Research on optimization method of deep neural network

Pengfei Liu; Huaici Zhao; Feidao Cao

doi:10.1117/12.2294481

15 November 2017 Research on optimization method of deep neural network

Pengfei Liu, Huaici Zhao, Feidao Cao

Proceedings Volume 10605, LIDAR Imaging Detection and Target Recognition 2017; 106052T (2017) https://doi.org/10.1117/12.2294481
Event: LIDAR Imaging Detection and Target Recognition 2017, 2017, Changchun, China

Abstract

Image recognition technology has been widely applied and played an important role in various fields nowadays. Because of multi-layer structure of deep network can use a more concise way to express complex functions, deep neural network (DNN) will be applied to the image recognition to improve the accuracy of image classification. Analysis the existing problems of deep neural network. Then put forward new approaches to solve the gradient vanishing and over-fitting problems. The experimental results which verified on the MNIST, show that our proposed approaches can improve the classification accuracy greatly and accelerate the convergence speed. Compared to support vector machine (SVM), the optimized model of the neural network is not only effective, but also converged quickly.

Citation Download Citation

Pengfei Liu, Huaici Zhao, and Feidao Cao "Research on optimization method of deep neural network", Proc. SPIE 10605, LIDAR Imaging Detection and Target Recognition 2017, 106052T (15 November 2017); https://doi.org/10.1117/12.2294481

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