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train完之后模型使用及性能评估 #2452

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Damon-wyg opened this issue Jun 13, 2017 · 3 comments

Damon-wyg commented Jun 13, 2017 •

edited by lcy-seso

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在模型跑完之后，在output会有每个pass训练出来的w和bias的二进制文件。请教两个问题：

怎么评估模型的auc？
线上使用这个模型时，具体应该怎么操作？如果是直接加载这个w和bias到线上，线上配置相应的网络结构，然后train的网络修改了，线上也要修改网络结构？

Contributor

qingqing01 commented Jun 13, 2017

线下评估auc
可以使用AUC evalutor，在网络配置中使用该evalutor即可。
线上使用模型
- 线上预测：可以参考C-API
- 配置： train的网络修改了，线上需要对应的修改配置和重新加载模型

Author

Damon-wyg commented Jun 13, 2017

我用的是v1 api训练的模型，有针对v1的auc评估方法么？

Contributor

qingqing01 commented Jun 13, 2017

v1的auc用法相同，接口是这个：https://github.com/PaddlePaddle/Paddle/blob/develop/python/paddle/trainer_config_helpers/evaluators.py#L196

Damon-wyg closed this as completed

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