caca2348bd54ec25d71ee05fd2a58e12db39f2e4,texar/modules/decoders/rnn_decoder_base.py,RNNDecoderBase,__init__,#RNNDecoderBase#Any#Any#Any#Any#,35

Before Change

                    "Either `output_layer` or `vocab_size` must be provided. "
                    "Set `output_layer=tf.identity` if no output layer is "
                    "wanted.")
            with tf.variable_scope(self.variable_scope):
                self._output_layer = tf.layers.Dense(units=self._vocab_size)
        elif output_layer is not tf.identity:
            if not isinstance(output_layer, tf.layers.Layer):
                raise ValueError(
                    "`output_layer` must be either `tf.identity` or "

After Change

        return self._helper.batch_size

    @property
    def output_size(self):
        Output size of one step.
        
        raise NotImplementedError

    @property
    def output_dtype(self):
        Types of output of one step.
        
        raise NotImplementedError

    def initialize(self, name=None):
        // Inherits from TFDecoder
        // All RNN decoder classes must implement this
        raise NotImplementedError

    def step(self, time, inputs, state, name=None):
        // Inherits from TFDecoder
        // All RNN decoder classes must implement this
        raise NotImplementedError

    def finalize(self, outputs, final_state, sequence_lengths):
        // Inherits from TFDecoder
        // All RNN decoder classes must implement this
        raise NotImplementedError

    @property
    def cell(self):
        The RNN cell.
        
        return self._cell

    def zero_state(self, batch_size, dtype):
        Zero state of the rnn cell.

        Same as :attr:`decoder.cell.zero_state`.

Instances