Penalize high confident false negative classifications?

[callenilsson]

❓ Questions & Help

I added the line logits = torch.nn.functional.softmax(logits) to convert binary classifications to a confidence score between 0.0 - 1.0. However, the predictions are very harsh being really close to either 0.0 or 1.0 and not somewhere in between. Is there a way to penalize the model from being so categorical? I especially want to minimize high confidence scores on false negatives.

[Torec]

The softmax function specifically uses exponentiation to exacerbate the differences in scores (to get the soft 'max'). You can normalize scores by other means than a softmax.

Related to your title: using a log loss will penalize wrong predictions with high confidence more (e.g. BCE).

[thomwolf]

Related read is the section entitled "Don’t Mistake Class Probabilities for Confidence" here: https://www.inovex.de/blog/uncertainty-quantification-deep-learning/

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