From 1f5d86305a6e0ea586fd83cc1a1735257a341c07 Mon Sep 17 00:00:00 2001 From: sunshine-2015 Date: Mon, 16 Apr 2018 16:47:42 +0800 Subject: [PATCH] Update README_EN.md update link for figure 3.4 --- ltr/README_EN.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/ltr/README_EN.md b/ltr/README_EN.md index f36c5f08..e6248249 100644 --- a/ltr/README_EN.md +++ b/ltr/README_EN.md @@ -75,7 +75,7 @@ The meaning of the expression is the increase or decrease of the document $U_i$ Based on the above inference, the RankNet network structure is constructed, which is composed of several layers of hidden layers and full connected layers. As shown in the figure, the document features are used in the hidden layers, and the all connected layer is transformed by layer by layer,completing the transformation from the underlying feature space to the high-level feature space. The structure of docA and docB is symmetrical and they are input into the final RankCost layer. -![image](https://github.com/PaddlePaddle/models/blob/develop/ltr/images/ranknet.jpg?raw=true) +![image](https://github.com/sunshine-2015/models/blob/patch-4/ltr/images/ranknet_en.png?raw=true) Figure.3 The structure diagram of RankNet network @@ -174,7 +174,7 @@ Replace the gradient representation in RankNet and get the ranking model called From the above derivation we can see that the LambdaRank network structure is very similar to the RankNet structure. as the picture shows -![image](https://github.com/PaddlePaddle/models/blob/develop/ltr/images/lambdarank.jpg?raw=true) +![image](https://github.com/sunshine-2015/models/blob/patch-4/ltr/images/LambdaRank_EN.png?raw=true) Figure 4. Network structure of LambdaRank -- GitLab