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import cv2 as cv import numpy as np import matplotlib.pyplot as plt from tensorflow.keras import datasets, layers, models (training_images, training_labels), (testing_images, testing_labels) = datasets.cifar10.load_data() training_images, testing_images = training_images / 255, testing_images / 255 class_names = ['Plane', 'Car', 'Bird', 'Cat', 'Deer', 'Dog', 'Frog', 'Horse', 'Ship', 'Truck'] for i in range(16): plt.subplot(4, 4, i+1) plt.xsticks([]) plt.ysticks([]) plt.imshow(training_images[i], cmap=plt.cm.binary) plt.xlabel(class_names[training_labels[i][0]]) plt.show()