You signed in with another tab or window. Reload to refresh your session.You signed out in another tab or window. Reload to refresh your session.You switched accounts on another tab or window. Reload to refresh your session.Dismiss alert
In the documentation it is stated that partial training can be used to reduce memory consumption.
I tried to train a Softmax classifier with several datasets and partial methods.
But only the first labels of the train() method are known. If new labels are present in the dataset given to the partial() method, they are not taken into account.
Can Dataset object retain set of all labels after Labeled::fold() method?
Regards.
The text was updated successfully, but these errors were encountered:
Yes, the first training set defines all the possible labels for the model. If you want to fold your dataset such that each fold has samples that correspond to all possible classes in the master dataset then you can use the straftifiedFold() method.
Hi,
In the documentation it is stated that partial training can be used to reduce memory consumption.
I tried to train a Softmax classifier with several datasets and partial methods.
But only the first labels of the
train()
method are known. If new labels are present in the dataset given to thepartial()
method, they are not taken into account.Can Dataset object retain set of all labels after
Labeled::fold()
method?Regards.
The text was updated successfully, but these errors were encountered: