ubuntu16.04初始安装+无gpu+caffe+python2+opencv2+matlab2016+tensorflow

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