pytorch 使用lenet训练Mnist数据集的代码

以下是用PyTorch实现LeNet模型并训练Mnist数据集的示例代码：

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

# 定义LeNet模型
class LeNet(nn.Module):
    def __init__(self):
        super(LeNet, self).__init__()
        self.conv1 = nn.Conv2d(1, 6, 5)
        self.pool1 = nn.MaxPool2d(kernel_size=2)
        self.conv2 = nn.Conv2d(6, 16, 5)
        self.pool2 = nn.MaxPool2d(kernel_size=2)
        self.fc1 = nn.Linear(16*4*4, 120)
        self.fc2 = nn.Linear(120, 84)
        self.fc3 = nn.Linear(84, 10)

    def forward(self, x):
        x = self.pool1(torch.relu(self.conv1(x)))
        x = self.pool2(torch.relu(self.conv2(x)))
        x = x.view(-1, 16*4*4)
        x = torch.relu(self.fc1(x))
        x = torch.relu(self.fc2(x))
        x = self.fc3(x)
        return x

# 设置超参数
batch_size = 100
learning_rate = 0.001
num_epochs = 5

# 加载Mnist数据集
train_dataset = dsets.MNIST(root='./data',
                            train=True,
                            transform=transforms.ToTensor(),
                            download=True)
test_dataset = dsets.MNIST(root='./data',
                            train=False,
                            transform=transforms.ToTensor(),
                            download=True)

# 创建数据加载器
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)

# 创建LeNet模型和优化器
model = LeNet()
optimizer = optim.Adam(model.parameters(), lr=learning_rate)

# 训练模型
total_step = len(train_loader)
for epoch in range(num_epochs):
    for i, (images, labels) in enumerate(train_loader):
        images = images
        labels = labels

        # 前向传播
        outputs = model(images)
        loss = nn.CrossEntropyLoss()(outputs, labels)

        # 反向传播和优化
        optimizer.zero_grad()
        loss.backward()
        optimizer.step()

        # 输出训练状态
        if (i+1) % 100 == 0:
            print('Epoch [{}/{}], Step [{}/{}], Loss: {:.4f}'
                  .format(epoch+1, num_epochs, i+1, total_step, loss.item()))

# 测试模型
model.eval()
with torch.no_grad():
    correct = 0
    total = 0
    for images, labels in test_loader:
        images = images
        labels = labels
        outputs = model(images)
        _, predicted = torch.max(outputs.data, 1)
        total += labels.size(0)
        correct += (predicted == labels).sum().item()

    print('Test Accuracy of the model on the 10000 test images: {} %'.format(100 * correct / total))

这个代码可以在Mnist数据集上训练LeNet模型，并在测试集上计算模型的准确率。