请选择实现网络模型LeNet、并在Mnist数据集和Fashion Mnist数据集上进行训练和验证。

好的，我将为您提供一个LeNet模型的实现示例，并在Mnist数据集和Fashion Mnist数据集上进行训练和验证。

LeNet模型的结构如下所示：

LeNet(
  (conv1): Conv2d(1, 6, kernel_size=(5, 5), stride=(1, 1))
  (pool1): AvgPool2d(kernel_size=2, stride=2, padding=0)
  (conv2): Conv2d(6, 16, kernel_size=(5, 5), stride=(1, 1))
  (pool2): AvgPool2d(kernel_size=2, stride=2, padding=0)
  (fc1): Linear(in_features=400, out_features=120, bias=True)
  (fc2): Linear(in_features=120, out_features=84, bias=True)
  (fc3): Linear(in_features=84, out_features=10, bias=True)
)

其中，conv1和conv2分别为两个卷积层，pool1和pool2分别为两个池化层，fc1、fc2和fc3分别为三个全连接层。

首先，我们需要导入必要的库和Mnist数据集：

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

# 设置随机种子，保证实验结果的可重复性
torch.manual_seed(2021)

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

# 加载Mnist数据集
trainset = torchvision.datasets.MNIST(root='./data', train=True, download=True, transform=transform)
testset = torchvision.datasets.MNIST(root='./data', train=False, download=True, transform=transform)

# 加载Fashion Mnist数据集
# trainset = torchvision.datasets.FashionMNIST(root='./data', train=True, download=True, transform=transform)
# testset = torchvision.datasets.FashionMNIST(root='./data', train=False, download=True, transform=transform)

# 定义数据加载器
trainloader = torch.utils.data.DataLoader(trainset, batch_size=128, shuffle=True, num_workers=2)
testloader = torch.utils.data.DataLoader(testset, batch_size=128, shuffle=False, num_workers=2)

然后，我们可以定义LeNet模型并进行训练和验证：

# 定义LeNet模型
class LeNet(nn.Module):
    def __init__(self):
        super(LeNet, self).__init__()
        self.conv1 = nn.Conv2d(1, 6, kernel_size=5)
        self.pool1 = nn.AvgPool2d(kernel_size=2, stride=2)
        self.conv2 = nn.Conv2d(6, 16, kernel_size=5)
        self.pool2 = nn.AvgPool2d(kernel_size=2, stride=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

# 定义训练函数
def train(net, criterion, optimizer, trainloader, num_epochs):
    for epoch in range(num_epochs):
        running_loss = 0.0
        for i, data in enumerate(trainloader, 0):
            inputs, labels = data
            optimizer.zero_grad()
            outputs = net(inputs)
            loss = criterion(outputs, labels)
            loss.backward()
            optimizer.step()
            running_loss += loss.item()
            if (i+1) % 100 == 0:
                print('[Epoch %d, Batch %d] loss: %.3f' % (epoch+1, i+1, running_loss/100))
                running_loss = 0.0

# 定义测试函数
def test(net, dataloader):
    correct = 0
    total = 0
    with torch.no_grad():
        for data in dataloader:
            images, labels = data
            outputs = net(images)
            _, predicted = torch.max(outputs.data, 1)
            total += labels.size(0)
            correct += (predicted == labels).sum().item()
    accuracy = 100 * correct / total
    print('Accuracy on test set: %.2f%%' % (accuracy))

# 实例化LeNet模型、损失函数和优化器
net = LeNet()
criterion = nn.CrossEntropyLoss()
optimizer = optim.SGD(net.parameters(), lr=0.01, momentum=0.9)

# 在Mnist数据集上进行训练和验证
num_epochs = 10
train(net, criterion, optimizer, trainloader, num_epochs)
test(net, testloader)

# 在Fashion Mnist数据集上进行训练和验证
# num_epochs = 10
# train(net, criterion, optimizer, trainloader, num_epochs)
# test(net, testloader)

最后，我们可以得到在Mnist数据集和Fashion Mnist数据集上的准确率。如果您想要进一步提高模型的表现，可以考虑调节超参数或使用其他更复杂的网络模型。