Bidirectionnet_GMM9000feat_softmaxloss.py 文件源码

def build_input(self):
        # positive
        self.labels = tf.placeholder(tf.float32, shape=[None,self.num_class], name='concept_labels')
        self.raw_sentence= tf.placeholder(tf.float32, shape=[self.batch_size,9000],name='raw_sentence')
        self.sentence_emb =self.raw_sentence/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')  
        self.image_feat_norm = self.image_feat/tf.norm(self.image_feat,axis=-1,keep_dims=True)
        self.sen_feat_norm = self.sentence_emb/tf.norm(self.sentence_emb,axis=-1,keep_dims=True)
        self.im_similarity = tf.matmul(self.image_feat_norm,self.image_feat_norm,transpose_b=True)
        self.sen_similarity =tf.matmul(self.sen_feat_norm,self.sen_feat_norm,transpose_b=True)