Analytical Calculation of Weights Convolutional Neural Network

IF 1 Q4 OPTICS Optical Memory and Neural Networks Pub Date : 2024-07-04 DOI:10.3103/s1060992x24700061

P. Sh. Geidarov

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Abstract

In this paper proposes an algorithm for the analytical calculation of convolutional neural networks without using neural network training algorithms. A description of the algorithm is given, on the basis of which the weights and threshold values of a convolutional neural network are analytically calculated. In this case, to calculate the parameters of the convolutional neural network, only 10 selected samples were used from the MNIST digit database, each of which is an image of one of the recognizable classes of digits from 0 to 9, and was randomly selected from the MNIST digit database. As a result of the operation of this algorithm, the number of channels of the convolutional neural network layers is also determined analytically. Based on the proposed algorithm, a software module was implemented in the Builder environment C++, on the basis of which a number of experiments were carried out with recognition of the MNIST database. The results of the experiments described in the work showed that the computation time of convolutional neural networks is very short and amounts to fractions of a second or a minute. Analytically computed convolutional neural networks were tested on the MNIST digit database, consisting of 1000 images of handwritten digits. The experimental results showed that already using only 10 selected images from the MNIST database, analytically calculated convolutional neural networks are able to recognize more than half of the images of the MNIST database, without application of neural network training algorithms. In general, the study showed that artificial neural networks, and in particular convolutional neural networks, are capable of not only being trained by learning algorithms, but also recognizing images almost instantly, without the use of learning algorithms using preliminary analytical calculation of the values of the neural network’s weights.

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权重分析计算卷积神经网络

摘要本文提出了一种不使用神经网络训练算法的卷积神经网络分析计算算法。本文介绍了该算法，并在此基础上分析计算了卷积神经网络的权值和阈值。在本例中，为了计算卷积神经网络的参数，只使用了从 MNIST 数字数据库中选取的 10 个样本，每个样本都是从 MNIST 数字数据库中随机选取的 0 至 9 可识别数字类别之一的图像。由于该算法的运行，卷积神经网络层的通道数也是通过分析确定的。根据所提出的算法，在 C++ Builder 环境中实现了一个软件模块，并在此基础上对 MNIST 数据库进行了多次识别实验。实验结果表明，卷积神经网络的计算时间非常短，仅为几分之一秒或一分钟。分析计算的卷积神经网络在 MNIST 数字数据库中进行了测试，该数据库由 1000 幅手写数字图像组成。实验结果表明，仅使用从 MNIST 数据库中选出的 10 幅图像，分析计算卷积神经网络就能识别 MNIST 数据库中一半以上的图像，而无需使用神经网络训练算法。总之，研究表明，人工神经网络，特别是卷积神经网络，不仅能够通过学习算法进行训练，而且几乎能够在不使用学习算法的情况下通过初步分析计算神经网络的权重值立即识别图像。

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来源期刊

Optical Memory and Neural Networks OPTICS-

CiteScore

1.50

自引率

11.10%

发文量

期刊介绍： The journal covers a wide range of issues in information optics such as optical memory, mechanisms for optical data recording and processing, photosensitive materials, optical, optoelectronic and holographic nanostructures, and many other related topics. Papers on memory systems using holographic and biological structures and concepts of brain operation are also included. The journal pays particular attention to research in the field of neural net systems that may lead to a new generation of computional technologies by endowing them with intelligence.