layers.py 文件源码

def answer_start_pred(context_encoding, question_attention_vector, context_mask, W, dropout_rate):
    """Answer start prediction layer."""

    answer_start = Lambda(lambda arg:
                          concatenate([arg[0], arg[1], arg[2]]))([
        context_encoding,
        question_attention_vector,
        multiply([context_encoding, question_attention_vector])])

    answer_start = TimeDistributed(Dense(W, activation='relu'))(answer_start)
    answer_start = Dropout(rate=dropout_rate)(answer_start)
    answer_start = TimeDistributed(Dense(1))(answer_start)

    # apply masking
    answer_start = Lambda(lambda q: masked_softmax(q[0], q[1]))([answer_start, context_mask])
    answer_start = Lambda(lambda q: flatten(q))(answer_start)
    return answer_start