Module keras.layers.legacy_rnn.rnn_cell_impl
Module implementing RNN Cells.
This module provides a number of basic commonly used RNN cells, such as LSTM
(Long Short Term Memory) or GRU (Gated Recurrent Unit), and a number of
operators that allow adding dropouts, projections, or embeddings for inputs.
Constructing multi-layer cells is supported by the class MultiRNNCell
, or by
calling the rnn
ops several times.
Expand source code
# 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=g-classes-have-attributes
"""Module implementing RNN Cells.
This module provides a number of basic commonly used RNN cells, such as LSTM
(Long Short Term Memory) or GRU (Gated Recurrent Unit), and a number of
operators that allow adding dropouts, projections, or embeddings for inputs.
Constructing multi-layer cells is supported by the class `MultiRNNCell`, or by
calling the `rnn` ops several times.
"""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import tensorflow.compat.v2 as tf
import collections
import warnings
from keras import activations
from keras import backend
from keras import initializers
from keras.engine import base_layer_utils
from keras.engine import input_spec
from keras.layers.legacy_rnn import rnn_cell_wrapper_impl
from keras.legacy_tf_layers import base as base_layer
from keras.utils import tf_utils
from tensorflow.python.platform import tf_logging as logging
from tensorflow.python.util.tf_export import keras_export
from tensorflow.python.util.tf_export import tf_export
_BIAS_VARIABLE_NAME = "bias"
_WEIGHTS_VARIABLE_NAME = "kernel"
# This can be used with self.assertRaisesRegexp for assert_like_rnncell.
ASSERT_LIKE_RNNCELL_ERROR_REGEXP = "is not an RNNCell"
def _hasattr(obj, attr_name):
try:
getattr(obj, attr_name)
except AttributeError:
return False
else:
return True
def assert_like_rnncell(cell_name, cell):
"""Raises a TypeError if cell is not like an RNNCell.
NOTE: Do not rely on the error message (in particular in tests) which can be
subject to change to increase readability. Use
ASSERT_LIKE_RNNCELL_ERROR_REGEXP.
Args:
cell_name: A string to give a meaningful error referencing to the name of
the functionargument.
cell: The object which should behave like an RNNCell.
Raises:
TypeError: A human-friendly exception.
"""
conditions = [
_hasattr(cell, "output_size"),
_hasattr(cell, "state_size"),
_hasattr(cell, "get_initial_state") or _hasattr(cell, "zero_state"),
callable(cell),
]
errors = [
"'output_size' property is missing", "'state_size' property is missing",
"either 'zero_state' or 'get_initial_state' method is required",
"is not callable"
]
if not all(conditions):
errors = [error for error, cond in zip(errors, conditions) if not cond]
raise TypeError("The argument {!r} ({}) is not an RNNCell: {}.".format(
cell_name, cell, ", ".join(errors)))
def _concat(prefix, suffix, static=False):
"""Concat that enables int, Tensor, or TensorShape values.
This function takes a size specification, which can be an integer, a
TensorShape, or a Tensor, and converts it into a concatenated Tensor
(if static = False) or a list of integers (if static = True).
Args:
prefix: The prefix; usually the batch size (and/or time step size).
(TensorShape, int, or Tensor.)
suffix: TensorShape, int, or Tensor.
static: If `True`, return a python list with possibly unknown dimensions.
Otherwise return a `Tensor`.
Returns:
shape: the concatenation of prefix and suffix.
Raises:
ValueError: if `suffix` is not a scalar or vector (or TensorShape).
ValueError: if prefix or suffix was `None` and asked for dynamic
Tensors out.
"""
if isinstance(prefix, tf.Tensor):
p = prefix
p_static = tf.get_static_value(prefix)
if p.shape.ndims == 0:
p = tf.compat.v1.expand_dims(p, 0)
elif p.shape.ndims != 1:
raise ValueError("prefix tensor must be either a scalar or vector, "
"but saw tensor: %s" % p)
else:
p = tf.TensorShape(prefix)
p_static = p.as_list() if p.ndims is not None else None
p = (
tf.constant(p.as_list(), dtype=tf.int32)
if p.is_fully_defined() else None)
if isinstance(suffix, tf.Tensor):
s = suffix
s_static = tf.get_static_value(suffix)
if s.shape.ndims == 0:
s = tf.compat.v1.expand_dims(s, 0)
elif s.shape.ndims != 1:
raise ValueError("suffix tensor must be either a scalar or vector, "
"but saw tensor: %s" % s)
else:
s = tf.TensorShape(suffix)
s_static = s.as_list() if s.ndims is not None else None
s = (
tf.constant(s.as_list(), dtype=tf.int32)
if s.is_fully_defined() else None)
if static:
shape = tf.TensorShape(p_static).concatenate(s_static)
shape = shape.as_list() if shape.ndims is not None else None
else:
if p is None or s is None:
raise ValueError("Provided a prefix or suffix of None: %s and %s" %
(prefix, suffix))
shape = tf.concat((p, s), 0)
return shape
def _zero_state_tensors(state_size, batch_size, dtype):
"""Create tensors of zeros based on state_size, batch_size, and dtype."""
def get_state_shape(s):
"""Combine s with batch_size to get a proper tensor shape."""
c = _concat(batch_size, s)
size = tf.zeros(c, dtype=dtype)
if not tf.executing_eagerly():
c_static = _concat(batch_size, s, static=True)
size.set_shape(c_static)
return size
return tf.nest.map_structure(get_state_shape, state_size)
@keras_export(v1=["keras.__internal__.legacy.rnn_cell.RNNCell"])
@tf_export(v1=["nn.rnn_cell.RNNCell"])
class RNNCell(base_layer.Layer):
"""Abstract object representing an RNN cell.
Every `RNNCell` must have the properties below and implement `call` with
the signature `(output, next_state) = call(input, state)`. The optional
third input argument, `scope`, is allowed for backwards compatibility
purposes; but should be left off for new subclasses.
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 __init__(self, trainable=True, name=None, dtype=None, **kwargs):
super(RNNCell, self).__init__(
trainable=trainable, name=name, dtype=dtype, **kwargs)
# Attribute that indicates whether the cell is a TF RNN cell, due the slight
# difference between TF and Keras RNN cell. Notably the state is not wrapped
# in a list for TF cell where they are single tensor state, whereas keras
# cell will wrap the state into a list, and call() will have to unwrap them.
self._is_tf_rnn_cell = True
def __call__(self, inputs, state, scope=None):
"""Run this RNN cell on inputs, starting from the given state.
Args:
inputs: `2-D` tensor with shape `[batch_size, input_size]`.
state: if `self.state_size` is an integer, this should be a `2-D Tensor`
with shape `[batch_size, self.state_size]`. Otherwise, if
`self.state_size` is a tuple of integers, this should be a tuple with
shapes `[batch_size, s] for s in self.state_size`.
scope: VariableScope for the created subgraph; defaults to class name.
Returns:
A pair containing:
- Output: A `2-D` tensor with shape `[batch_size, self.output_size]`.
- New state: Either a single `2-D` tensor, or a tuple of tensors matching
the arity and shapes of `state`.
"""
if scope is not None:
with tf.compat.v1.variable_scope(
scope, custom_getter=self._rnn_get_variable) as scope:
return super(RNNCell, self).__call__(inputs, state, scope=scope)
else:
scope_attrname = "rnncell_scope"
scope = getattr(self, scope_attrname, None)
if scope is None:
scope = tf.compat.v1.variable_scope(
tf.compat.v1.get_variable_scope(), custom_getter=self._rnn_get_variable)
setattr(self, scope_attrname, scope)
with scope:
return super(RNNCell, self).__call__(inputs, state)
def _rnn_get_variable(self, getter, *args, **kwargs):
variable = getter(*args, **kwargs)
if tf.compat.v1.executing_eagerly_outside_functions():
trainable = variable.trainable
else:
trainable = (
variable in tf.compat.v1.trainable_variables() or
(base_layer_utils.is_split_variable(variable) and
list(variable)[0] in tf.compat.v1.trainable_variables()))
if trainable and all(variable is not v for v in self._trainable_weights):
self._trainable_weights.append(variable)
elif not trainable and all(
variable is not v for v in self._non_trainable_weights):
self._non_trainable_weights.append(variable)
return variable
@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 build(self, _):
# This tells the parent Layer object that it's OK to call
# self.add_variable() inside the call() method.
pass
def get_initial_state(self, inputs=None, batch_size=None, dtype=None):
if inputs is not None:
# Validate the given batch_size and dtype against inputs if provided.
inputs = tf.convert_to_tensor(inputs, name="inputs")
if batch_size is not None:
if tf.is_tensor(batch_size):
static_batch_size = tf.get_static_value(
batch_size, partial=True)
else:
static_batch_size = batch_size
if inputs.shape.dims[0].value != static_batch_size:
raise ValueError(
"batch size from input tensor is different from the "
"input param. Input tensor batch: {}, batch_size: {}".format(
inputs.shape.dims[0].value, batch_size))
if dtype is not None and inputs.dtype != dtype:
raise ValueError(
"dtype from input tensor is different from the "
"input param. Input tensor dtype: {}, dtype: {}".format(
inputs.dtype, dtype))
batch_size = inputs.shape.dims[0].value or tf.compat.v1.shape(inputs)[0]
dtype = inputs.dtype
if batch_size 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, dtype))
return self.zero_state(batch_size, dtype)
def zero_state(self, batch_size, dtype):
"""Return zero-filled state tensor(s).
Args:
batch_size: int, float, or unit Tensor representing the batch size.
dtype: the data type to use for the state.
Returns:
If `state_size` is an int or TensorShape, then the return value is a
`N-D` tensor of shape `[batch_size, state_size]` filled with zeros.
If `state_size` is a nested list or tuple, then the return value is
a nested list or tuple (of the same structure) of `2-D` tensors with
the shapes `[batch_size, s]` for each s in `state_size`.
"""
# Try to use the last cached zero_state. This is done to avoid recreating
# zeros, especially when eager execution is enabled.
state_size = self.state_size
is_eager = tf.executing_eagerly()
if is_eager and _hasattr(self, "_last_zero_state"):
(last_state_size, last_batch_size, last_dtype,
last_output) = getattr(self, "_last_zero_state")
if (last_batch_size == batch_size and last_dtype == dtype and
last_state_size == state_size):
return last_output
with backend.name_scope(type(self).__name__ + "ZeroState"):
output = _zero_state_tensors(state_size, batch_size, dtype)
if is_eager:
self._last_zero_state = (state_size, batch_size, dtype, output)
return output
# TODO(b/134773139): Remove when contrib RNN cells implement `get_config`
def get_config(self): # pylint: disable=useless-super-delegation
return super(RNNCell, self).get_config()
@property
def _use_input_spec_as_call_signature(self):
# We do not store the shape information for the state argument in the call
# function for legacy RNN cells, so do not generate an input signature.
return False
class LayerRNNCell(RNNCell):
"""Subclass of RNNCells that act like proper `tf.Layer` objects.
For backwards compatibility purposes, most `RNNCell` instances allow their
`call` methods to instantiate variables via `tf.compat.v1.get_variable`. The
underlying
variable scope thus keeps track of any variables, and returning cached
versions. This is atypical of `tf.layer` objects, which separate this
part of layer building into a `build` method that is only called once.
Here we provide a subclass for `RNNCell` objects that act exactly as
`Layer` objects do. They must provide a `build` method and their
`call` methods do not access Variables `tf.compat.v1.get_variable`.
"""
def __call__(self, inputs, state, scope=None, *args, **kwargs):
"""Run this RNN cell on inputs, starting from the given state.
Args:
inputs: `2-D` tensor with shape `[batch_size, input_size]`.
state: if `self.state_size` is an integer, this should be a `2-D Tensor`
with shape `[batch_size, self.state_size]`. Otherwise, if
`self.state_size` is a tuple of integers, this should be a tuple with
shapes `[batch_size, s] for s in self.state_size`.
scope: optional cell scope.
*args: Additional positional arguments.
**kwargs: Additional keyword arguments.
Returns:
A pair containing:
- Output: A `2-D` tensor with shape `[batch_size, self.output_size]`.
- New state: Either a single `2-D` tensor, or a tuple of tensors matching
the arity and shapes of `state`.
"""
# Bypass RNNCell's variable capturing semantics for LayerRNNCell.
# Instead, it is up to subclasses to provide a proper build
# method. See the class docstring for more details.
return base_layer.Layer.__call__(
self, inputs, state, scope=scope, *args, **kwargs)
@keras_export(v1=["keras.__internal__.legacy.rnn_cell.BasicRNNCell"])
@tf_export(v1=["nn.rnn_cell.BasicRNNCell"])
class BasicRNNCell(LayerRNNCell):
"""The most basic RNN cell.
Note that this cell is not optimized for performance. Please use
`tf.contrib.cudnn_rnn.CudnnRNNTanh` for better performance on GPU.
Args:
num_units: int, The number of units in the RNN cell.
activation: Nonlinearity to use. Default: `tanh`. It could also be string
that is within Keras activation function names.
reuse: (optional) Python boolean describing whether to reuse variables in an
existing scope. If not `True`, and the existing scope already has the
given variables, an error is raised.
name: String, the name of the layer. Layers with the same name will share
weights, but to avoid mistakes we require reuse=True in such cases.
dtype: Default dtype of the layer (default of `None` means use the type of
the first input). Required when `build` is called before `call`.
**kwargs: Dict, keyword named properties for common layer attributes, like
`trainable` etc when constructing the cell from configs of get_config().
"""
def __init__(self,
num_units,
activation=None,
reuse=None,
name=None,
dtype=None,
**kwargs):
warnings.warn("`tf.nn.rnn_cell.BasicRNNCell` is deprecated and will be "
"removed in a future version. This class "
"is equivalent as `tf.keras.layers.SimpleRNNCell`, "
"and will be replaced by that in Tensorflow 2.0.")
super(BasicRNNCell, self).__init__(
_reuse=reuse, name=name, dtype=dtype, **kwargs)
_check_supported_dtypes(self.dtype)
if tf.executing_eagerly() and tf.config.list_logical_devices("GPU"):
logging.warning(
"%s: Note that this cell is not optimized for performance. "
"Please use tf.contrib.cudnn_rnn.CudnnRNNTanh for better "
"performance on GPU.", self)
# Inputs must be 2-dimensional.
self.input_spec = input_spec.InputSpec(ndim=2)
self._num_units = num_units
if activation:
self._activation = activations.get(activation)
else:
self._activation = tf.tanh
@property
def state_size(self):
return self._num_units
@property
def output_size(self):
return self._num_units
@tf_utils.shape_type_conversion
def build(self, inputs_shape):
if inputs_shape[-1] is None:
raise ValueError("Expected inputs.shape[-1] to be known, saw shape: %s" %
str(inputs_shape))
_check_supported_dtypes(self.dtype)
input_depth = inputs_shape[-1]
self._kernel = self.add_variable(
_WEIGHTS_VARIABLE_NAME,
shape=[input_depth + self._num_units, self._num_units])
self._bias = self.add_variable(
_BIAS_VARIABLE_NAME,
shape=[self._num_units],
initializer=tf.compat.v1.zeros_initializer(dtype=self.dtype))
self.built = True
def call(self, inputs, state):
"""Most basic RNN: output = new_state = act(W * input + U * state + B)."""
_check_rnn_cell_input_dtypes([inputs, state])
gate_inputs = tf.matmul(
tf.concat([inputs, state], 1), self._kernel)
gate_inputs = tf.nn.bias_add(gate_inputs, self._bias)
output = self._activation(gate_inputs)
return output, output
def get_config(self):
config = {
"num_units": self._num_units,
"activation": activations.serialize(self._activation),
"reuse": self._reuse,
}
base_config = super(BasicRNNCell, self).get_config()
return dict(list(base_config.items()) + list(config.items()))
@keras_export(v1=["keras.__internal__.legacy.rnn_cell.GRUCell"])
@tf_export(v1=["nn.rnn_cell.GRUCell"])
class GRUCell(LayerRNNCell):
"""Gated Recurrent Unit cell.
Note that this cell is not optimized for performance. Please use
`tf.contrib.cudnn_rnn.CudnnGRU` for better performance on GPU, or
`tf.contrib.rnn.GRUBlockCellV2` for better performance on CPU.
Args:
num_units: int, The number of units in the GRU cell.
activation: Nonlinearity to use. Default: `tanh`.
reuse: (optional) Python boolean describing whether to reuse variables in an
existing scope. If not `True`, and the existing scope already has the
given variables, an error is raised.
kernel_initializer: (optional) The initializer to use for the weight and
projection matrices.
bias_initializer: (optional) The initializer to use for the bias.
name: String, the name of the layer. Layers with the same name will share
weights, but to avoid mistakes we require reuse=True in such cases.
dtype: Default dtype of the layer (default of `None` means use the type of
the first input). Required when `build` is called before `call`.
**kwargs: Dict, keyword named properties for common layer attributes, like
`trainable` etc when constructing the cell from configs of get_config().
References:
Learning Phrase Representations using RNN Encoder Decoder for Statistical
Machine Translation:
[Cho et al., 2014]
(https://aclanthology.coli.uni-saarland.de/papers/D14-1179/d14-1179)
([pdf](http://emnlp2014.org/papers/pdf/EMNLP2014179.pdf))
"""
def __init__(self,
num_units,
activation=None,
reuse=None,
kernel_initializer=None,
bias_initializer=None,
name=None,
dtype=None,
**kwargs):
warnings.warn("`tf.nn.rnn_cell.GRUCell` is deprecated and will be removed "
"in a future version. This class "
"is equivalent as `tf.keras.layers.GRUCell`, "
"and will be replaced by that in Tensorflow 2.0.")
super(GRUCell, self).__init__(
_reuse=reuse, name=name, dtype=dtype, **kwargs)
_check_supported_dtypes(self.dtype)
if tf.executing_eagerly() and tf.config.list_logical_devices("GPU"):
logging.warning(
"%s: Note that this cell is not optimized for performance. "
"Please use tf.contrib.cudnn_rnn.CudnnGRU for better "
"performance on GPU.", self)
# Inputs must be 2-dimensional.
self.input_spec = input_spec.InputSpec(ndim=2)
self._num_units = num_units
if activation:
self._activation = activations.get(activation)
else:
self._activation = tf.tanh
self._kernel_initializer = initializers.get(kernel_initializer)
self._bias_initializer = initializers.get(bias_initializer)
@property
def state_size(self):
return self._num_units
@property
def output_size(self):
return self._num_units
@tf_utils.shape_type_conversion
def build(self, inputs_shape):
if inputs_shape[-1] is None:
raise ValueError("Expected inputs.shape[-1] to be known, saw shape: %s" %
str(inputs_shape))
_check_supported_dtypes(self.dtype)
input_depth = inputs_shape[-1]
self._gate_kernel = self.add_variable(
"gates/%s" % _WEIGHTS_VARIABLE_NAME,
shape=[input_depth + self._num_units, 2 * self._num_units],
initializer=self._kernel_initializer)
self._gate_bias = self.add_variable(
"gates/%s" % _BIAS_VARIABLE_NAME,
shape=[2 * self._num_units],
initializer=(self._bias_initializer
if self._bias_initializer is not None else
tf.compat.v1.constant_initializer(1.0, dtype=self.dtype)))
self._candidate_kernel = self.add_variable(
"candidate/%s" % _WEIGHTS_VARIABLE_NAME,
shape=[input_depth + self._num_units, self._num_units],
initializer=self._kernel_initializer)
self._candidate_bias = self.add_variable(
"candidate/%s" % _BIAS_VARIABLE_NAME,
shape=[self._num_units],
initializer=(self._bias_initializer
if self._bias_initializer is not None else
tf.compat.v1.zeros_initializer(dtype=self.dtype)))
self.built = True
def call(self, inputs, state):
"""Gated recurrent unit (GRU) with nunits cells."""
_check_rnn_cell_input_dtypes([inputs, state])
gate_inputs = tf.matmul(
tf.concat([inputs, state], 1), self._gate_kernel)
gate_inputs = tf.nn.bias_add(gate_inputs, self._gate_bias)
value = tf.sigmoid(gate_inputs)
r, u = tf.split(value=value, num_or_size_splits=2, axis=1)
r_state = r * state
candidate = tf.matmul(
tf.concat([inputs, r_state], 1), self._candidate_kernel)
candidate = tf.nn.bias_add(candidate, self._candidate_bias)
c = self._activation(candidate)
new_h = u * state + (1 - u) * c
return new_h, new_h
def get_config(self):
config = {
"num_units": self._num_units,
"kernel_initializer": initializers.serialize(self._kernel_initializer),
"bias_initializer": initializers.serialize(self._bias_initializer),
"activation": activations.serialize(self._activation),
"reuse": self._reuse,
}
base_config = super(GRUCell, self).get_config()
return dict(list(base_config.items()) + list(config.items()))
_LSTMStateTuple = collections.namedtuple("LSTMStateTuple", ("c", "h"))
@keras_export(v1=["keras.__internal__.legacy.rnn_cell.LSTMStateTuple"])
@tf_export(v1=["nn.rnn_cell.LSTMStateTuple"])
class LSTMStateTuple(_LSTMStateTuple):
"""Tuple used by LSTM Cells for `state_size`, `zero_state`, and output state.
Stores two elements: `(c, h)`, in that order. Where `c` is the hidden state
and `h` is the output.
Only used when `state_is_tuple=True`.
"""
__slots__ = ()
@property
def dtype(self):
(c, h) = self
if c.dtype != h.dtype:
raise TypeError("Inconsistent internal state: %s vs %s" %
(str(c.dtype), str(h.dtype)))
return c.dtype
@keras_export(v1=["keras.__internal__.legacy.rnn_cell.BasicLSTMCell"])
@tf_export(v1=["nn.rnn_cell.BasicLSTMCell"])
class BasicLSTMCell(LayerRNNCell):
"""DEPRECATED: Please use `tf.compat.v1.nn.rnn_cell.LSTMCell` instead.
Basic LSTM recurrent network cell.
The implementation is based on
We add forget_bias (default: 1) to the biases of the forget gate in order to
reduce the scale of forgetting in the beginning of the training.
It does not allow cell clipping, a projection layer, and does not
use peep-hole connections: it is the basic baseline.
For advanced models, please use the full `tf.compat.v1.nn.rnn_cell.LSTMCell`
that follows.
Note that this cell is not optimized for performance. Please use
`tf.contrib.cudnn_rnn.CudnnLSTM` for better performance on GPU, or
`tf.contrib.rnn.LSTMBlockCell` and `tf.contrib.rnn.LSTMBlockFusedCell` for
better performance on CPU.
"""
def __init__(self,
num_units,
forget_bias=1.0,
state_is_tuple=True,
activation=None,
reuse=None,
name=None,
dtype=None,
**kwargs):
"""Initialize the basic LSTM cell.
Args:
num_units: int, The number of units in the LSTM cell.
forget_bias: float, The bias added to forget gates (see above). Must set
to `0.0` manually when restoring from CudnnLSTM-trained checkpoints.
state_is_tuple: If True, accepted and returned states are 2-tuples of the
`c_state` and `m_state`. If False, they are concatenated along the
column axis. The latter behavior will soon be deprecated.
activation: Activation function of the inner states. Default: `tanh`. It
could also be string that is within Keras activation function names.
reuse: (optional) Python boolean describing whether to reuse variables in
an existing scope. If not `True`, and the existing scope already has
the given variables, an error is raised.
name: String, the name of the layer. Layers with the same name will share
weights, but to avoid mistakes we require reuse=True in such cases.
dtype: Default dtype of the layer (default of `None` means use the type of
the first input). Required when `build` is called before `call`.
**kwargs: Dict, keyword named properties for common layer attributes, like
`trainable` etc when constructing the cell from configs of get_config().
When restoring from CudnnLSTM-trained checkpoints, must use
`CudnnCompatibleLSTMCell` instead.
"""
warnings.warn("`tf.nn.rnn_cell.BasicLSTMCell` is deprecated and will be "
"removed in a future version. This class "
"is equivalent as `tf.keras.layers.LSTMCell`, "
"and will be replaced by that in Tensorflow 2.0.")
super(BasicLSTMCell, self).__init__(
_reuse=reuse, name=name, dtype=dtype, **kwargs)
_check_supported_dtypes(self.dtype)
if not state_is_tuple:
logging.warning(
"%s: Using a concatenated state is slower and will soon be "
"deprecated. Use state_is_tuple=True.", self)
if tf.executing_eagerly() and tf.config.list_logical_devices("GPU"):
logging.warning(
"%s: Note that this cell is not optimized for performance. "
"Please use tf.contrib.cudnn_rnn.CudnnLSTM for better "
"performance on GPU.", self)
# Inputs must be 2-dimensional.
self.input_spec = input_spec.InputSpec(ndim=2)
self._num_units = num_units
self._forget_bias = forget_bias
self._state_is_tuple = state_is_tuple
if activation:
self._activation = activations.get(activation)
else:
self._activation = tf.tanh
@property
def state_size(self):
return (LSTMStateTuple(self._num_units, self._num_units)
if self._state_is_tuple else 2 * self._num_units)
@property
def output_size(self):
return self._num_units
@tf_utils.shape_type_conversion
def build(self, inputs_shape):
if inputs_shape[-1] is None:
raise ValueError("Expected inputs.shape[-1] to be known, saw shape: %s" %
str(inputs_shape))
_check_supported_dtypes(self.dtype)
input_depth = inputs_shape[-1]
h_depth = self._num_units
self._kernel = self.add_variable(
_WEIGHTS_VARIABLE_NAME,
shape=[input_depth + h_depth, 4 * self._num_units])
self._bias = self.add_variable(
_BIAS_VARIABLE_NAME,
shape=[4 * self._num_units],
initializer=tf.compat.v1.zeros_initializer(dtype=self.dtype))
self.built = True
def call(self, inputs, state):
"""Long short-term memory cell (LSTM).
Args:
inputs: `2-D` tensor with shape `[batch_size, input_size]`.
state: An `LSTMStateTuple` of state tensors, each shaped `[batch_size,
num_units]`, if `state_is_tuple` has been set to `True`. Otherwise, a
`Tensor` shaped `[batch_size, 2 * num_units]`.
Returns:
A pair containing the new hidden state, and the new state (either a
`LSTMStateTuple` or a concatenated state, depending on
`state_is_tuple`).
"""
_check_rnn_cell_input_dtypes([inputs, state])
sigmoid = tf.sigmoid
one = tf.constant(1, dtype=tf.int32)
# Parameters of gates are concatenated into one multiply for efficiency.
if self._state_is_tuple:
c, h = state
else:
c, h = tf.split(value=state, num_or_size_splits=2, axis=one)
gate_inputs = tf.matmul(
tf.concat([inputs, h], 1), self._kernel)
gate_inputs = tf.nn.bias_add(gate_inputs, self._bias)
# i = input_gate, j = new_input, f = forget_gate, o = output_gate
i, j, f, o = tf.split(
value=gate_inputs, num_or_size_splits=4, axis=one)
forget_bias_tensor = tf.constant(self._forget_bias, dtype=f.dtype)
# Note that using `add` and `multiply` instead of `+` and `*` gives a
# performance improvement. So using those at the cost of readability.
add = tf.add
multiply = tf.multiply
new_c = add(
multiply(c, sigmoid(add(f, forget_bias_tensor))),
multiply(sigmoid(i), self._activation(j)))
new_h = multiply(self._activation(new_c), sigmoid(o))
if self._state_is_tuple:
new_state = LSTMStateTuple(new_c, new_h)
else:
new_state = tf.concat([new_c, new_h], 1)
return new_h, new_state
def get_config(self):
config = {
"num_units": self._num_units,
"forget_bias": self._forget_bias,
"state_is_tuple": self._state_is_tuple,
"activation": activations.serialize(self._activation),
"reuse": self._reuse,
}
base_config = super(BasicLSTMCell, self).get_config()
return dict(list(base_config.items()) + list(config.items()))
@keras_export(v1=["keras.__internal__.legacy.rnn_cell.LSTMCell"])
@tf_export(v1=["nn.rnn_cell.LSTMCell"])
class LSTMCell(LayerRNNCell):
"""Long short-term memory unit (LSTM) recurrent network cell.
The default non-peephole implementation is based on (Gers et al., 1999).
The peephole implementation is based on (Sak et al., 2014).
The class uses optional peep-hole connections, optional cell clipping, and
an optional projection layer.
Note that this cell is not optimized for performance. Please use
`tf.contrib.cudnn_rnn.CudnnLSTM` for better performance on GPU, or
`tf.contrib.rnn.LSTMBlockCell` and `tf.contrib.rnn.LSTMBlockFusedCell` for
better performance on CPU.
References:
Long short-term memory recurrent neural network architectures for large
scale acoustic modeling:
[Sak et al., 2014]
(https://www.isca-speech.org/archive/interspeech_2014/i14_0338.html)
([pdf]
(https://www.isca-speech.org/archive/archive_papers/interspeech_2014/i14_0338.pdf))
Learning to forget:
[Gers et al., 1999]
(http://digital-library.theiet.org/content/conferences/10.1049/cp_19991218)
([pdf](https://arxiv.org/pdf/1409.2329.pdf))
Long Short-Term Memory:
[Hochreiter et al., 1997]
(https://www.mitpressjournals.org/doi/abs/10.1162/neco.1997.9.8.1735)
([pdf](http://ml.jku.at/publications/older/3504.pdf))
"""
def __init__(self,
num_units,
use_peepholes=False,
cell_clip=None,
initializer=None,
num_proj=None,
proj_clip=None,
num_unit_shards=None,
num_proj_shards=None,
forget_bias=1.0,
state_is_tuple=True,
activation=None,
reuse=None,
name=None,
dtype=None,
**kwargs):
"""Initialize the parameters for an LSTM cell.
Args:
num_units: int, The number of units in the LSTM cell.
use_peepholes: bool, set True to enable diagonal/peephole connections.
cell_clip: (optional) A float value, if provided the cell state is clipped
by this value prior to the cell output activation.
initializer: (optional) The initializer to use for the weight and
projection matrices.
num_proj: (optional) int, The output dimensionality for the projection
matrices. If None, no projection is performed.
proj_clip: (optional) A float value. If `num_proj > 0` and `proj_clip` is
provided, then the projected values are clipped elementwise to within
`[-proj_clip, proj_clip]`.
num_unit_shards: Deprecated, will be removed by Jan. 2017. Use a
variable_scope partitioner instead.
num_proj_shards: Deprecated, will be removed by Jan. 2017. Use a
variable_scope partitioner instead.
forget_bias: Biases of the forget gate are initialized by default to 1 in
order to reduce the scale of forgetting at the beginning of the
training. Must set it manually to `0.0` when restoring from CudnnLSTM
trained checkpoints.
state_is_tuple: If True, accepted and returned states are 2-tuples of the
`c_state` and `m_state`. If False, they are concatenated along the
column axis. This latter behavior will soon be deprecated.
activation: Activation function of the inner states. Default: `tanh`. It
could also be string that is within Keras activation function names.
reuse: (optional) Python boolean describing whether to reuse variables in
an existing scope. If not `True`, and the existing scope already has
the given variables, an error is raised.
name: String, the name of the layer. Layers with the same name will share
weights, but to avoid mistakes we require reuse=True in such cases.
dtype: Default dtype of the layer (default of `None` means use the type of
the first input). Required when `build` is called before `call`.
**kwargs: Dict, keyword named properties for common layer attributes, like
`trainable` etc when constructing the cell from configs of get_config().
When restoring from CudnnLSTM-trained checkpoints, use
`CudnnCompatibleLSTMCell` instead.
"""
warnings.warn("`tf.nn.rnn_cell.LSTMCell` is deprecated and will be "
"removed in a future version. This class "
"is equivalent as `tf.keras.layers.LSTMCell`, "
"and will be replaced by that in Tensorflow 2.0.")
super(LSTMCell, self).__init__(
_reuse=reuse, name=name, dtype=dtype, **kwargs)
_check_supported_dtypes(self.dtype)
if not state_is_tuple:
logging.warning(
"%s: Using a concatenated state is slower and will soon be "
"deprecated. Use state_is_tuple=True.", self)
if num_unit_shards is not None or num_proj_shards is not None:
logging.warning(
"%s: The num_unit_shards and proj_unit_shards parameters are "
"deprecated and will be removed in Jan 2017. "
"Use a variable scope with a partitioner instead.", self)
if tf.executing_eagerly() and tf.config.list_logical_devices("GPU"):
logging.warning(
"%s: Note that this cell is not optimized for performance. "
"Please use tf.contrib.cudnn_rnn.CudnnLSTM for better "
"performance on GPU.", self)
# Inputs must be 2-dimensional.
self.input_spec = input_spec.InputSpec(ndim=2)
self._num_units = num_units
self._use_peepholes = use_peepholes
self._cell_clip = cell_clip
self._initializer = initializers.get(initializer)
self._num_proj = num_proj
self._proj_clip = proj_clip
self._num_unit_shards = num_unit_shards
self._num_proj_shards = num_proj_shards
self._forget_bias = forget_bias
self._state_is_tuple = state_is_tuple
if activation:
self._activation = activations.get(activation)
else:
self._activation = tf.tanh
if num_proj:
self._state_size = (
LSTMStateTuple(num_units, num_proj) if state_is_tuple else num_units +
num_proj)
self._output_size = num_proj
else:
self._state_size = (
LSTMStateTuple(num_units, num_units) if state_is_tuple else 2 *
num_units)
self._output_size = num_units
@property
def state_size(self):
return self._state_size
@property
def output_size(self):
return self._output_size
@tf_utils.shape_type_conversion
def build(self, inputs_shape):
if inputs_shape[-1] is None:
raise ValueError("Expected inputs.shape[-1] to be known, saw shape: %s" %
str(inputs_shape))
_check_supported_dtypes(self.dtype)
input_depth = inputs_shape[-1]
h_depth = self._num_units if self._num_proj is None else self._num_proj
maybe_partitioner = (
tf.compat.v1.fixed_size_partitioner(self._num_unit_shards)
if self._num_unit_shards is not None else None)
self._kernel = self.add_variable(
_WEIGHTS_VARIABLE_NAME,
shape=[input_depth + h_depth, 4 * self._num_units],
initializer=self._initializer,
partitioner=maybe_partitioner)
if self.dtype is None:
initializer = tf.compat.v1.zeros_initializer
else:
initializer = tf.compat.v1.zeros_initializer(dtype=self.dtype)
self._bias = self.add_variable(
_BIAS_VARIABLE_NAME,
shape=[4 * self._num_units],
initializer=initializer)
if self._use_peepholes:
self._w_f_diag = self.add_variable(
"w_f_diag", shape=[self._num_units], initializer=self._initializer)
self._w_i_diag = self.add_variable(
"w_i_diag", shape=[self._num_units], initializer=self._initializer)
self._w_o_diag = self.add_variable(
"w_o_diag", shape=[self._num_units], initializer=self._initializer)
if self._num_proj is not None:
maybe_proj_partitioner = (
tf.compat.v1.fixed_size_partitioner(self._num_proj_shards)
if self._num_proj_shards is not None else None)
self._proj_kernel = self.add_variable(
"projection/%s" % _WEIGHTS_VARIABLE_NAME,
shape=[self._num_units, self._num_proj],
initializer=self._initializer,
partitioner=maybe_proj_partitioner)
self.built = True
def call(self, inputs, state):
"""Run one step of LSTM.
Args:
inputs: input Tensor, must be 2-D, `[batch, input_size]`.
state: if `state_is_tuple` is False, this must be a state Tensor, `2-D,
[batch, state_size]`. If `state_is_tuple` is True, this must be a tuple
of state Tensors, both `2-D`, with column sizes `c_state` and `m_state`.
Returns:
A tuple containing:
- A `2-D, [batch, output_dim]`, Tensor representing the output of the
LSTM after reading `inputs` when previous state was `state`.
Here output_dim is:
num_proj if num_proj was set,
num_units otherwise.
- Tensor(s) representing the new state of LSTM after reading `inputs` when
the previous state was `state`. Same type and shape(s) as `state`.
Raises:
ValueError: If input size cannot be inferred from inputs via
static shape inference.
"""
_check_rnn_cell_input_dtypes([inputs, state])
num_proj = self._num_units if self._num_proj is None else self._num_proj
sigmoid = tf.sigmoid
if self._state_is_tuple:
(c_prev, m_prev) = state
else:
c_prev = tf.slice(state, [0, 0], [-1, self._num_units])
m_prev = tf.slice(state, [0, self._num_units], [-1, num_proj])
input_size = inputs.get_shape().with_rank(2).dims[1].value
if input_size is None:
raise ValueError("Could not infer input size from inputs.get_shape()[-1]")
# i = input_gate, j = new_input, f = forget_gate, o = output_gate
lstm_matrix = tf.matmul(
tf.concat([inputs, m_prev], 1), self._kernel)
lstm_matrix = tf.nn.bias_add(lstm_matrix, self._bias)
i, j, f, o = tf.split(
value=lstm_matrix, num_or_size_splits=4, axis=1)
# Diagonal connections
if self._use_peepholes:
c = (
sigmoid(f + self._forget_bias + self._w_f_diag * c_prev) * c_prev +
sigmoid(i + self._w_i_diag * c_prev) * self._activation(j))
else:
c = (
sigmoid(f + self._forget_bias) * c_prev +
sigmoid(i) * self._activation(j))
if self._cell_clip is not None:
# pylint: disable=invalid-unary-operand-type
c = tf.clip_by_value(c, -self._cell_clip, self._cell_clip)
# pylint: enable=invalid-unary-operand-type
if self._use_peepholes:
m = sigmoid(o + self._w_o_diag * c) * self._activation(c)
else:
m = sigmoid(o) * self._activation(c)
if self._num_proj is not None:
m = tf.matmul(m, self._proj_kernel)
if self._proj_clip is not None:
# pylint: disable=invalid-unary-operand-type
m = tf.clip_by_value(m, -self._proj_clip, self._proj_clip)
# pylint: enable=invalid-unary-operand-type
new_state = (
LSTMStateTuple(c, m)
if self._state_is_tuple else tf.concat([c, m], 1))
return m, new_state
def get_config(self):
config = {
"num_units": self._num_units,
"use_peepholes": self._use_peepholes,
"cell_clip": self._cell_clip,
"initializer": initializers.serialize(self._initializer),
"num_proj": self._num_proj,
"proj_clip": self._proj_clip,
"num_unit_shards": self._num_unit_shards,
"num_proj_shards": self._num_proj_shards,
"forget_bias": self._forget_bias,
"state_is_tuple": self._state_is_tuple,
"activation": activations.serialize(self._activation),
"reuse": self._reuse,
}
base_config = super(LSTMCell, self).get_config()
return dict(list(base_config.items()) + list(config.items()))
class _RNNCellWrapperV1(RNNCell):
"""Base class for cells wrappers V1 compatibility.
This class along with `_RNNCellWrapperV2` allows to define cells wrappers that
are compatible with V1 and V2, and defines helper methods for this purpose.
"""
def __init__(self, cell, *args, **kwargs):
super(_RNNCellWrapperV1, self).__init__(*args, **kwargs)
assert_like_rnncell("cell", cell)
self.cell = cell
if isinstance(cell, tf.__internal__.tracking.Trackable):
self._track_trackable(self.cell, name="cell")
def _call_wrapped_cell(self, inputs, state, cell_call_fn, **kwargs):
"""Calls the wrapped cell and performs the wrapping logic.
This method is called from the wrapper's `call` or `__call__` methods.
Args:
inputs: A tensor with wrapped cell's input.
state: A tensor or tuple of tensors with wrapped cell's state.
cell_call_fn: Wrapped cell's method to use for step computation (cell's
`__call__` or 'call' method).
**kwargs: Additional arguments.
Returns:
A pair containing:
- Output: A tensor with cell's output.
- New state: A tensor or tuple of tensors with new wrapped cell's state.
"""
raise NotImplementedError
def __call__(self, inputs, state, scope=None):
"""Runs the RNN cell step computation.
We assume that the wrapped RNNCell is being built within its `__call__`
method. We directly use the wrapped cell's `__call__` in the overridden
wrapper `__call__` method.
This allows to use the wrapped cell and the non-wrapped cell equivalently
when using `__call__`.
Args:
inputs: A tensor with wrapped cell's input.
state: A tensor or tuple of tensors with wrapped cell's state.
scope: VariableScope for the subgraph created in the wrapped cells'
`__call__`.
Returns:
A pair containing:
- Output: A tensor with cell's output.
- New state: A tensor or tuple of tensors with new wrapped cell's state.
"""
return self._call_wrapped_cell(
inputs, state, cell_call_fn=self.cell.__call__, scope=scope)
def get_config(self):
config = {
"cell": {
"class_name": self.cell.__class__.__name__,
"config": self.cell.get_config()
},
}
base_config = super(_RNNCellWrapperV1, self).get_config()
return dict(list(base_config.items()) + list(config.items()))
@classmethod
def from_config(cls, config, custom_objects=None):
config = config.copy()
cell = config.pop("cell")
try:
assert_like_rnncell("cell", cell)
return cls(cell, **config)
except TypeError:
raise ValueError("RNNCellWrapper cannot reconstruct the wrapped cell. "
"Please overwrite the cell in the config with a RNNCell "
"instance.")
@keras_export(v1=["keras.__internal__.legacy.rnn_cell.DropoutWrapper"])
@tf_export(v1=["nn.rnn_cell.DropoutWrapper"])
class DropoutWrapper(rnn_cell_wrapper_impl.DropoutWrapperBase,
_RNNCellWrapperV1):
"""Operator adding dropout to inputs and outputs of the given cell."""
def __init__(self, *args, **kwargs): # pylint: disable=useless-super-delegation
super(DropoutWrapper, self).__init__(*args, **kwargs)
__init__.__doc__ = rnn_cell_wrapper_impl.DropoutWrapperBase.__init__.__doc__
@keras_export(v1=["keras.__internal__.legacy.rnn_cell.ResidualWrapper"])
@tf_export(v1=["nn.rnn_cell.ResidualWrapper"])
class ResidualWrapper(rnn_cell_wrapper_impl.ResidualWrapperBase,
_RNNCellWrapperV1):
"""RNNCell wrapper that ensures cell inputs are added to the outputs."""
def __init__(self, *args, **kwargs): # pylint: disable=useless-super-delegation
super(ResidualWrapper, self).__init__(*args, **kwargs)
__init__.__doc__ = rnn_cell_wrapper_impl.ResidualWrapperBase.__init__.__doc__
@keras_export(v1=["keras.__internal__.legacy.rnn_cell.DeviceWrapper"])
@tf_export(v1=["nn.rnn_cell.DeviceWrapper"])
class DeviceWrapper(rnn_cell_wrapper_impl.DeviceWrapperBase,
_RNNCellWrapperV1):
def __init__(self, *args, **kwargs): # pylint: disable=useless-super-delegation
super(DeviceWrapper, self).__init__(*args, **kwargs)
__init__.__doc__ = rnn_cell_wrapper_impl.DeviceWrapperBase.__init__.__doc__
@keras_export(v1=["keras.__internal__.legacy.rnn_cell.MultiRNNCell"])
@tf_export(v1=["nn.rnn_cell.MultiRNNCell"])
class MultiRNNCell(RNNCell):
"""RNN cell composed sequentially of multiple simple cells.
Example:
```python
num_units = [128, 64]
cells = [BasicLSTMCell(num_units=n) for n in num_units]
stacked_rnn_cell = MultiRNNCell(cells)
```
"""
def __init__(self, cells, state_is_tuple=True):
"""Create a RNN cell composed sequentially of a number of RNNCells.
Args:
cells: list of RNNCells that will be composed in this order.
state_is_tuple: If True, accepted and returned states are n-tuples, where
`n = len(cells)`. If False, the states are all concatenated along the
column axis. This latter behavior will soon be deprecated.
Raises:
ValueError: if cells is empty (not allowed), or at least one of the cells
returns a state tuple but the flag `state_is_tuple` is `False`.
"""
logging.warning("`tf.nn.rnn_cell.MultiRNNCell` is deprecated. This class "
"is equivalent as `tf.keras.layers.StackedRNNCells`, "
"and will be replaced by that in Tensorflow 2.0.")
super(MultiRNNCell, self).__init__()
if not cells:
raise ValueError("Must specify at least one cell for MultiRNNCell.")
if not tf.nest.is_nested(cells):
raise TypeError("cells must be a list or tuple, but saw: %s." % cells)
if len(set(id(cell) for cell in cells)) < len(cells):
logging.log_first_n(
logging.WARN, "At least two cells provided to MultiRNNCell "
"are the same object and will share weights.", 1)
self._cells = cells
for cell_number, cell in enumerate(self._cells):
# Add Trackable dependencies on these cells so their variables get
# saved with this object when using object-based saving.
if isinstance(cell, tf.__internal__.tracking.Trackable):
# TODO(allenl): Track down non-Trackable callers.
self._track_trackable(cell, name="cell-%d" % (cell_number,))
self._state_is_tuple = state_is_tuple
if not state_is_tuple:
if any(tf.nest.is_nested(c.state_size) for c in self._cells):
raise ValueError("Some cells return tuples of states, but the flag "
"state_is_tuple is not set. State sizes are: %s" %
str([c.state_size for c in self._cells]))
@property
def state_size(self):
if self._state_is_tuple:
return tuple(cell.state_size for cell in self._cells)
else:
return sum(cell.state_size for cell in self._cells)
@property
def output_size(self):
return self._cells[-1].output_size
def zero_state(self, batch_size, dtype):
with backend.name_scope(type(self).__name__ + "ZeroState"):
if self._state_is_tuple:
return tuple(cell.zero_state(batch_size, dtype) for cell in self._cells)
else:
# We know here that state_size of each cell is not a tuple and
# presumably does not contain TensorArrays or anything else fancy
return super(MultiRNNCell, self).zero_state(batch_size, dtype)
@property
def trainable_weights(self):
if not self.trainable:
return []
weights = []
for cell in self._cells:
if isinstance(cell, base_layer.Layer):
weights += cell.trainable_weights
return weights
@property
def non_trainable_weights(self):
weights = []
for cell in self._cells:
if isinstance(cell, base_layer.Layer):
weights += cell.non_trainable_weights
if not self.trainable:
trainable_weights = []
for cell in self._cells:
if isinstance(cell, base_layer.Layer):
trainable_weights += cell.trainable_weights
return trainable_weights + weights
return weights
def call(self, inputs, state):
"""Run this multi-layer cell on inputs, starting from state."""
cur_state_pos = 0
cur_inp = inputs
new_states = []
for i, cell in enumerate(self._cells):
with tf.compat.v1.variable_scope("cell_%d" % i):
if self._state_is_tuple:
if not tf.nest.is_nested(state):
raise ValueError(
"Expected state to be a tuple of length %d, but received: %s" %
(len(self.state_size), state))
cur_state = state[i]
else:
cur_state = tf.slice(state, [0, cur_state_pos],
[-1, cell.state_size])
cur_state_pos += cell.state_size
cur_inp, new_state = cell(cur_inp, cur_state)
new_states.append(new_state)
new_states = (
tuple(new_states) if self._state_is_tuple else tf.concat(
new_states, 1))
return cur_inp, new_states
def _check_rnn_cell_input_dtypes(inputs):
"""Check whether the input tensors are with supported dtypes.
Default RNN cells only support floats and complex as its dtypes since the
activation function (tanh and sigmoid) only allow those types. This function
will throw a proper error message if the inputs is not in a supported type.
Args:
inputs: tensor or nested structure of tensors that are feed to RNN cell as
input or state.
Raises:
ValueError: if any of the input tensor are not having dtypes of float or
complex.
"""
for t in tf.nest.flatten(inputs):
_check_supported_dtypes(t.dtype)
def _check_supported_dtypes(dtype):
if dtype is None:
return
dtype = tf.as_dtype(dtype)
if not (dtype.is_floating or dtype.is_complex):
raise ValueError("RNN cell only supports floating point inputs, "
"but saw dtype: %s" % dtype)
Functions
def assert_like_rnncell(cell_name, cell)
-
Raises a TypeError if cell is not like an RNNCell.
NOTE: Do not rely on the error message (in particular in tests) which can be subject to change to increase readability. Use ASSERT_LIKE_RNNCELL_ERROR_REGEXP.
Args
cell_name
- A string to give a meaningful error referencing to the name of the functionargument.
cell
- The object which should behave like an RNNCell.
Raises
TypeError
- A human-friendly exception.
Expand source code
def assert_like_rnncell(cell_name, cell): """Raises a TypeError if cell is not like an RNNCell. NOTE: Do not rely on the error message (in particular in tests) which can be subject to change to increase readability. Use ASSERT_LIKE_RNNCELL_ERROR_REGEXP. Args: cell_name: A string to give a meaningful error referencing to the name of the functionargument. cell: The object which should behave like an RNNCell. Raises: TypeError: A human-friendly exception. """ conditions = [ _hasattr(cell, "output_size"), _hasattr(cell, "state_size"), _hasattr(cell, "get_initial_state") or _hasattr(cell, "zero_state"), callable(cell), ] errors = [ "'output_size' property is missing", "'state_size' property is missing", "either 'zero_state' or 'get_initial_state' method is required", "is not callable" ] if not all(conditions): errors = [error for error, cond in zip(errors, conditions) if not cond] raise TypeError("The argument {!r} ({}) is not an RNNCell: {}.".format( cell_name, cell, ", ".join(errors)))
Classes
class BasicLSTMCell (num_units, forget_bias=1.0, state_is_tuple=True, activation=None, reuse=None, name=None, dtype=None, **kwargs)
-
DEPRECATED: Please use
tf.compat.v1.nn.rnn_cell.LSTMCell
instead.Basic LSTM recurrent network cell.
The implementation is based on
We add forget_bias (default: 1) to the biases of the forget gate in order to reduce the scale of forgetting in the beginning of the training.
It does not allow cell clipping, a projection layer, and does not use peep-hole connections: it is the basic baseline.
For advanced models, please use the full
tf.compat.v1.nn.rnn_cell.LSTMCell
that follows.Note that this cell is not optimized for performance. Please use
tf.contrib.cudnn_rnn.CudnnLSTM
for better performance on GPU, ortf.contrib.rnn.LSTMBlockCell
andtf.contrib.rnn.LSTMBlockFusedCell
for better performance on CPU.Initialize the basic LSTM cell.
Args
num_units
- int, The number of units in the LSTM cell.
forget_bias
- float, The bias added to forget gates (see above). Must set
to
0.0
manually when restoring from CudnnLSTM-trained checkpoints. state_is_tuple
- If True, accepted and returned states are 2-tuples of the
c_state
andm_state
. If False, they are concatenated along the column axis. The latter behavior will soon be deprecated. activation
- Activation function of the inner states.
Default:
tanh
. It could also be string that is within Keras activation function names. reuse
- (optional) Python boolean describing whether to reuse variables in
an existing scope.
If not
True
, and the existing scope already has the given variables, an error is raised. name
- String, the name of the layer. Layers with the same name will share weights, but to avoid mistakes we require reuse=True in such cases.
dtype
- Default dtype of the layer (default of
None
means use the type of the first input). Required whenbuild
is called beforecall
. **kwargs
- Dict, keyword named properties for common layer attributes, like
trainable
etc when constructing the cell from configs of get_config(). When restoring from CudnnLSTM-trained checkpoints, must useCudnnCompatibleLSTMCell
instead.
Expand source code
class BasicLSTMCell(LayerRNNCell): """DEPRECATED: Please use `tf.compat.v1.nn.rnn_cell.LSTMCell` instead. Basic LSTM recurrent network cell. The implementation is based on We add forget_bias (default: 1) to the biases of the forget gate in order to reduce the scale of forgetting in the beginning of the training. It does not allow cell clipping, a projection layer, and does not use peep-hole connections: it is the basic baseline. For advanced models, please use the full `tf.compat.v1.nn.rnn_cell.LSTMCell` that follows. Note that this cell is not optimized for performance. Please use `tf.contrib.cudnn_rnn.CudnnLSTM` for better performance on GPU, or `tf.contrib.rnn.LSTMBlockCell` and `tf.contrib.rnn.LSTMBlockFusedCell` for better performance on CPU. """ def __init__(self, num_units, forget_bias=1.0, state_is_tuple=True, activation=None, reuse=None, name=None, dtype=None, **kwargs): """Initialize the basic LSTM cell. Args: num_units: int, The number of units in the LSTM cell. forget_bias: float, The bias added to forget gates (see above). Must set to `0.0` manually when restoring from CudnnLSTM-trained checkpoints. state_is_tuple: If True, accepted and returned states are 2-tuples of the `c_state` and `m_state`. If False, they are concatenated along the column axis. The latter behavior will soon be deprecated. activation: Activation function of the inner states. Default: `tanh`. It could also be string that is within Keras activation function names. reuse: (optional) Python boolean describing whether to reuse variables in an existing scope. If not `True`, and the existing scope already has the given variables, an error is raised. name: String, the name of the layer. Layers with the same name will share weights, but to avoid mistakes we require reuse=True in such cases. dtype: Default dtype of the layer (default of `None` means use the type of the first input). Required when `build` is called before `call`. **kwargs: Dict, keyword named properties for common layer attributes, like `trainable` etc when constructing the cell from configs of get_config(). When restoring from CudnnLSTM-trained checkpoints, must use `CudnnCompatibleLSTMCell` instead. """ warnings.warn("`tf.nn.rnn_cell.BasicLSTMCell` is deprecated and will be " "removed in a future version. This class " "is equivalent as `tf.keras.layers.LSTMCell`, " "and will be replaced by that in Tensorflow 2.0.") super(BasicLSTMCell, self).__init__( _reuse=reuse, name=name, dtype=dtype, **kwargs) _check_supported_dtypes(self.dtype) if not state_is_tuple: logging.warning( "%s: Using a concatenated state is slower and will soon be " "deprecated. Use state_is_tuple=True.", self) if tf.executing_eagerly() and tf.config.list_logical_devices("GPU"): logging.warning( "%s: Note that this cell is not optimized for performance. " "Please use tf.contrib.cudnn_rnn.CudnnLSTM for better " "performance on GPU.", self) # Inputs must be 2-dimensional. self.input_spec = input_spec.InputSpec(ndim=2) self._num_units = num_units self._forget_bias = forget_bias self._state_is_tuple = state_is_tuple if activation: self._activation = activations.get(activation) else: self._activation = tf.tanh @property def state_size(self): return (LSTMStateTuple(self._num_units, self._num_units) if self._state_is_tuple else 2 * self._num_units) @property def output_size(self): return self._num_units @tf_utils.shape_type_conversion def build(self, inputs_shape): if inputs_shape[-1] is None: raise ValueError("Expected inputs.shape[-1] to be known, saw shape: %s" % str(inputs_shape)) _check_supported_dtypes(self.dtype) input_depth = inputs_shape[-1] h_depth = self._num_units self._kernel = self.add_variable( _WEIGHTS_VARIABLE_NAME, shape=[input_depth + h_depth, 4 * self._num_units]) self._bias = self.add_variable( _BIAS_VARIABLE_NAME, shape=[4 * self._num_units], initializer=tf.compat.v1.zeros_initializer(dtype=self.dtype)) self.built = True def call(self, inputs, state): """Long short-term memory cell (LSTM). Args: inputs: `2-D` tensor with shape `[batch_size, input_size]`. state: An `LSTMStateTuple` of state tensors, each shaped `[batch_size, num_units]`, if `state_is_tuple` has been set to `True`. Otherwise, a `Tensor` shaped `[batch_size, 2 * num_units]`. Returns: A pair containing the new hidden state, and the new state (either a `LSTMStateTuple` or a concatenated state, depending on `state_is_tuple`). """ _check_rnn_cell_input_dtypes([inputs, state]) sigmoid = tf.sigmoid one = tf.constant(1, dtype=tf.int32) # Parameters of gates are concatenated into one multiply for efficiency. if self._state_is_tuple: c, h = state else: c, h = tf.split(value=state, num_or_size_splits=2, axis=one) gate_inputs = tf.matmul( tf.concat([inputs, h], 1), self._kernel) gate_inputs = tf.nn.bias_add(gate_inputs, self._bias) # i = input_gate, j = new_input, f = forget_gate, o = output_gate i, j, f, o = tf.split( value=gate_inputs, num_or_size_splits=4, axis=one) forget_bias_tensor = tf.constant(self._forget_bias, dtype=f.dtype) # Note that using `add` and `multiply` instead of `+` and `*` gives a # performance improvement. So using those at the cost of readability. add = tf.add multiply = tf.multiply new_c = add( multiply(c, sigmoid(add(f, forget_bias_tensor))), multiply(sigmoid(i), self._activation(j))) new_h = multiply(self._activation(new_c), sigmoid(o)) if self._state_is_tuple: new_state = LSTMStateTuple(new_c, new_h) else: new_state = tf.concat([new_c, new_h], 1) return new_h, new_state def get_config(self): config = { "num_units": self._num_units, "forget_bias": self._forget_bias, "state_is_tuple": self._state_is_tuple, "activation": activations.serialize(self._activation), "reuse": self._reuse, } base_config = super(BasicLSTMCell, self).get_config() return dict(list(base_config.items()) + list(config.items()))
Ancestors
- LayerRNNCell
- RNNCell
- Layer
- Layer
- tensorflow.python.module.module.Module
- tensorflow.python.training.tracking.tracking.AutoTrackable
- tensorflow.python.training.tracking.base.Trackable
- LayerVersionSelector
Methods
def call(self, inputs, state)
-
Long short-term memory cell (LSTM).
Args
inputs
2-D
tensor with shape[batch_size, input_size]
.state
- An
LSTMStateTuple
of state tensors, each shaped[batch_size, num_units]<code>, if </code>state_is_tuple<code> has been set to </code>True
. Otherwise, aTensor
shaped[batch_size, 2 * num_units]
.
Returns
A pair containing the new hidden state, and the new state (either a
LSTMStateTuple
or a concatenated state, depending onstate_is_tuple
).Expand source code
def call(self, inputs, state): """Long short-term memory cell (LSTM). Args: inputs: `2-D` tensor with shape `[batch_size, input_size]`. state: An `LSTMStateTuple` of state tensors, each shaped `[batch_size, num_units]`, if `state_is_tuple` has been set to `True`. Otherwise, a `Tensor` shaped `[batch_size, 2 * num_units]`. Returns: A pair containing the new hidden state, and the new state (either a `LSTMStateTuple` or a concatenated state, depending on `state_is_tuple`). """ _check_rnn_cell_input_dtypes([inputs, state]) sigmoid = tf.sigmoid one = tf.constant(1, dtype=tf.int32) # Parameters of gates are concatenated into one multiply for efficiency. if self._state_is_tuple: c, h = state else: c, h = tf.split(value=state, num_or_size_splits=2, axis=one) gate_inputs = tf.matmul( tf.concat([inputs, h], 1), self._kernel) gate_inputs = tf.nn.bias_add(gate_inputs, self._bias) # i = input_gate, j = new_input, f = forget_gate, o = output_gate i, j, f, o = tf.split( value=gate_inputs, num_or_size_splits=4, axis=one) forget_bias_tensor = tf.constant(self._forget_bias, dtype=f.dtype) # Note that using `add` and `multiply` instead of `+` and `*` gives a # performance improvement. So using those at the cost of readability. add = tf.add multiply = tf.multiply new_c = add( multiply(c, sigmoid(add(f, forget_bias_tensor))), multiply(sigmoid(i), self._activation(j))) new_h = multiply(self._activation(new_c), sigmoid(o)) if self._state_is_tuple: new_state = LSTMStateTuple(new_c, new_h) else: new_state = tf.concat([new_c, new_h], 1) return new_h, new_state
Inherited members
LayerRNNCell
:activity_regularizer
add_loss
add_metric
add_update
add_variable
add_weight
apply
build
compute_dtype
compute_mask
compute_output_shape
compute_output_signature
count_params
dtype
dtype_policy
dynamic
finalize_state
from_config
get_config
get_input_at
get_input_mask_at
get_input_shape_at
get_losses_for
get_output_at
get_output_mask_at
get_output_shape_at
get_updates_for
get_weights
inbound_nodes
input
input_mask
input_shape
input_spec
losses
metrics
name
non_trainable_variables
non_trainable_weights
outbound_nodes
output
output_mask
output_shape
output_size
set_weights
state_size
supports_masking
trainable_variables
trainable_weights
variable_dtype
variables
weights
zero_state
class BasicRNNCell (num_units, activation=None, reuse=None, name=None, dtype=None, **kwargs)
-
The most basic RNN cell.
Note that this cell is not optimized for performance. Please use
tf.contrib.cudnn_rnn.CudnnRNNTanh
for better performance on GPU.Args
num_units
- int, The number of units in the RNN cell.
activation
- Nonlinearity to use.
Default:
tanh
. It could also be string that is within Keras activation function names. reuse
- (optional) Python boolean describing whether to reuse variables in an
existing scope.
If not
True
, and the existing scope already has the given variables, an error is raised. name
- String, the name of the layer. Layers with the same name will share weights, but to avoid mistakes we require reuse=True in such cases.
dtype
- Default dtype of the layer (default of
None
means use the type of the first input). Required whenbuild
is called beforecall
. **kwargs
- Dict, keyword named properties for common layer attributes, like
trainable
etc when constructing the cell from configs of get_config().
Expand source code
class BasicRNNCell(LayerRNNCell): """The most basic RNN cell. Note that this cell is not optimized for performance. Please use `tf.contrib.cudnn_rnn.CudnnRNNTanh` for better performance on GPU. Args: num_units: int, The number of units in the RNN cell. activation: Nonlinearity to use. Default: `tanh`. It could also be string that is within Keras activation function names. reuse: (optional) Python boolean describing whether to reuse variables in an existing scope. If not `True`, and the existing scope already has the given variables, an error is raised. name: String, the name of the layer. Layers with the same name will share weights, but to avoid mistakes we require reuse=True in such cases. dtype: Default dtype of the layer (default of `None` means use the type of the first input). Required when `build` is called before `call`. **kwargs: Dict, keyword named properties for common layer attributes, like `trainable` etc when constructing the cell from configs of get_config(). """ def __init__(self, num_units, activation=None, reuse=None, name=None, dtype=None, **kwargs): warnings.warn("`tf.nn.rnn_cell.BasicRNNCell` is deprecated and will be " "removed in a future version. This class " "is equivalent as `tf.keras.layers.SimpleRNNCell`, " "and will be replaced by that in Tensorflow 2.0.") super(BasicRNNCell, self).__init__( _reuse=reuse, name=name, dtype=dtype, **kwargs) _check_supported_dtypes(self.dtype) if tf.executing_eagerly() and tf.config.list_logical_devices("GPU"): logging.warning( "%s: Note that this cell is not optimized for performance. " "Please use tf.contrib.cudnn_rnn.CudnnRNNTanh for better " "performance on GPU.", self) # Inputs must be 2-dimensional. self.input_spec = input_spec.InputSpec(ndim=2) self._num_units = num_units if activation: self._activation = activations.get(activation) else: self._activation = tf.tanh @property def state_size(self): return self._num_units @property def output_size(self): return self._num_units @tf_utils.shape_type_conversion def build(self, inputs_shape): if inputs_shape[-1] is None: raise ValueError("Expected inputs.shape[-1] to be known, saw shape: %s" % str(inputs_shape)) _check_supported_dtypes(self.dtype) input_depth = inputs_shape[-1] self._kernel = self.add_variable( _WEIGHTS_VARIABLE_NAME, shape=[input_depth + self._num_units, self._num_units]) self._bias = self.add_variable( _BIAS_VARIABLE_NAME, shape=[self._num_units], initializer=tf.compat.v1.zeros_initializer(dtype=self.dtype)) self.built = True def call(self, inputs, state): """Most basic RNN: output = new_state = act(W * input + U * state + B).""" _check_rnn_cell_input_dtypes([inputs, state]) gate_inputs = tf.matmul( tf.concat([inputs, state], 1), self._kernel) gate_inputs = tf.nn.bias_add(gate_inputs, self._bias) output = self._activation(gate_inputs) return output, output def get_config(self): config = { "num_units": self._num_units, "activation": activations.serialize(self._activation), "reuse": self._reuse, } base_config = super(BasicRNNCell, self).get_config() return dict(list(base_config.items()) + list(config.items()))
Ancestors
- LayerRNNCell
- RNNCell
- Layer
- Layer
- tensorflow.python.module.module.Module
- tensorflow.python.training.tracking.tracking.AutoTrackable
- tensorflow.python.training.tracking.base.Trackable
- LayerVersionSelector
Methods
def call(self, inputs, state)
-
Most basic RNN: output = new_state = act(W * input + U * state + B).
Expand source code
def call(self, inputs, state): """Most basic RNN: output = new_state = act(W * input + U * state + B).""" _check_rnn_cell_input_dtypes([inputs, state]) gate_inputs = tf.matmul( tf.concat([inputs, state], 1), self._kernel) gate_inputs = tf.nn.bias_add(gate_inputs, self._bias) output = self._activation(gate_inputs) return output, output
Inherited members
LayerRNNCell
:activity_regularizer
add_loss
add_metric
add_update
add_variable
add_weight
apply
build
compute_dtype
compute_mask
compute_output_shape
compute_output_signature
count_params
dtype
dtype_policy
dynamic
finalize_state
from_config
get_config
get_input_at
get_input_mask_at
get_input_shape_at
get_losses_for
get_output_at
get_output_mask_at
get_output_shape_at
get_updates_for
get_weights
inbound_nodes
input
input_mask
input_shape
input_spec
losses
metrics
name
non_trainable_variables
non_trainable_weights
outbound_nodes
output
output_mask
output_shape
output_size
set_weights
state_size
supports_masking
trainable_variables
trainable_weights
variable_dtype
variables
weights
zero_state
class DeviceWrapper (*args, **kwargs)
-
Operator that ensures an RNNCell runs on a particular device.
Construct a
DeviceWrapper
forcell
with devicedevice
.Ensures the wrapped
cell
is called withtf.device(device)
.Args
cell
- An instance of
RNNCell
. device
- A device string or function, for passing to
tf.device
. **kwargs
- dict of keyword arguments for base layer.
Expand source code
class DeviceWrapper(rnn_cell_wrapper_impl.DeviceWrapperBase, _RNNCellWrapperV1): def __init__(self, *args, **kwargs): # pylint: disable=useless-super-delegation super(DeviceWrapper, self).__init__(*args, **kwargs) __init__.__doc__ = rnn_cell_wrapper_impl.DeviceWrapperBase.__init__.__doc__
Ancestors
- DeviceWrapperBase
- keras.layers.legacy_rnn.rnn_cell_impl._RNNCellWrapperV1
- RNNCell
- Layer
- Layer
- tensorflow.python.module.module.Module
- tensorflow.python.training.tracking.tracking.AutoTrackable
- tensorflow.python.training.tracking.base.Trackable
- LayerVersionSelector
Inherited members
RNNCell
:activity_regularizer
add_loss
add_metric
add_update
add_variable
add_weight
apply
build
call
compute_dtype
compute_mask
compute_output_shape
compute_output_signature
count_params
dtype
dtype_policy
dynamic
finalize_state
from_config
get_config
get_input_at
get_input_mask_at
get_input_shape_at
get_losses_for
get_output_at
get_output_mask_at
get_output_shape_at
get_updates_for
get_weights
inbound_nodes
input
input_mask
input_shape
input_spec
losses
metrics
name
non_trainable_variables
non_trainable_weights
outbound_nodes
output
output_mask
output_shape
output_size
set_weights
state_size
supports_masking
trainable_variables
trainable_weights
variable_dtype
variables
weights
zero_state
class DropoutWrapper (*args, **kwargs)
-
Operator adding dropout to inputs and outputs of the given cell.
Create a cell with added input, state, and/or output dropout.
If
variational_recurrent
is set toTrue
(NOT the default behavior), then the same dropout mask is applied at every step, as described in: A Theoretically Grounded Application of Dropout in Recurrent Neural Networks. Y. Gal, Z. Ghahramani.Otherwise a different dropout mask is applied at every time step.
Note, by default (unless a custom
dropout_state_filter
is provided), the memory state (c
component of anyLSTMStateTuple
) passing through aDropoutWrapper
is never modified. This behavior is described in the above article.Args
cell
- an RNNCell, a projection to output_size is added to it.
input_keep_prob
- unit Tensor or float between 0 and 1, input keep probability; if it is constant and 1, no input dropout will be added.
output_keep_prob
- unit Tensor or float between 0 and 1, output keep probability; if it is constant and 1, no output dropout will be added.
state_keep_prob
- unit Tensor or float between 0 and 1, output keep
probability; if it is constant and 1, no output dropout will be added.
State dropout is performed on the outgoing states of the cell. Note
the state components to which dropout is applied when
state_keep_prob
is in(0, 1)
are also determined by the argumentdropout_state_filter_visitor
(e.g. by default dropout is never applied to thec
component of anLSTMStateTuple
). variational_recurrent
- Python bool.
If
True
, then the same dropout pattern is applied across all time steps per run call. If this parameter is set,input_size
must be provided. input_size
- (optional) (possibly nested tuple of)
TensorShape
objects containing the depth(s) of the input tensors expected to be passed in to theDropoutWrapper
. Required and used iffvariational_recurrent = True<code> and </code>input_keep_prob < 1
. dtype
- (optional) The
dtype
of the input, state, and output tensors. Required and used iffvariational_recurrent = True
. seed
- (optional) integer, the randomness seed.
dropout_state_filter_visitor
- (optional), default: (see below).
Function
that takes any hierarchical level of the state and returns a scalar or
depth=1 structure of Python booleans describing which terms in the state
should be dropped out.
In addition, if the function returns
True
, dropout is applied across this sublevel. If the function returnsFalse
, dropout is not applied across this entire sublevel. Default behavior: perform dropout on all terms except the memory (c
) state ofLSTMCellState
objects, and don't try to apply dropout toTensorArray
objects:def dropout_state_filter_visitor(s): if isinstance(s, LSTMCellState): # Never perform dropout on the c state. return LSTMCellState(c=False, h=True) elif isinstance(s, TensorArray): return False return True
**kwargs
- dict of keyword arguments for base layer.
Raises
TypeError
- if
cell
is not anRNNCell
, orkeep_state_fn
is provided but notcallable
. ValueError
- if any of the keep_probs are not between 0 and 1.
Expand source code
class DropoutWrapper(rnn_cell_wrapper_impl.DropoutWrapperBase, _RNNCellWrapperV1): """Operator adding dropout to inputs and outputs of the given cell.""" def __init__(self, *args, **kwargs): # pylint: disable=useless-super-delegation super(DropoutWrapper, self).__init__(*args, **kwargs) __init__.__doc__ = rnn_cell_wrapper_impl.DropoutWrapperBase.__init__.__doc__
Ancestors
- DropoutWrapperBase
- keras.layers.legacy_rnn.rnn_cell_impl._RNNCellWrapperV1
- RNNCell
- Layer
- Layer
- tensorflow.python.module.module.Module
- tensorflow.python.training.tracking.tracking.AutoTrackable
- tensorflow.python.training.tracking.base.Trackable
- LayerVersionSelector
Inherited members
DropoutWrapperBase
:RNNCell
:activity_regularizer
add_loss
add_metric
add_update
add_variable
add_weight
apply
build
call
compute_dtype
compute_mask
compute_output_shape
compute_output_signature
count_params
dtype
dtype_policy
dynamic
finalize_state
from_config
get_input_at
get_input_mask_at
get_input_shape_at
get_losses_for
get_output_at
get_output_mask_at
get_output_shape_at
get_updates_for
get_weights
inbound_nodes
input
input_mask
input_shape
input_spec
losses
metrics
name
non_trainable_variables
non_trainable_weights
outbound_nodes
output
output_mask
output_shape
output_size
set_weights
state_size
supports_masking
trainable_variables
trainable_weights
variable_dtype
variables
weights
zero_state
class GRUCell (num_units, activation=None, reuse=None, kernel_initializer=None, bias_initializer=None, name=None, dtype=None, **kwargs)
-
Gated Recurrent Unit cell.
Note that this cell is not optimized for performance. Please use
tf.contrib.cudnn_rnn.CudnnGRU
for better performance on GPU, ortf.contrib.rnn.GRUBlockCellV2
for better performance on CPU.Args
num_units
- int, The number of units in the GRU cell.
activation
- Nonlinearity to use.
Default:
tanh
. reuse
- (optional) Python boolean describing whether to reuse variables in an
existing scope.
If not
True
, and the existing scope already has the given variables, an error is raised. kernel_initializer
- (optional) The initializer to use for the weight and projection matrices.
bias_initializer
- (optional) The initializer to use for the bias.
name
- String, the name of the layer. Layers with the same name will share weights, but to avoid mistakes we require reuse=True in such cases.
dtype
- Default dtype of the layer (default of
None
means use the type of the first input). Required whenbuild
is called beforecall
. **kwargs
-
Dict, keyword named properties for common layer attributes, like
trainable
etc when constructing the cell from configs of get_config().References:
Learning Phrase Representations using RNN Encoder Decoder for Statistical Machine Translation: [Cho et al., 2014] (https://aclanthology.coli.uni-saarland.de/papers/D14-1179/d14-1179) (pdf)
Expand source code
class GRUCell(LayerRNNCell): """Gated Recurrent Unit cell. Note that this cell is not optimized for performance. Please use `tf.contrib.cudnn_rnn.CudnnGRU` for better performance on GPU, or `tf.contrib.rnn.GRUBlockCellV2` for better performance on CPU. Args: num_units: int, The number of units in the GRU cell. activation: Nonlinearity to use. Default: `tanh`. reuse: (optional) Python boolean describing whether to reuse variables in an existing scope. If not `True`, and the existing scope already has the given variables, an error is raised. kernel_initializer: (optional) The initializer to use for the weight and projection matrices. bias_initializer: (optional) The initializer to use for the bias. name: String, the name of the layer. Layers with the same name will share weights, but to avoid mistakes we require reuse=True in such cases. dtype: Default dtype of the layer (default of `None` means use the type of the first input). Required when `build` is called before `call`. **kwargs: Dict, keyword named properties for common layer attributes, like `trainable` etc when constructing the cell from configs of get_config(). References: Learning Phrase Representations using RNN Encoder Decoder for Statistical Machine Translation: [Cho et al., 2014] (https://aclanthology.coli.uni-saarland.de/papers/D14-1179/d14-1179) ([pdf](http://emnlp2014.org/papers/pdf/EMNLP2014179.pdf)) """ def __init__(self, num_units, activation=None, reuse=None, kernel_initializer=None, bias_initializer=None, name=None, dtype=None, **kwargs): warnings.warn("`tf.nn.rnn_cell.GRUCell` is deprecated and will be removed " "in a future version. This class " "is equivalent as `tf.keras.layers.GRUCell`, " "and will be replaced by that in Tensorflow 2.0.") super(GRUCell, self).__init__( _reuse=reuse, name=name, dtype=dtype, **kwargs) _check_supported_dtypes(self.dtype) if tf.executing_eagerly() and tf.config.list_logical_devices("GPU"): logging.warning( "%s: Note that this cell is not optimized for performance. " "Please use tf.contrib.cudnn_rnn.CudnnGRU for better " "performance on GPU.", self) # Inputs must be 2-dimensional. self.input_spec = input_spec.InputSpec(ndim=2) self._num_units = num_units if activation: self._activation = activations.get(activation) else: self._activation = tf.tanh self._kernel_initializer = initializers.get(kernel_initializer) self._bias_initializer = initializers.get(bias_initializer) @property def state_size(self): return self._num_units @property def output_size(self): return self._num_units @tf_utils.shape_type_conversion def build(self, inputs_shape): if inputs_shape[-1] is None: raise ValueError("Expected inputs.shape[-1] to be known, saw shape: %s" % str(inputs_shape)) _check_supported_dtypes(self.dtype) input_depth = inputs_shape[-1] self._gate_kernel = self.add_variable( "gates/%s" % _WEIGHTS_VARIABLE_NAME, shape=[input_depth + self._num_units, 2 * self._num_units], initializer=self._kernel_initializer) self._gate_bias = self.add_variable( "gates/%s" % _BIAS_VARIABLE_NAME, shape=[2 * self._num_units], initializer=(self._bias_initializer if self._bias_initializer is not None else tf.compat.v1.constant_initializer(1.0, dtype=self.dtype))) self._candidate_kernel = self.add_variable( "candidate/%s" % _WEIGHTS_VARIABLE_NAME, shape=[input_depth + self._num_units, self._num_units], initializer=self._kernel_initializer) self._candidate_bias = self.add_variable( "candidate/%s" % _BIAS_VARIABLE_NAME, shape=[self._num_units], initializer=(self._bias_initializer if self._bias_initializer is not None else tf.compat.v1.zeros_initializer(dtype=self.dtype))) self.built = True def call(self, inputs, state): """Gated recurrent unit (GRU) with nunits cells.""" _check_rnn_cell_input_dtypes([inputs, state]) gate_inputs = tf.matmul( tf.concat([inputs, state], 1), self._gate_kernel) gate_inputs = tf.nn.bias_add(gate_inputs, self._gate_bias) value = tf.sigmoid(gate_inputs) r, u = tf.split(value=value, num_or_size_splits=2, axis=1) r_state = r * state candidate = tf.matmul( tf.concat([inputs, r_state], 1), self._candidate_kernel) candidate = tf.nn.bias_add(candidate, self._candidate_bias) c = self._activation(candidate) new_h = u * state + (1 - u) * c return new_h, new_h def get_config(self): config = { "num_units": self._num_units, "kernel_initializer": initializers.serialize(self._kernel_initializer), "bias_initializer": initializers.serialize(self._bias_initializer), "activation": activations.serialize(self._activation), "reuse": self._reuse, } base_config = super(GRUCell, self).get_config() return dict(list(base_config.items()) + list(config.items()))
Ancestors
- LayerRNNCell
- RNNCell
- Layer
- Layer
- tensorflow.python.module.module.Module
- tensorflow.python.training.tracking.tracking.AutoTrackable
- tensorflow.python.training.tracking.base.Trackable
- LayerVersionSelector
Methods
def call(self, inputs, state)
-
Gated recurrent unit (GRU) with nunits cells.
Expand source code
def call(self, inputs, state): """Gated recurrent unit (GRU) with nunits cells.""" _check_rnn_cell_input_dtypes([inputs, state]) gate_inputs = tf.matmul( tf.concat([inputs, state], 1), self._gate_kernel) gate_inputs = tf.nn.bias_add(gate_inputs, self._gate_bias) value = tf.sigmoid(gate_inputs) r, u = tf.split(value=value, num_or_size_splits=2, axis=1) r_state = r * state candidate = tf.matmul( tf.concat([inputs, r_state], 1), self._candidate_kernel) candidate = tf.nn.bias_add(candidate, self._candidate_bias) c = self._activation(candidate) new_h = u * state + (1 - u) * c return new_h, new_h
Inherited members
LayerRNNCell
:activity_regularizer
add_loss
add_metric
add_update
add_variable
add_weight
apply
build
compute_dtype
compute_mask
compute_output_shape
compute_output_signature
count_params
dtype
dtype_policy
dynamic
finalize_state
from_config
get_config
get_input_at
get_input_mask_at
get_input_shape_at
get_losses_for
get_output_at
get_output_mask_at
get_output_shape_at
get_updates_for
get_weights
inbound_nodes
input
input_mask
input_shape
input_spec
losses
metrics
name
non_trainable_variables
non_trainable_weights
outbound_nodes
output
output_mask
output_shape
output_size
set_weights
state_size
supports_masking
trainable_variables
trainable_weights
variable_dtype
variables
weights
zero_state
class LSTMCell (num_units, use_peepholes=False, cell_clip=None, initializer=None, num_proj=None, proj_clip=None, num_unit_shards=None, num_proj_shards=None, forget_bias=1.0, state_is_tuple=True, activation=None, reuse=None, name=None, dtype=None, **kwargs)
-
Long short-term memory unit (LSTM) recurrent network cell.
The default non-peephole implementation is based on (Gers et al., 1999). The peephole implementation is based on (Sak et al., 2014).
The class uses optional peep-hole connections, optional cell clipping, and an optional projection layer.
Note that this cell is not optimized for performance. Please use
tf.contrib.cudnn_rnn.CudnnLSTM
for better performance on GPU, ortf.contrib.rnn.LSTMBlockCell
andtf.contrib.rnn.LSTMBlockFusedCell
for better performance on CPU.References
Long short-term memory recurrent neural network architectures for large scale acoustic modeling: [Sak et al., 2014] (https://www.isca-speech.org/archive/interspeech_2014/i14_0338.html) ([pdf] (https://www.isca-speech.org/archive/archive_papers/interspeech_2014/i14_0338.pdf)) Learning to forget: [Gers et al., 1999] (http://digital-library.theiet.org/content/conferences/10.1049/cp_19991218) (pdf) Long Short-Term Memory: [Hochreiter et al., 1997] (https://www.mitpressjournals.org/doi/abs/10.1162/neco.1997.9.8.1735) (pdf)
Initialize the parameters for an LSTM cell.
Args
num_units
- int, The number of units in the LSTM cell.
use_peepholes
- bool, set True to enable diagonal/peephole connections.
cell_clip
- (optional) A float value, if provided the cell state is clipped by this value prior to the cell output activation.
initializer
- (optional) The initializer to use for the weight and projection matrices.
num_proj
- (optional) int, The output dimensionality for the projection matrices. If None, no projection is performed.
proj_clip
- (optional) A float value.
If
num_proj > 0
andproj_clip
is provided, then the projected values are clipped elementwise to within[-proj_clip, proj_clip]
. num_unit_shards
- Deprecated, will be removed by Jan. 2017. Use a variable_scope partitioner instead.
num_proj_shards
- Deprecated, will be removed by Jan. 2017. Use a variable_scope partitioner instead.
forget_bias
- Biases of the forget gate are initialized by default to 1 in
order to reduce the scale of forgetting at the beginning of the
training. Must set it manually to
0.0
when restoring from CudnnLSTM trained checkpoints. state_is_tuple
- If True, accepted and returned states are 2-tuples of the
c_state
andm_state
. If False, they are concatenated along the column axis. This latter behavior will soon be deprecated. activation
- Activation function of the inner states.
Default:
tanh
. It could also be string that is within Keras activation function names. reuse
- (optional) Python boolean describing whether to reuse variables in
an existing scope.
If not
True
, and the existing scope already has the given variables, an error is raised. name
- String, the name of the layer. Layers with the same name will share weights, but to avoid mistakes we require reuse=True in such cases.
dtype
- Default dtype of the layer (default of
None
means use the type of the first input). Required whenbuild
is called beforecall
. **kwargs
- Dict, keyword named properties for common layer attributes, like
trainable
etc when constructing the cell from configs of get_config(). When restoring from CudnnLSTM-trained checkpoints, useCudnnCompatibleLSTMCell
instead.
Expand source code
class LSTMCell(LayerRNNCell): """Long short-term memory unit (LSTM) recurrent network cell. The default non-peephole implementation is based on (Gers et al., 1999). The peephole implementation is based on (Sak et al., 2014). The class uses optional peep-hole connections, optional cell clipping, and an optional projection layer. Note that this cell is not optimized for performance. Please use `tf.contrib.cudnn_rnn.CudnnLSTM` for better performance on GPU, or `tf.contrib.rnn.LSTMBlockCell` and `tf.contrib.rnn.LSTMBlockFusedCell` for better performance on CPU. References: Long short-term memory recurrent neural network architectures for large scale acoustic modeling: [Sak et al., 2014] (https://www.isca-speech.org/archive/interspeech_2014/i14_0338.html) ([pdf] (https://www.isca-speech.org/archive/archive_papers/interspeech_2014/i14_0338.pdf)) Learning to forget: [Gers et al., 1999] (http://digital-library.theiet.org/content/conferences/10.1049/cp_19991218) ([pdf](https://arxiv.org/pdf/1409.2329.pdf)) Long Short-Term Memory: [Hochreiter et al., 1997] (https://www.mitpressjournals.org/doi/abs/10.1162/neco.1997.9.8.1735) ([pdf](http://ml.jku.at/publications/older/3504.pdf)) """ def __init__(self, num_units, use_peepholes=False, cell_clip=None, initializer=None, num_proj=None, proj_clip=None, num_unit_shards=None, num_proj_shards=None, forget_bias=1.0, state_is_tuple=True, activation=None, reuse=None, name=None, dtype=None, **kwargs): """Initialize the parameters for an LSTM cell. Args: num_units: int, The number of units in the LSTM cell. use_peepholes: bool, set True to enable diagonal/peephole connections. cell_clip: (optional) A float value, if provided the cell state is clipped by this value prior to the cell output activation. initializer: (optional) The initializer to use for the weight and projection matrices. num_proj: (optional) int, The output dimensionality for the projection matrices. If None, no projection is performed. proj_clip: (optional) A float value. If `num_proj > 0` and `proj_clip` is provided, then the projected values are clipped elementwise to within `[-proj_clip, proj_clip]`. num_unit_shards: Deprecated, will be removed by Jan. 2017. Use a variable_scope partitioner instead. num_proj_shards: Deprecated, will be removed by Jan. 2017. Use a variable_scope partitioner instead. forget_bias: Biases of the forget gate are initialized by default to 1 in order to reduce the scale of forgetting at the beginning of the training. Must set it manually to `0.0` when restoring from CudnnLSTM trained checkpoints. state_is_tuple: If True, accepted and returned states are 2-tuples of the `c_state` and `m_state`. If False, they are concatenated along the column axis. This latter behavior will soon be deprecated. activation: Activation function of the inner states. Default: `tanh`. It could also be string that is within Keras activation function names. reuse: (optional) Python boolean describing whether to reuse variables in an existing scope. If not `True`, and the existing scope already has the given variables, an error is raised. name: String, the name of the layer. Layers with the same name will share weights, but to avoid mistakes we require reuse=True in such cases. dtype: Default dtype of the layer (default of `None` means use the type of the first input). Required when `build` is called before `call`. **kwargs: Dict, keyword named properties for common layer attributes, like `trainable` etc when constructing the cell from configs of get_config(). When restoring from CudnnLSTM-trained checkpoints, use `CudnnCompatibleLSTMCell` instead. """ warnings.warn("`tf.nn.rnn_cell.LSTMCell` is deprecated and will be " "removed in a future version. This class " "is equivalent as `tf.keras.layers.LSTMCell`, " "and will be replaced by that in Tensorflow 2.0.") super(LSTMCell, self).__init__( _reuse=reuse, name=name, dtype=dtype, **kwargs) _check_supported_dtypes(self.dtype) if not state_is_tuple: logging.warning( "%s: Using a concatenated state is slower and will soon be " "deprecated. Use state_is_tuple=True.", self) if num_unit_shards is not None or num_proj_shards is not None: logging.warning( "%s: The num_unit_shards and proj_unit_shards parameters are " "deprecated and will be removed in Jan 2017. " "Use a variable scope with a partitioner instead.", self) if tf.executing_eagerly() and tf.config.list_logical_devices("GPU"): logging.warning( "%s: Note that this cell is not optimized for performance. " "Please use tf.contrib.cudnn_rnn.CudnnLSTM for better " "performance on GPU.", self) # Inputs must be 2-dimensional. self.input_spec = input_spec.InputSpec(ndim=2) self._num_units = num_units self._use_peepholes = use_peepholes self._cell_clip = cell_clip self._initializer = initializers.get(initializer) self._num_proj = num_proj self._proj_clip = proj_clip self._num_unit_shards = num_unit_shards self._num_proj_shards = num_proj_shards self._forget_bias = forget_bias self._state_is_tuple = state_is_tuple if activation: self._activation = activations.get(activation) else: self._activation = tf.tanh if num_proj: self._state_size = ( LSTMStateTuple(num_units, num_proj) if state_is_tuple else num_units + num_proj) self._output_size = num_proj else: self._state_size = ( LSTMStateTuple(num_units, num_units) if state_is_tuple else 2 * num_units) self._output_size = num_units @property def state_size(self): return self._state_size @property def output_size(self): return self._output_size @tf_utils.shape_type_conversion def build(self, inputs_shape): if inputs_shape[-1] is None: raise ValueError("Expected inputs.shape[-1] to be known, saw shape: %s" % str(inputs_shape)) _check_supported_dtypes(self.dtype) input_depth = inputs_shape[-1] h_depth = self._num_units if self._num_proj is None else self._num_proj maybe_partitioner = ( tf.compat.v1.fixed_size_partitioner(self._num_unit_shards) if self._num_unit_shards is not None else None) self._kernel = self.add_variable( _WEIGHTS_VARIABLE_NAME, shape=[input_depth + h_depth, 4 * self._num_units], initializer=self._initializer, partitioner=maybe_partitioner) if self.dtype is None: initializer = tf.compat.v1.zeros_initializer else: initializer = tf.compat.v1.zeros_initializer(dtype=self.dtype) self._bias = self.add_variable( _BIAS_VARIABLE_NAME, shape=[4 * self._num_units], initializer=initializer) if self._use_peepholes: self._w_f_diag = self.add_variable( "w_f_diag", shape=[self._num_units], initializer=self._initializer) self._w_i_diag = self.add_variable( "w_i_diag", shape=[self._num_units], initializer=self._initializer) self._w_o_diag = self.add_variable( "w_o_diag", shape=[self._num_units], initializer=self._initializer) if self._num_proj is not None: maybe_proj_partitioner = ( tf.compat.v1.fixed_size_partitioner(self._num_proj_shards) if self._num_proj_shards is not None else None) self._proj_kernel = self.add_variable( "projection/%s" % _WEIGHTS_VARIABLE_NAME, shape=[self._num_units, self._num_proj], initializer=self._initializer, partitioner=maybe_proj_partitioner) self.built = True def call(self, inputs, state): """Run one step of LSTM. Args: inputs: input Tensor, must be 2-D, `[batch, input_size]`. state: if `state_is_tuple` is False, this must be a state Tensor, `2-D, [batch, state_size]`. If `state_is_tuple` is True, this must be a tuple of state Tensors, both `2-D`, with column sizes `c_state` and `m_state`. Returns: A tuple containing: - A `2-D, [batch, output_dim]`, Tensor representing the output of the LSTM after reading `inputs` when previous state was `state`. Here output_dim is: num_proj if num_proj was set, num_units otherwise. - Tensor(s) representing the new state of LSTM after reading `inputs` when the previous state was `state`. Same type and shape(s) as `state`. Raises: ValueError: If input size cannot be inferred from inputs via static shape inference. """ _check_rnn_cell_input_dtypes([inputs, state]) num_proj = self._num_units if self._num_proj is None else self._num_proj sigmoid = tf.sigmoid if self._state_is_tuple: (c_prev, m_prev) = state else: c_prev = tf.slice(state, [0, 0], [-1, self._num_units]) m_prev = tf.slice(state, [0, self._num_units], [-1, num_proj]) input_size = inputs.get_shape().with_rank(2).dims[1].value if input_size is None: raise ValueError("Could not infer input size from inputs.get_shape()[-1]") # i = input_gate, j = new_input, f = forget_gate, o = output_gate lstm_matrix = tf.matmul( tf.concat([inputs, m_prev], 1), self._kernel) lstm_matrix = tf.nn.bias_add(lstm_matrix, self._bias) i, j, f, o = tf.split( value=lstm_matrix, num_or_size_splits=4, axis=1) # Diagonal connections if self._use_peepholes: c = ( sigmoid(f + self._forget_bias + self._w_f_diag * c_prev) * c_prev + sigmoid(i + self._w_i_diag * c_prev) * self._activation(j)) else: c = ( sigmoid(f + self._forget_bias) * c_prev + sigmoid(i) * self._activation(j)) if self._cell_clip is not None: # pylint: disable=invalid-unary-operand-type c = tf.clip_by_value(c, -self._cell_clip, self._cell_clip) # pylint: enable=invalid-unary-operand-type if self._use_peepholes: m = sigmoid(o + self._w_o_diag * c) * self._activation(c) else: m = sigmoid(o) * self._activation(c) if self._num_proj is not None: m = tf.matmul(m, self._proj_kernel) if self._proj_clip is not None: # pylint: disable=invalid-unary-operand-type m = tf.clip_by_value(m, -self._proj_clip, self._proj_clip) # pylint: enable=invalid-unary-operand-type new_state = ( LSTMStateTuple(c, m) if self._state_is_tuple else tf.concat([c, m], 1)) return m, new_state def get_config(self): config = { "num_units": self._num_units, "use_peepholes": self._use_peepholes, "cell_clip": self._cell_clip, "initializer": initializers.serialize(self._initializer), "num_proj": self._num_proj, "proj_clip": self._proj_clip, "num_unit_shards": self._num_unit_shards, "num_proj_shards": self._num_proj_shards, "forget_bias": self._forget_bias, "state_is_tuple": self._state_is_tuple, "activation": activations.serialize(self._activation), "reuse": self._reuse, } base_config = super(LSTMCell, self).get_config() return dict(list(base_config.items()) + list(config.items()))
Ancestors
- LayerRNNCell
- RNNCell
- Layer
- Layer
- tensorflow.python.module.module.Module
- tensorflow.python.training.tracking.tracking.AutoTrackable
- tensorflow.python.training.tracking.base.Trackable
- LayerVersionSelector
Methods
def call(self, inputs, state)
-
Run one step of LSTM.
Args
inputs
- input Tensor, must be 2-D,
[batch, input_size]
. state
- if
state_is_tuple
is False, this must be a state Tensor,2-D, [batch, state_size]<code>. If </code>state_is_tuple
is True, this must be a tuple of state Tensors, both2-D
, with column sizesc_state
andm_state
.
Returns
A tuple containing:
- A
2-D, [batch, output_dim]
, Tensor representing the output of the LSTM after readinginputs
when previous state wasstate
. Here output_dim is: num_proj if num_proj was set, num_units otherwise. - Tensor(s) representing the new state of LSTM after reading
inputs
when the previous state wasstate
. Same type and shape(s) asstate
.
Raises
ValueError
- If input size cannot be inferred from inputs via static shape inference.
Expand source code
def call(self, inputs, state): """Run one step of LSTM. Args: inputs: input Tensor, must be 2-D, `[batch, input_size]`. state: if `state_is_tuple` is False, this must be a state Tensor, `2-D, [batch, state_size]`. If `state_is_tuple` is True, this must be a tuple of state Tensors, both `2-D`, with column sizes `c_state` and `m_state`. Returns: A tuple containing: - A `2-D, [batch, output_dim]`, Tensor representing the output of the LSTM after reading `inputs` when previous state was `state`. Here output_dim is: num_proj if num_proj was set, num_units otherwise. - Tensor(s) representing the new state of LSTM after reading `inputs` when the previous state was `state`. Same type and shape(s) as `state`. Raises: ValueError: If input size cannot be inferred from inputs via static shape inference. """ _check_rnn_cell_input_dtypes([inputs, state]) num_proj = self._num_units if self._num_proj is None else self._num_proj sigmoid = tf.sigmoid if self._state_is_tuple: (c_prev, m_prev) = state else: c_prev = tf.slice(state, [0, 0], [-1, self._num_units]) m_prev = tf.slice(state, [0, self._num_units], [-1, num_proj]) input_size = inputs.get_shape().with_rank(2).dims[1].value if input_size is None: raise ValueError("Could not infer input size from inputs.get_shape()[-1]") # i = input_gate, j = new_input, f = forget_gate, o = output_gate lstm_matrix = tf.matmul( tf.concat([inputs, m_prev], 1), self._kernel) lstm_matrix = tf.nn.bias_add(lstm_matrix, self._bias) i, j, f, o = tf.split( value=lstm_matrix, num_or_size_splits=4, axis=1) # Diagonal connections if self._use_peepholes: c = ( sigmoid(f + self._forget_bias + self._w_f_diag * c_prev) * c_prev + sigmoid(i + self._w_i_diag * c_prev) * self._activation(j)) else: c = ( sigmoid(f + self._forget_bias) * c_prev + sigmoid(i) * self._activation(j)) if self._cell_clip is not None: # pylint: disable=invalid-unary-operand-type c = tf.clip_by_value(c, -self._cell_clip, self._cell_clip) # pylint: enable=invalid-unary-operand-type if self._use_peepholes: m = sigmoid(o + self._w_o_diag * c) * self._activation(c) else: m = sigmoid(o) * self._activation(c) if self._num_proj is not None: m = tf.matmul(m, self._proj_kernel) if self._proj_clip is not None: # pylint: disable=invalid-unary-operand-type m = tf.clip_by_value(m, -self._proj_clip, self._proj_clip) # pylint: enable=invalid-unary-operand-type new_state = ( LSTMStateTuple(c, m) if self._state_is_tuple else tf.concat([c, m], 1)) return m, new_state
Inherited members
LayerRNNCell
:activity_regularizer
add_loss
add_metric
add_update
add_variable
add_weight
apply
build
compute_dtype
compute_mask
compute_output_shape
compute_output_signature
count_params
dtype
dtype_policy
dynamic
finalize_state
from_config
get_config
get_input_at
get_input_mask_at
get_input_shape_at
get_losses_for
get_output_at
get_output_mask_at
get_output_shape_at
get_updates_for
get_weights
inbound_nodes
input
input_mask
input_shape
input_spec
losses
metrics
name
non_trainable_variables
non_trainable_weights
outbound_nodes
output
output_mask
output_shape
output_size
set_weights
state_size
supports_masking
trainable_variables
trainable_weights
variable_dtype
variables
weights
zero_state
class LSTMStateTuple (c, h)
-
Tuple used by LSTM Cells for
state_size
,zero_state
, and output state.Stores two elements:
(c, h)
, in that order. Wherec
is the hidden state andh
is the output.Only used when
state_is_tuple=True
.Expand source code
class LSTMStateTuple(_LSTMStateTuple): """Tuple used by LSTM Cells for `state_size`, `zero_state`, and output state. Stores two elements: `(c, h)`, in that order. Where `c` is the hidden state and `h` is the output. Only used when `state_is_tuple=True`. """ __slots__ = () @property def dtype(self): (c, h) = self if c.dtype != h.dtype: raise TypeError("Inconsistent internal state: %s vs %s" % (str(c.dtype), str(h.dtype))) return c.dtype
Ancestors
- builtins.tuple
Instance variables
var dtype
-
Expand source code
@property def dtype(self): (c, h) = self if c.dtype != h.dtype: raise TypeError("Inconsistent internal state: %s vs %s" % (str(c.dtype), str(h.dtype))) return c.dtype
class LayerRNNCell (trainable=True, name=None, dtype=None, **kwargs)
-
Subclass of RNNCells that act like proper
tf.Layer
objects.For backwards compatibility purposes, most
RNNCell
instances allow theircall
methods to instantiate variables viatf.compat.v1.get_variable
. The underlying variable scope thus keeps track of any variables, and returning cached versions. This is atypical oftf.layer
objects, which separate this part of layer building into abuild
method that is only called once.Here we provide a subclass for
RNNCell
objects that act exactly asLayer
objects do. They must provide abuild
method and theircall
methods do not access Variablestf.compat.v1.get_variable
.Expand source code
class LayerRNNCell(RNNCell): """Subclass of RNNCells that act like proper `tf.Layer` objects. For backwards compatibility purposes, most `RNNCell` instances allow their `call` methods to instantiate variables via `tf.compat.v1.get_variable`. The underlying variable scope thus keeps track of any variables, and returning cached versions. This is atypical of `tf.layer` objects, which separate this part of layer building into a `build` method that is only called once. Here we provide a subclass for `RNNCell` objects that act exactly as `Layer` objects do. They must provide a `build` method and their `call` methods do not access Variables `tf.compat.v1.get_variable`. """ def __call__(self, inputs, state, scope=None, *args, **kwargs): """Run this RNN cell on inputs, starting from the given state. Args: inputs: `2-D` tensor with shape `[batch_size, input_size]`. state: if `self.state_size` is an integer, this should be a `2-D Tensor` with shape `[batch_size, self.state_size]`. Otherwise, if `self.state_size` is a tuple of integers, this should be a tuple with shapes `[batch_size, s] for s in self.state_size`. scope: optional cell scope. *args: Additional positional arguments. **kwargs: Additional keyword arguments. Returns: A pair containing: - Output: A `2-D` tensor with shape `[batch_size, self.output_size]`. - New state: Either a single `2-D` tensor, or a tuple of tensors matching the arity and shapes of `state`. """ # Bypass RNNCell's variable capturing semantics for LayerRNNCell. # Instead, it is up to subclasses to provide a proper build # method. See the class docstring for more details. return base_layer.Layer.__call__( self, inputs, state, scope=scope, *args, **kwargs)
Ancestors
- RNNCell
- Layer
- Layer
- tensorflow.python.module.module.Module
- tensorflow.python.training.tracking.tracking.AutoTrackable
- tensorflow.python.training.tracking.base.Trackable
- LayerVersionSelector
Subclasses
Inherited members
RNNCell
:activity_regularizer
add_loss
add_metric
add_update
add_variable
add_weight
apply
build
call
compute_dtype
compute_mask
compute_output_shape
compute_output_signature
count_params
dtype
dtype_policy
dynamic
finalize_state
from_config
get_config
get_input_at
get_input_mask_at
get_input_shape_at
get_losses_for
get_output_at
get_output_mask_at
get_output_shape_at
get_updates_for
get_weights
inbound_nodes
input
input_mask
input_shape
input_spec
losses
metrics
name
non_trainable_variables
non_trainable_weights
outbound_nodes
output
output_mask
output_shape
output_size
set_weights
state_size
supports_masking
trainable_variables
trainable_weights
variable_dtype
variables
weights
zero_state
class MultiRNNCell (cells, state_is_tuple=True)
-
RNN cell composed sequentially of multiple simple cells.
Example:
num_units = [128, 64] cells = [BasicLSTMCell(num_units=n) for n in num_units] stacked_rnn_cell = MultiRNNCell(cells)
Create a RNN cell composed sequentially of a number of RNNCells.
Args
cells
- list of RNNCells that will be composed in this order.
state_is_tuple
- If True, accepted and returned states are n-tuples, where
n = len(cells)
. If False, the states are all concatenated along the column axis. This latter behavior will soon be deprecated.
Raises
ValueError
- if cells is empty (not allowed), or at least one of the cells
returns a state tuple but the flag
state_is_tuple
isFalse
.
Expand source code
class MultiRNNCell(RNNCell): """RNN cell composed sequentially of multiple simple cells. Example: ```python num_units = [128, 64] cells = [BasicLSTMCell(num_units=n) for n in num_units] stacked_rnn_cell = MultiRNNCell(cells) ``` """ def __init__(self, cells, state_is_tuple=True): """Create a RNN cell composed sequentially of a number of RNNCells. Args: cells: list of RNNCells that will be composed in this order. state_is_tuple: If True, accepted and returned states are n-tuples, where `n = len(cells)`. If False, the states are all concatenated along the column axis. This latter behavior will soon be deprecated. Raises: ValueError: if cells is empty (not allowed), or at least one of the cells returns a state tuple but the flag `state_is_tuple` is `False`. """ logging.warning("`tf.nn.rnn_cell.MultiRNNCell` is deprecated. This class " "is equivalent as `tf.keras.layers.StackedRNNCells`, " "and will be replaced by that in Tensorflow 2.0.") super(MultiRNNCell, self).__init__() if not cells: raise ValueError("Must specify at least one cell for MultiRNNCell.") if not tf.nest.is_nested(cells): raise TypeError("cells must be a list or tuple, but saw: %s." % cells) if len(set(id(cell) for cell in cells)) < len(cells): logging.log_first_n( logging.WARN, "At least two cells provided to MultiRNNCell " "are the same object and will share weights.", 1) self._cells = cells for cell_number, cell in enumerate(self._cells): # Add Trackable dependencies on these cells so their variables get # saved with this object when using object-based saving. if isinstance(cell, tf.__internal__.tracking.Trackable): # TODO(allenl): Track down non-Trackable callers. self._track_trackable(cell, name="cell-%d" % (cell_number,)) self._state_is_tuple = state_is_tuple if not state_is_tuple: if any(tf.nest.is_nested(c.state_size) for c in self._cells): raise ValueError("Some cells return tuples of states, but the flag " "state_is_tuple is not set. State sizes are: %s" % str([c.state_size for c in self._cells])) @property def state_size(self): if self._state_is_tuple: return tuple(cell.state_size for cell in self._cells) else: return sum(cell.state_size for cell in self._cells) @property def output_size(self): return self._cells[-1].output_size def zero_state(self, batch_size, dtype): with backend.name_scope(type(self).__name__ + "ZeroState"): if self._state_is_tuple: return tuple(cell.zero_state(batch_size, dtype) for cell in self._cells) else: # We know here that state_size of each cell is not a tuple and # presumably does not contain TensorArrays or anything else fancy return super(MultiRNNCell, self).zero_state(batch_size, dtype) @property def trainable_weights(self): if not self.trainable: return [] weights = [] for cell in self._cells: if isinstance(cell, base_layer.Layer): weights += cell.trainable_weights return weights @property def non_trainable_weights(self): weights = [] for cell in self._cells: if isinstance(cell, base_layer.Layer): weights += cell.non_trainable_weights if not self.trainable: trainable_weights = [] for cell in self._cells: if isinstance(cell, base_layer.Layer): trainable_weights += cell.trainable_weights return trainable_weights + weights return weights def call(self, inputs, state): """Run this multi-layer cell on inputs, starting from state.""" cur_state_pos = 0 cur_inp = inputs new_states = [] for i, cell in enumerate(self._cells): with tf.compat.v1.variable_scope("cell_%d" % i): if self._state_is_tuple: if not tf.nest.is_nested(state): raise ValueError( "Expected state to be a tuple of length %d, but received: %s" % (len(self.state_size), state)) cur_state = state[i] else: cur_state = tf.slice(state, [0, cur_state_pos], [-1, cell.state_size]) cur_state_pos += cell.state_size cur_inp, new_state = cell(cur_inp, cur_state) new_states.append(new_state) new_states = ( tuple(new_states) if self._state_is_tuple else tf.concat( new_states, 1)) return cur_inp, new_states
Ancestors
- RNNCell
- Layer
- Layer
- tensorflow.python.module.module.Module
- tensorflow.python.training.tracking.tracking.AutoTrackable
- tensorflow.python.training.tracking.base.Trackable
- LayerVersionSelector
Methods
def call(self, inputs, state)
-
Run this multi-layer cell on inputs, starting from state.
Expand source code
def call(self, inputs, state): """Run this multi-layer cell on inputs, starting from state.""" cur_state_pos = 0 cur_inp = inputs new_states = [] for i, cell in enumerate(self._cells): with tf.compat.v1.variable_scope("cell_%d" % i): if self._state_is_tuple: if not tf.nest.is_nested(state): raise ValueError( "Expected state to be a tuple of length %d, but received: %s" % (len(self.state_size), state)) cur_state = state[i] else: cur_state = tf.slice(state, [0, cur_state_pos], [-1, cell.state_size]) cur_state_pos += cell.state_size cur_inp, new_state = cell(cur_inp, cur_state) new_states.append(new_state) new_states = ( tuple(new_states) if self._state_is_tuple else tf.concat( new_states, 1)) return cur_inp, new_states
Inherited members
RNNCell
:activity_regularizer
add_loss
add_metric
add_update
add_variable
add_weight
apply
build
compute_dtype
compute_mask
compute_output_shape
compute_output_signature
count_params
dtype
dtype_policy
dynamic
finalize_state
from_config
get_config
get_input_at
get_input_mask_at
get_input_shape_at
get_losses_for
get_output_at
get_output_mask_at
get_output_shape_at
get_updates_for
get_weights
inbound_nodes
input
input_mask
input_shape
input_spec
losses
metrics
name
non_trainable_variables
non_trainable_weights
outbound_nodes
output
output_mask
output_shape
output_size
set_weights
state_size
supports_masking
trainable_variables
trainable_weights
variable_dtype
variables
weights
zero_state
class RNNCell (trainable=True, name=None, dtype=None, **kwargs)
-
Abstract object representing an RNN cell.
Every
RNNCell
must have the properties below and implementcall
with the signature(output, next_state) = call(input, state)
. The optional third input argument,scope
, is allowed for backwards compatibility purposes; but should be left off for new subclasses.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. Ifself.state_size
is an integer, this operation also results in a new state matrix withself.state_size
columns. Ifself.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 eachs
inself.batch_size
.Expand source code
class RNNCell(base_layer.Layer): """Abstract object representing an RNN cell. Every `RNNCell` must have the properties below and implement `call` with the signature `(output, next_state) = call(input, state)`. The optional third input argument, `scope`, is allowed for backwards compatibility purposes; but should be left off for new subclasses. 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 __init__(self, trainable=True, name=None, dtype=None, **kwargs): super(RNNCell, self).__init__( trainable=trainable, name=name, dtype=dtype, **kwargs) # Attribute that indicates whether the cell is a TF RNN cell, due the slight # difference between TF and Keras RNN cell. Notably the state is not wrapped # in a list for TF cell where they are single tensor state, whereas keras # cell will wrap the state into a list, and call() will have to unwrap them. self._is_tf_rnn_cell = True def __call__(self, inputs, state, scope=None): """Run this RNN cell on inputs, starting from the given state. Args: inputs: `2-D` tensor with shape `[batch_size, input_size]`. state: if `self.state_size` is an integer, this should be a `2-D Tensor` with shape `[batch_size, self.state_size]`. Otherwise, if `self.state_size` is a tuple of integers, this should be a tuple with shapes `[batch_size, s] for s in self.state_size`. scope: VariableScope for the created subgraph; defaults to class name. Returns: A pair containing: - Output: A `2-D` tensor with shape `[batch_size, self.output_size]`. - New state: Either a single `2-D` tensor, or a tuple of tensors matching the arity and shapes of `state`. """ if scope is not None: with tf.compat.v1.variable_scope( scope, custom_getter=self._rnn_get_variable) as scope: return super(RNNCell, self).__call__(inputs, state, scope=scope) else: scope_attrname = "rnncell_scope" scope = getattr(self, scope_attrname, None) if scope is None: scope = tf.compat.v1.variable_scope( tf.compat.v1.get_variable_scope(), custom_getter=self._rnn_get_variable) setattr(self, scope_attrname, scope) with scope: return super(RNNCell, self).__call__(inputs, state) def _rnn_get_variable(self, getter, *args, **kwargs): variable = getter(*args, **kwargs) if tf.compat.v1.executing_eagerly_outside_functions(): trainable = variable.trainable else: trainable = ( variable in tf.compat.v1.trainable_variables() or (base_layer_utils.is_split_variable(variable) and list(variable)[0] in tf.compat.v1.trainable_variables())) if trainable and all(variable is not v for v in self._trainable_weights): self._trainable_weights.append(variable) elif not trainable and all( variable is not v for v in self._non_trainable_weights): self._non_trainable_weights.append(variable) return variable @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 build(self, _): # This tells the parent Layer object that it's OK to call # self.add_variable() inside the call() method. pass def get_initial_state(self, inputs=None, batch_size=None, dtype=None): if inputs is not None: # Validate the given batch_size and dtype against inputs if provided. inputs = tf.convert_to_tensor(inputs, name="inputs") if batch_size is not None: if tf.is_tensor(batch_size): static_batch_size = tf.get_static_value( batch_size, partial=True) else: static_batch_size = batch_size if inputs.shape.dims[0].value != static_batch_size: raise ValueError( "batch size from input tensor is different from the " "input param. Input tensor batch: {}, batch_size: {}".format( inputs.shape.dims[0].value, batch_size)) if dtype is not None and inputs.dtype != dtype: raise ValueError( "dtype from input tensor is different from the " "input param. Input tensor dtype: {}, dtype: {}".format( inputs.dtype, dtype)) batch_size = inputs.shape.dims[0].value or tf.compat.v1.shape(inputs)[0] dtype = inputs.dtype if batch_size 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, dtype)) return self.zero_state(batch_size, dtype) def zero_state(self, batch_size, dtype): """Return zero-filled state tensor(s). Args: batch_size: int, float, or unit Tensor representing the batch size. dtype: the data type to use for the state. Returns: If `state_size` is an int or TensorShape, then the return value is a `N-D` tensor of shape `[batch_size, state_size]` filled with zeros. If `state_size` is a nested list or tuple, then the return value is a nested list or tuple (of the same structure) of `2-D` tensors with the shapes `[batch_size, s]` for each s in `state_size`. """ # Try to use the last cached zero_state. This is done to avoid recreating # zeros, especially when eager execution is enabled. state_size = self.state_size is_eager = tf.executing_eagerly() if is_eager and _hasattr(self, "_last_zero_state"): (last_state_size, last_batch_size, last_dtype, last_output) = getattr(self, "_last_zero_state") if (last_batch_size == batch_size and last_dtype == dtype and last_state_size == state_size): return last_output with backend.name_scope(type(self).__name__ + "ZeroState"): output = _zero_state_tensors(state_size, batch_size, dtype) if is_eager: self._last_zero_state = (state_size, batch_size, dtype, output) return output # TODO(b/134773139): Remove when contrib RNN cells implement `get_config` def get_config(self): # pylint: disable=useless-super-delegation return super(RNNCell, self).get_config() @property def _use_input_spec_as_call_signature(self): # We do not store the shape information for the state argument in the call # function for legacy RNN cells, so do not generate an input signature. return False
Ancestors
- Layer
- Layer
- tensorflow.python.module.module.Module
- tensorflow.python.training.tracking.tracking.AutoTrackable
- tensorflow.python.training.tracking.base.Trackable
- LayerVersionSelector
Subclasses
- LayerRNNCell
- MultiRNNCell
- keras.layers.legacy_rnn.rnn_cell_impl._RNNCellWrapperV1
Instance variables
var output_size
-
Integer or TensorShape: size of outputs produced by this cell.
Expand source code
@property def output_size(self): """Integer or TensorShape: size of outputs produced by this cell.""" raise NotImplementedError("Abstract method")
var state_size
-
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.
Expand source code
@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")
Methods
def get_initial_state(self, inputs=None, batch_size=None, dtype=None)
-
Expand source code
def get_initial_state(self, inputs=None, batch_size=None, dtype=None): if inputs is not None: # Validate the given batch_size and dtype against inputs if provided. inputs = tf.convert_to_tensor(inputs, name="inputs") if batch_size is not None: if tf.is_tensor(batch_size): static_batch_size = tf.get_static_value( batch_size, partial=True) else: static_batch_size = batch_size if inputs.shape.dims[0].value != static_batch_size: raise ValueError( "batch size from input tensor is different from the " "input param. Input tensor batch: {}, batch_size: {}".format( inputs.shape.dims[0].value, batch_size)) if dtype is not None and inputs.dtype != dtype: raise ValueError( "dtype from input tensor is different from the " "input param. Input tensor dtype: {}, dtype: {}".format( inputs.dtype, dtype)) batch_size = inputs.shape.dims[0].value or tf.compat.v1.shape(inputs)[0] dtype = inputs.dtype if batch_size 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, dtype)) return self.zero_state(batch_size, dtype)
def zero_state(self, batch_size, dtype)
-
Return zero-filled state tensor(s).
Args
batch_size
- int, float, or unit Tensor representing the batch size.
dtype
- the data type to use for the state.
Returns
If
state_size
is an int or TensorShape, then the return value is aN-D
tensor of shape[batch_size, state_size]
filled with zeros.If
state_size
is a nested list or tuple, then the return value is a nested list or tuple (of the same structure) of2-D
tensors with the shapes[batch_size, s]
for each s instate_size
.Expand source code
def zero_state(self, batch_size, dtype): """Return zero-filled state tensor(s). Args: batch_size: int, float, or unit Tensor representing the batch size. dtype: the data type to use for the state. Returns: If `state_size` is an int or TensorShape, then the return value is a `N-D` tensor of shape `[batch_size, state_size]` filled with zeros. If `state_size` is a nested list or tuple, then the return value is a nested list or tuple (of the same structure) of `2-D` tensors with the shapes `[batch_size, s]` for each s in `state_size`. """ # Try to use the last cached zero_state. This is done to avoid recreating # zeros, especially when eager execution is enabled. state_size = self.state_size is_eager = tf.executing_eagerly() if is_eager and _hasattr(self, "_last_zero_state"): (last_state_size, last_batch_size, last_dtype, last_output) = getattr(self, "_last_zero_state") if (last_batch_size == batch_size and last_dtype == dtype and last_state_size == state_size): return last_output with backend.name_scope(type(self).__name__ + "ZeroState"): output = _zero_state_tensors(state_size, batch_size, dtype) if is_eager: self._last_zero_state = (state_size, batch_size, dtype, output) return output
Inherited members
Layer
:activity_regularizer
add_loss
add_metric
add_update
add_variable
add_weight
apply
build
call
compute_dtype
compute_mask
compute_output_shape
compute_output_signature
count_params
dtype
dtype_policy
dynamic
finalize_state
from_config
get_config
get_input_at
get_input_mask_at
get_input_shape_at
get_losses_for
get_output_at
get_output_mask_at
get_output_shape_at
get_updates_for
get_weights
inbound_nodes
input
input_mask
input_shape
input_spec
losses
metrics
name
non_trainable_variables
non_trainable_weights
outbound_nodes
output
output_mask
output_shape
set_weights
supports_masking
trainable_variables
trainable_weights
variable_dtype
variables
weights
class ResidualWrapper (*args, **kwargs)
-
RNNCell wrapper that ensures cell inputs are added to the outputs.
Constructs a
ResidualWrapper
forcell
.Args
cell
- An instance of
RNNCell
. residual_fn
- (Optional) The function to map raw cell inputs and raw cell outputs to the actual cell outputs of the residual network. Defaults to calling nest.map_structure on (lambda i, o: i + o), inputs and outputs.
**kwargs
- dict of keyword arguments for base layer.
Expand source code
class ResidualWrapper(rnn_cell_wrapper_impl.ResidualWrapperBase, _RNNCellWrapperV1): """RNNCell wrapper that ensures cell inputs are added to the outputs.""" def __init__(self, *args, **kwargs): # pylint: disable=useless-super-delegation super(ResidualWrapper, self).__init__(*args, **kwargs) __init__.__doc__ = rnn_cell_wrapper_impl.ResidualWrapperBase.__init__.__doc__
Ancestors
- ResidualWrapperBase
- keras.layers.legacy_rnn.rnn_cell_impl._RNNCellWrapperV1
- RNNCell
- Layer
- Layer
- tensorflow.python.module.module.Module
- tensorflow.python.training.tracking.tracking.AutoTrackable
- tensorflow.python.training.tracking.base.Trackable
- LayerVersionSelector
Inherited members
ResidualWrapperBase
:RNNCell
:activity_regularizer
add_loss
add_metric
add_update
add_variable
add_weight
apply
build
call
compute_dtype
compute_mask
compute_output_shape
compute_output_signature
count_params
dtype
dtype_policy
dynamic
finalize_state
from_config
get_input_at
get_input_mask_at
get_input_shape_at
get_losses_for
get_output_at
get_output_mask_at
get_output_shape_at
get_updates_for
get_weights
inbound_nodes
input
input_mask
input_shape
input_spec
losses
metrics
name
non_trainable_variables
non_trainable_weights
outbound_nodes
output
output_mask
output_shape
output_size
set_weights
state_size
supports_masking
trainable_variables
trainable_weights
variable_dtype
variables
weights
zero_state