基于CIFAR-10的图像分类模型优化

基于CIFAR-10的图像分类模型优化
张占军;彭艳兵;程光
【期刊名称】《计算机应用与软件》
【年(卷),期】2018(035)003
【摘要】With the research and application of convolutional neural network in image processing, the accuracy of image classification has been greatly improved, but the problem of over-fitting has always existed and has become an important factor affecting the classification accuracy.In this paper,starting from the source of over-fitting,we increased the amount of data and reduced the number of parameters in order to reduce the over-fitting purposes.Based on the classical model LeNet-5, this paper made input data enhancement and split the convolution layer to reduce the parameters.At the same time,it used L1 and L2 mixed constraints and adjusted the proportion of the two to achieve the best effect.Experimental results showed that the optimized network achieved 91.2%accuracy on the CIFAR-10 dataset. Compared with the original LeNet-5 model,it was increased by 23%.It greatly reduced the over-fitting,and improved the classification accuracy of the model.%随着卷积神经网络在图像处理的研究与应用,图像的分类准确度得到了大幅提升,但是过拟合的问题却一直存在并成为影响分类准确率的重要因素.从过拟合的产生源头出发,增加数据量并减少参数数量以达到降低过拟合的目的.基于经典模型LeNet-5,对输入数据进行数据增强,并对卷积层进行拆分以减少参数,同时采用L1、L2混合约束的方法,并灵活调整两者的占比以
达到最佳效果.实验结果表明,在CIFAR-10数据集上,优化后的网络达到了91.2%的准确率,相比最初的LeNet-5模型提高了23%,极大地降低了过拟合,提高了模型的分类准确率.
【总页数】5页(P177-181)
【作者】张占军;彭艳兵;程光
【作者单位】武汉邮电科学研究院湖北武汉430074;烽火通信科技股份有限公司南京研发江苏南京210019;烽火通信科技股份有限公司南京研发江苏南京210019;烽火通信科技股份有限公司南京研发江苏南京210019
【正文语种】中文
【中图分类】TP183
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