pytorch训练模型写法

在PyTorch中，训练模型的写法通常包括以下几个步骤：
1. 导入必要的库和模块：

import torch
import torchvision
import torch.nn as nn
import torch.optim as optim

2. 定义模型结构：

model = torchvision.models.resnet50(pretrained=True)

3. 定义损失函数和优化器：

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

4. 进行训练循环：

for epoch in range(num_epochs):
    model.train()  # 将模型切换至训练模式
    running_loss = 0.0
    for images, labels in train_loader:
        # 前向传播
        outputs = model(images)
        loss = criterion(outputs, labels)
        
        # 反向传播和优化
        optimizer.zero_grad()
        loss.backward()
        optimizer.step()
        
        running_loss += loss.item()
    
    # 计算并打印每个epoch的平均损失
    epoch_loss = running_loss / len(train_loader)
    print(f"Epoch [{epoch+1}/{num_epochs}], Loss: {epoch_loss:.4f}")

5. 在验证集上评估模型性能：

model.eval()  # 将模型切换至验证模式
with torch.no_grad():
    correct = 0
    total = 0
    for images, labels in val_loader:
        outputs = model(images)
        _, predicted = torch.max(outputs.data, 1)
        total += labels.size(0)
        correct += (predicted == labels).sum().item()
    
    accuracy = 100 * correct / total
    print(f"Validation Accuracy: {accuracy:.2f}%")