Electronic Science and Technology ›› 2019, Vol. 32 ›› Issue (1): 72-75.doi: 10.16180/j.cnki.issn1007-7820.2019.01.0015

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Classification Performance of Compressing Dimensionality of Hidden Layer of Deep Neural Network

CHENG Lingfei1,HE Yang2,ZHANG Peiling1,LI Yan2   

  1. 1. School of Physics and Electronic Information,Henan Polytechnic University,Jiaozuo 454000,China
    2. School of Electrical Engineering and Automation,Henan Polytechnic University,Jiaozuo 454000,China
  • Received:2017-12-19 Online:2019-01-15 Published:2018-12-29
  • Supported by:
    National Natural Science Foundation of China(61501175);Key Project of Science and Technology Research of Henan Educational Committee(15A510008);Doctoral Fund of Henan Polytechnic University(B2015-33)


In order to make deep neural network get better generalization property and reduce training time. Inspired by compressed neural network, compressing dimension of the hidden layers according to different compression ratio, and based on the traditional compressed deep neural network, adding classification layer to the top layer, then deep neural network gets the ability of classification. Experiments performed on MNIST handwritten dataset show that using properly compress deep neural network instead of uncompressed network would get better classification results, and saving lots of training time.

Key words: deep neural network, classification, compression ratio, generalization property, training time, deep learning

CLC Number: 

  • TP183