<p>Formally, a single hidden layer is sufficient to approximate a continuous function to any desired degree of accuracy, so in that sense, you never need more than 1. This is called the <a href="https://en.wikipedia.org/wiki/Universal_approximation_theorem" rel="noreferrer">Universal Approximation Theorem</a>.</p>

<p>Finding the best topology for a given problem is an open research problem. As far as I know, there are few universal 'rules of thumb' for this.</p>

<p>For a given problem, one option is to apply a <em>neuroevolutionary</em> approach such as <a href="https://www.cs.ucf.edu/~kstanley/neat.html" rel="noreferrer">NEAT</a>, which attempts to find a topology that works well for the problem at hand.</p>

CC BY-SA 3.0
2016-08-03T10:26:17.493Z
2017-11-05T17:58:56.897Z
2017-11-05T17:58:56.897Z
NietzscheanAI
<p>I've read that the most of the problems can be solved with 1-2 hidden layers.</p>

<p>How do you know you need more than 2? For what kind of problems you would need them (give me an example)?</p>

<deep-neural-networks><hidden-layers>
What kind of problems require more than 2 hidden layers?
kenorb
# What kind of problems require more than 2 hidden layers?

(Asked by: username_0 on 2016-08-02)

I've read that the most of the problems can be solved with 1-2 hidden layers.

How do you know you need more than 2? For what kind of problems you would need them (give me an example)?

**Tags:** deep-neural-networks, hidden-layers


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## 1 answers

### Answer by username_1 on 2016-08-03. Score: 12 | Accepted answer

Formally, a single hidden layer is sufficient to approximate a continuous function to any desired degree of accuracy, so in that sense, you never need more than 1. This is called the [Universal Approximation Theorem](https://en.wikipedia.org/wiki/Universal_approximation_theorem).

Finding the best topology for a given problem is an open research problem. As far as I know, there are few universal 'rules of thumb' for this.

For a given problem, one option is to apply a *neuroevolutionary* approach such as [NEAT](https://www.cs.ucf.edu/~kstanley/neat.html), which attempts to find a topology that works well for the problem at hand.


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