transfer learning doubt
I am training a SGD Classifier with some training dataset which is temporary and will be lost after sometime. So I am planning to save the model in pickle file and reuse it and train again with some another dataset that arrives. But It forgets the previously learned data. As far as I researched in google, tensorflow model allows transfer learning and not forgetting the previous learning but is there any other way with sklearn model to achieve this?? any help would be appreciated
I am training a SGD Classifier with some training dataset which is temporary and will be lost after sometime. So I am planning to save the model in pickle file and reuse it and train again with some another dataset that arrives. But It forgets the previously learned data.
As far as I researched in google, tensorflow model allows transfer learning and not forgetting the previous learning but is there any other way with sklearn model to achieve this?? any help would be appreciated _______________________________________________ scikit-learn mailing list scikit-learn@python.org https://mail.python.org/mailman/listinfo/scikit-learn Did you use incremental estimator and partial _fit? If not, try to use
On 2020-03-19 00:11, Praneet Singh wrote: them. Should work. Another option is to us deep learning and store the weights for the first model and initialize the second model with that weight and keep doing it for the rest of the models. -- Best Regards, Farzana Anowar, PhD Candidate Department of Computer Science University of Regina
On 19/03/2020 14:19, Farzana Anowar wrote:
Another option is to us deep learning and store the weights for the first model and initialize the second model with that weight and keep doing it for the rest of the models.
This can also be done in scikit-learn with models that support warm_start=True init parameter (including SGDClassifier). Roman
participants (3)
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Farzana Anowar -
Praneet Singh -
Roman Yurchak