Repository for experiments with Hopfield and related networks.
A Hopfield network is a form of recurrent artificial neural network popularized by John Hopfield in 1982, but described earlier by Little in 1974 -- Wikipedia
Ι'm studying these networks for their simplicity and potential to represent a memory in another project.
The script takes images from a given directory and trains a network to remember them. Then it adds noise to the images and attempts to retrieve them using the Hopfield network. It can be run from the command line.
main.py [-h] [--directory DIRECTORY] [--noise NOISE] [--output OUTPUT]The arguments are all optional:
- --directory -- Directory to obtain the sample images from -- default: samples
- --noise -- Fraction of bits randomized for evaluation -- default: 0.1
- --output -- Output directory -- default: output
An example output is shown on the image above, with three images displayed side-by-side. The left image shows John Hopfield, the middle one has added noise, and the right is the retrieved image.
If required, the tests can be run from the command line.
python -m pytest