nicoledjy / Self-Balanced-Dropout

A modified dropout which can reduce the co-adaptation problem.

Self-Balanced-Dropout

This project shows an example to apply Self-Balanced Dropout to CNN on SST-1 dataset.

Requirements and preprocessing

Code is written in Python3 and requires Tensorflow (>=1.12.0).

The data preprocessing and hyper-parameter setting strictly follow the implementation in https://github.com/yoonkim/CNN_sentence [1], whose preprocessing code is reused in our project.

Pre-trained embedding 'GoogleNews-vectors-negative300.bin' is available at https://drive.google.com/file/d/0B7XkCwpI5KDYNlNUTTlSS21pQmM/edit?usp=sharing.

How to use

To process the raw data:

python process_data.py vectors_path

This will create a pickle object called 'sst1.p' in the same folder, which contains the dataset in the right format.

To train the model:

python cnn.py sst1.p

This will train and test the model.

Reference

[1] Yoon Kim. Convolutional neural networks for sentence classification. EMNLP 2014.