True. However, the resulting gradients make the net parameters. So, if I want to find the optimal combination of the two gradients (of c_out and f_out) as opposed to the plain accumulation at f2, I should be able to pose it as a classification loss minimization achieved by the resulting parameters (after the scaled backprop operation). In other words, I want to compute the gradient for alpha during the backward operation. I am sure its a bit loopy in mind as of now.