@@ -74,7 +74,7 @@ A: MindSpore has basic support for common training scenarios, please refer to [R
Q: What are the available recommendation or text generation networks or models provided by MindSpore?
A: Currently, recommendation models such as Wide & Deep, DeepFM, and NCF are under development. In the natural language processing (NLP) field, Bert\_NEZHA is available and models such as MASS are under development. You can rebuild the network into a text generation network based on the scenario requirements. Please stay tuned for updates on the [MindSpore Model Zoo](https://gitee.com/mindspore/mindspore/tree/master/mindspore/model_zoo).
A: Currently, recommendation models such as Wide & Deep, DeepFM, and NCF are under development. In the natural language processing (NLP) field, Bert\_NEZHA is available and models such as MASS are under development. You can rebuild the network into a text generation network based on the scenario requirements. Please stay tuned for updates on the [MindSpore Model Zoo](https://gitee.com/mindspore/mindspore/tree/master/model_zoo).
A：目前正在开发Wide & Deep、DeepFM、NCF等推荐类模型，NLP领域已经支持Bert_NEZHA，正在开发MASS等模型，用户可根据场景需要改造为生成类网络，可以关注[MindSpore Model Zoo](https://gitee.com/mindspore/mindspore/tree/master/mindspore/model_zoo)。
A：目前正在开发Wide & Deep、DeepFM、NCF等推荐类模型，NLP领域已经支持Bert_NEZHA，正在开发MASS等模型，用户可根据场景需要改造为生成类网络，可以关注[MindSpore Model Zoo](https://gitee.com/mindspore/mindspore/tree/master/model_zoo)。
@@ -79,7 +79,7 @@ The ResNet-50 network migration and training on the Ascend 910 is used as an exa
num_shards=device_num,shard_id=rank_id)
```
Then, perform data augmentation, data cleaning, and batch processing. For details about the code, see <https://gitee.com/mindspore/mindspore/blob/master/example/resnet50_cifar10/dataset.py>.
Then, perform data augmentation, data cleaning, and batch processing. For details about the code, see <https://gitee.com/mindspore/mindspore/blob/master/model_zoo/resnet/src/dataset.py>.
3. Build a network.
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@@ -214,7 +214,7 @@ The ResNet-50 network migration and training on the Ascend 910 is used as an exa
6. Build the entire network.
The [ResNet-50](https://gitee.com/mindspore/mindspore/blob/master/mindspore/model_zoo/resnet.py) network structure is formed by connecting multiple defined subnets. Follow the rule of defining subnets before using them and define all the subnets used in the `__init__` and connect subnets in the `construct`.
The [ResNet-50](https://gitee.com/mindspore/mindspore/blob/master/model_zoo/resnet/src/resnet.py) network structure is formed by connecting multiple defined subnets. Follow the rule of defining subnets before using them and define all the subnets used in the `__init__` and connect subnets in the `construct`.
7. Define a loss function and an optimizer.
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@@ -272,9 +272,7 @@ Models trained on the Ascend 910 AI processor can be used for inference on diffe
@@ -16,7 +16,7 @@ Models based on MindSpore training can be used for inference on different hardwa
1. Inference on the Ascend 910 AI processor
MindSpore provides the `model.eval` API for model validation. You only need to import the validation dataset. The processing method of the validation dataset is the same as that of the training dataset. For details about the complete code, see <https://gitee.com/mindspore/mindspore/blob/master/example/resnet50_cifar10/eval.py>.
MindSpore provides the `model.eval` API for model validation. You only need to import the validation dataset. The processing method of the validation dataset is the same as that of the training dataset. For details about the complete code, see <https://gitee.com/mindspore/mindspore/blob/master/model_zoo/lenet/eval.py>.
