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.
| Mualliflar | Primbetov, Abbaz, Akbarov, Navruz |
|---|---|
| Jurnal | Al-Farg'oniy avlodlari |
| Nashr sanasi | 2025-10-03 |
| Son | 3 |
| Betlar | 80-84 |
| Til | Rus |
Activation Functions, Spline Activation, Convolutional Neural Networks, CIFAR-10, Image Classification, ReLU, Swish, Deep Learning
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