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
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:
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.
(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.
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)
VariableScope for the created subgraph; defaults to "rnn".
A tuple (emit_ta, final_state, final_loop_state) where:
emit_ta: The RNN output TensorArray.
If loop_fn returns a (possibly nested) set of Tensors for
emit_output during initialization, (inputs time = 0,
cell_output = None, and loop_state = None), then emit_ta will
have the same structure, dtypes, and shapes as emit_output instead.
If loop_fn returns emit_output = None during this call,
the structure of cell.output_size is used:
If cell.output_size is a (possibly nested) tuple of integers
or TensorShape objects, then emit_ta will be a tuple having the
same structure as cell.output_size, containing TensorArrays whose
elements' shapes correspond to the shape data in cell.output_size.
final_state: The final cell state. If cell.state_size is an int, this
will be shaped [batch_size, cell.state_size]. If it is a
TensorShape, this will be shaped [batch_size] + cell.state_size.
If it is a (possibly nested) tuple of ints or TensorShape, this will
be a tuple having the corresponding shapes.
final_loop_state: The final loop state as returned by loop_fn.
If cell is not an instance of RNNCell, or loop_fn is not
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