"_maxK-" + str(max_k)
training_log_dir = os.path.join(
"./test_log/", test_name)
if not os.path.exists(
training_log_dir):
os.makedirs(training_log_dir)
printParOnFile(
test_name=test_name,
log_dir=training_log_dir,
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,
"aggregator": aggregator,
"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_4_reservoir(
test_type_folder = os.path.join(
"./test_log/", test_type)
if not os.path.exists(test_type_folder):
os.makedirs(test_type_folder)
training_log_dir = os.path.join(
test_type_folder, test_name)
print(test_name)
if not os.path.exists(training_log_dir):
os.makedirs(training_log_dir)
printParOnFile(test_name=test_name,
log_dir=training_log_dir,
par_list={
"dataset_name": dataset_name,
"learning_rate": lr,
"dropout": dropout,
"weight_decay": weight_decay,
"k": k,
"test_epoch": test_epoch,
"self_loops": self_loops
})
graph, features, labels, n_classes, train_mask, test_mask, valid_mask = DGLDatasetReader(
dataset_name, self_loops, device)
model = GCNetwork(g=graph,
in_feats=features.shape[1],
n_classes=n_classes,
dropout=dropout,
k=k,
convLayer=LGConv,