How to set class_weight in keras package of R?

Question

I am using keras package in R to train a deep learning model. My data set is highly imbalanced. Therefore, I want to set class_weight argument in t

Answer 1

Class_weight needs to be a list, so

    history <- model %>% fit(
        trainData, trainClass, 
        epochs = 5, batch_size = 1000, 
        class_weight = list("0"=1,"1"=30),
        validation_split = 0.2
    )

seems to work. Keras internally uses a function called as_class_weights to change the list to a python-dictionary (see https://rdrr.io/cran/keras/src/R/model.R).

     class_weight <- dict(list('0'=1,'1'=10))
     class_weight
     >>> {0: 1.0, 1: 10.0}

Looks just like the python dictionary that you mentioned above.

Answer 2

I found a generic solution in Python solution, so I converted into R:

counter=funModeling::freq(Y_data_aux_tr, plot=F) %>% select(var, frequency)
majority=max(counter$frequency)
counter$weight=ceil(majority/counter$frequency)


l_weights=setNames(as.list(counter$weight), counter$var)

Using it:

 fit(..., class_weight = l_weights)

An advice if you are using fit_generator: since the weights are based on frequency, having a different number of training-validation samples may bias the validation results. They should be equally-sized.