from lib.model.configs import cfg from data_test.ant.util import get_model_list # Parameters # ================================================== emb = Embedding(cfg) if cfg.test_data is None: print("test_data is empty.") exit() test_data = pd.read_csv(cfg.test_data, sep='\t') x_test, y_test = emb.generate_sentence_token_ind(test_data) x1_test, x2_test = zip(*x_test) model_list = get_model_list(cfg.model_directory) # print checkpoint_file graph = tf.Graph() with graph.as_default(): session_conf = tf.ConfigProto( allow_soft_placement=True, log_device_placement=False) sess = tf.Session(config=session_conf) with sess.as_default(), tf.device("/gpu:%s" % GPU): report_list = [] for model in model_list: checkpoint_file = model # Load the saved meta graph and restore variables saver = tf.train.import_meta_graph( "{}.meta".format(checkpoint_file))
print("test_data is empty.") exit() emb = Embedding(cfg) train_data = pd.read_csv(cfg.train_data, sep='\t') x_train, y_train = emb.generate_sentence_token_ind(train_data) test_data = pd.read_csv(cfg.test_data, sep='\t') x_test, y_test = emb.generate_sentence_token_ind(test_data) disan_model_path = os.path.join(root_path, 'model/disan_models') bimpm_model_path = os.path.join(root_path, 'model/bimpm_models') bimpm_pinyin_model_path = os.path.join(root_path, 'model/bimpm_pinyin_models') disan_pingying_model_path = os.path.join(root_path, 'model/disan_pinyin_models') disan_models = get_model_list(disan_model_path)[1:2] bimpm_models = get_model_list(bimpm_model_path)[1:3] bimpm_pinyin_models = get_model_list(bimpm_pinyin_model_path) disan_pingying_models = get_model_list(disan_pingying_model_path) models_count = len(disan_models) + len(bimpm_models)\ + len(bimpm_pinyin_models) + len(disan_pingying_models) # print checkpoint_file all_train_scores = [] all_test_scores = [] cfg.dropout = 1 cfg.dropout_rate = 0.0 graph = tf.Graph() with graph.as_default():