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# Copyright 2023 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.
"""Implementation of fully connected network."""
import tensorflow as tf, tf_keras
class FeedForwardNetwork(tf_keras.layers.Layer):
"""Fully connected feedforward network."""
def __init__(self, hidden_size, filter_size, relu_dropout):
"""Initialize FeedForwardNetwork.
Args:
hidden_size: int, output dim of hidden layer.
filter_size: int, filter size for the inner (first) dense layer.
relu_dropout: float, dropout rate for training.
"""
super(FeedForwardNetwork, self).__init__()
self.hidden_size = hidden_size
self.filter_size = filter_size
self.relu_dropout = relu_dropout
def build(self, input_shape):
self.filter_dense_layer = tf_keras.layers.Dense(
self.filter_size,
use_bias=True,
activation=tf.nn.relu,
name="filter_layer")
self.output_dense_layer = tf_keras.layers.Dense(
self.hidden_size, use_bias=True, name="output_layer")
super(FeedForwardNetwork, self).build(input_shape)
def get_config(self):
return {
"hidden_size": self.hidden_size,
"filter_size": self.filter_size,
"relu_dropout": self.relu_dropout,
}
def call(self, x, training):
"""Return outputs of the feedforward network.
Args:
x: tensor with shape [batch_size, length, hidden_size]
training: boolean, whether in training mode or not.
Returns:
Output of the feedforward network.
tensor with shape [batch_size, length, hidden_size]
"""
# Retrieve dynamically known shapes
output = self.filter_dense_layer(x)
if training:
output = tf.nn.dropout(output, rate=self.relu_dropout)
output = self.output_dense_layer(output)
return output
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