modify readme

model_zoo/official/cv/alexnet/README.md

@@ -71,8 +71,7 @@ sh run_standalone_eval_ascend.sh [DATA_PATH] [CKPT_NAME]
 ## [Script and Sample Code](#contents)
 
 ```
-├── model_zoo
-    ├── README.md                          // descriptions about all the models
+├── cv
     ├── alexnet        
         ├── README.md                    // descriptions about alexnet
         ├── requirements.txt             // package needed
@@ -116,8 +115,8 @@ sh run_standalone_train_ascend.sh cifar-10-batches-bin ckpt
 
 After training, the loss value will be achieved as follows:
 
-# grep "loss is " train.log
 ```
+# grep "loss is " train.log
 epoch: 1 step: 1, loss is 2.2791853
 ...
 epoch: 1 step: 1536, loss is 1.9366643
@@ -171,7 +170,7 @@ You can view the results through the file "log.txt". The accuracy of the test da
 
 # [Description of Random Situation](#contents)
 
-In dataset.py, we set the seed inside “create_dataset" function.
+In dataset.py, we set the seed inside ```create_dataset``` function.
 
 # [ModelZoo Homepage](#contents)  
  Please check the official [homepage](https://gitee.com/mindspore/mindspore/tree/master/model_zoo).  

model_zoo/official/cv/lenet/README.md

@@ -77,8 +77,7 @@ sh run_standalone_eval_ascend.sh [DATA_PATH] [CKPT_NAME]
 ## [Script and Sample Code](#contents)
 
 ```
-├── model_zoo
-    ├── README.md                          // descriptions about all the models
+├── cv
     ├── lenet        
         ├── README.md                    // descriptions about lenet
         ├── requirements.txt             // package needed
@@ -181,7 +180,7 @@ You can view the results through the file "log.txt". The accuracy of the test da
 
 # [Description of Random Situation](#contents)
 
-In dataset.py, we set the seed inside “create_dataset" function.
+In dataset.py, we set the seed inside ```create_dataset``` function.
 
 # [ModelZoo Homepage](#contents)  
  Please check the official [homepage](https://gitee.com/mindspore/mindspore/tree/master/model_zoo).  

model_zoo/official/cv/mobilenetv2/Readme.md

@@ -175,7 +175,7 @@ result: {'acc': 0.71976314102564111} ckpt=/path/to/checkpoint/mobilenet-200_625.
 | Parameters                 |                               |                           |                      |
 | -------------------------- | ----------------------------- | ------------------------- | -------------------- |
 | Model Version              | V1                            |                           |                      |
-| Resource                   | Huawei 910                    | NV SMX2 V100-32G          | Huawei 310           |
+| Resource                   | Ascend 910                    | NV SMX2 V100-32G          | Ascend 310           |
 | uploaded Date              | 05/06/2020                    | 05/22/2020                |                      |
 | MindSpore Version          | 0.2.0                         | 0.2.0                     | 0.2.0                | 
 | Dataset                    | ImageNet, 1.2W                | ImageNet, 1.2W            | ImageNet, 1.2W       |

model_zoo/official/cv/resnext50/README.md

@@ -47,7 +47,8 @@ Dataset used: [imagenet](http://www.image-net.org/)
 
 ## [Mixed Precision](#contents)
 
-The [mixed precision](https://www.mindspore.cn/tutorial/zh-CN/master/advanced_use/mixed_precision.html) training method accelerates the deep learning neural network training process by using both the single-precision and half-precision data formats, and maintains the network precision achieved by the single-precision training at the same time. Mixed precision training can accelerate the computation process, reduce memory usage, and enable a larger model or batch size to be trained on specific hardware. 
+The [mixed precision](https://www.mindspore.cn/tutorial/zh-CN/master/advanced_use/mixed_precision.html) training method accelerates the deep learning neural network training process by using both the single-precision and half-precision data formats, and maintains the network precision achieved by the single-precision training at the same time. Mixed precision training can accelerate the computation process, reduce memory usage, and enable a larger model or batch size to be trained on specific hardware.
+ 
 For FP16 operators, if the input data type is FP32, the backend of MindSpore will automatically handle it with reduced precision. Users could check the reduced-precision operators by enabling INFO log and then searching ‘reduce precision’.
 
 # [Environment Requirements](#contents)
@@ -228,7 +229,7 @@ acc=93.88%(TOP5)
 
 | Parameters                 |                               |                           |                      |
 | -------------------------- | ----------------------------- | ------------------------- | -------------------- |
-| Resource                   | Huawei 910                    | NV SMX2 V100-32G          | Huawei 310           |
+| Resource                   | Ascend 910                    | NV SMX2 V100-32G          | Ascend 310           |
 | uploaded Date              | 06/30/2020                    | 07/23/2020                | 07/23/2020           |
 | MindSpore Version          | 0.5.0                         | 0.6.0                     | 0.6.0                | 
 | Dataset                    | ImageNet, 1.2W                | ImageNet, 1.2W            | ImageNet, 1.2W       |