MLP.rar_MLP_matlab MLP_mlp for xor_perceptron_xor perceptron

%--------------------------------------------------------- % MATLAB neural network backprop code % by Phil Brierley % www.philbrierley.com % 29 March 2006 % % This code implements the basic backpropagation of % error learning algorithm. The network has tanh hidden % neurons and a linear output neuron. % % adjust the learning rate with the slider % % feel free to improve! % %-------------------------------------------------------- %user specified values hidden_neurons = 3; epochs = 10000; % ------- load in the data ------- % XOR data train_inp = [1 1; 1 0; 0 1; 0 0]; train_out = [1; 0; 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 mu_inp = mean(train_inp); sigma_inp = std(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(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) < 0.001 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); act_pred_err = [a b b-a]