!98 modify callback infomation

Merge pull request !98 from changzherui/mod_callback

tutorials/source_en/advanced_use/customized_debugging_information.md

@@ -41,6 +41,7 @@ model.train(epoch, dataset, callbacks=[ckpt_cb, loss_cb, summary_cb])
 ModelCheckpoint can save model parameters for retraining or inference.
 LossMonitor can output loss information in logs for users to view. In addition, LossMonitor monitors the loss value change during training. When the loss value is `Nan` or `Inf`, the training terminates.
 SummaryStep can save the training information to a file for later use.
+During the training process, the callback list will execute the callback function in the defined order. Therefore, in the definition process, the dependency between callbacks needs to be considered.
 
 ### Custom Callback
 

tutorials/source_zh_cn/advanced_use/customized_debugging_information.md

@@ -40,6 +40,7 @@ model.train(epoch, dataset, callbacks=[ckpt_cb, loss_cb, summary_cb])
 ModelCheckpoint可以保存模型参数，以便进行再训练或推理。
 LossMonitor可以在日志中输出loss，方便用户查看，同时它还会监控训练过程中的loss值变化情况，当loss值为`Nan`或`Inf`时终止训练。
 SummaryStep可以把训练过程中的信息存储到文件中，以便后续进行查看或可视化展示。
+在训练过程中，Callback列表会按照定义的顺序执行Callback函数。因此在定义过程中，需考虑Callback之间的依赖关系。
 
 ### 自定义Callback