[R] use nnet

[Wensui Liu]

no, it is called regression. ^_^.

On 3/9/07, Aimin Yan <aiminy at iastate.edu> wrote:
> thank you very much.
> I have a another question about nnet
> if I set size=0, and skip=TRUE.
> Then this network has just input layer and out layer.
> Is this also called perceptron network?
>
> thanks,
>
> Aimin Yan
>
>
> At 12:39 PM 3/9/2007, Wensui Liu wrote:
> >AM,
> >Sorry. please ignore the top box in the code. It is not actually a cv
> >validation but just a simple split-sample validation.
> >sorry for confusion.
> >
> >On 3/9/07, Wensui Liu <liuwensui at gmail.com> wrote:
> >>AM,
> >>I have a pieice of junk on my blog. Here it is.
> >>#################################################
> >># USE CROSS-VALIDATION TO DO A GRID-SEARCH FOR  #
> >># THE OPTIMAL SETTINGS (WEIGHT DECAY AND NUMBER #
> >># OF HIDDEN UNITS) OF NEURAL NETS               #
> >>#################################################
> >>
> >>library(nnet);
> >>library(MASS);
> >>data(Boston);
> >>X <- I(as.matrix(Boston[-14]));
> >># STANDARDIZE PREDICTORS
> >>st.X <- scale(X);
> >>Y <- I(as.matrix(Boston[14]));
> >>boston <- data.frame(X = st.X, Y);
> >>
> >># DIVIDE DATA INTO TESTING AND TRAINING SETS
> >>set.seed(2005);
> >>test.rows <- sample(1:nrow(boston), 100);
> >>test.set <- boston[test.rows, ];
> >>train.set <- boston[-test.rows, ];
> >>
> >># INITIATE A NULL TABLE
> >>sse.table <- NULL;
> >>
> >># SEARCH FOR OPTIMAL WEIGHT DECAY
> >># RANGE OF WEIGHT DECAYS SUGGESTED BY B. RIPLEY
> >>for (w in c(0.0001, 0.001, 0.01))
> >>{
> >>   # SEARCH FOR OPTIMAL NUMBER OF HIDDEN UNITS
> >>   for (n in 1:10)
> >>   {
> >>     # UNITIATE A NULL VECTOR
> >>     sse <- NULL;
> >>     # FOR EACH SETTING, RUN NEURAL NET MULTIPLE TIMES
> >>     for (i in 1:10)
> >>     {
> >>       # INITIATE THE RANDOM STATE FOR EACH NET
> >>       set.seed(i);
> >>       # TRAIN NEURAL NETS
> >>       net <- nnet(Y~X, size = n, data = train.set, rang = 0.00001,
> >>                        linout = TRUE, maxit = 10000, decay = w,
> >>                        skip = FALSE, trace = FALSE);
> >>       # CALCULATE SSE FOR TESTING SET
> >>       test.sse <- sum((test.set$Y - predict(net, test.set))^2);
> >>       # APPEND EACH SSE TO A VECTOR
> >>       if (i == 1) sse <- test.sse else sse <- rbind(sse, test.sse);
> >>     }
> >>     # APPEND AVERAGED SSE WITH RELATED PARAMETERS TO A TABLE
> >>     sse.table <- rbind(sse.table, c(WT = w, UNIT = n, SSE = mean(sse)));
> >>   }
> >>}
> >># PRINT OUT THE RESULT
> >>print(sse.table);http://statcompute.spaces.live.com/Blog/cns!39C8032DBD1321B7!290.entry
> >>
> >>
> >>On 3/9/07, Aimin Yan <aiminy at iastate.edu> wrote:
> >> > I want to adjust weight decay and number of hidden units for nnet by
> >> > a loop like
> >> > for(decay)
> >> > {
> >> >   for(number of unit)
> >> >    {
> >> >     for(#run)
> >> >      {model<-nnet()
> >> >        test.error<-....
> >> >      }
> >> >    }
> >> > }
> >> >
> >> > for example:
> >> > I set decay=0.1, size=3, maxit=200, for this set I run 10 times, and
> >> > calculate test error
> >> >
> >> > after that I want to get a matrix like this
> >> >
> >> > decay  size   maxit  #run  test_error
> >> > 0.1        3        200   1       1.2
> >> > 0.1        3        200   2       1.1
> >> > 0.1        3        200   3       1.0
> >> > 0.1        3        200   4       3.4
> >> > 0.1        3        200   5        ..
> >> > 0.1        3        200   6         ..
> >> > 0.1        3        200   7       ..
> >> > 0.1        3        200   8      ..
> >> > 0.1        3        200   9       ..
> >> > 0.1        3        200   10       ..
> >> > 0.2        3        200   1       1.2
> >> > 0.2        3        200   2       1.1
> >> > 0.2        3        200   3       1.0
> >> > 0.2        3        200   4       3.4
> >> > 0.2        3        200   5        ..
> >> > 0.2        3        200   6         ..
> >> > 0.2        3        200   7       ..
> >> > 0.2        3        200   8      ..
> >> > 0.2        3        200   9       ..
> >> > 0.2        3        200   10       ..
> >> >
> >> > I am not sure if this is correct way to do this?
> >> > Does anyone tune these parameters like this before?
> >> > thanks,
> >> >
> >> > Aimin
> >> >
> >> > ______________________________________________
> >> > R-help at stat.math.ethz.ch mailing list
> >> > https://stat.ethz.ch/mailman/listinfo/r-help
> >> > PLEASE do read the posting guide
> >> http://www.R-project.org/posting-guide.html
> >> > and provide commented, minimal, self-contained, reproducible code.
> >> >
> >>
> >>
> >>--
> >>WenSui Liu
> >>A lousy statistician who happens to know a little programming
> >>(http://spaces.msn.com/statcompute/blog)
> >
> >
> >--
> >WenSui Liu
> >A lousy statistician who happens to know a little programming
> >(http://spaces.msn.com/statcompute/blog)
>
>
>


-- 
WenSui Liu
A lousy statistician who happens to know a little programming
(http://spaces.msn.com/statcompute/blog)