计算机工程与应用2017，Vol.53Issue(8)：1-7,14,8.DOI:10.3778/j.issn.1002-8331.1610-0244
分布式环境下卷积神经网络并行策略研究
Research on parallel strategy of convolution neural network in distributed environment
摘要
Abstract
Convolutional neural networks usually use standard error back propagation algorithm to do serial training. With the growth of data size, the single machine serial training is time-consuming and takes up more system resources. In order to realize the convolution neural network training of massive data, a parallel training model of BP neural network based on MapReduce framework is proposed. The model combines the standard error back-propagation algorithm and error back-propagation algorithm and divides large data sets into several sub sets. Parallel processing is carried out in the condition of loss of a small amount of accuracy, and the MNIST data set is extended to carry out the image recognition test. Experi-mental results show that the algorithm has a good adaptability to the data size, and can improve the training efficiency of the convolution neural network.关键词
卷积神经网络/后向传播(BP)算法/Hadoop并行策略Key words
convolutional neural networks/Back Propagation(BP)algorithm/Hadoop parallel processing分类
信息技术与安全科学引用本文复制引用
张任其,李建华,范磊..分布式环境下卷积神经网络并行策略研究[J].计算机工程与应用,2017,53(8):1-7,14,8.基金项目
上海市科委基础研究重点项目(No.13JC1403501). （No.13JC1403501）
