Same feeling as Andy. I'd favor implementing averaging instead.
Mathieu
On Sun, May 4, 2014 at 12:23 AM, Andy <t3k...@gmail.com> wrote:
> Hi Sean.
> For linear classifiers I'm not really aware of benefits in doing
> mini-batch training, and I don't think it is widely used (someone correct
> me if I'm wrong).
> Usually we only like to add features that have a clear benefit for the
> users, to prevent scikit-learn from becoming bloated.
>
> Do you have a particular use-case where it is important?
>
> Cheers,
> Andy
>
>
> On 05/01/2014 02:04 AM, Sean Violante wrote:
>
> Hi
>
> I was wondering if there is any interest in implementing minibatch/batch
> for the SGD algorithm. As I understand it, this is not implemented
>
> "There is a compromise between the two forms, which is often called
> "mini-batches", where the true gradient is approximated by a sum over a
> small number of training examples."
>
> http://en.wikipedia.org/wiki/Stochastic_gradient_descent
>
>
> This would be doing a partial_fit (on small number of training examples)
> but updating the weights only after each epoch rather than after each
> training sample
>
>
> as far as I can see it would only require a flag in the sgd_fast.pyx
> code.
>
> thanks
>
> Sean
>
>
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