def get_default_hparams():
"""Returns copy of default HParams for LSTM models."""
hparams_map = base_model.get_default_hparams().values()
hparams_map.update({
'conditional': True,
'dec_rnn_size': [512], # Decoder RNN: number of units per layer.
'enc_rnn_size': [256], # Encoder RNN: number of units per layer per dir.
'dropout_keep_prob': 1.0, # Probability all dropout keep.
'sampling_schedule': 'constant', # constant, exponential, inverse_sigmoid
'sampling_rate': 0.0, # Interpretation is based on `sampling_schedule`.
'use_cudnn': False, # Uses faster CudnnLSTM to train. For GPU only.
})
return tf.contrib.training.HParams(**hparams_map)
def get_default_hparams():
"""Returns copy of default HParams for LSTM models."""
hparams_map = base_model.get_default_hparams().values()
hparams_map.update({
'conditional': True,
'dec_rnn_size': [512], # Decoder RNN: number of units per layer.
'enc_rnn_size': [256], # Encoder RNN: number of units per layer per dir.
'dropout_keep_prob': 1.0, # Probability all dropout keep.
'sampling_schedule': 'constant', # constant, exponential, inverse_sigmoid
'sampling_rate': 0.0, # Interpretation is based on `sampling_schedule`.
'use_cudnn_dec': False, # Uses faster CudnnLstm to train. For GPU only.
'use_cudnn_enc': False, # Only enable for sequences w/ equal length.
})
return tf.contrib.training.HParams(**hparams_map)
def get_default_hparams():
"""Returns copy of default HParams for LSTM models."""
hparams_map = base_model.get_default_hparams().values()
hparams_map.update({
'conditional': True,
'dec_rnn_size': [512], # Decoder RNN: number of units per layer.
'enc_rnn_size': [256], # Encoder RNN: number of units per layer per dir.
'dropout_keep_prob': 1.0, # Probability all dropout keep.
'sampling_schedule': 'constant', # constant, exponential, inverse_sigmoid
'sampling_rate': 0.0, # Interpretation is based on `sampling_schedule`.
'use_cudnn': False, # DEPRECATED
'residual_encoder': False, # Use residual connections in encoder.
'residual_decoder': False, # Use residual connections in decoder.
'control_preprocessing_rnn_size': [256], # Decoder control preprocessing.
})
return contrib_training.HParams(**hparams_map)
