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Abstract <p>This paper presents a description of an improved algorithm for computing the weights of a convolutional neural network, as well as the results of comparative experiments on training a convolutional neural network on a handwritten digit dataset MNIST using both precomputed and randomly initialized weights. The experimental results demonstrate the advantages of preliminary analytical computation of neural network weight values compared to random weight initialization. The weights of the convolutional neural network were computed using only 10 digit images, randomly selected from the dataset MNIST. Experiments showed that the time spent on the analytical computation of weight values was negligible. The testing results on the test dataset with the computed but yet untrained neural network showed an accuracy of more than 50% in correctly recognizing the images from the test dataset MNIST. The results of numerous training experiments on the same convolutional neural network, using both computed and random weights, showed that training with precomputed weights yields better results and requires less training time.</p>

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Comparison of Training Results of a Convolutional Neural Network with Computed Weights and Random Weight Initialization

  • 期刊论文

  • 发表日期：

    2025年12月24日

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  • P. Sh. Geidarov

摘要

Abstract

This paper presents a description of an improved algorithm for computing the weights of a convolutional neural network, as well as the results of comparative experiments on training a convolutional neural network on a handwritten digit dataset MNIST using both precomputed and randomly initialized weights. The experimental results demonstrate the advantages of preliminary analytical computation of neural network weight values compared to random weight initialization. The weights of the convolutional neural network were computed using only 10 digit images, randomly selected from the dataset MNIST. Experiments showed that the time spent on the analytical computation of weight values was negligible. The testing results on the test dataset with the computed but yet untrained neural network showed an accuracy of more than 50% in correctly recognizing the images from the test dataset MNIST. The results of numerous training experiments on the same convolutional neural network, using both computed and random weights, showed that training with precomputed weights yields better results and requires less training time.