多个graph训练VGAE代码示例

下面是一个多个graph训练VGAE的代码示例：

import dgl
import torch
import torch.nn.functional as F
from dgl.nn import GraphConv
from dgl.data import MiniGCDataset

class VGAE(torch.nn.Module):
    def __init__(self, in_feats, hidden_size):
        super(VGAE, self).__init__()
        self.conv1 = GraphConv(in_feats, hidden_size)
        self.conv2 = GraphConv(hidden_size, hidden_size)

        self.mean_fc = torch.nn.Linear(hidden_size, hidden_size)
        self.logstd_fc = torch.nn.Linear(hidden_size, hidden_size)

    def forward(self, g):
        h = g.ndata['feat']
        h = F.relu(self.conv1(g, h))
        h = F.relu(self.conv2(g, h))

        mean = self.mean_fc(h)
        logstd = self.logstd_fc(h)

        return mean, logstd

def train(dataset):
    model = VGAE(10, 5)
    optimizer = torch.optim.Adam(model.parameters(), lr=0.01)

    for epoch in range(10):
        for graph, _ in dataset:
            optimizer.zero_grad()
            h = graph.ndata['feat']
            mean, logstd = model(graph)
            z = mean + torch.randn([graph.num_nodes(), 5]) * torch.exp(logstd)
            recon = torch.sigmoid(torch.matmul(z, z.t()))
            loss = -torch.mean(
                torch.sum(graph.adjacency_matrix().to_dense() * torch.log(recon)
                          + (1 - graph.adjacency_matrix().to_dense()) * torch.log(1 - recon), dim=1))
            kl_loss = -0.5 * torch.mean(torch.sum(1 + 2 * logstd - mean.pow(2) - torch.exp(logstd).pow(2), dim=1))
            loss += kl_loss
            loss.backward()
            optimizer.step()

if __name__ == '__main__':
    dataset = MiniGCDataset(80, 10, 20)
    graphs = []
    for graph, _ in dataset:
        graphs.append(graph)
    batched_graph = dgl.batch(graphs)
    train(batched_graph)

在这个示例中，我们首先定义了一个VGAE模型，然后在`train()`函数中，我们对数据集中的每个图进行了训练。注意，这里我们将所有图合并成一个大图，然后在训练时使用这个大图进行训练。这里的损失函数包括重构误差和KL散度，用于衡量生成的图与原始图之间的相似度。最后，我们将所有图合并到一个大图中，然后使用`train()`函数进行训练。