https://github.com/dilawar/moden-hopfield-network

Modern Hopfield Networ aka Dense Associative Memory In Rust
https://github.com/dilawar/moden-hopfield-network

Last synced: about 2 months ago
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Modern Hopfield Networ aka Dense Associative Memory In Rust

• Host: GitHub
• URL: https://github.com/dilawar/moden-hopfield-network
• Owner: dilawar
• Created: 2024-11-11T23:28:06.000Z (about 2 months ago)
• Default Branch: main
• Last Pushed: 2024-11-11T23:37:37.000Z (about 2 months ago)
• Last Synced: 2024-11-12T00:28:17.317Z (about 2 months ago)
• Language: Rust
• Size: 0 Bytes
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
# Modern Hopfield Network

This crate implements Modern Hopfield Netowrk aka Dense Associative Memory [1].

## TODO

- [ ] Docs

- [ ] More tests

- [ ] (Stupid) Python bindings

## Usage

`cargo run --example mnist --release` runs the net on MNIST dataset.

On my machine, I get the following results. When I use 1000 images for training,

my accuracy is roughly 76%. With a small training set of 10, accuracy is about

70%! The speed of inference decreases linearly with number of stored patterns.

```

Results

none  : stored memories=1000, classified=10000, correct=76.62%, wrong=4.82%, unclassified=18.56%, speed=374.2 patterns/sec

mean  : stored memories=10, classified=10000, correct=69.14%, wrong=14.35%, unclassified=16.51%, speed=29304.4 patterns/sec

median: stored memories=10, classified=10000, correct=69.14%, wrong=14.35%, unclassified=16.51%, speed=29477.0 patterns/sec

auto  : stored memories=40, classified=10000, correct=69.6%, wrong=6.44%, unclassified=23.96%, speed=10354.1 patterns/sec

```

## References

1. Krotov, D., & Hopfield, J. J. (2016). Dense associative memory for pattern

   recognition. Advances in neural information processing systems, 29.

   https://proceedings.neurips.cc/paper_files/paper/2016/file/eaae339c4d89fc102edd9dbdb6a28915-Paper.pdf

# Demo

A simple demo of pattern recognition: https://meena.subcom.link/