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

@article{Watanabe2021HyperparameterTO,
  title={Hyperparameter tuning of optical neural network classifiers for high-order Gaussian beams.},
  author={S. Watanabe and Tomoyoshi Shimobaba and Takashi Kakue and Tomoyoshi Ito},
  journal={Optics express},
  year={2021},
  volume={30 7},
  pages={
          11079-11089
        }
}
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… 
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