Biological Learning

A GPU implementation of Biological Learning. This is a fork from this repo by D. Krotov and J. Hopfield.


MNIST	CIFAR-10	Real/Fake Faces

Structure

Notebooks:

Unsupervised_learning_algorithm_MNIST.ipynb: Compares the BIO algorithm with backprop (BP) model on the MNIST dataset for digit recognition.
CIFAR-10, + gpu implementation for bio-unsupervised.ipynb: A comparison between BIO and BP models for object classification on CIFAR-10 dataset.
Face detection.ipynb: Real/Fake Face classification.
Unsupervised learning on Face (Gender, Ethicity): Gender and ethnicity classification.

Installation

We create a conda environment using environment.yml. Feel free to change the package versions. To install the environment:

conda env create -f environment.yml
conda env list 
conda activate hebbian

Note: To get the best of GPU functionalities, you should have a cuda-compatible gpu. Please change the cudatoolkit, tensorflow, and pytorch versions to match your GPU settings.

Running the Code

To run any of the notebooks, simply run:

jupyter notebook [NOTEBOOK NAME]

Results

MNIST:

N_hid	BIO Validation Accuracy	BP Validation Accuracy
200	95.7	97.9
500	96.5	97.9
1000	97.2	98.1
2000	97.8	98.15
4000	98.1	98.29
8000	98.1	98.3


BIO vs. BP Accuracies for MNIST	BIO Weights Visualization

CIFAR-10:

N_hid	BIO Validation Accuracy	BP Validation Accuracy
1000	46.5	54.2
2000	49.0	54.2
4000	50.3	54.6
8000	52.0	55.0


BIO vs. BP Accuracies for CIFAR	BIO Weights Visualization

Resources

Example of "biological" learning for MNIST based on the paper Unsupervised Learning by Competing Hidden Units by D.Krotov and J.Hopfield. If you want to learn more about this work you can also check out this lecture from MIT's 6.S191 course.

Author and License

This fork is the work of Yana Kuznetcov and Amin Heydarshahi and was initially developed as the course project for the graduate class Computational Visual Perception, instructed and supervised by Prof. Dr. Bernhard Egger and Dr. Patrick Krauß.

heydarshahi / biological_learning Goto Github PK