I followed the paper : https://arxiv.org/pdf/1511.06434.pdf to build a GAN model from scratch. The version is very similar to the tutorial available on PyTorch: DCGAN Tutorial — PyTorch Tutorials 2.0.0+cu117 documentation
There is one thing that confuses me though, the Generator transforms noise into a fake image, however, the Generator uses the tanh activation function, thus the values of the fake image are in the range between 1 and 1. Displaying this as an RGB image, don’t we require values between 0 and 255? On the tutorial I cannot really detect that step from (1, 1) to (0, 255).
After training the Generator for some time, how can we recover the pixels from those values to actually see a real image? I think I lack some knowledge here, so sorry if that question is rather stupid or basic!
The tutorial uses torchvision.utils.make_grid
with normalize=True
, which will  according to the docs  normalize the image to the range [0, 1]
using the min
and max
values from value_range
. By default value_range
will be set to the min/max
values of the input tensor unless its set to specific values:

normalize (bool, optional) – If True, shift the image to the range (0, 1), by the min and max values specified by
value_range
. Default: False
.

value_range (tuple, optional) – tuple (min, max) where min and max are numbers, then these numbers are used to normalize the image. By default, min and max are computed from the tensor.
Here is a code example showing this behavior:
output = (torch.rand(3, 224, 224) * 2.)  1.
print(output.min())
# tensor(1.0000)
print(output.max())
#tensor(1.0000)
out = make_grid(output)
print(out.min())
# tensor(1.0000)
print(out.max())
# tensor(1.0000)
out = make_grid(output, normalize=True)
print(out.min())
# tensor(0.)
print(out.max())
# tensor(1.0000)