Setting hidden layers and neurons in neuralnet and caret (R)

Question

I would like to cross-validate a neural network using the package neuralnet and caret.

The data df can be copied from this post.

When running the neuralnet() function, there is an argument called hidden where you can set the hidden layers and neurons in each. Let's say I want 2 hidden layers with 3 and 2 neurons respectively. It would be written as hidden = c(3, 2).

However, as I want to cross-validate it, I decided to use the fantastic caret package. But when using the function train(), I do not know how to set the number of layers and neurons.

Does anyone know where can I add these numbers?

This is the code I ran:

nn <- caret::train(DC1 ~ ., data=df, 
                   method = "neuralnet", 
                   #tuneGrid = tune.grid.neuralnet,
                   metric = "RMSE",
                   trControl = trainControl (
                     method = "cv", number = 10,
                     verboseIter = TRUE
))

By the way, I am getting some warnings with the previous code:

predictions failed for Fold01: layer1=3, layer2=0, layer3=0 Error in cbind(1, pred) %*% weights[[num_hidden_layers + 1]] : 
  requires numeric/complex matrix/vector arguments

Ideas on how to solve it?

Answer 1

When using neural net model in caret in order to specify the number of hidden units in each of the three supported layers you can use the parameters layer1, layer2 and layer3. I found out by checking the source.

library(caret)

grid <-  expand.grid(layer1 = c(32, 16),
                     layer2 = c(32, 16),
                     layer3 = 8)

Use case with BostonHousing data:

library(mlbench)

data(BostonHousing)

lets just select numerical columns for the example to make it simple:

BostonHousing[,sapply(BostonHousing, is.numeric)] -> df

nn <- train(medv ~ ., 
            data = df, 
            method = "neuralnet", 
            tuneGrid = grid,
            metric = "RMSE",
            preProc = c("center", "scale", "nzv"), #good idea to do this with neural nets - your error is due to non scaled data
            trControl = trainControl(
              method = "cv",
              number = 5,
              verboseIter = TRUE)
            )

The part

preProc = c("center", "scale", "nzv")

is essential for the algorithm to converge, neural nets don't like unscaled features

Its super slow though.

nn
#output
Neural Network 

506 samples
 12 predictor

Pre-processing: centered (12), scaled (12) 
Resampling: Cross-Validated (5 fold) 
Summary of sample sizes: 405, 404, 404, 405, 406 
Resampling results across tuning parameters:

  layer1  layer2  RMSE      Rsquared   MAE     
  16      16           NaN        NaN       NaN
  16      32      4.177368  0.8113711  2.978918
  32      16      3.978955  0.8275479  2.822114
  32      32      3.923646  0.8266605  2.783526

Tuning parameter 'layer3' was held constant at a value of 8
RMSE was used to select the optimal model using the smallest value.
The final values used for the model were layer1 = 32, layer2 = 32 and layer3 = 8.