def sentencenet(self, sentence_emb, reuse=False):
with tf.variable_scope('sentence_net', reuse=reuse) as scope:
wd = tf.contrib.layers.l2_regularizer(self.weight_decay)
sentence_fc1 =tf.nn.dropout(tf.contrib.layers.fully_connected(sentence_emb,2048, \
weights_regularizer=wd, scope='s_fc1'),keep_prob=self.keep_prob) # 20*10*256
sentence_fc2 = tf.contrib.layers.fully_connected(sentence_fc1, 512,activation_fn=None,normalizer_fn=tf.contrib.layers.batch_norm,\
normalizer_params={'is_training':self.is_training,'updates_collections':None}, weights_regularizer=wd, scope='s_fc2')
sentence_fc2 = sentence_fc2/tf.norm(sentence_fc2,axis= -1,keep_dims=True)
self.endpoint['sentence_fc1'] = sentence_fc1
self.endpoint['sentence_fc2'] = sentence_fc2
return sentence_fc2
Bidirectionnet_GMM_norm.py 文件源码
python
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