models.py 文件源码

def _compute_vert_context_soft(self, edge_factor, vert_factor, reuse=False):
        """
        attention-based vertex(node) message pooling
        """

        out_edge = utils.pad_and_gather(edge_factor, self.edge_pair_mask_inds[:,0])
        in_edge = utils.pad_and_gather(edge_factor, self.edge_pair_mask_inds[:,1])
        # gather correspounding vert factors
        vert_factor_gathered = tf.gather(vert_factor, self.edge_pair_segment_inds)

        # concat outgoing edges and ingoing edges with gathered vert_factors
        out_edge_w_input = tf.concat(concat_dim=1, values=[out_edge, vert_factor_gathered])
        in_edge_w_input = tf.concat(concat_dim=1, values=[in_edge, vert_factor_gathered])

        # compute compatibility scores
        (self.feed(out_edge_w_input)
             .fc(1, relu=False, reuse=reuse, name='out_edge_w_fc')
             .sigmoid(name='out_edge_score'))
        (self.feed(in_edge_w_input)
             .fc(1, relu=False, reuse=reuse, name='in_edge_w_fc')
             .sigmoid(name='in_edge_score'))

        out_edge_w = self.get_output('out_edge_score')
        in_edge_w = self.get_output('in_edge_score')

        # weight the edge factors with computed weigths
        out_edge_weighted = tf.mul(out_edge, out_edge_w)
        in_edge_weighted = tf.mul(in_edge, in_edge_w)


        edge_sum = out_edge_weighted + in_edge_weighted
        vert_ctx = tf.segment_sum(edge_sum, self.edge_pair_segment_inds)
        return vert_ctx