4ce9a9d078d41af1a960f0e5bf16e373f69d5117,open_seq2seq/encoders/rnn_encoders.py,BidirectionalRNNEncoderWithEmbedding,_encode,#BidirectionalRNNEncoderWithEmbedding#Any#,204

Before Change


      dtype=tf.float32
    )

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

    if self._mode == "train":
      dp_input_keep_prob = self.params["encoder_dp_input_keep_prob"]
      dp_output_keep_prob = self.params["encoder_dp_output_keep_prob"]
    else:
      dp_input_keep_prob = 1.0
      dp_output_keep_prob = 1.0

    with tf.variable_scope("FW"):
      self._encoder_cell_fw = create_rnn_cell(
        cell_type=self.params["encoder_cell_type"],
        cell_params=cell_params,
        num_layers=self.params["encoder_layers"],
        dp_input_keep_prob=dp_input_keep_prob,
        dp_output_keep_prob=dp_output_keep_prob,
        residual_connections=self.params["encoder_use_skip_connections"]
      )

    with tf.variable_scope("BW"):
      self._encoder_cell_bw = create_rnn_cell(
        cell_type=self.params["encoder_cell_type"],

After Change


      dp_input_keep_prob = 1.0
      dp_output_keep_prob = 1.0

    fwd_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=self.params["encoder_use_skip_connections"]
                  ) for _ in range(self.params["encoder_layers"])]
    bwd_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=self.params["encoder_use_skip_connections"]
                  ) for _ in range(self.params["encoder_layers"])]


    with tf.variable_scope("FW"):
      self._encoder_cell_fw = tf.contrib.rnn.MultiRNNCell(fwd_cells)

    with tf.variable_scope("BW"):
      self._encoder_cell_bw = tf.contrib.rnn.MultiRNNCell(bwd_cells)
Italian Trulli
In pattern: SUPERPATTERN

Frequency: 3

Non-data size: 13

Instances


Project Name: NVIDIA/OpenSeq2Seq
Commit Name: 4ce9a9d078d41af1a960f0e5bf16e373f69d5117
Time: 2018-06-13
Author: okuchaiev@nvidia.com
File Name: open_seq2seq/encoders/rnn_encoders.py
Class Name: BidirectionalRNNEncoderWithEmbedding
Method Name: _encode


Project Name: NVIDIA/OpenSeq2Seq
Commit Name: 4ce9a9d078d41af1a960f0e5bf16e373f69d5117
Time: 2018-06-13
Author: okuchaiev@nvidia.com
File Name: open_seq2seq/decoders/rnn_decoders.py
Class Name: BeamSearchRNNDecoderWithAttention
Method Name: _decode


Project Name: NVIDIA/OpenSeq2Seq
Commit Name: 4ce9a9d078d41af1a960f0e5bf16e373f69d5117
Time: 2018-06-13
Author: okuchaiev@nvidia.com
File Name: open_seq2seq/decoders/rnn_decoders.py
Class Name: RNNDecoderWithAttention
Method Name: _decode