R help - Jun 2009 - Inf in nnet final value for validation data

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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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.

 

 

___________________________________________
Jude Ryan
Director, Client Analytical Services
Strategy & Business Development
UBS Financial Services Inc.
1200 Harbor Boulevard, 4th Floor
Weehawken, NJ 07086-6791
Tel. 201-352-1935
Fax 201-272-2914
Email: jude.ryan at ubs.com



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