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docs/source_en/benchmark.md
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......@@ -16,7 +16,7 @@ For details about the MindSpore pre-trained model, see [Model Zoo](https://gitee
| | | | | Ascend: 16 * Ascend 910 </br> CPU：384 Cores | Mixed | 256 | 32768 images/sec | 0.96 |
1. The preceding performance is obtained based on ModelArts, the HUAWEI CLOUD AI development platform. It is the average performance obtained by the Ascend 910 AI processor during the overall training process.
2. For details about other open source frameworks, see [ResNet-50 v1.5 for TensorFlow](https://github.com/NVIDIA/DeepLearningExamples/tree/master/TensorFlow/Classification/RN50v1.5#nvidia-dgx-2-16x-v100-32g).
2. For details about other open source frameworks, see [ResNet-50 v1.5 for TensorFlow](https://github.com/NVIDIA/DeepLearningExamples/tree/master/TensorFlow/Classification/ConvNets/resnet50v1.5).
### BERT
......
docs/source_en/network_list.md
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# Network List
<a href="https://gitee.com/mindspore/docs/tree/r0.5/docs/source_en/network_list.md" target="_blank"><img src="./_static/logo_source.png"></a>
| Domain | Sub Domain | Network | Ascend | GPU | CPU
|:------ |:------| :----------- |:------ |:------ |:-----
|Computer Version (CV) | Image Classification | [AlexNet](https://gitee.com/mindspore/mindspore/blob/r0.5/model_zoo/alexnet/src/alexnet.py) | Supported | Supported | Doing
| Computer Version (CV) | Image Classification | [GoogleNet](https://gitee.com/mindspore/mindspore/blob/r0.5/model_zoo/googlenet/src/googlenet.py) | Supported | Doing | Doing
| Computer Version (CV) | Image Classification | [LeNet](https://gitee.com/mindspore/mindspore/blob/r0.5/model_zoo/lenet/src/lenet.py) | Supported | Supported | Supported
| Computer Version (CV) | Image Classification | [ResNet-50](https://gitee.com/mindspore/mindspore/blob/r0.5/model_zoo/resnet/src/resnet.py) | Supported | Supported | Doing
|Computer Version (CV) | Image Classification | [ResNet-101](https://gitee.com/mindspore/mindspore/blob/r0.5/model_zoo/resnet/src/resnet.py) | Supported |Doing | Doing
| Computer Version (CV) | Image Classification | [VGG16](https://gitee.com/mindspore/mindspore/blob/r0.5/model_zoo/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/r0.5/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/r0.5/model_zoo/mobilenetv3/src/mobilenetV3.py) | Doing | Supported | Doing
|Computer Version (CV) | Targets Detection | [SSD](https://gitee.com/mindspore/mindspore/blob/r0.5/model_zoo/ssd/src/ssd.py) | Supported |Doing | Doing
| Computer Version (CV) | Targets Detection | [YoloV3](https://gitee.com/mindspore/mindspore/blob/r0.5/model_zoo/yolov3/src/yolov3.py) | Supported | Doing | Doing
| Computer Version (CV) | Targets Detection | [FasterRCNN](https://gitee.com/mindspore/mindspore/tree/r0.5/model_zoo/faster_rcnn/src/FasterRcnn) | Supported | Doing | Doing
| Computer Version (CV) | Semantic Segmentation | [Deeplabv3](https://gitee.com/mindspore/mindspore/blob/r0.5/model_zoo/deeplabv3/src/deeplabv3.py) | Supported | Doing | Doing
| Natural Language Processing (NLP) | Natural Language Understanding | [BERT](https://gitee.com/mindspore/mindspore/blob/r0.5/model_zoo/bert/src/bert_model.py) | Supported | Doing | Doing
| Natural Language Processing (NLP) | Natural Language Understanding | [Transformer](https://gitee.com/mindspore/mindspore/blob/r0.5/model_zoo/Transformer/src/transformer_model.py) | Supported | Doing | Doing
| Natural Language Processing (NLP) | Natural Language Understanding | [SentimentNet](https://gitee.com/mindspore/mindspore/blob/r0.5/model_zoo/lstm/src/lstm.py) | Doing | Supported | Supported
| Recommender | Recommender System, CTR prediction | [DeepFM](https://gitee.com/mindspore/mindspore/blob/r0.5/model_zoo/deepfm/src/deepfm.py) | Supported | Doing | Doing
| Recommender | Recommender System, Search ranking | [Wide&Deep](https://gitee.com/mindspore/mindspore/blob/r0.5/model_zoo/wide_and_deep/src/wide_and_deep.py) | Supported | Doing | Doing
| Graph Neural Networks（GNN）| Text Classification | [GCN](https://gitee.com/mindspore/mindspore/tree/master/model_zoo/gcn/src/gcn.py) | Supported | Doing | Doing
| Graph Neural Networks（GNN）| Text Classification | [GAT](https://gitee.com/mindspore/mindspore/tree/master/model_zoo/gat/src/gat.py) | Supported | Doing | Doing
# Network List
<a href="https://gitee.com/mindspore/docs/tree/r0.5/docs/source_en/network_list.md" target="_blank"><img src="./_static/logo_source.png"></a>
| Domain | Sub Domain | Network | Ascend | GPU | CPU
|:------ |:------| :----------- |:------ |:------ |:-----
|Computer Version (CV) | Image Classification | [AlexNet](https://gitee.com/mindspore/mindspore/blob/r0.5/model_zoo/alexnet/src/alexnet.py) | Supported | Supported | Doing
| Computer Version (CV) | Image Classification | [GoogleNet](https://gitee.com/mindspore/mindspore/blob/r0.5/model_zoo/googlenet/src/googlenet.py) | Supported | Doing | Doing
| Computer Version (CV) | Image Classification | [LeNet](https://gitee.com/mindspore/mindspore/blob/r0.5/model_zoo/lenet/src/lenet.py) | Supported | Supported | Supported
| Computer Version (CV) | Image Classification | [ResNet-50](https://gitee.com/mindspore/mindspore/blob/r0.5/model_zoo/resnet/src/resnet.py) | Supported | Supported | Doing
|Computer Version (CV) | Image Classification | [ResNet-101](https://gitee.com/mindspore/mindspore/blob/r0.5/model_zoo/resnet/src/resnet.py) | Supported |Doing | Doing
|Computer Version (CV) | Image Classification | [ResNext50](https://gitee.com/mindspore/mindspore/blob/r0.5/model_zoo/resnext50/src/image_classification.py) | Supported |Doing | Doing
| Computer Version (CV) | Image Classification | [VGG16](https://gitee.com/mindspore/mindspore/blob/r0.5/model_zoo/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/r0.5/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/r0.5/model_zoo/mobilenetv3/src/mobilenetV3.py) | Doing | Supported | Doing
|Computer Version (CV) | Targets Detection | [SSD](https://gitee.com/mindspore/mindspore/blob/r0.5/model_zoo/ssd/src/ssd.py) | Supported |Doing | Doing
| Computer Version (CV) | Targets Detection | [YoloV3](https://gitee.com/mindspore/mindspore/blob/r0.5/model_zoo/yolov3/src/yolov3.py) | Supported | Doing | Doing
| Computer Version (CV) | Targets Detection | [FasterRCNN](https://gitee.com/mindspore/mindspore/tree/r0.5/model_zoo/faster_rcnn/src/FasterRcnn) | Supported | Doing | Doing
| Computer Version (CV) | Semantic Segmentation | [Deeplabv3](https://gitee.com/mindspore/mindspore/blob/r0.5/model_zoo/deeplabv3/src/deeplabv3.py) | Supported | Doing | Doing
| Natural Language Processing (NLP) | Natural Language Understanding | [BERT](https://gitee.com/mindspore/mindspore/blob/r0.5/model_zoo/bert/src/bert_model.py) | Supported | Doing | Doing
| Natural Language Processing (NLP) | Natural Language Understanding | [Transformer](https://gitee.com/mindspore/mindspore/blob/r0.5/model_zoo/Transformer/src/transformer_model.py) | Supported | Doing | Doing
| Natural Language Processing (NLP) | Natural Language Understanding | [SentimentNet](https://gitee.com/mindspore/mindspore/blob/r0.5/model_zoo/lstm/src/lstm.py) | Doing | Supported | Supported
| Natural Language Processing (NLP) | Natural Language Understanding | [MASS](https://gitee.com/mindspore/mindspore/tree/r0.5/model_zoo/mass/src/transformer) | Supported | Doing | Doing
| Recommender | Recommender System, CTR prediction | [DeepFM](https://gitee.com/mindspore/mindspore/blob/r0.5/model_zoo/deepfm/src/deepfm.py) | Supported | Doing | Doing
| Recommender | Recommender System, Search ranking | [Wide&Deep](https://gitee.com/mindspore/mindspore/blob/r0.5/model_zoo/wide_and_deep/src/wide_and_deep.py) | Supported | Doing | Doing
| Graph Neural Networks（GNN）| Text Classification | [GCN](https://gitee.com/mindspore/mindspore/blob/r0.5/model_zoo/gcn/src/gcn.py) | Supported | Doing | Doing
| Graph Neural Networks（GNN）| Text Classification | [GAT](https://gitee.com/mindspore/mindspore/blob/r0.5/model_zoo/gat/src/gat.py) | Supported | Doing | Doing
docs/source_en/operator_list.md
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| [mindspore.nn.BatchNorm2d](https://www.mindspore.cn/api/en/r0.5/api/python/mindspore/mindspore.nn.html#mindspore.nn.BatchNorm2d) | Supported | Supported | Doing |layer/normalization
| [mindspore.nn.GroupNorm](https://www.mindspore.cn/api/en/r0.5/api/python/mindspore/mindspore.nn.html#mindspore.nn.GroupNorm) | Supported | Doing | Doing |layer/normalization
| [mindspore.nn.LayerNorm](https://www.mindspore.cn/api/en/r0.5/api/python/mindspore/mindspore.nn.html#mindspore.nn.LayerNorm) | Supported | Supported | Doing |layer/normalization
| [mindspore.nn.MatrixDiag](https://www.mindspore.cn/api/en/r0.5/api/python/mindspore/mindspore.nn.html#mindspore.nn.MatrixDiag) | Supported | Doing | Doing | layer/normalization
| [mindspore.nn.MatrixDiagPart](https://www.mindspore.cn/api/en/r0.5/api/python/mindspore/mindspore.nn.html#mindspore.nn.MatrixDiagPart) | Supported | Doing | Doing | layer/normalization
| [mindspore.nn.MatrixSetDiag](https://www.mindspore.cn/api/en/r0.5/api/python/mindspore/mindspore.nn.html#mindspore.nn.MatrixSetDiag) | Supported | Doing | Doing | layer/normalization
| [mindspore.nn.LinSpace](https://www.mindspore.cn/api/en/r0.5/api/python/mindspore/mindspore.nn.html#mindspore.nn.LinSpace) | Supported | Doing | Doing | layer/normalization
| [mindspore.nn.MaxPool2d](https://www.mindspore.cn/api/en/r0.5/api/python/mindspore/mindspore.nn.html#mindspore.nn.MaxPool2d) | Supported | Supported | Supported |layer/pooling
| [mindspore.nn.AvgPool2d](https://www.mindspore.cn/api/en/r0.5/api/python/mindspore/mindspore.nn.html#mindspore.nn.AvgPool2d) |Doing | Supported | Doing |layer/pooling
......@@ -107,11 +104,8 @@
| [mindspore.ops.operations.Sigmoid](https://www.mindspore.cn/api/en/r0.5/api/python/mindspore/mindspore.ops.operations.html#mindspore.ops.operations.Sigmoid) | Supported | Supported |Doing | nn_ops
| [mindspore.ops.operations.Tanh](https://www.mindspore.cn/api/en/r0.5/api/python/mindspore/mindspore.ops.operations.html#mindspore.ops.operations.Tanh) | Supported | Supported |Doing | nn_ops
| [mindspore.ops.operations.BatchNorm](https://www.mindspore.cn/api/en/r0.5/api/python/mindspore/mindspore.ops.operations.html#mindspore.ops.operations.BatchNorm) | Supported | Doing |Doing | nn_ops
| [mindspore.ops.operations.LRN](https://www.mindspore.cn/api/en/r0.5/api/python/mindspore/mindspore.ops.operations.html#mindspore.ops.operations.LRN) | Supported | Doing |Doing | nn_ops
| [mindspore.ops.operations.Conv2D](https://www.mindspore.cn/api/en/r0.5/api/python/mindspore/mindspore.ops.operations.html#mindspore.ops.operations.Conv2D) | Supported | Supported | Supported | nn_ops
| [mindspore.ops.operations.DepthwiseConv2dNative](https://www.mindspore.cn/api/en/r0.5/api/python/mindspore/mindspore.ops.operations.html#mindspore.ops.operations.DepthwiseConv2dNative) | Supported | Doing |Doing | nn_ops
| [mindspore.ops.operations.DepthwiseConv2dNativeBackpropInput](https://www.mindspore.cn/api/en/r0.5/api/python/mindspore/mindspore.ops.operations.html#mindspore.ops.operations.DepthwiseConv2dNativeBackpropInput) | Supported | Doing |Doing | nn_ops
| [mindspore.ops.operations.DepthwiseConv2dNativeiBackpropFilter](https://www.mindspore.cn/api/en/r0.5/api/python/mindspore/mindspore.ops.operations.html#mindspore.ops.operations.DepthwiseConv2dNativeBackpropFilter) | Supported | Doing |Doing | nn_ops
| [mindspore.ops.operations.MaxPoolWithArgmax](https://www.mindspore.cn/api/en/r0.5/api/python/mindspore/mindspore.ops.operations.html#mindspore.ops.operations.MaxPoolWithArgmax) | Supported | Doing |Doing | nn_ops
| [mindspore.ops.operations.MaxPool](https://www.mindspore.cn/api/en/r0.5/api/python/mindspore/mindspore.ops.operations.html#mindspore.ops.operations.MaxPool) | Supported | Supported | Supported | nn_ops
| [mindspore.ops.operations.AvgPool](https://www.mindspore.cn/api/en/r0.5/api/python/mindspore/mindspore.ops.operations.html#mindspore.ops.operations.AvgPool) | Supported | Supported |Doing | nn_ops
......
docs/source_zh_cn/benchmark.md
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| | | | | Ascend: 16 * Ascend 910 </br> CPU：384 Cores | Mixed | 256 | 32768 images/sec | 0.96 |
1. 以上数据基于华为云AI开发平台ModelArts测试获得，是训练过程整体下沉至Ascend 910 AI处理器执行所得的平均性能。
2. 业界其他开源框架数据可参考：[ResNet-50 v1.5 for TensorFlow](https://github.com/NVIDIA/DeepLearningExamples/tree/master/TensorFlow/Classification/RN50v1.5#nvidia-dgx-2-16x-v100-32g)。
2. 业界其他开源框架数据可参考：[ResNet-50 v1.5 for TensorFlow](https://github.com/NVIDIA/DeepLearningExamples/tree/master/TensorFlow/Classification/ConvNets/resnet50v1.5)。
### BERT
......
docs/source_zh_cn/network_list.md
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# 网络支持
<a href="https://gitee.com/mindspore/docs/tree/r0.5/docs/source_zh_cn/network_list.md" target="_blank"><img src="./_static/logo_source.png"></a>
| 领域 | 子领域 | 网络 | Ascend | GPU | CPU
|:------ |:------| :----------- |:------ |:------ |:-----
|计算机视觉（CV） | 图像分类（Image Classification） | [AlexNet](https://gitee.com/mindspore/mindspore/blob/r0.5/model_zoo/alexnet/src/alexnet.py) | Supported | Supported | Doing
| 计算机视觉（CV） | 图像分类（Image Classification） | [GoogleNet](https://gitee.com/mindspore/mindspore/blob/r0.5/model_zoo/googlenet/src/googlenet.py) | Supported | Doing | Doing
| 计算机视觉（CV） | 图像分类（Image Classification） | [LeNet](https://gitee.com/mindspore/mindspore/blob/r0.5/model_zoo/lenet/src/lenet.py) | Supported | Supported | Supported
| 计算机视觉（CV） | 图像分类（Image Classification） | [ResNet-50](https://gitee.com/mindspore/mindspore/blob/r0.5/model_zoo/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/r0.5/model_zoo/resnet/src/resnet.py) | Supported |Doing | Doing
| 计算机视觉（CV） | 图像分类（Image Classification） | [VGG16](https://gitee.com/mindspore/mindspore/blob/r0.5/model_zoo/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/r0.5/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/r0.5/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/r0.5/model_zoo/ssd/src/ssd.py) | Supported |Doing | Doing
| 计算机视觉（CV） | 目标检测（Targets Detection） | [YoloV3](https://gitee.com/mindspore/mindspore/blob/r0.5/model_zoo/yolov3/src/yolov3.py) | Supported | Doing | Doing
| 计算机视觉（CV） | 目标检测（Targets Detection） | [FasterRCNN](https://gitee.com/mindspore/mindspore/tree/r0.5/model_zoo/faster_rcnn/src/FasterRcnn) | Supported | Doing | Doing
| 计算机视觉（CV） | 语义分割（Semantic Segmentation） | [Deeplabv3](https://gitee.com/mindspore/mindspore/blob/r0.5/model_zoo/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/r0.5/model_zoo/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/r0.5/model_zoo/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/r0.5/model_zoo/lstm/src/lstm.py) | Doing | Supported | Supported
| 推荐（Recommender） | 推荐系统、点击率预估（Recommender System, CTR prediction） | [DeepFM](https://gitee.com/mindspore/mindspore/blob/r0.5/model_zoo/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/r0.5/model_zoo/wide_and_deep/src/wide_and_deep.py) | Supported | Doing | Doing
| 图神经网络（GNN） | 文本分类（Text Classification） | [GCN](https://gitee.com/mindspore/mindspore/tree/master/model_zoo/gcn/src/gcn.py) | Supported | Doing | Doing
| 图神经网络（GNN） | 文本分类（Text Classification） | [GAT](https://gitee.com/mindspore/mindspore/tree/master/model_zoo/gat/src/gat.py) | Supported | Doing | Doing
# 网络支持
<a href="https://gitee.com/mindspore/docs/tree/r0.5/docs/source_zh_cn/network_list.md" target="_blank"><img src="./_static/logo_source.png"></a>
| 领域 | 子领域 | 网络 | Ascend | GPU | CPU
|:------ |:------| :----------- |:------ |:------ |:-----
|计算机视觉（CV） | 图像分类（Image Classification） | [AlexNet](https://gitee.com/mindspore/mindspore/blob/r0.5/model_zoo/alexnet/src/alexnet.py) | Supported | Supported | Doing
| 计算机视觉（CV） | 图像分类（Image Classification） | [GoogleNet](https://gitee.com/mindspore/mindspore/blob/r0.5/model_zoo/googlenet/src/googlenet.py) | Supported | Doing | Doing
| 计算机视觉（CV） | 图像分类（Image Classification） | [LeNet](https://gitee.com/mindspore/mindspore/blob/r0.5/model_zoo/lenet/src/lenet.py) | Supported | Supported | Supported
| 计算机视觉（CV） | 图像分类（Image Classification） | [ResNet-50](https://gitee.com/mindspore/mindspore/blob/r0.5/model_zoo/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/r0.5/model_zoo/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/r0.5/model_zoo/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/r0.5/model_zoo/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/r0.5/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/r0.5/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/r0.5/model_zoo/ssd/src/ssd.py) | Supported |Doing | Doing
| 计算机视觉（CV） | 目标检测（Targets Detection） | [YoloV3](https://gitee.com/mindspore/mindspore/blob/r0.5/model_zoo/yolov3/src/yolov3.py) | Supported | Doing | Doing
| 计算机视觉（CV） | 目标检测（Targets Detection） | [FasterRCNN](https://gitee.com/mindspore/mindspore/tree/r0.5/model_zoo/faster_rcnn/src/FasterRcnn) | Supported | Doing | Doing
| 计算机视觉（CV） | 语义分割（Semantic Segmentation） | [Deeplabv3](https://gitee.com/mindspore/mindspore/blob/r0.5/model_zoo/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/r0.5/model_zoo/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/r0.5/model_zoo/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/r0.5/model_zoo/lstm/src/lstm.py) | Doing | Supported | Supported
| 推荐（Recommender） | 推荐系统、点击率预估（Recommender System, CTR prediction） | [DeepFM](https://gitee.com/mindspore/mindspore/blob/r0.5/model_zoo/deepfm/src/deepfm.py) | 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/tree/r0.5/model_zoo/mass/src/transformer) | 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/r0.5/model_zoo/wide_and_deep/src/wide_and_deep.py) | Supported | Doing | Doing
| 图神经网络（GNN） | 文本分类（Text Classification） | [GCN](https://gitee.com/mindspore/mindspore/blob/r0.5/model_zoo/gcn/src/gcn.py) | Supported | Doing | Doing
| 图神经网络（GNN） | 文本分类（Text Classification） | [GAT](https://gitee.com/mindspore/mindspore/blob/r0.5/model_zoo/gat/src/gat.py) | Supported | Doing | Doing
docs/source_zh_cn/operator_list.md
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......@@ -49,9 +49,6 @@
| [mindspore.nn.BatchNorm2d](https://www.mindspore.cn/api/zh-CN/r0.5/api/python/mindspore/mindspore.nn.html#mindspore.nn.BatchNorm2d) | Supported | Supported | Doing |layer/normalization
| [mindspore.nn.GroupNorm](https://www.mindspore.cn/api/zh-CN/r0.5/api/python/mindspore/mindspore.nn.html#mindspore.nn.GroupNorm) | Supported | Doing | Doing |layer/normalization
| [mindspore.nn.LayerNorm](https://www.mindspore.cn/api/zh-CN/r0.5/api/python/mindspore/mindspore.nn.html#mindspore.nn.LayerNorm) | Supported | Supported | Doing |layer/normalization
| [mindspore.nn.MatrixDiag](https://www.mindspore.cn/api/zh-CN/r0.5/api/python/mindspore/mindspore.nn.html#mindspore.nn.MatrixDiag) | Supported | Doing | Doing | layer/normalization
| [mindspore.nn.MatrixDiagPart](https://www.mindspore.cn/api/zh-CN/r0.5/api/python/mindspore/mindspore.nn.html#mindspore.nn.MatrixDiagPart) | Supported | Doing | Doing | layer/normalization
| [mindspore.nn.MatrixSetDiag](https://www.mindspore.cn/api/zh-CN/r0.5/api/python/mindspore/mindspore.nn.html#mindspore.nn.MatrixSetDiag) | Supported | Doing | Doing | layer/normalization
| [mindspore.nn.LinSpace](https://www.mindspore.cn/api/zh-CN/r0.5/api/python/mindspore/mindspore.nn.html#mindspore.nn.LinSpace) | Supported | Doing | Doing | layer/normalization
| [mindspore.nn.MaxPool2d](https://www.mindspore.cn/api/zh-CN/r0.5/api/python/mindspore/mindspore.nn.html#mindspore.nn.MaxPool2d) | Supported | Supported | Supported |layer/pooling
| [mindspore.nn.AvgPool2d](https://www.mindspore.cn/api/zh-CN/r0.5/api/python/mindspore/mindspore.nn.html#mindspore.nn.AvgPool2d) |Doing | Supported | Doing |layer/pooling
......@@ -107,11 +104,8 @@
| [mindspore.ops.operations.Sigmoid](https://www.mindspore.cn/api/zh-CN/r0.5/api/python/mindspore/mindspore.ops.operations.html#mindspore.ops.operations.Sigmoid) | Supported | Supported |Doing | nn_ops
| [mindspore.ops.operations.Tanh](https://www.mindspore.cn/api/zh-CN/r0.5/api/python/mindspore/mindspore.ops.operations.html#mindspore.ops.operations.Tanh) | Supported | Supported |Doing | nn_ops
| [mindspore.ops.operations.BatchNorm](https://www.mindspore.cn/api/zh-CN/r0.5/api/python/mindspore/mindspore.ops.operations.html#mindspore.ops.operations.BatchNorm) | Supported | Doing |Doing | nn_ops
| [mindspore.ops.operations.LRN](https://www.mindspore.cn/api/zh-CN/r0.5/api/python/mindspore/mindspore.ops.operations.html#mindspore.ops.operations.LRN) | Supported | Doing |Doing | nn_ops
| [mindspore.ops.operations.Conv2D](https://www.mindspore.cn/api/zh-CN/r0.5/api/python/mindspore/mindspore.ops.operations.html#mindspore.ops.operations.Conv2D) | Supported | Supported | Supported | nn_ops
| [mindspore.ops.operations.DepthwiseConv2dNative](https://www.mindspore.cn/api/zh-CN/r0.5/api/python/mindspore/mindspore.ops.operations.html#mindspore.ops.operations.DepthwiseConv2dNative) | Supported | Doing |Doing | nn_ops
| [mindspore.ops.operations.DepthwiseConv2dNativeBackpropInput](https://www.mindspore.cn/api/zh-CN/r0.5/api/python/mindspore/mindspore.ops.operations.html#mindspore.ops.operations.DepthwiseConv2dNativeBackpropInput) | Supported | Doing |Doing | nn_ops
| [mindspore.ops.operations.DepthwiseConv2dNativeiBackpropFilter](https://www.mindspore.cn/api/zh-CN/r0.5/api/python/mindspore/mindspore.ops.operations.html#mindspore.ops.operations.DepthwiseConv2dNativeBackpropFilter) | Supported | Doing |Doing | nn_ops
| [mindspore.ops.operations.MaxPoolWithArgmax](https://www.mindspore.cn/api/zh-CN/r0.5/api/python/mindspore/mindspore.ops.operations.html#mindspore.ops.operations.MaxPoolWithArgmax) | Supported | Doing |Doing | nn_ops
| [mindspore.ops.operations.MaxPool](https://www.mindspore.cn/api/zh-CN/r0.5/api/python/mindspore/mindspore.ops.operations.html#mindspore.ops.operations.MaxPool) | Supported | Supported | Supported | nn_ops
| [mindspore.ops.operations.AvgPool](https://www.mindspore.cn/api/zh-CN/r0.5/api/python/mindspore/mindspore.ops.operations.html#mindspore.ops.operations.AvgPool) | Supported | Supported |Doing | nn_ops
......
resource/faq/FAQ_en.md
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......@@ -98,7 +98,7 @@ A: Currently, you can try out MindSpore through Docker images on laptops or in e
Q: Does MindSpore support Windows 10?
A: The MindSpore CPU version can be installed on Windows 10. For details about the installation procedure, see tutorials on the [MindSpore official website](https://www.mindspore.cn/tutorial/en/r0.5/advanced_use/mindspore_cpu_win_install.html).
A: The MindSpore CPU version can be installed on Windows 10.
### Programming Language
......
resource/faq/FAQ_zh_cn.md
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......@@ -97,7 +97,7 @@ A：目前笔记本电脑或者有GPU的环境，都可以通过Docker镜像来
Q：MindSpore是否支持Windows 10？
A：MindSpore CPU版本已经支持在Windows 10系统中安装，具体安装步骤可以查阅[MindSpore官网教程](https://www.mindspore.cn/tutorial/zh-CN/r0.5/advanced_use/mindspore_cpu_win_install.html)。
A：MindSpore CPU版本已经支持在Windows 10系统中安装。
### 编程语言扩展
......
tutorials/notebook/mindinsight/mindinsight_model_lineage_and_data_lineage.ipynb
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......@@ -43,7 +43,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
"本次体验将使用快速入门案例作为基础用例，将MindInsight的模型溯源和数据溯源的数据记录功能加入到案例中，快速入门案例的源码请参考：https://gitee.com/mindspore/docs/blob/master/tutorials/tutorial_code/lenet.py 。"
"本次体验将使用快速入门案例作为基础用例，将MindInsight的模型溯源和数据溯源的数据记录功能加入到案例中，快速入门案例的源码请参考：https://gitee.com/mindspore/docs/blob/r0.5/tutorials/tutorial_code/lenet.py 。"
]
},
{
......@@ -410,7 +410,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
"初始化`SummaryCollector`，使用`collect_specified_data`控制需要记录的数据，我们这里只需要记录模型溯源和数据溯源，所以将`collect_train_lineage`和`collect_eval_lineage`参数设置成`True`,其他的参数使用`keep_default_action`设置成`False`，SummaryCollector能够记录哪些数据，请参考官网：https://www.mindspore.cn/api/zh-CN/master/api/python/mindspore/mindspore.train.html?highlight=collector#mindspore.train.callback.SummaryCollector 。"
"初始化`SummaryCollector`，使用`collect_specified_data`控制需要记录的数据，我们这里只需要记录模型溯源和数据溯源，所以将`collect_train_lineage`和`collect_eval_lineage`参数设置成`True`,其他的参数使用`keep_default_action`设置成`False`，SummaryCollector能够记录哪些数据，请参考官网：<https://www.mindspore.cn/api/zh-CN/r0.5/api/python/mindspore/mindspore.train.html#mindspore.train.callback.SummaryCollector> 。"
]
},
{
......@@ -460,7 +460,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
"这里主要展示如何启用及关闭MindInsight，更多的命令集信息，请参考MindSpore官方网站：https://www.mindspore.cn/tutorial/zh-CN/master/advanced_use/visualization_tutorials.html 。"
"这里主要展示如何启用及关闭MindInsight，更多的命令集信息，请参考MindSpore官方网站：https://www.mindspore.cn/tutorial/zh-CN/r0.5/advanced_use/visualization_tutorials.html 。"
]
},
{
......
tutorials/source_en/advanced_use/mindinsight_commands.md
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# MindInsight Commands
<a href="https://gitee.com/mindspore/docs/blob/r0.5/tutorials/source_en/advanced_use/mindinsight_commands.md" target="_blank"><img src="../_static/logo_source.png"></a>
1. View the command help information.
```shell
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tutorials/source_en/use/data_preparation/data_processing_and_augmentation.md
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......@@ -229,6 +229,9 @@ The code output is as follows. Data values in each row of the dataset `ds2` is m
```
### zip
MindSpore provides the `zip` function to combine multiple datasets into one dataset.
![avatar](../images/zip.png)
> If the column names in the two datasets are the same, the two datasets are not combined. Therefore, pay attention to column names.
> If the number of rows in the two datasets is different, the number of rows after combination is the same as the smaller number.
```python
......
tutorials/source_zh_cn/advanced_use/mindinsight_commands.md
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# MindInsight相关命令
<a href="https://gitee.com/mindspore/docs/blob/r0.5/tutorials/source_zh_cn/advanced_use/mindinsight_commands.md" target="_blank"><img src="../_static/logo_source.png"></a>
1. 查看命令帮助信息
```shell
......
tutorials/source_zh_cn/use/data_preparation/data_processing_and_augmentation.md
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......@@ -228,6 +228,9 @@ for data in ds2.create_dict_iterator():
```
### zip
MindSpore提供`zip`函数，可将多个数据集合并成1个数据集。
![avatar](../images/zip.png)
> 如果两个数据集的列名相同，则不会合并，请注意列的命名。
> 如果两个数据集的行数不同，合并后的行数将和较小行数保持一致。
```python
......
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