Caffe Window環境下搭建（CPU）
1.Caffe for windows下載地址：https://github.com/initialneil/caffe-vs2013
2.安裝所需依賴：
CUDA6.5 for windows https://developer.nvidia.com/cuda-downloads
OpenCV 2.4.9 http://opencv.org/releases.html
Boost
google protoBuff
3ryparty.zip 下載解壓到3ryparty目錄下
gflags git clone https://github.com/gflags/gflags.git
hdf5 https://support.hdfgroup.org/HDF5/release/obtain518.html
glog http://download.csdn.net/download/chenguangxing3/6661667
此處CUDA安裝時，如果顯卡不是GT系列的，使用自定義安裝，不安裝驅動，只安裝sample和toolkit 這樣雖不能使用GPU但可以編譯工程；
OPENCV_3_0_0 = “你的安裝路徑”
BOOST_1_56_0 = “你的安裝路徑”
CUDA_PATH_V6_5 = “你的安裝路徑”
設置完系統環境變量後重啓機器，使其生效；
4.編譯工程：
根據你的機器，設置編譯平臺，我這裏選擇x64
(1)fatal error C1083: 無法打開包括文件: “gflags/gflags.h”: No such file or directory
(2)無法打開包括文件: “hdf5/hdf5.h”: No such file or directoryD:\Meachine_learning\caffe-vs2013\caffe\src\caffe\layers\hdf5_data_layer.cu
(3)fatal error C1083: 無法打開包括文件: “glog/logging.h”: No such file or directory
Caffe Linux install
轉載 :
https://www.zybuluo.com/hanxiaoyang/note/364737
參考：
https://github.com/BVLC/caffe
0.關於caffe(by@寒小陽)
caffe是深度學習在圖像領域廣泛使用的框架，其model zoo有大量的預訓練好的模型提供使用。圖像相關應用會大量使用到caffe。
牆裂建議大家使用linux系統，原因如下。
- linux系統(大部分公司爲CentOS或者Ubuntu)，纔是實際開發中用到的系統，提前熟悉一下命令行，對於實際應用和開發都很有必要。
- 相對於Windows而言，linux下的依賴包安裝非常簡單。
- Windows下編譯經常會出現大量問題，且現在網上Windows下caffe出現問題的解決方案非常少。
- 大量的二次開發和擴展都只有linux版本。
1.說明
下面是安裝caffe的一些筆記，寫的不明白的，歡迎在社區發帖問，也方便更多的同學查看相同的問題^_^
2.關於系統
這個說明是關於linux系統的，最好是centOS 7.0以上，或者ubuntu 14.04 以上
因爲低版本的裝不上兼容合適的boost，opencv等庫
3.安裝依賴的庫
要確認一下，所有的庫都裝上了，否則編譯出來可能不能使用。
其中protobuf是用來定義layers的，leveldb是訓練時存儲圖片數據的數據庫，opencv是圖像處理庫，boost是通用C++庫，等等...
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
4.安裝科學計算和python所需的部分庫
sudo apt-get install openblas-dev numpy scipy matplotlib lapack-dev freetype-dev libpng-dev openblas-dev
5.安裝其餘依賴
sudo apt-get install libgflags-dev libgoogle-glog-dev liblmdb-dev
6.安裝git，拉取源碼
apt-get install git
git clone https://github.com/BVLC/caffe.git
7.安裝python的pip和easy_install，方便安裝軟件包
wget --no-check-certificate
https://bootstrap.pypa.io/ez_setup.py
python ez_setup.py --insecure
wget https://bootstrap.pypa.io/get-pip.py
python get-pip.py
8.安裝python依賴(路徑根據自己的目錄可能要調一下)
cd caffe/python
執行 for req in $(cat requirements.txt); do pip install $req; done
這步安裝也有點慢，別急，等會兒，先去幹點別的 ^_^
9.編輯caffe所需的Makefile文件
cd caffe
cp Makefile.config.example Makefile.config
vim Makefile.config
Makefile.config裏面有依賴庫的路徑，及各種編譯配置，如果是沒有GPU的情況下，可以參照我下面幫你改的配置文件內容:
## 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 := atlasBLAS := open# 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)/libBLAS_INCLUDE := /usr/include/openblas# 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 \# 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/includeLIBRARY_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 := 1BUILD_DIR := buildDISTRIBUTE_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 ?= @
10.編譯caffe
make -j4
編譯可能會有點慢，你可以先去幹點別的事情
- 測試一下編譯結果
make test
make runtest
11.編譯pycaffe
make pycaffe -j4