Calculation for depth of deep belief network - Details

Author：

Pan, Guang-Yuan (Pan, Guang-Yuan.) | Chai, Wei (Chai, Wei.) | Qiao, Jun-Fei (Qiao, Jun-Fei.) (Scholars：乔俊飞)

Indexed by：

EI PKU CSCD

Abstract：

In　order　to　calculate　the　depth　of　deep　belief　network　(DBN)　in　its　applications,　the　reason　of　failure　in　training　by　using　random　initialization　in　gradient-based　is　analyzed　in　both　math　and　biology,　and　then　verified　by　the　test.　The　theorem　that　the　reconstruction　error　of　restricted　boltzmann　machine　(RBM)　is　related　to　network＇s　energy　function　is　proved.　After　that,　a　method　to　calculate　the　depth　by　using　restructure　error　in　RBM　is　proposed　based　on　the　relationship　between　hidden　layers　and　errors.　DBN　approaches　human-level　performance　in　AI　tasks　after　the　self-training.　The　experiment　of　hand　writing　digital　recognition　shows　that　the　proposed　method　can　improve　the　efficiency　and　lower　the　cost.　©,　2014,　Northeast　University.　All　right　reserved.

Keyword：

Unsupervised learning Computer simulation Errors Control engineering

Author Community：

[ 1 ] [Pan, Guang-Yuan]College of Electronic Information and Control Engineering, Beijing University of Technology, Beijing; 100124, China
[ 2 ] [Chai, Wei]College of Electronic Information and Control Engineering, Beijing University of Technology, Beijing; 100124, China
[ 3 ] [Qiao, Jun-Fei]College of Electronic Information and Control Engineering, Beijing University of Technology, Beijing; 100124, China