最簡單的三層神經網路Matlab實現
阿新 • • 發佈：2019-01-02
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;