在PyTorch中定义批量大小为1的手动排序的MNIST数据集

import torch
import torchvision
import torchvision.transforms as transforms

def load_mnist(batch_size):
    transform = transforms.Compose([
        transforms.ToTensor(),
        transforms.Normalize((0.5,), (0.5,))
    ])

    trainset = torchvision.datasets.MNIST(root='./data', train=True,
                                          download=True, transform=transform)
    trainloader = torch.utils.data.DataLoader(trainset, batch_size=batch_size,
                                              shuffle=True, num_workers=2)

    testset = torchvision.datasets.MNIST(root='./data', train=False,
                                         download=True, transform=transform)
    testloader = torch.utils.data.DataLoader(testset, batch_size=batch_size,
                                             shuffle=False, num_workers=2)

    classes = tuple(str(i) for i in range(10))

    return trainloader, testloader, classes

trainloader, testloader, classes = load_mnist(batch_size=1)