摘要
CNN框架中,如何对其模型的超参数进行自动化获取一直是一个重要问题。提出一种基于改进的贝叶斯优化算法的CNN超参数优化方法。该方法使用改进的汤普森采样方法作为采集函数,利用改进的马尔可夫链蒙特卡罗算法加速训练高斯代理模型。该方法可以在超参数空间不同的CNN框架下进行超参数优化。利用CIFAR-10、MRBI和SVHN测试集对算法进行性能测试,实验结果表明,改进后的CNN超参数优化算法比同类超参数优化算法具有更好的性能。
In the framework of convolutional neural network(CNN),how to obtain the hyper-parameters of its model automatically is an important and pressing research topic.This paper proposed a hyper-parameter optimization method of CNN based on improved Bayesian optimization algorithm.This method used the improved Thompson sampling method as the acquisition function.It used the improved Markov chain Monte Carlo algorithm to accelerate the Gaussian surrogate model.The proposed me- thod can be used to optimize hyper-parameters in frameworks of CNN with different hyper-parameter space.It tested the perfor- mance of the algorithm by using these testing sets:CIFAR-10,MRBI and SVHN.The experimental results show that the improved hyper-parameter optimization algorithm of CNN has better performance than the other algorithms.
作者
邓帅
Deng Shuai(Beijing Advanced Innovation Center for Future Internet Technology,Beijing University of Technology,Beijing 100124,China;Beijing Engineering Research Center for IoT Software & Systems,Beijing University of Technology,Beijing 100124,China)
出处
《计算机应用研究》
CSCD
北大核心
2019年第7期1984-1987,共4页
Application Research of Computers
基金
北京市自然科学基金资助项目(4122010)
国家自然科学基金资助项目(60773186)
关键词
贝叶斯优化
卷积神经网络
高斯过程
超参数优化
Bayesian optimization
convolutional neural network
Gaussian process
hyper-parameter optimization
