chunked/nltk_chunking/tasks_image_classification/chunk_11.txt

from tensorflow import keras
from tensorflow.keras import layers
size = (image_processor.size["height"], image_processor.size["width"])
train_data_augmentation = keras.Sequential(
     [
         layers.RandomCrop(size[0], size[1]),
         layers.Rescaling(scale=1.0 / 127.5, offset=-1),
         layers.RandomFlip("horizontal"),
         layers.RandomRotation(factor=0.02),
         layers.RandomZoom(height_factor=0.2, width_factor=0.2),
     ],
     name="train_data_augmentation",
 )
val_data_augmentation = keras.Sequential(
     [
         layers.CenterCrop(size[0], size[1]),
         layers.Rescaling(scale=1.0 / 127.5, offset=-1),
     ],
     name="val_data_augmentation",
 )

Next, create functions to apply appropriate transformations to a batch of images, instead of one image at a time.