An implementation of implementation of CoDeepNEAT, originally created by Risto Miikkulainen et al. with our own extensions. Implementation details were taken from their 2017 and 2019 paper.

Requires conda

conda create -n cdn --file requirements.txt
conda activate cdn
pip install tarjan wandb  # these are not available from conda

Directory: src/main/
ft.py Fully trains a run from evo.py
evo.py Does an evolutionary run
batch_run.py Running many different configurations all the way from evolution to fully training. (See note below)

All config options are in src/configuration/configuration.py
Example configs are in src/configuration/configs directory

python src/main/evo.py -g 1 -c base

Extensions are detailed in the paper linked above

If you use this code, please cite our paper:

@INPROCEEDINGS{9308151,
  author={S. {Acton} and S. {Abramowitz} and L. {Toledo} and G. {Nitschke}},
  booktitle={2020 IEEE Symposium Series on Computational Intelligence (SSCI)}, 
  title={Efficiently Coevolving Deep Neural Networks and Data Augmentations}, 
  year={2020},
  volume={},
  number={},
  pages={2543-2550},
  doi={10.1109/SSCI47803.2020.9308151}}

For detailed results see:
convergence
evolution

The accuracies obtained on CIFAR-10

The best data augmentations found

The best genotype found. Using config configuration/configs/experiments/mms_da_pop_25e.json and a feature multiplier of 5

And its corresponding phenotype

This system was developed for rapid tuning of CDN's own hyperparameters on a cluster with a limited number of GPUs. It should not be used for normal training as it was created for our very specific case. Rather do a single run on evo.py and then fully train it with ft.py.