Hi, Thomas:
Pylearn2 supports dropout:
https://github.com/lisa-lab/pylearn2/blob/master/pylearn2/costs/mlp/dropout.py
Regards,
Nick
On Wed, Feb 5, 2014 at 12:17 PM, Thomas Johnson
<thomas.j.john...@gmail.com>wrote:
> Apologies if this is slightly offtopic, but is there a high-quality Python
> implementation of DropOut / DropConnect available somewhere?
>
>
> On Wed, Feb 5, 2014 at 12:58 PM, Andy <t3k...@gmail.com> wrote:
>
>> On 02/05/2014 04:30 PM, Gael Varoquaux wrote:
>> > On Wed, Feb 05, 2014 at 03:02:24PM +0300, Issam wrote:
>> >> I have been working with scikit-learn for three pull requests -
>> namely,
>> >> Multi-layer Perceptron (MLP), Sparse Auto-encoders, and Gaussian
>> >> Restricted Boltzmann Machines.
>> > Yes, you have been doing good work here!
>> +1
>> >> For the upcoming GSoC, I propose to ensure completing these three pull
>> >> requests. I also would develop Greedy layer-wise training algorithm for
>> >> deep learning, extending MLP to allow for more than one hidden layer,
>> >> where weights are initialized using Sparse Auto-encoders or RBM.
>> >> How will this suit for GSoC?
>> > The MLP is almost finished. I would hope that it would be finished
>> before
>> > the GSoC. Actually, I was hoping that it could be finished before next
>> > release.
>> I'm also still hopeful there.
>> Unfortunately I will definitely be unable to mentor.
>>
>> About pretraining: that is really out of style now ;)
>> Afaik "everybody" is now doing purely supervised training using drop-out.
>>
>> Implementing pretrained deep nets should be fairly easy for a user if we
>> support more than one hidden layer,
>> but just doing a pipeline of RBMs / Autoencoders. As that is not that
>> popular any more, I don't think we should put much effort there.
>>
>> Deeper nets might be interesting but I'm quite sceptical about doing
>> without GPUs.
>>
>> On the other hand I think it should be possible for you to find a topic
>> around these general concepts.
>> But I'm not sure who could mentor.
>>
>> Cheers,
>> Andy
>>
>>
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