matlab列优先与高维矩阵重构
由于matlab在列化a(:)以及reshape(a)等操作中是列优先的，所以要重构出新的高维度矩阵，通常要把reshape和permute结合起来使用。
先到 http://caffe.berkeleyvision.org/ 下载 训练好的model bvlc_reference_caffenet.caffemodel;
更多caffe使用也请参看上面的网址。
1 clear
2 close all
3
4
5 addpath ./matlab
6
7 model= './models/bvlc_reference_caffenet/deploy.prototxt';
8 weights= './models/bvlc_reference_caffenet/bvlc_reference_caffenet.caffemodel';
9
10
11 net = caffe.Net(model, weights, 'test'); % create net and load weights
12
13 %% obtain params in diff layers and show
14 pdata = net.params('conv1',1).get_data();
15
16 vis_square(pdata,2,0.5);
17
18
19 net.blobs('data').reshape([227 227 3 1]);
20 net.reshape();
21
22 %% prepare the image
23 im_data = caffe.io.load_image('./examples/images/cat.jpg');
24 mean = load('./matlab/+caffe/imagenet/ilsvrc_2012_mean.mat');
25
26 %% subtract mean_data (already in W x H x C, BGR)
27 mean_data = mean.mean_data;
28 im_data = im_data - mean_data;
29
30 width = 227; height = 227;
31 im_data = imresize(im_data, [width, height]); % resize using Matlab's imresize
32 res = net.forward({im_data});
33
34 prob = res{1};
35
36 %% obtain features and show
37 ddata = net.blobs('conv2').get_data();
38 vis_square(ddata,2,0);
MPCA可用于高维数据进行降维可与LDA结合
1 function vis_square(data,padsize,padval)
2
3 data=net_data_normalize(data);
4
5 if ~ exist('padsize', 'var')
6 padsize=1;
7 end
8 if ~ exist('padval', 'var')
9 padval=0;
10 end
11 ndim=ndims(data);
12 % w*num*h*chanel
13 if ndim==4
14 fprintf('visualize params\n');
15 data=permute(data,[1,4,2,3]);
16 else ndim==3
17 fprintf('visualize maps\n');
18 data=permute(data,[1,3,2]);
19 end
20
21 n = (ceil(sqrt(size(data,2))));
22 data=padarray(data,[padsize n^2-size(data,2) padsize 0],'post');
23 data=reshape(data,size(data,1),n,n,size(data,3),size(data,4));
24 data=permute(data,[1,3,4,2,5]);
25 data=reshape(data,[size(data,1)*n,size(data,3)*n,size(data,5)]);
26
27 figure
28 if ndim==4
29 ;
30 else ndim==3
31 data=imrotate(data,-90);
32 end
33
34 imshow(imresize(data,[500,500],'nearest'))
35
36 end