Sieć neuronowa 2w

clear all
NNTWARN OFF
format compact


P = -1:0.1:-0.5;
T = [-0.9602 -0.5770 -0.0729 0.3771 0.6405 0.6600];


[R,Q] = size(P);

%%%
S1 = 2;
%[S1,Q] = size(T);
[S2,Q] = size(T);
%%%
[W1,B1] = nwtan(S1,R);
[W2,B2] = rands(S2,S1);
%%%

disp_freq = 100;
max_epoch = 20000;
err_goal = 1e-30;
lr = 0.1;
error = [];
A1 = tansig(W1*P,B1);
%%%
A2 = purelin(W2*A1,B2);
%%%
%%%E = T-A1;
E = T-A2;
%%%
SSE = sumsqr(E);


for epoch = 1:max_epoch

    if (SSE < err_goal)
        epoch = epoch-1;
        break;
    end

    D2 = deltalin(A2,E);
    %%%D1 = deltatan(A1,E);
    D1 = deltatan(A1,D2,W2);
    [dW2,dB2] = learnbp(A1,D2,lr);
    %%%%
    [dW1,dB1] = learnbp(P,D1,lr);
    %%%
    W2 = W2 + dW2;
    W1 = W1 + dW1;
    B2 = B2 + dB2;
    B1 = B1 + dB1;
    %%%

    A1 = tansig(W1*P,B1);
    %%%
    A2 = purelin(W2*A1,B2);
    %%%E = T-A1;
    E = T-A2;
    %%%
    SSE = sumsqr(E);
    error = [error SSE];

if(rem(epoch,disp_freq)==0)
%%epoch
%%SSE
%%%plot(P,T,'r',P,A1,'g')
plot(P,T,'r',P,A2,'g') %wyjscie 2

pause(1e-50)
end

end