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.
  # ==============================================================================
  """Exp Tests."""
  
  from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  
  import numpy as np
  
  from tensorflow.contrib.distributions.python.ops.bijectors.exp import Exp
  from tensorflow.python.ops.distributions.bijector_test_util import assert_bijective_and_finite
  from tensorflow.python.ops.distributions.bijector_test_util import assert_scalar_congruency
  from tensorflow.python.platform import test
  
  
  class ExpBijectorTest(test.TestCase):
    """Tests correctness of the Y = g(X) = exp(X) transformation."""
  
    def testBijector(self):
      with self.test_session():
        bijector = Exp(event_ndims=1)
        self.assertEqual("exp", bijector.name)
        x = [[[1.], [2.]]]
        y = np.exp(x)
        self.assertAllClose(y, bijector.forward(x).eval())
        self.assertAllClose(x, bijector.inverse(y).eval())
        self.assertAllClose(
            -np.sum(np.log(y), axis=-1),
            bijector.inverse_log_det_jacobian(y).eval())
        self.assertAllClose(-bijector.inverse_log_det_jacobian(np.exp(x)).eval(),
                            bijector.forward_log_det_jacobian(x).eval())
  
    def testScalarCongruency(self):
      with self.test_session():
        bijector = Exp()
        assert_scalar_congruency(
            bijector, lower_x=-2., upper_x=1.5, rtol=0.05)
  
    def testBijectiveAndFinite(self):
      with self.test_session():
        bijector = Exp(event_ndims=0)
        x = np.linspace(-10, 10, num=10).astype(np.float32)
        y = np.logspace(-10, 10, num=10).astype(np.float32)
        assert_bijective_and_finite(bijector, x, y)
  
  
  if __name__ == "__main__":
    test.main()
  