On 2019-09-09 17:53, Daniel Sullivan wrote:
Hey Farzana,
The algorithm only keeps one batch in memory at a time. Between
processing over each batch, SGD keeps a set of weights that it alters
with each iteration of a data point or instance within a batch. This
set of weights functions as the persisted state between calls of
partial_fit. That means you will get the same results with SGD
regardless of your batch size and you can choose your batch size
according to your memory constraints. Hope that helps.
- Danny
On Mon, Sep 9, 2019 at 5:53 PM Farzana Anowar <fad...@uregina.ca>
wrote:
Hello Sir/Madam,
I am going through the incremental learning algorithm in
Scikit-learn.
SGD in sci-kit learn is such a kind of algorithm that allows
learning
incrementally by passing chunks/batches. Now my question is: does
sci-kit learn keeps all the batches for training data in memory? Or
it
keeps chunks/batches in memory up to a certain amount of size? Or it
keeps only one chunk/batch while training in memory and removes the
other trained chunks/batches after training? Does that mean it
suffers
from catastrophic forgetting?
Thanks!
--
Regards,
Farzana Anowar
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Thanks a lot!
--
Regards,
Farzana Anowar
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