improved-googlenet-master(googlenet代码,亲测可运行)

上传者: 44603934 | 上传时间: 2022-12-07 12:27:40 | 文件大小: 31.19MB | 文件类型: RAR
GoogLeNet是2014年Christian Szegedy提出的一种全新的深度学习结构,在这之前的AlexNet、VGG等结构都是通过增大网络的深度(层数)来获得更好的训练效果,但层数的增加会带来很多负作用,比如overfit、梯度消失、梯度爆炸等。inception的提出则从另一种角度来提升训练结果:能更高效的利用计算资源,在相同的计算量下能提取到更多的特征,从而提升训练结果。 外文名GoogLeNet类 型神经网络 结构介绍 inception模块的基本机构如图1,整个inception结构就是由多个这样的inception模块串联起来的。inception结构的主要贡献有两个:一是使用1x1的卷积来进行升降维;二是在多个尺寸上同时进行卷积再聚合。 图1 图1 1x1卷积 作用1:在相同尺寸的感受野中叠加更多的卷积,能提取到更丰富的特征。这个观点来自于Network in Network,图1里三个1x1卷积都起到了该作用。 图2 图2 图2左侧是是传统的卷积层结构(线性卷积),在一个尺度上只有一次卷积;图2右图是Network in Network结构(NIN结构),

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