Parallel Implementation of Multilayered Neural Networks Based on Map-Reduce on Cloud Computing Clusters
In order to meet the requirements of big data processing,this paper presents an efficient mapping scheme for a fully connected multilayered neural network, which is trained by using back-propagation (BP) algorithm based on Map-Reduce of cloud computing clusters (MRBP).The batch-training (or epoch-training)regimes are used by effective segmentation of samples on the clusters, and are adopted in the separated training method, weight summary to achieve convergence by iterating.For a parallel BP algorithm on the clusters and a serial BP algorithm on an uniprocessor, the required time for implementing the algorithms is derived.The performance parameters,such as speed-up, optimal number and minimum of data nodes are evaluated for the parallel BP algorithm on the clusters.Experiment results demonstrate that the proposed parallel BP algorithm in this paper has better speed-up, faster convergence rate, less iterationsthan that of the existed algorithms.
Multilayered neural networks Performance analysis Back-propagation Cloud computing Map-Reduce Hadoop
Hai-jun Zhang Nan-feng Xiao
School of Computer Science and Engineering, South China University of Technology, Guangzhou510006, China
国内会议
广州
英文
504-516
2015-05-01（万方平台首次上网日期，不代表论文的发表时间）