How to implement semi-supervised learning in PyTorch?

In PyTorch, implementing semi-supervised learning can utilize existing methods such as self-training, pseudo-labeling, and generative adversarial networks (GAN).

Here is an example of self-training implemented in PyTorch.

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

# 定义模型
class Model(nn.Module):
    def __init__(self):
        super(Model, self).__init__()
        self.fc = nn.Linear(10, 2)
    
    def forward(self, x):
        return self.fc(x)

# 定义数据集
class MyDataset(torch.utils.data.Dataset):
    def __init__(self, data, labels):
        self.data = data
        self.labels = labels

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

    def __getitem__(self, idx):
        return self.data[idx], self.labels[idx]

# 加载数据
data = torch.randn(100, 10)
labels = torch.randint(0, 2, (100,))
dataset = MyDataset(data, labels)
dataloader = DataLoader(dataset, batch_size=10, shuffle=True)

# 初始化模型和优化器
model = Model()
optimizer = optim.Adam(model.parameters(), lr=0.001)

# 自训练
for epoch in range(10):
    for inputs, labels in dataloader:
        outputs = model(inputs)
        loss = nn.CrossEntropyLoss()(outputs, labels)

        optimizer.zero_grad()
        loss.backward()
        optimizer.step()

# 使用训练好的模型对未标记数据进行预测
unlabeled_data = torch.randn(50, 10)
predicted_labels = torch.argmax(model(unlabeled_data), dim=1)

In the examples above, we defined a simple model and dataset, then trained it with labeled data using self-training methods, and finally made predictions on unlabeled data using the trained model. This is just a basic example; in practice, one can choose more suitable semi-supervised learning methods based on specific problems and datasets.