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提交 150d61be 编写于 作者: C CaoJian
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modify the vgg16/lstm path to offical/{cv/npl}

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docs/source_en/network_list.md
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| 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-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 | [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 | [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 | [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) | 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 |Computer Version (CV) | Targets Detection | [SSD](https://gitee.com/mindspore/mindspore/blob/master/model_zoo/official/cv/ssd/src/ssd.py) | Supported |Doing | Doing
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| Computer Version (CV) | Semantic Segmentation | [DeeplabV3](https://gitee.com/mindspore/mindspore/blob/master/model_zoo/official/cv/deeplabv3/src/deeplabv3.py) | Supported | Doing | Doing | 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 | [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 | [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 | 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, 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 | 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
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docs/source_zh_cn/network_list.md
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| 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-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） | [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） | [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） | [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） | 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 |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
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| 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 | 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） | [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） | [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 | 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、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 | 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
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tutorials/source_en/advanced_use/nlp_application.md
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...@@ -86,7 +86,7 @@ Currently, MindSpore GPU and CPU supports SentimentNet network based on the long ...@@ -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. 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. 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/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/dataset.py`：dataset related definition，include MindRecord file convert and data-preprocess, etc.
> - `src/imdb.py`： the util class for parsing IMDB dataset. > - `src/imdb.py`： the util class for parsing IMDB dataset.
...@@ -156,7 +156,7 @@ if args.preprocess == "true": ...@@ -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. > 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: > 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. >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], ...@@ -176,7 +176,7 @@ network = SentimentNet(vocab_size=embedding_table.shape[0],
weight=Tensor(embedding_table), weight=Tensor(embedding_table),
batch_size=cfg.batch_size) 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 ### Pre-Training
...@@ -215,7 +215,7 @@ else: ...@@ -215,7 +215,7 @@ else:
model.train(cfg.num_epochs, ds_train, callbacks=[time_cb, ckpoint_cb, loss_cb]) model.train(cfg.num_epochs, ds_train, callbacks=[time_cb, ckpoint_cb, loss_cb])
print("============== Training Success ==============") 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 ### Validating the Model
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tutorials/source_zh_cn/advanced_use/nlp_application.md
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...@@ -87,7 +87,7 @@ $F1分数 = (2 * Precision * Recall) / (Precision + Recall)$ ...@@ -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。 > 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。 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/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/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。 > - `src/imdb.py`： 解析IMDB数据集的工具。
...@@ -156,7 +156,7 @@ if args.preprocess == "true": ...@@ -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。 > 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： > 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。 > 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], ...@@ -176,7 +176,7 @@ network = SentimentNet(vocab_size=embedding_table.shape[0],
weight=Tensor(embedding_table), weight=Tensor(embedding_table),
batch_size=cfg.batch_size) 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 ### 预训练模型
...@@ -215,7 +215,7 @@ else: ...@@ -215,7 +215,7 @@ else:
model.train(cfg.num_epochs, ds_train, callbacks=[time_cb, ckpoint_cb, loss_cb]) model.train(cfg.num_epochs, ds_train, callbacks=[time_cb, ckpoint_cb, loss_cb])
print("============== Training Success ==============") 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 ### 模型验证
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