wrencanfly / unsupervised_bio_classifier

PyTorch Implementation of “Unsupervised learning by competing hidden units” MNIST classifier

PyTorch Implementation of “Unsupervised learning by competing hidden units” MNIST classifier, combining with Feedback alignment.

This reprository is a PyTorch implementation of the paper "Unsupervised learning by competing hidden units". By Dmitry Krotov and John J. Hopfield.

And a simple implementation of "Random synaptic feedback weights support error backpropagation for deep learning" by Timothy P. Lillicrap, Daniel Cownden, Douglas B. Tweed & Colin J. Akerman.

We also very brielfy explore adversarial robustness of models implemented with this method.

Environment

torch 2.1 + cu118

python 3.9

Explanation

• bioLearning.ipynb: comparison of different CNN, Bio net, and Feedback alignment architecture.
• fa_linear.py: implementation of the feedback alignment function
• bio_learn.py: core codes implement the different architecture, detailed can be found in inline comments
• 0030_adverserial_1.ipynb: other metric comparison

unlisted files are for test use.

Reference code

https://github.com/gatapia/unsupervised_bio_classifier

https://github.com/L0SG/feedback-alignment-pytorch