()
return contrib_training.HParams(**hparams_map)
Returns copy of default HParams for LSTM models.
Returns copy of default HParams for LSTM models.
Returns copy of default HParams for LSTM models .
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)
def get_default_hparams ( ) : 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 )