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"""Recurrent layers backed by cuDNN.
"""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from .. import backend as K
from .. import initializers
from .. import regularizers
from .. import constraints
from .recurrent import RNN
from ..layers import InputSpec
from collections import namedtuple
class _CuDNNRNN(RNN):
"""Private base class for CuDNNGRU and CuDNNLSTM.
# Arguments
return_sequences: Boolean. Whether to return the last output.
in the output sequence, or the full sequence.
return_state: Boolean. Whether to return the last state
in addition to the output.
stateful: Boolean (default False). If True, the last state
for each sample at index i in a batch will be used as initial
state for the sample of index i in the following batch.
"""
def __init__(self,
return_sequences=False,
return_state=False,
go_backwards=False,
stateful=False,
**kwargs):
if K.backend() != 'tensorflow':
raise RuntimeError('CuDNN RNNs are only available '
'with the TensorFlow backend.')
super(RNN, self).__init__(**kwargs)
self.return_sequences = return_sequences
self.return_state = return_state
self.go_backwards = go_backwards
self.stateful = stateful
self.supports_masking = False
self.input_spec = [InputSpec(ndim=3)]
if hasattr(self.cell.state_size, '__len__'):
state_size = self.cell.state_size
else:
state_size = [self.cell.state_size]
self.state_spec = [InputSpec(shape=(None, dim))
for dim in state_size]
self.constants_spec = None
self._states = None
self._num_constants = None
def _canonical_to_params(self, weights, biases):
import tensorflow as tf
weights = [tf.reshape(x, (-1,)) for x in weights]
biases = [tf.reshape(x, (-1,)) for x in biases]
return tf.concat(weights + biases, 0)
def call(self, inputs, mask=None, training=None, initial_state=None):
if isinstance(mask, list):
mask = mask[0]
if mask is not None:
raise ValueError('Masking is not supported for CuDNN RNNs.')
# input shape: `(samples, time (padded with zeros), input_dim)`
# note that the .build() method of subclasses MUST define
# self.input_spec and self.state_spec with complete input shapes.
if isinstance(inputs, list):
initial_state = inputs[1:]
inputs = inputs[0]
elif initial_state is not None:
pass
elif self.stateful:
initial_state = self.states
else:
initial_state = self.get_initial_state(inputs)
if len(initial_state) != len(self.states):
raise ValueError('Layer has ' + str(len(self.states)) +
' states but was passed ' +
str(len(initial_state)) +
' initial states.')
if self.go_backwards:
# Reverse time axis.
inputs = K.reverse(inputs, 1)
output, states = self._process_batch(inputs, initial_state)
if self.stateful:
updates = []
for i in range(len(states)):
updates.append((self.states[i], states[i]))
self.add_update(updates, inputs)
if self.return_state:
return [output] + states
else:
return output
def get_config(self):
config = {'return_sequences': self.return_sequences,
'return_state': self.return_state,
'go_backwards': self.go_backwards,
'stateful': self.stateful}
base_config = super(RNN, self).get_config()
return dict(list(base_config.items()) + list(config.items()))
@classmethod
def from_config(cls, config):
return cls(**config)
@property
def trainable_weights(self):
if self.trainable and self.built:
return [self.kernel, self.recurrent_kernel, self.bias]
return []
@property
def non_trainable_weights(self):
if not self.trainable and self.built:
return [self.kernel, self.recurrent_kernel, self.bias]
return []
@property
def losses(self):
return super(RNN, self).losses
def get_losses_for(self, inputs=None):
return super(RNN, self).get_losses_for(inputs=inputs)
class CuDNNGRU(_CuDNNRNN):
"""Fast GRU implementation backed by [CuDNN](https://developer.nvidia.com/cudnn).
Can only be run on GPU, with the TensorFlow backend.
# Arguments
units: Positive integer, dimensionality of the output space.
kernel_initializer: Initializer for the `kernel` weights matrix,
used for the linear transformation of the inputs.
(see [initializers](../initializers.md)).
recurrent_initializer: Initializer for the `recurrent_kernel`
weights matrix,
used for the linear transformation of the recurrent state.
(see [initializers](../initializers.md)).
bias_initializer: Initializer for the bias vector
(see [initializers](../initializers.md)).
kernel_regularizer: Regularizer function applied to
the `kernel` weights matrix
(see [regularizer](../regularizers.md)).
recurrent_regularizer: Regularizer function applied to
the `recurrent_kernel` weights matrix
(see [regularizer](../regularizers.md)).
bias_regularizer: Regularizer function applied to the bias vector
(see [regularizer](../regularizers.md)).
activity_regularizer: Regularizer function applied to
the output of the layer (its "activation").
(see [regularizer](../regularizers.md)).
kernel_constraint: Constraint function applied to
the `kernel` weights matrix
(see [constraints](../constraints.md)).
recurrent_constraint: Constraint function applied to
the `recurrent_kernel` weights matrix
(see [constraints](../constraints.md)).
bias_constraint: Constraint function applied to the bias vector
(see [constraints](../constraints.md)).
return_sequences: Boolean. Whether to return the last output.
in the output sequence, or the full sequence.
return_state: Boolean. Whether to return the last state
in addition to the output.
stateful: Boolean (default False). If True, the last state
for each sample at index i in a batch will be used as initial
state for the sample of index i in the following batch.
"""
def __init__(self, units,
kernel_initializer='glorot_uniform',
recurrent_initializer='orthogonal',
bias_initializer='zeros',
kernel_regularizer=None,
recurrent_regularizer=None,
bias_regularizer=None,
activity_regularizer=None,
kernel_constraint=None,
recurrent_constraint=None,
bias_constraint=None,
return_sequences=False,
return_state=False,
stateful=False,
**kwargs):
self.units = units
super(CuDNNGRU, self).__init__(
return_sequences=return_sequences,
return_state=return_state,
stateful=stateful,
**kwargs)
self.kernel_initializer = initializers.get(kernel_initializer)
self.recurrent_initializer = initializers.get(recurrent_initializer)
self.bias_initializer = initializers.get(bias_initializer)
self.kernel_regularizer = regularizers.get(kernel_regularizer)
self.recurrent_regularizer = regularizers.get(recurrent_regularizer)
self.bias_regularizer = regularizers.get(bias_regularizer)
self.activity_regularizer = regularizers.get(activity_regularizer)
self.kernel_constraint = constraints.get(kernel_constraint)
self.recurrent_constraint = constraints.get(recurrent_constraint)
self.bias_constraint = constraints.get(bias_constraint)
@property
def cell(self):
Cell = namedtuple('cell', 'state_size')
cell = Cell(state_size=self.units)
return cell
def build(self, input_shape):
super(CuDNNGRU, self).build(input_shape)
if isinstance(input_shape, list):
input_shape = input_shape[0]
input_dim = input_shape[-1]
from tensorflow.contrib.cudnn_rnn.python.ops import cudnn_rnn_ops
self._cudnn_gru = cudnn_rnn_ops.CudnnGRU(
num_layers=1,
num_units=self.units,
input_size=input_dim,
input_mode='linear_input')
self.kernel = self.add_weight(shape=(input_dim, self.units * 3),
name='kernel',
initializer=self.kernel_initializer,
regularizer=self.kernel_regularizer,
constraint=self.kernel_constraint)
self.recurrent_kernel = self.add_weight(
shape=(self.units, self.units * 3),
name='recurrent_kernel',
initializer=self.recurrent_initializer,
regularizer=self.recurrent_regularizer,
constraint=self.recurrent_constraint)
self.bias = self.add_weight(shape=(self.units * 6,),
name='bias',
initializer=self.bias_initializer,
regularizer=self.bias_regularizer,
constraint=self.bias_constraint)
self.kernel_z = self.kernel[:, :self.units]
self.recurrent_kernel_z = self.recurrent_kernel[:, :self.units]
self.kernel_r = self.kernel[:, self.units: self.units * 2]
self.recurrent_kernel_r = self.recurrent_kernel[:,
self.units:
self.units * 2]
self.kernel_h = self.kernel[:, self.units * 2:]
self.recurrent_kernel_h = self.recurrent_kernel[:, self.units * 2:]
self.bias_z_i = self.bias[:self.units]
self.bias_r_i = self.bias[self.units: self.units * 2]
self.bias_h_i = self.bias[self.units * 2: self.units * 3]
self.bias_z = self.bias[self.units * 3: self.units * 4]
self.bias_r = self.bias[self.units * 4: self.units * 5]
self.bias_h = self.bias[self.units * 5:]
self.built = True
def _process_batch(self, inputs, initial_state):
import tensorflow as tf
inputs = tf.transpose(inputs, (1, 0, 2))
input_h = initial_state[0]
input_h = tf.expand_dims(input_h, axis=0)
params = self._canonical_to_params(
weights=[
self.kernel_r,
self.kernel_z,
self.kernel_h,
self.recurrent_kernel_r,
self.recurrent_kernel_z,
self.recurrent_kernel_h,
],
biases=[
self.bias_r_i,
self.bias_z_i,
self.bias_h_i,
self.bias_r,
self.bias_z,
self.bias_h,
],
)
outputs, h = self._cudnn_gru(
inputs,
input_h=input_h,
params=params,
is_training=True)
if self.stateful or self.return_state:
h = h[0]
if self.return_sequences:
output = tf.transpose(outputs, (1, 0, 2))
else:
output = outputs[-1]
return output, [h]
def get_config(self):
config = {
'units': self.units,
'kernel_initializer': initializers.serialize(self.kernel_initializer),
'recurrent_initializer':
initializers.serialize(self.recurrent_initializer),
'bias_initializer': initializers.serialize(self.bias_initializer),
'kernel_regularizer': regularizers.serialize(self.kernel_regularizer),
'recurrent_regularizer':
regularizers.serialize(self.recurrent_regularizer),
'bias_regularizer': regularizers.serialize(self.bias_regularizer),
'activity_regularizer':
regularizers.serialize(self.activity_regularizer),
'kernel_constraint': constraints.serialize(self.kernel_constraint),
'recurrent_constraint':
constraints.serialize(self.recurrent_constraint),
'bias_constraint': constraints.serialize(self.bias_constraint)}
base_config = super(CuDNNGRU, self).get_config()
return dict(list(base_config.items()) + list(config.items()))
class CuDNNLSTM(_CuDNNRNN):
"""Fast LSTM implementation with [CuDNN](https://developer.nvidia.com/cudnn).
Can only be run on GPU, with the TensorFlow backend.
# Arguments
units: Positive integer, dimensionality of the output space.
kernel_initializer: Initializer for the `kernel` weights matrix,
used for the linear transformation of the inputs.
(see [initializers](../initializers.md)).
unit_forget_bias: Boolean.
If True, add 1 to the bias of the forget gate at initialization.
Setting it to true will also force `bias_initializer="zeros"`.
This is recommended in [Jozefowicz et al. (2015)](
http://www.jmlr.org/proceedings/papers/v37/jozefowicz15.pdf).
recurrent_initializer: Initializer for the `recurrent_kernel`
weights matrix,
used for the linear transformation of the recurrent state.
(see [initializers](../initializers.md)).
bias_initializer: Initializer for the bias vector
(see [initializers](../initializers.md)).
kernel_regularizer: Regularizer function applied to
the `kernel` weights matrix
(see [regularizer](../regularizers.md)).
recurrent_regularizer: Regularizer function applied to
the `recurrent_kernel` weights matrix
(see [regularizer](../regularizers.md)).
bias_regularizer: Regularizer function applied to the bias vector
(see [regularizer](../regularizers.md)).
activity_regularizer: Regularizer function applied to
the output of the layer (its "activation").
(see [regularizer](../regularizers.md)).
kernel_constraint: Constraint function applied to
the `kernel` weights matrix
(see [constraints](../constraints.md)).
recurrent_constraint: Constraint function applied to
the `recurrent_kernel` weights matrix
(see [constraints](../constraints.md)).
bias_constraint: Constraint function applied to the bias vector
(see [constraints](../constraints.md)).
return_sequences: Boolean. Whether to return the last output.
in the output sequence, or the full sequence.
return_state: Boolean. Whether to return the last state
in addition to the output.
stateful: Boolean (default False). If True, the last state
for each sample at index i in a batch will be used as initial
state for the sample of index i in the following batch.
"""
def __init__(self, units,
kernel_initializer='glorot_uniform',
recurrent_initializer='orthogonal',
bias_initializer='zeros',
unit_forget_bias=True,
kernel_regularizer=None,
recurrent_regularizer=None,
bias_regularizer=None,
activity_regularizer=None,
kernel_constraint=None,
recurrent_constraint=None,
bias_constraint=None,
return_sequences=False,
return_state=False,
stateful=False,
**kwargs):
self.units = units
super(CuDNNLSTM, self).__init__(
return_sequences=return_sequences,
return_state=return_state,
stateful=stateful,
**kwargs)
self.kernel_initializer = initializers.get(kernel_initializer)
self.recurrent_initializer = initializers.get(recurrent_initializer)
self.bias_initializer = initializers.get(bias_initializer)
self.unit_forget_bias = unit_forget_bias
self.kernel_regularizer = regularizers.get(kernel_regularizer)
self.recurrent_regularizer = regularizers.get(recurrent_regularizer)
self.bias_regularizer = regularizers.get(bias_regularizer)
self.activity_regularizer = regularizers.get(activity_regularizer)
self.kernel_constraint = constraints.get(kernel_constraint)
self.recurrent_constraint = constraints.get(recurrent_constraint)
self.bias_constraint = constraints.get(bias_constraint)
@property
def cell(self):
Cell = namedtuple('cell', 'state_size')
cell = Cell(state_size=(self.units, self.units))
return cell
def build(self, input_shape):
super(CuDNNLSTM, self).build(input_shape)
if isinstance(input_shape, list):
input_shape = input_shape[0]
input_dim = input_shape[-1]
from tensorflow.contrib.cudnn_rnn.python.ops import cudnn_rnn_ops
self._cudnn_lstm = cudnn_rnn_ops.CudnnLSTM(
num_layers=1,
num_units=self.units,
input_size=input_dim,
input_mode='linear_input')
self.kernel = self.add_weight(shape=(input_dim, self.units * 4),
name='kernel',
initializer=self.kernel_initializer,
regularizer=self.kernel_regularizer,
constraint=self.kernel_constraint)
self.recurrent_kernel = self.add_weight(
shape=(self.units, self.units * 4),
name='recurrent_kernel',
initializer=self.recurrent_initializer,
regularizer=self.recurrent_regularizer,
constraint=self.recurrent_constraint)
if self.unit_forget_bias:
def bias_initializer(shape, *args, **kwargs):
return K.concatenate([
self.bias_initializer((self.units * 5,), *args, **kwargs),
initializers.Ones()((self.units,), *args, **kwargs),
self.bias_initializer((self.units * 2,), *args, **kwargs),
])
else:
bias_initializer = self.bias_initializer
self.bias = self.add_weight(shape=(self.units * 8,),
name='bias',
initializer=bias_initializer,
regularizer=self.bias_regularizer,
constraint=self.bias_constraint)
self.kernel_i = self.kernel[:, :self.units]
self.kernel_f = self.kernel[:, self.units: self.units * 2]
self.kernel_c = self.kernel[:, self.units * 2: self.units * 3]
self.kernel_o = self.kernel[:, self.units * 3:]
self.recurrent_kernel_i = self.recurrent_kernel[:, :self.units]
self.recurrent_kernel_f = (
self.recurrent_kernel[:, self.units: self.units * 2])
self.recurrent_kernel_c = (
self.recurrent_kernel[:, self.units * 2: self.units * 3])
self.recurrent_kernel_o = self.recurrent_kernel[:, self.units * 3:]
self.bias_i_i = self.bias[:self.units]
self.bias_f_i = self.bias[self.units: self.units * 2]
self.bias_c_i = self.bias[self.units * 2: self.units * 3]
self.bias_o_i = self.bias[self.units * 3: self.units * 4]
self.bias_i = self.bias[self.units * 4: self.units * 5]
self.bias_f = self.bias[self.units * 5: self.units * 6]
self.bias_c = self.bias[self.units * 6: self.units * 7]
self.bias_o = self.bias[self.units * 7:]
self.built = True
def _process_batch(self, inputs, initial_state):
import tensorflow as tf
inputs = tf.transpose(inputs, (1, 0, 2))
input_h = initial_state[0]
input_c = initial_state[1]
input_h = tf.expand_dims(input_h, axis=0)
input_c = tf.expand_dims(input_c, axis=0)
params = self._canonical_to_params(
weights=[
self.kernel_i,
self.kernel_f,
self.kernel_c,
self.kernel_o,
self.recurrent_kernel_i,
self.recurrent_kernel_f,
self.recurrent_kernel_c,
self.recurrent_kernel_o,
],
biases=[
self.bias_i_i,
self.bias_f_i,
self.bias_c_i,
self.bias_o_i,
self.bias_i,
self.bias_f,
self.bias_c,
self.bias_o,
],
)
outputs, h, c = self._cudnn_lstm(
inputs,
input_h=input_h,
input_c=input_c,
params=params,
is_training=True)
if self.stateful or self.return_state:
h = h[0]
c = c[0]
if self.return_sequences:
output = tf.transpose(outputs, (1, 0, 2))
else:
output = outputs[-1]
return output, [h, c]
def get_config(self):
config = {
'units': self.units,
'kernel_initializer': initializers.serialize(self.kernel_initializer),
'recurrent_initializer':
initializers.serialize(self.recurrent_initializer),
'bias_initializer': initializers.serialize(self.bias_initializer),
'unit_forget_bias': self.unit_forget_bias,
'kernel_regularizer': regularizers.serialize(self.kernel_regularizer),
'recurrent_regularizer':
regularizers.serialize(self.recurrent_regularizer),
'bias_regularizer': regularizers.serialize(self.bias_regularizer),
'activity_regularizer':
regularizers.serialize(self.activity_regularizer),
'kernel_constraint': constraints.serialize(self.kernel_constraint),
'recurrent_constraint': constraints.serialize(self.recurrent_constraint),
'bias_constraint': constraints.serialize(self.bias_constraint)}
base_config = super(CuDNNLSTM, self).get_config()
return dict(list(base_config.items()) + list(config.items()))