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An Image Classification Method Based on Deep Neural Network with Energy Model

Yang Yang1,*, Jinbao Duan1, Haitao Yu1, Zhipeng Gao1, Xuesong Qiu1
State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications, Beijing, 100876, China.
*Corresponding Author: Yang Yang. Email: .

Computer Modeling in Engineering & Sciences 2018, 117(3), 555-575. https://doi.org/10.31614/cmes.2018.04249

Abstract

The development of deep learning has revolutionized image recognition technology. How to design faster and more accurate image classification algorithms has become our research interests. In this paper, we propose a new algorithm called stochastic depth networks with deep energy model (SADIE), and the model improves stochastic depth neural network with deep energy model to provide attributes of images and analysis their characteristics. First, the Bernoulli distribution probability is used to select the current layer of the neural network to prevent gradient dispersion during training. Then in the backpropagation process, the energy function is designed to optimize the target loss function of the neural network. We also explored the possibility of using Adam and SGD combination optimization in deep neural networks. Finally, we use training data to train our network based on deep energy model and testing data to verify the performance of the model. The results we finally obtained in this research include the Classified labels of images. The impacts of our obtained results show that our model has high accuracy and performance.

Keywords

Image classification, deep energy model, deep neural network, stochastic depth, deep learning.

Cite This Article

Yang, Y., Duan, J., Yu, H., Gao, Z., Qiu, X. (2018). An Image Classification Method Based on Deep Neural Network with Energy Model. CMES-Computer Modeling in Engineering & Sciences, 117(3), 555–575.



This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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