Fig. 5: Handwriting and fashion recognition using the IDNN.
From: Space-efficient optical computing with an integrated chip diffractive neural network
a The network consists of a hidden layer W and an output layer Wout. The outputs of the output layer are recognition results. The input layer is calculated by the traditional computer and the complex output results of the input layer are converted into amplitude and phase information as the input of the chip. b The numerical testing results of accuracy and loss versus epoch number for the MNIST dataset. c The confusion matrix for our experimental results, using 500 different handwritten digits. d The output intensity distribution of the IDNN for a handwritten input of “2” is demonstrated. e The numerical testing results of accuracy and loss versus epoch number for the MNIST-Fashion dataset. f The confusion matrix in the experiment. g As an example, the output intensity distribution of the IDNN for a fashion product input of “pullover” is demonstrated.
