fix error links in modelzoo files for master

model_zoo/official/cv/MCNN/README.md

@@ -62,8 +62,8 @@ Dataset used: [ShanghaitechA](<https://www.dropbox.com/s/fipgjqxl7uj8hd5/Shangha
 - Framework
     - [MindSpore](https://www.mindspore.cn/install/en)
 - For more information, please check the resources below：
-    - [MindSpore Tutorials](https://www.mindspore.cn/tutorial/training/en/master/index.html)
-    - [MindSpore Python API](https://www.mindspore.cn/doc/api_python/en/master/index.html)
+    - [MindSpore Tutorials](https://www.mindspore.cn/tutorials/en/master/index.html)
+    - [MindSpore Python API](https://www.mindspore.cn/docs/api/en/master/index.html)
 
 # [Quick Start](#contents)
 

model_zoo/research/cv/inception_resnet_v2/README.md

@@ -44,14 +44,14 @@ Dataset used can refer to paper.
 
 ## [Mixed Precision(Ascend)](#contents)
 
-The [mixed precision](https://www.mindspore.cn/tutorial/training/en/master/advanced_use/enable_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/docs/programming_guide/en/master/enable_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)
 
 - Hardware（Ascend）
-    - Prepare hardware environment with Ascend processor. If you want to try Ascend  , please send the [application form](https://obs-9be7.obs.cn-east-2.myhuaweicloud.com/file/other/Ascend%20Model%20Zoo%E4%BD%93%E9%AA%8C%E8%B5%84%E6%BA%90%E7%94%B3%E8%AF%B7%E8%A1%A8.docx) to ascend@huawei.com. Once approved, you can get the resources.
+    - Prepare hardware environment with Ascend processor.
 - Framework
     - [MindSpore](https://www.mindspore.cn/install)
 - For more information, please check the resources below：
@@ -121,7 +121,7 @@ bash scripts/run_standalone_train_ascend.sh DEVICE_ID DATA_DIR
 ```
 
 > Notes:
-> RANK_TABLE_FILE can refer to [Link](https://www.mindspore.cn/tutorial/training/en/master/advanced_use/distributed_training_ascend.html) , and the device_ip can be got as [Link](https://gitee.com/mindspore/mindspore/tree/master/model_zoo/utils/hccl_tools). For large models like InceptionV4, it's better to export an external environment variable `export HCCL_CONNECT_TIMEOUT=600` to extend hccl connection checking time from the default 120 seconds to 600 seconds. Otherwise, the connection could be timeout since compiling time increases with the growth of model size.
+> RANK_TABLE_FILE can refer to [Link](https://www.mindspore.cn/docs/programming_guide/en/master/distributed_training_ascend.html) , and the device_ip can be got as [Link](https://gitee.com/mindspore/mindspore/tree/master/model_zoo/utils/hccl_tools). For large models like InceptionV4, it's better to export an external environment variable `export HCCL_CONNECT_TIMEOUT=600` to extend hccl connection checking time from the default 120 seconds to 600 seconds. Otherwise, the connection could be timeout since compiling time increases with the growth of model size.
 >
 > This is processor cores binding operation regarding the `device_num` and total processor numbers. If you are not expect to do it, remove the operations `taskset` in `scripts/run_distribute_train.sh`
 

model_zoo/research/cv/inception_resnet_v2/README_CN.md

@@ -55,7 +55,7 @@ Inception_ResNet_v2的总体网络架构如下：
 
 ## 混合精度（Ascend）
 
-采用[混合精度](https://www.mindspore.cn/tutorial/training/zh-CN/master/advanced_use/enable_mixed_precision.html)的训练方法使用支持单精度和半精度数据来提高深度学习神经网络的训练速度，同时保持单精度训练所能达到的网络精度。混合精度训练提高计算速度、减少内存使用的同时，支持在特定硬件上训练更大的模型或实现更大批次的训练。
+采用[混合精度](https://www.mindspore.cn/docs/programming_guide/zh-CN/master/enable_mixed_precision.html)的训练方法使用支持单精度和半精度数据来提高深度学习神经网络的训练速度，同时保持单精度训练所能达到的网络精度。混合精度训练提高计算速度、减少内存使用的同时，支持在特定硬件上训练更大的模型或实现更大批次的训练。
 
 以FP16算子为例，如果输入数据类型为FP32，MindSpore后台会自动降低精度来处理数据。用户可打开INFO日志，搜索“reduce precision”查看精度降低的算子。
 
@@ -131,7 +131,7 @@ Major parameters in train.py and config.py are:
     bash scripts/run_standalone_train_ascend.sh DEVICE_ID DATA_DIR
     ```
 
-> 注：RANK_TABLE_FILE可参考[链接](https://www.mindspore.cn/tutorial/training/zh-CN/master/advanced_use/distributed_training_ascend.html)。device_ip可以通过[链接](https://gitee.com/mindspore/mindspore/tree/master/model_zoo/utils/hccl_tools)获取
+> 注：RANK_TABLE_FILE可参考[链接]( https://www.mindspore.cn/docs/programming_guide/zh-CN/master/distributed_training_ascend.html)。device_ip可以通过[链接](https://gitee.com/mindspore/mindspore/tree/master/model_zoo/utils/hccl_tools)获取
 
 ### 结果