[mlpack] Tutorial for HyperParameterTuning for FFNs (Ambica Prasad)

[Benson Muite]

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-bounces at lists.mlpack.org> On Behalf Of Benson Muite
> Sent: 08 November 2020 00:04
> To: mlpack at 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_1Dropout.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-request at lists.mlpack.org
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>>          1. Tutorial for HyperParameterTuning for FFNs (Ambica Prasad)
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>>      ---------- Forwarded message ----------
>>      From: Ambica Prasad <Ambica.Prasad at arm.com
>>      <mailto:Ambica.Prasad at arm.com>>
>>      To: "mlpack at lists.mlpack.org <mailto:mlpack at lists.mlpack.org>"
>>      <mlpack at lists.mlpack.org <mailto:mlpack at 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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