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

摘要： 卷积神经网络通常使用标准误差逆传播算法进行串行训练，随着数据规模的增长，单机串行训练存在耗时长且占有较多的系统资源的问题。为有效实现海量数据的卷积神经网络训练，提出一种基于MapReduce框架的BP神经网络并行化训练模型。该模型结合了标准误差逆传播算法和累积误差逆传播算法，将大数据集分割成若干个子集，在损失少量准确率的条件下进行并行化处理，并扩展MNIST数据集进行图像识别测试。实验结果表明，该算法对数据规模有较好的适应性，能够提高卷积神经网络的训练效率。

关键词: 卷积神经网络, 后向传播（BP）算法, Hadoop并行策略