Function _dynamic_rnn_loop

tensorflow/python/ops/rnn.py:719–936

Internal implementation of Dynamic RNN. Args: cell: An instance of RNNCell. inputs: A `Tensor` of shape [time, batch_size, input_size], or a nested tuple of such elements. initial_state: A `Tensor` of shape `[batch_size, state_size]`, or if `cell.state_size` is a tuple, th

(cell,
                      inputs,
                      initial_state,
                      parallel_iterations,
                      swap_memory,
                      sequence_length=None,
                      dtype=None)

def _dynamic_rnn_loop(cell,
                      inputs,
                      initial_state,
                      parallel_iterations,
                      swap_memory,
                      sequence_length=None,
                      dtype=None):
  """Internal implementation of Dynamic RNN.

  Args:
    cell: An instance of RNNCell.
    inputs: A `Tensor` of shape [time, batch_size, input_size], or a nested
      tuple of such elements.
    initial_state: A `Tensor` of shape `[batch_size, state_size]`, or if
      `cell.state_size` is a tuple, then this should be a tuple of tensors
      having shapes `[batch_size, s] for s in cell.state_size`.
    parallel_iterations: Positive Python int.
    swap_memory: A Python boolean
    sequence_length: (optional) An `int32` `Tensor` of shape [batch_size].
    dtype: (optional) Expected dtype of output. If not specified, inferred from
      initial_state.

  Returns:
    Tuple `(final_outputs, final_state)`.
    final_outputs:
      A `Tensor` of shape `[time, batch_size, cell.output_size]`.  If
      `cell.output_size` is a (possibly nested) tuple of ints or `TensorShape`
      objects, then this returns a (possibly nested) tuple of Tensors matching
      the corresponding shapes.
    final_state:
      A `Tensor`, or possibly nested tuple of Tensors, matching in length
      and shapes to `initial_state`.

  Raises:
    ValueError: If the input depth cannot be inferred via shape inference
      from the inputs.
    ValueError: If time_step is not the same for all the elements in the
      inputs.
    ValueError: If batch_size is not the same for all the elements in the
      inputs.
  """
  state = initial_state
  assert isinstance(parallel_iterations, int), "parallel_iterations must be int"

  state_size = cell.state_size

  flat_input = nest.flatten(inputs)
  flat_output_size = nest.flatten(cell.output_size)

  # Construct an initial output
  input_shape = array_ops.shape(flat_input[0])
  time_steps = input_shape[0]
  batch_size = _best_effort_input_batch_size(flat_input)

  inputs_got_shape = tuple(
      input_.get_shape().with_rank_at_least(3) for input_ in flat_input)

  const_time_steps, const_batch_size = inputs_got_shape[0].as_list()[:2]