不多说了，经历了很多莫名其妙的错误最后终于安装好了，直接放安装脚本：
#!/bin/bash
#安装时要注意有些库可能安装失败以及安装caffe有和protobuf相关错误时可能需要重新对protobuf进行make install
cd /home/zw/softwares #需要事先下载对应版本的cuda
sudo dpkg -i cuda-repo-ubuntu1604---local-ga2_8.0.61-1_amd64.deb
sudo apt-get update
sudo apt-get install cudacd /home/zw/git_home/ #我存放git项目的目录
git clone https://github.com/google/protobuf.git
sudo apt-get install autoconf automake libtool curl make g++ unzip
cd protobuf
./autogen.sh
./configure --prefix=/usr
make -j8
make check -j8
sudo make install -j8
sudo ldconfig # refresh shared library cache.cd /home/zw/git_home/
git clone https://github.com/BVLC/caffe.git
cd 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
sudo apt-get install libatlas-base-dev
sudo apt-get install libgflags-dev libgoogle-glog-dev liblmdb-dev
cp Makefile.config.example Makefile.config #config中如果启用anaconda目录改成anaconda2(安装时默认名称),否则sudo make pycaffe无法编译成功。不过建议不需要启用anaconda目录，因为没这个必要，后续只要在PYTHONPATH路径中加入caffe和安装protobuf即可。另外，如果事先安装了opencv3.0需要在Makefile.cinfig中修改对应选项read -rsp $'更改你的Makefile.config, 完成后Press any key to continue...\n' -n1 keymake all -j8
make test -j8
make runtestmake pycaffe -j8cd /home/zw/git_home/protobuf/python
~/anaconda2/bin/python setup.py install #安装对应版本的protobuf，这里要特别注意，如果使用conda安装最新版本的protobuf，可能出现不兼容问题的，因为上面的caffe是用这个版本的protobuf编译的，切记！这里是我自己尝试出来的，花了不少时间
#echo "export PYTHONPATH=~/git_home/protobuf/python:$PYTHONPATH" >> ~/.bashrc #如果你用的时zsh，那么应该导入到～/.zshrc
echo "export PYTHONPATH=~/git_home/caffe/python:$PYTHONPATH" >> ~/.bashrc
echo "export PATH=~/git_home/caffe/build/tools:$PATH" >> ~/.bashrc
Makefile.config如下：
## 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 := # CPU-only switch (uncomment to build without GPU support).
# CPU_ONLY := # uncomment to disable IO dependencies and corresponding data layers
# USE_OPENCV :=
# USE_LEVELDB :=
# USE_LMDB := # 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 := # Uncomment if you're using OpenCV 3
OPENCV_VERSION := 3 #事先安装了使用了opencv3，这里要启用# 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 #使用了cuda，这里要启用
# 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 := /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. \
/usr/lib/python2./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. \
# $(ANACONDA_HOME)/lib/python2./site-packages/numpy/core/include# Uncomment to use Python (default is Python )
# PYTHON_LIBRARIES := boost_python3 python3.5m
# PYTHON_INCLUDE := /usr/include/python3.5m \
# /usr/lib/python3./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 := # 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/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 := # 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 := # 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 := # The ID of the GPU that 'make runtest' will use to run unit tests.
TEST_GPUID := # enable pretty build (comment to see full commands)
Q ?= @
