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I trained a model with 2500000 samples and 900 features. But the result Wi were all NaN. I randomly chose 1000 samples from the dataset, and the reault seems ok.
Does it have limitation on the size of dataset or feature values?
The text was updated successfully, but these errors were encountered:
I encountered a similar overflow problem with SoftMax. And I fix the code as follows.
val margins = ws.map { w =>
VectorUtil.wxpb(w,x,1d)
}
val maxMargin = margins.max
val adjustedMargins = margins.map { margin =>
margin - maxMargin
}
val mes = adjustedMargins.map {...
I trained a model with 2500000 samples and 900 features. But the result Wi were all NaN. I randomly chose 1000 samples from the dataset, and the reault seems ok.
Does it have limitation on the size of dataset or feature values?
The text was updated successfully, but these errors were encountered: