/GAKeras

Genetic algorithm to optimize Keras Sequential model

Primary LanguagePython

GAKeras

Search for an optimal KERAS architecture using genetic algorithms. Uses DEAP for GA.

Tested on air pollution prediction task and MNIST.

Edit config.py or use --config to set up the configuration.

Usage

main.py [-h] [--trainset TRAINSET] [--testset TESTSET] [--id ID]
               [--checkpoint CHECKPOINT]

optional arguments:
  -h, --help            show this help message and exit
  --trainset TRAINSET   filename of training set 
  --testset TESTSET     filename of test set
  --id ID               computation id
  --checkpoint CHECKPOINT
                        checkpoint file to load the initial state from
  --config              config file name
			

TRAINSET and TESTSET are either filenames or keywords mnist.train | mnist.test | mnist2d.train | mnist2d.test. Use either 1D data or main_conv.py for 2D data.

Examples

main.py --trainset mnist.train --testset mnist.test
main_es.py --trainset mnist.train --testset mnist.test 
main_conv.py --trainset mnist2d.train --testset mnist2d.test --config config_mnist.ini

Citation

Vidnerová, Petra and Neruda, Roman Evolving Keras Architectures for Sensor Data Analysis. Proceedings of the 2017 Federated Conference on Computer Science and Information Systems. Warszawa: Polish Information Processing Society, 2017 - (Ganzha, M.; Maciaszek, L.; Paprzycki, M.), s. 109-112. Annals of Computer Science and Information Systems, 11. ISBN 978-83-946253-7-5. ISSN 2300-5963. [FedCSIS 2017. Federated Conference on Computer Science and Information Systems. Prague (CZ), 03.09.2017-06.09.2017]

Vidnerová, Petra and Neruda, Roman Evolution Strategies for Deep Neural Network Models Design. Proceedings ITAT 2017: Information Technologies - Applications and Theory. Aachen & Charleston: Technical University & CreateSpace Independent Publishing Platform, 2017 - (Hlaváčová, J.), s. 159-166. CEUR Workshop Proceedings, V-1885. ISBN 978-1974274741. ISSN 1613-0073. [ITAT 2017. Conference on Theory and Practice of Information Technologies - Applications and Theory/17./. Martinské hole (SK), 22.09.2017-26.09.2017] http://ceur-ws.org/Vol-1885/159.pdf

Vidnerová, Petra and Neruda, Roman Asynchronous Evolution of Convolutional Networks. ITAT 2018: Information Technologies – Applications and Theory. Proceedings of the 18th conference ITAT 2018. Aachen: Technical University & CreateSpace Independent Publishing Platform, 2018 - (Krajči, S.), s. 80-85. CEUR Workshop Proceedings, V-2203. ISSN 1613-0073. [ITAT 2018. Conference on Information Technologies – Applications and Theory /18./. Plejsy (SK), 21.09.2018-25.09.2018] http://ceur-ws.org/Vol-2203/80.pdf