VGAE 损失函数设置 图分类任务（不是节点分类） 并根据损失函数写出相应的训练代码

在VGAE模型中，损失函数的设置与任务类型相关，如果是图分类任务，可以使用如下代码进行训练：

import torch
import torch.nn.functional as F
import dgl

# 定义VGAE模型
class VGAE(nn.Module):
    def __init__(self, in_feats, hidden_size, out_feats):
        super(VGAE, self).__init__()
        self.en1 = nn.Linear(in_feats, hidden_size)
        self.en2_mu = nn.Linear(hidden_size, hidden_size)
        self.en2_logvar = nn.Linear(hidden_size, hidden_size)
        self.de1 = nn.Linear(hidden_size, hidden_size)
        self.de2 = nn.Linear(hidden_size, out_feats)
    
    def encode(self, x):
        h = F.relu(self.en1(x))
        mu = self.en2_mu(h)
        logvar = self.en2_logvar(h)
        return mu, logvar
    
    def reparameterize(self, mu, logvar):
        std = torch.exp(0.5 * logvar)
        eps = torch.randn_like(std)
        return eps * std + mu
    
    def decode(self, z):
        h = F.relu(self.de1(z))
        return torch.sigmoid(self.de2(h))

    def forward(self, x):
        mu, logvar = self.encode(x)
        z = self.reparameterize(mu, logvar)
        return self.decode(z), mu, logvar

# 定义损失函数
def loss_function(output, label, mu, logvar, adj):
    criterion = nn.CrossEntropyLoss() # 交叉熵损失函数
    recon_loss = dgl.losses.binary_cross_entropy(output, adj) # 重构误差
    kl_loss = -0.5 * torch.sum(1 + logvar - mu.pow(2) - logvar.exp()) # KL散度损失
    total_loss = recon_loss + kl_loss # 总损失
    class_loss = criterion(output, label) # 分类损失
    total_loss += class_loss # 增加分类损失
    return total_loss

# 定义训练函数
def train(model, optimizer, train_loader, device):
    model.train()
    for batch, data in enumerate(train_loader):
        adj, features, labels = data
        adj, features, labels = adj.to(device), features.to(device), labels.to(device)
        optimizer.zero_grad()
        output, mu, logvar = model(features)
        loss = loss_function(output, labels, mu, logvar, adj)
        loss.backward()
        optimizer.step()
        
# 定义测试函数
def test(model, test_loader, device):
    model.eval()
    correct = 0
    total = 0
    with torch.no_grad():
        for batch, data in enumerate(test_loader):
            adj, features, labels = data
            adj, features, labels = adj.to(device), features.to(device), labels.to(device)
            output, _, _ = model(features)
            _, predicted = torch.max(output.data, 1)
            total += labels.size(0)
            correct += (predicted == labels).sum().item()
    acc = 100 * correct / total
    print('Accuracy: {:.2f}%'.format(acc))

# 定义数据集
dataset = dgl.data.CoraGraphDataset()
g = dataset[0]
features = g.ndata['feat']
labels = g.ndata['label']
train_mask = g.ndata['train_mask']
test_mask = g.ndata['test_mask']

# 构建数据加载器
train_loader = dgl.dataloading.GraphDataLoader(
    g, train_mask, sampler=dgl.dataloading.MultiLayerFullNeighborSampler(2), batch_size=64, shuffle=True, drop_last=False)
test_loader = dgl.dataloading.GraphDataLoader(
    g, test_mask, sampler=dgl.dataloading.MultiLayerFullNeighborSampler(2), batch_size=64, shuffle=False, drop_last=False)

# 定义模型和优化器
model = VGAE(in_feats=features.shape[1], hidden_size=64, out_feats=dataset.num_classes)
optimizer = torch.optim.Adam(model.parameters(), lr=0.01)

# 训练模型
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
model.to(device)
for epoch in range(100):
    train(model, optimizer, train_loader, device)
    test(model, test_loader, device)

在该代码中，首先定义了一个VGAE模型，然后定义了损失函数和训练函数。在训练函数中，使用`GraphDataLoader`来进行数据加载，然后将数据传入模型进行训练。在测试函数中，根据模型在测试集上的表现计算预测准确率。在训练过程中，使用Adam优化器来更新模型参数。