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  # Copyright 2019 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.
  # ==============================================================================
  """Functional tests for BiasAdd."""
  
  from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  
  from tensorflow.python.kernel_tests import bias_op_base
  from tensorflow.python.platform import test
  
  BiasAddTest = bias_op_base.BiasAddTestBase
  
  if __name__ == "__main__":
    test.main()
  =======
  """Functional tests for BiasAdd."""
  import tensorflow.python.platform
  
  import numpy as np
  import tensorflow as tf
  
  from tensorflow.python.kernel_tests import gradient_checker
  
  
  class BiasAddTest(tf.test.TestCase):
  
    def _npBias(self, inputs, bias):
      assert len(bias.shape) == 1
      print inputs.shape
      print bias.shape
      assert inputs.shape[-1] == bias.shape[0]
      return inputs + bias.reshape(([1] * (len(inputs.shape) - 1))
                                   + [bias.shape[0]])
  
    def testNpBias(self):
      self.assertAllClose(np.array([[11, 22, 33], [41, 52, 63]]),
                          self._npBias(np.array([[10, 20, 30], [40, 50, 60]]),
                                       np.array([1, 2, 3])))
  
    def _testBias(self, np_inputs, np_bias, use_gpu=False):
      np_val = self._npBias(np_inputs, np_bias)
      with self.test_session(use_gpu=use_gpu):
        tf_val = tf.nn.bias_add(np_inputs, np_bias).eval()
      self.assertAllClose(np_val, tf_val)
  
    def _testAll(self, np_inputs, np_bias):
      self._testBias(np_inputs, np_bias, use_gpu=False)
      if np_inputs.dtype == np.float32 or np_inputs.dtype == np.float64:
        self._testBias(np_inputs, np_bias, use_gpu=True)
  
    def testInputDims(self):
      with self.assertRaises(ValueError):
        tf.nn.bias_add([1, 2], [1])
  
    def testBiasVec(self):
      with self.assertRaises(ValueError):
        tf.nn.bias_add(tf.reshape([1, 2], shape=[1, 2]),
                        tf.reshape([1, 2], shape=[1, 2]))
  
    def testBiasInputsMatch(self):
      with self.assertRaises(ValueError):
        tf.nn.bias_add(tf.reshape([1, 2], shape=[1, 2]),
                        tf.reshape([1], shape=[1]))
  
    def testIntTypes(self):
      for t in [np.int8, np.int16, np.int32, np.int64]:
        self._testAll(np.array([[10, 20, 30], [40, 50, 60]]).astype(t),
                      np.array([1, 2, 3]).astype(t))
  
    def testFloatTypes(self):
      for t in [np.float32, np.float64]:
        self._testAll(np.random.rand(4, 3, 3).astype(t),
                      np.random.rand(3).astype(t))
  
    def testGradientTensor(self):
      with self.test_session():
        t = tf.constant([1.0, 2.0, 3.0, 4.0, 5.0, 6.0], shape=[3, 2],
                                 dtype=tf.float64)
        b = tf.constant([1.3, 2.4], dtype=tf.float64)
        bo = tf.nn.bias_add(t, b)
        err = gradient_checker.ComputeGradientError(t, [3, 2], bo, [3, 2])
      print "bias add tensor gradient err = ", err
      self.assertLess(err, 1e-10)
  
    def testGradientBias(self):
      with self.test_session():
        t = tf.constant([1.0, 2.0, 3.0, 4.0, 5.0, 6.0], shape=[3, 2],
                                 dtype=tf.float64)
        b = tf.constant([1.3, 2.4], dtype=tf.float64)
        bo = tf.nn.bias_add(t, b)
        err = gradient_checker.ComputeGradientError(b, [2], bo, [3, 2])
      print "bias add bias gradient err = ", err
      self.assertLess(err, 1e-10)
  
    def testGradientTensor4D(self):
      with self.test_session():
        s = [2, 3, 4, 2]
        x = np.arange(1.0, 49.0).reshape(s).astype(np.float32)
        t = tf.constant(x, shape=s, dtype=tf.float32)
        b = tf.constant([1.3, 2.4], dtype=tf.float32)
        bo = tf.nn.bias_add(t, b)
        err = gradient_checker.ComputeGradientError(t, s, bo, s, x_init_value=x)
      print "bias add tensor gradient err = ", err
      self.assertLess(err, 1e-3)
  
  
  if __name__ == "__main__":
    tf.test.main()
  >>>>>>> f41959ccb2... TensorFlow: Initial commit of TensorFlow library.
  