def _get_config():
config = get_default_config()
params = {
"model.py": "example_model.model_multimodal:GCN",
"save_result_test": "../result/test.multimodal.csv",
"save_result_train": "../result/train.multimodal.csv",
"load_model": "../model/model.sample_multimodal.ckpt",
"save_model": "../model/model.sample_multimodal.ckpt",
"validation_data_rate": 0.3,
"embedding_dim": 4,
"epoch": 1,
"with_feature": True,
"batch_size": 10,
"save_interval": 10,
"learning_rate": 0.3,
"with_node_embedding": False,
"save_model_path": "model",
"patience": 0,
"dataset": "../example_jbl/sample.jbl"
}
for k, v in params.items():
config[k] = v
return config
default=None,
nargs='?',
help='config json file')
parser.add_argument('--save-config',
default=None,
nargs='?',
help='save config json file')
parser.add_argument('--no-config',
action='store_true',
help='use default setting')
parser.add_argument('--model', type=str, default=None, help='model')
parser.add_argument('--dataset', type=str, default=None, help='dataset')
args = parser.parse_args()
# config
config = get_default_config()
if args.config is None:
pass
#parser.print_help()
#quit()
else:
print("[LOAD] ", args.config)
fp = open(args.config, 'r')
config.update(json.load(fp))
# option
if args.model is not None:
config["load_model"] = args.model
if args.dataset is not None:
config["dataset"] = args.dataset
# setup
with tf.Graph().as_default():