# 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.
# ==============================================================================
"""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 collections
import hashlib
import numbers
from tensorflow.python.framework import constant_op
from tensorflow.python.framework import dtypes
from tensorflow.python.framework import ops
from tensorflow.python.framework import tensor_shape
from tensorflow.python.framework import tensor_util
from tensorflow.python.layers import base as base_layer
from tensorflow.python.ops import array_ops
from tensorflow.python.ops import clip_ops
from tensorflow.python.ops import init_ops
from tensorflow.python.ops import math_ops
from tensorflow.python.ops import nn_ops
from tensorflow.python.ops import partitioned_variables
from tensorflow.python.ops import random_ops
from tensorflow.python.ops import variable_scope as vs
from tensorflow.python.ops import variables as tf_variables
from tensorflow.python.platform import tf_logging as logging
from tensorflow.python.util import nest
_BIAS_VARIABLE_NAME = "bias"
_WEIGHTS_VARIABLE_NAME = "kernel"
def _like_rnncell(cell):
"""Checks that a given object is an RNNCell by using duck typing."""
conditions = [hasattr(cell, "output_size"), hasattr(cell, "state_size"),
hasattr(cell, "zero_state"), callable(cell)]
return all(conditions)
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, ops.Tensor):
p = prefix
p_static = tensor_util.constant_value(prefix)
if p.shape.ndims == 0:
p = array_ops.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 = tensor_shape.as_shape(prefix)
p_static = p.as_list() if p.ndims is not None else None
p = (constant_op.constant(p.as_list(), dtype=dtypes.int32)
if p.is_fully_defined() else None)
if isinstance(suffix, ops.Tensor):
s = suffix
s_static = tensor_util.constant_value(suffix)
if s.shape.ndims == 0:
s = array_ops.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 = tensor_shape.as_shape(suffix)
s_static = s.as_list() if s.ndims is not None else None
s = (constant_op.constant(s.as_list(), dtype=dtypes.int32)
if s.is_fully_defined() else None)
if static:
shape = tensor_shape.as_shape(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 = array_ops.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)
c_static = _concat(batch_size, s, static=True)
size = array_ops.zeros(c, dtype=dtype)
size.set_shape(c_static)
return size
return nest.map_structure(get_state_shape, state_size)
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 __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 x input_size]`.
state: if `self.state_size` is an integer, this should be a `2-D Tensor`
with shape `[batch_size x self.state_size]`. Otherwise, if
`self.state_size` is a tuple of integers, this should be a tuple
with shapes `[batch_size x 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 x 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 vs.variable_scope(scope,
custom_getter=self._rnn_get_variable) as scope:
return super(RNNCell, self).__call__(inputs, state, scope=scope)
else:
with vs.variable_scope(vs.get_variable_scope(),
custom_getter=self._rnn_get_variable):
return super(RNNCell, self).__call__(inputs, state)
def _rnn_get_variable(self, getter, *args, **kwargs):
variable = getter(*args, **kwargs)
trainable = (variable in tf_variables.trainable_variables() or
(isinstance(variable, tf_variables.PartitionedVariable) and
list(variable)[0] in tf_variables.trainable_variables()))
if trainable and variable not in self._trainable_weights:
self._trainable_weights.append(variable)
elif not trainable and variable not 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 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 x 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 x s]` for each s in `state_size`.
"""
with ops.name_scope(type(self).__name__ + "ZeroState", values=[batch_size]):
state_size = self.state_size
return _zero_state_tensors(state_size, batch_size, dtype)
class BasicRNNCell(RNNCell):
"""The most basic RNN cell.
Args:
num_units: int, The number of units in the RNN 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.
"""
def __init__(self, num_units, activation=None, reuse=None):
super(BasicRNNCell, self).__init__(_reuse=reuse)
self._num_units = num_units
self._activation = activation or math_ops.tanh
@property
def state_size(self):
return self._num_units
@property
def output_size(self):
return self._num_units
def call(self, inputs, state):
"""Most basic RNN: output = new_state = act(W * input + U * state + B)."""
output = self._activation(_linear([inputs, state], self._num_units, True))
return output, output
class GRUCell(RNNCell):
"""Gated Recurrent Unit cell (cf. http://arxiv.org/abs/1406.1078)."""
def __init__(self,
num_units,
activation=None,
reuse=None,
kernel_initializer=None,
bias_initializer=None):
super(GRUCell, self).__init__(_reuse=reuse)
self._num_units = num_units
self._activation = activation or math_ops.tanh
self._kernel_initializer = kernel_initializer
self._bias_initializer = bias_initializer
@property
def state_size(self):
return self._num_units
@property
def output_size(self):
return self._num_units
def call(self, inputs, state):
"""Gated recurrent unit (GRU) with nunits cells."""
with vs.variable_scope("gates"): # Reset gate and update gate.
# We start with bias of 1.0 to not reset and not update.
bias_ones = self._bias_initializer
if self._bias_initializer is None:
dtype = [a.dtype for a in [inputs, state]][0]
bias_ones = init_ops.constant_initializer(1.0, dtype=dtype)
value = math_ops.sigmoid(
_linear([inputs, state], 2 * self._num_units, True, bias_ones,
self._kernel_initializer))
r, u = array_ops.split(value=value, num_or_size_splits=2, axis=1)
with vs.variable_scope("candidate"):
c = self._activation(
_linear([inputs, r * state], self._num_units, True,
self._bias_initializer, self._kernel_initializer))
new_h = u * state + (1 - u) * c
return new_h, new_h
_LSTMStateTuple = collections.namedtuple("LSTMStateTuple", ("c", "h"))
class LSTMStateTuple(_LSTMStateTuple):
"""Tuple used by LSTM Cells for `state_size`, `zero_state`, and output state.
Stores two elements: `(c, h)`, in that order.
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
class BasicLSTMCell(RNNCell):
"""Basic LSTM recurrent network cell.
The implementation is based on: http://arxiv.org/abs/1409.2329.
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.nn.rnn_cell.LSTMCell}
that follows.
"""
def __init__(self, num_units, forget_bias=1.0,
state_is_tuple=True, activation=None, reuse=None):
"""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).
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`.
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.
"""
super(BasicLSTMCell, self).__init__(_reuse=reuse)
if not state_is_tuple:
logging.warn("%s: Using a concatenated state is slower and will soon be "
"deprecated. Use state_is_tuple=True.", self)
self._num_units = num_units
self._forget_bias = forget_bias
self._state_is_tuple = state_is_tuple
self._activation = activation or math_ops.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
def call(self, inputs, state):
"""Long short-term memory cell (LSTM)."""
sigmoid = math_ops.sigmoid
# Parameters of gates are concatenated into one multiply for efficiency.
if self._state_is_tuple:
c, h = state
else:
c, h = array_ops.split(value=state, num_or_size_splits=2, axis=1)
concat = _linear([inputs, h], 4 * self._num_units, True)
# i = input_gate, j = new_input, f = forget_gate, o = output_gate
i, j, f, o = array_ops.split(value=concat, num_or_size_splits=4, axis=1)
new_c = (
c * sigmoid(f + self._forget_bias) + sigmoid(i) * self._activation(j))
new_h = self._activation(new_c) * sigmoid(o)
if self._state_is_tuple:
new_state = LSTMStateTuple(new_c, new_h)
else:
new_state = array_ops.concat([new_c, new_h], 1)
return new_h, new_state
class LSTMCell(RNNCell):
"""Long short-term memory unit (LSTM) recurrent network cell.
The default non-peephole implementation is based on:
http://deeplearning.cs.cmu.edu/pdfs/Hochreiter97_lstm.pdf
S. Hochreiter and J. Schmidhuber.
"Long Short-Term Memory". Neural Computation, 9(8):1735-1780, 1997.
The peephole implementation is based on:
https://research.google.com/pubs/archive/43905.pdf
Hasim Sak, Andrew Senior, and Francoise Beaufays.
"Long short-term memory recurrent neural network architectures for
large scale acoustic modeling." INTERSPEECH, 2014.
The class uses optional peep-hole connections, optional cell clipping, and
an optional projection layer.
"""
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):
"""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.
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`.
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.
"""
super(LSTMCell, self).__init__(_reuse=reuse)
if not state_is_tuple:
logging.warn("%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.warn(
"%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)
self._num_units = num_units
self._use_peepholes = use_peepholes
self._cell_clip = cell_clip
self._initializer = 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
self._activation = activation or math_ops.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
def call(self, inputs, state):
"""Run one step of LSTM.
Args:
inputs: input Tensor, 2D, batch x num_units.
state: if `state_is_tuple` is False, this must be a state Tensor,
`2-D, batch x 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 x 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.
"""
num_proj = self._num_units if self._num_proj is None else self._num_proj
sigmoid = math_ops.sigmoid
if self._state_is_tuple:
(c_prev, m_prev) = state
else:
c_prev = array_ops.slice(state, [0, 0], [-1, self._num_units])
m_prev = array_ops.slice(state, [0, self._num_units], [-1, num_proj])
dtype = inputs.dtype
input_size = inputs.get_shape().with_rank(2)[1]
if input_size.value is None:
raise ValueError("Could not infer input size from inputs.get_shape()[-1]")
scope = vs.get_variable_scope()
with vs.variable_scope(scope, initializer=self._initializer) as unit_scope:
if self._num_unit_shards is not None:
unit_scope.set_partitioner(
partitioned_variables.fixed_size_partitioner(
self._num_unit_shards))
# i = input_gate, j = new_input, f = forget_gate, o = output_gate
lstm_matrix = _linear([inputs, m_prev], 4 * self._num_units, bias=True)
i, j, f, o = array_ops.split(
value=lstm_matrix, num_or_size_splits=4, axis=1)
# Diagonal connections
if self._use_peepholes:
with vs.variable_scope(unit_scope) as projection_scope:
if self._num_unit_shards is not None:
projection_scope.set_partitioner(None)
w_f_diag = vs.get_variable(
"w_f_diag", shape=[self._num_units], dtype=dtype)
w_i_diag = vs.get_variable(
"w_i_diag", shape=[self._num_units], dtype=dtype)
w_o_diag = vs.get_variable(
"w_o_diag", shape=[self._num_units], dtype=dtype)
if self._use_peepholes:
c = (sigmoid(f + self._forget_bias + w_f_diag * c_prev) * c_prev +
sigmoid(i + 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 = clip_ops.clip_by_value(c, -self._cell_clip, self._cell_clip)
# pylint: enable=invalid-unary-operand-type
if self._use_peepholes:
m = sigmoid(o + w_o_diag * c) * self._activation(c)
else:
m = sigmoid(o) * self._activation(c)
if self._num_proj is not None:
with vs.variable_scope("projection") as proj_scope:
if self._num_proj_shards is not None:
proj_scope.set_partitioner(
partitioned_variables.fixed_size_partitioner(
self._num_proj_shards))
m = _linear(m, self._num_proj, bias=False)
if self._proj_clip is not None:
# pylint: disable=invalid-unary-operand-type
m = clip_ops.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
array_ops.concat([c, m], 1))
return m, new_state
def _enumerated_map_structure(map_fn, *args, **kwargs):
ix = [0]
def enumerated_fn(*inner_args, **inner_kwargs):
r = map_fn(ix[0], *inner_args, **inner_kwargs)
ix[0] += 1
return r
return nest.map_structure(enumerated_fn, *args, **kwargs)
class DropoutWrapper(RNNCell):
"""Operator adding dropout to inputs and outputs of the given cell."""
def __init__(self, cell, input_keep_prob=1.0, output_keep_prob=1.0,
state_keep_prob=1.0, variational_recurrent=False,
input_size=None, dtype=None, seed=None):
"""Create a cell with added input, state, and/or output dropout.
If `variational_recurrent` is set to `True` (**NOT** the default behavior),
then the the same dropout mask is applied at every step, as described in:
Y. Gal, Z Ghahramani. "A Theoretically Grounded Application of Dropout in
Recurrent Neural Networks". https://arxiv.org/abs/1512.05287
Otherwise a different dropout mask is applied at every time step.
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 *output* states of the cell.
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
the `DropoutWrapper`. Required and used **iff**
`variational_recurrent = True` and `input_keep_prob < 1`.
dtype: (optional) The `dtype` of the input, state, and output tensors.
Required and used **iff** `variational_recurrent = True`.
seed: (optional) integer, the randomness seed.
Raises:
TypeError: if cell is not an RNNCell.
ValueError: if any of the keep_probs are not between 0 and 1.
"""
if not _like_rnncell(cell):
raise TypeError("The parameter cell is not a RNNCell.")
with ops.name_scope("DropoutWrapperInit"):
def tensor_and_const_value(v):
tensor_value = ops.convert_to_tensor(v)
const_value = tensor_util.constant_value(tensor_value)
return (tensor_value, const_value)
for prob, attr in [(input_keep_prob, "input_keep_prob"),
(state_keep_prob, "state_keep_prob"),
(output_keep_prob, "output_keep_prob")]:
tensor_prob, const_prob = tensor_and_const_value(prob)
if const_prob is not None:
if const_prob < 0 or const_prob > 1:
raise ValueError("Parameter %s must be between 0 and 1: %d"
% (attr, const_prob))
setattr(self, "_%s" % attr, float(const_prob))
else:
setattr(self, "_%s" % attr, tensor_prob)
# Set cell, variational_recurrent, seed before running the code below
self._cell = cell
self._variational_recurrent = variational_recurrent
self._seed = seed
self._recurrent_input_noise = None
self._recurrent_state_noise = None
self._recurrent_output_noise = None
if variational_recurrent:
if dtype is None:
raise ValueError(
"When variational_recurrent=True, dtype must be provided")
def convert_to_batch_shape(s):
# Prepend a 1 for the batch dimension; for recurrent
# variational dropout we use the same dropout mask for all
# batch elements.
return array_ops.concat(
([1], tensor_shape.TensorShape(s).as_list()), 0)
def batch_noise(s, inner_seed):
shape = convert_to_batch_shape(s)
return random_ops.random_uniform(shape, seed=inner_seed, dtype=dtype)
if (not isinstance(self._input_keep_prob, numbers.Real) or
self._input_keep_prob < 1.0):
if input_size is None:
raise ValueError(
"When variational_recurrent=True and input_keep_prob < 1.0 or "
"is unknown, input_size must be provided")
self._recurrent_input_noise = _enumerated_map_structure(
lambda i, s: batch_noise(s, inner_seed=self._gen_seed("input", i)),
input_size)
self._recurrent_state_noise = _enumerated_map_structure(
lambda i, s: batch_noise(s, inner_seed=self._gen_seed("state", i)),
cell.state_size)
self._recurrent_output_noise = _enumerated_map_structure(
lambda i, s: batch_noise(s, inner_seed=self._gen_seed("output", i)),
cell.output_size)
def _gen_seed(self, salt_prefix, index):
if self._seed is None:
return None
salt = "%s_%d" % (salt_prefix, index)
string = (str(self._seed) + salt).encode("utf-8")
return int(hashlib.md5(string).hexdigest()[:8], 16) & 0x7FFFFFFF
@property
def state_size(self):
return self._cell.state_size
@property
def output_size(self):
return self._cell.output_size
def zero_state(self, batch_size, dtype):
with ops.name_scope(type(self).__name__ + "ZeroState", values=[batch_size]):
return self._cell.zero_state(batch_size, dtype)
def _variational_recurrent_dropout_value(
self, index, value, noise, keep_prob):
"""Performs dropout given the pre-calculated noise tensor."""
# uniform [keep_prob, 1.0 + keep_prob)
random_tensor = keep_prob + noise
# 0. if [keep_prob, 1.0) and 1. if [1.0, 1.0 + keep_prob)
binary_tensor = math_ops.floor(random_tensor)
ret = math_ops.div(value, keep_prob) * binary_tensor
ret.set_shape(value.get_shape())
return ret
def _dropout(self, values, salt_prefix, recurrent_noise, keep_prob):
"""Decides whether to perform standard dropout or recurrent dropout."""
if not self._variational_recurrent:
def dropout(i, v):
return nn_ops.dropout(
v, keep_prob=keep_prob, seed=self._gen_seed(salt_prefix, i))
return _enumerated_map_structure(dropout, values)
else:
def dropout(i, v, n):
return self._variational_recurrent_dropout_value(i, v, n, keep_prob)
return _enumerated_map_structure(dropout, values, recurrent_noise)
def __call__(self, inputs, state, scope=None):
"""Run the cell with the declared dropouts."""
def _should_dropout(p):
return (not isinstance(p, float)) or p < 1
if _should_dropout(self._input_keep_prob):
inputs = self._dropout(inputs, "input",
self._recurrent_input_noise,
self._input_keep_prob)
output, new_state = self._cell(inputs, state, scope)
if _should_dropout(self._state_keep_prob):
new_state = self._dropout(new_state, "state",
self._recurrent_state_noise,
self._state_keep_prob)
if _should_dropout(self._output_keep_prob):
output = self._dropout(output, "output",
self._recurrent_output_noise,
self._output_keep_prob)
return output, new_state
class ResidualWrapper(RNNCell):
"""RNNCell wrapper that ensures cell inputs are added to the outputs."""
def __init__(self, cell):
"""Constructs a `ResidualWrapper` for `cell`.
Args:
cell: An instance of `RNNCell`.
"""
self._cell = cell
@property
def state_size(self):
return self._cell.state_size
@property
def output_size(self):
return self._cell.output_size
def zero_state(self, batch_size, dtype):
with ops.name_scope(type(self).__name__ + "ZeroState", values=[batch_size]):
return self._cell.zero_state(batch_size, dtype)
def __call__(self, inputs, state, scope=None):
"""Run the cell and add its inputs to its outputs.
Args:
inputs: cell inputs.
state: cell state.
scope: optional cell scope.
Returns:
Tuple of cell outputs and new state.
Raises:
TypeError: If cell inputs and outputs have different structure (type).
ValueError: If cell inputs and outputs have different structure (value).
"""
outputs, new_state = self._cell(inputs, state, scope=scope)
nest.assert_same_structure(inputs, outputs)
# Ensure shapes match
def assert_shape_match(inp, out):
inp.get_shape().assert_is_compatible_with(out.get_shape())
nest.map_structure(assert_shape_match, inputs, outputs)
res_outputs = nest.map_structure(
lambda inp, out: inp + out, inputs, outputs)
return (res_outputs, new_state)
class DeviceWrapper(RNNCell):
"""Operator that ensures an RNNCell runs on a particular device."""
def __init__(self, cell, device):
"""Construct a `DeviceWrapper` for `cell` with device `device`.
Ensures the wrapped `cell` is called with `tf.device(device)`.
Args:
cell: An instance of `RNNCell`.
device: A device string or function, for passing to `tf.device`.
"""
self._cell = cell
self._device = device
@property
def state_size(self):
return self._cell.state_size
@property
def output_size(self):
return self._cell.output_size
def zero_state(self, batch_size, dtype):
with ops.name_scope(type(self).__name__ + "ZeroState", values=[batch_size]):
with ops.device(self._device):
return self._cell.zero_state(batch_size, dtype)
def __call__(self, inputs, state, scope=None):
"""Run the cell on specified device."""
with ops.device(self._device):
return self._cell(inputs, state, scope=scope)
class MultiRNNCell(RNNCell):
"""RNN cell composed sequentially of multiple simple 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`.
"""
super(MultiRNNCell, self).__init__()
if not cells:
raise ValueError("Must specify at least one cell for MultiRNNCell.")
if not nest.is_sequence(cells):
raise TypeError(
"cells must be a list or tuple, but saw: %s." % cells)
self._cells = cells
self._state_is_tuple = state_is_tuple
if not state_is_tuple:
if any(nest.is_sequence(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 ops.name_scope(type(self).__name__ + "ZeroState", values=[batch_size]):
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)
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 vs.variable_scope("cell_%d" % i):
if self._state_is_tuple:
if not nest.is_sequence(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 = array_ops.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
array_ops.concat(new_states, 1))
return cur_inp, new_states
class _SlimRNNCell(RNNCell):
"""A simple wrapper for slim.rnn_cells."""
def __init__(self, cell_fn):
"""Create a SlimRNNCell from a cell_fn.
Args:
cell_fn: a function which takes (inputs, state, scope) and produces the
outputs and the new_state. Additionally when called with inputs=None and
state=None it should return (initial_outputs, initial_state).
Raises:
TypeError: if cell_fn is not callable
ValueError: if cell_fn cannot produce a valid initial state.
"""
if not callable(cell_fn):
raise TypeError("cell_fn %s needs to be callable", cell_fn)
self._cell_fn = cell_fn
self._cell_name = cell_fn.func.__name__
init_output, init_state = self._cell_fn(None, None)
output_shape = init_output.get_shape()
state_shape = init_state.get_shape()
self._output_size = output_shape.with_rank(2)[1].value
self._state_size = state_shape.with_rank(2)[1].value
if self._output_size is None:
raise ValueError("Initial output created by %s has invalid shape %s" %
(self._cell_name, output_shape))
if self._state_size is None:
raise ValueError("Initial state created by %s has invalid shape %s" %
(self._cell_name, state_shape))
@property
def state_size(self):
return self._state_size
@property
def output_size(self):
return self._output_size
def __call__(self, inputs, state, scope=None):
scope = scope or self._cell_name
output, state = self._cell_fn(inputs, state, scope=scope)
return output, state
def _linear(args,
output_size,
bias,
bias_initializer=None,
kernel_initializer=None):
"""Linear map: sum_i(args[i] * W[i]), where W[i] is a variable.
Args:
args: a 2D Tensor or a list of 2D, batch x n, Tensors.
output_size: int, second dimension of W[i].
bias: boolean, whether to add a bias term or not.
bias_initializer: starting value to initialize the bias
(default is all zeros).
kernel_initializer: starting value to initialize the weight.
Returns:
A 2D Tensor with shape [batch x output_size] equal to
sum_i(args[i] * W[i]), where W[i]s are newly created matrices.
Raises:
ValueError: if some of the arguments has unspecified or wrong shape.
"""
if args is None or (nest.is_sequence(args) and not args):
raise ValueError("`args` must be specified")
if not nest.is_sequence(args):
args = [args]
# Calculate the total size of arguments on dimension 1.
total_arg_size = 0
shapes = [a.get_shape() for a in args]
for shape in shapes:
if shape.ndims != 2:
raise ValueError("linear is expecting 2D arguments: %s" % shapes)
if shape[1].value is None:
raise ValueError("linear expects shape[1] to be provided for shape %s, "
"but saw %s" % (shape, shape[1]))
else:
total_arg_size += shape[1].value
dtype = [a.dtype for a in args][0]
# Now the computation.
scope = vs.get_variable_scope()
with vs.variable_scope(scope) as outer_scope:
weights = vs.get_variable(
_WEIGHTS_VARIABLE_NAME, [total_arg_size, output_size],
dtype=dtype,
initializer=kernel_initializer)
if len(args) == 1:
res = math_ops.matmul(args[0], weights)
else:
res = math_ops.matmul(array_ops.concat(args, 1), weights)
if not bias:
return res
with vs.variable_scope(outer_scope) as inner_scope:
inner_scope.set_partitioner(None)
if bias_initializer is None:
bias_initializer = init_ops.constant_initializer(0.0, dtype=dtype)
biases = vs.get_variable(
_BIAS_VARIABLE_NAME, [output_size],
dtype=dtype,
initializer=bias_initializer)
return nn_ops.bias_add(res, biases)
来源：CSDN
作者：JonyeLH
链接：https://blog.csdn.net/JonyeLH/article/details/103598085