MATLAB: ??? Error using ==> mtimes MTIMES is not fully supported for integer classes. At least one input must be scalar. (How to solve this??)

%stop if error is small if err(iter) < 0.01 fprintf('converged at epoch: %d\n'iter); break end end %here is the problem

%user specified values
hidden_neurons = 100;
epochs = 1000000000;
% ------- load in the data -------
% for getting row image into a single variable
%for i=1:3
%tg_in=imread(1);
%r_in=reshape(tg_in,4800,1);
%tg_out()=r_in();
%end
temp1=imread('im1.JPG');
temp2=imread('im2.JPG');
tr1=reshape(temp1,1,4800);
tr2=reshape(temp2,1,4800);
%train_inp = [192 210 123 143;132 168 124 136];
%train_out = [1;2];
train_inp=[tr1;tr2];
train_out=[0;1];
% check same number of patterns in each
if size(train_inp,1) ~= size(train_out,1)
disp('ERROR: data mismatch')
return
end
%standardise the data to mean=0 and standard deviation=1
%inputs problem
mu_inp = mean(train_inp);
sigma_inp = std(double(train_inp));
train_inp = (train_inp(:,:) - mu_inp(:,1)) / sigma_inp(:,1);
%outputs
train_out = train_out';
mu_out = mean(train_out);
sigma_out = std(double(train_out));
train_out = (train_out(:,:) - mu_out(:,1)) / sigma_out(:,1);
train_out = train_out';
%read how many patterns
patterns = size(train_inp,1);
%add a bias as an input
bias = ones(patterns,1);
train_inp = [train_inp bias];
%read how many inputs
inputs = size(train_inp,2);
%---------- data loaded ------------
%--------- add some control buttons ---------
%add button for early stopping
hstop = uicontrol('Style','PushButton','String','Stop', 'Position', [5 5 70 20],'callback','earlystop = 1;');
earlystop = 0;
%add button for resetting weights
hreset = uicontrol('Style','PushButton','String','Reset Wts', 'Position', get(hstop,'position')+[75 0 0 0],'callback','reset = 1;');
reset = 0;
%add slider to adjust the learning rate
hlr = uicontrol('Style','slider','value',.1,'Min',.01,'Max',1,'SliderStep',[0.01 0.1],'Position', get(hreset,'position')+[75 0 100 0]);
% ---------- set weights -----------------
%set initial random weights
weight_input_hidden = (randn(inputs,hidden_neurons) - 0.5)/10;
weight_hidden_output = (randn(1,hidden_neurons) - 0.5)/10;
%-----------------------------------

%--- Learning Starts Here! ---------
%-----------------------------------
%do a number of epochs
for iter = 1:epochs
%get the learning rate from the slider
alr = get(hlr,'value');
blr = alr / 10;
%loop through the patterns, selecting randomly
for j = 1:patterns
%select a random pattern
patnum = round((rand * patterns) + 0.5);
if patnum > patterns
patnum = patterns;
elseif patnum < 1
patnum = 1;
end
%set the current pattern
this_pat = train_inp(patnum,:);
act = train_out(patnum,1);
%calculate the current error for this pattern
hval = (tanh(this_pat*weight_input_hidden))';
pred = hval'*weight_hidden_output';
error = pred - act;
% adjust weight hidden - output
delta_HO = error.*blr .*hval;
weight_hidden_output = weight_hidden_output - delta_HO';
% adjust the weights input - hidden
delta_IH= alr.*error.*weight_hidden_output'.*(1-(hval.^2))*this_pat;
weight_input_hidden = weight_input_hidden - delta_IH';
end
% -- another epoch finished
%plot overall network error at end of each epoch
pred = weight_hidden_output*tanh(train_inp*weight_input_hidden)';
error = pred' - train_out;
err((iter)) = (sum(error.^2))^0.5;
figure(1);
plot(err)
%reset weights if requested
if reset
weight_input_hidden = (randn(inputs,hidden_neurons) - 0.5)/10;
weight_hidden_output = (randn(1,hidden_neurons) - 0.5)/10;
fprintf('weights reaset after %d epochs\n',iter);
reset = 0;
end
%stop if requested
if earlystop
fprintf('stopped at epoch: %d\n',iter);
break
end
%stop if error is small
if err(iter) < 0.01
fprintf('converged at epoch: %d\n',iter);
break
end
end
%-----FINISHED---------
%display actual,predicted & error
fprintf('state after %d epochs\n',iter);
a = (train_out* sigma_out(:,1)) + mu_out(:,1);
b = (pred'* sigma_out(:,1)) + mu_out(:,1);
Result=[b]

Best Answer

  • Replace
    temp1=imread('im1.JPG');
    temp2=imread('im2.JPG');
    with
    temp1 = double(imread('im1.JPG'));
    temp2 = double(imread('im2.JPG'));
    I address the issue there rather than later because you had other places in the code where it was going to be a problem that you were working with uint8();