def train_siamese():
# 读取配置
# conf = Config()
cfg_path = "./configs/config.yml"
cfg = yaml.load(open(cfg_path, encoding='utf-8'), Loader=yaml.FullLoader)
# 读取数据
data_train, data_val, data_test = data_input.get_lcqmc()
# data_train = data_train[:100]
print("train size:{},val size:{}, test size:{}".format(
len(data_train), len(data_val), len(data_test)))
model = SiamenseRNN(cfg)
model.fit(data_train, data_val, data_test)
pass
def predict_siamese(file_='./results/'):
# 加载配置
cfg_path = "./configs/config.yml"
cfg = yaml.load(open(cfg_path, encoding='utf-8'), Loader=yaml.FullLoader)
os.environ["CUDA_VISIBLE_DEVICES"] = "4"
# 将 seq转为id，
vocab = Vocabulary(meta_file='./data/vocab.txt', max_len=cfg['max_seq_len'], allow_unk=1, unk='[UNK]', pad='[PAD]')
test_arr, query_arr = get_test(file_, vocab)
# 加载模型
model = SiamenseRNN(cfg)
model.restore_session(cfg["checkpoint_dir"])
test_label, test_prob = model.predict(test_arr)
out_arr = [x + [test_label[i]] + [test_prob[i]] for i, x in enumerate(query_arr)]
write_file(out_arr, file_ + '.siamese.predict', )
pass