title: Multiple Output Neural Network
body: <p>I have built my first neural network in python, and i've been playing around with a few datasets; it's going well so far !</p>
  
  <p>I have a quick question regarding modelling events with multiple outcomes: -</p>
  
  <p>Say i wish to train a network to tell me the probability of each runner winning a 100m sprint. I would give the network all of the relevant data regarding each runner, and the number of outputs would be equal to the number of runners in the race. </p>
  
  <p>My question is, using a sigmoid function, how can i ensure the sum of the outputs will be equal to 1.0 ? Will the network naturally learn to do this, or will i have to somehow make this happen explicitly ? If so, how would i go about doing this ?</p>
  
  <p>Many Thanks.</p>