da37438735fd4b845bb0874562bd071865c480bb,texar/modules/encoders/rnn_encoders.py,RNNEncoderBase,__init__,#RNNEncoderBase#Any#Any#Any#,49

Before Change

        self._vocab_size = vocab_size
        self._embedding = None
        if self._hparams.use_embedding:
            if embedding is None and self._vocab_size is None:
                raise ValueError(
                    "`vocab_szie` is required if embedding is enabled and "
                    "`embedding` is not provided")
            if isinstance(embedding, tf.Variable):
                self._embedding = embedding
            else:
                self._embedding = embedder_utils.get_embedding(
                    self._hparams.embedding, embedding, self._vocab_size,
                    self.variable_scope)
            if self._hparams.embedding.trainable:
                self._add_trainable_variable(self._embedding)
            if self._vocab_size is None:
                self._vocab_size = self._embedding.get_shape().as_list()[0]

    @staticmethod
    def default_hparams():
        Returns a dictionary of hyperparameters with default values.

After Change

        // Make RNN cells
        with tf.variable_scope(self.variable_scope):
            if cell_fw is not None:
                self._cell_fw = cell_fw
            else:
                self._cell_fw = layers.get_rnn_cell(self._hparams.rnn_cell_fw)

            if cell_bw is not None:
                self._cell_bw = cell_bw
            elif self.hparams.rnn_cell_share_config:
                self._cell_bw = layers.get_rnn_cell(self._hparams.rnn_cell_fw)

Instances