【转】[caffe]深度学习之图像分类模型AlexNet解读
[caffe]深度学习之图像分类模型AlexNet解读
版权声明:本文为博主原创文章,未经博主允许不得转载。
在imagenet上的图像分类challenge上Alex提出的alexnet网络结构模型赢得了2012届的冠军。要研究CNN类型DL网络模型在图像分类上的应用,就逃不开研究alexnet,这是CNN在图像分类上的经典模型(DL火起来之后)。
在DL开源实现caffe的model样例中,它也给出了alexnet的复现,具体网络配置文件如下https://github.com/BVLC/caffe/blob/master/models/bvlc_reference_caffenet/train_val.prototxt:
接下来本文将一步步对该网络配置结构中各个层进行详细的解读(训练阶段):
1. conv1阶段DFD(data flow diagram):
2. conv2阶段DFD(data flow diagram):
3. conv3阶段DFD(data flow diagram):
4. conv4阶段DFD(data flow diagram):
5. conv5阶段DFD(data flow diagram):
6. fc6阶段DFD(data flow diagram):
7. fc7阶段DFD(data flow diagram):
8. fc8阶段DFD(data flow diagram):
各种layer的operation更多解释可以参考http://caffe.berkeleyvision.org/tutorial/layers.html
从计算该模型的数据流过程中,该模型参数大概5kw+。
caffe的输出中也有包含这块的内容日志,详情如下:
- I0721 10:38:15.326920 4692 net.cpp:125] Top shape: 256 3 227 227 (39574272)
- I0721 10:38:15.326971 4692 net.cpp:125] Top shape: 256 1 1 1 (256)
- I0721 10:38:15.326982 4692 net.cpp:156] data does not need backward computation.
- I0721 10:38:15.327003 4692 net.cpp:74] Creating Layer conv1
- I0721 10:38:15.327011 4692 net.cpp:84] conv1 <- data
- I0721 10:38:15.327033 4692 net.cpp:110] conv1 -> conv1
- I0721 10:38:16.721956 4692 net.cpp:125] Top shape: 256 96 55 55 (74342400)
- I0721 10:38:16.722030 4692 net.cpp:151] conv1 needs backward computation.
- I0721 10:38:16.722059 4692 net.cpp:74] Creating Layer relu1
- I0721 10:38:16.722070 4692 net.cpp:84] relu1 <- conv1
- I0721 10:38:16.722082 4692 net.cpp:98] relu1 -> conv1 (in-place)
- I0721 10:38:16.722096 4692 net.cpp:125] Top shape: 256 96 55 55 (74342400)
- I0721 10:38:16.722105 4692 net.cpp:151] relu1 needs backward computation.
- I0721 10:38:16.722116 4692 net.cpp:74] Creating Layer pool1
- I0721 10:38:16.722125 4692 net.cpp:84] pool1 <- conv1
- I0721 10:38:16.722133 4692 net.cpp:110] pool1 -> pool1
- I0721 10:38:16.722167 4692 net.cpp:125] Top shape: 256 96 27 27 (17915904)
- I0721 10:38:16.722187 4692 net.cpp:151] pool1 needs backward computation.
- I0721 10:38:16.722205 4692 net.cpp:74] Creating Layer norm1
- I0721 10:38:16.722221 4692 net.cpp:84] norm1 <- pool1
- I0721 10:38:16.722234 4692 net.cpp:110] norm1 -> norm1
- I0721 10:38:16.722251 4692 net.cpp:125] Top shape: 256 96 27 27 (17915904)
- I0721 10:38:16.722260 4692 net.cpp:151] norm1 needs backward computation.
- I0721 10:38:16.722272 4692 net.cpp:74] Creating Layer conv2
- I0721 10:38:16.722280 4692 net.cpp:84] conv2 <- norm1
- I0721 10:38:16.722290 4692 net.cpp:110] conv2 -> conv2
- I0721 10:38:16.725225 4692 net.cpp:125] Top shape: 256 256 27 27 (47775744)
- I0721 10:38:16.725242 4692 net.cpp:151] conv2 needs backward computation.
- I0721 10:38:16.725253 4692 net.cpp:74] Creating Layer relu2
- I0721 10:38:16.725261 4692 net.cpp:84] relu2 <- conv2
- I0721 10:38:16.725270 4692 net.cpp:98] relu2 -> conv2 (in-place)
- I0721 10:38:16.725280 4692 net.cpp:125] Top shape: 256 256 27 27 (47775744)
- I0721 10:38:16.725288 4692 net.cpp:151] relu2 needs backward computation.
- I0721 10:38:16.725298 4692 net.cpp:74] Creating Layer pool2
- I0721 10:38:16.725307 4692 net.cpp:84] pool2 <- conv2
- I0721 10:38:16.725317 4692 net.cpp:110] pool2 -> pool2
- I0721 10:38:16.725329 4692 net.cpp:125] Top shape: 256 256 13 13 (11075584)
- I0721 10:38:16.725338 4692 net.cpp:151] pool2 needs backward computation.
- I0721 10:38:16.725358 4692 net.cpp:74] Creating Layer norm2
- I0721 10:38:16.725368 4692 net.cpp:84] norm2 <- pool2
- I0721 10:38:16.725378 4692 net.cpp:110] norm2 -> norm2
- I0721 10:38:16.725389 4692 net.cpp:125] Top shape: 256 256 13 13 (11075584)
- I0721 10:38:16.725399 4692 net.cpp:151] norm2 needs backward computation.
- I0721 10:38:16.725409 4692 net.cpp:74] Creating Layer conv3
- I0721 10:38:16.725419 4692 net.cpp:84] conv3 <- norm2
- I0721 10:38:16.725427 4692 net.cpp:110] conv3 -> conv3
- I0721 10:38:16.735193 4692 net.cpp:125] Top shape: 256 384 13 13 (16613376)
- I0721 10:38:16.735213 4692 net.cpp:151] conv3 needs backward computation.
- I0721 10:38:16.735224 4692 net.cpp:74] Creating Layer relu3
- I0721 10:38:16.735234 4692 net.cpp:84] relu3 <- conv3
- I0721 10:38:16.735242 4692 net.cpp:98] relu3 -> conv3 (in-place)
- I0721 10:38:16.735250 4692 net.cpp:125] Top shape: 256 384 13 13 (16613376)
- I0721 10:38:16.735258 4692 net.cpp:151] relu3 needs backward computation.
- I0721 10:38:16.735302 4692 net.cpp:74] Creating Layer conv4
- I0721 10:38:16.735312 4692 net.cpp:84] conv4 <- conv3
- I0721 10:38:16.735321 4692 net.cpp:110] conv4 -> conv4
- I0721 10:38:16.743952 4692 net.cpp:125] Top shape: 256 384 13 13 (16613376)
- I0721 10:38:16.743988 4692 net.cpp:151] conv4 needs backward computation.
- I0721 10:38:16.744000 4692 net.cpp:74] Creating Layer relu4
- I0721 10:38:16.744010 4692 net.cpp:84] relu4 <- conv4
- I0721 10:38:16.744020 4692 net.cpp:98] relu4 -> conv4 (in-place)
- I0721 10:38:16.744030 4692 net.cpp:125] Top shape: 256 384 13 13 (16613376)
- I0721 10:38:16.744038 4692 net.cpp:151] relu4 needs backward computation.
- I0721 10:38:16.744050 4692 net.cpp:74] Creating Layer conv5
- I0721 10:38:16.744057 4692 net.cpp:84] conv5 <- conv4
- I0721 10:38:16.744067 4692 net.cpp:110] conv5 -> conv5
- I0721 10:38:16.748935 4692 net.cpp:125] Top shape: 256 256 13 13 (11075584)
- I0721 10:38:16.748955 4692 net.cpp:151] conv5 needs backward computation.
- I0721 10:38:16.748965 4692 net.cpp:74] Creating Layer relu5
- I0721 10:38:16.748975 4692 net.cpp:84] relu5 <- conv5
- I0721 10:38:16.748983 4692 net.cpp:98] relu5 -> conv5 (in-place)
- I0721 10:38:16.748998 4692 net.cpp:125] Top shape: 256 256 13 13 (11075584)
- I0721 10:38:16.749011 4692 net.cpp:151] relu5 needs backward computation.
- I0721 10:38:16.749022 4692 net.cpp:74] Creating Layer pool5
- I0721 10:38:16.749030 4692 net.cpp:84] pool5 <- conv5
- I0721 10:38:16.749039 4692 net.cpp:110] pool5 -> pool5
- I0721 10:38:16.749050 4692 net.cpp:125] Top shape: 256 256 6 6 (2359296)
- I0721 10:38:16.749058 4692 net.cpp:151] pool5 needs backward computation.
- I0721 10:38:16.749074 4692 net.cpp:74] Creating Layer fc6
- I0721 10:38:16.749083 4692 net.cpp:84] fc6 <- pool5
- I0721 10:38:16.749091 4692 net.cpp:110] fc6 -> fc6
- I0721 10:38:17.160079 4692 net.cpp:125] Top shape: 256 4096 1 1 (1048576)
- I0721 10:38:17.160148 4692 net.cpp:151] fc6 needs backward computation.
- I0721 10:38:17.160166 4692 net.cpp:74] Creating Layer relu6
- I0721 10:38:17.160177 4692 net.cpp:84] relu6 <- fc6
- I0721 10:38:17.160190 4692 net.cpp:98] relu6 -> fc6 (in-place)
- I0721 10:38:17.160202 4692 net.cpp:125] Top shape: 256 4096 1 1 (1048576)
- I0721 10:38:17.160212 4692 net.cpp:151] relu6 needs backward computation.
- I0721 10:38:17.160222 4692 net.cpp:74] Creating Layer drop6
- I0721 10:38:17.160230 4692 net.cpp:84] drop6 <- fc6
- I0721 10:38:17.160238 4692 net.cpp:98] drop6 -> fc6 (in-place)
- I0721 10:38:17.160258 4692 net.cpp:125] Top shape: 256 4096 1 1 (1048576)
- I0721 10:38:17.160265 4692 net.cpp:151] drop6 needs backward computation.
- I0721 10:38:17.160277 4692 net.cpp:74] Creating Layer fc7
- I0721 10:38:17.160286 4692 net.cpp:84] fc7 <- fc6
- I0721 10:38:17.160295 4692 net.cpp:110] fc7 -> fc7
- I0721 10:38:17.342094 4692 net.cpp:125] Top shape: 256 4096 1 1 (1048576)
- I0721 10:38:17.342157 4692 net.cpp:151] fc7 needs backward computation.
- I0721 10:38:17.342175 4692 net.cpp:74] Creating Layer relu7
- I0721 10:38:17.342185 4692 net.cpp:84] relu7 <- fc7
- I0721 10:38:17.342198 4692 net.cpp:98] relu7 -> fc7 (in-place)
- I0721 10:38:17.342208 4692 net.cpp:125] Top shape: 256 4096 1 1 (1048576)
- I0721 10:38:17.342217 4692 net.cpp:151] relu7 needs backward computation.
- I0721 10:38:17.342228 4692 net.cpp:74] Creating Layer drop7
- I0721 10:38:17.342236 4692 net.cpp:84] drop7 <- fc7
- I0721 10:38:17.342245 4692 net.cpp:98] drop7 -> fc7 (in-place)
- I0721 10:38:17.342254 4692 net.cpp:125] Top shape: 256 4096 1 1 (1048576)
- I0721 10:38:17.342262 4692 net.cpp:151] drop7 needs backward computation.
- I0721 10:38:17.342274 4692 net.cpp:74] Creating Layer fc8
- I0721 10:38:17.342283 4692 net.cpp:84] fc8 <- fc7
- I0721 10:38:17.342291 4692 net.cpp:110] fc8 -> fc8
- I0721 10:38:17.343199 4692 net.cpp:125] Top shape: 256 22 1 1 (5632)
- I0721 10:38:17.343214 4692 net.cpp:151] fc8 needs backward computation.
- I0721 10:38:17.343231 4692 net.cpp:74] Creating Layer loss
- I0721 10:38:17.343240 4692 net.cpp:84] loss <- fc8
- I0721 10:38:17.343250 4692 net.cpp:84] loss <- label
- I0721 10:38:17.343264 4692 net.cpp:151] loss needs backward computation.
- I0721 10:38:17.343305 4692 net.cpp:173] Collecting Learning Rate and Weight Decay.
- I0721 10:38:17.343327 4692 net.cpp:166] Network initialization done.
- I0721 10:38:17.343335 4692 net.cpp:167] Memory required for Data 1073760256
转载于:https://www.cnblogs.com/Crysaty/p/6245711.html
【转】[caffe]深度学习之图像分类模型AlexNet解读相关推荐
- [caffe]深度学习之图像分类模型AlexNet解读
在imagenet上的图像分类challenge上Alex提出的alexnet网络结构模型赢得了2012届的冠军.要研究CNN类型DL网络模型在图像分类上的应用,就逃不开研究alexnet,这是CNN ...
- [caffe]深度学习之图像分类模型VGG解读
一.简单介绍 vgg和googlenet是2014年imagenet竞赛的双雄,这两类模型结构有一个共同特点是go deeper.跟googlenet不同的是.vgg继承了lenet以及alexnet ...
- Caffe深度学习框架上手教程
Caffe是一个清晰而高效的深度学习框架,本文详细介绍了caffe的优势.架构,网络定义.各层定义,Caffe的安装与配置,解读了Caffe实现的图像分类模型AlexNet,并演示了CIFAR-10在 ...
- Caffe 深度学习框架上手教程
转载自: Caffe 深度学习框架上手教程 - OPEN 开发经验库 http://www.open-open.com/lib/view/open1421995285109.html 阅读目录 Caf ...
- 深度学习100+经典模型TensorFlow与Pytorch代码实现大合集
关注上方"深度学习技术前沿",选择"星标公众号", 资源干货,第一时间送达! [导读]深度学习在过去十年获得了极大进展,出现很多新的模型,并且伴随TensorF ...
- 深度学习在图像分类中的发展
深度学习是一门比较年轻的研究方向,从机器视觉到语音识别,以及自然语言识别等领域都有它的身影.说实话,喵哥此前只是知道有这个学科,但是并不清楚它到底是什么,怎么使用它.其实现在也是一无所知,但是我越发觉 ...
- 深度学习之图像分类(九)--ResNeXt 网络结构
深度学习之图像分类(九)ResNeXt 网络结构 目录 深度学习之图像分类(九)ResNeXt 网络结构 1. 前言 2. 组卷积 3. ResNeXt block 分析 4. 代码 本节学习 Res ...
- 基于深度学习的图像识别模型研究综述
基于深度学习的图像识别模型研究综述 摘要:深度学习是机器学习研究中的一个新的领域,其目的在于训练计算机完成自主学习.判断.决策等人类行为并建立.模拟人脑进行分析学习的神经网络,它模仿人类大脑的机制来解 ...
- 深度学习CTR预估模型凭什么成为互联网增长的关键?
本文是王喆在InfoQ开设的原创技术专栏"深度学习CTR预估模型实践"的第一篇文章(以下"深度学习CTR预估模型实践"简称"深度CTR模型" ...
最新文章
- EAFP vs LBYL
- Spring框架中有哪些不同类型的事件
- 投资学习网课笔记(part9)--基金第九课
- 一起来庆祝 .NET 20 周年!
- 如何在 Mac 上切换语言
- 由左右像点坐标反求世界坐标点
- 超详细:Springboot连接centos7下redis6的必要配置和失败分析
- pytorch torch.utils.data.TensorDataset
- 进阶03 System、StringBuilder类
- python:使用strip()去掉首尾空格
- oracle数据库connectionstring,oracle数据库 connectionstring
- 集丰照明|如何深度解读 LED 标准?
- hdu 5455 Fang Fang 坑题
- 跨越opengl和d3d的鸿沟(四):完结篇,平台和未来
- python时间序列因果检验_用python做时间序列预测8:Granger causality test(格兰杰因果检验)...
- 【自媒体必备】AI文字转语音,支持多种人声选择,在线生成一键导出【电脑永久版】
- 项目总结,彻底掌握如何在NodeJs中使用Sequelize
- cygwin上的telnet安装
- 初识大数据(三)旅游大数据
- linux系统使用命令一次显示日历,Linux命令行上如何使用日历详解
热门文章
- python决策树逻辑回归_百面机器学习(逻辑回归以及决策树,leetcode)
- Qt笔记-QTcpSocket跨线程调用(官方推荐方法,非百度烂大街方法)
- Android笔记-雷电模拟器(Android5.1.1)安装Xposed
- Spring Boot笔记-发送纯字符串邮件及带附件邮件
- Java笔记-spring-rabbit框架中消息中header的添加
- 前端工作笔记-element ui弹窗嵌套并获取输入
- Qt工作笔记-使用QRegExp实现QString的split【读取文件时候,过滤各种字符】
- C/C++轻松写基于UDP的远程控制
- linux python3安装教程_linux python3安装
- python访问数据库统一方法_Python 3.x 连接数据库(pymysql 方式)