www.pudn.com > Classification_toolbox.part01.rar > Backpropagation_SM.m

function [test_targets, Wh, Wo, J] = Backpropagation_SM(train_patterns, train_targets, test_patterns, params) 
 
% Classify using a backpropagation network with stochastic learning algorithm with momentum 
% Inputs: 
% 	training_patterns   - Train patterns 
%	training_targets	- Train targets 
%   test_patterns       - Test  patterns 
%	params              - Number of hidden units, Convergence criterion, alpha, Convergence rate 
% 
% Outputs 
%	test_targets        - Predicted targets 
%   Wh                  - Hidden unit weights 
%   Wo                  - Output unit weights 
%   J                   - Error throughout the training 
 
[Nh, Theta, alpha, eta] = process_params(params); 
iter	= 1; 
 
[Ni, M]     = size(train_patterns); 
No		    = 1; 
Uc          = length(unique(train_targets)); 
 
%If there are only two classes, remap to {-1,1} 
if (Uc == 2) 
    train_targets    = (train_targets>0)*2-1; 
end 
 
%Initialize the net: In this implementation there is only one output unit, so there 
%will be a weight vector from the hidden units to the output units, and a weight matrix 
%from the input units to the hidden units. 
%The matrices are defined with one more weight so that there will be a bias 
w0		= max(abs(std(train_patterns')')); 
Wh		= rand(Nh, Ni+1).*w0*2-w0; %Hidden weights 
Wo		= rand(1,  Nh+1).*w0*2-w0; %Output weights 
Wo    = Wo/mean(std(Wo'))*(Nh+1)^(-0.5); 
Wh    = Wh/mean(std(Wh'))*(Ni+1)^(-0.5); 
 
Bh    = zeros(size(Wh)); 
Bo    = zeros(size(Wo)); 
 
rate  = 10*Theta; 
J(1)  = 1e3; 
 
while (rate > Theta), 
    %Randomally choose an example 
    i	= randperm(M); 
    m	= i(1); 
    Xm = train_patterns(:,m); 
    tk = train_targets(m); 
     
    %Forward propagate the input: 
    %First to the hidden units 
    gh				= Wh*[Xm; 1]; 
    [y, dfh]		= activation(gh); 
    %Now to the output unit 
    go				= Wo*[y; 1]; 
    [zk, dfo]	= activation(go); 
     
    %Now, evaluate delta_k at the output: delta_k = (tk-zk)*f'(net) 
    delta_k		= (tk - zk).*dfo; 
     
    %...and delta_j: delta_j = f'(net)*w_j*delta_k 
    delta_j		= dfh'.*Wo(1:end-1).*delta_k; 
     
    %B_kj <- eta*(1-alpha)*delta_k*y_j + alpha*B_kj 
    Bo				= eta*(1-alpha)*delta_k*[y;1]'+alpha*Bo; 
     
    %B_ij <- eta*(1-alpha)*eta*delta_j*[Xm;1] + alpha*B_ij 
    Bh				= eta*(1-alpha)*delta_j'*[Xm;1]' + alpha*Bh; 
     
    %w_kj <- w_kj + B_kj 
    Wo				= Wo + Bo; 
     
    %w_ji <- w_ji + B_ji 
    Wh				= Wh + Bh; 
     
    iter 			= iter + 1; 
 
    %Calculate total error 
    J(iter)    = 0; 
    for i = 1:M, 
        J(iter) = J(iter) + (train_targets(i) - activation(Wo*[activation(Wh*[train_patterns(:,i); 1]); 1])).^2; 
    end 
    J(iter) = J(iter)/M;  
    rate  = abs(J(iter) - J(iter-1))/J(iter-1)*100; 
     
    if (iter/100 == floor(iter/100)), 
        disp(['Iteration ' num2str(iter) ': Total error is ' num2str(J(iter))]) 
    end 
     
end 
 
disp(['Backpropagation converged after ' num2str(iter) ' iterations.']) 
 
%Classify the test patterns 
test_targets = zeros(1, size(test_patterns,2)); 
for i = 1:size(test_patterns,2), 
    test_targets(i) = activation(Wo*[activation(Wh*[test_patterns(:,i); 1]); 1]); 
end 
 
if (Uc == 2) 
    test_targets  = test_targets >0; 
end 
 
function [f, df] = activation(x) 
 
a = 1.716; 
b = 2/3; 
f	= a*tanh(b*x); 
df	= a*b*sech(b*x).^2;