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

Question

%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]

Answer 1

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();