Method zero_state

tensorflow/python/ops/rnn_cell_impl.py:311–340

Return zero-filled state tensor(s). Args: batch_size: int, float, or unit Tensor representing the batch size. dtype: the data type to use for the state. Returns: If `state_size` is an int or TensorShape, then the return value is a `N-D` tensor of shape `[batch_size,

(self, batch_size, dtype)

    return self.zero_state(batch_size, dtype)

  def zero_state(self, batch_size, dtype):
    """Return zero-filled state tensor(s).

    Args:
      batch_size: int, float, or unit Tensor representing the batch size.
      dtype: the data type to use for the state.

    Returns:
      If `state_size` is an int or TensorShape, then the return value is a
      `N-D` tensor of shape `[batch_size, state_size]` filled with zeros.

      If `state_size` is a nested list or tuple, then the return value is
      a nested list or tuple (of the same structure) of `2-D` tensors with
      the shapes `[batch_size, s]` for each s in `state_size`.
    """
    # Try to use the last cached zero_state. This is done to avoid recreating
    # zeros, especially when eager execution is enabled.
    state_size = self.state_size
    is_eager = context.executing_eagerly()
    if is_eager and _hasattr(self, "_last_zero_state"):
      (last_state_size, last_batch_size, last_dtype,
       last_output) = getattr(self, "_last_zero_state")
      if (last_batch_size == batch_size and last_dtype == dtype and
          last_state_size == state_size):
        return last_output
    with ops.name_scope(type(self).__name__ + "ZeroState", values=[batch_size]):
      output = _zero_state_tensors(state_size, batch_size, dtype)
    if is_eager:
      self._last_zero_state = (state_size, batch_size, dtype, output)
    return output

  # TODO(b/134773139): Remove when contrib RNN cells implement `get_config`
  def get_config(self):  # pylint: disable=useless-super-delegation