def _build_encoded_sequences(self, sequences, length, mode, hparams):
num_bi_layers = 1
num_uni_layers = hparams.enc_num_layers - num_bi_layers
# Build bidirectional layers.
bi_outputs, bi_state = model_utils.build_bidirectional_rnn(
sequences=sequences,
length=length,
num_layers=num_bi_layers,
num_residual_layers=0, # no residual connection
num_units=hparams.hidden_size,
unit_type=hparams.rnn_unit_type,
forget_bias=hparams.rnn_forget_bias,
dropout=hparams.dropout,
mode=mode)
# Build unidirectional layers.
outputs, state = model_utils.build_unidirectional_rnn(
sequences=bi_outputs,
length=length,
num_layers=num_uni_layers,
num_residual_layers=hparams.enc_num_residual_layers,
num_units=hparams.hidden_size,
unit_type=hparams.rnn_unit_type,
forget_bias=hparams.rnn_forget_bias,
dropout=hparams.dropout,
mode=mode)
# Pass all encoder states to the decoder except the first in the bi-layer.
state = (bi_state[1], ) + ((state, ) if num_uni_layers == 1 else state)
return outputs, state
def _build_encoded_sequences(self, sequences, length, mode, hparams):
return model_utils.build_unidirectional_rnn(
sequences=sequences,
length=length,
num_layers=hparams.enc_num_layers,
num_residual_layers=hparams.enc_num_residual_layers,
num_units=hparams.hidden_size,
unit_type=hparams.rnn_unit_type,
forget_bias=hparams.rnn_forget_bias,
dropout=hparams.dropout,
mode=mode)