cffa4034f580e33fe4295e9f1b54217e7fa724eb,examples/pytorch/gcn/gcn.py,GCN,forward,#GCN#Any#,66

Before Change

                self.g.ndata["h"] = self.dropout(self.g.ndata["h"])
            // normalization by square root of src degree
            self.g.ndata["h"] = self.g.ndata["h"] * self.g.ndata["norm"]
            self.g.update_all(gcn_msg, gcn_reduce, layer)
        return self.g.ndata.pop("h")

def evaluate(model, features, labels, mask):
    model.eval()

After Change

        self.layers.append(GCNLayer(g, n_hidden, n_classes, None, dropout))

    def forward(self, features):
        h = features
        for layer in self.layers:
            h = layer(h)
        return h

def evaluate(model, features, labels, mask):
    model.eval()
    with torch.no_grad():

Instances