pytorch-deep-learning.mdc

# PyTorch 深度学习指南

## 模型定义

### 使用Module定义模型结构
```python
import torch.nn as nn

class CNNClassifier(nn.Module):
    def __init__(self):
        super().__init__()
        self.conv1 = nn.Conv2d(3, 16, kernel_size=3, padding=1)
        self.conv2 = nn.Conv2d(16, 32, kernel_size=3, padding=1)
        self.fc = nn.Linear(32 * 8 * 8, 10)
        
    def forward(self, x):
        x = F.relu(self.conv1(x))
        x = F.max_pool2d(x, 2)
        x = F.relu(self.conv2(x))
        x = F.max_pool2d(x, 2)
        x = x.view(-1, 32 * 8 * 8)
        x = self.fc(x)
        return x
```

## 训练循环

### 标准训练步骤
```python
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model = CNNClassifier().to(device)
criterion = nn.CrossEntropyLoss()
optimizer = torch.optim.Adam(model.parameters(), lr=0.001)

for epoch in range(10):
    for inputs, labels in train_loader:
        inputs, labels = inputs.to(device), labels.to(device)
        
        optimizer.zero_grad()
        outputs = model(inputs)
        loss = criterion(outputs, labels)
        loss.backward()
        optimizer.step()
```