Skip to content

Lintianqianjin/GCN-CNN-in-accusation-prediction-a-multi-labels-text-classfication

Folders and files

NameName
Last commit message
Last commit date

Latest commit

 

History

17 Commits
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 

Repository files navigation

GCN CNN in accusation prediction

a multi-labels text classfication

2019/5/13

add a GCN baseline based on tensorfow

the model is composed of three graph convolutional layer and a dense layer
the dataset contains 499 texts of criminal fact and involves 5 labels, which are 盗窃,故意伤害,诈骗,危险驾驶,抢劫(Theft, intentional injury, fraud, dangerous driving, robbery), and all the texts contains 8182 different words.
However, as a result, the loss(sigmoid_cross_entropy) is nearly no longer reduced when it is not a very small value(0.6002855,after 200 epoch).
The reason may be limited by the size of the data set(only 499 samples).

·try 998 texts, 12491 words, 5 labels, out of memory·

todo: figure out sparseTensor multiplication to make it possible to operate on more larger matrices

About

No description, website, or topics provided.

Resources

Stars

Watchers

Forks

Releases

No releases published

Packages

No packages published