258 lines
9.8 KiB
Python
258 lines
9.8 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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"""Fast LSTM layer backed by cuDNN."""
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import collections
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import tensorflow.compat.v2 as tf
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from keras import constraints
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from keras import initializers
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from keras import regularizers
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from keras.layers.rnn import gru_lstm_utils
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from keras.layers.rnn.base_cudnn_rnn import _CuDNNRNN
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# isort: off
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from tensorflow.python.util.tf_export import keras_export
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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
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for 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
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the output.
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go_backwards: Boolean (default False). If True, process the input
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sequence backwards and return the reversed sequence.
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stateful: Boolean (default False). If True, the last state for each
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sample at index i in a batch will be used as initial state for the
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sample of index i in the following batch.
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"""
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def __init__(
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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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):
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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().__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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)
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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().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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)
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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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)
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if self.unit_forget_bias:
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def bias_initializer(_, *args, **kwargs):
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return tf.concat(
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[
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self.bias_initializer(
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(self.units * 5,), *args, **kwargs
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),
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tf.compat.v1.ones_initializer()(
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(self.units,), *args, **kwargs
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),
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self.bias_initializer(
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(self.units * 2,), *args, **kwargs
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),
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],
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axis=0,
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)
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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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)
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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 = tf.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 = tf.expand_dims(input_h, axis=0)
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input_c = tf.expand_dims(input_c, axis=0)
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params = gru_lstm_utils.canonical_to_params(
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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],
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self.recurrent_kernel[:, self.units * 2 : self.units * 3],
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self.recurrent_kernel[:, self.units * 3 :],
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],
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biases=[
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self.bias[: self.units],
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self.bias[self.units : self.units * 2],
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self.bias[self.units * 2 : self.units * 3],
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self.bias[self.units * 3 : self.units * 4],
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self.bias[self.units * 4 : self.units * 5],
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self.bias[self.units * 5 : self.units * 6],
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self.bias[self.units * 6 : self.units * 7],
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self.bias[self.units * 7 :],
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],
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shape=self._vector_shape,
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)
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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": input_c,
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"params": params,
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"is_training": True,
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}
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outputs, h, c, _, _ = tf.raw_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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c = c[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 = tf.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, c]
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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(
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self.kernel_initializer
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),
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"recurrent_initializer": initializers.serialize(
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self.recurrent_initializer
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),
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"bias_initializer": initializers.serialize(self.bias_initializer),
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"unit_forget_bias": self.unit_forget_bias,
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"kernel_regularizer": regularizers.serialize(
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self.kernel_regularizer
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),
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"recurrent_regularizer": regularizers.serialize(
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self.recurrent_regularizer
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),
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"bias_regularizer": regularizers.serialize(self.bias_regularizer),
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"activity_regularizer": regularizers.serialize(
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self.activity_regularizer
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),
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"kernel_constraint": constraints.serialize(self.kernel_constraint),
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"recurrent_constraint": constraints.serialize(
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self.recurrent_constraint
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),
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"bias_constraint": constraints.serialize(self.bias_constraint),
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}
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base_config = super().get_config()
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return dict(list(base_config.items()) + list(config.items()))
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