text: # Copyright 2018 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.
  # ==============================================================================
  """Tests for class Step."""
  
  from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  
  from absl.testing import parameterized
  import numpy
  
  from tensorflow.contrib.distribute.python import combinations
  from tensorflow.contrib.distribute.python.single_loss_example import single_loss_example
  from tensorflow.python.eager import context
  from tensorflow.python.eager import test
  from tensorflow.python.ops import variables
  
  
  class SingleLossStepTest(test.TestCase, parameterized.TestCase):
  
    @combinations.generate(
        combinations.times(
            combinations.distributions_and_v1_optimizers(),
            combinations.combine(mode=combinations.graph_and_eager_modes)))
    def testTrainNetwork(self, distribution, optimizer_fn):
      with distribution.scope():
        single_loss_step, layer = single_loss_example(
            optimizer_fn, distribution, use_bias=True)
  
        if context.executing_eagerly():
          run_step = single_loss_step
        else:
          with self.test_session() as sess:
            run_step = sess.make_callable(single_loss_step())
        self.evaluate(variables.global_variables_initializer())
  
        weights, biases = [], []
        for _ in range(10):
          run_step()
  
          weights.append(self.evaluate(layer.kernel))
          biases.append(self.evaluate(layer.bias))
  
        error = abs(numpy.add(numpy.squeeze(weights), numpy.squeeze(biases)) - 1)
        is_not_increasing = all(y <= x for x, y in zip(error, error[1:]))
        self.assertTrue(is_not_increasing)
  
  
  if __name__ == "__main__":
    test.main()
  