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# coding=utf-8

# Copyright 2018 The Google AI Language Team Authors.

#

# 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.

from __future__ import absolute_import

from __future__ import division

from __future__ import print_function

import optimization

import tensorflow as tf

class OptimizationTest(tf.test.TestCase):

def test_adam(self):

with self.test_session() as sess:

w = tf.get_variable(

"w",

shape=[3],

initializer=tf.constant_initializer([0.1, -0.2, -0.1]))

x = tf.constant([0.4, 0.2, -0.5])

loss = tf.reduce_mean(tf.square(x - w))

tvars = tf.trainable_variables()

grads = tf.gradients(loss, tvars)

global_step = tf.train.get_or_create_global_step()

optimizer = optimization.AdamWeightDecayOptimizer(learning_rate=0.2)

train_op = optimizer.apply_gradients(zip(grads, tvars), global_step)

init_op = tf.group(tf.global_variables_initializer(),

tf.local_variables_initializer())

sess.run(init_op)

for _ in range(100):

sess.run(train_op)

w_np = sess.run(w)

self.assertAllClose(w_np.flat, [0.4, 0.2, -0.5], rtol=1e-2, atol=1e-2)

if __name__ == "__main__":

tf.test.main()