# 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.
#modified by Stephanie (@corcra) to enable initializing the bias term in lstm """
# ==============================================================================
"""Module implementing RNN Cells."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import collections
import contextlib
import hashlib
import math
import numbers
from tensorflow.python.framework import ops
from tensorflow.python.framework import tensor_shape
from tensorflow.python.framework import tensor_util
from tensorflow.python.ops import array_ops
from tensorflow.python.ops import clip_ops
from tensorflow.python.ops import embedding_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.math_ops import sigmoid
from tensorflow.python.ops.math_ops import tanh
#from tensorflow.python.ops.rnn_cell_impl import _RNNCell as RNNCell
from tensorflow.python.ops.rnn_cell_impl import RNNCell
from tensorflow.python.platform import tf_logging as logging
from tensorflow.python.util import nest
_BIAS_VARIABLE_NAME = "biases"
_WEIGHTS_VARIABLE_NAME = "weights"
@contextlib.contextmanager
def _checked_scope(cell, scope, reuse=None, **kwargs):
if reuse is not None:
kwargs["reuse"] = reuse
with vs.variable_scope(scope, **kwargs) as checking_scope:
scope_name = checking_scope.name
if hasattr(cell, "_scope"):
cell_scope = cell._scope # pylint: disable=protected-access
if cell_scope.name != checking_scope.name:
raise ValueError(
"Attempt to reuse RNNCell %s with a different variable scope than "
"its first use. First use of cell was with scope '%s', this "
"attempt is with scope '%s'. Please create a new instance of the "
"cell if you would like it to use a different set of weights. "
"If before you were using: MultiRNNCell([%s(...)] * num_layers), "
"change to: MultiRNNCell([%s(...) for _ in range(num_layers)]). "
"If before you were using the same cell instance as both the "
"forward and reverse cell of a bidirectional RNN, simply create "
"two instances (one for forward, one for reverse). "
"In May 2017, we will start transitioning this cell's behavior "
"to use existing stored weights, if any, when it is called "
"with scope=None (which can lead to silent model degradation, so "
"this error will remain until then.)"
% (cell, cell_scope.name, scope_name, type(cell).__name__,
type(cell).__name__))
else:
weights_found = False
try:
with vs.variable_scope(checking_scope, reuse=True):
vs.get_variable(_WEIGHTS_VARIABLE_NAME)
weights_found = True
except ValueError:
pass
if weights_found and reuse is None:
raise ValueError(
"Attempt to have a second RNNCell use the weights of a variable "
"scope that already has weights: '%s'; and the cell was not "
"constructed as %s(..., reuse=True). "
"To share the weights of an RNNCell, simply "
"reuse it in your second calculation, or create a new one with "
"the argument reuse=True." % (scope_name, type(cell).__name__))
# Everything is OK. Update the cell's scope and yield it.
cell._scope = checking_scope # pylint: disable=protected-access
yield checking_scope
class BasicRNNCell(RNNCell):
"""The most basic RNN cell."""
def __init__(self, num_units, input_size=None, activation=tanh, reuse=None):
if input_size is not None:
logging.warn("%s: The input_size parameter is deprecated.", self)
self._num_units = num_units
self._activation = activation
self._reuse = reuse
@property
def state_size(self):
return self._num_units
@property
def output_size(self):
return self._num_units
def __call__(self, inputs, state, scope=None):
"""Most basic RNN: output = new_state = act(W * input + U * state + B)."""
with _checked_scope(self, scope or "basic_rnn_cell", reuse=self._reuse):
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, input_size=None, activation=tanh, reuse=None):
if input_size is not None:
logging.warn("%s: The input_size parameter is deprecated.", self)
self._num_units = num_units
self._activation = activation
self._reuse = reuse
@property
def state_size(self):
return self._num_units
@property
def output_size(self):
return self._num_units
def __call__(self, inputs, state, scope=None):
"""Gated recurrent unit (GRU) with nunits cells."""
with _checked_scope(self, scope or "gru_cell", reuse=self._reuse):
with vs.variable_scope("gates"): # Reset gate and update gate.
# We start with bias of 1.0 to not reset and not update.
value = sigmoid(_linear(
[inputs, state], 2 * self._num_units, True, 1.0))
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))
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 not 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 LSTMCell that follows.
"""
def __init__(self, num_units, forget_bias=1.0, input_size=None,
state_is_tuple=True, activation=tanh, 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).
input_size: Deprecated and unused.
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.
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.
"""
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 input_size is not None:
logging.warn("%s: The input_size parameter is deprecated.", self)
self._num_units = num_units
self._forget_bias = forget_bias
self._state_is_tuple = state_is_tuple
self._activation = activation
self._reuse = reuse
@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, scope=None):
"""Long short-term memory cell (LSTM)."""
with _checked_scope(self, scope or "basic_lstm_cell", reuse=self._reuse):
# 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, input_size=None,
use_peepholes=False, cell_clip=None,
initializer=None, bias_start=0.0, num_proj=None, proj_clip=None,
num_unit_shards=None, num_proj_shards=None,
forget_bias=1.0, state_is_tuple=True,
activation=tanh, reuse=None):
"""Initialize the parameters for an LSTM cell.
Args:
num_units: int, The number of units in the LSTM cell
input_size: Deprecated and unused.
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.
bias_start: (optional) The VALUE to initialize the bias to, in
the linear call
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.
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.
"""
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 input_size is not None:
logging.warn("%s: The input_size parameter is deprecated.", 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._bias_start = bias_start
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
self._reuse = reuse
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, scope=None):
"""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`.
scope: VariableScope for the created subgraph; defaults to "lstm_cell".
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
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]")
with _checked_scope(self, scope or "lstm_cell",
initializer=self._initializer,
reuse=self._reuse) 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, bias_start=self._bias_start)
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
class OutputProjectionWrapper(RNNCell):
"""Operator adding an output projection to the given cell.
Note: in many cases it may be more efficient to not use this wrapper,
but instead concatenate the whole sequence of your outputs in time,
do the projection on this batch-concatenated sequence, then split it
if needed or directly feed into a softmax.
"""
def __init__(self, cell, output_size, reuse=None):
"""Create a cell with output projection.
Args:
cell: an RNNCell, a projection to output_size is added to it.
output_size: integer, the size of the output after projection.
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.
Raises:
TypeError: if cell is not an RNNCell.
ValueError: if output_size is not positive.
"""
if not isinstance(cell, RNNCell):
raise TypeError("The parameter cell is not RNNCell.")
if output_size < 1:
raise ValueError("Parameter output_size must be > 0: %d." % output_size)
self._cell = cell
self._output_size = output_size
self._reuse = reuse
@property
def state_size(self):
return self._cell.state_size
@property
def output_size(self):
return self._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 output projection on inputs, starting from state."""
output, res_state = self._cell(inputs, state)
# Default scope: "OutputProjectionWrapper"
with _checked_scope(self, scope or "output_projection_wrapper",
reuse=self._reuse):
projected = _linear(output, self._output_size, True)
return projected, res_state
class InputProjectionWrapper(RNNCell):
"""Operator adding an input projection to the given cell.
Note: in many cases it may be more efficient to not use this wrapper,
but instead concatenate the whole sequence of your inputs in time,
do the projection on this batch-concatenated sequence, then split it.
"""
def __init__(self, cell, num_proj, input_size=None):
"""Create a cell with input projection.
Args:
cell: an RNNCell, a projection of inputs is added before it.
num_proj: Python integer. The dimension to project to.
input_size: Deprecated and unused.
Raises:
TypeError: if cell is not an RNNCell.
"""
if input_size is not None:
logging.warn("%s: The input_size parameter is deprecated.", self)
if not isinstance(cell, RNNCell):
raise TypeError("The parameter cell is not RNNCell.")
self._cell = cell
self._num_proj = num_proj
@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 input projection and then the cell."""
# Default scope: "InputProjectionWrapper"
with vs.variable_scope(scope or "input_projection_wrapper"):
projected = _linear(inputs, self._num_proj, True)
return self._cell(projected, 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 isinstance(cell, RNNCell):
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]):
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 EmbeddingWrapper(RNNCell):
"""Operator adding input embedding to the given cell.
Note: in many cases it may be more efficient to not use this wrapper,
but instead concatenate the whole sequence of your inputs in time,
do the embedding on this batch-concatenated sequence, then split it and
feed into your RNN.
"""
def __init__(self, cell, embedding_classes, embedding_size, initializer=None,
reuse=None):
"""Create a cell with an added input embedding.
Args:
cell: an RNNCell, an embedding will be put before its inputs.
embedding_classes: integer, how many symbols will be embedded.
embedding_size: integer, the size of the vectors we embed into.
initializer: an initializer to use when creating the embedding;
if None, the initializer from variable scope or a default one is used.
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.
Raises:
TypeError: if cell is not an RNNCell.
ValueError: if embedding_classes is not positive.
"""
if not isinstance(cell, RNNCell):
raise TypeError("The parameter cell is not RNNCell.")
if embedding_classes <= 0 or embedding_size <= 0:
raise ValueError("Both embedding_classes and embedding_size must be > 0: "
"%d, %d." % (embedding_classes, embedding_size))
self._cell = cell
self._embedding_classes = embedding_classes
self._embedding_size = embedding_size
self._initializer = initializer
self._reuse = reuse
@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 on embedded inputs."""
with _checked_scope(self, scope or "embedding_wrapper", reuse=self._reuse):
with ops.device("/cpu:0"):
if self._initializer:
initializer = self._initializer
elif vs.get_variable_scope().initializer:
initializer = vs.get_variable_scope().initializer
else:
# Default initializer for embeddings should have variance=1.
sqrt3 = math.sqrt(3) # Uniform(-sqrt(3), sqrt(3)) has variance=1.
initializer = init_ops.random_uniform_initializer(-sqrt3, sqrt3)
if type(state) is tuple:
data_type = state[0].dtype
else:
data_type = state.dtype
embedding = vs.get_variable(
"embedding", [self._embedding_classes, self._embedding_size],
initializer=initializer,
dtype=data_type)
embedded = embedding_ops.embedding_lookup(
embedding, array_ops.reshape(inputs, [-1]))
return self._cell(embedded, state)
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`.
"""
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, scope=None):
"""Run this multi-layer cell on inputs, starting from state."""
with vs.variable_scope(scope or "multi_rnn_cell"):
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_start=0.0, scope=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_start: starting value to initialize the bias; 0 by default.
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)
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)
biases = vs.get_variable(
_BIAS_VARIABLE_NAME, [output_size],
dtype=dtype,
initializer=init_ops.constant_initializer(bias_start, dtype=dtype))
return nn_ops.bias_add(res, biases)
