给我一段手写数字识别的pytorch代码

下面是一段简单的手写数字识别的pytorch代码：

import torch
import torch.nn as nn
import torch.optim as optim
from torch.autograd import Variable
from torchvision import datasets, transforms

# 定义数据预处理方式
transform = transforms.Compose([
                       transforms.ToTensor(),
                       transforms.Normalize((0.1307,), (0.3081,))
                   ])

# 加载MNIST数据集
train_dataset = datasets.MNIST(root='./data/',
                               train=True,
                               transform=transform,
                               download=True)

test_dataset = datasets.MNIST(root='./data/',
                              train=False,
                              transform=transform)

# 定义数据加载器
batch_size = 64

train_loader = torch.utils.data.DataLoader(dataset=train_dataset,
                                           batch_size=batch_size,
                                           shuffle=True)

test_loader = torch.utils.data.DataLoader(dataset=test_dataset,
                                          batch_size=batch_size,
                                          shuffle=False)

# 定义模型
class Net(nn.Module):
    def __init__(self):
        super(Net, self).__init__()
        self.conv1 = nn.Conv2d(1, 10, kernel_size=5)
        self.conv2 = nn.Conv2d(10, 20, kernel_size=5)
        self.fc1 = nn.Linear(320, 50)
        self.fc2 = nn.Linear(50, 10)

    def forward(self, x):
        x = nn.functional.relu(nn.functional.max_pool2d(self.conv1(x), 2))
        x = nn.functional.relu(nn.functional.max_pool2d(self.conv2(x), 2))
        x = x.view(-1, 320)
        x = nn.functional.relu(self.fc1(x))
        x = self.fc2(x)
        return nn.functional.log_softmax(x, dim=1)

# 定义模型、损失函数和优化器
model = Net()
criterion = nn.CrossEntropyLoss()
optimizer = optim.SGD(model.parameters(), lr=0.01, momentum=0.5)

# 训练模型
epochs = 10

for epoch in range(epochs):
    running_loss = 0.0
    for i, (inputs, labels) in enumerate(train_loader):
        inputs, labels = Variable(inputs), Variable(labels)
        optimizer.zero_grad()
        outputs = model(inputs)
        loss = criterion(outputs, labels)
        loss.backward()
        optimizer.step()
        running_loss += loss.item()
        if i % 100 == 99:
            print('[%d, %5d] loss: %.3f' %
                  (epoch + 1, i + 1, running_loss / 100))
            running_loss = 0.0

# 测试模型
correct = 0
total = 0

with torch.no_grad():
    for data in test_loader:
        inputs, labels = data
        outputs = model(inputs)
        _, predicted = torch.max(outputs.data, 1)
        total += labels.size(0)
        correct += (predicted == labels).sum().item()

print('Accuracy of the model on the test images: %d %%' % (100 * correct / total))

这段代码使用了卷积神经网络（Convolutional Neural Network，CNN）来对手写数字进行识别。具体来说，它使用了两个卷积层和两个全连接层，其中每个卷积层都有一个ReLU激活函数和一个最大池化层。在训练过程中，使用了随机梯度下降（Stochastic Gradient Descent，SGD）优化器来最小化交叉熵损失函数。在测试过程中，用模型对测试集的数据进行预测，并计算模型的准确率。