NEURAL NETWORKS OPTIMIZATION: METHODS AND THEIR COMPARISON BASED OFF TEXT INTELLECTUAL ANALYSIS

Abstract

The research resulted in the development of software that implements various algorithms of neural networks optimization, which allowed to carry out their comparative analysis in terms of optimization quality. The article takes a detailed look at artificial neural networks and methods of their optimization: quantization, overcutting, distillation, Tucker’s dissolution. Algorithms and optimization tools of neural networks were explained, as well as comparative analysis of different methods was conducted with their advantages and disadvantages listed. Calculation values were given as well as recommendations on how to execute each method. Optimization is studied by text classification performance: peculiarities were removed, models were chosen and taught, parameters were adjusted. The set task was completed with the use of the following technologies: Python programming language, Pytorch framework and Jupyter Notebook developing environment. The results that were acquired can be used to reduce the demand on computing power while preserving the same level of detection and classification abilities.

Keywords

• artificial neural networks
• optimization
• compression and accelerating of neural networks
• text classification
• quantization
• tucker’s dissolution
• distillation