Andrea,
You can calculate predictions for your validation data based on nnet objects
using the predict function (the predict function can also be used for
regressions, quantile regressions, etc.)
If you create a neural net with the following code:
library(nnet)
# 3 hidden neurons, for classification (linout = F), and not a skip layer
network (skip = F, or T if you want)
mynet.nn <- nnet(dependent_variable ~ ., data = train, size = 3, decay = 1e-3,
linout = F, skip = F, maxit = 1000, Hess = T)
# calculate predictions for your training data and append to data frame called
train
train$predictions <- predict(mynet.nn)
# calculate predictions for your validation data and append to data frame
called valid
valid$predictions <- predict(mynet.nn, valid) # you need to pass your neural
net object and your validation dataset to the predict function
To just get the predictions for your validation dataset this is all you need. I
do not know why you need to calculate the log likelihood.
Hope this helps.
Jude
Andrea wrote:
Hi,
I use nnet for my classification problem and have a problem concerning the
calculation of the final value for my validation data.(nnet only calculates the
final value for the training data). I made my own final value formula (for the
training data I get the same value as nnet):
# prob-matrix
pmatrix <- cat*fittedValues
tmp <- rowSums(pmatrix)
# -log likelihood
finalValue <- sum(-log(tmp))
# add penalty term
finalValue + sum(decay * weights^2)
where cat is a matrix with cols for each possible category and a row for each
data record. The values are 1 for the target categories of a data record and 0
otherwise.
My problem is, that I get Inf-values for some validation data records, because
the rowsum of cat*fittedValues gets 0 and the log gets Inf.
Has anyone an idea how to deal with that problem properly? How does nnet?
I´m thinking of a penalty value for those values. That means if
cat*fittedValues == 0 not to calculate the log but add e.g. 100 instead of
"-log(tmp)" to the finalValue-sum??
But how to determine the penalty value???
I´m looking forwar for all suggestions,
Andrea.
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