776f891afa3e7bfe2ae456c064e0867c4987c762,examples/rgcn.py,,test,#,42

Before Change

    model.eval()
    out = model(data.edge_index, data.edge_type, data.edge_norm)
    pred = out[data.test_idx].max(1)[1]
    acc = pred.eq(data.test_y).sum().item() / data.test_y.size(0)
    return acc


for epoch in range(1, 51):

After Change

def test():
    model.eval()
    pred = model(data.edge_index, data.edge_type).argmax(dim=-1)
    train_acc = pred[data.train_idx].eq(data.train_y).to(torch.float).mean()
    test_acc = pred[data.test_idx].eq(data.test_y).to(torch.float).mean()
    return train_acc.item(), test_acc.item()


for epoch in range(1, 51):
    loss = train()

Instances