dynamic_rnn_estimator.py 文件源码

def _to_rnn_cell(cell_or_type, num_units, num_layers):
  """Constructs and return an `RNNCell`.

  Args:
    cell_or_type: Either a string identifying the `RNNCell` type, a subclass of
      `RNNCell` or an instance of an `RNNCell`.
    num_units: The number of units in the `RNNCell`.
    num_layers: The number of layers in the RNN.
  Returns:
    An initialized `RNNCell`.
  Raises:
    ValueError: `cell_or_type` is an invalid `RNNCell` name.
    TypeError: `cell_or_type` is not a string or a subclass of `RNNCell`.
  """
  if isinstance(cell_or_type, contrib_rnn.RNNCell):
    return cell_or_type
  if isinstance(cell_or_type, str):
    cell_or_type = _CELL_TYPES.get(cell_or_type)
    if cell_or_type is None:
      raise ValueError('The supported cell types are {}; got {}'.format(
          list(_CELL_TYPES.keys()), cell_or_type))
  if not issubclass(cell_or_type, contrib_rnn.RNNCell):
    raise TypeError(
        'cell_or_type must be a subclass of RNNCell or one of {}.'.format(
            list(_CELL_TYPES.keys())))
  single_cell = lambda: cell_or_type(num_units=num_units)
  if num_layers > 1:
    cell = contrib_rnn.MultiRNNCell(
        [single_cell() for _ in range(num_layers)], state_is_tuple=True)
  else:
    cell = single_cell()
  return cell