def build(self, hp, inputs=None): input_node = nest.flatten(inputs)[0] output_node = input_node block_type = self.block_type or hp.Choice( 'block_type', ['resnet', 'xception', 'vanilla'], default='vanilla') normalize = self.normalize if normalize is None: normalize = hp.Boolean('normalize', default=False) augment = self.augment if augment is None: augment = hp.Boolean('augment', default=False) if normalize: output_node = preprocessing.Normalization().build(hp, output_node) if augment: output_node = preprocessing.ImageAugmentation().build( hp, output_node) if block_type == 'resnet': output_node = basic.ResNetBlock().build(hp, output_node) elif block_type == 'xception': output_node = basic.XceptionBlock().build(hp, output_node) elif block_type == 'vanilla': output_node = basic.ConvBlock().build(hp, output_node) return output_node
def test_imag_augmentation(): input_shape = (32, 32, 3) block = preprocessing.ImageAugmentation() hp = kerastuner.HyperParameters() block = graph_module.deserialize(graph_module.serialize(block)) block.build(hp, ak.Input(shape=input_shape).build()) assert utils.name_in_hps('vertical_flip', hp) assert utils.name_in_hps('horizontal_flip', hp)
def test_image_augmentation(): utils.block_basic_exam( preprocessing.ImageAugmentation(), tf.keras.Input(shape=(32, 32, 3), dtype=tf.float32), ['vertical_flip', 'horizontal_flip'], )