摘要
针对神经网络模型在剪枝操作中Dropout率下调造成的过拟合问题,提出一种剪枝模型泛化能力的验证方法。研究人为遮挡数据集模拟图像范围的变化情况,分析不同Dropout值和剪枝比例对模型准确率的影响,进而得到剪枝操作后模型泛化能力变化的原因。在卷积神经网络模型lenet-5上进行实验,结果表明,剪枝模型泛化能力减弱是因为Dropout率下调和剪枝操作时参数量的变化。
To address the over-fitting problem caused by the down-regulation of the Dropout rate in the pruning operation of the neural network model,a verification method for the generalization ability of the pruning model is proposed.By artificially occluding the dataset to simulate the change of the image range,the effects of different Dropout values and pruning ratios on the accuracy of the model are analyzed,and the reasons for the change of the generalization ability of the model after pruning operation are obtained.Experiments on the convolutional neural network model lenet-5 show that the generalization ability of the pruning model is weakened because of the drop of the Dropout rate and the change of the parameter amount during the pruning operation.
作者
刘崇阳
刘勤让
LIU Chongyang;LIU Qinrang(China National Digital Switching System Engineering and Technological R&D Center,Zhengzhou 450002,China)
出处
《计算机工程》
CAS
CSCD
北大核心
2019年第10期234-238,共5页
Computer Engineering
基金
国家科技重大专项(2016ZX01012101)
国家自然科学基金面上项目(61572520)
国家自然科学基金创新研究群体项目(61521003)
关键词
深度神经网络
模型剪枝
深度学习
泛化能力
遮挡数据集
Deep Neural Networks(DNN)
model pruning
Deep Learning(DL)
generalization ability
occlusion datasets
