arpita2512 / hopfield_experiments

Hopfield Experiments

This is an implementation of the Hopfield Network for COMP5400M Bio-Inspired Computing: Coursework 2. The task is to evaluate image reconstruction using synchronous pattern recovery, and compare results with the asynchronous approach and Self-Organising Map. Experiments are performed using the MNIST dataset.

For implementation details and results, please see the documentation.

Directory Structure

├───hopfield_experiments
├   ├───docs
├   ├───hopfield_experiments
├       ├─── hopfield.py
├       └─── utils.py
├   ├─── Comparison_Digits.ipynb
├   ├─── Comparison_Plots.ipynb
├   ├─── Hopfield_Evaluation.ipynb
├   ├─── SOM_Evaluation.ipynb
├   ├─── README.md
├   ├─── mse_sync.pkl
├   ├─── mse_async.pkl
├   ├─── mse_som.pkl
├   ├─── psnr_sync.pkl
├   ├─── psnr_async.pkl
├   ├─── psnr_som.pkl
├   ├─── ssim_sync.pkl
├   ├─── ssim_async.pkl
├   ├─── ssim_som.pkl
└───└─── requirements.txt

Description of Files

Comparison_Digits.ipynb --> Code for digit wise comparison of metrics

Comparison_Plots.ipynb --> Code for metric comparison aggregated across all digits

Hopfield_Evaluation.ipynb --> Code to generate a demo of reconstruction, a demo of spurious states, and evaluate metrics for synchronous hopfield network

SOM_Evaluation.ipynb --> Code to generate a demo of reconstruction and evaluate metrics for self organising map

Reproduce Results

1. Unzip folder
2. Navigate to folder

cd hopfield_experiments

3. Install requirements

!pip install -r requirements.txt

4. Run desired ipynb file (see decriptions above)

Statement of Contributions

The comparison with asynchronous approach was done using code from Ruchita Mijagiri.