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

Before Change

      dp_input_keep_prob = 1.0
      dp_output_keep_prob = 1.0

    if self.params["attention_type"].startswith("gnmt"):
      residual_connections = False
      wrap_to_multi_rnn = False
    else:

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,

Instances