minor fixes

exercise4.m

@@ -81,7 +81,11 @@ function exercise4(varargin)
 
 % Load the sentence
 [im,cmap] = imread('data/sentence-lato.png') ;
-im = im2single(ind2gray(im,cmap)) ;
+if isempty(cmap)
+  im = im2single(im) ;
+else
+  im = im2single(ind2gray(p,cmap)) ;
+end
 im = 256 * (im - net.imageMean) ;
 
 % Apply the CNN to the larger image

exercise5.m

@@ -17,8 +17,8 @@ function exercise5()
 % obtain and preprocess an image
 im = imread('peppers.png') ;
 im_ = single(im) ; % note: 255 range
-im_ = imresize(im_, net.normalization.imageSize(1:2)) ;
-im_ = im_ - net.normalization.averageImage ;
+im_ = imresize(im_, net.meta.normalization.imageSize(1:2)) ;
+im_ = bsxfun(@minus,im_,net.meta.normalization.averageImage) ;
 
 % run the CNN
 res = vl_simplenn(net, im_) ;
@@ -28,4 +28,4 @@ function exercise5()
 [bestScore, best] = max(scores) ;
 figure(1) ; clf ; imagesc(im) ; axis image ;
 title(sprintf('%s (%d), score %.3f',...
-net.classes.description{best}, best, bestScore)) ;
+   net.meta.classes.description{best}, best, bestScore)) ;

extra/packFonts.m

@@ -12,7 +12,11 @@ function packFonts()
 for i = 1:numel(fonts)
   for j = 1:numel(chars)
     [p,cmap] = imread(fullfile('extra', 'fonts', fonts{i}, [chars(j) '.png'])) ;
-    im{i,j} = im2single(ind2gray(p,cmap)) ;
+    if isempty(cmap)
+      im{i,j} = im2single(p) ;
+    else
+      im{i,j} = im2single(ind2gray(p,cmap)) ;
+    end
     labels{i,j} = j ;
   end
 end