我只改了兩個數字，然後，所有錯誤，不翼而飛，兩天折騰，全是窮折騰。

事情是這樣的，除了官方說法，其他不帶官方doc的教程都是耍流氓。

有人說，官方說anaconda+python非常簡單好配置，為什麽，我這麽多錯誤，最後不得不用pip，因為官方配置文檔，就是makefile.config裏面是anaconda2+python2.7，如果你安裝的是以上版本，那你的確很簡單，但是舊版本是註定要被淘汰的，你看現在誰用windows xp？

沒有教程，或者沒有最新的針對anaconda3+python3.6的，中麽辦？

我告訴你，配置的時候只需按照你的anaconda安裝包裏面的路徑，原本的改了那個針對anaconda2的路徑即可，所有前置，所有版本，全都給你弄好了，你只要改了比如我的python在anaconda中的

那我需要把config中的python2.7改成3.6m總之，你既然用了anaconda，你就要精確的告訴你的caffe去哪裏找我的庫，而不是瞎改，改完了出錯，到處去搜索（是我）我看了那麽多doc，唯一一塊自由發揮，就把自己給坑了（的確看臉，可能最近照鏡子有點多）

最後附上我的config，我這片終極教程，是建立在你看了官網教程的基礎上的，配置最後的config時的。另外提醒一句，GPU cudnn要求你的顯卡加速在3以上，我的機子不到，而且bantu16.04要求裝cuda8.0,我裝了9.1。我顯然是好奇又傻大膽，在犯錯的邊緣試探，就愛嘗試最新版，等我裝回cuda8,再整個GPU版本的。

請註意cudnn與cuda是不一定一起的，具體看管網，配置的時候說了三種情況。

另外如果裝cudnn那麽請註意時差，對面工作時間非常準時，我們只能在早上還有晚上訪問觀望。其他時間都是維護。我就奇怪了，運維都請不起嗎？？？

## 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/python3.5 \
        /usr/lib/python3.6/dist-packages/numpy/core/include
# Anaconda Python distribution is quite popular. Include path:
# Verify anaconda location, sometimes it‘s in root.
 ANACONDA_HOME := $(HOME)/anaconda3

 PYTHON_INCLUDE := $(ANACONDA_HOME)/include          $(ANACONDA_HOME)/include/python3.6m          $(ANACONDA_HOME)/lib/python3.6/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
#INCLUDE_DIRS := $(PYTHON_INCLUDE) /usr/local/include /usr/include/hdf5/serial/
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 ?= @

最後，如果有問題歡迎留言，我現在還比較熟悉，你在晚點我就忘了。

另外，萬一火了，問得太多了，我就該高冷了（想太多）

caffe makefile.config anaconda2 python3 所有問題一種解決方式