tensorflow/python/keras/layers/recurrent.py
# 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.
# ==============================================================================
# pylint: disable=protected-access
# pylint: disable=g-classes-have-attributes
"""Recurrent layers and their base classes."""
import collections
import warnings
import numpy as np
from tensorflow.python.distribute import distribute_lib
from tensorflow.python.eager import context
from tensorflow.python.framework import ops
from tensorflow.python.framework import tensor_shape
from tensorflow.python.keras import activations
from tensorflow.python.keras import backend
from tensorflow.python.keras import constraints
from tensorflow.python.keras import initializers
from tensorflow.python.keras import regularizers
from tensorflow.python.keras.engine.base_layer import Layer
from tensorflow.python.keras.engine.input_spec import InputSpec
from tensorflow.python.keras.saving.saved_model import layer_serialization
from tensorflow.python.keras.utils import control_flow_util
from tensorflow.python.keras.utils import generic_utils
from tensorflow.python.keras.utils import tf_utils
from tensorflow.python.ops import array_ops
from tensorflow.python.ops import array_ops_stack
from tensorflow.python.ops import cond
from tensorflow.python.ops import math_ops
from tensorflow.python.ops import state_ops
from tensorflow.python.platform import tf_logging as logging
from tensorflow.python.trackable import base as trackable
from tensorflow.python.util import nest
from tensorflow.tools.docs import doc_controls
RECURRENT_DROPOUT_WARNING_MSG = (
'RNN `implementation=2` is not supported when `recurrent_dropout` is set. '
'Using `implementation=1`.')
class StackedRNNCells(Layer):
"""Wrapper allowing a stack of RNN cells to behave as a single cell.
Used to implement efficient stacked RNNs.
Args:
cells: List of RNN cell instances.
Examples:
```python
batch_size = 3
sentence_max_length = 5
n_features = 2
new_shape = (batch_size, sentence_max_length, n_features)
x = tf.constant(np.reshape(np.arange(30), new_shape), dtype = tf.float32)
rnn_cells = [tf.keras.layers.LSTMCell(128) for _ in range(2)]
stacked_lstm = tf.keras.layers.StackedRNNCells(rnn_cells)
lstm_layer = tf.keras.layers.RNN(stacked_lstm)
result = lstm_layer(x)
```
"""
def __init__(self, cells, **kwargs):
for cell in cells:
if not 'call' in dir(cell):
raise ValueError('All cells must have a `call` method. '
'received cells:', cells)
if not 'state_size' in dir(cell):
raise ValueError('All cells must have a '
'`state_size` attribute. '
'received cells:', cells)
self.cells = cells
# reverse_state_order determines whether the state size will be in a reverse
# order of the cells' state. User might want to set this to True to keep the
# existing behavior. This is only useful when use RNN(return_state=True)
# since the state will be returned as the same order of state_size.
self.reverse_state_order = kwargs.pop('reverse_state_order', False)
if self.reverse_state_order:
logging.warning('reverse_state_order=True in StackedRNNCells will soon '
'be deprecated. Please update the code to work with the '
'natural order of states if you rely on the RNN states, '
'eg RNN(return_state=True).')
super(StackedRNNCells, self).__init__(**kwargs)
@property
def state_size(self):
return tuple(c.state_size for c in
(self.cells[::-1] if self.reverse_state_order else self.cells))
@property
def output_size(self):
if getattr(self.cells[-1], 'output_size', None) is not None:
return self.cells[-1].output_size
elif _is_multiple_state(self.cells[-1].state_size):
return self.cells[-1].state_size[0]
else:
return self.cells[-1].state_size
def get_initial_state(self, inputs=None, batch_size=None, dtype=None):
initial_states = []
for cell in self.cells[::-1] if self.reverse_state_order else self.cells:
get_initial_state_fn = getattr(cell, 'get_initial_state', None)
if get_initial_state_fn:
initial_states.append(get_initial_state_fn(
inputs=inputs, batch_size=batch_size, dtype=dtype))
else:
initial_states.append(_generate_zero_filled_state_for_cell(
cell, inputs, batch_size, dtype))
return tuple(initial_states)
def call(self, inputs, states, constants=None, training=None, **kwargs):
# Recover per-cell states.
state_size = (self.state_size[::-1]
if self.reverse_state_order else self.state_size)
nested_states = nest.pack_sequence_as(state_size, nest.flatten(states))
# Call the cells in order and store the returned states.
new_nested_states = []
for cell, states in zip(self.cells, nested_states):
states = states if nest.is_nested(states) else [states]
# TF cell does not wrap the state into list when there is only one state.
is_tf_rnn_cell = getattr(cell, '_is_tf_rnn_cell', None) is not None
states = states[0] if len(states) == 1 and is_tf_rnn_cell else states
if generic_utils.has_arg(cell.call, 'training'):
kwargs['training'] = training
else:
kwargs.pop('training', None)
# Use the __call__ function for callable objects, eg layers, so that it
# will have the proper name scopes for the ops, etc.
cell_call_fn = cell.__call__ if callable(cell) else cell.call
if generic_utils.has_arg(cell.call, 'constants'):
inputs, states = cell_call_fn(inputs, states,
constants=constants, **kwargs)
else:
inputs, states = cell_call_fn(inputs, states, **kwargs)
new_nested_states.append(states)
return inputs, nest.pack_sequence_as(state_size,
nest.flatten(new_nested_states))
@tf_utils.shape_type_conversion
def build(self, input_shape):
if isinstance(input_shape, list):
input_shape = input_shape[0]
for cell in self.cells:
if isinstance(cell, Layer) and not cell.built:
with backend.name_scope(cell.name):
cell.build(input_shape)
cell.built = True
if getattr(cell, 'output_size', None) is not None:
output_dim = cell.output_size
elif _is_multiple_state(cell.state_size):
output_dim = cell.state_size[0]
else:
output_dim = cell.state_size
input_shape = tuple([input_shape[0]] +
tensor_shape.TensorShape(output_dim).as_list())
self.built = True
def get_config(self):
cells = []
for cell in self.cells:
cells.append(generic_utils.serialize_keras_object(cell))
config = {'cells': cells}
base_config = super(StackedRNNCells, self).get_config()
return dict(list(base_config.items()) + list(config.items()))
@classmethod
def from_config(cls, config, custom_objects=None):
from tensorflow.python.keras.layers import deserialize as deserialize_layer # pylint: disable=g-import-not-at-top
cells = []
for cell_config in config.pop('cells'):
cells.append(
deserialize_layer(cell_config, custom_objects=custom_objects))
return cls(cells, **config)
class RNN(Layer):
"""Base class for recurrent layers.
See [the Keras RNN API guide](https://www.tensorflow.org/guide/keras/rnn)
for details about the usage of RNN API.
Args:
cell: A RNN cell instance or a list of RNN cell instances.
A RNN cell is a class that has:
- A `call(input_at_t, states_at_t)` method, returning
`(output_at_t, states_at_t_plus_1)`. The call method of the
cell can also take the optional argument `constants`, see
section "Note on passing external constants" below.
- A `state_size` attribute. This can be a single integer
(single state) in which case it is the size of the recurrent
state. This can also be a list/tuple of integers (one size per state).
The `state_size` can also be TensorShape or tuple/list of
TensorShape, to represent high dimension state.
- A `output_size` attribute. This can be a single integer or a
TensorShape, which represent the shape of the output. For backward
compatible reason, if this attribute is not available for the
cell, the value will be inferred by the first element of the
`state_size`.
- A `get_initial_state(inputs=None, batch_size=None, dtype=None)`
method that creates a tensor meant to be fed to `call()` as the
initial state, if the user didn't specify any initial state via other
means. The returned initial state should have a shape of
[batch_size, cell.state_size]. The cell might choose to create a
tensor full of zeros, or full of other values based on the cell's
implementation.
`inputs` is the input tensor to the RNN layer, which should
contain the batch size as its shape[0], and also dtype. Note that
the shape[0] might be `None` during the graph construction. Either
the `inputs` or the pair of `batch_size` and `dtype` are provided.
`batch_size` is a scalar tensor that represents the batch size
of the inputs. `dtype` is `tf.DType` that represents the dtype of
the inputs.
For backward compatibility, if this method is not implemented
by the cell, the RNN layer will create a zero filled tensor with the
size of [batch_size, cell.state_size].
In the case that `cell` is a list of RNN cell instances, the cells
will be stacked on top of each other in the RNN, resulting in an
efficient stacked RNN.
return_sequences: Boolean (default `False`). Whether to return the last
output in the output sequence, or the full sequence.
return_state: Boolean (default `False`). Whether to return the last state
in addition to the output.
go_backwards: Boolean (default `False`).
If True, process the input sequence backwards and return the
reversed sequence.
stateful: Boolean (default `False`). If True, the last state
for each sample at index i in a batch will be used as initial
state for the sample of index i in the following batch.
unroll: Boolean (default `False`).
If True, the network will be unrolled, else a symbolic loop will be used.
Unrolling can speed-up a RNN, although it tends to be more
memory-intensive. Unrolling is only suitable for short sequences.
time_major: The shape format of the `inputs` and `outputs` tensors.
If True, the inputs and outputs will be in shape
`(timesteps, batch, ...)`, whereas in the False case, it will be
`(batch, timesteps, ...)`. 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.
zero_output_for_mask: Boolean (default `False`).
Whether the output should use zeros for the masked timesteps. Note that
this field is only used when `return_sequences` is True and mask is
provided. It can useful if you want to reuse the raw output sequence of
the RNN without interference from the masked timesteps, eg, merging
bidirectional RNNs.
Call arguments:
inputs: Input tensor.
mask: Binary tensor of shape `[batch_size, timesteps]` indicating whether
a given timestep should be masked. An individual `True` entry indicates
that the corresponding timestep should be utilized, while a `False`
entry indicates that the corresponding timestep should be ignored.
training: Python boolean indicating whether the layer should behave in
training mode or in inference mode. This argument is passed to the cell
when calling it. This is for use with cells that use dropout.
initial_state: List of initial state tensors to be passed to the first
call of the cell.
constants: List of constant tensors to be passed to the cell at each
timestep.
Input shape:
N-D tensor with shape `[batch_size, timesteps, ...]` or
`[timesteps, batch_size, ...]` when time_major is True.
Output shape:
- If `return_state`: a list of tensors. The first tensor is
the output. The remaining tensors are the last states,
each with shape `[batch_size, state_size]`, where `state_size` could
be a high dimension tensor shape.
- If `return_sequences`: N-D tensor with shape
`[batch_size, timesteps, output_size]`, where `output_size` could
be a high dimension tensor shape, or
`[timesteps, batch_size, output_size]` when `time_major` is True.
- Else, N-D tensor with shape `[batch_size, output_size]`, where
`output_size` could be a high dimension tensor shape.
Masking:
This layer supports masking for input data with a variable number
of timesteps. To introduce masks to your data,
use an [tf.keras.layers.Embedding] layer with the `mask_zero` parameter
set to `True`.
Note on using statefulness in RNNs:
You can set RNN layers to be 'stateful', which means that the states
computed for the samples in one batch will be reused as initial states
for the samples in the next batch. This assumes a one-to-one mapping
between samples in different successive batches.
To enable statefulness:
- Specify `stateful=True` in the layer constructor.
- Specify a fixed batch size for your model, by passing
If sequential model:
`batch_input_shape=(...)` to the first layer in your model.
Else for functional model with 1 or more Input layers:
`batch_shape=(...)` to all the first layers in your model.
This is the expected shape of your inputs
*including the batch size*.
It should be a tuple of integers, e.g. `(32, 10, 100)`.
- Specify `shuffle=False` when calling `fit()`.
To reset the states of your model, call `.reset_states()` on either
a specific layer, or on your entire model.
Note on specifying the initial state of RNNs:
You can specify the initial state of RNN layers symbolically by
calling them with the keyword argument `initial_state`. The value of
`initial_state` should be a tensor or list of tensors representing
the initial state of the RNN layer.
You can specify the initial state of RNN layers numerically by
calling `reset_states` with the keyword argument `states`. The value of
`states` should be a numpy array or list of numpy arrays representing
the initial state of the RNN layer.
Note on passing external constants to RNNs:
You can pass "external" constants to the cell using the `constants`
keyword argument of `RNN.__call__` (as well as `RNN.call`) method. This
requires that the `cell.call` method accepts the same keyword argument
`constants`. Such constants can be used to condition the cell
transformation on additional static inputs (not changing over time),
a.k.a. an attention mechanism.
Examples:
```python
# First, let's define a RNN Cell, as a layer subclass.
class MinimalRNNCell(keras.layers.Layer):
def __init__(self, units, **kwargs):
self.units = units
self.state_size = units
super(MinimalRNNCell, self).__init__(**kwargs)
def build(self, input_shape):
self.kernel = self.add_weight(shape=(input_shape[-1], self.units),
initializer='uniform',
name='kernel')
self.recurrent_kernel = self.add_weight(
shape=(self.units, self.units),
initializer='uniform',
name='recurrent_kernel')
self.built = True
def call(self, inputs, states):
prev_output = states[0]
h = backend.dot(inputs, self.kernel)
output = h + backend.dot(prev_output, self.recurrent_kernel)
return output, [output]
# Let's use this cell in a RNN layer:
cell = MinimalRNNCell(32)
x = keras.Input((None, 5))
layer = RNN(cell)
y = layer(x)
# Here's how to use the cell to build a stacked RNN:
cells = [MinimalRNNCell(32), MinimalRNNCell(64)]
x = keras.Input((None, 5))
layer = RNN(cells)
y = layer(x)
```
"""
def __init__(self,
cell,
return_sequences=False,
return_state=False,
go_backwards=False,
stateful=False,
unroll=False,
time_major=False,
**kwargs):
if isinstance(cell, (list, tuple)):
cell = StackedRNNCells(cell)
if not 'call' in dir(cell):
raise ValueError('`cell` should have a `call` method. '
'The RNN was passed:', cell)
if not 'state_size' in dir(cell):
raise ValueError('The RNN cell should have '
'an attribute `state_size` '
'(tuple of integers, '
'one integer per RNN state).')
# If True, the output for masked timestep will be zeros, whereas in the
# False case, output from previous timestep is returned for masked timestep.
self.zero_output_for_mask = kwargs.pop('zero_output_for_mask', False)
if 'input_shape' not in kwargs and (
'input_dim' in kwargs or 'input_length' in kwargs):
input_shape = (kwargs.pop('input_length', None),
kwargs.pop('input_dim', None))
kwargs['input_shape'] = input_shape
super(RNN, self).__init__(**kwargs)
self.cell = cell
self.return_sequences = return_sequences
self.return_state = return_state
self.go_backwards = go_backwards
self.stateful = stateful
self.unroll = unroll
self.time_major = time_major
self.supports_masking = True
# The input shape is unknown yet, it could have nested tensor inputs, and
# the input spec will be the list of specs for nested inputs, the structure
# of the input_spec will be the same as the input.
self.input_spec = None
self.state_spec = None
self._states = None
self.constants_spec = None
self._num_constants = 0
if stateful:
if distribute_lib.has_strategy():
raise ValueError('RNNs with stateful=True not yet supported with '
'tf.distribute.Strategy.')
@property
def _use_input_spec_as_call_signature(self):
if self.unroll:
# When the RNN layer is unrolled, the time step shape cannot be unknown.
# The input spec does not define the time step (because this layer can be
# called with any time step value, as long as it is not None), so it
# cannot be used as the call function signature when saving to SavedModel.
return False
return super(RNN, self)._use_input_spec_as_call_signature
@property
def states(self):
if self._states is None:
state = nest.map_structure(lambda _: None, self.cell.state_size)
return state if nest.is_nested(self.cell.state_size) else [state]
return self._states
@states.setter
# Automatic tracking catches "self._states" which adds an extra weight and
# breaks HDF5 checkpoints.
@trackable.no_automatic_dependency_tracking
def states(self, states):
self._states = states
def compute_output_shape(self, input_shape):
if isinstance(input_shape, list):
input_shape = input_shape[0]
# Check whether the input shape contains any nested shapes. It could be
# (tensor_shape(1, 2), tensor_shape(3, 4)) or (1, 2, 3) which is from numpy
# inputs.
try:
input_shape = tensor_shape.TensorShape(input_shape)
except (ValueError, TypeError):
# A nested tensor input
input_shape = nest.flatten(input_shape)[0]
batch = input_shape[0]
time_step = input_shape[1]
if self.time_major:
batch, time_step = time_step, batch
if _is_multiple_state(self.cell.state_size):
state_size = self.cell.state_size
else:
state_size = [self.cell.state_size]
def _get_output_shape(flat_output_size):
output_dim = tensor_shape.TensorShape(flat_output_size).as_list()
if self.return_sequences:
if self.time_major:
output_shape = tensor_shape.TensorShape(
[time_step, batch] + output_dim)
else:
output_shape = tensor_shape.TensorShape(
[batch, time_step] + output_dim)
else:
output_shape = tensor_shape.TensorShape([batch] + output_dim)
return output_shape
if getattr(self.cell, 'output_size', None) is not None:
# cell.output_size could be nested structure.
output_shape = nest.flatten(nest.map_structure(
_get_output_shape, self.cell.output_size))
output_shape = output_shape[0] if len(output_shape) == 1 else output_shape
else:
# Note that state_size[0] could be a tensor_shape or int.
output_shape = _get_output_shape(state_size[0])
if self.return_state:
def _get_state_shape(flat_state):
state_shape = [batch] + tensor_shape.TensorShape(flat_state).as_list()
return tensor_shape.TensorShape(state_shape)
state_shape = nest.map_structure(_get_state_shape, state_size)
return generic_utils.to_list(output_shape) + nest.flatten(state_shape)
else:
return output_shape
def compute_mask(self, inputs, mask):
# Time step masks must be the same for each input.
# This is because the mask for an RNN is of size [batch, time_steps, 1],
# and specifies which time steps should be skipped, and a time step
# must be skipped for all inputs.
# TODO(scottzhu): Should we accept multiple different masks?
mask = nest.flatten(mask)[0]
output_mask = mask if self.return_sequences else None
if self.return_state:
state_mask = [None for _ in self.states]
return [output_mask] + state_mask
else:
return output_mask
def build(self, input_shape):
if isinstance(input_shape, list):
input_shape = input_shape[0]
# The input_shape here could be a nest structure.
# do the tensor_shape to shapes here. The input could be single tensor, or a
# nested structure of tensors.
def get_input_spec(shape):
"""Convert input shape to InputSpec."""
if isinstance(shape, tensor_shape.TensorShape):
input_spec_shape = shape.as_list()
else:
input_spec_shape = list(shape)
batch_index, time_step_index = (1, 0) if self.time_major else (0, 1)
if not self.stateful:
input_spec_shape[batch_index] = None
input_spec_shape[time_step_index] = None
return InputSpec(shape=tuple(input_spec_shape))
def get_step_input_shape(shape):
if isinstance(shape, tensor_shape.TensorShape):
shape = tuple(shape.as_list())
# remove the timestep from the input_shape
return shape[1:] if self.time_major else (shape[0],) + shape[2:]
# Check whether the input shape contains any nested shapes. It could be
# (tensor_shape(1, 2), tensor_shape(3, 4)) or (1, 2, 3) which is from numpy
# inputs.
try:
input_shape = tensor_shape.TensorShape(input_shape)
except (ValueError, TypeError):
# A nested tensor input
pass
if not nest.is_nested(input_shape):
# This indicates the there is only one input.
if self.input_spec is not None:
self.input_spec[0] = get_input_spec(input_shape)
else:
self.input_spec = [get_input_spec(input_shape)]
step_input_shape = get_step_input_shape(input_shape)
else:
if self.input_spec is not None:
self.input_spec[0] = nest.map_structure(get_input_spec, input_shape)
else:
self.input_spec = generic_utils.to_list(
nest.map_structure(get_input_spec, input_shape))
step_input_shape = nest.map_structure(get_step_input_shape, input_shape)
# allow cell (if layer) to build before we set or validate state_spec.
if isinstance(self.cell, Layer) and not self.cell.built:
with backend.name_scope(self.cell.name):
self.cell.build(step_input_shape)
self.cell.built = True
# set or validate state_spec
if _is_multiple_state(self.cell.state_size):
state_size = list(self.cell.state_size)
else:
state_size = [self.cell.state_size]
if self.state_spec is not None:
# initial_state was passed in call, check compatibility
self._validate_state_spec(state_size, self.state_spec)
else:
self.state_spec = [
InputSpec(shape=[None] + tensor_shape.TensorShape(dim).as_list())
for dim in state_size
]
if self.stateful:
self.reset_states()
self.built = True
@staticmethod
def _validate_state_spec(cell_state_sizes, init_state_specs):
"""Validate the state spec between the initial_state and the state_size.
Args:
cell_state_sizes: list, the `state_size` attribute from the cell.
init_state_specs: list, the `state_spec` from the initial_state that is
passed in `call()`.
Raises:
ValueError: When initial state spec is not compatible with the state size.
"""
validation_error = ValueError(
'An `initial_state` was passed that is not compatible with '
'`cell.state_size`. Received `state_spec`={}; '
'however `cell.state_size` is '
'{}'.format(init_state_specs, cell_state_sizes))
flat_cell_state_sizes = nest.flatten(cell_state_sizes)
flat_state_specs = nest.flatten(init_state_specs)
if len(flat_cell_state_sizes) != len(flat_state_specs):
raise validation_error
for cell_state_spec, cell_state_size in zip(flat_state_specs,
flat_cell_state_sizes):
if not tensor_shape.TensorShape(
# Ignore the first axis for init_state which is for batch
cell_state_spec.shape[1:]).is_compatible_with(
tensor_shape.TensorShape(cell_state_size)):
raise validation_error
@doc_controls.do_not_doc_inheritable
def get_initial_state(self, inputs):
get_initial_state_fn = getattr(self.cell, 'get_initial_state', None)
if nest.is_nested(inputs):
# The input are nested sequences. Use the first element in the seq to get
# batch size and dtype.
inputs = nest.flatten(inputs)[0]
input_shape = array_ops.shape(inputs)
batch_size = input_shape[1] if self.time_major else input_shape[0]
dtype = inputs.dtype
if get_initial_state_fn:
init_state = get_initial_state_fn(
inputs=None, batch_size=batch_size, dtype=dtype)
else:
init_state = _generate_zero_filled_state(batch_size, self.cell.state_size,
dtype)
# Keras RNN expect the states in a list, even if it's a single state tensor.
if not nest.is_nested(init_state):
init_state = [init_state]
# Force the state to be a list in case it is a namedtuple eg LSTMStateTuple.
return list(init_state)
def __call__(self, inputs, initial_state=None, constants=None, **kwargs):
inputs, initial_state, constants = _standardize_args(inputs,
initial_state,
constants,
self._num_constants)
if initial_state is None and constants is None:
return super(RNN, self).__call__(inputs, **kwargs)
# If any of `initial_state` or `constants` are specified and are Keras
# tensors, then add them to the inputs and temporarily modify the
# input_spec to include them.
additional_inputs = []
additional_specs = []
if initial_state is not None:
additional_inputs += initial_state
self.state_spec = nest.map_structure(
lambda s: InputSpec(shape=backend.int_shape(s)), initial_state)
additional_specs += self.state_spec
if constants is not None:
additional_inputs += constants
self.constants_spec = [
InputSpec(shape=backend.int_shape(constant)) for constant in constants
]
self._num_constants = len(constants)
additional_specs += self.constants_spec
# additional_inputs can be empty if initial_state or constants are provided
# but empty (e.g. the cell is stateless).
flat_additional_inputs = nest.flatten(additional_inputs)
is_keras_tensor = backend.is_keras_tensor(
flat_additional_inputs[0]) if flat_additional_inputs else True
for tensor in flat_additional_inputs:
if backend.is_keras_tensor(tensor) != is_keras_tensor:
raise ValueError('The initial state or constants of an RNN'
' layer cannot be specified with a mix of'
' Keras tensors and non-Keras tensors'
' (a "Keras tensor" is a tensor that was'
' returned by a Keras layer, or by `Input`)')
if is_keras_tensor:
# Compute the full input spec, including state and constants
full_input = [inputs] + additional_inputs
if self.built:
# Keep the input_spec since it has been populated in build() method.
full_input_spec = self.input_spec + additional_specs
else:
# The original input_spec is None since there could be a nested tensor
# input. Update the input_spec to match the inputs.
full_input_spec = generic_utils.to_list(
nest.map_structure(lambda _: None, inputs)) + additional_specs
# Perform the call with temporarily replaced input_spec
self.input_spec = full_input_spec
output = super(RNN, self).__call__(full_input, **kwargs)
# Remove the additional_specs from input spec and keep the rest. It is
# important to keep since the input spec was populated by build(), and
# will be reused in the stateful=True.
self.input_spec = self.input_spec[:-len(additional_specs)]
return output
else:
if initial_state is not None:
kwargs['initial_state'] = initial_state
if constants is not None:
kwargs['constants'] = constants
return super(RNN, self).__call__(inputs, **kwargs)
def call(self,
inputs,
mask=None,
training=None,
initial_state=None,
constants=None):
# The input should be dense, padded with zeros. If a ragged input is fed
# into the layer, it is padded and the row lengths are used for masking.
inputs, row_lengths = backend.convert_inputs_if_ragged(inputs)
is_ragged_input = (row_lengths is not None)
self._validate_args_if_ragged(is_ragged_input, mask)
inputs, initial_state, constants = self._process_inputs(
inputs, initial_state, constants)
self._maybe_reset_cell_dropout_mask(self.cell)
if isinstance(self.cell, StackedRNNCells):
for cell in self.cell.cells:
self._maybe_reset_cell_dropout_mask(cell)
if mask is not None:
# Time step masks must be the same for each input.
# TODO(scottzhu): Should we accept multiple different masks?
mask = nest.flatten(mask)[0]
if nest.is_nested(inputs):
# In the case of nested input, use the first element for shape check.
input_shape = backend.int_shape(nest.flatten(inputs)[0])
else:
input_shape = backend.int_shape(inputs)
timesteps = input_shape[0] if self.time_major else input_shape[1]
if self.unroll and timesteps is None:
raise ValueError('Cannot unroll a RNN if the '
'time dimension is undefined. \n'
'- If using a Sequential model, '
'specify the time dimension by passing '
'an `input_shape` or `batch_input_shape` '
'argument to your first layer. If your '
'first layer is an Embedding, you can '
'also use the `input_length` argument.\n'
'- If using the functional API, specify '
'the time dimension by passing a `shape` '
'or `batch_shape` argument to your Input layer.')
kwargs = {}
if generic_utils.has_arg(self.cell.call, 'training'):
kwargs['training'] = training
# TF RNN cells expect single tensor as state instead of list wrapped tensor.
is_tf_rnn_cell = getattr(self.cell, '_is_tf_rnn_cell', None) is not None
# Use the __call__ function for callable objects, eg layers, so that it
# will have the proper name scopes for the ops, etc.
cell_call_fn = self.cell.__call__ if callable(self.cell) else self.cell.call
if constants:
if not generic_utils.has_arg(self.cell.call, 'constants'):
raise ValueError('RNN cell does not support constants')
def step(inputs, states):
constants = states[-self._num_constants:] # pylint: disable=invalid-unary-operand-type
states = states[:-self._num_constants] # pylint: disable=invalid-unary-operand-type
states = states[0] if len(states) == 1 and is_tf_rnn_cell else states
output, new_states = cell_call_fn(
inputs, states, constants=constants, **kwargs)
if not nest.is_nested(new_states):
new_states = [new_states]
return output, new_states
else:
def step(inputs, states):
states = states[0] if len(states) == 1 and is_tf_rnn_cell else states
output, new_states = cell_call_fn(inputs, states, **kwargs)
if not nest.is_nested(new_states):
new_states = [new_states]
return output, new_states
last_output, outputs, states = backend.rnn(
step,
inputs,
initial_state,
constants=constants,
go_backwards=self.go_backwards,
mask=mask,
unroll=self.unroll,
input_length=row_lengths if row_lengths is not None else timesteps,
time_major=self.time_major,
zero_output_for_mask=self.zero_output_for_mask)
if self.stateful:
updates = [
state_ops.assign(self_state, state) for self_state, state in zip(
nest.flatten(self.states), nest.flatten(states))
]
self.add_update(updates)
if self.return_sequences:
output = backend.maybe_convert_to_ragged(
is_ragged_input, outputs, row_lengths, go_backwards=self.go_backwards)
else:
output = last_output
if self.return_state:
if not isinstance(states, (list, tuple)):
states = [states]
else:
states = list(states)
return generic_utils.to_list(output) + states
else:
return output
def _process_inputs(self, inputs, initial_state, constants):
# input shape: `(samples, time (padded with zeros), input_dim)`
# note that the .build() method of subclasses MUST define
# self.input_spec and self.state_spec with complete input shapes.
if (isinstance(inputs, collections.abc.Sequence)
and not isinstance(inputs, tuple)):
# get initial_state from full input spec
# as they could be copied to multiple GPU.
if not self._num_constants:
initial_state = inputs[1:]
else:
initial_state = inputs[1:-self._num_constants]
constants = inputs[-self._num_constants:]
if len(initial_state) == 0:
initial_state = None
inputs = inputs[0]
if self.stateful:
if initial_state is not None:
# When layer is stateful and initial_state is provided, check if the
# recorded state is same as the default value (zeros). Use the recorded
# state if it is not same as the default.
non_zero_count = math_ops.add_n([math_ops.count_nonzero_v2(s)
for s in nest.flatten(self.states)])
# Set strict = True to keep the original structure of the state.
initial_state = cond.cond(non_zero_count > 0,
true_fn=lambda: self.states,
false_fn=lambda: initial_state,
strict=True)
else:
initial_state = self.states
elif initial_state is None:
initial_state = self.get_initial_state(inputs)
if len(initial_state) != len(self.states):
raise ValueError('Layer has ' + str(len(self.states)) +
' states but was passed ' + str(len(initial_state)) +
' initial states.')
return inputs, initial_state, constants
def _validate_args_if_ragged(self, is_ragged_input, mask):
if not is_ragged_input:
return
if mask is not None:
raise ValueError('The mask that was passed in was ' + str(mask) +
' and cannot be applied to RaggedTensor inputs. Please '
'make sure that there is no mask passed in by upstream '
'layers.')
if self.unroll:
raise ValueError('The input received contains RaggedTensors and does '
'not support unrolling. Disable unrolling by passing '
'`unroll=False` in the RNN Layer constructor.')
def _maybe_reset_cell_dropout_mask(self, cell):
if isinstance(cell, DropoutRNNCellMixin):
cell.reset_dropout_mask()
cell.reset_recurrent_dropout_mask()
def reset_states(self, states=None):
"""Reset the recorded states for the stateful RNN layer.
Can only be used when RNN layer is constructed with `stateful` = `True`.
Args:
states: Numpy arrays that contains the value for the initial state, which
will be feed to cell at the first time step. When the value is None,
zero filled numpy array will be created based on the cell state size.
Raises:
AttributeError: When the RNN layer is not stateful.
ValueError: When the batch size of the RNN layer is unknown.
ValueError: When the input numpy array is not compatible with the RNN
layer state, either size wise or dtype wise.
"""
if not self.stateful:
raise AttributeError('Layer must be stateful.')
spec_shape = None
if self.input_spec is not None:
spec_shape = nest.flatten(self.input_spec[0])[0].shape
if spec_shape is None:
# It is possible to have spec shape to be None, eg when construct a RNN
# with a custom cell, or standard RNN layers (LSTM/GRU) which we only know
# it has 3 dim input, but not its full shape spec before build().
batch_size = None
else:
batch_size = spec_shape[1] if self.time_major else spec_shape[0]
if not batch_size:
raise ValueError('If a RNN is stateful, it needs to know '
'its batch size. Specify the batch size '
'of your input tensors: \n'
'- If using a Sequential model, '
'specify the batch size by passing '
'a `batch_input_shape` '
'argument to your first layer.\n'
'- If using the functional API, specify '
'the batch size by passing a '
'`batch_shape` argument to your Input layer.')
# initialize state if None
if nest.flatten(self.states)[0] is None:
if getattr(self.cell, 'get_initial_state', None):
flat_init_state_values = nest.flatten(self.cell.get_initial_state(
inputs=None, batch_size=batch_size,
dtype=self.dtype or backend.floatx()))
else:
flat_init_state_values = nest.flatten(_generate_zero_filled_state(
batch_size, self.cell.state_size, self.dtype or backend.floatx()))
flat_states_variables = nest.map_structure(
backend.variable, flat_init_state_values)
self.states = nest.pack_sequence_as(self.cell.state_size,
flat_states_variables)
if not nest.is_nested(self.states):
self.states = [self.states]
elif states is None:
for state, size in zip(nest.flatten(self.states),
nest.flatten(self.cell.state_size)):
backend.set_value(
state,
np.zeros([batch_size] + tensor_shape.TensorShape(size).as_list()))
else:
flat_states = nest.flatten(self.states)
flat_input_states = nest.flatten(states)
if len(flat_input_states) != len(flat_states):
raise ValueError('Layer ' + self.name + ' expects ' +
str(len(flat_states)) + ' states, '
'but it received ' + str(len(flat_input_states)) +
' state values. Input received: ' + str(states))
set_value_tuples = []
for i, (value, state) in enumerate(zip(flat_input_states,
flat_states)):
if value.shape != state.shape:
raise ValueError(
'State ' + str(i) + ' is incompatible with layer ' +
self.name + ': expected shape=' + str(
(batch_size, state)) + ', found shape=' + str(value.shape))
set_value_tuples.append((state, value))
backend.batch_set_value(set_value_tuples)
def get_config(self):
config = {
'return_sequences': self.return_sequences,
'return_state': self.return_state,
'go_backwards': self.go_backwards,
'stateful': self.stateful,
'unroll': self.unroll,
'time_major': self.time_major
}
if self._num_constants:
config['num_constants'] = self._num_constants
if self.zero_output_for_mask:
config['zero_output_for_mask'] = self.zero_output_for_mask
config['cell'] = generic_utils.serialize_keras_object(self.cell)
base_config = super(RNN, self).get_config()
return dict(list(base_config.items()) + list(config.items()))
@classmethod
def from_config(cls, config, custom_objects=None):
from tensorflow.python.keras.layers import deserialize as deserialize_layer # pylint: disable=g-import-not-at-top
cell = deserialize_layer(config.pop('cell'), custom_objects=custom_objects)
num_constants = config.pop('num_constants', 0)
layer = cls(cell, **config)
layer._num_constants = num_constants
return layer
@property
def _trackable_saved_model_saver(self):
return layer_serialization.RNNSavedModelSaver(self)
class AbstractRNNCell(Layer):
"""Abstract object representing an RNN cell.
See [the Keras RNN API guide](https://www.tensorflow.org/guide/keras/rnn)
for details about the usage of RNN API.
This is the base class for implementing RNN cells with custom behavior.
Every `RNNCell` must have the properties below and implement `call` with
the signature `(output, next_state) = call(input, state)`.
Examples:
```python
class MinimalRNNCell(AbstractRNNCell):
def __init__(self, units, **kwargs):
self.units = units
super(MinimalRNNCell, self).__init__(**kwargs)
@property
def state_size(self):
return self.units
def build(self, input_shape):
self.kernel = self.add_weight(shape=(input_shape[-1], self.units),
initializer='uniform',
name='kernel')
self.recurrent_kernel = self.add_weight(
shape=(self.units, self.units),
initializer='uniform',
name='recurrent_kernel')
self.built = True
def call(self, inputs, states):
prev_output = states[0]
h = backend.dot(inputs, self.kernel)
output = h + backend.dot(prev_output, self.recurrent_kernel)
return output, output
```
This definition of cell differs from the definition used in the literature.
In the literature, 'cell' refers to an object with a single scalar output.
This definition refers to a horizontal array of such units.
An RNN cell, in the most abstract setting, is anything that has
a state and performs some operation that takes a matrix of inputs.
This operation results in an output matrix with `self.output_size` columns.
If `self.state_size` is an integer, this operation also results in a new
state matrix with `self.state_size` columns. If `self.state_size` is a
(possibly nested tuple of) TensorShape object(s), then it should return a
matching structure of Tensors having shape `[batch_size].concatenate(s)`
for each `s` in `self.batch_size`.
"""
def call(self, inputs, states):
"""The function that contains the logic for one RNN step calculation.
Args:
inputs: the input tensor, which is a slide from the overall RNN input by
the time dimension (usually the second dimension).
states: the state tensor from previous step, which has the same shape
as `(batch, state_size)`. In the case of timestep 0, it will be the
initial state user specified, or zero filled tensor otherwise.
Returns:
A tuple of two tensors:
1. output tensor for the current timestep, with size `output_size`.
2. state tensor for next step, which has the shape of `state_size`.
"""
raise NotImplementedError('Abstract method')
@property
def state_size(self):
"""size(s) of state(s) used by this cell.
It can be represented by an Integer, a TensorShape or a tuple of Integers
or TensorShapes.
"""
raise NotImplementedError('Abstract method')
@property
def output_size(self):
"""Integer or TensorShape: size of outputs produced by this cell."""
raise NotImplementedError('Abstract method')
def get_initial_state(self, inputs=None, batch_size=None, dtype=None):
return _generate_zero_filled_state_for_cell(self, inputs, batch_size, dtype)
@doc_controls.do_not_generate_docs
class DropoutRNNCellMixin(object):
"""Object that hold dropout related fields for RNN Cell.
This class is not a standalone RNN cell. It suppose to be used with a RNN cell
by multiple inheritance. Any cell that mix with class should have following
fields:
dropout: a float number within range [0, 1). The ratio that the input
tensor need to dropout.
recurrent_dropout: a float number within range [0, 1). The ratio that the
recurrent state weights need to dropout.
This object will create and cache created dropout masks, and reuse them for
the incoming data, so that the same mask is used for every batch input.
"""
def __init__(self, *args, **kwargs):
self._create_non_trackable_mask_cache()
super(DropoutRNNCellMixin, self).__init__(*args, **kwargs)
@trackable.no_automatic_dependency_tracking
def _create_non_trackable_mask_cache(self):
"""Create the cache for dropout and recurrent dropout mask.
Note that the following two masks will be used in "graph function" mode,
e.g. these masks are symbolic tensors. In eager mode, the `eager_*_mask`
tensors will be generated differently than in the "graph function" case,
and they will be cached.
Also note that in graph mode, we still cache those masks only because the
RNN could be created with `unroll=True`. In that case, the `cell.call()`
function will be invoked multiple times, and we want to ensure same mask
is used every time.
Also the caches are created without tracking. Since they are not picklable
by python when deepcopy, we don't want `layer._obj_reference_counts_dict`
to track it by default.
"""
self._dropout_mask_cache = backend.ContextValueCache(
self._create_dropout_mask)
self._recurrent_dropout_mask_cache = backend.ContextValueCache(
self._create_recurrent_dropout_mask)
def reset_dropout_mask(self):
"""Reset the cached dropout masks if any.
This is important for the RNN layer to invoke this in it `call()` method so
that the cached mask is cleared before calling the `cell.call()`. The mask
should be cached across the timestep within the same batch, but shouldn't
be cached between batches. Otherwise it will introduce unreasonable bias
against certain index of data within the batch.
"""
self._dropout_mask_cache.clear()
def reset_recurrent_dropout_mask(self):
"""Reset the cached recurrent dropout masks if any.
This is important for the RNN layer to invoke this in it call() method so
that the cached mask is cleared before calling the cell.call(). The mask
should be cached across the timestep within the same batch, but shouldn't
be cached between batches. Otherwise it will introduce unreasonable bias
against certain index of data within the batch.
"""
self._recurrent_dropout_mask_cache.clear()
def _create_dropout_mask(self, inputs, training, count=1):
return _generate_dropout_mask(
array_ops.ones_like(inputs),
self.dropout,
training=training,
count=count)
def _create_recurrent_dropout_mask(self, inputs, training, count=1):
return _generate_dropout_mask(
array_ops.ones_like(inputs),
self.recurrent_dropout,
training=training,
count=count)
def get_dropout_mask_for_cell(self, inputs, training, count=1):
"""Get the dropout mask for RNN cell's input.
It will create mask based on context if there isn't any existing cached
mask. If a new mask is generated, it will update the cache in the cell.
Args:
inputs: The input tensor whose shape will be used to generate dropout
mask.
training: Boolean tensor, whether its in training mode, dropout will be
ignored in non-training mode.
count: Int, how many dropout mask will be generated. It is useful for cell
that has internal weights fused together.
Returns:
List of mask tensor, generated or cached mask based on context.
"""
if self.dropout == 0:
return None
init_kwargs = dict(inputs=inputs, training=training, count=count)
return self._dropout_mask_cache.setdefault(kwargs=init_kwargs)
def get_recurrent_dropout_mask_for_cell(self, inputs, training, count=1):
"""Get the recurrent dropout mask for RNN cell.
It will create mask based on context if there isn't any existing cached
mask. If a new mask is generated, it will update the cache in the cell.
Args:
inputs: The input tensor whose shape will be used to generate dropout
mask.
training: Boolean tensor, whether its in training mode, dropout will be
ignored in non-training mode.
count: Int, how many dropout mask will be generated. It is useful for cell
that has internal weights fused together.
Returns:
List of mask tensor, generated or cached mask based on context.
"""
if self.recurrent_dropout == 0:
return None
init_kwargs = dict(inputs=inputs, training=training, count=count)
return self._recurrent_dropout_mask_cache.setdefault(kwargs=init_kwargs)
def __getstate__(self):
# Used for deepcopy. The caching can't be pickled by python, since it will
# contain tensor and graph.
state = super(DropoutRNNCellMixin, self).__getstate__()
state.pop('_dropout_mask_cache', None)
state.pop('_recurrent_dropout_mask_cache', None)
return state
def __setstate__(self, state):
state['_dropout_mask_cache'] = backend.ContextValueCache(
self._create_dropout_mask)
state['_recurrent_dropout_mask_cache'] = backend.ContextValueCache(
self._create_recurrent_dropout_mask)
super(DropoutRNNCellMixin, self).__setstate__(state)
class SimpleRNNCell(DropoutRNNCellMixin, Layer):
"""Cell class for SimpleRNN.
See [the Keras RNN API guide](https://www.tensorflow.org/guide/keras/rnn)
for details about the usage of RNN API.
This class processes one step within the whole time sequence input, whereas
`tf.keras.layer.SimpleRNN` processes the whole sequence.
Args:
units: Positive integer, dimensionality of the output space.
activation: Activation function to use.
Default: hyperbolic tangent (`tanh`).
If you pass `None`, no activation is applied
(ie. "linear" activation: `a(x) = x`).
use_bias: Boolean, (default `True`), whether the layer uses a bias vector.
kernel_initializer: Initializer for the `kernel` weights matrix,
used for the linear transformation of the inputs. Default:
`glorot_uniform`.
recurrent_initializer: Initializer for the `recurrent_kernel`
weights matrix, used for the linear transformation of the recurrent state.
Default: `orthogonal`.
bias_initializer: Initializer for the bias vector. Default: `zeros`.
kernel_regularizer: Regularizer function applied to the `kernel` weights
matrix. Default: `None`.
recurrent_regularizer: Regularizer function applied to the
`recurrent_kernel` weights matrix. Default: `None`.
bias_regularizer: Regularizer function applied to the bias vector. Default:
`None`.
kernel_constraint: Constraint function applied to the `kernel` weights
matrix. Default: `None`.
recurrent_constraint: Constraint function applied to the `recurrent_kernel`
weights matrix. Default: `None`.
bias_constraint: Constraint function applied to the bias vector. Default:
`None`.
dropout: Float between 0 and 1. Fraction of the units to drop for the linear
transformation of the inputs. Default: 0.
recurrent_dropout: Float between 0 and 1. Fraction of the units to drop for
the linear transformation of the recurrent state. Default: 0.
Call arguments:
inputs: A 2D tensor, with shape of `[batch, feature]`.
states: A 2D tensor with shape of `[batch, units]`, which is the state from
the previous time step. For timestep 0, the initial state provided by user
will be feed to cell.
training: Python boolean indicating whether the layer should behave in
training mode or in inference mode. Only relevant when `dropout` or
`recurrent_dropout` is used.
Examples:
```python
inputs = np.random.random([32, 10, 8]).astype(np.float32)
rnn = tf.keras.layers.RNN(tf.keras.layers.SimpleRNNCell(4))
output = rnn(inputs) # The output has shape `[32, 4]`.
rnn = tf.keras.layers.RNN(
tf.keras.layers.SimpleRNNCell(4),
return_sequences=True,
return_state=True)
# whole_sequence_output has shape `[32, 10, 4]`.
# final_state has shape `[32, 4]`.
whole_sequence_output, final_state = rnn(inputs)
```
"""
def __init__(self,
units,
activation='tanh',
use_bias=True,
kernel_initializer='glorot_uniform',
recurrent_initializer='orthogonal',
bias_initializer='zeros',
kernel_regularizer=None,
recurrent_regularizer=None,
bias_regularizer=None,
kernel_constraint=None,
recurrent_constraint=None,
bias_constraint=None,
dropout=0.,
recurrent_dropout=0.,
**kwargs):
if units < 0:
raise ValueError(f'Received an invalid value for units, expected '
f'a positive integer, got {units}.')
# By default use cached variable under v2 mode, see b/143699808.
if ops.executing_eagerly_outside_functions():
self._enable_caching_device = kwargs.pop('enable_caching_device', True)
else:
self._enable_caching_device = kwargs.pop('enable_caching_device', False)
super(SimpleRNNCell, self).__init__(**kwargs)
self.units = units
self.activation = activations.get(activation)
self.use_bias = use_bias
self.kernel_initializer = initializers.get(kernel_initializer)
self.recurrent_initializer = initializers.get(recurrent_initializer)
self.bias_initializer = initializers.get(bias_initializer)
self.kernel_regularizer = regularizers.get(kernel_regularizer)
self.recurrent_regularizer = regularizers.get(recurrent_regularizer)
self.bias_regularizer = regularizers.get(bias_regularizer)
self.kernel_constraint = constraints.get(kernel_constraint)
self.recurrent_constraint = constraints.get(recurrent_constraint)
self.bias_constraint = constraints.get(bias_constraint)
self.dropout = min(1., max(0., dropout))
self.recurrent_dropout = min(1., max(0., recurrent_dropout))
self.state_size = self.units
self.output_size = self.units
@tf_utils.shape_type_conversion
def build(self, input_shape):
default_caching_device = _caching_device(self)
self.kernel = self.add_weight(
shape=(input_shape[-1], self.units),
name='kernel',
initializer=self.kernel_initializer,
regularizer=self.kernel_regularizer,
constraint=self.kernel_constraint,
caching_device=default_caching_device)
self.recurrent_kernel = self.add_weight(
shape=(self.units, self.units),
name='recurrent_kernel',
initializer=self.recurrent_initializer,
regularizer=self.recurrent_regularizer,
constraint=self.recurrent_constraint,
caching_device=default_caching_device)
if self.use_bias:
self.bias = self.add_weight(
shape=(self.units,),
name='bias',
initializer=self.bias_initializer,
regularizer=self.bias_regularizer,
constraint=self.bias_constraint,
caching_device=default_caching_device)
else:
self.bias = None
self.built = True
def call(self, inputs, states, training=None):
prev_output = states[0] if nest.is_nested(states) else states
dp_mask = self.get_dropout_mask_for_cell(inputs, training)
rec_dp_mask = self.get_recurrent_dropout_mask_for_cell(
prev_output, training)
if dp_mask is not None:
h = backend.dot(inputs * dp_mask, self.kernel)
else:
h = backend.dot(inputs, self.kernel)
if self.bias is not None:
h = backend.bias_add(h, self.bias)
if rec_dp_mask is not None:
prev_output = prev_output * rec_dp_mask
output = h + backend.dot(prev_output, self.recurrent_kernel)
if self.activation is not None:
output = self.activation(output)
new_state = [output] if nest.is_nested(states) else output
return output, new_state
def get_initial_state(self, inputs=None, batch_size=None, dtype=None):
return _generate_zero_filled_state_for_cell(self, inputs, batch_size, dtype)
def get_config(self):
config = {
'units':
self.units,
'activation':
activations.serialize(self.activation),
'use_bias':
self.use_bias,
'kernel_initializer':
initializers.serialize(self.kernel_initializer),
'recurrent_initializer':
initializers.serialize(self.recurrent_initializer),
'bias_initializer':
initializers.serialize(self.bias_initializer),
'kernel_regularizer':
regularizers.serialize(self.kernel_regularizer),
'recurrent_regularizer':
regularizers.serialize(self.recurrent_regularizer),
'bias_regularizer':
regularizers.serialize(self.bias_regularizer),
'kernel_constraint':
constraints.serialize(self.kernel_constraint),
'recurrent_constraint':
constraints.serialize(self.recurrent_constraint),
'bias_constraint':
constraints.serialize(self.bias_constraint),
'dropout':
self.dropout,
'recurrent_dropout':
self.recurrent_dropout
}
config.update(_config_for_enable_caching_device(self))
base_config = super(SimpleRNNCell, self).get_config()
return dict(list(base_config.items()) + list(config.items()))
class SimpleRNN(RNN):
"""Fully-connected RNN where the output is to be fed back to input.
See [the Keras RNN API guide](https://www.tensorflow.org/guide/keras/rnn)
for details about the usage of RNN API.
Args:
units: Positive integer, dimensionality of the output space.
activation: Activation function to use.
Default: hyperbolic tangent (`tanh`).
If you pass None, no activation is applied
(ie. "linear" activation: `a(x) = x`).
use_bias: Boolean, (default `True`), whether the layer uses a bias vector.
kernel_initializer: Initializer for the `kernel` weights matrix,
used for the linear transformation of the inputs. Default:
`glorot_uniform`.
recurrent_initializer: Initializer for the `recurrent_kernel`
weights matrix, used for the linear transformation of the recurrent state.
Default: `orthogonal`.
bias_initializer: Initializer for the bias vector. Default: `zeros`.
kernel_regularizer: Regularizer function applied to the `kernel` weights
matrix. Default: `None`.
recurrent_regularizer: Regularizer function applied to the
`recurrent_kernel` weights matrix. Default: `None`.
bias_regularizer: Regularizer function applied to the bias vector. Default:
`None`.
activity_regularizer: Regularizer function applied to the output of the
layer (its "activation"). Default: `None`.
kernel_constraint: Constraint function applied to the `kernel` weights
matrix. Default: `None`.
recurrent_constraint: Constraint function applied to the `recurrent_kernel`
weights matrix. Default: `None`.
bias_constraint: Constraint function applied to the bias vector. Default:
`None`.
dropout: Float between 0 and 1.
Fraction of the units to drop for the linear transformation of the inputs.
Default: 0.
recurrent_dropout: Float between 0 and 1.
Fraction of the units to drop for the linear transformation of the
recurrent state. Default: 0.
return_sequences: Boolean. Whether to return the last output
in the output sequence, or the full sequence. Default: `False`.
return_state: Boolean. Whether to return the last state
in addition to the output. Default: `False`
go_backwards: Boolean (default False).
If True, process the input sequence backwards and return the
reversed sequence.
stateful: Boolean (default False). If True, the last state
for each sample at index i in a batch will be used as initial
state for the sample of index i in the following batch.
unroll: Boolean (default False).
If True, the network will be unrolled,
else a symbolic loop will be used.
Unrolling can speed-up a RNN,
although it tends to be more memory-intensive.
Unrolling is only suitable for short sequences.
Call arguments:
inputs: A 3D tensor, with shape `[batch, timesteps, feature]`.
mask: Binary tensor of shape `[batch, timesteps]` indicating whether
a given timestep should be masked. An individual `True` entry indicates
that the corresponding timestep should be utilized, while a `False` entry
indicates that the corresponding timestep should be ignored.
training: Python boolean indicating whether the layer should behave in
training mode or in inference mode. This argument is passed to the cell
when calling it. This is only relevant if `dropout` or
`recurrent_dropout` is used.
initial_state: List of initial state tensors to be passed to the first
call of the cell.
Examples:
```python
inputs = np.random.random([32, 10, 8]).astype(np.float32)
simple_rnn = tf.keras.layers.SimpleRNN(4)
output = simple_rnn(inputs) # The output has shape `[32, 4]`.
simple_rnn = tf.keras.layers.SimpleRNN(
4, return_sequences=True, return_state=True)
# whole_sequence_output has shape `[32, 10, 4]`.
# final_state has shape `[32, 4]`.
whole_sequence_output, final_state = simple_rnn(inputs)
```
"""
def __init__(self,
units,
activation='tanh',
use_bias=True,
kernel_initializer='glorot_uniform',
recurrent_initializer='orthogonal',
bias_initializer='zeros',
kernel_regularizer=None,
recurrent_regularizer=None,
bias_regularizer=None,
activity_regularizer=None,
kernel_constraint=None,
recurrent_constraint=None,
bias_constraint=None,
dropout=0.,
recurrent_dropout=0.,
return_sequences=False,
return_state=False,
go_backwards=False,
stateful=False,
unroll=False,
**kwargs):
if 'implementation' in kwargs:
kwargs.pop('implementation')
logging.warning('The `implementation` argument '
'in `SimpleRNN` has been deprecated. '
'Please remove it from your layer call.')
if 'enable_caching_device' in kwargs:
cell_kwargs = {'enable_caching_device':
kwargs.pop('enable_caching_device')}
else:
cell_kwargs = {}
cell = SimpleRNNCell(
units,
activation=activation,
use_bias=use_bias,
kernel_initializer=kernel_initializer,
recurrent_initializer=recurrent_initializer,
bias_initializer=bias_initializer,
kernel_regularizer=kernel_regularizer,
recurrent_regularizer=recurrent_regularizer,
bias_regularizer=bias_regularizer,
kernel_constraint=kernel_constraint,
recurrent_constraint=recurrent_constraint,
bias_constraint=bias_constraint,
dropout=dropout,
recurrent_dropout=recurrent_dropout,
dtype=kwargs.get('dtype'),
trainable=kwargs.get('trainable', True),
**cell_kwargs)
super(SimpleRNN, self).__init__(
cell,
return_sequences=return_sequences,
return_state=return_state,
go_backwards=go_backwards,
stateful=stateful,
unroll=unroll,
**kwargs)
self.activity_regularizer = regularizers.get(activity_regularizer)
self.input_spec = [InputSpec(ndim=3)]
def call(self, inputs, mask=None, training=None, initial_state=None):
return super(SimpleRNN, self).call(
inputs, mask=mask, training=training, initial_state=initial_state)
@property
def units(self):
return self.cell.units
@property
def activation(self):
return self.cell.activation
@property
def use_bias(self):
return self.cell.use_bias
@property
def kernel_initializer(self):
return self.cell.kernel_initializer
@property
def recurrent_initializer(self):
return self.cell.recurrent_initializer
@property
def bias_initializer(self):
return self.cell.bias_initializer
@property
def kernel_regularizer(self):
return self.cell.kernel_regularizer
@property
def recurrent_regularizer(self):
return self.cell.recurrent_regularizer
@property
def bias_regularizer(self):
return self.cell.bias_regularizer
@property
def kernel_constraint(self):
return self.cell.kernel_constraint
@property
def recurrent_constraint(self):
return self.cell.recurrent_constraint
@property
def bias_constraint(self):
return self.cell.bias_constraint
@property
def dropout(self):
return self.cell.dropout
@property
def recurrent_dropout(self):
return self.cell.recurrent_dropout
def get_config(self):
config = {
'units':
self.units,
'activation':
activations.serialize(self.activation),
'use_bias':
self.use_bias,
'kernel_initializer':
initializers.serialize(self.kernel_initializer),
'recurrent_initializer':
initializers.serialize(self.recurrent_initializer),
'bias_initializer':
initializers.serialize(self.bias_initializer),
'kernel_regularizer':
regularizers.serialize(self.kernel_regularizer),
'recurrent_regularizer':
regularizers.serialize(self.recurrent_regularizer),
'bias_regularizer':
regularizers.serialize(self.bias_regularizer),
'activity_regularizer':
regularizers.serialize(self.activity_regularizer),
'kernel_constraint':
constraints.serialize(self.kernel_constraint),
'recurrent_constraint':
constraints.serialize(self.recurrent_constraint),
'bias_constraint':
constraints.serialize(self.bias_constraint),
'dropout':
self.dropout,
'recurrent_dropout':
self.recurrent_dropout
}
base_config = super(SimpleRNN, self).get_config()
config.update(_config_for_enable_caching_device(self.cell))
del base_config['cell']
return dict(list(base_config.items()) + list(config.items()))
@classmethod
def from_config(cls, config):
if 'implementation' in config:
config.pop('implementation')
return cls(**config)
class GRUCell(DropoutRNNCellMixin, Layer):
"""Cell class for the GRU layer.
Args:
units: Positive integer, dimensionality of the output space.
activation: Activation function to use.
Default: hyperbolic tangent (`tanh`).
If you pass None, no activation is applied
(ie. "linear" activation: `a(x) = x`).
recurrent_activation: Activation function to use
for the recurrent step.
Default: hard sigmoid (`hard_sigmoid`).
If you pass `None`, no activation is applied
(ie. "linear" activation: `a(x) = x`).
use_bias: Boolean, whether the layer uses a bias vector.
kernel_initializer: Initializer for the `kernel` weights matrix,
used for the linear transformation of the inputs.
recurrent_initializer: Initializer for the `recurrent_kernel`
weights matrix,
used for the linear transformation of the recurrent state.
bias_initializer: Initializer for the bias vector.
kernel_regularizer: Regularizer function applied to
the `kernel` weights matrix.
recurrent_regularizer: Regularizer function applied to
the `recurrent_kernel` weights matrix.
bias_regularizer: Regularizer function applied to the bias vector.
kernel_constraint: Constraint function applied to
the `kernel` weights matrix.
recurrent_constraint: Constraint function applied to
the `recurrent_kernel` weights matrix.
bias_constraint: Constraint function applied to the bias vector.
dropout: Float between 0 and 1.
Fraction of the units to drop for the linear transformation of the inputs.
recurrent_dropout: Float between 0 and 1.
Fraction of the units to drop for
the linear transformation of the recurrent state.
reset_after: GRU convention (whether to apply reset gate after or
before matrix multiplication). False = "before" (default),
True = "after" (CuDNN compatible).
Call arguments:
inputs: A 2D tensor.
states: List of state tensors corresponding to the previous timestep.
training: Python boolean indicating whether the layer should behave in
training mode or in inference mode. Only relevant when `dropout` or
`recurrent_dropout` is used.
"""
def __init__(self,
units,
activation='tanh',
recurrent_activation='hard_sigmoid',
use_bias=True,
kernel_initializer='glorot_uniform',
recurrent_initializer='orthogonal',
bias_initializer='zeros',
kernel_regularizer=None,
recurrent_regularizer=None,
bias_regularizer=None,
kernel_constraint=None,
recurrent_constraint=None,
bias_constraint=None,
dropout=0.,
recurrent_dropout=0.,
reset_after=False,
**kwargs):
if units < 0:
raise ValueError(f'Received an invalid value for units, expected '
f'a positive integer, got {units}.')
# By default use cached variable under v2 mode, see b/143699808.
if ops.executing_eagerly_outside_functions():
self._enable_caching_device = kwargs.pop('enable_caching_device', True)
else:
self._enable_caching_device = kwargs.pop('enable_caching_device', False)
super(GRUCell, self).__init__(**kwargs)
self.units = units
self.activation = activations.get(activation)
self.recurrent_activation = activations.get(recurrent_activation)
self.use_bias = use_bias
self.kernel_initializer = initializers.get(kernel_initializer)
self.recurrent_initializer = initializers.get(recurrent_initializer)
self.bias_initializer = initializers.get(bias_initializer)
self.kernel_regularizer = regularizers.get(kernel_regularizer)
self.recurrent_regularizer = regularizers.get(recurrent_regularizer)
self.bias_regularizer = regularizers.get(bias_regularizer)
self.kernel_constraint = constraints.get(kernel_constraint)
self.recurrent_constraint = constraints.get(recurrent_constraint)
self.bias_constraint = constraints.get(bias_constraint)
self.dropout = min(1., max(0., dropout))
self.recurrent_dropout = min(1., max(0., recurrent_dropout))
implementation = kwargs.pop('implementation', 1)
if self.recurrent_dropout != 0 and implementation != 1:
logging.debug(RECURRENT_DROPOUT_WARNING_MSG)
self.implementation = 1
else:
self.implementation = implementation
self.reset_after = reset_after
self.state_size = self.units
self.output_size = self.units
@tf_utils.shape_type_conversion
def build(self, input_shape):
input_dim = input_shape[-1]
default_caching_device = _caching_device(self)
self.kernel = self.add_weight(
shape=(input_dim, self.units * 3),
name='kernel',
initializer=self.kernel_initializer,
regularizer=self.kernel_regularizer,
constraint=self.kernel_constraint,
caching_device=default_caching_device)
self.recurrent_kernel = self.add_weight(
shape=(self.units, self.units * 3),
name='recurrent_kernel',
initializer=self.recurrent_initializer,
regularizer=self.recurrent_regularizer,
constraint=self.recurrent_constraint,
caching_device=default_caching_device)
if self.use_bias:
if not self.reset_after:
bias_shape = (3 * self.units,)
else:
# separate biases for input and recurrent kernels
# Note: the shape is intentionally different from CuDNNGRU biases
# `(2 * 3 * self.units,)`, so that we can distinguish the classes
# when loading and converting saved weights.
bias_shape = (2, 3 * self.units)
self.bias = self.add_weight(shape=bias_shape,
name='bias',
initializer=self.bias_initializer,
regularizer=self.bias_regularizer,
constraint=self.bias_constraint,
caching_device=default_caching_device)
else:
self.bias = None
self.built = True
def call(self, inputs, states, training=None):
h_tm1 = states[0] if nest.is_nested(states) else states # previous memory
dp_mask = self.get_dropout_mask_for_cell(inputs, training, count=3)
rec_dp_mask = self.get_recurrent_dropout_mask_for_cell(
h_tm1, training, count=3)
if self.use_bias:
if not self.reset_after:
input_bias, recurrent_bias = self.bias, None
else:
input_bias, recurrent_bias = array_ops_stack.unstack(self.bias)
if self.implementation == 1:
if 0. < self.dropout < 1.:
inputs_z = inputs * dp_mask[0]
inputs_r = inputs * dp_mask[1]
inputs_h = inputs * dp_mask[2]
else:
inputs_z = inputs
inputs_r = inputs
inputs_h = inputs
x_z = backend.dot(inputs_z, self.kernel[:, :self.units])
x_r = backend.dot(inputs_r, self.kernel[:, self.units:self.units * 2])
x_h = backend.dot(inputs_h, self.kernel[:, self.units * 2:])
if self.use_bias:
x_z = backend.bias_add(x_z, input_bias[:self.units])
x_r = backend.bias_add(x_r, input_bias[self.units: self.units * 2])
x_h = backend.bias_add(x_h, input_bias[self.units * 2:])
if 0. < self.recurrent_dropout < 1.:
h_tm1_z = h_tm1 * rec_dp_mask[0]
h_tm1_r = h_tm1 * rec_dp_mask[1]
h_tm1_h = h_tm1 * rec_dp_mask[2]
else:
h_tm1_z = h_tm1
h_tm1_r = h_tm1
h_tm1_h = h_tm1
recurrent_z = backend.dot(h_tm1_z, self.recurrent_kernel[:, :self.units])
recurrent_r = backend.dot(
h_tm1_r, self.recurrent_kernel[:, self.units:self.units * 2])
if self.reset_after and self.use_bias:
recurrent_z = backend.bias_add(recurrent_z, recurrent_bias[:self.units])
recurrent_r = backend.bias_add(
recurrent_r, recurrent_bias[self.units:self.units * 2])
z = self.recurrent_activation(x_z + recurrent_z)
r = self.recurrent_activation(x_r + recurrent_r)
# reset gate applied after/before matrix multiplication
if self.reset_after:
recurrent_h = backend.dot(
h_tm1_h, self.recurrent_kernel[:, self.units * 2:])
if self.use_bias:
recurrent_h = backend.bias_add(
recurrent_h, recurrent_bias[self.units * 2:])
recurrent_h = r * recurrent_h
else:
recurrent_h = backend.dot(
r * h_tm1_h, self.recurrent_kernel[:, self.units * 2:])
hh = self.activation(x_h + recurrent_h)
else:
if 0. < self.dropout < 1.:
inputs = inputs * dp_mask[0]
# inputs projected by all gate matrices at once
matrix_x = backend.dot(inputs, self.kernel)
if self.use_bias:
# biases: bias_z_i, bias_r_i, bias_h_i
matrix_x = backend.bias_add(matrix_x, input_bias)
x_z, x_r, x_h = array_ops.split(matrix_x, 3, axis=-1)
if self.reset_after:
# hidden state projected by all gate matrices at once
matrix_inner = backend.dot(h_tm1, self.recurrent_kernel)
if self.use_bias:
matrix_inner = backend.bias_add(matrix_inner, recurrent_bias)
else:
# hidden state projected separately for update/reset and new
matrix_inner = backend.dot(
h_tm1, self.recurrent_kernel[:, :2 * self.units])
recurrent_z, recurrent_r, recurrent_h = array_ops.split(
matrix_inner, [self.units, self.units, -1], axis=-1)
z = self.recurrent_activation(x_z + recurrent_z)
r = self.recurrent_activation(x_r + recurrent_r)
if self.reset_after:
recurrent_h = r * recurrent_h
else:
recurrent_h = backend.dot(
r * h_tm1, self.recurrent_kernel[:, 2 * self.units:])
hh = self.activation(x_h + recurrent_h)
# previous and candidate state mixed by update gate
h = z * h_tm1 + (1 - z) * hh
new_state = [h] if nest.is_nested(states) else h
return h, new_state
def get_config(self):
config = {
'units': self.units,
'activation': activations.serialize(self.activation),
'recurrent_activation':
activations.serialize(self.recurrent_activation),
'use_bias': self.use_bias,
'kernel_initializer': initializers.serialize(self.kernel_initializer),
'recurrent_initializer':
initializers.serialize(self.recurrent_initializer),
'bias_initializer': initializers.serialize(self.bias_initializer),
'kernel_regularizer': regularizers.serialize(self.kernel_regularizer),
'recurrent_regularizer':
regularizers.serialize(self.recurrent_regularizer),
'bias_regularizer': regularizers.serialize(self.bias_regularizer),
'kernel_constraint': constraints.serialize(self.kernel_constraint),
'recurrent_constraint':
constraints.serialize(self.recurrent_constraint),
'bias_constraint': constraints.serialize(self.bias_constraint),
'dropout': self.dropout,
'recurrent_dropout': self.recurrent_dropout,
'implementation': self.implementation,
'reset_after': self.reset_after
}
config.update(_config_for_enable_caching_device(self))
base_config = super(GRUCell, self).get_config()
return dict(list(base_config.items()) + list(config.items()))
def get_initial_state(self, inputs=None, batch_size=None, dtype=None):
return _generate_zero_filled_state_for_cell(self, inputs, batch_size, dtype)
class GRU(RNN):
"""Gated Recurrent Unit - Cho et al. 2014.
There are two variants. The default one is based on 1406.1078v3 and
has reset gate applied to hidden state before matrix multiplication. The
other one is based on original 1406.1078v1 and has the order reversed.
The second variant is compatible with CuDNNGRU (GPU-only) and allows
inference on CPU. Thus it has separate biases for `kernel` and
`recurrent_kernel`. Use `'reset_after'=True` and
`recurrent_activation='sigmoid'`.
Args:
units: Positive integer, dimensionality of the output space.
activation: Activation function to use.
Default: hyperbolic tangent (`tanh`).
If you pass `None`, no activation is applied
(ie. "linear" activation: `a(x) = x`).
recurrent_activation: Activation function to use
for the recurrent step.
Default: hard sigmoid (`hard_sigmoid`).
If you pass `None`, no activation is applied
(ie. "linear" activation: `a(x) = x`).
use_bias: Boolean, whether the layer uses a bias vector.
kernel_initializer: Initializer for the `kernel` weights matrix,
used for the linear transformation of the inputs.
recurrent_initializer: Initializer for the `recurrent_kernel`
weights matrix, used for the linear transformation of the recurrent state.
bias_initializer: Initializer for the bias vector.
kernel_regularizer: Regularizer function applied to
the `kernel` weights matrix.
recurrent_regularizer: Regularizer function applied to
the `recurrent_kernel` weights matrix.
bias_regularizer: Regularizer function applied to the bias vector.
activity_regularizer: Regularizer function applied to
the output of the layer (its "activation")..
kernel_constraint: Constraint function applied to
the `kernel` weights matrix.
recurrent_constraint: Constraint function applied to
the `recurrent_kernel` weights matrix.
bias_constraint: Constraint function applied to the bias vector.
dropout: Float between 0 and 1.
Fraction of the units to drop for
the linear transformation of the inputs.
recurrent_dropout: Float between 0 and 1.
Fraction of the units to drop for
the linear transformation of the recurrent state.
return_sequences: Boolean. Whether to return the last output
in the output sequence, or the full sequence.
return_state: Boolean. Whether to return the last state
in addition to the output.
go_backwards: Boolean (default False).
If True, process the input sequence backwards and return the
reversed sequence.
stateful: Boolean (default False). If True, the last state
for each sample at index i in a batch will be used as initial
state for the sample of index i in the following batch.
unroll: Boolean (default False).
If True, the network will be unrolled,
else a symbolic loop will be used.
Unrolling can speed-up a RNN,
although it tends to be more memory-intensive.
Unrolling is only suitable for short sequences.
time_major: The shape format of the `inputs` and `outputs` tensors.
If True, the inputs and outputs will be in shape
`(timesteps, batch, ...)`, whereas in the False case, it will be
`(batch, timesteps, ...)`. 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.
reset_after: GRU convention (whether to apply reset gate after or
before matrix multiplication). False = "before" (default),
True = "after" (CuDNN compatible).
Call arguments:
inputs: A 3D tensor.
mask: Binary tensor of shape `(samples, timesteps)` indicating whether
a given timestep should be masked. An individual `True` entry indicates
that the corresponding timestep should be utilized, while a `False`
entry indicates that the corresponding timestep should be ignored.
training: Python boolean indicating whether the layer should behave in
training mode or in inference mode. This argument is passed to the cell
when calling it. This is only relevant if `dropout` or
`recurrent_dropout` is used.
initial_state: List of initial state tensors to be passed to the first
call of the cell.
"""
def __init__(self,
units,
activation='tanh',
recurrent_activation='hard_sigmoid',
use_bias=True,
kernel_initializer='glorot_uniform',
recurrent_initializer='orthogonal',
bias_initializer='zeros',
kernel_regularizer=None,
recurrent_regularizer=None,
bias_regularizer=None,
activity_regularizer=None,
kernel_constraint=None,
recurrent_constraint=None,
bias_constraint=None,
dropout=0.,
recurrent_dropout=0.,
return_sequences=False,
return_state=False,
go_backwards=False,
stateful=False,
unroll=False,
reset_after=False,
**kwargs):
implementation = kwargs.pop('implementation', 1)
if implementation == 0:
logging.warning('`implementation=0` has been deprecated, '
'and now defaults to `implementation=1`.'
'Please update your layer call.')
if 'enable_caching_device' in kwargs:
cell_kwargs = {'enable_caching_device':
kwargs.pop('enable_caching_device')}
else:
cell_kwargs = {}
cell = GRUCell(
units,
activation=activation,
recurrent_activation=recurrent_activation,
use_bias=use_bias,
kernel_initializer=kernel_initializer,
recurrent_initializer=recurrent_initializer,
bias_initializer=bias_initializer,
kernel_regularizer=kernel_regularizer,
recurrent_regularizer=recurrent_regularizer,
bias_regularizer=bias_regularizer,
kernel_constraint=kernel_constraint,
recurrent_constraint=recurrent_constraint,
bias_constraint=bias_constraint,
dropout=dropout,
recurrent_dropout=recurrent_dropout,
implementation=implementation,
reset_after=reset_after,
dtype=kwargs.get('dtype'),
trainable=kwargs.get('trainable', True),
**cell_kwargs)
super(GRU, self).__init__(
cell,
return_sequences=return_sequences,
return_state=return_state,
go_backwards=go_backwards,
stateful=stateful,
unroll=unroll,
**kwargs)
self.activity_regularizer = regularizers.get(activity_regularizer)
self.input_spec = [InputSpec(ndim=3)]
def call(self, inputs, mask=None, training=None, initial_state=None):
return super(GRU, self).call(
inputs, mask=mask, training=training, initial_state=initial_state)
@property
def units(self):
return self.cell.units
@property
def activation(self):
return self.cell.activation
@property
def recurrent_activation(self):
return self.cell.recurrent_activation
@property
def use_bias(self):
return self.cell.use_bias
@property
def kernel_initializer(self):
return self.cell.kernel_initializer
@property
def recurrent_initializer(self):
return self.cell.recurrent_initializer
@property
def bias_initializer(self):
return self.cell.bias_initializer
@property
def kernel_regularizer(self):
return self.cell.kernel_regularizer
@property
def recurrent_regularizer(self):
return self.cell.recurrent_regularizer
@property
def bias_regularizer(self):
return self.cell.bias_regularizer
@property
def kernel_constraint(self):
return self.cell.kernel_constraint
@property
def recurrent_constraint(self):
return self.cell.recurrent_constraint
@property
def bias_constraint(self):
return self.cell.bias_constraint
@property
def dropout(self):
return self.cell.dropout
@property
def recurrent_dropout(self):
return self.cell.recurrent_dropout
@property
def implementation(self):
return self.cell.implementation
@property
def reset_after(self):
return self.cell.reset_after
def get_config(self):
config = {
'units':
self.units,
'activation':
activations.serialize(self.activation),
'recurrent_activation':
activations.serialize(self.recurrent_activation),
'use_bias':
self.use_bias,
'kernel_initializer':
initializers.serialize(self.kernel_initializer),
'recurrent_initializer':
initializers.serialize(self.recurrent_initializer),
'bias_initializer':
initializers.serialize(self.bias_initializer),
'kernel_regularizer':
regularizers.serialize(self.kernel_regularizer),
'recurrent_regularizer':
regularizers.serialize(self.recurrent_regularizer),
'bias_regularizer':
regularizers.serialize(self.bias_regularizer),
'activity_regularizer':
regularizers.serialize(self.activity_regularizer),
'kernel_constraint':
constraints.serialize(self.kernel_constraint),
'recurrent_constraint':
constraints.serialize(self.recurrent_constraint),
'bias_constraint':
constraints.serialize(self.bias_constraint),
'dropout':
self.dropout,
'recurrent_dropout':
self.recurrent_dropout,
'implementation':
self.implementation,
'reset_after':
self.reset_after
}
config.update(_config_for_enable_caching_device(self.cell))
base_config = super(GRU, self).get_config()
del base_config['cell']
return dict(list(base_config.items()) + list(config.items()))
@classmethod
def from_config(cls, config):
if 'implementation' in config and config['implementation'] == 0:
config['implementation'] = 1
return cls(**config)
class LSTMCell(DropoutRNNCellMixin, Layer):
"""Cell class for the LSTM layer.
Args:
units: Positive integer, dimensionality of the output space.
activation: Activation function to use.
Default: hyperbolic tangent (`tanh`).
If you pass `None`, no activation is applied
(ie. "linear" activation: `a(x) = x`).
recurrent_activation: Activation function to use
for the recurrent step.
Default: hard sigmoid (`hard_sigmoid`).
If you pass `None`, no activation is applied
(ie. "linear" activation: `a(x) = x`).
use_bias: Boolean, whether the layer uses a bias vector.
kernel_initializer: Initializer for the `kernel` weights matrix,
used for the linear transformation of the inputs.
recurrent_initializer: Initializer for the `recurrent_kernel`
weights matrix,
used for the linear transformation of the recurrent state.
bias_initializer: Initializer for the bias vector.
unit_forget_bias: Boolean.
If True, add 1 to the bias of the forget gate at initialization.
Setting it to true will also force `bias_initializer="zeros"`.
This is recommended in [Jozefowicz et al., 2015](
http://www.jmlr.org/proceedings/papers/v37/jozefowicz15.pdf)
kernel_regularizer: Regularizer function applied to
the `kernel` weights matrix.
recurrent_regularizer: Regularizer function applied to
the `recurrent_kernel` weights matrix.
bias_regularizer: Regularizer function applied to the bias vector.
kernel_constraint: Constraint function applied to
the `kernel` weights matrix.
recurrent_constraint: Constraint function applied to
the `recurrent_kernel` weights matrix.
bias_constraint: Constraint function applied to the bias vector.
dropout: Float between 0 and 1.
Fraction of the units to drop for
the linear transformation of the inputs.
recurrent_dropout: Float between 0 and 1.
Fraction of the units to drop for
the linear transformation of the recurrent state.
Call arguments:
inputs: A 2D tensor.
states: List of state tensors corresponding to the previous timestep.
training: Python boolean indicating whether the layer should behave in
training mode or in inference mode. Only relevant when `dropout` or
`recurrent_dropout` is used.
"""
def __init__(self,
units,
activation='tanh',
recurrent_activation='hard_sigmoid',
use_bias=True,
kernel_initializer='glorot_uniform',
recurrent_initializer='orthogonal',
bias_initializer='zeros',
unit_forget_bias=True,
kernel_regularizer=None,
recurrent_regularizer=None,
bias_regularizer=None,
kernel_constraint=None,
recurrent_constraint=None,
bias_constraint=None,
dropout=0.,
recurrent_dropout=0.,
**kwargs):
if units < 0:
raise ValueError(f'Received an invalid value for units, expected '
f'a positive integer, got {units}.')
# By default use cached variable under v2 mode, see b/143699808.
if ops.executing_eagerly_outside_functions():
self._enable_caching_device = kwargs.pop('enable_caching_device', True)
else:
self._enable_caching_device = kwargs.pop('enable_caching_device', False)
super(LSTMCell, self).__init__(**kwargs)
self.units = units
self.activation = activations.get(activation)
self.recurrent_activation = activations.get(recurrent_activation)
self.use_bias = use_bias
self.kernel_initializer = initializers.get(kernel_initializer)
self.recurrent_initializer = initializers.get(recurrent_initializer)
self.bias_initializer = initializers.get(bias_initializer)
self.unit_forget_bias = unit_forget_bias
self.kernel_regularizer = regularizers.get(kernel_regularizer)
self.recurrent_regularizer = regularizers.get(recurrent_regularizer)
self.bias_regularizer = regularizers.get(bias_regularizer)
self.kernel_constraint = constraints.get(kernel_constraint)
self.recurrent_constraint = constraints.get(recurrent_constraint)
self.bias_constraint = constraints.get(bias_constraint)
self.dropout = min(1., max(0., dropout))
self.recurrent_dropout = min(1., max(0., recurrent_dropout))
implementation = kwargs.pop('implementation', 1)
if self.recurrent_dropout != 0 and implementation != 1:
logging.debug(RECURRENT_DROPOUT_WARNING_MSG)
self.implementation = 1
else:
self.implementation = implementation
self.state_size = [self.units, self.units]
self.output_size = self.units
@tf_utils.shape_type_conversion
def build(self, input_shape):
default_caching_device = _caching_device(self)
input_dim = input_shape[-1]
self.kernel = self.add_weight(
shape=(input_dim, self.units * 4),
name='kernel',
initializer=self.kernel_initializer,
regularizer=self.kernel_regularizer,
constraint=self.kernel_constraint,
caching_device=default_caching_device)
self.recurrent_kernel = self.add_weight(
shape=(self.units, self.units * 4),
name='recurrent_kernel',
initializer=self.recurrent_initializer,
regularizer=self.recurrent_regularizer,
constraint=self.recurrent_constraint,
caching_device=default_caching_device)
if self.use_bias:
if self.unit_forget_bias:
def bias_initializer(_, *args, **kwargs):
return backend.concatenate([
self.bias_initializer((self.units,), *args, **kwargs),
initializers.get('ones')((self.units,), *args, **kwargs),
self.bias_initializer((self.units * 2,), *args, **kwargs),
])
else:
bias_initializer = self.bias_initializer
self.bias = self.add_weight(
shape=(self.units * 4,),
name='bias',
initializer=bias_initializer,
regularizer=self.bias_regularizer,
constraint=self.bias_constraint,
caching_device=default_caching_device)
else:
self.bias = None
self.built = True
def _compute_carry_and_output(self, x, h_tm1, c_tm1):
"""Computes carry and output using split kernels."""
x_i, x_f, x_c, x_o = x
h_tm1_i, h_tm1_f, h_tm1_c, h_tm1_o = h_tm1
i = self.recurrent_activation(
x_i + backend.dot(h_tm1_i, self.recurrent_kernel[:, :self.units]))
f = self.recurrent_activation(x_f + backend.dot(
h_tm1_f, self.recurrent_kernel[:, self.units:self.units * 2]))
c = f * c_tm1 + i * self.activation(x_c + backend.dot(
h_tm1_c, self.recurrent_kernel[:, self.units * 2:self.units * 3]))
o = self.recurrent_activation(
x_o + backend.dot(h_tm1_o, self.recurrent_kernel[:, self.units * 3:]))
return c, o
def _compute_carry_and_output_fused(self, z, c_tm1):
"""Computes carry and output using fused kernels."""
z0, z1, z2, z3 = z
i = self.recurrent_activation(z0)
f = self.recurrent_activation(z1)
c = f * c_tm1 + i * self.activation(z2)
o = self.recurrent_activation(z3)
return c, o
def call(self, inputs, states, training=None):
h_tm1 = states[0] # previous memory state
c_tm1 = states[1] # previous carry state
dp_mask = self.get_dropout_mask_for_cell(inputs, training, count=4)
rec_dp_mask = self.get_recurrent_dropout_mask_for_cell(
h_tm1, training, count=4)
if self.implementation == 1:
if 0 < self.dropout < 1.:
inputs_i = inputs * dp_mask[0]
inputs_f = inputs * dp_mask[1]
inputs_c = inputs * dp_mask[2]
inputs_o = inputs * dp_mask[3]
else:
inputs_i = inputs
inputs_f = inputs
inputs_c = inputs
inputs_o = inputs
k_i, k_f, k_c, k_o = array_ops.split(
self.kernel, num_or_size_splits=4, axis=1)
x_i = backend.dot(inputs_i, k_i)
x_f = backend.dot(inputs_f, k_f)
x_c = backend.dot(inputs_c, k_c)
x_o = backend.dot(inputs_o, k_o)
if self.use_bias:
b_i, b_f, b_c, b_o = array_ops.split(
self.bias, num_or_size_splits=4, axis=0)
x_i = backend.bias_add(x_i, b_i)
x_f = backend.bias_add(x_f, b_f)
x_c = backend.bias_add(x_c, b_c)
x_o = backend.bias_add(x_o, b_o)
if 0 < self.recurrent_dropout < 1.:
h_tm1_i = h_tm1 * rec_dp_mask[0]
h_tm1_f = h_tm1 * rec_dp_mask[1]
h_tm1_c = h_tm1 * rec_dp_mask[2]
h_tm1_o = h_tm1 * rec_dp_mask[3]
else:
h_tm1_i = h_tm1
h_tm1_f = h_tm1
h_tm1_c = h_tm1
h_tm1_o = h_tm1
x = (x_i, x_f, x_c, x_o)
h_tm1 = (h_tm1_i, h_tm1_f, h_tm1_c, h_tm1_o)
c, o = self._compute_carry_and_output(x, h_tm1, c_tm1)
else:
if 0. < self.dropout < 1.:
inputs = inputs * dp_mask[0]
z = backend.dot(inputs, self.kernel)
z += backend.dot(h_tm1, self.recurrent_kernel)
if self.use_bias:
z = backend.bias_add(z, self.bias)
z = array_ops.split(z, num_or_size_splits=4, axis=1)
c, o = self._compute_carry_and_output_fused(z, c_tm1)
h = o * self.activation(c)
return h, [h, c]
def get_config(self):
config = {
'units':
self.units,
'activation':
activations.serialize(self.activation),
'recurrent_activation':
activations.serialize(self.recurrent_activation),
'use_bias':
self.use_bias,
'kernel_initializer':
initializers.serialize(self.kernel_initializer),
'recurrent_initializer':
initializers.serialize(self.recurrent_initializer),
'bias_initializer':
initializers.serialize(self.bias_initializer),
'unit_forget_bias':
self.unit_forget_bias,
'kernel_regularizer':
regularizers.serialize(self.kernel_regularizer),
'recurrent_regularizer':
regularizers.serialize(self.recurrent_regularizer),
'bias_regularizer':
regularizers.serialize(self.bias_regularizer),
'kernel_constraint':
constraints.serialize(self.kernel_constraint),
'recurrent_constraint':
constraints.serialize(self.recurrent_constraint),
'bias_constraint':
constraints.serialize(self.bias_constraint),
'dropout':
self.dropout,
'recurrent_dropout':
self.recurrent_dropout,
'implementation':
self.implementation
}
config.update(_config_for_enable_caching_device(self))
base_config = super(LSTMCell, self).get_config()
return dict(list(base_config.items()) + list(config.items()))
def get_initial_state(self, inputs=None, batch_size=None, dtype=None):
return list(_generate_zero_filled_state_for_cell(
self, inputs, batch_size, dtype))
class PeepholeLSTMCell(LSTMCell):
"""Equivalent to LSTMCell class but adds peephole connections.
Peephole connections allow the gates to utilize the previous internal state as
well as the previous hidden state (which is what LSTMCell is limited to).
This allows PeepholeLSTMCell to better learn precise timings over LSTMCell.
From [Gers et al., 2002](
http://www.jmlr.org/papers/volume3/gers02a/gers02a.pdf):
"We find that LSTM augmented by 'peephole connections' from its internal
cells to its multiplicative gates can learn the fine distinction between
sequences of spikes spaced either 50 or 49 time steps apart without the help
of any short training exemplars."
The peephole implementation is based on:
[Sak et al., 2014](https://research.google.com/pubs/archive/43905.pdf)
Example:
```python
# Create 2 PeepholeLSTMCells
peephole_lstm_cells = [PeepholeLSTMCell(size) for size in [128, 256]]
# Create a layer composed sequentially of the peephole LSTM cells.
layer = RNN(peephole_lstm_cells)
input = keras.Input((timesteps, input_dim))
output = layer(input)
```
"""
def __init__(self,
units,
activation='tanh',
recurrent_activation='hard_sigmoid',
use_bias=True,
kernel_initializer='glorot_uniform',
recurrent_initializer='orthogonal',
bias_initializer='zeros',
unit_forget_bias=True,
kernel_regularizer=None,
recurrent_regularizer=None,
bias_regularizer=None,
kernel_constraint=None,
recurrent_constraint=None,
bias_constraint=None,
dropout=0.,
recurrent_dropout=0.,
**kwargs):
warnings.warn('`tf.keras.experimental.PeepholeLSTMCell` is deprecated '
'and will be removed in a future version. '
'Please use tensorflow_addons.rnn.PeepholeLSTMCell '
'instead.')
super(PeepholeLSTMCell, self).__init__(
units=units,
activation=activation,
recurrent_activation=recurrent_activation,
use_bias=use_bias,
kernel_initializer=kernel_initializer,
recurrent_initializer=recurrent_initializer,
bias_initializer=bias_initializer,
unit_forget_bias=unit_forget_bias,
kernel_regularizer=kernel_regularizer,
recurrent_regularizer=recurrent_regularizer,
bias_regularizer=bias_regularizer,
kernel_constraint=kernel_constraint,
recurrent_constraint=recurrent_constraint,
bias_constraint=bias_constraint,
dropout=dropout,
recurrent_dropout=recurrent_dropout,
implementation=kwargs.pop('implementation', 1),
**kwargs)
def build(self, input_shape):
super(PeepholeLSTMCell, self).build(input_shape)
# The following are the weight matrices for the peephole connections. These
# are multiplied with the previous internal state during the computation of
# carry and output.
self.input_gate_peephole_weights = self.add_weight(
shape=(self.units,),
name='input_gate_peephole_weights',
initializer=self.kernel_initializer)
self.forget_gate_peephole_weights = self.add_weight(
shape=(self.units,),
name='forget_gate_peephole_weights',
initializer=self.kernel_initializer)
self.output_gate_peephole_weights = self.add_weight(
shape=(self.units,),
name='output_gate_peephole_weights',
initializer=self.kernel_initializer)
def _compute_carry_and_output(self, x, h_tm1, c_tm1):
x_i, x_f, x_c, x_o = x
h_tm1_i, h_tm1_f, h_tm1_c, h_tm1_o = h_tm1
i = self.recurrent_activation(
x_i + backend.dot(h_tm1_i, self.recurrent_kernel[:, :self.units]) +
self.input_gate_peephole_weights * c_tm1)
f = self.recurrent_activation(x_f + backend.dot(
h_tm1_f, self.recurrent_kernel[:, self.units:self.units * 2]) +
self.forget_gate_peephole_weights * c_tm1)
c = f * c_tm1 + i * self.activation(x_c + backend.dot(
h_tm1_c, self.recurrent_kernel[:, self.units * 2:self.units * 3]))
o = self.recurrent_activation(
x_o + backend.dot(h_tm1_o, self.recurrent_kernel[:, self.units * 3:]) +
self.output_gate_peephole_weights * c)
return c, o
def _compute_carry_and_output_fused(self, z, c_tm1):
z0, z1, z2, z3 = z
i = self.recurrent_activation(z0 +
self.input_gate_peephole_weights * c_tm1)
f = self.recurrent_activation(z1 +
self.forget_gate_peephole_weights * c_tm1)
c = f * c_tm1 + i * self.activation(z2)
o = self.recurrent_activation(z3 + self.output_gate_peephole_weights * c)
return c, o
class LSTM(RNN):
"""Long Short-Term Memory layer - Hochreiter 1997.
Note that this cell is not optimized for performance on GPU. Please use
`tf.compat.v1.keras.layers.CuDNNLSTM` for better performance on GPU.
Args:
units: Positive integer, dimensionality of the output space.
activation: Activation function to use.
Default: hyperbolic tangent (`tanh`).
If you pass `None`, no activation is applied
(ie. "linear" activation: `a(x) = x`).
recurrent_activation: Activation function to use
for the recurrent step.
Default: hard sigmoid (`hard_sigmoid`).
If you pass `None`, no activation is applied
(ie. "linear" activation: `a(x) = x`).
use_bias: Boolean, whether the layer uses a bias vector.
kernel_initializer: Initializer for the `kernel` weights matrix,
used for the linear transformation of the inputs..
recurrent_initializer: Initializer for the `recurrent_kernel`
weights matrix,
used for the linear transformation of the recurrent state.
bias_initializer: Initializer for the bias vector.
unit_forget_bias: Boolean.
If True, add 1 to the bias of the forget gate at initialization.
Setting it to true will also force `bias_initializer="zeros"`.
This is recommended in [Jozefowicz et al., 2015](
http://www.jmlr.org/proceedings/papers/v37/jozefowicz15.pdf).
kernel_regularizer: Regularizer function applied to
the `kernel` weights matrix.
recurrent_regularizer: Regularizer function applied to
the `recurrent_kernel` weights matrix.
bias_regularizer: Regularizer function applied to the bias vector.
activity_regularizer: Regularizer function applied to
the output of the layer (its "activation").
kernel_constraint: Constraint function applied to
the `kernel` weights matrix.
recurrent_constraint: Constraint function applied to
the `recurrent_kernel` weights matrix.
bias_constraint: Constraint function applied to the bias vector.
dropout: Float between 0 and 1.
Fraction of the units to drop for
the linear transformation of the inputs.
recurrent_dropout: Float between 0 and 1.
Fraction of the units to drop for
the linear transformation of the recurrent state.
return_sequences: Boolean. Whether to return the last output.
in the output sequence, or the full sequence.
return_state: Boolean. Whether to return the last state
in addition to the output.
go_backwards: Boolean (default False).
If True, process the input sequence backwards and return the
reversed sequence.
stateful: Boolean (default False). If True, the last state
for each sample at index i in a batch will be used as initial
state for the sample of index i in the following batch.
unroll: Boolean (default False).
If True, the network will be unrolled,
else a symbolic loop will be used.
Unrolling can speed-up a RNN,
although it tends to be more memory-intensive.
Unrolling is only suitable for short sequences.
time_major: The shape format of the `inputs` and `outputs` tensors.
If True, the inputs and outputs will be in shape
`(timesteps, batch, ...)`, whereas in the False case, it will be
`(batch, timesteps, ...)`. 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.
Call arguments:
inputs: A 3D tensor.
mask: Binary tensor of shape `(samples, timesteps)` indicating whether
a given timestep should be masked. An individual `True` entry indicates
that the corresponding timestep should be utilized, while a `False`
entry indicates that the corresponding timestep should be ignored.
training: Python boolean indicating whether the layer should behave in
training mode or in inference mode. This argument is passed to the cell
when calling it. This is only relevant if `dropout` or
`recurrent_dropout` is used.
initial_state: List of initial state tensors to be passed to the first
call of the cell.
"""
def __init__(self,
units,
activation='tanh',
recurrent_activation='hard_sigmoid',
use_bias=True,
kernel_initializer='glorot_uniform',
recurrent_initializer='orthogonal',
bias_initializer='zeros',
unit_forget_bias=True,
kernel_regularizer=None,
recurrent_regularizer=None,
bias_regularizer=None,
activity_regularizer=None,
kernel_constraint=None,
recurrent_constraint=None,
bias_constraint=None,
dropout=0.,
recurrent_dropout=0.,
return_sequences=False,
return_state=False,
go_backwards=False,
stateful=False,
unroll=False,
**kwargs):
implementation = kwargs.pop('implementation', 1)
if implementation == 0:
logging.warning('`implementation=0` has been deprecated, '
'and now defaults to `implementation=1`.'
'Please update your layer call.')
if 'enable_caching_device' in kwargs:
cell_kwargs = {'enable_caching_device':
kwargs.pop('enable_caching_device')}
else:
cell_kwargs = {}
cell = LSTMCell(
units,
activation=activation,
recurrent_activation=recurrent_activation,
use_bias=use_bias,
kernel_initializer=kernel_initializer,
recurrent_initializer=recurrent_initializer,
unit_forget_bias=unit_forget_bias,
bias_initializer=bias_initializer,
kernel_regularizer=kernel_regularizer,
recurrent_regularizer=recurrent_regularizer,
bias_regularizer=bias_regularizer,
kernel_constraint=kernel_constraint,
recurrent_constraint=recurrent_constraint,
bias_constraint=bias_constraint,
dropout=dropout,
recurrent_dropout=recurrent_dropout,
implementation=implementation,
dtype=kwargs.get('dtype'),
trainable=kwargs.get('trainable', True),
**cell_kwargs)
super(LSTM, self).__init__(
cell,
return_sequences=return_sequences,
return_state=return_state,
go_backwards=go_backwards,
stateful=stateful,
unroll=unroll,
**kwargs)
self.activity_regularizer = regularizers.get(activity_regularizer)
self.input_spec = [InputSpec(ndim=3)]
def call(self, inputs, mask=None, training=None, initial_state=None):
return super(LSTM, self).call(
inputs, mask=mask, training=training, initial_state=initial_state)
@property
def units(self):
return self.cell.units
@property
def activation(self):
return self.cell.activation
@property
def recurrent_activation(self):
return self.cell.recurrent_activation
@property
def use_bias(self):
return self.cell.use_bias
@property
def kernel_initializer(self):
return self.cell.kernel_initializer
@property
def recurrent_initializer(self):
return self.cell.recurrent_initializer
@property
def bias_initializer(self):
return self.cell.bias_initializer
@property
def unit_forget_bias(self):
return self.cell.unit_forget_bias
@property
def kernel_regularizer(self):
return self.cell.kernel_regularizer
@property
def recurrent_regularizer(self):
return self.cell.recurrent_regularizer
@property
def bias_regularizer(self):
return self.cell.bias_regularizer
@property
def kernel_constraint(self):
return self.cell.kernel_constraint
@property
def recurrent_constraint(self):
return self.cell.recurrent_constraint
@property
def bias_constraint(self):
return self.cell.bias_constraint
@property
def dropout(self):
return self.cell.dropout
@property
def recurrent_dropout(self):
return self.cell.recurrent_dropout
@property
def implementation(self):
return self.cell.implementation
def get_config(self):
config = {
'units':
self.units,
'activation':
activations.serialize(self.activation),
'recurrent_activation':
activations.serialize(self.recurrent_activation),
'use_bias':
self.use_bias,
'kernel_initializer':
initializers.serialize(self.kernel_initializer),
'recurrent_initializer':
initializers.serialize(self.recurrent_initializer),
'bias_initializer':
initializers.serialize(self.bias_initializer),
'unit_forget_bias':
self.unit_forget_bias,
'kernel_regularizer':
regularizers.serialize(self.kernel_regularizer),
'recurrent_regularizer':
regularizers.serialize(self.recurrent_regularizer),
'bias_regularizer':
regularizers.serialize(self.bias_regularizer),
'activity_regularizer':
regularizers.serialize(self.activity_regularizer),
'kernel_constraint':
constraints.serialize(self.kernel_constraint),
'recurrent_constraint':
constraints.serialize(self.recurrent_constraint),
'bias_constraint':
constraints.serialize(self.bias_constraint),
'dropout':
self.dropout,
'recurrent_dropout':
self.recurrent_dropout,
'implementation':
self.implementation
}
config.update(_config_for_enable_caching_device(self.cell))
base_config = super(LSTM, self).get_config()
del base_config['cell']
return dict(list(base_config.items()) + list(config.items()))
@classmethod
def from_config(cls, config):
if 'implementation' in config and config['implementation'] == 0:
config['implementation'] = 1
return cls(**config)
def _generate_dropout_mask(ones, rate, training=None, count=1):
def dropped_inputs():
return backend.dropout(ones, rate)
if count > 1:
return [
backend.in_train_phase(dropped_inputs, ones, training=training)
for _ in range(count)
]
return backend.in_train_phase(dropped_inputs, ones, training=training)
def _standardize_args(inputs, initial_state, constants, num_constants):
"""Standardizes `__call__` to a single list of tensor inputs.
When running a model loaded from a file, the input tensors
`initial_state` and `constants` can be passed to `RNN.__call__()` as part
of `inputs` instead of by the dedicated keyword arguments. This method
makes sure the arguments are separated and that `initial_state` and
`constants` are lists of tensors (or None).
Args:
inputs: Tensor or list/tuple of tensors. which may include constants
and initial states. In that case `num_constant` must be specified.
initial_state: Tensor or list of tensors or None, initial states.
constants: Tensor or list of tensors or None, constant tensors.
num_constants: Expected number of constants (if constants are passed as
part of the `inputs` list.
Returns:
inputs: Single tensor or tuple of tensors.
initial_state: List of tensors or None.
constants: List of tensors or None.
"""
if isinstance(inputs, list):
# There are several situations here:
# In the graph mode, __call__ will be only called once. The initial_state
# and constants could be in inputs (from file loading).
# In the eager mode, __call__ will be called twice, once during
# rnn_layer(inputs=input_t, constants=c_t, ...), and second time will be
# model.fit/train_on_batch/predict with real np data. In the second case,
# the inputs will contain initial_state and constants as eager tensor.
#
# For either case, the real input is the first item in the list, which
# could be a nested structure itself. Then followed by initial_states, which
# could be a list of items, or list of list if the initial_state is complex
# structure, and finally followed by constants which is a flat list.
assert initial_state is None and constants is None
if num_constants:
constants = inputs[-num_constants:]
inputs = inputs[:-num_constants]
if len(inputs) > 1:
initial_state = inputs[1:]
inputs = inputs[:1]
if len(inputs) > 1:
inputs = tuple(inputs)
else:
inputs = inputs[0]
def to_list_or_none(x):
if x is None or isinstance(x, list):
return x
if isinstance(x, tuple):
return list(x)
return [x]
initial_state = to_list_or_none(initial_state)
constants = to_list_or_none(constants)
return inputs, initial_state, constants
def _is_multiple_state(state_size):
"""Check whether the state_size contains multiple states."""
return (hasattr(state_size, '__len__') and
not isinstance(state_size, tensor_shape.TensorShape))
def _generate_zero_filled_state_for_cell(cell, inputs, batch_size, dtype):
if inputs is not None:
batch_size = array_ops.shape(inputs)[0]
dtype = inputs.dtype
return _generate_zero_filled_state(batch_size, cell.state_size, dtype)
def _generate_zero_filled_state(batch_size_tensor, state_size, dtype):
"""Generate a zero filled tensor with shape [batch_size, state_size]."""
if batch_size_tensor is None or dtype is None:
raise ValueError(
'batch_size and dtype cannot be None while constructing initial state: '
'batch_size={}, dtype={}'.format(batch_size_tensor, dtype))
def create_zeros(unnested_state_size):
flat_dims = tensor_shape.TensorShape(unnested_state_size).as_list()
init_state_size = [batch_size_tensor] + flat_dims
return array_ops.zeros(init_state_size, dtype=dtype)
if nest.is_nested(state_size):
return nest.map_structure(create_zeros, state_size)
else:
return create_zeros(state_size)
def _caching_device(rnn_cell):
"""Returns the caching device for the RNN variable.
This is useful for distributed training, when variable is not located as same
device as the training worker. By enabling the device cache, this allows
worker to read the variable once and cache locally, rather than read it every
time step from remote when it is needed.
Note that this is assuming the variable that cell needs for each time step is
having the same value in the forward path, and only gets updated in the
backprop. It is true for all the default cells (SimpleRNN, GRU, LSTM). If the
cell body relies on any variable that gets updated every time step, then
caching device will cause it to read the stall value.
Args:
rnn_cell: the rnn cell instance.
"""
if context.executing_eagerly():
# caching_device is not supported in eager mode.
return None
if not getattr(rnn_cell, '_enable_caching_device', False):
return None
# 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.
if control_flow_util.IsInWhileLoop(ops.get_default_graph()):
logging.warning(
'Variable read device caching has been disabled because the '
'RNN is in tf.while_loop loop context, which will cause '
'reading stalled value in forward path. This could slow down '
'the training due to duplicated variable reads. Please '
'consider updating your code to remove tf.while_loop if possible.')
return None
if (rnn_cell._dtype_policy.compute_dtype !=
rnn_cell._dtype_policy.variable_dtype):
logging.warning(
'Variable read device caching has been disabled since it '
'doesn\'t work with the mixed precision API. This is '
'likely to cause a slowdown for RNN training due to '
'duplicated read of variable for each timestep, which '
'will be significant in a multi remote worker setting. '
'Please consider disabling mixed precision API if '
'the performance has been affected.')
return None
# Cache the value on the device that access the variable.
return lambda op: op.device
def _config_for_enable_caching_device(rnn_cell):
"""Return the dict config for RNN cell wrt to enable_caching_device field.
Since enable_caching_device is a internal implementation detail for speed up
the RNN variable read when running on the multi remote worker setting, we
don't want this config to be serialized constantly in the JSON. We will only
serialize this field when a none default value is used to create the cell.
Args:
rnn_cell: the RNN cell for serialize.
Returns:
A dict which contains the JSON config for enable_caching_device value or
empty dict if the enable_caching_device value is same as the default value.
"""
default_enable_caching_device = ops.executing_eagerly_outside_functions()
if rnn_cell._enable_caching_device != default_enable_caching_device:
return {'enable_caching_device': rnn_cell._enable_caching_device}
return {}