CNN特征图可视化

% load pre-trained CNN net = load('imagenet-vgg-f.mat'); % load and preprocess an image origin_img = imread('input.jpg'); img = imresize(single(origin_img), net.meta.normalization.imageSize(1:2)); imshow(img); img = img - net.meta.normalization.averageImage; % run the VGG res = vl_simplenn(net, img); % show the classification result scores = squeeze(gather(res(end).x)); [bestScore, best] = max(scores); figure(1);clf; imagesc(origin_img); title(sprintf('%s(%d),score %.3f', net.meta.classes.description{best},best,bestScore)); % show first conv kernel figure(2); clf; colormap gray; f1 = squeeze(net.layers{1}.weights{1}); vl_imarraysc(f1,'spacing',2); axis equal ; title('filters in the first layer') ; % show first feature map figure(3); clf; colormap gray; f_t = squeeze(res(3).x); f2 = zeros(54,54,3,64); for i = 1:64 f2(:,:,:,i) = color_remap(f_t(:,:,i)); end vl_imarraysc(f2); axis equal ; title('the first layer feature map') ; % show second feature map figure(4); clf; colormap gray; f_t = squeeze(res(7).x); minVal = min(min(min(min(f_t)))); maxVal = max(max(max(max(f_t)))); f_t = (f_t - minVal) / (maxVal - minVal) * 255; f3 = zeros(27,27,3,64); for i = 1:64 f3(:,:,:,i) = color_remap(f_t(:,:,i)); end vl_imarraysc(f3); axis equal ; title('the second layer feature map') ; % show third feature map figure(5); clf; colormap gray; f_t = squeeze(res(11).x); minVal = min(min(min(min(f_t)))); maxVal = max(max(max(max(f_t)))); f_t = (f_t - minVal) / (maxVal - minVal) * 255; f4 = zeros(13,13,3,64); for i = 1:64 f4(:,:,:,i) = color_remap(f_t(:,:,i)); end vl_imarraysc(f4); axis equal ; title('the third layer feature map') ; % show fourth feature map figure(6); clf; colormap gray; f_t = squeeze(res(13).x); minVal = min(min(min(min(f_t)))); maxVal = max(max(max(max(f_t)))); f_t = (f_t - minVal) / (maxVal - minVal) * 255; f5 = zeros(13,13,3,64); for i = 1:64 f5(:,:,:,i) = color_remap(f_t(:,:,i)); end vl_imarraysc(f5); axis equal ; title('the fourth layer feature map') ; % show fifth feature map figure(7); clf; colormap gray; f_t = squeeze(res(15).x); minVal = min(min(min(min(f_t)))); maxVal = max(max(max(max(f_t)))); f_t = (f_t - minVal) / (maxVal - minVal) * 255; f6 = zeros(13,13,3,64); for i = 1:64 f6(:,:,:,i) = color_remap(f_t(:,:,i)); end vl_imarraysc(f6); axis equal ; title('the fifth layer feature map') ;