ubuntu16.04 显卡是AMD 因此使用cpu安装吧(其实好像可以使用opencl-caffe)
1.搜狗输入法：
http://blog.csdn.net/blueheart20/article/details/51901867 http://blog.csdn.net/iamplane/article/details/70447517
2. notepadqq
http://blog.sina.com.cn/s/blog_636a55070102w83y.html
3. win qq
4.python查看版本
查看opencv
pkg-config --modversion opencv
5.matlab2016b
http://blog.csdn.net/generallc/article/details/52793820
命令行启动MATLAB
sudo ln -s /usr/local/MATLAB/R2016b/bin/matlab /usr/local/bin/matlab
6.安装caffe
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BLAS(注意没有更换目录)
sudo apt-get install libatlas-base-dev
apt-get install python-dev 安装的是python2.7.12(不想安装了)
安装谷歌、gflags、lmdb（一些兼容性依赖库）
sudo apt-get install libgflags-dev libgoogle-glog-dev liblmdb-dev
由于用到了ｇｉｔ，如果没有安装ｇｉｔ的话，首先需要安装ｇｉｔ sudo apt-get install git
利用ｇｉｔ下载ｃａｆｆｅ源码 git clone git://github.com/BVLC/caffe.git
sudo apt-get install python-numpy python-scipy python-matplotlib ipython ipython-notebook python-pandas python-sympy python-nose
下面的总是出错 所以试着 加上上面的这一句看看是否有效
安装pip及Ｐｙｔｈｏｎ的依赖库（利用ｐｉｐ安装Ｐｙｔｈｏｎ的依赖包,两种方法）
到caffe/python目录下
cd /home/zzh/caffe/python apt-get install python-pip pip install --upgrade pip for req in $(cat requirements.txt); do pip install $req; done
复制Makefile.config 并且修改
cd ~/caffe cp Makefile.config.example Makefile.config
## 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 := 2.4.13 # 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 := /usr/local/MATLAB/R2016b #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 INCLUDE_DIRS := $(PYTHON_INCLUDE) /usr/local/include /usr/include/hdf5/serial LIBRARY_DIRS := $(PYTHON_LIB) /usr/local/lib /usr/lib /usr/lib /usr/lib/x86_64-linux-gnu /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 ?= @
编译
make pycaffe
make all
make test
make runtest
make matcaffe
make mattest
出错：
MEX-file '/home/zzh/caffe/matlab/+caffe/private/caffe_.mexa64' 无效:
/usr/local/MATLAB/R2016b/bin/glnxa64/../../sys/os/glnxa64/libstdc++.so.6:
version `GLIBCXX_3.4.21' not found (required by
/home/zzh/caffe/matlab/+caffe/private/caffe_.mexa64)。
出错 caffe.set_mode_cpu (line 5)
caffe_('set_mode_cpu');
出错 caffe.run_tests (line 6)
caffe.set_mode_cpu();
输入exit()退出
然后
sudo rm /usr/local/MATLAB/R2016b/sys/os/glnxa64/libstdc++.so.6 sudo ln -s /usr/lib/x86_64-linux-gnu/libstdc++.so.6 /usr/local/Matlab/R2013a/sys/os/glnxa64/libstdc++.so.6
make mattest
成功！
7. sudo su切换到root
su 用户名 切换到自己用户 或是 Ctrl+d
8.安装opencv2.4.13
http://blog.csdn.net/u011557212/article/details/54706966?utm_source=itdadao&utm_medium=referral
9.安装tensorflow
sudo pip install --upgrade https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-0.8.0-cp27-none-linux_x86_64.whl
出错：IOError: [Errno 2] No such file or directory: '/tmp/pip-YCI5uL-build/setup.py'
解决办法：升级pip
sudo pip install --upgrade pip
然后再
sudo pip install --upgrade https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-0.8.0-cp27-none-linux_x86_64.whl
测试：
python
>>> import tensorflow as tf
>>> hello = tf.constant('Hello, TensorFlow!')
>>> sess = tf.Session()
>>> print(sess.run(hello))
Hello, TensorFlow!
>>> a = tf.constant(10)
>>> b = tf.constant(32)
>>> print(sess.run(a + b))
42
在 import tensorflow as tf时有警告 意思是numexpr版本不高不能用
解决方法：sudo pip install --upgrade numexpr即可
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