关键词: 卷积神经网络, 后向传播（BP）算法, Hadoop并行策略

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. Experimental results show that the algorithm has a good adaptability to the data size, and can improve the training efficiency of the convolution neural network.

Key words: convolutional neural networks, Back Propagation（BP） algorithm, Hadoop parallel processing