TY  - JOUR
T1  - Tam Bağlı İleri Beslemeli Sinir Ağlarda Softplus Aktivasyon Fonksiyonu İçin Yeni Bir Yol Ağırlık Başlatma Düzeni
TT  - A New Weight Initialization Scheme for Softplus Activation Function in Fully Connected FeedForward Neural Networks
AU  - Dönmez, Emrah
AU  - Turhan, Mehmet Murat
AU  - Talu, Muhammed Fatih
PY  - 2025
DA  - October
Y2  - 2025
DO  - 10.46387/bjesr.1717780
JF  - Mühendislik Bilimleri ve Araştırmaları Dergisi
JO  - BJESR
PB  - Bandırma Onyedi Eylül Üniversitesi
WT  - DergiPark
SN  - 2687-4415
SP  - 193
EP  - 202
VL  - 7
IS  - 2
LA  - tr
AB  - Derin sinir ağlarının eğitilebilirliği, özellikle katman sayısı arttığında, gradyan sönümlenmesi veya gradyan patlaması gibi sorunlar nedeniyle zorlaşmaktadır. Bu çalışmada, Softplus aktivasyon fonksiyonu kullanılan tam bağlı ileri beslemeli sinir ağları için, merkezi limit teoremine dayalı iki yeni yol ağırlık başlatma düzeni önerilmiştir. İlk düzen yalnızca ileri yönde sinyal istatistiklerini, ikincisi ise hem ileri hem de geri yönde istatistikleri korumayı hedeflemektedir. CIFAR-10 ve CIFAR-100 veri kümeleri üzerinde yapılan deneylerde, derin mimarilerde yalnızca ileri yöndeki istatistik korumanın yeterli olmadığı, iki yönlü korumanın ise ağın eğitilebilirliğini anlamlı şekilde artırdığı gözlemlenmiştir. Özellikle 25 gizli katmanlı ağlarda, yalnızca iki yönlü koruma sağlayan başlatma düzeniyle başarılı eğitim gerçekleştirilebilmiştir. Elde edilen sonuçlar, aktivasyon fonksiyonu dinamiklerine uygun başlatma stratejilerinin, derin sinir ağlarının eğitilebilmesinde belirleyici rol oynadığını göstermektedir.
KW  - Aktivasyon Fonksiyonu
KW  - Derin Sinir Ağları
KW  - Softplus
KW  - Yol Ağırlık Başlatma
N2  - The trainability of deep neural networks becomes challenging as the number of layers increases, primarily due to issues such as vanishing or exploding gradients. In this study, two new weight initialization schemes based on the central limit theorem are proposed for fully connected feedforward neural networks using the Softplus activation function. The first scheme aims to preserve signal statistics only in the forward direction, while the second aims to preserve them in both forward and backward directions. Experiments conducted on the CIFAR-10 and CIFAR-100 datasets demonstrate that preserving only forward signal statistics is insufficient in deep architectures, whereas preserving statistics in both directions significantly improves trainability. Particularly in architectures with 25 hidden layers, successful training was achieved only with the bidirectional initialization scheme. The results reveal that initialization strategies compatible with the dynamics of the activation function play a critical role in enabling the effective training of deep neural networks.
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UR  - https://doi.org/10.46387/bjesr.1717780
L1  - https://dergipark.org.tr/tr/download/article-file/4950837
ER  -