tensorflow_probability/python/vi/csiszar_divergence.py
def kl_reverse(logu, self_normalized=False, name=None):
  """The reverse Kullback-Leibler Csiszar-function in log-space.

  A Csiszar-function is a member of,

  ```none
  F = { f:R_+ to R : f convex }.
  ```

  When `self_normalized = True`, the KL-reverse Csiszar-function is:

  ```none
  f(u) = -log(u) + (u - 1)
  ```

  When `self_normalized = False` the `(u - 1)` term is omitted.

  Observe that as an f-Divergence, this Csiszar-function implies:

  ```none
  D_f[p, q] = KL[q, p]
  ```

  The KL is "reverse" because in maximum likelihood we think of minimizing `q`
  as in `KL[p, q]`.

  Warning: when self_normalized = True` this function makes non-log-space
  calculations and may therefore be numerically unstable for `|logu| >> 0`.

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

  Raises:
    TypeError: if `self_normalized` is `None` or a `Tensor`.
  """

  with tf.compat.v1.name_scope(name, "kl_reverse", [logu]):
    return amari_alpha(logu, alpha=0., self_normalized=self_normalized)
def kl_reverse(logu, self_normalized=False, name=None):
  with tf.compat.v1.name_scope(name, "kl_reverse", [logu]):
    return amari_alpha(logu, alpha=0., self_normalized=self_normalized)
def kl_reverse ( logu , self_normalized = False , name = None ) : with tf . compat . v1 . name_scope ( name , "kl_reverse" , [ logu ] ) : return amari_alpha ( logu , alpha = 0. , self_normalized = self_normalized )
The reverse Kullback-Leibler Csiszar-function in log-space.

  A Csiszar-function is a member of,

  ```none
  F = { f:R_+ to R : f convex }.
  ```

  When `self_normalized = True`, the KL-reverse Csiszar-function is:

  ```none
  f(u) = -log(u) + (u - 1)
  ```

  When `self_normalized = False` the `(u - 1)` term is omitted.

  Observe that as an f-Divergence, this Csiszar-function implies:

  ```none
  D_f[p, q] = KL[q, p]
  ```

  The KL is "reverse" because in maximum likelihood we think of minimizing `q`
  as in `KL[p, q]`.

  Warning: when self_normalized = True` this function makes non-log-space
  calculations and may therefore be numerically unstable for `|logu| >> 0`.

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

  Raises:
    TypeError: if `self_normalized` is `None` or a `Tensor`.
The reverse Kullback - Leibler Csiszar - function in log - space .