| Issue |
EPJ Web Conf.
Volume 248, 2021
V International Conference “Modeling of Nonlinear Processes and Systems“ (MNPS-2020)
|
|
|---|---|---|
| Article Number | 01012 | |
| Number of page(s) | 6 | |
| Section | Mathematical Models in Natural Sciences | |
| DOI | https://doi.org/10.1051/epjconf/202124801012 | |
| Published online | 26 April 2021 | |
https://doi.org/10.1051/epjconf/202124801012
Gradient-Based Algorithm for Tracking the Activity of Neural Network Weights Changing
Moscow State University of Technology “STANKIN”, RU-127055, Moscow, Russia
* Corresponding author: starodub_a_v@mail.ru
Published online: 26 April 2021
The research conducted in this paper is in the field of machine learning. The main object of the research is the learning process of an artificial neural network in order to increase its efficiency. The algorithm based on the analysis of retrospective learning data. The dynamics of changes in the values of the weights of an artificial neural network during training is an important indicator of training efficiency. The algorithm proposed in this work is based on changing the weight gradients values. Changing of the gradients weights makes it possible to understand how actively the network weights change during training. This knowledge helps to diagnose the training process and makes an adjusting the training parameters. The results of the algorithm can be used to train an artificial neural network. The network will help to determine the set of measures (actions) needed to optimize the learning process by the algorithm results.
© The Authors, published by EDP Sciences, 2021
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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