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The Modified-GAN Csiszar-function in log-space.

A Csiszar-function is a member of,

F = { f:R_+ to R : f convex }.

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).

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.

chi_square_of_u float-like Tensor of the Csiszar-function evaluated at u = exp(logu).