【前期準備工作】

1. 確保模型訓練成功，生成模型檔案：cifar10_quick_iter_4000.caffemodel及均值檔案：mean.binaryproto。注意，此處一定是生成caffemodel格式的模型檔案，而非.h5模型檔案，否則會導致Matlab執行崩潰。如何生成caffemodel檔案請參考上篇帖子。

也可以利用Matlab生成cifar10_quick_iter_4000.caffemodel，方法是進入caffe根目錄，例如我的電腦上為D:\caffe-master\caffe-master，然後在matlab中執行以下命令，即可對模型進行訓練：

solver = caffe.Solver('./examples/cifar10/cifar10_quick_solver.prototxt');
solver.solve()
 

2. 在caffe-master\matlab路徑下新建cifar資料夾用於案例除錯

3. 拷貝classification_demo.m檔案到cifar資料夾下，並更名為classification_cifar.m

【基於mean.binaryproto檔案生成.mat 檔案】

在matlab command line中輸入以下命令，對mean.binaryproto檔案進行轉換：

mean_file = 'D:\caffe-master\caffe-master\examples\cifar10\test\mean.binaryproto';
image_mean = caffe_('read_mean', mean_file);
save 'D:\caffe-master\caffe-master\matlab\cifar\image_mean.mat' image_mean
 

【對classification_cifar.m檔案進行修改】

1. 修改dir路徑、model路徑和weight路徑：

2. 修改prepare.image()函式

修改後的classification_cifar.m檔案程式碼：

function [scores, maxlabel] = classification_cifar(im, use_gpu)

% 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 = '../../examples/cifar10/';
net_model = [model_dir 'cifar10_quick.prototxt'];
net_weights = [model_dir 'cifar10_quick_iter_4000.caffemodel'];
phase = 'test'; % run with phase test (so that dropout isn't applied)
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);

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');
end

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

scores = scores{1};
scores = mean(scores, 2);  % take average scores over 10 crops

[~, maxlabel] = max(scores);

% call caffe.reset_all() to reset caffe
caffe.reset_all();

% ------------------------------------------------------------------------
function im_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('D:\caffe-master\caffe-master\matlab\cifar\image_mean.mat');
mean_data = d.mean_data;
IMAGE_DIM = 32;

% 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)
 

編寫test.m檔案，用於模型測試，test.m檔案程式碼：

clear;clc  

im = imread('D:\caffe-master\caffe-master\examples\images\cat.jpg');
[scores, maxlabel] = classification_cifar(im,0)
index = importdata('synset_words.txt');
name = index(maxlabel);

figure;imshow(im);
str=strcat('分類結果：',name,'   得分：',num2str(max(scores)));
title(str);

使用上述命令完成模型測試，並對貓做出了正確分類：

【檔案下載】

上述資料夾中的4個檔案：classification.m、test.m、image_mean.mat、synset_words.txt打包下載地址：

訓練的cifar10_quick_iter_4000.caffemodel檔案下載地址：