542 lines
20 KiB
Python
542 lines
20 KiB
Python
# Copyright 2018 The TensorFlow Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# ==============================================================================
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"""Recurrent layers backed by cuDNN.
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"""
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from __future__ import absolute_import
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from __future__ import division
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from __future__ import print_function
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import collections
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from tensorflow.python.framework import constant_op
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from tensorflow.python.keras import backend as K
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from tensorflow.python.keras import constraints
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from tensorflow.python.keras import initializers
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from tensorflow.python.keras import regularizers
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from tensorflow.python.keras.engine.input_spec import InputSpec
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from tensorflow.python.keras.layers import recurrent_v2
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from tensorflow.python.keras.layers.recurrent import RNN
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from tensorflow.python.ops import array_ops
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from tensorflow.python.ops import gen_cudnn_rnn_ops
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from tensorflow.python.ops import state_ops
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from tensorflow.python.util.tf_export import keras_export
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class _CuDNNRNN(RNN):
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"""Private base class for CuDNNGRU and CuDNNLSTM layers.
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Args:
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return_sequences: Boolean. Whether to return the last output
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in the output sequence, or the full sequence.
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return_state: Boolean. Whether to return the last state
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in addition to the output.
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go_backwards: Boolean (default False).
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If True, process the input sequence backwards and return the
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reversed sequence.
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stateful: Boolean (default False). If True, the last state
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for each sample at index i in a batch will be used as initial
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state for the sample of index i in the following batch.
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time_major: Boolean (default False). If true, the inputs and outputs will be
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in shape `(timesteps, batch, ...)`, whereas in the False case, it will
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be `(batch, timesteps, ...)`.
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"""
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def __init__(self,
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return_sequences=False,
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return_state=False,
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go_backwards=False,
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stateful=False,
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time_major=False,
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**kwargs):
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# We invoke the base layer's initializer directly here because we do not
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# want to create RNN cell instance.
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super(RNN, self).__init__(**kwargs) # pylint: disable=bad-super-call
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self.return_sequences = return_sequences
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self.return_state = return_state
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self.go_backwards = go_backwards
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self.stateful = stateful
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self.time_major = time_major
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self.supports_masking = False
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self.input_spec = [InputSpec(ndim=3)]
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if hasattr(self.cell.state_size, '__len__'):
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state_size = self.cell.state_size
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else:
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state_size = [self.cell.state_size]
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self.state_spec = [InputSpec(shape=(None, dim)) for dim in state_size]
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self.constants_spec = None
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self._states = None
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self._num_constants = 0
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self._vector_shape = constant_op.constant([-1])
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def call(self, inputs, mask=None, training=None, initial_state=None):
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if isinstance(mask, list):
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mask = mask[0]
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if mask is not None:
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raise ValueError('Masking is not supported for CuDNN RNNs.')
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# input shape: `(samples, time (padded with zeros), input_dim)`
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# note that the .build() method of subclasses MUST define
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# self.input_spec and self.state_spec with complete input shapes.
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if isinstance(inputs, list):
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initial_state = inputs[1:]
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inputs = inputs[0]
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elif initial_state is not None:
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pass
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elif self.stateful:
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initial_state = self.states
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else:
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initial_state = self.get_initial_state(inputs)
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if len(initial_state) != len(self.states):
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raise ValueError('Layer has ' + str(len(self.states)) +
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' states but was passed ' + str(len(initial_state)) +
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' initial states.')
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if self.go_backwards:
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# Reverse time axis.
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inputs = K.reverse(inputs, 1)
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output, states = self._process_batch(inputs, initial_state)
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if self.stateful:
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updates = [
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state_ops.assign(self_state, state)
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for self_state, state in zip(self.states, states)
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]
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self.add_update(updates)
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if self.return_state:
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return [output] + states
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else:
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return output
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def get_config(self):
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config = {
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'return_sequences': self.return_sequences,
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'return_state': self.return_state,
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'go_backwards': self.go_backwards,
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'stateful': self.stateful,
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'time_major': self.time_major,
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}
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base_config = super( # pylint: disable=bad-super-call
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RNN, self).get_config()
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return dict(list(base_config.items()) + list(config.items()))
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@classmethod
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def from_config(cls, config):
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return cls(**config)
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@property
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def trainable_weights(self):
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if self.trainable and self.built:
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return [self.kernel, self.recurrent_kernel, self.bias]
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return []
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@property
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def non_trainable_weights(self):
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if not self.trainable and self.built:
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return [self.kernel, self.recurrent_kernel, self.bias]
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return []
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@property
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def losses(self):
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return super(RNN, self).losses
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def get_losses_for(self, inputs=None):
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return super( # pylint: disable=bad-super-call
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RNN, self).get_losses_for(inputs=inputs)
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@keras_export(v1=['keras.layers.CuDNNGRU'])
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class CuDNNGRU(_CuDNNRNN):
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"""Fast GRU implementation backed by cuDNN.
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More information about cuDNN can be found on the [NVIDIA
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developer website](https://developer.nvidia.com/cudnn).
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Can only be run on GPU.
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Args:
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units: Positive integer, dimensionality of the output space.
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kernel_initializer: Initializer for the `kernel` weights matrix, used for
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the linear transformation of the inputs.
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recurrent_initializer: Initializer for the `recurrent_kernel` weights
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matrix, used for the linear transformation of the recurrent state.
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bias_initializer: Initializer for the bias vector.
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kernel_regularizer: Regularizer function applied to the `kernel` weights
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matrix.
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recurrent_regularizer: Regularizer function applied to the
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`recurrent_kernel` weights matrix.
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bias_regularizer: Regularizer function applied to the bias vector.
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activity_regularizer: Regularizer function applied to the output of the
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layer (its "activation").
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kernel_constraint: Constraint function applied to the `kernel` weights
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matrix.
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recurrent_constraint: Constraint function applied to the
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`recurrent_kernel` weights matrix.
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bias_constraint: Constraint function applied to the bias vector.
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return_sequences: Boolean. Whether to return the last output in the output
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sequence, or the full sequence.
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return_state: Boolean. Whether to return the last state in addition to the
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output.
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go_backwards: Boolean (default False). If True, process the input sequence
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backwards and return the reversed sequence.
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stateful: Boolean (default False). If True, the last state for each sample
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at index i in a batch will be used as initial state for the sample of
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index i in the following batch.
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"""
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def __init__(self,
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units,
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kernel_initializer='glorot_uniform',
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recurrent_initializer='orthogonal',
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bias_initializer='zeros',
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kernel_regularizer=None,
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recurrent_regularizer=None,
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bias_regularizer=None,
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activity_regularizer=None,
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kernel_constraint=None,
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recurrent_constraint=None,
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bias_constraint=None,
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return_sequences=False,
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return_state=False,
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go_backwards=False,
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stateful=False,
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**kwargs):
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self.units = units
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cell_spec = collections.namedtuple('cell', 'state_size')
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self._cell = cell_spec(state_size=self.units)
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super(CuDNNGRU, self).__init__(
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return_sequences=return_sequences,
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return_state=return_state,
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go_backwards=go_backwards,
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stateful=stateful,
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**kwargs)
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self.kernel_initializer = initializers.get(kernel_initializer)
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self.recurrent_initializer = initializers.get(recurrent_initializer)
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self.bias_initializer = initializers.get(bias_initializer)
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self.kernel_regularizer = regularizers.get(kernel_regularizer)
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self.recurrent_regularizer = regularizers.get(recurrent_regularizer)
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self.bias_regularizer = regularizers.get(bias_regularizer)
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self.activity_regularizer = regularizers.get(activity_regularizer)
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self.kernel_constraint = constraints.get(kernel_constraint)
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self.recurrent_constraint = constraints.get(recurrent_constraint)
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self.bias_constraint = constraints.get(bias_constraint)
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@property
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def cell(self):
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return self._cell
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def build(self, input_shape):
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super(CuDNNGRU, self).build(input_shape)
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if isinstance(input_shape, list):
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input_shape = input_shape[0]
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input_dim = int(input_shape[-1])
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self.kernel = self.add_weight(
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shape=(input_dim, self.units * 3),
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name='kernel',
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initializer=self.kernel_initializer,
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regularizer=self.kernel_regularizer,
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constraint=self.kernel_constraint)
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self.recurrent_kernel = self.add_weight(
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shape=(self.units, self.units * 3),
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name='recurrent_kernel',
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initializer=self.recurrent_initializer,
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regularizer=self.recurrent_regularizer,
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constraint=self.recurrent_constraint)
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self.bias = self.add_weight(
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shape=(self.units * 6,),
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name='bias',
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initializer=self.bias_initializer,
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regularizer=self.bias_regularizer,
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constraint=self.bias_constraint)
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self.built = True
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def _process_batch(self, inputs, initial_state):
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if not self.time_major:
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inputs = array_ops.transpose(inputs, perm=(1, 0, 2))
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input_h = initial_state[0]
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input_h = array_ops.expand_dims(input_h, axis=0)
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params = recurrent_v2._canonical_to_params( # pylint: disable=protected-access
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weights=[
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self.kernel[:, self.units:self.units * 2],
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self.kernel[:, :self.units],
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self.kernel[:, self.units * 2:],
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self.recurrent_kernel[:, self.units:self.units * 2],
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self.recurrent_kernel[:, :self.units],
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self.recurrent_kernel[:, self.units * 2:],
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],
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biases=[
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self.bias[self.units:self.units * 2],
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self.bias[:self.units],
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self.bias[self.units * 2:self.units * 3],
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self.bias[self.units * 4:self.units * 5],
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self.bias[self.units * 3:self.units * 4],
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self.bias[self.units * 5:],
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],
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shape=self._vector_shape)
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args = {
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'input': inputs,
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'input_h': input_h,
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'input_c': 0,
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'params': params,
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'is_training': True,
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'rnn_mode': 'gru',
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}
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outputs, h, _, _, _ = gen_cudnn_rnn_ops.CudnnRNNV2(**args)
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if self.stateful or self.return_state:
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h = h[0]
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if self.return_sequences:
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if self.time_major:
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output = outputs
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else:
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output = array_ops.transpose(outputs, perm=(1, 0, 2))
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else:
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output = outputs[-1]
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return output, [h]
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def get_config(self):
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config = {
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'units': self.units,
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'kernel_initializer': initializers.serialize(self.kernel_initializer),
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'recurrent_initializer':
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initializers.serialize(self.recurrent_initializer),
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'bias_initializer': initializers.serialize(self.bias_initializer),
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'kernel_regularizer': regularizers.serialize(self.kernel_regularizer),
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'recurrent_regularizer':
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regularizers.serialize(self.recurrent_regularizer),
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'bias_regularizer': regularizers.serialize(self.bias_regularizer),
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'activity_regularizer':
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regularizers.serialize(self.activity_regularizer),
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'kernel_constraint': constraints.serialize(self.kernel_constraint),
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'recurrent_constraint':
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constraints.serialize(self.recurrent_constraint),
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'bias_constraint': constraints.serialize(self.bias_constraint)
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}
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base_config = super(CuDNNGRU, self).get_config()
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return dict(list(base_config.items()) + list(config.items()))
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@keras_export(v1=['keras.layers.CuDNNLSTM'])
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class CuDNNLSTM(_CuDNNRNN):
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"""Fast LSTM implementation backed by cuDNN.
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More information about cuDNN can be found on the [NVIDIA
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developer website](https://developer.nvidia.com/cudnn).
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Can only be run on GPU.
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Args:
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units: Positive integer, dimensionality of the output space.
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kernel_initializer: Initializer for the `kernel` weights matrix, used for
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the linear transformation of the inputs.
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unit_forget_bias: Boolean. If True, add 1 to the bias of the forget gate
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at initialization. Setting it to true will also force
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`bias_initializer="zeros"`. This is recommended in [Jozefowicz et
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al.](http://www.jmlr.org/proceedings/papers/v37/jozefowicz15.pdf)
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recurrent_initializer: Initializer for the `recurrent_kernel` weights
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matrix, used for the linear transformation of the recurrent state.
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bias_initializer: Initializer for the bias vector.
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kernel_regularizer: Regularizer function applied to the `kernel` weights
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matrix.
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recurrent_regularizer: Regularizer function applied to the
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`recurrent_kernel` weights matrix.
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bias_regularizer: Regularizer function applied to the bias vector.
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activity_regularizer: Regularizer function applied to the output of the
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layer (its "activation").
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kernel_constraint: Constraint function applied to the `kernel` weights
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matrix.
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recurrent_constraint: Constraint function applied to the
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`recurrent_kernel` weights matrix.
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bias_constraint: Constraint function applied to the bias vector.
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return_sequences: Boolean. Whether to return the last output. in the
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output sequence, or the full sequence.
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return_state: Boolean. Whether to return the last state in addition to the
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output.
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go_backwards: Boolean (default False). If True, process the input sequence
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backwards and return the reversed sequence.
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stateful: Boolean (default False). If True, the last state for each sample
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at index i in a batch will be used as initial state for the sample of
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index i in the following batch.
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"""
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def __init__(self,
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units,
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kernel_initializer='glorot_uniform',
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recurrent_initializer='orthogonal',
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bias_initializer='zeros',
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unit_forget_bias=True,
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kernel_regularizer=None,
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recurrent_regularizer=None,
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bias_regularizer=None,
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activity_regularizer=None,
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kernel_constraint=None,
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recurrent_constraint=None,
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bias_constraint=None,
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return_sequences=False,
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return_state=False,
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go_backwards=False,
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stateful=False,
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**kwargs):
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self.units = units
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cell_spec = collections.namedtuple('cell', 'state_size')
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self._cell = cell_spec(state_size=(self.units, self.units))
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super(CuDNNLSTM, self).__init__(
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return_sequences=return_sequences,
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return_state=return_state,
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go_backwards=go_backwards,
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stateful=stateful,
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**kwargs)
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self.kernel_initializer = initializers.get(kernel_initializer)
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self.recurrent_initializer = initializers.get(recurrent_initializer)
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self.bias_initializer = initializers.get(bias_initializer)
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self.unit_forget_bias = unit_forget_bias
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self.kernel_regularizer = regularizers.get(kernel_regularizer)
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self.recurrent_regularizer = regularizers.get(recurrent_regularizer)
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self.bias_regularizer = regularizers.get(bias_regularizer)
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self.activity_regularizer = regularizers.get(activity_regularizer)
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self.kernel_constraint = constraints.get(kernel_constraint)
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self.recurrent_constraint = constraints.get(recurrent_constraint)
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self.bias_constraint = constraints.get(bias_constraint)
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@property
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def cell(self):
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return self._cell
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def build(self, input_shape):
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super(CuDNNLSTM, self).build(input_shape)
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if isinstance(input_shape, list):
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input_shape = input_shape[0]
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input_dim = int(input_shape[-1])
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self.kernel = self.add_weight(
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shape=(input_dim, self.units * 4),
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name='kernel',
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initializer=self.kernel_initializer,
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regularizer=self.kernel_regularizer,
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constraint=self.kernel_constraint)
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self.recurrent_kernel = self.add_weight(
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shape=(self.units, self.units * 4),
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name='recurrent_kernel',
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initializer=self.recurrent_initializer,
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regularizer=self.recurrent_regularizer,
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constraint=self.recurrent_constraint)
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if self.unit_forget_bias:
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def bias_initializer(_, *args, **kwargs):
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return array_ops.concat([
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self.bias_initializer((self.units * 5,), *args, **kwargs),
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initializers.Ones()((self.units,), *args, **kwargs),
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self.bias_initializer((self.units * 2,), *args, **kwargs),
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], axis=0)
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else:
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bias_initializer = self.bias_initializer
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self.bias = self.add_weight(
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shape=(self.units * 8,),
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name='bias',
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initializer=bias_initializer,
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regularizer=self.bias_regularizer,
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constraint=self.bias_constraint)
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self.built = True
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def _process_batch(self, inputs, initial_state):
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if not self.time_major:
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inputs = array_ops.transpose(inputs, perm=(1, 0, 2))
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input_h = initial_state[0]
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input_c = initial_state[1]
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input_h = array_ops.expand_dims(input_h, axis=0)
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input_c = array_ops.expand_dims(input_c, axis=0)
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params = recurrent_v2._canonical_to_params( # pylint: disable=protected-access
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weights=[
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self.kernel[:, :self.units],
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self.kernel[:, self.units:self.units * 2],
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self.kernel[:, self.units * 2:self.units * 3],
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self.kernel[:, self.units * 3:],
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self.recurrent_kernel[:, :self.units],
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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, _, _ = gen_cudnn_rnn_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 = array_ops.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()))
|