def testFunctionalConv2DTransposeNoReuse(self):
   height, width = 7, 9
   images = random_ops.random_uniform((5, height, width, 3), seed=1)
   conv_layers.conv2d_transpose(images, 32, [3, 3])
   self.assertEqual(len(variables.trainable_variables()), 2)
   conv_layers.conv2d_transpose(images, 32, [3, 3])
   self.assertEqual(len(variables.trainable_variables()), 4)

  def testInvalidKernelSize(self):
    height, width = 7, 9
    images = random_ops.random_uniform((5, height, width, 3), seed=1)
    with self.assertRaisesRegexp(ValueError, 'kernel_size'):
      conv_layers.conv2d_transpose(images, 32, (1, 2, 3))

    with self.assertRaisesRegexp(ValueError, 'kernel_size'):
      conv_layers.conv2d_transpose(images, 32, None)

  def testInvalidStrides(self):
    height, width = 7, 9
    images = random_ops.random_uniform((5, height, width, 3), seed=1)
    with self.assertRaisesRegexp(ValueError, 'strides'):
      conv_layers.conv2d_transpose(images, 32, 3, strides=(1, 2, 3))

    with self.assertRaisesRegexp(ValueError, 'strides'):
      conv_layers.conv2d_transpose(images, 32, 3, strides=None)

 def testFunctionalConv2DTransposeReuseFromScope(self):
   with variable_scope.variable_scope('scope'):
     height, width = 7, 9
     images = random_ops.random_uniform((5, height, width, 3), seed=1)
     conv_layers.conv2d_transpose(images, 32, [3, 3], name='deconv1')
     self.assertEqual(len(variables.trainable_variables()), 2)
   with variable_scope.variable_scope('scope', reuse=True):
     conv_layers.conv2d_transpose(images, 32, [3, 3], name='deconv1')
     self.assertEqual(len(variables.trainable_variables()), 2)

 def testFunctionalConv2DTransposeNoReuse(self):
   height, width = 7, 9
   images = random_ops.random_uniform((5, height, width, 3), seed=1)
   conv_layers.conv2d_transpose(images, 32, [3, 3])
   self.assertEqual(
       len(ops.get_collection(ops.GraphKeys.TRAINABLE_VARIABLES)), 2)
   conv_layers.conv2d_transpose(images, 32, [3, 3])
   self.assertEqual(
       len(ops.get_collection(ops.GraphKeys.TRAINABLE_VARIABLES)), 4)

 def testConv2DTransposeFloat16(self):
   height, width = 7, 9
   images = random_ops.random_uniform((5, height, width, 4), dtype='float16')
   output = conv_layers.conv2d_transpose(images, 32, [3, 3],
                                         activation=nn_ops.relu)
   self.assertListEqual(output.get_shape().as_list(),
                        [5, height + 2, width + 2, 32])

 def testFunctionalConv2DTransposeInitializerFromScope(self):
   with self.test_session() as sess:
     with variable_scope.variable_scope(
         'scope', initializer=init_ops.ones_initializer()):
       height, width = 7, 9
       images = random_ops.random_uniform((5, height, width, 3), seed=1)
       conv_layers.conv2d_transpose(images, 32, [3, 3], name='deconv1')
       weights = variables.trainable_variables()
       # Check the names of weights in order.
       self.assertTrue('kernel' in weights[0].name)
       self.assertTrue('bias' in weights[1].name)
       sess.run(variables.global_variables_initializer())
       weights = sess.run(weights)
       # Check that the kernel weights got initialized to ones (from scope)
       self.assertAllClose(weights[0], np.ones((3, 3, 32, 3)))
       # Check that the bias still got initialized to zeros.
       self.assertAllClose(weights[1], np.zeros((32)))

 def testInvalidDataFormat(self):
   height, width = 7, 9
   images = random_ops.random_uniform((5, height, width, 3), seed=1)
   with self.assertRaisesRegexp(ValueError, 'data_format'):
     conv_layers.conv2d_transpose(images, 32, 3, data_format='invalid')