Why doesn't Keras need the gradient of a custom loss function?
To my understanding, as long as each operator that you will use in your Error function has already a predefined gradient. the underlying framework will manage to calculate the gradient of you loss function.
To my understanding, in order to update model parameters through gradient descend, the algorithm needs to calculate at some point the derivative of the error function E with respect of the output y: dE/dy. Nevertheless, I've seen that if you want to use a custom loss function in Keras, you simply need to define E and you don't need to define its derivative. What am I missing?
Each lost function will have a different derivative, for example:
If loss function is the mean square error: dE/dy = 2(y_true - y)
If loss function is cross entropy: dE/dy = y_true/y
Again, how is it possible that the model does not ask me what the derivative is? How does the model calculate the gradient of the loss function with respect of parameters from just the value of E?
Thanks