Update link for markdown

docs/source_en/network_list.md

@@ -10,7 +10,7 @@
 
 <!-- /TOC -->
 
-<a href="https://gitee.com/mindspore/docs/tree/master/docs/source_en/network_list.md" target="_blank"><img src="./_static/logo_source.png"></a>
+<a href="https://gitee.com/mindspore/docs/blob/master/docs/source_en/network_list.md" target="_blank"><img src="./_static/logo_source.png"></a>
 
 ## Model Zoo
 
@@ -53,7 +53,7 @@ Domain | Sub Domain| Network | Dataset | CPU   | GPU | Ascend | 0.5.0-beta*
 |Computer Vision (CV) | Image Classification| [LeNet](https://gitee.com/mindspore/mindspore/blob/master/model_zoo/official/cv/lenet/src/lenet.py)| MNIST |   |   | ✓  | [Download](http://download.mindspore.cn/model_zoo/official/cv/lenet/lenet_ascend_0.5.0_mnist_official_classification_20200716.tar.gz)
 |Computer Vision (CV) | Image Classification| [VGG16](https://gitee.com/mindspore/mindspore/blob/master/model_zoo/official/cv/vgg16/src/vgg.py)|    CIFAR-10 | |   | ✓ | [Download](http://download.mindspore.cn/model_zoo/official/cv/vgg/vgg16_ascend_0.5.0_cifar10_official_classification_20200715.tar.gz)
 |Computer Vision (CV) | Image Classification| [ResNet-50](https://gitee.com/mindspore/mindspore/blob/master/model_zoo/official/cv/resnet/src/resnet.py) | CIFAR-10|   |    | ✓ |[Download](http://download.mindspore.cn/model_zoo/official/cv/resnet/resnet50_v1.5_ascend_0.3.0_cifar10_official_classification_20200718.tar.gz)
-|Computer Vision (CV)  | Targets Detection| [YoloV3-DarkNet53](https://gitee.com/mindspore/mindspore/tree/master/model_zoo/official/cv/yolov3_darknet53/src/yolo.py) | COCO 2014|   |    | ✓  | [Download](http://download.mindspore.cn/model_zoo/official/cv/yolo/yolov3_darknet53_ascend_0.5.0_coco2014_official_object_detection_20200717.tar.gz) 
+|Computer Vision (CV)  | Targets Detection| [YoloV3-DarkNet53](https://gitee.com/mindspore/mindspore/blob/master/model_zoo/official/cv/yolov3_darknet53/src/yolo.py) | COCO 2014|   |    | ✓  | [Download](http://download.mindspore.cn/model_zoo/official/cv/yolo/yolov3_darknet53_ascend_0.5.0_coco2014_official_object_detection_20200717.tar.gz) 
 | Natural Language Processing (NLP) | Natural Language Understanding| [BERT_Base](https://gitee.com/mindspore/mindspore/blob/master/model_zoo/official/nlp/bert/src/bert_model.py) | zhwiki |   |    | ✓  |  [Download](http://download.mindspore.cn/model_zoo/official/nlp/bert/bert_base_ascend_0.5.0_cn-wiki_official_nlp_20200720.tar.gz)
 | Natural Language Processing (NLP) | Natural Language Understanding| [BERT_NEZHA](https://gitee.com/mindspore/mindspore/blob/master/model_zoo/official/nlp/bert/src/bert_model.py)| zhwiki|  |    | ✓  |  [Download](http://download.mindspore.cn/model_zoo/official/nlp/bert/bert_nezha_ascend_0.5.0_cn-wiki_official_nlp_20200720.tar.gz) 
 | Natural Language Processing (NLP) | Natural Language Understanding| [Transformer](https://gitee.com/mindspore/mindspore/blob/master/model_zoo/official/nlp/transformer/src/transformer_model.py)| WMT English-German|   |   | ✓  | [Download](http://download.mindspore.cn/model_zoo/official/nlp/transformer/transformer_ascend_0.5.0_wmtende_official_machine_translation_20200713.tar.gz)

docs/source_zh_cn/network_list.md

@@ -10,7 +10,7 @@
 
 <!-- /TOC -->
 
-<a href="https://gitee.com/mindspore/docs/tree/master/docs/source_zh_cn/network_list.md" target="_blank"><img src="./_static/logo_source.png"></a>
+<a href="https://gitee.com/mindspore/docs/blob/master/docs/source_zh_cn/network_list.md" target="_blank"><img src="./_static/logo_source.png"></a>
 
 ## Model Zoo
 

lite/tutorials/source_zh_cn/quick_start/quick_start.md

@@ -28,7 +28,7 @@
 2. 将模型转换成MindSpore Lite模型格式。
 3. 在端侧使用MindSpore Lite推理模型。详细说明如何在端侧利用MindSpore Lite C++ API（Android JNI）和MindSpore Lite图像分类模型完成端侧推理，实现对设备摄像头捕获的内容进行分类，并在APP图像预览界面中，显示出最可能的分类结果。
    
-> 你可以在这里找到[Android图像分类模型](https://download.mindspore.cn/model_zoo/official/lite/mobilenetv2_openimage_lite)和[示例代码](https://gitee.com/mindspore/mindspore/blob/master/model_zoo/official/lite/image_classification)。
+> 你可以在这里找到[Android图像分类模型](https://download.mindspore.cn/model_zoo/official/lite/mobilenetv2_openimage_lite)和[示例代码](https://gitee.com/mindspore/mindspore/tree/master/model_zoo/official/lite/image_classification)。
 
 ## 选择模型
 

tutorials/source_en/advanced_use/computer_vision_application.md

@@ -61,7 +61,7 @@ Next, let's use MindSpore to solve the image classification task. The overall pr
 5. Call the high-level `Model` API to train and save the model file.
 6. Load the saved model for inference.
 
-> This example is for the hardware platform of the Ascend 910 AI processor. You can find the complete executable sample code at： <https://gitee.com/mindspore/docs/blob/master/tutorials/tutorial_code/resnet>.
+> This example is for the hardware platform of the Ascend 910 AI processor. You can find the complete executable sample code at： <https://gitee.com/mindspore/docs/tree/master/tutorials/tutorial_code/resnet>.
 
 The key parts of the task process code are explained below.
 

tutorials/source_en/advanced_use/network_migration.md

@@ -19,7 +19,7 @@
 
 <!-- /TOC -->
 
-<a href="https://gitee.com/mindspore/docs/tree/master/tutorials/source_en/advanced_use/network_migration.md" target="_blank"><img src="../_static/logo_source.png"></a>
+<a href="https://gitee.com/mindspore/docs/blob/master/tutorials/source_en/advanced_use/network_migration.md" target="_blank"><img src="../_static/logo_source.png"></a>
 
 ## Overview
 

tutorials/source_en/advanced_use/nlp_application.md

@@ -88,7 +88,7 @@ Currently, MindSpore GPU and CPU supports SentimentNet network based on the long
     Long short-term memory (LSTM) is an artificial recurrent neural network (RNN) architecture used for processing and predicting an important event with a long interval and delay in a time sequence. For details, refer to online documentation.
 3. After the model is obtained, use the validation dataset to check the accuracy of model.
 
-> The current sample is for the Ascend 910 AI processor. You can find the complete executable sample code at：<https://gitee.com/mindspore/mindspore/blob/master/model_zoo/official/nlp/lstm>
+> The current sample is for the Ascend 910 AI processor. You can find the complete executable sample code at：<https://gitee.com/mindspore/mindspore/tree/master/model_zoo/official/nlp/lstm>
 > - `src/config.py`：some configurations on the network, including the batch size and number of training epochs.
 > - `src/dataset.py`：dataset related definition，include MindRecord file convert and data-preprocess, etc.
 > - `src/imdb.py`： the util class for parsing IMDB dataset.

tutorials/source_en/use/custom_operator.md

@@ -38,7 +38,7 @@ This section takes a Square operator as an example to describe how to customize
 The primitive of an operator is a subclass inherited from `PrimitiveWithInfer`. The type name of the subclass is the operator name.
 
 The definition of the custom operator primitive is the same as that of the built-in operator primitive.  
-- The attribute is defined by the input parameter of the constructor function `__init__`. The operator in this test case has no attribute. Therefore, `__init__` has only one input parameter. For details about test cases in which operators have attributes, see [custom add3](https://gitee.com/mindspore/mindspore/tree/master/tests/st/ops/custom_ops_tbe/cus_add3.py) in the MindSpore source code.
+- The attribute is defined by the input parameter of the constructor function `__init__`. The operator in this test case has no attribute. Therefore, `__init__` has only one input parameter. For details about test cases in which operators have attributes, see [custom add3](https://gitee.com/mindspore/mindspore/blob/master/tests/st/ops/custom_ops_tbe/cus_add3.py) in the MindSpore source code.
 - The input and output names are defined by the `init_prim_io_names` function.
 - The shape inference method of the output tensor is defined in the `infer_shape` function, and the dtype inference method of the output tensor is defined in the `infer_dtype` function.
 

tutorials/source_zh_cn/advanced_use/computer_vision_application.md

@@ -63,7 +63,7 @@ MindSpore当前支持的图像分类网络包括：典型网络LeNet、AlexNet
 6. 加载保存的模型进行推理
 
 
-> 本例面向Ascend 910 AI处理器硬件平台，你可以在这里下载完整的样例代码：<https://gitee.com/mindspore/docs/blob/master/tutorials/tutorial_code/resnet>
+> 本例面向Ascend 910 AI处理器硬件平台，你可以在这里下载完整的样例代码：<https://gitee.com/mindspore/docs/tree/master/tutorials/tutorial_code/resnet>
 
 下面对任务流程中各个环节及代码关键片段进行解释说明。
 

tutorials/source_zh_cn/advanced_use/gradient_accumulation.md

@@ -59,11 +59,11 @@ from model_zoo.official.cv.lenet.src.lenet import LeNet5
 
 ### 加载数据集
 
-利用MindSpore的dataset提供的`MnistDataset`接口加载MNIST数据集，此部分代码由model_zoo中lenet目录下的[dataset.py](<https://gitee.com/mindspore/mindspore/tree/master/model_zoo/official/cv/lenet/src/dataset.py>)导入。
+利用MindSpore的dataset提供的`MnistDataset`接口加载MNIST数据集，此部分代码由model_zoo中lenet目录下的[dataset.py](<https://gitee.com/mindspore/mindspore/blob/master/model_zoo/official/cv/lenet/src/dataset.py>)导入。
 
 ### 定义网络
 
-这里以LeNet网络为例进行介绍，当然也可以使用其它的网络，如ResNet-50、BERT等, 此部分代码由model_zoo中lenet目录下的[lenet.py](<https://gitee.com/mindspore/mindspore/tree/master/model_zoo/official/cv/lenet/src/lenet.py>)导入。
+这里以LeNet网络为例进行介绍，当然也可以使用其它的网络，如ResNet-50、BERT等, 此部分代码由model_zoo中lenet目录下的[lenet.py](<https://gitee.com/mindspore/mindspore/blob/master/model_zoo/official/cv/lenet/src/lenet.py>)导入。
 
 ### 定义训练模型
 将训练流程拆分为正向反向训练、参数更新和累积梯度清理三个部分：
@@ -253,7 +253,7 @@ if __name__ == "__main__":
 
 **验证模型**
 
-通过model_zoo中lenet目录下的[eval.py](<https://gitee.com/mindspore/mindspore/tree/master/model_zoo/official/cv/lenet/train.py>)，使用保存的CheckPoint文件，加载验证数据集，进行验证。
+通过model_zoo中lenet目录下的[eval.py](<https://gitee.com/mindspore/mindspore/blob/master/model_zoo/official/cv/lenet/train.py>)，使用保存的CheckPoint文件，加载验证数据集，进行验证。
 
 ```shell
 $ python eval.py --data_path=./MNIST_Data --ckpt_path=./gradient_accumulation.ckpt

tutorials/source_zh_cn/advanced_use/nlp_application.md

@@ -89,7 +89,7 @@ $F1分数 = (2 * Precision * Recall) / (Precision + Recall)$
     > LSTM（Long short-term memory，长短期记忆）网络是一种时间循环神经网络，适合于处理和预测时间序列中间隔和延迟非常长的重要事件。具体介绍可参考网上资料，在此不再赘述。
 3. 得到模型之后，使用验证数据集，查看模型精度情况。
 
-> 本例面向GPU或CPU硬件平台，你可以在这里下载完整的样例代码：<https://gitee.com/mindspore/mindspore/blob/master/model_zoo/official/nlp/lstm>
+> 本例面向GPU或CPU硬件平台，你可以在这里下载完整的样例代码：<https://gitee.com/mindspore/mindspore/tree/master/model_zoo/official/nlp/lstm>
 > - `src/config.py`：网络中的一些配置，包括`batch size`、进行几次epoch训练等。
 > - `src/dataset.py`：数据集相关，包括转换成MindRecord文件，数据预处理等。
 > - `src/imdb.py`： 解析IMDB数据集的工具。

tutorials/source_zh_cn/use/custom_operator.md

@@ -38,7 +38,7 @@
 每个算子的原语是一个继承于`PrimitiveWithInfer`的子类，其类型名称即是算子名称。
 
 自定义算子原语与内置算子原语的接口定义完全一致：  
-- 属性由构造函数`__init__`的入参定义。本用例的算子没有属性，因此`__init__`没有额外的入参。带属性的用例可参考MindSpore源码中的[custom add3](https://gitee.com/mindspore/mindspore/tree/master/tests/st/ops/custom_ops_tbe/cus_add3.py)用例。
+- 属性由构造函数`__init__`的入参定义。本用例的算子没有属性，因此`__init__`没有额外的入参。带属性的用例可参考MindSpore源码中的[custom add3](https://gitee.com/mindspore/mindspore/blob/master/tests/st/ops/custom_ops_tbe/cus_add3.py)用例。
 - 输入输出的名称通过`init_prim_io_names`函数定义。
 - 输出Tensor的shape推理方法在`infer_shape`函数中定义，输出Tensor的dtype推理方法在`infer_dtype`函数中定义。