def main(arg, min_edge_distance=0.5, max_edge_distance=1.5):
dataset_param = arg2dataset_param(arg[0])
model_param = arg2model_param(arg[1])
test_dataset_param = arg2dataset_param(arg[2])
layer_num = None if len(arg) <= 3 else int(arg[3])
dataset_param.size = 100000
dataset_param.min_num_node = 4
dataset_param.num_num_node = 30
test_dataset_param.size = 1000
test_dataset_param.min_num_node = 4
test_dataset_param.num_num_node = 30
test_dataset_param.min_edge_distance = min_edge_distance
test_dataset_param.max_edge_distance = max_edge_distance
metric_name_list = get_metric_names(test_dataset_param.dataset_name)
test_model(dataset_param,
model_param,
test_dataset_param,
batch_size=max(
min(
int(16 * 1000 / test_dataset_param.min_num_node * 10 /
(layer_num or model_param.layer_num)), 50), 1),
metric_name_list=metric_name_list,
layer_num=layer_num)
def main(arg):
dataset_param = arg2dataset_param(arg[0])
model_param = arg2model_param(arg[1])
test_dataset_param = arg2dataset_param(arg[2])
layer_num = int(arg[3])
model_path = arg[4]
print(model_path)
dataset_param.size = 100000
dataset_param.min_num_node = 4
dataset_param.num_num_node = 30
test_dataset_param.size = 1000
metric_name_list = get_metric_names(test_dataset_param.dataset_name)
test_model(model_path,
dataset_param, model_param, test_dataset_param,
batch_size=max(min(int(16*1000/test_dataset_param.min_num_node
*10/(layer_num or model_param.layer_num)), 50), 1),
metric_name_list=metric_name_list, layer_num=layer_num)
def main(args):
test_dataset_param = arg2dataset_param(args.test_dataset_param)
test_dataset_param.size = 1000
if not args.metric:
metric_name_list = get_metric_names(test_dataset_param.dataset_name)
else:
metric_name_list = args.metric
best_parameters(test_dataset_param,
metric_name_list=metric_name_list,
keys=args.key,
no_keys=args.no_key)
def main(arg):
dataset_param = arg2dataset_param(arg[0])
model_param = arg2model_param(arg[1])
resume_flag = len(arg) > 2
dataset_param.size = 100000
dataset_param.min_num_node = 4
dataset_param.num_num_node = 30
general_param = get_general_param()
general_param.save_freq = int(general_param.save_freq*10000/dataset_param.size)
general_param.epoch_num = int(general_param.epoch_num*10000*2/dataset_param.size)
general_param.resume_flag = resume_flag
train_model(dataset_param, model_param, general_param)