What's wrong with my neural network?

I am trying to debug and figure out why my code is not getting better with each epoch, rather it's getting progressively worse.

I am trying to make it so that I get better training of my training data each time I run the code. What do I do to fix it?

In general, I am aiming for 11 inputs, 1 single layer, and 5 output layers. I'm not sure what's the problem.

Here is the lines of code:

x = rand(75,11);
y = randi(5, 5, 75);

P = x';
T = y;

net = linearlayer;
net = configure(net,P,T);
net.IW{1}(5,11);
net.b{1};
[net,a,e,pf] = adapt(net,P,T);
net = train(net,P,T);

Answer 1

[Amanjit Dulai]

I managed to get this to work by reducing the learning rate:

x = rand(75,11);
y = randi(5, 5, 75);

P = x';
T = y;

% Set the learning rate to 0.001
net = linearlayer(0,0.001);
net = configure(net,P,T);
net.IW{1}(5,11);
net.b{1};
[net,a,e,pf] = adapt(net,P,T);
net = train(net,P,T);

By default, linearlayer does not use backpropagation when training. Instead, it uses the Widrow-Hoff learning rule, so the training might behave differently compared to other networks (fitnet for example uses backpropagation by default).

It's also worth noting that linearlayer is learning only a linear function (that is, a weight and a bias), so it won't fit data with a non-linear relationship very well.