On 05/03/2014 05:37 PM, Mathieu Blondel wrote:
Same feeling as Andy. I'd favor implementing averaging instead.
I totally forgot that we don't have averaging yet ^^
I'd be in favor of geometric averaging: http://arxiv.org/abs/1212.2002
Mathieu
On Sun, May 4, 2014 at 12:23 AM, Andy <t3k...@gmail.com
<mailto: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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