(X_train, y_train), (X_test, y_test) = cifar10.load_data()

`(X_train, y_train), (X_test, y_test) = cifar10.load_data()` 这行代码是从Keras自带的CIFAR-10数据集中加载训练集和测试集。CIFAR-10数据集是一个常用的图像分类数据集，包含10个类别，每个类别有5000张尺寸为32\*32的彩色图像。数据集中的图像已经被预处理为像素值在0-1之间的浮点数。其中，`(X_train, y_train)`表示训练集数据和对应的标签，`(X_test, y_test)`表示测试集数据和对应的标签。

相关问题

(x_train, y_train), (x_test, y_test) = keras.datasets.cifar10.load_data()

As an AI language model, I do not have the ability to access external files or libraries. However, I can explain what the above code does.

The code is using the Keras library to load the CIFAR-10 dataset. The CIFAR-10 dataset is a collection of 60,000 32x32 color images in 10 classes, with 6,000 images per class. The dataset is divided into 50,000 training images and 10,000 test images.

The `load_data()` function is used to load the data from the dataset. It returns two tuples: `(x_train, y_train)` and `(x_test, y_test)`. The `x_train` and `x_test` variables contain the images as numpy arrays, while `y_train` and `y_test` contain the corresponding labels for each image. The training data is used to train the model, while the testing data is used to evaluate the performance of the model.

(x_train, y_train), (x_test, y_test) = cifar10.load_data()

这行代码是用于加载 CIFAR-10 数据集 的。CIFAR-10 数据集是一个包含 60000 张 32x32 像素的彩色图片的数据集，其中包括了 10 个不同的类别，每个类别包含 6000 张图片。其中 50000 张图片被用作训练集，10000 张图片被用作测试集。这行代码将训练集和测试集分别存储在变量 x_train、y_train、x_test、y_test 中。其中，x_train 和 x_test 是图片数据，y_train 和 y_test 是对应的标签。