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【转】[caffe]深度学习之图像分类模型AlexNet解读
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[caffe]深度學習之圖像分類模型AlexNet解讀
原文地址：http://blog.csdn.net/sunbaigui/article/details/39938097
本文章已收錄于：? ?
深度學習知識庫 ?分類： deep learning（28）?
版權聲明：本文為博主原創文章，未經博主允許不得轉載。
在imagenet上的圖像分類challenge上Alex提出的alexnet網絡結構模型贏得了2012屆的冠軍。要研究CNN類型DL網絡模型在圖像分類上的應用，就逃不開研究alexnet，這是CNN在圖像分類上的經典模型（DL火起來之后）。
在DL開源實現caffe的model樣例中，它也給出了alexnet的復現，具體網絡配置文件如下https://github.com/BVLC/caffe/blob/master/models/bvlc_reference_caffenet/train_val.prototxt：
接下來本文將一步步對該網絡配置結構中各個層進行詳細的解讀（訓練階段）： ?
1. conv1階段DFD（data flow diagram）：
2. conv2階段DFD（data flow diagram）：
3. conv3階段DFD（data flow diagram）：
4. conv4階段DFD（data flow diagram）：
5. conv5階段DFD（data flow diagram）：
6. fc6階段DFD（data flow diagram）：
7. fc7階段DFD（data flow diagram）：
8. fc8階段DFD（data flow diagram）：
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各種layer的operation更多解釋可以參考http://caffe.berkeleyvision.org/tutorial/layers.html
從計算該模型的數據流過程中，該模型參數大概5kw+。
caffe的輸出中也有包含這塊的內容日志，詳情如下：
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[cpp]?view plaincopy print?
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??
轉載于:https://www.cnblogs.com/Crysaty/p/6245711.html
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