对于想了解CAFFE安装笔记【windows和ubuntu】的读者，本文将提供新的信息，我们将详细介绍caffe安装教程windows，并且为您提供关于Buildcaffeusingqmake(ubu

对于想了解CAFFE 安装笔记【windows 和 ubuntu】的读者，本文将提供新的信息，我们将详细介绍caffe安装教程windows，并且为您提供关于Build caffe using qmake (ubuntu&windows)、caffe 安装（Ubuntu 14.04 +caffe+GTX 1080+CUDA8.0 +cuDNN v5）、Caffe+Ubuntu 16.04 安装教程、Caffe+Ubuntu14.04+CUDA7.5安装笔记的有价值信息。

• CAFFE 安装笔记【windows 和 ubuntu】（caffe安装教程windows）
• Build caffe using qmake (ubuntu&windows)
• caffe 安装（Ubuntu 14.04 +caffe+GTX 1080+CUDA8.0 +cuDNN v5）
• Caffe+Ubuntu 16.04 安装教程
• Caffe+Ubuntu14.04+CUDA7.5安装笔记

CAFFE 安装笔记【windows 和 ubuntu】（caffe安装教程windows）

第一部分是 windows 下安装 caffe 的心得；第二部分是 ubuntu 下安装 caffe 的心得：

第一部分：【Win10+VS2017 环境】

老版的 caffe 在 BVLC 的 github 上已经找不到，如果要想下载老版 caffe 可以下载微软的 caffe 版本：https://github.com/Microsoft/caffe

网上的大多安装 caffe 教程都是基于老版的 caffe。

常规错误参考：https://www.cnblogs.com/cxyxbk/p/5902034.html

 win10+VS2017

如果只编译 libcaffe 项目按照网上的流程，基本可以 OK

如果还编译其他项目，可能会出现以下问题，以编译 upgrade_net_proto_binary 项目为例

一、错误 C2976 “std::array”: 模板 参数太少 caffe.managed      \caffe-master-micorsoft\windows\caffe.managed\caffelib.cpp 68 

类似这种错误，双击进去，将 array<array<float>^>^ 中的 array 改成 cli::array

即改成：cli::array<cli::array<float>^>^ 

参考：https://blog.csdn.net/m0_37287643/article/details/83020441

二、错误 LNK1104 无法打开文件 “libboost_date_time-vc140-mt-gd-1_59.lib” upgrade_net_proto_binary     \caffe-master-micorsoft\windows\upgrade_net_proto_binary\LINK 1 

缺少 boost 库 。注意版本号是 1.59

这里有 boost 安装流程：https://blog.csdn.net/u011054333/article/details/78648294

这里关联 boost 库： https://www.cnblogs.com/denggelin/p/5769480.html

三、错误 LNK2019 无法解析的外部符号 "__declspec (dllimport) void __cdecl google::InitGoogleLogging (char const *)" (__imp_?InitGoogleLogging@google@@YAXPEBD@Z)，该符号在函数 main 中被引用 upgrade_net_proto_binary     \caffe-master-micorsoft\windows\upgrade_net_proto_binary\upgrade_net_proto_binary.obj 1

没有安装 Google GLOG 库，这里有 https://download.csdn.net/download/colorsky100/10776566 下载，预编译版本的下载（实际上这个包同时提供了 GLOG 和 GFLAGS 库的包）

下载后分别在【项目属性页库 | VC++ 目录 | 库目录】添加 glog.dll 和 glog.lib 所在目录，在【项目属性页库 | VC++ 目录 | 包含目录】添加 include 所在目录，在【项目属性页库 | 链接器 | 输入 | 附加依赖型】添加 glog.lib

说明：glog 的预编译包下有三个目录 bin、share、static；其中 bin 中是 dll，share 是和 bin 配套的 lib，static 是不需要 dll 的完整的 lib。如果配的是 share 对应的 glog.lib，则必须配 bin，如果用 static 目录下的 glog.lib，那么库目录只需要添加一个 static 对应的目录

上面的描述是有错误的，库目录只需要配 lib 所在的路径，如果这个 lib 是个完全的 lib（即 static 目录下的 glog.lib），则编译完直接使用，如果这个 lib 还需要配 dll 使用（即 share 下的 glog.lib）。编译正常编译，但是在使用时，要么需要将 dll 所在目录设成环境变量，要么需要将 dll 文件（即 bin 下的 glog.dll）拷贝到项目目录下。 不论如何，dll 目录不需要配置在库目录中，都可以正常编译通过

注意，需要添加 debug 版本的动静态链接库文件，否则会报错误如下：

错误 LNK2038 检测到 “_ITERATOR_DEBUG_LEVEL” 的不匹配项：值 “0” 不匹配值 “2”(upgrade_net_proto_binary.obj 中) upgrade_net_proto_binary             \caffe-master-micorsoft\windows\upgrade_net_proto_binary\glog.lib (logging.obj) 1 

上述配置完后可能依然有错误，需要在【项目属性页库 | C/C++ 目录 | 预处理器 | 预处理器定义】中添加 GOOGLE_GLOG_DLL_DECL = 宏后解决，参考：https://www.cnblogs.com/21207-iHome/p/9297329.html

四、错误 LNK2001 无法解析的外部符号 "public: __cdecl google::protobuf::internal::LogMessage::LogMessage (enum google::protobuf::LogLevel,char const *,int)" (??0LogMessage@internal@protobuf@google@@QEAA@W4LogLevel@23@PEBDH@Z) upgrade_net_proto_binary              \caffe-master-micorsoft\windows\upgrade_net_proto_binary\sgd_solver.obj 1

缺少 protobuff 库。这里有 protobuff 的下载、编译，比较麻烦。参考：https://blog.csdn.net/hp_cpp/article/details/81561310

编译完，回过来配置 caffe 的 upgrade_net_proto_binary 项目，继续添加 libprotocd.lib 和 libprotobufd.lib 库目录，依赖项添加 libprotocd.lib 和 libprotobufd.lib

如果出现错误：LNK2038 检测到 “RuntimeLibrary” 的不匹配项：值 “MTd_StaticDebug” 不匹配值 “MDd_DynamicDebug”(upgrade_net_proto_binary.obj 中) upgrade_net_proto_binary         \caffe-master-micorsoft\windows\upgrade_net_proto_binary\libprotocd.lib (ruby_generator.obj) 1

则在 CMAKE 配置 protobuff 不能像帖子 https://blog.csdn.net/hp_cpp/article/details/81561310 推荐的那样，而是需要将 protobuff_BUILD_SHARED_LIBS 对应的复选框给勾上，即在 protobuff 编译完后的 debug 目录下要有 dll 文件 

安装上面的安装还是会报无法解析外部符号问题，网上说是 protobuff 版本问题，改为 2.5.0 版本后还是有问题，又改为 2.6.1 版本，问题得到解决

需要注意的是：官网下载的 2.6.1 是 win32 的，需要改成 x64, 然后需要做以下修改：

只要编译 libprotobuf 项目和 libprotoc 项目，需要在属性页中改动以下几处：1.【常规 | Windows SDK 版本】下拉，选择合适的版本；2.【常规 | 配置类型】下拉为静态库；3.【C/C++| 常规 | 附加包含目录】设为../src;.;%(AdditionalIncludeDirectories)；4.【C/C++| 代码生成 | 运行库】设为 MDd。 

五、错误 LNK2019 无法解析的外部符号 "unsigned int __cdecl google::ParseCommandLineFlags (int *,char * * *,bool)" (?ParseCommandLineFlags@google@@YAIPEAHPEAPEAPEAD_N@Z)，该符号在函数 "void __cdecl caffe::GlobalInit (int *,char * * *)" (?GlobalInit@caffe@@YAXPEAHPEAPEAPEAD@Z) 中被引用 upgrade_net_proto_binary               \caffe-master-micorsoft\windows\upgrade_net_proto_binary\common.obj 1

缺少 gflags 库，在三中提供的预编译包中同时有 glog 和 gflags，正常配置到项目中即可

六、错误 LNK2019 无法解析的外部符号 "public: __cdecl cv::Exception::Exception (int,class std::basic_string<char,struct std::char_traits<char>,class std::allocator<char> > const &,class std::basic_string<char,struct std::char_traits<char>,class std::allocator<char> > const &,class std::basic_string<char,struct std::char_traits<char>,class std::allocator<char> > const &,int)" (??0Exception@cv@@QEAA@HAEBV?$basic_string@DU?$char_traits@D@std@@V?$allocator@D@2@@std@@00H@Z)，该符号在函数 "public: unsigned char * __cdecl cv::Mat::ptr<unsigned char>(int)" (??$ptr@E@Mat@cv@@QEAAPEAEH@Z) 中被引用 upgrade_net_proto_binary                   \caffe-master-micorsoft\windows\upgrade_net_proto_binary\data_transformer.obj 1

这个明显是缺少 opencv 库，但是非常纠结的是官网提供的 opencv2.14 版本只提供 VC14（VS2015）的预编译版本，然后下载官网的 opencv3.4.5 版本，这个提供 VC15 版本。但是配置后还是报无法解析外部符号的问题，配置是没有问题的。

后来转过头直接用 opencv2.14.13 版本，居然没有问题，需要添加的依赖项为 opencv_core2413d.lib;opencv_highgui2413d.lib;opencv_imgproc2413d.lib;

 七、错误 LNK2019 无法解析的外部符号 "public: __cdecl leveldb::WriteBatch::~WriteBatch (void)" (??1WriteBatch@leveldb@@QEAA@XZ)，该符号在函数 "int`public: __cdecl caffe::db::LevelDBTransaction::LevelDBTransaction (class leveldb::DB *)''::`1''::dtor$1" (?dtor$1@?0???0LevelDBTransaction@db@caffe@@QEAA@PEAVDB@leveldb@@@Z@4HA) 中被引用 upgrade_net_proto_binary    \caffe-master-micorsoft\windows\upgrade_net_proto_binary\db.obj 1

错误 LNK2001 无法解析的外部符号 "public: class std::basic_string<char,struct std::char_traits<char>,class std::allocator<char> > __cdecl leveldb::Status::ToString (void) const" (?ToString@Status@leveldb@@QEBA?AV?$basic_string@DU?$char_traits@D@std@@V?$allocator@D@2@@std@@XZ) upgrade_net_proto_binary        \caffe-master-micorsoft\windows\upgrade_net_proto_binary\db_leveldb.obj 1

缺少 LevelDB 库，在官网 https://github.com/google/leveldb/ 下载好后，用 CMAKE-GUI 配置总报错  The C++ compiler      "D:/Program Files (x86)/Microsoft Visual Studio/2017/Enterprise/VC/Tools/MSVC/14.15.26726/bin/Hostx86/x64/cl.exe"    is not able to compile a simple test program.

后来按照官网的指示在 cmd 中用 cmake 指令，成功了

官网说明：

Building for Windows
First generate the Visual Studio 2017 project/solution files:

mkdir -p build
cd build
cmake -G "Visual Studio 15" ..
The default default will build for x86. For 64-bit run:

cmake -G "Visual Studio 15 Win64" ..
To compile the Windows solution from the command-line:

devenv /build Debug leveldb.sln
or open leveldb.sln in Visual Studio and build from within.

Please see the CMake documentation and CMakeLists.txt for more advanced usage.

 如下图，其中 build 是自己建的目录

CMAKE 后，直接双击 leveldb.sln 进 VS，然后只编译 leveldb 项目，回过头配置 caffe 的 upgrade_net_proto_binary。搞定

总结：

在 upgrade_net_proto_binary 项目中，需要的附加库包括：

1.BOOST  2.GLOG  3.PROTOBUFF  4.GFLAGS   5.OPENCV   6.LEVELDB 等一系列第三方库

最后的属性配置页如下：

包含目录：

库目录：

依赖项

2019.3.24

++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++

第二部分：【Ubuntu16.04 环境】

基本安装帖子 https://www.cnblogs.com/sunshineatnoon/p/4535329.html 操作

1. 安装 glog 库

直接 git clone https://github.com/google/glog/

然后进入 glog 目录:

./autogen.sh && ./configure && make && make install

如果报错：

./autogen.sh: 5: ./autogen.sh: autoreconf: not found

按照帖子 https://blog.csdn.net/mybelief321/article/details/9208073 操作

apt-get install autoconf
apt-get install automake
apt-get install libtool

　　2019.5.27

2. libhdf5 包相关错误：

src/caffe/layers/hdf5_output_layer.cpp:4:18: fatal error: hdf5.h: 没有那个文件或目录
#include "hdf5.h"

在上面的帖子链接中，我没有按照他的步骤安装 libhdf5-serial-dev 包

正常 apt install  libhdf5-serial-dev。安装后，依然报这个错误，此时按照帖子 https://www.cnblogs.com/xiangfeidemengzhu/p/7058391.html 操作。

首先查找 hdf5.h 的位置，终端输入： locate hdf5.h

可以看到在 /usr/include/hdf5/serial/ 下有，把这个路径添加到 Makefile.config 的 INCLUDE_DIRS 中，即：

INCLUDE_DIRS := $(PYTHON_INCLUDE) /usr/local/include /usr/include/hdf5/serial/

继续 make all，报错：

AR -o .build_release/lib/libcaffe.a
LD -o .build_release/lib/libcaffe.so.1.0.0
/usr/bin/ld: 找不到 -lhdf5_hl
/usr/bin/ld: 找不到 -lhdf5
collect2: error: ld returned 1 exit status
Makefile:582: recipe for target ''.build_release/lib/libcaffe.so.1.0.0'' failed
make: *** [.build_release/lib/libcaffe.so.1.0.0] Error 1 

这个应该是找不到 hdf5 的 lib 文件。其实这个和上面找不到头文件是一个问题，就是 apt install libhdf5-serial-dev 库的时候安装的路径不在 caffe 库默认的编译路径下。

所以第一步还是 locate

locate libhdf5

然后可以看到有个结果是

/usr/lib/x86_64-linux-gnu/hdf5/serial/libhdf5.so

将这个路径添加到 Makefile.config 中去，即：

LIBRARY_DIRS := $(PYTHON_LIB) /usr/local/lib /usr/lib /usr/lib/x86_64-linux-gnu/hdf5/serial/

再 make all 解决。

3. gflags 包的问题：

AR -o .build_release/lib/libcaffe.a
LD -o .build_release/lib/libcaffe.so.1.0.0
/usr/bin/ld: /usr/local/lib/libgflags.a(gflags.cc.o): relocation R_X86_64_32 against `.rodata.str1.1'' can not be used when making a shared object; recompile with -fPIC
/usr/local/lib/libgflags.a: 无法添加符号：错误的值
collect2: error: ld returned 1 exit status
Makefile:572: recipe for target ''.build_release/lib/libcaffe.so.1.0.0'' failed
make: *** [.build_release/lib/libcaffe.so.1.0.0] Error 1

from：https://github.com/BVLC/caffe/issues/6317 追踪到 #6200，#2171，一路看过去

其中在 #2171 的讨论中，有一层楼说：

just recompile the gflags with CXXFLAGS += -fPIC makes things work

该层回复较多

再回过头看前面的帖子 https://blog.csdn.net/mybelief321/article/details/9208073

这个帖子在安装 gflags 库时明确说了要 export 环境变量，即

export CXXFLAGS="-fPIC"

2019.5.29

4. gcc 版本问题

[ 89%] Linking CXX executable compute_image_mean
CMakeFiles/compute_image_mean.dir/compute_image_mean.cpp.o：在函数‘std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> >* google::MakeCheckOpString<int, int>(int const&, int const&, char const*)’中：
compute_image_mean.cpp:(.text._ZN6google17MakeCheckOpStringIiiEEPNSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEERKT_RKT0_PKc [_ZN6google17MakeCheckOpStringIiiEEPNSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEERKT_RKT0_PKc]+0x50)：对‘google::base::CheckOpMessageBuilder::NewString [abi:cxx11]()’未定义的引用
CMakeFiles/compute_image_mean.dir/compute_image_mean.cpp.o：在函数‘std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> >* google::MakeCheckOpString<unsigned long, int>(unsigned long const&, int const&, char const*)’中：
compute_image_mean.cpp:(.text._ZN6google17MakeCheckOpStringImiEEPNSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEERKT_RKT0_PKc [_ZN6google17MakeCheckOpStringImiEEPNSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEERKT_RKT0_PKc]+0x50)：对‘google::base::CheckOpMessageBuilder::NewString [abi:cxx11]()’未定义的引用
collect2: error: ld returned 1 exit status
tools/CMakeFiles/compute_image_mean.dir/build.make:133: recipe for target ''tools/compute_image_mean'' failed
make[2]: *** [tools/compute_image_mean] Error 1
CMakeFiles/Makefile2:457: recipe for target ''tools/CMakeFiles/compute_image_mean.dir/all'' failed
make[1]: *** [tools/CMakeFiles/compute_image_mean.dir/all] Error 2
Makefile:127: recipe for target ''all'' failed
make: *** [all] Error 2

这里的本质原因是 gcc 版本问题，因为前期编译 cuda 的时候对 gcc 降低了版本。所以需要将 gcc 的版本提升到 5 以上，按照这个帖子的方法进行 https://blog.csdn.net/xuezhisdc/article/details/48650015 (虽然这个帖子是降版本）即：

cd /usr/bin
ls gcc*
sudo rm gcc
sudo ln -s gcc-5 gcc
sudo rm g++
sudo ln -s g++-5 g++
# 查看是否连接到5
ls –al gcc g++
gcc --version
g++ --version　　

完了之后，make clean 

make all 还是报同样的错误。

然后再按照最开始的帖子 https://blog.csdn.net/mybelief321/article/details/9208073 的方法再安装一遍 glog，即

cd glog-0.3.3
./configure
make
sudo make install

　　再 cd 到 caffe 目录

make clean

make all 

2019.6.2 

5. make pycaffe 相关问题

CXX/LD -o python/caffe/_caffe.so python/caffe/_caffe.cpp
python/caffe/_caffe.cpp:10:31: fatal error: numpy/arrayobject.h: 没有那个文件或目录
compilation terminated.
Makefile:517: recipe for target ''python/caffe/_caffe.so'' failed
make: *** [python/caffe/_caffe.so] Error 1

首先看自己机器是否已经安装了。因为 numpy 库是必装的，所以因为还是路径设置的问题

终端输入 locate arrayobject.h

找到 

/home/qian/.local/lib/python3.5/site-packages/numpy/core/include/numpy/arrayobject.h

注意上面的错误提示是缺少 numpy/arrayobject.h，而不是缺少 arrayobject.h。所以在添加路径的时候，最后的 numpy 不要加上

修改 Makefile.config 如下：

PYTHON_INCLUDE := /usr/include/python3.5m \
/usr/lib/python3.5/dist-packages/numpy/core/include \
/home/qian/.local/lib/python3.5/site-packages/numpy/core/include

解决

继续 make，找不到 lib：

CXX/LD -o python/caffe/_caffe.so python/caffe/_caffe.cpp
/usr/bin/ld: 找不到 -lboost_python3
collect2: error: ld returned 1 exit status
Makefile:517: recipe for target ''python/caffe/_caffe.so'' failed
make: *** [python/caffe/_caffe.so] Error 1

首先终端查找：locate libboost_python3.so

找不到文件

再 locate libboost_python

大概结果如下：

/usr/lib/x86_64-linux-gnu/libboost_python-py27.a
/usr/lib/x86_64-linux-gnu/libboost_python-py27.so
/usr/lib/x86_64-linux-gnu/libboost_python-py27.so.1.58.0
/usr/lib/x86_64-linux-gnu/libboost_python-py35.a
/usr/lib/x86_64-linux-gnu/libboost_python-py35.so
/usr/lib/x86_64-linux-gnu/libboost_python-py35.so.1.58.0
/usr/lib/x86_64-linux-gnu/libboost_python.a
/usr/lib/x86_64-linux-gnu/libboost_python.so

因为我编译的是 python3 版本。所以很明显我需要创建一个指向 libboost_python_py35.so 的软链接，名为 libboost_python3.so

root@qian-desktop:/usr/lib/x86_64-linux-gnu# ln -s libboost_python-py35.so libboost_python3.so

make pycaffe 解决

6. pip3 install -r requirements.txt 相关问题

ERROR: matplotlib 3.0.3 has requirement python-dateutil>=2.1, but you''ll have python-dateutil 1.5 which is incompatible.
ERROR: pandas 0.24.2 has requirement python-dateutil>=2.5.0, but you''ll have python-dateutil 1.5 which is incompatible.

已经终端 pip3 install --upgrade python-dateutil 依然报此错误

解决办法：https://blog.csdn.net/CAU_Ayao/article/details/83538024

gedit requirements.txt

python-dateutil>=1.4,<2

改为：

python-dateutil // 不加版本号就是默认为最新版本

继续 pip3 install -r requirements.txt，问题解决。

2019.6.4

如果升级 pip3 后之后出现：from pip import main ImportError: cannot import name ''main'' 错误

解决办法 https://blog.csdn.net/qq_38522539/article/details/80678412

7. import caffe 相关错误

caffe 模块找不到错误

解决办法：

export PYTHONPATH=/home/qian/libraries/caffe/python

需要升级 matplotlib 错误

>>> import caffe
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/home/qian/libraries/caffe/python/caffe/__init__.py", line 1, in <module>
from .pycaffe import Net, SGDSolver, NesterovSolver, AdaGradSolver, RMSPropSolver, AdaDeltaSolver, AdamSolver, NCCL, Timer
File "/home/qian/libraries/caffe/python/caffe/pycaffe.py", line 15, in <module>
import caffe.io
File "/home/qian/libraries/caffe/python/caffe/io.py", line 2, in <module>
import skimage.io
File "/usr/local/lib/python3.5/dist-packages/skimage/__init__.py", line 135, in <module>
from .data import data_dir
File "/usr/local/lib/python3.5/dist-packages/skimage/data/__init__.py", line 13, in <module>
from ..io import imread, use_plugin
File "/usr/local/lib/python3.5/dist-packages/skimage/io/__init__.py", line 15, in <module>
reset_plugins()
File "/usr/local/lib/python3.5/dist-packages/skimage/io/manage_plugins.py", line 91, in reset_plugins
_load_preferred_plugins()
File "/usr/local/lib/python3.5/dist-packages/skimage/io/manage_plugins.py", line 71, in _load_preferred_plugins
_set_plugin(p_type, preferred_plugins[''all''])
File "/usr/local/lib/python3.5/dist-packages/skimage/io/manage_plugins.py", line 83, in _set_plugin
use_plugin(plugin, kind=plugin_type)
File "/usr/local/lib/python3.5/dist-packages/skimage/io/manage_plugins.py", line 254, in use_plugin
_load(name)
File "/usr/local/lib/python3.5/dist-packages/skimage/io/manage_plugins.py", line 298, in _load
fromlist=[modname])
File "/usr/local/lib/python3.5/dist-packages/skimage/io/_plugins/matplotlib_plugin.py", line 3, in <module>
from mpl_toolkits.axes_grid1 import make_axes_locatable
File "/usr/local/lib/python3.5/dist-packages/mpl_toolkits/axes_grid1/__init__.py", line 1, in <module>
from . import axes_size as Size
File "/usr/local/lib/python3.5/dist-packages/mpl_toolkits/axes_grid1/axes_size.py", line 15, in <module>
from matplotlib.axes import Axes
File "/usr/local/lib/python3.5/dist-packages/matplotlib/axes/__init__.py", line 1, in <module>
from ._subplots import *
File "/usr/local/lib/python3.5/dist-packages/matplotlib/axes/_subplots.py", line 6, in <module>
from matplotlib.axes._axes import Axes
File "/usr/local/lib/python3.5/dist-packages/matplotlib/axes/_axes.py", line 20, in <module>
import matplotlib.dates as _ # <-registers a date unit converter
File "/usr/local/lib/python3.5/dist-packages/matplotlib/dates.py", line 145, in <module>
from dateutil.rrule import (rrule, MO, TU, WE, TH, FR, SA, SU, YEARLY,
File "/usr/local/lib/python3.5/dist-packages/dateutil/rrule.py", line 55
raise ValueError, "Can''t create weekday with n == 0"

^
SyntaxError: invalid syntax

解决办法：https://blog.csdn.net/quantum7/article/details/83475530

2019.6.5

终于搞定。。。 

Build caffe using qmake (ubuntu&windows)

TEMPLATE = lib
CONfig(debug,debug|release): TARGET = libcaffed
CONfig(release,debug|release): TARGET = libcaffe
INCLUDEPATH += include src include/caffe/proto
DEFInes += USE_OPENCV cpu_ONLY
CONfig += dll staticlib

# Input
HEADERS += \     include/caffe/blob.hpp\     include/caffe/caffe.hpp\     include/caffe/common.hpp\     include/caffe/data_reader.hpp\     include/caffe/data_transformer.hpp\     include/caffe/filler.hpp\     include/caffe/internal_thread.hpp\     include/caffe/layer.hpp\     include/caffe/layers/absval_layer.hpp\     include/caffe/layers/accuracy_layer.hpp\     include/caffe/layers/argmax_layer.hpp\     include/caffe/layers/base_conv_layer.hpp\     include/caffe/layers/base_data_layer.hpp\     include/caffe/layers/batch_norm_layer.hpp\     include/caffe/layers/batch_reindex_layer.hpp\     include/caffe/layers/bias_layer.hpp\     include/caffe/layers/bnll_layer.hpp\     include/caffe/layers/Box_annotator_ohem_layer.hpp\     include/caffe/layers/concat_layer.hpp\     include/caffe/layers/contrastive_loss_layer.hpp\     include/caffe/layers/conv_layer.hpp\     include/caffe/layers/crop_layer.hpp\     include/caffe/layers/cudnn_conv_layer.hpp\     include/caffe/layers/cudnn_lcn_layer.hpp\     include/caffe/layers/cudnn_lrn_layer.hpp\     include/caffe/layers/cudnn_pooling_layer.hpp\     include/caffe/layers/cudnn_relu_layer.hpp\     include/caffe/layers/cudnn_sigmoid_layer.hpp\     include/caffe/layers/cudnn_softmax_layer.hpp\     include/caffe/layers/cudnn_tanh_layer.hpp\     include/caffe/layers/data_layer.hpp\     include/caffe/layers/deconv_layer.hpp\     include/caffe/layers/dropout_layer.hpp\     include/caffe/layers/dummy_data_layer.hpp\     include/caffe/layers/eltwise_layer.hpp\     include/caffe/layers/elu_layer.hpp\     include/caffe/layers/embed_layer.hpp\     include/caffe/layers/euclidean_loss_layer.hpp\     include/caffe/layers/exp_layer.hpp\     include/caffe/layers/filter_layer.hpp\     include/caffe/layers/flatten_layer.hpp\     include/caffe/layers/hdf5_data_layer/.hpp\     include/caffe/layers/hdf5_output_layer.hpp\     include/caffe/layers/hinge_loss_layer.hpp\     include/caffe/layers/im2col_layer.hpp\     include/caffe/layers/image_data_layer.hpp\     include/caffe/layers/infogain_loss_layer.hpp\     include/caffe/layers/inner_product_layer.hpp\     include/caffe/layers/input_layer.hpp\     include/caffe/layers/log_layer.hpp\     include/caffe/layers/loss_layer.hpp\     include/caffe/layers/lrn_layer.hpp\     include/caffe/layers/lstm_layer.hpp\     include/caffe/layers/memory_data_layer.hpp\     include/caffe/layers/multinomial_logistic_loss_layer.hpp\     include/caffe/layers/mvn_layer.hpp\     include/caffe/layers/neuron_layer.hpp\     include/caffe/layers/parameter_layer.hpp\     include/caffe/layers/pooling_layer.hpp\     include/caffe/layers/power_layer.hpp\     include/caffe/layers/prelu_layer.hpp\     include/caffe/layers/psroi_pooling_layer.hpp\     include/caffe/layers/python_layer.hpp\     include/caffe/layers/recurrent_layer.hpp\     include/caffe/layers/reduction_layer.hpp\     include/caffe/layers/relu_layer.hpp\     include/caffe/layers/reshape_layer.hpp\     include/caffe/layers/rnn_layer.hpp\     include/caffe/layers/roi_pooling_layer.hpp\     include/caffe/layers/scale_layer.hpp\     include/caffe/layers/sigmoid_cross_entropy_loss_layer.hpp\     include/caffe/layers/sigmoid_layer.hpp\     include/caffe/layers/silence_layer.hpp\     include/caffe/layers/slice_layer.hpp\     include/caffe/layers/smooth_l1_loss_layer.hpp\     include/caffe/layers/smooth_l1_loss_ohem_layer.hpp\     include/caffe/layers/softmax_layer.hpp\     include/caffe/layers/softmax_loss_layer.hpp\     include/caffe/layers/softmax_loss_ohem_layer.hpp\     include/caffe/layers/split_layer.hpp\     include/caffe/layers/spp_layer.hpp\     include/caffe/layers/tanh_layer.hpp\     include/caffe/layers/threshold_layer.hpp\     include/caffe/layers/tile_layer.hpp\     include/caffe/layers/window_data_layer.hpp\     include/caffe/layer_factory.hpp\     include/caffe/net.hpp\     include/caffe/parallel.hpp\     include/caffe/proto/caffe.pb.h\     include/caffe/sgd_solvers.hpp\     include/caffe/solver.hpp\     include/caffe/solver_factory.hpp\     include/caffe/syncedmem.hpp\     include/caffe/util/benchmark.hpp\     include/caffe/util/blocking_queue.hpp\     include/caffe/util/cudnn.hpp\     include/caffe/util/db.hpp\     include/caffe/util/db_leveldb.hpp\     include/caffe/util/db_lmdb.hpp\     include/caffe/util/device_alternate.hpp\     include/caffe/util/format.hpp\     include/caffe/util/hdf5.hpp\     include/caffe/util/im2col.hpp\     include/caffe/util/insert_splits.hpp\     include/caffe/util/io.hpp\     include/caffe/util/math_functions.hpp\     include/caffe/util/mkl_alternate.hpp\     include/caffe/util/rng.hpp\     include/caffe/util/signal_handler.h\     include/caffe/util/upgrade_proto.hpp

SOURCES += \    src/caffe/blob.cpp \    src/caffe/common.cpp \    src/caffe/data_reader.cpp \    src/caffe/data_transformer.cpp \    src/caffe/internal_thread.cpp \    src/caffe/layer.cpp \    src/caffe/layers/absval_layer.cpp \    src/caffe/layers/accuracy_layer.cpp \    src/caffe/layers/argmax_layer.cpp \    src/caffe/layers/base_conv_layer.cpp \    src/caffe/layers/base_data_layer.cpp \    src/caffe/layers/batch_norm_layer.cpp \    src/caffe/layers/batch_reindex_layer.cpp \    src/caffe/layers/bias_layer.cpp \    src/caffe/layers/bnll_layer.cpp \    src/caffe/layers/Box_annotator_ohem_layer.cpp \    src/caffe/layers/concat_layer.cpp \    src/caffe/layers/contrastive_loss_layer.cpp \    src/caffe/layers/conv_layer.cpp \    src/caffe/layers/crop_layer.cpp \    src/caffe/layers/cudnn_conv_layer.cpp \    src/caffe/layers/cudnn_lcn_layer.cpp \    src/caffe/layers/cudnn_lrn_layer.cpp \    src/caffe/layers/cudnn_pooling_layer.cpp \    src/caffe/layers/cudnn_relu_layer.cpp \    src/caffe/layers/cudnn_sigmoid_layer.cpp \    src/caffe/layers/cudnn_softmax_layer.cpp \    src/caffe/layers/cudnn_tanh_layer.cpp \    src/caffe/layers/data_layer.cpp \    src/caffe/layers/deconv_layer.cpp \    src/caffe/layers/dropout_layer.cpp \    src/caffe/layers/dummy_data_layer.cpp \    src/caffe/layers/eltwise_layer.cpp \    src/caffe/layers/elu_layer.cpp \    src/caffe/layers/embed_layer.cpp \    src/caffe/layers/euclidean_loss_layer.cpp \    src/caffe/layers/exp_layer.cpp \    src/caffe/layers/filter_layer.cpp \    src/caffe/layers/flatten_layer.cpp \    src/caffe/layers/hdf5_data_layer.cpp \    src/caffe/layers/hdf5_output_layer.cpp \    src/caffe/layers/hinge_loss_layer.cpp \    src/caffe/layers/im2col_layer.cpp \    src/caffe/layers/image_data_layer.cpp \    src/caffe/layers/infogain_loss_layer.cpp \    src/caffe/layers/inner_product_layer.cpp \    src/caffe/layers/input_layer.cpp \    src/caffe/layers/log_layer.cpp \    src/caffe/layers/loss_layer.cpp \    src/caffe/layers/lrn_layer.cpp \    src/caffe/layers/lstm_layer.cpp \    src/caffe/layers/lstm_unit_layer.cpp \    src/caffe/layers/memory_data_layer.cpp \    src/caffe/layers/multinomial_logistic_loss_layer.cpp \    src/caffe/layers/mvn_layer.cpp \    src/caffe/layers/neuron_layer.cpp \    src/caffe/layers/parameter_layer.cpp \    src/caffe/layers/pooling_layer.cpp \    src/caffe/layers/power_layer.cpp \    src/caffe/layers/prelu_layer.cpp \    src/caffe/layers/psroi_pooling_layer.cpp \    src/caffe/layers/recurrent_layer.cpp \    src/caffe/layers/reduction_layer.cpp \    src/caffe/layers/relu_layer.cpp \    src/caffe/layers/reshape_layer.cpp \    src/caffe/layers/rnn_layer.cpp \    src/caffe/layers/roi_pooling_layer.cpp \    src/caffe/layers/scale_layer.cpp \    src/caffe/layers/sigmoid_cross_entropy_loss_layer.cpp \    src/caffe/layers/sigmoid_layer.cpp \    src/caffe/layers/silence_layer.cpp \    src/caffe/layers/slice_layer.cpp \    src/caffe/layers/smooth_l1_loss_layer.cpp \    src/caffe/layers/smooth_L1_loss_ohem_layer.cpp \    src/caffe/layers/softmax_layer.cpp \    src/caffe/layers/softmax_loss_layer.cpp \    src/caffe/layers/softmax_loss_ohem_layer.cpp \    src/caffe/layers/split_layer.cpp \    src/caffe/layers/spp_layer.cpp \    src/caffe/layers/tanh_layer.cpp \    src/caffe/layers/threshold_layer.cpp \    src/caffe/layers/tile_layer.cpp \    src/caffe/layers/window_data_layer.cpp \    src/caffe/layer_factory.cpp \    src/caffe/net.cpp \    src/caffe/parallel.cpp \    src/caffe/solver.cpp \    src/caffe/solvers/adadelta_solver.cpp \    src/caffe/solvers/adagrad_solver.cpp \    src/caffe/solvers/adam_solver.cpp \    src/caffe/solvers/nesterov_solver.cpp \    src/caffe/solvers/rmsprop_solver.cpp \    src/caffe/solvers/sgd_solver.cpp \    src/caffe/syncedmem.cpp \    src/caffe/util/benchmark.cpp \    src/caffe/util/blocking_queue.cpp \    src/caffe/util/cudnn.cpp \    src/caffe/util/db.cpp \    src/caffe/util/db_leveldb.cpp \    src/caffe/util/db_lmdb.cpp \    src/caffe/util/hdf5.cpp \    src/caffe/util/im2col.cpp \    src/caffe/util/insert_splits.cpp \    src/caffe/util/io.cpp \    src/caffe/util/math_functions.cpp \    src/caffe/util/signal_handler.cpp \    src/caffe/util/upgrade_proto.cpp \    src/caffe/proto/caffe.pb.cc

win32{

# opencv
PATH_OPENCV_INCLUDE   = "H:\3rdparty\OpenCV\opencv310\build\include"
PATH_OPENCV_LIBRARIES = "H:\3rdparty\OpenCV\opencv310\build\x64\vc12\lib"
VERSION_OPENCV        = 310
INCLUDEPATH += $${PATH_OPENCV_INCLUDE} CONfig(debug,debug|release){ LIBS += -L$${PATH_OPENCV_LIBRARIES} -lopencv_core$${VERSION_OPENCV}d LIBS += -L$${PATH_OPENCV_LIBRARIES} -lopencv_highgui$${VERSION_OPENCV}d LIBS += -L$${PATH_OPENCV_LIBRARIES} -lopencv_imgcodecs$${VERSION_OPENCV}d LIBS += -L$${PATH_OPENCV_LIBRARIES} -lopencv_imgproc$${VERSION_OPENCV}d } CONfig(release,debug|release){ LIBS += -L$${PATH_OPENCV_LIBRARIES} -lopencv_core$${VERSION_OPENCV} LIBS += -L$${PATH_OPENCV_LIBRARIES} -lopencv_highgui$${VERSION_OPENCV} LIBS += -L$${PATH_OPENCV_LIBRARIES} -lopencv_imgcodecs$${VERSION_OPENCV} LIBS += -L$${PATH_OPENCV_LIBRARIES} -lopencv_imgproc$${VERSION_OPENCV} } #glog INCLUDEPATH += H:\3rdparty\glog\include LIBS += -LH:\3rdparty\glog\lib\x64\v120\Debug\dynamic -llibglog #boost INCLUDEPATH += H:\3rdparty\boost\boost_1_59_0 CONfig(debug,debug|release): BOOST_VERSION = "-vc120-mt-gd-1_59" CONfig(release,debug|release): BOOST_VERSION = "-vc120-mt-1_59" LIBS += -LH:\3rdparty\boost\boost_1_59_0\lib64-msvc-12.0 \  -llibboost_system$${BOOST_VERSION} \     -llibboost_date_time$${BOOST_VERSION} \  -llibboost_filesystem$${BOOST_VERSION} \     -llibboost_thread$${BOOST_VERSION} \  -llibboost_regex$${BOOST_VERSION}

#gflags
INCLUDEPATH += H:\3rdparty\gflags\include
CONfig(debug,debug|release): LIBS += -LH:\3rdparty\gflags\x64\v120\dynamic\Lib -lgflagsd
CONfig(release,debug|release): LIBS += -LH:\3rdparty\gflags\x64\v120\dynamic\Lib -lgflags

#protobuf
INCLUDEPATH += H:\3rdparty\protobuf\include
CONfig(debug,debug|release): LIBS += -LH:\3rdparty\protobuf\lib\x64\v120\Debug -llibprotobuf
CONfig(release,debug|release): LIBS += -LH:\3rdparty\protobuf\lib\x64\v120\Release -llibprotobuf

# hdf5
INCLUDEPATH += H:\3rdparty\hdf5\include
LIBS += -LH:\3rdparty\hdf5\lib\x64 -lhdf5 -lhdf5_hl -lhdf5_tools -lhdf5_cpp

# levelDb
INCLUDEPATH += H:\3rdparty\LevelDB\include
CONfig(debug,debug|release): LIBS += -LH:\3rdparty\LevelDB\lib\x64\v120\Debug -lLevelDb
CONfig(release,debug|release): LIBS += -LH:\3rdparty\LevelDB\lib\x64\v120\Release -lLevelDb

# lmdb
INCLUDEPATH += H:\3rdparty\lmdb\include
CONfig(debug,debug|release): LIBS += -LH:\3rdparty\lmdb\lib\x64 -llmdbD
CONfig(release,debug|release): LIBS += -LH:\3rdparty\lmdb\lib\x64 -llmdb

#openblas
INCLUDEPATH += H:\3rdparty\openblas\include
LIBS += -LH:\3rdparty\openblas\lib\x64 -llibopenblas
}

caffe 安装（Ubuntu 14.04 +caffe+GTX 1080+CUDA8.0 +cuDNN v5）

sudo apt-get install --no-install-recommends libboost-all-dev
sudo apt-get install libprotobuf-dev libleveldb-dev libsnappy-dev libopencv-dev libhdf5-serial-dev protobuf-compiler
sudo apt-get install libgflags-dev libgoogle-glog-dev liblmdb-dev

sudo apt-get install libatlas-base-dev

sudo apt-get install build-essential
sudo apt-get install cmake git libgtk2.0-dev pkg-config libavcodec-dev libavformat-dev libswscale-dev
sudo apt-get install python-dev python-numpy libtbb2 libtbb-dev libjpeg-dev libpng-dev libtiff-dev libjasper-dev libdc1394-22-dev

mkdir ~/opencv
cd ~/opencv
wget https://github.com/Itseez/opencv/archive/3.0.0-alpha.zip -O opencv-3.0.0-alpha.zip
unzip opencv-3.0.0-alpha.zip
cd opencv-3.0.0-alpha

mkdir build
cd build
cmake ..
sudo make
sudo make install
sudo /bin/bash -c 'echo "/usr/local/lib" > /etc/ld.so.conf.d/opencv.conf'
sudo ldconfig

cd /etc/modprobe.d/
sudo vim nvidia-installer-disable-nouveau.conf

blacklist nouveau options nouveau modeset=0 

rdblacklist nouveau nouveau.modeset=0 

sudo sh ./NVIDIA-Linux-x86_64-352.30.run

sudo start lightdm

cd ~
sudo tar xvf cudnn-8.0-linux-x64-v5.1.tgz
cd cuda/include
sudo cp *.h /usr/local/include/
cd ../lib64
sudo cp lib* /usr/local/lib/
cd /usr/local/lib# sudo chmod +r libcudnn.so.5.1.5
sudo ln -sf libcudnn.so.5.1.5 libcudnn.so.5
sudo ln -sf libcudnn.so.5 libcudnn.so
sudo ldconfig

cd ~
git clone https://github.com/BVLC/caffe.git

sudo apt-get install libatlas-base-dev libprotobuf-dev libleveldb-dev libsnappy-dev libopencv-dev libboost-all-dev libhdf5-serial-dev libgflags-dev libgoogle-glog-dev liblmdb-dev protobuf-compiler

cd ~/caffe
cp Makefile.config.example Makefile.config
vim Makefile.config

make all
make test
make runtest

Caffe+Ubuntu 16.04 安装教程

sudo apt-get update

sudo apt-get upgrade

sudo apt-get install git

sudo apt-get install  libprotobuf-dev libleveldb-dev libsnappy-dev libopencv-dev libhdf5-serial-dev protobuf-compiler

sudo apt-get install --no-install-recommends libboost-all-dev

sudo apt-get install libatlas-base-dev

sudo apt-get install python-dev 

sudo apt-get install libgflags-dev libgoogle-glog-dev liblamdb-dev

git clone https://github.com/bvlc/caffe.git

 cd caffe/

 mv Makefile.config.example Makefile.config

find . -type f -exec sed -i -e 's^"hdf5.h"^"hdf5/serial/hdf5.h"^g' -e 's^"hdf5_hl.h"^"hdf5/serial/hdf5_hl.h"^g' '{}' \;

cd /usr/lib/x86_64-linux-gnu

sudo ln -s libhdf5_serial.so.10.1.0 libhdf5.so
sudo ln -s libhdf5_serial_hl.so.10.0.2 libhdf5_hl.so

cd 
cd caffe/


make all -j4

make test -j4

make runtest -j4

Caffe+Ubuntu14.04+CUDA7.5安装笔记

ubuntu 14.04安装

• 先到官网下载ubuntu14.04，网址：http://www.ubuntu.com/download/desktop
  ubuntu-14.04.4-desktop-amd64.iso

• 参考：Ubuntu14.04 安装及使用：[1]制作安装U盘制作安装U盘
  然后参考：Ubantu14.04安装教程安装系统

• 分区：

  1. boot 设置 200M 主分区
  2. / 设置 50000M
  3. swap 设置 4000M
  4. home 设置 剩余M

cuda7.5安装

1. cuda7.5下载：地址https://developer.nvidia.com/cuda-downloads
   文件： cuda_7.5.18_linux.run
2. 登录界面前按Ctrl+Alt+F1进入命令提示符 【禁用nouveau驱动】
3. 执行命令： sudo vi /etc/modprobe.d/blacklist-nouveau.conf

4. 输入以下内容

   blacklist nouveau
   options nouveau modset=0

   最后保存退出（:wq)

5. 执行命令： sudo update-initramfs -u
   再执行命令： lspci | grep nouveau 查看是否有内容
   如果没有内容 ，说明禁用成功，如果有内容，就重启一下再查看
   sudo reboot
   重启后，进入登录界面的时候，不要登录进入桌面，直接按Ctrl+Alt+F1进入命令提示符。

6. 重启后，登录界面时直接按Ctrl+Alt+F1进入命令提示符

7. 安装依赖项：
   sudo service lightdm stop
   sudo apt-get install g++
   sudo apt-get installGit
   sudo apt-get install freeglut3-dev

8. 假设cuda_7.5.18_linux.run位于 ~ 目录，切换到~目录： cd ~

9. 执行命令： sudo sh cude_7.5.18_linux.run

10. 安装的时候，要让你先看一堆文字（EULA)，我们直接不停的按空格键到100%，然后输入一堆accept,yes,yes或回车进行安装。安装完成后，重启，然后用ls查看一下：
    ls /dev/nvidia*
    会看到/dev目录下生成多个nvidia开头文件(夹)
    或者输入命令： sudo nvcc –version 会显示类似以下信息

       
       
    
        
        
    1
        
        
    2
        
        
    3
        
        
    4
        
        
    5
       
       
       
       
    
        
        
    1
        
        
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    5
       
       
    dl@dl-Z170X-Gaming-3:~$ nvcc --version
    nvcc: NVIDIA (R) Cuda compiler driver
    copyright (c) 2005-2015 NVIDIA Corporation
    Built on Tue_Aug_11_14:2732_CDT_2015
    Cuda compilation tools,release 7.5,V7.5.17

11. 配置环境变量
    执行命令： sudo vi /etc/profile
    文件底部添加以下内容：

       
       
    
        
        
    1
        
        
    2
       
       
       
       
    
        
        
    1
        
        
    2
       
       
    export PATH=/usr/local/cuda-7.5/bin:$PATH export LD_LIBRARY_PATH=/usr/local/cuda-7.5/lib64:$LD_LIBRARY_PATH//注意这里的问题lib64而不是lib

12. 编译samples
    安装成功后在～目录下可以看到一个NVIDIA_CUDA-7.5_Samples文件夹，切换到目录
    输入sudo make， 大概等个十多分钟后就会把全部的samples编译完毕。生成的可执行文件位于
    NVIDIA_CUDA-7.5_Samples/bin/x86_64/Linux/release 目录下
    比如运行 ./nbody可以看到以下demo
    

cuda安装过程中遇到的问题

Q1

–

1. 在执行命令： sudo apt-get install g++ 时出现以下错误
   g++ : Depends: g++-4.8 (>= 4.8.2-5~) but it is not going to be installed

2. 是因为ubuntu 14.04的源过旧或不可访问导致，可以通过更新源解决。

3. 首先，备份原始源文件source.list
   sudo cp /etc/apt/sources.list /etc/apt/sources.list_backup

4. 然后
   sudo gedit /etc/apt/source.list
   在文件尾部添加以下内容

       
       
   1
       
       
   2
       
       
   3
       
       
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   5
       
       
   6
       
       
   7
       
       
   8
       
       
   9
       
       
   10
      
      
      
      
   
       
       
   1
       
       
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   deb http://archive.ubuntu.com/ubuntu/ trusty main restricted universe multiverse
   deb http://archive.com/ubuntu/ trusty-security main restricted universe multiverse
   deb http://archive.com/ubuntu/ trusty-updates main restricted universe multiverse
   deb http://archive.com/ubuntu/ trusty-proposed main restricted universe multiverse
   deb http://archive.com/ubuntu/ trusty-backports main restricted universe multiverse
   deb-src http://archive.com/ubuntu/ trusty main restricted universe multiverse
   deb-src http://archive.com/ubuntu/ trusty-security main restricted universe multiverse
   deb-src http://archive.com/ubuntu/ trusty-updates main restricted universe multiverse
   deb-src http://archive.com/ubuntu/ trusty-proposed main restricted universe multiverse
   deb-src http://archive.com/ubuntu/ trusty-backports main restricted universe multiverse

5. 最后 sudo apt-get update

Q2

 
 

  
  
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W: GPG 错误：http://archive.ubuntukylin.com:10006/ubuntukylin xenial InRelease: 由于没有公钥，无法验证下列签名： NO_PUBKEY 8D5A09DC9B929006
W: 仓库 “http://archive10006/ubuntukylin xenial InRelease” 没有数字签名。
N: 无法认证来自该源的数据，所以使用它会带来潜在风险。
N: 参见 apt-secure(8) 手册以了解仓库创建和用户配置方面的细节。
W: 以下 ID 的密钥没有可用的公钥：
8D5A09DC9B929006

solution:

  
  
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sudo apt-key adv -keyserver keyserver.ubuntucom recvkeys 8D5A09DC9B929006

注意最后的一串密钥就是报错信息里的,每个人的不一样

安装caffe

1. 下载caffe：执行命令： sudo git clonehttps://github.com/BVLC/caffe.git

2. 安装依赖项：

   sudo apt-get install libatlas-base-dev
   sudo apt-get install libprotobuf-dev
   sudo apt-get install libleveldb-dev
   sudo apt-get install libsnappy-dev
   sudo apt-get install libopencv-dev
   sudo apt-get install libboost-all-dev
   sudo apt-get install libhdf5-serial-dev
   sudo apt-get install libgflags-dev
   sudo apt-get install libgoogle-glog-dev
   sudo apt-get install liblmdb-dev
   sudo apt-get install protobuf-compiler

3. 编译caffe
   cd ~/caffe
   sudo cp Makefile.config.example Makefile.config
   make all

4. 配置运行环境
   sudo vi /etc/ld.so.conf.d/caffe.conf
   添加内容：
   /usr/local/cuda/lib64

5. 更新配置
   sudo ldconfig
6. caffe测试，执行以下命令：
   cd ~/caffe
   sudo sh data/mnist/get_mnist.sh
   sudo sh examples/mnist/create_mnist.sh
   最后测试：
   sudo sh examples/mnist/train_lenet.sh

运行结果如下：

其他依赖项

我们查看caffe目录下 Makefile.config 内容如下：

  
  
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## Refer to http://caffe.berkeleyvision.org/installation.html
# Contributions simplifying and improving our build system are welcome!

# cuDNN acceleration switch (uncomment to build with cuDNN).
 USE_CUDNN := 1

# cpu-only switch (uncomment to build without GPU support).
# cpu_ONLY := 1

# uncomment to disable IO dependencies and corresponding data layers
# USE_OPENCV := 0
# USE_LEVELDB := 0
# USE_LMDB := 0

# uncomment to allow MDB_NOLOCK when reading LMDB files (only if necessary)
# You should not set this flag if you will be reading LMDBs with any
# possibility of simultaneous read and write
# ALLOW_LMDB_NOLOCK := 1

# Uncomment if you're using OpenCV 3
 OPENCV_VERSION := 3

# To customize your choice of compiler,uncomment and set the following.
# N.B. the default for Linux is g++ and the default for OSX is clang++
# CUSTOM_CXX := g++

# CUDA directory contains bin/ and lib/ directories that we need.
CUDA_DIR := /usr/local/cuda
# On Ubuntu 14.04,if cuda tools are installed via
# "sudo apt-get install nvidia-cuda-toolkit" then use this instead:
# CUDA_DIR := /usr

# CUDA architecture setting: going with all of them.
# For CUDA < 6.0,comment the *_50 lines for compatibility.
CUDA_ARCH := -gencode arch=compute_20,code=sm_20 \
        -gencode arch=compute_20,code=sm_21 \
        -gencode arch=compute_30,code=sm_30 \
        -gencode arch=compute_35,code=sm_35 \
        -gencode arch=compute_50,code=sm_50 \
        -gencode arch=compute_50,code=compute_50

# BLAS choice:
# atlas for ATLAS (default)
# mkl for MKL
# open for OpenBlas
BLAS := mkl
# Custom (MKL/ATLAS/OpenBLAS) include and lib directories.
# Leave commented to accept the defaults for your choice of BLAS
# (which should work)!
# BLAS_INCLUDE := /path/to/your/blas
# BLAS_LIB := /path/to/your/blas

# Homebrew puts openblas in a directory that is not on the standard search path
# BLAS_INCLUDE := $(shell brew --prefix openblas)/include
# BLAS_LIB := $(shell brew --prefix openblas)/lib

# This is required only if you will compile the matlab interface.
# MATLAB directory should contain the mex binary in /bin.
# MATLAB_DIR := /usr/local
# MATLAB_DIR := /Applications/MATLAB_R2012b.app

# NOTE: this is required only if you will compile the python interface.
# We need to be able to find Python.h and numpy/arrayobject.h.
PYTHON_INCLUDE := /usr/include/python2.7 \
        /usr/lib/python2.7/dist-packages/numpy/core/include
# Anaconda Python distribution is quite popular. Include path:
# Verify anaconda location,sometimes it's in root.
# ANACONDA_HOME := $(HOME)/anaconda
# PYTHON_INCLUDE := $(ANACONDA_HOME)/include \
        # $(ANACONDA_HOME)/include/python2.7 \
        # $(ANACONDA_HOME)/lib/python2.7/site-packages/numpy/core/include \

# Uncomment to use Python 3 (default is Python 2)
# PYTHON_LIBRARIES := boost_python3 python3.5m
# PYTHON_INCLUDE := /usr/include/python3.5m \
# /usr/lib/python3.5/dist-packages/numpy/core/include

# We need to be able to find libpythonX.X.so or .dylib.
PYTHON_LIB := /usr/lib
# PYTHON_LIB := $(ANACONDA_HOME)/lib

# Homebrew installs numpy in a non standard path (keg only)
# PYTHON_INCLUDE += $(dir $(shell python -c 'import numpy.core; print(numpy.core.__file__)'))/include
# PYTHON_LIB += $(shell brew --prefix numpy)/lib

# Uncomment to support layers written in Python (will link against Python libs)
# WITH_PYTHON_LAYER := 1

# Whatever else you find you need goes here.
INCLUDE_Dirs := $(PYTHON_INCLUDE) /usr/local/include
LIBRARY_Dirs := $(PYTHON_LIB) /usr/local/lib /usr/lib

# If Homebrew is installed at a non standard location (for example your home directory) and you use it for general dependencies
# INCLUDE_Dirs += $(shell brew --prefix)/include
# LIBRARY_Dirs += $(shell brew --prefix)/lib

# Uncomment to use `pkg-config` to specify OpenCV library paths.
# (Usually not necessary -- OpenCV libraries are normally installed in one of the above $LIBRARY_Dirs.)
# USE_PKG_CONfig := 1

BUILD_DIR := build
distribute_DIR := distribute

# Uncomment for debugging. Does not work on OSX due to https://github.com/BVLC/caffe/issues/171
# DEBUG := 1

# The ID of the GPU that 'make runtest' will use to run unit tests.
TEST_GPUID := 0

# enable pretty build (comment to see full commands)
Q ?= @

可以看到诸如

  
  
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# cuDNN acceleration switch (uncomment to build with cuDNN).
# USE_CUDNN := 1

# Uncomment if you're using OpenCV 3
# OPENCV_VERSION := 3

# open for OpenBlas
BLAS := atlas

都是使用默认的设置，我们可以安装其他依赖项提高caffe运行效率

opencv3.0安装

1. github上有人写好完整的运行脚本自动下载OpenCV，编译，安装，配置等

2. Caffe + Ubuntu 15.04 + CUDA 7.5 新手安装配置指南作者 Xin-Yu Ou（欧新宇） 可以到他的网盘中下载
   PS：为了方便大家使用，我提供一个百度云盘，用于分享部分安装过程中需要用到的软件包和链接地址（所有软件包仅供学术交流使用，请大家尽量去官网下载。）。百度云盘链接:http://pan.baidu.com/s/1qX1uFHa密码:wysa

3. 在Install-OpenCV-master文件夹中包含安装各个版本opencv脚本

4. 切换到目录执行：
   sudo sh Ubuntu/dependencies.sh
   安装依赖项

5. 执行opencv3.0安装脚本
   sudo sh Ubuntu/3.0/opencv3_0_0.sh
   等待安装完成即可

6. 修改Makefile.config

       
       
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   # Uncomment if you're using OpenCV 3
   
   OPENCV_VERSION := 3

7. (可选）opencv3.1已经发布，如果要安装最新的opencv3.1，我们可以先执行
   sudo sh get_latest_version_download_file.sh
   获取最新的地址，然后更新opencv3_0_0.sh中的下载地址，同时需要修正文件名等

       
       
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   arch=$(uname -m)
   if [ "$arch" == "i686" -o "i386" -o "i486" -o "i586" ]; then
   flag=1
   else
   flag=0
   fi
   echo "Installing OpenCV 3.0.0"
   mkdir OpenCV
   cd OpenCV
   "Removing any pre-installed ffmpeg and x264"
   sudo apt-get -y remove ffmpeg x264 libx264-dev
   "Installing Dependenices"
   sudo apt-get -y install libopencv-dev
   sudo apt-get -y install build-essential checkinstall cmake pkg-config yasm
   sudo apt-get -y install libtiff4-dev libjpeg-dev libjasper-dev
   sudo apt-get -y install libavcodec-dev libavformat-dev libswscale-dev libdc1394-22-dev libxine-dev libgstreamer0.10-dev libgstreamer-plugins-base0.10-dev libv4l-dev
   sudo apt-get -y install python-dev python-numpy
   sudo apt-get -y install libtbb-dev
   sudo apt-get -y install libqt4-dev libgtk2.0-dev
   sudo apt-get -y install libfaac-dev libmp3lame-dev libopencore-amrnb-dev libopencore-amrwb-dev libtheora-dev libvorbis-dev libxvidcore-dev
   sudo apt-get -y install x264 v4l-utils ffmpeg
   sudo apt-get -y install libgtk2."Downloading OpenCV 3.0.0"
   wget -O opencv-3.0.0.zip http://sourceforge.net/projects/opencvlibrary/files/opencv-unix/0/opencv-0.zip/download
   "Installing OpenCV 3.0.0"
   unzip opencv-0.zip
   cd opencv-0
   mkdir build
   cd build
   cmake -D CMAKE_BUILD_TYPE=RELEASE -D CMAKE_INSTALL_PREFIX=/usr/local -D WITH_TBB=ON -D BUILD_NEW_PYTHON_SUPPORT=ON -D WITH_V4L=ON -D INSTALL_C_EXAMPLES=ON -D INSTALL_PYTHON_EXAMPLES=ON -D BUILD_EXAMPLES=ON -D WITH_QT=ON -D WITH_OPENGL=ON ..
   make -j8
   sudo make install
   sudo sh -c 'echo "/usr/local/lib" > /etc/ld.so.conf.d/opencv.conf'
   sudo ldconfig
   "OpenCV 3.0.0 ready to be used"

安装opencv3遇到的问题

1. 在执行
   sudo sh Ubuntu/3.0/opencv3_0_0.sh
   出现有个地方一直卡住了，显示在下载一个文件： ippicv_linux_20141027.tgz
   因为墙的原因，这个文件无法下载下来

2. [其他文档] ippicv_linux_20141027.tgz处下载文件 ippicv_linux_20141027.tgz

3. 下载后拷贝到opencv/3rdparty/ippicv/downloads/linux-8b449a536a2157bcad08a2b9f266828b/ 目录下即

4. http://stackoverflow.com/questions/25726768/opencv-3-0-trouble-with-installation

安装BLAS——选择MKL

1. 首先下载 MKL(Intel(R) Parallel Studio XE Cluster Edition for Linux 2016)
   网址：https://software.intel.com/en-us/intel-education-offerings
   Caffe + Ubuntu 15.04 + CUDA 7.5 新手安装配置指南作者 Xin-Yu Ou（欧新宇） 可以到他的网盘中下载， 需要自己申请序列号

2. 下载完成后： parallel_studio_xe_2016.tgz

3. 执行以下命令：
   $ tar zxvf parallel_studio_xe_2016.tar.gz

   $ chmod a+x parallel_studio_xe_2016 -R

   $ sh install_GUI.sh

4. 环境配置：
   $ sudo gedit /etc/ld.so.conf.d/intel_mkl.conf
   然后添加以下内容

   /opt/intel/lib/intel64
   /opt/intel/mkl/lib/intel64

   配置生效： sudo ldconfig -v
   安装MKL完成

5. 修改Makefile.config

       
       
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      # BLAS choice:
       # atlas for ATLAS (default)
       # mkl for MKL
       # open for OpenBlas
       BLAS := mkl

cuDNN安装

1. cudnn下载
   下载地址：https://developer.nvidia.com/cudnn
   或者到网盘：http://pan.baidu.com/s/1bnOKBO下载
   下载相应文件cudnn-7.0-linux-x64-v4.0-rc.tgz,放到~根目录下

2. 切换到～目录，执行命令

       
       
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      sudo tar xvf cudnn-7.0-linux-x64-v4.0-rc.tgz
   cd cuda/include
   sudo cp *.h /usr/local/include/
   cd ../lib64
   sudo cp lib* /usr/local/lib/
   cd /usr/local/lib
   sudo chmod +r libcudnn.so.4.0.4
   sudo ln -sf libcudnn.so.4 libcudnn.so.4 libcudnn.so
   sudo ldconfig

3. 修改Makefile.config

       
       
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      # cuDNN acceleration switch (uncomment to build with cuDNN).
   
   USE_CUDNN := 1

cudnn版本问题

在make工程的时候出现以下错误：

  
  
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...

NVCC src/caffe/layers/deconv_layer.cu
NVCC src/caffe/layers/cudnn_conv_layer.cu
src/caffe/layers/cudnn_conv_layer.cu(81): error: argument of type "cudnnAddMode_t" is incompatible with parameter of type "const void *"
          detected during instantiation of "void caffe::CuDNNConvolutionLayer<Dtype>::Forward_gpu(const std::vector<caffe::Blob<Dtype> *,std::allocator<caffe::Blob<Dtype> *>> &,const std::vector<caffe::Blob<Dtype> *,std::allocator<caffe::Blob<Dtype> *>> &) [with Dtype=float]" 
(157): here

...

20 errors detected in the compilation of "/tmp/tmpxft_00002703_00000000-16_cudnn_conv_layer.compute_50.cpp1.ii".
make: *** [.build_release/cuda/src/caffe/layers/cudnn_conv_layer.o] Error 1
make: *** Waiting for unfinished jobs....

解决方案：

更换V3版本cudnnCaffe 工程的一些编译错误以及解决方案

  
  
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$ cd lib64/
$ sudo cp lib* /usr/local/cuda/lib64/
$ cd ../include/
$ sudo cp cudnn.h /usr/local/cuda/$ cd /usr/local/cuda/lib64/
$ sudo rm -r libcudnn.so libcudnn.so.7.0
$ sudo ln -sf libcudnn.so.7.0.64 libcudnn.so.7.0 libcudnn.so
$ sudo ldconfig

重新编译测试caffe

1. 编译

   sudo make clean
   sudo make all

2. sample测试： （ 比不使用cudnn快很多）
   sh data/mnist/get_mnist.sh
   sh examples/mnist/create_mnist.sh

3. 我们可以将迭代次数增加到50000次
   sudo gedit examples/mnist/lenet_solver.prototxt
   修改max_iter: 50000
   最后：
   sh examples/mnist/train_lenet.sh
   

编译Python接口

依赖项

  
  
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sudo apt-get install -y python-numpy python-scipy python-matplotlib python-sklearn python-skimage python-h5py python-protobuf python-leveldb python-networkx python-nose python-pandas python-gflags cython ipython

  
  
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sudo vi ~/.bashrc
添加： export PYTHONPATH=/home/dl/caffe/python:$PYTHONPATH
sudo make pycaffe -j8

编译matlab接口

1. 安装matlab2014
   sh /usr/local/MATLAB/R2014a/bin/matlab
2. Makefile.config 中修改 ： MATLAB_DIR := /usr/local/MATLAB/R2014a
3. sudo make matcaffe -j8

其他

1. Vi编辑命令常用vi编辑器命令行

  
  
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A:当前行的尾部追加内容

i:游标前插入内容

I:游标后插入内容

o:在鼠标所在行的下面添加内容

O:在鼠标所在行的上面添加内容

ESC:退出编辑模式

Ctrl-T:移动到下一个tab

Backspace:向后移动一个字符

Ctrl-U:删除当前

cw:删除游标所在的字符，然后进入编辑模式

cc:删除游标所在的行，然后进入编辑模式

C:删除从游标所在的位置到行尾的字符，然后进入编辑模式

dd:删除当前行

ndd:删除第n行

D:删除当前行游标所在的位置后面的字符

dw:删除邮编所在的字符

d}:删除当前段剩余的字符

d^:删除游标前到行首的字符

c/pat:删除游标后面到第一次匹配字符间的内容

dn:删除游标后面到下一个匹配字符间的内容

dfa:删除当前行游标到匹配字符间的内容（匹配的字符也将被删）

dta:删除当前行游标到匹配字符间的内容（匹配的字符不被删）

dL:删除从游标到屏幕的最后一行之间的内容

dG:删除从游标到文件末尾之间的内容
J:连结上下两行的内容

p:在游标后面插入buffer中的内容

P:在游标前面插入buffer中的内容

rx:用x替换字符

Rtext:用text从游标开始处进行替换

u:撤销最后的改变

U:还原当前行的内容

x:向后删除游标所在位置的字符

X:向前删除游标前面的字符

nX:删除前面的n个字符，游标所在的字符将不会被删

.:还原最后的改变

~:反转字母的大小写

y:拷贝当前行到新的buffer

yy:拷贝当前行

"xyy:拷贝当前行的buffer名为x的buffer ye:拷贝当单词的末尾

1. 搜狗输入法安装
   Ubuntu14.04安装搜狗输入法

2. im-config 然后 ibus选取fcitx

3. fcitx-config-gtk3

参考资料

1. Caffe学习系列(1)：安装配置ubuntu14.04+cuda7.5+caffe+cudnn
2. Caffe + Ubuntu 15.04 + CUDA 7.5 新手安装配置指南
3. ubuntu 14.04 install g++问题＂g++:Depends:g++-4.8(>= 4.8.2-5
4. ippicv_linux_20141027.tgz
5. http://stackoverflow.com/questions/25726768/opencv-3-0-trouble-with-installation

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