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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.
"""Utils for testing."""
import tensorflow as tf, tf_keras
class FakeKerasModel(tf_keras.Model):
"""Fake keras model for testing."""
def __init__(self):
super().__init__()
self.dense = tf_keras.layers.Dense(4, activation=tf.nn.relu)
self.dense2 = tf_keras.layers.Dense(4, activation=tf.nn.relu)
def call(self, inputs): # pytype: disable=signature-mismatch # overriding-parameter-count-checks
return self.dense2(self.dense(inputs))
class _Dense(tf.Module):
"""A dense layer."""
def __init__(self, input_dim, output_size, name=None):
super().__init__(name=name)
with self.name_scope:
self.w = tf.Variable(
tf.random.normal([input_dim, output_size]), name='w')
self.b = tf.Variable(tf.zeros([output_size]), name='b')
@tf.Module.with_name_scope
def __call__(self, x):
y = tf.matmul(x, self.w) + self.b
return tf.nn.relu(y)
class FakeModule(tf.Module):
"""Fake model using tf.Module for testing."""
def __init__(self, input_size, name=None):
super().__init__(name=name)
with self.name_scope:
self.dense = _Dense(input_size, 4, name='dense')
self.dense2 = _Dense(4, 4, name='dense_1')
@tf.Module.with_name_scope
def __call__(self, x):
return self.dense2(self.dense(x))
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