textClassifierHATT.py has the implementation of [Hierarchical Attention Networks for Document Classification](https://www.cs.cmu.edu/~diyiy/docs/naacl16.pdf). Please see the [my blog](https://richliao.github.io/supervised/classification/2016/12/26/textclassifier-HATN/) for full detail. Also see [Keras Google group discussion](https://groups.google.com/forum/#!topic/keras-users/IWK9opMFavQ)

textClassifierConv has implemented [Convolutional Neural Networks for Sentence Classification - Yoo Kim](https://www.cs.cmu.edu/~diyiy/docs/naacl16.pdf). Please see the [my blog](https://richliao.github.io/supervised/classification/2016/11/26/textclassifier-convolutional/) for full detail.

textClassifierRNN has implemented bidirectional LSTM and one level attentional RNN. Please see the [my blog](https://richliao.github.io/supervised/classification/2016/12/26/textclassifier-RNN/) for full detail.

To derive the attention weight which can be useful to identify important words for the classification. Please see my latest update on the post. All you need to do is run a forward pass right before attention layer output. The result is not very promising. I will update the post once I have further result.