[R] nnet learning
Prof Brian Ripley
ripley at stats.ox.ac.uk
Mon Oct 18 14:48:55 CEST 2004
On Mon, 18 Oct 2004, Samuel Kemp wrote:
> I am trying to make a neural network learning a "noisy sine wave".
> Suppose I generate my data like so..
>
> x <- seq(-2*pi, 2*pi, length=500)
> y <- sin(x) + rnorm(500, sd=sqrt(0.075))
>
> I then train the neural net on the first 400 points using
>
> c <- nnet(as.matrix(x[1:400]),as.matrix(y[1:400]), size=3, maxit=10000,
> abstol=0.075, decay=0.007)
>
> Inspecting the fit of the training set against the actual values using:
>
> pmat<- plot(y[1:400])
> lines(c$fitted.values, col="blue", lwd=2)
>
> It seems as though neural net is not learning the negative values. I
> have tried running nnet several times, but each time I get the same
> problem. I have also tried upsampling, but no joy.
>
> I suspect that I am not using nnet correctly. Can anyone provide any
> hints/solutions?
Yes, please read the documentation, as the posting guide asks you to.
Hint: it's on p. 246. Compare your example with that given there.
--
Brian D. Ripley, ripley at stats.ox.ac.uk
Professor of Applied Statistics, http://www.stats.ox.ac.uk/~ripley/
University of Oxford, Tel: +44 1865 272861 (self)
1 South Parks Road, +44 1865 272866 (PA)
Oxford OX1 3TG, UK Fax: +44 1865 272595
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