源码:

import torch 
import torchvision as tv
from torch.utils import data
import matplotlib.pyplot as plt
import time

def get_fashion_mnist_labels(labels):
    text_labels = ['T-shirt/top', 'Trouser', 'Pullover', 'Dress', 'Coat', 'Sandal', 'Shirt', 'Sneaker', 'Bag', 'Ankle boot']
    return [text_labels[int(i)] for i in labels]

def show_fashion_mnist(imgs, num_rows, num_cols, titles=None, scale=0.5):
    figsize = (num_cols*scale, num_rows*scale)
    _, axes = plt.subplots(num_rows, num_cols, figsize=figsize)
    axes = axes.flatten()
    for i,(ax, img) in enumerate(zip(axes, imgs)):
        if torch.is_tensor(img):
            ax.imshow(img.numpy())
        else:
            ax.imshow(img)
        ax.axis('off')
        if titles:
            ax.set_title(titles[i])
    plt.show()
    return axes


def get_dataloader_workers():  #@save
    """使用4个进程来读取数据"""
    return 4

def load_data_fashion_mnist(batch_size, resize=None):
    trans = [tv.transforms.ToTensor()]  # 创建一个将图像转换为张量的变换
    if resize:
        trans.insert(0, tv.transforms.Resize(resize))
    trans = tv.transforms.Compose(trans)
    mnist_train = tv.datasets.FashionMNIST(root='./data', train=True, download=True, transform=trans)  # 加载FashionMNIST训练数据集，并应用变换
    mnist_test = tv.datasets.FashionMNIST(root='./data', train=False, download=True, transform=trans)  # 加载FashionMNIST测试数据集，并应用变换
    return (data.DataLoader(mnist_train, batch_size, shuffle=True, num_workers=get_dataloader_workers()),
            data.DataLoader(mnist_test, batch_size, shuffle=False, num_workers=get_dataloader_workers()))

def softmax(X):
    X_exp = torch.exp(X)
    partition = X_exp.sum(1,keepdim=True)
    return X_exp / partition

def net(X):
    return softmax(torch.matmul(X.reshape(-1, W.shape[0]), W) +b)

def cross_entropy(y_hat, y):
    return -torch.log(y_hat[range(len(y_hat)), y])


def accuracy(y_hat, y):
    if len(y_hat.shape)>1 and y_hat.shape[1]>1:
        y_hat = y_hat.argmax(axis=1)
    cmp = y_hat.type(y.dtype) == y
    return float(cmp.type(y.dtype).sum())

def evaluate_accuracy(net, data_iter):
    if isinstance(net, torch.nn.Module):
        net.eval() # 评估模式, 这会关闭dropout
    metric = Accumulator(2) # 正确预测数、预测总数
    with torch.no_grad():
        for X, y in data_iter:
            metric.add(accuracy(net(X), y), y.numel())
    return metric[0]/metric[1]

def sgd(params, lr, batch_size):  #@save
    """小批量随机梯度下降"""
    with torch.no_grad():
        for param in params:
            param -= lr * param.grad / batch_size
            param.grad.zero_()
        
def updater(batch_size):
    return sgd([W, b], lr, batch_size)


class Accumulator:  #@save
    """在n个变量上累加"""
    def __init__(self, n):
        self.data = [0.0] * n       #self.data 是一个列表，初始化为 n 个 0.0，用于存储累加的值。

    def add(self, *args):
        self.data = [a + float(b) for a, b in zip(self.data, args)] #一个列表推导式，它遍历每一对 (a, b)，并将 a 和 b 相加的结果生成一个新的列表。

    def reset(self):
        self.data = [0.0] * len(self.data)

    def __getitem__(self, idx):
        return self.data[idx]
def train_epoch_ch3(net, train_iter, loss, updater):  #@save
    """训练模型一个迭代周期（定义见第3章）"""
    # 将模型设置为训练模式
    if isinstance(net, torch.nn.Module):
        net.train()
    # 训练损失总和、训练准确度总和、样本数
    metric = Accumulator(3)
    for X, y in train_iter:
        # 计算梯度并更新参数
        y_hat = net(X)
        l = loss(y_hat, y)
        if isinstance(updater, torch.optim.Optimizer):
            # 使用PyTorch内置的优化器和损失函数
            updater.zero_grad()
            l.mean().backward()
            updater.step()
        else:
            # 使用定制的优化器和损失函数
            l.sum().backward()
            updater(X.shape[0])
        metric.add(float(l.sum()), accuracy(y_hat, y), y.numel())
    # 返回训练损失和训练精度
    return metric[0] / metric[2], metric[1] / metric[2]




def train_ch3(net, train_iter, test_iter, loss, num_epochs, updater):  #@save
    """训练模型（定义见第3章）"""
    for epoch in range(num_epochs):
        train_metrics = train_epoch_ch3(net, train_iter, loss, updater)
        test_acc = evaluate_accuracy(net, test_iter)
        print('epoch %d, loss %.4f, train acc %.3f, test acc %.3f' % ( epoch + 1, train_metrics[0], train_metrics[1], test_acc))



def predict_ch3(net, test_iter, n=6):  #@save
    for X, y in test_iter:
        break
    trues = get_fashion_mnist_labels(y)
    preds = get_fashion_mnist_labels(net(X).argmax(axis=1))
    titles = [true +'\n' + pred for true, pred in zip(trues, preds)]

    show_fashion_mnist(X[:n].reshape(-1,28,28), 1, n, titles[:n])



if __name__ == '__main__':
    batch_size = 256
    train_iter, test_iter = load_data_fashion_mnist(batch_size)

    num_inputs = 784
    num_outputs = 10
    W = torch.normal(0, 0.1, size=(num_inputs, num_outputs), requires_grad=True)
    b = torch.zeros(num_outputs, requires_grad=True)

    lr = 0.1
    num_epochs = 10
    loss = cross_entropy    
    # updater = lambda batch_size: sgd([W, b], lr, batch_size)

    train_ch3(net, train_iter, test_iter, loss, num_epochs, updater)
    predict_ch3(net, test_iter)

另外感慨一下MNIST数据集下载速度真是比CIFAR快太多了