def _build_encoder()

in nmt/gnmt_model.py [0:0]

• 35 lines of code
• 8 McCabe index (conditional complexity)

  def _build_encoder(self, hparams):
    """Build a GNMT encoder."""
    if hparams.encoder_type == "uni" or hparams.encoder_type == "bi":
      return super(GNMTModel, self)._build_encoder(hparams)

    if hparams.encoder_type != "gnmt":
      raise ValueError("Unknown encoder_type %s" % hparams.encoder_type)

    # Build GNMT encoder.
    num_bi_layers = 1
    num_uni_layers = self.num_encoder_layers - num_bi_layers
    utils.print_out("# Build a GNMT encoder")
    utils.print_out("  num_bi_layers = %d" % num_bi_layers)
    utils.print_out("  num_uni_layers = %d" % num_uni_layers)

    iterator = self.iterator
    source = iterator.source
    if self.time_major:
      source = tf.transpose(source)

    with tf.variable_scope("encoder") as scope:
      dtype = scope.dtype

      self.encoder_emb_inp = self.encoder_emb_lookup_fn(
          self.embedding_encoder, source)

      # Execute _build_bidirectional_rnn from Model class
      bi_encoder_outputs, bi_encoder_state = self._build_bidirectional_rnn(
          inputs=self.encoder_emb_inp,
          sequence_length=iterator.source_sequence_length,
          dtype=dtype,
          hparams=hparams,
          num_bi_layers=num_bi_layers,
          num_bi_residual_layers=0,  # no residual connection
      )

      # Build unidirectional layers
      if self.extract_encoder_layers:
        encoder_state, encoder_outputs = self._build_individual_encoder_layers(
            bi_encoder_outputs, num_uni_layers, dtype, hparams)
      else:
        encoder_state, encoder_outputs = self._build_all_encoder_layers(
            bi_encoder_outputs, num_uni_layers, dtype, hparams)

      # Pass all encoder states to the decoder
      #   except the first bi-directional layer
      encoder_state = (bi_encoder_state[1],) + (
          (encoder_state,) if num_uni_layers == 1 else encoder_state)

    return encoder_outputs, encoder_state