最简单的三层神经网络Matlab实现

%function output = ANN_Training(X, t, H, max_iter) %Train ANN with one hidden layer and one output unit for classification %input : %X: attributes. Every column represents a sample. %y: target. should be 0 or 1. length(y) == size(X,2) is assumed. %H: size of hidden layer. %max_iter: maximum iterates %tol: convergence tolerate %output: % output: a structure containing all network parameters. %Created by Ranch Y.Q. Lai on Mar 22, 2011 %yqlai@comp.hkbu.edu.hk function output = ANN_Training(X, t, H, max_iter,tol) [n,N] = size(X); if N~= length(t) error('inconsistent sample size'); end W = randn(n,H); % weight for hidden layer, W(:,i) is the weight vector for unit i b = randn(H,1); % bias for hidden layer wo = randn(H,1); %weight for output layer bo = randn(1,1); %bias, output y = zeros(H,1); %output of hidden layer iter = 0; cost_v = [inf]; fprintf('###################################\n'); fprintf('ANN TRAINING STARTED\n'); fprintf('###################################\n'); fprintf('Iterate \t training error\n'); while iter < max_iter delta_wo = zeros(H,1); delta_bo = 0; delta_W = zeros(n,H); delta_b = zeros(H,1); for i=1:N for j=1:H y(j) = s(X(:,i),W(:,j),b(j)); end delta_wo = delta_wo + y*(s(y,wo,bo)-t(i))*s(y,wo,bo)*(1-s(y,wo,bo)); delta_bo = delta_bo + (s(y,wo,bo)-t(i))*s(y,wo,bo)*(1-s(y,wo,bo)); for j=1:H delta_W(:,j) = delta_W(:,j) + X(:,i)*(s(y,wo,bo)-t(i))*s(y,wo,bo)*(1-s(y,wo,bo))*wo(j)*s(X(:,i),W(:,j),b(j))*(1-s(X(:,i),W(:,j),b(j))); delta_b(j) = delta_b(j) + (s(y,wo,bo)-t(i))*s(y,wo,bo)*(1-s(y,wo,bo))*wo(j)*s(X(:,i),W(:,j),b(j))*(1-s(X(:,i),W(:,j),b(j))); end end step = 1; cost = training_error(N,H,X,t,W,b,wo,bo); while step > 1e-10 wo1 = wo - step*delta_wo; bo1 = bo - step*delta_bo; W1 = W - step.*delta_W; b1 = b - step.*delta_b; cost1 = training_error(N,H,X,t,W1,b1,wo1,bo1); if cost1 < cost break; end step = step * 0.1; end if step <=1e-10 disp('cannot descend anymore'); break; end % update wo,bo,W,b wo = wo1; bo = bo1; W = W1; b = b1; cost = cost1; if iter>0 fprintf('\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b'); end fprintf('%4d\t%1.10f\n',iter,cost); cost_v = [cost_v;cost]; if abs(cost - cost_v(end-1))< tol break; end iter = iter + 1; end output.W = W; output.b= b; output.wo = wo; output.bo =bo; output.cost = cost_v; function cost = training_error(N,H,X,t,W,b,wo,bo) %total cost y = zeros(H,1); cost = 0; for i=1:N for j=1:H y(j) = s(X(:,i),W(:,j),b(j)); end cost = cost + ( s(y,wo,bo) - t(i)) ^2; end cost = cost / N;