Computer Science ›› 2017, Vol. 44 ›› Issue (Z6): 146-150.doi: 10.11896/j.issn.1002-137X.2017.6A.034

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Introduce Numerical Solution to Visualize Convolutional Neuron Networks Based on Numerical Solution

YU Hai-bao, SHEN Qi and FENG Guo-can   

  • Online:2017-12-01 Published:2018-12-01

Abstract: Zeiler’s visualization model restore the feature maps to original image space,by unpooling and deconvolution,to visualize what the node learn from the image.It helps to research the convolutional neural networks mechanism,but the result is not apparent for the vague method.Based on the Zeiler’s deconvolutional visualiztion model,numerical solution method was introduced to replace the vague method that just use convolutional kernel.The database was constructed firstly.The triangle and rectangle was generated with random size,shape and location,which have simple structure and apparent vertex.Based on the database,we constructed hierarchy database and took out experiment.The experi-ment results show that the improvement model extracts more apparent features and has less noise,which has more precise result.Experiment on bigger database was taken to verify our result,and the result to guide how to construct the network’s stucture.

Key words: Convolutional neural networks(CNN),Visualization,Deconvolution,Numerical solution

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