Caffe 深度学习框架上手教程

Posted 米罗西

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Caffe 深度学习框架上手教程

 

 

 
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15年1月
 

Caffe (CNN, deep learning) 介绍

Caffe -----------Convolution Architecture For Feature Embedding (Extraction)

  1. Caffe 是什么东东？
    • CNN (Deep Learning) 工具箱
    • C++ 语言架构
    • CPU 和GPU 无缝交换
    • Python 和matlab的封装
    • 但是，Decaf只是CPU 版本。

  2. 为什么要用Caffe？

    • 运算速度快。简单 友好的架构 用到的一些库：
    • Google Logging library (Glog): 一个C++语言的应用级日志记录框架，提供了C++风格的流操作和各种助手宏.
    • lebeldb(数据存储)： 是一个google实现的非常高效的kv数据库，单进程操作。
    • CBLAS library（CPU版本的矩阵操作）
    • CUBLAS library (GPU 版本的矩阵操作)

  3. Caffe 架构

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  1. 预处理图像的leveldb构建
    输入：一批图像和label （2和3）
    输出：leveldb （4）
    指令里包含如下信息：
    • conver_imageset （构建leveldb的可运行程序）
    • train/ （此目录放处理的jpg或者其他格式的图像)
    • label.txt (图像文件名及其label信息)
    • 输出的leveldb文件夹的名字
    • CPU/GPU (指定是在cpu上还是在gpu上运行code)

  2. CNN网络配置文件

    • Imagenet_solver.prototxt （包含全局参数的配置的文件）
    • Imagenet.prototxt （包含训练网络的配置的文件）
    • Imagenet_val.prototxt （包含测试网络的配置文件）
 
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15年1月
 

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train_val.prototxt452

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）：
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  2. conv2阶段DFD（data flow diagram）：
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  3. conv3阶段DFD（data flow diagram）：

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    SouthEast2455x274

     

  4. conv4阶段DFD（data flow diagram）：
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  5. conv5阶段DFD（data flow diagram）：
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  6. fc6阶段DFD（data flow diagram）：
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  7. fc7阶段DFD（data flow diagram）：
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  8. fc8阶段DFD（data flow diagram）：
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各种layer的operation更多解释可以参考Caffe Layer Catalogue356

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
 
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15年1月
 

CIFAR-10在caffe上进行训练与学习

使用数据库：CIFAR-10

60000张 32X32 彩色图像 10类，50000张训练，10000张测试

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准备

在终端运行以下指令：

 

cd $CAFFE_ROOT/data/cifar10
./get_cifar10.sh
cd $CAFFE_ROOT/examples/cifar10
./create_cifar10.sh

其中CAFFE_ROOT是caffe-master在你机子的地址

运行之后，将会在examples中出现数据库文件./cifar10-leveldb和数据库图像均值二进制文件./mean.binaryproto

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模型

该CNN由卷积层，POOLing层，非线性变换层，在顶端的局部对比归一化线性分类器组成。该模型的定义在CAFFE_ROOT/examples/cifar10 directory’s cifar10_quick_train.prototxt中，可以进行修改。其实后缀为prototxt很多都是用来修改配置的。

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训练和测试

训练这个模型非常简单，当我们写好参数设置的文件cifar10_quick_solver.prototxt和定义的文件cifar10_quick_train.prototxt和cifar10_quick_test.prototxt后，运行train_quick.sh或者在终端输入下面的命令：

 

cd $CAFFE_ROOT/examples/cifar10
./train_quick.sh

即可，train_quick.sh是一个简单的脚本，会把执行的信息显示出来，培训的工具是train_net.bin,cifar10_quick_solver.prototxt作为参数。

然后出现类似以下的信息：这是搭建模型的相关信息

I0317 21:52:48.945710 2008298256 net.cpp:74] Creating Layer conv1
I0317 21:52:48.945716 2008298256 net.cpp:84] conv1 <- data
I0317 21:52:48.945725 2008298256 net.cpp:110] conv1 -> conv1
I0317 21:52:49.298691 2008298256 net.cpp:125] Top shape: 100 32 32 32 (3276800)
I0317 21:52:49.298719 2008298256 net.cpp:151] conv1 needs backward computation.

接着：

0317 21:52:49.309370 2008298256 net.cpp:166] Network initialization done.
I0317 21:52:49.309376 2008298256 net.cpp:167] Memory required for Data 23790808
I0317 21:52:49.309422 2008298256 solver.cpp:36] Solver scaffolding done.
I0317 21:52:49.309447 2008298256 solver.cpp:47] Solving CIFAR10_quick_train

之后，训练开始

I0317 21:53:12.179772 2008298256 solver.cpp:208] Iteration 100, lr = 0.001
I0317 21:53:12.185698 2008298256 solver.cpp:65] Iteration 100, loss = 1.73643
...
I0317 21:54:41.150030 2008298256 solver.cpp:87] Iteration 500, Testing net
I0317 21:54:47.129461 2008298256 solver.cpp:114] Test score #0: 0.5504
I0317 21:54:47.129500 2008298256 solver.cpp:114] Test score #1: 1.27805

其中每100次迭代次数显示一次训练时lr(learningrate),和loss（训练损失函数），每500次测试一次，输出score 0（准确率）和score 1（测试损失函数）

当5000次迭代之后，正确率约为75%，模型的参数存储在二进制protobuf格式在cifar10_quick_iter_5000

然后，这个模型就可以用来运行在新数据上了。

其他

另外，更改cifar*solver.prototxt文件可以使用CPU训练，

 

# solver mode: CPU or GPU
solver_mode: CPU

可以看看CPU和GPU训练的差别。

主要资料来源：caffe官网教程











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