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arXiv · 1806.02460

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

Lech Szymanski·Brendan McCane·Michael Albert
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Abstract

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

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Lech Szymanski, Brendan McCane, Michael Albert. 2018-06-06. The effect of the choice of neural network depth and breadth on the size of its hypothesis space. https://arxiv.org/abs/1806.02460

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