Package: nianet
Designing and constructing neural network topologies using nature-inspired algorithmsDescription 📝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 libraryNiaPyto navigate efficiently in waste search-space.What it can do? 👀Construct novel AE's architectureusing nature-inspired algorithms.It can be utilized forany kind of dataset, which hasnumericalvalues.Installation ✅Installing NiaNet with pip3:pip3installnianetDocumentation 📘Annals of Computer Science and Information Systems, Volume 30:NiaNet: A framework for constructing Autoencoder architectures using nature-inspired algorithmsExamplesUsage examples can be foundhere.Getting started 🔨Create your own example:Inexamplesfolder create the Python file based on the existingevolve_for_diabetes_dataset.py.Change dataset:Change the dataset import function as follows:fromsklearn.datasetsimportload_diabetesdataset=load_diabetes()Specify the search space:Set the boundaries of your search space withautoencoder.py.The following dimensions can be modified:Topology shape (symmetrical, asymmetrical)Size of input, hidden and output layersNumber of hidden layersNumber of neurons in hidden layersActivation functionsNumber of epochsLearning rateOptimizerYou can run the NiaNet script once your setup is complete.Running NiaNet script:python evolve_for_diabetes_dataset.pyHELP ⚠️saso.pavlic@student.um.siAcknowledgments 🎓NiaNet was developed under the supervision
ofdoc. dr Iztok Fister ml.atUniversity of Maribor.This code is a fork ofNiaPy. I am grateful that the authors chose to
open-source their work for future use.Cite usAre you using NiaNet in your project or research? Please cite us!Plain formatS. 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.htmlBibtex 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 formatTY  - 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.htmlLicenseThis package is distributed under the MIT License. This license can be found online athttp://www.opensource.org/licenses/MIT.DisclaimerThis 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!