from tensorflow.keras.preprocessing.image import ImageDataGenerator
import matplotlib.pyplot as plt

train_datagen = ImageDataGenerator(
    validation_split=0.25,
    rescale=1. / 255,
    horizontal_flip=True,
    vertical_flip=True,
    width_shift_range=0.2,
    height_shift_range=0.2,
    rotation_range=90)

validation_datagen = ImageDataGenerator(
    validation_split=0.25,
    rescale=1. / 255)

train_datagen_flow = train_datagen.flow_from_directory(
    '/datasets/fruits_small/',
    target_size=(150, 150),
    batch_size=16,
    class_mode='sparse',
    subset='training',
    seed=12345)

val_datagen_flow = validation_datagen.flow_from_directory(
    '/datasets/fruits_small/',
    target_size=(150, 150),
    batch_size=16,
    class_mode='sparse',
    subset='validation',
    seed=12345)

features, target = next(train_datagen_flow)

fig = plt.figure(figsize=(10, 10))
for i in range(16):
    fig.add_subplot(4, 4, i + 1)
    plt.imshow(features[i])
    # для компактности удаляем оси и прижимаем изображения друг к другу
    plt.xticks([])
    plt.yticks([])
    plt.tight_layout()