利用lenet网络实现手写数字识别，可以给出具体的pytorch代码吗

当然可以！以下是基于PyTorch实现手写数字识别的LeNet代码示例：

import torch
import torch.nn as nn
import torch.optim as optim
import torch.nn.functional as F
from torchvision import datasets, transforms

# 定义LeNet神经网络模型
class LeNet(nn.Module):
    def __init__(self):
        super(LeNet, self).__init__()
        self.conv1 = nn.Conv2d(1, 6, kernel_size=5)
        self.conv2 = nn.Conv2d(6, 16, kernel_size=5)
        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 = F.relu(self.conv1(x))
        x = F.max_pool2d(x, 2)
        x = F.relu(self.conv2(x))
        x = F.max_pool2d(x, 2)
        x = x.view(-1, 16*4*4)
        x = F.relu(self.fc1(x))
        x = F.relu(self.fc2(x))
        x = self.fc3(x)
        return x

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

# 加载数据集
trainset = datasets.MNIST(root='MNIST_data/', train=True, download=True, transform=transform)
testset = datasets.MNIST(root='MNIST_data/', train=False, download=True, transform=transform)
trainloader = torch.utils.data.DataLoader(trainset, batch_size=64, shuffle=True)
testloader = torch.utils.data.DataLoader(testset, batch_size=64, shuffle=False)

# 初始化模型、损失函数和优化器
model = LeNet()
criterion = nn.CrossEntropyLoss()
optimizer = optim.Adam(model.parameters(),lr=0.001)

# 训练模型
for epoch in range(10):
    running_loss = 0.0
    for i, data in enumerate(trainloader, 0):
        inputs, labels = data
        optimizer.zero_grad()
        outputs = model(inputs)
        loss = criterion(outputs, labels)
        loss.backward()
        optimizer.step()
        running_loss += loss.item()
    print('Epoch [%d/%d], Loss: %.4f' % (epoch + 1, 10, running_loss / len(trainloader)))

# 测试模型
correct = 0
total = 0
with torch.no_grad():
    for data in testloader:
        images, labels = data
        outputs = model(images)
        _, predicted = torch.max(outputs.data, 1)
        total += labels.size(0)
        correct += (predicted == labels).sum().item()
    print('Accuracy of the network on the 10000 test images: %d %%' % (100 * correct / total))

此代码演示了如何使用PyTorch实现手写数字识别的LeNet神经网络，并在MNIST数据集上进行训练和测试。请注意，MNIST数据集是一个包含手写数字的标准数据集，用于训练和评估机器学习模型。

希望这可以帮助你开始理解和构建基于深度学习的手写数字识别模型。