#数据集加载from torch_geometric.datasets import Planetoiddataset = Planetoid(root='/tmp/Cora', name='Cora')

#网络定义class Net(torch.nn.Module):    def __init__(self):        super(Net, self).__init__()        self.conv1 = GCNConv(dataset.num_node_features, 16)        self.conv2 = GCNConv(16, dataset.num_classes)

   def forward(self, data):        x, edge_index = data.x, data.edge_index

       x = self.conv1(x, edge_index)        x = F.relu(x)        x = F.dropout(x, training=self.training)        x = self.conv2(x, edge_index)

       return F.log_softmax(x, dim=1)

device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')model = Net().to(device)data = dataset[0].to(device)optimizer = torch.optim.Adam(model.parameters(), lr=0.01, weight_decay=5e-4)

#网络训练model.train()for epoch in range(200):    optimizer.zero_grad()    out = model(data)    loss = F.nll_loss(out[data.train_mask], data.y[data.train_mask])    loss.backward()    optimizer.step()

#测试model.eval()_, pred = model(data).max(dim=1)correct = float(pred[data.test_mask].eq(data.y[data.test_mask]).sum().item())acc = correct / data.test_mask.sum().item()print('Accuracy: {:.4f}'.format(acc))