layers.py 文件源码

def question_attn_vector(question_encoding, question_mask, context_encoding, repeat=True):
    """Attention over question."""

    question_attention_vector = TimeDistributed(Dense(1))(question_encoding)
    # apply masking
    question_attention_vector = Lambda(lambda q: masked_softmax(q[0], q[1]))([question_attention_vector, question_mask])
    # apply the attention
    question_attention_vector = Lambda(lambda q: q[0] * q[1])([question_encoding, question_attention_vector])
    question_attention_vector = Lambda(lambda q: K.sum(q, axis=1))(question_attention_vector)
    if repeat==True:
        question_attention_vector = Lambda(lambda q: repeat_vector(q[0], q[1]))([question_attention_vector, context_encoding])
    return question_attention_vector