给我写一个图像分类训练和预测函数

布满荆棘的人生 2023-10-07 23:18 5阅读 0赞

好的，下面是一个简单的图像分类训练和预测函数的示例。这个函数使用了 PyTorch 深度学习框架。

\`\`\`python import torch import torch.nn as nn import torch.optim as optim

def train\_and\_predict(model, train\_dataloader, val\_dataloader, num\_epochs=10): \# 定义损失函数和优化器 criterion = nn.CrossEntropyLoss() optimizer = optim.SGD(model.parameters(), lr=0.001, momentum=0.9)

# 训练模型
    for epoch in range(num_epochs):
        # 训练阶段
        model.train()
        for data, target in train_dataloader:
            optimizer.zero_grad()
            output = model(data)
            loss = criterion(output, target)
            loss.backward()
            optimizer.step()
    
        # 验证阶段
        model.eval()
        correct = 0
        total = 0
        with torch.no_grad():
            for data, target in val_dataloader:
                output = model(data)
                _, predicted = torch.max(output.data, 1)
                total += target.size(0)
                correct += (predicted == target).sum().item()
    
        print(f'Epoch {epoch+1}/{num_epochs}, Validation Accuracy: {
         100.0 * correct / total:.2f}%')

## 定义模型 ##

class SimpleCNN(nn.Module): def **init**(self): super(SimpleCNN, self).**init**() self.conv1 = nn.Conv2d(3, 6, 5) self.pool = nn.MaxPool2d(2, 2) self.conv2 = nn.Conv2d(6, 16, 5) self.fc1 = nn.Linear(16 \* 5 \* 5, 120) self.fc2 = nn.Linear(120, 84) self.fc3 = nn.Linear(84, 10)

def forward(self, x):
        x = self.pool(F.relu(self.conv1(x)))
        x = self.pool(F.relu(self.conv2(x)))
        x = x.view(-1, 16 * 5 * 5)
        x = F.relu(self.fc1(x))
        x = F.relu(self.fc2(x))
        x = self.fc3(x)
        return x

## 初始化模型和数据加载器 ##

model = SimpleCNN