关键词: 神经网络, MapReduce, 输入分片, 收敛

Abstract: During the training of a BP neural network with MapReduce, its convergence with the current intermediate weight matrix is just got by sample data slices on the specific map task node. Therefore, the converge of the BP network to the whole training sample set is hard to be fulfilled. The approach to training BP networks with MapReduce based on sample slice disruptions is proposed. Based on systematic sampling to the whole training sample data, new input slice can be produced for each training map task. Such sample slices are used for the specific map tasks as new input during future training. This can accelerate the process of convergence of the BP network. Moreover, in order to speed up the local convergence of the map training tasks, the intermediate matrix with minimum global error is taken as the initial weight matrix during the future training. The experimental results on Hadoop clusters show that the approach can improve the efficiency of BP neural network training with MapReduce.

Key words: neural network, MapReduce, sample slices, convergence