CODEWITHSUNDEEP
rmatrixneural-network
Elnaz
Elnaz

Reputation: 111

neuralnet: requires numeric/complex matrix/vector arguments

I used the package neuralnet to build a classification model in R. However, I encountered the famous error:

Error in cbind(1, pred) %*% weights [[num_hidden_layers + 1]]:    
requires numeric/complex matrix/vector arguments

There are many other similar questions here, but none of them solved my problem. These are the steps I am taking:

  1. Using model.matrix to create dummy columns (ensuring no column is factor or string)
  2. Creating a manual formula by paste function
  3. Training neuralnet using the one-hot-encoded dataset of step 1 and the formula created in step 2

Up to here, everything is fine. No error. The model converges after 5000 iterations. However, when I use either compute or predict functions to have a prediction on the test data, it gives me the above error.

I am pretty sure that the columns are the same and have the same name. Also, the class is numeric for each and every attribute. I told myself that maybe the test set is not transformed well using model.matrix, so I used the same training set in the predict/compute function! Surprisingly, it gives a similar error for the same training set! If the data is not a numeric/complex matrix, how is it trained at first and cannot be predicted now?

PS: I cannot share the data due to a privacy issue. This is the simplified code:

trainset = model.matrix(~., data=train_roig)
NN_model = neuralnet(f, trainset[,-c(1:2)], hidden = c(4,2))
# NO ERROR
compute(NN_model, trainset[,-c(1:2)])
# GIVES ME THE ERROR
predict(NN_model, trainset[,-c(1:2)])
# GIVES ME THE SAME ERROR

Double-checking column names:

NN_model$model.list$variables == colnames(trainset[,-c(1:2)])
# TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE

Checking structure of trainset after applying model.matrix:

str(trainset)
# num [1:134260, 1:19] 0 0 0 0 0 0 0 0 0 ... 
# -attr(*, "dimnames") = list of 2
# ..$ : chr [1:134260] "1" "2" "3" "4" ...
# ..$ : chr [1:19] "Y" "n_trips" "age" "sexM" ...

Upvotes: 1

Views: 5407

Answers (1)

Hadij
Hadij

Reputation: 4600

One possible issue is convergence. If the number of stepmax reaches but the default threshold does not meet, then the model will not generate any weight.

Use plot(trainset) to make sure your weights are generated. If it was unable to plot your network due to weight absence, then you need to increase the threshold or stepmax to let the model fit.

This issue happens only with neuralnet. Other functions I worked with, even if they do not converge, will give you the last weight before the final step. However, this package only releases weights if it converges.

Upvotes: 2

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