  Express Wavenet is an improved optical diffractive neural network. At each
layer, it uses wavelet-like pattern to modulate the phase of optical waves. For
input image with n2 pixels, express wavenet reduce parameter number from O(n2)
to O(n). Only need one percent of the parameters, and the accuracy is still
very high. In the MNIST dataset, it only needs 1229 parameters to get accuracy
of 92%, while the standard optical network needs 125440 parameters. The random
shift wavelets show the characteristics of optical network more vividly.
Especially the vanishing gradient phenomenon in the training process. We
present a modified expressway structure for this problem. Experiments verified
the effect of random shift wavelet and expressway structure. Our work shows
optical diffractive network would use much fewer parameters than other neural
networks. The source codes are available at
https://github.com/closest-git/ONNet.
