import sys import numpy as np import pandas as pd import tensorflow as tf from sklearn.metrics import f1_score root_path = os.path.dirname( os.path.dirname(os.path.dirname(os.path.abspath(__file__)))) sys.path.append(root_path) from lib.model.configs import cfg from lib.model.fast_disan.model_fast_disan import ModelFastDiSAN from data_test.ant.embedding import Embedding from common.data_helper import DataHelper emb = Embedding(cfg) GPU = cfg.gpu os.environ["CUDA_VISIBLE_DEVICES"] = GPU logging.basicConfig(filename="ant_fast_disan.log" + cfg.log_name, filemode="w", format="%(asctime)s-%(name)s-%(levelname)s-%(message)s", level=logging.INFO) train_data = pd.read_csv(cfg.train_data, sep='\t') x_train, y_train = emb.generate_sentence_token_ind(train_data) train_data_emb = list(zip(x_train, y_train)) valid_data = pd.read_csv(cfg.validate_data, sep='\t') x_valid, y_valid = emb.generate_sentence_token_ind(valid_data)
root_path = os.path.dirname(os.path.dirname(os.path.dirname(__file__))) sys.path.append(root_path) from data_test.ant.embedding import Embedding GPU = '2' os.environ["CUDA_VISIBLE_DEVICES"] = GPU from common.data_helper import DataHelper 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(