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View Code? Open in Web Editor NEWEasy & Effective Application Framework for PaddlePaddle
License: Apache License 2.0
Easy & Effective Application Framework for PaddlePaddle
License: Apache License 2.0
希望能够提供功能:训练时,每隔n个batch进行一次自定义的操作。比如保存模型,比如跑一次预测。
实现了一个ranknet,网络是fc-bn-fc-bn-fc-bn-fc-bn-fc-bn-fc,fc神经元的数量除了最后一层是1,其他都是32. fc没有激活,bn用的relu。 输入是 样本label永远是1,左边是pos,右边是neg,左边永远大于右边;设计是希望pos和neg走同一个上面的网络,计算出最后一层结果,然后进入margin_rank_loss,所以永远是pos-neg,然后做sigmoid和二元交叉熵logloss,最后取均值作为loss。
逻辑是,希望网络能够根据特征计算相关性得分,然后pos-neg差值越大越好。
现在问题是,训练时loss迅速降为0.001,预测时效果很差。加载训练好的模型再进行训练,loss 0.001,但是一旦bn使用全局状态(use_global_stats设为True,不更新bn参数,模拟预测),立刻loss暴涨。 辛苦帮忙看下是bn层有异常么?
其中一层FC-bn
训练中难免会有中断的情况,希望可以加载之前的模型,继续训练
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