The effect of the choice of neural network depth and breadth on the size of its hypothesis space

We show that the number of unique function mappings in a neural network hypothesis space is inversely proportional to ${\prod }_l{U}_l!$, where $U_l$ is the number of neurons in the hidden layer $l$.