How works linear neural network ?

Hi there,

I'm creating a simple 1-layer NN, using the 'purelin' function. My net has 2 inputs and 1 output. The weights are approximately 0.3339 and -0.1704, and the bias is 0.1152. When I launch net([0.5 ; 0.5]), I get 2.51 as a result, but when I compute myself 0.5*0.3339+0.5-0.1704+0.1152=0.196. I don't understand how this 'purelin' function works, can you help me ? The same problem appears when I use the 'satlins' function : the result shouldn't be greater than 1 but it is. I can't explain that.

Net creation

learningRate = '0.000001';
structure = [];
net = feedforwardnet(structure);
net.trainFcn = 'trainlm';
net.trainParam.lr = learningRate;
for i = 1:net.numLayers-1 
    net.layers{i}.transferFcn = 'satlins';
end 
net.layers{net.numLayers}.transferFcn = 'purelin';

Training data creation

x = rand(2, 10000);
x1 = x(1, :);
x2 = x(2, :);
t = sin(x1*10)./(x1*10)+exp(x1)+cos(x2.*x2)./(x1+1);
figure
plot3(x1, x2, t, '.')

Net training

net = train(net, x, t, 'useGpu','yes');

Net application

xtest = rand(2, 5000);
x1test = xtest(1, :);
x2test = xtest(2, :);
hold on
plot3(x1test, x2test, net(xtest), '.r')

Thanks in advance for your help