def predict(path): np.random.seed(42) tf.set_random_seed(42) config = Config.get_default_config(args) model = Model(config) predictor = InteractivePredictor(config, model, path) predictor.predict() model.close_session()
args = parser.parse_args() replace_tokens = ["@R_%d@"%i for i in range(1, args.num_replace_tokens+1)] if args.debug: config = Config.get_debug_config(args) else: config = Config.get_default_config(args) # Composite training loss lamb = args.lamb print('Lamb :=' + str(lamb)) model = Model(config, replace_tokens) print('Created model') if config.TRAIN_PATH: model.train(lamb=lamb) if config.TEST_PATH and not args.data_path: results, precision, recall, f1 = model.evaluate() print('Accuracy: ' + str(results)) print('Precision: ' + str(precision) + ', recall: ' + str(recall) + ', F1: ' + str(f1)) if args.predict: predictor = InteractivePredictor(config, model) predictor.predict() if args.adv_eval: model.adv_eval_batched() if args.release and args.load_path: model.evaluate(release=True) model.close_session()
tf.random.set_seed(args.seed) if args.debug: config = Config.get_debug_config(args) tf.config.experimental_run_functions_eagerly(True) else: config = Config.get_default_config(args) print('Created model') if config.TRAIN_PATH: model = ModelRunner(config) model.train() #if config.TEST_PATH and not args.data_path: #model = ModelRunner(config) #results, precision, recall, f1, rouge, bleu = model.evaluate('test') #print('Accuracy: ' + str(results)) #print('Precision: ' + str(precision) + ', recall: ' + str(recall) + ', F1: ' + str(f1)) #print('Rouge: ', rouge) #print('Bleu: ', bleu) if args.predict: model = ModelRunner(config) predictor = InteractivePredictor(config, model) paths = glob(args.predict) if args.shard: paths = [ path for path in paths if path[args.predict.find('*') + 1:].startswith(str(args.shard)) ] predictor.predict(paths)