导入必要的库

import numpy as np import matplotlib.pyplot as plt

获取整个验证集的图片和标签

test_images, test_labels = validation_generator.next() predicted_labels = model.predict(test_images)

随机选择15张图片，并展示它们的预测结果

fig, ax = plt.subplots(3, 5, figsize=(12, 8)) fig.suptitle('Random Test Images with Predictions')

for i, axis in enumerate(ax.flat): index = np.random.randint(len(test_images)) image = test_images[index] label = test_labels[index].argmax(axis=-1) predicted_label = predicted_labels[index].argmax(axis=-1) # 显示图片和它的预测标签 axis.imshow(image) axis.axis('off') class_names =['cane', 'cavallo', 'elefante', 'farfalla', 'gallina', 'gatto', 'mucca', 'pecora', 'ragno', 'scoiattolo'] # 显示原始图像以及它的实际标签和模型预测的标签 axis.set_title('Actual: {} Predicted: {}'.format(class_names[label], class_names[predicted_label]))

plt.show()