BidirectionNet_tfidf.py 文件源码

def select_negtive(self, i_feat, s_feat, sess, topN=50):
        '''
        Select the triplets with the largest losses \n
        return i_feat_pos, s_feat_pos, i_feat_neg, s_feat_neg
        '''
        feed_dict = {self.image_feat: i_feat, self.sentence_feat:s_feat}
        i_embed, s_embed = sess.run([self.image_fc2, self.sentence_fc2], feed_dict=feed_dict)
        S = np.matmul(i_embed, s_embed.T)
        i_feat_pos = i_feat.repeat(topN, axis=0)
        s_feat_pos = s_feat.repeat(topN, axis=0)
        N = S.shape[0]
        np.fill_diagonal(S, -2*np.ones(N))
        neg_s_idx = S.argsort(axis=1)[:, -topN:]
        neg_i_idx = S.argsort(axis=0)[-topN:, :]
        s_feat_neg = s_feat[neg_s_idx.flatten('C')]
        i_feat_neg = i_feat[neg_i_idx.flatten('F')]
        return i_feat_pos, s_feat_pos, i_feat_neg, s_feat_neg