一种改进的LeNet网络

doi:10.16180/j.cnki.issn1007-7820.2019.03.011

Abstract

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

HU Demin,CHENG Pufang. An Improved LeNet Network[J].Electronic Science and Technology, 2019, 32(3): 53-57.

Figures/Tables 6

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References 16

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参数	数值
处理器	英特尔第四代酷睿 i5-4210H @2.90 GHz
内存	12.0 GB
操作系统	WIN10 64位
编程软件	Python3.5 + TensorFlow框架(纯CPU模式)

LeNet层	LeNet参数	本文层级	本文结构参数
C1	156	C1	156+2+6*2
S2	0	C2	0+(33+1)6+2+6*2
C3	1516	C3	1516+2+16*2
S4	0	C4	0+(33+1)16+2+16*2
C5	48 120	C5	1 516+2+1*2
F6	10 164	F6	10 164+2+1*2

结构	Mnist准确率	Cifar-10准确率
LeNet	90.58%	73.46%
只去除池化层	91.80%	75.78%
只加BN	94.36%	78.69%
本文结构	96.42%	80.08%

An Improved LeNet Network

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