[R] use nnet

[Aimin Yan]

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)