mlp3.m

bpnn详细算法

clear,clc,close all 
% MATLAB neural network back propagation code
% by AliReza KashaniPour & Phil Brierley
% first version 29 March 2006
% 2's edition 14 agust 2007
%
% This code implements the basic backpropagation of
% error learning algorithm. The network has tanh hidden  
% neurons and a linear output neuron.
% and applied for predicting y=sin(2piX1)*sin(2piX2)
%
% adjust the learning rate with the slider
%
% feel free to improve! and notic us
% [e-mail]
% Special thank from Phil
%
%--------------------------------------------------------
%% Initializing
hidden_neurons = 20;
epochs = 300;

% ------- load in the data -------

X1 = linspace(0,5,100); %input
X2 = 1.2*linspace(0,5,100); %input
 Y_train = 200*exp(-1*X1).*sin(pi*X2); %satisfy output

train_inp = [X1'; X2'];   %setting input
train_out = [Y_train'; Y_train']; %seting Out put

% 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;

%% Learining

%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

%% Testing
X3 =  linspace(0,5,100)';
X4 = 1.2*linspace(0,5,100)';

train_test = [X3; X4];

mu_test = mean(train_test);
sigma_test = std(train_test);
train_test = (train_test(:,:) - mu_test(:,1)) / sigma_test(:,1);
train_test = [train_test bias];

pred = weight_hidden_output*tanh(train_test*weight_input_hidden)';

%% Finish
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]  ;%display actual,predicted & error

%figure,plot3(X1',X2',act_pred_err((1:(size(train_inp,1)/2)),2),'color','red','linewidth',2)
%grid on,title(' Approximatly result (using Neural Networks)');
%figure,plot3(X1,X2,Y_train, 'color','green','linewidth',2)
%grid on,title(' Sin(2*pi.*X1)*Sin(2*pi.*X2) Y = Orginal result');
figure,plot(sqrt(2.44)*X1,act_pred_err((1:(size(train_inp,1)/2)),2))
grid on,title(' Approximatly result (using Neural Networks)');
figure,plot(sqrt(2.44)*X1,Y_train)
grid on,title( 'Orginal result');