Every example neural network for image recognition I've read about produces a simple "yes" or "no" answer. One exit node corresponds to "Yes, this is a human face," and one corresponds to "No, this is not a human face."
I understand that this is likely for simplicity of explanation, but I'm wondering how such a neural network could be programmed to give a more specific output. For example, let's say I was classifying animals. Instead of it saying "Animal" or "Not an animal", I would want responses like "Dog", "Fish", "Bird", "Snake", etc., with one final exit node being "Not an animal/I don't recognize this".
I'm sure this must be possible, but I'm having trouble understanding how. It seems like due to the training algorithm of backpropogation of error, as you train up one exit node (i.e., "This is a dog") and the weights of the neurons are changed, then the ideal state for another exit node that you previously trained (i.e., "This is a bird") will begin to deviate, and vice versa. So training the network to recognize one category would sabotage any training done for another category, thus limiting us to a simple "Yes" or "No" design.
Does this make such a recognizer impossible? Or am I misunderstanding the algorithm? The only two things I can think of are that:
Either we could train one neural network for each thing we want classified and somehow use those to construct a greater, super-network (so for example, a network for "dog", a network for "bird", etc., which we somehow add together to create the super-network for "animals"); or,
Create some kind of ridiculously complicated training methodology which would require incredibly advanced mathematics and would somehow produce an ideal neuron-weight-state for all possible outputs (in other words,
insert math magic here).
(Side note 1: I am specifically looking at multilayer perceptrons as a kind of neural network.)
(Side note 2: For the first bulleted "possible solution", having each specific neural network and iterating through them until we receive a "Yes" response is not good enough. I know this could be done fairly easily, but that is simple functional programming rather than machine learning. I want to know if it's possible to have one neural network to feed the information to and receive the appropriate response.)