How can I specify a loss function to be quadratic weighted kappa in Keras?

Question

My understanding is that keras requires loss functions to have the signature:

def custom_loss(y_true, y_pred):

I am trying to use skl

Answer 1

You can define it as a custom loss and yes you are right that keras accepts only two arguments in the loss function. Here is how you can define your loss:

def get_cohen_kappa(weights=None):
    def cohen_kappa_score(y_true, y_pred):
        """
        Define your code here. You can now use `weights` directly
        in this function
        """
        return score
    return cohen_kappa_score

Now you can pass this function to your model as:

model.compile(loss=get_cohen_kappa_score(weights=weights),
              optimizer='adam')
model.fit(...)