I'm working on with an imbalanced dataset in Keras, and would like to give a higher weight to the samples from my minority class. The
fit() function has a nice
sample_weight argument, however because of the size of my data I have to use
fit_generator() has a
class_weight argument, which seems useful for this purpose and is already discussed in Another question. However, in this case the labels are not one-hot-encoded/categorical and I could not find whether using
class_weight also allows for categorical data.
Can use the
class_weight argument for one-hot-encoded/categorical labels and if so how? Or do I have to resort to a custom weighted loss function?