45a03ac258234ba49c0a43d46ae022493190591e,torch_geometric/nn/models/autoencoder.py,VGAE,kl_loss,#VGAE#Any#Any#,168

Before Change

        torch.nn.init.xavier_uniform(self.z_var.weight)

    def kl_loss(self, mean, logvar):
        loss = torch.mean(0.5 * torch.sum(
            torch.exp(logvar) + mean**2 - 1. - logvar, 1))
        print(loss)
        return loss

    def reconstruction_loss(self, adj, edge_index, neg_adj_mask):
        row, col = edge_index
        loss = -torch.log(torch.sigmoid(adj[row, col])).mean()

After Change

        return mu + torch.randn_like(logvar) * torch.exp(0.5 * logvar)

    def kl_loss(self, mu, logvar):
        return -0.5 * torch.mean(1 + logvar - mu.pow(2) - logvar.exp())

    def loss(self, mu, logvar, pos_edge_index, neg_adj_mask):
        z = self.sample(mu, logvar)
        recon_loss = self.reconstruction_loss(z, pos_edge_index, neg_adj_mask)

Instances