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例子中。它也给出了alexnet的复现。详细网络配置文件例如以下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具体的解读（训练阶段）：

1. conv1阶段DFD（data flow diagram）：

watermark/2/text/aHR0cDovL2Jsb2cuY3Nkbi5uZXQvc3VuYmFpZ3Vp/font/5a6L5L2T/fontsize/400/fill/I0JBQkFCMA==/dissolve/70/gravity/SouthEast" alt="">

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）：

watermark/2/text/aHR0cDovL2Jsb2cuY3Nkbi5uZXQvc3VuYmFpZ3Vp/font/5a6L5L2T/fontsize/400/fill/I0JBQkFCMA==/dissolve/70/gravity/SouthEast" alt="">

6. fc6阶段DFD（data flow diagram）：

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7. fc7阶段DFD（data flow diagram）：

8. fc8阶段DFD（data flow diagram）：

watermark/2/text/aHR0cDovL2Jsb2cuY3Nkbi5uZXQvc3VuYmFpZ3Vp/font/5a6L5L2T/fontsize/400/fill/I0JBQkFCMA==/dissolve/70/gravity/SouthEast" alt="">

各种layer的operation很多其它解释能够參考http://caffe.berkeleyvision.org/tutorial/layers.html

从计算该模型的数据流过程中。该模型參数大概5kw+。

caffe的输出中也有包括这块的内容日志，详情例如以下：

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

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