For example consider object localization problem. Here NN will have 5 ouputs.
output will tell probability of object present in image, other 4 will tell bounding box coordinates.
As we see that
output has to use classification loss like cross entropy and
output will have to use regression loss like Mean-squared-error.
So Total loss is something like this:
loss=Cross_entropy(output,Y)+MSE(output[1:5],Y[1:5]) #Y is true value
Are loss like that backprogationable in vectorised form? Can I implement that kind of loss in tensorflow? If yes, how does tensorflow do that? Does it perform differentiation on each element of vector or matrix instead of whole thing at once?