Comparative Analysis of the Results of Training a Neural Network with Calculated Weights and with Random Generation of t
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BUST, ADAPTIVE, AND NETWORK CONTROL
Comparative Analysis of the Results of Training a Neural Network with Calculated Weights and with Random Generation of the Weights P. Sh. Geidarov Institute of Control Systems, Azerbaijan National Academy of Sciences, Baku, Azerbaijan e-mail: [email protected] Received December 3, 2018 Revised October 24, 2019 Accepted November 28, 2019
Abstract—Neural networks based on metric recognition methods allow, based on the initial conditions of the computer vision task such as the number of images and samples, to determine the structure of the neural network (the number of neurons, layers, connections), and also allow to analytically calculate the values of the weights on the connections of the neural network. As feedforward neural networks, they can also be trained by classical learning algorithms. The possibility of precomputation of the values of the neural network weights allows us to say that the procedure for creating and training a feedforward neural network is accelerated in comparison with the classical scheme for creating and training a neural network where values of the weights are randomly generated. In this work, we conduct two experiments based on the handwritten numbers dataset MNIST that confirm this statement. Keywords: neural networks, metric recognition methods, nearest neighbor method, backpropagation algorithms, random weight initialization DOI: 10.1134/S0005117920070048
1. INTRODUCTION Neural networks are being widely used in the modern world, especially in computer vision problems. Despite this, in practice, the creation and training of neural networks remains a complex and often unpredictable operation. This is mainly due to the fact that the process of creating and training neural networks [1, 2] is not strictly defined, which leads to a number of difficulties and makes this process time-consuming. The difficulties lie in the choice of the structure of the neural network itself, as well as in the choice of training parameters. The works [3, 4] propose the architecture of a neural network that implements metric recognition methods [5]. The structure of these neural networks, i.e., the number of neurons, connections, and layers, is strictly determined based on the initial conditions of the problem for metric recognition methods [5], such as the number of samples and the number of patterns being recognized. The values of connection weights for these networks are also calculated analytically based on metric proximity measures [5]. This possibility already allows to get a working neural network without the use of training algorithms. Based on metric recognition methods, neural networks are a special case of the classical three/four-layered multilayered perceptron, but the architectures of these networks allow to determine the structure of a neural network and analytically determine the values of weights. In addition, the architecture of these networks allows to add new samples and patterns to the neural network in a cascade fashion without changing the previous weight
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