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| On the Relationship Between the Complexity and Accuracy of Convolutional Neural Networks |
| WANG Guangbo, SUN Rencheng, SUI Yi, SHAO Fengjing
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| School of Computer Science and Technology, Qingdao University, Qingdao 266071, China |
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Abstract The structure of the convolutional neural network will also affect its performance. The design of the convolutional neural network relies more on experience and powerful computing power. How to design a neural network with better performance lacks effective theoretical support. In order to solve this problem, based on the analysis of the complexity of the typical convolutional neural network topology, in order to quickly realize the convolutional neural network that meets the given complexity characteristics, the generation from complex network topology to convolutional neural network is given. The algorithm, through the establishment of a series of convolutional neural networks with different topological features, uses the Cifar10 and Cifar100 data sets to analyze the relationship between the average clustering coefficient, average path length, graph density, modularity and other topological properties on the recognition effectiveness of the convolutional neural network. Experiments show that when the number of parameters of the neural network is basically equal, the average clustering coefficient will affect the performance of the convolutional neural network. The final conclusion is that in a statistical sense, a network structure with a small average clustering coefficient will have better performance, which provides a theoretical basis for further designing a better convolutional neural network.
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Received: 20 August 2020
Published: 10 May 2021
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