def transform(image, label, training):
if training:
image = random_crop(image, (32, 32), (4, 4))
image = tf.image.random_flip_left_right(image)
image, label = to_tensor(image, label)
image = normalize(image, [0.491, 0.482, 0.447], [0.247, 0.243, 0.262])
label = tf.one_hot(label, 100)
return image, label
def transform(image, label, training):
if training:
image = random_crop(image, (32, 32), (4, 4))
image = tf.image.random_flip_left_right(image)
image = autoaugment(image, "CIFAR10")
image, label = to_tensor(image, label)
image = normalize(image, [0.491, 0.482, 0.447], [0.247, 0.243, 0.262])
if training:
image = random_apply(cutout(length=16), params['cutout_prob'], image)
label = tf.one_hot(label, 100)
return image, label
def transform(image, label, training):
if training:
image = random_crop(image, (32, 32), (4, 4))
image = tf.image.random_flip_left_right(image)
# image = autoaugment(image, "CIFAR10")
image, label = to_tensor(image, label)
image = normalize(image, [0.491, 0.482, 0.447], [0.247, 0.243, 0.262])
# if training:
# image = cutout(image, 16)
label = tf.one_hot(label, 10)
return image, label
