I am using Keras'
class_weight parameter to deal with an imbalanced class problem. I am doing this to define the weights :
weights = class_weight.compute_class_weight('balanced',np.unique(trainY),trainY)
then, in my network:
However, I also use a callbacks list to prevent overfitting, by imposing EarlyStopping based on
earlystop = EarlyStopping(monitor='val_acc', min_delta=0.001, patience=5, verbose=1, mode='auto') callbacks_list = [earlystop]
However, my weights are only defined based on my training set... But my validation set also contains imbalanced data, in different proportions than the training set, and I would also like to give appropriate weights to have a fair evaluation of
So my question is
With which weights is the
val_losscomputed ? With the weights given in
class_weightparameter ? Or is this parameter only used for training loss ?
How could I define weights that could be used for the validation set, to have a more accurate value of