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

Before Change

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():

After Change

    return pred.eq(data.target.data[mask]).sum() / mask.size(0)


acc = []
for run in range(1, 101):
    model.conv1.reset_parameters()
    model.conv2.reset_parameters()

Instances