AUTOTUNE = tf.data.AUTOTUNE

def prepare(ds, shuffle=False):
    # Rescale and resize all datasets.
    ds = ds.map(lambda x, y: (resize_and_rescale(x), y), num_parallel_calls=AUTOTUNE)

    if shuffle:
        ds = ds.shuffle(1000)

    # Use buffered prefetching on all datasets.
    return ds.prefetch(buffer_size=AUTOTUNE)

train_ds = prepare(training_set, shuffle=True)
val_ds = prepare(validation_set)
evaluation_set = prepare(eval_set)

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