diff --git a/docs/source_en/network_list.md b/docs/source_en/network_list.md index 3360fc6b2c9436aa68978761b21258ada580c5ff..774bcafadb358a44c4471fad231845e9f8f54100 100644 --- a/docs/source_en/network_list.md +++ b/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
Image Classification
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
Image Classification
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 diff --git a/docs/source_zh_cn/network_list.md b/docs/source_zh_cn/network_list.md index 8c45050e716fdeb4d1ad5fb87b0acc9e9c178737..1c0b15c44ddbb81dee062e33a4bf1db76743d841 100644 --- a/docs/source_zh_cn/network_list.md +++ b/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）
目标检测（Image Classification）
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）
目标检测（Image Classification）
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 diff --git a/tutorials/source_en/advanced_use/nlp_application.md b/tutorials/source_en/advanced_use/nlp_application.md index bc531f49635c78ad50092691f1657f9ffeb50c8a..4e56d1116a3ffb33aadd2dd61658e4825cdac440 100644 --- a/tutorials/source_en/advanced_use/nlp_application.md +++ b/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： +> The current sample is for the Ascend 910 AI processor. You can find the complete executable sample code at： > - `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: +> `convert_to_mindrecord` You can find the complete definition at: > 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: +> `SentimentNet` You can find the complete definition at: ### 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: +> `lstm_create_dataset` You can find the complete definition at: ### Validating the Model diff --git a/tutorials/source_zh_cn/advanced_use/nlp_application.md b/tutorials/source_zh_cn/advanced_use/nlp_application.md index 540f4e19096fcf08db98f43541b397cb5cc6bf7f..7e1b05b9d3b1bbda6d013d56fa15aba23621431e 100644 --- a/tutorials/source_zh_cn/advanced_use/nlp_application.md +++ b/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： +> 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： > - `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 +> `convert_to_mindrecord`函数的具体实现请参考 > 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 +> `SentimentNet`网络结构的具体实现请参考 ### 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 +> `lstm_create_dataset`函数的具体实现请参考 ### 模型验证