# More output neurons than labels?

When we train a neural network model for a classification problem, we usually have a dense output layer of size equal to the number of labels we have.

If the layer size was greater, the model can still be trained and have output

How can we interpret such output? are there any applications for this?

• Are you looking for a solution for the target which is not yet introduced but probably will come in furlturw? – vipin bansal Jul 16 '19 at 13:38
• @vipinbansal not really – Abdulrahman Bres Jul 16 '19 at 13:58

One kind-of similar case are the advantage actor-critic networks commonly used in reinforcement learning. The ultimate goal of a reinforcement-learning agent is to choose an action from a set of $$n$$ possible actions, so traditionally we might try a network with $$n$$ outputs. Actor critic methods actually have $$n+1$$ outputs. The first $$n$$ choose an action, and the "extra" neuron tries to estimate the value of the chosen action.