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Merge pull request #777 from yinhaofeng/bug2.3
infer auc
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‎README_CN.md‎

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<h2 align="center">最新动态<img src="./doc/imgs/rec_new_icon.png" width="40"/></h2>
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* [2022/5/18] 新增3个前沿算法：[aitm](models/multitask/aitm),[sign](models/rank/sign),[dsin](models/rank/dsin)
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* [2022/3/21] 新增[paper](./paper)目录，发布我们对21年的推荐顶会论文的分析，以及工业界的推荐论文列表，供大家参考。
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* [2022/3/10] 新增5个前沿算法: [DCN_V2](models/rank/dcn_v2), [MHCN](models/recall/mhcn), [FLEN](models/rank/flen), [Dselect_K](models/multitask/dselect_k), [AutoFIS](models/rank/autofis)。
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* [2022/1/12] 新增AI Studio一键[在线运行](https://aistudio.baidu.com/aistudio/projectdetail/3240640)功能，可以方便快捷的在AI Studio平台上在线体验我们的模型。
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| 排序 | [DeepRec](models/rank/deeprec/) | - | ✓ | ✓ | >=2.1.0 | [2017][Training Deep AutoEncoders for Collaborative Filtering](https://arxiv.org/pdf/1708.01715v3.pdf) |
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| 排序 | [AutoFIS](models/rank/autofis/) | - | ✓ | ✓ | >=2.1.0 | [KDD 2020][AutoFIS: Automatic Feature Interaction Selection in Factorization Models for Click-Through Rate Prediction](https://arxiv.org/pdf/2003.11235v3.pdf) |
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| 排序 | [DCN_V2](models/rank/dcn_v2/) | - | ✓ | ✓ | >=2.1.0 | [WWW 2021][DCN V2: Improved Deep & Cross Network and Practical Lessons for Web-scale Learning to Rank Systems](https://arxiv.org/pdf/2008.13535v2.pdf)|
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| 排序 | [AITM](models/rank/aitm/) | - | ✓ | ✓ | >=2.1.0 | [KDD 2021][Modeling the Sequential Dependence among Audience Multi-step Conversions withMulti-task Learning in Targeted Display Advertising](https://arxiv.org/pdf/2105.08489v2.pdf) |
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| 排序 | [DSIN](models/rank/dsin/) | - | ✓ | ✓ | >=2.1.0 | [IJCAI 2019][Deep Session Interest Network for Click-Through Rate Prediction](https://arxiv.org/pdf/1905.06482v1.pdf) |
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| 排序 | [SIGN](models/rank/sign/)([文档](https://paddl7erec.readthedocs.io/en/latest/models/rank/sign.html)) | [Python CPU/GPU](https://aistudio.baidu.com/aistudio/projectdetail/3869111) | ✓ | ✓ | >=2.1.0 | [AAAI 2021][Detecting Beneficial Feature Interactions for Recommender Systems](https://arxiv.org/pdf/2008.00404v6.pdf) |
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| 多任务 | [AITM](models/rank/aitm/) | - | ✓ | ✓ | >=2.1.0 | [KDD 2021][Modeling the Sequential Dependence among Audience Multi-step Conversions with Multi-task Learning in Targeted Display Advertising](https://arxiv.org/pdf/2105.08489v2.pdf) |
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| 多任务 | [PLE](models/multitask/ple/)([文档](https://paddlerec.readthedocs.io/en/latest/models/multitask/ple.html)) | [Python CPU/GPU](https://aistudio.baidu.com/aistudio/projectdetail/3238938) | ✓ | ✓ | >=2.1.0 | [RecSys 2020][Progressive Layered Extraction (PLE): A Novel Multi-Task Learning (MTL) Model for Personalized Recommendations](https://dl.acm.org/doi/abs/10.1145/3383313.3412236) |
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| 多任务 | [ESMM](models/multitask/esmm/)([文档](https://paddlerec.readthedocs.io/en/latest/models/multitask/esmm.html)) | [Python CPU/GPU](https://aistudio.baidu.com/aistudio/projectdetail/3238583) | ✓ | ✓ | >=2.1.0 | [SIGIR 2018][Entire Space Multi-Task Model: An Effective Approach for Estimating Post-Click Conversion Rate](https://arxiv.org/abs/1804.07931) |
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| 多任务 | [MMOE](models/multitask/mmoe/)([文档](https://paddlerec.readthedocs.io/en/latest/models/multitask/mmoe.html)) | [Python CPU/GPU](https://aistudio.baidu.com/aistudio/projectdetail/3238934) | ✓ | ✓ | >=2.1.0 | [KDD 2018][Modeling Task Relationships in Multi-task Learning with Multi-gate Mixture-of-Experts](https://dl.acm.org/doi/abs/10.1145/3219819.3220007) |

‎README_EN.md‎

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<h2 align="center">News<img src="./doc/imgs/rec_new_icon.png" width="40"/></h2>
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* [2022/5/18] Add 3 algorithms:：[aitm](models/multitask/aitm),[sign](models/rank/sign),[dsin](models/rank/dsin)
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* [2022/3/21] Add a new [paper](./paper) directory , show our analysis of the top meeting papers of the recommendation system in 2021 years and the list of recommendation system papers in the industry for your reference.
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* [2022/3/10] Add 5 algorithms: [DCN_V2](models/rank/dcn_v2), [MHCN](models/recall/mhcn), [FLEN](models/rank/flen), [Dselect_K](models/multitask/dselect_k)，[AutoFIS](models/rank/autofis)。
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* [2022/1/12] Add AI Studio [Online running](https://aistudio.baidu.com/aistudio/projectdetail/3240640) function, you can easily and quickly online experience our model on AI studio platform.
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| Rank | [DeepRec](models/rank/deeprec/) | - | ✓ | ✓ | >=2.1.0 | [2017][Training Deep AutoEncoders for Collaborative Filtering](https://arxiv.org/pdf/1708.01715v3.pdf) |
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| Rank | [AutoFIS](models/rank/autofis/) | - | ✓ | ✓ | >=2.1.0 | [KDD 2020][AutoFIS: Automatic Feature Interaction Selection in Factorization Models for Click-Through Rate Prediction](https://arxiv.org/pdf/2003.11235v3.pdf) |
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| Rank | [DCN_V2](models/rank/dcn_v2/) | - | ✓ | ✓ | >=2.1.0 | [WWW 2021][DCN V2: Improved Deep & Cross Network and Practical Lessons for Web-scale Learning to Rank Systems](https://arxiv.org/pdf/2008.13535v2.pdf)|
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| Rank | [AITM](models/rank/aitm/) | - | ✓ | ✓ | >=2.1.0 | [KDD 2021][Modeling the Sequential Dependence among Audience Multi-step Conversions withMulti-task Learning in Targeted Display Advertising](https://arxiv.org/pdf/2105.08489v2.pdf) |
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| Rank | [DSIN](models/rank/dsin/) | - | ✓ | ✓ | >=2.1.0 | [IJCAI 2019][Deep Session Interest Network for Click-Through Rate Prediction](https://arxiv.org/pdf/1905.06482v1.pdf) |
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| Rank | [SIGN](models/rank/sign/)([doc](https://paddlerec.readthedocs.io/en/latest/models/rank/sign.html)) | [Python CPU/GPU](https://aistudio.baidu.com/aistudio/projectdetail/3869111) | ✓ | ✓ | >=2.1.0 | [AAAI 2021][Detecting Beneficial Feature Interactions for Recommender Systems](https://arxiv.org/pdf/2008.00404v6.pdf) |
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| Multi-Task | [AITM](models/rank/aitm/) | - | ✓ | ✓ | >=2.1.0 | [KDD 2021][Modeling the Sequential Dependence among Audience Multi-step Conversions with Multi-task Learning in Targeted Display Advertising](https://arxiv.org/pdf/2105.08489v2.pdf) |
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| Multi-Task | [PLE](models/multitask/ple/)<br>([doc](https://paddlerec.readthedocs.io/en/latest/models/multitask/ple.html)) | [Python CPU/GPU](https://aistudio.baidu.com/aistudio/projectdetail/3238938) | ✓ | ✓ | >=2.1.0 | [RecSys 2020][Progressive Layered Extraction (PLE): A Novel Multi-Task Learning (MTL) Model for Personalized Recommendations](https://dl.acm.org/doi/abs/10.1145/3383313.3412236) |
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| Multi-Task | [ESMM](models/multitask/esmm/)<br>([doc](https://paddlerec.readthedocs.io/en/latest/models/multitask/esmm.html)) | [Python CPU/GPU](https://aistudio.baidu.com/aistudio/projectdetail/3238583) | ✓ | ✓ | >=2.1.0 | [SIGIR 2018][Entire Space Multi-Task Model: An Effective Approach for Estimating Post-Click Conversion Rate](https://arxiv.org/abs/1804.07931) |
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| Multi-Task | [MMOE](models/multitask/mmoe/)<br>([doc](https://paddlerec.readthedocs.io/en/latest/models/multitask/mmoe.html)) | [Python CPU/GPU](https://aistudio.baidu.com/aistudio/projectdetail/3238934) | ✓ | ✓ | >=2.1.0 | [KDD 2018][Modeling Task Relationships in Multi-task Learning with Multi-gate Mixture-of-Experts](https://dl.acm.org/doi/abs/10.1145/3219819.3220007) |

‎models/rank/sign/README.md‎

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cd - # 切回模型目录
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# 动态图训练
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python -u ../../../tools/trainer.py -m config_bigdata.yaml # 全量数据运行
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python -u .././../tools/infer.py -m config_bigdata.yaml # 全量数据预测
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python -u ../../../tools/infer.py -m config_bigdata.yaml # 全量数据预测
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```
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## 进阶使用

‎tools/infer.py‎

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# tools.vars
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use_gpu = config.get("runner.use_gpu", True)
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use_auc = config.get("runner.use_auc", False)
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use_xpu = config.get("runner.use_xpu", False)
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use_npu = config.get("runner.use_npu", False)
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use_visual = config.get("runner.use_visual", False)
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metric_str += (
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": {:.6f},".format(metric_list[metric_id].accumulate()))
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if use_auc:
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metric_list[metric_id].reset()
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if tensor_print_dict is not None:

‎tools/trainer.py‎

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# tools.vars
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use_gpu = config.get("runner.use_gpu", True)
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use_auc = config.get("runner.use_auc", False)
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use_npu = config.get("runner.use_npu", False)
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metric_list_name[metric_id] +
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if use_auc:
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metric_list[metric_id].reset()
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tensor_print_str = ""
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if tensor_print_dict is not None:

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