A method to improve the computational performance of nonlinear all—optical diffractive deep neural network model

Abstract

AbstractTo further improve the computational performance of the diffractive deep neural network (D2NN) model, we use the ReLU function to limit the phase parameters, which effectively solves the problem of vanishing gradient that occurs in the mitigation model. We add various commonly used nonlinear activation functions to the hidden layer of the model and establish the ReLU phase-limit nonlinear diffractive deep neural network (ReLU phase-limit N-D2NN) model. We evaluate the model by comparing the performance of various nonlinear activation functions, where confusion matrix and accuracy are used as evaluation methods. The numerical simulation results show that the model achieves better classification performance on the MNIST and Fashion-MNIST datasets, respectively. In particular, the highest classification performance is obtained by the ReLU phase-limit N-D2NN model, in which the hidden layer uses PReLU, with 98.38% and 90.14%, respectively. This paper provides a theoretical basis for applying the nonlinear D2NN systems in natural scenes.

Keywords

• Diffractive deep neural network
• Nonlinear functions
• Optical neural network
• Rectified linear unit (ReLU)