default=1.0) parser.add_argument( '--negative_sampling', dest='negative_sampling', type=str, help='choose unit or bern to generate negative examples', default='bern') parser.add_argument( '--score_func', dest='score_func', type=str, default='l1', help='choose l1 or l2 to calculate distance of vectors') args = parser.parse_args() print(args) KG = KnowledgeGraph(data_dir=args.data_dir, negative_sampling=args.negative_sampling) model = TransE(num_entity=KG.num_entity, num_relation=KG.num_relation, learning_rate=args.learning_rate, batch_size=args.batch_size, num_epochs=args.num_epochs, margin=args.margin, dimension=args.dimension, score_func=args.score_func) model.compile() tp, tn = KG.get_training_data() train_model(model, tp, tn) model.save_embeddings() test_model(model, KG.get_test_data())