I know what’s your mean now. I have to admit that I haven’t seen anyone do this before. As far as I know, SVM can only change its hyperplane without changing the input features. In other words，it only assumes that its hyperplane is not perfect and would not think that the input was wrong.
We can think it in a different way. Suppose that SVM has the ability to change the representation of sample, which we call it as feats, we do not need DNN any more.
Sorry for not being able to help you.Maybe you can try other loss functions except Softmax.