[R] use nnet

[Wensui Liu]

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
>
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>


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