I'm trying to implement cross-entropy as an error function in RBF neural networks instead of hinge loss error function. I need to find cross-entropy error for each output neuron, like hinge loss error function as you can see in the formula below
but when it comes to cross-entropy, it seems meaningless. because the formulation of cross-entropy returns 1 scaler. I look at some of the recent papers using cross-entropy and soft-max but can't find any relative information to my problem.
Is there any way to finding cross-entropy value for each neuron of output?