Distributed Training Method of Artificial Neural Network Based on Big Data Analysis
In order to reduce the complexity of artificial neural network during the training and reduce the communication over-head of traditional distributed training methods,this paper proposes a distributed training method of artificial neural network based on big data analysis.Specifically,the dynamic model averaging method is used to synchronize the local model only when it deviates significantly from the global model.Therefore,compared with the distributed training framework based on period averaging,the unnecessary overhead in communication is reduced.In the experiment part,based on the actual scene of large data sets and depth of the convolution neural networks,proves that communication time required by the model synchronization is significantly shortened,and the dynamic model of average method can achieve the precision of the method to be equal to the static cycle average.Otherwise,it proves that the increase of computing nodes can scale out the end strengthen which shows the effectiveness of the proposed method.
big data analysisartificial neural networkdistributed training
万方数据
维普
