Something like this should work:

x = torch.randn(10, 10, 10)
y = x - x.min()
y = y / y.max() * 2 - 1
print(y.min(), y.max())
> tensor(-1.) tensor(1.)



Normalize subtracts the mean and divides by the stddev to create normalized/standardized outputs, which are also known as z-scores.


It depends on your use case and yes, you can also load the already pretrained model and continue the training with the lower learning rate.