Image classification model based on spark and CNN

School of Information Engineering, Zhengzhou University, Zhengzhou, China

^* Corresponding author: shenyueliu@163.com

Abstract

Convolution neural network is a commonly used image classification model, but when the network nodes of the training process are too many, it will have a great influence on the training complexity. At the same time, when the size of the image data is large, many problems will appear on the single node, such as convergence slowly, frequently disk reading and writing. In order to overcome the above problems, this paper proposes a distributed convolution neural network based on Spark (Distribution Convolution neural network, Dis-CNN) model. The model first improves the initialization mode of convolution kernel parameters, then eliminates the redundancy of feature maps, and finally optimizes the distributed gradient descent by reducing the synchronous traffic between master and slave, so as to improve the convergence speed and performance. The experimental results show that the model not only improves the accuracy and recall of image classification, but also performs excellent in parallelism.