  Generative Networks have shown great promise in generating photo-realistic
images. Despite this, the theory surrounding them is still an active research
area. Much of the useful work with Generative networks rely on heuristics that
tend to produce good results. One of these heuristics is the advice not to use
Batch Normalization in the final layer of the generator network. Many of the
state-of-the-art generative network architectures use this heuristic, but the
reasons for doing so are inconsistent. This paper will show that this is not
necessarily a good heuristic and that Batch Normalization can be beneficial in
the final layer of the generator network either by placing it before the final
non-linear activation, usually a $tanh$ or replacing the final $tanh$
activation altogether with Batch Normalization and clipping. We show that this
can lead to the faster training of Generator networks by matching the generator
to the mean and standard deviation of the target distribution's image colour
values.
