https://github.com/sash-a/codeepneat
An implementation of CoDeepNEAT using pytorch with extensions
https://github.com/sash-a/codeepneat
automl machine-learning neat neat-algorithm neural-architecture-search neuroevolution
Last synced: 11 months ago
JSON representation
An implementation of CoDeepNEAT using pytorch with extensions
- Host: GitHub
- URL: https://github.com/sash-a/codeepneat
- Owner: sash-a
- License: mit
- Created: 2019-06-08T09:03:59.000Z (almost 7 years ago)
- Default Branch: master
- Last Pushed: 2021-04-28T09:00:06.000Z (about 5 years ago)
- Last Synced: 2025-03-30T09:31:26.857Z (about 1 year ago)
- Topics: automl, machine-learning, neat, neat-algorithm, neural-architecture-search, neuroevolution
- Language: Python
- Homepage:
- Size: 116 MB
- Stars: 31
- Watchers: 1
- Forks: 10
- Open Issues: 2
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
# CoDeepNEAT
An implementation of implementation of CoDeepNEAT, originally created by Risto Miikkulainen et al. with our own extensions. Implementation details were taken from their [2017](https://arxiv.org/pdf/1703.00548/) and [2019](https://arxiv.org/pdf/1902.06827.pdf) paper.
## Setup
Requires [conda](https://docs.conda.io/en/latest/)
```
conda create -n cdn --file requirements.txt
conda activate cdn
pip install tarjan wandb # these are not available from conda
```
## Entry points
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)
## Config
All config options are in ```src/configuration/configuration.py```
Example configs are in ```src/configuration/configs``` directory
## How to run
```python src/main/evo.py -g 1 -c base```
## Extensions
Extensions are detailed in the paper linked above
# Paper
If you use this code, please cite [our paper](https://ieeexplore.ieee.org/abstract/document/9308151):
```
@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}}
```
## Results
For detailed results see:
[convergence](https://app.wandb.ai/codeepneat/cdn_fully_train/reports/CoDeepNEAT-convergence-results--VmlldzoyMTIyMjY?accessToken=86xwfnm0f8tko6spt71oharczveqgv388hzojcuei7g3z4wonshr4uy5n24bbga6)
[evolution](https://app.wandb.ai/codeepneat/cdn/reports/CoDeepNEAT-evolution-results--VmlldzoyMTIyNDI?accessToken=bvjugcdbb1qdgn7czmcyxct60cxkgatapk8nldg1bt7gwy4a4kovlavdt5sy7bz5)
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
#### Note about batch runs
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```.