Since GAN's are totally supervised learning,  It is totally on the author or 
programmer of the GAN 
to pick the CNN for it. The CNN could be a  birds or a morph between time in a 
local area, or sequence
through time. When the generator  NN is generating something between a chicken 
and a turkey the 
 CNN fire 0.5 for chicken and 0.5 for turkey. A 1.0 means it is a chicken on 
the chicken output one the 
the other side of the CNN. Or 1.0 for turkey for the dedicated output for 
turkey.

 The generator NN has a value you input for chicken, let say the value five. 
And turkey has a value of 
eight. 
 You put a the value of five into the input of the generator network and image 
of a chicken pop out 
on the other side and the CNN. That activates it dedicated chicken output when 
it see the chicken 
appear on the output of the generator NN.
 A six inputted into the generator NN will generate some thing in between the 
two birds. The CNN
will activate a  0.5 on the chicken line and 0.5 on the turkey line. 

 You need a register for each morph, hair, color, and etc.....
Which are inputted into the front end of the Generator NN.

 The training algorithm is needed for the generator NN.
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