When self_normalized = True the modified-GAN (Generative/Adversarial
Network) Csiszar-function is:
f(u) = log(1 + u) - log(u) + 0.5 (u - 1)
When self_normalized = False the 0.5 (u - 1) is omitted.
The unmodified GAN Csiszar-function is identical to Jensen-Shannon (with
self_normalized = False).
Args
logu
float-like Tensor representing log(u) from above.
self_normalized
Python bool indicating whether f'(u=1)=0. When
f'(u=1)=0 the implied Csiszar f-Divergence remains non-negative even
when p, q are unnormalized measures.
name
Python str name prefixed to Ops created by this function.
Returns
chi_square_of_u
float-like Tensor of the Csiszar-function evaluated
at u = exp(logu).