google-research

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# coding=utf-8
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# Copyright 2024 The Google Research Authors.
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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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"""Tests for optimization."""
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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 tensorflow.compat.v1 as tf
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from mobilebert import optimization
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class OptimizationTest(tf.test.TestCase):
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  def test_adam(self):
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    with self.test_session() as sess:
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      w = tf.get_variable(
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          "w",
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          shape=[3],
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          initializer=tf.constant_initializer([0.1, -0.2, -0.1]))
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      x = tf.constant([0.4, 0.2, -0.5])
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      loss = tf.reduce_mean(tf.square(x - w))
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      tvars = tf.trainable_variables()
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      grads = tf.gradients(loss, tvars)
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      global_step = tf.train.get_or_create_global_step()
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      optimizer = optimization.AdamWeightDecayOptimizer(learning_rate=0.2)
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      train_op = optimizer.apply_gradients(zip(grads, tvars), global_step)
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      init_op = tf.group(tf.global_variables_initializer(),
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                         tf.local_variables_initializer())
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      sess.run(init_op)
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      for _ in range(100):
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        sess.run(train_op)
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      w_np = sess.run(w)
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      self.assertAllClose(w_np.flat, [0.4, 0.2, -0.5], rtol=1e-2, atol=1e-2)
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if __name__ == "__main__":
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  tf.test.main()
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