d2l.get_fashion_mnist_labels(y)

As an AI language model, I cannot execute code directly. However, I can explain the function `d2l.get_fashion_mnist_labels(y)`.

`d2l` is a module that provides various functions and classes for deep learning. `get_fashion_mnist_labels()` is a function defined in this module that takes an array of integers `y` (representing the labels of the Fashion-MNIST dataset) as input and returns an array of corresponding class labels as output.

For instance, if `y = [0, 1, 2, 3, 4]`, then `d2l.get_fashion_mnist_labels(y)` would return `['t-shirt', 'trouser', 'pullover', 'dress', 'coat']`, which are the corresponding class names for the five labels in the Fashion-MNIST dataset.

相关问题

d2l.load_data_fashion_mnist

fashion-mnist数据集是一个用于机器学习的图像识别数据集，其中包含10个类别的60,000个训练图像和10,000个测试图像。该数据集可以通过d2l包轻松载入，只需调用d2l.load_data_fashion_mnist()即可。

解释train_iter, test_iter = d2l.load_data_fashion_mnist(batch_size)

train_iter和test_iter是用于训练和测试深度学习模型的数据迭代器。d2l.load_data_fashion_mnist(batch_size)函数返回一个二元组，其中第一个元素（train_iter）是训练集的数据迭代器，第二个元素（test_iter）是测试集的数据迭代器。batch_size参数表示每个数据批次的大小。在训练模型时，将一批次的数据输入模型进行训练，在测试模型时，也将一批次的数据输入模型进行测试并计算准确率。