: 98.707, Time: 44.31
[2020-12-15 19:54:44,870][__main__][INFO] - [2560] Loss: 1.656, Running accuracy: 98.669, Time: 45.02
[2020-12-15 19:55:24,584][__main__][INFO] - [2880] Loss: 1.337, Running accuracy: 98.673, Time: 39.71
[2020-12-15 19:56:06,386][__main__][INFO] - [3200] Loss: 1.363, Running accuracy: 98.690, Time: 41.80
[2020-12-15 19:56:40,205][__main__][INFO] - [3520] Loss: 1.228, Running accuracy: 98.695, Time: 33.82
[2020-12-15 19:57:14,499][__main__][INFO] - [3840] Loss: 1.410, Running accuracy: 98.676, Time: 34.29
[2020-12-15 19:57:53,699][__main__][INFO] - [4160] Loss: 1.253, Running accuracy: 98.685, Time: 39.20
[2020-12-15 19:58:34,457][__main__][INFO] - [4480] Loss: 1.567, Running accuracy: 98.674, Time: 40.76
[2020-12-15 19:59:09,099][__main__][INFO] - [4800] Loss: 1.270, Running accuracy: 98.674, Time: 34.64
[2020-12-15 19:59:46,208][__main__][INFO] - [5120] Loss: 1.327, Running accuracy: 98.674, Time: 37.11
[2020-12-15 20:00:25,590][__main__][INFO] - [5440] Loss: 1.778, Running accuracy: 98.656, Time: 39.38
[2020-12-15 20:01:05,058][__main__][INFO] - [5760] Loss: 1.409, Running accuracy: 98.654, Time: 39.47
[2020-12-15 20:01:40,916][__main__][INFO] - [6080] Loss: 1.433, Running accuracy: 98.648, Time: 35.86
[2020-12-15 20:02:17,993][__main__][INFO] - [6400] Loss: 1.355, Running accuracy: 98.649, Time: 37.08
[2020-12-15 20:02:56,970][__main__][INFO] - [6720] Loss: 1.871, Running accuracy: 98.616, Time: 38.98
[2020-12-15 20:03:28,642][__main__][INFO] - [7040] Loss: 1.315, Running accuracy: 98.613, Time: 31.67
[2020-12-15 20:04:04,294][__main__][INFO] - [7360] Loss: 1.466, Running accuracy: 98.613, Time: 35.65
[2020-12-15 20:04:44,692][__main__][INFO] - [7680] Loss: 1.398, Running accuracy: 98.618, Time: 40.40
[2020-12-15 20:05:17,882][__main__][INFO] - [8000] Loss: 1.237, Running accuracy: 98.621, Time: 33.19
[2020-12-15 20:05:55,686][__main__][INFO] - [8320] Loss: 1.653, Running accuracy: 98.607, Time: 37.80
[2020-12-15 20:06:37,281][__main__][INFO] - [8640] Loss: 1.792, Running accuracy: 98.599, Time: 41.59
[2020-12-15 20:07:12,753][__main__][INFO] - [8960] Loss: 1.513, Running accuracy: 98.593, Time: 35.47
[2020-12-15 20:07:50,289][__main__][INFO] - [9280] Loss: 1.183, Running accuracy: 98.606, Time: 37.54
[2020-12-15 20:08:30,083][__main__][INFO] - [9600] Loss: 1.551, Running accuracy: 98.603, Time: 39.79
[2020-12-15 20:09:04,982][__main__][INFO] - [9920] Loss: 1.299, Running accuracy: 98.607, Time: 34.90
[2020-12-15 20:09:43,674][__main__][INFO] - [10240] Loss: 1.398, Running accuracy: 98.605, Time: 38.69
[2020-12-15 20:10:22,973][__main__][INFO] - [10560] Loss: 1.401, Running accuracy: 98.608, Time: 39.29
[2020-12-15 20:10:58,780][__main__][INFO] - [10880] Loss: 1.263, Running accuracy: 98.606, Time: 35.81
[2020-12-15 20:11:36,580][__main__][INFO] - [11200] Loss: 1.543, Running accuracy: 98.602, Time: 37.80
[2020-12-15 20:12:12,360][__main__][INFO] - [11520] Loss: 1.451, Running accuracy: 98.597, Time: 35.78
[2020-12-15 20:12:52,200][__main__][INFO] - [11840] Loss: 1.370, Running accuracy: 98.599, Time: 39.84
[2020-12-15 20:13:27,402][__main__][INFO] - [12160] Loss: 1.451, Running accuracy: 98.596, Time: 35.20
[2020-12-15 20:14:01,881][__main__][INFO] - [12480] Loss: 1.392, Running accuracy: 98.593, Time: 34.48
[2020-12-15 20:14:43,680][__main__][INFO] - [12800] Loss: 1.491, Running accuracy: 98.593, Time: 41.80
[2020-12-15 20:15:22,618][__main__][INFO] - [13120] Loss: 1.308, Running accuracy: 98.598, Time: 38.94
[2020-12-15 20:15:59,394][__main__][INFO] - [13440] Loss: 1.614, Running accuracy: 98.592, Time: 36.77
[2020-12-15 20:16:43,461][__main__][INFO] - [13760] Loss: 1.487, Running accuracy: 98.592, Time: 44.07
[2020-12-15 20:17:23,630][__main__][INFO] - [14080] Loss: 1.452, Running accuracy: 98.587, Time: 40.17
[2020-12-15 20:18:04,752][__main__][INFO] - [14400] Loss: 1.361, Running accuracy: 98.590, Time: 41.12
[2020-12-15 20:18:43,323][__main__][INFO] - [14720] Loss: 1.430, Running accuracy: 98.592, Time: 38.57
[2020-12-15 20:19:23,729][__main__][INFO] - [15040] Loss: 1.365, Running accuracy: 98.589, Time: 40.31
[2020-12-15 20:20:00,597][__main__][INFO] - [15360] Loss: 1.416, Running accuracy: 98.591, Time: 36.78
[2020-12-15 20:20:37,172][__main__][INFO] - [15680] Loss: 1.245, Running accuracy: 98.592, Time: 36.49
[2020-12-15 20:21:12,781][__main__][INFO] - [16000] Loss: 1.494, Running accuracy: 98.590, Time: 35.61
[2020-12-15 20:21:51,465][__main__][INFO] - [16320] Loss: 1.587, Running accuracy: 98.587, Time: 38.68
[2020-12-15 20:22:34,197][__main__][INFO] - [16640] Loss: 1.490, Running accuracy: 98.589, Time: 42.73
[2020-12-15 20:23:12,625][__main__][INFO] - [16960] Loss: 1.268, Running accuracy: 98.592, Time: 38.43
[2020-12-15 20:23:57,320][__main__][INFO] - [17280] Loss: 1.661, Running accuracy: 98.589, Time: 44.69
[2020-12-15 20:24:26,997][__main__][INFO] - Action accuracy: 98.585, Loss: 1.588
[2020-12-15 20:24:26,998][__main__][INFO] - Validating..
[2020-12-15 20:24:33,727][test][INFO] - Time elapsed: 5.428026
[2020-12-15 20:24:33,729][__main__][INFO] - Validation F1 score: 94.490, Exact match: 58.810, Precision: 94.500, Recall: 94.480
[2020-12-15 20:24:45,614][__main__][INFO] - Checkpoint saved to checkpoints/model_latest.pth
[2020-12-15 20:24:45,946][__main__][INFO] - Epoch #14
[2020-12-15 20:24:45,946][__main__][INFO] - Training..
[2020-12-15 20:25:22,072][__main__][INFO] - [320] Loss: 1.071, Running accuracy: 98.925, Time: 35.13
[2020-12-15 20:26:00,704][__main__][INFO] - [640] Loss: 1.260, Running accuracy: 98.849, Time: 38.63
[2020-12-15 20:26:39,950][__main__][INFO] - [960] Loss: 1.261, Running accuracy: 98.847, Time: 39.25
[2020-12-15 20:27:16,775][__main__][INFO] - [1280] Loss: 1.098, Running accuracy: 98.873, Time: 36.82
[2020-12-15 20:27:55,609][__main__][INFO] - [1600] Loss: 1.344, Running accuracy: 98.839, Time: 38.83
[2020-12-15 20:28:34,829][__main__][INFO] - [1920] Loss: 1.134, Running accuracy: 98.848, Time: 39.22
[2020-12-15 20:29:11,403][__main__][INFO] - [2240] Loss: 1.075, Running accuracy: 98.849, Time: 36.57
[2020-12-15 20:29:51,040][__main__][INFO] - [2560] Loss: 1.342, Running accuracy: 98.834, Time: 39.64
[2020-12-15 20:30:25,836][__main__][INFO] - [2880] Loss: 1.289, Running accuracy: 98.838, Time: 34.79
[2020-12-15 20:31:04,959][__main__][INFO] - [3200] Loss: 1.265, Running accuracy: 98.843, Time: 39.12
[2020-12-15 20:31:49,410][__main__][INFO] - [3520] Loss: 1.459, Running accuracy: 98.830, Time: 44.45
[2020-12-15 20:32:37,017][__main__][INFO] - [3840] Loss: 1.488, Running accuracy: 98.828, Time: 47.61
[2020-12-15 20:33:13,724][__main__][INFO] - [4160] Loss: 1.131, Running accuracy: 98.832, Time: 36.71
[2020-12-15 20:33:51,027][__main__][INFO] - [4480] Loss: 1.475, Running accuracy: 98.813, Time: 37.30
[2020-12-15 20:34:30,071][__main__][INFO] - [4800] Loss: 1.116, Running accuracy: 98.824, Time: 39.04
[2020-12-15 20:35:13,860][__main__][INFO] - [5120] Loss: 1.220, Running accuracy: 98.828, Time: 43.79
[2020-12-15 20:35:47,642][__main__][INFO] - [5440] Loss: 1.142, Running accuracy: 98.833, Time: 33.78
[2020-12-15 20:36:27,562][__main__][INFO] - [5760] Loss: 1.208, Running accuracy: 98.832, Time: 39.92
[2020-12-15 20:37:06,775][__main__][INFO] - [6080] Loss: 1.158, Running accuracy: 98.831, Time: 39.21
[2020-12-15 20:37:47,197][__main__][INFO] - [6400] Loss: 1.310, Running accuracy: 98.831, Time: 40.42
[2020-12-15 20:38:26,007][__main__][INFO] - [6720] Loss: 1.126, Running accuracy: 98.836, Time: 38.81
[2020-12-15 20:39:04,000][__main__][INFO] - [7040] Loss: 1.307, Running accuracy: 98.836, Time: 37.99
[2020-12-15 20:39:43,161][__main__][INFO] - [7360] Loss: 1.305, Running accuracy: 98.836, Time: 39.16
[2020-12-15 20:40:19,041][__main__][INFO] - [7680] Loss: 1.133, Running accuracy: 98.829, Time: 35.88
[2020-12-15 20:40:54,401][__main__][INFO] - [8000] Loss: 0.970, Running accuracy: 98.837, Time: 35.36
[2020-12-15 20:41:28,258][__main__][INFO] - [8320] Loss: 1.230, Running accuracy: 98.833, Time: 33.86
[2020-12-15 20:42:08,990][__main__][INFO] - [8640] Loss: 1.191, Running accuracy: 98.836, Time: 40.73
[2020-12-15 20:42:46,600][__main__][INFO] - [8960] Loss: 1.516, Running accuracy: 98.826, Time: 37.61
[2020-12-15 20:43:23,091][__main__][INFO] - [9280] Loss: 1.238, Running accuracy: 98.823, Time: 36.49
[2020-12-15 20:44:02,880][__main__][INFO] - [9600] Loss: 1.322, Running accuracy: 98.823, Time: 39.79
[2020-12-15 20:44:41,148][__main__][INFO] - [9920] Loss: 1.249, Running accuracy: 98.818, Time: 38.27
[2020-12-15 20:45:21,752][__main__][INFO] - [10240] Loss: 1.215, Running accuracy: 98.819, Time: 40.60
[2020-12-15 20:45:57,482][__main__][INFO] - [10560] Loss: 1.167, Running accuracy: 98.821, Time: 35.73
[2020-12-15 20:46:34,307][__main__][INFO] - [10880] Loss: 0.924, Running accuracy: 98.826, Time: 36.82
[2020-12-15 20:47:14,084][__main__][INFO] - [11200] Loss: 1.262, Running accuracy: 98.822, Time: 39.78
[2020-12-15 20:47:52,158][__main__][INFO] - [11520] Loss: 1.280, Running accuracy: 98.820, Time: 38.07
[2020-12-15 20:48:34,499][__main__][INFO] - [11840] Loss: 1.458, Running accuracy: 98.816, Time: 42.34
[2020-12-15 20:49:08,161][__main__][INFO] - [12160] Loss: 1.417, Running accuracy: 98.811, Time: 33.66
[2020-12-15 20:49:48,083][__main__][INFO] - [12480] Loss: 1.075, Running accuracy: 98.813, Time: 39.92
[2020-12-15 20:50:25,496][__main__][INFO] - [12800] Loss: 1.250, Running accuracy: 98.809, Time: 37.41
[2020-12-15 20:51:01,144][__main__][INFO] - [13120] Loss: 1.225, Running accuracy: 98.802, Time: 35.65
[2020-12-15 20:51:40,860][__main__][INFO] - [13440] Loss: 1.194, Running accuracy: 98.802, Time: 39.72
[2020-12-15 20:52:16,483][__main__][INFO] - [13760] Loss: 1.195, Running accuracy: 98.803, Time: 35.62
[2020-12-15 20:52:59,715][__main__][INFO] - [14080] Loss: 1.276, Running accuracy: 98.805, Time: 43.23
[2020-12-15 20:53:36,208][__main__][INFO] - [14400] Loss: 1.233, Running accuracy: 98.800, Time: 36.49
[2020-12-15 20:54:10,867][__main__][INFO] - [14720] Loss: 1.357, Running accuracy: 98.796, Time: 34.57
[2020-12-15 20:54:47,746][__main__][INFO] - [15040] Loss: 1.124, Running accuracy: 98.798, Time: 36.84
[2020-12-15 20:55:24,722][__main__][INFO] - [15360] Loss: 1.084, Running accuracy: 98.800, Time: 36.98
[2020-12-15 20:56:00,110][__main__][INFO] - [15680] Loss: 1.350, Running accuracy: 98.794, Time: 35.39
[2020-12-15 20:56:40,316][__main__][INFO] - [16000] Loss: 1.176, Running accuracy: 98.797, Time: 40.21
[2020-12-15 20:57:18,014][__main__][INFO] - [16320] Loss: 1.427, Running accuracy: 98.792, Time: 37.70
[2020-12-15 20:57:53,917][__main__][INFO] - [16640] Loss: 1.172, Running accuracy: 98.794, Time: 35.90
[2020-12-15 20:58:32,266][__main__][INFO] - [16960] Loss: 1.112, Running accuracy: 98.797, Time: 38.35
[2020-12-15 20:59:10,316][__main__][INFO] - [17280] Loss: 1.293, Running accuracy: 98.796, Time: 38.05
[2020-12-15 20:59:38,142][__main__][INFO] - Action accuracy: 98.795, Loss: 1.372
[2020-12-15 20:59:38,143][__main__][INFO] - Validating..
[2020-12-15 20:59:44,711][test][INFO] - Time elapsed: 5.157638
[2020-12-15 20:59:44,713][__main__][INFO] - Validation F1 score: 94.520, Exact match: 57.950, Precision: 94.630, Recall: 94.410
[2020-12-15 20:59:57,547][__main__][INFO] - Checkpoint saved to checkpoints/model_latest.pth
[2020-12-15 20:59:57,860][__main__][INFO] - Epoch #15
[2020-12-15 20:59:57,860][__main__][INFO] - Training..
[2020-12-15 21:00:35,746][__main__][INFO] - [320] Loss: 0.972, Running accuracy: 99.022, Time: 36.69
[2020-12-15 21:01:12,910][__main__][INFO] - [640] Loss: 1.015, Running accuracy: 98.998, Time: 37.16
[2020-12-15 21:01:48,651][__main__][INFO] - [960] Loss: 1.244, Running accuracy: 98.974, Time: 35.74
[2020-12-15 21:02:23,020][__main__][INFO] - [1280] Loss: 1.093, Running accuracy: 98.954, Time: 34.37
[2020-12-15 21:03:05,169][__main__][INFO] - [1600] Loss: 1.102, Running accuracy: 98.978, Time: 42.15
[2020-12-15 21:03:45,086][__main__][INFO] - [1920] Loss: 1.060, Running accuracy: 98.966, Time: 39.92
[2020-12-15 21:04:20,718][__main__][INFO] - [2240] Loss: 0.872, Running accuracy: 98.995, Time: 35.63
[2020-12-15 21:04:59,280][__main__][INFO] - [2560] Loss: 1.061, Running accuracy: 98.991, Time: 38.56
[2020-12-15 21:05:36,287][__main__][INFO] - [2880] Loss: 1.294, Running accuracy: 98.957, Time: 37.01
[2020-12-15 21:06:10,642][__main__][INFO] - [3200] Loss: 1.039, Running accuracy: 98.938, Time: 34.35
[2020-12-15 21:06:44,131][__main__][INFO] - [3520] Loss: 1.035, Running accuracy: 98.945, Time: 33.49
[2020-12-15 21:07:23,129][__main__][INFO] - [3840] Loss: 1.102, Running accuracy: 98.944, Time: 39.00
[2020-12-15 21:08:00,375][__main__][INFO] - [4160] Loss: 1.158, Running accuracy: 98.930, Time: 37.24
[2020-12-15 21:08:35,406][__main__][INFO] - [4480] Loss: 1.212, Running accuracy: 98.925, Time: 35.03
[2020-12-15 21:09:18,042][__main__][INFO] - [4800] Loss: 1.113, Running accuracy: 98.917, Time: 42.63
[2020-12-15 21:09:58,098][__main__][INFO] - [5120] Loss: 1.068, Running accuracy: 98.923, Time: 40.06
[2020-12-15 21:10:33,553][__main__][INFO] - [5440] Loss: 0.958, Running accuracy: 98.925, Time: 35.45
[2020-12-15 21:11:11,258][__main__][INFO] - [5760] Loss: 1.135, Running accuracy: 98.921, Time: 37.70
[2020-12-15 21:11:44,567][__main__][INFO] - [6080] Loss: 1.042, Running accuracy: 98.916, Time: 33.31
[2020-12-15 21:12:24,929][__main__][INFO] - [6400] Loss: 1.149, Running accuracy: 98.917, Time: 40.36
[2020-12-15 21:12:58,599][__main__][INFO] - [6720] Loss: 0.890, Running accuracy: 98.923, Time: 33.67
[2020-12-15 21:13:42,843][__main__][INFO] - [7040] Loss: 1.188, Running accuracy: 98.916, Time: 44.24
[2020-12-15 21:14:21,223][__main__][INFO] - [7360] Loss: 1.300, Running accuracy: 98.907, Time: 38.38
[2020-12-15 21:15:06,094][__main__][INFO] - [7680] Loss: 1.300, Running accuracy: 98.904, Time: 44.87
[2020-12-15 21:15:45,545][__main__][INFO] - [8000] Loss: 1.124, Running accuracy: 98.907, Time: 39.45
[2020-12-15 21:16:25,642][__main__][INFO] - [8320] Loss: 1.215, Running accuracy: 98.905, Time: 40.10
[2020-12-15 21:17:06,298][__main__][INFO] - [8640] Loss: 1.171, Running accuracy: 98.906, Time: 40.66
[2020-12-15 21:17:47,433][__main__][INFO] - [8960] Loss: 1.043, Running accuracy: 98.912, Time: 41.13
[2020-12-15 21:18:25,023][__main__][INFO] - [9280] Loss: 1.114, Running accuracy: 98.909, Time: 37.59
[2020-12-15 21:19:03,347][__main__][INFO] - [9600] Loss: 1.272, Running accuracy: 98.909, Time: 38.32
[2020-12-15 21:19:36,346][__main__][INFO] - [9920] Loss: 0.984, Running accuracy: 98.914, Time: 33.00
[2020-12-15 21:20:17,607][__main__][INFO] - [10240] Loss: 1.247, Running accuracy: 98.914, Time: 41.26
[2020-12-15 21:20:52,557][__main__][INFO] - [10560] Loss: 1.017, Running accuracy: 98.922, Time: 34.95
[2020-12-15 21:21:32,889][__main__][INFO] - [10880] Loss: 1.115, Running accuracy: 98.924, Time: 40.33
[2020-12-15 21:22:14,679][__main__][INFO] - [11200] Loss: 1.104, Running accuracy: 98.924, Time: 41.79
[2020-12-15 21:22:48,382][__main__][INFO] - [11520] Loss: 1.104, Running accuracy: 98.922, Time: 33.70
[2020-12-15 21:23:25,874][__main__][INFO] - [11840] Loss: 0.999, Running accuracy: 98.925, Time: 37.49
[2020-12-15 21:24:07,503][__main__][INFO] - [12160] Loss: 1.255, Running accuracy: 98.924, Time: 41.63
[2020-12-15 21:24:42,135][__main__][INFO] - [12480] Loss: 1.093, Running accuracy: 98.927, Time: 34.63
[2020-12-15 21:25:18,195][__main__][INFO] - [12800] Loss: 1.060, Running accuracy: 98.927, Time: 36.06
[2020-12-15 21:25:55,442][__main__][INFO] - [13120] Loss: 1.073, Running accuracy: 98.926, Time: 37.24
[2020-12-15 21:26:32,582][__main__][INFO] - [13440] Loss: 1.214, Running accuracy: 98.923, Time: 37.14
[2020-12-15 21:27:09,241][__main__][INFO] - [13760] Loss: 1.094, Running accuracy: 98.921, Time: 36.66
[2020-12-15 21:27:49,884][__main__][INFO] - [14080] Loss: 1.041, Running accuracy: 98.922, Time: 40.64
[2020-12-15 21:28:30,508][__main__][INFO] - [14400] Loss: 0.988, Running accuracy: 98.924, Time: 40.62
[2020-12-15 21:29:09,137][__main__][INFO] - [14720] Loss: 1.194, Running accuracy: 98.922, Time: 38.63
[2020-12-15 21:29:43,947][__main__][INFO] - [15040] Loss: 0.840, Running accuracy: 98.923, Time: 34.81
[2020-12-15 21:30:20,895][__main__][INFO] - [15360] Loss: 1.036, Running accuracy: 98.922, Time: 36.95
[2020-12-15 21:30:57,688][__main__][INFO] - [15680] Loss: 1.075, Running accuracy: 98.924, Time: 36.79
[2020-12-15 21:31:34,050][__main__][INFO] - [16000] Loss: 1.036, Running accuracy: 98.924, Time: 36.36
[2020-12-15 21:32:12,428][__main__][INFO] - [16320] Loss: 1.121, Running accuracy: 98.924, Time: 38.38
[2020-12-15 21:32:54,326][__main__][INFO] - [16640] Loss: 1.109, Running accuracy: 98.922, Time: 41.90
[2020-12-15 21:33:37,838][__main__][INFO] - [16960] Loss: 0.965, Running accuracy: 98.926, Time: 43.51
[2020-12-15 21:34:17,760][__main__][INFO] - [17280] Loss: 1.160, Running accuracy: 98.925, Time: 39.92
[2020-12-15 21:34:46,497][__main__][INFO] - Action accuracy: 98.926, Loss: 1.220
[2020-12-15 21:34:46,497][__main__][INFO] - Validating..
[2020-12-15 21:34:55,585][test][INFO] - Time elapsed: 5.236627
[2020-12-15 21:34:55,587][__main__][INFO] - Validation F1 score: 94.670, Exact match: 58.810, Precision: 94.830, Recall: 94.520
[2020-12-15 21:34:55,587][__main__][INFO] - F1 score has improved
[2020-12-15 21:35:08,320][__main__][INFO] - Checkpoint saved to checkpoints/model_latest.pth
[2020-12-15 21:35:08,634][__main__][INFO] - Epoch #16
[2020-12-15 21:35:08,635][__main__][INFO] - Training..
[2020-12-15 21:35:44,795][__main__][INFO] - [320] Loss: 0.945, Running accuracy: 98.997, Time: 35.07
[2020-12-15 21:36:27,938][__main__][INFO] - [640] Loss: 0.934, Running accuracy: 99.097, Time: 43.14
[2020-12-15 21:37:03,186][__main__][INFO] - [960] Loss: 0.832, Running accuracy: 99.113, Time: 35.25
[2020-12-15 21:37:46,277][__main__][INFO] - [1280] Loss: 1.116, Running accuracy: 99.090, Time: 43.09
[2020-12-15 21:38:25,779][__main__][INFO] - [1600] Loss: 0.803, Running accuracy: 99.133, Time: 39.50
[2020-12-15 21:39:06,400][__main__][INFO] - [1920] Loss: 0.959, Running accuracy: 99.136, Time: 40.62
[2020-12-15 21:39:47,373][__main__][INFO] - [2240] Loss: 0.837, Running accuracy: 99.142, Time: 40.97
[2020-12-15 21:40:20,650][__main__][INFO] - [2560] Loss: 0.844, Running accuracy: 99.148, Time: 33.28
[2020-12-15 21:40:58,603][__main__][INFO] - [2880] Loss: 0.897, Running accuracy: 99.142, Time: 37.95
[2020-12-15 21:41:35,207][__main__][INFO] - [3200] Loss: 0.808, Running accuracy: 99.144, Time: 36.60
[2020-12-15 21:42:14,838][__main__][INFO] - [3520] Loss: 1.055, Running accuracy: 99.120, Time: 39.63
[2020-12-15 21:42:54,141][__main__][INFO] - [3840] Loss: 0.934, Running accuracy: 99.120, Time: 39.30
[2020-12-15 21:43:33,993][__main__][INFO] - [4160] Loss: 1.187, Running accuracy: 99.103, Time: 39.85
[2020-12-15 21:44:11,653][__main__][INFO] - [4480] Loss: 0.917, Running accuracy: 99.105, Time: 37.66
[2020-12-15 21:44:50,015][__main__][INFO] - [4800] Loss: 0.945, Running accuracy: 99.107, Time: 38.36
[2020-12-15 21:45:30,128][__main__][INFO] - [5120] Loss: 1.114, Running accuracy: 99.095, Time: 40.11
[2020-12-15 21:46:08,686][__main__][INFO] - [5440] Loss: 1.049, Running accuracy: 99.095, Time: 38.56
[2020-12-15 21:46:43,121][__main__][INFO] - [5760] Loss: 0.888, Running accuracy: 99.100, Time: 34.43
[2020-12-15 21:47:25,597][__main__][INFO] - [6080] Loss: 0.933, Running accuracy: 99.103, Time: 42.48
[2020-12-15 21:48:05,563][__main__][INFO] - [6400] Loss: 1.011, Running accuracy: 99.107, Time: 39.96
[2020-12-15 21:48:44,811][__main__][INFO] - [6720] Loss: 0.787, Running accuracy: 99.116, Time: 39.25
[2020-12-15 21:49:20,668][__main__][INFO] - [7040] Loss: 0.832, Running accuracy: 99.121, Time: 35.86
[2020-12-15 21:50:00,323][__main__][INFO] - [7360] Loss: 1.063, Running accuracy: 99.116, Time: 39.65
[2020-12-15 21:50:36,446][__main__][INFO] - [7680] Loss: 0.922, Running accuracy: 99.118, Time: 36.12
[2020-12-15 21:51:13,833][__main__][INFO] - [8000] Loss: 0.937, Running accuracy: 99.116, Time: 37.39
[2020-12-15 21:51:56,820][__main__][INFO] - [8320] Loss: 0.985, Running accuracy: 99.113, Time: 42.99
[2020-12-15 21:52:36,785][__main__][INFO] - [8640] Loss: 0.994, Running accuracy: 99.112, Time: 39.96
[2020-12-15 21:53:14,425][__main__][INFO] - [8960] Loss: 0.957, Running accuracy: 99.108, Time: 37.64
[2020-12-15 21:53:56,564][__main__][INFO] - [9280] Loss: 1.126, Running accuracy: 99.105, Time: 42.14
[2020-12-15 21:54:39,508][__main__][INFO] - [9600] Loss: 1.206, Running accuracy: 99.097, Time: 42.94
[2020-12-15 21:55:18,343][__main__][INFO] - [9920] Loss: 1.041, Running accuracy: 99.089, Time: 38.83
[2020-12-15 21:55:56,770][__main__][INFO] - [10240] Loss: 0.990, Running accuracy: 99.089, Time: 38.43
[2020-12-15 21:56:38,988][__main__][INFO] - [10560] Loss: 0.891, Running accuracy: 99.093, Time: 42.22
[2020-12-15 21:57:17,081][__main__][INFO] - [10880] Loss: 1.143, Running accuracy: 99.086, Time: 38.09
[2020-12-15 21:57:53,209][__main__][INFO] - [11200] Loss: 1.045, Running accuracy: 99.085, Time: 36.13
[2020-12-15 21:58:30,763][__main__][INFO] - [11520] Loss: 1.224, Running accuracy: 99.077, Time: 37.55
[2020-12-15 21:59:09,899][__main__][INFO] - [11840] Loss: 1.022, Running accuracy: 99.075, Time: 39.13
[2020-12-15 21:59:45,158][__main__][INFO] - [12160] Loss: 1.088, Running accuracy: 99.069, Time: 35.26
[2020-12-15 22:00:23,986][__main__][INFO] - [12480] Loss: 0.942, Running accuracy: 99.068, Time: 38.83
[2020-12-15 22:01:00,844][__main__][INFO] - [12800] Loss: 1.006, Running accuracy: 99.066, Time: 36.86
[2020-12-15 22:01:34,645][__main__][INFO] - [13120] Loss: 0.936, Running accuracy: 99.065, Time: 33.80
[2020-12-15 22:02:13,893][__main__][INFO] - [13440] Loss: 0.872, Running accuracy: 99.065, Time: 39.25
[2020-12-15 22:02:49,310][__main__][INFO] - [13760] Loss: 0.940, Running accuracy: 99.061, Time: 35.42
[2020-12-15 22:03:24,196][__main__][INFO] - [14080] Loss: 0.853, Running accuracy: 99.059, Time: 34.89
[2020-12-15 22:04:06,164][__main__][INFO] - [14400] Loss: 1.128, Running accuracy: 99.055, Time: 41.97
[2020-12-15 22:04:42,265][__main__][INFO] - [14720] Loss: 1.023, Running accuracy: 99.052, Time: 36.10
[2020-12-15 22:05:16,393][__main__][INFO] - [15040] Loss: 1.023, Running accuracy: 99.046, Time: 34.13
[2020-12-15 22:05:50,665][__main__][INFO] - [15360] Loss: 1.014, Running accuracy: 99.045, Time: 34.27
[2020-12-15 22:06:25,572][__main__][INFO] - [15680] Loss: 0.953, Running accuracy: 99.043, Time: 34.91
[2020-12-15 22:07:05,730][__main__][INFO] - [16000] Loss: 1.173, Running accuracy: 99.038, Time: 40.16
[2020-12-15 22:07:39,143][__main__][INFO] - [16320] Loss: 0.939, Running accuracy: 99.035, Time: 33.41
[2020-12-15 22:08:21,993][__main__][INFO] - [16640] Loss: 1.215, Running accuracy: 99.031, Time: 42.85
[2020-12-15 22:08:59,971][__main__][INFO] - [16960] Loss: 1.021, Running accuracy: 99.030, Time: 37.98
[2020-12-15 22:09:38,780][__main__][INFO] - [17280] Loss: 0.913, Running accuracy: 99.033, Time: 38.81
[2020-12-15 22:10:07,764][__main__][INFO] - Action accuracy: 99.030, Loss: 1.091
[2020-12-15 22:10:07,765][__main__][INFO] - Validating..
[2020-12-15 22:10:14,450][test][INFO] - Time elapsed: 5.338603
[2020-12-15 22:10:14,452][__main__][INFO] - Validation F1 score: 94.390, Exact match: 56.820, Precision: 94.570, Recall: 94.220
[2020-12-15 22:10:27,271][__main__][INFO] - Checkpoint saved to checkpoints/model_latest.pth
[2020-12-15 22:10:27,624][__main__][INFO] - Epoch #17
[2020-12-15 22:10:27,624][__main__][INFO] - Training..
[2020-12-15 22:11:02,064][__main__][INFO] - [320] Loss: 0.808, Running accuracy: 99.103, Time: 33.28
[2020-12-15 22:11:39,523][__main__][INFO] - [640] Loss: 0.857, Running accuracy: 99.132, Time: 37.46
[2020-12-15 22:12:18,411][__main__][INFO] - [960] Loss: 0.822, Running accuracy: 99.122, Time: 38.89
[2020-12-15 22:12:58,956][__main__][INFO] - [1280] Loss: 0.824, Running accuracy: 99.173, Time: 40.54
[2020-12-15 22:13:36,761][__main__][INFO] - [1600] Loss: 0.855, Running accuracy: 99.160, Time: 37.80
[2020-12-15 22:14:15,326][__main__][INFO] - [1920] Loss: 0.901, Running accuracy: 99.170, Time: 38.56
[2020-12-15 22:14:54,528][__main__][INFO] - [2240] Loss: 0.967, Running accuracy: 99.166, Time: 39.20
[2020-12-15 22:15:32,416][__main__][INFO] - [2560] Loss: 0.920, Running accuracy: 99.164, Time: 37.89
[2020-12-15 22:16:07,497][__main__][INFO] - [2880] Loss: 0.845, Running accuracy: 99.163, Time: 35.08
[2020-12-15 22:16:45,985][__main__][INFO] - [3200] Loss: 1.057, Running accuracy: 99.141, Time: 38.48
[2020-12-15 22:17:18,328][__main__][INFO] - [3520] Loss: 0.863, Running accuracy: 99.125, Time: 32.34
[2020-12-15 22:18:03,432][__main__][INFO] - [3840] Loss: 0.953, Running accuracy: 99.117, Time: 45.10
[2020-12-15 22:18:45,443][__main__][INFO] - [4160] Loss: 0.907, Running accuracy: 99.117, Time: 42.01
[2020-12-15 22:19:23,125][__main__][INFO] - [4480] Loss: 0.737, Running accuracy: 99.136, Time: 37.68
[2020-12-15 22:19:59,835][__main__][INFO] - [4800] Loss: 0.984, Running accuracy: 99.131, Time: 36.71
[2020-12-15 22:20:38,294][__main__][INFO] - [5120] Loss: 0.794, Running accuracy: 99.136, Time: 38.45
[2020-12-15 22:21:18,878][__main__][INFO] - [5440] Loss: 0.887, Running accuracy: 99.138, Time: 40.58
[2020-12-15 22:21:54,978][__main__][INFO] - [5760] Loss: 1.040, Running accuracy: 99.130, Time: 36.10
[2020-12-15 22:22:29,615][__main__][INFO] - [6080] Loss: 0.815, Running accuracy: 99.131, Time: 34.63
[2020-12-15 22:23:06,207][__main__][INFO] - [6400] Loss: 0.778, Running accuracy: 99.138, Time: 36.59
[2020-12-15 22:23:46,456][__main__][INFO] - [6720] Loss: 0.625, Running accuracy: 99.146, Time: 40.24
[2020-12-15 22:24:27,299][__main__][INFO] - [7040] Loss: 0.997, Running accuracy: 99.141, Time: 40.84
[2020-12-15 22:25:13,437][__main__][INFO] - [7360] Loss: 0.831, Running accuracy: 99.142, Time: 46.14
[2020-12-15 22:25:51,062][__main__][INFO] - [7680] Loss: 0.904, Running accuracy: 99.138, Time: 37.62
[2020-12-15 22:26:30,841][__main__][INFO] - [8000] Loss: 0.805, Running accuracy: 99.143, Time: 39.78
[2020-12-15 22:27:07,665][__main__][INFO] - [8320] Loss: 0.907, Running accuracy: 99.145, Time: 36.82
[2020-12-15 22:27:45,123][__main__][INFO] - [8640] Loss: 0.874, Running accuracy: 99.145, Time: 37.46
[2020-12-15 22:28:22,446][__main__][INFO] - [8960] Loss: 0.861, Running accuracy: 99.147, Time: 37.32
[2020-12-15 22:29:01,414][__main__][INFO] - [9280] Loss: 0.902, Running accuracy: 99.148, Time: 38.97
[2020-12-15 22:29:35,899][__main__][INFO] - [9600] Loss: 0.911, Running accuracy: 99.144, Time: 34.48
[2020-12-15 22:30:18,297][__main__][INFO] - [9920] Loss: 0.981, Running accuracy: 99.144, Time: 42.40
[2020-12-15 22:30:54,274][__main__][INFO] - [10240] Loss: 0.867, Running accuracy: 99.142, Time: 35.98
[2020-12-15 22:31:34,178][__main__][INFO] - [10560] Loss: 0.960, Running accuracy: 99.142, Time: 39.90
[2020-12-15 22:32:12,749][__main__][INFO] - [10880] Loss: 0.768, Running accuracy: 99.146, Time: 38.57
[2020-12-15 22:32:47,910][__main__][INFO] - [11200] Loss: 0.892, Running accuracy: 99.148, Time: 35.16
[2020-12-15 22:33:22,927][__main__][INFO] - [11520] Loss: 0.926, Running accuracy: 99.145, Time: 35.02
[2020-12-15 22:34:04,474][__main__][INFO] - [11840] Loss: 1.015, Running accuracy: 99.147, Time: 41.55
[2020-12-15 22:34:46,009][__main__][INFO] - [12160] Loss: 0.684, Running accuracy: 99.150, Time: 41.53
[2020-12-15 22:35:23,280][__main__][INFO] - [12480] Loss: 1.023, Running accuracy: 99.147, Time: 37.27
[2020-12-15 22:36:00,800][__main__][INFO] - [12800] Loss: 0.802, Running accuracy: 99.146, Time: 37.52
[2020-12-15 22:36:39,376][__main__][INFO] - [13120] Loss: 0.949, Running accuracy: 99.145, Time: 38.57
[2020-12-15 22:37:20,704][__main__][INFO] - [13440] Loss: 0.891, Running accuracy: 99.146, Time: 41.33
[2020-12-15 22:38:03,556][__main__][INFO] - [13760] Loss: 0.824, Running accuracy: 99.148, Time: 42.85
[2020-12-15 22:38:38,996][__main__][INFO] - [14080] Loss: 0.895, Running accuracy: 99.148, Time: 35.35
[2020-12-15 22:39:18,234][__main__][INFO] - [14400] Loss: 1.043, Running accuracy: 99.146, Time: 39.24
[2020-12-15 22:39:55,301][__main__][INFO] - [14720] Loss: 0.787, Running accuracy: 99.148, Time: 37.07
[2020-12-15 22:40:33,861][__main__][INFO] - [15040] Loss: 0.997, Running accuracy: 99.142, Time: 38.56
[2020-12-15 22:41:10,428][__main__][INFO] - [15360] Loss: 0.799, Running accuracy: 99.143, Time: 36.56
[2020-12-15 22:41:49,534][__main__][INFO] - [15680] Loss: 1.077, Running accuracy: 99.139, Time: 39.11
[2020-12-15 22:42:25,395][__main__][INFO] - [16000] Loss: 0.907, Running accuracy: 99.138, Time: 35.86
[2020-12-15 22:43:06,817][__main__][INFO] - [16320] Loss: 0.839, Running accuracy: 99.140, Time: 41.42
[2020-12-15 22:43:45,893][__main__][INFO] - [16640] Loss: 0.939, Running accuracy: 99.138, Time: 39.07
[2020-12-15 22:44:22,655][__main__][INFO] - [16960] Loss: 0.846, Running accuracy: 99.136, Time: 36.76
[2020-12-15 22:45:01,458][__main__][INFO] - [17280] Loss: 0.795, Running accuracy: 99.138, Time: 38.80
[2020-12-15 22:45:26,633][__main__][INFO] - Action accuracy: 99.137, Loss: 0.978
[2020-12-15 22:45:26,634][__main__][INFO] - Validating..
[2020-12-15 22:45:33,312][test][INFO] - Time elapsed: 5.235347
[2020-12-15 22:45:33,313][__main__][INFO] - Validation F1 score: 94.330, Exact match: 54.830, Precision: 94.710, Recall: 93.960
[2020-12-15 22:45:46,273][__main__][INFO] - Checkpoint saved to checkpoints/model_latest.pth
[2020-12-15 22:45:46,587][__main__][INFO] - Epoch #18
[2020-12-15 22:45:46,587][__main__][INFO] - Training..
[2020-12-15 22:46:28,332][__main__][INFO] - [320] Loss: 0.868, Running accuracy: 99.187, Time: 40.57
[2020-12-15 22:47:02,860][__main__][INFO] - [640] Loss: 0.675, Running accuracy: 99.286, Time: 34.53
[2020-12-15 22:47:41,734][__main__][INFO] - [960] Loss: 0.671, Running accuracy: 99.291, Time: 38.87
[2020-12-15 22:48:21,099][__main__][INFO] - [1280] Loss: 0.824, Running accuracy: 99.275, Time: 39.36
[2020-12-15 22:48:59,315][__main__][INFO] - [1600] Loss: 0.740, Running accuracy: 99.271, Time: 38.21
[2020-12-15 22:49:36,141][__main__][INFO] - [1920] Loss: 0.877, Running accuracy: 99.254, Time: 36.82
[2020-12-15 22:50:15,313][__main__][INFO] - [2240] Loss: 0.797, Running accuracy: 99.243, Time: 39.17
[2020-12-15 22:50:55,124][__main__][INFO] - [2560] Loss: 0.820, Running accuracy: 99.259, Time: 39.81
[2020-12-15 22:51:34,015][__main__][INFO] - [2880] Loss: 0.694, Running accuracy: 99.261, Time: 38.89
[2020-12-15 22:52:14,846][__main__][INFO] - [3200] Loss: 0.643, Running accuracy: 99.284, Time: 40.83
[2020-12-15 22:52:54,326][__main__][INFO] - [3520] Loss: 0.745, Running accuracy: 99.290, Time: 39.48
[2020-12-15 22:53:29,711][__main__][INFO] - [3840] Loss: 0.673, Running accuracy: 99.294, Time: 35.38
[2020-12-15 22:54:08,474][__main__][INFO] - [4160] Loss: 0.746, Running accuracy: 99.296, Time: 38.76
[2020-12-15 22:54:47,913][__main__][INFO] - [4480] Loss: 0.886, Running accuracy: 99.290, Time: 39.44
[2020-12-15 22:55:22,403][__main__][INFO] - [4800] Loss: 0.727, Running accuracy: 99.287, Time: 34.49
[2020-12-15 22:55:59,842][__main__][INFO] - [5120] Loss: 0.762, Running accuracy: 99.289, Time: 37.44
[2020-12-15 22:56:39,868][__main__][INFO] - [5440] Loss: 0.816, Running accuracy: 99.285, Time: 40.02
[2020-12-15 22:57:22,152][__main__][INFO] - [5760] Loss: 0.870, Running accuracy: 99.274, Time: 42.28
[2020-12-15 22:58:04,229][__main__][INFO] - [6080] Loss: 0.746, Running accuracy: 99.272, Time: 42.07
[2020-12-15 22:58:44,120][__main__][INFO] - [6400] Loss: 0.722, Running accuracy: 99.275, Time: 39.89
[2020-12-15 22:59:18,539][__main__][INFO] - [6720] Loss: 0.796, Running accuracy: 99.277, Time: 34.42
[2020-12-15 22:59:53,028][__main__][INFO] - [7040] Loss: 0.767, Running accuracy: 99.278, Time: 34.49
[2020-12-15 23:00:27,136][__main__][INFO] - [7360] Loss: 0.692, Running accuracy: 99.279, Time: 34.10
[2020-12-15 23:01:03,459][__main__][INFO] - [7680] Loss: 0.804, Running accuracy: 99.280, Time: 36.32
[2020-12-15 23:01:40,230][__main__][INFO] - [8000] Loss: 0.854, Running accuracy: 99.273, Time: 36.77
[2020-12-15 23:02:19,724][__main__][INFO] - [8320] Loss: 0.959, Running accuracy: 99.266, Time: 39.49
[2020-12-15 23:03:03,374][__main__][INFO] - [8640] Loss: 0.822, Running accuracy: 99.264, Time: 43.65
[2020-12-15 23:03:44,396][__main__][INFO] - [8960] Loss: 0.816, Running accuracy: 99.262, Time: 41.02
[2020-12-15 23:04:25,563][__main__][INFO] - [9280] Loss: 0.744, Running accuracy: 99.265, Time: 41.16
[2020-12-15 23:05:05,733][__main__][INFO] - [9600] Loss: 0.665, Running accuracy: 99.267, Time: 40.17
[2020-12-15 23:05:41,713][__main__][INFO] - [9920] Loss: 0.809, Running accuracy: 99.263, Time: 35.97
[2020-12-15 23:06:16,693][__main__][INFO] - [10240] Loss: 0.691, Running accuracy: 99.264, Time: 34.98
[2020-12-15 23:06:50,050][__main__][INFO] - [10560] Loss: 0.680, Running accuracy: 99.264, Time: 33.36
[2020-12-15 23:07:27,319][__main__][INFO] - [10880] Loss: 0.825, Running accuracy: 99.261, Time: 37.27
[2020-12-15 23:08:03,776][__main__][INFO] - [11200] Loss: 0.793, Running accuracy: 99.260, Time: 36.46
[2020-12-15 23:08:45,187][__main__][INFO] - [11520] Loss: 0.816, Running accuracy: 99.257, Time: 41.41
[2020-12-15 23:09:23,297][__main__][INFO] - [11840] Loss: 0.746, Running accuracy: 99.257, Time: 38.11
[2020-12-15 23:10:02,872][__main__][INFO] - [12160] Loss: 0.780, Running accuracy: 99.256, Time: 39.57
[2020-12-15 23:10:42,920][__main__][INFO] - [12480] Loss: 0.823, Running accuracy: 99.255, Time: 40.05
[2020-12-15 23:11:19,847][__main__][INFO] - [12800] Loss: 0.688, Running accuracy: 99.256, Time: 36.93
[2020-12-15 23:12:00,078][__main__][INFO] - [13120] Loss: 0.841, Running accuracy: 99.254, Time: 40.23
[2020-12-15 23:12:44,315][__main__][INFO] - [13440] Loss: 0.933, Running accuracy: 99.256, Time: 44.24
[2020-12-15 23:13:19,955][__main__][INFO] - [13760] Loss: 0.918, Running accuracy: 99.251, Time: 35.55
[2020-12-15 23:14:00,883][__main__][INFO] - [14080] Loss: 0.891, Running accuracy: 99.246, Time: 40.93
[2020-12-15 23:14:35,639][__main__][INFO] - [14400] Loss: 0.754, Running accuracy: 99.247, Time: 34.67
[2020-12-15 23:15:11,983][__main__][INFO] - [14720] Loss: 0.836, Running accuracy: 99.245, Time: 36.34
[2020-12-15 23:15:51,364][__main__][INFO] - [15040] Loss: 0.805, Running accuracy: 99.245, Time: 39.38
[2020-12-15 23:16:30,465][__main__][INFO] - [15360] Loss: 0.859, Running accuracy: 99.243, Time: 39.10
[2020-12-15 23:17:13,873][__main__][INFO] - [15680] Loss: 0.770, Running accuracy: 99.243, Time: 43.41
[2020-12-15 23:17:55,062][__main__][INFO] - [16000] Loss: 0.778, Running accuracy: 99.242, Time: 41.19
[2020-12-15 23:18:29,964][__main__][INFO] - [16320] Loss: 0.709, Running accuracy: 99.243, Time: 34.90
[2020-12-15 23:19:08,805][__main__][INFO] - [16640] Loss: 0.724, Running accuracy: 99.244, Time: 38.84
[2020-12-15 23:19:55,685][__main__][INFO] - [16960] Loss: 0.928, Running accuracy: 99.242, Time: 46.88
[2020-12-15 23:20:30,142][__main__][INFO] - [17280] Loss: 0.771, Running accuracy: 99.242, Time: 34.46
[2020-12-15 23:20:57,233][__main__][INFO] - Action accuracy: 99.239, Loss: 0.870
[2020-12-15 23:20:57,234][__main__][INFO] - Validating..
[2020-12-15 23:21:03,958][test][INFO] - Time elapsed: 5.289134
[2020-12-15 23:21:03,959][__main__][INFO] - Validation F1 score: 94.440, Exact match: 54.830, Precision: 94.790, Recall: 94.100
[2020-12-15 23:21:16,916][__main__][INFO] - Checkpoint saved to checkpoints/model_latest.pth
[2020-12-15 23:21:17,229][__main__][INFO] - Epoch #19
[2020-12-15 23:21:17,229][__main__][INFO] - Training..
[2020-12-15 23:21:55,783][__main__][INFO] - [320] Loss: 0.710, Running accuracy: 99.371, Time: 37.39
[2020-12-15 23:22:33,640][__main__][INFO] - [640] Loss: 0.622, Running accuracy: 99.393, Time: 37.85
[2020-12-15 23:23:08,798][__main__][INFO] - [960] Loss: 0.614, Running accuracy: 99.400, Time: 35.16
[2020-12-15 23:23:45,886][__main__][INFO] - [1280] Loss: 0.607, Running accuracy: 99.371, Time: 37.09
[2020-12-15 23:24:23,448][__main__][INFO] - [1600] Loss: 0.759, Running accuracy: 99.322, Time: 37.56
[2020-12-15 23:25:00,820][__main__][INFO] - [1920] Loss: 0.668, Running accuracy: 99.322, Time: 37.37
[2020-12-15 23:25:41,142][__main__][INFO] - [2240] Loss: 0.694, Running accuracy: 99.330, Time: 40.32
[2020-12-15 23:26:17,618][__main__][INFO] - [2560] Loss: 0.646, Running accuracy: 99.340, Time: 36.47
[2020-12-15 23:26:57,041][__main__][INFO] - [2880] Loss: 0.765, Running accuracy: 99.340, Time: 39.42
[2020-12-15 23:27:37,043][__main__][INFO] - [3200] Loss: 0.655, Running accuracy: 99.349, Time: 40.00
[2020-12-15 23:28:17,141][__main__][INFO] - [3520] Loss: 0.738, Running accuracy: 99.350, Time: 40.10
[2020-12-15 23:28:56,387][__main__][INFO] - [3840] Loss: 0.705, Running accuracy: 99.342, Time: 39.24
[2020-12-15 23:29:36,358][__main__][INFO] - [4160] Loss: 0.660, Running accuracy: 99.341, Time: 39.97
[2020-12-15 23:30:15,984][__main__][INFO] - [4480] Loss: 0.776, Running accuracy: 99.342, Time: 39.63
[2020-12-15 23:30:55,279][__main__][INFO] - [4800] Loss: 0.881, Running accuracy: 99.329, Time: 39.29
[2020-12-15 23:31:36,251][__main__][INFO] - [5120] Loss: 0.673, Running accuracy: 99.339, Time: 40.97
[2020-12-15 23:32:11,793][__main__][INFO] - [5440] Loss: 0.683, Running accuracy: 99.336, Time: 35.54
[2020-12-15 23:32:45,188][__main__][INFO] - [5760] Loss: 0.617, Running accuracy: 99.338, Time: 33.39
[2020-12-15 23:33:26,628][__main__][INFO] - [6080] Loss: 0.859, Running accuracy: 99.329, Time: 41.44
[2020-12-15 23:34:00,981][__main__][INFO] - [6400] Loss: 0.737, Running accuracy: 99.327, Time: 34.35
[2020-12-15 23:34:45,690][__main__][INFO] - [6720] Loss: 0.849, Running accuracy: 99.325, Time: 44.71
[2020-12-15 23:35:21,200][__main__][INFO] - [7040] Loss: 0.639, Running accuracy: 99.320, Time: 35.51
[2020-12-15 23:36:01,503][__main__][INFO] - [7360] Loss: 0.733, Running accuracy: 99.324, Time: 40.30
[2020-12-15 23:36:43,025][__main__][INFO] - [7680] Loss: 0.673, Running accuracy: 99.326, Time: 41.52
[2020-12-15 23:37:20,606][__main__][INFO] - [8000] Loss: 0.798, Running accuracy: 99.325, Time: 37.58
[2020-12-15 23:37:59,154][__main__][INFO] - [8320] Loss: 0.714, Running accuracy: 99.325, Time: 38.55
[2020-12-15 23:38:39,530][__main__][INFO] - [8640] Loss: 0.925, Running accuracy: 99.318, Time: 40.37
[2020-12-15 23:39:08,504][__main__][INFO] - [8960] Loss: 0.715, Running accuracy: 99.314, Time: 28.97
[2020-12-15 23:39:43,734][__main__][INFO] - [9280] Loss: 0.618, Running accuracy: 99.315, Time: 35.23
[2020-12-15 23:40:19,922][__main__][INFO] - [9600] Loss: 0.743, Running accuracy: 99.310, Time: 36.19
[2020-12-15 23:40:56,157][__main__][INFO] - [9920] Loss: 0.604, Running accuracy: 99.315, Time: 36.23
[2020-12-15 23:41:36,897][__main__][INFO] - [10240] Loss: 0.792, Running accuracy: 99.312, Time: 40.74
[2020-12-15 23:42:13,973][__main__][INFO] - [10560] Loss: 0.684, Running accuracy: 99.314, Time: 37.07
[2020-12-15 23:42:51,231][__main__][INFO] - [10880] Loss: 0.627, Running accuracy: 99.314, Time: 37.26
[2020-12-15 23:43:32,964][__main__][INFO] - [11200] Loss: 0.633, Running accuracy: 99.315, Time: 41.73
[2020-12-15 23:44:15,253][__main__][INFO] - [11520] Loss: 0.895, Running accuracy: 99.309, Time: 42.29
[2020-12-15 23:44:53,322][__main__][INFO] - [11840] Loss: 0.697, Running accuracy: 99.308, Time: 38.07
[2020-12-15 23:45:28,631][__main__][INFO] - [12160] Loss: 0.682, Running accuracy: 99.310, Time: 35.31
[2020-12-15 23:46:06,775][__main__][INFO] - [12480] Loss: 0.769, Running accuracy: 99.307, Time: 38.14
[2020-12-15 23:46:47,432][__main__][INFO] - [12800] Loss: 0.779, Running accuracy: 99.305, Time: 40.66
[2020-12-15 23:47:23,658][__main__][INFO] - [13120] Loss: 0.692, Running accuracy: 99.307, Time: 36.22
[2020-12-15 23:48:00,827][__main__][INFO] - [13440] Loss: 0.684, Running accuracy: 99.307, Time: 37.17
[2020-12-15 23:48:38,770][__main__][INFO] - [13760] Loss: 0.651, Running accuracy: 99.308, Time: 37.94
[2020-12-15 23:49:18,469][__main__][INFO] - [14080] Loss: 0.661, Running accuracy: 99.311, Time: 39.69
[2020-12-15 23:49:53,956][__main__][INFO] - [14400] Loss: 0.843, Running accuracy: 99.307, Time: 35.49
[2020-12-15 23:50:30,185][__main__][INFO] - [14720] Loss: 0.767, Running accuracy: 99.306, Time: 36.23
[2020-12-15 23:51:08,614][__main__][INFO] - [15040] Loss: 0.672, Running accuracy: 99.304, Time: 38.43
[2020-12-15 23:51:51,410][__main__][INFO] - [15360] Loss: 0.694, Running accuracy: 99.304, Time: 42.79
[2020-12-15 23:52:30,444][__main__][INFO] - [15680] Loss: 0.991, Running accuracy: 99.302, Time: 39.03
[2020-12-15 23:53:05,774][__main__][INFO] - [16000] Loss: 0.739, Running accuracy: 99.302, Time: 35.33
[2020-12-15 23:53:43,857][__main__][INFO] - [16320] Loss: 0.754, Running accuracy: 99.302, Time: 38.08
[2020-12-15 23:54:23,942][__main__][INFO] - [16640] Loss: 0.650, Running accuracy: 99.303, Time: 40.08
[2020-12-15 23:55:04,767][__main__][INFO] - [16960] Loss: 0.805, Running accuracy: 99.303, Time: 40.82
[2020-12-15 23:55:43,761][__main__][INFO] - [17280] Loss: 0.749, Running accuracy: 99.303, Time: 38.99
[2020-12-15 23:56:15,021][__main__][INFO] - Action accuracy: 99.301, Loss: 0.798
[2020-12-15 23:56:15,022][__main__][INFO] - Validating..
[2020-12-15 23:56:21,858][test][INFO] - Time elapsed: 5.379122
[2020-12-15 23:56:21,860][__main__][INFO] - Validation F1 score: 94.160, Exact match: 54.550, Precision: 94.260, Recall: 94.060
[2020-12-15 23:56:34,813][__main__][INFO] - Checkpoint saved to checkpoints/model_latest.pth
[2020-12-15 23:56:35,129][__main__][INFO] - Epoch #20
[2020-12-15 23:56:35,130][__main__][INFO] - Training..
[2020-12-15 23:57:21,164][__main__][INFO] - [320] Loss: 0.707, Running accuracy: 99.413, Time: 44.80
[2020-12-15 23:57:54,767][__main__][INFO] - [640] Loss: 0.598, Running accuracy: 99.411, Time: 33.60
[2020-12-15 23:58:32,869][__main__][INFO] - [960] Loss: 0.766, Running accuracy: 99.392, Time: 38.10
[2020-12-15 23:59:07,518][__main__][INFO] - [1280] Loss: 0.646, Running accuracy: 99.383, Time: 34.65
[2020-12-15 23:59:43,296][__main__][INFO] - [1600] Loss: 0.514, Running accuracy: 99.403, Time: 35.78
[2020-12-16 00:00:21,613][__main__][INFO] - [1920] Loss: 0.682, Running accuracy: 99.388, Time: 38.31
[2020-12-16 00:00:57,909][__main__][INFO] - [2240] Loss: 0.643, Running accuracy: 99.392, Time: 36.29
[2020-12-16 00:01:32,130][__main__][INFO] - [2560] Loss: 0.497, Running accuracy: 99.408, Time: 34.22
[2020-12-16 00:02:11,056][__main__][INFO] - [2880] Loss: 0.622, Running accuracy: 99.416, Time: 38.92
[2020-12-16 00:02:47,032][__main__][INFO] - [3200] Loss: 0.700, Running accuracy: 99.408, Time: 35.97
[2020-12-16 00:03:22,743][__main__][INFO] - [3520] Loss: 0.669, Running accuracy: 99.400, Time: 35.71
[2020-12-16 00:04:10,551][__main__][INFO] - [3840] Loss: 0.737, Running accuracy: 99.403, Time: 47.81
[2020-12-16 00:04:48,153][__main__][INFO] - [4160] Loss: 0.635, Running accuracy: 99.401, Time: 37.60
[2020-12-16 00:05:24,001][__main__][INFO] - [4480] Loss: 0.621, Running accuracy: 99.400, Time: 35.85
[2020-12-16 00:06:07,303][__main__][INFO] - [4800] Loss: 0.727, Running accuracy: 99.401, Time: 43.30
[2020-12-16 00:06:45,886][__main__][INFO] - [5120] Loss: 0.680, Running accuracy: 99.399, Time: 38.58
[2020-12-16 00:07:21,118][__main__][INFO] - [5440] Loss: 0.665, Running accuracy: 99.395, Time: 35.23
[2020-12-16 00:08:05,067][__main__][INFO] - [5760] Loss: 0.578, Running accuracy: 99.398, Time: 43.95
[2020-12-16 00:08:44,814][__main__][INFO] - [6080] Loss: 0.757, Running accuracy: 99.386, Time: 39.74
[2020-12-16 00:09:22,033][__main__][INFO] - [6400] Loss: 0.743, Running accuracy: 99.386, Time: 37.22
[2020-12-16 00:10:03,795][__main__][INFO] - [6720] Loss: 0.698, Running accuracy: 99.385, Time: 41.76
[2020-12-16 00:10:39,157][__main__][INFO] - [7040] Loss: 0.529, Running accuracy: 99.387, Time: 35.36
[2020-12-16 00:11:14,759][__main__][INFO] - [7360] Loss: 0.662, Running accuracy: 99.382, Time: 35.60
[2020-12-16 00:11:55,817][__main__][INFO] - [7680] Loss: 0.692, Running accuracy: 99.384, Time: 41.06
[2020-12-16 00:12:28,471][__main__][INFO] - [8000] Loss: 0.525, Running accuracy: 99.389, Time: 32.65
[2020-12-16 00:13:04,309][__main__][INFO] - [8320] Loss: 0.666, Running accuracy: 99.386, Time: 35.84
[2020-12-16 00:13:44,053][__main__][INFO] - [8640] Loss: 0.573, Running accuracy: 99.389, Time: 39.74
[2020-12-16 00:14:29,732][__main__][INFO] - [8960] Loss: 0.693, Running accuracy: 99.384, Time: 45.68
[2020-12-16 00:15:07,091][__main__][INFO] - [9280] Loss: 0.641, Running accuracy: 99.382, Time: 37.36
[2020-12-16 00:15:46,061][__main__][INFO] - [9600] Loss: 0.759, Running accuracy: 99.378, Time: 38.97
[2020-12-16 00:16:24,576][__main__][INFO] - [9920] Loss: 0.602, Running accuracy: 99.381, Time: 38.51
[2020-12-16 00:17:04,121][__main__][INFO] - [10240] Loss: 0.747, Running accuracy: 99.379, Time: 39.54
[2020-12-16 00:17:44,926][__main__][INFO] - [10560] Loss: 0.811, Running accuracy: 99.377, Time: 40.80
[2020-12-16 00:18:24,422][__main__][INFO] - [10880] Loss: 0.776, Running accuracy: 99.370, Time: 39.49
[2020-12-16 00:19:06,654][__main__][INFO] - [11200] Loss: 0.646, Running accuracy: 99.370, Time: 42.23
[2020-12-16 00:19:40,893][__main__][INFO] - [11520] Loss: 0.623, Running accuracy: 99.370, Time: 34.24
[2020-12-16 00:20:19,308][__main__][INFO] - [11840] Loss: 0.628, Running accuracy: 99.370, Time: 38.41
[2020-12-16 00:20:54,739][__main__][INFO] - [12160] Loss: 0.617, Running accuracy: 99.371, Time: 35.43
[2020-12-16 00:21:36,464][__main__][INFO] - [12480] Loss: 0.675, Running accuracy: 99.372, Time: 41.72
[2020-12-16 00:22:18,129][__main__][INFO] - [12800] Loss: 0.690, Running accuracy: 99.370, Time: 41.66
[2020-12-16 00:22:54,537][__main__][INFO] - [13120] Loss: 0.891, Running accuracy: 99.365, Time: 36.41
[2020-12-16 00:23:31,173][__main__][INFO] - [13440] Loss: 0.692, Running accuracy: 99.364, Time: 36.64
[2020-12-16 00:24:06,371][__main__][INFO] - [13760] Loss: 0.552, Running accuracy: 99.365, Time: 35.19
[2020-12-16 00:24:44,761][__main__][INFO] - [14080] Loss: 0.676, Running accuracy: 99.366, Time: 38.39
[2020-12-16 00:25:20,786][__main__][INFO] - [14400] Loss: 0.776, Running accuracy: 99.365, Time: 36.02
[2020-12-16 00:26:02,306][__main__][INFO] - [14720] Loss: 0.727, Running accuracy: 99.361, Time: 41.52
[2020-12-16 00:26:41,063][__main__][INFO] - [15040] Loss: 0.710, Running accuracy: 99.358, Time: 38.76
[2020-12-16 00:27:15,352][__main__][INFO] - [15360] Loss: 0.738, Running accuracy: 99.354, Time: 34.29
[2020-12-16 00:27:56,093][__main__][INFO] - [15680] Loss: 0.872, Running accuracy: 99.352, Time: 40.74
[2020-12-16 00:28:33,632][__main__][INFO] - [16000] Loss: 0.622, Running accuracy: 99.350, Time: 37.54
[2020-12-16 00:29:13,557][__main__][INFO] - [16320] Loss: 0.630, Running accuracy: 99.352, Time: 39.92
[2020-12-16 00:29:51,947][__main__][INFO] - [16640] Loss: 0.722, Running accuracy: 99.350, Time: 38.39
[2020-12-16 00:30:28,102][__main__][INFO] - [16960] Loss: 0.654, Running accuracy: 99.349, Time: 36.15
[2020-12-16 00:31:05,851][__main__][INFO] - [17280] Loss: 0.619, Running accuracy: 99.349, Time: 37.75
[2020-12-16 00:31:35,897][__main__][INFO] - Action accuracy: 99.348, Loss: 0.740
[2020-12-16 00:31:35,898][__main__][INFO] - Validating..
[2020-12-16 00:31:42,552][test][INFO] - Time elapsed: 5.225715
[2020-12-16 00:31:42,554][__main__][INFO] - Validation F1 score: 94.750, Exact match: 56.530, Precision: 94.920, Recall: 94.590
[2020-12-16 00:31:42,554][__main__][INFO] - F1 score has improved
[2020-12-16 00:31:54,117][__main__][INFO] - Checkpoint saved to checkpoints/model_latest.pth
[2020-12-16 00:31:54,438][__main__][INFO] - Epoch #21
[2020-12-16 00:31:54,438][__main__][INFO] - Training..
[2020-12-16 00:32:29,836][__main__][INFO] - [320] Loss: 0.520, Running accuracy: 99.558, Time: 34.29
[2020-12-16 00:33:06,891][__main__][INFO] - [640] Loss: 0.599, Running accuracy: 99.495, Time: 37.05
[2020-12-16 00:33:41,410][__main__][INFO] - [960] Loss: 0.564, Running accuracy: 99.479, Time: 34.52
[2020-12-16 00:34:23,314][__main__][INFO] - [1280] Loss: 0.494, Running accuracy: 99.495, Time: 41.90
[2020-12-16 00:35:03,193][__main__][INFO] - [1600] Loss: 0.560, Running accuracy: 99.496, Time: 39.88
[2020-12-16 00:35:45,178][__main__][INFO] - [1920] Loss: 0.557, Running accuracy: 99.502, Time: 41.98
[2020-12-16 00:36:25,909][__main__][INFO] - [2240] Loss: 0.541, Running accuracy: 99.506, Time: 40.73
[2020-12-16 00:37:00,648][__main__][INFO] - [2560] Loss: 0.572, Running accuracy: 99.503, Time: 34.74
[2020-12-16 00:37:42,154][__main__][INFO] - [2880] Loss: 0.646, Running accuracy: 99.505, Time: 41.50
[2020-12-16 00:38:20,199][__main__][INFO] - [3200] Loss: 0.696, Running accuracy: 99.492, Time: 38.04
[2020-12-16 00:38:57,445][__main__][INFO] - [3520] Loss: 0.531, Running accuracy: 99.490, Time: 37.24
[2020-12-16 00:39:38,740][__main__][INFO] - [3840] Loss: 0.596, Running accuracy: 99.483, Time: 41.29
[2020-12-16 00:40:13,893][__main__][INFO] - [4160] Loss: 0.655, Running accuracy: 99.475, Time: 35.15
[2020-12-16 00:40:54,796][__main__][INFO] - [4480] Loss: 0.563, Running accuracy: 99.467, Time: 40.90
[2020-12-16 00:41:28,645][__main__][INFO] - [4800] Loss: 0.513, Running accuracy: 99.462, Time: 33.85
[2020-12-16 00:42:03,099][__main__][INFO] - [5120] Loss: 0.628, Running accuracy: 99.452, Time: 34.45
[2020-12-16 00:42:39,375][__main__][INFO] - [5440] Loss: 0.555, Running accuracy: 99.454, Time: 36.27
[2020-12-16 00:43:27,498][__main__][INFO] - [5760] Loss: 0.658, Running accuracy: 99.451, Time: 48.12
[2020-12-16 00:44:05,491][__main__][INFO] - [6080] Loss: 0.681, Running accuracy: 99.442, Time: 37.99
[2020-12-16 00:44:42,202][__main__][INFO] - [6400] Loss: 0.557, Running accuracy: 99.447, Time: 36.71
[2020-12-16 00:45:23,256][__main__][INFO] - [6720] Loss: 0.527, Running accuracy: 99.446, Time: 41.05
[2020-12-16 00:45:59,961][__main__][INFO] - [7040] Loss: 0.463, Running accuracy: 99.452, Time: 36.70
[2020-12-16 00:46:37,968][__main__][INFO] - [7360] Loss: 0.564, Running accuracy: 99.448, Time: 38.01
[2020-12-16 00:47:14,330][__main__][INFO] - [7680] Loss: 0.599, Running accuracy: 99.444, Time: 36.36
[2020-12-16 00:47:52,227][__main__][INFO] - [8000] Loss: 0.607, Running accuracy: 99.441, Time: 37.90
[2020-12-16 00:48:30,561][__main__][INFO] - [8320] Loss: 0.612, Running accuracy: 99.440, Time: 38.33
[2020-12-16 00:49:06,041][__main__][INFO] - [8640] Loss: 0.813, Running accuracy: 99.436, Time: 35.48
[2020-12-16 00:49:44,416][__main__][INFO] - [8960] Loss: 0.516, Running accuracy: 99.438, Time: 38.37
[2020-12-16 00:50:23,562][__main__][INFO] - [9280] Loss: 0.501, Running accuracy: 99.443, Time: 39.14
[2020-12-16 00:50:56,692][__main__][INFO] - [9600] Loss: 0.631, Running accuracy: 99.440, Time: 33.13
[2020-12-16 00:51:36,818][__main__][INFO] - [9920] Loss: 0.630, Running accuracy: 99.435, Time: 40.12
[2020-12-16 00:52:13,445][__main__][INFO] - [10240] Loss: 0.550, Running accuracy: 99.434, Time: 36.63
[2020-12-16 00:52:51,486][__main__][INFO] - [10560] Loss: 0.620, Running accuracy: 99.433, Time: 38.04
[2020-12-16 00:53:33,938][__main__][INFO] - [10880] Loss: 0.533, Running accuracy: 99.435, Time: 42.37
[2020-12-16 00:54:07,172][__main__][INFO] - [11200] Loss: 0.416, Running accuracy: 99.439, Time: 33.23
[2020-12-16 00:54:45,034][__main__][INFO] - [11520] Loss: 0.672, Running accuracy: 99.435, Time: 37.86
[2020-12-16 00:55:20,584][__main__][INFO] - [11840] Loss: 0.638, Running accuracy: 99.433, Time: 35.55
[2020-12-16 00:55:59,726][__main__][INFO] - [12160] Loss: 0.589, Running accuracy: 99.432, Time: 39.14
[2020-12-16 00:56:37,472][__main__][INFO] - [12480] Loss: 0.697, Running accuracy: 99.430, Time: 37.74
[2020-12-16 00:57:14,557][__main__][INFO] - [12800] Loss: 0.561, Running accuracy: 99.431, Time: 37.08
[2020-12-16 00:57:54,098][__main__][INFO] - [13120] Loss: 0.647, Running accuracy: 99.434, Time: 39.54
[2020-12-16 00:58:32,640][__main__][INFO] - [13440] Loss: 0.703, Running accuracy: 99.432, Time: 38.54
[2020-12-16 00:59:11,563][__main__][INFO] - [13760] Loss: 0.613, Running accuracy: 99.431, Time: 38.92
[2020-12-16 00:59:48,820][__main__][INFO] - [14080] Loss: 0.584, Running accuracy: 99.429, Time: 37.25
[2020-12-16 01:00:29,498][__main__][INFO] - [14400] Loss: 0.581, Running accuracy: 99.432, Time: 40.68
[2020-12-16 01:01:07,791][__main__][INFO] - [14720] Loss: 0.547, Running accuracy: 99.436, Time: 38.29
[2020-12-16 01:01:44,432][__main__][INFO] - [15040] Loss: 0.746, Running accuracy: 99.432, Time: 36.64
[2020-12-16 01:02:26,984][__main__][INFO] - [15360] Loss: 0.638, Running accuracy: 99.432, Time: 42.55
[2020-12-16 01:03:03,071][__main__][INFO] - [15680] Loss: 0.552, Running accuracy: 99.433, Time: 36.09
[2020-12-16 01:03:39,521][__main__][INFO] - [16000] Loss: 0.821, Running accuracy: 99.430, Time: 36.45
[2020-12-16 01:04:28,896][__main__][INFO] - [16320] Loss: 0.756, Running accuracy: 99.428, Time: 49.37
[2020-12-16 01:05:10,204][__main__][INFO] - [16640] Loss: 0.620, Running accuracy: 99.427, Time: 41.31
[2020-12-16 01:05:48,611][__main__][INFO] - [16960] Loss: 0.606, Running accuracy: 99.429, Time: 38.41
[2020-12-16 01:06:24,367][__main__][INFO] - [17280] Loss: 0.691, Running accuracy: 99.428, Time: 35.75
[2020-12-16 01:06:55,592][__main__][INFO] - Action accuracy: 99.429, Loss: 0.671
[2020-12-16 01:06:55,592][__main__][INFO] - Validating..
[2020-12-16 01:07:02,223][test][INFO] - Time elapsed: 5.212502
[2020-12-16 01:07:02,225][__main__][INFO] - Validation F1 score: 94.460, Exact match: 55.400, Precision: 94.710, Recall: 94.220
[2020-12-16 01:07:15,818][__main__][INFO] - Checkpoint saved to checkpoints/model_latest.pth
[2020-12-16 01:07:16,134][__main__][INFO] - Epoch #22
[2020-12-16 01:07:16,135][__main__][INFO] - Training..
[2020-12-16 01:07:57,541][__main__][INFO] - [320] Loss: 0.531, Running accuracy: 99.456, Time: 40.23
[2020-12-16 01:08:38,098][__main__][INFO] - [640] Loss: 0.480, Running accuracy: 99.448, Time: 40.55
[2020-12-16 01:09:19,114][__main__][INFO] - [960] Loss: 0.568, Running accuracy: 99.459, Time: 41.02
[2020-12-16 01:09:59,413][__main__][INFO] - [1280] Loss: 0.549, Running accuracy: 99.473, Time: 40.30
[2020-12-16 01:10:39,386][__main__][INFO] - [1600] Loss: 0.491, Running accuracy: 99.468, Time: 39.97
[2020-12-16 01:11:16,677][__main__][INFO] - [1920] Loss: 0.489, Running accuracy: 99.484, Time: 37.29
[2020-12-16 01:11:51,197][__main__][INFO] - [2240] Loss: 0.564, Running accuracy: 99.476, Time: 34.52
[2020-12-16 01:12:34,044][__main__][INFO] - [2560] Loss: 0.526, Running accuracy: 99.488, Time: 42.85
[2020-12-16 01:13:13,895][__main__][INFO] - [2880] Loss: 0.534, Running accuracy: 99.491, Time: 39.85
[2020-12-16 01:13:49,445][__main__][INFO] - [3200] Loss: 0.624, Running accuracy: 99.475, Time: 35.55
[2020-12-16 01:14:26,145][__main__][INFO] - [3520] Loss: 0.548, Running accuracy: 99.474, Time: 36.69
[2020-12-16 01:15:03,439][__main__][INFO] - [3840] Loss: 0.583, Running accuracy: 99.475, Time: 37.29
[2020-12-16 01:15:47,141][__main__][INFO] - [4160] Loss: 0.522, Running accuracy: 99.479, Time: 43.70
[2020-12-16 01:16:24,075][__main__][INFO] - [4480] Loss: 0.523, Running accuracy: 99.480, Time: 36.93
[2020-12-16 01:17:03,436][__main__][INFO] - [4800] Loss: 0.582, Running accuracy: 99.474, Time: 39.36
[2020-12-16 01:17:42,062][__main__][INFO] - [5120] Loss: 0.611, Running accuracy: 99.467, Time: 38.62
[2020-12-16 01:18:16,665][__main__][INFO] - [5440] Loss: 0.467, Running accuracy: 99.472, Time: 34.60
[2020-12-16 01:18:58,216][__main__][INFO] - [5760] Loss: 0.565, Running accuracy: 99.474, Time: 41.55
[2020-12-16 01:19:36,611][__main__][INFO] - [6080] Loss: 0.505, Running accuracy: 99.478, Time: 38.39
[2020-12-16 01:20:14,457][__main__][INFO] - [6400] Loss: 0.482, Running accuracy: 99.481, Time: 37.84
[2020-12-16 01:20:53,959][__main__][INFO] - [6720] Loss: 0.541, Running accuracy: 99.482, Time: 39.50
[2020-12-16 01:21:40,202][__main__][INFO] - [7040] Loss: 0.613, Running accuracy: 99.480, Time: 46.24
[2020-12-16 01:22:19,127][__main__][INFO] - [7360] Loss: 0.533, Running accuracy: 99.480, Time: 38.92
[2020-12-16 01:23:00,067][__main__][INFO] - [7680] Loss: 0.666, Running accuracy: 99.475, Time: 40.94
[2020-12-16 01:23:42,338][__main__][INFO] - [8000] Loss: 0.584, Running accuracy: 99.477, Time: 42.27
[2020-12-16 01:24:12,332][__main__][INFO] - [8320] Loss: 0.478, Running accuracy: 99.477, Time: 29.99
[2020-12-16 01:24:51,109][__main__][INFO] - [8640] Loss: 0.592, Running accuracy: 99.474, Time: 38.78
[2020-12-16 01:25:32,454][__main__][INFO] - [8960] Loss: 0.691, Running accuracy: 99.472, Time: 41.34
[2020-12-16 01:26:04,734][__main__][INFO] - [9280] Loss: 0.562, Running accuracy: 99.472, Time: 32.28
[2020-12-16 01:26:40,673][__main__][INFO] - [9600] Loss: 0.525, Running accuracy: 99.474, Time: 35.94
[2020-12-16 01:27:19,026][__main__][INFO] - [9920] Loss: 0.582, Running accuracy: 99.469, Time: 38.35
[2020-12-16 01:27:59,662][__main__][INFO] - [10240] Loss: 0.566, Running accuracy: 99.470, Time: 40.64
[2020-12-16 01:28:31,498][__main__][INFO] - [10560] Loss: 0.448, Running accuracy: 99.472, Time: 31.83
[2020-12-16 01:29:12,178][__main__][INFO] - [10880] Loss: 0.484, Running accuracy: 99.477, Time: 40.68
[2020-12-16 01:29:54,845][__main__][INFO] - [11200] Loss: 0.498, Running accuracy: 99.478, Time: 42.67
[2020-12-16 01:30:33,131][__main__][INFO] - [11520] Loss: 0.613, Running accuracy: 99.476, Time: 38.29
[2020-12-16 01:31:12,437][__main__][INFO] - [11840] Loss: 0.551, Running accuracy: 99.477, Time: 39.30
[2020-12-16 01:31:48,027][__main__][INFO] - [12160] Loss: 0.456, Running accuracy: 99.480, Time: 35.59
[2020-12-16 01:32:27,840][__main__][INFO] - [12480] Loss: 0.599, Running accuracy: 99.481, Time: 39.79
[2020-12-16 01:33:05,853][__main__][INFO] - [12800] Loss: 0.566, Running accuracy: 99.478, Time: 38.01
[2020-12-16 01:33:37,948][__main__][INFO] - [13120] Loss: 0.512, Running accuracy: 99.477, Time: 32.00
[2020-12-16 01:34:16,199][__main__][INFO] - [13440] Loss: 0.732, Running accuracy: 99.475, Time: 38.25
[2020-12-16 01:34:52,368][__main__][INFO] - [13760] Loss: 0.531, Running accuracy: 99.475, Time: 36.17
[2020-12-16 01:35:28,776][__main__][INFO] - [14080] Loss: 0.499, Running accuracy: 99.477, Time: 36.41
[2020-12-16 01:36:01,748][__main__][INFO] - [14400] Loss: 0.533, Running accuracy: 99.478, Time: 32.97
[2020-12-16 01:36:35,508][__main__][INFO] - [14720] Loss: 0.571, Running accuracy: 99.476, Time: 33.76
[2020-12-16 01:37:13,072][__main__][INFO] - [15040] Loss: 0.571, Running accuracy: 99.476, Time: 37.56
[2020-12-16 01:37:56,977][__main__][INFO] - [15360] Loss: 0.764, Running accuracy: 99.474, Time: 43.90
[2020-12-16 01:38:31,220][__main__][INFO] - [15680] Loss: 0.482, Running accuracy: 99.474, Time: 34.24
[2020-12-16 01:39:11,012][__main__][INFO] - [16000] Loss: 0.693, Running accuracy: 99.470, Time: 39.79
[2020-12-16 01:39:47,368][__main__][INFO] - [16320] Loss: 0.524, Running accuracy: 99.471, Time: 36.35
[2020-12-16 01:40:29,094][__main__][INFO] - [16640] Loss: 0.552, Running accuracy: 99.472, Time: 41.73
[2020-12-16 01:41:07,855][__main__][INFO] - [16960] Loss: 0.644, Running accuracy: 99.469, Time: 38.76
[2020-12-16 01:41:46,946][__main__][INFO] - [17280] Loss: 0.590, Running accuracy: 99.469, Time: 39.09
[2020-12-16 01:42:14,471][__main__][INFO] - Action accuracy: 99.469, Loss: 0.616
[2020-12-16 01:42:14,472][__main__][INFO] - Validating..
[2020-12-16 01:42:23,255][test][INFO] - Time elapsed: 7.450333
[2020-12-16 01:42:23,259][__main__][INFO] - Validation F1 score: 94.640, Exact match: 57.950, Precision: 94.370, Recall: 94.900
[2020-12-16 01:42:35,874][__main__][INFO] - Checkpoint saved to checkpoints/model_latest.pth
[2020-12-16 01:42:36,194][__main__][INFO] - Epoch #23
[2020-12-16 01:42:36,194][__main__][INFO] - Training..
[2020-12-16 01:43:08,932][__main__][INFO] - [320] Loss: 0.415, Running accuracy: 99.489, Time: 31.75
[2020-12-16 01:43:41,360][__main__][INFO] - [640] Loss: 0.386, Running accuracy: 99.518, Time: 32.43
[2020-12-16 01:44:14,927][__main__][INFO] - [960] Loss: 0.462, Running accuracy: 99.521, Time: 33.56
[2020-12-16 01:44:55,072][__main__][INFO] - [1280] Loss: 0.493, Running accuracy: 99.534, Time: 40.14
[2020-12-16 01:45:33,600][__main__][INFO] - [1600] Loss: 0.441, Running accuracy: 99.553, Time: 38.53
[2020-12-16 01:46:11,701][__main__][INFO] - [1920] Loss: 0.450, Running accuracy: 99.559, Time: 38.10
[2020-12-16 01:46:55,708][__main__][INFO] - [2240] Loss: 0.604, Running accuracy: 99.556, Time: 44.00
[2020-12-16 01:47:30,881][__main__][INFO] - [2560] Loss: 0.542, Running accuracy: 99.544, Time: 35.17
[2020-12-16 01:48:06,699][__main__][INFO] - [2880] Loss: 0.476, Running accuracy: 99.542, Time: 35.82
[2020-12-16 01:48:44,572][__main__][INFO] - [3200] Loss: 0.473, Running accuracy: 99.540, Time: 37.87
[2020-12-16 01:49:18,523][__main__][INFO] - [3520] Loss: 0.526, Running accuracy: 99.531, Time: 33.95
[2020-12-16 01:49:58,618][__main__][INFO] - [3840] Loss: 0.503, Running accuracy: 99.539, Time: 40.09
[2020-12-16 01:50:37,422][__main__][INFO] - [4160] Loss: 0.504, Running accuracy: 99.537, Time: 38.80
[2020-12-16 01:51:18,952][__main__][INFO] - [4480] Loss: 0.544, Running accuracy: 99.530, Time: 41.53
[2020-12-16 01:51:57,827][__main__][INFO] - [4800] Loss: 0.372, Running accuracy: 99.545, Time: 38.87
[2020-12-16 01:52:37,815][__main__][INFO] - [5120] Loss: 0.541, Running accuracy: 99.546, Time: 39.99
[2020-12-16 01:53:15,754][__main__][INFO] - [5440] Loss: 0.520, Running accuracy: 99.545, Time: 37.94
[2020-12-16 01:54:00,039][__main__][INFO] - [5760] Loss: 0.509, Running accuracy: 99.544, Time: 44.28
[2020-12-16 01:54:34,839][__main__][INFO] - [6080] Loss: 0.461, Running accuracy: 99.547, Time: 34.80
[2020-12-16 01:55:14,524][__main__][INFO] - [6400] Loss: 0.469, Running accuracy: 99.545, Time: 39.68
[2020-12-16 01:55:52,567][__main__][INFO] - [6720] Loss: 0.539, Running accuracy: 99.547, Time: 38.04
[2020-12-16 01:56:31,240][__main__][INFO] - [7040] Loss: 0.519, Running accuracy: 99.544, Time: 38.67
[2020-12-16 01:57:06,851][__main__][INFO] - [7360] Loss: 0.535, Running accuracy: 99.542, Time: 35.61
[2020-12-16 01:57:44,373][__main__][INFO] - [7680] Loss: 0.509, Running accuracy: 99.537, Time: 37.52
[2020-12-16 01:58:20,325][__main__][INFO] - [8000] Loss: 0.458, Running accuracy: 99.539, Time: 35.95
[2020-12-16 01:58:59,028][__main__][INFO] - [8320] Loss: 0.411, Running accuracy: 99.539, Time: 38.70
[2020-12-16 01:59:34,040][__main__][INFO] - [8640] Loss: 0.503, Running accuracy: 99.537, Time: 35.01
[2020-12-16 02:00:11,346][__main__][INFO] - [8960] Loss: 0.509, Running accuracy: 99.532, Time: 37.30
[2020-12-16 02:00:50,927][__main__][INFO] - [9280] Loss: 0.518, Running accuracy: 99.532, Time: 39.58
[2020-12-16 02:01:32,233][__main__][INFO] - [9600] Loss: 0.542, Running accuracy: 99.531, Time: 41.30
[2020-12-16 02:02:09,225][__main__][INFO] - [9920] Loss: 0.656, Running accuracy: 99.529, Time: 36.99
[2020-12-16 02:02:46,358][__main__][INFO] - [10240] Loss: 0.410, Running accuracy: 99.531, Time: 37.13
[2020-12-16 02:03:32,553][__main__][INFO] - [10560] Loss: 0.517, Running accuracy: 99.530, Time: 46.19
[2020-12-16 02:04:10,257][__main__][INFO] - [10880] Loss: 0.535, Running accuracy: 99.528, Time: 37.70
[2020-12-16 02:04:47,049][__main__][INFO] - [11200] Loss: 0.356, Running accuracy: 99.532, Time: 36.79
[2020-12-16 02:05:28,677][__main__][INFO] - [11520] Loss: 0.521, Running accuracy: 99.532, Time: 41.63
[2020-12-16 02:06:06,933][__main__][INFO] - [11840] Loss: 0.521, Running accuracy: 99.531, Time: 38.26
[2020-12-16 02:06:49,824][__main__][INFO] - [12160] Loss: 0.526, Running accuracy: 99.532, Time: 42.89
[2020-12-16 02:07:29,336][__main__][INFO] - [12480] Loss: 0.502, Running accuracy: 99.532, Time: 39.51
[2020-12-16 02:08:03,307][__main__][INFO] - [12800] Loss: 0.419, Running accuracy: 99.533, Time: 33.97
[2020-12-16 02:08:41,590][__main__][INFO] - [13120] Loss: 0.486, Running accuracy: 99.533, Time: 38.28
[2020-12-16 02:09:23,685][__main__][INFO] - [13440] Loss: 0.589, Running accuracy: 99.529, Time: 42.09
[2020-12-16 02:10:02,497][__main__][INFO] - [13760] Loss: 0.471, Running accuracy: 99.530, Time: 38.81
[2020-12-16 02:10:41,222][__main__][INFO] - [14080] Loss: 0.545, Running accuracy: 99.529, Time: 38.72
[2020-12-16 02:11:14,755][__main__][INFO] - [14400] Loss: 0.485, Running accuracy: 99.527, Time: 33.53
[2020-12-16 02:11:54,576][__main__][INFO] - [14720] Loss: 0.435, Running accuracy: 99.528, Time: 39.82
[2020-12-16 02:12:33,066][__main__][INFO] - [15040] Loss: 0.580, Running accuracy: 99.527, Time: 38.49
[2020-12-16 02:13:15,072][__main__][INFO] - [15360] Loss: 0.558, Running accuracy: 99.527, Time: 42.01
[2020-12-16 02:13:54,084][__main__][INFO] - [15680] Loss: 0.560, Running accuracy: 99.524, Time: 39.01
[2020-12-16 02:14:29,621][__main__][INFO] - [16000] Loss: 0.616, Running accuracy: 99.521, Time: 35.54
[2020-12-16 02:15:07,780][__main__][INFO] - [16320] Loss: 0.466, Running accuracy: 99.522, Time: 38.15
[2020-12-16 02:15:45,178][__main__][INFO] - [16640] Loss: 0.510, Running accuracy: 99.523, Time: 37.40
[2020-12-16 02:16:28,548][__main__][INFO] - [16960] Loss: 0.501, Running accuracy: 99.523, Time: 43.37
[2020-12-16 02:17:10,067][__main__][INFO] - [17280] Loss: 0.565, Running accuracy: 99.525, Time: 41.52
[2020-12-16 02:17:38,291][__main__][INFO] - Action accuracy: 99.525, Loss: 0.558
[2020-12-16 02:17:38,292][__main__][INFO] - Validating..
[2020-12-16 02:17:44,890][test][INFO] - Time elapsed: 5.173347
[2020-12-16 02:17:44,892][__main__][INFO] - Validation F1 score: 94.290, Exact match: 57.100, Precision: 94.420, Recall: 94.160
[2020-12-16 02:17:57,657][__main__][INFO] - Checkpoint saved to checkpoints/model_latest.pth
[2020-12-16 02:17:58,021][__main__][INFO] - Epoch #24
[2020-12-16 02:17:58,021][__main__][INFO] - Training..
[2020-12-16 02:18:35,484][__main__][INFO] - [320] Loss: 0.434, Running accuracy: 99.570, Time: 36.35
[2020-12-16 02:19:17,403][__main__][INFO] - [640] Loss: 0.459, Running accuracy: 99.545, Time: 41.92
[2020-12-16 02:19:56,462][__main__][INFO] - [960] Loss: 0.350, Running accuracy: 99.584, Time: 39.06
[2020-12-16 02:20:39,271][__main__][INFO] - [1280] Loss: 0.539, Running accuracy: 99.559, Time: 42.81
[2020-12-16 02:21:21,564][__main__][INFO] - [1600] Loss: 0.431, Running accuracy: 99.582, Time: 42.29
[2020-12-16 02:21:58,088][__main__][INFO] - [1920] Loss: 0.397, Running accuracy: 99.586, Time: 36.52
[2020-12-16 02:22:39,638][__main__][INFO] - [2240] Loss: 0.642, Running accuracy: 99.564, Time: 41.55
[2020-12-16 02:23:17,958][__main__][INFO] - [2560] Loss: 0.454, Running accuracy: 99.561, Time: 38.32
[2020-12-16 02:23:54,588][__main__][INFO] - [2880] Loss: 0.410, Running accuracy: 99.572, Time: 36.63
[2020-12-16 02:24:34,474][__main__][INFO] - [3200] Loss: 0.451, Running accuracy: 99.567, Time: 39.88
[2020-12-16 02:25:16,425][__main__][INFO] - [3520] Loss: 0.416, Running accuracy: 99.575, Time: 41.95
[2020-12-16 02:25:54,779][__main__][INFO] - [3840] Loss: 0.484, Running accuracy: 99.579, Time: 38.35
[2020-12-16 02:26:32,490][__main__][INFO] - [4160] Loss: 0.529, Running accuracy: 99.572, Time: 37.71
[2020-12-16 02:27:15,527][__main__][INFO] - [4480] Loss: 0.414, Running accuracy: 99.572, Time: 43.03
[2020-12-16 02:27:56,680][__main__][INFO] - [4800] Loss: 0.541, Running accuracy: 99.574, Time: 41.15
[2020-12-16 02:28:38,810][__main__][INFO] - [5120] Loss: 0.497, Running accuracy: 99.574, Time: 42.13
[2020-12-16 02:29:14,646][__main__][INFO] - [5440] Loss: 0.390, Running accuracy: 99.578, Time: 35.84
[2020-12-16 02:29:53,810][__main__][INFO] - [5760] Loss: 0.464, Running accuracy: 99.576, Time: 39.16
[2020-12-16 02:30:34,590][__main__][INFO] - [6080] Loss: 0.455, Running accuracy: 99.576, Time: 40.78
[2020-12-16 02:31:12,548][__main__][INFO] - [6400] Loss: 0.388, Running accuracy: 99.580, Time: 37.96
[2020-12-16 02:31:52,244][__main__][INFO] - [6720] Loss: 0.435, Running accuracy: 99.578, Time: 39.69
[2020-12-16 02:32:26,259][__main__][INFO] - [7040] Loss: 0.429, Running accuracy: 99.578, Time: 34.01
[2020-12-16 02:33:05,752][__main__][INFO] - [7360] Loss: 0.443, Running accuracy: 99.581, Time: 39.49
[2020-12-16 02:33:43,774][__main__][INFO] - [7680] Loss: 0.443, Running accuracy: 99.581, Time: 38.02
[2020-12-16 02:34:20,200][__main__][INFO] - [8000] Loss: 0.482, Running accuracy: 99.577, Time: 36.43
[2020-12-16 02:34:58,459][__main__][INFO] - [8320] Loss: 0.550, Running accuracy: 99.574, Time: 38.26
[2020-12-16 02:35:33,982][__main__][INFO] - [8640] Loss: 0.428, Running accuracy: 99.573, Time: 35.52
[2020-12-16 02:36:11,899][__main__][INFO] - [8960] Loss: 0.379, Running accuracy: 99.576, Time: 37.92
[2020-12-16 02:36:46,582][__main__][INFO] - [9280] Loss: 0.401, Running accuracy: 99.576, Time: 34.68
[2020-12-16 02:37:27,319][__main__][INFO] - [9600] Loss: 0.406, Running accuracy: 99.577, Time: 40.74
[2020-12-16 02:38:04,175][__main__][INFO] - [9920] Loss: 0.544, Running accuracy: 99.575, Time: 36.86
[2020-12-16 02:38:41,647][__main__][INFO] - [10240] Loss: 0.432, Running accuracy: 99.576, Time: 37.47
[2020-12-16 02:39:18,877][__main__][INFO] - [10560] Loss: 0.479, Running accuracy: 99.576, Time: 37.23
[2020-12-16 02:39:55,826][__main__][INFO] - [10880] Loss: 0.425, Running accuracy: 99.575, Time: 36.95
[2020-12-16 02:40:29,918][__main__][INFO] - [11200] Loss: 0.421, Running accuracy: 99.576, Time: 34.09
[2020-12-16 02:41:07,352][__main__][INFO] - [11520] Loss: 0.472, Running accuracy: 99.575, Time: 37.34
[2020-12-16 02:41:46,253][__main__][INFO] - [11840] Loss: 0.597, Running accuracy: 99.572, Time: 38.90
[2020-12-16 02:42:26,781][__main__][INFO] - [12160] Loss: 0.543, Running accuracy: 99.569, Time: 40.53
[2020-12-16 02:43:08,957][__main__][INFO] - [12480] Loss: 0.526, Running accuracy: 99.567, Time: 42.18
[2020-12-16 02:43:44,224][__main__][INFO] - [12800] Loss: 0.407, Running accuracy: 99.568, Time: 35.17
[2020-12-16 02:44:28,070][__main__][INFO] - [13120] Loss: 0.524, Running accuracy: 99.566, Time: 43.84
[2020-12-16 02:45:05,753][__main__][INFO] - [13440] Loss: 0.500, Running accuracy: 99.564, Time: 37.68
[2020-12-16 02:45:41,612][__main__][INFO] - [13760] Loss: 0.510, Running accuracy: 99.563, Time: 35.86
[2020-12-16 02:46:19,360][__main__][INFO] - [14080] Loss: 0.380, Running accuracy: 99.562, Time: 37.74
[2020-12-16 02:46:57,454][__main__][INFO] - [14400] Loss: 0.551, Running accuracy: 99.559, Time: 38.09
[2020-12-16 02:47:30,623][__main__][INFO] - [14720] Loss: 0.496, Running accuracy: 99.558, Time: 33.17
[2020-12-16 02:48:09,402][__main__][INFO] - [15040] Loss: 0.476, Running accuracy: 99.557, Time: 38.78
[2020-12-16 02:48:48,804][__main__][INFO] - [15360] Loss: 0.573, Running accuracy: 99.555, Time: 39.40
[2020-12-16 02:49:30,397][__main__][INFO] - [15680] Loss: 0.489, Running accuracy: 99.556, Time: 41.59
[2020-12-16 02:50:10,916][__main__][INFO] - [16000] Loss: 0.474, Running accuracy: 99.557, Time: 40.52
[2020-12-16 02:50:45,455][__main__][INFO] - [16320] Loss: 0.518, Running accuracy: 99.555, Time: 34.54
[2020-12-16 02:51:22,842][__main__][INFO] - [16640] Loss: 0.501, Running accuracy: 99.556, Time: 37.39
[2020-12-16 02:52:02,726][__main__][INFO] - [16960] Loss: 0.461, Running accuracy: 99.555, Time: 39.88
[2020-12-16 02:52:42,155][__main__][INFO] - [17280] Loss: 0.474, Running accuracy: 99.556, Time: 39.43
[2020-12-16 02:53:14,620][__main__][INFO] - Action accuracy: 99.558, Loss: 0.525
[2020-12-16 02:53:14,620][__main__][INFO] - Validating..
[2020-12-16 02:53:21,182][test][INFO] - Time elapsed: 5.272622
[2020-12-16 02:53:21,184][__main__][INFO] - Validation F1 score: 94.400, Exact match: 56.820, Precision: 94.640, Recall: 94.160
[2020-12-16 02:53:33,098][__main__][INFO] - Checkpoint saved to checkpoints/model_latest.pth
[2020-12-16 02:53:33,413][__main__][INFO] - Epoch #25
[2020-12-16 02:53:33,413][__main__][INFO] - Training..
[2020-12-16 02:54:15,269][__main__][INFO] - [320] Loss: 0.504, Running accuracy: 99.602, Time: 40.70
[2020-12-16 02:54:53,093][__main__][INFO] - [640] Loss: 0.351, Running accuracy: 99.646, Time: 37.82
[2020-12-16 02:55:34,707][__main__][INFO] - [960] Loss: 0.437, Running accuracy: 99.598, Time: 41.61
[2020-12-16 02:56:12,985][__main__][INFO] - [1280] Loss: 0.541, Running accuracy: 99.573, Time: 38.28
[2020-12-16 02:56:50,306][__main__][INFO] - [1600] Loss: 0.410, Running accuracy: 99.598, Time: 37.32
[2020-12-16 02:57:23,425][__main__][INFO] - [1920] Loss: 0.468, Running accuracy: 99.591, Time: 33.12
[2020-12-16 02:58:04,903][__main__][INFO] - [2240] Loss: 0.416, Running accuracy: 99.597, Time: 41.48
[2020-12-16 02:58:45,235][__main__][INFO] - [2560] Loss: 0.513, Running accuracy: 99.581, Time: 40.33
[2020-12-16 02:59:23,374][__main__][INFO] - [2880] Loss: 0.461, Running accuracy: 99.576, Time: 38.14
[2020-12-16 02:59:57,699][__main__][INFO] - [3200] Loss: 0.357, Running accuracy: 99.591, Time: 34.32
[2020-12-16 03:00:35,884][__main__][INFO] - [3520] Loss: 0.493, Running accuracy: 99.582, Time: 38.18
[2020-12-16 03:01:17,661][__main__][INFO] - [3840] Loss: 0.529, Running accuracy: 99.576, Time: 41.78
[2020-12-16 03:02:01,684][__main__][INFO] - [4160] Loss: 0.411, Running accuracy: 99.582, Time: 44.02
[2020-12-16 03:02:39,305][__main__][INFO] - [4480] Loss: 0.396, Running accuracy: 99.582, Time: 37.62
[2020-12-16 03:03:21,809][__main__][INFO] - [4800] Loss: 0.453, Running accuracy: 99.583, Time: 42.50
[2020-12-16 03:04:00,258][__main__][INFO] - [5120] Loss: 0.367, Running accuracy: 99.586, Time: 38.45
[2020-12-16 03:04:39,253][__main__][INFO] - [5440] Loss: 0.454, Running accuracy: 99.583, Time: 38.99
[2020-12-16 03:05:12,910][__main__][INFO] - [5760] Loss: 0.427, Running accuracy: 99.580, Time: 33.64
[2020-12-16 03:05:48,294][__main__][INFO] - [6080] Loss: 0.416, Running accuracy: 99.580, Time: 35.38
[2020-12-16 03:06:28,598][__main__][INFO] - [6400] Loss: 0.416, Running accuracy: 99.584, Time: 40.30
[2020-12-16 03:07:04,747][__main__][INFO] - [6720] Loss: 0.446, Running accuracy: 99.584, Time: 36.15
[2020-12-16 03:07:45,278][__main__][INFO] - [7040] Loss: 0.538, Running accuracy: 99.587, Time: 40.53
[2020-12-16 03:08:21,795][__main__][INFO] - [7360] Loss: 0.414, Running accuracy: 99.589, Time: 36.52
[2020-12-16 03:09:06,414][__main__][INFO] - [7680] Loss: 0.471, Running accuracy: 99.589, Time: 44.62
[2020-12-16 03:09:45,971][__main__][INFO] - [8000] Loss: 0.462, Running accuracy: 99.588, Time: 39.55
[2020-12-16 03:10:26,212][__main__][INFO] - [8320] Loss: 0.430, Running accuracy: 99.587, Time: 40.24
[2020-12-16 03:11:03,220][__main__][INFO] - [8640] Loss: 0.467, Running accuracy: 99.582, Time: 37.01
[2020-12-16 03:11:36,285][__main__][INFO] - [8960] Loss: 0.455, Running accuracy: 99.581, Time: 33.06
[2020-12-16 03:12:11,458][__main__][INFO] - [9280] Loss: 0.458, Running accuracy: 99.581, Time: 35.17
[2020-12-16 03:12:51,152][__main__][INFO] - [9600] Loss: 0.444, Running accuracy: 99.578, Time: 39.66
[2020-12-16 03:13:32,289][__main__][INFO] - [9920] Loss: 0.504, Running accuracy: 99.576, Time: 41.13
[2020-12-16 03:14:10,347][__main__][INFO] - [10240] Loss: 0.409, Running accuracy: 99.576, Time: 38.06
[2020-12-16 03:14:48,075][__main__][INFO] - [10560] Loss: 0.361, Running accuracy: 99.579, Time: 37.73
[2020-12-16 03:15:26,211][__main__][INFO] - [10880] Loss: 0.391, Running accuracy: 99.582, Time: 38.13
[2020-12-16 03:16:03,109][__main__][INFO] - [11200] Loss: 0.490, Running accuracy: 99.582, Time: 36.87
[2020-12-16 03:16:40,093][__main__][INFO] - [11520] Loss: 0.386, Running accuracy: 99.583, Time: 36.98
[2020-12-16 03:17:19,175][__main__][INFO] - [11840] Loss: 0.519, Running accuracy: 99.581, Time: 39.08
[2020-12-16 03:17:52,744][__main__][INFO] - [12160] Loss: 0.333, Running accuracy: 99.580, Time: 33.57
[2020-12-16 03:18:27,405][__main__][INFO] - [12480] Loss: 0.448, Running accuracy: 99.578, Time: 34.66
[2020-12-16 03:19:06,796][__main__][INFO] - [12800] Loss: 0.464, Running accuracy: 99.580, Time: 39.39
[2020-12-16 03:19:48,036][__main__][INFO] - [13120] Loss: 0.388, Running accuracy: 99.580, Time: 41.24
[2020-12-16 03:20:25,362][__main__][INFO] - [13440] Loss: 0.412, Running accuracy: 99.580, Time: 37.32
[2020-12-16 03:21:00,106][__main__][INFO] - [13760] Loss: 0.476, Running accuracy: 99.579, Time: 34.74
[2020-12-16 03:21:34,772][__main__][INFO] - [14080] Loss: 0.389, Running accuracy: 99.580, Time: 34.66
[2020-12-16 03:22:13,524][__main__][INFO] - [14400] Loss: 0.519, Running accuracy: 99.578, Time: 38.75
[2020-12-16 03:22:47,749][__main__][INFO] - [14720] Loss: 0.461, Running accuracy: 99.577, Time: 34.22
[2020-12-16 03:23:24,523][__main__][INFO] - [15040] Loss: 0.434, Running accuracy: 99.576, Time: 36.77
[2020-12-16 03:24:04,264][__main__][INFO] - [15360] Loss: 0.464, Running accuracy: 99.573, Time: 39.74
[2020-12-16 03:24:40,395][__main__][INFO] - [15680] Loss: 0.409, Running accuracy: 99.572, Time: 36.12
[2020-12-16 03:25:16,128][__main__][INFO] - [16000] Loss: 0.435, Running accuracy: 99.571, Time: 35.72
[2020-12-16 03:25:58,452][__main__][INFO] - [16320] Loss: 0.468, Running accuracy: 99.573, Time: 42.31
[2020-12-16 03:26:38,811][__main__][INFO] - [16640] Loss: 0.474, Running accuracy: 99.574, Time: 40.36
[2020-12-16 03:27:13,003][__main__][INFO] - [16960] Loss: 0.478, Running accuracy: 99.575, Time: 34.19
[2020-12-16 03:27:52,824][__main__][INFO] - [17280] Loss: 0.387, Running accuracy: 99.577, Time: 39.82
[2020-12-16 03:28:20,900][__main__][INFO] - Action accuracy: 99.578, Loss: 0.490
[2020-12-16 03:28:20,901][__main__][INFO] - Validating..
[2020-12-16 03:28:27,741][test][INFO] - Time elapsed: 5.404875
[2020-12-16 03:28:27,743][__main__][INFO] - Validation F1 score: 94.670, Exact match: 55.680, Precision: 94.940, Recall: 94.400
[2020-12-16 03:28:47,939][__main__][INFO] - Checkpoint saved to checkpoints/model_latest.pth
[2020-12-16 03:28:48,266][__main__][INFO] - Epoch #26
[2020-12-16 03:28:48,266][__main__][INFO] - Training..
[2020-12-16 03:29:30,628][__main__][INFO] - [320] Loss: 0.416, Running accuracy: 99.693, Time: 41.12
[2020-12-16 03:30:11,915][__main__][INFO] - [640] Loss: 0.410, Running accuracy: 99.656, Time: 41.28
[2020-12-16 03:30:52,194][__main__][INFO] - [960] Loss: 0.401, Running accuracy: 99.663, Time: 40.28
[2020-12-16 03:31:31,228][__main__][INFO] - [1280] Loss: 0.395, Running accuracy: 99.637, Time: 39.03
[2020-12-16 03:32:08,858][__main__][INFO] - [1600] Loss: 0.421, Running accuracy: 99.637, Time: 37.60
[2020-12-16 03:32:48,957][__main__][INFO] - [1920] Loss: 0.486, Running accuracy: 99.627, Time: 40.10
[2020-12-16 03:33:31,682][__main__][INFO] - [2240] Loss: 0.509, Running accuracy: 99.617, Time: 42.72
[2020-12-16 03:34:07,971][__main__][INFO] - [2560] Loss: 0.331, Running accuracy: 99.624, Time: 36.29
[2020-12-16 03:34:43,347][__main__][INFO] - [2880] Loss: 0.451, Running accuracy: 99.622, Time: 35.37
[2020-12-16 03:35:21,225][__main__][INFO] - [3200] Loss: 0.401, Running accuracy: 99.623, Time: 37.86
[2020-12-16 03:35:56,444][__main__][INFO] - [3520] Loss: 0.504, Running accuracy: 99.618, Time: 35.22
[2020-12-16 03:36:33,815][__main__][INFO] - [3840] Loss: 0.294, Running accuracy: 99.621, Time: 37.37
[2020-12-16 03:37:08,204][__main__][INFO] - [4160] Loss: 0.410, Running accuracy: 99.618, Time: 34.39
[2020-12-16 03:37:45,328][__main__][INFO] - [4480] Loss: 0.367, Running accuracy: 99.616, Time: 37.12
[2020-12-16 03:38:25,660][__main__][INFO] - [4800] Loss: 0.402, Running accuracy: 99.617, Time: 40.33
[2020-12-16 03:38:57,051][__main__][INFO] - [5120] Loss: 0.284, Running accuracy: 99.626, Time: 31.39
[2020-12-16 03:39:37,781][__main__][INFO] - [5440] Loss: 0.347, Running accuracy: 99.630, Time: 40.73
[2020-12-16 03:40:23,612][__main__][INFO] - [5760] Loss: 0.366, Running accuracy: 99.635, Time: 45.83
[2020-12-16 03:41:00,122][__main__][INFO] - [6080] Loss: 0.296, Running accuracy: 99.641, Time: 36.51
[2020-12-16 03:41:38,748][__main__][INFO] - [6400] Loss: 0.375, Running accuracy: 99.640, Time: 38.62
[2020-12-16 03:42:21,820][__main__][INFO] - [6720] Loss: 0.376, Running accuracy: 99.640, Time: 43.07
[2020-12-16 03:42:58,623][__main__][INFO] - [7040] Loss: 0.379, Running accuracy: 99.639, Time: 36.80
[2020-12-16 03:43:37,106][__main__][INFO] - [7360] Loss: 0.421, Running accuracy: 99.638, Time: 38.44
[2020-12-16 03:44:13,705][__main__][INFO] - [7680] Loss: 0.425, Running accuracy: 99.636, Time: 36.60
[2020-12-16 03:44:52,408][__main__][INFO] - [8000] Loss: 0.408, Running accuracy: 99.632, Time: 38.69
[2020-12-16 03:45:24,631][__main__][INFO] - [8320] Loss: 0.400, Running accuracy: 99.632, Time: 32.22
[2020-12-16 03:46:01,569][__main__][INFO] - [8640] Loss: 0.416, Running accuracy: 99.633, Time: 36.92
[2020-12-16 03:46:40,924][__main__][INFO] - [8960] Loss: 0.418, Running accuracy: 99.633, Time: 39.35
[2020-12-16 03:47:22,811][__main__][INFO] - [9280] Loss: 0.412, Running accuracy: 99.634, Time: 41.89
[2020-12-16 03:47:56,028][__main__][INFO] - [9600] Loss: 0.504, Running accuracy: 99.630, Time: 33.21
[2020-12-16 03:48:36,710][__main__][INFO] - [9920] Loss: 0.529, Running accuracy: 99.628, Time: 40.66
[2020-12-16 03:49:15,472][__main__][INFO] - [10240] Loss: 0.392, Running accuracy: 99.626, Time: 38.76
[2020-12-16 03:49:55,471][__main__][INFO] - [10560] Loss: 0.447, Running accuracy: 99.624, Time: 39.99
[2020-12-16 03:50:32,220][__main__][INFO] - [10880] Loss: 0.415, Running accuracy: 99.621, Time: 36.56
[2020-12-16 03:51:12,123][__main__][INFO] - [11200] Loss: 0.383, Running accuracy: 99.622, Time: 39.90
[2020-12-16 03:51:51,467][__main__][INFO] - [11520] Loss: 0.400, Running accuracy: 99.622, Time: 39.24
[2020-12-16 03:52:26,642][__main__][INFO] - [11840] Loss: 0.369, Running accuracy: 99.622, Time: 35.17
[2020-12-16 03:53:05,882][__main__][INFO] - [12160] Loss: 0.362, Running accuracy: 99.621, Time: 39.24
[2020-12-16 03:53:46,691][__main__][INFO] - [12480] Loss: 0.497, Running accuracy: 99.619, Time: 40.81
[2020-12-16 03:54:25,374][__main__][INFO] - [12800] Loss: 0.410, Running accuracy: 99.619, Time: 38.68
[2020-12-16 03:55:11,370][__main__][INFO] - [13120] Loss: 0.311, Running accuracy: 99.622, Time: 45.99
[2020-12-16 03:55:50,264][__main__][INFO] - [13440] Loss: 0.410, Running accuracy: 99.620, Time: 38.89
[2020-12-16 03:56:26,980][__main__][INFO] - [13760] Loss: 0.458, Running accuracy: 99.619, Time: 36.71
[2020-12-16 03:56:59,816][__main__][INFO] - [14080] Loss: 0.358, Running accuracy: 99.617, Time: 32.84
[2020-12-16 03:57:38,943][__main__][INFO] - [14400] Loss: 0.423, Running accuracy: 99.616, Time: 39.13
[2020-12-16 03:58:13,472][__main__][INFO] - [14720] Loss: 0.417, Running accuracy: 99.615, Time: 34.53
[2020-12-16 03:58:51,781][__main__][INFO] - [15040] Loss: 0.491, Running accuracy: 99.612, Time: 38.31
[2020-12-16 03:59:35,980][__main__][INFO] - [15360] Loss: 0.467, Running accuracy: 99.611, Time: 44.20
[2020-12-16 04:00:17,177][__main__][INFO] - [15680] Loss: 0.376, Running accuracy: 99.611, Time: 41.20
[2020-12-16 04:00:55,031][__main__][INFO] - [16000] Loss: 0.413, Running accuracy: 99.612, Time: 37.85
[2020-12-16 04:01:32,133][__main__][INFO] - [16320] Loss: 0.364, Running accuracy: 99.613, Time: 37.10
[2020-12-16 04:02:09,845][__main__][INFO] - [16640] Loss: 0.422, Running accuracy: 99.612, Time: 37.71
[2020-12-16 04:02:43,779][__main__][INFO] - [16960] Loss: 0.454, Running accuracy: 99.612, Time: 33.93
[2020-12-16 04:03:22,315][__main__][INFO] - [17280] Loss: 0.454, Running accuracy: 99.612, Time: 38.53
[2020-12-16 04:03:55,514][__main__][INFO] - Action accuracy: 99.612, Loss: 0.456
[2020-12-16 04:03:55,515][__main__][INFO] - Validating..
[2020-12-16 04:04:02,161][test][INFO] - Time elapsed: 5.244759
[2020-12-16 04:04:02,163][__main__][INFO] - Validation F1 score: 94.150, Exact match: 53.690, Precision: 94.460, Recall: 93.840
Epoch    27: reducing learning rate of group 0 to 1.0000e-05.
[2020-12-16 04:04:14,525][__main__][INFO] - Checkpoint saved to checkpoints/model_latest.pth
[2020-12-16 04:04:14,867][__main__][INFO] - Epoch #27
[2020-12-16 04:04:14,868][__main__][INFO] - Training..
[2020-12-16 04:04:52,261][__main__][INFO] - [320] Loss: 0.280, Running accuracy: 99.769, Time: 36.17
[2020-12-16 04:05:28,871][__main__][INFO] - [640] Loss: 0.363, Running accuracy: 99.739, Time: 36.61
[2020-12-16 04:06:07,072][__main__][INFO] - [960] Loss: 0.264, Running accuracy: 99.744, Time: 38.20
[2020-12-16 04:06:43,960][__main__][INFO] - [1280] Loss: 0.293, Running accuracy: 99.749, Time: 36.89
[2020-12-16 04:07:19,630][__main__][INFO] - [1600] Loss: 0.280, Running accuracy: 99.741, Time: 35.67
[2020-12-16 04:07:55,650][__main__][INFO] - [1920] Loss: 0.286, Running accuracy: 99.737, Time: 36.02
[2020-12-16 04:08:31,870][__main__][INFO] - [2240] Loss: 0.343, Running accuracy: 99.733, Time: 36.22
[2020-12-16 04:09:08,883][__main__][INFO] - [2560] Loss: 0.343, Running accuracy: 99.726, Time: 37.01
[2020-12-16 04:09:49,086][__main__][INFO] - [2880] Loss: 0.322, Running accuracy: 99.717, Time: 40.20
[2020-12-16 04:10:29,858][__main__][INFO] - [3200] Loss: 0.321, Running accuracy: 99.715, Time: 40.77
[2020-12-16 04:11:04,488][__main__][INFO] - [3520] Loss: 0.307, Running accuracy: 99.714, Time: 34.63
[2020-12-16 04:11:48,214][__main__][INFO] - [3840] Loss: 0.298, Running accuracy: 99.713, Time: 43.73
[2020-12-16 04:12:24,467][__main__][INFO] - [4160] Loss: 0.316, Running accuracy: 99.713, Time: 36.25
[2020-12-16 04:13:04,533][__main__][INFO] - [4480] Loss: 0.280, Running accuracy: 99.718, Time: 40.06
[2020-12-16 04:13:36,710][__main__][INFO] - [4800] Loss: 0.318, Running accuracy: 99.717, Time: 32.18
[2020-12-16 04:14:14,337][__main__][INFO] - [5120] Loss: 0.317, Running accuracy: 99.720, Time: 37.63
[2020-12-16 04:14:51,101][__main__][INFO] - [5440] Loss: 0.332, Running accuracy: 99.721, Time: 36.76
[2020-12-16 04:15:29,859][__main__][INFO] - [5760] Loss: 0.276, Running accuracy: 99.723, Time: 38.76
[2020-12-16 04:16:04,031][__main__][INFO] - [6080] Loss: 0.304, Running accuracy: 99.723, Time: 34.17
[2020-12-16 04:16:40,798][__main__][INFO] - [6400] Loss: 0.320, Running accuracy: 99.721, Time: 36.77
[2020-12-16 04:17:16,382][__main__][INFO] - [6720] Loss: 0.256, Running accuracy: 99.724, Time: 35.58
[2020-12-16 04:17:57,897][__main__][INFO] - [7040] Loss: 0.298, Running accuracy: 99.726, Time: 41.51
[2020-12-16 04:18:38,208][__main__][INFO] - [7360] Loss: 0.249, Running accuracy: 99.729, Time: 40.31
[2020-12-16 04:19:12,790][__main__][INFO] - [7680] Loss: 0.359, Running accuracy: 99.726, Time: 34.58
[2020-12-16 04:19:51,318][__main__][INFO] - [8000] Loss: 0.277, Running accuracy: 99.728, Time: 38.53
[2020-12-16 04:20:24,272][__main__][INFO] - [8320] Loss: 0.277, Running accuracy: 99.730, Time: 32.95
[2020-12-16 04:20:59,600][__main__][INFO] - [8640] Loss: 0.268, Running accuracy: 99.731, Time: 35.33
[2020-12-16 04:21:35,747][__main__][INFO] - [8960] Loss: 0.255, Running accuracy: 99.734, Time: 36.14
[2020-12-16 04:22:14,366][__main__][INFO] - [9280] Loss: 0.275, Running accuracy: 99.736, Time: 38.62
[2020-12-16 04:22:52,668][__main__][INFO] - [9600] Loss: 0.244, Running accuracy: 99.738, Time: 38.30
[2020-12-16 04:23:31,622][__main__][INFO] - [9920] Loss: 0.262, Running accuracy: 99.742, Time: 38.95
[2020-12-16 04:24:07,590][__main__][INFO] - [10240] Loss: 0.304, Running accuracy: 99.742, Time: 35.97
[2020-12-16 04:24:50,041][__main__][INFO] - [10560] Loss: 0.335, Running accuracy: 99.740, Time: 42.45
[2020-12-16 04:25:30,531][__main__][INFO] - [10880] Loss: 0.308, Running accuracy: 99.740, Time: 40.49
[2020-12-16 04:26:02,660][__main__][INFO] - [11200] Loss: 0.269, Running accuracy: 99.742, Time: 32.13
[2020-12-16 04:26:46,102][__main__][INFO] - [11520] Loss: 0.311, Running accuracy: 99.742, Time: 43.34
[2020-12-16 04:27:19,453][__main__][INFO] - [11840] Loss: 0.290, Running accuracy: 99.742, Time: 33.35
[2020-12-16 04:27:58,467][__main__][INFO] - [12160] Loss: 0.323, Running accuracy: 99.743, Time: 39.01
[2020-12-16 04:28:35,495][__main__][INFO] - [12480] Loss: 0.292, Running accuracy: 99.743, Time: 37.03
[2020-12-16 04:29:15,498][__main__][INFO] - [12800] Loss: 0.219, Running accuracy: 99.745, Time: 40.00
[2020-12-16 04:29:53,028][__main__][INFO] - [13120] Loss: 0.351, Running accuracy: 99.744, Time: 37.53
[2020-12-16 04:30:32,751][__main__][INFO] - [13440] Loss: 0.448, Running accuracy: 99.741, Time: 39.72
[2020-12-16 04:31:13,344][__main__][INFO] - [13760] Loss: 0.274, Running accuracy: 99.742, Time: 40.59
[2020-12-16 04:31:53,262][__main__][INFO] - [14080] Loss: 0.259, Running accuracy: 99.744, Time: 39.92
[2020-12-16 04:32:30,323][__main__][INFO] - [14400] Loss: 0.341, Running accuracy: 99.742, Time: 37.06
[2020-12-16 04:33:11,587][__main__][INFO] - [14720] Loss: 0.271, Running accuracy: 99.743, Time: 41.26
[2020-12-16 04:33:50,059][__main__][INFO] - [15040] Loss: 0.310, Running accuracy: 99.742, Time: 38.47
[2020-12-16 04:34:31,221][__main__][INFO] - [15360] Loss: 0.264, Running accuracy: 99.743, Time: 41.16
[2020-12-16 04:35:08,357][__main__][INFO] - [15680] Loss: 0.278, Running accuracy: 99.744, Time: 37.13
[2020-12-16 04:35:45,813][__main__][INFO] - [16000] Loss: 0.321, Running accuracy: 99.743, Time: 37.46
[2020-12-16 04:36:21,524][__main__][INFO] - [16320] Loss: 0.290, Running accuracy: 99.742, Time: 35.71
[2020-12-16 04:36:58,613][__main__][INFO] - [16640] Loss: 0.270, Running accuracy: 99.741, Time: 37.09
[2020-12-16 04:37:36,720][__main__][INFO] - [16960] Loss: 0.359, Running accuracy: 99.741, Time: 38.11
[2020-12-16 04:38:16,599][__main__][INFO] - [17280] Loss: 0.268, Running accuracy: 99.742, Time: 39.87
[2020-12-16 04:38:53,049][__main__][INFO] - Action accuracy: 99.744, Loss: 0.329
[2020-12-16 04:38:53,049][__main__][INFO] - Validating..
[2020-12-16 04:38:59,890][test][INFO] - Time elapsed: 5.369356
[2020-12-16 04:38:59,891][__main__][INFO] - Validation F1 score: 94.060, Exact match: 54.260, Precision: 94.410, Recall: 93.720
[2020-12-16 04:39:12,634][__main__][INFO] - Checkpoint saved to checkpoints/model_latest.pth
[2020-12-16 04:39:12,947][__main__][INFO] - Epoch #28
[2020-12-16 04:39:12,947][__main__][INFO] - Training..
[2020-12-16 04:39:56,509][__main__][INFO] - [320] Loss: 0.218, Running accuracy: 99.807, Time: 39.89
[2020-12-16 04:40:39,035][__main__][INFO] - [640] Loss: 0.226, Running accuracy: 99.842, Time: 42.52
[2020-12-16 04:41:17,612][__main__][INFO] - [960] Loss: 0.245, Running accuracy: 99.814, Time: 38.58
[2020-12-16 04:41:56,109][__main__][INFO] - [1280] Loss: 0.243, Running accuracy: 99.811, Time: 38.50
[2020-12-16 04:42:30,861][__main__][INFO] - [1600] Loss: 0.245, Running accuracy: 99.794, Time: 34.75
[2020-12-16 04:43:10,368][__main__][INFO] - [1920] Loss: 0.250, Running accuracy: 99.794, Time: 39.50
[2020-12-16 04:43:44,081][__main__][INFO] - [2240] Loss: 0.170, Running accuracy: 99.802, Time: 33.71
[2020-12-16 04:44:20,219][__main__][INFO] - [2560] Loss: 0.257, Running accuracy: 99.795, Time: 36.14
[2020-12-16 04:44:53,857][__main__][INFO] - [2880] Loss: 0.200, Running accuracy: 99.802, Time: 33.64
[2020-12-16 04:45:32,760][__main__][INFO] - [3200] Loss: 0.277, Running accuracy: 99.802, Time: 38.90
[2020-12-16 04:46:06,028][__main__][INFO] - [3520] Loss: 0.247, Running accuracy: 99.798, Time: 33.27
[2020-12-16 04:46:45,275][__main__][INFO] - [3840] Loss: 0.224, Running accuracy: 99.799, Time: 39.24
[2020-12-16 04:47:26,314][__main__][INFO] - [4160] Loss: 0.196, Running accuracy: 99.802, Time: 41.04
[2020-12-16 04:48:07,929][__main__][INFO] - [4480] Loss: 0.263, Running accuracy: 99.797, Time: 41.61
[2020-12-16 04:48:47,510][__main__][INFO] - [4800] Loss: 0.263, Running accuracy: 99.797, Time: 39.58
[2020-12-16 04:49:22,780][__main__][INFO] - [5120] Loss: 0.212, Running accuracy: 99.800, Time: 35.27
[2020-12-16 04:50:00,393][__main__][INFO] - [5440] Loss: 0.188, Running accuracy: 99.802, Time: 37.61
[2020-12-16 04:50:35,618][__main__][INFO] - [5760] Loss: 0.270, Running accuracy: 99.802, Time: 35.22
[2020-12-16 04:51:11,148][__main__][INFO] - [6080] Loss: 0.300, Running accuracy: 99.794, Time: 35.53
[2020-12-16 04:51:48,741][__main__][INFO] - [6400] Loss: 0.231, Running accuracy: 99.795, Time: 37.59
[2020-12-16 04:52:23,429][__main__][INFO] - [6720] Loss: 0.219, Running accuracy: 99.797, Time: 34.69
[2020-12-16 04:53:03,548][__main__][INFO] - [7040] Loss: 0.221, Running accuracy: 99.799, Time: 40.12
[2020-12-16 04:53:41,588][__main__][INFO] - [7360] Loss: 0.239, Running accuracy: 99.801, Time: 38.04
[2020-12-16 04:54:16,862][__main__][INFO] - [7680] Loss: 0.221, Running accuracy: 99.803, Time: 35.27
[2020-12-16 04:54:56,387][__main__][INFO] - [8000] Loss: 0.233, Running accuracy: 99.803, Time: 39.52
[2020-12-16 04:55:34,818][__main__][INFO] - [8320] Loss: 0.293, Running accuracy: 99.802, Time: 38.43
[2020-12-16 04:56:12,351][__main__][INFO] - [8640] Loss: 0.256, Running accuracy: 99.802, Time: 37.53
[2020-12-16 04:56:51,221][__main__][INFO] - [8960] Loss: 0.222, Running accuracy: 99.802, Time: 38.87
[2020-12-16 04:57:28,732][__main__][INFO] - [9280] Loss: 0.316, Running accuracy: 99.798, Time: 37.51
[2020-12-16 04:58:02,855][__main__][INFO] - [9600] Loss: 0.231, Running accuracy: 99.799, Time: 34.12
[2020-12-16 04:58:39,567][__main__][INFO] - [9920] Loss: 0.293, Running accuracy: 99.796, Time: 36.71
[2020-12-16 04:59:17,734][__main__][INFO] - [10240] Loss: 0.292, Running accuracy: 99.795, Time: 38.17
[2020-12-16 04:59:53,352][__main__][INFO] - [10560] Loss: 0.219, Running accuracy: 99.796, Time: 35.53
[2020-12-16 05:00:36,169][__main__][INFO] - [10880] Loss: 0.279, Running accuracy: 99.794, Time: 42.82
[2020-12-16 05:01:14,793][__main__][INFO] - [11200] Loss: 0.226, Running accuracy: 99.796, Time: 38.62
[2020-12-16 05:01:52,566][__main__][INFO] - [11520] Loss: 0.271, Running accuracy: 99.793, Time: 37.77
[2020-12-16 05:02:24,800][__main__][INFO] - [11840] Loss: 0.186, Running accuracy: 99.794, Time: 32.23
[2020-12-16 05:03:00,199][__main__][INFO] - [12160] Loss: 0.268, Running accuracy: 99.794, Time: 35.40
[2020-12-16 05:03:41,183][__main__][INFO] - [12480] Loss: 0.271, Running accuracy: 99.795, Time: 40.98
[2020-12-16 05:04:16,860][__main__][INFO] - [12800] Loss: 0.242, Running accuracy: 99.795, Time: 35.67
[2020-12-16 05:04:56,394][__main__][INFO] - [13120] Loss: 0.253, Running accuracy: 99.795, Time: 39.53
[2020-12-16 05:05:37,380][__main__][INFO] - [13440] Loss: 0.252, Running accuracy: 99.795, Time: 40.99
[2020-12-16 05:06:15,269][__main__][INFO] - [13760] Loss: 0.264, Running accuracy: 99.793, Time: 37.89
[2020-12-16 05:06:55,967][__main__][INFO] - [14080] Loss: 0.210, Running accuracy: 99.796, Time: 40.70
[2020-12-16 05:07:34,207][__main__][INFO] - [14400] Loss: 0.177, Running accuracy: 99.799, Time: 38.24
[2020-12-16 05:08:17,889][__main__][INFO] - [14720] Loss: 0.220, Running accuracy: 99.798, Time: 43.68
[2020-12-16 05:08:52,966][__main__][INFO] - [15040] Loss: 0.237, Running accuracy: 99.800, Time: 35.08
[2020-12-16 05:09:28,180][__main__][INFO] - [15360] Loss: 0.261, Running accuracy: 99.800, Time: 35.21
[2020-12-16 05:10:07,311][__main__][INFO] - [15680] Loss: 0.242, Running accuracy: 99.801, Time: 39.13
[2020-12-16 05:10:50,288][__main__][INFO] - [16000] Loss: 0.205, Running accuracy: 99.801, Time: 42.97
[2020-12-16 05:11:35,706][__main__][INFO] - [16320] Loss: 0.303, Running accuracy: 99.801, Time: 45.42
[2020-12-16 05:12:16,515][__main__][INFO] - [16640] Loss: 0.277, Running accuracy: 99.801, Time: 40.81
[2020-12-16 05:12:56,307][__main__][INFO] - [16960] Loss: 0.283, Running accuracy: 99.801, Time: 39.79
[2020-12-16 05:13:35,146][__main__][INFO] - [17280] Loss: 0.243, Running accuracy: 99.801, Time: 38.84
[2020-12-16 05:14:07,740][__main__][INFO] - Action accuracy: 99.802, Loss: 0.271
[2020-12-16 05:14:07,741][__main__][INFO] - Validating..
[2020-12-16 05:14:14,444][test][INFO] - Time elapsed: 5.233523
[2020-12-16 05:14:14,446][__main__][INFO] - Validation F1 score: 94.240, Exact match: 55.970, Precision: 94.440, Recall: 94.040
[2020-12-16 05:14:26,555][__main__][INFO] - Checkpoint saved to checkpoints/model_latest.pth
[2020-12-16 05:14:26,890][__main__][INFO] - Epoch #29
[2020-12-16 05:14:26,891][__main__][INFO] - Training..
[2020-12-16 05:15:09,951][__main__][INFO] - [320] Loss: 0.226, Running accuracy: 99.817, Time: 41.73
[2020-12-16 05:15:46,348][__main__][INFO] - [640] Loss: 0.174, Running accuracy: 99.856, Time: 36.40
[2020-12-16 05:16:19,854][__main__][INFO] - [960] Loss: 0.173, Running accuracy: 99.860, Time: 33.50
[2020-12-16 05:16:59,751][__main__][INFO] - [1280] Loss: 0.216, Running accuracy: 99.854, Time: 39.90
[2020-12-16 05:17:37,756][__main__][INFO] - [1600] Loss: 0.269, Running accuracy: 99.839, Time: 38.00
[2020-12-16 05:18:15,264][__main__][INFO] - [1920] Loss: 0.197, Running accuracy: 99.833, Time: 37.50
[2020-12-16 05:18:54,321][__main__][INFO] - [2240] Loss: 0.216, Running accuracy: 99.838, Time: 39.06
[2020-12-16 05:19:28,438][__main__][INFO] - [2560] Loss: 0.219, Running accuracy: 99.831, Time: 34.11
[2020-12-16 05:20:03,314][__main__][INFO] - [2880] Loss: 0.244, Running accuracy: 99.826, Time: 34.87
[2020-12-16 05:20:47,220][__main__][INFO] - [3200] Loss: 0.211, Running accuracy: 99.827, Time: 43.90
[2020-12-16 05:21:27,799][__main__][INFO] - [3520] Loss: 0.182, Running accuracy: 99.832, Time: 40.58
[2020-12-16 05:22:10,089][__main__][INFO] - [3840] Loss: 0.224, Running accuracy: 99.827, Time: 42.29
[2020-12-16 05:22:47,276][__main__][INFO] - [4160] Loss: 0.247, Running accuracy: 99.823, Time: 37.19
[2020-12-16 05:23:26,467][__main__][INFO] - [4480] Loss: 0.259, Running accuracy: 99.820, Time: 39.19
[2020-12-16 05:24:00,787][__main__][INFO] - [4800] Loss: 0.143, Running accuracy: 99.827, Time: 34.32
[2020-12-16 05:24:43,346][__main__][INFO] - [5120] Loss: 0.233, Running accuracy: 99.825, Time: 42.56
[2020-12-16 05:25:20,535][__main__][INFO] - [5440] Loss: 0.247, Running accuracy: 99.822, Time: 37.19
[2020-12-16 05:26:03,811][__main__][INFO] - [5760] Loss: 0.242, Running accuracy: 99.820, Time: 43.28
[2020-12-16 05:26:37,865][__main__][INFO] - [6080] Loss: 0.164, Running accuracy: 99.821, Time: 34.05
[2020-12-16 05:27:09,152][__main__][INFO] - [6400] Loss: 0.213, Running accuracy: 99.817, Time: 31.29
[2020-12-16 05:27:53,000][__main__][INFO] - [6720] Loss: 0.226, Running accuracy: 99.821, Time: 43.85
[2020-12-16 05:28:36,359][__main__][INFO] - [7040] Loss: 0.273, Running accuracy: 99.819, Time: 43.35
[2020-12-16 05:29:17,404][__main__][INFO] - [7360] Loss: 0.229, Running accuracy: 99.819, Time: 41.04
[2020-12-16 05:29:58,968][__main__][INFO] - [7680] Loss: 0.286, Running accuracy: 99.817, Time: 41.56
[2020-12-16 05:30:39,234][__main__][INFO] - [8000] Loss: 0.247, Running accuracy: 99.816, Time: 40.27
[2020-12-16 05:31:18,361][__main__][INFO] - [8320] Loss: 0.253, Running accuracy: 99.815, Time: 39.13
[2020-12-16 05:31:58,948][__main__][INFO] - [8640] Loss: 0.209, Running accuracy: 99.814, Time: 40.59
[2020-12-16 05:32:32,685][__main__][INFO] - [8960] Loss: 0.204, Running accuracy: 99.814, Time: 33.74
[2020-12-16 05:33:09,333][__main__][INFO] - [9280] Loss: 0.193, Running accuracy: 99.815, Time: 36.65
[2020-12-16 05:33:49,479][__main__][INFO] - [9600] Loss: 0.219, Running accuracy: 99.817, Time: 40.14
[2020-12-16 05:34:28,962][__main__][INFO] - [9920] Loss: 0.245, Running accuracy: 99.815, Time: 39.48
[2020-12-16 05:35:08,353][__main__][INFO] - [10240] Loss: 0.272, Running accuracy: 99.817, Time: 39.39
[2020-12-16 05:35:44,778][__main__][INFO] - [10560] Loss: 0.193, Running accuracy: 99.816, Time: 36.42
[2020-12-16 05:36:25,639][__main__][INFO] - [10880] Loss: 0.277, Running accuracy: 99.816, Time: 40.73
[2020-12-16 05:37:07,286][__main__][INFO] - [11200] Loss: 0.184, Running accuracy: 99.817, Time: 41.65
[2020-12-16 05:37:43,661][__main__][INFO] - [11520] Loss: 0.266, Running accuracy: 99.815, Time: 36.37
[2020-12-16 05:38:22,329][__main__][INFO] - [11840] Loss: 0.218, Running accuracy: 99.815, Time: 38.67
[2020-12-16 05:38:58,132][__main__][INFO] - [12160] Loss: 0.196, Running accuracy: 99.815, Time: 35.80
[2020-12-16 05:39:35,697][__main__][INFO] - [12480] Loss: 0.232, Running accuracy: 99.815, Time: 37.56
[2020-12-16 05:40:07,916][__main__][INFO] - [12800] Loss: 0.175, Running accuracy: 99.818, Time: 32.22
[2020-12-16 05:40:45,668][__main__][INFO] - [13120] Loss: 0.225, Running accuracy: 99.819, Time: 37.75
[2020-12-16 05:41:25,228][__main__][INFO] - [13440] Loss: 0.224, Running accuracy: 99.819, Time: 39.56
[2020-12-16 05:42:01,097][__main__][INFO] - [13760] Loss: 0.206, Running accuracy: 99.818, Time: 35.87
[2020-12-16 05:42:38,979][__main__][INFO] - [14080] Loss: 0.211, Running accuracy: 99.818, Time: 37.88
[2020-12-16 05:43:10,085][__main__][INFO] - [14400] Loss: 0.235, Running accuracy: 99.818, Time: 31.10
[2020-12-16 05:43:46,776][__main__][INFO] - [14720] Loss: 0.230, Running accuracy: 99.816, Time: 36.69
[2020-12-16 05:44:21,625][__main__][INFO] - [15040] Loss: 0.240, Running accuracy: 99.815, Time: 34.85
[2020-12-16 05:44:56,534][__main__][INFO] - [15360] Loss: 0.243, Running accuracy: 99.816, Time: 34.91
[2020-12-16 05:45:35,196][__main__][INFO] - [15680] Loss: 0.262, Running accuracy: 99.813, Time: 38.66
[2020-12-16 05:46:15,999][__main__][INFO] - [16000] Loss: 0.191, Running accuracy: 99.814, Time: 40.80
[2020-12-16 05:46:54,803][__main__][INFO] - [16320] Loss: 0.218, Running accuracy: 99.814, Time: 38.80
[2020-12-16 05:47:34,209][__main__][INFO] - [16640] Loss: 0.208, Running accuracy: 99.814, Time: 39.40
[2020-12-16 05:48:12,302][__main__][INFO] - [16960] Loss: 0.208, Running accuracy: 99.814, Time: 38.09
[2020-12-16 05:48:48,143][__main__][INFO] - [17280] Loss: 0.219, Running accuracy: 99.814, Time: 35.84
[2020-12-16 05:49:17,191][__main__][INFO] - Action accuracy: 99.814, Loss: 0.245
[2020-12-16 05:49:17,192][__main__][INFO] - Validating..
[2020-12-16 05:49:23,847][test][INFO] - Time elapsed: 5.380993
[2020-12-16 05:49:23,848][__main__][INFO] - Validation F1 score: 94.320, Exact match: 56.250, Precision: 94.620, Recall: 94.020
[2020-12-16 05:49:35,445][__main__][INFO] - Checkpoint saved to checkpoints/model_latest.pth
[2020-12-16 05:49:35,758][__main__][INFO] - Epoch #30
[2020-12-16 05:49:35,758][__main__][INFO] - Training..
[2020-12-16 05:50:16,305][__main__][INFO] - [320] Loss: 0.207, Running accuracy: 99.851, Time: 39.32
[2020-12-16 05:50:52,261][__main__][INFO] - [640] Loss: 0.184, Running accuracy: 99.851, Time: 35.95
[2020-12-16 05:51:26,748][__main__][INFO] - [960] Loss: 0.192, Running accuracy: 99.839, Time: 34.49
[2020-12-16 05:52:03,586][__main__][INFO] - [1280] Loss: 0.276, Running accuracy: 99.836, Time: 36.84
[2020-12-16 05:52:42,489][__main__][INFO] - [1600] Loss: 0.162, Running accuracy: 99.852, Time: 38.90
[2020-12-16 05:53:18,671][__main__][INFO] - [1920] Loss: 0.169, Running accuracy: 99.850, Time: 36.18
[2020-12-16 05:53:50,063][__main__][INFO] - [2240] Loss: 0.168, Running accuracy: 99.849, Time: 31.39
[2020-12-16 05:54:26,454][__main__][INFO] - [2560] Loss: 0.171, Running accuracy: 99.851, Time: 36.39
[2020-12-16 05:55:03,878][__main__][INFO] - [2880] Loss: 0.220, Running accuracy: 99.847, Time: 37.42
[2020-12-16 05:55:40,537][__main__][INFO] - [3200] Loss: 0.198, Running accuracy: 99.847, Time: 36.66
[2020-12-16 05:56:20,003][__main__][INFO] - [3520] Loss: 0.172, Running accuracy: 99.853, Time: 39.46
[2020-12-16 05:56:55,682][__main__][INFO] - [3840] Loss: 0.152, Running accuracy: 99.857, Time: 35.68
[2020-12-16 05:57:36,518][__main__][INFO] - [4160] Loss: 0.217, Running accuracy: 99.855, Time: 40.83
[2020-12-16 05:58:14,423][__main__][INFO] - [4480] Loss: 0.291, Running accuracy: 99.853, Time: 37.90
[2020-12-16 05:58:56,339][__main__][INFO] - [4800] Loss: 0.212, Running accuracy: 99.851, Time: 41.92
[2020-12-16 05:59:39,051][__main__][INFO] - [5120] Loss: 0.212, Running accuracy: 99.849, Time: 42.71
[2020-12-16 06:00:17,959][__main__][INFO] - [5440] Loss: 0.178, Running accuracy: 99.850, Time: 38.91
[2020-12-16 06:01:01,596][__main__][INFO] - [5760] Loss: 0.149, Running accuracy: 99.853, Time: 43.63
[2020-12-16 06:01:36,496][__main__][INFO] - [6080] Loss: 0.196, Running accuracy: 99.851, Time: 34.90
[2020-12-16 06:02:11,699][__main__][INFO] - [6400] Loss: 0.148, Running accuracy: 99.855, Time: 35.20
[2020-12-16 06:02:53,367][__main__][INFO] - [6720] Loss: 0.153, Running accuracy: 99.856, Time: 41.67
[2020-12-16 06:03:25,053][__main__][INFO] - [7040] Loss: 0.166, Running accuracy: 99.856, Time: 31.68
[2020-12-16 06:04:06,126][__main__][INFO] - [7360] Loss: 0.197, Running accuracy: 99.857, Time: 41.07
[2020-12-16 06:04:47,525][__main__][INFO] - [7680] Loss: 0.196, Running accuracy: 99.857, Time: 41.40
[2020-12-16 06:05:27,152][__main__][INFO] - [8000] Loss: 0.214, Running accuracy: 99.857, Time: 39.63
[2020-12-16 06:06:00,184][__main__][INFO] - [8320] Loss: 0.264, Running accuracy: 99.855, Time: 33.03
[2020-12-16 06:06:36,987][__main__][INFO] - [8640] Loss: 0.210, Running accuracy: 99.854, Time: 36.80
[2020-12-16 06:07:16,063][__main__][INFO] - [8960] Loss: 0.243, Running accuracy: 99.853, Time: 39.08
[2020-12-16 06:07:54,621][__main__][INFO] - [9280] Loss: 0.221, Running accuracy: 99.851, Time: 38.56
[2020-12-16 06:08:35,442][__main__][INFO] - [9600] Loss: 0.221, Running accuracy: 99.850, Time: 40.82
[2020-12-16 06:09:17,981][__main__][INFO] - [9920] Loss: 0.261, Running accuracy: 99.849, Time: 42.54
[2020-12-16 06:09:57,644][__main__][INFO] - [10240] Loss: 0.157, Running accuracy: 99.851, Time: 39.66
[2020-12-16 06:10:37,623][__main__][INFO] - [10560] Loss: 0.213, Running accuracy: 99.850, Time: 39.98
[2020-12-16 06:11:15,089][__main__][INFO] - [10880] Loss: 0.173, Running accuracy: 99.851, Time: 37.46
[2020-12-16 06:11:54,059][__main__][INFO] - [11200] Loss: 0.208, Running accuracy: 99.851, Time: 38.97
[2020-12-16 06:12:34,153][__main__][INFO] - [11520] Loss: 0.192, Running accuracy: 99.851, Time: 39.98
[2020-12-16 06:13:12,566][__main__][INFO] - [11840] Loss: 0.208, Running accuracy: 99.851, Time: 38.41
[2020-12-16 06:13:53,612][__main__][INFO] - [12160] Loss: 0.216, Running accuracy: 99.850, Time: 41.04
[2020-12-16 06:14:32,069][__main__][INFO] - [12480] Loss: 0.204, Running accuracy: 99.850, Time: 38.46
[2020-12-16 06:15:09,957][__main__][INFO] - [12800] Loss: 0.189, Running accuracy: 99.851, Time: 37.89
[2020-12-16 06:15:47,925][__main__][INFO] - [13120] Loss: 0.219, Running accuracy: 99.850, Time: 37.97
[2020-12-16 06:16:32,615][__main__][INFO] - [13440] Loss: 0.206, Running accuracy: 99.850, Time: 44.69
[2020-12-16 06:17:15,082][__main__][INFO] - [13760] Loss: 0.156, Running accuracy: 99.851, Time: 42.47
[2020-12-16 06:17:49,716][__main__][INFO] - [14080] Loss: 0.178, Running accuracy: 99.851, Time: 34.63
[2020-12-16 06:18:24,176][__main__][INFO] - [14400] Loss: 0.196, Running accuracy: 99.851, Time: 34.46
[2020-12-16 06:19:01,865][__main__][INFO] - [14720] Loss: 0.244, Running accuracy: 99.852, Time: 37.69
[2020-12-16 06:19:44,658][__main__][INFO] - [15040] Loss: 0.196, Running accuracy: 99.852, Time: 42.79
[2020-12-16 06:20:19,065][__main__][INFO] - [15360] Loss: 0.160, Running accuracy: 99.853, Time: 34.41
[2020-12-16 06:20:55,048][__main__][INFO] - [15680] Loss: 0.180, Running accuracy: 99.852, Time: 35.98
[2020-12-16 06:21:30,362][__main__][INFO] - [16000] Loss: 0.183, Running accuracy: 99.852, Time: 35.31
[2020-12-16 06:22:07,577][__main__][INFO] - [16320] Loss: 0.249, Running accuracy: 99.850, Time: 37.21
[2020-12-16 06:22:46,293][__main__][INFO] - [16640] Loss: 0.168, Running accuracy: 99.851, Time: 38.71
[2020-12-16 06:23:27,324][__main__][INFO] - [16960] Loss: 0.219, Running accuracy: 99.849, Time: 41.03
[2020-12-16 06:24:08,437][__main__][INFO] - [17280] Loss: 0.185, Running accuracy: 99.849, Time: 41.11
[2020-12-16 06:24:38,506][__main__][INFO] - Action accuracy: 99.850, Loss: 0.222
[2020-12-16 06:24:38,506][__main__][INFO] - Validating..
[2020-12-16 06:24:45,200][test][INFO] - Time elapsed: 5.253397
[2020-12-16 06:24:45,202][__main__][INFO] - Validation F1 score: 94.390, Exact match: 57.100, Precision: 94.640, Recall: 94.140
[2020-12-16 06:24:58,064][__main__][INFO] - Checkpoint saved to checkpoints/model_latest.pth
[2020-12-16 06:24:58,378][__main__][INFO] - Epoch #31
[2020-12-16 06:24:58,379][__main__][INFO] - Training..
[2020-12-16 06:25:37,987][__main__][INFO] - [320] Loss: 0.145, Running accuracy: 99.899, Time: 38.35
[2020-12-16 06:26:17,933][__main__][INFO] - [640] Loss: 0.250, Running accuracy: 99.848, Time: 39.94
[2020-12-16 06:26:56,670][__main__][INFO] - [960] Loss: 0.206, Running accuracy: 99.855, Time: 38.73
[2020-12-16 06:27:38,460][__main__][INFO] - [1280] Loss: 0.191, Running accuracy: 99.864, Time: 41.79
[2020-12-16 06:28:14,348][__main__][INFO] - [1600] Loss: 0.152, Running accuracy: 99.865, Time: 35.89
[2020-12-16 06:28:53,244][__main__][INFO] - [1920] Loss: 0.199, Running accuracy: 99.861, Time: 38.89
[2020-12-16 06:29:31,664][__main__][INFO] - [2240] Loss: 0.139, Running accuracy: 99.869, Time: 38.42
[2020-12-16 06:30:08,073][__main__][INFO] - [2560] Loss: 0.205, Running accuracy: 99.869, Time: 36.41
[2020-12-16 06:30:44,600][__main__][INFO] - [2880] Loss: 0.192, Running accuracy: 99.871, Time: 36.53
[2020-12-16 06:31:21,009][__main__][INFO] - [3200] Loss: 0.162, Running accuracy: 99.874, Time: 36.41
[2020-12-16 06:31:59,433][__main__][INFO] - [3520] Loss: 0.148, Running accuracy: 99.879, Time: 38.42
[2020-12-16 06:32:35,539][__main__][INFO] - [3840] Loss: 0.201, Running accuracy: 99.877, Time: 36.10
[2020-12-16 06:33:13,869][__main__][INFO] - [4160] Loss: 0.183, Running accuracy: 99.877, Time: 38.33
[2020-12-16 06:33:55,039][__main__][INFO] - [4480] Loss: 0.176, Running accuracy: 99.877, Time: 41.17
[2020-12-16 06:34:34,930][__main__][INFO] - [4800] Loss: 0.181, Running accuracy: 99.877, Time: 39.89
[2020-12-16 06:35:09,227][__main__][INFO] - [5120] Loss: 0.159, Running accuracy: 99.878, Time: 34.29
[2020-12-16 06:35:44,542][__main__][INFO] - [5440] Loss: 0.173, Running accuracy: 99.876, Time: 35.31
[2020-12-16 06:36:27,249][__main__][INFO] - [5760] Loss: 0.140, Running accuracy: 99.878, Time: 42.71
[2020-12-16 06:37:05,015][__main__][INFO] - [6080] Loss: 0.180, Running accuracy: 99.877, Time: 37.76
[2020-12-16 06:37:41,613][__main__][INFO] - [6400] Loss: 0.181, Running accuracy: 99.874, Time: 36.59
[2020-12-16 06:38:20,279][__main__][INFO] - [6720] Loss: 0.162, Running accuracy: 99.877, Time: 38.66
[2020-12-16 06:38:57,610][__main__][INFO] - [7040] Loss: 0.192, Running accuracy: 99.877, Time: 37.33
[2020-12-16 06:39:34,393][__main__][INFO] - [7360] Loss: 0.183, Running accuracy: 99.878, Time: 36.78
[2020-12-16 06:40:11,774][__main__][INFO] - [7680] Loss: 0.156, Running accuracy: 99.878, Time: 37.38
[2020-12-16 06:40:48,218][__main__][INFO] - [8000] Loss: 0.136, Running accuracy: 99.879, Time: 36.44
[2020-12-16 06:41:22,321][__main__][INFO] - [8320] Loss: 0.186, Running accuracy: 99.877, Time: 34.10
[2020-12-16 06:42:01,655][__main__][INFO] - [8640] Loss: 0.240, Running accuracy: 99.878, Time: 39.33
[2020-12-16 06:42:31,395][__main__][INFO] - [8960] Loss: 0.118, Running accuracy: 99.877, Time: 29.74
[2020-12-16 06:43:13,196][__main__][INFO] - [9280] Loss: 0.219, Running accuracy: 99.876, Time: 41.80
[2020-12-16 06:43:50,892][__main__][INFO] - [9600] Loss: 0.150, Running accuracy: 99.876, Time: 37.42
[2020-12-16 06:44:27,871][__main__][INFO] - [9920] Loss: 0.191, Running accuracy: 99.873, Time: 36.98
[2020-12-16 06:45:04,072][__main__][INFO] - [10240] Loss: 0.262, Running accuracy: 99.871, Time: 36.20
[2020-12-16 06:45:46,599][__main__][INFO] - [10560] Loss: 0.165, Running accuracy: 99.871, Time: 42.44
[2020-12-16 06:46:22,901][__main__][INFO] - [10880] Loss: 0.175, Running accuracy: 99.871, Time: 36.30
[2020-12-16 06:47:05,460][__main__][INFO] - [11200] Loss: 0.189, Running accuracy: 99.871, Time: 42.56
[2020-12-16 06:47:41,076][__main__][INFO] - [11520] Loss: 0.191, Running accuracy: 99.870, Time: 35.62
[2020-12-16 06:48:16,734][__main__][INFO] - [11840] Loss: 0.160, Running accuracy: 99.870, Time: 35.66
[2020-12-16 06:48:55,083][__main__][INFO] - [12160] Loss: 0.173, Running accuracy: 99.870, Time: 38.35
[2020-12-16 06:49:34,843][__main__][INFO] - [12480] Loss: 0.217, Running accuracy: 99.868, Time: 39.76
[2020-12-16 06:50:11,240][__main__][INFO] - [12800] Loss: 0.253, Running accuracy: 99.866, Time: 36.40
[2020-12-16 06:50:50,282][__main__][INFO] - [13120] Loss: 0.189, Running accuracy: 99.866, Time: 39.04
[2020-12-16 06:51:29,150][__main__][INFO] - [13440] Loss: 0.184, Running accuracy: 99.865, Time: 38.87
[2020-12-16 06:52:07,142][__main__][INFO] - [13760] Loss: 0.164, Running accuracy: 99.866, Time: 37.99
[2020-12-16 06:52:43,659][__main__][INFO] - [14080] Loss: 0.199, Running accuracy: 99.866, Time: 36.51
[2020-12-16 06:53:26,280][__main__][INFO] - [14400] Loss: 0.240, Running accuracy: 99.865, Time: 42.62
[2020-12-16 06:54:00,391][__main__][INFO] - [14720] Loss: 0.176, Running accuracy: 99.865, Time: 34.11
[2020-12-16 06:54:38,554][__main__][INFO] - [15040] Loss: 0.183, Running accuracy: 99.865, Time: 38.16
[2020-12-16 06:55:15,819][__main__][INFO] - [15360] Loss: 0.203, Running accuracy: 99.864, Time: 37.26
[2020-12-16 06:55:55,461][__main__][INFO] - [15680] Loss: 0.126, Running accuracy: 99.866, Time: 39.64
[2020-12-16 06:56:32,443][__main__][INFO] - [16000] Loss: 0.244, Running accuracy: 99.865, Time: 36.98
[2020-12-16 06:57:14,351][__main__][INFO] - [16320] Loss: 0.201, Running accuracy: 99.865, Time: 41.91
[2020-12-16 06:57:56,195][__main__][INFO] - [16640] Loss: 0.204, Running accuracy: 99.865, Time: 41.84
[2020-12-16 06:58:35,799][__main__][INFO] - [16960] Loss: 0.190, Running accuracy: 99.865, Time: 39.60
[2020-12-16 06:59:16,766][__main__][INFO] - [17280] Loss: 0.188, Running accuracy: 99.865, Time: 40.96
[2020-12-16 06:59:49,999][__main__][INFO] - Action accuracy: 99.864, Loss: 0.205
[2020-12-16 06:59:50,000][__main__][INFO] - Validating..
[2020-12-16 06:59:56,720][test][INFO] - Time elapsed: 5.316995
[2020-12-16 06:59:56,722][__main__][INFO] - Validation F1 score: 94.520, Exact match: 56.530, Precision: 94.510, Recall: 94.530
[2020-12-16 07:00:09,668][__main__][INFO] - Checkpoint saved to checkpoints/model_latest.pth
[2020-12-16 07:00:09,981][__main__][INFO] - Epoch #32
[2020-12-16 07:00:09,982][__main__][INFO] - Training..
[2020-12-16 07:00:56,400][__main__][INFO] - [320] Loss: 0.219, Running accuracy: 99.864, Time: 45.20
[2020-12-16 07:01:40,125][__main__][INFO] - [640] Loss: 0.151, Running accuracy: 99.898, Time: 43.72
[2020-12-16 07:02:19,743][__main__][INFO] - [960] Loss: 0.152, Running accuracy: 99.886, Time: 39.62
[2020-12-16 07:02:51,406][__main__][INFO] - [1280] Loss: 0.131, Running accuracy: 99.884, Time: 31.66
[2020-12-16 07:03:27,813][__main__][INFO] - [1600] Loss: 0.157, Running accuracy: 99.877, Time: 36.41
[2020-12-16 07:04:07,276][__main__][INFO] - [1920] Loss: 0.175, Running accuracy: 99.881, Time: 39.46
[2020-12-16 07:04:44,691][__main__][INFO] - [2240] Loss: 0.145, Running accuracy: 99.892, Time: 37.41
[2020-12-16 07:05:23,776][__main__][INFO] - [2560] Loss: 0.151, Running accuracy: 99.890, Time: 39.08
[2020-12-16 07:06:01,469][__main__][INFO] - [2880] Loss: 0.191, Running accuracy: 99.883, Time: 37.69
[2020-12-16 07:06:40,264][__main__][INFO] - [3200] Loss: 0.187, Running accuracy: 99.883, Time: 38.79
[2020-12-16 07:07:15,965][__main__][INFO] - [3520] Loss: 0.152, Running accuracy: 99.883, Time: 35.70
[2020-12-16 07:07:46,873][__main__][INFO] - [3840] Loss: 0.173, Running accuracy: 99.879, Time: 30.91
[2020-12-16 07:08:26,630][__main__][INFO] - [4160] Loss: 0.164, Running accuracy: 99.878, Time: 39.76
[2020-12-16 07:08:58,967][__main__][INFO] - [4480] Loss: 0.123, Running accuracy: 99.882, Time: 32.34
[2020-12-16 07:09:37,370][__main__][INFO] - [4800] Loss: 0.122, Running accuracy: 99.886, Time: 38.40
[2020-12-16 07:10:14,084][__main__][INFO] - [5120] Loss: 0.144, Running accuracy: 99.883, Time: 36.71
[2020-12-16 07:10:51,984][__main__][INFO] - [5440] Loss: 0.155, Running accuracy: 99.883, Time: 37.90
[2020-12-16 07:11:29,835][__main__][INFO] - [5760] Loss: 0.175, Running accuracy: 99.883, Time: 37.85
[2020-12-16 07:12:09,034][__main__][INFO] - [6080] Loss: 0.167, Running accuracy: 99.884, Time: 39.20
[2020-12-16 07:12:46,113][__main__][INFO] - [6400] Loss: 0.213, Running accuracy: 99.880, Time: 37.08
[2020-12-16 07:13:26,933][__main__][INFO] - [6720] Loss: 0.179, Running accuracy: 99.880, Time: 40.82
[2020-12-16 07:14:06,658][__main__][INFO] - [7040] Loss: 0.198, Running accuracy: 99.877, Time: 39.72
[2020-12-16 07:14:47,519][__main__][INFO] - [7360] Loss: 0.199, Running accuracy: 99.873, Time: 40.86
[2020-12-16 07:15:22,309][__main__][INFO] - [7680] Loss: 0.165, Running accuracy: 99.872, Time: 34.79
[2020-12-16 07:15:58,094][__main__][INFO] - [8000] Loss: 0.209, Running accuracy: 99.872, Time: 35.78
[2020-12-16 07:16:34,793][__main__][INFO] - [8320] Loss: 0.185, Running accuracy: 99.872, Time: 36.70
[2020-12-16 07:17:08,151][__main__][INFO] - [8640] Loss: 0.198, Running accuracy: 99.872, Time: 33.36
[2020-12-16 07:17:48,078][__main__][INFO] - [8960] Loss: 0.153, Running accuracy: 99.872, Time: 39.93
[2020-12-16 07:18:22,875][__main__][INFO] - [9280] Loss: 0.169, Running accuracy: 99.871, Time: 34.80
[2020-12-16 07:18:59,752][__main__][INFO] - [9600] Loss: 0.190, Running accuracy: 99.872, Time: 36.88
[2020-12-16 07:19:43,835][__main__][INFO] - [9920] Loss: 0.201, Running accuracy: 99.871, Time: 44.08
[2020-12-16 07:20:24,100][__main__][INFO] - [10240] Loss: 0.184, Running accuracy: 99.871, Time: 40.16
[2020-12-16 07:20:59,283][__main__][INFO] - [10560] Loss: 0.147, Running accuracy: 99.872, Time: 35.18
[2020-12-16 07:21:36,426][__main__][INFO] - [10880] Loss: 0.220, Running accuracy: 99.871, Time: 37.14
[2020-12-16 07:22:15,153][__main__][INFO] - [11200] Loss: 0.225, Running accuracy: 99.869, Time: 38.63
[2020-12-16 07:22:56,898][__main__][INFO] - [11520] Loss: 0.202, Running accuracy: 99.870, Time: 41.74
[2020-12-16 07:23:34,954][__main__][INFO] - [11840] Loss: 0.155, Running accuracy: 99.869, Time: 38.06
[2020-12-16 07:24:15,379][__main__][INFO] - [12160] Loss: 0.168, Running accuracy: 99.870, Time: 40.42
[2020-12-16 07:24:54,310][__main__][INFO] - [12480] Loss: 0.129, Running accuracy: 99.871, Time: 38.93
[2020-12-16 07:25:29,658][__main__][INFO] - [12800] Loss: 0.194, Running accuracy: 99.871, Time: 35.35
[2020-12-16 07:26:04,273][__main__][INFO] - [13120] Loss: 0.155, Running accuracy: 99.870, Time: 34.61
[2020-12-16 07:26:40,676][__main__][INFO] - [13440] Loss: 0.187, Running accuracy: 99.869, Time: 36.40
[2020-12-16 07:27:13,604][__main__][INFO] - [13760] Loss: 0.136, Running accuracy: 99.869, Time: 32.93
[2020-12-16 07:27:50,430][__main__][INFO] - [14080] Loss: 0.244, Running accuracy: 99.868, Time: 36.83
[2020-12-16 07:28:30,114][__main__][INFO] - [14400] Loss: 0.189, Running accuracy: 99.868, Time: 39.68
[2020-12-16 07:29:17,388][__main__][INFO] - [14720] Loss: 0.212, Running accuracy: 99.868, Time: 47.27
[2020-12-16 07:29:56,882][__main__][INFO] - [15040] Loss: 0.217, Running accuracy: 99.868, Time: 39.49
[2020-12-16 07:30:37,489][__main__][INFO] - [15360] Loss: 0.223, Running accuracy: 99.866, Time: 40.61
[2020-12-16 07:31:15,156][__main__][INFO] - [15680] Loss: 0.166, Running accuracy: 99.867, Time: 37.67
[2020-12-16 07:31:50,857][__main__][INFO] - [16000] Loss: 0.169, Running accuracy: 99.867, Time: 35.70
[2020-12-16 07:32:29,244][__main__][INFO] - [16320] Loss: 0.187, Running accuracy: 99.868, Time: 38.39
[2020-12-16 07:33:09,458][__main__][INFO] - [16640] Loss: 0.141, Running accuracy: 99.868, Time: 40.21
[2020-12-16 07:33:47,490][__main__][INFO] - [16960] Loss: 0.157, Running accuracy: 99.869, Time: 38.03
[2020-12-16 07:34:22,468][__main__][INFO] - [17280] Loss: 0.141, Running accuracy: 99.869, Time: 34.98
[2020-12-16 07:34:55,220][__main__][INFO] - Action accuracy: 99.869, Loss: 0.192
[2020-12-16 07:34:55,220][__main__][INFO] - Validating..
[2020-12-16 07:35:01,829][test][INFO] - Time elapsed: 5.191540
[2020-12-16 07:35:01,830][__main__][INFO] - Validation F1 score: 94.440, Exact match: 55.400, Precision: 94.580, Recall: 94.300
Epoch    33: reducing learning rate of group 0 to 5.0000e-06.
[2020-12-16 07:35:14,566][__main__][INFO] - Checkpoint saved to checkpoints/model_latest.pth
[2020-12-16 07:35:14,902][__main__][INFO] - Epoch #33
[2020-12-16 07:35:14,902][__main__][INFO] - Training..
[2020-12-16 07:35:52,938][__main__][INFO] - [320] Loss: 0.150, Running accuracy: 99.873, Time: 36.73
[2020-12-16 07:36:31,052][__main__][INFO] - [640] Loss: 0.121, Running accuracy: 99.877, Time: 38.11
[2020-12-16 07:37:05,945][__main__][INFO] - [960] Loss: 0.154, Running accuracy: 99.867, Time: 34.89
[2020-12-16 07:37:39,551][__main__][INFO] - [1280] Loss: 0.137, Running accuracy: 99.885, Time: 33.61
[2020-12-16 07:38:18,425][__main__][INFO] - [1600] Loss: 0.132, Running accuracy: 99.897, Time: 38.87
[2020-12-16 07:38:59,062][__main__][INFO] - [1920] Loss: 0.146, Running accuracy: 99.899, Time: 40.64
[2020-12-16 07:39:37,470][__main__][INFO] - [2240] Loss: 0.153, Running accuracy: 99.899, Time: 38.41
[2020-12-16 07:40:12,959][__main__][INFO] - [2560] Loss: 0.141, Running accuracy: 99.904, Time: 35.49
[2020-12-16 07:40:51,393][__main__][INFO] - [2880] Loss: 0.142, Running accuracy: 99.904, Time: 38.43
[2020-12-16 07:41:28,670][__main__][INFO] - [3200] Loss: 0.140, Running accuracy: 99.908, Time: 37.27
[2020-12-16 07:42:04,942][__main__][INFO] - [3520] Loss: 0.155, Running accuracy: 99.901, Time: 36.27
[2020-12-16 07:42:45,348][__main__][INFO] - [3840] Loss: 0.117, Running accuracy: 99.904, Time: 40.40
[2020-12-16 07:43:20,468][__main__][INFO] - [4160] Loss: 0.151, Running accuracy: 99.902, Time: 35.12
[2020-12-16 07:43:59,112][__main__][INFO] - [4480] Loss: 0.099, Running accuracy: 99.905, Time: 38.64
[2020-12-16 07:44:39,181][__main__][INFO] - [4800] Loss: 0.124, Running accuracy: 99.905, Time: 40.07
[2020-12-16 07:45:19,458][__main__][INFO] - [5120] Loss: 0.136, Running accuracy: 99.904, Time: 40.27
[2020-12-16 07:46:00,372][__main__][INFO] - [5440] Loss: 0.141, Running accuracy: 99.903, Time: 40.90
[2020-12-16 07:46:38,930][__main__][INFO] - [5760] Loss: 0.152, Running accuracy: 99.903, Time: 38.56
[2020-12-16 07:47:20,640][__main__][INFO] - [6080] Loss: 0.138, Running accuracy: 99.904, Time: 41.71
[2020-12-16 07:47:59,486][__main__][INFO] - [6400] Loss: 0.162, Running accuracy: 99.903, Time: 38.84
[2020-12-16 07:48:38,014][__main__][INFO] - [6720] Loss: 0.135, Running accuracy: 99.904, Time: 38.53
[2020-12-16 07:49:10,657][__main__][INFO] - [7040] Loss: 0.123, Running accuracy: 99.904, Time: 32.64
[2020-12-16 07:49:53,503][__main__][INFO] - [7360] Loss: 0.183, Running accuracy: 99.906, Time: 42.84
[2020-12-16 07:50:30,876][__main__][INFO] - [7680] Loss: 0.128, Running accuracy: 99.905, Time: 37.37
[2020-12-16 07:51:10,180][__main__][INFO] - [8000] Loss: 0.102, Running accuracy: 99.907, Time: 39.30
[2020-12-16 07:51:48,930][__main__][INFO] - [8320] Loss: 0.155, Running accuracy: 99.905, Time: 38.75
[2020-12-16 07:52:33,765][__main__][INFO] - [8640] Loss: 0.167, Running accuracy: 99.904, Time: 44.83
[2020-12-16 07:53:14,326][__main__][INFO] - [8960] Loss: 0.144, Running accuracy: 99.903, Time: 40.48
[2020-12-16 07:53:55,540][__main__][INFO] - [9280] Loss: 0.108, Running accuracy: 99.904, Time: 41.21
[2020-12-16 07:54:32,297][__main__][INFO] - [9600] Loss: 0.159, Running accuracy: 99.904, Time: 36.75
[2020-12-16 07:55:07,863][__main__][INFO] - [9920] Loss: 0.138, Running accuracy: 99.903, Time: 35.46
[2020-12-16 07:55:51,938][__main__][INFO] - [10240] Loss: 0.097, Running accuracy: 99.904, Time: 44.07
[2020-12-16 07:56:27,501][__main__][INFO] - [10560] Loss: 0.184, Running accuracy: 99.901, Time: 35.56
[2020-12-16 07:57:02,113][__main__][INFO] - [10880] Loss: 0.131, Running accuracy: 99.901, Time: 34.61
[2020-12-16 07:57:37,878][__main__][INFO] - [11200] Loss: 0.134, Running accuracy: 99.900, Time: 35.76
[2020-12-16 07:58:20,168][__main__][INFO] - [11520] Loss: 0.157, Running accuracy: 99.899, Time: 42.29
[2020-12-16 07:58:56,925][__main__][INFO] - [11840] Loss: 0.144, Running accuracy: 99.900, Time: 36.75
[2020-12-16 07:59:34,451][__main__][INFO] - [12160] Loss: 0.162, Running accuracy: 99.899, Time: 37.52
[2020-12-16 08:00:12,672][__main__][INFO] - [12480] Loss: 0.197, Running accuracy: 99.900, Time: 38.22
[2020-12-16 08:00:47,227][__main__][INFO] - [12800] Loss: 0.126, Running accuracy: 99.899, Time: 34.55
[2020-12-16 08:01:25,957][__main__][INFO] - [13120] Loss: 0.146, Running accuracy: 99.900, Time: 38.73
[2020-12-16 08:02:02,715][__main__][INFO] - [13440] Loss: 0.124, Running accuracy: 99.899, Time: 36.76
[2020-12-16 08:02:38,496][__main__][INFO] - [13760] Loss: 0.162, Running accuracy: 99.899, Time: 35.77
[2020-12-16 08:03:15,484][__main__][INFO] - [14080] Loss: 0.170, Running accuracy: 99.897, Time: 36.99
[2020-12-16 08:03:52,683][__main__][INFO] - [14400] Loss: 0.126, Running accuracy: 99.898, Time: 37.20
[2020-12-16 08:04:31,550][__main__][INFO] - [14720] Loss: 0.164, Running accuracy: 99.898, Time: 38.86
[2020-12-16 08:05:09,248][__main__][INFO] - [15040] Loss: 0.122, Running accuracy: 99.899, Time: 37.70
[2020-12-16 08:05:50,745][__main__][INFO] - [15360] Loss: 0.218, Running accuracy: 99.898, Time: 41.49
[2020-12-16 08:06:29,982][__main__][INFO] - [15680] Loss: 0.145, Running accuracy: 99.899, Time: 39.24
[2020-12-16 08:07:09,485][__main__][INFO] - [16000] Loss: 0.153, Running accuracy: 99.899, Time: 39.50
[2020-12-16 08:07:49,193][__main__][INFO] - [16320] Loss: 0.129, Running accuracy: 99.900, Time: 39.71
[2020-12-16 08:08:27,389][__main__][INFO] - [16640] Loss: 0.133, Running accuracy: 99.901, Time: 38.20
[2020-12-16 08:09:00,879][__main__][INFO] - [16960] Loss: 0.138, Running accuracy: 99.901, Time: 33.49
[2020-12-16 08:09:39,140][__main__][INFO] - [17280] Loss: 0.176, Running accuracy: 99.900, Time: 38.26
[2020-12-16 08:10:09,367][__main__][INFO] - Action accuracy: 99.900, Loss: 0.159
[2020-12-16 08:10:09,368][__main__][INFO] - Validating..
[2020-12-16 08:10:15,929][test][INFO] - Time elapsed: 5.241420
[2020-12-16 08:10:15,931][__main__][INFO] - Validation F1 score: 94.600, Exact match: 57.390, Precision: 94.800, Recall: 94.410
[2020-12-16 08:10:28,717][__main__][INFO] - Checkpoint saved to checkpoints/model_latest.pth
[2020-12-16 08:10:29,031][__main__][INFO] - Epoch #34
[2020-12-16 08:10:29,032][__main__][INFO] - Training..
[2020-12-16 08:11:11,475][__main__][INFO] - [320] Loss: 0.122, Running accuracy: 99.946, Time: 41.40
[2020-12-16 08:11:49,594][__main__][INFO] - [640] Loss: 0.108, Running accuracy: 99.928, Time: 38.12
[2020-12-16 08:12:29,716][__main__][INFO] - [960] Loss: 0.149, Running accuracy: 99.925, Time: 40.12
[2020-12-16 08:13:07,519][__main__][INFO] - [1280] Loss: 0.120, Running accuracy: 99.922, Time: 37.80
[2020-12-16 08:13:43,756][__main__][INFO] - [1600] Loss: 0.202, Running accuracy: 99.906, Time: 36.24
[2020-12-16 08:14:23,878][__main__][INFO] - [1920] Loss: 0.126, Running accuracy: 99.906, Time: 40.12
[2020-12-16 08:15:04,689][__main__][INFO] - [2240] Loss: 0.122, Running accuracy: 99.910, Time: 40.81
[2020-12-16 08:15:40,721][__main__][INFO] - [2560] Loss: 0.145, Running accuracy: 99.910, Time: 36.03
[2020-12-16 08:16:17,110][__main__][INFO] - [2880] Loss: 0.100, Running accuracy: 99.915, Time: 36.39
[2020-12-16 08:16:50,446][__main__][INFO] - [3200] Loss: 0.089, Running accuracy: 99.917, Time: 33.33
[2020-12-16 08:17:28,459][__main__][INFO] - [3520] Loss: 0.140, Running accuracy: 99.919, Time: 38.01
[2020-12-16 08:18:07,395][__main__][INFO] - [3840] Loss: 0.187, Running accuracy: 99.913, Time: 38.93
[2020-12-16 08:18:45,052][__main__][INFO] - [4160] Loss: 0.134, Running accuracy: 99.912, Time: 37.66
[2020-12-16 08:19:23,957][__main__][INFO] - [4480] Loss: 0.108, Running accuracy: 99.916, Time: 38.90
[2020-12-16 08:20:02,740][__main__][INFO] - [4800] Loss: 0.131, Running accuracy: 99.917, Time: 38.78
[2020-12-16 08:20:44,834][__main__][INFO] - [5120] Loss: 0.111, Running accuracy: 99.916, Time: 42.09
[2020-12-16 08:21:21,901][__main__][INFO] - [5440] Loss: 0.099, Running accuracy: 99.917, Time: 37.06
[2020-12-16 08:22:05,238][__main__][INFO] - [5760] Loss: 0.143, Running accuracy: 99.919, Time: 43.34
[2020-12-16 08:22:44,763][__main__][INFO] - [6080] Loss: 0.135, Running accuracy: 99.919, Time: 39.52
[2020-12-16 08:23:23,677][__main__][INFO] - [6400] Loss: 0.097, Running accuracy: 99.922, Time: 38.91
[2020-12-16 08:24:01,961][__main__][INFO] - [6720] Loss: 0.119, Running accuracy: 99.924, Time: 38.28
[2020-12-16 08:24:40,225][__main__][INFO] - [7040] Loss: 0.091, Running accuracy: 99.925, Time: 38.26
[2020-12-16 08:25:19,615][__main__][INFO] - [7360] Loss: 0.109, Running accuracy: 99.926, Time: 39.39
[2020-12-16 08:25:58,538][__main__][INFO] - [7680] Loss: 0.159, Running accuracy: 99.923, Time: 38.92
[2020-12-16 08:26:35,490][__main__][INFO] - [8000] Loss: 0.137, Running accuracy: 99.923, Time: 36.95
[2020-12-16 08:27:14,356][__main__][INFO] - [8320] Loss: 0.103, Running accuracy: 99.924, Time: 38.86
[2020-12-16 08:27:50,951][__main__][INFO] - [8640] Loss: 0.084, Running accuracy: 99.926, Time: 36.59
[2020-12-16 08:28:28,890][__main__][INFO] - [8960] Loss: 0.177, Running accuracy: 99.927, Time: 37.94
[2020-12-16 08:29:13,300][__main__][INFO] - [9280] Loss: 0.159, Running accuracy: 99.926, Time: 44.41
[2020-12-16 08:29:46,418][__main__][INFO] - [9600] Loss: 0.168, Running accuracy: 99.923, Time: 33.02
[2020-12-16 08:30:26,032][__main__][INFO] - [9920] Loss: 0.119, Running accuracy: 99.923, Time: 39.61
[2020-12-16 08:31:03,455][__main__][INFO] - [10240] Loss: 0.127, Running accuracy: 99.922, Time: 37.42
[2020-12-16 08:31:41,232][__main__][INFO] - [10560] Loss: 0.137, Running accuracy: 99.921, Time: 37.68
[2020-12-16 08:32:21,342][__main__][INFO] - [10880] Loss: 0.126, Running accuracy: 99.920, Time: 40.11
[2020-12-16 08:33:03,672][__main__][INFO] - [11200] Loss: 0.102, Running accuracy: 99.920, Time: 42.33
[2020-12-16 08:33:38,744][__main__][INFO] - [11520] Loss: 0.141, Running accuracy: 99.920, Time: 35.07
[2020-12-16 08:34:13,461][__main__][INFO] - [11840] Loss: 0.118, Running accuracy: 99.921, Time: 34.72
[2020-12-16 08:34:50,607][__main__][INFO] - [12160] Loss: 0.116, Running accuracy: 99.921, Time: 37.15
[2020-12-16 08:35:25,711][__main__][INFO] - [12480] Loss: 0.118, Running accuracy: 99.922, Time: 35.10
[2020-12-16 08:36:02,538][__main__][INFO] - [12800] Loss: 0.125, Running accuracy: 99.922, Time: 36.83
[2020-12-16 08:36:44,406][__main__][INFO] - [13120] Loss: 0.123, Running accuracy: 99.923, Time: 41.87
[2020-12-16 08:37:21,082][__main__][INFO] - [13440] Loss: 0.121, Running accuracy: 99.924, Time: 36.67
[2020-12-16 08:37:57,727][__main__][INFO] - [13760] Loss: 0.125, Running accuracy: 99.924, Time: 36.64
[2020-12-16 08:38:33,948][__main__][INFO] - [14080] Loss: 0.112, Running accuracy: 99.924, Time: 36.22
[2020-12-16 08:39:07,274][__main__][INFO] - [14400] Loss: 0.094, Running accuracy: 99.925, Time: 33.32
[2020-12-16 08:39:45,917][__main__][INFO] - [14720] Loss: 0.198, Running accuracy: 99.923, Time: 38.64
[2020-12-16 08:40:22,329][__main__][INFO] - [15040] Loss: 0.155, Running accuracy: 99.922, Time: 36.41
[2020-12-16 08:41:03,851][__main__][INFO] - [15360] Loss: 0.142, Running accuracy: 99.922, Time: 41.52
[2020-12-16 08:41:43,758][__main__][INFO] - [15680] Loss: 0.136, Running accuracy: 99.922, Time: 39.91
[2020-12-16 08:42:19,301][__main__][INFO] - [16000] Loss: 0.123, Running accuracy: 99.922, Time: 35.54
[2020-12-16 08:42:51,614][__main__][INFO] - [16320] Loss: 0.151, Running accuracy: 99.920, Time: 32.31
[2020-12-16 08:43:29,409][__main__][INFO] - [16640] Loss: 0.144, Running accuracy: 99.920, Time: 37.79
[2020-12-16 08:44:09,018][__main__][INFO] - [16960] Loss: 0.114, Running accuracy: 99.920, Time: 39.61
[2020-12-16 08:44:44,365][__main__][INFO] - [17280] Loss: 0.145, Running accuracy: 99.919, Time: 35.35
[2020-12-16 08:45:18,216][__main__][INFO] - Action accuracy: 99.918, Loss: 0.143
[2020-12-16 08:45:18,217][__main__][INFO] - Validating..
[2020-12-16 08:45:27,325][test][INFO] - Time elapsed: 7.673506
[2020-12-16 08:45:27,327][__main__][INFO] - Validation F1 score: 94.560, Exact match: 57.670, Precision: 94.790, Recall: 94.330
[2020-12-16 08:45:40,385][__main__][INFO] - Checkpoint saved to checkpoints/model_latest.pth
[2020-12-16 08:45:40,695][__main__][INFO] - Epoch #35
[2020-12-16 08:45:40,696][__main__][INFO] - Training..
[2020-12-16 08:46:18,624][__main__][INFO] - [320] Loss: 0.129, Running accuracy: 99.928, Time: 36.91
[2020-12-16 08:47:00,071][__main__][INFO] - [640] Loss: 0.113, Running accuracy: 99.937, Time: 41.45
[2020-12-16 08:47:36,322][__main__][INFO] - [960] Loss: 0.094, Running accuracy: 99.935, Time: 36.25
[2020-12-16 08:48:12,671][__main__][INFO] - [1280] Loss: 0.147, Running accuracy: 99.923, Time: 36.35
[2020-12-16 08:48:50,325][__main__][INFO] - [1600] Loss: 0.108, Running accuracy: 99.922, Time: 37.65
[2020-12-16 08:49:24,802][__main__][INFO] - [1920] Loss: 0.111, Running accuracy: 99.925, Time: 34.48
[2020-12-16 08:49:59,267][__main__][INFO] - [2240] Loss: 0.105, Running accuracy: 99.928, Time: 34.46
[2020-12-16 08:50:35,942][__main__][INFO] - [2560] Loss: 0.145, Running accuracy: 99.929, Time: 36.67
[2020-12-16 08:51:14,447][__main__][INFO] - [2880] Loss: 0.107, Running accuracy: 99.932, Time: 38.50
[2020-12-16 08:51:46,885][__main__][INFO] - [3200] Loss: 0.086, Running accuracy: 99.935, Time: 32.44
[2020-12-16 08:52:23,658][__main__][INFO] - [3520] Loss: 0.107, Running accuracy: 99.936, Time: 36.77
[2020-12-16 08:53:01,869][__main__][INFO] - [3840] Loss: 0.139, Running accuracy: 99.933, Time: 38.21
[2020-12-16 08:53:35,680][__main__][INFO] - [4160] Loss: 0.106, Running accuracy: 99.933, Time: 33.80
[2020-12-16 08:54:05,921][__main__][INFO] - [4480] Loss: 0.096, Running accuracy: 99.932, Time: 30.24
[2020-12-16 08:54:38,605][__main__][INFO] - [4800] Loss: 0.139, Running accuracy: 99.928, Time: 32.68
[2020-12-16 08:55:16,795][__main__][INFO] - [5120] Loss: 0.104, Running accuracy: 99.929, Time: 38.19
[2020-12-16 08:55:53,534][__main__][INFO] - [5440] Loss: 0.121, Running accuracy: 99.930, Time: 36.74
[2020-12-16 08:56:25,797][__main__][INFO] - [5760] Loss: 0.117, Running accuracy: 99.932, Time: 32.26
[2020-12-16 08:56:56,443][__main__][INFO] - [6080] Loss: 0.111, Running accuracy: 99.930, Time: 30.64
[2020-12-16 08:57:37,146][__main__][INFO] - [6400] Loss: 0.114, Running accuracy: 99.932, Time: 40.70
[2020-12-16 08:58:10,193][__main__][INFO] - [6720] Loss: 0.108, Running accuracy: 99.930, Time: 33.05
[2020-12-16 08:58:42,461][__main__][INFO] - [7040] Loss: 0.101, Running accuracy: 99.931, Time: 32.27
[2020-12-16 08:59:21,026][__main__][INFO] - [7360] Loss: 0.142, Running accuracy: 99.931, Time: 38.56
[2020-12-16 08:59:55,477][__main__][INFO] - [7680] Loss: 0.118, Running accuracy: 99.930, Time: 34.45
[2020-12-16 09:00:28,685][__main__][INFO] - [8000] Loss: 0.124, Running accuracy: 99.929, Time: 33.21
[2020-12-16 09:01:06,350][__main__][INFO] - [8320] Loss: 0.124, Running accuracy: 99.929, Time: 37.66
[2020-12-16 09:01:44,330][__main__][INFO] - [8640] Loss: 0.132, Running accuracy: 99.931, Time: 37.98
[2020-12-16 09:02:19,043][__main__][INFO] - [8960] Loss: 0.152, Running accuracy: 99.928, Time: 34.71
[2020-12-16 09:02:54,496][__main__][INFO] - [9280] Loss: 0.105, Running accuracy: 99.929, Time: 35.45
[2020-12-16 09:03:31,327][__main__][INFO] - [9600] Loss: 0.136, Running accuracy: 99.929, Time: 36.83
[2020-12-16 09:04:07,414][__main__][INFO] - [9920] Loss: 0.103, Running accuracy: 99.928, Time: 36.09
[2020-12-16 09:04:42,842][__main__][INFO] - [10240] Loss: 0.111, Running accuracy: 99.928, Time: 35.43
[2020-12-16 09:05:24,683][__main__][INFO] - [10560] Loss: 0.117, Running accuracy: 99.929, Time: 41.84
[2020-12-16 09:06:01,878][__main__][INFO] - [10880] Loss: 0.115, Running accuracy: 99.929, Time: 37.19
[2020-12-16 09:06:37,065][__main__][INFO] - [11200] Loss: 0.116, Running accuracy: 99.929, Time: 35.19
[2020-12-16 09:07:08,667][__main__][INFO] - [11520] Loss: 0.137, Running accuracy: 99.928, Time: 31.60
[2020-12-16 09:07:40,158][__main__][INFO] - [11840] Loss: 0.104, Running accuracy: 99.928, Time: 31.49
[2020-12-16 09:08:14,863][__main__][INFO] - [12160] Loss: 0.125, Running accuracy: 99.928, Time: 34.70
[2020-12-16 09:08:45,298][__main__][INFO] - [12480] Loss: 0.120, Running accuracy: 99.926, Time: 30.43
[2020-12-16 09:09:19,920][__main__][INFO] - [12800] Loss: 0.104, Running accuracy: 99.927, Time: 34.62
[2020-12-16 09:09:54,496][__main__][INFO] - [13120] Loss: 0.121, Running accuracy: 99.927, Time: 34.57
[2020-12-16 09:10:25,662][__main__][INFO] - [13440] Loss: 0.169, Running accuracy: 99.925, Time: 31.16
[2020-12-16 09:10:58,791][__main__][INFO] - [13760] Loss: 0.107, Running accuracy: 99.926, Time: 33.13
[2020-12-16 09:11:31,458][__main__][INFO] - [14080] Loss: 0.111, Running accuracy: 99.927, Time: 32.66
[2020-12-16 09:12:06,926][__main__][INFO] - [14400] Loss: 0.115, Running accuracy: 99.927, Time: 35.47
[2020-12-16 09:12:42,815][__main__][INFO] - [14720] Loss: 0.092, Running accuracy: 99.927, Time: 35.89
[2020-12-16 09:13:15,349][__main__][INFO] - [15040] Loss: 0.087, Running accuracy: 99.928, Time: 32.53
[2020-12-16 09:13:47,481][__main__][INFO] - [15360] Loss: 0.169, Running accuracy: 99.927, Time: 32.13
[2020-12-16 09:14:20,840][__main__][INFO] - [15680] Loss: 0.120, Running accuracy: 99.927, Time: 33.36
[2020-12-16 09:14:54,320][__main__][INFO] - [16000] Loss: 0.166, Running accuracy: 99.927, Time: 33.47
[2020-12-16 09:15:30,916][__main__][INFO] - [16320] Loss: 0.096, Running accuracy: 99.927, Time: 36.59
[2020-12-16 09:16:10,564][__main__][INFO] - [16640] Loss: 0.142, Running accuracy: 99.927, Time: 39.65
[2020-12-16 09:16:52,221][__main__][INFO] - [16960] Loss: 0.125, Running accuracy: 99.927, Time: 41.65
[2020-12-16 09:17:30,446][__main__][INFO] - [17280] Loss: 0.104, Running accuracy: 99.928, Time: 38.22
[2020-12-16 09:17:58,064][__main__][INFO] - Action accuracy: 99.927, Loss: 0.132
[2020-12-16 09:17:58,065][__main__][INFO] - Validating..
[2020-12-16 09:18:04,224][test][INFO] - Time elapsed: 4.800493
[2020-12-16 09:18:04,225][__main__][INFO] - Validation F1 score: 94.420, Exact match: 56.530, Precision: 94.580, Recall: 94.260
[2020-12-16 09:18:16,113][__main__][INFO] - Checkpoint saved to checkpoints/model_latest.pth
[2020-12-16 09:18:16,460][__main__][INFO] - Epoch #36
[2020-12-16 09:18:16,460][__main__][INFO] - Training..
[2020-12-16 09:18:53,690][__main__][INFO] - [320] Loss: 0.094, Running accuracy: 99.967, Time: 35.91
[2020-12-16 09:19:26,058][__main__][INFO] - [640] Loss: 0.163, Running accuracy: 99.937, Time: 32.36
[2020-12-16 09:20:05,151][__main__][INFO] - [960] Loss: 0.089, Running accuracy: 99.948, Time: 39.08
[2020-12-16 09:20:43,565][__main__][INFO] - [1280] Loss: 0.097, Running accuracy: 99.947, Time: 38.41
[2020-12-16 09:21:17,869][__main__][INFO] - [1600] Loss: 0.124, Running accuracy: 99.935, Time: 34.29
[2020-12-16 09:21:51,410][__main__][INFO] - [1920] Loss: 0.159, Running accuracy: 99.925, Time: 33.54
[2020-12-16 09:22:30,171][__main__][INFO] - [2240] Loss: 0.117, Running accuracy: 99.927, Time: 38.76
[2020-12-16 09:23:01,843][__main__][INFO] - [2560] Loss: 0.099, Running accuracy: 99.930, Time: 31.67
[2020-12-16 09:23:34,763][__main__][INFO] - [2880] Loss: 0.108, Running accuracy: 99.927, Time: 32.92
[2020-12-16 09:24:07,475][__main__][INFO] - [3200] Loss: 0.088, Running accuracy: 99.930, Time: 32.71
[2020-12-16 09:24:39,206][__main__][INFO] - [3520] Loss: 0.109, Running accuracy: 99.930, Time: 31.73
[2020-12-16 09:25:12,977][__main__][INFO] - [3840] Loss: 0.125, Running accuracy: 99.925, Time: 33.77
[2020-12-16 09:25:46,492][__main__][INFO] - [4160] Loss: 0.083, Running accuracy: 99.925, Time: 33.51
[2020-12-16 09:26:21,159][__main__][INFO] - [4480] Loss: 0.101, Running accuracy: 99.925, Time: 34.67
[2020-12-16 09:26:54,210][__main__][INFO] - [4800] Loss: 0.088, Running accuracy: 99.926, Time: 33.05
[2020-12-16 09:27:29,412][__main__][INFO] - [5120] Loss: 0.126, Running accuracy: 99.927, Time: 35.20
[2020-12-16 09:28:06,702][__main__][INFO] - [5440] Loss: 0.126, Running accuracy: 99.924, Time: 37.29
[2020-12-16 09:28:37,550][__main__][INFO] - [5760] Loss: 0.088, Running accuracy: 99.926, Time: 30.85
[2020-12-16 09:29:10,401][__main__][INFO] - [6080] Loss: 0.094, Running accuracy: 99.928, Time: 32.85
[2020-12-16 09:29:47,124][__main__][INFO] - [6400] Loss: 0.148, Running accuracy: 99.927, Time: 36.72
[2020-12-16 09:30:26,512][__main__][INFO] - [6720] Loss: 0.106, Running accuracy: 99.927, Time: 39.39
[2020-12-16 09:31:02,802][__main__][INFO] - [7040] Loss: 0.100, Running accuracy: 99.927, Time: 36.29
[2020-12-16 09:31:36,019][__main__][INFO] - [7360] Loss: 0.122, Running accuracy: 99.925, Time: 33.22
[2020-12-16 09:32:08,087][__main__][INFO] - [7680] Loss: 0.098, Running accuracy: 99.924, Time: 32.07
[2020-12-16 09:32:39,224][__main__][INFO] - [8000] Loss: 0.105, Running accuracy: 99.924, Time: 31.14
[2020-12-16 09:33:14,640][__main__][INFO] - [8320] Loss: 0.130, Running accuracy: 99.924, Time: 35.41
[2020-12-16 09:33:48,076][__main__][INFO] - [8640] Loss: 0.109, Running accuracy: 99.924, Time: 33.43
[2020-12-16 09:34:26,699][__main__][INFO] - [8960] Loss: 0.127, Running accuracy: 99.924, Time: 38.62
[2020-12-16 09:35:00,393][__main__][INFO] - [9280] Loss: 0.083, Running accuracy: 99.926, Time: 33.69
[2020-12-16 09:35:33,165][__main__][INFO] - [9600] Loss: 0.125, Running accuracy: 99.926, Time: 32.67
[2020-12-16 09:36:06,529][__main__][INFO] - [9920] Loss: 0.095, Running accuracy: 99.927, Time: 33.36
[2020-12-16 09:36:45,338][__main__][INFO] - [10240] Loss: 0.118, Running accuracy: 99.928, Time: 38.81
[2020-12-16 09:37:16,755][__main__][INFO] - [10560] Loss: 0.090, Running accuracy: 99.927, Time: 31.42
[2020-12-16 09:37:47,774][__main__][INFO] - [10880] Loss: 0.162, Running accuracy: 99.927, Time: 31.02
[2020-12-16 09:38:22,323][__main__][INFO] - [11200] Loss: 0.136, Running accuracy: 99.926, Time: 34.55
[2020-12-16 09:38:59,772][__main__][INFO] - [11520] Loss: 0.100, Running accuracy: 99.927, Time: 37.45
[2020-12-16 09:39:34,436][__main__][INFO] - [11840] Loss: 0.099, Running accuracy: 99.929, Time: 34.66
[2020-12-16 09:40:07,745][__main__][INFO] - [12160] Loss: 0.108, Running accuracy: 99.929, Time: 33.31
[2020-12-16 09:40:44,004][__main__][INFO] - [12480] Loss: 0.110, Running accuracy: 99.930, Time: 36.26
[2020-12-16 09:41:21,681][__main__][INFO] - [12800] Loss: 0.100, Running accuracy: 99.930, Time: 37.68
[2020-12-16 09:41:54,795][__main__][INFO] - [13120] Loss: 0.119, Running accuracy: 99.930, Time: 33.11
[2020-12-16 09:42:26,705][__main__][INFO] - [13440] Loss: 0.097, Running accuracy: 99.930, Time: 31.91
[2020-12-16 09:43:05,694][__main__][INFO] - [13760] Loss: 0.133, Running accuracy: 99.929, Time: 38.99
[2020-12-16 09:43:43,417][__main__][INFO] - [14080] Loss: 0.099, Running accuracy: 99.929, Time: 37.72
[2020-12-16 09:44:18,797][__main__][INFO] - [14400] Loss: 0.125, Running accuracy: 99.929, Time: 35.38
[2020-12-16 09:44:50,471][__main__][INFO] - [14720] Loss: 0.096, Running accuracy: 99.930, Time: 31.67
[2020-12-16 09:45:22,970][__main__][INFO] - [15040] Loss: 0.125, Running accuracy: 99.928, Time: 32.50
[2020-12-16 09:46:00,329][__main__][INFO] - [15360] Loss: 0.166, Running accuracy: 99.927, Time: 37.36
[2020-12-16 09:46:35,153][__main__][INFO] - [15680] Loss: 0.086, Running accuracy: 99.927, Time: 34.82
[2020-12-16 09:47:12,956][__main__][INFO] - [16000] Loss: 0.103, Running accuracy: 99.928, Time: 37.80
[2020-12-16 09:47:49,910][__main__][INFO] - [16320] Loss: 0.132, Running accuracy: 99.927, Time: 36.95
[2020-12-16 09:48:28,345][__main__][INFO] - [16640] Loss: 0.173, Running accuracy: 99.926, Time: 38.43
[2020-12-16 09:49:03,980][__main__][INFO] - [16960] Loss: 0.134, Running accuracy: 99.926, Time: 35.63
[2020-12-16 09:49:42,699][__main__][INFO] - [17280] Loss: 0.116, Running accuracy: 99.926, Time: 38.72
[2020-12-16 09:50:15,935][__main__][INFO] - Action accuracy: 99.925, Loss: 0.127
[2020-12-16 09:50:15,936][__main__][INFO] - Validating..
[2020-12-16 09:50:21,984][test][INFO] - Time elapsed: 4.851779
[2020-12-16 09:50:21,986][__main__][INFO] - Validation F1 score: 94.460, Exact match: 56.250, Precision: 94.570, Recall: 94.340
[2020-12-16 09:50:34,164][__main__][INFO] - Checkpoint saved to checkpoints/model_latest.pth
[2020-12-16 09:50:34,477][__main__][INFO] - Epoch #37
[2020-12-16 09:50:34,477][__main__][INFO] - Training..
[2020-12-16 09:51:14,995][__main__][INFO] - [320] Loss: 0.102, Running accuracy: 99.932, Time: 39.36
[2020-12-16 09:51:45,191][__main__][INFO] - [640] Loss: 0.083, Running accuracy: 99.941, Time: 30.19
[2020-12-16 09:52:20,185][__main__][INFO] - [960] Loss: 0.097, Running accuracy: 99.942, Time: 34.99
[2020-12-16 09:52:55,304][__main__][INFO] - [1280] Loss: 0.080, Running accuracy: 99.936, Time: 35.12
[2020-12-16 09:53:28,951][__main__][INFO] - [1600] Loss: 0.099, Running accuracy: 99.932, Time: 33.65
[2020-12-16 09:54:07,675][__main__][INFO] - [1920] Loss: 0.099, Running accuracy: 99.930, Time: 38.72
[2020-12-16 09:54:44,429][__main__][INFO] - [2240] Loss: 0.105, Running accuracy: 99.933, Time: 36.75
[2020-12-16 09:55:19,117][__main__][INFO] - [2560] Loss: 0.085, Running accuracy: 99.932, Time: 34.69
[2020-12-16 09:55:52,455][__main__][INFO] - [2880] Loss: 0.106, Running accuracy: 99.932, Time: 33.34
[2020-12-16 09:56:29,608][__main__][INFO] - [3200] Loss: 0.133, Running accuracy: 99.933, Time: 37.15
[2020-12-16 09:57:07,822][__main__][INFO] - [3520] Loss: 0.079, Running accuracy: 99.937, Time: 38.21
[2020-12-16 09:57:39,979][__main__][INFO] - [3840] Loss: 0.107, Running accuracy: 99.936, Time: 32.16
[2020-12-16 09:58:13,379][__main__][INFO] - [4160] Loss: 0.090, Running accuracy: 99.939, Time: 33.40
[2020-12-16 09:58:49,317][__main__][INFO] - [4480] Loss: 0.104, Running accuracy: 99.940, Time: 35.94
[2020-12-16 09:59:26,145][__main__][INFO] - [4800] Loss: 0.077, Running accuracy: 99.942, Time: 36.83
[2020-12-16 10:00:03,506][__main__][INFO] - [5120] Loss: 0.165, Running accuracy: 99.941, Time: 37.36
[2020-12-16 10:00:39,036][__main__][INFO] - [5440] Loss: 0.102, Running accuracy: 99.942, Time: 35.53
[2020-12-16 10:01:14,989][__main__][INFO] - [5760] Loss: 0.070, Running accuracy: 99.945, Time: 35.95
[2020-12-16 10:01:52,477][__main__][INFO] - [6080] Loss: 0.088, Running accuracy: 99.946, Time: 37.49
[2020-12-16 10:02:26,698][__main__][INFO] - [6400] Loss: 0.119, Running accuracy: 99.945, Time: 34.22
[2020-12-16 10:03:04,315][__main__][INFO] - [6720] Loss: 0.094, Running accuracy: 99.946, Time: 37.62
[2020-12-16 10:03:38,569][__main__][INFO] - [7040] Loss: 0.073, Running accuracy: 99.949, Time: 34.25
[2020-12-16 10:04:15,906][__main__][INFO] - [7360] Loss: 0.087, Running accuracy: 99.949, Time: 37.34
[2020-12-16 10:04:48,612][__main__][INFO] - [7680] Loss: 0.096, Running accuracy: 99.949, Time: 32.60
[2020-12-16 10:05:24,736][__main__][INFO] - [8000] Loss: 0.097, Running accuracy: 99.948, Time: 36.12
[2020-12-16 10:06:00,901][__main__][INFO] - [8320] Loss: 0.090, Running accuracy: 99.948, Time: 36.16
[2020-12-16 10:06:37,332][__main__][INFO] - [8640] Loss: 0.088, Running accuracy: 99.948, Time: 36.43
[2020-12-16 10:07:16,503][__main__][INFO] - [8960] Loss: 0.130, Running accuracy: 99.947, Time: 39.17
[2020-12-16 10:07:48,707][__main__][INFO] - [9280] Loss: 0.082, Running accuracy: 99.948, Time: 32.20
[2020-12-16 10:08:17,328][__main__][INFO] - [9600] Loss: 0.076, Running accuracy: 99.948, Time: 28.62
[2020-12-16 10:08:52,654][__main__][INFO] - [9920] Loss: 0.140, Running accuracy: 99.947, Time: 35.33
[2020-12-16 10:09:28,814][__main__][INFO] - [10240] Loss: 0.117, Running accuracy: 99.946, Time: 36.16
[2020-12-16 10:10:03,574][__main__][INFO] - [10560] Loss: 0.171, Running accuracy: 99.945, Time: 34.76
[2020-12-16 10:10:39,250][__main__][INFO] - [10880] Loss: 0.089, Running accuracy: 99.945, Time: 35.68
[2020-12-16 10:11:12,573][__main__][INFO] - [11200] Loss: 0.104, Running accuracy: 99.945, Time: 33.32
[2020-12-16 10:11:44,038][__main__][INFO] - [11520] Loss: 0.084, Running accuracy: 99.945, Time: 31.46
[2020-12-16 10:12:15,555][__main__][INFO] - [11840] Loss: 0.115, Running accuracy: 99.944, Time: 31.51
[2020-12-16 10:12:51,899][__main__][INFO] - [12160] Loss: 0.100, Running accuracy: 99.943, Time: 36.34
[2020-12-16 10:13:21,211][__main__][INFO] - [12480] Loss: 0.090, Running accuracy: 99.942, Time: 29.31
[2020-12-16 10:13:53,072][__main__][INFO] - [12800] Loss: 0.097, Running accuracy: 99.942, Time: 31.86
[2020-12-16 10:14:27,962][__main__][INFO] - [13120] Loss: 0.081, Running accuracy: 99.942, Time: 34.89
[2020-12-16 10:15:05,395][__main__][INFO] - [13440] Loss: 0.105, Running accuracy: 99.942, Time: 37.43
[2020-12-16 10:15:38,917][__main__][INFO] - [13760] Loss: 0.125, Running accuracy: 99.941, Time: 33.52
[2020-12-16 10:16:11,897][__main__][INFO] - [14080] Loss: 0.098, Running accuracy: 99.942, Time: 32.98
[2020-12-16 10:16:42,738][__main__][INFO] - [14400] Loss: 0.112, Running accuracy: 99.941, Time: 30.84
[2020-12-16 10:17:20,837][__main__][INFO] - [14720] Loss: 0.121, Running accuracy: 99.940, Time: 38.10
[2020-12-16 10:17:57,229][__main__][INFO] - [15040] Loss: 0.091, Running accuracy: 99.940, Time: 36.39
[2020-12-16 10:18:33,500][__main__][INFO] - [15360] Loss: 0.069, Running accuracy: 99.941, Time: 36.27
[2020-12-16 10:19:05,965][__main__][INFO] - [15680] Loss: 0.145, Running accuracy: 99.940, Time: 32.46
[2020-12-16 10:19:40,025][__main__][INFO] - [16000] Loss: 0.156, Running accuracy: 99.940, Time: 34.06
[2020-12-16 10:20:15,416][__main__][INFO] - [16320] Loss: 0.118, Running accuracy: 99.940, Time: 35.39
[2020-12-16 10:20:56,205][__main__][INFO] - [16640] Loss: 0.111, Running accuracy: 99.940, Time: 40.79
[2020-12-16 10:21:35,613][__main__][INFO] - [16960] Loss: 0.100, Running accuracy: 99.938, Time: 39.41
[2020-12-16 10:22:12,939][__main__][INFO] - [17280] Loss: 0.092, Running accuracy: 99.939, Time: 37.33
[2020-12-16 10:22:38,596][__main__][INFO] - Action accuracy: 99.938, Loss: 0.114
[2020-12-16 10:22:38,597][__main__][INFO] - Validating..
[2020-12-16 10:22:44,655][test][INFO] - Time elapsed: 4.747133
[2020-12-16 10:22:44,657][__main__][INFO] - Validation F1 score: 94.530, Exact match: 56.530, Precision: 94.640, Recall: 94.410
[2020-12-16 10:22:57,180][__main__][INFO] - Checkpoint saved to checkpoints/model_latest.pth
[2020-12-16 10:22:57,533][__main__][INFO] - Epoch #38
[2020-12-16 10:22:57,533][__main__][INFO] - Training..
[2020-12-16 10:23:33,567][__main__][INFO] - [320] Loss: 0.130, Running accuracy: 99.932, Time: 34.78
[2020-12-16 10:24:05,298][__main__][INFO] - [640] Loss: 0.095, Running accuracy: 99.930, Time: 31.73
[2020-12-16 10:24:41,427][__main__][INFO] - [960] Loss: 0.106, Running accuracy: 99.924, Time: 36.13
[2020-12-16 10:25:17,304][__main__][INFO] - [1280] Loss: 0.100, Running accuracy: 99.923, Time: 35.88
[2020-12-16 10:25:56,976][__main__][INFO] - [1600] Loss: 0.099, Running accuracy: 99.925, Time: 39.67
[2020-12-16 10:26:31,566][__main__][INFO] - [1920] Loss: 0.098, Running accuracy: 99.926, Time: 34.59
[2020-12-16 10:27:07,310][__main__][INFO] - [2240] Loss: 0.082, Running accuracy: 99.928, Time: 35.74
[2020-12-16 10:27:42,735][__main__][INFO] - [2560] Loss: 0.104, Running accuracy: 99.927, Time: 35.42
[2020-12-16 10:28:18,330][__main__][INFO] - [2880] Loss: 0.088, Running accuracy: 99.929, Time: 35.59
[2020-12-16 10:28:54,515][__main__][INFO] - [3200] Loss: 0.082, Running accuracy: 99.932, Time: 36.18
[2020-12-16 10:29:31,240][__main__][INFO] - [3520] Loss: 0.079, Running accuracy: 99.935, Time: 36.72
[2020-12-16 10:30:06,752][__main__][INFO] - [3840] Loss: 0.092, Running accuracy: 99.938, Time: 35.51
[2020-12-16 10:30:41,414][__main__][INFO] - [4160] Loss: 0.072, Running accuracy: 99.940, Time: 34.66
[2020-12-16 10:31:15,349][__main__][INFO] - [4480] Loss: 0.062, Running accuracy: 99.942, Time: 33.93
[2020-12-16 10:31:53,334][__main__][INFO] - [4800] Loss: 0.088, Running accuracy: 99.944, Time: 37.98
[2020-12-16 10:32:26,968][__main__][INFO] - [5120] Loss: 0.109, Running accuracy: 99.944, Time: 33.63
[2020-12-16 10:33:06,860][__main__][INFO] - [5440] Loss: 0.089, Running accuracy: 99.944, Time: 39.89
[2020-12-16 10:33:43,717][__main__][INFO] - [5760] Loss: 0.085, Running accuracy: 99.942, Time: 36.86
[2020-12-16 10:34:14,436][__main__][INFO] - [6080] Loss: 0.135, Running accuracy: 99.939, Time: 30.72
[2020-12-16 10:34:46,788][__main__][INFO] - [6400] Loss: 0.068, Running accuracy: 99.940, Time: 32.35
[2020-12-16 10:35:23,370][__main__][INFO] - [6720] Loss: 0.109, Running accuracy: 99.938, Time: 36.58
[2020-12-16 10:35:56,758][__main__][INFO] - [7040] Loss: 0.078, Running accuracy: 99.939, Time: 33.39
[2020-12-16 10:36:31,532][__main__][INFO] - [7360] Loss: 0.148, Running accuracy: 99.939, Time: 34.77
[2020-12-16 10:37:06,543][__main__][INFO] - [7680] Loss: 0.106, Running accuracy: 99.939, Time: 35.01
[2020-12-16 10:37:41,061][__main__][INFO] - [8000] Loss: 0.116, Running accuracy: 99.935, Time: 34.51
[2020-12-16 10:38:10,469][__main__][INFO] - [8320] Loss: 0.086, Running accuracy: 99.935, Time: 29.41
[2020-12-16 10:38:43,556][__main__][INFO] - [8640] Loss: 0.065, Running accuracy: 99.936, Time: 33.09
[2020-12-16 10:39:23,742][__main__][INFO] - [8960] Loss: 0.101, Running accuracy: 99.937, Time: 40.09
[2020-12-16 10:40:01,423][__main__][INFO] - [9280] Loss: 0.107, Running accuracy: 99.938, Time: 37.68
[2020-12-16 10:40:33,780][__main__][INFO] - [9600] Loss: 0.084, Running accuracy: 99.939, Time: 32.29
[2020-12-16 10:41:08,905][__main__][INFO] - [9920] Loss: 0.120, Running accuracy: 99.939, Time: 35.12
[2020-12-16 10:41:45,192][__main__][INFO] - [10240] Loss: 0.087, Running accuracy: 99.938, Time: 36.28
[2020-12-16 10:42:18,224][__main__][INFO] - [10560] Loss: 0.084, Running accuracy: 99.939, Time: 33.03
[2020-12-16 10:42:52,771][__main__][INFO] - [10880] Loss: 0.131, Running accuracy: 99.939, Time: 34.55
[2020-12-16 10:43:27,306][__main__][INFO] - [11200] Loss: 0.101, Running accuracy: 99.939, Time: 34.53
[2020-12-16 10:44:01,054][__main__][INFO] - [11520] Loss: 0.100, Running accuracy: 99.939, Time: 33.75
[2020-12-16 10:44:35,711][__main__][INFO] - [11840] Loss: 0.106, Running accuracy: 99.938, Time: 34.66
[2020-12-16 10:45:11,999][__main__][INFO] - [12160] Loss: 0.082, Running accuracy: 99.939, Time: 36.29
[2020-12-16 10:45:44,331][__main__][INFO] - [12480] Loss: 0.086, Running accuracy: 99.939, Time: 32.33
[2020-12-16 10:46:20,433][__main__][INFO] - [12800] Loss: 0.112, Running accuracy: 99.940, Time: 36.10
[2020-12-16 10:46:53,349][__main__][INFO] - [13120] Loss: 0.099, Running accuracy: 99.940, Time: 32.91
[2020-12-16 10:47:28,618][__main__][INFO] - [13440] Loss: 0.085, Running accuracy: 99.941, Time: 35.27
[2020-12-16 10:48:01,573][__main__][INFO] - [13760] Loss: 0.093, Running accuracy: 99.942, Time: 32.95
[2020-12-16 10:48:36,340][__main__][INFO] - [14080] Loss: 0.069, Running accuracy: 99.942, Time: 34.76
[2020-12-16 10:49:15,559][__main__][INFO] - [14400] Loss: 0.107, Running accuracy: 99.942, Time: 39.22
[2020-12-16 10:49:44,239][__main__][INFO] - [14720] Loss: 0.076, Running accuracy: 99.943, Time: 28.68
[2020-12-16 10:50:19,186][__main__][INFO] - [15040] Loss: 0.096, Running accuracy: 99.943, Time: 34.94
[2020-12-16 10:50:56,268][__main__][INFO] - [15360] Loss: 0.102, Running accuracy: 99.943, Time: 37.08
[2020-12-16 10:51:30,959][__main__][INFO] - [15680] Loss: 0.066, Running accuracy: 99.943, Time: 34.69
[2020-12-16 10:52:02,470][__main__][INFO] - [16000] Loss: 0.089, Running accuracy: 99.943, Time: 31.50
[2020-12-16 10:52:42,722][__main__][INFO] - [16320] Loss: 0.153, Running accuracy: 99.942, Time: 40.25
[2020-12-16 10:53:23,153][__main__][INFO] - [16640] Loss: 0.123, Running accuracy: 99.942, Time: 40.43
[2020-12-16 10:53:56,715][__main__][INFO] - [16960] Loss: 0.131, Running accuracy: 99.941, Time: 33.56
[2020-12-16 10:54:29,214][__main__][INFO] - [17280] Loss: 0.096, Running accuracy: 99.941, Time: 32.50
[2020-12-16 10:54:56,138][__main__][INFO] - Action accuracy: 99.942, Loss: 0.108
[2020-12-16 10:54:56,138][__main__][INFO] - Validating..
[2020-12-16 10:55:02,167][test][INFO] - Time elapsed: 4.716413
[2020-12-16 10:55:02,169][__main__][INFO] - Validation F1 score: 94.490, Exact match: 56.820, Precision: 94.600, Recall: 94.380
Epoch    39: reducing learning rate of group 0 to 2.5000e-06.
[2020-12-16 10:55:14,891][__main__][INFO] - Checkpoint saved to checkpoints/model_latest.pth
[2020-12-16 10:55:15,200][__main__][INFO] - Epoch #39
[2020-12-16 10:55:15,201][__main__][INFO] - Training..
[2020-12-16 10:55:48,316][__main__][INFO] - [320] Loss: 0.081, Running accuracy: 99.929, Time: 32.11
[2020-12-16 10:56:28,481][__main__][INFO] - [640] Loss: 0.076, Running accuracy: 99.936, Time: 40.16
[2020-12-16 10:57:06,456][__main__][INFO] - [960] Loss: 0.091, Running accuracy: 99.943, Time: 37.97
[2020-12-16 10:57:38,651][__main__][INFO] - [1280] Loss: 0.087, Running accuracy: 99.949, Time: 32.19
[2020-12-16 10:58:08,172][__main__][INFO] - [1600] Loss: 0.104, Running accuracy: 99.944, Time: 29.51
[2020-12-16 10:58:40,094][__main__][INFO] - [1920] Loss: 0.076, Running accuracy: 99.946, Time: 31.92
[2020-12-16 10:59:13,431][__main__][INFO] - [2240] Loss: 0.103, Running accuracy: 99.938, Time: 33.34
[2020-12-16 10:59:46,197][__main__][INFO] - [2560] Loss: 0.077, Running accuracy: 99.940, Time: 32.76
[2020-12-16 11:00:24,423][__main__][INFO] - [2880] Loss: 0.075, Running accuracy: 99.942, Time: 38.21
[2020-12-16 11:01:03,926][__main__][INFO] - [3200] Loss: 0.102, Running accuracy: 99.942, Time: 39.50
[2020-12-16 11:01:36,708][__main__][INFO] - [3520] Loss: 0.093, Running accuracy: 99.941, Time: 32.77
[2020-12-16 11:02:08,499][__main__][INFO] - [3840] Loss: 0.082, Running accuracy: 99.945, Time: 31.79
[2020-12-16 11:02:49,015][__main__][INFO] - [4160] Loss: 0.086, Running accuracy: 99.946, Time: 40.51
[2020-12-16 11:03:26,275][__main__][INFO] - [4480] Loss: 0.081, Running accuracy: 99.947, Time: 37.26
[2020-12-16 11:04:02,199][__main__][INFO] - [4800] Loss: 0.106, Running accuracy: 99.947, Time: 35.92
[2020-12-16 11:04:39,503][__main__][INFO] - [5120] Loss: 0.111, Running accuracy: 99.944, Time: 37.30
[2020-12-16 11:05:12,792][__main__][INFO] - [5440] Loss: 0.095, Running accuracy: 99.944, Time: 33.28
[2020-12-16 11:05:42,446][__main__][INFO] - [5760] Loss: 0.063, Running accuracy: 99.946, Time: 29.65
[2020-12-16 11:06:20,301][__main__][INFO] - [6080] Loss: 0.099, Running accuracy: 99.944, Time: 37.85
[2020-12-16 11:06:55,276][__main__][INFO] - [6400] Loss: 0.087, Running accuracy: 99.945, Time: 34.97
[2020-12-16 11:07:32,861][__main__][INFO] - [6720] Loss: 0.072, Running accuracy: 99.946, Time: 37.58
[2020-12-16 11:08:07,266][__main__][INFO] - [7040] Loss: 0.098, Running accuracy: 99.946, Time: 34.32
[2020-12-16 11:08:42,440][__main__][INFO] - [7360] Loss: 0.084, Running accuracy: 99.946, Time: 35.17
[2020-12-16 11:09:17,524][__main__][INFO] - [7680] Loss: 0.085, Running accuracy: 99.947, Time: 35.08
[2020-12-16 11:09:51,282][__main__][INFO] - [8000] Loss: 0.081, Running accuracy: 99.947, Time: 33.76
[2020-12-16 11:10:25,897][__main__][INFO] - [8320] Loss: 0.056, Running accuracy: 99.949, Time: 34.61
[2020-12-16 11:10:59,179][__main__][INFO] - [8640] Loss: 0.086, Running accuracy: 99.949, Time: 33.28
[2020-12-16 11:11:34,431][__main__][INFO] - [8960] Loss: 0.070, Running accuracy: 99.950, Time: 35.25
[2020-12-16 11:12:09,562][__main__][INFO] - [9280] Loss: 0.078, Running accuracy: 99.951, Time: 35.13
[2020-12-16 11:12:44,355][__main__][INFO] - [9600] Loss: 0.117, Running accuracy: 99.950, Time: 34.79
[2020-12-16 11:13:19,976][__main__][INFO] - [9920] Loss: 0.100, Running accuracy: 99.948, Time: 35.62
[2020-12-16 11:13:55,771][__main__][INFO] - [10240] Loss: 0.098, Running accuracy: 99.947, Time: 35.79
[2020-12-16 11:14:31,455][__main__][INFO] - [10560] Loss: 0.069, Running accuracy: 99.947, Time: 35.68
[2020-12-16 11:15:05,392][__main__][INFO] - [10880] Loss: 0.072, Running accuracy: 99.947, Time: 33.94
[2020-12-16 11:15:41,461][__main__][INFO] - [11200] Loss: 0.100, Running accuracy: 99.946, Time: 36.07
[2020-12-16 11:16:17,912][__main__][INFO] - [11520] Loss: 0.083, Running accuracy: 99.947, Time: 36.45
[2020-12-16 11:16:51,946][__main__][INFO] - [11840] Loss: 0.078, Running accuracy: 99.948, Time: 34.03
[2020-12-16 11:17:27,600][__main__][INFO] - [12160] Loss: 0.082, Running accuracy: 99.949, Time: 35.65
[2020-12-16 11:17:54,889][__main__][INFO] - [12480] Loss: 0.103, Running accuracy: 99.948, Time: 27.29
[2020-12-16 11:18:27,617][__main__][INFO] - [12800] Loss: 0.077, Running accuracy: 99.948, Time: 32.73
[2020-12-16 11:19:01,219][__main__][INFO] - [13120] Loss: 0.093, Running accuracy: 99.947, Time: 33.60
[2020-12-16 11:19:36,557][__main__][INFO] - [13440] Loss: 0.089, Running accuracy: 99.947, Time: 35.33
[2020-12-16 11:20:10,306][__main__][INFO] - [13760] Loss: 0.090, Running accuracy: 99.947, Time: 33.75
[2020-12-16 11:20:47,885][__main__][INFO] - [14080] Loss: 0.090, Running accuracy: 99.947, Time: 37.58
[2020-12-16 11:21:20,513][__main__][INFO] - [14400] Loss: 0.144, Running accuracy: 99.946, Time: 32.63
[2020-12-16 11:21:54,394][__main__][INFO] - [14720] Loss: 0.084, Running accuracy: 99.946, Time: 33.88
[2020-12-16 11:22:37,148][__main__][INFO] - [15040] Loss: 0.094, Running accuracy: 99.946, Time: 42.75
[2020-12-16 11:23:13,175][__main__][INFO] - [15360] Loss: 0.111, Running accuracy: 99.945, Time: 36.02
[2020-12-16 11:23:54,951][__main__][INFO] - [15680] Loss: 0.079, Running accuracy: 99.945, Time: 41.78
[2020-12-16 11:24:29,433][__main__][INFO] - [16000] Loss: 0.062, Running accuracy: 99.946, Time: 34.48
[2020-12-16 11:25:00,621][__main__][INFO] - [16320] Loss: 0.053, Running accuracy: 99.947, Time: 31.19
[2020-12-16 11:25:32,917][__main__][INFO] - [16640] Loss: 0.090, Running accuracy: 99.947, Time: 32.29
[2020-12-16 11:26:03,975][__main__][INFO] - [16960] Loss: 0.102, Running accuracy: 99.947, Time: 31.06
[2020-12-16 11:26:35,146][__main__][INFO] - [17280] Loss: 0.119, Running accuracy: 99.946, Time: 31.17
[2020-12-16 11:27:01,996][__main__][INFO] - Action accuracy: 99.946, Loss: 0.097
[2020-12-16 11:27:01,996][__main__][INFO] - Validating..
[2020-12-16 11:27:10,315][test][INFO] - Time elapsed: 7.088203
[2020-12-16 11:27:10,317][__main__][INFO] - Validation F1 score: 94.430, Exact match: 55.970, Precision: 94.620, Recall: 94.250
[2020-12-16 11:27:23,120][__main__][INFO] - Checkpoint saved to checkpoints/model_latest.pth
[2020-12-16 11:27:23,432][__main__][INFO] - Epoch #40
[2020-12-16 11:27:23,432][__main__][INFO] - Training..
[2020-12-16 11:28:05,182][__main__][INFO] - [320] Loss: 0.077, Running accuracy: 99.967, Time: 40.86
[2020-12-16 11:28:41,691][__main__][INFO] - [640] Loss: 0.124, Running accuracy: 99.940, Time: 36.51
[2020-12-16 11:29:17,207][__main__][INFO] - [960] Loss: 0.074, Running accuracy: 99.952, Time: 35.52
[2020-12-16 11:29:49,853][__main__][INFO] - [1280] Loss: 0.085, Running accuracy: 99.952, Time: 32.65
[2020-12-16 11:30:22,878][__main__][INFO] - [1600] Loss: 0.071, Running accuracy: 99.957, Time: 33.02
[2020-12-16 11:30:59,195][__main__][INFO] - [1920] Loss: 0.078, Running accuracy: 99.956, Time: 36.32
[2020-12-16 11:31:37,163][__main__][INFO] - [2240] Loss: 0.086, Running accuracy: 99.955, Time: 37.97
[2020-12-16 11:32:09,612][__main__][INFO] - [2560] Loss: 0.115, Running accuracy: 99.953, Time: 32.45
[2020-12-16 11:32:43,200][__main__][INFO] - [2880] Loss: 0.078, Running accuracy: 99.953, Time: 33.59
[2020-12-16 11:33:15,728][__main__][INFO] - [3200] Loss: 0.060, Running accuracy: 99.955, Time: 32.53
[2020-12-16 11:33:51,675][__main__][INFO] - [3520] Loss: 0.076, Running accuracy: 99.956, Time: 35.95
[2020-12-16 11:34:28,793][__main__][INFO] - [3840] Loss: 0.060, Running accuracy: 99.960, Time: 37.11
[2020-12-16 11:35:00,740][__main__][INFO] - [4160] Loss: 0.069, Running accuracy: 99.961, Time: 31.94
[2020-12-16 11:35:37,020][__main__][INFO] - [4480] Loss: 0.089, Running accuracy: 99.960, Time: 36.28
[2020-12-16 11:36:08,606][__main__][INFO] - [4800] Loss: 0.112, Running accuracy: 99.958, Time: 31.58
[2020-12-16 11:36:42,899][__main__][INFO] - [5120] Loss: 0.119, Running accuracy: 99.956, Time: 34.29
[2020-12-16 11:37:20,936][__main__][INFO] - [5440] Loss: 0.085, Running accuracy: 99.958, Time: 38.03
[2020-12-16 11:37:54,569][__main__][INFO] - [5760] Loss: 0.071, Running accuracy: 99.958, Time: 33.63
[2020-12-16 11:38:32,602][__main__][INFO] - [6080] Loss: 0.087, Running accuracy: 99.957, Time: 38.03
[2020-12-16 11:39:04,660][__main__][INFO] - [6400] Loss: 0.075, Running accuracy: 99.957, Time: 32.06
[2020-12-16 11:39:36,825][__main__][INFO] - [6720] Loss: 0.086, Running accuracy: 99.958, Time: 32.16
[2020-12-16 11:40:13,332][__main__][INFO] - [7040] Loss: 0.113, Running accuracy: 99.957, Time: 36.51
[2020-12-16 11:40:43,513][__main__][INFO] - [7360] Loss: 0.108, Running accuracy: 99.955, Time: 30.18
[2020-12-16 11:41:22,790][__main__][INFO] - [7680] Loss: 0.084, Running accuracy: 99.954, Time: 39.27
[2020-12-16 11:42:01,338][__main__][INFO] - [8000] Loss: 0.084, Running accuracy: 99.953, Time: 38.55
[2020-12-16 11:42:35,954][__main__][INFO] - [8320] Loss: 0.067, Running accuracy: 99.954, Time: 34.61
[2020-12-16 11:43:11,190][__main__][INFO] - [8640] Loss: 0.077, Running accuracy: 99.955, Time: 35.24
[2020-12-16 11:43:42,905][__main__][INFO] - [8960] Loss: 0.070, Running accuracy: 99.955, Time: 31.71
[2020-12-16 11:44:15,996][__main__][INFO] - [9280] Loss: 0.065, Running accuracy: 99.956, Time: 33.09
[2020-12-16 11:44:52,812][__main__][INFO] - [9600] Loss: 0.087, Running accuracy: 99.956, Time: 36.81
[2020-12-16 11:45:25,545][__main__][INFO] - [9920] Loss: 0.090, Running accuracy: 99.956, Time: 32.73
[2020-12-16 11:46:04,072][__main__][INFO] - [10240] Loss: 0.056, Running accuracy: 99.956, Time: 38.53
[2020-12-16 11:46:37,031][__main__][INFO] - [10560] Loss: 0.094, Running accuracy: 99.956, Time: 32.96
[2020-12-16 11:47:09,752][__main__][INFO] - [10880] Loss: 0.062, Running accuracy: 99.956, Time: 32.72
[2020-12-16 11:47:48,521][__main__][INFO] - [11200] Loss: 0.081, Running accuracy: 99.956, Time: 38.77
[2020-12-16 11:48:23,353][__main__][INFO] - [11520] Loss: 0.079, Running accuracy: 99.956, Time: 34.83
[2020-12-16 11:48:55,576][__main__][INFO] - [11840] Loss: 0.067, Running accuracy: 99.957, Time: 32.22
[2020-12-16 11:49:31,264][__main__][INFO] - [12160] Loss: 0.091, Running accuracy: 99.957, Time: 35.69
[2020-12-16 11:50:05,564][__main__][INFO] - [12480] Loss: 0.085, Running accuracy: 99.957, Time: 34.29
[2020-12-16 11:50:40,364][__main__][INFO] - [12800] Loss: 0.094, Running accuracy: 99.956, Time: 34.80
[2020-12-16 11:51:12,399][__main__][INFO] - [13120] Loss: 0.076, Running accuracy: 99.956, Time: 32.03
[2020-12-16 11:51:47,562][__main__][INFO] - [13440] Loss: 0.088, Running accuracy: 99.956, Time: 35.16
[2020-12-16 11:52:19,577][__main__][INFO] - [13760] Loss: 0.103, Running accuracy: 99.955, Time: 32.01
[2020-12-16 11:52:55,516][__main__][INFO] - [14080] Loss: 0.059, Running accuracy: 99.955, Time: 35.94
[2020-12-16 11:53:26,261][__main__][INFO] - [14400] Loss: 0.076, Running accuracy: 99.954, Time: 30.74
[2020-12-16 11:54:06,230][__main__][INFO] - [14720] Loss: 0.093, Running accuracy: 99.954, Time: 39.97
[2020-12-16 11:54:43,648][__main__][INFO] - [15040] Loss: 0.090, Running accuracy: 99.954, Time: 37.42
[2020-12-16 11:55:17,962][__main__][INFO] - [15360] Loss: 0.123, Running accuracy: 99.954, Time: 34.31
[2020-12-16 11:55:48,809][__main__][INFO] - [15680] Loss: 0.075, Running accuracy: 99.954, Time: 30.85
[2020-12-16 11:56:28,809][__main__][INFO] - [16000] Loss: 0.096, Running accuracy: 99.954, Time: 40.00
[2020-12-16 11:57:04,371][__main__][INFO] - [16320] Loss: 0.081, Running accuracy: 99.955, Time: 35.56
[2020-12-16 11:57:38,842][__main__][INFO] - [16640] Loss: 0.096, Running accuracy: 99.954, Time: 34.47
[2020-12-16 11:58:12,095][__main__][INFO] - [16960] Loss: 0.092, Running accuracy: 99.953, Time: 33.25
[2020-12-16 11:58:46,452][__main__][INFO] - [17280] Loss: 0.090, Running accuracy: 99.954, Time: 34.36
[2020-12-16 11:59:19,758][__main__][INFO] - Action accuracy: 99.954, Loss: 0.094
[2020-12-16 11:59:19,758][__main__][INFO] - Validating..
[2020-12-16 11:59:25,976][test][INFO] - Time elapsed: 4.878847
[2020-12-16 11:59:25,978][__main__][INFO] - Validation F1 score: 94.610, Exact match: 57.100, Precision: 94.650, Recall: 94.570
[2020-12-16 11:59:42,465][__main__][INFO] - Checkpoint saved to checkpoints/model_latest.pth
[2020-12-16 11:59:42,775][__main__][INFO] - Epoch #41
[2020-12-16 11:59:42,776][__main__][INFO] - Training..
[2020-12-16 12:00:19,256][__main__][INFO] - [320] Loss: 0.066, Running accuracy: 99.979, Time: 35.20
[2020-12-16 12:00:54,375][__main__][INFO] - [640] Loss: 0.091, Running accuracy: 99.972, Time: 35.12
[2020-12-16 12:01:26,550][__main__][INFO] - [960] Loss: 0.064, Running accuracy: 99.969, Time: 32.17
[2020-12-16 12:01:58,250][__main__][INFO] - [1280] Loss: 0.082, Running accuracy: 99.968, Time: 31.70
[2020-12-16 12:02:31,219][__main__][INFO] - [1600] Loss: 0.070, Running accuracy: 99.965, Time: 32.97
[2020-12-16 12:03:05,985][__main__][INFO] - [1920] Loss: 0.077, Running accuracy: 99.963, Time: 34.76
[2020-12-16 12:03:45,082][__main__][INFO] - [2240] Loss: 0.097, Running accuracy: 99.964, Time: 39.09
[2020-12-16 12:04:23,764][__main__][INFO] - [2560] Loss: 0.113, Running accuracy: 99.963, Time: 38.68
[2020-12-16 12:05:00,427][__main__][INFO] - [2880] Loss: 0.061, Running accuracy: 99.965, Time: 36.66
[2020-12-16 12:05:34,785][__main__][INFO] - [3200] Loss: 0.070, Running accuracy: 99.968, Time: 34.36
[2020-12-16 12:06:09,080][__main__][INFO] - [3520] Loss: 0.077, Running accuracy: 99.969, Time: 34.29
[2020-12-16 12:06:45,585][__main__][INFO] - [3840] Loss: 0.065, Running accuracy: 99.969, Time: 36.50
[2020-12-16 12:07:21,182][__main__][INFO] - [4160] Loss: 0.062, Running accuracy: 99.969, Time: 35.60
[2020-12-16 12:07:58,443][__main__][INFO] - [4480] Loss: 0.097, Running accuracy: 99.966, Time: 37.26
[2020-12-16 12:08:32,777][__main__][INFO] - [4800] Loss: 0.057, Running accuracy: 99.969, Time: 34.33
[2020-12-16 12:09:12,680][__main__][INFO] - [5120] Loss: 0.091, Running accuracy: 99.967, Time: 39.90
[2020-12-16 12:09:49,362][__main__][INFO] - [5440] Loss: 0.085, Running accuracy: 99.966, Time: 36.68
[2020-12-16 12:10:22,778][__main__][INFO] - [5760] Loss: 0.081, Running accuracy: 99.964, Time: 33.41
[2020-12-16 12:10:56,839][__main__][INFO] - [6080] Loss: 0.064, Running accuracy: 99.964, Time: 34.06
[2020-12-16 12:11:29,076][__main__][INFO] - [6400] Loss: 0.106, Running accuracy: 99.963, Time: 32.24
[2020-12-16 12:12:05,750][__main__][INFO] - [6720] Loss: 0.097, Running accuracy: 99.962, Time: 36.67
[2020-12-16 12:12:36,680][__main__][INFO] - [7040] Loss: 0.101, Running accuracy: 99.961, Time: 30.93
[2020-12-16 12:13:10,343][__main__][INFO] - [7360] Loss: 0.094, Running accuracy: 99.960, Time: 33.66
[2020-12-16 12:13:46,358][__main__][INFO] - [7680] Loss: 0.102, Running accuracy: 99.958, Time: 35.92
[2020-12-16 12:14:24,877][__main__][INFO] - [8000] Loss: 0.087, Running accuracy: 99.956, Time: 38.52
[2020-12-16 12:14:56,382][__main__][INFO] - [8320] Loss: 0.093, Running accuracy: 99.956, Time: 31.38
[2020-12-16 12:15:34,392][__main__][INFO] - [8640] Loss: 0.081, Running accuracy: 99.957, Time: 38.01
[2020-12-16 12:16:11,670][__main__][INFO] - [8960] Loss: 0.092, Running accuracy: 99.956, Time: 37.28
[2020-12-16 12:16:52,068][__main__][INFO] - [9280] Loss: 0.074, Running accuracy: 99.957, Time: 40.40
[2020-12-16 12:17:27,384][__main__][INFO] - [9600] Loss: 0.070, Running accuracy: 99.957, Time: 35.32
[2020-12-16 12:18:01,746][__main__][INFO] - [9920] Loss: 0.088, Running accuracy: 99.957, Time: 34.36
[2020-12-16 12:18:33,832][__main__][INFO] - [10240] Loss: 0.088, Running accuracy: 99.957, Time: 32.08
[2020-12-16 12:19:05,072][__main__][INFO] - [10560] Loss: 0.073, Running accuracy: 99.957, Time: 31.24
[2020-12-16 12:19:44,331][__main__][INFO] - [10880] Loss: 0.080, Running accuracy: 99.958, Time: 39.26
[2020-12-16 12:20:15,697][__main__][INFO] - [11200] Loss: 0.090, Running accuracy: 99.958, Time: 31.36
[2020-12-16 12:20:51,468][__main__][INFO] - [11520] Loss: 0.063, Running accuracy: 99.958, Time: 35.77
[2020-12-16 12:21:25,507][__main__][INFO] - [11840] Loss: 0.091, Running accuracy: 99.958, Time: 34.04
[2020-12-16 12:21:59,285][__main__][INFO] - [12160] Loss: 0.088, Running accuracy: 99.958, Time: 33.78
[2020-12-16 12:22:32,065][__main__][INFO] - [12480] Loss: 0.069, Running accuracy: 99.958, Time: 32.77
[2020-12-16 12:23:03,435][__main__][INFO] - [12800] Loss: 0.067, Running accuracy: 99.958, Time: 31.37
[2020-12-16 12:23:42,047][__main__][INFO] - [13120] Loss: 0.081, Running accuracy: 99.959, Time: 38.61
[2020-12-16 12:24:13,451][__main__][INFO] - [13440] Loss: 0.075, Running accuracy: 99.958, Time: 31.40
[2020-12-16 12:24:52,359][__main__][INFO] - [13760] Loss: 0.077, Running accuracy: 99.958, Time: 38.90
[2020-12-16 12:25:26,944][__main__][INFO] - [14080] Loss: 0.074, Running accuracy: 99.959, Time: 34.58
[2020-12-16 12:26:02,513][__main__][INFO] - [14400] Loss: 0.092, Running accuracy: 99.959, Time: 35.57
[2020-12-16 12:26:37,137][__main__][INFO] - [14720] Loss: 0.075, Running accuracy: 99.959, Time: 34.62
[2020-12-16 12:27:14,133][__main__][INFO] - [15040] Loss: 0.063, Running accuracy: 99.959, Time: 36.99
[2020-12-16 12:27:54,419][__main__][INFO] - [15360] Loss: 0.073, Running accuracy: 99.960, Time: 40.28
[2020-12-16 12:28:27,721][__main__][INFO] - [15680] Loss: 0.096, Running accuracy: 99.959, Time: 33.30
[2020-12-16 12:29:04,461][__main__][INFO] - [16000] Loss: 0.091, Running accuracy: 99.959, Time: 36.74
[2020-12-16 12:29:36,926][__main__][INFO] - [16320] Loss: 0.068, Running accuracy: 99.959, Time: 32.46
[2020-12-16 12:30:11,500][__main__][INFO] - [16640] Loss: 0.122, Running accuracy: 99.958, Time: 34.57
[2020-12-16 12:30:51,805][__main__][INFO] - [16960] Loss: 0.179, Running accuracy: 99.957, Time: 40.30
[2020-12-16 12:31:30,011][__main__][INFO] - [17280] Loss: 0.082, Running accuracy: 99.957, Time: 38.21
[2020-12-16 12:32:01,640][__main__][INFO] - Action accuracy: 99.957, Loss: 0.092
[2020-12-16 12:32:01,641][__main__][INFO] - Validating..
[2020-12-16 12:32:08,326][test][INFO] - Time elapsed: 5.364194
[2020-12-16 12:32:08,328][__main__][INFO] - Validation F1 score: 94.580, Exact match: 56.820, Precision: 94.730, Recall: 94.440
[2020-12-16 12:32:20,825][__main__][INFO] - Checkpoint saved to checkpoints/model_latest.pth
[2020-12-16 12:32:21,151][__main__][INFO] - Epoch #42
[2020-12-16 12:32:21,151][__main__][INFO] - Training..
[2020-12-16 12:33:02,119][__main__][INFO] - [320] Loss: 0.064, Running accuracy: 99.967, Time: 37.32
[2020-12-16 12:33:37,616][__main__][INFO] - [640] Loss: 0.097, Running accuracy: 99.942, Time: 35.49
[2020-12-16 12:34:16,489][__main__][INFO] - [960] Loss: 0.098, Running accuracy: 99.939, Time: 38.87
[2020-12-16 12:34:58,369][__main__][INFO] - [1280] Loss: 0.091, Running accuracy: 99.929, Time: 41.88
[2020-12-16 12:35:39,564][__main__][INFO] - [1600] Loss: 0.122, Running accuracy: 99.928, Time: 41.19
[2020-12-16 12:36:19,530][__main__][INFO] - [1920] Loss: 0.070, Running accuracy: 99.936, Time: 39.97
[2020-12-16 12:36:56,286][__main__][INFO] - [2240] Loss: 0.069, Running accuracy: 99.937, Time: 36.75
[2020-12-16 12:37:33,325][__main__][INFO] - [2560] Loss: 0.103, Running accuracy: 99.936, Time: 37.04
[2020-12-16 12:38:11,074][__main__][INFO] - [2880] Loss: 0.077, Running accuracy: 99.941, Time: 37.75
[2020-12-16 12:38:46,072][__main__][INFO] - [3200] Loss: 0.080, Running accuracy: 99.940, Time: 35.00
[2020-12-16 12:39:32,568][__main__][INFO] - [3520] Loss: 0.081, Running accuracy: 99.942, Time: 46.49
[2020-12-16 12:40:13,029][__main__][INFO] - [3840] Loss: 0.072, Running accuracy: 99.942, Time: 40.46
[2020-12-16 12:40:53,110][__main__][INFO] - [4160] Loss: 0.084, Running accuracy: 99.941, Time: 40.08
[2020-12-16 12:41:37,167][__main__][INFO] - [4480] Loss: 0.076, Running accuracy: 99.941, Time: 44.05
[2020-12-16 12:42:11,860][__main__][INFO] - [4800] Loss: 0.079, Running accuracy: 99.944, Time: 34.69
[2020-12-16 12:42:49,631][__main__][INFO] - [5120] Loss: 0.067, Running accuracy: 99.947, Time: 37.77
[2020-12-16 12:43:28,848][__main__][INFO] - [5440] Loss: 0.077, Running accuracy: 99.946, Time: 39.21
[2020-12-16 12:43:58,893][__main__][INFO] - [5760] Loss: 0.054, Running accuracy: 99.948, Time: 30.04
[2020-12-16 12:44:41,317][__main__][INFO] - [6080] Loss: 0.060, Running accuracy: 99.950, Time: 42.42
[2020-12-16 12:45:18,779][__main__][INFO] - [6400] Loss: 0.059, Running accuracy: 99.951, Time: 37.46
[2020-12-16 12:45:57,899][__main__][INFO] - [6720] Loss: 0.064, Running accuracy: 99.951, Time: 39.12
[2020-12-16 12:46:36,630][__main__][INFO] - [7040] Loss: 0.068, Running accuracy: 99.951, Time: 38.73
[2020-12-16 12:47:14,427][__main__][INFO] - [7360] Loss: 0.100, Running accuracy: 99.950, Time: 37.79
[2020-12-16 12:47:49,672][__main__][INFO] - [7680] Loss: 0.097, Running accuracy: 99.950, Time: 35.24
[2020-12-16 12:48:30,010][__main__][INFO] - [8000] Loss: 0.100, Running accuracy: 99.950, Time: 40.34
[2020-12-16 12:49:14,208][__main__][INFO] - [8320] Loss: 0.079, Running accuracy: 99.951, Time: 44.20
[2020-12-16 12:49:56,287][__main__][INFO] - [8640] Loss: 0.066, Running accuracy: 99.951, Time: 42.08
[2020-12-16 12:50:32,833][__main__][INFO] - [8960] Loss: 0.100, Running accuracy: 99.951, Time: 36.54
[2020-12-16 12:51:13,774][__main__][INFO] - [9280] Loss: 0.076, Running accuracy: 99.951, Time: 40.94
[2020-12-16 12:51:53,753][__main__][INFO] - [9600] Loss: 0.097, Running accuracy: 99.951, Time: 39.98
[2020-12-16 12:52:36,237][__main__][INFO] - [9920] Loss: 0.083, Running accuracy: 99.951, Time: 42.48
[2020-12-16 12:53:10,747][__main__][INFO] - [10240] Loss: 0.072, Running accuracy: 99.952, Time: 34.51
[2020-12-16 12:53:50,864][__main__][INFO] - [10560] Loss: 0.067, Running accuracy: 99.952, Time: 40.11
[2020-12-16 12:54:31,487][__main__][INFO] - [10880] Loss: 0.085, Running accuracy: 99.952, Time: 40.62
[2020-12-16 12:55:07,063][__main__][INFO] - [11200] Loss: 0.092, Running accuracy: 99.952, Time: 35.58
[2020-12-16 12:55:45,737][__main__][INFO] - [11520] Loss: 0.094, Running accuracy: 99.952, Time: 38.67
[2020-12-16 12:56:24,084][__main__][INFO] - [11840] Loss: 0.083, Running accuracy: 99.952, Time: 38.35
[2020-12-16 12:57:00,672][__main__][INFO] - [12160] Loss: 0.075, Running accuracy: 99.952, Time: 36.59
[2020-12-16 12:57:41,031][__main__][INFO] - [12480] Loss: 0.085, Running accuracy: 99.953, Time: 40.36
[2020-12-16 12:58:24,603][__main__][INFO] - [12800] Loss: 0.079, Running accuracy: 99.954, Time: 43.57
[2020-12-16 12:59:01,161][__main__][INFO] - [13120] Loss: 0.079, Running accuracy: 99.953, Time: 36.56
[2020-12-16 12:59:40,350][__main__][INFO] - [13440] Loss: 0.082, Running accuracy: 99.953, Time: 39.19
[2020-12-16 13:00:17,652][__main__][INFO] - [13760] Loss: 0.066, Running accuracy: 99.953, Time: 37.30
[2020-12-16 13:00:49,569][__main__][INFO] - [14080] Loss: 0.061, Running accuracy: 99.954, Time: 31.92
[2020-12-16 13:01:22,552][__main__][INFO] - [14400] Loss: 0.083, Running accuracy: 99.954, Time: 32.98
[2020-12-16 13:01:55,697][__main__][INFO] - [14720] Loss: 0.058, Running accuracy: 99.955, Time: 33.14
[2020-12-16 13:02:27,642][__main__][INFO] - [15040] Loss: 0.070, Running accuracy: 99.955, Time: 31.94
[2020-12-16 13:03:05,202][__main__][INFO] - [15360] Loss: 0.059, Running accuracy: 99.955, Time: 37.56
[2020-12-16 13:03:39,089][__main__][INFO] - [15680] Loss: 0.096, Running accuracy: 99.955, Time: 33.89
[2020-12-16 13:04:15,589][__main__][INFO] - [16000] Loss: 0.085, Running accuracy: 99.955, Time: 36.50
[2020-12-16 13:04:50,857][__main__][INFO] - [16320] Loss: 0.082, Running accuracy: 99.955, Time: 35.27
[2020-12-16 13:05:22,890][__main__][INFO] - [16640] Loss: 0.092, Running accuracy: 99.955, Time: 32.03
[2020-12-16 13:06:02,327][__main__][INFO] - [16960] Loss: 0.055, Running accuracy: 99.955, Time: 39.44
[2020-12-16 13:06:40,363][__main__][INFO] - [17280] Loss: 0.086, Running accuracy: 99.955, Time: 38.03
[2020-12-16 13:07:09,287][__main__][INFO] - Action accuracy: 99.956, Loss: 0.087
[2020-12-16 13:07:09,288][__main__][INFO] - Validating..
[2020-12-16 13:07:15,831][test][INFO] - Time elapsed: 5.121580
[2020-12-16 13:07:15,833][__main__][INFO] - Validation F1 score: 94.640, Exact match: 56.530, Precision: 94.840, Recall: 94.450
[2020-12-16 13:07:28,560][__main__][INFO] - Checkpoint saved to checkpoints/model_latest.pth
[2020-12-16 13:07:28,875][__main__][INFO] - Epoch #43
[2020-12-16 13:07:28,876][__main__][INFO] - Training..
[2020-12-16 13:08:13,425][__main__][INFO] - [320] Loss: 0.106, Running accuracy: 99.927, Time: 43.41
[2020-12-16 13:08:49,680][__main__][INFO] - [640] Loss: 0.058, Running accuracy: 99.956, Time: 36.25
[2020-12-16 13:09:28,166][__main__][INFO] - [960] Loss: 0.106, Running accuracy: 99.934, Time: 38.48
[2020-12-16 13:10:05,360][__main__][INFO] - [1280] Loss: 0.072, Running accuracy: 99.935, Time: 37.19
[2020-12-16 13:10:42,020][__main__][INFO] - [1600] Loss: 0.078, Running accuracy: 99.939, Time: 36.66
[2020-12-16 13:11:19,834][__main__][INFO] - [1920] Loss: 0.060, Running accuracy: 99.943, Time: 37.81
[2020-12-16 13:11:56,212][__main__][INFO] - [2240] Loss: 0.055, Running accuracy: 99.948, Time: 36.38
[2020-12-16 13:12:35,660][__main__][INFO] - [2560] Loss: 0.082, Running accuracy: 99.949, Time: 39.45
[2020-12-16 13:13:12,876][__main__][INFO] - [2880] Loss: 0.095, Running accuracy: 99.948, Time: 37.22
[2020-12-16 13:13:53,211][__main__][INFO] - [3200] Loss: 0.066, Running accuracy: 99.950, Time: 40.33
[2020-12-16 13:14:29,210][__main__][INFO] - [3520] Loss: 0.061, Running accuracy: 99.953, Time: 36.00
[2020-12-16 13:15:05,212][__main__][INFO] - [3840] Loss: 0.073, Running accuracy: 99.955, Time: 36.00
[2020-12-16 13:15:42,216][__main__][INFO] - [4160] Loss: 0.068, Running accuracy: 99.955, Time: 37.00
[2020-12-16 13:16:21,713][__main__][INFO] - [4480] Loss: 0.088, Running accuracy: 99.954, Time: 39.50
[2020-12-16 13:16:59,136][__main__][INFO] - [4800] Loss: 0.078, Running accuracy: 99.955, Time: 37.42
[2020-12-16 13:17:40,200][__main__][INFO] - [5120] Loss: 0.086, Running accuracy: 99.952, Time: 41.06
[2020-12-16 13:18:20,524][__main__][INFO] - [5440] Loss: 0.071, Running accuracy: 99.953, Time: 40.32
[2020-12-16 13:18:53,168][__main__][INFO] - [5760] Loss: 0.077, Running accuracy: 99.952, Time: 32.55
[2020-12-16 13:19:32,255][__main__][INFO] - [6080] Loss: 0.050, Running accuracy: 99.954, Time: 39.08
[2020-12-16 13:20:08,177][__main__][INFO] - [6400] Loss: 0.056, Running accuracy: 99.956, Time: 35.92
[2020-12-16 13:20:47,653][__main__][INFO] - [6720] Loss: 0.126, Running accuracy: 99.956, Time: 39.48
[2020-12-16 13:21:21,999][__main__][INFO] - [7040] Loss: 0.079, Running accuracy: 99.955, Time: 34.34
[2020-12-16 13:22:03,006][__main__][INFO] - [7360] Loss: 0.069, Running accuracy: 99.956, Time: 41.01
[2020-12-16 13:22:38,083][__main__][INFO] - [7680] Loss: 0.119, Running accuracy: 99.954, Time: 35.08
[2020-12-16 13:23:17,587][__main__][INFO] - [8000] Loss: 0.066, Running accuracy: 99.954, Time: 39.50
[2020-12-16 13:23:53,871][__main__][INFO] - [8320] Loss: 0.087, Running accuracy: 99.954, Time: 36.28
[2020-12-16 13:24:32,897][__main__][INFO] - [8640] Loss: 0.102, Running accuracy: 99.954, Time: 39.02
[2020-12-16 13:25:09,339][__main__][INFO] - [8960] Loss: 0.095, Running accuracy: 99.953, Time: 36.44
[2020-12-16 13:25:42,838][__main__][INFO] - [9280] Loss: 0.073, Running accuracy: 99.953, Time: 33.50
[2020-12-16 13:26:23,576][__main__][INFO] - [9600] Loss: 0.067, Running accuracy: 99.953, Time: 40.74
[2020-12-16 13:27:00,666][__main__][INFO] - [9920] Loss: 0.066, Running accuracy: 99.953, Time: 37.09
[2020-12-16 13:27:37,606][__main__][INFO] - [10240] Loss: 0.058, Running accuracy: 99.954, Time: 36.94
[2020-12-16 13:28:18,252][__main__][INFO] - [10560] Loss: 0.065, Running accuracy: 99.955, Time: 40.65
[2020-12-16 13:29:00,750][__main__][INFO] - [10880] Loss: 0.070, Running accuracy: 99.955, Time: 42.50
[2020-12-16 13:29:36,474][__main__][INFO] - [11200] Loss: 0.071, Running accuracy: 99.955, Time: 35.72
[2020-12-16 13:30:15,880][__main__][INFO] - [11520] Loss: 0.080, Running accuracy: 99.956, Time: 39.39
[2020-12-16 13:30:53,056][__main__][INFO] - [11840] Loss: 0.093, Running accuracy: 99.955, Time: 37.17
[2020-12-16 13:31:29,920][__main__][INFO] - [12160] Loss: 0.101, Running accuracy: 99.955, Time: 36.86
[2020-12-16 13:32:10,956][__main__][INFO] - [12480] Loss: 0.073, Running accuracy: 99.955, Time: 41.03
[2020-12-16 13:32:51,440][__main__][INFO] - [12800] Loss: 0.075, Running accuracy: 99.955, Time: 40.48
[2020-12-16 13:33:33,201][__main__][INFO] - [13120] Loss: 0.078, Running accuracy: 99.956, Time: 41.75
[2020-12-16 13:34:12,527][__main__][INFO] - [13440] Loss: 0.059, Running accuracy: 99.956, Time: 39.32
[2020-12-16 13:34:49,982][__main__][INFO] - [13760] Loss: 0.091, Running accuracy: 99.956, Time: 37.45
[2020-12-16 13:35:28,623][__main__][INFO] - [14080] Loss: 0.088, Running accuracy: 99.956, Time: 38.64
[2020-12-16 13:36:08,085][__main__][INFO] - [14400] Loss: 0.067, Running accuracy: 99.956, Time: 39.46
[2020-12-16 13:36:46,229][__main__][INFO] - [14720] Loss: 0.061, Running accuracy: 99.957, Time: 38.14
[2020-12-16 13:37:23,692][__main__][INFO] - [15040] Loss: 0.071, Running accuracy: 99.957, Time: 37.46
[2020-12-16 13:38:00,297][__main__][INFO] - [15360] Loss: 0.069, Running accuracy: 99.957, Time: 36.60
[2020-12-16 13:38:35,283][__main__][INFO] - [15680] Loss: 0.077, Running accuracy: 99.958, Time: 34.98
[2020-12-16 13:39:15,167][__main__][INFO] - [16000] Loss: 0.072, Running accuracy: 99.957, Time: 39.88
[2020-12-16 13:39:57,231][__main__][INFO] - [16320] Loss: 0.076, Running accuracy: 99.957, Time: 42.06
[2020-12-16 13:40:34,523][__main__][INFO] - [16640] Loss: 0.091, Running accuracy: 99.957, Time: 37.29
[2020-12-16 13:41:08,921][__main__][INFO] - [16960] Loss: 0.076, Running accuracy: 99.957, Time: 34.40
[2020-12-16 13:41:47,259][__main__][INFO] - [17280] Loss: 0.067, Running accuracy: 99.957, Time: 38.34
[2020-12-16 13:42:15,336][__main__][INFO] - Action accuracy: 99.957, Loss: 0.086
[2020-12-16 13:42:15,337][__main__][INFO] - Validating..
[2020-12-16 13:42:22,017][test][INFO] - Time elapsed: 5.226555
[2020-12-16 13:42:22,018][__main__][INFO] - Validation F1 score: 94.660, Exact match: 57.390, Precision: 94.850, Recall: 94.480
[2020-12-16 13:42:34,806][__main__][INFO] - Checkpoint saved to checkpoints/model_latest.pth
[2020-12-16 13:42:35,121][__main__][INFO] - Epoch #44
[2020-12-16 13:42:35,121][__main__][INFO] - Training..
[2020-12-16 13:43:13,969][__main__][INFO] - [320] Loss: 0.066, Running accuracy: 99.949, Time: 37.68
[2020-12-16 13:43:52,174][__main__][INFO] - [640] Loss: 0.085, Running accuracy: 99.940, Time: 38.20
[2020-12-16 13:44:31,921][__main__][INFO] - [960] Loss: 0.083, Running accuracy: 99.946, Time: 39.75
[2020-12-16 13:45:08,269][__main__][INFO] - [1280] Loss: 0.064, Running accuracy: 99.954, Time: 36.35
[2020-12-16 13:45:50,704][__main__][INFO] - [1600] Loss: 0.096, Running accuracy: 99.949, Time: 42.43
[2020-12-16 13:46:25,679][__main__][INFO] - [1920] Loss: 0.056, Running accuracy: 99.954, Time: 34.97
[2020-12-16 13:47:05,233][__main__][INFO] - [2240] Loss: 0.064, Running accuracy: 99.957, Time: 39.55
[2020-12-16 13:47:46,421][__main__][INFO] - [2560] Loss: 0.063, Running accuracy: 99.961, Time: 41.19
[2020-12-16 13:48:21,073][__main__][INFO] - [2880] Loss: 0.065, Running accuracy: 99.958, Time: 34.65
[2020-12-16 13:48:54,925][__main__][INFO] - [3200] Loss: 0.068, Running accuracy: 99.956, Time: 33.85
[2020-12-16 13:49:26,792][__main__][INFO] - [3520] Loss: 0.062, Running accuracy: 99.956, Time: 31.87
[2020-12-16 13:50:04,230][__main__][INFO] - [3840] Loss: 0.080, Running accuracy: 99.956, Time: 37.44
[2020-12-16 13:50:43,946][__main__][INFO] - [4160] Loss: 0.072, Running accuracy: 99.957, Time: 39.71
[2020-12-16 13:51:20,954][__main__][INFO] - [4480] Loss: 0.050, Running accuracy: 99.960, Time: 37.01
[2020-12-16 13:52:01,314][__main__][INFO] - [4800] Loss: 0.061, Running accuracy: 99.962, Time: 40.36
[2020-12-16 13:52:38,831][__main__][INFO] - [5120] Loss: 0.077, Running accuracy: 99.962, Time: 37.52
[2020-12-16 13:53:17,711][__main__][INFO] - [5440] Loss: 0.053, Running accuracy: 99.964, Time: 38.88
[2020-12-16 13:53:53,558][__main__][INFO] - [5760] Loss: 0.090, Running accuracy: 99.964, Time: 35.85
[2020-12-16 13:54:27,461][__main__][INFO] - [6080] Loss: 0.054, Running accuracy: 99.965, Time: 33.90
[2020-12-16 13:55:06,212][__main__][INFO] - [6400] Loss: 0.084, Running accuracy: 99.963, Time: 38.75
[2020-12-16 13:55:51,567][__main__][INFO] - [6720] Loss: 0.096, Running accuracy: 99.963, Time: 45.26
[2020-12-16 13:56:30,630][__main__][INFO] - [7040] Loss: 0.074, Running accuracy: 99.963, Time: 39.06
[2020-12-16 13:57:09,220][__main__][INFO] - [7360] Loss: 0.065, Running accuracy: 99.964, Time: 38.59
[2020-12-16 13:57:49,201][__main__][INFO] - [7680] Loss: 0.052, Running accuracy: 99.964, Time: 39.98
[2020-12-16 13:58:24,736][__main__][INFO] - [8000] Loss: 0.100, Running accuracy: 99.964, Time: 35.53
[2020-12-16 13:59:00,324][__main__][INFO] - [8320] Loss: 0.078, Running accuracy: 99.963, Time: 35.59
[2020-12-16 13:59:35,709][__main__][INFO] - [8640] Loss: 0.077, Running accuracy: 99.963, Time: 35.38
[2020-12-16 14:00:14,957][__main__][INFO] - [8960] Loss: 0.059, Running accuracy: 99.964, Time: 39.25
[2020-12-16 14:00:53,919][__main__][INFO] - [9280] Loss: 0.098, Running accuracy: 99.963, Time: 38.96
[2020-12-16 14:01:28,300][__main__][INFO] - [9600] Loss: 0.050, Running accuracy: 99.964, Time: 34.38
[2020-12-16 14:02:09,627][__main__][INFO] - [9920] Loss: 0.076, Running accuracy: 99.964, Time: 41.32
[2020-12-16 14:02:51,093][__main__][INFO] - [10240] Loss: 0.062, Running accuracy: 99.965, Time: 41.46
[2020-12-16 14:03:31,515][__main__][INFO] - [10560] Loss: 0.069, Running accuracy: 99.965, Time: 40.42
[2020-12-16 14:04:08,862][__main__][INFO] - [10880] Loss: 0.109, Running accuracy: 99.963, Time: 37.35
[2020-12-16 14:04:43,765][__main__][INFO] - [11200] Loss: 0.069, Running accuracy: 99.963, Time: 34.90
[2020-12-16 14:05:19,763][__main__][INFO] - [11520] Loss: 0.065, Running accuracy: 99.963, Time: 36.00
[2020-12-16 14:05:56,106][__main__][INFO] - [11840] Loss: 0.087, Running accuracy: 99.963, Time: 36.34
[2020-12-16 14:06:34,189][__main__][INFO] - [12160] Loss: 0.061, Running accuracy: 99.963, Time: 38.08
[2020-12-16 14:07:15,912][__main__][INFO] - [12480] Loss: 0.091, Running accuracy: 99.962, Time: 41.72
[2020-12-16 14:07:57,366][__main__][INFO] - [12800] Loss: 0.095, Running accuracy: 99.962, Time: 41.45
[2020-12-16 14:08:37,355][__main__][INFO] - [13120] Loss: 0.099, Running accuracy: 99.962, Time: 39.99
[2020-12-16 14:09:13,217][__main__][INFO] - [13440] Loss: 0.083, Running accuracy: 99.961, Time: 35.86
[2020-12-16 14:09:51,390][__main__][INFO] - [13760] Loss: 0.063, Running accuracy: 99.962, Time: 38.17
[2020-12-16 14:10:25,397][__main__][INFO] - [14080] Loss: 0.062, Running accuracy: 99.962, Time: 34.01
[2020-12-16 14:10:59,512][__main__][INFO] - [14400] Loss: 0.055, Running accuracy: 99.963, Time: 34.11
[2020-12-16 14:11:33,526][__main__][INFO] - [14720] Loss: 0.061, Running accuracy: 99.963, Time: 34.01
[2020-12-16 14:12:11,562][__main__][INFO] - [15040] Loss: 0.090, Running accuracy: 99.962, Time: 38.03
[2020-12-16 14:12:51,031][__main__][INFO] - [15360] Loss: 0.079, Running accuracy: 99.962, Time: 39.47
[2020-12-16 14:13:28,462][__main__][INFO] - [15680] Loss: 0.116, Running accuracy: 99.962, Time: 37.43
[2020-12-16 14:14:09,368][__main__][INFO] - [16000] Loss: 0.079, Running accuracy: 99.962, Time: 40.91
[2020-12-16 14:14:43,559][__main__][INFO] - [16320] Loss: 0.076, Running accuracy: 99.962, Time: 34.19
[2020-12-16 14:15:19,794][__main__][INFO] - [16640] Loss: 0.083, Running accuracy: 99.961, Time: 36.23
[2020-12-16 14:16:00,394][__main__][INFO] - [16960] Loss: 0.087, Running accuracy: 99.961, Time: 40.60
[2020-12-16 14:16:42,624][__main__][INFO] - [17280] Loss: 0.079, Running accuracy: 99.962, Time: 42.23
[2020-12-16 14:17:11,220][__main__][INFO] - Action accuracy: 99.961, Loss: 0.084
[2020-12-16 14:17:11,221][__main__][INFO] - Validating..
[2020-12-16 14:17:17,768][test][INFO] - Time elapsed: 5.118990
[2020-12-16 14:17:17,770][__main__][INFO] - Validation F1 score: 94.640, Exact match: 56.820, Precision: 94.850, Recall: 94.440
Epoch    45: reducing learning rate of group 0 to 1.2500e-06.
[2020-12-16 14:17:30,439][__main__][INFO] - Checkpoint saved to checkpoints/model_latest.pth
[2020-12-16 14:17:30,827][__main__][INFO] - Epoch #45
[2020-12-16 14:17:30,827][__main__][INFO] - Training..
[2020-12-16 14:18:11,841][__main__][INFO] - [320] Loss: 0.062, Running accuracy: 99.967, Time: 40.06
[2020-12-16 14:18:46,869][__main__][INFO] - [640] Loss: 0.077, Running accuracy: 99.955, Time: 35.03
[2020-12-16 14:19:22,956][__main__][INFO] - [960] Loss: 0.060, Running accuracy: 99.962, Time: 36.09
[2020-12-16 14:19:57,768][__main__][INFO] - [1280] Loss: 0.054, Running accuracy: 99.963, Time: 34.81
[2020-12-16 14:20:36,433][__main__][INFO] - [1600] Loss: 0.054, Running accuracy: 99.965, Time: 38.66
[2020-12-16 14:21:11,958][__main__][INFO] - [1920] Loss: 0.087, Running accuracy: 99.963, Time: 35.52
[2020-12-16 14:21:46,525][__main__][INFO] - [2240] Loss: 0.050, Running accuracy: 99.965, Time: 34.57
[2020-12-16 14:22:21,534][__main__][INFO] - [2560] Loss: 0.065, Running accuracy: 99.963, Time: 35.01
[2020-12-16 14:23:03,283][__main__][INFO] - [2880] Loss: 0.075, Running accuracy: 99.963, Time: 41.75
[2020-12-16 14:23:42,436][__main__][INFO] - [3200] Loss: 0.081, Running accuracy: 99.959, Time: 39.15
[2020-12-16 14:24:16,980][__main__][INFO] - [3520] Loss: 0.051, Running accuracy: 99.962, Time: 34.54
[2020-12-16 14:24:58,392][__main__][INFO] - [3840] Loss: 0.076, Running accuracy: 99.964, Time: 41.41
[2020-12-16 14:25:40,099][__main__][INFO] - [4160] Loss: 0.095, Running accuracy: 99.963, Time: 41.71
[2020-12-16 14:26:12,349][__main__][INFO] - [4480] Loss: 0.067, Running accuracy: 99.963, Time: 32.25
[2020-12-16 14:26:51,566][__main__][INFO] - [4800] Loss: 0.048, Running accuracy: 99.963, Time: 39.22
[2020-12-16 14:27:31,389][__main__][INFO] - [5120] Loss: 0.063, Running accuracy: 99.965, Time: 39.82
[2020-12-16 14:28:10,629][__main__][INFO] - [5440] Loss: 0.058, Running accuracy: 99.965, Time: 39.24
[2020-12-16 14:28:49,023][__main__][INFO] - [5760] Loss: 0.063, Running accuracy: 99.966, Time: 38.39
[2020-12-16 14:29:28,512][__main__][INFO] - [6080] Loss: 0.056, Running accuracy: 99.967, Time: 39.49
[2020-12-16 14:30:08,791][__main__][INFO] - [6400] Loss: 0.050, Running accuracy: 99.968, Time: 40.18
[2020-12-16 14:30:44,879][__main__][INFO] - [6720] Loss: 0.050, Running accuracy: 99.969, Time: 36.09
[2020-12-16 14:31:22,398][__main__][INFO] - [7040] Loss: 0.076, Running accuracy: 99.968, Time: 37.52
[2020-12-16 14:31:59,191][__main__][INFO] - [7360] Loss: 0.059, Running accuracy: 99.969, Time: 36.79
[2020-12-16 14:32:37,736][__main__][INFO] - [7680] Loss: 0.067, Running accuracy: 99.969, Time: 38.54
[2020-12-16 14:33:20,189][__main__][INFO] - [8000] Loss: 0.106, Running accuracy: 99.967, Time: 42.45
[2020-12-16 14:33:58,690][__main__][INFO] - [8320] Loss: 0.080, Running accuracy: 99.966, Time: 38.50
[2020-12-16 14:34:35,690][__main__][INFO] - [8640] Loss: 0.074, Running accuracy: 99.966, Time: 37.00
[2020-12-16 14:35:07,837][__main__][INFO] - [8960] Loss: 0.066, Running accuracy: 99.966, Time: 32.15
[2020-12-16 14:35:45,742][__main__][INFO] - [9280] Loss: 0.075, Running accuracy: 99.966, Time: 37.90
[2020-12-16 14:36:21,195][__main__][INFO] - [9600] Loss: 0.080, Running accuracy: 99.966, Time: 35.45
[2020-12-16 14:37:04,518][__main__][INFO] - [9920] Loss: 0.075, Running accuracy: 99.966, Time: 43.32
[2020-12-16 14:37:42,731][__main__][INFO] - [10240] Loss: 0.078, Running accuracy: 99.965, Time: 38.21
[2020-12-16 14:38:21,708][__main__][INFO] - [10560] Loss: 0.087, Running accuracy: 99.965, Time: 38.98
[2020-12-16 14:38:57,874][__main__][INFO] - [10880] Loss: 0.063, Running accuracy: 99.965, Time: 36.17
[2020-12-16 14:39:38,314][__main__][INFO] - [11200] Loss: 0.066, Running accuracy: 99.965, Time: 40.44
[2020-12-16 14:40:19,817][__main__][INFO] - [11520] Loss: 0.060, Running accuracy: 99.965, Time: 41.50
[2020-12-16 14:40:53,414][__main__][INFO] - [11840] Loss: 0.061, Running accuracy: 99.965, Time: 33.60
[2020-12-16 14:41:33,329][__main__][INFO] - [12160] Loss: 0.089, Running accuracy: 99.964, Time: 39.91
[2020-12-16 14:42:07,490][__main__][INFO] - [12480] Loss: 0.055, Running accuracy: 99.964, Time: 34.16
[2020-12-16 14:42:44,933][__main__][INFO] - [12800] Loss: 0.076, Running accuracy: 99.963, Time: 37.44
[2020-12-16 14:43:21,018][__main__][INFO] - [13120] Loss: 0.058, Running accuracy: 99.963, Time: 36.08
[2020-12-16 14:43:56,950][__main__][INFO] - [13440] Loss: 0.048, Running accuracy: 99.964, Time: 35.93
[2020-12-16 14:44:30,854][__main__][INFO] - [13760] Loss: 0.084, Running accuracy: 99.963, Time: 33.90
[2020-12-16 14:45:13,979][__main__][INFO] - [14080] Loss: 0.070, Running accuracy: 99.962, Time: 43.12
[2020-12-16 14:45:51,137][__main__][INFO] - [14400] Loss: 0.067, Running accuracy: 99.962, Time: 37.16
[2020-12-16 14:46:26,610][__main__][INFO] - [14720] Loss: 0.084, Running accuracy: 99.962, Time: 35.47
[2020-12-16 14:47:08,665][__main__][INFO] - [15040] Loss: 0.062, Running accuracy: 99.962, Time: 42.05
[2020-12-16 14:47:45,200][__main__][INFO] - [15360] Loss: 0.073, Running accuracy: 99.960, Time: 36.53
[2020-12-16 14:48:24,034][__main__][INFO] - [15680] Loss: 0.073, Running accuracy: 99.961, Time: 38.83
[2020-12-16 14:49:00,123][__main__][INFO] - [16000] Loss: 0.064, Running accuracy: 99.961, Time: 36.09
[2020-12-16 14:49:40,241][__main__][INFO] - [16320] Loss: 0.063, Running accuracy: 99.961, Time: 40.12
[2020-12-16 14:50:15,330][__main__][INFO] - [16640] Loss: 0.042, Running accuracy: 99.961, Time: 35.09
[2020-12-16 14:50:52,754][__main__][INFO] - [16960] Loss: 0.090, Running accuracy: 99.961, Time: 37.42
[2020-12-16 14:51:30,054][__main__][INFO] - [17280] Loss: 0.052, Running accuracy: 99.961, Time: 37.30
[2020-12-16 14:52:01,150][__main__][INFO] - Action accuracy: 99.961, Loss: 0.075
[2020-12-16 14:52:01,151][__main__][INFO] - Validating..
[2020-12-16 14:52:07,695][test][INFO] - Time elapsed: 5.146278
[2020-12-16 14:52:07,697][__main__][INFO] - Validation F1 score: 94.600, Exact match: 57.390, Precision: 94.800, Recall: 94.410
[2020-12-16 14:52:19,464][__main__][INFO] - Checkpoint saved to checkpoints/model_latest.pth
[2020-12-16 14:52:19,776][__main__][INFO] - Epoch #46
[2020-12-16 14:52:19,776][__main__][INFO] - Training..
[2020-12-16 14:52:53,736][__main__][INFO] - [320] Loss: 0.068, Running accuracy: 99.936, Time: 32.66
[2020-12-16 14:53:30,798][__main__][INFO] - [640] Loss: 0.049, Running accuracy: 99.957, Time: 37.06
[2020-12-16 14:54:01,366][__main__][INFO] - [960] Loss: 0.062, Running accuracy: 99.954, Time: 30.57
[2020-12-16 14:54:37,840][__main__][INFO] - [1280] Loss: 0.061, Running accuracy: 99.960, Time: 36.47
[2020-12-16 14:55:14,002][__main__][INFO] - [1600] Loss: 0.060, Running accuracy: 99.959, Time: 36.16
[2020-12-16 14:55:48,170][__main__][INFO] - [1920] Loss: 0.047, Running accuracy: 99.964, Time: 34.17
[2020-12-16 14:56:25,452][__main__][INFO] - [2240] Loss: 0.086, Running accuracy: 99.957, Time: 37.28
[2020-12-16 14:57:03,278][__main__][INFO] - [2560] Loss: 0.054, Running accuracy: 99.961, Time: 37.83
[2020-12-16 14:57:42,171][__main__][INFO] - [2880] Loss: 0.069, Running accuracy: 99.960, Time: 38.89
[2020-12-16 14:58:21,445][__main__][INFO] - [3200] Loss: 0.076, Running accuracy: 99.961, Time: 39.27
[2020-12-16 14:59:00,412][__main__][INFO] - [3520] Loss: 0.050, Running accuracy: 99.963, Time: 38.97
[2020-12-16 14:59:34,707][__main__][INFO] - [3840] Loss: 0.052, Running accuracy: 99.965, Time: 34.29
[2020-12-16 15:00:09,247][__main__][INFO] - [4160] Loss: 0.056, Running accuracy: 99.967, Time: 34.54
[2020-12-16 15:00:47,595][__main__][INFO] - [4480] Loss: 0.053, Running accuracy: 99.968, Time: 38.35
[2020-12-16 15:01:27,144][__main__][INFO] - [4800] Loss: 0.053, Running accuracy: 99.969, Time: 39.55
[2020-12-16 15:02:06,982][__main__][INFO] - [5120] Loss: 0.072, Running accuracy: 99.970, Time: 39.84
[2020-12-16 15:02:40,234][__main__][INFO] - [5440] Loss: 0.056, Running accuracy: 99.970, Time: 33.16
[2020-12-16 15:03:19,605][__main__][INFO] - [5760] Loss: 0.054, Running accuracy: 99.971, Time: 39.37
[2020-12-16 15:03:54,168][__main__][INFO] - [6080] Loss: 0.060, Running accuracy: 99.970, Time: 34.56
[2020-12-16 15:04:32,210][__main__][INFO] - [6400] Loss: 0.081, Running accuracy: 99.970, Time: 37.95
[2020-12-16 15:05:06,736][__main__][INFO] - [6720] Loss: 0.071, Running accuracy: 99.969, Time: 34.53
[2020-12-16 15:05:41,950][__main__][INFO] - [7040] Loss: 0.077, Running accuracy: 99.968, Time: 35.21
[2020-12-16 15:06:18,489][__main__][INFO] - [7360] Loss: 0.058, Running accuracy: 99.969, Time: 36.54
[2020-12-16 15:06:53,562][__main__][INFO] - [7680] Loss: 0.059, Running accuracy: 99.969, Time: 35.07
[2020-12-16 15:07:31,821][__main__][INFO] - [8000] Loss: 0.063, Running accuracy: 99.969, Time: 38.26
[2020-12-16 15:08:10,295][__main__][INFO] - [8320] Loss: 0.058, Running accuracy: 99.970, Time: 38.47
[2020-12-16 15:08:50,990][__main__][INFO] - [8640] Loss: 0.081, Running accuracy: 99.969, Time: 40.69
[2020-12-16 15:09:32,467][__main__][INFO] - [8960] Loss: 0.074, Running accuracy: 99.969, Time: 41.48
[2020-12-16 15:10:15,051][__main__][INFO] - [9280] Loss: 0.077, Running accuracy: 99.968, Time: 42.58
[2020-12-16 15:10:50,120][__main__][INFO] - [9600] Loss: 0.072, Running accuracy: 99.968, Time: 35.07
[2020-12-16 15:11:34,457][__main__][INFO] - [9920] Loss: 0.084, Running accuracy: 99.968, Time: 44.34
[2020-12-16 15:12:10,319][__main__][INFO] - [10240] Loss: 0.089, Running accuracy: 99.966, Time: 35.86
[2020-12-16 15:12:50,564][__main__][INFO] - [10560] Loss: 0.054, Running accuracy: 99.966, Time: 40.24
[2020-12-16 15:13:29,292][__main__][INFO] - [10880] Loss: 0.087, Running accuracy: 99.966, Time: 38.73
[2020-12-16 15:14:10,973][__main__][INFO] - [11200] Loss: 0.094, Running accuracy: 99.965, Time: 41.68
[2020-12-16 15:14:46,119][__main__][INFO] - [11520] Loss: 0.080, Running accuracy: 99.964, Time: 35.14
[2020-12-16 15:15:18,271][__main__][INFO] - [11840] Loss: 0.064, Running accuracy: 99.964, Time: 32.15
[2020-12-16 15:15:57,267][__main__][INFO] - [12160] Loss: 0.065, Running accuracy: 99.965, Time: 39.00
[2020-12-16 15:16:32,580][__main__][INFO] - [12480] Loss: 0.060, Running accuracy: 99.965, Time: 35.31
[2020-12-16 15:17:13,542][__main__][INFO] - [12800] Loss: 0.079, Running accuracy: 99.965, Time: 40.96
[2020-12-16 15:17:50,920][__main__][INFO] - [13120] Loss: 0.043, Running accuracy: 99.965, Time: 37.38
[2020-12-16 15:18:30,905][__main__][INFO] - [13440] Loss: 0.095, Running accuracy: 99.965, Time: 39.98
[2020-12-16 15:19:07,680][__main__][INFO] - [13760] Loss: 0.072, Running accuracy: 99.965, Time: 36.77
[2020-12-16 15:19:47,392][__main__][INFO] - [14080] Loss: 0.064, Running accuracy: 99.965, Time: 39.71
[2020-12-16 15:20:27,513][__main__][INFO] - [14400] Loss: 0.072, Running accuracy: 99.965, Time: 40.12
[2020-12-16 15:21:06,710][__main__][INFO] - [14720] Loss: 0.061, Running accuracy: 99.965, Time: 39.20
[2020-12-16 15:21:48,162][__main__][INFO] - [15040] Loss: 0.088, Running accuracy: 99.964, Time: 41.45
[2020-12-16 15:22:28,826][__main__][INFO] - [15360] Loss: 0.072, Running accuracy: 99.964, Time: 40.66
[2020-12-16 15:23:07,453][__main__][INFO] - [15680] Loss: 0.066, Running accuracy: 99.965, Time: 38.63
[2020-12-16 15:23:47,780][__main__][INFO] - [16000] Loss: 0.067, Running accuracy: 99.965, Time: 40.33
[2020-12-16 15:24:25,099][__main__][INFO] - [16320] Loss: 0.049, Running accuracy: 99.966, Time: 37.32
[2020-12-16 15:25:02,722][__main__][INFO] - [16640] Loss: 0.072, Running accuracy: 99.965, Time: 37.62
[2020-12-16 15:25:42,558][__main__][INFO] - [16960] Loss: 0.071, Running accuracy: 99.965, Time: 39.84
[2020-12-16 15:26:23,146][__main__][INFO] - [17280] Loss: 0.062, Running accuracy: 99.966, Time: 40.59
[2020-12-16 15:26:50,531][__main__][INFO] - Action accuracy: 99.965, Loss: 0.074
[2020-12-16 15:26:50,532][__main__][INFO] - Validating..
[2020-12-16 15:26:59,561][test][INFO] - Time elapsed: 5.328074
[2020-12-16 15:26:59,563][__main__][INFO] - Validation F1 score: 94.570, Exact match: 56.820, Precision: 94.650, Recall: 94.490
[2020-12-16 15:27:12,253][__main__][INFO] - Checkpoint saved to checkpoints/model_latest.pth
[2020-12-16 15:27:12,574][__main__][INFO] - Epoch #47
[2020-12-16 15:27:12,574][__main__][INFO] - Training..
[2020-12-16 15:27:53,704][__main__][INFO] - [320] Loss: 0.068, Running accuracy: 99.956, Time: 39.85
[2020-12-16 15:28:26,940][__main__][INFO] - [640] Loss: 0.059, Running accuracy: 99.959, Time: 33.23
[2020-12-16 15:29:04,709][__main__][INFO] - [960] Loss: 0.099, Running accuracy: 99.953, Time: 37.77
[2020-12-16 15:29:42,078][__main__][INFO] - [1280] Loss: 0.071, Running accuracy: 99.953, Time: 37.37
[2020-12-16 15:30:21,966][__main__][INFO] - [1600] Loss: 0.057, Running accuracy: 99.961, Time: 39.89
[2020-12-16 15:31:00,644][__main__][INFO] - [1920] Loss: 0.060, Running accuracy: 99.963, Time: 38.68
[2020-12-16 15:31:36,838][__main__][INFO] - [2240] Loss: 0.072, Running accuracy: 99.962, Time: 36.19
[2020-12-16 15:32:10,623][__main__][INFO] - [2560] Loss: 0.072, Running accuracy: 99.956, Time: 33.78
[2020-12-16 15:32:46,873][__main__][INFO] - [2880] Loss: 0.071, Running accuracy: 99.957, Time: 36.25
[2020-12-16 15:33:26,490][__main__][INFO] - [3200] Loss: 0.087, Running accuracy: 99.956, Time: 39.62
[2020-12-16 15:34:03,103][__main__][INFO] - [3520] Loss: 0.051, Running accuracy: 99.959, Time: 36.61
[2020-12-16 15:34:38,082][__main__][INFO] - [3840] Loss: 0.052, Running accuracy: 99.961, Time: 34.98
[2020-12-16 15:35:20,745][__main__][INFO] - [4160] Loss: 0.083, Running accuracy: 99.958, Time: 42.66
[2020-12-16 15:35:55,962][__main__][INFO] - [4480] Loss: 0.048, Running accuracy: 99.961, Time: 35.22
[2020-12-16 15:36:27,871][__main__][INFO] - [4800] Loss: 0.060, Running accuracy: 99.962, Time: 31.91
[2020-12-16 15:37:01,161][__main__][INFO] - [5120] Loss: 0.074, Running accuracy: 99.961, Time: 33.19
[2020-12-16 15:37:39,140][__main__][INFO] - [5440] Loss: 0.078, Running accuracy: 99.961, Time: 37.98
[2020-12-16 15:38:19,010][__main__][INFO] - [5760] Loss: 0.076, Running accuracy: 99.961, Time: 39.87
[2020-12-16 15:39:01,614][__main__][INFO] - [6080] Loss: 0.083, Running accuracy: 99.962, Time: 42.60
[2020-12-16 15:39:36,166][__main__][INFO] - [6400] Loss: 0.059, Running accuracy: 99.962, Time: 34.55
[2020-12-16 15:40:13,260][__main__][INFO] - [6720] Loss: 0.101, Running accuracy: 99.961, Time: 37.09
[2020-12-16 15:40:46,831][__main__][INFO] - [7040] Loss: 0.048, Running accuracy: 99.962, Time: 33.57
[2020-12-16 15:41:25,497][__main__][INFO] - [7360] Loss: 0.103, Running accuracy: 99.961, Time: 38.67
[2020-12-16 15:42:03,174][__main__][INFO] - [7680] Loss: 0.065, Running accuracy: 99.961, Time: 37.68
[2020-12-16 15:42:43,570][__main__][INFO] - [8000] Loss: 0.054, Running accuracy: 99.963, Time: 40.40
[2020-12-16 15:43:25,046][__main__][INFO] - [8320] Loss: 0.072, Running accuracy: 99.962, Time: 41.47
[2020-12-16 15:44:04,912][__main__][INFO] - [8640] Loss: 0.048, Running accuracy: 99.963, Time: 39.86
[2020-12-16 15:44:46,722][__main__][INFO] - [8960] Loss: 0.093, Running accuracy: 99.963, Time: 41.81
[2020-12-16 15:45:27,591][__main__][INFO] - [9280] Loss: 0.058, Running accuracy: 99.963, Time: 40.87
[2020-12-16 15:46:02,913][__main__][INFO] - [9600] Loss: 0.054, Running accuracy: 99.963, Time: 35.32
[2020-12-16 15:46:38,885][__main__][INFO] - [9920] Loss: 0.048, Running accuracy: 99.964, Time: 35.97
[2020-12-16 15:47:13,410][__main__][INFO] - [10240] Loss: 0.071, Running accuracy: 99.965, Time: 34.52
[2020-12-16 15:47:53,852][__main__][INFO] - [10560] Loss: 0.065, Running accuracy: 99.965, Time: 40.44
[2020-12-16 15:48:33,135][__main__][INFO] - [10880] Loss: 0.050, Running accuracy: 99.966, Time: 39.28
[2020-12-16 15:49:13,207][__main__][INFO] - [11200] Loss: 0.053, Running accuracy: 99.967, Time: 40.07
[2020-12-16 15:49:48,860][__main__][INFO] - [11520] Loss: 0.052, Running accuracy: 99.968, Time: 35.65
[2020-12-16 15:50:27,382][__main__][INFO] - [11840] Loss: 0.059, Running accuracy: 99.968, Time: 38.52
[2020-12-16 15:51:07,854][__main__][INFO] - [12160] Loss: 0.079, Running accuracy: 99.968, Time: 40.47
[2020-12-16 15:51:45,868][__main__][INFO] - [12480] Loss: 0.101, Running accuracy: 99.966, Time: 38.01
[2020-12-16 15:52:20,209][__main__][INFO] - [12800] Loss: 0.041, Running accuracy: 99.967, Time: 34.34
[2020-12-16 15:53:00,748][__main__][INFO] - [13120] Loss: 0.048, Running accuracy: 99.967, Time: 40.54
[2020-12-16 15:53:37,526][__main__][INFO] - [13440] Loss: 0.079, Running accuracy: 99.966, Time: 36.78
[2020-12-16 15:54:19,644][__main__][INFO] - [13760] Loss: 0.089, Running accuracy: 99.966, Time: 42.12
[2020-12-16 15:55:00,402][__main__][INFO] - [14080] Loss: 0.073, Running accuracy: 99.966, Time: 40.76
[2020-12-16 15:55:41,651][__main__][INFO] - [14400] Loss: 0.062, Running accuracy: 99.967, Time: 41.25
[2020-12-16 15:56:15,499][__main__][INFO] - [14720] Loss: 0.081, Running accuracy: 99.965, Time: 33.85
[2020-12-16 15:56:52,850][__main__][INFO] - [15040] Loss: 0.068, Running accuracy: 99.965, Time: 37.35
[2020-12-16 15:57:34,929][__main__][INFO] - [15360] Loss: 0.049, Running accuracy: 99.965, Time: 42.08
[2020-12-16 15:58:08,399][__main__][INFO] - [15680] Loss: 0.063, Running accuracy: 99.965, Time: 33.47
[2020-12-16 15:58:45,859][__main__][INFO] - [16000] Loss: 0.077, Running accuracy: 99.965, Time: 37.46
[2020-12-16 15:59:22,448][__main__][INFO] - [16320] Loss: 0.065, Running accuracy: 99.965, Time: 36.59
[2020-12-16 16:00:01,523][__main__][INFO] - [16640] Loss: 0.074, Running accuracy: 99.965, Time: 39.07
[2020-12-16 16:00:37,579][__main__][INFO] - [16960] Loss: 0.063, Running accuracy: 99.965, Time: 36.06
[2020-12-16 16:01:14,437][__main__][INFO] - [17280] Loss: 0.066, Running accuracy: 99.965, Time: 36.86
[2020-12-16 16:01:44,494][__main__][INFO] - Action accuracy: 99.964, Loss: 0.074
[2020-12-16 16:01:44,495][__main__][INFO] - Validating..
[2020-12-16 16:01:51,139][test][INFO] - Time elapsed: 5.213742
[2020-12-16 16:01:51,140][__main__][INFO] - Validation F1 score: 94.520, Exact match: 57.390, Precision: 94.740, Recall: 94.300
[2020-12-16 16:02:03,868][__main__][INFO] - Checkpoint saved to checkpoints/model_latest.pth
[2020-12-16 16:02:04,180][__main__][INFO] - Epoch #48
[2020-12-16 16:02:04,180][__main__][INFO] - Training..
[2020-12-16 16:02:41,006][__main__][INFO] - [320] Loss: 0.050, Running accuracy: 99.976, Time: 35.74
[2020-12-16 16:03:14,406][__main__][INFO] - [640] Loss: 0.051, Running accuracy: 99.988, Time: 33.40
[2020-12-16 16:03:57,786][__main__][INFO] - [960] Loss: 0.070, Running accuracy: 99.969, Time: 43.38
[2020-12-16 16:04:34,514][__main__][INFO] - [1280] Loss: 0.072, Running accuracy: 99.968, Time: 36.73
[2020-12-16 16:05:11,123][__main__][INFO] - [1600] Loss: 0.068, Running accuracy: 99.970, Time: 36.61
[2020-12-16 16:05:50,737][__main__][INFO] - [1920] Loss: 0.049, Running accuracy: 99.969, Time: 39.61
[2020-12-16 16:06:25,090][__main__][INFO] - [2240] Loss: 0.050, Running accuracy: 99.969, Time: 34.35
[2020-12-16 16:07:06,179][__main__][INFO] - [2560] Loss: 0.062, Running accuracy: 99.968, Time: 41.09
[2020-12-16 16:07:46,622][__main__][INFO] - [2880] Loss: 0.091, Running accuracy: 99.967, Time: 40.44
[2020-12-16 16:08:25,656][__main__][INFO] - [3200] Loss: 0.085, Running accuracy: 99.964, Time: 39.03
[2020-12-16 16:09:00,212][__main__][INFO] - [3520] Loss: 0.095, Running accuracy: 99.961, Time: 34.55
[2020-12-16 16:09:36,147][__main__][INFO] - [3840] Loss: 0.086, Running accuracy: 99.956, Time: 35.93
[2020-12-16 16:10:12,430][__main__][INFO] - [4160] Loss: 0.048, Running accuracy: 99.958, Time: 36.28
[2020-12-16 16:10:55,814][__main__][INFO] - [4480] Loss: 0.050, Running accuracy: 99.960, Time: 43.38
[2020-12-16 16:11:33,947][__main__][INFO] - [4800] Loss: 0.069, Running accuracy: 99.960, Time: 38.04
[2020-12-16 16:12:14,757][__main__][INFO] - [5120] Loss: 0.043, Running accuracy: 99.962, Time: 40.81
[2020-12-16 16:12:55,036][__main__][INFO] - [5440] Loss: 0.044, Running accuracy: 99.964, Time: 40.28
[2020-12-16 16:13:29,017][__main__][INFO] - [5760] Loss: 0.061, Running accuracy: 99.964, Time: 33.98
[2020-12-16 16:14:05,407][__main__][INFO] - [6080] Loss: 0.057, Running accuracy: 99.965, Time: 36.39
[2020-12-16 16:14:48,598][__main__][INFO] - [6400] Loss: 0.069, Running accuracy: 99.965, Time: 43.19
[2020-12-16 16:15:28,040][__main__][INFO] - [6720] Loss: 0.061, Running accuracy: 99.965, Time: 39.44
[2020-12-16 16:16:16,891][__main__][INFO] - [7040] Loss: 0.057, Running accuracy: 99.964, Time: 48.85
[2020-12-16 16:16:53,832][__main__][INFO] - [7360] Loss: 0.080, Running accuracy: 99.964, Time: 36.94
[2020-12-16 16:17:29,950][__main__][INFO] - [7680] Loss: 0.058, Running accuracy: 99.965, Time: 36.12
[2020-12-16 16:18:10,517][__main__][INFO] - [8000] Loss: 0.076, Running accuracy: 99.966, Time: 40.48
[2020-12-16 16:18:48,075][__main__][INFO] - [8320] Loss: 0.059, Running accuracy: 99.966, Time: 37.56
[2020-12-16 16:19:27,479][__main__][INFO] - [8640] Loss: 0.054, Running accuracy: 99.966, Time: 39.40
[2020-12-16 16:20:04,152][__main__][INFO] - [8960] Loss: 0.059, Running accuracy: 99.965, Time: 36.67
[2020-12-16 16:20:46,351][__main__][INFO] - [9280] Loss: 0.086, Running accuracy: 99.965, Time: 42.20
[2020-12-16 16:21:25,093][__main__][INFO] - [9600] Loss: 0.071, Running accuracy: 99.965, Time: 38.74
[2020-12-16 16:22:00,657][__main__][INFO] - [9920] Loss: 0.065, Running accuracy: 99.964, Time: 35.56
[2020-12-16 16:22:34,261][__main__][INFO] - [10240] Loss: 0.058, Running accuracy: 99.964, Time: 33.60
[2020-12-16 16:23:10,166][__main__][INFO] - [10560] Loss: 0.049, Running accuracy: 99.965, Time: 35.90
[2020-12-16 16:23:45,256][__main__][INFO] - [10880] Loss: 0.101, Running accuracy: 99.965, Time: 35.09
[2020-12-16 16:24:26,603][__main__][INFO] - [11200] Loss: 0.076, Running accuracy: 99.964, Time: 41.35
[2020-12-16 16:25:05,136][__main__][INFO] - [11520] Loss: 0.066, Running accuracy: 99.964, Time: 38.53
[2020-12-16 16:25:42,028][__main__][INFO] - [11840] Loss: 0.053, Running accuracy: 99.964, Time: 36.89
[2020-12-16 16:26:15,955][__main__][INFO] - [12160] Loss: 0.068, Running accuracy: 99.964, Time: 33.93
[2020-12-16 16:26:49,124][__main__][INFO] - [12480] Loss: 0.057, Running accuracy: 99.964, Time: 33.17
[2020-12-16 16:27:26,438][__main__][INFO] - [12800] Loss: 0.059, Running accuracy: 99.964, Time: 37.31
[2020-12-16 16:28:02,657][__main__][INFO] - [13120] Loss: 0.057, Running accuracy: 99.964, Time: 36.22
[2020-12-16 16:28:42,149][__main__][INFO] - [13440] Loss: 0.070, Running accuracy: 99.964, Time: 39.49
[2020-12-16 16:29:18,307][__main__][INFO] - [13760] Loss: 0.065, Running accuracy: 99.965, Time: 36.16
[2020-12-16 16:29:57,757][__main__][INFO] - [14080] Loss: 0.077, Running accuracy: 99.964, Time: 39.45
[2020-12-16 16:30:30,528][__main__][INFO] - [14400] Loss: 0.061, Running accuracy: 99.965, Time: 32.77
[2020-12-16 16:31:08,316][__main__][INFO] - [14720] Loss: 0.058, Running accuracy: 99.965, Time: 37.79
[2020-12-16 16:31:42,887][__main__][INFO] - [15040] Loss: 0.050, Running accuracy: 99.965, Time: 34.57
[2020-12-16 16:32:24,706][__main__][INFO] - [15360] Loss: 0.070, Running accuracy: 99.965, Time: 41.82
[2020-12-16 16:33:06,756][__main__][INFO] - [15680] Loss: 0.061, Running accuracy: 99.965, Time: 42.05
[2020-12-16 16:33:42,506][__main__][INFO] - [16000] Loss: 0.076, Running accuracy: 99.965, Time: 35.75
[2020-12-16 16:34:17,619][__main__][INFO] - [16320] Loss: 0.076, Running accuracy: 99.965, Time: 35.11
[2020-12-16 16:34:54,207][__main__][INFO] - [16640] Loss: 0.056, Running accuracy: 99.965, Time: 36.59
[2020-12-16 16:35:29,831][__main__][INFO] - [16960] Loss: 0.050, Running accuracy: 99.965, Time: 35.62
[2020-12-16 16:36:08,097][__main__][INFO] - [17280] Loss: 0.054, Running accuracy: 99.965, Time: 38.27
[2020-12-16 16:36:35,752][__main__][INFO] - Action accuracy: 99.965, Loss: 0.072
[2020-12-16 16:36:35,753][__main__][INFO] - Validating..
[2020-12-16 16:36:42,471][test][INFO] - Time elapsed: 5.281958
[2020-12-16 16:36:42,473][__main__][INFO] - Validation F1 score: 94.600, Exact match: 57.390, Precision: 94.800, Recall: 94.400
[2020-12-16 16:36:57,306][__main__][INFO] - Checkpoint saved to checkpoints/model_latest.pth
[2020-12-16 16:36:57,676][__main__][INFO] - Epoch #49
[2020-12-16 16:36:57,676][__main__][INFO] - Training..
[2020-12-16 16:37:37,074][__main__][INFO] - [320] Loss: 0.053, Running accuracy: 99.989, Time: 38.41
[2020-12-16 16:38:16,760][__main__][INFO] - [640] Loss: 0.072, Running accuracy: 99.983, Time: 39.69
[2020-12-16 16:38:55,000][__main__][INFO] - [960] Loss: 0.071, Running accuracy: 99.973, Time: 38.24
[2020-12-16 16:39:31,590][__main__][INFO] - [1280] Loss: 0.047, Running accuracy: 99.977, Time: 36.59
[2020-12-16 16:40:10,380][__main__][INFO] - [1600] Loss: 0.055, Running accuracy: 99.973, Time: 38.79
[2020-12-16 16:40:51,158][__main__][INFO] - [1920] Loss: 0.051, Running accuracy: 99.975, Time: 40.78
[2020-12-16 16:41:30,087][__main__][INFO] - [2240] Loss: 0.085, Running accuracy: 99.969, Time: 38.93
[2020-12-16 16:42:06,327][__main__][INFO] - [2560] Loss: 0.071, Running accuracy: 99.969, Time: 36.24
[2020-12-16 16:42:45,240][__main__][INFO] - [2880] Loss: 0.076, Running accuracy: 99.968, Time: 38.91
[2020-12-16 16:43:21,306][__main__][INFO] - [3200] Loss: 0.055, Running accuracy: 99.969, Time: 36.06
[2020-12-16 16:43:58,261][__main__][INFO] - [3520] Loss: 0.052, Running accuracy: 99.971, Time: 36.95
[2020-12-16 16:44:38,993][__main__][INFO] - [3840] Loss: 0.052, Running accuracy: 99.972, Time: 40.73
[2020-12-16 16:45:14,786][__main__][INFO] - [4160] Loss: 0.062, Running accuracy: 99.973, Time: 35.79
[2020-12-16 16:45:53,029][__main__][INFO] - [4480] Loss: 0.045, Running accuracy: 99.973, Time: 38.24
[2020-12-16 16:46:27,666][__main__][INFO] - [4800] Loss: 0.045, Running accuracy: 99.975, Time: 34.64
[2020-12-16 16:47:08,787][__main__][INFO] - [5120] Loss: 0.055, Running accuracy: 99.975, Time: 41.12
[2020-12-16 16:47:43,582][__main__][INFO] - [5440] Loss: 0.073, Running accuracy: 99.974, Time: 34.79
[2020-12-16 16:48:19,245][__main__][INFO] - [5760] Loss: 0.073, Running accuracy: 99.974, Time: 35.66
[2020-12-16 16:49:00,250][__main__][INFO] - [6080] Loss: 0.053, Running accuracy: 99.974, Time: 41.00
[2020-12-16 16:49:39,157][__main__][INFO] - [6400] Loss: 0.055, Running accuracy: 99.974, Time: 38.91
[2020-12-16 16:50:16,092][__main__][INFO] - [6720] Loss: 0.071, Running accuracy: 99.973, Time: 36.93
[2020-12-16 16:50:53,735][__main__][INFO] - [7040] Loss: 0.072, Running accuracy: 99.972, Time: 37.64
[2020-12-16 16:51:30,269][__main__][INFO] - [7360] Loss: 0.053, Running accuracy: 99.972, Time: 36.53
[2020-12-16 16:52:05,673][__main__][INFO] - [7680] Loss: 0.052, Running accuracy: 99.972, Time: 35.40
[2020-12-16 16:52:46,874][__main__][INFO] - [8000] Loss: 0.065, Running accuracy: 99.973, Time: 41.20
[2020-12-16 16:53:29,024][__main__][INFO] - [8320] Loss: 0.063, Running accuracy: 99.973, Time: 42.15
[2020-12-16 16:54:11,563][__main__][INFO] - [8640] Loss: 0.078, Running accuracy: 99.972, Time: 42.54
[2020-12-16 16:54:49,911][__main__][INFO] - [8960] Loss: 0.046, Running accuracy: 99.972, Time: 38.35
[2020-12-16 16:55:23,566][__main__][INFO] - [9280] Loss: 0.054, Running accuracy: 99.972, Time: 33.65
[2020-12-16 16:55:59,990][__main__][INFO] - [9600] Loss: 0.055, Running accuracy: 99.972, Time: 36.42
[2020-12-16 16:56:38,160][__main__][INFO] - [9920] Loss: 0.073, Running accuracy: 99.972, Time: 38.17
[2020-12-16 16:57:16,119][__main__][INFO] - [10240] Loss: 0.045, Running accuracy: 99.972, Time: 37.96
[2020-12-16 16:57:59,752][__main__][INFO] - [10560] Loss: 0.060, Running accuracy: 99.972, Time: 43.63
[2020-12-16 16:58:38,255][__main__][INFO] - [10880] Loss: 0.056, Running accuracy: 99.972, Time: 38.50
[2020-12-16 16:59:15,038][__main__][INFO] - [11200] Loss: 0.052, Running accuracy: 99.972, Time: 36.78
[2020-12-16 16:59:50,194][__main__][INFO] - [11520] Loss: 0.056, Running accuracy: 99.971, Time: 35.15
[2020-12-16 17:00:23,764][__main__][INFO] - [11840] Loss: 0.067, Running accuracy: 99.971, Time: 33.57
[2020-12-16 17:00:56,368][__main__][INFO] - [12160] Loss: 0.061, Running accuracy: 99.971, Time: 32.60
[2020-12-16 17:01:38,761][__main__][INFO] - [12480] Loss: 0.079, Running accuracy: 99.971, Time: 42.39
[2020-12-16 17:02:14,507][__main__][INFO] - [12800] Loss: 0.064, Running accuracy: 99.970, Time: 35.75
[2020-12-16 17:02:49,548][__main__][INFO] - [13120] Loss: 0.063, Running accuracy: 99.970, Time: 35.04
[2020-12-16 17:03:25,213][__main__][INFO] - [13440] Loss: 0.081, Running accuracy: 99.969, Time: 35.66
[2020-12-16 17:04:08,194][__main__][INFO] - [13760] Loss: 0.057, Running accuracy: 99.969, Time: 42.98
[2020-12-16 17:04:45,742][__main__][INFO] - [14080] Loss: 0.058, Running accuracy: 99.969, Time: 37.55
[2020-12-16 17:05:24,322][__main__][INFO] - [14400] Loss: 0.069, Running accuracy: 99.969, Time: 38.58
[2020-12-16 17:06:03,309][__main__][INFO] - [14720] Loss: 0.067, Running accuracy: 99.969, Time: 38.99
[2020-12-16 17:06:38,914][__main__][INFO] - [15040] Loss: 0.052, Running accuracy: 99.969, Time: 35.60
[2020-12-16 17:07:10,296][__main__][INFO] - [15360] Loss: 0.041, Running accuracy: 99.969, Time: 31.38
[2020-12-16 17:07:42,980][__main__][INFO] - [15680] Loss: 0.070, Running accuracy: 99.969, Time: 32.68
[2020-12-16 17:08:25,263][__main__][INFO] - [16000] Loss: 0.086, Running accuracy: 99.968, Time: 42.28
[2020-12-16 17:08:59,330][__main__][INFO] - [16320] Loss: 0.092, Running accuracy: 99.967, Time: 34.06
[2020-12-16 17:09:37,678][__main__][INFO] - [16640] Loss: 0.077, Running accuracy: 99.967, Time: 38.35
[2020-12-16 17:10:19,732][__main__][INFO] - [16960] Loss: 0.049, Running accuracy: 99.967, Time: 42.05
[2020-12-16 17:10:59,009][__main__][INFO] - [17280] Loss: 0.060, Running accuracy: 99.968, Time: 39.28
[2020-12-16 17:11:30,334][__main__][INFO] - Action accuracy: 99.968, Loss: 0.069
[2020-12-16 17:11:30,335][__main__][INFO] - Validating..
[2020-12-16 17:11:36,965][test][INFO] - Time elapsed: 5.225607
[2020-12-16 17:11:36,966][__main__][INFO] - Validation F1 score: 94.430, Exact match: 56.820, Precision: 94.610, Recall: 94.250
[2020-12-16 17:11:48,917][__main__][INFO] - Checkpoint saved to checkpoints/model_latest.pth
[2020-12-16 17:11:49,229][__main__][INFO] - Epoch #50
[2020-12-16 17:11:49,229][__main__][INFO] - Training..
[2020-12-16 17:12:25,869][__main__][INFO] - [320] Loss: 0.060, Running accuracy: 99.989, Time: 35.61
[2020-12-16 17:13:05,416][__main__][INFO] - [640] Loss: 0.054, Running accuracy: 99.989, Time: 39.55
[2020-12-16 17:13:46,621][__main__][INFO] - [960] Loss: 0.057, Running accuracy: 99.985, Time: 41.20
[2020-12-16 17:14:21,721][__main__][INFO] - [1280] Loss: 0.065, Running accuracy: 99.986, Time: 35.10
[2020-12-16 17:14:59,922][__main__][INFO] - [1600] Loss: 0.057, Running accuracy: 99.984, Time: 38.20
[2020-12-16 17:15:39,925][__main__][INFO] - [1920] Loss: 0.072, Running accuracy: 99.975, Time: 40.00
[2020-12-16 17:16:19,805][__main__][INFO] - [2240] Loss: 0.059, Running accuracy: 99.977, Time: 39.88
[2020-12-16 17:16:57,361][__main__][INFO] - [2560] Loss: 0.062, Running accuracy: 99.979, Time: 37.55
[2020-12-16 17:17:34,458][__main__][INFO] - [2880] Loss: 0.043, Running accuracy: 99.980, Time: 37.10
[2020-12-16 17:18:10,679][__main__][INFO] - [3200] Loss: 0.043, Running accuracy: 99.982, Time: 36.22
[2020-12-16 17:18:49,468][__main__][INFO] - [3520] Loss: 0.067, Running accuracy: 99.980, Time: 38.79
[2020-12-16 17:19:29,841][__main__][INFO] - [3840] Loss: 0.057, Running accuracy: 99.980, Time: 40.37
[2020-12-16 17:20:06,880][__main__][INFO] - [4160] Loss: 0.043, Running accuracy: 99.981, Time: 37.04
[2020-12-16 17:20:47,654][__main__][INFO] - [4480] Loss: 0.067, Running accuracy: 99.978, Time: 40.77
[2020-12-16 17:21:26,234][__main__][INFO] - [4800] Loss: 0.067, Running accuracy: 99.976, Time: 38.58
[2020-12-16 17:22:02,352][__main__][INFO] - [5120] Loss: 0.064, Running accuracy: 99.977, Time: 36.12
[2020-12-16 17:22:36,202][__main__][INFO] - [5440] Loss: 0.061, Running accuracy: 99.978, Time: 33.85
[2020-12-16 17:23:12,437][__main__][INFO] - [5760] Loss: 0.043, Running accuracy: 99.979, Time: 36.23
[2020-12-16 17:23:54,927][__main__][INFO] - [6080] Loss: 0.068, Running accuracy: 99.978, Time: 42.49
[2020-12-16 17:24:30,901][__main__][INFO] - [6400] Loss: 0.076, Running accuracy: 99.978, Time: 35.97
[2020-12-16 17:25:10,137][__main__][INFO] - [6720] Loss: 0.049, Running accuracy: 99.978, Time: 39.24
[2020-12-16 17:25:44,119][__main__][INFO] - [7040] Loss: 0.045, Running accuracy: 99.979, Time: 33.98
[2020-12-16 17:26:19,815][__main__][INFO] - [7360] Loss: 0.058, Running accuracy: 99.978, Time: 35.69
[2020-12-16 17:27:03,979][__main__][INFO] - [7680] Loss: 0.070, Running accuracy: 99.977, Time: 44.16
[2020-12-16 17:27:41,641][__main__][INFO] - [8000] Loss: 0.054, Running accuracy: 99.978, Time: 37.66
[2020-12-16 17:28:15,562][__main__][INFO] - [8320] Loss: 0.055, Running accuracy: 99.977, Time: 33.92
[2020-12-16 17:28:56,092][__main__][INFO] - [8640] Loss: 0.065, Running accuracy: 99.977, Time: 40.53
[2020-12-16 17:29:30,534][__main__][INFO] - [8960] Loss: 0.081, Running accuracy: 99.976, Time: 34.44
[2020-12-16 17:30:11,074][__main__][INFO] - [9280] Loss: 0.080, Running accuracy: 99.975, Time: 40.54
[2020-12-16 17:30:47,545][__main__][INFO] - [9600] Loss: 0.044, Running accuracy: 99.975, Time: 36.47
[2020-12-16 17:31:24,983][__main__][INFO] - [9920] Loss: 0.091, Running accuracy: 99.974, Time: 37.44
[2020-12-16 17:32:05,928][__main__][INFO] - [10240] Loss: 0.064, Running accuracy: 99.974, Time: 40.94
[2020-12-16 17:32:45,264][__main__][INFO] - [10560] Loss: 0.052, Running accuracy: 99.974, Time: 39.34
[2020-12-16 17:33:27,614][__main__][INFO] - [10880] Loss: 0.066, Running accuracy: 99.973, Time: 42.35
[2020-12-16 17:34:07,124][__main__][INFO] - [11200] Loss: 0.070, Running accuracy: 99.973, Time: 39.51
[2020-12-16 17:34:48,418][__main__][INFO] - [11520] Loss: 0.065, Running accuracy: 99.973, Time: 41.29
[2020-12-16 17:35:25,600][__main__][INFO] - [11840] Loss: 0.059, Running accuracy: 99.973, Time: 37.18
[2020-12-16 17:36:02,032][__main__][INFO] - [12160] Loss: 0.065, Running accuracy: 99.973, Time: 36.43
[2020-12-16 17:36:40,689][__main__][INFO] - [12480] Loss: 0.047, Running accuracy: 99.973, Time: 38.66
[2020-12-16 17:37:16,949][__main__][INFO] - [12800] Loss: 0.053, Running accuracy: 99.973, Time: 36.26
[2020-12-16 17:37:54,416][__main__][INFO] - [13120] Loss: 0.068, Running accuracy: 99.972, Time: 37.47
[2020-12-16 17:38:33,672][__main__][INFO] - [13440] Loss: 0.074, Running accuracy: 99.972, Time: 39.26
[2020-12-16 17:39:17,016][__main__][INFO] - [13760] Loss: 0.073, Running accuracy: 99.972, Time: 43.34
[2020-12-16 17:39:53,692][__main__][INFO] - [14080] Loss: 0.044, Running accuracy: 99.972, Time: 36.67
[2020-12-16 17:40:31,909][__main__][INFO] - [14400] Loss: 0.061, Running accuracy: 99.972, Time: 38.22
[2020-12-16 17:41:03,767][__main__][INFO] - [14720] Loss: 0.069, Running accuracy: 99.972, Time: 31.86
[2020-12-16 17:41:47,654][__main__][INFO] - [15040] Loss: 0.071, Running accuracy: 99.973, Time: 43.89
[2020-12-16 17:42:23,709][__main__][INFO] - [15360] Loss: 0.048, Running accuracy: 99.973, Time: 36.05
[2020-12-16 17:43:03,405][__main__][INFO] - [15680] Loss: 0.052, Running accuracy: 99.973, Time: 39.69
[2020-12-16 17:43:40,210][__main__][INFO] - [16000] Loss: 0.056, Running accuracy: 99.973, Time: 36.80
[2020-12-16 17:44:17,541][__main__][INFO] - [16320] Loss: 0.069, Running accuracy: 99.973, Time: 37.33
[2020-12-16 17:44:52,731][__main__][INFO] - [16640] Loss: 0.051, Running accuracy: 99.973, Time: 35.19
[2020-12-16 17:45:29,678][__main__][INFO] - [16960] Loss: 0.059, Running accuracy: 99.972, Time: 36.95
[2020-12-16 17:46:04,151][__main__][INFO] - [17280] Loss: 0.043, Running accuracy: 99.972, Time: 34.47
[2020-12-16 17:46:30,011][__main__][INFO] - Action accuracy: 99.972, Loss: 0.066
[2020-12-16 17:46:30,012][__main__][INFO] - Validating..
[2020-12-16 17:46:36,752][test][INFO] - Time elapsed: 5.223603
[2020-12-16 17:46:36,754][__main__][INFO] - Validation F1 score: 94.470, Exact match: 56.820, Precision: 94.670, Recall: 94.270
Epoch    51: reducing learning rate of group 0 to 6.2500e-07.
[2020-12-16 17:46:49,501][__main__][INFO] - Checkpoint saved to checkpoints/model_latest.pth
[2020-12-16 17:46:49,813][__main__][INFO] - Epoch #51
[2020-12-16 17:46:49,813][__main__][INFO] - Training..
[2020-12-16 17:47:28,169][__main__][INFO] - [320] Loss: 0.046, Running accuracy: 99.965, Time: 37.30
[2020-12-16 17:48:06,654][__main__][INFO] - [640] Loss: 0.049, Running accuracy: 99.966, Time: 38.48
[2020-12-16 17:48:41,140][__main__][INFO] - [960] Loss: 0.086, Running accuracy: 99.962, Time: 34.49
[2020-12-16 17:49:18,628][__main__][INFO] - [1280] Loss: 0.062, Running accuracy: 99.960, Time: 37.49
[2020-12-16 17:49:56,501][__main__][INFO] - [1600] Loss: 0.058, Running accuracy: 99.964, Time: 37.87
[2020-12-16 17:50:32,129][__main__][INFO] - [1920] Loss: 0.047, Running accuracy: 99.966, Time: 35.63
[2020-12-16 17:51:20,744][__main__][INFO] - [2240] Loss: 0.065, Running accuracy: 99.968, Time: 48.61
[2020-12-16 17:51:51,762][__main__][INFO] - [2560] Loss: 0.063, Running accuracy: 99.966, Time: 31.02
[2020-12-16 17:52:31,423][__main__][INFO] - [2880] Loss: 0.068, Running accuracy: 99.966, Time: 39.66
[2020-12-16 17:53:08,283][__main__][INFO] - [3200] Loss: 0.044, Running accuracy: 99.968, Time: 36.86
[2020-12-16 17:53:46,250][__main__][INFO] - [3520] Loss: 0.049, Running accuracy: 99.970, Time: 37.97
[2020-12-16 17:54:27,143][__main__][INFO] - [3840] Loss: 0.046, Running accuracy: 99.971, Time: 40.89
[2020-12-16 17:55:03,748][__main__][INFO] - [4160] Loss: 0.045, Running accuracy: 99.972, Time: 36.60
[2020-12-16 17:55:38,294][__main__][INFO] - [4480] Loss: 0.070, Running accuracy: 99.970, Time: 34.55
[2020-12-16 17:56:19,260][__main__][INFO] - [4800] Loss: 0.061, Running accuracy: 99.970, Time: 40.97
[2020-12-16 17:57:00,449][__main__][INFO] - [5120] Loss: 0.060, Running accuracy: 99.968, Time: 41.19
[2020-12-16 17:57:39,572][__main__][INFO] - [5440] Loss: 0.060, Running accuracy: 99.968, Time: 39.12
[2020-12-16 17:58:14,200][__main__][INFO] - [5760] Loss: 0.039, Running accuracy: 99.968, Time: 34.63
[2020-12-16 17:58:51,489][__main__][INFO] - [6080] Loss: 0.041, Running accuracy: 99.970, Time: 37.29
[2020-12-16 17:59:33,323][__main__][INFO] - [6400] Loss: 0.050, Running accuracy: 99.971, Time: 41.83
[2020-12-16 18:00:06,438][__main__][INFO] - [6720] Loss: 0.056, Running accuracy: 99.972, Time: 33.11
[2020-12-16 18:00:43,490][__main__][INFO] - [7040] Loss: 0.055, Running accuracy: 99.971, Time: 37.05
[2020-12-16 18:01:21,760][__main__][INFO] - [7360] Loss: 0.069, Running accuracy: 99.972, Time: 38.27
[2020-12-16 18:02:02,351][__main__][INFO] - [7680] Loss: 0.052, Running accuracy: 99.972, Time: 40.59
[2020-12-16 18:02:45,022][__main__][INFO] - [8000] Loss: 0.069, Running accuracy: 99.972, Time: 42.67
[2020-12-16 18:03:24,983][__main__][INFO] - [8320] Loss: 0.088, Running accuracy: 99.971, Time: 39.96
[2020-12-16 18:04:01,740][__main__][INFO] - [8640] Loss: 0.049, Running accuracy: 99.972, Time: 36.76
[2020-12-16 18:04:36,501][__main__][INFO] - [8960] Loss: 0.059, Running accuracy: 99.971, Time: 34.76
[2020-12-16 18:05:12,586][__main__][INFO] - [9280] Loss: 0.055, Running accuracy: 99.971, Time: 36.08
[2020-12-16 18:05:48,793][__main__][INFO] - [9600] Loss: 0.053, Running accuracy: 99.971, Time: 36.21
[2020-12-16 18:06:24,291][__main__][INFO] - [9920] Loss: 0.047, Running accuracy: 99.971, Time: 35.50
[2020-12-16 18:07:08,230][__main__][INFO] - [10240] Loss: 0.047, Running accuracy: 99.972, Time: 43.94
[2020-12-16 18:07:48,157][__main__][INFO] - [10560] Loss: 0.062, Running accuracy: 99.971, Time: 39.93
[2020-12-16 18:08:26,573][__main__][INFO] - [10880] Loss: 0.047, Running accuracy: 99.972, Time: 38.41
[2020-12-16 18:09:00,402][__main__][INFO] - [11200] Loss: 0.063, Running accuracy: 99.971, Time: 33.83
[2020-12-16 18:09:36,171][__main__][INFO] - [11520] Loss: 0.070, Running accuracy: 99.970, Time: 35.77
[2020-12-16 18:10:13,442][__main__][INFO] - [11840] Loss: 0.064, Running accuracy: 99.971, Time: 37.27
[2020-12-16 18:10:56,515][__main__][INFO] - [12160] Loss: 0.066, Running accuracy: 99.970, Time: 43.07
[2020-12-16 18:11:33,328][__main__][INFO] - [12480] Loss: 0.075, Running accuracy: 99.970, Time: 36.81
[2020-12-16 18:12:13,782][__main__][INFO] - [12800] Loss: 0.080, Running accuracy: 99.969, Time: 40.45
[2020-12-16 18:12:52,334][__main__][INFO] - [13120] Loss: 0.051, Running accuracy: 99.969, Time: 38.55
[2020-12-16 18:13:28,191][__main__][INFO] - [13440] Loss: 0.079, Running accuracy: 99.969, Time: 35.86
[2020-12-16 18:14:08,498][__main__][INFO] - [13760] Loss: 0.060, Running accuracy: 99.969, Time: 40.31
[2020-12-16 18:14:42,360][__main__][INFO] - [14080] Loss: 0.070, Running accuracy: 99.970, Time: 33.86
[2020-12-16 18:15:19,064][__main__][INFO] - [14400] Loss: 0.050, Running accuracy: 99.970, Time: 36.70
[2020-12-16 18:15:58,810][__main__][INFO] - [14720] Loss: 0.057, Running accuracy: 99.970, Time: 39.75
[2020-12-16 18:16:38,317][__main__][INFO] - [15040] Loss: 0.062, Running accuracy: 99.970, Time: 39.51
[2020-12-16 18:17:20,306][__main__][INFO] - [15360] Loss: 0.065, Running accuracy: 99.970, Time: 41.99
[2020-12-16 18:18:00,670][__main__][INFO] - [15680] Loss: 0.082, Running accuracy: 99.969, Time: 40.36
[2020-12-16 18:18:35,157][__main__][INFO] - [16000] Loss: 0.056, Running accuracy: 99.969, Time: 34.49
[2020-12-16 18:19:16,915][__main__][INFO] - [16320] Loss: 0.075, Running accuracy: 99.969, Time: 41.76
[2020-12-16 18:19:51,661][__main__][INFO] - [16640] Loss: 0.062, Running accuracy: 99.969, Time: 34.75
[2020-12-16 18:20:29,504][__main__][INFO] - [16960] Loss: 0.086, Running accuracy: 99.968, Time: 37.84
[2020-12-16 18:21:07,123][__main__][INFO] - [17280] Loss: 0.080, Running accuracy: 99.968, Time: 37.62
[2020-12-16 18:21:35,391][__main__][INFO] - Action accuracy: 99.968, Loss: 0.068
[2020-12-16 18:21:35,392][__main__][INFO] - Validating..
[2020-12-16 18:21:41,990][test][INFO] - Time elapsed: 5.181721
[2020-12-16 18:21:41,992][__main__][INFO] - Validation F1 score: 94.490, Exact match: 56.820, Precision: 94.690, Recall: 94.300
[2020-12-16 18:21:54,714][__main__][INFO] - Checkpoint saved to checkpoints/model_latest.pth
[2020-12-16 18:21:55,066][__main__][INFO] - Epoch #52
[2020-12-16 18:21:55,066][__main__][INFO] - Training..
[2020-12-16 18:22:35,125][__main__][INFO] - [320] Loss: 0.041, Running accuracy: 99.989, Time: 39.02
[2020-12-16 18:23:08,811][__main__][INFO] - [640] Loss: 0.046, Running accuracy: 99.982, Time: 33.68
[2020-12-16 18:23:43,640][__main__][INFO] - [960] Loss: 0.084, Running accuracy: 99.960, Time: 34.83
[2020-12-16 18:24:27,405][__main__][INFO] - [1280] Loss: 0.056, Running accuracy: 99.968, Time: 43.76
[2020-12-16 18:25:05,999][__main__][INFO] - [1600] Loss: 0.065, Running accuracy: 99.965, Time: 38.59
[2020-12-16 18:25:40,407][__main__][INFO] - [1920] Loss: 0.089, Running accuracy: 99.963, Time: 34.41
[2020-12-16 18:26:20,363][__main__][INFO] - [2240] Loss: 0.065, Running accuracy: 99.967, Time: 39.96
[2020-12-16 18:27:01,157][__main__][INFO] - [2560] Loss: 0.079, Running accuracy: 99.966, Time: 40.79
[2020-12-16 18:27:38,873][__main__][INFO] - [2880] Loss: 0.051, Running accuracy: 99.969, Time: 37.71
[2020-12-16 18:28:13,961][__main__][INFO] - [3200] Loss: 0.062, Running accuracy: 99.969, Time: 35.09
[2020-12-16 18:28:52,128][__main__][INFO] - [3520] Loss: 0.065, Running accuracy: 99.968, Time: 38.17
[2020-12-16 18:29:27,742][__main__][INFO] - [3840] Loss: 0.050, Running accuracy: 99.970, Time: 35.61
[2020-12-16 18:30:08,200][__main__][INFO] - [4160] Loss: 0.067, Running accuracy: 99.970, Time: 40.46
[2020-12-16 18:30:42,409][__main__][INFO] - [4480] Loss: 0.065, Running accuracy: 99.968, Time: 34.21
[2020-12-16 18:31:18,600][__main__][INFO] - [4800] Loss: 0.072, Running accuracy: 99.968, Time: 36.19
[2020-12-16 18:31:54,947][__main__][INFO] - [5120] Loss: 0.091, Running accuracy: 99.968, Time: 36.35
[2020-12-16 18:32:33,394][__main__][INFO] - [5440] Loss: 0.064, Running accuracy: 99.967, Time: 38.45
[2020-12-16 18:33:17,501][__main__][INFO] - [5760] Loss: 0.044, Running accuracy: 99.968, Time: 44.11
[2020-12-16 18:33:52,994][__main__][INFO] - [6080] Loss: 0.079, Running accuracy: 99.965, Time: 35.40
[2020-12-16 18:34:29,007][__main__][INFO] - [6400] Loss: 0.053, Running accuracy: 99.966, Time: 36.01
[2020-12-16 18:35:10,025][__main__][INFO] - [6720] Loss: 0.062, Running accuracy: 99.966, Time: 41.02
[2020-12-16 18:35:47,360][__main__][INFO] - [7040] Loss: 0.057, Running accuracy: 99.966, Time: 37.33
[2020-12-16 18:36:28,628][__main__][INFO] - [7360] Loss: 0.051, Running accuracy: 99.966, Time: 41.27
[2020-12-16 18:37:03,231][__main__][INFO] - [7680] Loss: 0.064, Running accuracy: 99.967, Time: 34.60
[2020-12-16 18:37:42,335][__main__][INFO] - [8000] Loss: 0.048, Running accuracy: 99.967, Time: 39.10
[2020-12-16 18:38:20,459][__main__][INFO] - [8320] Loss: 0.062, Running accuracy: 99.968, Time: 38.12
[2020-12-16 18:38:55,575][__main__][INFO] - [8640] Loss: 0.054, Running accuracy: 99.968, Time: 35.11
[2020-12-16 18:39:30,483][__main__][INFO] - [8960] Loss: 0.061, Running accuracy: 99.968, Time: 34.91
[2020-12-16 18:40:08,573][__main__][INFO] - [9280] Loss: 0.067, Running accuracy: 99.968, Time: 38.09
[2020-12-16 18:40:45,255][__main__][INFO] - [9600] Loss: 0.051, Running accuracy: 99.968, Time: 36.68
[2020-12-16 18:41:27,915][__main__][INFO] - [9920] Loss: 0.049, Running accuracy: 99.969, Time: 42.66
[2020-12-16 18:42:05,913][__main__][INFO] - [10240] Loss: 0.050, Running accuracy: 99.970, Time: 38.00
[2020-12-16 18:42:46,751][__main__][INFO] - [10560] Loss: 0.080, Running accuracy: 99.970, Time: 40.84
[2020-12-16 18:43:23,877][__main__][INFO] - [10880] Loss: 0.062, Running accuracy: 99.970, Time: 37.12
[2020-12-16 18:44:00,177][__main__][INFO] - [11200] Loss: 0.039, Running accuracy: 99.971, Time: 36.30
[2020-12-16 18:44:36,720][__main__][INFO] - [11520] Loss: 0.054, Running accuracy: 99.969, Time: 36.54
[2020-12-16 18:45:11,181][__main__][INFO] - [11840] Loss: 0.045, Running accuracy: 99.969, Time: 34.46
[2020-12-16 18:45:49,425][__main__][INFO] - [12160] Loss: 0.051, Running accuracy: 99.970, Time: 38.24
[2020-12-16 18:46:27,824][__main__][INFO] - [12480] Loss: 0.074, Running accuracy: 99.970, Time: 38.40
[2020-12-16 18:47:05,051][__main__][INFO] - [12800] Loss: 0.045, Running accuracy: 99.969, Time: 37.23
[2020-12-16 18:47:43,000][__main__][INFO] - [13120] Loss: 0.107, Running accuracy: 99.968, Time: 37.95
[2020-12-16 18:48:19,709][__main__][INFO] - [13440] Loss: 0.047, Running accuracy: 99.968, Time: 36.71
[2020-12-16 18:48:57,872][__main__][INFO] - [13760] Loss: 0.043, Running accuracy: 99.968, Time: 38.16
[2020-12-16 18:49:36,641][__main__][INFO] - [14080] Loss: 0.082, Running accuracy: 99.967, Time: 38.77
[2020-12-16 18:50:13,684][__main__][INFO] - [14400] Loss: 0.051, Running accuracy: 99.967, Time: 37.04
[2020-12-16 18:50:53,856][__main__][INFO] - [14720] Loss: 0.053, Running accuracy: 99.968, Time: 40.17
[2020-12-16 18:51:31,176][__main__][INFO] - [15040] Loss: 0.047, Running accuracy: 99.968, Time: 37.32
[2020-12-16 18:52:08,400][__main__][INFO] - [15360] Loss: 0.054, Running accuracy: 99.968, Time: 37.22
[2020-12-16 18:52:47,045][__main__][INFO] - [15680] Loss: 0.049, Running accuracy: 99.968, Time: 38.64
[2020-12-16 18:53:29,596][__main__][INFO] - [16000] Loss: 0.057, Running accuracy: 99.968, Time: 42.55
[2020-12-16 18:54:10,711][__main__][INFO] - [16320] Loss: 0.067, Running accuracy: 99.967, Time: 41.11
[2020-12-16 18:54:51,993][__main__][INFO] - [16640] Loss: 0.067, Running accuracy: 99.968, Time: 41.28
[2020-12-16 18:55:27,847][__main__][INFO] - [16960] Loss: 0.052, Running accuracy: 99.968, Time: 35.85
[2020-12-16 18:56:03,591][__main__][INFO] - [17280] Loss: 0.078, Running accuracy: 99.967, Time: 35.74
[2020-12-16 18:56:33,228][__main__][INFO] - Action accuracy: 99.968, Loss: 0.066
[2020-12-16 18:56:33,229][__main__][INFO] - Validating..
[2020-12-16 18:56:39,949][test][INFO] - Time elapsed: 5.412812
[2020-12-16 18:56:39,950][__main__][INFO] - Validation F1 score: 94.520, Exact match: 56.820, Precision: 94.680, Recall: 94.360
[2020-12-16 18:56:52,716][__main__][INFO] - Checkpoint saved to checkpoints/model_latest.pth
[2020-12-16 18:56:53,028][__main__][INFO] - Epoch #53
[2020-12-16 18:56:53,029][__main__][INFO] - Training..
[2020-12-16 18:57:35,370][__main__][INFO] - [320] Loss: 0.051, Running accuracy: 99.989, Time: 41.18
[2020-12-16 18:58:15,253][__main__][INFO] - [640] Loss: 0.035, Running accuracy: 99.988, Time: 39.88
[2020-12-16 18:58:54,845][__main__][INFO] - [960] Loss: 0.080, Running accuracy: 99.973, Time: 39.59
[2020-12-16 18:59:24,005][__main__][INFO] - [1280] Loss: 0.050, Running accuracy: 99.976, Time: 29.16
[2020-12-16 18:59:56,701][__main__][INFO] - [1600] Loss: 0.058, Running accuracy: 99.969, Time: 32.69
[2020-12-16 19:00:34,788][__main__][INFO] - [1920] Loss: 0.063, Running accuracy: 99.968, Time: 38.09
[2020-12-16 19:01:12,909][__main__][INFO] - [2240] Loss: 0.060, Running accuracy: 99.971, Time: 38.12
[2020-12-16 19:01:54,844][__main__][INFO] - [2560] Loss: 0.051, Running accuracy: 99.972, Time: 41.93
[2020-12-16 19:02:29,774][__main__][INFO] - [2880] Loss: 0.060, Running accuracy: 99.971, Time: 34.93
[2020-12-16 19:03:12,577][__main__][INFO] - [3200] Loss: 0.054, Running accuracy: 99.972, Time: 42.80
[2020-12-16 19:03:52,471][__main__][INFO] - [3520] Loss: 0.051, Running accuracy: 99.973, Time: 39.89
[2020-12-16 19:04:30,780][__main__][INFO] - [3840] Loss: 0.047, Running accuracy: 99.971, Time: 38.31
[2020-12-16 19:05:05,198][__main__][INFO] - [4160] Loss: 0.055, Running accuracy: 99.970, Time: 34.42
[2020-12-16 19:05:41,165][__main__][INFO] - [4480] Loss: 0.064, Running accuracy: 99.970, Time: 35.97
[2020-12-16 19:06:20,742][__main__][INFO] - [4800] Loss: 0.067, Running accuracy: 99.968, Time: 39.58
[2020-12-16 19:06:57,123][__main__][INFO] - [5120] Loss: 0.042, Running accuracy: 99.968, Time: 36.38
[2020-12-16 19:07:38,637][__main__][INFO] - [5440] Loss: 0.097, Running accuracy: 99.965, Time: 41.51
[2020-12-16 19:08:12,669][__main__][INFO] - [5760] Loss: 0.044, Running accuracy: 99.965, Time: 34.03
[2020-12-16 19:08:45,843][__main__][INFO] - [6080] Loss: 0.049, Running accuracy: 99.965, Time: 33.17
[2020-12-16 19:09:22,367][__main__][INFO] - [6400] Loss: 0.057, Running accuracy: 99.965, Time: 36.52
[2020-12-16 19:09:58,429][__main__][INFO] - [6720] Loss: 0.056, Running accuracy: 99.965, Time: 36.06
[2020-12-16 19:10:36,310][__main__][INFO] - [7040] Loss: 0.084, Running accuracy: 99.964, Time: 37.88
[2020-12-16 19:11:18,456][__main__][INFO] - [7360] Loss: 0.048, Running accuracy: 99.964, Time: 42.15
[2020-12-16 19:11:58,021][__main__][INFO] - [7680] Loss: 0.064, Running accuracy: 99.965, Time: 39.56
[2020-12-16 19:12:42,893][__main__][INFO] - [8000] Loss: 0.076, Running accuracy: 99.963, Time: 44.87
[2020-12-16 19:13:23,393][__main__][INFO] - [8320] Loss: 0.046, Running accuracy: 99.964, Time: 40.50
[2020-12-16 19:14:06,324][__main__][INFO] - [8640] Loss: 0.053, Running accuracy: 99.964, Time: 42.93
[2020-12-16 19:14:43,252][__main__][INFO] - [8960] Loss: 0.056, Running accuracy: 99.964, Time: 36.93
[2020-12-16 19:15:21,594][__main__][INFO] - [9280] Loss: 0.055, Running accuracy: 99.965, Time: 38.34
[2020-12-16 19:15:59,104][__main__][INFO] - [9600] Loss: 0.062, Running accuracy: 99.965, Time: 37.51
[2020-12-16 19:16:38,320][__main__][INFO] - [9920] Loss: 0.087, Running accuracy: 99.965, Time: 39.21
[2020-12-16 19:17:17,214][__main__][INFO] - [10240] Loss: 0.051, Running accuracy: 99.965, Time: 38.89
[2020-12-16 19:17:55,722][__main__][INFO] - [10560] Loss: 0.055, Running accuracy: 99.966, Time: 38.51
[2020-12-16 19:18:30,704][__main__][INFO] - [10880] Loss: 0.040, Running accuracy: 99.967, Time: 34.98
[2020-12-16 19:19:08,662][__main__][INFO] - [11200] Loss: 0.051, Running accuracy: 99.967, Time: 37.96
[2020-12-16 19:19:44,523][__main__][INFO] - [11520] Loss: 0.056, Running accuracy: 99.967, Time: 35.86
[2020-12-16 19:20:21,222][__main__][INFO] - [11840] Loss: 0.090, Running accuracy: 99.967, Time: 36.70
[2020-12-16 19:20:53,047][__main__][INFO] - [12160] Loss: 0.065, Running accuracy: 99.967, Time: 31.82
[2020-12-16 19:21:30,029][__main__][INFO] - [12480] Loss: 0.045, Running accuracy: 99.968, Time: 36.98
[2020-12-16 19:22:08,285][__main__][INFO] - [12800] Loss: 0.040, Running accuracy: 99.968, Time: 38.26
[2020-12-16 19:22:49,502][__main__][INFO] - [13120] Loss: 0.085, Running accuracy: 99.968, Time: 41.22
[2020-12-16 19:23:25,364][__main__][INFO] - [13440] Loss: 0.057, Running accuracy: 99.968, Time: 35.86
[2020-12-16 19:24:04,792][__main__][INFO] - [13760] Loss: 0.058, Running accuracy: 99.968, Time: 39.43
[2020-12-16 19:24:42,434][__main__][INFO] - [14080] Loss: 0.046, Running accuracy: 99.968, Time: 37.64
[2020-12-16 19:25:22,412][__main__][INFO] - [14400] Loss: 0.061, Running accuracy: 99.968, Time: 39.98
[2020-12-16 19:25:59,778][__main__][INFO] - [14720] Loss: 0.046, Running accuracy: 99.968, Time: 37.37
[2020-12-16 19:26:37,944][__main__][INFO] - [15040] Loss: 0.036, Running accuracy: 99.969, Time: 38.16
[2020-12-16 19:27:18,805][__main__][INFO] - [15360] Loss: 0.058, Running accuracy: 99.968, Time: 40.86
[2020-12-16 19:27:56,041][__main__][INFO] - [15680] Loss: 0.064, Running accuracy: 99.969, Time: 37.24
[2020-12-16 19:28:34,980][__main__][INFO] - [16000] Loss: 0.075, Running accuracy: 99.968, Time: 38.94
[2020-12-16 19:29:10,713][__main__][INFO] - [16320] Loss: 0.055, Running accuracy: 99.968, Time: 35.73
[2020-12-16 19:29:49,554][__main__][INFO] - [16640] Loss: 0.064, Running accuracy: 99.969, Time: 38.84
[2020-12-16 19:30:33,025][__main__][INFO] - [16960] Loss: 0.054, Running accuracy: 99.969, Time: 43.47
[2020-12-16 19:31:07,058][__main__][INFO] - [17280] Loss: 0.043, Running accuracy: 99.969, Time: 34.03
[2020-12-16 19:31:39,182][__main__][INFO] - Action accuracy: 99.969, Loss: 0.065
[2020-12-16 19:31:39,183][__main__][INFO] - Validating..
[2020-12-16 19:31:45,824][test][INFO] - Time elapsed: 5.243823
[2020-12-16 19:31:45,825][__main__][INFO] - Validation F1 score: 94.450, Exact match: 56.820, Precision: 94.610, Recall: 94.290
[2020-12-16 19:31:57,709][__main__][INFO] - Checkpoint saved to checkpoints/model_latest.pth
[2020-12-16 19:31:58,093][__main__][INFO] - Epoch #54
[2020-12-16 19:31:58,093][__main__][INFO] - Training..
[2020-12-16 19:32:35,038][__main__][INFO] - [320] Loss: 0.042, Running accuracy: 99.989, Time: 35.83
[2020-12-16 19:33:11,879][__main__][INFO] - [640] Loss: 0.048, Running accuracy: 99.989, Time: 36.84
[2020-12-16 19:33:50,385][__main__][INFO] - [960] Loss: 0.058, Running accuracy: 99.970, Time: 38.50
[2020-12-16 19:34:25,064][__main__][INFO] - [1280] Loss: 0.066, Running accuracy: 99.963, Time: 34.68
[2020-12-16 19:35:01,197][__main__][INFO] - [1600] Loss: 0.044, Running accuracy: 99.970, Time: 36.13
[2020-12-16 19:35:37,737][__main__][INFO] - [1920] Loss: 0.057, Running accuracy: 99.973, Time: 36.54
[2020-12-16 19:36:16,722][__main__][INFO] - [2240] Loss: 0.082, Running accuracy: 99.969, Time: 38.98
[2020-12-16 19:36:54,543][__main__][INFO] - [2560] Loss: 0.067, Running accuracy: 99.966, Time: 37.82
[2020-12-16 19:37:30,905][__main__][INFO] - [2880] Loss: 0.046, Running accuracy: 99.963, Time: 36.36
[2020-12-16 19:38:08,576][__main__][INFO] - [3200] Loss: 0.065, Running accuracy: 99.963, Time: 37.67
[2020-12-16 19:38:45,861][__main__][INFO] - [3520] Loss: 0.054, Running accuracy: 99.962, Time: 37.28
[2020-12-16 19:39:27,460][__main__][INFO] - [3840] Loss: 0.051, Running accuracy: 99.965, Time: 41.51
[2020-12-16 19:40:06,092][__main__][INFO] - [4160] Loss: 0.077, Running accuracy: 99.962, Time: 38.63
[2020-12-16 19:40:41,038][__main__][INFO] - [4480] Loss: 0.054, Running accuracy: 99.961, Time: 34.95
[2020-12-16 19:41:21,022][__main__][INFO] - [4800] Loss: 0.048, Running accuracy: 99.964, Time: 39.98
[2020-12-16 19:41:57,095][__main__][INFO] - [5120] Loss: 0.044, Running accuracy: 99.966, Time: 36.07
[2020-12-16 19:42:31,714][__main__][INFO] - [5440] Loss: 0.036, Running accuracy: 99.966, Time: 34.62
[2020-12-16 19:43:08,472][__main__][INFO] - [5760] Loss: 0.065, Running accuracy: 99.967, Time: 36.76
[2020-12-16 19:43:48,486][__main__][INFO] - [6080] Loss: 0.076, Running accuracy: 99.967, Time: 40.01
[2020-12-16 19:44:28,196][__main__][INFO] - [6400] Loss: 0.051, Running accuracy: 99.968, Time: 39.71
[2020-12-16 19:45:13,901][__main__][INFO] - [6720] Loss: 0.049, Running accuracy: 99.969, Time: 45.70
[2020-12-16 19:45:53,679][__main__][INFO] - [7040] Loss: 0.074, Running accuracy: 99.967, Time: 39.78
[2020-12-16 19:46:36,603][__main__][INFO] - [7360] Loss: 0.087, Running accuracy: 99.965, Time: 42.92
[2020-12-16 19:47:17,394][__main__][INFO] - [7680] Loss: 0.059, Running accuracy: 99.964, Time: 40.79
[2020-12-16 19:47:56,966][__main__][INFO] - [8000] Loss: 0.052, Running accuracy: 99.964, Time: 39.57
[2020-12-16 19:48:40,532][__main__][INFO] - [8320] Loss: 0.045, Running accuracy: 99.966, Time: 43.57
[2020-12-16 19:49:17,702][__main__][INFO] - [8640] Loss: 0.048, Running accuracy: 99.967, Time: 37.17
[2020-12-16 19:49:54,116][__main__][INFO] - [8960] Loss: 0.046, Running accuracy: 99.968, Time: 36.41
[2020-12-16 19:50:36,089][__main__][INFO] - [9280] Loss: 0.065, Running accuracy: 99.967, Time: 41.97
[2020-12-16 19:51:16,079][__main__][INFO] - [9600] Loss: 0.069, Running accuracy: 99.966, Time: 39.99
[2020-12-16 19:51:52,570][__main__][INFO] - [9920] Loss: 0.057, Running accuracy: 99.967, Time: 36.49
[2020-12-16 19:52:33,626][__main__][INFO] - [10240] Loss: 0.049, Running accuracy: 99.967, Time: 41.06
[2020-12-16 19:53:08,638][__main__][INFO] - [10560] Loss: 0.063, Running accuracy: 99.968, Time: 35.01
[2020-12-16 19:53:48,125][__main__][INFO] - [10880] Loss: 0.089, Running accuracy: 99.967, Time: 39.49
[2020-12-16 19:54:24,841][__main__][INFO] - [11200] Loss: 0.039, Running accuracy: 99.968, Time: 36.71
[2020-12-16 19:55:01,797][__main__][INFO] - [11520] Loss: 0.060, Running accuracy: 99.967, Time: 36.96
[2020-12-16 19:55:36,928][__main__][INFO] - [11840] Loss: 0.069, Running accuracy: 99.967, Time: 35.13
[2020-12-16 19:56:13,163][__main__][INFO] - [12160] Loss: 0.054, Running accuracy: 99.967, Time: 36.23
[2020-12-16 19:56:56,184][__main__][INFO] - [12480] Loss: 0.057, Running accuracy: 99.968, Time: 43.02
[2020-12-16 19:57:31,676][__main__][INFO] - [12800] Loss: 0.063, Running accuracy: 99.967, Time: 35.49
[2020-12-16 19:58:05,873][__main__][INFO] - [13120] Loss: 0.042, Running accuracy: 99.968, Time: 34.20
[2020-12-16 19:58:41,264][__main__][INFO] - [13440] Loss: 0.055, Running accuracy: 99.968, Time: 35.39
[2020-12-16 19:59:17,378][__main__][INFO] - [13760] Loss: 0.095, Running accuracy: 99.968, Time: 36.11
[2020-12-16 19:59:58,123][__main__][INFO] - [14080] Loss: 0.070, Running accuracy: 99.967, Time: 40.74
[2020-12-16 20:00:34,571][__main__][INFO] - [14400] Loss: 0.052, Running accuracy: 99.968, Time: 36.45
[2020-12-16 20:01:12,425][__main__][INFO] - [14720] Loss: 0.047, Running accuracy: 99.968, Time: 37.85
[2020-12-16 20:01:49,236][__main__][INFO] - [15040] Loss: 0.049, Running accuracy: 99.968, Time: 36.81
[2020-12-16 20:02:27,731][__main__][INFO] - [15360] Loss: 0.067, Running accuracy: 99.969, Time: 38.49
[2020-12-16 20:03:01,731][__main__][INFO] - [15680] Loss: 0.051, Running accuracy: 99.969, Time: 34.00
[2020-12-16 20:03:37,329][__main__][INFO] - [16000] Loss: 0.088, Running accuracy: 99.969, Time: 35.60
[2020-12-16 20:04:14,067][__main__][INFO] - [16320] Loss: 0.067, Running accuracy: 99.969, Time: 36.74
[2020-12-16 20:04:54,549][__main__][INFO] - [16640] Loss: 0.056, Running accuracy: 99.969, Time: 40.48
[2020-12-16 20:05:32,256][__main__][INFO] - [16960] Loss: 0.067, Running accuracy: 99.968, Time: 37.71
[2020-12-16 20:06:10,869][__main__][INFO] - [17280] Loss: 0.050, Running accuracy: 99.969, Time: 38.61
[2020-12-16 20:06:41,638][__main__][INFO] - Action accuracy: 99.969, Loss: 0.066
[2020-12-16 20:06:41,639][__main__][INFO] - Validating..
[2020-12-16 20:06:48,337][test][INFO] - Time elapsed: 5.208622
[2020-12-16 20:06:48,338][__main__][INFO] - Validation F1 score: 94.430, Exact match: 56.820, Precision: 94.580, Recall: 94.270
[2020-12-16 20:07:01,092][__main__][INFO] - Checkpoint saved to checkpoints/model_latest.pth
[2020-12-16 20:07:01,405][__main__][INFO] - Epoch #55
[2020-12-16 20:07:01,405][__main__][INFO] - Training..
[2020-12-16 20:07:42,782][__main__][INFO] - [320] Loss: 0.049, Running accuracy: 99.989, Time: 40.03
[2020-12-16 20:08:20,715][__main__][INFO] - [640] Loss: 0.079, Running accuracy: 99.965, Time: 37.93
[2020-12-16 20:09:02,016][__main__][INFO] - [960] Loss: 0.055, Running accuracy: 99.961, Time: 41.30
[2020-12-16 20:09:39,044][__main__][INFO] - [1280] Loss: 0.052, Running accuracy: 99.966, Time: 37.03
[2020-12-16 20:10:15,671][__main__][INFO] - [1600] Loss: 0.049, Running accuracy: 99.968, Time: 36.63
[2020-12-16 20:10:52,706][__main__][INFO] - [1920] Loss: 0.089, Running accuracy: 99.964, Time: 37.03
[2020-12-16 20:11:36,462][__main__][INFO] - [2240] Loss: 0.058, Running accuracy: 99.966, Time: 43.75
[2020-12-16 20:12:13,574][__main__][INFO] - [2560] Loss: 0.053, Running accuracy: 99.965, Time: 37.11
[2020-12-16 20:12:56,188][__main__][INFO] - [2880] Loss: 0.063, Running accuracy: 99.965, Time: 42.61
[2020-12-16 20:13:36,729][__main__][INFO] - [3200] Loss: 0.051, Running accuracy: 99.967, Time: 40.54
[2020-12-16 20:14:10,236][__main__][INFO] - [3520] Loss: 0.050, Running accuracy: 99.968, Time: 33.51
[2020-12-16 20:14:48,332][__main__][INFO] - [3840] Loss: 0.046, Running accuracy: 99.969, Time: 38.10
[2020-12-16 20:15:27,460][__main__][INFO] - [4160] Loss: 0.067, Running accuracy: 99.969, Time: 39.13
[2020-12-16 20:16:07,424][__main__][INFO] - [4480] Loss: 0.045, Running accuracy: 99.971, Time: 39.96
[2020-12-16 20:16:44,029][__main__][INFO] - [4800] Loss: 0.070, Running accuracy: 99.971, Time: 36.60
[2020-12-16 20:17:23,143][__main__][INFO] - [5120] Loss: 0.065, Running accuracy: 99.971, Time: 39.11
[2020-12-16 20:17:58,751][__main__][INFO] - [5440] Loss: 0.056, Running accuracy: 99.971, Time: 35.61
[2020-12-16 20:18:37,563][__main__][INFO] - [5760] Loss: 0.082, Running accuracy: 99.969, Time: 38.81
[2020-12-16 20:19:13,459][__main__][INFO] - [6080] Loss: 0.120, Running accuracy: 99.969, Time: 35.89
[2020-12-16 20:19:45,798][__main__][INFO] - [6400] Loss: 0.068, Running accuracy: 99.968, Time: 32.34
[2020-12-16 20:20:27,352][__main__][INFO] - [6720] Loss: 0.082, Running accuracy: 99.967, Time: 41.55
[2020-12-16 20:21:03,067][__main__][INFO] - [7040] Loss: 0.057, Running accuracy: 99.966, Time: 35.71
[2020-12-16 20:21:43,564][__main__][INFO] - [7360] Loss: 0.065, Running accuracy: 99.966, Time: 40.50
[2020-12-16 20:22:21,304][__main__][INFO] - [7680] Loss: 0.049, Running accuracy: 99.967, Time: 37.74
[2020-12-16 20:23:00,251][__main__][INFO] - [8000] Loss: 0.097, Running accuracy: 99.966, Time: 38.95
[2020-12-16 20:23:37,328][__main__][INFO] - [8320] Loss: 0.069, Running accuracy: 99.965, Time: 37.08
[2020-12-16 20:24:12,765][__main__][INFO] - [8640] Loss: 0.055, Running accuracy: 99.966, Time: 35.44
[2020-12-16 20:24:49,072][__main__][INFO] - [8960] Loss: 0.069, Running accuracy: 99.966, Time: 36.31
[2020-12-16 20:25:30,126][__main__][INFO] - [9280] Loss: 0.061, Running accuracy: 99.966, Time: 41.05
[2020-12-16 20:26:03,442][__main__][INFO] - [9600] Loss: 0.047, Running accuracy: 99.967, Time: 33.31
[2020-12-16 20:26:40,172][__main__][INFO] - [9920] Loss: 0.043, Running accuracy: 99.967, Time: 36.73
[2020-12-16 20:27:16,067][__main__][INFO] - [10240] Loss: 0.042, Running accuracy: 99.968, Time: 35.89
[2020-12-16 20:27:53,247][__main__][INFO] - [10560] Loss: 0.056, Running accuracy: 99.969, Time: 37.18
[2020-12-16 20:28:26,951][__main__][INFO] - [10880] Loss: 0.044, Running accuracy: 99.969, Time: 33.70
[2020-12-16 20:29:04,498][__main__][INFO] - [11200] Loss: 0.056, Running accuracy: 99.969, Time: 37.55
[2020-12-16 20:29:41,273][__main__][INFO] - [11520] Loss: 0.047, Running accuracy: 99.969, Time: 36.77
[2020-12-16 20:30:19,018][__main__][INFO] - [11840] Loss: 0.070, Running accuracy: 99.969, Time: 37.74
[2020-12-16 20:30:58,471][__main__][INFO] - [12160] Loss: 0.062, Running accuracy: 99.968, Time: 39.45
[2020-12-16 20:31:33,236][__main__][INFO] - [12480] Loss: 0.052, Running accuracy: 99.969, Time: 34.76
[2020-12-16 20:32:17,423][__main__][INFO] - [12800] Loss: 0.089, Running accuracy: 99.968, Time: 44.19
[2020-12-16 20:32:56,520][__main__][INFO] - [13120] Loss: 0.051, Running accuracy: 99.969, Time: 39.10
[2020-12-16 20:33:34,949][__main__][INFO] - [13440] Loss: 0.046, Running accuracy: 99.969, Time: 38.43
[2020-12-16 20:34:13,187][__main__][INFO] - [13760] Loss: 0.066, Running accuracy: 99.969, Time: 38.24
[2020-12-16 20:34:49,502][__main__][INFO] - [14080] Loss: 0.061, Running accuracy: 99.969, Time: 36.31
[2020-12-16 20:35:28,486][__main__][INFO] - [14400] Loss: 0.055, Running accuracy: 99.969, Time: 38.98
[2020-12-16 20:36:07,043][__main__][INFO] - [14720] Loss: 0.062, Running accuracy: 99.969, Time: 38.56
[2020-12-16 20:36:44,629][__main__][INFO] - [15040] Loss: 0.068, Running accuracy: 99.968, Time: 37.59
[2020-12-16 20:37:21,214][__main__][INFO] - [15360] Loss: 0.048, Running accuracy: 99.969, Time: 36.58
[2020-12-16 20:38:06,376][__main__][INFO] - [15680] Loss: 0.066, Running accuracy: 99.968, Time: 45.16
[2020-12-16 20:38:48,517][__main__][INFO] - [16000] Loss: 0.039, Running accuracy: 99.969, Time: 42.14
[2020-12-16 20:39:24,613][__main__][INFO] - [16320] Loss: 0.054, Running accuracy: 99.969, Time: 36.10
[2020-12-16 20:40:00,646][__main__][INFO] - [16640] Loss: 0.048, Running accuracy: 99.969, Time: 36.03
[2020-12-16 20:40:44,101][__main__][INFO] - [16960] Loss: 0.070, Running accuracy: 99.969, Time: 43.45
[2020-12-16 20:41:21,929][__main__][INFO] - [17280] Loss: 0.085, Running accuracy: 99.969, Time: 37.83
[2020-12-16 20:41:48,414][__main__][INFO] - Action accuracy: 99.969, Loss: 0.067
[2020-12-16 20:41:48,415][__main__][INFO] - Validating..
[2020-12-16 20:41:57,332][test][INFO] - Time elapsed: 7.378886
[2020-12-16 20:41:57,334][__main__][INFO] - Validation F1 score: 94.600, Exact match: 57.100, Precision: 94.770, Recall: 94.420
[2020-12-16 20:42:10,045][__main__][INFO] - Checkpoint saved to checkpoints/model_latest.pth
[2020-12-16 20:42:10,357][__main__][INFO] - Epoch #56
[2020-12-16 20:42:10,357][__main__][INFO] - Training..
[2020-12-16 20:42:52,583][__main__][INFO] - [320] Loss: 0.059, Running accuracy: 99.988, Time: 41.29
[2020-12-16 20:43:32,848][__main__][INFO] - [640] Loss: 0.050, Running accuracy: 99.989, Time: 40.26
[2020-12-16 20:44:13,200][__main__][INFO] - [960] Loss: 0.104, Running accuracy: 99.977, Time: 40.35
[2020-12-16 20:44:48,093][__main__][INFO] - [1280] Loss: 0.048, Running accuracy: 99.980, Time: 34.89
[2020-12-16 20:45:22,162][__main__][INFO] - [1600] Loss: 0.078, Running accuracy: 99.975, Time: 34.07
[2020-12-16 20:46:08,159][__main__][INFO] - [1920] Loss: 0.073, Running accuracy: 99.975, Time: 46.00
[2020-12-16 20:46:48,961][__main__][INFO] - [2240] Loss: 0.054, Running accuracy: 99.977, Time: 40.80
[2020-12-16 20:47:28,104][__main__][INFO] - [2560] Loss: 0.049, Running accuracy: 99.977, Time: 39.14
[2020-12-16 20:48:06,966][__main__][INFO] - [2880] Loss: 0.058, Running accuracy: 99.979, Time: 38.86
[2020-12-16 20:48:44,913][__main__][INFO] - [3200] Loss: 0.051, Running accuracy: 99.978, Time: 37.95
[2020-12-16 20:49:27,616][__main__][INFO] - [3520] Loss: 0.085, Running accuracy: 99.976, Time: 42.70
[2020-12-16 20:50:03,792][__main__][INFO] - [3840] Loss: 0.065, Running accuracy: 99.976, Time: 36.18
[2020-12-16 20:50:41,969][__main__][INFO] - [4160] Loss: 0.049, Running accuracy: 99.978, Time: 38.18
[2020-12-16 20:51:19,853][__main__][INFO] - [4480] Loss: 0.051, Running accuracy: 99.978, Time: 37.88
[2020-12-16 20:51:58,139][__main__][INFO] - [4800] Loss: 0.061, Running accuracy: 99.978, Time: 38.20
[2020-12-16 20:52:36,128][__main__][INFO] - [5120] Loss: 0.060, Running accuracy: 99.977, Time: 37.99
[2020-12-16 20:53:13,332][__main__][INFO] - [5440] Loss: 0.055, Running accuracy: 99.976, Time: 37.20
[2020-12-16 20:53:52,742][__main__][INFO] - [5760] Loss: 0.049, Running accuracy: 99.976, Time: 39.41
[2020-12-16 20:54:31,152][__main__][INFO] - [6080] Loss: 0.098, Running accuracy: 99.974, Time: 38.41
[2020-12-16 20:55:10,557][__main__][INFO] - [6400] Loss: 0.055, Running accuracy: 99.974, Time: 39.32
[2020-12-16 20:55:44,724][__main__][INFO] - [6720] Loss: 0.054, Running accuracy: 99.974, Time: 34.17
[2020-12-16 20:56:18,177][__main__][INFO] - [7040] Loss: 0.061, Running accuracy: 99.974, Time: 33.45
[2020-12-16 20:57:00,219][__main__][INFO] - [7360] Loss: 0.084, Running accuracy: 99.973, Time: 42.04
[2020-12-16 20:57:35,668][__main__][INFO] - [7680] Loss: 0.061, Running accuracy: 99.971, Time: 35.45
[2020-12-16 20:58:14,019][__main__][INFO] - [8000] Loss: 0.071, Running accuracy: 99.970, Time: 38.35
[2020-12-16 20:58:53,985][__main__][INFO] - [8320] Loss: 0.065, Running accuracy: 99.970, Time: 39.97
[2020-12-16 20:59:30,880][__main__][INFO] - [8640] Loss: 0.055, Running accuracy: 99.970, Time: 36.89
[2020-12-16 21:00:07,690][__main__][INFO] - [8960] Loss: 0.057, Running accuracy: 99.970, Time: 36.81
[2020-12-16 21:00:45,435][__main__][INFO] - [9280] Loss: 0.046, Running accuracy: 99.971, Time: 37.74
[2020-12-16 21:01:26,940][__main__][INFO] - [9600] Loss: 0.052, Running accuracy: 99.971, Time: 41.50
[2020-12-16 21:02:04,166][__main__][INFO] - [9920] Loss: 0.051, Running accuracy: 99.971, Time: 37.22
[2020-12-16 21:02:41,070][__main__][INFO] - [10240] Loss: 0.050, Running accuracy: 99.972, Time: 36.90
[2020-12-16 21:03:19,816][__main__][INFO] - [10560] Loss: 0.048, Running accuracy: 99.973, Time: 38.74
[2020-12-16 21:04:00,971][__main__][INFO] - [10880] Loss: 0.056, Running accuracy: 99.973, Time: 41.15
[2020-12-16 21:04:40,236][__main__][INFO] - [11200] Loss: 0.052, Running accuracy: 99.973, Time: 39.26
[2020-12-16 21:05:19,849][__main__][INFO] - [11520] Loss: 0.045, Running accuracy: 99.974, Time: 39.61
[2020-12-16 21:05:51,993][__main__][INFO] - [11840] Loss: 0.049, Running accuracy: 99.974, Time: 32.14
[2020-12-16 21:06:31,866][__main__][INFO] - [12160] Loss: 0.083, Running accuracy: 99.973, Time: 39.87
[2020-12-16 21:07:07,150][__main__][INFO] - [12480] Loss: 0.052, Running accuracy: 99.973, Time: 35.28
[2020-12-16 21:07:41,327][__main__][INFO] - [12800] Loss: 0.049, Running accuracy: 99.974, Time: 34.18
[2020-12-16 21:08:13,329][__main__][INFO] - [13120] Loss: 0.053, Running accuracy: 99.974, Time: 32.00
[2020-12-16 21:08:49,581][__main__][INFO] - [13440] Loss: 0.063, Running accuracy: 99.973, Time: 36.25
[2020-12-16 21:09:25,882][__main__][INFO] - [13760] Loss: 0.048, Running accuracy: 99.973, Time: 36.30
[2020-12-16 21:10:05,121][__main__][INFO] - [14080] Loss: 0.047, Running accuracy: 99.973, Time: 39.24
[2020-12-16 21:10:42,462][__main__][INFO] - [14400] Loss: 0.049, Running accuracy: 99.973, Time: 37.34
[2020-12-16 21:11:21,245][__main__][INFO] - [14720] Loss: 0.057, Running accuracy: 99.973, Time: 38.78
[2020-12-16 21:12:00,123][__main__][INFO] - [15040] Loss: 0.052, Running accuracy: 99.973, Time: 38.88
[2020-12-16 21:12:36,744][__main__][INFO] - [15360] Loss: 0.052, Running accuracy: 99.973, Time: 36.62
[2020-12-16 21:13:15,300][__main__][INFO] - [15680] Loss: 0.052, Running accuracy: 99.973, Time: 38.56
[2020-12-16 21:13:48,981][__main__][INFO] - [16000] Loss: 0.040, Running accuracy: 99.973, Time: 33.68
[2020-12-16 21:14:27,043][__main__][INFO] - [16320] Loss: 0.063, Running accuracy: 99.972, Time: 38.06
[2020-12-16 21:14:59,060][__main__][INFO] - [16640] Loss: 0.033, Running accuracy: 99.972, Time: 32.02
[2020-12-16 21:15:42,167][__main__][INFO] - [16960] Loss: 0.057, Running accuracy: 99.973, Time: 43.11
[2020-12-16 21:16:22,116][__main__][INFO] - [17280] Loss: 0.052, Running accuracy: 99.973, Time: 39.95
[2020-12-16 21:16:53,393][__main__][INFO] - Action accuracy: 99.973, Loss: 0.064
[2020-12-16 21:16:53,394][__main__][INFO] - Validating..
[2020-12-16 21:16:59,977][test][INFO] - Time elapsed: 5.164924
[2020-12-16 21:16:59,979][__main__][INFO] - Validation F1 score: 94.580, Exact match: 57.100, Precision: 94.790, Recall: 94.370
Epoch    57: reducing learning rate of group 0 to 3.1250e-07.
[2020-12-16 21:17:12,586][__main__][INFO] - Checkpoint saved to checkpoints/model_latest.pth
[2020-12-16 21:17:12,949][__main__][INFO] - Epoch #57
[2020-12-16 21:17:12,949][__main__][INFO] - Training..
[2020-12-16 21:17:44,373][__main__][INFO] - [320] Loss: 0.051, Running accuracy: 99.988, Time: 30.34
[2020-12-16 21:18:23,474][__main__][INFO] - [640] Loss: 0.053, Running accuracy: 99.988, Time: 39.10
[2020-12-16 21:18:57,746][__main__][INFO] - [960] Loss: 0.075, Running accuracy: 99.976, Time: 34.27
[2020-12-16 21:19:32,586][__main__][INFO] - [1280] Loss: 0.050, Running accuracy: 99.976, Time: 34.84
[2020-12-16 21:20:09,808][__main__][INFO] - [1600] Loss: 0.061, Running accuracy: 99.974, Time: 37.22
[2020-12-16 21:20:46,951][__main__][INFO] - [1920] Loss: 0.050, Running accuracy: 99.974, Time: 37.14
[2020-12-16 21:21:26,464][__main__][INFO] - [2240] Loss: 0.049, Running accuracy: 99.977, Time: 39.51
[2020-12-16 21:22:04,706][__main__][INFO] - [2560] Loss: 0.045, Running accuracy: 99.977, Time: 38.24
[2020-12-16 21:22:47,693][__main__][INFO] - [2880] Loss: 0.056, Running accuracy: 99.978, Time: 42.99
[2020-12-16 21:23:26,684][__main__][INFO] - [3200] Loss: 0.081, Running accuracy: 99.978, Time: 38.99
[2020-12-16 21:24:09,786][__main__][INFO] - [3520] Loss: 0.054, Running accuracy: 99.978, Time: 43.10
[2020-12-16 21:24:43,321][__main__][INFO] - [3840] Loss: 0.047, Running accuracy: 99.977, Time: 33.53
[2020-12-16 21:25:17,502][__main__][INFO] - [4160] Loss: 0.062, Running accuracy: 99.974, Time: 34.18
[2020-12-16 21:25:58,137][__main__][INFO] - [4480] Loss: 0.055, Running accuracy: 99.972, Time: 40.63
[2020-12-16 21:26:32,557][__main__][INFO] - [4800] Loss: 0.074, Running accuracy: 99.971, Time: 34.32
[2020-12-16 21:27:08,942][__main__][INFO] - [5120] Loss: 0.044, Running accuracy: 99.972, Time: 36.38
[2020-12-16 21:27:48,169][__main__][INFO] - [5440] Loss: 0.054, Running accuracy: 99.972, Time: 39.23
[2020-12-16 21:28:31,668][__main__][INFO] - [5760] Loss: 0.051, Running accuracy: 99.971, Time: 43.50
[2020-12-16 21:29:08,817][__main__][INFO] - [6080] Loss: 0.053, Running accuracy: 99.970, Time: 37.15
[2020-12-16 21:29:43,683][__main__][INFO] - [6400] Loss: 0.059, Running accuracy: 99.970, Time: 34.86
[2020-12-16 21:30:19,557][__main__][INFO] - [6720] Loss: 0.055, Running accuracy: 99.970, Time: 35.87
[2020-12-16 21:30:58,692][__main__][INFO] - [7040] Loss: 0.071, Running accuracy: 99.970, Time: 39.13
[2020-12-16 21:31:39,735][__main__][INFO] - [7360] Loss: 0.063, Running accuracy: 99.969, Time: 41.04
[2020-12-16 21:32:20,396][__main__][INFO] - [7680] Loss: 0.059, Running accuracy: 99.969, Time: 40.66
[2020-12-16 21:32:58,693][__main__][INFO] - [8000] Loss: 0.052, Running accuracy: 99.970, Time: 38.30
[2020-12-16 21:33:37,100][__main__][INFO] - [8320] Loss: 0.053, Running accuracy: 99.970, Time: 38.41
[2020-12-16 21:34:14,370][__main__][INFO] - [8640] Loss: 0.039, Running accuracy: 99.971, Time: 37.27
[2020-12-16 21:34:54,426][__main__][INFO] - [8960] Loss: 0.059, Running accuracy: 99.971, Time: 40.05
[2020-12-16 21:35:32,941][__main__][INFO] - [9280] Loss: 0.054, Running accuracy: 99.971, Time: 38.51
[2020-12-16 21:36:10,950][__main__][INFO] - [9600] Loss: 0.054, Running accuracy: 99.972, Time: 38.01
[2020-12-16 21:36:46,733][__main__][INFO] - [9920] Loss: 0.047, Running accuracy: 99.972, Time: 35.78
[2020-12-16 21:37:24,217][__main__][INFO] - [10240] Loss: 0.051, Running accuracy: 99.972, Time: 37.48
[2020-12-16 21:38:03,906][__main__][INFO] - [10560] Loss: 0.070, Running accuracy: 99.972, Time: 39.69
[2020-12-16 21:38:41,196][__main__][INFO] - [10880] Loss: 0.044, Running accuracy: 99.972, Time: 37.29
[2020-12-16 21:39:16,844][__main__][INFO] - [11200] Loss: 0.047, Running accuracy: 99.972, Time: 35.65
[2020-12-16 21:39:54,575][__main__][INFO] - [11520] Loss: 0.061, Running accuracy: 99.972, Time: 37.73
[2020-12-16 21:40:33,250][__main__][INFO] - [11840] Loss: 0.052, Running accuracy: 99.972, Time: 38.67
[2020-12-16 21:41:11,856][__main__][INFO] - [12160] Loss: 0.050, Running accuracy: 99.973, Time: 38.61
[2020-12-16 21:41:48,996][__main__][INFO] - [12480] Loss: 0.056, Running accuracy: 99.973, Time: 37.14
[2020-12-16 21:42:31,573][__main__][INFO] - [12800] Loss: 0.051, Running accuracy: 99.974, Time: 42.58
[2020-12-16 21:43:07,086][__main__][INFO] - [13120] Loss: 0.048, Running accuracy: 99.974, Time: 35.51
[2020-12-16 21:43:47,514][__main__][INFO] - [13440] Loss: 0.049, Running accuracy: 99.974, Time: 40.43
[2020-12-16 21:44:28,862][__main__][INFO] - [13760] Loss: 0.056, Running accuracy: 99.974, Time: 41.35
[2020-12-16 21:45:03,006][__main__][INFO] - [14080] Loss: 0.064, Running accuracy: 99.974, Time: 34.14
[2020-12-16 21:45:42,867][__main__][INFO] - [14400] Loss: 0.063, Running accuracy: 99.973, Time: 39.86
[2020-12-16 21:46:17,866][__main__][INFO] - [14720] Loss: 0.068, Running accuracy: 99.973, Time: 35.00
[2020-12-16 21:46:58,676][__main__][INFO] - [15040] Loss: 0.054, Running accuracy: 99.973, Time: 40.81
[2020-12-16 21:47:36,093][__main__][INFO] - [15360] Loss: 0.051, Running accuracy: 99.973, Time: 37.42
[2020-12-16 21:48:12,892][__main__][INFO] - [15680] Loss: 0.052, Running accuracy: 99.973, Time: 36.80
[2020-12-16 21:48:47,525][__main__][INFO] - [16000] Loss: 0.039, Running accuracy: 99.973, Time: 34.63
[2020-12-16 21:49:24,388][__main__][INFO] - [16320] Loss: 0.055, Running accuracy: 99.973, Time: 36.86
[2020-12-16 21:50:01,816][__main__][INFO] - [16640] Loss: 0.069, Running accuracy: 99.972, Time: 37.43
[2020-12-16 21:50:39,435][__main__][INFO] - [16960] Loss: 0.045, Running accuracy: 99.973, Time: 37.62
[2020-12-16 21:51:21,046][__main__][INFO] - [17280] Loss: 0.062, Running accuracy: 99.972, Time: 41.61
[2020-12-16 21:51:52,753][__main__][INFO] - Action accuracy: 99.972, Loss: 0.061
[2020-12-16 21:51:52,755][__main__][INFO] - Validating..
[2020-12-16 21:51:59,322][test][INFO] - Time elapsed: 5.174685
[2020-12-16 21:51:59,324][__main__][INFO] - Validation F1 score: 94.510, Exact match: 57.100, Precision: 94.680, Recall: 94.340
[2020-12-16 21:52:11,767][__main__][INFO] - Checkpoint saved to checkpoints/model_latest.pth
[2020-12-16 21:52:12,112][__main__][INFO] - Epoch #58
[2020-12-16 21:52:12,112][__main__][INFO] - Training..
[2020-12-16 21:52:56,930][__main__][INFO] - [320] Loss: 0.046, Running accuracy: 100.000, Time: 43.56
[2020-12-16 21:53:34,113][__main__][INFO] - [640] Loss: 0.047, Running accuracy: 99.978, Time: 37.18
[2020-12-16 21:54:14,574][__main__][INFO] - [960] Loss: 0.063, Running accuracy: 99.959, Time: 40.46
[2020-12-16 21:54:52,367][__main__][INFO] - [1280] Loss: 0.048, Running accuracy: 99.966, Time: 37.79
[2020-12-16 21:55:24,937][__main__][INFO] - [1600] Loss: 0.056, Running accuracy: 99.964, Time: 32.57
[2020-12-16 21:56:03,343][__main__][INFO] - [1920] Loss: 0.053, Running accuracy: 99.966, Time: 38.41
[2020-12-16 21:56:42,642][__main__][INFO] - [2240] Loss: 0.048, Running accuracy: 99.969, Time: 39.30
[2020-12-16 21:57:25,494][__main__][INFO] - [2560] Loss: 0.053, Running accuracy: 99.970, Time: 42.77
[2020-12-16 21:58:04,346][__main__][INFO] - [2880] Loss: 0.060, Running accuracy: 99.967, Time: 38.85
[2020-12-16 21:58:40,339][__main__][INFO] - [3200] Loss: 0.058, Running accuracy: 99.967, Time: 35.99
[2020-12-16 21:59:17,070][__main__][INFO] - [3520] Loss: 0.072, Running accuracy: 99.967, Time: 36.73
[2020-12-16 21:59:57,568][__main__][INFO] - [3840] Loss: 0.057, Running accuracy: 99.968, Time: 40.50
[2020-12-16 22:00:39,812][__main__][INFO] - [4160] Loss: 0.061, Running accuracy: 99.968, Time: 42.24
[2020-12-16 22:01:15,382][__main__][INFO] - [4480] Loss: 0.054, Running accuracy: 99.969, Time: 35.57
[2020-12-16 22:01:54,195][__main__][INFO] - [4800] Loss: 0.073, Running accuracy: 99.964, Time: 38.81
[2020-12-16 22:02:34,769][__main__][INFO] - [5120] Loss: 0.074, Running accuracy: 99.963, Time: 40.57
[2020-12-16 22:03:08,519][__main__][INFO] - [5440] Loss: 0.062, Running accuracy: 99.963, Time: 33.75
[2020-12-16 22:03:49,248][__main__][INFO] - [5760] Loss: 0.071, Running accuracy: 99.963, Time: 40.73
[2020-12-16 22:04:27,496][__main__][INFO] - [6080] Loss: 0.039, Running accuracy: 99.965, Time: 38.25
[2020-12-16 22:05:06,336][__main__][INFO] - [6400] Loss: 0.064, Running accuracy: 99.965, Time: 38.84
[2020-12-16 22:05:41,489][__main__][INFO] - [6720] Loss: 0.050, Running accuracy: 99.966, Time: 35.15
[2020-12-16 22:06:24,774][__main__][INFO] - [7040] Loss: 0.069, Running accuracy: 99.967, Time: 43.28
[2020-12-16 22:07:03,946][__main__][INFO] - [7360] Loss: 0.068, Running accuracy: 99.967, Time: 39.17
[2020-12-16 22:07:41,709][__main__][INFO] - [7680] Loss: 0.046, Running accuracy: 99.967, Time: 37.76
[2020-12-16 22:08:27,905][__main__][INFO] - [8000] Loss: 0.095, Running accuracy: 99.966, Time: 46.19
[2020-12-16 22:09:02,524][__main__][INFO] - [8320] Loss: 0.045, Running accuracy: 99.967, Time: 34.62
[2020-12-16 22:09:38,329][__main__][INFO] - [8640] Loss: 0.046, Running accuracy: 99.967, Time: 35.80
[2020-12-16 22:10:20,221][__main__][INFO] - [8960] Loss: 0.046, Running accuracy: 99.968, Time: 41.89
[2020-12-16 22:10:54,440][__main__][INFO] - [9280] Loss: 0.050, Running accuracy: 99.969, Time: 34.22
[2020-12-16 22:11:23,897][__main__][INFO] - [9600] Loss: 0.042, Running accuracy: 99.969, Time: 29.46
[2020-12-16 22:11:57,112][__main__][INFO] - [9920] Loss: 0.042, Running accuracy: 99.970, Time: 33.21
[2020-12-16 22:12:38,088][__main__][INFO] - [10240] Loss: 0.087, Running accuracy: 99.969, Time: 40.97
[2020-12-16 22:13:17,576][__main__][INFO] - [10560] Loss: 0.042, Running accuracy: 99.970, Time: 39.49
[2020-12-16 22:13:54,870][__main__][INFO] - [10880] Loss: 0.042, Running accuracy: 99.970, Time: 37.29
[2020-12-16 22:14:32,292][__main__][INFO] - [11200] Loss: 0.048, Running accuracy: 99.970, Time: 37.42
[2020-12-16 22:15:08,892][__main__][INFO] - [11520] Loss: 0.060, Running accuracy: 99.970, Time: 36.60
[2020-12-16 22:15:48,285][__main__][INFO] - [11840] Loss: 0.047, Running accuracy: 99.970, Time: 39.39
[2020-12-16 22:16:26,787][__main__][INFO] - [12160] Loss: 0.074, Running accuracy: 99.968, Time: 38.50
[2020-12-16 22:17:05,589][__main__][INFO] - [12480] Loss: 0.053, Running accuracy: 99.969, Time: 38.80
[2020-12-16 22:17:44,829][__main__][INFO] - [12800] Loss: 0.080, Running accuracy: 99.968, Time: 39.24
[2020-12-16 22:18:26,230][__main__][INFO] - [13120] Loss: 0.066, Running accuracy: 99.969, Time: 41.40
[2020-12-16 22:18:59,536][__main__][INFO] - [13440] Loss: 0.063, Running accuracy: 99.968, Time: 33.30
[2020-12-16 22:19:37,869][__main__][INFO] - [13760] Loss: 0.085, Running accuracy: 99.968, Time: 38.33
[2020-12-16 22:20:15,590][__main__][INFO] - [14080] Loss: 0.046, Running accuracy: 99.968, Time: 37.72
[2020-12-16 22:20:52,039][__main__][INFO] - [14400] Loss: 0.049, Running accuracy: 99.969, Time: 36.45
[2020-12-16 22:21:32,551][__main__][INFO] - [14720] Loss: 0.051, Running accuracy: 99.969, Time: 40.51
[2020-12-16 22:22:07,642][__main__][INFO] - [15040] Loss: 0.048, Running accuracy: 99.969, Time: 35.09
[2020-12-16 22:22:46,640][__main__][INFO] - [15360] Loss: 0.038, Running accuracy: 99.970, Time: 39.00
[2020-12-16 22:23:19,945][__main__][INFO] - [15680] Loss: 0.052, Running accuracy: 99.970, Time: 33.30
[2020-12-16 22:23:58,754][__main__][INFO] - [16000] Loss: 0.044, Running accuracy: 99.971, Time: 38.81
[2020-12-16 22:24:34,002][__main__][INFO] - [16320] Loss: 0.065, Running accuracy: 99.970, Time: 35.25
[2020-12-16 22:25:05,690][__main__][INFO] - [16640] Loss: 0.050, Running accuracy: 99.970, Time: 31.69
[2020-12-16 22:25:39,273][__main__][INFO] - [16960] Loss: 0.047, Running accuracy: 99.970, Time: 33.58
[2020-12-16 22:26:17,930][__main__][INFO] - [17280] Loss: 0.055, Running accuracy: 99.970, Time: 38.66
[2020-12-16 22:26:48,957][__main__][INFO] - Action accuracy: 99.970, Loss: 0.062
[2020-12-16 22:26:48,958][__main__][INFO] - Validating..
[2020-12-16 22:26:57,946][test][INFO] - Time elapsed: 5.211335
[2020-12-16 22:26:57,948][__main__][INFO] - Validation F1 score: 94.500, Exact match: 57.100, Precision: 94.680, Recall: 94.330
[2020-12-16 22:27:10,601][__main__][INFO] - Checkpoint saved to checkpoints/model_latest.pth
[2020-12-16 22:27:11,021][__main__][INFO] - Epoch #59
[2020-12-16 22:27:11,022][__main__][INFO] - Training..
[2020-12-16 22:27:55,302][__main__][INFO] - [320] Loss: 0.045, Running accuracy: 99.989, Time: 42.88
[2020-12-16 22:28:37,311][__main__][INFO] - [640] Loss: 0.074, Running accuracy: 99.978, Time: 42.01
[2020-12-16 22:29:10,114][__main__][INFO] - [960] Loss: 0.062, Running accuracy: 99.974, Time: 32.80
[2020-12-16 22:29:49,178][__main__][INFO] - [1280] Loss: 0.056, Running accuracy: 99.970, Time: 39.06
[2020-12-16 22:30:23,940][__main__][INFO] - [1600] Loss: 0.052, Running accuracy: 99.971, Time: 34.76
[2020-12-16 22:30:57,104][__main__][INFO] - [1920] Loss: 0.042, Running accuracy: 99.971, Time: 33.16
[2020-12-16 22:31:33,766][__main__][INFO] - [2240] Loss: 0.070, Running accuracy: 99.969, Time: 36.66
[2020-12-16 22:32:11,272][__main__][INFO] - [2560] Loss: 0.053, Running accuracy: 99.967, Time: 37.51
[2020-12-16 22:32:46,754][__main__][INFO] - [2880] Loss: 0.060, Running accuracy: 99.963, Time: 35.48
[2020-12-16 22:33:23,880][__main__][INFO] - [3200] Loss: 0.049, Running accuracy: 99.966, Time: 37.12
[2020-12-16 22:34:07,196][__main__][INFO] - [3520] Loss: 0.055, Running accuracy: 99.967, Time: 43.31
[2020-12-16 22:34:45,610][__main__][INFO] - [3840] Loss: 0.041, Running accuracy: 99.968, Time: 38.41
[2020-12-16 22:35:20,368][__main__][INFO] - [4160] Loss: 0.034, Running accuracy: 99.970, Time: 34.76
[2020-12-16 22:36:01,284][__main__][INFO] - [4480] Loss: 0.051, Running accuracy: 99.972, Time: 40.92
[2020-12-16 22:36:36,150][__main__][INFO] - [4800] Loss: 0.070, Running accuracy: 99.971, Time: 34.86
[2020-12-16 22:37:13,724][__main__][INFO] - [5120] Loss: 0.047, Running accuracy: 99.971, Time: 37.57
[2020-12-16 22:37:53,421][__main__][INFO] - [5440] Loss: 0.036, Running accuracy: 99.973, Time: 39.69
[2020-12-16 22:38:27,144][__main__][INFO] - [5760] Loss: 0.043, Running accuracy: 99.973, Time: 33.72
[2020-12-16 22:39:01,029][__main__][INFO] - [6080] Loss: 0.042, Running accuracy: 99.975, Time: 33.88
[2020-12-16 22:39:34,837][__main__][INFO] - [6400] Loss: 0.071, Running accuracy: 99.973, Time: 33.71
[2020-12-16 22:40:12,730][__main__][INFO] - [6720] Loss: 0.054, Running accuracy: 99.973, Time: 37.89
[2020-12-16 22:40:47,212][__main__][INFO] - [7040] Loss: 0.047, Running accuracy: 99.973, Time: 34.48
[2020-12-16 22:41:26,114][__main__][INFO] - [7360] Loss: 0.064, Running accuracy: 99.973, Time: 38.90
[2020-12-16 22:42:05,158][__main__][INFO] - [7680] Loss: 0.065, Running accuracy: 99.971, Time: 39.04
[2020-12-16 22:42:39,842][__main__][INFO] - [8000] Loss: 0.040, Running accuracy: 99.972, Time: 34.68
[2020-12-16 22:43:18,066][__main__][INFO] - [8320] Loss: 0.064, Running accuracy: 99.971, Time: 38.22
[2020-12-16 22:43:59,174][__main__][INFO] - [8640] Loss: 0.063, Running accuracy: 99.972, Time: 41.11
[2020-12-16 22:44:34,927][__main__][INFO] - [8960] Loss: 0.038, Running accuracy: 99.973, Time: 35.75
[2020-12-16 22:45:11,877][__main__][INFO] - [9280] Loss: 0.074, Running accuracy: 99.972, Time: 36.95
[2020-12-16 22:45:57,408][__main__][INFO] - [9600] Loss: 0.054, Running accuracy: 99.972, Time: 45.53
[2020-12-16 22:46:34,007][__main__][INFO] - [9920] Loss: 0.048, Running accuracy: 99.973, Time: 36.60
[2020-12-16 22:47:18,227][__main__][INFO] - [10240] Loss: 0.062, Running accuracy: 99.973, Time: 44.22
[2020-12-16 22:47:55,602][__main__][INFO] - [10560] Loss: 0.052, Running accuracy: 99.973, Time: 37.37
[2020-12-16 22:48:32,297][__main__][INFO] - [10880] Loss: 0.041, Running accuracy: 99.974, Time: 36.69
[2020-12-16 22:49:06,114][__main__][INFO] - [11200] Loss: 0.046, Running accuracy: 99.975, Time: 33.82
[2020-12-16 22:49:44,874][__main__][INFO] - [11520] Loss: 0.062, Running accuracy: 99.974, Time: 38.76
[2020-12-16 22:50:23,605][__main__][INFO] - [11840] Loss: 0.119, Running accuracy: 99.974, Time: 38.73
[2020-12-16 22:51:01,184][__main__][INFO] - [12160] Loss: 0.047, Running accuracy: 99.975, Time: 37.58
[2020-12-16 22:51:41,517][__main__][INFO] - [12480] Loss: 0.041, Running accuracy: 99.975, Time: 40.33
[2020-12-16 22:52:17,007][__main__][INFO] - [12800] Loss: 0.078, Running accuracy: 99.974, Time: 35.49
[2020-12-16 22:52:50,410][__main__][INFO] - [13120] Loss: 0.051, Running accuracy: 99.974, Time: 33.40
[2020-12-16 22:53:28,910][__main__][INFO] - [13440] Loss: 0.053, Running accuracy: 99.974, Time: 38.50
[2020-12-16 22:54:04,556][__main__][INFO] - [13760] Loss: 0.046, Running accuracy: 99.974, Time: 35.64
[2020-12-16 22:54:43,799][__main__][INFO] - [14080] Loss: 0.045, Running accuracy: 99.974, Time: 39.24
[2020-12-16 22:55:19,300][__main__][INFO] - [14400] Loss: 0.142, Running accuracy: 99.973, Time: 35.50
[2020-12-16 22:55:55,314][__main__][INFO] - [14720] Loss: 0.054, Running accuracy: 99.973, Time: 36.01
[2020-12-16 22:56:39,985][__main__][INFO] - [15040] Loss: 0.061, Running accuracy: 99.973, Time: 44.67
[2020-12-16 22:57:11,940][__main__][INFO] - [15360] Loss: 0.066, Running accuracy: 99.973, Time: 31.95
[2020-12-16 22:57:49,823][__main__][INFO] - [15680] Loss: 0.069, Running accuracy: 99.972, Time: 37.88
[2020-12-16 22:58:29,158][__main__][INFO] - [16000] Loss: 0.074, Running accuracy: 99.971, Time: 39.33
[2020-12-16 22:59:04,790][__main__][INFO] - [16320] Loss: 0.076, Running accuracy: 99.971, Time: 35.63
[2020-12-16 22:59:42,734][__main__][INFO] - [16640] Loss: 0.052, Running accuracy: 99.971, Time: 37.94
[2020-12-16 23:00:22,421][__main__][INFO] - [16960] Loss: 0.059, Running accuracy: 99.971, Time: 39.69
[2020-12-16 23:00:58,878][__main__][INFO] - [17280] Loss: 0.047, Running accuracy: 99.971, Time: 36.46
[2020-12-16 23:01:27,552][__main__][INFO] - Action accuracy: 99.971, Loss: 0.064
[2020-12-16 23:01:27,553][__main__][INFO] - Validating..
[2020-12-16 23:01:34,070][test][INFO] - Time elapsed: 5.129817
[2020-12-16 23:01:34,071][__main__][INFO] - Validation F1 score: 94.490, Exact match: 57.100, Precision: 94.690, Recall: 94.300
[2020-12-16 23:01:47,098][__main__][INFO] - Checkpoint saved to checkpoints/model_latest.pth
[2020-12-16 23:01:47,482][__main__][INFO] - Training completed. Launching the testing script..
[2020-12-16 23:01:51,949][__main__][INFO] - python test.py model_path=runs/ctb_bert_graph_seed2/checkpoints/model_latest.pth dataset=ctb
/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 23:02:00,394][__main__][INFO] - 
exp_id: test
model_path: runs/ctb_bert_graph_seed2/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/ctb_train.txt
path_val: data/ctb_dev.txt
path_test: data/ctb_test.txt
max_sentence_len: 250

[2020-12-16 23:02:00,395][__main__][INFO] - Loading the model from /n/fs/pvl-mathqa/attach-juxtapose-parser/runs/ctb_bert_graph_seed2/checkpoints/model_latest.pth
[2020-12-16 23:02:04,895][__main__][INFO] - Restoring hyperparameters from the saved model checkpoint..
[2020-12-16 23:02:04,896][__main__][INFO] - d_model: 2048
[2020-12-16 23:02:04,896][__main__][INFO] - encoder: bert-base-chinese
[2020-12-16 23:02:04,896][__main__][INFO] - use_words: False
[2020-12-16 23:02:04,897][__main__][INFO] - use_tags: True
[2020-12-16 23:02:04,897][__main__][INFO] - d_kqv: 64
[2020-12-16 23:02:04,897][__main__][INFO] - d_ff: 2048
[2020-12-16 23:02:04,897][__main__][INFO] - word_emb_dropout: 0
[2020-12-16 23:02:04,898][__main__][INFO] - tag_emb_dropout: 0.2
[2020-12-16 23:02:04,898][__main__][INFO] - relu_dropout: 0.4
[2020-12-16 23:02:04,898][__main__][INFO] - residual_dropout: 0.2
[2020-12-16 23:02:04,898][__main__][INFO] - attention_dropout: 0
[2020-12-16 23:02:04,898][__main__][INFO] - num_attn_layers: 4
[2020-12-16 23:02:04,899][__main__][INFO] - num_attn_heads: 8
[2020-12-16 23:02:04,899][__main__][INFO] - decoder: graph
[2020-12-16 23:02:04,899][__main__][INFO] - num_gcn_layers: 5
[2020-12-16 23:02:04,899][__main__][WARNING] - Missing: d_decoder
[2020-12-16 23:02:04,900][__main__][INFO] - max_sentence_len: 250
[2020-12-16 23:02:04,900][dataloader][INFO] - Loading constituency trees from /n/fs/pvl-mathqa/attach-juxtapose-parser/data/ctb_dev.txt
[2020-12-16 23:02:05,668][dataloader][INFO] - Loading constituency trees from /n/fs/pvl-mathqa/attach-juxtapose-parser/data/ctb_test.txt
[2020-12-16 23:02:10,150][__main__][INFO] - 
Parser(
  (encoder): Encoder(
    (word_embedding): TransformerEmbedding(
      (contextual_embedding): BertModel(
        (embeddings): BertEmbeddings(
          (word_embeddings): Embedding(21128, 768, padding_idx=0)
          (position_embeddings): Embedding(512, 768)
          (token_type_embeddings): Embedding(2, 768)
          (LayerNorm): LayerNorm((768,), 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=768, out_features=768, bias=True)
                  (key): Linear(in_features=768, out_features=768, bias=True)
                  (value): Linear(in_features=768, out_features=768, bias=True)
                  (dropout): Dropout(p=0.1, inplace=False)
                )
                (output): BertSelfOutput(
                  (dense): Linear(in_features=768, out_features=768, bias=True)
                  (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)
                  (dropout): Dropout(p=0.1, inplace=False)
                )
              )
              (intermediate): BertIntermediate(
                (dense): Linear(in_features=768, out_features=3072, bias=True)
              )
              (output): BertOutput(
                (dense): Linear(in_features=3072, out_features=768, bias=True)
                (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)
                (dropout): Dropout(p=0.1, inplace=False)
              )
            )
            (1): BertLayer(
              (attention): BertAttention(
                (self): BertSelfAttention(
                  (query): Linear(in_features=768, out_features=768, bias=True)
                  (key): Linear(in_features=768, out_features=768, bias=True)
                  (value): Linear(in_features=768, out_features=768, bias=True)
                  (dropout): Dropout(p=0.1, inplace=False)
                )
                (output): BertSelfOutput(
                  (dense): Linear(in_features=768, out_features=768, bias=True)
                  (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)
                  (dropout): Dropout(p=0.1, inplace=False)
                )
              )
              (intermediate): BertIntermediate(
                (dense): Linear(in_features=768, out_features=3072, bias=True)
              )
              (output): BertOutput(
                (dense): Linear(in_features=3072, out_features=768, bias=True)
                (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)
                (dropout): Dropout(p=0.1, inplace=False)
              )
            )
            (2): BertLayer(
              (attention): BertAttention(
                (self): BertSelfAttention(
                  (query): Linear(in_features=768, out_features=768, bias=True)
                  (key): Linear(in_features=768, out_features=768, bias=True)
                  (value): Linear(in_features=768, out_features=768, bias=True)
                  (dropout): Dropout(p=0.1, inplace=False)
                )
                (output): BertSelfOutput(
                  (dense): Linear(in_features=768, out_features=768, bias=True)
                  (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)
                  (dropout): Dropout(p=0.1, inplace=False)
                )
              )
              (intermediate): BertIntermediate(
                (dense): Linear(in_features=768, out_features=3072, bias=True)
              )
              (output): BertOutput(
                (dense): Linear(in_features=3072, out_features=768, bias=True)
                (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)
                (dropout): Dropout(p=0.1, inplace=False)
              )
            )
            (3): BertLayer(
              (attention): BertAttention(
                (self): BertSelfAttention(
                  (query): Linear(in_features=768, out_features=768, bias=True)
                  (key): Linear(in_features=768, out_features=768, bias=True)
                  (value): Linear(in_features=768, out_features=768, bias=True)
                  (dropout): Dropout(p=0.1, inplace=False)
                )
                (output): BertSelfOutput(
                  (dense): Linear(in_features=768, out_features=768, bias=True)
                  (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)
                  (dropout): Dropout(p=0.1, inplace=False)
                )
              )
              (intermediate): BertIntermediate(
                (dense): Linear(in_features=768, out_features=3072, bias=True)
              )
              (output): BertOutput(
                (dense): Linear(in_features=3072, out_features=768, bias=True)
                (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)
                (dropout): Dropout(p=0.1, inplace=False)
              )
            )
            (4): BertLayer(
              (attention): BertAttention(
                (self): BertSelfAttention(
                  (query): Linear(in_features=768, out_features=768, bias=True)
                  (key): Linear(in_features=768, out_features=768, bias=True)
                  (value): Linear(in_features=768, out_features=768, bias=True)
                  (dropout): Dropout(p=0.1, inplace=False)
                )
                (output): BertSelfOutput(
                  (dense): Linear(in_features=768, out_features=768, bias=True)
                  (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)
                  (dropout): Dropout(p=0.1, inplace=False)
                )
              )
              (intermediate): BertIntermediate(
                (dense): Linear(in_features=768, out_features=3072, bias=True)
              )
              (output): BertOutput(
                (dense): Linear(in_features=3072, out_features=768, bias=True)
                (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)
                (dropout): Dropout(p=0.1, inplace=False)
              )
            )
            (5): BertLayer(
              (attention): BertAttention(
                (self): BertSelfAttention(
                  (query): Linear(in_features=768, out_features=768, bias=True)
                  (key): Linear(in_features=768, out_features=768, bias=True)
                  (value): Linear(in_features=768, out_features=768, bias=True)
                  (dropout): Dropout(p=0.1, inplace=False)
                )
                (output): BertSelfOutput(
                  (dense): Linear(in_features=768, out_features=768, bias=True)
                  (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)
                  (dropout): Dropout(p=0.1, inplace=False)
                )
              )
              (intermediate): BertIntermediate(
                (dense): Linear(in_features=768, out_features=3072, bias=True)
              )
              (output): BertOutput(
                (dense): Linear(in_features=3072, out_features=768, bias=True)
                (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)
                (dropout): Dropout(p=0.1, inplace=False)
              )
            )
            (6): BertLayer(
              (attention): BertAttention(
                (self): BertSelfAttention(
                  (query): Linear(in_features=768, out_features=768, bias=True)
                  (key): Linear(in_features=768, out_features=768, bias=True)
                  (value): Linear(in_features=768, out_features=768, bias=True)
                  (dropout): Dropout(p=0.1, inplace=False)
                )
                (output): BertSelfOutput(
                  (dense): Linear(in_features=768, out_features=768, bias=True)
                  (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)
                  (dropout): Dropout(p=0.1, inplace=False)
                )
              )
              (intermediate): BertIntermediate(
                (dense): Linear(in_features=768, out_features=3072, bias=True)
              )
              (output): BertOutput(
                (dense): Linear(in_features=3072, out_features=768, bias=True)
                (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)
                (dropout): Dropout(p=0.1, inplace=False)
              )
            )
            (7): BertLayer(
              (attention): BertAttention(
                (self): BertSelfAttention(
                  (query): Linear(in_features=768, out_features=768, bias=True)
                  (key): Linear(in_features=768, out_features=768, bias=True)
                  (value): Linear(in_features=768, out_features=768, bias=True)
                  (dropout): Dropout(p=0.1, inplace=False)
                )
                (output): BertSelfOutput(
                  (dense): Linear(in_features=768, out_features=768, bias=True)
                  (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)
                  (dropout): Dropout(p=0.1, inplace=False)
                )
              )
              (intermediate): BertIntermediate(
                (dense): Linear(in_features=768, out_features=3072, bias=True)
              )
              (output): BertOutput(
                (dense): Linear(in_features=3072, out_features=768, bias=True)
                (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)
                (dropout): Dropout(p=0.1, inplace=False)
              )
            )
            (8): BertLayer(
              (attention): BertAttention(
                (self): BertSelfAttention(
                  (query): Linear(in_features=768, out_features=768, bias=True)
                  (key): Linear(in_features=768, out_features=768, bias=True)
                  (value): Linear(in_features=768, out_features=768, bias=True)
                  (dropout): Dropout(p=0.1, inplace=False)
                )
                (output): BertSelfOutput(
                  (dense): Linear(in_features=768, out_features=768, bias=True)
                  (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)
                  (dropout): Dropout(p=0.1, inplace=False)
                )
              )
              (intermediate): BertIntermediate(
                (dense): Linear(in_features=768, out_features=3072, bias=True)
              )
              (output): BertOutput(
                (dense): Linear(in_features=3072, out_features=768, bias=True)
                (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)
                (dropout): Dropout(p=0.1, inplace=False)
              )
            )
            (9): BertLayer(
              (attention): BertAttention(
                (self): BertSelfAttention(
                  (query): Linear(in_features=768, out_features=768, bias=True)
                  (key): Linear(in_features=768, out_features=768, bias=True)
                  (value): Linear(in_features=768, out_features=768, bias=True)
                  (dropout): Dropout(p=0.1, inplace=False)
                )
                (output): BertSelfOutput(
                  (dense): Linear(in_features=768, out_features=768, bias=True)
                  (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)
                  (dropout): Dropout(p=0.1, inplace=False)
                )
              )
              (intermediate): BertIntermediate(
                (dense): Linear(in_features=768, out_features=3072, bias=True)
              )
              (output): BertOutput(
                (dense): Linear(in_features=3072, out_features=768, bias=True)
                (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)
                (dropout): Dropout(p=0.1, inplace=False)
              )
            )
            (10): BertLayer(
              (attention): BertAttention(
                (self): BertSelfAttention(
                  (query): Linear(in_features=768, out_features=768, bias=True)
                  (key): Linear(in_features=768, out_features=768, bias=True)
                  (value): Linear(in_features=768, out_features=768, bias=True)
                  (dropout): Dropout(p=0.1, inplace=False)
                )
                (output): BertSelfOutput(
                  (dense): Linear(in_features=768, out_features=768, bias=True)
                  (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)
                  (dropout): Dropout(p=0.1, inplace=False)
                )
              )
              (intermediate): BertIntermediate(
                (dense): Linear(in_features=768, out_features=3072, bias=True)
              )
              (output): BertOutput(
                (dense): Linear(in_features=3072, out_features=768, bias=True)
                (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)
                (dropout): Dropout(p=0.1, inplace=False)
              )
            )
            (11): BertLayer(
              (attention): BertAttention(
                (self): BertSelfAttention(
                  (query): Linear(in_features=768, out_features=768, bias=True)
                  (key): Linear(in_features=768, out_features=768, bias=True)
                  (value): Linear(in_features=768, out_features=768, bias=True)
                  (dropout): Dropout(p=0.1, inplace=False)
                )
                (output): BertSelfOutput(
                  (dense): Linear(in_features=768, out_features=768, bias=True)
                  (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)
                  (dropout): Dropout(p=0.1, inplace=False)
                )
              )
              (intermediate): BertIntermediate(
                (dense): Linear(in_features=768, out_features=3072, bias=True)
              )
              (output): BertOutput(
                (dense): Linear(in_features=3072, out_features=768, bias=True)
                (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)
                (dropout): Dropout(p=0.1, inplace=False)
              )
            )
          )
        )
        (pooler): BertPooler(
          (dense): Linear(in_features=768, out_features=768, bias=True)
          (activation): Tanh()
        )
      )
      (linear): Linear(in_features=768, out_features=1024, bias=False)
    )
    (word_dropout): FeatureDropout()
    (tag_embedding): OneHotEmbedding(
      (embedding): Embedding(36, 1024)
    )
    (tag_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(709, 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)
      (conv_4): SeparateGCNConv(
        (linear_c): Linear(in_features=1024, out_features=1024, bias=True)
        (linear_p): Linear(in_features=1024, out_features=1024, bias=True)
      )
      (layernorm_4): 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=1418, bias=True)
      )
    )
  )
)
[2020-12-16 23:02:10,154][__main__][INFO] - #parameters = 147562636
[2020-12-16 23:02:10,154][__main__][INFO] - Validating..
[2020-12-16 23:02:16,210][__main__][INFO] - Time elapsed: 3.880277
[2020-12-16 23:02:16,212][__main__][INFO] - Validation F1 score: 94.490, Exact match: 57.100, Precision: 94.690, Recall: 94.300
[2020-12-16 23:02:16,212][__main__][INFO] - Testing..
[2020-12-16 23:02:16,213][__main__][INFO] - Running without beam search..
[2020-12-16 23:02:23,388][__main__][INFO] - Time elapsed: 5.166594
[2020-12-16 23:02:23,390][__main__][INFO] - Testing F1 score: 93.520, Exact match: 49.430, Precision: 93.660, Recall: 93.380