!431 modify the vgg16/lstm path to offical/{cv/npl}

Merge pull request !431 from caojian05/ms_master

docs/source_en/network_list.md

@@ -12,7 +12,7 @@
 | Computer Version (CV)  | Image Classification  | [ResNet-50](https://gitee.com/mindspore/mindspore/blob/master/model_zoo/official/cv/resnet/src/resnet.py)          |  Supported |  Supported | Doing
 |Computer Version (CV)  | Image Classification  | [ResNet-101](https://gitee.com/mindspore/mindspore/blob/master/model_zoo/official/cv/resnet/src/resnet.py)                                              |  Supported |Doing | Doing
 |Computer Version (CV)  | Image Classification  | [ResNext50](https://gitee.com/mindspore/mindspore/blob/master/model_zoo/official/cv/resnext50/src/image_classification.py)                                             |  Supported |Doing | Doing
-| Computer Version (CV)  | Image Classification  | [VGG16](https://gitee.com/mindspore/mindspore/blob/master/model_zoo/vgg16/src/vgg.py)                |  Supported |  Doing | Doing
+| Computer Version (CV)  | Image Classification  | [VGG16](https://gitee.com/mindspore/mindspore/blob/master/model_zoo/official/cv/vgg16/src/vgg.py)                |  Supported |  Doing | Doing
 | Computer Version (CV)  | Mobile Image Classification<br>Image Classification<br>Semantic Tegmentation  | [MobileNetV2](https://gitee.com/mindspore/mindspore/blob/master/model_zoo/mobilenetv2/src/mobilenetV2.py)                                            |  Supported |  Supported | Doing
 | Computer Version (CV)  | Mobile Image Classification<br>Image Classification<br>Semantic Tegmentation  | [MobileNetV3](https://gitee.com/mindspore/mindspore/blob/master/model_zoo/mobilenetv3/src/mobilenetV3.py)                                            |  Doing |  Supported | Doing
 |Computer Version (CV)  | Targets Detection  | [SSD](https://gitee.com/mindspore/mindspore/blob/master/model_zoo/official/cv/ssd/src/ssd.py)                   |  Supported |Doing | Doing
@@ -21,7 +21,7 @@
 | Computer Version (CV) | Semantic Segmentation  | [DeeplabV3](https://gitee.com/mindspore/mindspore/blob/master/model_zoo/official/cv/deeplabv3/src/deeplabv3.py)                                           |  Supported |  Doing | Doing
 | Natural Language Processing (NLP) | Natural Language Understanding  | [BERT](https://gitee.com/mindspore/mindspore/blob/master/model_zoo/official/nlp/bert/src/bert_model.py)                                          |  Supported |  Doing | Doing
 | Natural Language Processing (NLP) | Natural Language Understanding  | [Transformer](https://gitee.com/mindspore/mindspore/blob/master/model_zoo/official/nlp/transformer/src/transformer_model.py)                                          |  Supported |  Doing | Doing
-| Natural Language Processing (NLP) | Natural Language Understanding  | [SentimentNet](https://gitee.com/mindspore/mindspore/blob/master/model_zoo/lstm/src/lstm.py)                                          |  Doing |  Supported | Supported
+| Natural Language Processing (NLP) | Natural Language Understanding  | [SentimentNet](https://gitee.com/mindspore/mindspore/blob/master/model_zoo/official/nlp/lstm/src/lstm.py)                                          |  Doing |  Supported | Supported
 | Natural Language Processing (NLP) | Natural Language Understanding  | [MASS](https://gitee.com/mindspore/mindspore/blob/master/model_zoo/official/nlp/mass/src/transformer)                                          |  Supported |  Doing | Doing
 | Recommender | Recommender System, CTR prediction  | [DeepFM](https://gitee.com/mindspore/mindspore/blob/master/model_zoo/official/recommend/deepfm/src/deepfm.py)                                          |  Supported |  Doing | Doing
 | Recommender | Recommender System, Search ranking  | [Wide&Deep](https://gitee.com/mindspore/mindspore/blob/master/model_zoo/official/recommend/wide_and_deep/src/wide_and_deep.py)                                          |  Supported |  Doing | Doing

docs/source_zh_cn/network_list.md

@@ -12,7 +12,7 @@
 | 计算机视觉（CV）  | 图像分类（Image Classification）  | [ResNet-50](https://gitee.com/mindspore/mindspore/blob/master/model_zoo/official/cv/resnet/src/resnet.py)          |  Supported |  Supported | Doing
 |计算机视觉（CV）  | 图像分类（Image Classification）  | [ResNet-101](https://gitee.com/mindspore/mindspore/blob/master/model_zoo/official/cv/resnet/src/resnet.py)                                              |  Supported |Doing | Doing
 |计算机视觉（CV）  | 图像分类（Image Classification）  | [ResNext50](https://gitee.com/mindspore/mindspore/blob/master/model_zoo/official/cv/resnext50/src/image_classification.py)                                             |  Supported |Doing | Doing
-| 计算机视觉（CV）  | 图像分类（Image Classification）  | [VGG16](https://gitee.com/mindspore/mindspore/blob/master/model_zoo/vgg16/src/vgg.py)                |  Supported |  Doing | Doing
+| 计算机视觉（CV）  | 图像分类（Image Classification）  | [VGG16](https://gitee.com/mindspore/mindspore/blob/master/model_zoo/official/cv/vgg16/src/vgg.py)                |  Supported |  Doing | Doing
 | 计算机视觉（CV）  | 移动端图像分类（Mobile Image Classification）<br>目标检测（Image Classification）<br>语义分割（Semantic Tegmentation）  | [MobileNetV2](https://gitee.com/mindspore/mindspore/blob/master/model_zoo/mobilenetv2/src/mobilenetV2.py)                                            |  Supported |  Supported | Doing
 | 计算机视觉（CV）  | 移动端图像分类（Mobile Image Classification）<br>目标检测（Image Classification）<br>语义分割（Semantic Tegmentation）  | [MobileNetV3](https://gitee.com/mindspore/mindspore/blob/master/model_zoo/mobilenetv3/src/mobilenetV3.py)                                            |  Doing |  Supported | Doing
 |计算机视觉（CV）  | 目标检测（Targets Detection）  | [SSD](https://gitee.com/mindspore/mindspore/blob/master/model_zoo/official/cv/ssd/src/ssd.py)                   |  Supported |Doing | Doing
@@ -21,7 +21,7 @@
 | 计算机视觉（CV） | 语义分割（Semantic Segmentation）  | [DeeplabV3](https://gitee.com/mindspore/mindspore/blob/master/model_zoo/official/cv/deeplabv3/src/deeplabv3.py)                                      |  Supported |  Doing | Doing
 | 自然语言处理（NLP） | 自然语言理解（Natural Language Understanding）  | [BERT](https://gitee.com/mindspore/mindspore/blob/master/model_zoo/official/nlp/bert/src/bert_model.py)                                          |  Supported |  Doing | Doing
 | 自然语言处理（NLP） | 自然语言理解（Natural Language Understanding）  | [Transformer](https://gitee.com/mindspore/mindspore/blob/master/model_zoo/official/nlp/transformer/src/transformer_model.py)                                          |  Supported |  Doing | Doing
-| 自然语言处理（NLP） | 自然语言理解（Natural Language Understanding）  | [SentimentNet](https://gitee.com/mindspore/mindspore/blob/master/model_zoo/lstm/src/lstm.py)                                          |  Doing |  Supported | Supported
+| 自然语言处理（NLP） | 自然语言理解（Natural Language Understanding）  | [SentimentNet](https://gitee.com/mindspore/mindspore/blob/master/model_zoo/official/nlp/lstm/src/lstm.py)                                          |  Doing |  Supported | Supported
 | 自然语言处理（NLP） | 自然语言理解（Natural Language Understanding）  | [MASS](https://gitee.com/mindspore/mindspore/blob/master/model_zoo/official/nlp/mass/src/transformer)                                          |  Supported |  Doing | Doing
 | 推荐（Recommender） | 推荐系统、点击率预估（Recommender System, CTR prediction）  | [DeepFM](https://gitee.com/mindspore/mindspore/blob/master/model_zoo/official/recommend/deepfm/src/deepfm.py)                                          |  Supported |  Doing | Doing
 | 推荐（Recommender） | 推荐系统、搜索、排序（Recommender System, Search ranking）  | [Wide&Deep](https://gitee.com/mindspore/mindspore/blob/master/model_zoo/official/recommend/wide_and_deep/src/wide_and_deep.py)                                          |  Supported |  Doing | Doing

tutorials/source_en/advanced_use/nlp_application.md

@@ -86,7 +86,7 @@ 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/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/blob/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.
@@ -156,7 +156,7 @@ if args.preprocess == "true":
 ```
 > After convert success, we can file `mindrecord` files under the directory `preprocess_path`. Usually, this operation does not need to be performed every time while the data set is unchanged.
 
-> `convert_to_mindrecord` You can find the complete definition at: <https://gitee.com/mindspore/mindspore/blob/master/model_zoo/lstm/src/dataset.py>
+> `convert_to_mindrecord` You can find the complete definition at: <https://gitee.com/mindspore/mindspore/blob/master/model_zoo/official/nlp/lstm/src/dataset.py>
 
 > It consists of two steps:
 >1. Process the text dataset, including encoding, word segmentation, alignment, and processing the original GloVe data to adapt to the network structure.
@@ -176,7 +176,7 @@ network = SentimentNet(vocab_size=embedding_table.shape[0],
                        weight=Tensor(embedding_table),
                        batch_size=cfg.batch_size)
 ```
-> `SentimentNet` You can find the complete definition at: <https://gitee.com/mindspore/mindspore/blob/master/model_zoo/lstm/src/lstm.py>
+> `SentimentNet` You can find the complete definition at: <https://gitee.com/mindspore/mindspore/blob/master/model_zoo/official/nlp/lstm/src/lstm.py>
 
 ### Pre-Training
 
@@ -215,7 +215,7 @@ else:
     model.train(cfg.num_epochs, ds_train, callbacks=[time_cb, ckpoint_cb, loss_cb])
 print("============== Training Success ==============")
 ```
-> `lstm_create_dataset` You can find the complete definition at: <https://gitee.com/mindspore/mindspore/blob/master/model_zoo/lstm/src/dataset.py>
+> `lstm_create_dataset` You can find the complete definition at: <https://gitee.com/mindspore/mindspore/blob/master/model_zoo/official/nlp/lstm/src/dataset.py>
 
 ### Validating the Model
 

tutorials/source_zh_cn/advanced_use/nlp_application.md

@@ -87,7 +87,7 @@ $F1分数 = (2 * Precision * Recall) / (Precision + Recall)$
     > 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/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/blob/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。
@@ -156,7 +156,7 @@ if args.preprocess == "true":
 ```
 > 转换成功后会在`preprocess_path`路径下生成`mindrecord`文件； 通常该操作在数据集不变的情况下，无需每次训练都执行。
 
-> `convert_to_mindrecord`函数的具体实现请参考<https://gitee.com/mindspore/mindspore/blob/master/model_zoo/lstm/src/dataset.py>
+> `convert_to_mindrecord`函数的具体实现请参考<https://gitee.com/mindspore/mindspore/blob/master/model_zoo/official/nlp/lstm/src/dataset.py>
 
 > 其中包含两大步骤：
 > 1. 解析文本数据集，包括编码、分词、对齐、处理GloVe原始数据，使之能够适应网络结构。
@@ -176,7 +176,7 @@ network = SentimentNet(vocab_size=embedding_table.shape[0],
                        weight=Tensor(embedding_table),
                        batch_size=cfg.batch_size)
 ```
-> `SentimentNet`网络结构的具体实现请参考<https://gitee.com/mindspore/mindspore/blob/master/model_zoo/lstm/src/lstm.py>
+> `SentimentNet`网络结构的具体实现请参考<https://gitee.com/mindspore/mindspore/blob/master/model_zoo/official/nlp/lstm/src/lstm.py>
 
 ### 预训练模型
 
@@ -215,7 +215,7 @@ else:
     model.train(cfg.num_epochs, ds_train, callbacks=[time_cb, ckpoint_cb, loss_cb])
 print("============== Training Success ==============")
 ```
-> `lstm_create_dataset`函数的具体实现请参考<https://gitee.com/mindspore/mindspore/blob/master/model_zoo/lstm/src/dataset.py>
+> `lstm_create_dataset`函数的具体实现请参考<https://gitee.com/mindspore/mindspore/blob/master/model_zoo/official/nlp/lstm/src/dataset.py>
 
 ### 模型验证