import tensorflow as tf from tensorflow.keras.preprocessing import image_dataset_from_directory

BATCH_SIZE = 32 IMG_HEIGHT = 180 IMG_WIDTH = 180 BASE_PATH = "./traffic_Data/DATA" AUTOTUNE = tf.data.AUTOTUNE

train_ds = image_dataset_from_directory(BASE_PATH, image_size=(IMG_HEIGHT, IMG_WIDTH), batch_size=BATCH_SIZE)

class_names = train_ds.class_names NUM_CLASSES = len(class_names)

def normalize_and_center(image, label): image = tf.cast(image/255. - 0.5, tf.float32) return image, label

train_ds_norm = train_ds.map(normalize_and_center)

train_ds_norm = train_ds_norm.cache().shuffle(1000).prefetch(buffer_size=AUTOTUNE)