text: # Copyright 2015 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 Relu and ReluGrad."""
  from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  
  import numpy as np
  import tensorflow as tf
  
  
  class ReluTest(tf.test.TestCase):
  
    def _npRelu(self, np_features):
      return np.maximum(np_features, np.zeros(np_features.shape))
  
    def testNpRelu(self):
      self.assertAllClose(
          np.array([[0.0, 0.7, 0.0, 0.3, 0.0],
                    [0.1, 0.0, 0.5, 0.0, 0.9]]),
          self._npRelu(np.array([[-0.9, 0.7, -0.5, 0.3, -0.1],
                                 [0.1, -0.3, 0.5, -0.7, 0.9]])))
  
    def _testRelu(self, np_features, use_gpu=False):
      np_relu = self._npRelu(np_features)
      with self.test_session(use_gpu=use_gpu):
        relu = tf.nn.relu(np_features)
        tf_relu = relu.eval()
      self.assertAllClose(np_relu, tf_relu)
      self.assertShapeEqual(np_relu, relu)
  
    def testNumbers(self):
      for t in [np.int32, np.int64, np.float32, np.float64]:
        self._testRelu(
            np.array([[-9, 7, -5, 3, -1], [1, -3, 5, -7, 9]]).astype(t),
            use_gpu=False)
        if t in [np.float32, np.float64]:
          self._testRelu(
              np.array([[-9, 7, -5, 3, -1], [1, -3, 5, -7, 9]]).astype(t),
              use_gpu=True)
  
    # The gradient test for ReLU is a bit tricky as the derivative is not well
    # defined at around zero and we want to avoid that in terms of input values.
    def testGradientFloat32(self):
      with self.test_session():
        x = tf.constant(
            [-0.9, -0.7, -0.5, -0.3, -0.1, 0.1, 0.3, 0.5, 0.7, 0.9],
            shape=[2, 5], name="x")
        y = tf.nn.relu(x, name="relu")
        x_init = np.asarray(
            [[-0.9, -0.7, -0.5, -0.3, -0.1], [0.1, 0.3, 0.5, 0.7, 0.9]],
            dtype=np.float32, order="F")
        err = tf.test.compute_gradient_error(x,
                                             [2, 5],
                                             y,
                                             [2, 5],
                                             x_init_value=x_init)
      print("relu (float32) gradient err = ", err)
      self.assertLess(err, 1e-4)
  
    def testGradientFloat64(self):
      with self.test_session():
        x = tf.constant(
            [-0.9, -0.7, -0.5, -0.3, -0.1, 0.1, 0.3, 0.5, 0.7, 0.9],
            shape=[2, 5], dtype=tf.float64, name="x")
        y = tf.nn.relu(x, name="relu")
        x_init = np.asarray(
            [[-0.9, -0.7, -0.5, -0.3, -0.1], [0.1, 0.3, 0.5, 0.7, 0.9]],
            dtype=np.float64, order="F")
        err = tf.test.compute_gradient_error(x,
                                             [2, 5],
                                             y,
                                             [2, 5],
                                             x_init_value=x_init)
      print("relu (float64) gradient err = ", err)
      self.assertLess(err, 1e-10)
  
    def testGradGradFloat32(self):
      with self.test_session():
        x = tf.constant(
            [-0.9, -0.7, -0.5, -0.3, -0.1, 0.1, 0.3, 0.5, 0.7, 0.9],
            shape=[2, 5], name="x")
        y = tf.nn.relu(x, name="relu")
        z = tf.gradients(y, x)
        x_init = np.asarray(
            [[-0.9, -0.7, -0.5, -0.3, -0.1], [0.1, 0.3, 0.5, 0.7, 0.9]],
            dtype=np.float32, order="F")
        err = tf.test.compute_gradient_error(x,
                                             [2, 5],
                                             z[0],
                                             [2, 5],
                                             x_init_value=x_init)
      print("relu (float32) gradient of gradient err = ", err)
      self.assertLess(err, 1e-4)
  
    def testGradGradFloat64(self):
      with self.test_session():
        x = tf.constant(
            [-0.9, -0.7, -0.5, -0.3, -0.1, 0.1, 0.3, 0.5, 0.7, 0.9],
            shape=[2, 5], dtype=tf.float64, name="x")
        y = tf.nn.relu(x, name="relu")
        z = tf.gradients(y, x)
        x_init = np.asarray(
            [[-0.9, -0.7, -0.5, -0.3, -0.1], [0.1, 0.3, 0.5, 0.7, 0.9]],
            dtype=np.float64, order="F")
        err = tf.test.compute_gradient_error(x,
                                             [2, 5],
                                             z[0],
                                             [2, 5],
                                             x_init_value=x_init)
      print("relu (float64) gradient of gradient err = ", err)
      self.assertLess(err, 1e-10)
  
    def testGradientScalar(self):
      with self.test_session() as sess:
        x = tf.Variable(100.)
        y = tf.nn.relu(x)
        loss = y**2
        optimizer = tf.train.GradientDescentOptimizer(learning_rate=0.25)
        train_op = optimizer.minimize(loss)
        sess.run(tf.initialize_all_variables())
        sess.run(train_op)
        self.assertAllClose(x.eval(), 50.0)
  
  
  class Relu6Test(tf.test.TestCase):
  
    def _npRelu6(self, np_features):
      sixes = np.copy(np_features)
      sixes.fill(6.0)
      return np.minimum(np.maximum(np_features, np.zeros(np_features.shape)),
                        sixes)
  
    def testNpRelu6(self):
      self.assertAllClose(
          np.array([[0.0, 0.7, 0.0, 0.3, 6.0],
                    [0.1, 0.0, 6.0, 0.0, 0.9]]),
          self._npRelu6(np.array([[-0.9, 0.7, -0.5, 0.3, 6.0],
                                  [0.1, -0.3, 6.5, -0.7, 0.9]])))
  
    def _testRelu6(self, np_features, use_gpu=False):
      np_relu6 = self._npRelu6(np_features)
      with self.test_session(use_gpu=use_gpu):
        relu6 = tf.nn.relu6(np_features)
        tf_relu6 = relu6.eval()
      self.assertAllClose(np_relu6, tf_relu6)
      self.assertShapeEqual(np_relu6, relu6)
  
    def testNumbers(self):
      for t in [np.int32, np.int64, np.float32, np.float64]:
        self._testRelu6(
            np.array([[-9, 7, -5, 3, -1], [1, -3, 5, -7, 9]]).astype(t),
            use_gpu=False)
        if t in [np.float, np.double]:
          self._testRelu6(
              np.array([[-9, 7, -5, 3, -1], [1, -3, 5, -7, 9]]).astype(t),
              use_gpu=True)
  
    # The gradient test for ReLU6 is a bit tricky as the derivative is
    # not well defined at around zero and six and we want to avoid that
    # in terms of input values.
    def testGradientFloat32(self):
      with self.test_session():
        x = tf.constant(
            [-0.9, -0.7, -0.5, -0.3, -0.1, 6.1, 6.3, 6.5, 6.7, 6.9],
            shape=[2, 5], name="x")
        y = tf.nn.relu6(x, name="relu6")
        x_init = np.asarray(
            [[-0.9, -0.7, -0.5, -0.3, -0.1], [6.1, 6.3, 6.5, 6.7, 6.9]],
            dtype=np.float32, order="F")
        err = tf.test.compute_gradient_error(x,
                                             [2, 5],
                                             y,
                                             [2, 5],
                                             x_init_value=x_init)
      print("relu6 (float32) gradient err = ", err)
      self.assertLess(err, 1e-4)
  
    def testGradientFloat64(self):
      with self.test_session():
        x = tf.constant(
            [-0.9, -0.7, -0.5, -0.3, -0.1, 6.1, 6.3, 6.5, 6.7, 6.9],
            shape=[2, 5], dtype=tf.float64, name="x")
        y = tf.nn.relu6(x, name="relu6")
        x_init = np.asarray(
            [[-0.9, -0.7, -0.5, -0.3, -0.1], [6.1, 6.3, 6.5, 6.7, 6.9]],
            dtype=np.float64, order="F")
        err = tf.test.compute_gradient_error(x,
                                             [2, 5],
                                             y,
                                             [2, 5],
                                             x_init_value=x_init)
      print("relu6 (float64) gradient err = ", err)
      self.assertLess(err, 1e-10)
  
  
  class EluTest(tf.test.TestCase):
  
    def _npElu(self, np_features):
      return np.where(np_features < 0, np.exp(np_features) - 1, np_features)
  
    def testNpElu(self):
      self.assertAllClose(
          np.array([[-0.59343034025, 0.7, -0.39346934028, 0.3, -0.09516258196],
                    [0.1, -0.25918177931, 0.5, -0.5034146962, 0.9]]),
          self._npElu(np.array([[-0.9, 0.7, -0.5, 0.3, -0.1], [0.1, -0.3, 0.5, -
                                                               0.7, 0.9]])))
  
    def _testElu(self, np_features, use_gpu=False):
      np_elu = self._npElu(np_features)
      with self.test_session(use_gpu=use_gpu):
        elu = tf.nn.elu(np_features)
        tf_elu = elu.eval()
      self.assertAllClose(np_elu, tf_elu)
      self.assertShapeEqual(np_elu, elu)
  
    def testNumbers(self):
      for t in [np.float32, np.float64]:
        self._testElu(
            np.array([[-9, 7, -5, 3, -1], [1, -3, 5, -7, 9]]).astype(t),
            use_gpu=False)
        self._testElu(
            np.array([[-9, 7, -5, 3, -1], [1, -3, 5, -7, 9]]).astype(t),
            use_gpu=True)
  
    def testGradientFloat32(self):
      with self.test_session():
        x_val = [[-0.9, -0.7, -0.5, -0.3, -0.1], [0.1, 0.3, 0.5, 0.7, 0.9]]
        x = tf.constant(x_val, name="x")
        y = tf.nn.elu(x, name="elu")
        x_init = np.asarray(x_val, dtype=np.float32, order="F")
        err = tf.test.compute_gradient_error(x,
                                             [2, 5],
                                             y,
                                             [2, 5],
                                             x_init_value=x_init)
      print("elu (float32) gradient err = ", err)
      self.assertLess(err, 1e-4)
  
    def testGradientFloat64(self):
      with self.test_session():
        x_val = [[-0.9, -0.7, -0.5, -0.3, -0.1], [0.1, 0.3, 0.5, 0.7, 0.9]]
        x = tf.constant(x_val, dtype=tf.float64, name="x")
        y = tf.nn.elu(x, name="elu")
        x_init = np.asarray(x_val, dtype=np.float64, order="F")
        err = tf.test.compute_gradient_error(x,
                                             [2, 5],
                                             y,
                                             [2, 5],
                                             x_init_value=x_init)
      print("elu (float64) gradient err = ", err)
      self.assertLess(err, 1e-6)
  
  
  if __name__ == "__main__":
    tf.test.main()
  