tdlm_model.py 文件源码

def get_topics(self, sess, topn):
        topics = []
        entropy = []
        tw_dist = sess.run(tf.nn.softmax(tf.matmul(self.topic_output_embedding, self.tm_softmax_w) + self.tm_softmax_b))
        for ti in xrange(self.config.topic_number):
            best = matutils.argsort(tw_dist[ti], topn=topn, reverse=True)
            topics.append(best)
            entropy.append(scipy.stats.entropy(tw_dist[ti]))

        return topics, entropy

    #get top topics and words given a doc