def arg_parse(): parser = argparse.ArgumentParser(description='GNN arguments.') utils.parse_optimizer(parser) parser.add_argument('--model_type', type=str, help='Type of GNN model.') parser.add_argument('--batch_size', type=int, help='Training batch size') parser.add_argument('--num_layers', type=int, help='Number of graph conv layers') parser.add_argument('--hidden_dim', type=int, help='Training hidden size') parser.add_argument('--dropout', type=float, help='Dropout rate') parser.add_argument('--epochs', type=int, help='Number of training epochs') parser.add_argument('--dataset', type=str, help='Dataset') parser.set_defaults( model_type='GCN', dataset='cora', num_layers=2, batch_size=32, hidden_dim=32, dropout=0.0, epochs=200, opt='adam', # opt_parser opt_scheduler='none', weight_decay=0.0, lr=0.01) return parser.parse_args()
def arg_parse(): parser = argparse.ArgumentParser(description='GNN arguments.') utils.parse_optimizer(parser) parser.add_argument('--model_type', type=str, help='Type of GNN model.') parser.add_argument('--batch_size', type=int, help='Training batch size') parser.add_argument('--num_layers', type=int, help='Number of graph conv layers') parser.add_argument('--hidden_dim', type=int, help='Training hidden size') parser.add_argument('--dropout', type=float, help='Dropout rate') parser.add_argument('--dataset', type=str, help='Dataset, either hate or suspended') parser.add_argument('--glove_only', action='store_true', help='Whether to use only glove features') parser.set_defaults( model_type='GraphSage', dataset='hate', num_layers=1, batch_size=128, hidden_dim=256, dropout=0.0, opt='adam', # opt_parser opt_scheduler='none', weight_decay=0.0, lr=0.01) return parser.parse_args()