Accompanying code for the paper:
Structured Weight Priors for Convolutional Neural Networks
Tim Pearce, Andrew Y. K. Foong, Alexandra Brintrup
ICML workshop, Uncertainty & Robustness in Deep Learning, 2020.
https://arxiv.org/abs/2007.14235
http://www.gatsby.ucl.ac.uk/~balaji/udl2020/accepted-papers/UDL2020-paper-094.pdf
utils.py - general file with most useful stuff
general_entropy_02.py - 4.1 prior predictive diversity
logits_corr_02.py - 4.2 prior activation correlation
tsne_03.py - 4.3 CAPPA
training_curve_02.py - 4.4 training curves
vis_filters_02.py - visualises first layer CNN filters of pretrained models
vis_gabors_02.py - visualises draws from probabilistic Gabor prior
The following package versions were used in developing and running the code. Figures etc are designed to open when run through ipython.
- Python 3.7.0
- Keras version 2.2.4
- Numpy version 1.18.1
- Matplotlib version 3.0.1
The purpose of this repo is to provide some depth behind the paper. It's not production ready. Feel free to fork/fix etc, but I won't be actively maintaining/developing/updating this repo, nor responding to minor bugs.