数据可视化环境（Python接口)配置

　　anaconda把与python有关的库（numpy,scipy等等）都打成一个包，建议用anaconda来进行安装。下载对应系统和对应版本的anaconda来安装，减少安装过程的问题。
　　pycaffe 的安装以及 notebook 环境配置是为了更方便的去使用深度学习框架caffe。notebook 使用浏览器作为界面使用，可以编写和执行 python 代码。

一、查看Python版本

linux系统都自带python，用version查看版本

python --version

二、利用anaconda来配置python环境

1.在continuum官网下载anaconda
现在的版本有python3.6版本和python2.7版本
vesion
2.下载好对应版本、对应系统的anaconda

sudo Anaconda2-2.5.0-Linux-x86_64.sh

2.将anaconda安装路径加入到环境变量（.bashrc)中

3.安装成功后，在当前用户根目录下生成一个anaconda2的文件夹

三、编译python接口

1、将caffe根目录下的python文件夹加入到环境变量，打开配置文件bashrc

sudo gedit ~/.bashrc

2.在最后面加入

export PYTHONPATH=/home/caffe/python:$PYTHONPATH

3.保存退出，更新配置文件

sudo ldconfig

4.修改编译配置文件Makefile.config
　　4.1修改ANACONDAHOME :=(HOME)/anaconda2对PYTHON_INCLUDE取消注释
step1
　　4.2注释掉原PYTHON_LIB路径，使用$(SNACONDA_HOME)/lib
step2
　　4.3取消对WITH_PYTHON_layer :=1 的注释
step3
Makefile.config文件

## 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_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 /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 := 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 ?= @

5.修改完编译配置文件后，最后进行编译：

sudo make pycaffe

pycaffe

sudo make test

sudo make runtest

runtest
6.查看python接口是否编译成功，进入python环境

python

7.导入caffe

import caffe

四、启动jupyter notebook

1.anaconda已集成jupyter notebook

2.把caffe中的和python相关的内容的路径添加到python的编译路径中

import sys
sys.path.append("/home/cow/caffe/python")
sys.path.append("/home/cow/caffe/python/caffe")

pythonimportcaffe
3.之前安装过protobuf在编译hadoop源码的时候，不过并没有添加对python的支持，这里要对python进行支持才可以安装pycaffe

4.protobuf建议不要用conda install来安装，使用pip命令安装

~/anaconda2/bin/pip install protobuf

　　4.1不然import caffe会出现ImportError: No module named google.protobuf.internal
　　
5.运行jupyter notebook

jupyter notebook

jupyter
6.新建ipynb文件

自动在浏览器中打开notebook，点击New-python2按钮，新建一个扩展名为ipynb的文件。在这个网页上，可以像在命令行下面一样运行python代码
jupytertest1
7.查看更多快捷键

输入代码后，按shift+enter运行。
更多的快捷键，可点击上方的help-Keyboard shortcuts查看，或者先按esc退出编辑状态，再按h键查看
jupytertest2