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.

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.

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

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