4ce9a9d078d41af1a960f0e5bf16e373f69d5117,open_seq2seq/decoders/rnn_decoders.py,BeamSearchRNNDecoderWithAttention,_decode,#BeamSearchRNNDecoderWithAttention#Any#,345

Before Change

      self._tgt_vocab_size, use_bias=False,
    )

    cell_params = copy.deepcopy(self.params)
    cell_params["num_units"] = self.params["decoder_cell_units"]

    if self._mode == "train":
      dp_input_keep_prob = self.params["decoder_dp_input_keep_prob"]
      dp_output_keep_prob = self.params["decoder_dp_output_keep_prob"]

After Change

    //)
    residual_connections = self.params["decoder_use_skip_connections"]
    // list of cells
    self._decoder_cells = [
      single_cell(cell_class=self.params["core_cell"],
                  cell_params=self.params.get("core_cell_params", {}),
                  dp_input_keep_prob=dp_input_keep_prob,
                  dp_output_keep_prob=dp_output_keep_prob,
                  // residual connections are added a little differently for GNMT
                  residual_connections=False if self.params[
                    "attention_type"].startswith(
                    "gnmt") else residual_connections,
                  ) for _ in range(self.params["decoder_layers"])]

    tiled_enc_outputs = tf.contrib.seq2seq.tile_batch(
      encoder_outputs,
      multiplier=self._beam_width,
    )
    tiled_enc_src_lengths = tf.contrib.seq2seq.tile_batch(
      enc_src_lengths,
      multiplier=self._beam_width,
    )
    attention_mechanism = self._build_attention(
      tiled_enc_outputs,
      tiled_enc_src_lengths,
    )

    if self.params["attention_type"].startswith("gnmt"):
      attention_cell = self._decoder_cells.pop(0)
      attention_cell = AttentionWrapper(
        attention_cell,
        attention_mechanism=attention_mechanism,
        attention_layer_size=None,  // don"t use attention layer.
        output_attention=False,
        name="gnmt_attention")
      attentive_decoder_cell = GNMTAttentionMultiCell(
        attention_cell, self._add_residual_wrapper(self._decoder_cells) if residual_connections else self._decoder_cells,
        use_new_attention=(self.params["attention_type"] == "gnmt_v2"))
    else: // non-GNMT
      attentive_decoder_cell = AttentionWrapper(
        cell=tf.contrib.rnn.MultiRNNCell(self._decoder_cells),
        attention_mechanism=attention_mechanism,
      )
    batch_size_tensor = tf.constant(self._batch_size)

Instances