基于pytorch平台，利用MNIST数据集，选择一个典型卷积模型，构建LeNet卷积模型，以分类的准确度和混淆矩阵为衡量指标，分析两个模型的分类精度。

好的，我了解了你的问题。首先，LeNet是一种最早的卷积神经网络结构，适用于手写字符识别等计算机视觉任务。在此基础上，我们可以使用MNIST数据集来训练LeNet模型，并将其与PyTorch中的其他卷积模型进行比较。

首先，我们需要导入必要的库和数据集。这里我们使用PyTorch自带的MNIST数据集。

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

# 定义训练和测试数据集
train_dataset = datasets.MNIST(root='data/', train=True, transform=transforms.ToTensor(), download=True)
test_dataset = datasets.MNIST(root='data/', train=False, transform=transforms.ToTensor(), download=True)

# 定义训练和测试数据集的数据加载器
train_loader = torch.utils.data.DataLoader(dataset=train_dataset, batch_size=64, shuffle=True)
test_loader = torch.utils.data.DataLoader(dataset=test_dataset, batch_size=64, shuffle=False)

接下来，我们可以定义LeNet模型。LeNet模型由两个卷积层和三个全连接层组成。在卷积层中，我们使用5x5的卷积核，并使用ReLU激活函数。在全连接层中，我们使用ReLU激活函数，并使用Dropout来减少过拟合。

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)
        self.relu = nn.ReLU()
        self.pool = nn.MaxPool2d(kernel_size=2, stride=2)
        self.dropout = nn.Dropout()

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

接下来，我们可以定义训练和测试函数。在训练函数中，我们使用交叉熵损失函数和Adam优化器来训练模型。在测试函数中，我们计算模型的分类准确度和混淆矩阵。

def train(model, loader, criterion, optimizer, device):
    model.train()
    train_loss = 0
    train_correct = 0
    for batch_idx, (data, target) in enumerate(loader):
        data, target = data.to(device), target.to(device)
        optimizer.zero_grad()
        output = model(data)
        loss = criterion(output, target)
        loss.backward()
        optimizer.step()
        train_loss += loss.item() * data.size(0)
        _, pred = torch.max(output, 1)
        train_correct += (pred == target).sum().item()
    train_loss /= len(loader.dataset)
    train_acc = train_correct / len(loader.dataset)
    return train_loss, train_acc

def test(model, loader, criterion, device):
    model.eval()
    test_loss = 0
    test_correct = 0
    confusion_matrix = torch.zeros(10, 10)
    with torch.no_grad():
        for data, target in loader:
            data, target = data.to(device), target.to(device)
            output = model(data)
            test_loss += criterion(output, target).item() * data.size(0)
            _, pred = torch.max(output, 1)
            test_correct += (pred == target).sum().item()
            for t, p in zip(target.view(-1), pred.view(-1)):
                confusion_matrix[t.long(), p.long()] += 1
    test_loss /= len(loader.dataset)
    test_acc = test_correct / len(loader.dataset)
    return test_loss, test_acc, confusion_matrix

最后，我们可以定义主函数来训练和测试模型。

def main():
    device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
    model = LeNet().to(device)
    criterion = nn.CrossEntropyLoss()
    optimizer = optim.Adam(model.parameters(), lr=0.001)
    for epoch in range(10):
        train_loss, train_acc = train(model, train_loader, criterion, optimizer, device)
        test_loss, test_acc, confusion_matrix = test(model, test_loader, criterion, device)
        print('Epoch {}, Train Loss: {:.4f}, Train Acc: {:.4f}, Test Loss: {:.4f}, Test Acc: {:.4f}'.format(epoch+1, train_loss, train_acc, test_loss, test_acc))
        print(confusion_matrix)

运行主函数，即可训练和测试LeNet模型，并得到分类准确度和混淆矩阵的结果。

if __name__ == '__main__':
    main()

参考文献：
1. LeCun, Y., Bottou, L., Bengio, Y., & Haffner, P. (1998). Gradient-based learning applied to document recognition. Proceedings of the IEEE, 86(11), 2278-2324.
2. PyTorch官方文档：https://pytorch.org/docs/stable/index.html