A COMPARATIVE STUDY OF ACTIVATION FUNCTIONS IN DEEP LEARNING MODELS

Primbetov, Abbaz, Akbarov, Navruz

Al-Farg'oniy avlodlari · 2025-yil

Annotatsiya

Activation functions play a vital role in the training dynamics and generalization performance of deep learning models. This study presents a comparative analysis of ten widely used activation functions—ReLU, Sigmoid, Tanh, ELU, SELU, Softplus, Softsign, Swish, GELU, and a custom spline-based function—within a unified convolutional neural network (CNN) architecture. All models were trained and evaluated on the CIFAR-10 dataset under identical experimental settings, including fixed learning rate, batch size, number of epochs, and architecture configuration. 

Maqola ma’lumotlari
MualliflarPrimbetov, Abbaz, Akbarov, Navruz
JurnalAl-Farg'oniy avlodlari
Nashr sanasi2025-10-03
Son3
Betlar80-84
TilRus

Kalit so‘zlar

Activation Functions, Spline Activation, Convolutional Neural Networks, CIFAR-10, Image Classification, ReLU, Swish, Deep Learning

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