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zOpAdCYi{oUfZGpp3{m^><}4fZ-{G@=O+VzmrT;Gr_A4Hv^Q$6oeQ696|3c~BmNJb6kf&v literal 0 HcmV?d00001 diff --git a/models/rank/deepfm/readme.md b/models/rank/deepfm/readme.md index 09101909..98ded483 100644 --- a/models/rank/deepfm/readme.md +++ b/models/rank/deepfm/readme.md @@ -102,7 +102,7 @@ PaddleRec Finish deepFM模型的组网本质是一个二分类任务,代码参考`model.py`。模型主要组成是一阶项部分,二阶项部分,dnn部分以及相应的分类任务的loss计算和auc计算。模型的组网可以看做FM部分和dnn部分的结合,其中FM部分主要的工作是通过特征间交叉得到低阶特征,以二阶特征为主。FM的表达式如下,可观察到,只是在线性表达式后面加入了新的交叉项特征及对应的权值。 -$$Out=sigmoid(b + \sum^{N}_{i=1}W_iX_i + \sum^{N-1}_{i=1}\sum^{N}_{j=i+1}W_{ij}X_iX_j)$$ + ### 一阶项部分 一阶项部分类似于我们rank下的logistic_regression模型。主要由embedding层和reduce_sum层组成 @@ -111,12 +111,17 @@ $$Out=sigmoid(b + \sum^{N}_{i=1}W_iX_i + \sum^{N-1}_{i=1}\sum^{N}_{j=i+1}W_{ij}X 将离散数据通过embedding查表得到的值,与连续数据的输入进行相乘再累加的操作,合为一个一阶项的整体。 用公式表示如下: -$$\sum^{N}_{i=1}W_iX_i$$ + ### 二阶项部分 -二阶项部分主要实现了公式中的交叉项部分,也就是特征的组合部分。Wij求解的思路是通过矩阵分解的方法。所有的二次项参数Wij可以组成一个对称阵W,那么这个矩阵就可以分解为 $W=V^TV$,V 的第 i 列便是第 i 维特征的隐向量。交叉项的展开式如下: +二阶项部分主要实现了公式中的交叉项部分,也就是特征的组合部分。Wij求解的思路是通过矩阵分解的方法。所有的二次项参数Wij可以组成一个对称阵W,那么这个矩阵就可以如下分解: -$$\sum^{N-1}_{i=1}\sum^{N}_{j=i+1}W_{ij}X_iX_j =1/2\sum^{k}_{j=1}((\sum^{N}_{i=1}W_iX_i)^2-\sum^{N}_{i=1}W_i^2X_i^2)$$ + + +V 的第 i 列便是第 i 维特征的隐向量。特征分量Xi与Xj的交叉项系数就等于Xi对应的隐向量与Xj对应的隐向量的内积,即每个参数 wij=⟨vi,vj⟩ +交叉项的展开式如下: + + ### Loss及Auc计算 - 预测的结果将FM的一阶项部分,二阶项部分以及dnn部分相加,再通过激活函数sigmoid给出,为了得到每条样本分属于正负样本的概率,我们将预测结果和`1-predict`合并起来得到predict_2d,以便接下来计算auc。 @@ -130,7 +135,7 @@ $$\sum^{N-1}_{i=1}\sum^{N}_{j=i+1}W_{ij}X_iX_j =1/2\sum^{k}_{j=1}((\sum^{N}_{i=1 在全量数据下模型的指标如下: | 模型 | auc | batch_size | thread_num| epoch_num| Time of each epoch | -| :------| :------ | :------| :------ | :------| :------ | :------ +| :------| :------ | :------| :------ | :------| :------ | | deepFM | 0.8044 | 1024 | 10 | 2 | 约3.5小时 | 1. 确认您当前所在目录为PaddleRec/models/rank/deepfm -- GitLab