GitHub - MSusik/Inhibited-softmax: Repository with code for paper "Inhibited Softmax for Uncertainty Estimation in Neural Networks"

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Repository with code for paper "Inhibited Softmax for Uncertainty Estimation in Neural Networks"

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Name		Name	Last commit message	Last commit date
Latest commit History 6 Commits
images		images
models_code		models_code
movies		movies
nonmovies		nonmovies
utilities		utilities
.gitattributes		.gitattributes
.gitignore		.gitignore
CIFAR experiments.ipynb		CIFAR experiments.ipynb
Dense autoencoder.ipynb		Dense autoencoder.ipynb
MNIST experiments.ipynb		MNIST experiments.ipynb
README		README
Sentiment Experiments.ipynb		Sentiment Experiments.ipynb
Sentiment_preprocessing.ipynb		Sentiment_preprocessing.ipynb
download.sh		download.sh
indices.json		indices.json
requirements.txt		requirements.txt

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Update (12.03.2019):
* We updated the repo so that it supports Pytorch 1.0.
* We updated the methodology - please see the updated paper on Arxiv.
* We removed the implementation of other methods - they are still available in the older commits.
* The repository contains now the matrices for running the sentiment experiments. They are on LFS, so you need to fetch them separately.

This repository contains source code that can reproduce results from the paper "Inhibited Softmax for Uncertainty Estimation in Neural Networks".

Please note:
* requirements.txt install CPU version of Pytorch that might be too slow for you
* In order to run experiments you have to download datasets and put them in correct directories

I provide a script download.sh that downloads all the required files.

The datasets that have to be downloaded:
* NOTMNIST - place in ./notMNIST_small - in case some of the images are corrupted, remove them
* Omniglot - ./omniglot
* LFW-a - ./lfw-a
* Reuters-21578 - ./nonmovies/reuters21578
* Customer Review data - ./nonmovies/customer\ review\ data
* Review Polarity - ./nonmovies/review_polarity
* Movie Reviews (IMDB) - ./movies/aclImdb