Electronic Science and Technology ›› 2019, Vol. 32 ›› Issue (3): 53-57.doi: 10.16180/j.cnki.issn1007-7820.2019.03.011

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An Improved LeNet Network

HU Demin,CHENG Pufang   

  1. School of Optical-Electrical and Computer Engineering,University of Shanghai for Science and Technology,Shanghai 200093,China
  • Received:2018-03-18 Online:2019-03-15 Published:2019-03-01
  • Supported by:
    National Natural Science Foundation of China(61170277);National Natural Science Foundation of China(61472256);Key Project of Scientific Research and Innovation of Shanghai Municipal Education Commission(12zz17);First Class Construction Project of Shanghai(S1201YLXK)

Abstract: Aim

ing at the problems of low learning efficiency, slow convergence and long training time in convolutional neural networks, this paper presented an improved LeNet convolutional neural network model. The model used a convolutional kernel whose convolution scale was set as 3 and stride was set as 2 instead of the original pooled layer, and added a batch normalization layer before each activation function layer. Experiments on the Mnist dataset showed that compared with the traditional LeNet network, the convolutional neural network proposed in this paper improved the accuracy rate and had faster convergence speed and shorter training time.

Key words: image classification, convolutional neural network, batch normalization, pooling layer, convolution kernel, stochastic gradient descent.

CLC Number: 

  • TP183