首先呢，博主是想搞tensorflow的，但是呢，博主的機子是32位的，tensorflow支援64位的，所以呢，只能玩玩caffe了，然後呢，博主肯定首先是想搞GPU的，但是呢，驅動麼去官網下，安裝提示核心不對，可是沒有很早以前的驅動了呀，其次呢，CUDA7.5現階段下載不了，原因你去問英偉達。。。

所以只能搞無GPU加速的caffe了，這也好，省去了一大片的雷區。。。

首先，你需要下載anaconda，為啥要下這個，裡面是關於矩陣計算的python。。。我們用這個代替原有的python2.7，這是連結https://www.continuum.io/downloads 

然後，去下對應的系列，我下的是2.7版本的32位linux系列。。。

然後開啟終端，輸入

bash ~/Downloads/Anaconda2-5.1.0-Linux-x86.sh

這是我儲存檔案的地址，請對應。。。

然後就不停地按回車和選yes，直到提示安裝成功

然後source一下環境變數

source ～ /.bashrc

這樣就安裝完成，這時，請你開啟新的終端，這很重要，可以通過python版本檢視

python --version

如果顯示有帶有anaconda的字樣說明安裝成功，沒有的話麼，仔細對照以上操作，博主也是裝了幾遍才好了的，並不存在其他會報錯的可能

然後就是caffe的安裝了，比較簡單，先下載依賴項

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

然後開啟終端，進入到你要的目錄clone一個

    cd /home/f/ Downloads
    git clone https://github.com/BVLC/caffe.git  

    cd caffe  
    sudo cp Makefile.config.example Makefile.config  
    sudo gedit  Makefile.config  

請你們對照我的配置檔案修改

## 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.
# For CUDA >= 9.0, comment the *_20 and *_21 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)/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
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

# 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 ?= @
 

ANACONDA_HOME := $(HOME)/anaconda2
 PYTHON_INCLUDE := $(ANACONDA_HOME)/include \
		 $(ANACONDA_HOME)/include/python2.7 \
		 $(ANACONDA_HOME)/lib/python2.7/site-packages/numpy/core/include

然後

 make all  
 make test  
 make runtest  

一般最後一部會報錯，提示什麼檔案沒有，請按如下操作

cp /home/f/anaconda2/lib/libhdf5.so.101 /usr/lib/i386-linux-gnu/libhdf5.so.101

注意，這裡的檔案請你和報錯檔案對應起來，我的只是我報錯的檔名

然後配置pycaffe，關於python的介面

sudo apt-get install 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  
sudo apt-get install protobuf-c-compiler protobuf-compiler  

然後cd到caffe目錄

make pycaffe 

然後修改一下python的環境，因為我們用的是anaconda

gedit ~/.bashrc  

然後請在最後新增地址

export PYTHONPATH="/home/f/Downloads/caffe/python:$PYTHONPATH"

一般這裡就沒問題了，輸入

cd ~  
python  
import caffe 

如果這裡有報錯請輸入

    import sys  
    >>> sys.path.append('/usr/lib/python2.7/dist-packages/')  
    >>> import caffe  

這樣就沒問題了。

我第二遍安裝caffe有bug 關於opencv的 國外友人給結果：

add "opencv_imgcodecs" in Makefile.(LIBRARIES += glog gflags protobuf leveldb snappy \
lmdb boost_system hdf5_hl hdf5 m \
opencv_core opencv_highgui opencv_imgproc opencv_imgcodecs)
If you input "make all",the problem is the same again.But if you delete all the file in build(rm -rf ./build/*) before "make all"(I use make clean ),you will success.I just success