Caffe本身是C++、CUDA语言编写的。在调试模型、参数时，根据运行log、snapshot很难实时反馈当前训练的权值情况，也难以捕捉算法存在的bug。

MATLAB则是非常适合算法设计、快速迭代的利器，只需要做少量工作就能编写出复杂的算法，调试非常方便，位于workspace中的变量随时都能打印，无论是一维、二维还是三维数据，都能直观显示，从而有利于定位算法设计问题，减少调试时间。

Caffe中有两种Wrapper：Python和MATLAB。Python是开源工具，用户无需付费即可使用，缺点是语法不够灵活，尤其算法描述，与商业软件不能比。MATLAB支持几乎你所知道的所有矩阵变换、数值计算、随机过程、概率论、最优化、自适应滤波、图像处理、神经网络等算法。

下面介绍如何用MATLAB调试Caffe。本文假设操作系统为Ubuntu 14.04.1  64bit .

1. 安装MATLAB R2014A

可以到这里下载（，提取码：dxBxMJ）

安装步骤类似Windows，不表。安装到~/MATLAB/，~/.bashrc中添加 export PATH=~/MATLAB/bin:$PATH

2.  安装Caffe

参考步骤：。其他OS请参考。

如果你希望自己编译依赖，可以到这里下载Caffe所有依赖包（，提取码：yuqZm1）

3. 编译 MatCaffe

修改Makefile.config，加上这一句：

MATLAB_DIR := ～/MATLAB

之后

make matcaffe

生成了 matlab/+caffe/private/caffe_.mex64，可以直接被MATLAB调用。

4. 运行MATLAB例子

在命令行中，配置好Caffe运行所需要的环境变量后（否则matcaffe会运行失败），输入matlab&，这样就启动了MATLAB窗口。

在MATLAB命令窗口中进行以下步骤。

>> cd Caffe_root_directory/

切换到了Caffe根目录。

>> addpath('./matlab/+caffe/private');

添加matcaffe模块所在路径到MATLAB搜索路径，便于加载。

>> cd matlab/demo/

切到demo目录。

>> im = imread('../../examples/images/cat.jpg');

读取一张测试图片。

>> figure;imshow(im);

弹出一个窗口，显示猫的测试图片如下：

>> [scores, maxlabel] = classification_demo(im, 1);

Elapsed time is 0.533388 seconds.

Elapsed time is 0.511420 seconds. Cleared 0 solvers and 1 stand-alone nets

运行分类demo程序。分类的结果返回到scores，maxlabel两个工作空间变量中。

>> maxlabel

maxlabel =    282

说明最大分类概率的标签号为282，查找ImageNet标签，对应的是n02123045 tabby, tabby cat（data/ilsvrc2012/synset_words.txt）

>> figure;plot(scores);

>> axis([0, 999, -0.1, 0.5]);

>> grid on

打印scores，一维图像如下：

说明这张图片被分到第282类的概率为0.2985。

到这里我们只是运行了简单的demo，接下来分析classification_demo.m这个文件内容。

function [scores, maxlabel] = classification_demo(im, use_gpu)% [scores, maxlabel] = classification_demo(im, use_gpu)% 使用BVLC CaffeNet进行图像分类的示例% 重要：运行前，应首先从Model Zoo（http://caffe.berkeleyvision.org/model_zoo.html) 下载BVLC CaffeNet训练好的权值%% ****************************************************************************% For detailed documentation and usage on Caffe's Matlab interface, please% refer to Caffe Interface Tutorial at% http://caffe.berkeleyvision.org/tutorial/interfaces.html#matlab% ****************************************************************************%% input%   im       color image as uint8 HxWx3%   use_gpu  1 to use the GPU, 0 to use the CPU%% output%   scores   1000-dimensional ILSVRC score vector%   maxlabel the label of the highest score%% You may need to do the following before you start matlab:%  $ export LD_LIBRARY_PATH=/opt/intel/mkl/lib/intel64:/usr/local/cuda-5.5/lib64%  $ export LD_PRELOAD=/usr/lib/x86_64-linux-gnu/libstdc++.so.6% Or the equivalent based on where things are installed on your system%% Usage:%  im = imread('../../examples/images/cat.jpg');%  scores = classification_demo(im, 1);%  [score, class] = max(scores);% Five things to be aware of:%   caffe uses row-major order%   matlab uses column-major order%   caffe uses BGR color channel order%   matlab uses RGB color channel order%   images need to have the data mean subtracted% Data coming in from matlab needs to be in the order%   [width, height, channels, images]% where width is the fastest dimension.% Here is the rough matlab for putting image data into the correct% format in W x H x C with BGR channels:%   % permute channels from RGB to BGR%   im_data = im(:, :, [3, 2, 1]);%   % flip width and height to make width the fastest dimension%   im_data = permute(im_data, [2, 1, 3]);%   % convert from uint8 to single%   im_data = single(im_data);%   % reshape to a fixed size (e.g., 227x227).%   im_data = imresize(im_data, [IMAGE_DIM IMAGE_DIM], 'bilinear');%   % subtract mean_data (already in W x H x C with BGR channels)%   im_data = im_data - mean_data;% If you have multiple images, cat them with cat(4, ...)% Add caffe/matlab to you Matlab search PATH to use matcaffeif exist('../+caffe', 'dir')  addpath('..');else  error('Please run this demo from caffe/matlab/demo');end% Set caffe modeif exist('use_gpu', 'var') && use_gpu  caffe.set_mode_gpu();  gpu_id = 0;  % we will use the first gpu in this demo  caffe.set_device(gpu_id);else  caffe.set_mode_cpu();end% Initialize the network using BVLC CaffeNet for image classification% Weights (parameter) file needs to be downloaded from Model Zoo.model_dir = '../../models/bvlc_reference_caffenet/';    % 模型所在目录net_model = [model_dir 'deploy.prototxt'];              % 模型描述文件，注意是deploy.prototxt，不包含data layersnet_weights = [model_dir 'bvlc_reference_caffenet.caffemodel'];   % 模型权值文件，需要预先下载到这里phase = 'test'; % run with phase test (so that dropout isn't applied)   % 只进行分类，不做训练if ~exist(net_weights, 'file')  error('Please download CaffeNet from Model Zoo before you run this demo');end% Initialize a networknet = caffe.Net(net_model, net_weights, phase);   % 初始化网络if nargin < 1  % For demo purposes we will use the cat image  fprintf('using caffe/examples/images/cat.jpg as input image\n');  im = imread('../../examples/images/cat.jpg');    % 获取输入图像end% prepare oversampled input% input_data is Height x Width x Channel x Numtic;input_data = {prepare_image(im)};         % 图像冗余处理toc;% do forward pass to get scores% scores are now Channels x Num, where Channels == 1000tic;% The net forward function. It takes in a cell array of N-D arrays% (where N == 4 here) containing data of input blob(s) and outputs a cell% array containing data from output blob(s)scores = net.forward(input_data);      %  分类，得到scorestoc;scores = scores{1};scores = mean(scores, 2);  % 取所有分类结果的平均值[~, maxlabel] = max(scores);  % 找到最大概率对应的标签号% call caffe.reset_all() to reset caffecaffe.reset_all();% ------------------------------------------------------------------------function crops_data = prepare_image(im)% ------------------------------------------------------------------------% caffe/matlab/+caffe/imagenet/ilsvrc_2012_mean.mat contains mean_data that% is already in W x H x C with BGR channelsd = load('../+caffe/imagenet/ilsvrc_2012_mean.mat');mean_data = d.mean_data;IMAGE_DIM = 256;CROPPED_DIM = 227;% Convert an image returned by Matlab's imread to im_data in caffe's data% format: W x H x C with BGR channelsim_data = im(:, :, [3, 2, 1]);  % permute channels from RGB to BGRim_data = permute(im_data, [2, 1, 3]);  % flip width and heightim_data = single(im_data);  % convert from uint8 to singleim_data = imresize(im_data, [IMAGE_DIM IMAGE_DIM], 'bilinear');  % resize im_dataim_data = im_data - mean_data;  % subtract mean_data (already in W x H x C, BGR)% oversample (4 corners, center, and their x-axis flips)crops_data = zeros(CROPPED_DIM, CROPPED_DIM, 3, 10, 'single');indices = [0 IMAGE_DIM-CROPPED_DIM] + 1;n = 1;for i = indices  for j = indices    crops_data(:, :, :, n) = im_data(i:i+CROPPED_DIM-1, j:j+CROPPED_DIM-1, :);    crops_data(:, :, :, n+5) = crops_data(end:-1:1, :, :, n);    n = n + 1;  endendcenter = floor(indices(2) / 2) + 1;crops_data(:,:,:,5) = ...  im_data(center:center+CROPPED_DIM-1,center:center+CROPPED_DIM-1,:);crops_data(:,:,:,10) = crops_data(end:-1:1, :, :, 5);