BidirectionNet_conv_ltp.py 文件源码

def sentencenet(self, input_tensor, reuse=False):
        with tf.variable_scope('sentence_net', reuse=reuse) as scope:
            wd = tf.contrib.layers.l2_regularizer(self.weight_decay)

            sentence_fc1 = tf.contrib.layers.fully_connected(input_tensor, 2048, weights_regularizer=wd, scope='s_fc1')
            #drop_fc1 = tf.nn.dropout(sentence_fc1, self.keep_prob, name='drop_fc1')
            sentence_fc2 = tf.contrib.layers.fully_connected(sentence_fc1, 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['sentence_fc1'] = sentence_fc1
        self.endpoint['sentence_fc2'] = embed
        return embed