深度学习优化算法研究  

Research on Optimization Algorithm of Deep Learning

作　　者：仝卫国 李敏霞 张一可 TONG Wei-guo;LI Min-xia;ZHANG Yi-ke(Department of Automation,North China Electric Power University,Baoding,Hebei 071003,China)

机构地区：[1]华北电力大学(保定)自动化系,河北保定071003

出　　处：《计算机科学》2018年第B11期155-159,共5页Computer Science

基　　金：河北省自然基金资助

摘　　要：深度学习是机器学习领域热门的研究方向,深度学习中的训练和优化算法也受到了较高的关注和研究,已成为人工智能发展的重要推动力。基于卷积神经网络的基本结构,介绍了网络训练中激活函数和网络结构的选择、超参数的设置和优化算法,分析了各算法的优劣,并以Cifar-10数据集为训练样本进行了验证。实验结果表明,合适的训练方式和优化算法能够有效提高网络的准确性和收敛性。最后,在实际输电线图像识别中对最优算法进行了应用并取得了良好的效果。Deep learning is a hot research field in machine learning.Training and optimization algorithm of deep lear-ning have also been high concern and studied,and has become an important driving force for the development of artificial intelligence.Based on the basic structure of convolution neural network,the selection of activation function,the setting of hyperparameters and optimization algorithms in network training were introduced in this paper.The advantages and disadvantages of each training and optimization algorithm were analyzed and verified by Cifar-10 data set as training samples.Experimental results show that the appropriate training methods and optimization algorithms can effectively improve the accuracy and convergence of the network.Finally,the optimal algorithm was applied in the image recognition of actual transmission line and achieved good result.

关 键 词：深度学习 卷积神经网络 激活函数 正则化 超参数 优化算法 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]