pytorch模拟letnet5模型构建卷积神经网络模型训练fashionmnist数据集

好的，我可以为您提供一些代码示例来构建LeNet-5模型并使用PyTorch进行训练FashionMNIST数据集。

首先，您需要导入必要的库和数据集：

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

# 定义训练集和测试集的转换
transform_train = transforms.Compose([
    transforms.RandomHorizontalFlip(),
    transforms.RandomCrop(28, padding=4),
    transforms.ToTensor(),
    transforms.Normalize((0.1307,), (0.3081,))
])
transform_test = transforms.Compose([
    transforms.ToTensor(),
    transforms.Normalize((0.1307,), (0.3081,))
])

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

# 定义数据加载器
trainloader = torch.utils.data.DataLoader(trainset, batch_size=64, shuffle=True, num_workers=2)
testloader = torch.utils.data.DataLoader(testset, batch_size=64, shuffle=False, num_workers=2)

接下来，您需要定义LeNet-5模型的架构：

class LeNet5(nn.Module):
    def __init__(self, num_classes=10):
        super(LeNet5, self).__init__()
        self.conv1 = nn.Conv2d(1, 6, kernel_size=5, stride=1)
        self.pool1 = nn.MaxPool2d(kernel_size=2, stride=2)
        self.conv2 = nn.Conv2d(6, 16, kernel_size=5, stride=1)
        self.pool2 = nn.MaxPool2d(kernel_size=2, stride=2)
        self.fc1 = nn.Linear(16*4*4, 120)
        self.fc2 = nn.Linear(120, 84)
        self.fc3 = nn.Linear(84, num_classes)
        
    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

然后，您可以定义损失函数和优化器：

device = torch.device("cuda" if torch.cuda.is_available() else "cpu")

model = LeNet5().to(device)
criterion = nn.CrossEntropyLoss()
optimizer = optim.SGD(model.parameters(), lr=0.001, momentum=0.9)

最后，您可以使用以下代码进行训练和测试：

num_epochs = 10

for epoch in range(num_epochs):
    running_loss = 0.0
    for i, data in enumerate(trainloader, 0):
        inputs, labels = data
        inputs, labels = inputs.to(device), labels.to(device)
        
        optimizer.zero_grad()
        
        outputs = model(inputs)
        loss = criterion(outputs, labels)
        loss.backward()
        optimizer.step()
        
        running_loss += loss.item()
        if i % 100 == 99:
            print('[%d, %5d] loss: %.3f' %
                  (epoch + 1, i + 1, running_loss / 100))
            running_loss = 0.0

    # 在测试集上进行测试
    correct = 0
    total = 0
    with torch.no_grad():
        for data in testloader:
            images, labels = data
            images, labels = images.to(device), labels.to(device)
            outputs = model(images)
            _, predicted = torch.max(outputs.data, 1)
            total += labels.size(0)
            correct += (predicted == labels).sum().item()

    print('Epoch %d, Test accuracy: %d %%' % (epoch+1, 100 * correct / total))

这样，您就可以使用PyTorch实现LeNet-5模型并使用FashionMNIST数据集进行训练和测试了。