clamp cross-entropy loss

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Matt Fetterman
Matt Fetterman 2020-9-3
评论： Matt Fetterman 2020-9-6
the Matlab cross-entropy loss has this form:
loss = -sum(W*(T.*log(Y)))/N;
I would like to "clamp" it so that the log function output is bounded, for example it cannot be less than 100.
See here: https://pytorch.org/docs/stable/generated/torch.nn.BCELoss.html
Can we do it?

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David Goodmanson
David Goodmanson 2020-9-3
编辑：David Goodmanson 2020-9-6
Hi Matt,
z = log(Y);
z(z<100) = 100;
loss = -sum(W*(T.*z))/N;
In the link you provided, they talk about a limit of -100 rather than +100. The former appears to make more sense. Lots of possibilities for a smooth differentiable cutoff, here is one, assuming Y>=0
Ylimit = -100;
loss = -sum(W*(T.*log(Y+exp(Ylimit)))/N;
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David Goodmanson
David Goodmanson 2020-9-6
Hi Matt,
see amended answer.
Matt Fetterman
Matt Fetterman 2020-9-6
probably a smart approach.

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