ccrc_model.py 文件源码

def get_candidates_representations_in_sentence(self, sentence_candidate_answers, sentence_attentioned_hidden_states):
        candidate_answer_num=tf.gather(tf.shape(sentence_candidate_answers), 0)
        logging.warn('candidate_answer_num:{}'.format(candidate_answer_num))
        logging.warn('sentence_candidate_answers:{}'.format(sentence_candidate_answers))
        candidate_answer_nodeids=tf.gather(sentence_candidate_answers, 0) #a node idx list
        candidate_answer_hidden_list=tf.gather(sentence_attentioned_hidden_states, candidate_answer_nodeids)
        candidate_final_representations=self.get_candidate_answer_final_representations(candidate_answer_hidden_list)
        candidates_final_representations=tf.expand_dims(candidate_final_representations, 0)
        idx_cand=tf.constant(1)
        def _recurse_candidate_answer(candidate_final_representations, idx_cand):
            cur_candidate_answer_nodeids=tf.gather(sentence_candidate_answers, idx_cand)
            cur_candidate_answer_hidden_list=tf.gather(sentence_attentioned_hidden_states, cur_candidate_answer_nodeids)
            cur_candidate_final_representations=tf.expand_dims( 
                self.get_candidate_answer_final_representations(cur_candidate_answer_hidden_list), 0)
            candidate_final_representations=tf.concat([candidate_final_representations, cur_candidate_final_representations], axis=0)
            idx_cand=tf.add(idx_cand,1)
            return candidate_final_representations, idx_cand
        loop_cond=lambda a1,idx:tf.less(idx, candidate_answer_num)
        loop_vars=[candidates_final_representations, idx_cand]
        candidates_final_representations, idx_cand=tf.while_loop(loop_cond, _recurse_candidate_answer, loop_vars,
            shape_invariants=[tf.TensorShape([None, 2*self.config.hidden_dim]),idx_cand.get_shape()])
        return candidates_final_representations