Hyperparameter tuning of optical neural network classifiers for high-order gaussian beams

TL;DR

This paper demonstrates that automatic hyperparameter tuning of a diffractive deep neural network improves classification accuracy of high-order Gaussian beams, especially as the number of modes increases.

Contribution

It introduces an automatic hyperparameter tuning method for D2NNs used in classifying high-order Gaussian beams, enhancing accuracy over manual tuning.

Findings

Auto-tuning hyperparameters yields higher accuracy than manual settings.

Accuracy improves with increasing number of classification modes.

Auto-tuning effectively optimizes interlayer distances in D2NNs.

Abstract

High-order Gaussian beams with multiple propagation modes have been studied for free-space optical communications. Fast classification of beams using a diffractive deep neural network, D2NN, has been proposed. D2NN optimization is important because it has numerous hyperparameters, such as interlayer distances and mode combinations. In this study, we classify Hermite-Gaussian beams, which are high-order Gaussian beams, using a D2NN, and automatically tune one of its hyperparameters known as the interlayer distance. We used the tree-structured Parzen estimator, a hyperparameter auto-tuning algorithm, to search for the best model. Results indicated that classification accuracy obtained by auto-tuning hyperparameters was higher than that obtained by manually setting interlayer distances at equal intervals. In addition, we confirmed that accuracy by auto-tuning improves as the number of classification modes increases.