帮我生成python代码用torch包，针对手写图像识别数据集，参考BP神经网络模型的代码，完成手写图像识别任务

当使用PyTorch处理手写数字识别任务时，我们可以构建一个基本的BP（反向传播）神经网络模型，例如LeNet或类似结构。以下是一个简化的LeNet示例，用于MNIST手写数字数据集：

import torch
import torch.nn as nn
import torchvision.datasets as datasets
import torchvision.transforms as transforms

# 数据预处理
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, download=True, transform=transform)
test_dataset = datasets.MNIST(root='./data', train=False, download=True, transform=transform)

train_loader = torch.utils.data.DataLoader(train_dataset, batch_size=64, shuffle=True)
test_loader = torch.utils.data.DataLoader(test_dataset, batch_size=64, shuffle=False)

# 定义LeNet模型
class LeNet(nn.Module):
    def __init__(self):
        super(LeNet, self).__init__()
        self.conv1 = nn.Conv2d(1, 6, 5) # 卷积层1
        self.pool = nn.MaxPool2d(2, 2) # 池化层
        self.conv2 = nn.Conv2d(6, 16, 5) # 卷积层2
        self.fc1 = nn.Linear(16 * 5 * 5, 120) # 全连接层1
        self.fc2 = nn.Linear(120, 84) # 全连接层2
        self.fc3 = nn.Linear(84, 10) # 输出层

    def forward(self, x):
        x = self.pool(F.relu(self.conv1(x)))
        x = self.pool(F.relu(self.conv2(x)))
        x = x.view(-1, 16 * 5 * 5) # 展平
        x = F.relu(self.fc1(x))
        x = F.relu(self.fc2(x))
        x = self.fc3(x)
        return x

model = LeNet()
criterion = nn.CrossEntropyLoss() # 使用交叉熵损失函数
optimizer = torch.optim.SGD(model.parameters(), lr=0.001, momentum=0.9) # 使用SGD优化器

# 训练过程
num_epochs = 10
for epoch in range(num_epochs):
    for images, labels in train_loader:
        optimizer.zero_grad()
        outputs = model(images)
        loss = criterion(outputs, labels)
        loss.backward()
        optimizer.step()

# 测试过程
model.eval()
with torch.no_grad():
    correct = 0
    total = 0
    for images, labels in test_loader:
        outputs = model(images)
        _, predicted = torch.max(outputs.data, 1)
        total += labels.size(0)
        correct += (predicted == labels).sum().item()
    print(f'Accuracy of the model on the 10000 test images: {100 * correct / total}%')