52e40cb89b5757b1616d3f561c75c36a3fd36674,examples/trials/ga_squad/trial.py,,run_epoch,#Any#Any#Any#,172

Before Change

            loss, _, = sess.run(
                [answer_net.loss, answer_net.train_op], feed_dict=feed_dict)
            if count % 100 == 0:
                logger.debug("%d %g except:%g, loss:%g" %
                             (count, used, used / count * len(batches), loss))
            loss_sum += loss
        else:
            feed_dict = {answer_net.query_word: query,
                         answer_net.query_mask: query_mask,
                         answer_net.query_lengths: query_lengths,
                         answer_net.passage_word: passage,
                         answer_net.passage_mask: passage_mask,
                         answer_net.passage_lengths: passage_lengths,
                         answer_net.query_char_ids: query_char,
                         answer_net.query_char_lengths: query_char_lengths,
                         answer_net.passage_char_ids: passage_char,
                         answer_net.passage_char_lengths: passage_char_lengths}
            position1, position2 = sess.run(
                [answer_net.begin_prob, answer_net.end_prob], feed_dict=feed_dict)
            position1_result += position1.tolist()
            position2_result += position2.tolist()
            contexts += context
            ids = np.concatenate((ids, sample_id))
            if count % 100 == 0:
                logger.debug("%d %g except:%g" %
                             (count, used, used / count * len(batches)))
    loss = loss_sum / len(batches)
    if is_training:
        return loss

After Change

    timer = Timer()
    count = 0
    for batch in batches:
        used = timer.get_elapsed(False)
        count += 1
        qps = batch["qp_pairs"]
        question_tokens = [qp["question_tokens"] for qp in qps]
        passage_tokens = [qp["passage_tokens"] for qp in qps]
        context = [(qp["passage"], qp["passage_tokens"]) for qp in qps]
        sample_id = [qp["id"] for qp in qps]

        _, query, query_mask, query_lengths = data.get_word_input(
            data=question_tokens, word_dict=word_vcb, embed=embed, embed_dim=cfg.word_embed_dim)
        _, passage, passage_mask, passage_lengths = data.get_word_input(
            data=passage_tokens, word_dict=word_vcb, embed=embed, embed_dim=cfg.word_embed_dim)

        query_char, query_char_lengths = data.get_char_input(
            data=question_tokens, char_dict=char_vcb, max_char_length=cfg.max_char_length)

        passage_char, passage_char_lengths = data.get_char_input(
            data=passage_tokens, char_dict=char_vcb, max_char_length=cfg.max_char_length)

        if is_training:
            answer_begin, answer_end = data.get_answer_begin_end(qps)

        if is_training:
            feed_dict = {answer_net.query_word: query,
                         answer_net.query_mask: query_mask,
                         answer_net.query_lengths: query_lengths,
                         answer_net.passage_word: passage,
                         answer_net.passage_mask: passage_mask,
                         answer_net.passage_lengths: passage_lengths,
                         answer_net.query_char_ids: query_char,
                         answer_net.query_char_lengths: query_char_lengths,
                         answer_net.passage_char_ids: passage_char,
                         answer_net.passage_char_lengths: passage_char_lengths,
                         answer_net.answer_begin: answer_begin,
                         answer_net.answer_end: answer_end}
            loss, _, = sess.run(
                [answer_net.loss, answer_net.train_op], feed_dict=feed_dict)
            if count % 100 == 0:
                logger.debug("%d %g except:%g, loss:%g", count, used, used / count * len(batches), loss)
            loss_sum += loss
        else:
            feed_dict = {answer_net.query_word: query,
                         answer_net.query_mask: query_mask,

Instances