From dfe3ee7445c4634c700a32072215e713cc91a560 Mon Sep 17 00:00:00 2001
From: wukesong <wukesong1@huawei.com>
Date: Fri, 31 Jul 2020 11:41:37 +0800
Subject: [PATCH] modify lossmonitor location

---
 tutorials/source_en/quick_start/quick_start.md    | 6 +++---
 tutorials/source_zh_cn/quick_start/quick_start.md | 6 +++---
 2 files changed, 6 insertions(+), 6 deletions(-)

diff --git a/tutorials/source_en/quick_start/quick_start.md b/tutorials/source_en/quick_start/quick_start.md
index 7599bd4b..700c262d 100644
--- a/tutorials/source_en/quick_start/quick_start.md
+++ b/tutorials/source_en/quick_start/quick_start.md
@@ -316,8 +316,8 @@ if __name__ == "__main__":
 
 ### Saving the Configured Model
 
-MindSpore provides the callback mechanism to execute customized logic during training. `ModelCheckpoint` and `LossMonitor` provided by the framework are used in this example.
-`ModelCheckpoint` can save network models and parameters for subsequent fine-tuning. `LossMonitor` can monitor the changes of the `loss` value during training.
+MindSpore provides the callback mechanism to execute customized logic during training. `ModelCheckpoint` provided by the framework is used in this example.
+`ModelCheckpoint` can save network models and parameters for subsequent fine-tuning.
 
 ```python
 from mindspore.train.callback import ModelCheckpoint, CheckpointConfig
@@ -333,7 +333,7 @@ if __name__ == "__main__":
 
 ### Configuring the Network Training
 
-Use the `model.train` API provided by MindSpore to easily train the network.
+Use the `model.train` API provided by MindSpore to easily train the network. `LossMonitor` can monitor the changes of the `loss` value during training.
 In this example, set `epoch_size` to 1 to train the dataset for five iterations.
 
 ```python
diff --git a/tutorials/source_zh_cn/quick_start/quick_start.md b/tutorials/source_zh_cn/quick_start/quick_start.md
index 365c41ea..1e188772 100644
--- a/tutorials/source_zh_cn/quick_start/quick_start.md
+++ b/tutorials/source_zh_cn/quick_start/quick_start.md
@@ -317,8 +317,8 @@ if __name__ == "__main__":
 
 ### 配置模型保存
 
-MindSpore提供了callback机制，可以在训练过程中执行自定义逻辑，这里使用框架提供的`ModelCheckpoint`和`LossMonitor`为例。
-`ModelCheckpoint`可以保存网络模型和参数，以便进行后续的fine-tuning（微调）操作，`LossMonitor`可以监控训练过程中`loss`值的变化。
+MindSpore提供了callback机制，可以在训练过程中执行自定义逻辑，这里使用框架提供的`ModelCheckpoint`为例。
+`ModelCheckpoint`可以保存网络模型和参数，以便进行后续的fine-tuning（微调）操作。
 
 ```python
 from mindspore.train.callback import ModelCheckpoint, CheckpointConfig
@@ -334,7 +334,7 @@ if __name__ == "__main__":
 
 ### 配置训练网络
 
-通过MindSpore提供的`model.train`接口可以方便地进行网络的训练。
+通过MindSpore提供的`model.train`接口可以方便地进行网络的训练。`LossMonitor`可以监控训练过程中`loss`值的变化。
 这里把`epoch_size`设置为1，对数据集进行1个迭代的训练。
 
 
-- 
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