Hi Jeshua!



 3DTOPO:

Using:
mean=0.488738864660263
std=0.247761115431785

Considering:
(sample - mean) / std

Expanded:
(0.3415 - 0.488738864660263) / 0.247761115431785 = -0.5942775338400



Yes, this is what Normalize does (and how it is supposed to work).


I was under the impression that the standard practice with PyTorch normalization is to use the range of 0.0 to 1.0 (or some other range
of order 1).


Not necessarily.
If your input values were approximately uniformly distributed over
the range 100 to 200, you might choose to transform them so that
they would lie in the range 0 to 1.
But if your values were approximately from a Gaussian distribution
with a mean of 150 and a standard deviation of 50, you might
choose to normalize them so they had a mean of 0 and a standard
deviation of 1.


Could -0.5942775338400 be correct?


Yes.  After normalization (to a mean of 0 and standard deviation of 1),
one of your sample values is 0.59 standard deviations away from the
mean.  This is perfectly reasonable, and, indeed, to be expected.
Best.
K. Frank