使用lwf算法训练模型具体代码示例

LWF（Learning without Forgetting）算法是一种用于避免神经网络在学习新任务时忘记以前任务的方法。下面是使用LWF算法训练模型的示例代码：

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

# 定义一个神经网络模型
class Net(nn.Module):
    def __init__(self):
        super(Net, self).__init__()
        self.fc1 = nn.Linear(784, 512)
        self.fc2 = nn.Linear(512, 256)
        self.fc3 = nn.Linear(256, 10)

    def forward(self, x):
        x = x.view(-1, 784)
        x = nn.functional.relu(self.fc1(x))
        x = nn.functional.relu(self.fc2(x))
        x = self.fc3(x)
        return x

# 定义一个数据集
class MyDataset(torch.utils.data.Dataset):
    def __init__(self, data, targets):
        self.data = data
        self.targets = targets

    def __len__(self):
        return len(self.data)

    def __getitem__(self, index):
        x = self.data[index]
        y = self.targets[index]
        return x, y

# 定义训练函数
def train(net, trainloader, optimizer, criterion, epoch):
    net.train()
    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()
    print('Epoch %d loss: %.3f' % (epoch + 1, running_loss / len(trainloader)))

# 定义测试函数
def test(net, testloader):
    net.eval()
    correct = 0
    total = 0
    with torch.no_grad():
        for data in testloader:
            inputs, labels = data
            outputs = net(inputs)
            _, predicted = torch.max(outputs.data, 1)
            total += labels.size(0)
            correct += (predicted == labels).sum().item()
    print('Accuracy: %d %%' % (100 * correct / total))

# 初始化模型和数据集
net = Net()
criterion = nn.CrossEntropyLoss()
optimizer = optim.SGD(net.parameters(), lr=0.01, momentum=0.9)
trainset = MyDataset(train_data, train_targets)
testset = MyDataset(test_data, test_targets)

# 第一轮训练
trainloader = DataLoader(trainset, batch_size=64, shuffle=True)
testloader = DataLoader(testset, batch_size=64, shuffle=True)
for epoch in range(5):
    train(net, trainloader, optimizer, criterion, epoch)
test(net, testloader)

# 定义LWF训练函数
def LWF_train(net, trainloader, optimizer, criterion, prev_net=None, alpha=0.5, T=2):
    net.train()
    running_loss = 0.0
    for i, data in enumerate(trainloader, 0):
        inputs, labels = data
        optimizer.zero_grad()
        outputs = net(inputs)
        loss = criterion(outputs, labels)
        if prev_net is not None:
            prev_outputs = prev_net(inputs)
            soft_targets = nn.functional.softmax(prev_outputs / T, dim=1)
            loss += alpha * criterion(outputs, soft_targets)
        loss.backward()
        optimizer.step()
        running_loss += loss.item()
    print('LWF Epoch loss: %.3f' % (running_loss / len(trainloader)))

# 第二轮训练（LWF）
prev_net = net
trainloader = DataLoader(trainset, batch_size=64, shuffle=True)
for epoch in range(5):
    LWF_train(net, trainloader, optimizer, criterion, prev_net=prev_net)
test(net, testloader)

在上面的代码中，我们首先定义了一个简单的神经网络模型和一个数据集。然后，我们定义了一个train函数和一个test函数来训练和测试模型。接下来，我们使用第一组数据对模型进行了5轮训练，并在测试集上进行了测试。现在，我们要使用LWF算法进行第二轮训练。在第二轮训练中，我们将使用之前训练过的模型（prev_net）来生成软目标，并将其与硬目标一起用于计算损失。我们还可以使用alpha和T参数来调整模型的学习率和温度。最后，我们再次在测试集上测试模型的性能。