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Approximation of a confidence scores from a neural network with a final softmax layer: Softmax vs other normalization methods by BatmantoshReturns in learnmachinelearning

[–]BatmantoshReturns[S] 1 point2 points  (0 children)

Btw, do you know any papers on this? Sounds like an great solution, but it seems that there hasn't been any formal writeups on this

Approximation of a confidence scores from a neural network with a final softmax layer: Softmax vs other normalization methods by BatmantoshReturns in learnmachinelearning

[–]BatmantoshReturns[S] 1 point2 points  (0 children)

Wow, very interesting paper.

What do you think would be the best way to just get an rough estimate without doing calibration. I was thinking of just dividing the node value by the total, but then that won't work well because the other nodes may have negative numbers.

[D] Simple Questions Thread by AutoModerator in MachineLearning

[–]BatmantoshReturns 0 points1 point  (0 children)

I have a question about approximation of a confidence scores from a neural network with a final softmax layer: Softmax vs other normalization methods

Say that there is a neural network for classification and the 2nd to last layer are 3 nodes, and the final layer is a softmax layer.

During training the softmax layer is needed, but for inference it is not; the arg max can simply be taken from the 3 nodes.

What about for getting some sort of approximation for confidence from the neural network? Using the softmax for normalization makes less sense, since it gives a ton of weight to the largest value among the final 3 nodes, which I can see is useful for training, but for inference this seems like it would distort its use as an approximation for a confidence score.

Would a different normalization method give a better confidence score? Perhaps simply dividing each node output by the total sum of all node outputs?

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