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Optimization of deep network models through fine tuning

International Journal of Intelligent Computing and Cybernetics 2018年第3期11卷 386-403页

作者： M.Arif Wani Saduf Afzal Postgraduate Department of Computer Science University of KashmirSrinagarIndia University of Kashmir SrinagarIndia

Purpose–Many strategies have been put forward for training deep network models,however,stacking of several layers of non-linearities typically results in poor propagation of gradients and *** purpose of this paper is to explore the use of two steps strategy where initial deep learning model is obtained first by unsupervised learning and then optimizing the initial deep learning model by fine tuning.A number of fine tuning algorithms are explored in this work for optimizing deep learning *** includes proposing a new algorithm where Backpropagation with adaptive gain algorithm is integrated with Dropout technique and the authors evaluate its performance in the fine tuning of the pretrained deep ***/methodology/approach–The parameters of deep neural networks are first learnt using greedy layer-wise unsupervised *** proposed technique is then used to perform supervised fine tuning of the deep neural network *** experimental study is performed to evaluate the performance of the proposed fine tuning technique on three benchmark data sets:USPS,Gisette and *** authors have tested the approach on varying size data sets which include randomly chosen training samples of size 20,50,70 and 100 percent from the original data ***–Through extensive experimental study,it is concluded that the two steps strategy and the proposed fine tuning technique significantly yield promising results in optimization of deep network ***/value–This paper proposes employing several algorithms for fine tuning of deep network model.A new approach that integrates adaptive gain Backpropagation(BP)algorithm with Dropout technique is proposed for fine tuning of deep *** and comparison of various algorithms proposed for fine tuning on three benchmark data sets is presented in the paper.

关键词： Dropout deep neural network Contrastive divergence fine tuning of deep neural network Restricted Boltzmann machine Unsupervised pretraining Backpropagation

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