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"]
    valid_acc = evaluator.eval({
        "y_true": data.y[split_idx["valid"]],
        "y_pred": pred[split_idx["valid"]],
    })["acc"]
    test_acc = evaluator.eval({
        "y_true": data.y[split_idx["test"]],
        "y_pred": pred[split_idx["test"]],
    })["acc"]

    return train_acc, valid_acc, test_acc


for epoch in range(1, 1001):
    loss = train()

After Change

    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
for epoch in range(1, 1001):

Instances