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# Copyright 2015 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
"""RNN helpers for TensorFlow models."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from tensorflow.python.eager import context
from tensorflow.python.framework import constant_op
from tensorflow.python.framework import dtypes
from tensorflow.python.framework import ops
from tensorflow.python.framework import tensor_shape
from tensorflow.python.framework import tensor_util
from tensorflow.python.keras.engine import base_layer
from tensorflow.python.ops import array_ops
from tensorflow.python.ops import control_flow_ops
from tensorflow.python.ops import control_flow_util
from tensorflow.python.ops import math_ops
from tensorflow.python.ops import rnn_cell_impl
from tensorflow.python.ops import tensor_array_ops
from tensorflow.python.ops import variable_scope as vs
from tensorflow.python.util import nest
from tensorflow.python.util.tf_export import tf_export
# pylint: disable=protected-access
_concat = rnn_cell_impl._concat
# pylint: enable=protected-access
def _transpose_batch_time(x):
"""Transposes the batch and time dimensions of a Tensor.
If the input tensor has rank < 2 it returns the original tensor. Retains as
much of the static shape information as possible.
Args:
x: A Tensor.
Returns:
x transposed along the first two dimensions.
"""
x_static_shape = x.get_shape()
if x_static_shape.ndims is not None and x_static_shape.ndims < 2:
return x
x_rank = array_ops.rank(x)
x_t = array_ops.transpose(
x, array_ops.concat(
([1, 0], math_ops.range(2, x_rank)), axis=0))
x_t.set_shape(
tensor_shape.TensorShape([
x_static_shape[1].value, x_static_shape[0].value
]).concatenate(x_static_shape[2:]))
return x_t
def _best_effort_input_batch_size(flat_input):
"""Get static input batch size if available, with fallback to the dynamic one.
Args:
flat_input: An iterable of time major input Tensors of shape
`[max_time, batch_size, ...]`.
All inputs should have compatible batch sizes.
Returns:
The batch size in Python integer if available, or a scalar Tensor otherwise.
Raises:
ValueError: if there is any input with an invalid shape.
"""
for input_ in flat_input:
shape = input_.shape
if shape.ndims is None:
continue
if shape.ndims < 2:
raise ValueError(
"Expected input tensor %s to have rank at least 2" % input_)
batch_size = shape[1].value
if batch_size is not None:
return batch_size
# Fallback to the dynamic batch size of the first input.
return array_ops.shape(flat_input[0])[1]
def _infer_state_dtype(explicit_dtype, state):
"""Infer the dtype of an RNN state.
Args:
explicit_dtype: explicitly declared dtype or None.
state: RNN's hidden state. Must be a Tensor or a nested iterable containing
Tensors.
Returns:
dtype: inferred dtype of hidden state.
Raises:
ValueError: if `state` has heterogeneous dtypes or is empty.
"""
if explicit_dtype is not None:
return explicit_dtype
elif nest.is_sequence(state):
inferred_dtypes = [element.dtype for element in nest.flatten(state)]
if not inferred_dtypes:
raise ValueError("Unable to infer dtype from empty state.")
all_same = all([x == inferred_dtypes[0] for x in inferred_dtypes])
if not all_same:
raise ValueError(
"State has tensors of different inferred_dtypes. Unable to infer a "
"single representative dtype.")
return inferred_dtypes[0]
else:
return state.dtype
def _maybe_tensor_shape_from_tensor(shape):
if isinstance(shape, ops.Tensor):
return tensor_shape.as_shape(tensor_util.constant_value(shape))
else:
return shape
def _should_cache():
"""Returns True if a default caching device should be set, otherwise False."""
if context.executing_eagerly():
return False
# Don't set a caching device when running in a loop, since it is possible that
# train steps could be wrapped in a tf.while_loop. In that scenario caching
# prevents forward computations in loop iterations from re-reading the
# updated weights.
ctxt = ops.get_default_graph()._get_control_flow_context() # pylint: disable=protected-access
return control_flow_util.GetContainingWhileContext(ctxt) is None
def _is_keras_rnn_cell(rnn_cell):
"""Check whether the cell is a Keras RNN cell.
The Keras RNN cell accept the state as a list even the state is a single
tensor, whereas the TF RNN cell does not wrap single state tensor in list.
This behavior difference should be unified in future version.
Args:
rnn_cell: An RNN cell instance that either follow the Keras interface or TF
RNN interface.
Returns:
Boolean, whether the cell is an Keras RNN cell.
"""
# Cell type check is not strict enough since there are cells created by other
# library like Deepmind that didn't inherit tf.nn.rnn_cell.RNNCell.
# Keras cells never had zero_state method, which was from the original
# interface from TF RNN cell.
return (not isinstance(rnn_cell, rnn_cell_impl.RNNCell)
and isinstance(rnn_cell, base_layer.Layer)
and getattr(rnn_cell, "zero_state", None) is None)
# pylint: disable=unused-argument
def _rnn_step(
time, sequence_length, min_sequence_length, max_sequence_length,
zero_output, state, call_cell, state_size, skip_conditionals=False):
"""Calculate one step of a dynamic RNN minibatch.
Returns an (output, state) pair conditioned on `sequence_length`.
When skip_conditionals=False, the pseudocode is something like:
if t >= max_sequence_length:
return (zero_output, state)
if t < min_sequence_length:
return call_cell()
# Selectively output zeros or output, old state or new state depending
# on whether we've finished calculating each row.
new_output, new_state = call_cell()
final_output = np.vstack([
zero_output if time >= sequence_length[r] else new_output_r
for r, new_output_r in enumerate(new_output)
])
final_state = np.vstack([
state[r] if time >= sequence_length[r] else new_state_r
for r, new_state_r in enumerate(new_state)
])
return (final_output, final_state)
Args:
time: int32 `Tensor` scalar.
sequence_length: int32 `Tensor` vector of size [batch_size].
min_sequence_length: int32 `Tensor` scalar, min of sequence_length.
max_sequence_length: int32 `Tensor` scalar, max of sequence_length.
zero_output: `Tensor` vector of shape [output_size].
state: Either a single `Tensor` matrix of shape `[batch_size, state_size]`,
or a list/tuple of such tensors.
call_cell: lambda returning tuple of (new_output, new_state) where
new_output is a `Tensor` matrix of shape `[batch_size, output_size]`.
new_state is a `Tensor` matrix of shape `[batch_size, state_size]`.
state_size: The `cell.state_size` associated with the state.
skip_conditionals: Python bool, whether to skip using the conditional
calculations. This is useful for `dynamic_rnn`, where the input tensor
matches `max_sequence_length`, and using conditionals just slows
everything down.
Returns:
A tuple of (`final_output`, `final_state`) as given by the pseudocode above:
final_output is a `Tensor` matrix of shape [batch_size, output_size]
final_state is either a single `Tensor` matrix, or a tuple of such
matrices (matching length and shapes of input `state`).
Raises:
ValueError: If the cell returns a state tuple whose length does not match
that returned by `state_size`.
"""
# Convert state to a list for ease of use
flat_state = nest.flatten(state)
flat_zero_output = nest.flatten(zero_output)
# Vector describing which batch entries are finished.
copy_cond = time >= sequence_length
def _copy_one_through(output, new_output):
# TensorArray and scalar get passed through.
if isinstance(output, tensor_array_ops.TensorArray):
return new_output
if output.shape.ndims == 0:
return new_output
# Otherwise propagate the old or the new value.
with ops.colocate_with(new_output):
return array_ops.where(copy_cond, output, new_output)
def _copy_some_through(flat_new_output, flat_new_state):
# Use broadcasting select to determine which values should get
# the previous state & zero output, and which values should get
# a calculated state & output.
flat_new_output = [
_copy_one_through(zero_output, new_output)
for zero_output, new_output in zip(flat_zero_output, flat_new_output)]
flat_new_state = [
_copy_one_through(state, new_state)
for state, new_state in zip(flat_state, flat_new_state)]
return flat_new_output + flat_new_state
def _maybe_copy_some_through():
"""Run RNN step. Pass through either no or some past state."""
new_output, new_state = call_cell()
nest.assert_same_structure(state, new_state)
flat_new_state = nest.flatten(new_state)
flat_new_output = nest.flatten(new_output)
return control_flow_ops.cond(
# if t < min_seq_len: calculate and return everything
time < min_sequence_length, lambda: flat_new_output + flat_new_state,
# else copy some of it through
lambda: _copy_some_through(flat_new_output, flat_new_state))
# TODO(ebrevdo): skipping these conditionals may cause a slowdown,
# but benefits from removing cond() and its gradient. We should
# profile with and without this switch here.
if skip_conditionals:
# Instead of using conditionals, perform the selective copy at all time
# steps. This is faster when max_seq_len is equal to the number of unrolls
# (which is typical for dynamic_rnn).
new_output, new_state = call_cell()
nest.assert_same_structure(state, new_state)
new_state = nest.flatten(new_state)
new_output = nest.flatten(new_output)
final_output_and_state = _copy_some_through(new_output, new_state)
else:
empty_update = lambda: flat_zero_output + flat_state
final_output_and_state = control_flow_ops.cond(
# if t >= max_seq_len: copy all state through, output zeros
time >= max_sequence_length, empty_update,
# otherwise calculation is required: copy some or all of it through
_maybe_copy_some_through)
if len(final_output_and_state) != len(flat_zero_output) + len(flat_state):
raise ValueError("Internal error: state and output were not concatenated "
"correctly.")
final_output = final_output_and_state[:len(flat_zero_output)]
final_state = final_output_and_state[len(flat_zero_output):]
for output, flat_output in zip(final_output, flat_zero_output):
output.set_shape(flat_output.get_shape())
for substate, flat_substate in zip(final_state, flat_state):
if not isinstance(substate, tensor_array_ops.TensorArray):
substate.set_shape(flat_substate.get_shape())
final_output = nest.pack_sequence_as(
structure=zero_output, flat_sequence=final_output)
final_state = nest.pack_sequence_as(
structure=state, flat_sequence=final_state)
return final_output, final_state
def _reverse_seq(input_seq, lengths):
"""Reverse a list of Tensors up to specified lengths.
Args:
input_seq: Sequence of seq_len tensors of dimension (batch_size, n_features)
or nested tuples of tensors.
lengths: A `Tensor` of dimension batch_size, containing lengths for each
sequence in the batch. If "None" is specified, simply reverses
the list.
Returns:
time-reversed sequence
"""
if lengths is None:
return list(reversed(input_seq))
flat_input_seq = tuple(nest.flatten(input_) for input_ in input_seq)
flat_results = [[] for _ in range(len(input_seq))]
for sequence in zip(*flat_input_seq):
input_shape = tensor_shape.unknown_shape(
ndims=sequence[0].get_shape().ndims)
for input_ in sequence:
input_shape.merge_with(input_.get_shape())
input_.set_shape(input_shape)
# Join into (time, batch_size, depth)
s_joined = array_ops.stack(sequence)
# Reverse along dimension 0
s_reversed = array_ops.reverse_sequence(s_joined, lengths, 0, 1)
# Split again into list
result = array_ops.unstack(s_reversed)
for r, flat_result in zip(result, flat_results):
r.set_shape(input_shape)
flat_result.append(r)
results = [nest.pack_sequence_as(structure=input_, flat_sequence=flat_result)
for input_, flat_result in zip(input_seq, flat_results)]
return results
@tf_export("nn.bidirectional_dynamic_rnn")
def bidirectional_dynamic_rnn(cell_fw, cell_bw, inputs, sequence_length=None,
initial_state_fw=None, initial_state_bw=None,
dtype=None, parallel_iterations=None,
swap_memory=False, time_major=False, scope=None):
"""Creates a dynamic version of bidirectional recurrent neural network.
Takes input and builds independent forward and backward RNNs. The input_size
of forward and backward cell must match. The initial state for both directions
is zero by default (but can be set optionally) and no intermediate states are
ever returned -- the network is fully unrolled for the given (passed in)
length(s) of the sequence(s) or completely unrolled if length(s) is not
given.
Args:
cell_fw: An instance of RNNCell, to be used for forward direction.
cell_bw: An instance of RNNCell, to be used for backward direction.
inputs: The RNN inputs.
If time_major == False (default), this must be a tensor of shape:
`[batch_size, max_time, ...]`, or a nested tuple of such elements.
If time_major == True, this must be a tensor of shape:
`[max_time, batch_size, ...]`, or a nested tuple of such elements.
sequence_length: (optional) An int32/int64 vector, size `[batch_size]`,
containing the actual lengths for each of the sequences in the batch.
If not provided, all batch entries are assumed to be full sequences; and
time reversal is applied from time `0` to `max_time` for each sequence.
initial_state_fw: (optional) An initial state for the forward RNN.
This must be a tensor of appropriate type and shape
`[batch_size, cell_fw.state_size]`.
If `cell_fw.state_size` is a tuple, this should be a tuple of
tensors having shapes `[batch_size, s] for s in cell_fw.state_size`.
initial_state_bw: (optional) Same as for `initial_state_fw`, but using
the corresponding properties of `cell_bw`.
dtype: (optional) The data type for the initial states and expected output.
Required if initial_states are not provided or RNN states have a
heterogeneous dtype.
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.
time_major: The shape format of the `inputs` and `outputs` Tensors.
If true, these `Tensors` must be shaped `[max_time, batch_size, depth]`.
If false, these `Tensors` must be shaped `[batch_size, max_time, depth]`.
Using `time_major = True` is a bit more efficient because it avoids
transposes at the beginning and end of the RNN calculation. However,
most TensorFlow data is batch-major, so by default this function
accepts input and emits output in batch-major form.
scope: VariableScope for the created subgraph; defaults to
"bidirectional_rnn"
Returns:
A tuple (outputs, output_states) where:
outputs: A tuple (output_fw, output_bw) containing the forward and
the backward rnn output `Tensor`.
If time_major == False (default),
output_fw will be a `Tensor` shaped:
`[batch_size, max_time, cell_fw.output_size]`
and output_bw will be a `Tensor` shaped:
`[batch_size, max_time, cell_bw.output_size]`.
If time_major == True,
output_fw will be a `Tensor` shaped:
`[max_time, batch_size, cell_fw.output_size]`
and output_bw will be a `Tensor` shaped:
`[max_time, batch_size, cell_bw.output_size]`.
It returns a tuple instead of a single concatenated `Tensor`, unlike
in the `bidirectional_rnn`. If the concatenated one is preferred,
the forward and backward outputs can be concatenated as
`tf.concat(outputs, 2)`.
output_states: A tuple (output_state_fw, output_state_bw) containing
the forward and the backward final states of bidirectional rnn.
Raises:
TypeError: If `cell_fw` or `cell_bw` is not an instance of `RNNCell`.
"""
rnn_cell_impl.assert_like_rnncell("cell_fw", cell_fw)
rnn_cell_impl.assert_like_rnncell("cell_bw", cell_bw)
with vs.variable_scope(scope or "bidirectional_rnn"):
# Forward direction
with vs.variable_scope("fw") as fw_scope:
output_fw, output_state_fw = dynamic_rnn(
cell=cell_fw, inputs=inputs, sequence_length=sequence_length,
initial_state=initial_state_fw, dtype=dtype,
parallel_iterations=parallel_iterations, swap_memory=swap_memory,
time_major=time_major, scope=fw_scope)
# Backward direction
if not time_major:
time_axis = 1
batch_axis = 0
else:
time_axis = 0
batch_axis = 1
def _reverse(input_, seq_lengths, seq_axis, batch_axis):
if seq_lengths is not None:
return array_ops.reverse_sequence(
input=input_, seq_lengths=seq_lengths,
seq_axis=seq_axis, batch_axis=batch_axis)
else:
return array_ops.reverse(input_, axis=[seq_axis])
with vs.variable_scope("bw") as bw_scope:
def _map_reverse(inp):
return _reverse(
inp,
seq_lengths=sequence_length,
seq_axis=time_axis,
batch_axis=batch_axis)
inputs_reverse = nest.map_structure(_map_reverse, inputs)
tmp, output_state_bw = dynamic_rnn(
cell=cell_bw, inputs=inputs_reverse, sequence_length=sequence_length,
initial_state=initial_state_bw, dtype=dtype,
parallel_iterations=parallel_iterations, swap_memory=swap_memory,
time_major=time_major, scope=bw_scope)
output_bw = _reverse(
tmp, seq_lengths=sequence_length,
seq_axis=time_axis, batch_axis=batch_axis)
outputs = (output_fw, output_bw)
output_states = (output_state_fw, output_state_bw)
return (outputs, output_states)
@tf_export("nn.dynamic_rnn")
def dynamic_rnn(cell, inputs, sequence_length=None, initial_state=None,
dtype=None, parallel_iterations=None, swap_memory=False,
time_major=False, scope=None):
"""Creates a recurrent neural network specified by RNNCell `cell`.
Performs fully dynamic unrolling of `inputs`.
Example:
```python
# create a BasicRNNCell
rnn_cell = tf.nn.rnn_cell.BasicRNNCell(hidden_size)
# 'outputs' is a tensor of shape [batch_size, max_time, cell_state_size]
# defining initial state
initial_state = rnn_cell.zero_state(batch_size, dtype=tf.float32)
# 'state' is a tensor of shape [batch_size, cell_state_size]
outputs, state = tf.nn.dynamic_rnn(rnn_cell, input_data,
initial_state=initial_state,
dtype=tf.float32)
```
```python
# create 2 LSTMCells
rnn_layers = [tf.nn.rnn_cell.LSTMCell(size) for size in [128, 256]]
# create a RNN cell composed sequentially of a number of RNNCells
multi_rnn_cell = tf.nn.rnn_cell.MultiRNNCell(rnn_layers)
# 'outputs' is a tensor of shape [batch_size, max_time, 256]
# 'state' is a N-tuple where N is the number of LSTMCells containing a
# tf.contrib.rnn.LSTMStateTuple for each cell
outputs, state = tf.nn.dynamic_rnn(cell=multi_rnn_cell,
inputs=data,
dtype=tf.float32)
```
Args:
cell: An instance of RNNCell.
inputs: The RNN inputs.
If `time_major == False` (default), this must be a `Tensor` of shape:
`[batch_size, max_time, ...]`, or a nested tuple of such
elements.
If `time_major == True`, this must be a `Tensor` of shape:
`[max_time, batch_size, ...]`, or a nested tuple of such
elements.
This may also be a (possibly nested) tuple of Tensors satisfying
this property. The first two dimensions must match across all the inputs,
but otherwise the ranks and other shape components may differ.
In this case, input to `cell` at each time-step will replicate the
structure of these tuples, except for the time dimension (from which the
time is taken).
The input to `cell` at each time step will be a `Tensor` or (possibly
nested) tuple of Tensors each with dimensions `[batch_size, ...]`.
sequence_length: (optional) An int32/int64 vector sized `[batch_size]`.
Used to copy-through state and zero-out outputs when past a batch
element's sequence length. So it's more for performance than correctness.
initial_state: (optional) An initial state for the RNN.
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 tuple, this should be a tuple of
tensors having shapes `[batch_size, s] for s in cell.state_size`.
dtype: (optional) The data type for the initial state and expected output.
Required if initial_state is not provided or RNN state has a heterogeneous
dtype.
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.
time_major: The shape format of the `inputs` and `outputs` Tensors.
If true, these `Tensors` must be shaped `[max_time, batch_size, depth]`.
If false, these `Tensors` must be shaped `[batch_size, max_time, depth]`.
Using `time_major = True` is a bit more efficient because it avoids
transposes at the beginning and end of the RNN calculation. However,
most TensorFlow data is batch-major, so by default this function
accepts input and emits output in batch-major form.
scope: VariableScope for the created subgraph; defaults to "rnn".
Returns:
A pair (outputs, state) where:
outputs: The RNN output `Tensor`.
If time_major == False (default), this will be a `Tensor` shaped:
`[batch_size, max_time, cell.output_size]`.
If time_major == True, this will be a `Tensor` shaped:
`[max_time, batch_size, cell.output_size]`.
Note, if `cell.output_size` is a (possibly nested) tuple of integers
or `TensorShape` objects, then `outputs` will be a tuple having the
same structure as `cell.output_size`, containing Tensors having shapes
corresponding to the shape data in `cell.output_size`.
state: The final 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. If cells are `LSTMCells`
`state` will be a tuple containing a `LSTMStateTuple` for each cell.
Raises:
TypeError: If `cell` is not an instance of RNNCell.
ValueError: If inputs is None or an empty list.
"""
rnn_cell_impl.assert_like_rnncell("cell", cell)
with vs.variable_scope(scope or "rnn") as varscope:
# Create a new scope in which the caching device is either
# determined by the parent scope, or is set to place the cached
# Variable using the same placement as for the rest of the RNN.
if _should_cache():
if varscope.caching_device is None:
varscope.set_caching_device(lambda op: op.device)
# By default, time_major==False and inputs are batch-major: shaped
# [batch, time, depth]
# For internal calculations, we transpose to [time, batch, depth]
flat_input = nest.flatten(inputs)
if not time_major:
# (B,T,D) => (T,B,D)
flat_input = [ops.convert_to_tensor(input_) for input_ in flat_input]
flat_input = tuple(_transpose_batch_time(input_) for input_ in flat_input)
parallel_iterations = parallel_iterations or 32
if sequence_length is not None:
sequence_length = math_ops.to_int32(sequence_length)
if sequence_length.get_shape().ndims not in (None, 1):
raise ValueError(
"sequence_length must be a vector of length batch_size, "
"but saw shape: %s" % sequence_length.get_shape())
sequence_length = array_ops.identity( # Just to find it in the graph.
sequence_length, name="sequence_length")
batch_size = _best_effort_input_batch_size(flat_input)
if initial_state is not None:
state = initial_state
else:
if not dtype:
raise ValueError("If there is no initial_state, you must give a dtype.")
if getattr(cell, "get_initial_state", None) is not None:
state = cell.get_initial_state(
inputs=None, batch_size=batch_size, dtype=dtype)
else:
state = cell.zero_state(batch_size, dtype)
def _assert_has_shape(x, shape):
x_shape = array_ops.shape(x)
packed_shape = array_ops.stack(shape)
return control_flow_ops.Assert(
math_ops.reduce_all(math_ops.equal(x_shape, packed_shape)),
["Expected shape for Tensor %s is " % x.name,
packed_shape, " but saw shape: ", x_shape])
if not context.executing_eagerly() and sequence_length is not None:
# Perform some shape validation
with ops.control_dependencies(
[_assert_has_shape(sequence_length, [batch_size])]):
sequence_length = array_ops.identity(
sequence_length, name="CheckSeqLen")
inputs = nest.pack_sequence_as(structure=inputs, flat_sequence=flat_input)
(outputs, final_state) = _dynamic_rnn_loop(
cell,
inputs,
state,
parallel_iterations=parallel_iterations,
swap_memory=swap_memory,
sequence_length=sequence_length,
dtype=dtype)
# Outputs of _dynamic_rnn_loop are always shaped [time, batch, depth].
# If we are performing batch-major calculations, transpose output back
# to shape [batch, time, depth]
if not time_major:
# (T,B,D) => (B,T,D)
outputs = nest.map_structure(_transpose_batch_time, outputs)
return (outputs, final_state)
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]
for shape in inputs_got_shape:
if not shape[2:].is_fully_defined():
raise ValueError(
"Input size (depth of inputs) must be accessible via shape inference,"
" but saw value None.")
got_time_steps = shape[0].value
got_batch_size = shape[1].value
if const_time_steps != got_time_steps:
raise ValueError(
"Time steps is not the same for all the elements in the input in a "
"batch.")
if const_batch_size != got_batch_size:
raise ValueError(
"Batch_size is not the same for all the elements in the input.")
# Prepare dynamic conditional copying of state & output
def _create_zero_arrays(size):
size = _concat(batch_size, size)
return array_ops.zeros(
array_ops.stack(size), _infer_state_dtype(dtype, state))
flat_zero_output = tuple(_create_zero_arrays(output)
for output in flat_output_size)
zero_output = nest.pack_sequence_as(structure=cell.output_size,
flat_sequence=flat_zero_output)
if sequence_length is not None:
min_sequence_length = math_ops.reduce_min(sequence_length)
max_sequence_length = math_ops.reduce_max(sequence_length)
else:
max_sequence_length = time_steps
time = array_ops.constant(0, dtype=dtypes.int32, name="time")
with ops.name_scope("dynamic_rnn") as scope:
base_name = scope
def _create_ta(name, element_shape, dtype):
return tensor_array_ops.TensorArray(dtype=dtype,
size=time_steps,
element_shape=element_shape,
tensor_array_name=base_name + name)
in_graph_mode = not context.executing_eagerly()
if in_graph_mode:
output_ta = tuple(
_create_ta(
"output_%d" % i,
element_shape=(tensor_shape.TensorShape([const_batch_size])
.concatenate(
_maybe_tensor_shape_from_tensor(out_size))),
dtype=_infer_state_dtype(dtype, state))
for i, out_size in enumerate(flat_output_size))
input_ta = tuple(
_create_ta(
"input_%d" % i,
element_shape=flat_input_i.shape[1:],
dtype=flat_input_i.dtype)
for i, flat_input_i in enumerate(flat_input))
input_ta = tuple(ta.unstack(input_)
for ta, input_ in zip(input_ta, flat_input))
else:
output_ta = tuple([0 for _ in range(time_steps.numpy())]
for i in range(len(flat_output_size)))
input_ta = flat_input
def _time_step(time, output_ta_t, state):
"""Take a time step of the dynamic RNN.
Args:
time: int32 scalar Tensor.
output_ta_t: List of `TensorArray`s that represent the output.
state: nested tuple of vector tensors that represent the state.
Returns:
The tuple (time + 1, output_ta_t with updated flow, new_state).
"""
if in_graph_mode:
input_t = tuple(ta.read(time) for ta in input_ta)
# Restore some shape information
for input_, shape in zip(input_t, inputs_got_shape):
input_.set_shape(shape[1:])
else:
input_t = tuple(ta[time.numpy()] for ta in input_ta)
input_t = nest.pack_sequence_as(structure=inputs, flat_sequence=input_t)
# Keras RNN cells only accept state as list, even if it's a single tensor.
is_keras_rnn_cell = _is_keras_rnn_cell(cell)
if is_keras_rnn_cell and not nest.is_sequence(state):
state = [state]
call_cell = lambda: cell(input_t, state)
if sequence_length is not None:
(output, new_state) = _rnn_step(
time=time,
sequence_length=sequence_length,
min_sequence_length=min_sequence_length,
max_sequence_length=max_sequence_length,
zero_output=zero_output,
state=state,
call_cell=call_cell,
state_size=state_size,
skip_conditionals=True)
else:
(output, new_state) = call_cell()
# Keras cells always wrap state as list, even if it's a single tensor.
if is_keras_rnn_cell and len(new_state) == 1:
new_state = new_state[0]
# Pack state if using state tuples
output = nest.flatten(output)
if in_graph_mode:
output_ta_t = tuple(
ta.write(time, out) for ta, out in zip(output_ta_t, output))
else:
for ta, out in zip(output_ta_t, output):
ta[time.numpy()] = out
return (time + 1, output_ta_t, new_state)
if in_graph_mode:
# Make sure that we run at least 1 step, if necessary, to ensure
# the TensorArrays pick up the dynamic shape.
loop_bound = math_ops.minimum(
time_steps, math_ops.maximum(1, max_sequence_length))
else:
# Using max_sequence_length isn't currently supported in the Eager branch.
loop_bound = time_steps
_, output_final_ta, final_state = control_flow_ops.while_loop(
cond=lambda time, *_: time < loop_bound,
body=_time_step,
loop_vars=(time, output_ta, state),
parallel_iterations=parallel_iterations,
maximum_iterations=time_steps,
swap_memory=swap_memory)
# Unpack final output if not using output tuples.
if in_graph_mode:
final_outputs = tuple(ta.stack() for ta in output_final_ta)
# Restore some shape information
for output, output_size in zip(final_outputs, flat_output_size):
shape = _concat(
[const_time_steps, const_batch_size], output_size, static=True)
output.set_shape(shape)
else:
final_outputs = output_final_ta
final_outputs = nest.pack_sequence_as(
structure=cell.output_size, flat_sequence=final_outputs)
if not in_graph_mode:
final_outputs = nest.map_structure_up_to(
cell.output_size, lambda x: array_ops.stack(x, axis=0), final_outputs)
return (final_outputs, final_state)
@tf_export("nn.raw_rnn")
def raw_rnn(cell, loop_fn,
parallel_iterations=None, swap_memory=False, scope=None):
"""Creates an `RNN` specified by RNNCell `cell` and loop function `loop_fn`.
**NOTE: This method is still in testing, and the API may change.**
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:
```python
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,
loop_state=loop_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:
```python
inputs = tf.placeholder(shape=(max_time, batch_size, input_depth),
dtype=tf.float32)
sequence_length = tf.placeholder(shape=(batch_size,), dtype=tf.int32)
inputs_ta = tf.TensorArray(dtype=tf.float32, size=max_time)
inputs_ta = inputs_ta.unstack(inputs)
cell = tf.contrib.rnn.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)
else:
next_cell_state = cell_state
elements_finished = (time >= sequence_length)
finished = tf.reduce_all(elements_finished)
next_input = tf.cond(
finished,
lambda: tf.zeros([batch_size, input_depth], dtype=tf.float32),
lambda: inputs_ta.read(time))
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()
```
Args:
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.
scope: VariableScope for the created subgraph; defaults to "rnn".
Returns:
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`.
Raises:
TypeError: If `cell` is not an instance of RNNCell, or `loop_fn` is not
a `callable`.
"""
rnn_cell_impl.assert_like_rnncell("cell", cell)
if not callable(loop_fn):
raise TypeError("loop_fn must be a callable")
parallel_iterations = parallel_iterations or 32
# Create a new scope in which the caching device is either
# determined by the parent scope, or is set to place the cached
# Variable using the same placement as for the rest of the RNN.
with vs.variable_scope(scope or "rnn") as varscope:
if _should_cache():
if varscope.caching_device is None:
varscope.set_caching_device(lambda op: op.device)
time = constant_op.constant(0, dtype=dtypes.int32)
(elements_finished, next_input, initial_state, emit_structure,
init_loop_state) = loop_fn(
time, None, None, None) # time, cell_output, cell_state, loop_state
flat_input = nest.flatten(next_input)
# Need a surrogate loop state for the while_loop if none is available.
loop_state = (init_loop_state if init_loop_state is not None
else constant_op.constant(0, dtype=dtypes.int32))
input_shape = [input_.get_shape() for input_ in flat_input]
static_batch_size = input_shape[0][0]
for input_shape_i in input_shape:
# Static verification that batch sizes all match
static_batch_size.merge_with(input_shape_i[0])
batch_size = static_batch_size.value
const_batch_size = batch_size
if batch_size is None:
batch_size = array_ops.shape(flat_input[0])[0]
nest.assert_same_structure(initial_state, cell.state_size)
state = initial_state
flat_state = nest.flatten(state)
flat_state = [ops.convert_to_tensor(s) for s in flat_state]
state = nest.pack_sequence_as(structure=state,
flat_sequence=flat_state)
if emit_structure is not None:
flat_emit_structure = nest.flatten(emit_structure)
flat_emit_size = [emit.shape if emit.shape.is_fully_defined() else
array_ops.shape(emit) for emit in flat_emit_structure]
flat_emit_dtypes = [emit.dtype for emit in flat_emit_structure]
else:
emit_structure = cell.output_size
flat_emit_size = nest.flatten(emit_structure)
flat_emit_dtypes = [flat_state[0].dtype] * len(flat_emit_size)
flat_emit_ta = [
tensor_array_ops.TensorArray(
dtype=dtype_i,
dynamic_size=True,
element_shape=(tensor_shape.TensorShape([const_batch_size])
.concatenate(
_maybe_tensor_shape_from_tensor(size_i))),
size=0,
name="rnn_output_%d" % i)
for i, (dtype_i, size_i)
in enumerate(zip(flat_emit_dtypes, flat_emit_size))]
emit_ta = nest.pack_sequence_as(structure=emit_structure,
flat_sequence=flat_emit_ta)
flat_zero_emit = [
array_ops.zeros(_concat(batch_size, size_i), dtype_i)
for size_i, dtype_i in zip(flat_emit_size, flat_emit_dtypes)]
zero_emit = nest.pack_sequence_as(structure=emit_structure,
flat_sequence=flat_zero_emit)
def condition(unused_time, elements_finished, *_):
return math_ops.logical_not(math_ops.reduce_all(elements_finished))
def body(time, elements_finished, current_input,
emit_ta, state, loop_state):
"""Internal while loop body for raw_rnn.
Args:
time: time scalar.
elements_finished: batch-size vector.
current_input: possibly nested tuple of input tensors.
emit_ta: possibly nested tuple of output TensorArrays.
state: possibly nested tuple of state tensors.
loop_state: possibly nested tuple of loop state tensors.
Returns:
Tuple having the same size as Args but with updated values.
"""
(next_output, cell_state) = cell(current_input, state)
nest.assert_same_structure(state, cell_state)
nest.assert_same_structure(cell.output_size, next_output)
next_time = time + 1
(next_finished, next_input, next_state, emit_output,
next_loop_state) = loop_fn(
next_time, next_output, cell_state, loop_state)
nest.assert_same_structure(state, next_state)
nest.assert_same_structure(current_input, next_input)
nest.assert_same_structure(emit_ta, emit_output)
# If loop_fn returns None for next_loop_state, just reuse the
# previous one.
loop_state = loop_state if next_loop_state is None else next_loop_state
def _copy_some_through(current, candidate):
"""Copy some tensors through via array_ops.where."""
def copy_fn(cur_i, cand_i):
# TensorArray and scalar get passed through.
if isinstance(cur_i, tensor_array_ops.TensorArray):
return cand_i
if cur_i.shape.ndims == 0:
return cand_i
# Otherwise propagate the old or the new value.
with ops.colocate_with(cand_i):
return array_ops.where(elements_finished, cur_i, cand_i)
return nest.map_structure(copy_fn, current, candidate)
emit_output = _copy_some_through(zero_emit, emit_output)
next_state = _copy_some_through(state, next_state)
emit_ta = nest.map_structure(
lambda ta, emit: ta.write(time, emit), emit_ta, emit_output)
elements_finished = math_ops.logical_or(elements_finished, next_finished)
return (next_time, elements_finished, next_input,
emit_ta, next_state, loop_state)
returned = control_flow_ops.while_loop(
condition, body, loop_vars=[
time, elements_finished, next_input,
emit_ta, state, loop_state],
parallel_iterations=parallel_iterations,
swap_memory=swap_memory)
(emit_ta, final_state, final_loop_state) = returned[-3:]
if init_loop_state is None:
final_loop_state = None
return (emit_ta, final_state, final_loop_state)
@tf_export("nn.static_rnn")
def static_rnn(cell,
inputs,
initial_state=None,
dtype=None,
sequence_length=None,
scope=None):
"""Creates a recurrent neural network specified by RNNCell `cell`.
The simplest form of RNN network generated is:
```python
state = cell.zero_state(...)
outputs = []
for input_ in inputs:
output, state = cell(input_, state)
outputs.append(output)
return (outputs, state)
```
However, a few other options are available:
An initial state can be provided.
If the sequence_length vector is provided, dynamic calculation is performed.
This method of calculation does not compute the RNN steps past the maximum
sequence length of the minibatch (thus saving computational time),
and properly propagates the state at an example's sequence length
to the final state output.
The dynamic calculation performed is, at time `t` for batch row `b`,
```python
(output, state)(b, t) =
(t >= sequence_length(b))
? (zeros(cell.output_size), states(b, sequence_length(b) - 1))
: cell(input(b, t), state(b, t - 1))
```
Args:
cell: An instance of RNNCell.
inputs: A length T list of inputs, each a `Tensor` of shape
`[batch_size, input_size]`, or a nested tuple of such elements.
initial_state: (optional) An initial state for the RNN.
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 tuple, this should be a tuple of
tensors having shapes `[batch_size, s] for s in cell.state_size`.
dtype: (optional) The data type for the initial state and expected output.
Required if initial_state is not provided or RNN state has a heterogeneous
dtype.
sequence_length: Specifies the length of each sequence in inputs.
An int32 or int64 vector (tensor) size `[batch_size]`, values in `[0, T)`.
scope: VariableScope for the created subgraph; defaults to "rnn".
Returns:
A pair (outputs, state) where:
- outputs is a length T list of outputs (one for each input), or a nested
tuple of such elements.
- state is the final state
Raises:
TypeError: If `cell` is not an instance of RNNCell.
ValueError: If `inputs` is `None` or an empty list, or if the input depth
(column size) cannot be inferred from inputs via shape inference.
"""
rnn_cell_impl.assert_like_rnncell("cell", cell)
if not nest.is_sequence(inputs):
raise TypeError("inputs must be a sequence")
if not inputs:
raise ValueError("inputs must not be empty")
outputs = []
# Create a new scope in which the caching device is either
# determined by the parent scope, or is set to place the cached
# Variable using the same placement as for the rest of the RNN.
with vs.variable_scope(scope or "rnn") as varscope:
if _should_cache():
if varscope.caching_device is None:
varscope.set_caching_device(lambda op: op.device)
# Obtain the first sequence of the input
first_input = inputs
while nest.is_sequence(first_input):
first_input = first_input[0]
# Temporarily avoid EmbeddingWrapper and seq2seq badness
# TODO(lukaszkaiser): remove EmbeddingWrapper
if first_input.get_shape().ndims != 1:
input_shape = first_input.get_shape().with_rank_at_least(2)
fixed_batch_size = input_shape[0]
flat_inputs = nest.flatten(inputs)
for flat_input in flat_inputs:
input_shape = flat_input.get_shape().with_rank_at_least(2)
batch_size, input_size = input_shape[0], input_shape[1:]
fixed_batch_size.merge_with(batch_size)
for i, size in enumerate(input_size):
if size.value is None:
raise ValueError(
"Input size (dimension %d of inputs) must be accessible via "
"shape inference, but saw value None." % i)
else:
fixed_batch_size = first_input.get_shape().with_rank_at_least(1)[0]
if fixed_batch_size.value:
batch_size = fixed_batch_size.value
else:
batch_size = array_ops.shape(first_input)[0]
if initial_state is not None:
state = initial_state
else:
if not dtype:
raise ValueError("If no initial_state is provided, "
"dtype must be specified")
if getattr(cell, "get_initial_state", None) is not None:
state = cell.get_initial_state(
inputs=None, batch_size=batch_size, dtype=dtype)
else:
state = cell.zero_state(batch_size, dtype)
if sequence_length is not None: # Prepare variables
sequence_length = ops.convert_to_tensor(
sequence_length, name="sequence_length")
if sequence_length.get_shape().ndims not in (None, 1):
raise ValueError(
"sequence_length must be a vector of length batch_size")
def _create_zero_output(output_size):
# convert int to TensorShape if necessary
size = _concat(batch_size, output_size)
output = array_ops.zeros(
array_ops.stack(size), _infer_state_dtype(dtype, state))
shape = _concat(fixed_batch_size.value, output_size, static=True)
output.set_shape(tensor_shape.TensorShape(shape))
return output
output_size = cell.output_size
flat_output_size = nest.flatten(output_size)
flat_zero_output = tuple(
_create_zero_output(size) for size in flat_output_size)
zero_output = nest.pack_sequence_as(
structure=output_size, flat_sequence=flat_zero_output)
sequence_length = math_ops.to_int32(sequence_length)
min_sequence_length = math_ops.reduce_min(sequence_length)
max_sequence_length = math_ops.reduce_max(sequence_length)
# Keras RNN cells only accept state as list, even if it's a single tensor.
is_keras_rnn_cell = _is_keras_rnn_cell(cell)
if is_keras_rnn_cell and not nest.is_sequence(state):
state = [state]
for time, input_ in enumerate(inputs):
if time > 0:
varscope.reuse_variables()
# pylint: disable=cell-var-from-loop
call_cell = lambda: cell(input_, state)
# pylint: enable=cell-var-from-loop
if sequence_length is not None:
(output, state) = _rnn_step(
time=time,
sequence_length=sequence_length,
min_sequence_length=min_sequence_length,
max_sequence_length=max_sequence_length,
zero_output=zero_output,
state=state,
call_cell=call_cell,
state_size=cell.state_size)
else:
(output, state) = call_cell()
outputs.append(output)
# Keras RNN cells only return state as list, even if it's a single tensor.
if is_keras_rnn_cell and len(state) == 1:
state = state[0]
return (outputs, state)
@tf_export("nn.static_state_saving_rnn")
def static_state_saving_rnn(cell,
inputs,
state_saver,
state_name,
sequence_length=None,
scope=None):
"""RNN that accepts a state saver for time-truncated RNN calculation.
Args:
cell: An instance of `RNNCell`.
inputs: A length T list of inputs, each a `Tensor` of shape
`[batch_size, input_size]`.
state_saver: A state saver object with methods `state` and `save_state`.
state_name: Python string or tuple of strings. The name to use with the
state_saver. If the cell returns tuples of states (i.e.,
`cell.state_size` is a tuple) then `state_name` should be a tuple of
strings having the same length as `cell.state_size`. Otherwise it should
be a single string.
sequence_length: (optional) An int32/int64 vector size [batch_size].
See the documentation for rnn() for more details about sequence_length.
scope: VariableScope for the created subgraph; defaults to "rnn".
Returns:
A pair (outputs, state) where:
outputs is a length T list of outputs (one for each input)
states is the final state
Raises:
TypeError: If `cell` is not an instance of RNNCell.
ValueError: If `inputs` is `None` or an empty list, or if the arity and
type of `state_name` does not match that of `cell.state_size`.
"""
state_size = cell.state_size
state_is_tuple = nest.is_sequence(state_size)
state_name_tuple = nest.is_sequence(state_name)
if state_is_tuple != state_name_tuple:
raise ValueError("state_name should be the same type as cell.state_size. "
"state_name: %s, cell.state_size: %s" % (str(state_name),
str(state_size)))
if state_is_tuple:
state_name_flat = nest.flatten(state_name)
state_size_flat = nest.flatten(state_size)
if len(state_name_flat) != len(state_size_flat):
raise ValueError("#elems(state_name) != #elems(state_size): %d vs. %d" %
(len(state_name_flat), len(state_size_flat)))
initial_state = nest.pack_sequence_as(
structure=state_size,
flat_sequence=[state_saver.state(s) for s in state_name_flat])
else:
initial_state = state_saver.state(state_name)
(outputs, state) = static_rnn(
cell,
inputs,
initial_state=initial_state,
sequence_length=sequence_length,
scope=scope)
if state_is_tuple:
flat_state = nest.flatten(state)
state_name = nest.flatten(state_name)
save_state = [
state_saver.save_state(name, substate)
for name, substate in zip(state_name, flat_state)
]
else:
save_state = [state_saver.save_state(state_name, state)]
with ops.control_dependencies(save_state):
last_output = outputs[-1]
flat_last_output = nest.flatten(last_output)
flat_last_output = [
array_ops.identity(output) for output in flat_last_output
]
outputs[-1] = nest.pack_sequence_as(
structure=last_output, flat_sequence=flat_last_output)
if state_is_tuple:
state = nest.pack_sequence_as(
structure=state,
flat_sequence=[array_ops.identity(s) for s in flat_state])
else:
state = array_ops.identity(state)
return (outputs, state)
@tf_export("nn.static_bidirectional_rnn")
def static_bidirectional_rnn(cell_fw,
cell_bw,
inputs,
initial_state_fw=None,
initial_state_bw=None,
dtype=None,
sequence_length=None,
scope=None):
"""Creates a bidirectional recurrent neural network.
Similar to the unidirectional case above (rnn) but takes input and builds
independent forward and backward RNNs with the final forward and backward
outputs depth-concatenated, such that the output will have the format
[time][batch][cell_fw.output_size + cell_bw.output_size]. The input_size of
forward and backward cell must match. The initial state for both directions
is zero by default (but can be set optionally) and no intermediate states are
ever returned -- the network is fully unrolled for the given (passed in)
length(s) of the sequence(s) or completely unrolled if length(s) is not given.
Args:
cell_fw: An instance of RNNCell, to be used for forward direction.
cell_bw: An instance of RNNCell, to be used for backward direction.
inputs: A length T list of inputs, each a tensor of shape
[batch_size, input_size], or a nested tuple of such elements.
initial_state_fw: (optional) An initial state for the forward RNN.
This must be a tensor of appropriate type and shape
`[batch_size, cell_fw.state_size]`.
If `cell_fw.state_size` is a tuple, this should be a tuple of
tensors having shapes `[batch_size, s] for s in cell_fw.state_size`.
initial_state_bw: (optional) Same as for `initial_state_fw`, but using
the corresponding properties of `cell_bw`.
dtype: (optional) The data type for the initial state. Required if
either of the initial states are not provided.
sequence_length: (optional) An int32/int64 vector, size `[batch_size]`,
containing the actual lengths for each of the sequences.
scope: VariableScope for the created subgraph; defaults to
"bidirectional_rnn"
Returns:
A tuple (outputs, output_state_fw, output_state_bw) where:
outputs is a length `T` list of outputs (one for each input), which
are depth-concatenated forward and backward outputs.
output_state_fw is the final state of the forward rnn.
output_state_bw is the final state of the backward rnn.
Raises:
TypeError: If `cell_fw` or `cell_bw` is not an instance of `RNNCell`.
ValueError: If inputs is None or an empty list.
"""
rnn_cell_impl.assert_like_rnncell("cell_fw", cell_fw)
rnn_cell_impl.assert_like_rnncell("cell_bw", cell_bw)
if not nest.is_sequence(inputs):
raise TypeError("inputs must be a sequence")
if not inputs:
raise ValueError("inputs must not be empty")
with vs.variable_scope(scope or "bidirectional_rnn"):
# Forward direction
with vs.variable_scope("fw") as fw_scope:
output_fw, output_state_fw = static_rnn(
cell_fw,
inputs,
initial_state_fw,
dtype,
sequence_length,
scope=fw_scope)
# Backward direction
with vs.variable_scope("bw") as bw_scope:
reversed_inputs = _reverse_seq(inputs, sequence_length)
tmp, output_state_bw = static_rnn(
cell_bw,
reversed_inputs,
initial_state_bw,
dtype,
sequence_length,
scope=bw_scope)
output_bw = _reverse_seq(tmp, sequence_length)
# Concat each of the forward/backward outputs
flat_output_fw = nest.flatten(output_fw)
flat_output_bw = nest.flatten(output_bw)
flat_outputs = tuple(
array_ops.concat([fw, bw], 1)
for fw, bw in zip(flat_output_fw, flat_output_bw))
outputs = nest.pack_sequence_as(
structure=output_fw, flat_sequence=flat_outputs)
return (outputs, output_state_fw, output_state_bw)