I don't know if the hyperparameter tuner will work with vector<int>, but
give it a shot and see what happens. :) In the worst case, you can
unpack all the elements into individual arguments (where 0 means "no
layer" I suppose).
On Fri, Nov 13, 2020 at 01:27:39PM +0000, Ambica Prasad wrote:
> 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 <[email protected]>
> Sent: 13 November 2020 08:20
> To: Ambica Prasad <[email protected]>
> Cc: Benson Muite <[email protected]>; [email protected]
> 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 <[email protected]>
> > Sent: 12 November 2020 00:50
> > To: Ambica Prasad <[email protected]>; [email protected]
> > 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 <[email protected]> On Behalf Of Benson
> > > Muite
> > > Sent: 08 November 2020 00:04
> > > To: [email protected]
> > > 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 <[email protected]
> > >> <mailto:[email protected]>> wrote:
> > >>
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> > >>
> > >> 1. Tutorial for HyperParameterTuning for FFNs (Ambica
> > >> Prasad)
> > >>
> > >>
> > >>
> > >> ---------- Forwarded message ----------
> > >> From: Ambica Prasad <[email protected]
> > >> <mailto:[email protected]>>
> > >> To: "[email protected] <mailto:[email protected]>"
> > >> <[email protected] <mailto:[email protected]>>
> > >> 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
> [email protected] | to squash me." - Kit Ramsey
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Ryan Curtin | "I don't really come from outer space."
[email protected] | - L.J. Washington
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