multi_perspective.py 文件源码

def _max_pooling_matching(self, h1, h2, w):
        """Max pooling matching operation.

        # Arguments
            h1: (batch_size, h1_timesteps, embedding_size)
            h2: (batch_size, h2_timesteps, embedding_size)
            w: weights of one direction, (mp_dim, embedding_size)

        # Output shape
            (batch_size, h1_timesteps, mp_dim)
        """
        # h1 * weights, (batch_size, h1_timesteps, mp_dim, embedding_size)
        h1 = self._time_distributed_multiply(h1, w)
        # h2 * weights, (batch_size, h2_timesteps, mp_dim, embedding_size)
        h2 = self._time_distributed_multiply(h2, w)
        # reshape v1 to (batch_size, h1_timesteps, 1, mp_dim, embedding_size)
        h1 = K.expand_dims(h1, axis=2)
        # reshape v1 to (batch_size, 1, h2_timesteps, mp_dim, embedding_size)
        h2 = K.expand_dims(h2, axis=1)
        # cosine similarity, (batch_size, h1_timesteps, h2_timesteps, mp_dim)
        cos = self._cosine_similarity(h1, h2)
        # (batch_size, h1_timesteps, mp_dim)
        matching = K.max(cos, axis=2)
        return matching