urikz/textCNN_public

CNN-DCNN text autoencoder

Implementations of the models in the paper "Deconvolutional Paragraph Representation Learning" by Yizhe Zhang, Dinghan Shen, Guoyin Wang, Zhe Gan, Ricardo Henao and Lawrence Carin, NIPS 2017

Prerequisite:

• Tensorflow (version >1.0)
• CUDA, cudnn

Run

• Run: python demo.py
• Options: options can be made by changing option class in the demo.py code.

• opt.n_hidden: number of hidden units.
• opt.layer: number of CNN/DCNN layer [2,3,4].
• opt.lr: learning rate.
• opt.batch_size: number of batchsize.

• Training roughly takes 6-7 hours (around 10 epochs) to converge on a K80 GPU machine.
• See output.txt for a sample of screen output.

Data:

• download from data

For any question or suggestions, feel free to contact yz196@duke.edu

Citation

• Arxiv link: https://arxiv.org/abs/1708.04729

@inproceedings{zhang2017deconvolutional,
  title={Deconvolutional Paragraph Representation Learning},
  author={Zhang, Yizhe and Shen, Dinghan and Wang, Guoyin and Gan, Zhe and Henao, Ricardo and Carin, Lawrence},
  Booktitle={NIPS},
  year={2017}
}