message: Update layers_test
  The Changes have been made as suggested in my previous pull request.
  The unit test case has been updated along with the update in layers.py with reference to the issue
  Please verify and get back.
diff: @@ -507,13 +507,13 @@ class ConvolutionTest(test.TestCase):
  self.assertEqual(
  len(ops.get_collection(ops.GraphKeys.REGULARIZATION_LOSSES)), 1)
  - def testConvWithBatchNorm(self):
  + def testConvWithNorm(self):
  height, width = 7, 9
  with self.test_session():
  images = random_ops.random_uniform((5, height, width, 32), seed=1)
  with arg_scope(
  [layers_lib.convolution2d],
  - normalizer_fn=_layers.batch_norm,
  + normalizer_fn=_layers._norm,
  normalizer_params={'decay': 0.9}):
  net = layers_lib.convolution2d(images, 32, [3, 3])
  net = layers_lib.convolution2d(net, 32, [3, 3])
  @@ -1739,6 +1739,11 @@ class BatchNormTest(test.TestCase):
  expected_var *= correction_factor
  return expected_var, correction_factor
  + def testBatchNormCenterFalse(self)
  + a = array_ops.placeholder(dtype.float32, shape=(10, 10, 10, 10))
  + b = _layers.batch_norm(a, center=False, data_format='NCHW',
  + zero_debias_moving_mean=True)
  +
  def testUnknownShape(self):
  with ops.Graph().as_default() as g, self.test_session(g):
  inputs = array_ops.placeholder(dtype=dtypes.float32)
  @@ -3689,16 +3694,6 @@ class LegacyFullyConnectedTest(test.TestCase):
  x = constant_op.constant([[]], shape=[0])
  _layers.legacy_fully_connected(x, 2, activation_fn=nn_ops.softmax)
  -class zero_debias_moving_mean(test.TestCase):
  -
  - def Error_in_tf.contrib.layers.batch_norm_when(self):
  - import tensorflow as tf
  - a = tf.placeholder(tf.float32, shape=(10, 10, 10, 10))
  - b = tf.contrib.layers.batch_norm(a, center=False, data_format='NCHW',
  - zero_debias_moving_mean=True)
  - sess = tf.Session()
  - sess.run(tf.global_variables_initializer())
  -
  if __name__ == '__main__':
  test.main()
  