b44364811b14bde01d84554624a9bffa0e4976e3,examples/cora_gcn.py,,,#,41

Before Change

        return F.log_softmax(x, dim=1)


model = Net()
if torch.cuda.is_available():
    train_mask, val_mask = train_mask.cuda(), val_mask.cuda()
    test_mask, model = test_mask.cuda(), model.cuda()

optimizer = torch.optim.Adam(model.parameters(), lr=0.01, weight_decay=0.005)


def train():
    model.train()

After Change

    old_val = 0
    cur_test = 0
    for _ in range(0, 200):
        train()
        val = test(val_mask)
        if val > old_val:
            old_val = val
            cur_test = test(test_mask)

    acc.append(cur_test)
    print("Run:", run, "Test Accuracy:", acc[-1])

acc = torch.FloatTensor(acc)

Instances