Unexpected Bayesian Regularization performance

I'm training a network to learn the sin function from a noisy input of 400 samples.

If I use a 1-30-1 feedforwardnet with 'trainlm' the network generalises well. If I use a 1-200-1 feedforwardnet the network overfits the training data, as expected. My understanding was that 'trainbr' on a network with too many neurons will not overfit. However if I run trainbr on a 1-200-1 network until convergence (Mu reaches maximum), the given network seems to overfit the data despite a strong reduction in "Effective # Param".

To me this is a strange behaviour. Have I misunderstood bayesian regularization? Can someone provide an explanation?

I can post my code if necessary, however first I want to know if the following is correct:

'trainbr' will not overfit with large networks if run to convergence

Thanks

[Greg Heath]

How many periods are covered by the 400 samples?

What minimum number of samples per period are necessary?

Greg

[Jonathan Lowe]

I use 100 samples per period and 4 periods.

x=-1:0.005:1;
y = sin(x*(4*pi))+0.25*randn(size(x));

trainbr is also 1-200-1 and runs to about 17 eff params. (the blue legend should read sin(4*pi*x))

Answer 1

[Shubham Rawat]

Hi Jonathan,

Given your dataset and number of neurons it might be possible that your model is overfitting.

I have reproduced your code with 20 neurons and "trainbr" training funtion and it is giving me these results attached here. With Effective # Param = 18.6.