text: # Copyright 2016 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 stochastic graphs."""
  
  from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  
  import tensorflow as tf
  
  st = tf.contrib.bayesflow.stochastic_tensor
  sge = tf.contrib.bayesflow.stochastic_gradient_estimators
  
  
  class StochasticGradientEstimatorsTest(tf.test.TestCase):
  
    def setUp(self):
      self._p = tf.constant(0.999999)
      self._final_loss = tf.constant(3.2)
  
    def _testScoreFunction(self, loss_fn, expected):
      x = st.BernoulliTensor(p=self._p, loss_fn=loss_fn)
      sf = x.loss(self._final_loss)
      with self.test_session() as sess:
        sess.run(tf.initialize_all_variables())
        self.assertAllClose(*sess.run([expected, sf]))
  
    def testScoreFunction(self):
      expected = tf.log(self._p) * self._final_loss
      self._testScoreFunction(sge.score_function, expected)
  
    def testScoreFunctionWithConstantBaseline(self):
      b = tf.constant(9.8)
      expected = tf.log(self._p) * (self._final_loss - b)
      self._testScoreFunction(
          sge.get_score_function_with_constant_baseline(b), expected)
  
    def testScoreFunctionWithBaselineFn(self):
      b = tf.constant(9.8)
  
      def baseline_fn(stoch_tensor, loss):
        self.assertTrue(isinstance(stoch_tensor, st.StochasticTensor))
        self.assertTrue(isinstance(loss, tf.Tensor))
        return b
  
      expected = tf.log(self._p) * (self._final_loss - b)
      self._testScoreFunction(
          sge.get_score_function_with_baseline(baseline_fn), expected)
  
    def testScoreFunctionWithMeanBaseline(self):
      ema_decay = 0.8
      x = st.BernoulliTensor(
          p=self._p,
          loss_fn=sge.get_score_function_with_baseline(
              sge.get_mean_baseline(ema_decay)))
      sf = x.loss(self._final_loss)
  
      expected = tf.log(self._p) * (self._final_loss -
                                    (1. - ema_decay) * self._final_loss)
  
      with self.test_session() as sess:
        sess.run(tf.initialize_all_variables())
        sess.run(sf)  # run to update EMA
        self.assertAllClose(*sess.run([expected, sf]))
  
    def testScoreFunctionWithAdvantageFn(self):
      b = tf.constant(9.8)
  
      def advantage_fn(stoch_tensor, loss):
        self.assertTrue(isinstance(stoch_tensor, st.StochasticTensor))
        self.assertTrue(isinstance(loss, tf.Tensor))
        return loss - b
  
      expected = tf.log(self._p) * (self._final_loss - b)
      self._testScoreFunction(
          sge.get_score_function_with_advantage(advantage_fn), expected)
  
  
  if __name__ == "__main__":
    tf.test.main()
  