Deep learning literature said that the more layers you have, the less
hidden nodes in one layer you need. But I agree one hidden layer would be
sufficient now.
On Thu, Jun 7, 2012 at 11:12 AM, David Warde-Farley <
warde...@iro.umontreal.ca> wrote:
> On 2012-06-05, at 1:51 PM, David Marek <h4wk...@gmail.com> wrote:
>
> > 1) Afaik all you need is one hidden layer,
>
> The universal approximator theorem says that any continuous function can
> be approximated arbitrarily well if you have one hidden layer with enough
> hidden units, but it says nothing about the ease of finding that solution,
> nor about the efficiency of the solution (you can prove that certain
> functions that can be compactly represented by a deep network require
> exponentially many more hidden units if you're restricted to one layer).
>
> However, with purely supervised training deeper networks are harder to fit
> (you can get to about 2 hidden layers if you're careful but beyond that it
> gets quite hard), so I wouldn't worry about it. In a "black box"
> implementation for scikit-learn, where the user isn't expected to be an
> expert in training neural nets, a single hidden layer is probably plenty.
>
> David
>
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--
Best Wishes
--------------------------------------------
Meng Xinfan（蒙新泛）
Institute of Computational Linguistics
Department of Computer Science & Technology
School of Electronic Engineering & Computer Science
Peking University
Beijing, 100871
China
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