先概括一下深度學習的幾大流行的框架：Pylearn2, Theano, Caffe, Torch, Cuda-covnet，Deeplarning4j等。
Caffe的網站上提供了安裝說明。由於其依賴的庫比較多，通常安裝過程會出現許多問題，在不同的機器和操作系統上可能遇到不同的問題。安裝時可以根據網站上提供的說明步驟進行，遇到有問題時用Google搜索一下基本都能找到。本文記錄了筆者在Mac上安裝遇到的問題和解決辦法。系統版本：OS X 10.9.5。
1.1 安裝CUDA。推薦7.0以上版本，6.*版本也可以。我安裝的是最新版CUDA 7.5。
1.2 安裝BLAS。這裡我使用了OpenBLAS。推薦使用brew安裝：brew install openblas
1.3 安裝Boost。
通過brew install boost默認安裝版本為1.60。但建議使用1.59。因為1.60編譯後可能會出現問題。
$ brew search boost boost homebrew/versions/boost-python159 ✔ boost-bcp homebrew/versions/boost149 boost-build homebrew/versions/boost150 boost-python homebrew/versions/boost155 homebrew/science/boost-compute homebrew/versions/boost159 ✔ Caskroom/cask/iboostup Caskroom/cask/turbo-boost-switcher Caskroom/cask/pivotalbooster $ brew install –build-from-source homebrew/versions/boost159
安裝好後可以後在/usr/local/opt/boost159下看到該庫。Caffe中把某些依賴庫所在的文件夾名字限定為boost，可以將/usr/local/opt/boost159復制粘貼產生備份，將備份改名為/usr/local/opt/boost。
1.4 安裝CuDNN。下載cuDNN v5.0版本。解壓後將include和bin文件夾中的內容分別復制到/usr/lcoal 下面的/include和/bin中。
1.5 使用brew install 分別安裝 protobuf, glog, gflags, hdf5, snappy, leveldb, szip, lmdb等。
如果使用python, protobuf安裝命令為
$ brew install --build-from-source --with-python -vd protobuf<code> </code><code></code>
1.6 （可選）OpenCV, 我使用2.4.6版本。
1.7 （可選）Python 版本：2.7。
需要安裝numpy。推薦使用Anaconda，裡面包含了一個python版本2.7.11並且包含了大多數所需要的庫，包括hdf5、numpy等。Anaconda默認安裝在$(HOME)/anaconda目錄下。
還需要安裝python-boost。與boost類似的方法，推薦1.59版本。
1.8 （可選）Matlab 版本 2015a
2.1 下載Caffe後在caffe-master文件夾下，以Makefile.config.example為模板根據第一步中的安裝情況，建立配置文件Makefile.config,內容如下：
## 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 := 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
# 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 := clang++
# 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 := 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 := /usr/local/opt/openblas/include
# BLAS_LIB := /usr/local/opt/openblas/lib
# 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_R2015a.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 \
/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/site-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 += $(shell brew --prefix numpy)/lib
#PYTHON_LIB +=/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/site-packages/numpy/core/lib
PYTHON_LIB +=$(ANACONDA_HOME)/lib/python2.7/site-packages/numpy/core/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/Cellar/boost159/1.59.0/include
LIBRARY_DIRS := $(PYTHON_LIB) /usr/local/cuda/lib /usr/local/Cellar/boost159/1.59.0/lib /usr/local/opt/boost-python159/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 := 1
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 ?= @
2.2，命令行進入caffe-master文件夾下，運行：
$ make all
出現問題：
PROTOC src/caffe/proto/caffe.proto make: protoc: No such file or directory
解決辦法： 需要用brew建立protobuf的鏈接。為此，運行
$ brew link protobuf
如果運行上述命令又出問題，比如brew的權限問題：permission denied for /usr/local。需要設置一下權限，更新一下brew, 為此，運行
$ sudo chown -R $USER:admin /usr/local $ cd /usr/local $ git reset --hard origin/master $ brew update
上述問題可以得到解決。
2.3 上一步通過後，運行
$ make test
這一步沒問題。將build_release/lib下的所有文件復制到/usr/local/lib
$ cp -a .build_release/lib/. /usr/local/lib/
再運行
$ make runtest
報錯：
.build_release/tools/caffe dyld: Library not loaded: @rpath/libcudart.7.5.dylib Referenced from: /Developer/caffe/.build_release/tools/caffe Reason: image not found
為此需要設置一下環境變量DYLD_FALLBACK_LIBRARY_PATH
$ export DYLD_FALLBACK_LIBRARY_PATH=/usr/local/cuda/lib:/usr/local/lib:$(HOME)/<span >anaconda/lib</span>
再運行make runtest，一切順利。
2.4 如果使用python,再運行
<pre name="code" class="html">$ make pycaffe $ make pytest
詳細步驟見：http://caffe.berkeleyvision.org/gathered/examples/mnist.html
3.1，下載mnist數據。在caffe-master目錄下運行
$ ./data/mnist/get_mnist.sh
3.2，建立訓練數據和測試數據，運行
$ ./examples/mnist/create_mnist.sh
出現以下錯誤，說convert_mnist_data.bin找不到：
Creating lmdb... ./examples/mnist/create_mnist.sh: line 16: build/examples/mnist/convert_mnist_data.bin: No such file or directory ./examples/mnist/create_mnist.sh: line 18: build/examples/mnist/convert_mnist_data.bin: No such file or directory Done.
解決辦法：搜索convert_mnist_data.bin發現該文件位於./distribute/bin目錄下，因此在在./examples/mnist/create_mnist.sh文件中將BUILD的值改為distribute/bin即可。
3.3，訓練和測試，運行：
$ ./examples/mnist/create_mnist.sh
如果出現和上面類似的錯誤，說caffe找不到 (caffe.bin位於./distribute/bin目錄下或者build/tools下)，檢查create_mnist.sh的內容，保證caffe.bin的路徑正確
./distribute/bin/caffe.bin train--solver=examples/mnist/lenet_solver.prototxt
然後就能看到運行結果了。
詳見：http://nbviewer.jupyter.org/github/BVLC/caffe/blob/master/examples/00-classification.ipynb
該例子用Caffe中已經訓練好的模型（基於Alexnet的結構）對圖像進行分類。並且可以顯示不同層中訓練得到的特征。
