Creates an RNN specified by RNNCell cell and loop function loop_fn.

This function is a more primitive version of dynamic_rnn that provides more direct access to the inputs each iteration. It also provides more control over when to start and finish reading the sequence, and what to emit for the output.

For example, it can be used to implement the dynamic decoder of a seq2seq model.

Instead of working with Tensor objects, most operations work with TensorArray objects directly.

The operation of raw_rnn, in pseudo-code, is basically the following:

time = tf.constant(0, dtype=tf.int32)
(finished, next_input, initial_state, emit_structure, loop_state) = loop_fn(
    time=time, cell_output=None, cell_state=None, loop_state=None)
emit_ta = TensorArray(dynamic_size=True, dtype=initial_state.dtype)
state = initial_state
while not all(finished):
  (output, cell_state) = cell(next_input, state)
  (next_finished, next_input, next_state, emit, loop_state) = loop_fn(
      time=time + 1, cell_output=output, cell_state=cell_state,
  # Emit zeros and copy forward state for minibatch entries that are finished.
  state = tf.where(finished, state, next_state)
  emit = tf.where(finished, tf.zeros_like(emit_structure), emit)
  emit_ta = emit_ta.write(time, emit)
  # If any new minibatch entries are marked as finished, mark these.
  finished = tf.logical_or(finished, next_finished)
  time += 1
return (emit_ta, state, loop_state)

with the additional properties that output and state may be (possibly nested) tuples, as determined by cell.output_size and cell.state_size, and as a result the final state and emit_ta may themselves be tuples.

A simple implementation of dynamic_rnn via raw_rnn looks like this:

inputs = tf.compat.v1.placeholder(shape=(max_time, batch_size, input_depth),
sequence_length = tf.compat.v1.placeholder(shape=(batch_size,),
inputs_ta = tf.TensorArray(dtype=tf.float32, size=max_time)
inputs_ta = inputs_ta.unstack(inputs)

cell = tf.compat.v1.nn.rnn_cell.LSTMCell(num_units)

def loop_fn(time, cell_output, cell_state, loop_state):
  emit_output = cell_output  # == None for time == 0
  if cell_output is None:  # time == 0
    next_cell_state = cell.zero_state(batch_size, tf.float32)
    next_cell_state = cell_state
  elements_finished = (time >= sequence_length)
  finished = tf.reduce_all(elements_finished)
  next_input = tf.cond(
      lambda: tf.zeros([batch_size, input_depth], dtype=tf.float32),
  next_loop_state = None
  return (elements_finished, next_input, next_cell_state,
          emit_output, next_loop_state)

outputs_ta, final_state, _ = raw_rnn(cell, loop_fn)
outputs = outputs_ta.stack()

cell An instance of RNNCell.
loop_fn A callable that takes inputs (time, cell_output, cell_state, loop_state) and returns the tuple (finished, next_input, next_cell_state, emit_output, next_loop_state). Here time is an int32 scalar Tensor, cell_output is a Tensor or (possibly nested) tuple of tensors as determined by cell.output_size, and cell_state is a Tensor or (possibly nested) tuple of tensors, as determined by the loop_fn on its first call (and should match cell.state_size). The outputs are: finished, a boolean Tensor of shape [batch_size], next_input: the next input to feed to cell, next_cell_state: the next state to feed to cell, and emit_output: the output to store for this iteration. Note that emit_output should be a Tensor or (possibly nested) tuple of tensors which is aggregated in the emit_ta inside the while_loop. For the first call to loop_fn, the emit_output corresponds to the emit_structure which is then used to determine the size of the zero_tensor for the emit_ta (defaults to cell.output_size). For the subsequent calls to the loop_fn, the emit_output corresponds to the actual output tensor that is to be aggregated in the emit_ta. The parameter cell_state and output next_cell_state may be either a single or (possibly nested) tuple of tensors. The parameter loop_state and output next_loop_state may be either a single or (possibly nested) tuple of Tensor and TensorArray objects. This last parameter may be ignored by loop_fn and the return value may be None. If it is not None, then the loop_state will be propagated through the RNN loop, for use purely by loop_fn to keep track of its own state. The next_loop_state parameter returned may be None. The first call to loop_fn will be time = 0, cell_output = None, cell_state = None, and loop_state = None. For this call: The next_cell_state value should be the value with which to initialize the cell's state. It may be a final state from a previous RNN or it may be the output of cell.zero_state(). It should be a (possibly nested) tuple structure of tensors. If cell.state_size is an integer, this must be a Tensor of appropriate type and shape [batch_size, cell.state_size]. If cell.state_size is a TensorShape, this must be a Tensor of appropriate type and shape [batch_size] + cell.state_size. If cell.state_size is a (possibly nested) tuple of ints or TensorShape, this will be a tuple having the corresponding shapes. The emit_output value may be either None or a (possibly nested) tuple structure of tensors, e.g., (tf.zeros(shape_0, dtype=dtype_0), tf.zeros(shape_1, dtype=dtype_1)). If this first emit_output return value is None, then the emit_ta result of raw_rnn will have the same structure and dtypes as cell.output_size. Otherwise emit_ta will have the same structure, shapes (prepended with a batch_size dimension), and dtypes as emit_output. The actual values returned for emit_output at this initializing call are ignored. Note, this emit structure must be consistent across all time steps.
parallel_iterations (Default: 32). The number of iterations to run in parallel. Those operations which do not have any temporal dependency and can be run in parallel, will be. This parameter trades off time for space. Values >> 1 use more memory but take less time, while smaller values use less memory but computations take longer.
swap_memory Transparently swap the tensors produced in forward inference but needed for back prop from GPU to CPU. This allows training RNNs which would typically not fit on a single GPU, with very minimal (or no) performance penalty.