[T_D_K]

First question: The network is trained to recognize a fixed set of outputs. That's what makes it a classifier -- it classifies an input into a single output. It does this by giving each output possibility a score, and the highest score is its guess for what the original image is. So if I have a network that I train to recognize cats, dogs, and cars, and I get an output like {cat: .13, dog: .85, car: .02} Then the input was most likely a dog. The network calculates all of those values simultaneously.

You can, of course, tell the network to output whatever you want: all of the guesses, best guess, top five guesses, all guesses over a threshold, etc.

Note, this is a gross oversimplification, but it gets the general concept across.