"log_softmax function" in pytorch tutorial example

I am studying the link below.

But I can’t understand “log_softmax” written in this document.

def log_softmax(x):
    return x - x.exp().sum(-1).log().unsqueeze(-1)

How this function match to the figure below?

Hi Dong Wook!

My guess is that you’re being thrown off by the “log-sum-exp trick”
that is being used to rewrite the “standard” expression for
log_softmax in a (mathematically-equivalent) form that avoids
floating-point overflow / underflow problems when evaluating
the expression numerically.

See the “log-sum-exp trick for log-domain calculations” section of
the LogSumExp Wikipedia article for an explanation. (This article
is not specifically about the log_softmax function, but instead
about the related LogSumExp function. log_softmax has the
same potential overflow problems, and they are avoided using
the same log-sum-exp trick.)

Good luck.

K. Frank

1 Like

Thank you very much for your explanation. It would be nice if I could find the same trick elsewhere, but it is not easy to find the same trick.