layers.py 文件源码

def apply(self, is_train, x, mask=None):
        if mask is not None:
            answer_mask = tf.expand_dims(tf.cast(tf.sequence_mask(mask, tf.shape(x)[1]), tf.float32), 2)
            if self.apply_mask:
                x *= answer_mask
        else:
            answer_mask = None

        if self.reduce == "max":
            if mask is not None:
                raise NotImplementedError()
            return tf.reduce_max(x, axis=1)
        elif self.reduce == "mean":
            if mask is not None:
                return tf.reduce_sum(x * answer_mask, axis=1) / tf.cast(tf.expand_dims(mask, 1), tf.float32)
            else:
                return tf.reduce_mean(x, axis=1)
        elif self.reduce == "sum":
            if mask is not None:
                return tf.reduce_sum(x * answer_mask, axis=1)
            else:
                return tf.reduce_sum(x, axis=1)
        else:
            raise ValueError()