给出一个以ResNet为模型的具有三特征输入{x,y,z}三输出{t1,t2,t3}的使用pytorch的训练代码示例

以下是一个以ResNet为模型的具有三特征输入{x,y,z}三输出{t1,t2,t3}的使用pytorch的训练代码示例：

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

# 定义ResNet模型
class ResNet(nn.Module):
    def __init__(self):
        super(ResNet, self).__init__()
        self.resnet = models.resnet18(pretrained=True)
        self.fc1 = nn.Linear(1000, 512)
        self.fc2 = nn.Linear(512, 256)
        self.fc3 = nn.Linear(256, 3)

    def forward(self, x, y, z):
        x = self.resnet(x)
        y = self.resnet(y)
        z = self.resnet(z)
        out = torch.cat((x, y, z), dim=1)
        out = self.fc1(out)
        out = self.fc2(out)
        out = self.fc3(out)
        return out

# 定义训练函数
def train(model, train_loader, criterion, optimizer):
    model.train()
    running_loss = 0.0
    for i, data in enumerate(train_loader, 0):
        inputs, labels = data
        x, y, z = inputs
        optimizer.zero_grad()
        outputs = model(x, y, z)
        loss = criterion(outputs, labels)
        loss.backward()
        optimizer.step()
        running_loss += loss.item()
    return running_loss / len(train_loader)

# 定义测试函数
def test(model, test_loader, criterion):
    model.eval()
    running_loss = 0.0
    correct = 0
    total = 0
    with torch.no_grad():
        for data in test_loader:
            inputs, labels = data
            x, y, z = inputs
            outputs = model(x, y, z)
            loss = criterion(outputs, labels)
            running_loss += loss.item()
            _, predicted = torch.max(outputs.data, 1)
            total += labels.size(0)
            correct += (predicted == labels).sum().item()
    return running_loss / len(test_loader), correct / total

# 加载数据
train_loader = torch.utils.data.DataLoader(train_dataset, batch_size=32, shuffle=True)
test_loader = torch.utils.data.DataLoader(test_dataset, batch_size=32, shuffle=False)

# 定义模型、损失函数和优化器
model = ResNet()
criterion = nn.CrossEntropyLoss()
optimizer = optim.SGD(model.parameters(), lr=0.001, momentum=0.9)

# 训练模型
for epoch in range(10):
    train_loss = train(model, train_loader, criterion, optimizer)
    test_loss, test_acc = test(model, test_loader, criterion)
    print('Epoch: {}, Train Loss: {:.4f}, Test Loss: {:.4f}, Test Acc: {:.4f}'.format(epoch+1, train_loss, test_loss, test_acc))