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neccam / nslt

Neural Sign Language Translation (CVPR'18)
Apache License 2.0
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Error " terminate called after throwing an instance of 'std::out_of_range' " #17

Open bbantita opened 5 years ago

bbantita commented 5 years ago

After running a code with

python -m nmt --src=sign --tgt=de --train_prefix=../Data/phoenix2014T.train --dev_prefix=../Data/phoenix2014T.dev --test_prefix=../Data/phoenix2014T.test --out_dir=../test_out/ --vocab_prefix=../Data/phoenix2014T.vocab --source_reverse=True --num_units=1000 --num_layers=4 --num_train_steps=150000 --residual=True --attention=luong --base_gpu=0 --unit_type=gru

I was encounter the error shown below

# Job id 0
# Set random seed to 285
# Loading hparams from ../test_out/hparams
  saving hparams to ../test_out/hparams
  saving hparams to ../test_out/best_bleu/hparams
  attention=luong
  attention_architecture=standard
  base_gpu=0
  batch_size=1
  beam_width=3
  best_bleu=0
  best_bleu_dir=../test_out/best_bleu
  bpe_delimiter=None
  colocate_gradients_with_ops=True
  decay_factor=0.98
  decay_steps=10000
  dev_prefix=../Data/phoenix2014T.dev
  dropout=0.2
  encoder_type=uni
  eos=</s>
  epoch_step=0
  eval_on_fly=True
  forget_bias=1.0
  infer_batch_size=32
  init_op=glorot_normal
  init_weight=0.1
  learning_rate=1e-05
  length_penalty_weight=0.0
  log_device_placement=False
  max_gradient_norm=5.0
  max_train=0
  metrics=[u'bleu']
  num_buckets=0
  num_embeddings_partitions=0
  num_gpus=1
  num_layers=4
  num_residual_layers=3
  num_train_steps=150000
  num_units=1000
  optimizer=adam
  out_dir=../test_out/
  pass_hidden_state=True
  random_seed=285
  residual=True
  snapshot_interval=1000
  sos=<s>
  source_reverse=True
  src=sign
  src_max_len=300
  src_max_len_infer=300
  start_decay_step=0
  steps_per_external_eval=None
  steps_per_stats=100
  test_prefix=../Data/phoenix2014T.test
  tgt=de
  tgt_max_len=50
  tgt_max_len_infer=None
  tgt_vocab_file=../Data/phoenix2014T.vocab.de
  tgt_vocab_size=2891
  time_major=True
  train_prefix=../Data/phoenix2014T.train
  unit_type=gru
  vocab_prefix=../Data/phoenix2014T.vocab
# creating train graph ...
  num_layers = 4, num_residual_layers=3
  cell 0  GRU  DropoutWrapper, dropout=0.2   DeviceWrapper, device=/gpu:0
  cell 1  GRU  DropoutWrapper, dropout=0.2   ResidualWrapper  DeviceWrapper, device=/gpu:0
  cell 2  GRU  DropoutWrapper, dropout=0.2   ResidualWrapper  DeviceWrapper, device=/gpu:0
  cell 3  GRU  DropoutWrapper, dropout=0.2   ResidualWrapper  DeviceWrapper, device=/gpu:0
  cell 0  GRU  DropoutWrapper, dropout=0.2   DeviceWrapper, device=/gpu:0
  cell 1  GRU  DropoutWrapper, dropout=0.2   ResidualWrapper  DeviceWrapper, device=/gpu:0
  cell 2  GRU  DropoutWrapper, dropout=0.2   ResidualWrapper  DeviceWrapper, device=/gpu:0
  cell 3  GRU  DropoutWrapper, dropout=0.2   ResidualWrapper  DeviceWrapper, device=/gpu:0
  start_decay_step=0, learning_rate=1e-05, decay_steps 10000, decay_factor 0.98
# Trainable variables
  conv1/weights:0, (11, 11, 3, 96), /device:GPU:0
  conv1/biases:0, (96,), /device:GPU:0
  conv2/weights:0, (5, 5, 48, 256), /device:GPU:0
  conv2/biases:0, (256,), /device:GPU:0
  conv3/weights:0, (3, 3, 256, 384), /device:GPU:0
  conv3/biases:0, (384,), /device:GPU:0
  conv4/weights:0, (3, 3, 192, 384), /device:GPU:0
  conv4/biases:0, (384,), /device:GPU:0
  conv5/weights:0, (3, 3, 192, 256), /device:GPU:0
  conv5/biases:0, (256,), /device:GPU:0
  fc6/weights:0, (9216, 4096), /device:GPU:0
  fc6/biases:0, (4096,), /device:GPU:0
  fc7/weights:0, (4096, 4096), /device:GPU:0
  fc7/biases:0, (4096,), /device:GPU:0
  embeddings/decoder/embedding_decoder:0, (2891, 1000), /device:GPU:0
  dynamic_seq2seq/encoder/rnn/multi_rnn_cell/cell_0/gru_cell/gates/kernel:0, (5096, 2000), /device:GPU:0
  dynamic_seq2seq/encoder/rnn/multi_rnn_cell/cell_0/gru_cell/gates/bias:0, (2000,), /device:GPU:0
  dynamic_seq2seq/encoder/rnn/multi_rnn_cell/cell_0/gru_cell/candidate/kernel:0, (5096, 1000), /device:GPU:0
  dynamic_seq2seq/encoder/rnn/multi_rnn_cell/cell_0/gru_cell/candidate/bias:0, (1000,), /device:GPU:0
  dynamic_seq2seq/encoder/rnn/multi_rnn_cell/cell_1/gru_cell/gates/kernel:0, (2000, 2000), /device:GPU:0
  dynamic_seq2seq/encoder/rnn/multi_rnn_cell/cell_1/gru_cell/gates/bias:0, (2000,), /device:GPU:0
  dynamic_seq2seq/encoder/rnn/multi_rnn_cell/cell_1/gru_cell/candidate/kernel:0, (2000, 1000), /device:GPU:0
  dynamic_seq2seq/encoder/rnn/multi_rnn_cell/cell_1/gru_cell/candidate/bias:0, (1000,), /device:GPU:0
  dynamic_seq2seq/encoder/rnn/multi_rnn_cell/cell_2/gru_cell/gates/kernel:0, (2000, 2000), /device:GPU:0
  dynamic_seq2seq/encoder/rnn/multi_rnn_cell/cell_2/gru_cell/gates/bias:0, (2000,), /device:GPU:0
  dynamic_seq2seq/encoder/rnn/multi_rnn_cell/cell_2/gru_cell/candidate/kernel:0, (2000, 1000), /device:GPU:0
  dynamic_seq2seq/encoder/rnn/multi_rnn_cell/cell_2/gru_cell/candidate/bias:0, (1000,), /device:GPU:0
  dynamic_seq2seq/encoder/rnn/multi_rnn_cell/cell_3/gru_cell/gates/kernel:0, (2000, 2000), /device:GPU:0
  dynamic_seq2seq/encoder/rnn/multi_rnn_cell/cell_3/gru_cell/gates/bias:0, (2000,), /device:GPU:0
  dynamic_seq2seq/encoder/rnn/multi_rnn_cell/cell_3/gru_cell/candidate/kernel:0, (2000, 1000), /device:GPU:0
  dynamic_seq2seq/encoder/rnn/multi_rnn_cell/cell_3/gru_cell/candidate/bias:0, (1000,), /device:GPU:0
  dynamic_seq2seq/decoder/memory_layer/kernel:0, (1000, 1000), /device:GPU:0
  dynamic_seq2seq/decoder/attention/multi_rnn_cell/cell_0/gru_cell/gates/kernel:0, (3000, 2000), /device:GPU:0
  dynamic_seq2seq/decoder/attention/multi_rnn_cell/cell_0/gru_cell/gates/bias:0, (2000,), /device:GPU:0
  dynamic_seq2seq/decoder/attention/multi_rnn_cell/cell_0/gru_cell/candidate/kernel:0, (3000, 1000), /device:GPU:0
  dynamic_seq2seq/decoder/attention/multi_rnn_cell/cell_0/gru_cell/candidate/bias:0, (1000,), /device:GPU:0
  dynamic_seq2seq/decoder/attention/multi_rnn_cell/cell_1/gru_cell/gates/kernel:0, (2000, 2000), /device:GPU:0
  dynamic_seq2seq/decoder/attention/multi_rnn_cell/cell_1/gru_cell/gates/bias:0, (2000,), /device:GPU:0
  dynamic_seq2seq/decoder/attention/multi_rnn_cell/cell_1/gru_cell/candidate/kernel:0, (2000, 1000), /device:GPU:0
  dynamic_seq2seq/decoder/attention/multi_rnn_cell/cell_1/gru_cell/candidate/bias:0, (1000,), /device:GPU:0
  dynamic_seq2seq/decoder/attention/multi_rnn_cell/cell_2/gru_cell/gates/kernel:0, (2000, 2000), /device:GPU:0
  dynamic_seq2seq/decoder/attention/multi_rnn_cell/cell_2/gru_cell/gates/bias:0, (2000,), /device:GPU:0
  dynamic_seq2seq/decoder/attention/multi_rnn_cell/cell_2/gru_cell/candidate/kernel:0, (2000, 1000), /device:GPU:0
  dynamic_seq2seq/decoder/attention/multi_rnn_cell/cell_2/gru_cell/candidate/bias:0, (1000,), /device:GPU:0
  dynamic_seq2seq/decoder/attention/multi_rnn_cell/cell_3/gru_cell/gates/kernel:0, (2000, 2000), /device:GPU:0
  dynamic_seq2seq/decoder/attention/multi_rnn_cell/cell_3/gru_cell/gates/bias:0, (2000,), /device:GPU:0
  dynamic_seq2seq/decoder/attention/multi_rnn_cell/cell_3/gru_cell/candidate/kernel:0, (2000, 1000), /device:GPU:0
  dynamic_seq2seq/decoder/attention/multi_rnn_cell/cell_3/gru_cell/candidate/bias:0, (1000,), /device:GPU:0
  dynamic_seq2seq/decoder/attention/attention_layer/kernel:0, (2000, 1000), /device:GPU:0
  dynamic_seq2seq/decoder/output_projection/kernel:0, (1000, 2891), /device:GPU:0
# creating eval graph ...
  num_layers = 4, num_residual_layers=3
  cell 0  GRU  DeviceWrapper, device=/gpu:0
  cell 1  GRU  ResidualWrapper  DeviceWrapper, device=/gpu:0
  cell 2  GRU  ResidualWrapper  DeviceWrapper, device=/gpu:0
  cell 3  GRU  ResidualWrapper  DeviceWrapper, device=/gpu:0
  cell 0  GRU  DeviceWrapper, device=/gpu:0
  cell 1  GRU  ResidualWrapper  DeviceWrapper, device=/gpu:0
  cell 2  GRU  ResidualWrapper  DeviceWrapper, device=/gpu:0
  cell 3  GRU  ResidualWrapper  DeviceWrapper, device=/gpu:0
  start_decay_step=0, learning_rate=1e-05, decay_steps 10000, decay_factor 0.98
# Trainable variables
  conv1/weights:0, (11, 11, 3, 96), /device:GPU:0
  conv1/biases:0, (96,), /device:GPU:0
  conv2/weights:0, (5, 5, 48, 256), /device:GPU:0
  conv2/biases:0, (256,), /device:GPU:0
  conv3/weights:0, (3, 3, 256, 384), /device:GPU:0
  conv3/biases:0, (384,), /device:GPU:0
  conv4/weights:0, (3, 3, 192, 384), /device:GPU:0
  conv4/biases:0, (384,), /device:GPU:0
  conv5/weights:0, (3, 3, 192, 256), /device:GPU:0
  conv5/biases:0, (256,), /device:GPU:0
  fc6/weights:0, (9216, 4096), /device:GPU:0
  fc6/biases:0, (4096,), /device:GPU:0
  fc7/weights:0, (4096, 4096), /device:GPU:0
  fc7/biases:0, (4096,), /device:GPU:0
  embeddings/decoder/embedding_decoder:0, (2891, 1000),
  dynamic_seq2seq/encoder/rnn/multi_rnn_cell/cell_0/gru_cell/gates/kernel:0, (5096, 2000), /device:GPU:0
  dynamic_seq2seq/encoder/rnn/multi_rnn_cell/cell_0/gru_cell/gates/bias:0, (2000,), /device:GPU:0
  dynamic_seq2seq/encoder/rnn/multi_rnn_cell/cell_0/gru_cell/candidate/kernel:0, (5096, 1000), /device:GPU:0
  dynamic_seq2seq/encoder/rnn/multi_rnn_cell/cell_0/gru_cell/candidate/bias:0, (1000,), /device:GPU:0
  dynamic_seq2seq/encoder/rnn/multi_rnn_cell/cell_1/gru_cell/gates/kernel:0, (2000, 2000), /device:GPU:0
  dynamic_seq2seq/encoder/rnn/multi_rnn_cell/cell_1/gru_cell/gates/bias:0, (2000,), /device:GPU:0
  dynamic_seq2seq/encoder/rnn/multi_rnn_cell/cell_1/gru_cell/candidate/kernel:0, (2000, 1000), /device:GPU:0
  dynamic_seq2seq/encoder/rnn/multi_rnn_cell/cell_1/gru_cell/candidate/bias:0, (1000,), /device:GPU:0
  dynamic_seq2seq/encoder/rnn/multi_rnn_cell/cell_2/gru_cell/gates/kernel:0, (2000, 2000), /device:GPU:0
  dynamic_seq2seq/encoder/rnn/multi_rnn_cell/cell_2/gru_cell/gates/bias:0, (2000,), /device:GPU:0
  dynamic_seq2seq/encoder/rnn/multi_rnn_cell/cell_2/gru_cell/candidate/kernel:0, (2000, 1000), /device:GPU:0
  dynamic_seq2seq/encoder/rnn/multi_rnn_cell/cell_2/gru_cell/candidate/bias:0, (1000,), /device:GPU:0
  dynamic_seq2seq/encoder/rnn/multi_rnn_cell/cell_3/gru_cell/gates/kernel:0, (2000, 2000), /device:GPU:0
  dynamic_seq2seq/encoder/rnn/multi_rnn_cell/cell_3/gru_cell/gates/bias:0, (2000,), /device:GPU:0
  dynamic_seq2seq/encoder/rnn/multi_rnn_cell/cell_3/gru_cell/candidate/kernel:0, (2000, 1000), /device:GPU:0
  dynamic_seq2seq/encoder/rnn/multi_rnn_cell/cell_3/gru_cell/candidate/bias:0, (1000,), /device:GPU:0
  dynamic_seq2seq/decoder/memory_layer/kernel:0, (1000, 1000),
  dynamic_seq2seq/decoder/attention/multi_rnn_cell/cell_0/gru_cell/gates/kernel:0, (3000, 2000), /device:GPU:0
  dynamic_seq2seq/decoder/attention/multi_rnn_cell/cell_0/gru_cell/gates/bias:0, (2000,), /device:GPU:0
  dynamic_seq2seq/decoder/attention/multi_rnn_cell/cell_0/gru_cell/candidate/kernel:0, (3000, 1000), /device:GPU:0
  dynamic_seq2seq/decoder/attention/multi_rnn_cell/cell_0/gru_cell/candidate/bias:0, (1000,), /device:GPU:0
  dynamic_seq2seq/decoder/attention/multi_rnn_cell/cell_1/gru_cell/gates/kernel:0, (2000, 2000), /device:GPU:0
  dynamic_seq2seq/decoder/attention/multi_rnn_cell/cell_1/gru_cell/gates/bias:0, (2000,), /device:GPU:0
  dynamic_seq2seq/decoder/attention/multi_rnn_cell/cell_1/gru_cell/candidate/kernel:0, (2000, 1000), /device:GPU:0
  dynamic_seq2seq/decoder/attention/multi_rnn_cell/cell_1/gru_cell/candidate/bias:0, (1000,), /device:GPU:0
  dynamic_seq2seq/decoder/attention/multi_rnn_cell/cell_2/gru_cell/gates/kernel:0, (2000, 2000), /device:GPU:0
  dynamic_seq2seq/decoder/attention/multi_rnn_cell/cell_2/gru_cell/gates/bias:0, (2000,), /device:GPU:0
  dynamic_seq2seq/decoder/attention/multi_rnn_cell/cell_2/gru_cell/candidate/kernel:0, (2000, 1000), /device:GPU:0
  dynamic_seq2seq/decoder/attention/multi_rnn_cell/cell_2/gru_cell/candidate/bias:0, (1000,), /device:GPU:0
  dynamic_seq2seq/decoder/attention/multi_rnn_cell/cell_3/gru_cell/gates/kernel:0, (2000, 2000), /device:GPU:0
  dynamic_seq2seq/decoder/attention/multi_rnn_cell/cell_3/gru_cell/gates/bias:0, (2000,), /device:GPU:0
  dynamic_seq2seq/decoder/attention/multi_rnn_cell/cell_3/gru_cell/candidate/kernel:0, (2000, 1000), /device:GPU:0
  dynamic_seq2seq/decoder/attention/multi_rnn_cell/cell_3/gru_cell/candidate/bias:0, (1000,), /device:GPU:0
  dynamic_seq2seq/decoder/attention/attention_layer/kernel:0, (2000, 1000), /device:GPU:0
  dynamic_seq2seq/decoder/output_projection/kernel:0, (1000, 2891), /device:GPU:0
# creating infer graph ...
  num_layers = 4, num_residual_layers=3
  cell 0  GRU  DeviceWrapper, device=/gpu:0
  cell 1  GRU  ResidualWrapper  DeviceWrapper, device=/gpu:0
  cell 2  GRU  ResidualWrapper  DeviceWrapper, device=/gpu:0
  cell 3  GRU  ResidualWrapper  DeviceWrapper, device=/gpu:0
  cell 0  GRU  DeviceWrapper, device=/gpu:0
  cell 1  GRU  ResidualWrapper  DeviceWrapper, device=/gpu:0
  cell 2  GRU  ResidualWrapper  DeviceWrapper, device=/gpu:0
  cell 3  GRU  ResidualWrapper  DeviceWrapper, device=/gpu:0
  start_decay_step=0, learning_rate=1e-05, decay_steps 10000, decay_factor 0.98
# Trainable variables
  conv1/weights:0, (11, 11, 3, 96), /device:GPU:0
  conv1/biases:0, (96,), /device:GPU:0
  conv2/weights:0, (5, 5, 48, 256), /device:GPU:0
  conv2/biases:0, (256,), /device:GPU:0
  conv3/weights:0, (3, 3, 256, 384), /device:GPU:0
  conv3/biases:0, (384,), /device:GPU:0
  conv4/weights:0, (3, 3, 192, 384), /device:GPU:0
  conv4/biases:0, (384,), /device:GPU:0
  conv5/weights:0, (3, 3, 192, 256), /device:GPU:0
  conv5/biases:0, (256,), /device:GPU:0
  fc6/weights:0, (9216, 4096), /device:GPU:0
  fc6/biases:0, (4096,), /device:GPU:0
  fc7/weights:0, (4096, 4096), /device:GPU:0
  fc7/biases:0, (4096,), /device:GPU:0
  embeddings/decoder/embedding_decoder:0, (2891, 1000),
  dynamic_seq2seq/encoder/rnn/multi_rnn_cell/cell_0/gru_cell/gates/kernel:0, (5096, 2000), /device:GPU:0
  dynamic_seq2seq/encoder/rnn/multi_rnn_cell/cell_0/gru_cell/gates/bias:0, (2000,), /device:GPU:0
  dynamic_seq2seq/encoder/rnn/multi_rnn_cell/cell_0/gru_cell/candidate/kernel:0, (5096, 1000), /device:GPU:0
  dynamic_seq2seq/encoder/rnn/multi_rnn_cell/cell_0/gru_cell/candidate/bias:0, (1000,), /device:GPU:0
  dynamic_seq2seq/encoder/rnn/multi_rnn_cell/cell_1/gru_cell/gates/kernel:0, (2000, 2000), /device:GPU:0
  dynamic_seq2seq/encoder/rnn/multi_rnn_cell/cell_1/gru_cell/gates/bias:0, (2000,), /device:GPU:0
  dynamic_seq2seq/encoder/rnn/multi_rnn_cell/cell_1/gru_cell/candidate/kernel:0, (2000, 1000), /device:GPU:0
  dynamic_seq2seq/encoder/rnn/multi_rnn_cell/cell_1/gru_cell/candidate/bias:0, (1000,), /device:GPU:0
  dynamic_seq2seq/encoder/rnn/multi_rnn_cell/cell_2/gru_cell/gates/kernel:0, (2000, 2000), /device:GPU:0
  dynamic_seq2seq/encoder/rnn/multi_rnn_cell/cell_2/gru_cell/gates/bias:0, (2000,), /device:GPU:0
  dynamic_seq2seq/encoder/rnn/multi_rnn_cell/cell_2/gru_cell/candidate/kernel:0, (2000, 1000), /device:GPU:0
  dynamic_seq2seq/encoder/rnn/multi_rnn_cell/cell_2/gru_cell/candidate/bias:0, (1000,), /device:GPU:0
  dynamic_seq2seq/encoder/rnn/multi_rnn_cell/cell_3/gru_cell/gates/kernel:0, (2000, 2000), /device:GPU:0
  dynamic_seq2seq/encoder/rnn/multi_rnn_cell/cell_3/gru_cell/gates/bias:0, (2000,), /device:GPU:0
  dynamic_seq2seq/encoder/rnn/multi_rnn_cell/cell_3/gru_cell/candidate/kernel:0, (2000, 1000), /device:GPU:0
  dynamic_seq2seq/encoder/rnn/multi_rnn_cell/cell_3/gru_cell/candidate/bias:0, (1000,), /device:GPU:0
  dynamic_seq2seq/decoder/memory_layer/kernel:0, (1000, 1000),
  dynamic_seq2seq/decoder/attention/multi_rnn_cell/cell_0/gru_cell/gates/kernel:0, (3000, 2000), /device:GPU:0
  dynamic_seq2seq/decoder/attention/multi_rnn_cell/cell_0/gru_cell/gates/bias:0, (2000,), /device:GPU:0
  dynamic_seq2seq/decoder/attention/multi_rnn_cell/cell_0/gru_cell/candidate/kernel:0, (3000, 1000), /device:GPU:0
  dynamic_seq2seq/decoder/attention/multi_rnn_cell/cell_0/gru_cell/candidate/bias:0, (1000,), /device:GPU:0
  dynamic_seq2seq/decoder/attention/multi_rnn_cell/cell_1/gru_cell/gates/kernel:0, (2000, 2000), /device:GPU:0
  dynamic_seq2seq/decoder/attention/multi_rnn_cell/cell_1/gru_cell/gates/bias:0, (2000,), /device:GPU:0
  dynamic_seq2seq/decoder/attention/multi_rnn_cell/cell_1/gru_cell/candidate/kernel:0, (2000, 1000), /device:GPU:0
  dynamic_seq2seq/decoder/attention/multi_rnn_cell/cell_1/gru_cell/candidate/bias:0, (1000,), /device:GPU:0
  dynamic_seq2seq/decoder/attention/multi_rnn_cell/cell_2/gru_cell/gates/kernel:0, (2000, 2000), /device:GPU:0
  dynamic_seq2seq/decoder/attention/multi_rnn_cell/cell_2/gru_cell/gates/bias:0, (2000,), /device:GPU:0
  dynamic_seq2seq/decoder/attention/multi_rnn_cell/cell_2/gru_cell/candidate/kernel:0, (2000, 1000), /device:GPU:0
  dynamic_seq2seq/decoder/attention/multi_rnn_cell/cell_2/gru_cell/candidate/bias:0, (1000,), /device:GPU:0
  dynamic_seq2seq/decoder/attention/multi_rnn_cell/cell_3/gru_cell/gates/kernel:0, (2000, 2000), /device:GPU:0
  dynamic_seq2seq/decoder/attention/multi_rnn_cell/cell_3/gru_cell/gates/bias:0, (2000,), /device:GPU:0
  dynamic_seq2seq/decoder/attention/multi_rnn_cell/cell_3/gru_cell/candidate/kernel:0, (2000, 1000), /device:GPU:0
  dynamic_seq2seq/decoder/attention/multi_rnn_cell/cell_3/gru_cell/candidate/bias:0, (1000,), /device:GPU:0
  dynamic_seq2seq/decoder/attention/attention_layer/kernel:0, (2000, 1000), /device:GPU:0
  dynamic_seq2seq/decoder/output_projection/kernel:0, (1000, 2891),
# log_file=../test_out/log_1557087524
  created train model with fresh parameters, time 5.24s
  created infer model with fresh parameters, time 0.96s
  # 301
    src: /home/ubuntu/fullFrame-227x227px/dev/20June_2011_Monday_heute-6514/
    ref: und eher wechselhaft geht es mit unserem wetter auch weiter .
    nmt: stunden bedeckt bedeckt bedeckt bedeckt bedeckt bedeckt informieren informieren informieren informieren informieren informieren informieren informieren informieren informieren informieren informieren informieren informieren informieren informieren informieren informieren informieren informieren informieren informieren informieren informieren informieren informieren informieren informieren informieren informieren informieren informieren informieren
  created eval model with fresh parameters, time 0.76s
  eval dev: perplexity 4663.06, time 3785s, Sun May  5 21:22:18 2019.
  eval test: perplexity 4627.91, time 4630s, Sun May  5 22:39:29 2019.
  created infer model with fresh parameters, time 0.85s
# Start step 0, lr 1e-05, Sun May  5 22:39:30 2019
# Init train iterator, skipping 0 elements

terminate called after throwing an instance of 'std::out_of_range'
what():  basic_string::substr: __pos (which is 140) > this->size() (which is 0)

Could you help me with this please?