辅助分类器pytorch代码

当我们使用PyTorch进行辅助分类时，可以使用多种和技术。以下是一个简单的示例代码，展示了如何使用PyTorch构建和训练一个辅助分类器：

import torch
import torch.nn as nn
import torch.optim as optim
from torch.utils.data import DataLoader

# 定义辅助分类器模型
class AuxiliaryClassifier(nn.Module):
    def __init__(self, input_size, num_classes):
        super(AuxiliaryClassifier, self).__init__()
        self.fc1 = nn.Linear(input_size, 64)
        self.fc2 = nn.Linear(64, num_classes)

    def forward(self, x):
        x = torch.relu(self.fc1(x))
        x = self.fc2(x)
        return x

# 定义训练函数
def train(model, train_loader, optimizer, criterion, device):
    model.train()
    for batch_idx, (data, target) in enumerate(train_loader):
        data, target = data.to(device), target.to(device)
        optimizer.zero_grad()
        output = model(data)
        loss = criterion(output, target)
        loss.backward()
        optimizer.step()

# 定义测试函数
def test(model, test_loader, criterion, device):
    model.eval()
    test_loss = 0
    correct = 0
    with torch.no_grad():
        for data, target in test_loader:
            data, target = data.to(device), target.to(device)
            output = model(data)
            test_loss += criterion(output, target).item()
            pred = output.argmax(dim=1, keepdim=True)
            correct += pred.eq(target.view_as(pred)).sum().item()

    test_loss /= len(test_loader.dataset)
    accuracy = 100. * correct / len(test_loader.dataset)
    print('Test set: Average loss: {:.4f}, Accuracy: {}/{} ({:.2f}%)'.format(
        test_loss, correct, len(test_loader.dataset), accuracy))

# 设置超参数
input_size = 784
num_classes = 10
batch_size = 64
learning_rate = 0.001
num_epochs = 10

# 加载数据集
train_dataset = torchvision.datasets.MNIST(root='./data', train=True, transform=transforms.ToTensor(), download=True)
test_dataset = torchvision.datasets.MNIST(root='./data', train=False, transform=transforms.ToTensor())
train_loader = DataLoader(dataset=train_dataset, batch_size=batch_size, shuffle=True)
test_loader = DataLoader(dataset=test_dataset, batch_size=batch_size, shuffle=False)

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

# 将模型移动到GPU（如果可用）
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model.to(device)

# 训练和测试模型
for epoch in range(num_epochs):
    train(model, train_loader, optimizer, criterion, device)
    test(model, test_loader, criterion, device)

这段代码展示了如何使用PyTorch构建一个简单的辅助分类器模型，并在MNIST数据集上进行训练和测试。你可以根据自己的需求进行修改和扩展。