Designing and constructing neural network topologies using nature-inspired algorithms

Description 📝

The proposed method NiaNet attempts to pick hyperparameters and AE architecture that will result in a successful encoding and decoding (minimal difference between input and output). NiaNet uses the collection of algorithms available in the library NiaPy to navigate efficiently in waste search-space.

What it can do? 👀

• Construct novel AE's architecture using nature-inspired algorithms.
• It can be utilized for any kind of dataset, which has numerical values.

Installation ✅

Installing NiaNet with pip3: sh pip3 install nianet

Documentation 📘

Annals of Computer Science and Information Systems, Volume 30: NiaNet: A framework for constructing Autoencoder architectures using nature-inspired algorithms

Examples

Usage examples can be found here.

Getting started 🔨

Create your own example:

In examples folder create the Python file based on the existing evolvefordiabetes_dataset.py.

Change dataset:

Change the dataset import function as follows: python from sklearn.datasets import load_diabetes dataset = load_diabetes()

Specify the search space:

Set the boundaries of your search space with autoencoder.py.

The following dimensions can be modified: * Topology shape (symmetrical, asymmetrical) * Size of input, hidden and output layers * Number of hidden layers * Number of neurons in hidden layers * Activation functions * Number of epochs * Learning rate * Optimizer

You can run the NiaNet script once your setup is complete.

Running NiaNet script:

python evolve_for_diabetes_dataset.py

HELP ⚠️

saso.pavlic@student.um.si

Acknowledgments 🎓

• NiaNet was developed under the supervision of doc. dr Iztok Fister ml. at University of Maribor.

• This code is a fork of NiaPy. I am grateful that the authors chose to open-source their work for future use.

Cite us

Are you using NiaNet in your project or research? Please cite us!

Plain format

S. Pavlič, I. F. Jr, and S. Karakatič, “NiaNet: A framework for constructing Autoencoder architectures using nature-inspired algorithms,” in Annals of Computer Science and Information Systems, 2022, vol. 30, pp. 109–116. Accessed: Oct. 08, 2022. [Online]. Available: https://annals-csis.org/Volume_30/drp/192.html

Bibtex format

@article{NiaPyJOSS2018, author = {Vrban{\v{c}}i{\v{c}}, Grega and Brezo{\v{c}}nik, Lucija and Mlakar, Uro{\v{s}} and Fister, Du{\v{s}}an and {Fister Jr.}, Iztok}, title = {{NiaPy: Python microframework for building nature-inspired algorithms}}, journal = {{Journal of Open Source Software}}, year = {2018}, volume = {3}, issue = {23}, issn = {2475-9066}, doi = {10.21105/joss.00613}, url = {https://doi.org/10.21105/joss.00613} }

RIS format

TY - CONF TI - NiaNet: A framework for constructing Autoencoder architectures using nature-inspired algorithms AU - Pavlič, Sašo AU - Jr, Iztok Fister AU - Karakatič, Sašo T2 - Proceedings of the 17th Conference on Computer Science and Intelligence Systems C3 - Annals of Computer Science and Information Systems DA - 2022/// PY - 2022 DP - annals-csis.org VL - 30 SP - 109 EP - 116 LA - en SN - 978-83-962423-9-6 ST - NiaNet UR - https://annals-csis.org/Volume_30/drp/192.html Y2 - 2022/10/08/19:08:20 L1 - https://annals-csis.org/Volume_30/drp/pdf/192.pdf L2 - https://annals-csis.org/Volume_30/drp/192.html

License

This package is distributed under the MIT License. This license can be found online at http://www.opensource.org/licenses/MIT.

Disclaimer

This framework is provided as-is, and there are no guarantees that it fits your purposes or that it is bug-free. Use it at your own risk!