answer_layer.py 文件源码

def bilinear_answer_layer(size, encoded_question, question_length, encoded_support, support_length,
                          support2question, answer2support, is_eval, beam_size=1,
                          max_span_size=10000):
    """Answer layer for multiple paragraph QA."""
    # computing single time attention over question
    size = encoded_support.get_shape()[-1].value
    question_state = compute_question_state(encoded_question, question_length)

    # compute logits
    hidden = tf.gather(tf.layers.dense(question_state, 2 * size, name="hidden"), support2question)
    hidden_start, hidden_end = tf.split(hidden, 2, 1)

    support_mask = misc.mask_for_lengths(support_length)

    start_scores = tf.einsum('ik,ijk->ij', hidden_start, encoded_support)
    start_scores = start_scores + support_mask

    end_scores = tf.einsum('ik,ijk->ij', hidden_end, encoded_support)
    end_scores = end_scores + support_mask

    return compute_spans(start_scores, end_scores, answer2support, is_eval, support2question,
                         beam_size, max_span_size)