【caffe学习笔记之4】利用MATLAB接口运行cifar数据集

【前期准备工作】

参考上篇帖子：http://write.blog.csdn.net/postedit/53964874

1. 确保模型训练成功，生成模型文件：cifar10_quick_iter_4000.caffemodel及均值文件：mean.binaryproto。注意，此处一定是生成caffemodel格式的模型文件，而非.h5模型文件，否则会导致Matlab运行崩溃。如何生成caffemodel文件请参考上篇帖子。

也可以利用Matlab生成cifar10_quick_iter_4000.caffemodel，方法是进入caffe根目录，例如我的电脑上为D:\caffe-master\caffe-master，然后在matlab中执行以下命令，即可对模型进行训练：

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

2. 在caffe-master\matlab路径下新建cifar文件夹用于案例调试

3. 拷贝classification_demo.m文件到cifar文件夹下，并更名为classification_cifar.m

【基于mean.binaryproto文件生成.mat 文件】

在matlab command line中输入以下命令，对mean.binaryproto文件进行转换：

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

于是在matlab/cifar文件夹下生成了image_mean.mat文件

【对classification_cifar.m文件进行修改】

1. 修改dir路径、model路径和weight路径：

2. 修改prepare.image()函数

修改后的classification_cifar.m文件代码：

function [scores, maxlabel] = classification_cifar(im, use_gpu)% 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 = '../../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 networknet = 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 Numtic;input_data = {prepare_image(im)};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);toc;scores = scores{1};scores = mean(scores, 2);  % take average scores over 10 crops[~, maxlabel] = max(scores);% call caffe.reset_all() to reset caffecaffe.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 channelsd = 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 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)

【模型测试】

编写test.m文件，用于模型测试，test.m文件代码：

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('分类结果：',name,'   得分：',num2str(max(scores)));title(str);

使用上述命令完成模型测试，并对猫做出了正确分类：

【文件下载】

上述文件夹中的4个文件：classification.m、test.m、image_mean.mat、synset_words.txt打包下载地址：

点击打开链接

训练的cifar10_quick_iter_4000.caffemodel文件下载地址：

点击打开链接