Abstract
Designing Neural Network Architectures requires expert knowledge and extensive parameter searches. Neural Architecture Search (NAS) aims to change that by automating the design process. It is important that these approaches are reproducible so they can be used in real-life scenarios. In our work, we reproduce a genetic programming approach to designing convolutional neural networks called CGP-CNN. We show that this is difficult and requires many changes to the training scheme, reducing real-life applicability. We achieve a final accuracy of \(90.6\% \pm 0.005\), substantially lower than the reported \(93.7\% \pm 0.005\). This negates some of the benefits of using CGP-CNN for NAS. We establish a random search as a consensus baseline and show that it produces similar results to the evolutionary method of CGP-CNN. To assess the adaptability and generality of the presented algorithm, it is applied to CIFAR-100 and SVHN with a final accuracy of 63.1% and 95.6%, respectively. We extend the investigated NAS by two methods for predicting candidate fitnesses from partial learning curves. This improves CGP-CNN runtime efficiency by a factor of 1.69.
This work has been partially funded by the “Bavarian Ministry of Economic Affairs, Regional Development and Energy” under the grant ’CrossAI’ (IUK593/002).
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Notes
- 1.
CIFAR-10 and CIFAR-100 [16] are image classification datasets of 32 \(\times \) 32 color images.
- 2.
Code and experimental setup: https://zenodo.org/record/2611575.
- 3.
Street View House Numbers (SVHN) [22] is an image classification task, “seen as similar in flavor to MNIST”, containing images of digits from house numbers.
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Wendlinger, L., Stier, J., Granitzer, M. (2021). Evofficient: Reproducing a Cartesian Genetic Programming Method. In: Hu, T., Lourenço, N., Medvet, E. (eds) Genetic Programming. EuroGP 2021. Lecture Notes in Computer Science(), vol 12691. Springer, Cham. https://doi.org/10.1007/978-3-030-72812-0_11
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