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)