Bidirectionnet_GMM_norm.py 文件源码

def build_input(self):
        # positive
        self.raw_sentence= tf.placeholder(tf.float32, shape=[self.batch_size,18000],name='raw_sentence')
        self.sentence_emb =tf.sign(self.raw_sentence)*tf.pow(tf.abs(self.raw_sentence),0.5)/tf.norm(self.raw_sentence,axis=1,keep_dims=True) #tf.nn.embedding_lookup(tf.get_variable('word_embedding',[4096,512]),self.raw_sentence)
        self.image_feat = tf.placeholder(tf.float32,shape=[self.batch_size,4096], name='image_features')