• utuntu16.04安装caffe+Matlab2017a+opencv3.1+CUDA8.0+cudnn6.0


    • 上午把tensorflow安装好了,下午和晚上装caffe的确很费劲。
    • 默认CUDA,cuDNN可以用了
    • caffe官方安装教程
    • 有些安装顺序自己也不清楚,简直就是碰运气

    1. 安装之前依赖项

    General dependencies

    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

    安装matlab见后面:

    为什么需要安装Matlab?
    caffe有Matlab的接口,因此如果需要使用Matlab调用caffe,进行编程,就需要安装Matlab。如果你觉得使用C或Python编程比较难,就请安装Matlab。当然如果不需要,并且后面不会编译caffe生成Matlab的接口,就不需要安装Matlab了。这个纯粹根据个人需求来定。
    
    
    为什么需要安装OpenCV?
    caffe是用来做深度学习的,深度学习的一大应用对象就是图像和视频。而OpenCV是目前最火的开源计算机视觉库,非常多的项目多用到了OpenCV,当然caffe也依赖OpenCV。所以,需要安装OpenCV,否则无法使用caffe哦
    
    OpenCV的版本和cuda的版本最好匹配。这样子安排的目的是为了减少错误出现的概率

    2.OpeCV安装

    从官网(http://opencv.org/downloads.html)下载Opencv,并将其解压到你要安装的位置,假设解压到了/home/opencv。 安装前准备,创建编译文件夹:

    cd ~/opencv
    mkdir build
    cd build

    配置:

    cmake -D CMAKE_BUILD_TYPE=Release -D CMAKE_INSTALL_PREFIX=/usr/local ..

    编译:

    make -j8  #-j8表示并行计算,根据自己电脑的配置进行设置,配置比较低的电脑可以将数字改小或不使用,直接输make。

    以上只是将opencv编译成功,还没将opencv安装,需要运行下面指令进行安装:

    sudo make install

    问题:由于CUDA 8.0不支持OpenCV的 GraphCut 算法,可能出现以下错误:

    /home/dsp/opencv-3.1.0/modules/core/include/opencv2/core/private.cuda.hpp:165:52: note: in definition of macro ‘nppSafeCall’
     #define nppSafeCall(expr)  cv::cuda::checkNppError(expr, __FILE__, __LINE__, CV_Func)
                                                        ^
    modules/cudalegacy/CMakeFiles/opencv_cudalegacy.dir/build.make:146: recipe for target 'modules/cudalegacy/CMakeFiles/opencv_cudalegacy.dir/src/graphcuts.cpp.o' failed
    make[2]: *** [modules/cudalegacy/CMakeFiles/opencv_cudalegacy.dir/src/graphcuts.cpp.o] Error 1
    make[2]: *** 正在等待未完成的任务....
    CMakeFiles/Makefile2:9226: recipe for target 'modules/cudalegacy/CMakeFiles/opencv_cudalegacy.dir/all' failed
    make[1]: *** [modules/cudalegacy/CMakeFiles/opencv_cudalegacy.dir/all] Error 2
    make[1]: *** 正在等待未完成的任务....
    [ 92%] Linking CXX shared library ../../lib/libopencv_photo.so
    [ 92%] Built target opencv_photo
    Makefile:160: recipe for target 'all' failed
    make: *** [all] Error 2

    进入opencv-3.1.0/modules/cudalegacy/src/目录,修改graphcuts.cpp文件,将:

    #include "precomp.hpp"
    
    #if !defined (HAVE_CUDA) || defined (CUDA_DISABLER)
    
    改为
    
    #include "precomp.hpp"
    
    #if !defined (HAVE_CUDA) || defined (CUDA_DISABLER) || (CUDART_VERSION >= 8000)
    
    然后make编译就可以了

    - 编译和安装完成

    装BLAS

    这里可以选择(ATLAS,MKL或者OpenBLAS):不知道这个,下载有问题,所以就没有搞这个,但是makefile.config文件里面有配置

    MKL首先下载并安装英特尔® 数学内核库 Linux* 版MKL(Intel(R) Parallel Studio XE Cluster Edition for Linux 2016),下载链接是:https://software.intel.com/en-us/qualify-for-free-software/student, 使用学生身份(邮件 + 学校)下载Student版,填好各种信息,可以直接下载,同时会给你一个邮件告知序列号。

    后面就直接:sudo apt-get install libatlas-base-dev -y  

    sudo apt-get install libatlas-base-dev 

     3.MATLAB2017a安装

    4.安装caffe

    (1)将终端cd到要安装caffe的位置。
    (2)从github上获取caffe:
    
    git clone https://github.com/BVLC/caffe.git
    
    注意:若没有安装Git,需要先安装Git:
    
    sudo apt-get install git
    3)因为make指令只能make Makefile.config文件,而Makefile.config.example是caffe给出的makefile例子,因此,首先将Makefile.config.example的内容复制到Makefile.config:
    
    sudo cp Makefile.config.example Makefile.config
    4)打开并修改配置文件:
    
    sudo gedit Makefile.config #打开Makefile.config文件
    • 自己按照改了一下,里面有写需要注意
    ## 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 := 1
    # 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 through *_61 lines for compatibility.
    # For CUDA < 8.0, comment the *_60 and *_61 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_52,code=sm_52 
            -gencode arch=compute_60,code=sm_60 
            -gencode arch=compute_61,code=sm_61 
            -gencode arch=compute_61,code=compute_61
    
    # BLAS choice:
    # atlas for ATLAS (default)
    # mkl for MKL
    # open for OpenBlas
    BLAS := atlas
    # 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 := /home/dsp
    #MATLAB_DIR := /home/dsp/bin
    
    # 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/dsp/anaconda2
    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 /usr/include/hdf5/serial/
    #LIBRARY_DIRS := $(PYTHON_LIB) /usr/local/lib /usr/lib
    INCLUDE_DIRS := $(PYTHON_INCLUDE) /usr/local/include /usr/include /usr/lib/x86_64-linux-gnu/hdf5/serial/include
    LIBRARY_DIRS := $(PYTHON_LIB) /usr/local/lib /usr/lib /usr/lib/x86_64-linux-gnu/hdf5/serial
    
    # 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
    
    # NCCL acceleration switch (uncomment to build with NCCL)
    # https://github.com/NVIDIA/nccl (last tested version: v1.2.3-1+cuda8.0)
    # USE_NCCL := 1
    
    # 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
    
    # N.B. both build and distribute dirs are cleared on `make clean`
    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 ?= @
    
    
    LINKFLAGS := -Wl,-rpath,$(HOME)/anaconda2/lib

    ## 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 through *_61 lines for compatibility.
    # For CUDA < 8.0, comment the *_60 and *_61 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_52,code=sm_52 
            -gencode arch=compute_60,code=sm_60 
            -gencode arch=compute_61,code=sm_61 
            -gencode arch=compute_61,code=compute_61
    
    # BLAS choice:
    # atlas for ATLAS (default)
    # mkl for MKL
    # open for OpenBlas
    BLAS := atlas
    # 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/dsp/anaconda2
    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 /usr/include/hdf5/serial/
    #LIBRARY_DIRS := $(PYTHON_LIB) /usr/local/lib /usr/lib
    INCLUDE_DIRS := $(PYTHON_INCLUDE) /usr/local/include /usr/include /usr/lib/x86_64-linux-gnu/hdf5/serial/include
    LIBRARY_DIRS := $(PYTHON_LIB) /usr/local/lib /usr/lib /usr/lib/x86_64-linux-gnu/hdf5/serial
    
    # 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
    
    # NCCL acceleration switch (uncomment to build with NCCL)
    # https://github.com/NVIDIA/nccl (last tested version: v1.2.3-1+cuda8.0)
    # USE_NCCL := 1
    
    # 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
    
    # N.B. both build and distribute dirs are cleared on `make clean`
    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 ?=

    问题:

    第一次编译:出错

    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
    
    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
    /usr/bin/ld: 找不到 -lcudnn
    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

    - hdf5的问题,通过修改Makefile.config文件

    在文件里面添加文本由于hdf5库目录更改,所以需要单独添加:
    #INCLUDE_DIRS := $(PYTHON_INCLUDE) /usr/local/include /usr/include/hdf5/serial/
    #LIBRARY_DIRS := $(PYTHON_LIB) /usr/local/lib /usr/lib
    INCLUDE_DIRS := $(PYTHON_INCLUDE) /usr/local/include /usr/include /usr/lib/x86_64-linux-gnu/hdf5/serial/include
    LIBRARY_DIRS := $(PYTHON_LIB) /usr/local/lib /usr/lib /usr/lib/x86_64-linux-gnu/hdf5/serial

    - 然后再次编译有一个问题:

    dsp@dsp:~/caffe$ make all -j16
    LD -o .build_release/lib/libcaffe.so.1.0.0
    /usr/bin/ld: 找不到 -lcudnn
    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
    • i found that in the path "/usr/local/cuda/lib64/" don't have the file liblcudnn.so

    该问题还在有待解决。

    - 这个问题其实挺简单的:后面自己想清楚了:就是cudnn的链接问题,重新拷贝cudnn文件;然后链接了一遍,后面就不报这个错了

    - 继续编译错误:

    //home/dsp/anaconda2/lib/libpng16.so.16:对‘inflateValidate@ZLIB_1.2.9’未定义的引用/
    /homecollect2: error: ld returned 1 exit status
    /dsp/anaconda2/lib/libpng16.so.16:对‘inflateValidate@ZLIB_1.2.9’�Makefile:625: recipe for target '.build_release/tools/upgrade_net_proto_text.bin' failed
    make: *** [.build_release/tools/upgrade_net_proto_text.bin] Error 1
    �make: *** 正在等待未完成的任务....
    �定义的引用
    collect2: error: ld returned 1 exit status
    Makefile:625: recipe for target '.build_release/tools/upgrade_net_proto_binary.bin' failed
    make: *** [.build_release/tools/upgrade_net_proto_binary.bin] Error 1
    //home/dsp/anaconda2/lib/libpng16.so.16:对‘inflateValidate@ZLIB_1.2.9’未定义的引用
    collect2: error: ld returned 1 exit status
    Makefile:625: recipe for target '.build_release/tools/upgrade_solver_proto_text.bin' failed
    make: *** [.build_release/tools/upgrade_solver_proto_text.bin] Error 1
    //home/dsp/anaconda2/lib/libpng16.so.16:对‘inflateValidate@ZLIB_1.2.9’未定义的引用
    collect2: error: ld returned 1 exit status

    - 然后我添加链接:sudo ln -s /home/username/anaconda2/lib/libpng16.so.16 libpng16.so.16 (方法不行)报另外的错:

    /usr/local/cuda-8.0/lib64/libpng16.so.16:对‘inflateValidate@ZLIB_1.2.9’未定义的引用
    collect2: error: ld returned 1 exit status
    Makefile:625: recipe for target '.build_release/tools/upgrade_net_proto_binary.bin' failed
    make: *** [.build_release/tools/upgrade_net_proto_binary.bin] Error 1
    make: *** 正在等待未完成的任务....
    /usr/local/cuda-8.0/lib64/libpng16.so.16:对‘inflateValidate@ZLIB_1.2.9’未定��/usr�的引用
    /localcollect2: error: ld returned 1 exit status
    /cuda-8.0/lib64/libpng16.so.16:对‘inflateValidate@ZLIB_1.2.9’未定义/的引用
    collect2: error: ld returned 1 exit status
    usr/local/cudaMakefile:630: recipe for target '.build_release/examples/siamese/convert_mnist_siamese_data.bin' failed
    -make: *** [.build_release/examples/siamese/convert_mnist_siamese_data.bin] Error 1
    8.0/lib64/libpng16.so.16:对‘inflateValidate@ZLIB_1.2.9’未定义的引用
    collect2: error: ld returned 1 exit status

    - 最后非常感谢:Caffe 编译错误记录:http://blog.csdn.net/ruotianxia/article/details/78437464 

    • 里面的几个错误有代表性,按照下面的方法就没有报这个错了
    在 Makefile.config 中,加入下一句
    LINKFLAGS := -Wl,-rpath,$(HOME)/anaconda2/lib

    - 然后执行:make all  报错:

    dsp@dsp:~/caffe$ make all -j16
    make: Nothing to be done for 'all'
    • 解决方法很简单:
    •  make: Nothing to be done for `all' 解决方法
      1.这句提示是说明你已经编译好了,而且没有对代码进行任何改动。
      若想重新编译,可以先删除以前编译产生的目标文件:
      
          make clean 
      
          make 

    5. 黎明的曙光

    • 按照如下编译顺序
    make all -j16
    make runtest -j16
    make pycaffe -j16
    make matcaffe -j16

    - 其中make all 和make runtest时间比较长;make pycaffe 很顺利

    [----------] Global test environment tear-down
    [==========] 2123 tests from 281 test cases ran. (285688 ms total)
    [  PASSED  ] 2123 tests.
    dsp@dsp:~/caffe$ make pycaffe -j16
    touch python/caffe/proto/__init__.py
    CXX/LD -o python/caffe/_caffe.so python/caffe/_caffe.cpp
    PROTOC (python) src/caffe/proto/caffe.proto

    - 实际使用pycaffe,出错:

    dsp@dsp:~/caffe$ python
    Python 2.7.14 |Anaconda, Inc.| (default, Oct 16 2017, 17:29:19) 
    [GCC 7.2.0] on linux2
    Type "help", "copyright", "credits" or "license" for more information.
    >>> import sys
    >>> caffe_root="/home/dsp/caffe/"
    >>> sys.path.insert(0,caffe_root+'python')
    >>> import caffe
    Traceback (most recent call last):
      File "<stdin>", line 1, in <module>
      File "/home/dsp/caffe/python/caffe/__init__.py", line 1, in <module>
        from .pycaffe import Net, SGDSolver, NesterovSolver, AdaGradSolver, RMSPropSolver, AdaDeltaSolver, AdamSolver, NCCL, Timer
      File "/home/dsp/caffe/python/caffe/pycaffe.py", line 15, in <module>
        import caffe.io
      File "/home/dsp/caffe/python/caffe/io.py", line 8, in <module>
        from caffe.proto import caffe_pb2
      File "/home/dsp/caffe/python/caffe/proto/caffe_pb2.py", line 6, in <module>
        from google.protobuf.internal import enum_type_wrapper
    ImportError: No module named google.protobuf.internal
    • 通过conda下安装protobuf即可
    • python caffe报错:No module named google.protobuf.internal
      
      我装的是anaconda2, 解决方法是在其中安装protobuf最新版本
      
      conda install protobuf

    6. MNIST数据集测试

    配置caffe完成后,我们可以利用MNIST数据集对caffe进行测试,过程如下:
    1.将终端定位到Caffe根目录

    cd ~/caffe

    2.下载MNIST数据库并解压缩

    ./data/mnist/get_mnist.sh

    3.将其转换成Lmdb数据库格式

    ./examples/mnist/create_mnist.sh

    4.训练网络

     ./examples/mnist/train_lenet.sh

    训练的时候可以看到损失与精度数值,如下图:

    - make matcaffe 有gcc版本问题

    dsp@dsp:~/caffe$ make matcaffe -j16
    MEX matlab/+caffe/private/caffe_.cpp
    使用 'g++' 编译。
    警告: 您使用的 gcc 版本为 '5.4.0'。不支持该版本的 gcc。MEX 当前支持的版本为 '4.9.x'。有关当前支持的编译器列表,请参阅: http://www.mathworks.com/support/compilers/current_release。
    MEX 已成功完成。
    • 解决办法是:
      
       在Makefile里面,大约第410行那一句话
      
      CXXFLAGS += -MMD -MP
      
      下面添加CXXFLAGS += -std=c++11,
      
      最后是这样 CXXFLAGS += -MMD -MP CXXFLAGS += -std=c++11
      
      然后在caffe根目录下make clean,make all

    - 执行 make mattest的时候,报错:

    .......
    
    b/+caffe/private/caffe_.mexa64'
    需要的符号 '_ZNSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEED1Ev'
    缺少
    '/usr/lib/x86_64-linux-gnu/libboost_python-py27.so.1.58.0->/home/dsp/caffe/matlab/+caffe/private/caffe_.mexa64'
    需要的符号
    '_ZNSt7__cxx1112basic_stringIwSt11char_traitsIwESaIwEE12_M_constructEmw'
    缺少
    '/usr/lib/x86_64-linux-gnu/libboost_python-py27.so.1.58.0->/home/dsp/caffe/matlab/+caffe/private/caffe_.mexa64'
    需要的符号
    '_ZNSt7__cxx1112basic_stringIwSt11char_traitsIwESaIwEE9_M_createERmm'。
    
    出错 caffe.set_mode_cpu (line 5)
    caffe_('set_mode_cpu');
    
    出错 caffe.run_tests (line 6)
    caffe.set_mode_cpu();

    - 参考:Caffe中使用MATLAB接口

    •  最后设置调用caffe/python的路径,可以在任意路径终端下导入caffe
    • 经过差不多两天的时间,安装了很多东西,情形庆幸没有重装系统,具体的内容如下:
    cuda: /usr/local/
    opencv_3.1: /usr/local/
    anaconda2,caffe: /home/dsp/
    
    python系统默认:2.7
    anaconda:2.7 ;虚拟环境下tensorflow_py3.5
    
    matlab2017a: /home/dsp/bin/matlab
    caffe: /home/dsp/caffe
    
    使用方法:
    ------
    matlab2017a: 终端输入: matlab即可,界面有问题,待解决
    
    ------
    默认终端python:
    dsp@dsp:~$ python
    Python 2.7.14 |Anaconda, Inc.| (default, Oct 16 2017, 17:29:19) 
    [GCC 7.2.0] on linux2
    
    ------
    终端输入:spyder
    python为:anaconda自带的python2.7
    
    ------
    tensorflow1.4 + python3.5使用:
    dsp@dsp:~$ source activate tensorflow_py3.5
    (tensorflow_py3.5) dsp@dsp:~$ spyder
    
    注: 1. 需要不同的python环境,需要自己创建虚拟环境
         2. 安装依赖项时注意,安装的位置
         3. 也可以通过:(tensorflow_py3.5) dsp@dsp:~$ anaconda-navigator 来安装和启动spyder
    
    
    ------
    pycharm 使用:
    
    1. 解压安装包可直接使用
    2. 运行:(tensorflow_py3.5) dsp@dsp:~$ sh ./pycharm/bin/pycharm.sh ;只要路径对即可
    3. 设置解释器为:python2.7 或者tensorflow_py3.5
    
    ------
    caffe 使用:
    
    1. 使用anaconda自带的python2.7即可
    2. 添加caffe的路径,再使用
    3. 本机可以在任意路径终端下:输入:python; 然后:import caffe

    Reference:

    Ubuntu16.04+CUDA8.0+caffe配置:

    安装ubuntu16.04+cuda8.0+caffe+python+matlab+opencv3.0
    http://blog.csdn.net/shiorioxy/article/details/52652831
    http://blog.csdn.net/u012841667/article/details/53572431(makefile.config各代码配置说明)

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  • 原文地址:https://www.cnblogs.com/ranjiewen/p/7788484.html
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