Bir Gizli Katmanlı Yapay Sinir Ağlarında Optimal Nöron Sayısının İncelenmesi

Year 2022, , 303 - 325, 25.11.2022

Tayfun Ünal , Ünver Çiftçi , Nurkut Nuray Urgan

https://doi.org/10.29233/sdufeffd.1056018

Cited By: 3

Abstract

Bu makalede, bir gizli katmanlı yapay sinir ağları için optimal nöron sayısı araştırılmıştır. Bunun için teorik ve istatiksel çalışmalar yapılmıştır. Optimal nöron sayısını bulmak için global minimum bulmak gereklidir. Ancak yapay sinir ağlarının eğitimi konveks olmayan bir problem olduğundan optimizasyon algoritmaları ile global minimum bulmak zordur. Bu çalışmada global minimumu dolayısıyla optimum nöron sayısını bulmak için baskı maliyet fonksiyonu önerilmiştir. Baskı maliyet fonksiyonu yardımıyla global minimumu veren yapay sinir ağı modelinin nöron sayısının, optimal nöron sayısını verdiği gösterilmiştir. Ayrıca baskı maliyet fonksiyonu XOR veri kümesi ve daire veri kümesi üzerinde test edilmiş ve XOR veri kümesi üzerinde %99, daire veri kümesi üzerinde ise %97 başarı elde edilmiştir. Bu veri kümeleri için optimal nöron sayısı tespit edilmiştir.

Keywords

Derin Öğrenme, Yapay sinir ağları, Tam bağlı katman, XOR problemi, Nöron sayısı

Investigation of the Optimal Number of Neuron in One-Hidden-Layer Artificial Neural Networks

Abstract

In this paper, optimal number of neurons in one-hidden-layer artificial neural networks is investigated. Theoretical and statistical studies are carried out for this goal. Finding the global minimum is necessary in order to determine the optimal number of neurons. However, since the training of artificial neural networks is a non-convex problem, it is difficult to find a global minimum with optimization algorithms. In this study, an augmented cost function is proposed to find the global minimum, hence the optimal number of neurons. It is shown that the optimal number of neurons is produced by the artificial neural network model, which gives the global minimum with the aid of the augmented cost function. Additionally, the XOR and circle datasets are used to test the augmented cost function, and 99% success was achieved on the XOR dataset and 97% on the circle dataset. The optimal number of neurons is determined for these datasets.

Keywords

Deep Learning, Artificial Neural Networks, Fully-Connected Layer, XOR Problem, Number of Neuron

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