tfp.vi.modified_gan

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

tfp.vi.modified_gan(
    logu,
    self_normalized=False,
    name=None
)

Defined in python/vi/csiszar_divergence.py.

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).

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).