Investigation of the Impact of Hyperparameters on the Accuracy of Neural Network Predictions Using the Fashion-MNIST Dataset

D. Klimenka and Ð . Kazlova ()

Digital Transformation, 2026, vol. 32, issue 2

Abstract: Machine learning and artificial intelligence (AI) are currently actively researching methods for optimizing and tuning model hyperparameters. One key area of research is analyzing the impact of varying hyperparameters, such as the number of two-dimensional convolution (Conv2D) layers and their parameters (number of filters, kernel size), the size and stride of maximum pooling (MaxPooling2D) layers, the number of neurons in fully connected layers, activation functions, batch size (batch_size), and the number of training epochs, on the prediction accuracy of machine learning models using a convolutional neural network architecture on the Fashion-MNIST

Date: 2026
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Persistent link: https://EconPapers.repec.org/RePEc:abx:journl:y:2026:id:1039

DOI: 10.35596/1729-7648-2026-32-2-44-52

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