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model = KRG({})
model.set_training_values(inputsincolumns,outputsincolumns)
model.train()
...
#============
The amount of data:inputsincolumns(6 rows and 3000 columns),outputsincolumns(7 rows and 3000 columns)
and the trainning takes almost 5mins
Is there some way to increase training speed?
The text was updated successfully, but these errors were encountered:
I suppose you meant 3000 rows with 6 or 7 columns as number of rows should be the same for inputs and outputs. You can try to remove some training samples and use them to validate the surrogate and see if it is good enough.
...
model = KRG({})
model.set_training_values(inputsincolumns,outputsincolumns)
model.train()
...
#============
The amount of data:inputsincolumns(6 rows and 3000 columns),outputsincolumns(7 rows and 3000 columns)
and the trainning takes almost 5mins
Is there some way to increase training speed?
The text was updated successfully, but these errors were encountered: