tensorflow：实现断点续训

saver = tf.train.Saver() #用于保存神经网络结构，构造方法可以传参数，参数可以是dict和list。不传参数时默认保存所有变量  
    with tf.Session() as sess:  
        tf.initialize_all_variables().run() #初始化所有变量  
        ckpt = tf.train.get_checkpoint_state(MODEL_SAVE_PATH) #获取checkpoints对象  
        if ckpt and ckpt.model_checkpoint_path:##判断ckpt是否为空，若不为空，才进行模型的加载，否则从头开始训练  
            saver.restore(sess,ckpt.model_checkpoint_path)#恢复保存的神经网络结构，实现断点续训  
        for i in range(TRAINING_STEPS):  
            xs, ys = mnist.train.next_batch(BATCH_SIZE) #产生这一轮的训练数据  
            _, loss_value, step = sess.run([train_op, loss, global_step], feed_dict={x: xs, y_: ys})  
            if i % 1000 == 0:  
                print("After %d training step(s), loss on training batch is %g." % (step, loss_value))  
                saver.save(sess, os.path.join(MODEL_SAVE_PATH, MODEL_NAME), global_step=global_step)#保存神经网络结构