TensorFlow的多模型部署，关键在于每个模型拥有一个独立的graph与session，各模型间互不干扰即可。最终直接依据各模型的结果，综合起来做决定。

import tensorflow as tfimport numpy as npclass Model:    def __init__(self,meta_path,ckpt_path,out_tensor_name,input_tensor_name):        self.graph = tf.Graph()        #恢复模型        with self.graph.as_default():            self.saver = tf.train.import_meta_graph(meta_path)            self.session = tf.Session(graph=self.graph)        with self.session.as_default():            with self.graph.as_default():                self.saver.restore(self.session,tf.train.latest_checkpoint(ckpt_path))                #获取输入输出tensor                self.out = self.graph.get_tensor_by_name(name=out_tensor_name)                self.input = self.graph.get_tensor_by_name(name=input_tensor_name)    #做预测    def predict(self,image):        result = self.session.run(self.out,feed_dict={self.input:image})        index = np.argmax(result,1)        return index[0]Age_pre = Model(meta_path='',ckpt_path='',out_tensor_name='softmax:0',input_tensor_name='input:0')Gender_pre = Model(meta_path='',ckpt_path='',out_tensor_name='softmax:0',input_tensor_name='input:0')with tf.Session() as session:    image = session.run(fetches='')    age = Age_pre.predict(image)    gender = Gender_pre.predict(image)