import tensorflow as tf import matplotlib.pyplot as plt # List of names for each CIFAR10 class cifar10_class_names = { 0: "Plane", 1: "Car", 2: "Bird", 3: "Cat", 4: "Deer", 5: "Dog", 6: "Frog", 7: "Horse", 8: "Boat", 9: "Truck" } # Load the entire data set (x_train, y_train), (x_test, y_test) = tf.keras.datasets.cifar10.load_data() # Loop through each picture in the data set for i in range(1000): # Grab an image from the data set sample_image = x_train[i] # Grab the image's expected class id image_class_number = y_train[i][0] # Look up the class name from the class id image_class_name = cifar10_class_names[image_class_number] # Draw the image as a plot plt.imshow(sample_image) # Label the image plt.title(image_class_name) # Show the plot on the screen plt.show()代码解释

这段代码是用来加载并展示CIFAR10数据集中的图像和对应的标签。首先，通过调用`tf.keras.datasets.cifar10.load_data()`函数来加载CIFAR10数据集，将训练集和测试集分别赋值给`(x_train, y_train)`和`(x_test, y_test)`。

然后，通过一个循环遍历数据集中的图像。在每次迭代中，从训练集中获取一张图像，并获取其对应的类别ID。使用`cifar10_class_names`字典将类别ID转换为对应的类别名称。

接下来，使用`plt.imshow()`函数绘制图像，并使用`plt.title()`函数添加图像的类别名称作为标题。最后，使用`plt.show()`函数将图像显示在屏幕上。

这段代码的目的是展示数据集中的前1000张图像和它们的类别名称。