Module keras.layers.cudnn_recurrent
Recurrent layers backed by cuDNN.
Expand source code
# Copyright 2018 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.
# ==============================================================================
"""Recurrent layers backed by cuDNN."""
import tensorflow.compat.v2 as tf
import collections
from keras import backend
from keras import constraints
from keras import initializers
from keras import regularizers
from keras.engine.input_spec import InputSpec
from keras.layers import recurrent_v2
from keras.layers.recurrent import RNN
from tensorflow.python.util.tf_export import keras_export
class _CuDNNRNN(RNN):
"""Private base class for CuDNNGRU and CuDNNLSTM layers.
Args:
return_sequences: Boolean. Whether to return the last output
in the output sequence, or the full sequence.
return_state: Boolean. Whether to return the last state
in addition to the output.
go_backwards: Boolean (default False).
If True, process the input sequence backwards and return the
reversed sequence.
stateful: Boolean (default False). If True, the last state
for each sample at index i in a batch will be used as initial
state for the sample of index i in the following batch.
time_major: Boolean (default False). If true, the inputs and outputs will be
in shape `(timesteps, batch, ...)`, whereas in the False case, it will
be `(batch, timesteps, ...)`.
"""
def __init__(self,
return_sequences=False,
return_state=False,
go_backwards=False,
stateful=False,
time_major=False,
**kwargs):
# We invoke the base layer's initializer directly here because we do not
# want to create RNN cell instance.
super(RNN, self).__init__(**kwargs) # pylint: disable=bad-super-call
self.return_sequences = return_sequences
self.return_state = return_state
self.go_backwards = go_backwards
self.stateful = stateful
self.time_major = time_major
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 = 0
self._vector_shape = tf.constant([-1])
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 = backend.reverse(inputs, 1)
output, states = self._process_batch(inputs, initial_state)
if self.stateful:
updates = [
tf.compat.v1.assign(self_state, state)
for self_state, state in zip(self.states, states)
]
self.add_update(updates)
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,
'time_major': self.time_major,
}
base_config = super( # pylint: disable=bad-super-call
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( # pylint: disable=bad-super-call
RNN, self).get_losses_for(inputs=inputs)
@keras_export(v1=['keras.layers.CuDNNGRU'])
class CuDNNGRU(_CuDNNRNN):
"""Fast GRU implementation backed by cuDNN.
More information about cuDNN can be found on the [NVIDIA
developer website](https://developer.nvidia.com/cudnn).
Can only be run on GPU.
Args:
units: Positive integer, dimensionality of the output space.
kernel_initializer: Initializer for the `kernel` weights matrix, used for
the linear transformation of the inputs.
recurrent_initializer: Initializer for the `recurrent_kernel` weights
matrix, used for the linear transformation of the recurrent state.
bias_initializer: Initializer for the bias vector.
kernel_regularizer: Regularizer function applied to the `kernel` weights
matrix.
recurrent_regularizer: Regularizer function applied to the
`recurrent_kernel` weights matrix.
bias_regularizer: Regularizer function applied to the bias vector.
activity_regularizer: Regularizer function applied to the output of the
layer (its "activation").
kernel_constraint: Constraint function applied to the `kernel` weights
matrix.
recurrent_constraint: Constraint function applied to the
`recurrent_kernel` weights matrix.
bias_constraint: Constraint function applied to the bias vector.
return_sequences: Boolean. Whether to return the last output in the output
sequence, or the full sequence.
return_state: Boolean. Whether to return the last state in addition to the
output.
go_backwards: Boolean (default False). If True, process the input sequence
backwards and return the reversed sequence.
stateful: Boolean (default False). If True, the last state for each sample
at index i in a batch will be used as initial state for the sample of
index i in the following batch.
"""
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,
go_backwards=False,
stateful=False,
**kwargs):
self.units = units
cell_spec = collections.namedtuple('cell', 'state_size')
self._cell = cell_spec(state_size=self.units)
super(CuDNNGRU, self).__init__(
return_sequences=return_sequences,
return_state=return_state,
go_backwards=go_backwards,
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):
return self._cell
def build(self, input_shape):
super(CuDNNGRU, self).build(input_shape)
if isinstance(input_shape, list):
input_shape = input_shape[0]
input_dim = int(input_shape[-1])
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.built = True
def _process_batch(self, inputs, initial_state):
if not self.time_major:
inputs = tf.transpose(inputs, perm=(1, 0, 2))
input_h = initial_state[0]
input_h = tf.expand_dims(input_h, axis=0)
params = recurrent_v2._canonical_to_params( # pylint: disable=protected-access
weights=[
self.kernel[:, self.units:self.units * 2],
self.kernel[:, :self.units],
self.kernel[:, self.units * 2:],
self.recurrent_kernel[:, self.units:self.units * 2],
self.recurrent_kernel[:, :self.units],
self.recurrent_kernel[:, self.units * 2:],
],
biases=[
self.bias[self.units:self.units * 2],
self.bias[:self.units],
self.bias[self.units * 2:self.units * 3],
self.bias[self.units * 4:self.units * 5],
self.bias[self.units * 3:self.units * 4],
self.bias[self.units * 5:],
],
shape=self._vector_shape)
args = {
'input': inputs,
'input_h': input_h,
'input_c': 0,
'params': params,
'is_training': True,
'rnn_mode': 'gru',
}
outputs, h, _, _, _ = tf.raw_ops.CudnnRNNV2(**args)
if self.stateful or self.return_state:
h = h[0]
if self.return_sequences:
if self.time_major:
output = outputs
else:
output = tf.transpose(outputs, perm=(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()))
@keras_export(v1=['keras.layers.CuDNNLSTM'])
class CuDNNLSTM(_CuDNNRNN):
"""Fast LSTM implementation backed by cuDNN.
More information about cuDNN can be found on the [NVIDIA
developer website](https://developer.nvidia.com/cudnn).
Can only be run on GPU.
Args:
units: Positive integer, dimensionality of the output space.
kernel_initializer: Initializer for the `kernel` weights matrix, used for
the linear transformation of the inputs.
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.](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.
bias_initializer: Initializer for the bias vector.
kernel_regularizer: Regularizer function applied to the `kernel` weights
matrix.
recurrent_regularizer: Regularizer function applied to the
`recurrent_kernel` weights matrix.
bias_regularizer: Regularizer function applied to the bias vector.
activity_regularizer: Regularizer function applied to the output of the
layer (its "activation").
kernel_constraint: Constraint function applied to the `kernel` weights
matrix.
recurrent_constraint: Constraint function applied to the
`recurrent_kernel` weights matrix.
bias_constraint: Constraint function applied to the bias vector.
return_sequences: Boolean. Whether to return the last output. in the
output sequence, or the full sequence.
return_state: Boolean. Whether to return the last state in addition to the
output.
go_backwards: Boolean (default False). If True, process the input sequence
backwards and return the reversed sequence.
stateful: Boolean (default False). If True, the last state for each sample
at index i in a batch will be used as initial state for the sample of
index i in the following batch.
"""
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,
go_backwards=False,
stateful=False,
**kwargs):
self.units = units
cell_spec = collections.namedtuple('cell', 'state_size')
self._cell = cell_spec(state_size=(self.units, self.units))
super(CuDNNLSTM, self).__init__(
return_sequences=return_sequences,
return_state=return_state,
go_backwards=go_backwards,
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):
return self._cell
def build(self, input_shape):
super(CuDNNLSTM, self).build(input_shape)
if isinstance(input_shape, list):
input_shape = input_shape[0]
input_dim = int(input_shape[-1])
self.kernel = self.add_weight(
shape=(input_dim, self.units * 4),
name='kernel',
initializer=self.kernel_initializer,
regularizer=self.kernel_regularizer,
constraint=self.kernel_constraint)
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(_, *args, **kwargs):
return tf.concat([
self.bias_initializer((self.units * 5,), *args, **kwargs),
tf.compat.v1.ones_initializer()((self.units,), *args, **kwargs),
self.bias_initializer((self.units * 2,), *args, **kwargs),
], axis=0)
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.built = True
def _process_batch(self, inputs, initial_state):
if not self.time_major:
inputs = tf.transpose(inputs, perm=(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 = recurrent_v2._canonical_to_params( # pylint: disable=protected-access
weights=[
self.kernel[:, :self.units],
self.kernel[:, self.units:self.units * 2],
self.kernel[:, self.units * 2:self.units * 3],
self.kernel[:, self.units * 3:],
self.recurrent_kernel[:, :self.units],
self.recurrent_kernel[:, self.units:self.units * 2],
self.recurrent_kernel[:, self.units * 2:self.units * 3],
self.recurrent_kernel[:, self.units * 3:],
],
biases=[
self.bias[:self.units],
self.bias[self.units:self.units * 2],
self.bias[self.units * 2:self.units * 3],
self.bias[self.units * 3:self.units * 4],
self.bias[self.units * 4:self.units * 5],
self.bias[self.units * 5:self.units * 6],
self.bias[self.units * 6:self.units * 7],
self.bias[self.units * 7:],
],
shape=self._vector_shape)
args = {
'input': inputs,
'input_h': input_h,
'input_c': input_c,
'params': params,
'is_training': True,
}
outputs, h, c, _, _ = tf.raw_ops.CudnnRNNV2(**args)
if self.stateful or self.return_state:
h = h[0]
c = c[0]
if self.return_sequences:
if self.time_major:
output = outputs
else:
output = tf.transpose(outputs, perm=(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()))
Classes
class CuDNNGRU (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, go_backwards=False, stateful=False, **kwargs)
-
Fast GRU implementation backed by cuDNN.
More information about cuDNN can be found on the NVIDIA developer website. Can only be run on GPU.
Args
units
- Positive integer, dimensionality of the output space.
kernel_initializer
- Initializer for the
kernel
weights matrix, used for the linear transformation of the inputs. recurrent_initializer
- Initializer for the
recurrent_kernel
weights matrix, used for the linear transformation of the recurrent state. bias_initializer
- Initializer for the bias vector.
kernel_regularizer
- Regularizer function applied to the
kernel
weights matrix. recurrent_regularizer
- Regularizer function applied to the
recurrent_kernel
weights matrix. bias_regularizer
- Regularizer function applied to the bias vector.
activity_regularizer
- Regularizer function applied to the output of the layer (its "activation").
kernel_constraint
- Constraint function applied to the
kernel
weights matrix. recurrent_constraint
- Constraint function applied to the
recurrent_kernel
weights matrix. bias_constraint
- Constraint function applied to the bias vector.
return_sequences
- Boolean. Whether to return the last output in the output sequence, or the full sequence.
return_state
- Boolean. Whether to return the last state in addition to the output.
go_backwards
- Boolean (default False). If True, process the input sequence backwards and return the reversed sequence.
stateful
- Boolean (default False). If True, the last state for each sample at index i in a batch will be used as initial state for the sample of index i in the following batch.
Expand source code
class CuDNNGRU(_CuDNNRNN): """Fast GRU implementation backed by cuDNN. More information about cuDNN can be found on the [NVIDIA developer website](https://developer.nvidia.com/cudnn). Can only be run on GPU. Args: units: Positive integer, dimensionality of the output space. kernel_initializer: Initializer for the `kernel` weights matrix, used for the linear transformation of the inputs. recurrent_initializer: Initializer for the `recurrent_kernel` weights matrix, used for the linear transformation of the recurrent state. bias_initializer: Initializer for the bias vector. kernel_regularizer: Regularizer function applied to the `kernel` weights matrix. recurrent_regularizer: Regularizer function applied to the `recurrent_kernel` weights matrix. bias_regularizer: Regularizer function applied to the bias vector. activity_regularizer: Regularizer function applied to the output of the layer (its "activation"). kernel_constraint: Constraint function applied to the `kernel` weights matrix. recurrent_constraint: Constraint function applied to the `recurrent_kernel` weights matrix. bias_constraint: Constraint function applied to the bias vector. return_sequences: Boolean. Whether to return the last output in the output sequence, or the full sequence. return_state: Boolean. Whether to return the last state in addition to the output. go_backwards: Boolean (default False). If True, process the input sequence backwards and return the reversed sequence. stateful: Boolean (default False). If True, the last state for each sample at index i in a batch will be used as initial state for the sample of index i in the following batch. """ 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, go_backwards=False, stateful=False, **kwargs): self.units = units cell_spec = collections.namedtuple('cell', 'state_size') self._cell = cell_spec(state_size=self.units) super(CuDNNGRU, self).__init__( return_sequences=return_sequences, return_state=return_state, go_backwards=go_backwards, 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): return self._cell def build(self, input_shape): super(CuDNNGRU, self).build(input_shape) if isinstance(input_shape, list): input_shape = input_shape[0] input_dim = int(input_shape[-1]) 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.built = True def _process_batch(self, inputs, initial_state): if not self.time_major: inputs = tf.transpose(inputs, perm=(1, 0, 2)) input_h = initial_state[0] input_h = tf.expand_dims(input_h, axis=0) params = recurrent_v2._canonical_to_params( # pylint: disable=protected-access weights=[ self.kernel[:, self.units:self.units * 2], self.kernel[:, :self.units], self.kernel[:, self.units * 2:], self.recurrent_kernel[:, self.units:self.units * 2], self.recurrent_kernel[:, :self.units], self.recurrent_kernel[:, self.units * 2:], ], biases=[ self.bias[self.units:self.units * 2], self.bias[:self.units], self.bias[self.units * 2:self.units * 3], self.bias[self.units * 4:self.units * 5], self.bias[self.units * 3:self.units * 4], self.bias[self.units * 5:], ], shape=self._vector_shape) args = { 'input': inputs, 'input_h': input_h, 'input_c': 0, 'params': params, 'is_training': True, 'rnn_mode': 'gru', } outputs, h, _, _, _ = tf.raw_ops.CudnnRNNV2(**args) if self.stateful or self.return_state: h = h[0] if self.return_sequences: if self.time_major: output = outputs else: output = tf.transpose(outputs, perm=(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()))
Ancestors
- keras.layers.cudnn_recurrent._CuDNNRNN
- RNN
- Layer
- tensorflow.python.module.module.Module
- tensorflow.python.training.tracking.tracking.AutoTrackable
- tensorflow.python.training.tracking.base.Trackable
- LayerVersionSelector
Instance variables
var cell
-
Expand source code
@property def cell(self): return self._cell
Inherited members
RNN
: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
reset_states
set_weights
supports_masking
trainable_variables
trainable_weights
variable_dtype
variables
weights
class CuDNNLSTM (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, go_backwards=False, stateful=False, **kwargs)
-
Fast LSTM implementation backed by cuDNN.
More information about cuDNN can be found on the NVIDIA developer website. Can only be run on GPU.
Args
units
- Positive integer, dimensionality of the output space.
kernel_initializer
- Initializer for the
kernel
weights matrix, used for the linear transformation of the inputs. 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. recurrent_initializer
- Initializer for the
recurrent_kernel
weights matrix, used for the linear transformation of the recurrent state. bias_initializer
- Initializer for the bias vector.
kernel_regularizer
- Regularizer function applied to the
kernel
weights matrix. recurrent_regularizer
- Regularizer function applied to the
recurrent_kernel
weights matrix. bias_regularizer
- Regularizer function applied to the bias vector.
activity_regularizer
- Regularizer function applied to the output of the layer (its "activation").
kernel_constraint
- Constraint function applied to the
kernel
weights matrix. recurrent_constraint
- Constraint function applied to the
recurrent_kernel
weights matrix. bias_constraint
- Constraint function applied to the bias vector.
return_sequences
- Boolean. Whether to return the last output. in the output sequence, or the full sequence.
return_state
- Boolean. Whether to return the last state in addition to the output.
go_backwards
- Boolean (default False). If True, process the input sequence backwards and return the reversed sequence.
stateful
- Boolean (default False). If True, the last state for each sample at index i in a batch will be used as initial state for the sample of index i in the following batch.
Expand source code
class CuDNNLSTM(_CuDNNRNN): """Fast LSTM implementation backed by cuDNN. More information about cuDNN can be found on the [NVIDIA developer website](https://developer.nvidia.com/cudnn). Can only be run on GPU. Args: units: Positive integer, dimensionality of the output space. kernel_initializer: Initializer for the `kernel` weights matrix, used for the linear transformation of the inputs. 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.](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. bias_initializer: Initializer for the bias vector. kernel_regularizer: Regularizer function applied to the `kernel` weights matrix. recurrent_regularizer: Regularizer function applied to the `recurrent_kernel` weights matrix. bias_regularizer: Regularizer function applied to the bias vector. activity_regularizer: Regularizer function applied to the output of the layer (its "activation"). kernel_constraint: Constraint function applied to the `kernel` weights matrix. recurrent_constraint: Constraint function applied to the `recurrent_kernel` weights matrix. bias_constraint: Constraint function applied to the bias vector. return_sequences: Boolean. Whether to return the last output. in the output sequence, or the full sequence. return_state: Boolean. Whether to return the last state in addition to the output. go_backwards: Boolean (default False). If True, process the input sequence backwards and return the reversed sequence. stateful: Boolean (default False). If True, the last state for each sample at index i in a batch will be used as initial state for the sample of index i in the following batch. """ 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, go_backwards=False, stateful=False, **kwargs): self.units = units cell_spec = collections.namedtuple('cell', 'state_size') self._cell = cell_spec(state_size=(self.units, self.units)) super(CuDNNLSTM, self).__init__( return_sequences=return_sequences, return_state=return_state, go_backwards=go_backwards, 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): return self._cell def build(self, input_shape): super(CuDNNLSTM, self).build(input_shape) if isinstance(input_shape, list): input_shape = input_shape[0] input_dim = int(input_shape[-1]) self.kernel = self.add_weight( shape=(input_dim, self.units * 4), name='kernel', initializer=self.kernel_initializer, regularizer=self.kernel_regularizer, constraint=self.kernel_constraint) 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(_, *args, **kwargs): return tf.concat([ self.bias_initializer((self.units * 5,), *args, **kwargs), tf.compat.v1.ones_initializer()((self.units,), *args, **kwargs), self.bias_initializer((self.units * 2,), *args, **kwargs), ], axis=0) 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.built = True def _process_batch(self, inputs, initial_state): if not self.time_major: inputs = tf.transpose(inputs, perm=(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 = recurrent_v2._canonical_to_params( # pylint: disable=protected-access weights=[ self.kernel[:, :self.units], self.kernel[:, self.units:self.units * 2], self.kernel[:, self.units * 2:self.units * 3], self.kernel[:, self.units * 3:], self.recurrent_kernel[:, :self.units], self.recurrent_kernel[:, self.units:self.units * 2], self.recurrent_kernel[:, self.units * 2:self.units * 3], self.recurrent_kernel[:, self.units * 3:], ], biases=[ self.bias[:self.units], self.bias[self.units:self.units * 2], self.bias[self.units * 2:self.units * 3], self.bias[self.units * 3:self.units * 4], self.bias[self.units * 4:self.units * 5], self.bias[self.units * 5:self.units * 6], self.bias[self.units * 6:self.units * 7], self.bias[self.units * 7:], ], shape=self._vector_shape) args = { 'input': inputs, 'input_h': input_h, 'input_c': input_c, 'params': params, 'is_training': True, } outputs, h, c, _, _ = tf.raw_ops.CudnnRNNV2(**args) if self.stateful or self.return_state: h = h[0] c = c[0] if self.return_sequences: if self.time_major: output = outputs else: output = tf.transpose(outputs, perm=(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()))
Ancestors
- keras.layers.cudnn_recurrent._CuDNNRNN
- RNN
- Layer
- tensorflow.python.module.module.Module
- tensorflow.python.training.tracking.tracking.AutoTrackable
- tensorflow.python.training.tracking.base.Trackable
- LayerVersionSelector
Instance variables
var cell
-
Expand source code
@property def cell(self): return self._cell
Inherited members
RNN
: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
reset_states
set_weights
supports_masking
trainable_variables
trainable_weights
variable_dtype
variables
weights