I have a simple question about the choice of activation function for the output layer in feed-forward neural networks.
I have seen several codes where the choice of the activation function for the output layer is linear.
Now, it might well be that I am wrong about this, but isn't that simply equivalent to a rescaling of the weights connecting the last hidden layer to the output layer? And following this point, aren't you just as well off with just using the identity function as your output activation function?