原作者：lynnandwei?原文地址：http://blog.csdn.net/lynnandwei/article/details/44458033
GoogLeNet, 2014年ILSVRC挑戰賽冠軍，將Top5 的錯誤率降低到6.67%. 一個22層的深度網絡，論文在http://arxiv.org/pdf/1409.4842v1.pdf，題目為：Going deeper with convolutions。（每次看這么簡潔優雅的題目，就想吐槽國內寫paper的 八股文題目）。GoogLeNet這個名字也是挺有意思的，為了像開山鼻祖的LeNet網絡致敬，他們選擇了這樣的名字。
BVLC在caffe中給出了網絡的實現：https://github.com/BVLC/caffe/tree/master/models/bvlc_googlenet
模型下載地址：http://dl.caffe.berkeleyvision.org/bvlc_googlenet.caffemodel
從論文里整理了一張這個22個層次的模型的圖出來（如果考慮pooling層是27層），先將模型跑了一遍結果，還是那只貓：?
直觀輸出是如下，覺得挺準確：
[287 281 285 282 283]
caffe的實現和原來論文的模型是有不同的：
not training with the relighting data-augmentation;
但是準確度還是達到了?a top-1 accuracy 68.7% (31.3% error) and a top-5 accuracy 88.9% (11.1% error)
我們來分析一下這個模型的層次關系:
原始數據，輸入為224*224*3
第一層卷積層 conv1 ，pad是3，64個特征，7*7 步長為2，輸出特征為 112*112*64，然后進行relu，經過pool1（紅色的max pool） 進行pooling 3*3的核，步長為2， [（112 - 3+1）/2]+1 = 56 ?特征為56*56*64 ， 然后進行norm?
第二層卷積層 conv2， pad是1，3*3，192個特征，輸出為56*56*192，然后進行relu，進行norm，經過pool2進行pooling，3*3的核，步長為2 輸出為28*28*192 然后進行split 分成四個支線
?
[html] ?view plaincopy
Setting?up?pool2/3x3_s2?? I0319?23:50:37.405478??5765?net.cpp:103]?Top?shape:?10?192?28?28?(1505280)?? I0319?23:50:37.405484??5765?net.cpp:113]?Memory?required?for?data:?174612480?? I0319?23:50:37.405495??5765?net.cpp:67]?Creating?Layer?pool2/3x3_s2_pool2/3x3_s2_0_split?? I0319?23:50:37.405503??5765?net.cpp:394]?pool2/3x3_s2_pool2/3x3_s2_0_split?<-?pool2/3x3_s2?? I0319?23:50:37.405515??5765?net.cpp:356]?pool2/3x3_s2_pool2/3x3_s2_0_split?->?pool2/3x3_s2_pool2/3x3_s2_0_split_0?? I0319?23:50:37.405531??5765?net.cpp:356]?pool2/3x3_s2_pool2/3x3_s2_0_split?->?pool2/3x3_s2_pool2/3x3_s2_0_split_1?? I0319?23:50:37.405545??5765?net.cpp:356]?pool2/3x3_s2_pool2/3x3_s2_0_split?->?pool2/3x3_s2_pool2/3x3_s2_0_split_2?? I0319?23:50:37.405557??5765?net.cpp:356]?pool2/3x3_s2_pool2/3x3_s2_0_split?->?pool2/3x3_s2_pool2/3x3_s2_0_split_3?? I0319?23:50:37.405567??5765?net.cpp:96]?Setting?up?pool2/3x3_s2_pool2/3x3_s2_0_split?? I0319?23:50:37.405577??5765?net.cpp:103]?Top?shape:?10?192?28?28?(1505280)?? I0319?23:50:37.405582??5765?net.cpp:103]?Top?shape:?10?192?28?28?(1505280)?? I0319?23:50:37.405587??5765?net.cpp:103]?Top?shape:?10?192?28?28?(1505280)?? I0319?23:50:37.405592??5765?net.cpp:103]?Top?shape:?10?192?28?28?(1505280)?? I0319?23:50:37.405597??5765?net.cpp:113]?Memory?required?for?data:?198696960?? I0319?23:50:37.405611??5765?net.cpp:67]?Creating?Layer?inception_3a/1x1^M??
?
1： 64個1*1的卷積核（之后進行RULE計算） 變成28*28*64
2： 96個1*1的卷積核 作為3*3卷積核之前的reduce，變成28*28*96， 進行relu計算后，再進行128個3*3的卷積，pad為1， 28*28*128
3：16個1*1的卷積核 作為5*5卷積核之前的reduce，變成28*28*16， 進行relu計算后，再進行32個5*5的卷積，pad為2，變成28*28*32
4：pool層，3*3的核，pad為1，輸出還是28*28*192，然后進行32個1*1的卷積，變成28*28*32。
將四個結果進行連接，輸出為28*28*256
?
?
?
?
然后將3a的結果又分成四條支線，開始建立3b的inception module
3b?
?
1：128個1*1的卷積核（之后進行RULE計算） 變成28*28*128
2：128個1*1的卷積核 作為3*3卷積核之前的reduce，變成28*28*128， 再進行192個3*3的卷積，pad為1， 28*28*192，進行relu計算
3：32個1*1的卷積核 作為5*5卷積核之前的reduce，變成28*28*32， 進行relu計算后，再進行96個5*5的卷積，pad為2，變成28*28*96
4：pool層，3*3的核，pad為1，輸出還是28*28*256，然后進行64個1*1的卷積，變成28*28*64。
?
將四個結果進行連接，輸出為28*28*480
同理依次推算，數據變化如下表：
?
一部分輸出結果如下：
I0319 22:27:51.257917 5080 net.cpp:208] This network produces output prob
?
[html] ?view plaincopy
I0319?22:27:51.258116??5080?net.cpp:467]?Collecting?Learning?Rate?and?Weight?Decay.?? I0319?22:27:51.258162??5080?net.cpp:219]?Network?initialization?done.?? I0319?22:27:51.258167??5080?net.cpp:220]?Memory?required?for?data:?545512320?? I0319?22:27:51.345417??5080?net.cpp:702]?Ignoring?source?layer?data?? I0319?22:27:51.345443??5080?net.cpp:702]?Ignoring?source?layer?label_data_1_split?? I0319?22:27:51.345448??5080?net.cpp:705]?Copying?source?layer?conv1/7x7_s2?? I0319?22:27:51.345542??5080?net.cpp:705]?Copying?source?layer?conv1/relu_7x7?? I0319?22:27:51.345548??5080?net.cpp:705]?Copying?source?layer?pool1/3x3_s2?? I0319?22:27:51.345553??5080?net.cpp:705]?Copying?source?layer?pool1/norm1?? I0319?22:27:51.345558??5080?net.cpp:705]?Copying?source?layer?conv2/3x3_reduce?? I0319?22:27:51.345600??5080?net.cpp:705]?Copying?source?layer?conv2/relu_3x3_reduce?? I0319?22:27:51.345607??5080?net.cpp:705]?Copying?source?layer?conv2/3x3?? I0319?22:27:51.346571??5080?net.cpp:705]?Copying?source?layer?conv2/relu_3x3?? I0319?22:27:51.346580??5080?net.cpp:705]?Copying?source?layer?conv2/norm2?? I0319?22:27:51.346585??5080?net.cpp:705]?Copying?source?layer?pool2/3x3_s2?? I0319?22:27:51.346590??5080?net.cpp:705]?Copying?source?layer?pool2/3x3_s2_pool2/3x3_s2_0_split?? I0319?22:27:51.346595??5080?net.cpp:705]?Copying?source?layer?inception_3a/1x1?? I0319?22:27:51.346706??5080?net.cpp:705]?Copying?source?layer?inception_3a/relu_1x1?? I0319?22:27:51.346712??5080?net.cpp:705]?Copying?source?layer?inception_3a/3x3_reduce?? I0319?22:27:51.346879??5080?net.cpp:705]?Copying?source?layer?inception_3a/relu_3x3_reduce?? I0319?22:27:51.346885??5080?net.cpp:705]?Copying?source?layer?inception_3a/3x3?? I0319?22:27:51.347844??5080?net.cpp:705]?Copying?source?layer?inception_3a/relu_3x3?? I0319?22:27:51.347851??5080?net.cpp:705]?Copying?source?layer?inception_3a/5x5_reduce?? I0319?22:27:51.347885??5080?net.cpp:705]?Copying?source?layer?inception_3a/relu_5x5_reduce?? I0319?22:27:51.347892??5080?net.cpp:705]?Copying?source?layer?inception_3a/5x5?? I0319?22:27:51.348008??5080?net.cpp:705]?Copying?source?layer?inception_3a/relu_5x5?? I0319?22:27:51.348014??5080?net.cpp:705]?Copying?source?layer?inception_3a/pool?? I0319?22:27:51.348019??5080?net.cpp:705]?Copying?source?layer?inception_3a/pool_proj?? I0319?22:27:51.348080??5080?net.cpp:705]?Copying?source?layer?inception_3a/relu_pool_proj?? I0319?22:27:51.348085??5080?net.cpp:705]?Copying?source?layer?inception_3a/output?? I0319?22:27:51.348091??5080?net.cpp:705]?Copying?source?layer?inception_3a/output_inception_3a/output_0_split?? I0319?22:27:51.348096??5080?net.cpp:705]?Copying?source?layer?inception_3b/1x1?? I0319?22:27:51.348398??5080?net.cpp:705]?Copying?source?layer?inception_3b/relu_1x1?? I0319?22:27:51.348405??5080?net.cpp:705]?Copying?source?layer?inception_3b/3x3_reduce?? I0319?22:27:51.348700??5080?net.cpp:705]?Copying?source?layer?inception_3b/relu_3x3_reduce?? I0319?22:27:51.348707??5080?net.cpp:705]?Copying?source?layer?inception_3b/3x3?? I0319?22:27:51.350611??5080?net.cpp:705]?Copying?source?layer?inception_3b/relu_3x3?? I0319?22:27:51.350620??5080?net.cpp:705]?Copying?source?layer?inception_3b/5x5_reduce?? I0319?22:27:51.350699??5080?net.cpp:705]?Copying?source?layer?inception_3b/relu_5x5_reduce?? I0319?22:27:51.350705??5080?net.cpp:705]?Copying?source?layer?inception_3b/5x5?? I0319?22:27:51.351372??5080?net.cpp:705]?Copying?source?layer?inception_3b/relu_5x5?? I0319?22:27:51.351378??5080?net.cpp:705]?Copying?source?layer?inception_3b/pool?? I0319?22:27:51.351384??5080?net.cpp:705]?Copying?source?layer?inception_3b/pool_proj?? I0319?22:27:51.351546??5080?net.cpp:705]?Copying?source?layer?inception_3b/relu_pool_proj?? I0319?22:27:51.351552??5080?net.cpp:705]?Copying?source?layer?inception_3b/output?? I0319?22:27:51.351558??5080?net.cpp:705]?Copying?source?layer?pool3/3x3_s2?? I0319?22:27:51.351563??5080?net.cpp:705]?Copying?source?layer?pool3/3x3_s2_pool3/3x3_s2_0_split?? I0319?22:27:51.351569??5080?net.cpp:705]?Copying?source?layer?inception_4a/1x1?? I0319?22:27:51.352367??5080?net.cpp:705]?Copying?source?layer?inception_4a/relu_1x1?? I0319?22:27:51.352375??5080?net.cpp:705]?Copying?source?layer?inception_4a/3x3_reduce?? I0319?22:27:51.352782??5080?net.cpp:705]?Copying?source?layer?inception_4a/relu_3x3_reduce?? I0319?22:27:51.352789??5080?net.cpp:705]?Copying?source?layer?inception_4a/3x3?? I0319?22:27:51.354333??5080?net.cpp:705]?Copying?source?layer?inception_4a/relu_3x3?? I0319?22:27:51.354341??5080?net.cpp:705]?Copying?source?layer?inception_4a/5x5_reduce?? I0319?22:27:51.354420??5080?net.cpp:705]?Copying?source?layer?inception_4a/relu_5x5_reduce?? I0319?22:27:51.354429??5080?net.cpp:705]?Copying?source?layer?inception_4a/5x5?? I0319?22:27:51.354601??5080?net.cpp:705]?Copying?source?layer?inception_4a/relu_5x5?? I0319?22:27:51.354609??5080?net.cpp:705]?Copying?source?layer?inception_4a/pool?? I0319?22:27:51.354614??5080?net.cpp:705]?Copying?source?layer?inception_4a/pool_proj?? I0319?22:27:51.354887??5080?net.cpp:705]?Copying?source?layer?inception_4a/relu_pool_proj?? I0319?22:27:51.354894??5080?net.cpp:705]?Copying?source?layer?inception_4a/output?? I0319?22:27:51.354900??5080?net.cpp:705]?Copying?source?layer?inception_4a/output_inception_4a/output_0_split?? I0319?22:27:51.354910??5080?net.cpp:702]?Ignoring?source?layer?loss1/ave_pool?? I0319?22:27:51.354918??5080?net.cpp:702]?Ignoring?source?layer?loss1/conv?? I0319?22:27:51.354923??5080?net.cpp:702]?Ignoring?source?layer?loss1/relu_conv?? I0319?22:27:51.354930??5080?net.cpp:702]?Ignoring?source?layer?loss1/fc?? I0319?22:27:51.354936??5080?net.cpp:702]?Ignoring?source?layer?loss1/relu_fc?? I0319?22:27:51.354943??5080?net.cpp:702]?Ignoring?source?layer?loss1/drop_fc?? I0319?22:27:51.354950??5080?net.cpp:702]?Ignoring?source?layer?loss1/classifier?? I0319?22:27:51.354956??5080?net.cpp:702]?Ignoring?source?layer?loss1/loss?? I0319?22:27:51.354962??5080?net.cpp:705]?Copying?source?layer?inception_4b/1x1?? I0319?22:27:51.355681??5080?net.cpp:705]?Copying?source?layer?inception_4b/relu_1x1?? I0319?22:27:51.355690??5080?net.cpp:705]?Copying?source?layer?inception_4b/3x3_reduce?? I0319?22:27:51.356190??5080?net.cpp:705]?Copying?source?layer?inception_4b/relu_3x3_reduce?? I0319?22:27:51.356199??5080?net.cpp:705]?Copying?source?layer?inception_4b/3x3?? I0319?22:27:51.358134??5080?net.cpp:705]?Copying?source?layer?inception_4b/relu_3x3?? I0319?22:27:51.358144??5080?net.cpp:705]?Copying?source?layer?inception_4b/5x5_reduce?? I0319?22:27:51.358256??5080?net.cpp:705]?Copying?source?layer?inception_4b/relu_5x5_reduce?? I0319?22:27:51.358263??5080?net.cpp:705]?Copying?source?layer?inception_4b/5x5?? I0319?22:27:51.358608??5080?net.cpp:705]?Copying?source?layer?inception_4b/relu_5x5?? I0319?22:27:51.358616??5080?net.cpp:705]?Copying?source?layer?inception_4b/pool?? I0319?22:27:51.358623??5080?net.cpp:705]?Copying?source?layer?inception_4b/pool_proj?? I0319?22:27:51.358917??5080?net.cpp:705]?Copying?source?layer?inception_4b/relu_pool_proj?? I0319?22:27:51.358925??5080?net.cpp:705]?Copying?source?layer?inception_4b/output?? I0319?22:27:51.358932??5080?net.cpp:705]?Copying?source?layer?inception_4b/output_inception_4b/output_0_split?? I0319?22:27:51.358937??5080?net.cpp:705]?Copying?source?layer?inception_4c/1x1?? I0319?22:27:51.359519??5080?net.cpp:705]?Copying?source?layer?inception_4c/relu_1x1?? I0319?22:27:51.359526??5080?net.cpp:705]?Copying?source?layer?inception_4c/3x3_reduce?? I0319?22:27:51.360097??5080?net.cpp:705]?Copying?source?layer?inception_4c/relu_3x3_reduce?? I0319?22:27:51.360105??5080?net.cpp:705]?Copying?source?layer?inception_4c/3x3?? I0319?22:27:51.362634??5080?net.cpp:705]?Copying?source?layer?inception_4c/relu_3x3?? I0319?22:27:51.362643??5080?net.cpp:705]?Copying?source?layer?inception_4c/5x5_reduce?? I0319?22:27:51.362757??5080?net.cpp:705]?Copying?source?layer?inception_4c/relu_5x5_reduce?? I0319?22:27:51.362764??5080?net.cpp:705]?Copying?source?layer?inception_4c/5x5?? I0319?22:27:51.363106??5080?net.cpp:705]?Copying?source?layer?inception_4c/relu_5x5?? I0319?22:27:51.363114??5080?net.cpp:705]?Copying?source?layer?inception_4c/pool?? I0319?22:27:51.363121??5080?net.cpp:705]?Copying?source?layer?inception_4c/pool_proj?? I0319?22:27:51.363415??5080?net.cpp:705]?Copying?source?layer?inception_4c/relu_pool_proj?? I0319?22:27:51.363423??5080?net.cpp:705]?Copying?source?layer?inception_4c/output?? I0319?22:27:51.363430??5080?net.cpp:705]?Copying?source?layer?inception_4c/output_inception_4c/output_0_split?? I0319?22:27:51.363436??5080?net.cpp:705]?Copying?source?layer?inception_4d/1x1?? I0319?22:27:51.363937??5080?net.cpp:705]?Copying?source?layer?inception_4d/relu_1x1?? I0319?22:27:51.363945??5080?net.cpp:705]?Copying?source?layer?inception_4d/3x3_reduce?? I0319?22:27:51.364591??5080?net.cpp:705]?Copying?source?layer?inception_4d/relu_3x3_reduce?? I0319?22:27:51.364600??5080?net.cpp:705]?Copying?source?layer?inception_4d/3x3?? I0319?22:27:51.367797??5080?net.cpp:705]?Copying?source?layer?inception_4d/relu_3x3?? I0319?22:27:51.367806??5080?net.cpp:705]?Copying?source?layer?inception_4d/5x5_reduce?? I0319?22:27:51.367959??5080?net.cpp:705]?Copying?source?layer?inception_4d/relu_5x5_reduce?? I0319?22:27:51.367966??5080?net.cpp:705]?Copying?source?layer?inception_4d/5x5?? I0319?22:27:51.368420??5080?net.cpp:705]?Copying?source?layer?inception_4d/relu_5x5?? I0319?22:27:51.368428??5080?net.cpp:705]?Copying?source?layer?inception_4d/pool?? I0319?22:27:51.368435??5080?net.cpp:705]?Copying?source?layer?inception_4d/pool_proj?? I0319?22:27:51.368726??5080?net.cpp:705]?Copying?source?layer?inception_4d/relu_pool_proj?? I0319?22:27:51.368733??5080?net.cpp:705]?Copying?source?layer?inception_4d/output?? I0319?22:27:51.368739??5080?net.cpp:705]?Copying?source?layer?inception_4d/output_inception_4d/output_0_split?? I0319?22:27:51.368748??5080?net.cpp:702]?Ignoring?source?layer?loss2/ave_pool?? I0319?22:27:51.368755??5080?net.cpp:702]?Ignoring?source?layer?loss2/conv?? I0319?22:27:51.368762??5080?net.cpp:702]?Ignoring?source?layer?loss2/relu_conv?? I0319?22:27:51.368768??5080?net.cpp:702]?Ignoring?source?layer?loss2/fc?? I0319?22:27:51.368774??5080?net.cpp:702]?Ignoring?source?layer?loss2/relu_fc?? I0319?22:27:51.368780??5080?net.cpp:702]?Ignoring?source?layer?loss2/drop_fc?? I0319?22:27:51.368788??5080?net.cpp:702]?Ignoring?source?layer?loss2/classifier?? I0319?22:27:51.368794??5080?net.cpp:702]?Ignoring?source?layer?loss2/loss?? I0319?22:27:51.368800??5080?net.cpp:705]?Copying?source?layer?inception_4e/1x1?? I0319?22:27:51.369971??5080?net.cpp:705]?Copying?source?layer?inception_4e/relu_1x1?? I0319?22:27:51.369981??5080?net.cpp:705]?Copying?source?layer?inception_4e/3x3_reduce?? I0319?22:27:51.370717??5080?net.cpp:705]?Copying?source?layer?inception_4e/relu_3x3_reduce?? I0319?22:27:51.370725??5080?net.cpp:705]?Copying?source?layer?inception_4e/3x3?? I0319?22:27:51.374668??5080?net.cpp:705]?Copying?source?layer?inception_4e/relu_3x3?? I0319?22:27:51.374678??5080?net.cpp:705]?Copying?source?layer?inception_4e/5x5_reduce?? I0319?22:27:51.374831??5080?net.cpp:705]?Copying?source?layer?inception_4e/relu_5x5_reduce?? I0319?22:27:51.374840??5080?net.cpp:705]?Copying?source?layer?inception_4e/5x5?? I0319?22:27:51.375728??5080?net.cpp:705]?Copying?source?layer?inception_4e/relu_5x5?? I0319?22:27:51.375737??5080?net.cpp:705]?Copying?source?layer?inception_4e/pool?? I0319?22:27:51.375744??5080?net.cpp:705]?Copying?source?layer?inception_4e/pool_proj?? I0319?22:27:51.376332??5080?net.cpp:705]?Copying?source?layer?inception_4e/relu_pool_proj?? I0319?22:27:51.376340??5080?net.cpp:705]?Copying?source?layer?inception_4e/output?? I0319?22:27:51.376346??5080?net.cpp:705]?Copying?source?layer?pool4/3x3_s2?? I0319?22:27:51.376353??5080?net.cpp:705]?Copying?source?layer?pool4/3x3_s2_pool4/3x3_s2_0_split?? I0319?22:27:51.376359??5080?net.cpp:705]?Copying?source?layer?inception_5a/1x1?? I0319?22:27:51.378186??5080?net.cpp:705]?Copying?source?layer?inception_5a/relu_1x1?? I0319?22:27:51.378196??5080?net.cpp:705]?Copying?source?layer?inception_5a/3x3_reduce?? I0319?22:27:51.379343??5080?net.cpp:705]?Copying?source?layer?inception_5a/relu_3x3_reduce?? I0319?22:27:51.379353??5080?net.cpp:705]?Copying?source?layer?inception_5a/3x3?? I0319?22:27:51.383293??5080?net.cpp:705]?Copying?source?layer?inception_5a/relu_3x3?? I0319?22:27:51.383303??5080?net.cpp:705]?Copying?source?layer?inception_5a/5x5_reduce?? I0319?22:27:51.383548??5080?net.cpp:705]?Copying?source?layer?inception_5a/relu_5x5_reduce?? I0319?22:27:51.383558??5080?net.cpp:705]?Copying?source?layer?inception_5a/5x5?? I0319?22:27:51.384449??5080?net.cpp:705]?Copying?source?layer?inception_5a/relu_5x5?? I0319?22:27:51.384459??5080?net.cpp:705]?Copying?source?layer?inception_5a/pool?? I0319?22:27:51.384465??5080?net.cpp:705]?Copying?source?layer?inception_5a/pool_proj?? I0319?22:27:51.385397??5080?net.cpp:705]?Copying?source?layer?inception_5a/relu_pool_proj?? I0319?22:27:51.385406??5080?net.cpp:705]?Copying?source?layer?inception_5a/output?? I0319?22:27:51.385414??5080?net.cpp:705]?Copying?source?layer?inception_5a/output_inception_5a/output_0_split?? I0319?22:27:51.385421??5080?net.cpp:705]?Copying?source?layer?inception_5b/1x1?? I0319?22:27:51.388157??5080?net.cpp:705]?Copying?source?layer?inception_5b/relu_1x1?? I0319?22:27:51.388166??5080?net.cpp:705]?Copying?source?layer?inception_5b/3x3_reduce?? I0319?22:27:51.389539??5080?net.cpp:705]?Copying?source?layer?inception_5b/relu_3x3_reduce?? I0319?22:27:51.389549??5080?net.cpp:705]?Copying?source?layer?inception_5b/3x3?? I0319?22:27:51.395212??5080?net.cpp:705]?Copying?source?layer?inception_5b/relu_3x3?? I0319?22:27:51.395225??5080?net.cpp:705]?Copying?source?layer?inception_5b/5x5_reduce?? I0319?22:27:51.395584??5080?net.cpp:705]?Copying?source?layer?inception_5b/relu_5x5_reduce?? I0319?22:27:51.395594??5080?net.cpp:705]?Copying?source?layer?inception_5b/5x5?? I0319?22:27:51.396921??5080?net.cpp:705]?Copying?source?layer?inception_5b/relu_5x5?? I0319?22:27:51.396931??5080?net.cpp:705]?Copying?source?layer?inception_5b/pool?? I0319?22:27:51.396939??5080?net.cpp:705]?Copying?source?layer?inception_5b/pool_proj?? I0319?22:27:51.397862??5080?net.cpp:705]?Copying?source?layer?inception_5b/relu_pool_proj?? I0319?22:27:51.397871??5080?net.cpp:705]?Copying?source?layer?inception_5b/output?? I0319?22:27:51.397879??5080?net.cpp:705]?Copying?source?layer?pool5/7x7_s1?? I0319?22:27:51.397886??5080?net.cpp:705]?Copying?source?layer?pool5/drop_7x7_s1?? I0319?22:27:51.397893??5080?net.cpp:705]?Copying?source?layer?loss3/classifier?? I0319?22:27:51.406652??5080?net.cpp:702]?Ignoring?source?layer?loss3/loss3?? ?
材料集合：
http://deeplearning.net/2014/09/19/googles-entry-to-imagenet-2014-challenge/
?
[1] Imagenet 2014 LSVRC results,?http://karpathy.github.io/2014/09/02/what-i-learned-from-competing-against-a-convnet-on-imagenet/,Last retrieved on: 19-09-2014.
[2] Christian Szegedy, Wei Liu, Yangqing Jia, Pierre Sermanet, Scott Reed, Dragomir Anguelov, Dumitru Erhan, Vincent Vanhoucke, Andrew Rabinovich, Going Deeper with Convolutions, Arxiv Link:?http://arxiv.org/abs/1409.4842.
[3] GoogLeNet presentation,?http://image-net.org/challenges/LSVRC/2014/slides/GoogLeNet.pptx, Last retrieved on: 19-09.2014..
[4] What I learned from competing against a convnet on imagenet,?http://karpathy.github.io/2014/09/02/what-i-learned-from-competing-against-a-convnet-on-imagenet/, Last retrieved on: 19-09-2014.
[5] Girshick, Ross, et al. “Rich feature hierarchies for accurate object detection and semantic segmentation.”?arXiv preprint arXiv:1311.2524?(2013).
?
?
?
版權聲明：本文為博主原創文章，未經博主允許不得轉載。
轉載于:https://www.cnblogs.com/gkwang/p/4625410.html
總結
以上是生活随笔 為你收集整理的【转】学习笔记：GoogLeNet 的全部內容，希望文章能夠幫你解決所遇到的問題。
如果覺得生活随笔 網站內容還不錯，歡迎將生活随笔 推薦給好友。
