parser.add_argument('--early_stopping', type=int, default=10)
parser.add_argument('--hidden', type=int, default=16)
parser.add_argument('--dropout', type=float, default=0.5)
parser.add_argument('--normalize_features', type=bool, default=True)
parser.add_argument('--num_hops', type=int, default=3)
args = parser.parse_args()
class Net(torch.nn.Module):
def __init__(self, dataset):
super(Net, self).__init__()
self.conv1 = ChebConv(dataset.num_features, args.hidden, args.num_hops)
self.conv2 = ChebConv(args.hidden, dataset.num_classes, args.num_hops)
def reset_parameters(self):
self.conv1.reset_parameters()
self.conv2.reset_parameters()
def forward(self, data):
x, edge_index = data.x, data.edge_index
x = F.relu(self.conv1(x, edge_index))
x = F.dropout(x, p=args.dropout, training=self.training)
x = self.conv2(x, edge_index)
return F.log_softmax(x, dim=1)
dataset = get_planetoid_dataset(args.dataset, args.normalize_features)
permute_masks = random_planetoid_splits if args.random_splits else None
run(dataset, Net(dataset), args.runs, args.epochs, args.lr, args.weight_decay,
args.early_stopping, permute_masks)
from citation import get_planetoid_dataset
def print_dataset(dataset):
data = dataset[0]
print('Name', dataset)
print('Nodes', data.num_nodes)
print('Edges', data.num_edges // 2)
print('Features', dataset.num_features)
print('Classes', dataset.num_classes)
print('Label rate', data.train_mask.sum().item() / data.num_nodes)
print()
for name in ['Cora', 'CiteSeer', 'PubMed']:
print_dataset(get_planetoid_dataset(name))
