def testConv(self): if 'GPU' in self.device: # TODO(b/32333178) self.skipTest( 'Current implementation of RandomStandardNormal kernel ' 'is very slow on GPU, and has been blacklisted.') with self.test_scope(): data_format = 'channels_last' conv = convolutional.Conv2D( filters=1, kernel_size=2, padding='VALID', data_format=data_format, activation=nn_ops.relu, kernel_initializer=init_ops.ones_initializer(), bias_initializer=init_ops.zeros_initializer()) pool = pooling.MaxPooling2D(2, 2, data_format=data_format) def model(x): x = conv(x) return pool(x) model = function.defun(model) x = array_ops.ones([1, 4, 4, 1]) y = model(x) self.assertAllEqual(y.numpy(), [[[[4.]]]])
def testMaxPooling2DPaddingSame(self): height, width = 7, 9 images = random_ops.random_uniform((5, height, width, 4), seed=1) layer = pooling_layers.MaxPooling2D( images.get_shape()[1:3], strides=2, padding='same') output = layer.apply(images) self.assertListEqual(output.get_shape().as_list(), [5, 4, 5, 4])
def testCreateMaxPooling2DChannelsFirst(self): height, width = 7, 9 images = random_ops.random_uniform((5, 2, height, width)) layer = pooling_layers.MaxPooling2D([2, 2], strides=1, data_format='channels_first') output = layer.apply(images) self.assertListEqual(output.get_shape().as_list(), [5, 2, 6, 8])
def testCreatePooling2DWithStrides(self): height, width = 6, 8 # Test strides tuple images = random_ops.random_uniform((5, height, width, 3), seed=1) layer = pooling_layers.MaxPooling2D([2, 2], strides=(2, 2), padding='same') output = layer.apply(images) self.assertListEqual(output.get_shape().as_list(), [5, height / 2, width / 2, 3]) # Test strides integer layer = pooling_layers.MaxPooling2D([2, 2], strides=2, padding='same') output = layer.apply(images) self.assertListEqual(output.get_shape().as_list(), [5, height / 2, width / 2, 3]) # Test unequal strides layer = pooling_layers.MaxPooling2D([2, 2], strides=(2, 1), padding='same') output = layer.apply(images) self.assertListEqual(output.get_shape().as_list(), [5, height / 2, width, 3])
def testCreateMaxPooling2D(self): height, width = 7, 9 images = random_ops.random_uniform((5, height, width, 4)) layer = pooling_layers.MaxPooling2D([2, 2], strides=2) output = layer.apply(images) self.assertListEqual(output.get_shape().as_list(), [5, 3, 4, 4])