225 lines
8.4 KiB
Python
225 lines
8.4 KiB
Python
|
# 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.
|
||
|
|
# ==============================================================================
|
||
|
|
"""Fast GRU layer backed by cuDNN."""
|
||
|
|
|
||
|
|
|
||
|
|
import collections
|
||
|
|
|
||
|
|
import tensorflow.compat.v2 as tf
|
||
|
|
|
||
|
|
from keras import constraints
|
||
|
|
from keras import initializers
|
||
|
|
from keras import regularizers
|
||
|
|
from keras.layers.rnn import gru_lstm_utils
|
||
|
|
from keras.layers.rnn.base_cudnn_rnn import _CuDNNRNN
|
||
|
|
|
||
|
|
# isort: off
|
||
|
|
from tensorflow.python.util.tf_export import keras_export
|
||
|
|
|
||
|
|
|
||
|
|
@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().__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().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 = gru_lstm_utils.canonical_to_params(
|
||
|
|
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().get_config()
|
||
|
|
return dict(list(base_config.items()) + list(config.items()))
|