how to change softmaxlayer with regression in matconvnet

I am trying to train MNIST data set with single output. It means when i give an 28*28 input (image) the model gives us a just number. For example i give '5', the model give me as a result 4.9,5, 5.002 or close to 5. So I have red some documents. People tells softmaxlayer have to be changed with regression layer. For doing do this. I am using matconvnet library and its mnist example. I have changed my network and written regression layer loss function. these are my codes:

net.layers = {} ;
net.layers{end+1} = struct('type', 'conv', ...
                           'weights', {{f*randn(5,5,1,20, 'single'), zeros(1, 20, 'single')}}, ...
                           'stride', 1, ...
                           'pad', 0) ;
net.layers{end+1} = struct('type', 'pool', ...
                           'method', 'max', ...
                           'pool', [2 2], ...
                           'stride', 2, ...
                           'pad', 0) ;
net.layers{end+1} = struct('type', 'conv', ...
                           'weights', {{f*randn(5,5,20,50, 'single'),zeros(1,50,'single')}}, ...
                           'stride', 1, ...
                           'pad', 0) ;
net.layers{end+1} = struct('type', 'pool', ...
                           'method', 'max', ...
                           'pool', [2 2], ...
                           'stride', 2, ...
                           'pad', 0) ;
net.layers{end+1} = struct('type', 'conv', ...
                           'weights', {{f*randn(4,4,50,500, 'single'),  zeros(1,500,'single')}}, ...
                           'stride', 1, ...
                           'pad', 0) ;
net.layers{end+1} = struct('type', 'relu') ;
net.layers{end+1} = struct('type', 'conv', ...
                           'weights', {{f*randn(1,1,500,1, 'single'), zeros(1,1,'single')}}, ...
                           'stride', 1, ...
                           'pad', 0) ;                         
 net.layers{end+1} = struct('type', 'normloss');

this is regression loss function:

function Y = vl_normloss(X,c,dzdy)
size(X)%1 1 1 100
size(c)%1 100

if nargin <= 2

Y = 0.5*sum((squeeze(X)'-c).^2);
size(Y)%1 1
Y      % 1.7361e+03
else
size(Y)
Y = +((squeeze(X)'-c))*dzdy;
Y = reshape(Y,size(X));
end

I changed opts.errorFunction = 'multiclass' ; to 'none' Also i add

case 'normloss'
      res(i+1).x = vl_normloss(res(i).x,l.class) ;

to vl_simplenn script

But when i run train this error occurs

Error using vl_nnconv DEROUTPUT dimensions are incompatible with X and FILTERS.

Error in vl_simplenn (line 415) [res(i).dzdx, dzdw{1}, dzdw{2}] = ...

what i have to do for solving this problem? thank you

I found the solution. I have made a mistake. There are 2 different line have to be changed in vl_simplenn script but i changed only one line. This code works.