module_cell.py 文件源码

def __call__(self, inputs, state, scope=None):
    """Run this RNN cell on inputs, starting from the given state.
    Args:
      inputs: `2-D` tensor with shape `[batch_size x input_size]`.
      state: if `self.state_size` is an integer, this should be a `2-D Tensor`
        with shape `[batch_size x self.state_size]`.  Otherwise, if
        `self.state_size` is a tuple of integers, this should be a tuple
        with shapes `[batch_size x s] for s in self.state_size`.
      scope: VariableScope for the created subgraph; defaults to class name.
    Returns:
      A pair containing:
      - Output: A `2-D` tensor with shape `[batch_size x self.output_size]`.
      - New state: Either a single `2-D` tensor, or a tuple of tensors matching
        the arity and shapes of `state`.
    """
    if scope is not None:
      with vs.variable_scope(scope,
                             custom_getter=self._rnn_get_variable) as scope:
        return super(RNNCell, self).__call__(inputs, state, scope=scope)
    else:
      with vs.variable_scope(vs.get_variable_scope(),
                             custom_getter=self._rnn_get_variable):
        return super(RNNCell, self).__call__(inputs, state)