par_list={"dataset_name": dataset_name, "n_fold": n_folds, "learning_rate": lr, "drop_prob": drop_prob, "weight_decay": weight_decay, "batch_size": batch_size, "n_hidden": n_units, "test_epoch": test_epoch, "output": output, "max_k": max_k}) device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') criterion = torch.nn.NLLLoss() dataset_cv_splits = getcross_validation_split(dataset_path, dataset_name, n_folds, batch_size) for split_id, split in enumerate(dataset_cv_splits): loader_train = split[0] loader_test = split[1] loader_valid = split[2] model = PGC_GNN(loader_train.dataset.num_features, n_units, n_units, n_classes, drop_prob, k=max_k, output=output).to(device) model_impl = modelImplementation_GraphBinClassifier(model, lr, criterion, device).to(device) model_impl.set_optimizer(weight_decay=weight_decay) model_impl.train_test_model(split_id, loader_train, loader_test, loader_valid,
"batch_size": batch_size, "n_hidden": n_units, "test_epoch": test_epoch, "output": output, "max_k": max_k }) device = torch.device( 'cuda' if torch.cuda.is_available( ) else 'cpu') criterion = torch.nn.NLLLoss() dataset_cv_splits = getcross_validation_split( dataset_path, dataset_name, n_folds, batch_size, use_node_attr=True) for split_id, split in enumerate( dataset_cv_splits): loader_train = split[0] loader_test = split[1] loader_valid = split[2] model = PGC_GNN( loader_train.dataset.num_features, n_units, n_units, n_classes, drop_prob, k=max_k,