Hi Ryan,
Thanks a lot. This is helpful. I will try something like this.
class FFNWrapper
{
...
template<typename MatType, typename LabelsType>
void Train(const MatType& data,
const LabelsType& labels,
const vector<int>& hidden_layers)
{
// Based on the vector, I will create the hidden_layers and start the training
}
...
};
Hope this works.
Thanks,
Ambica
-----Original Message-----
From: Ryan Curtin <r...@ratml.org>
Sent: 13 November 2020 08:20
To: Ambica Prasad <ambica.pra...@arm.com>
Cc: Benson Muite <benson_mu...@emailplus.org>; mlpack@lists.mlpack.org
Subject: Re: [mlpack] Tutorial for HyperParameterTuning for FFNs (Ambica Prasad)
Hi Ambica,
There's one more thing worth mentioning. The hyperparameter tuner works with
mlpack classifiers (or regressors) whose hyperparameters are specified in the
Train() call. So, for instance, you could implement a class that works a
little like this:
class FFNWrapper
{
...
template<typename MatType, typename LabelsType>
void Train(const MatType& data,
const LabelsType& labels,
const bool addSecondLayer)
{
// In this method you would build the network, and if
// `addSecondLayer` is true, you would add a second layer, then do
// the training.
}
...
};
Now that is just one idea for a single boolean parameter, but you could extend
that to do search over architectures, so long as you can keep the parameters of
the architecture as parameters to Train(). Then I think the hyperparameter
tuner could work for that situation.
I hope this is helpful! I know it would be a bit of implementation work, but
it should work (maybe with minor modifications). :)
On Wed, Nov 11, 2020 at 07:47:48PM +0000, Ambica Prasad wrote:
> Thanks Benson, I get it now.
>
> Thanks,
> Ambica
>
> -----Original Message-----
> From: Benson Muite <benson_mu...@emailplus.org>
> Sent: 12 November 2020 00:50
> To: Ambica Prasad <ambica.pra...@arm.com>; mlpack@lists.mlpack.org
> Subject: Re: [mlpack] Tutorial for HyperParameterTuning for FFNs
> (Ambica Prasad)
>
> Hi Ambica,
> If the aim is to avoid overfitting and choose a reasonable number of
> parameters, then DropOut might help reduce the size of grid search you need
> to do - in particular, will likely need to write code to change number of
> layers, but dropout changes layer size for you during training phase.
> Regards,
> Benson
> On 11/10/20 5:17 PM, Ambica Prasad wrote:
> > Hi Benson,
> >
> > I am not sure how I would use DropOut to perform a grid-search over my
> > parameters. Could you elaborate?
> >
> > Thanks,
> > Ambica
> >
> > -----Original Message-----
> > From: mlpack <mlpack-boun...@lists.mlpack.org> On Behalf Of Benson
> > Muite
> > Sent: 08 November 2020 00:04
> > To: mlpack@lists.mlpack.org
> > Subject: Re: [mlpack] Tutorial for HyperParameterTuning for FFNs
> > (Ambica Prasad)
> >
> > You may also want to examine the documentation on dropout:
> > https://www.mlpack.org/doc/mlpack-3.0.4/doxygen/classmlpack_1_1ann_1
> > _1
> > Dropout.html
> >
> > On 11/7/20 9:15 PM, Aakash kaushik wrote:
> >> Hey Ambica
> >>
> >> So There is not a specific tutorial available for that but you can
> >> always put the layer size in an array and loop over that for
> >> variable layers sizes or you can sample random integers from a
> >> range and for layer numbers I believe you have to change them
> >> manually every time but not totally sure about it.
> >>
> >> Best,
> >> Aakash
> >>
> >> On Sat, Nov 7, 2020 at 10:30 PM <mlpack-requ...@lists.mlpack.org
> >> <mailto:mlpack-requ...@lists.mlpack.org>> wrote:
> >>
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> >> 1. Tutorial for HyperParameterTuning for FFNs (Ambica
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> >>
> >>
> >>
> >> ---------- Forwarded message ----------
> >> From: Ambica Prasad <ambica.pra...@arm.com
> >> <mailto:ambica.pra...@arm.com>>
> >> To: "mlpack@lists.mlpack.org <mailto:mlpack@lists.mlpack.org>"
> >> <mlpack@lists.mlpack.org <mailto:mlpack@lists.mlpack.org>>
> >> Cc:
> >> Bcc:
> >> Date: Sat, 7 Nov 2020 02:36:39 +0000
> >> Subject: [mlpack] Tutorial for HyperParameterTuning for FFNs
> >>
> >> Hi Guys,____
> >>
> >> __ __
> >>
> >> Is there an example or a tutorial that explains how to perform the
> >> hyperparameter tuning for FFNs, where I can evaluate the network on
> >> different number of layers and layer-sizes?____
> >>
> >> __ __
> >>
> >> Thanks,____
> >>
> >> Ambica____
> >>
> >> __ __
> >>
> >> __ __
> >>
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--
Ryan Curtin | "Happy premise #2: There is no giant foot trying
r...@ratml.org | to squash me." - Kit Ramsey
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