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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