MATLAB則是非常適合演算法設計、快速迭代的利器，只需要做少量工作就能編寫出複雜的演算法，除錯非常方便，位於workspace中的變數隨時都能列印，無論是一維、二維還是三維資料，都能直觀顯示，從而有利於定位演算法設計問題，減少除錯時間。

Caffe中有兩種Wrapper：Python和MATLAB。Python是開源工具，使用者無需付費即可使用，缺點是語法不夠靈活，尤其演算法描述，與商業軟體不能比。MATLAB支援幾乎你所知道的所有矩陣變換、數值計算、隨機過程、概率論、最優化、自適應濾波、影象處理、神經網路等演算法。

下面介紹如何用MATLAB除錯Caffe。本文假設作業系統為Ubuntu 14.04.1 64bit .

1. 安裝MATLAB R2014A

可以到這裡下載（http://yunpan.taobao.com/s/ZFLGQjNABU，提取碼：dxBxMJ）

安裝步驟類似Windows，不表。安裝到~/MATLAB/，~/.bashrc中新增 export PATH=~/MATLAB/bin:$PATH

2. 安裝Caffe

參考步驟：

http://caffe.berkeleyvision.org/install_apt.html。其他OS請參考http://caffe.berkeleyvision.org/installation.html。

如果你希望自己編譯依賴，可以到這裡下載Caffe所有依賴包（http://yunpan.taobao.com/s/1I1TXcPYsk3，提取碼：yuqZm1）

3. 編譯 MatCaffe

修改Makefile.config，加上這一句：

MATLAB_DIR := ～/MATLAB

之後

make matcaffe

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

4. 執行MATLAB例子

在命令列中，配置好Caffe執行所需要的環境變數後（否則matcaffe會執行失敗），輸入matlab&，這樣就啟動了MATLAB視窗。

在MATLAB命令視窗中進行以下步驟。

>> cd Caffe_root_directory/

切換到了Caffe根目錄。

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

新增matcaffe模組所在路徑到MATLAB搜尋路徑，便於載入。

>> cd matlab/demo/

切到demo目錄。

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

讀取一張測試圖片。

>> figure;imshow(im);

彈出一個視窗，顯示貓的測試圖片如下：



>> [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

執行分類demo程式。分類的結果返回到scores，maxlabel兩個工作空間變數中。

>> maxlabel

maxlabel =

282

說明最大分類概率的標籤號為282，查詢ImageNet標籤，對應的是n02123045 tabby, tabby cat（data/ilsvrc2012/synset_words.txt）

>> figure;plot(scores);

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

>> grid on

列印scores，一維影象如下：



說明這張圖片被分到第282類的概率為0.2985。

到這裡我們只是運行了簡單的demo，接下來分析classification_demo.m這個檔案內容。

function [scores, maxlabel] = classification_demo(im, use_gpu)
% [scores, maxlabel] = classification_demo(im, use_gpu)
% 使用BVLC CaffeNet進行影象分類的示例
% 重要：執行前，應首先從Model Zoo（http://caffe.berkeleyvision.org/model_zoo.html) 下載BVLC CaffeNet訓練好的權值
%
% ****************************************************************************
% 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 matcaffe
if exist('../+caffe', 'dir')
  addpath('..');
else
  error('Please run this demo from caffe/matlab/demo');
end

% Set caffe mode
if 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/';    % 模型所在目錄
net_model = [model_dir 'deploy.prototxt'];              % 模型描述檔案，注意是deploy.prototxt，不包含data layers
net_weights = [model_dir 'bvlc_reference_caffenet.caffemodel'];   % 模型權值檔案，需要預先下載到這裡
phase = 'test'; % run with phase test (so that dropout isn't applied)   % 只進行分類，不做訓練
if ~exist(net_weights, 'file')
  error('Please download CaffeNet from Model Zoo before you run this demo');
end

% Initialize a network
net = caffe.Net(net_model, net_weights, phase);   % 初始化網路

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');    % 獲取輸入影象
end

% prepare oversampled input
% input_data is Height x Width x Channel x Num
tic;
input_data = {prepare_image(im)};         % 影象冗餘處理
toc;

% do forward pass to get scores
% scores are now Channels x Num, where Channels == 1000
tic;
% 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);      %  分類，得到scores
toc;

scores = scores{1};
scores = mean(scores, 2);  % 取所有分類結果的平均值

[~, maxlabel] = max(scores);  % 找到最大概率對應的標籤號

% call caffe.reset_all() to reset caffe
caffe.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 channels
d = 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 channels
im_data = im(:, :, [3, 2, 1]);  % permute channels from RGB to BGR
im_data = permute(im_data, [2, 1, 3]);  % flip width and height
im_data = single(im_data);  % convert from uint8 to single
im_data = imresize(im_data, [IMAGE_DIM IMAGE_DIM], 'bilinear');  % resize im_data
im_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;
  end
end
center = 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);