diff --git a/tutorials/source_en/advanced_use/distributed_training.md b/tutorials/source_en/advanced_use/distributed_training.md
index 20cd61d6253bb7392aa9e8c422aeedadfb102ee4..f3e67908ad61614b5a2698f7f905d6ac52462484 100644
--- a/tutorials/source_en/advanced_use/distributed_training.md
+++ b/tutorials/source_en/advanced_use/distributed_training.md
@@ -344,14 +344,14 @@ The running time is about 5 minutes, which is mainly occupied by operator compil
 Log files are saved in the `device` directory. The `env.log` file records environment variable information. The `train.log` file records the loss function information. The following is an example:
 
 ```
-epoch: 1 step: 156, loss is 2.0084016
-epoch: 2 step: 156, loss is 1.6407638
-epoch: 3 step: 156, loss is 1.6164391
-epoch: 4 step: 156, loss is 1.6838071
-epoch: 5 step: 156, loss is 1.6320667
-epoch: 6 step: 156, loss is 1.3098773
-epoch: 7 step: 156, loss is 1.3515002
-epoch: 8 step: 156, loss is 1.2943741
-epoch: 9 step: 156, loss is 1.2316195
-epoch: 10 step: 156, loss is 1.1533381
+Epoch: [1 /10], step: [156/156], loss: [2.0084], avg loss: [2.0000]
+Epoch: [2 /10], step: [156/156], loss: [1.6407], avg loss: [1.8000]
+Epoch: [3 /10], step: [156/156], loss: [1.6164], avg loss: [1.7066]
+Epoch: [4 /10], step: [156/156], loss: [1.6838], avg loss: [1.7597]
+Epoch: [5 /10], step: [156/156], loss: [1.6320], avg loss: [1.6504]
+Epoch: [6 /10], step: [156/156], loss: [1.3098], avg loss: [1.4504]
+Epoch: [7 /10], step: [156/156], loss: [1.3515], avg loss: [1.3940]
+Epoch: [8 /10], step: [156/156], loss: [1.2943], avg loss: [1.3089]
+Epoch: [9 /10], step: [156/156], loss: [1.2316], avg loss: [1.2694]
+Epoch: [10/10], step: [156/156], loss: [1.1533], avg loss: [1.1504]
 ```
diff --git a/tutorials/source_en/quick_start/quick_start.md b/tutorials/source_en/quick_start/quick_start.md
index 500c9614e3fa7e445a33a0822c73895495ead5d6..757d5bb416096332bb2176b74336bb9f68484157 100644
--- a/tutorials/source_en/quick_start/quick_start.md
+++ b/tutorials/source_en/quick_start/quick_start.md
@@ -373,23 +373,16 @@ In the preceding information:
 `Lenet. Py`: the script file you wrote.  
 `--device_target CPU`: Specify the hardware platform.The   parameters are 'CPU', 'GPU' or 'Ascend'.
 
-Loss values are output during training, as shown in the following figure. Although loss values may fluctuate, they gradually decrease and the accuracy gradually increases in general. Loss values displayed each time may be different because of their randomicity.
+Loss values are output during training. Although loss values may fluctuate, they gradually decrease and the accuracy gradually increases in general. Loss values displayed each time may be different because of their randomicity.
 
 The following is an example of loss values output during training:
 
 ```bash
 ...
-epoch: 1 step: 262, loss is 1.9212162
-epoch: 1 step: 263, loss is 1.8498616
-epoch: 1 step: 264, loss is 1.7990671
-epoch: 1 step: 265, loss is 1.9492403
-epoch: 1 step: 266, loss is 2.0305142
-epoch: 1 step: 267, loss is 2.0657792
-epoch: 1 step: 268, loss is 1.9582214
-epoch: 1 step: 269, loss is 0.9459006
-epoch: 1 step: 270, loss is 0.8167224
-epoch: 1 step: 271, loss is 0.7432692
-...
+Epoch: [1 /10], step: [262/900], loss: [1.9212], avg loss: [1.9222]
+Epoch: [1 /10], step: [263/900], loss: [1.8498], avg loss: [1.9200]
+Epoch: [1 /10], step: [264/900], loss: [1.7990], avg loss: [1.8542]
+Epoch: [1 /10], step: [265/900], loss: [1.9492], avg loss: [1.8640]
 ```
 
 The following is an example of model files saved after training:
@@ -419,7 +412,7 @@ def test_net(args,network,model,mnist_path):
     #load testing dataset
     ds_eval = create_dataset(os.path.join(mnist_path, "test")) # test
     acc = model.eval(ds_eval, dataset_sink_mode=False)
-    print("============== Accuracy:{} ==============".format(acc))
+    print("============== {} ==============".format(acc))
 
 if __name__ == "__main__":
     ...
@@ -444,7 +437,7 @@ Command output similar to the following is displayed:
 
 ```
 ============== Starting Testing ==============
-============== Accuracy:{'Accuracy': 0.9742588141025641} ==============
+============== {'Accuracy': 0.9742588141025641} ==============
 ```
 
 The model accuracy data is displayed in the output content. In the example, the accuracy reaches 97.4%, indicating a good model quality.
diff --git a/tutorials/source_zh_cn/advanced_use/distributed_training.md b/tutorials/source_zh_cn/advanced_use/distributed_training.md
index 52627db1c5abfa1614582dcab03a7571d2cbff70..22bc41e4a3ead4c55d6ee50cf097a02ef960c2ac 100644
--- a/tutorials/source_zh_cn/advanced_use/distributed_training.md
+++ b/tutorials/source_zh_cn/advanced_use/distributed_training.md
@@ -342,14 +342,14 @@ cd ../
 日志文件保存`device`目录下，`env.log`中记录了环境变量的相关信息，关于Loss部分结果保存在`train.log`中，示例如下：
 
 ```
-epoch: 1 step: 156, loss is 2.0084016
-epoch: 2 step: 156, loss is 1.6407638
-epoch: 3 step: 156, loss is 1.6164391
-epoch: 4 step: 156, loss is 1.6838071
-epoch: 5 step: 156, loss is 1.6320667
-epoch: 6 step: 156, loss is 1.3098773
-epoch: 7 step: 156, loss is 1.3515002
-epoch: 8 step: 156, loss is 1.2943741
-epoch: 9 step: 156, loss is 1.2316195
-epoch: 10 step: 156, loss is 1.1533381
-```
+Epoch: [1 /10], step: [156/156], loss: [2.0084], avg loss: [2.0000]
+Epoch: [2 /10], step: [156/156], loss: [1.6407], avg loss: [1.8000]
+Epoch: [3 /10], step: [156/156], loss: [1.6164], avg loss: [1.7066]
+Epoch: [4 /10], step: [156/156], loss: [1.6838], avg loss: [1.7597]
+Epoch: [5 /10], step: [156/156], loss: [1.6320], avg loss: [1.6504]
+Epoch: [6 /10], step: [156/156], loss: [1.3098], avg loss: [1.4504]
+Epoch: [7 /10], step: [156/156], loss: [1.3515], avg loss: [1.3940]
+Epoch: [8 /10], step: [156/156], loss: [1.2943], avg loss: [1.3089]
+Epoch: [9 /10], step: [156/156], loss: [1.2316], avg loss: [1.2694]
+Epoch: [10/10], step: [156/156], loss: [1.1533], avg loss: [1.1504]
+```
\ No newline at end of file
diff --git a/tutorials/source_zh_cn/quick_start/quick_start.md b/tutorials/source_zh_cn/quick_start/quick_start.md
index 19a7c8e7a442b7760079e5297f2f1eb8788c8c8c..606820414ac3934c3396b4d4c1bfb1a33ccd224f 100644
--- a/tutorials/source_zh_cn/quick_start/quick_start.md
+++ b/tutorials/source_zh_cn/quick_start/quick_start.md
@@ -380,16 +380,10 @@ python lenet.py --device_target=CPU
 
 ```bash
 ...
-epoch: 1 step: 262, loss is 1.9212162
-epoch: 1 step: 263, loss is 1.8498616
-epoch: 1 step: 264, loss is 1.7990671
-epoch: 1 step: 265, loss is 1.9492403
-epoch: 1 step: 266, loss is 2.0305142
-epoch: 1 step: 267, loss is 2.0657792
-epoch: 1 step: 268, loss is 1.9582214
-epoch: 1 step: 269, loss is 0.9459006
-epoch: 1 step: 270, loss is 0.8167224
-epoch: 1 step: 271, loss is 0.7432692
+Epoch: [1 /10], step: [262/900], loss: [1.9212], avg loss: [1.9222]
+Epoch: [1 /10], step: [263/900], loss: [1.8498], avg loss: [1.9200]
+Epoch: [1 /10], step: [264/900], loss: [1.7990], avg loss: [1.8542]
+Epoch: [1 /10], step: [265/900], loss: [1.9492], avg loss: [1.8640]
 ...
 ```