def preprocess_example(self, example, mode, unused_hparams): image = example["inputs"] image.set_shape([_CIFAR10_IMAGE_SIZE, _CIFAR10_IMAGE_SIZE, 3]) if mode == tf.estimator.ModeKeys.TRAIN: image = image_utils.cifar_image_augmentation(image) image = tf.image.per_image_standardization(image) example["inputs"] = image return example
def preprocess_example(self, example, mode, unused_hparams): image = example["inputs"] image.set_shape([_CIFAR100_IMAGE_SIZE, _CIFAR100_IMAGE_SIZE, 3]) if mode == tf.estimator.ModeKeys.TRAIN: image = image_utils.cifar_image_augmentation(image) image = tf.image.per_image_standardization(image) example["inputs"] = image return example