BidirectionNet_word2vec.py 文件源码

def sentence_concat(self, tfidf, lda, reuse=False):
    with tf.variable_scope('sentence_concat', reuse=reuse) as scope:
        wd = tf.contrib.layers.l2_regularizer(self.weight_decay)

        tfidf_fc1 = tf.contrib.layers.fully_connected(tfidf, 2048, weights_regularizer=wd, scope='tfidf_fc1')   
        lda_fc1 = tf.contrib.layers.fully_connected(lda, 64, scope='lda_fc1')
        feat_concat = tf.concat([tfidf_fc1, lda_fc1], axis=1)
        #drop_fc1 = tf.nn.dropout(feat_concat, self.keep_prob, name='drop_fc1')
        sentence_fc2 = tf.contrib.layers.fully_connected(feat_concat, 512,activation_fn=None, weights_regularizer=wd, scope='s_fc2')
        sentence_fc2_bn = tf.contrib.layers.batch_norm(sentence_fc2, center=True, scale=True, is_training=self.is_training,
                                                           reuse=reuse, decay=0.999, updates_collections=None, 
                                                           scope='s_fc2_bn')    
        embed = sentence_fc2_bn/tf.norm(sentence_fc2_bn, axis= -1, keep_dims=True)

    self.endpoint['tfidf_fc1'] = tfidf_fc1
    self.endpoint['lda_fc1'] = lda_fc1  
    self.endpoint['concat_embed'] = embed
    return embed