Implementasi Metode Optimasi Gradient Centralization untuk Pembuatan Model Klasifikasi Citra Pemandangan Alam

  • Pasha Renaisan Udayana University
  • I Gede Santi Astawa Universitas Udayana

Abstract

Optimization algorithms are algorithms that are needed to properly train Neural Networks. Optimization algorithms help improve model performance by modifying the attributes of the neural network, such as weights and learning rate to further enchant the model. Gradient Centralization is a new optimization algorithm that optimizes by centralizing gradient vectors to have zero mean. This paper focuses on finding the optimal learning rate for Gradient Centralization and uses that learning rate to create a classification model to classify natural scene images. The optimal learning rate obtained by this research is 2e-5 and the model obtained 84,17% mean recall, 84,39%  mean precision, and overall 83,60% accuracy.

References

[1] Yong, H., Huang, J., Hua, X., & Zhang, L. (2020). Gradient Centralization: A New Optimization Technique for Deep Neural Networks.

[2] O'Shea, Keiron., & Nash, Ryan. (2015). An Introduction to Convolutional Neural Networks.

[3] R. Poojary and A. Pai, "Comparative Study of Model Optimization Techniques in Fine-Tuned CNN Models," 2019 International Conference on Electrical and Computing Technologies and Applications (ICECTA), 2019, pp. 1-4, doi: 10.1109/ICECTA48151.2019.8959681.

[4] Ondieki, B. (2015). Convolutional Neural Networks for Scene Recognition.
Published
2022-11-25
How to Cite
RENAISAN, Pasha; ASTAWA, I Gede Santi. Implementasi Metode Optimasi Gradient Centralization untuk Pembuatan Model Klasifikasi Citra Pemandangan Alam. Jurnal Nasional Teknologi Informasi dan Aplikasinya (JNATIA), [S.l.], v. 1, n. 1, p. 389-396, nov. 2022. Available at: <https://ojs.unud.ac.id/index.php/jnatia/article/view/92632>. Date accessed: 26 jan. 2023.

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