matlab列优先与高维矩阵重构 及 CNN 逐层可视化 on Matlab

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。

  clear
  close all

 addpath ./matlab

 model= './models/bvlc_reference_caffenet/deploy.prototxt';
 weights= './models/bvlc_reference_caffenet/bvlc_reference_caffenet.caffemodel';

  net = caffe.Net(model, weights, 'test'); % create net and load weights

 %% obtain params in diff layers and show
 pdata = net.params('conv1',).get_data();

 vis_square(pdata,,0.5);

 net.blobs('data').reshape([   ]);
 net.reshape();

 %% prepare the image
 im_data = caffe.io.load_image('./examples/images/cat.jpg');
  mean = load('./matlab/+caffe/imagenet/ilsvrc_2012_mean.mat');

 %% subtract mean_data (already in W x H x C, BGR)
  mean_data = mean.mean_data;
  im_data = im_data - mean_data;  

  width = ; height = ;
  im_data = imresize(im_data, [width, height]); % resize using Matlab's imresize
  res = net.forward({im_data});

  prob = res{};

 %% obtain features and show
  ddata = net.blobs('conv2').get_data();
  vis_square(ddata,,);

 function vis_square(data,padsize,padval)

  data=net_data_normalize(data);

 if ~ exist('padsize', 'var')
   padsize=;
 end
 if ~ exist('padval', 'var')
   padval=;
 end
 ndim=ndims(data);
 % w*num*h*chanel
 if ndim==
     fprintf('visualize params\n');
     data=permute(data,[,,,]);
 else ndim==
     fprintf('visualize maps\n');
     data=permute(data,[,,]);
 end

     n = (ceil(sqrt(size(data,))));
     data=padarray(data,[padsize n^-size(data,) padsize ],'post');
     data=reshape(data,size(data,),n,n,size(data,),size(data,));
     data=permute(data,[,,,,]);
     data=reshape(data,[size(data,)*n,size(data,)*n,size(data,)]);

 figure
 if ndim==
     ;
 else ndim==
     data=imrotate(data,-);
 end   

 imshow(imresize(data,[,],'nearest'))

 end