MATEC Web Conf.
Volume 189, 20182018 2nd International Conference on Material Engineering and Advanced Manufacturing Technology (MEAMT 2018)
|Number of page(s)||9|
|Section||Cloud & Network|
|Published online||10 August 2018|
Image classification model based on spark and CNN
School of Information Engineering, Zhengzhou University, Zhengzhou, China
* Corresponding author: firstname.lastname@example.org
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.
© The Authors, published by EDP Sciences, 2018
This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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