新增加一个变量：

...

global_step = tf.Variable(0, name='global_step', trainable=False)

saver = tf.train.Saver(tf.global_variables(),max_to_keep=1)

加载 checkpoint 时读入变量 restored_global_step ：

restored_global_step = 0

# sess = tf.InteractiveSession()
with tf.Session() as sess:
    sess.run(tf.global_variables_initializer())
    
    ckpt = tf.train.get_checkpoint_state(model_dir)  # 注意此处是checkpoint存在的目录
    if ckpt and ckpt.model_checkpoint_path:
        saver.restore(sess,ckpt.model_checkpoint_path) # 自动恢复model_checkpoint_path保存模型,一般是最新
        print("Model restored ...")
        restored_global_step = global_step.eval()
        print('restored_global_step: ',restored_global_step)
    else:
        print('Start from scratch ...')



    for step in range(max_steps):

        ...
        
        if step % 10 == 0 or (step + 1) == max_steps:
        
            checkpoint_path = os.path.join(model_dir, 'model.ckpt')
            # 计算新的 global_step
            g_step = restored_global_step+step
            # 更新 global_step
            sess.run(tf.assign(global_step,g_step))
            # 保存 
            saver.save(sess, checkpoint_path, global_step=g_step)

这样一来每次保存 checkpoint 时，都会记录下 global_step，而且 checkpoint 的文件名也是累计次数的