e7512f6bd434ef89296298a8bd16044917d7ce68,examples/gcn2.py,,test,#,72

Before Change

    pred = model(data.x, data.adj_t).argmax(dim=-1, keepdim=True)

    train_acc = evaluator.eval({
        "y_true": data.y[split_idx["train"]],
        "y_pred": pred[split_idx["train"]],
    })["acc"]

After Change

    model.eval()
    pred, accs = model(data.x, data.adj_t).argmax(dim=-1), []
    for _, mask in data("train_mask", "val_mask", "test_mask"):
        accs.append(int((pred[mask] == data.y[mask]).sum()) / int(mask.sum()))
    return accs


best_val_acc = test_acc = 0

Instances