tf.contrib.distributions.quadrature_scheme_softmaxnormal_quantiles(
normal_loc,
normal_scale,
quadrature_size,
validate_args=False,
name=None
)
Defined in tensorflow/contrib/distributions/python/ops/vector_diffeomixture.py
.
Use SoftmaxNormal quantiles to form quadrature on K - 1
simplex. (deprecated)
A SoftmaxNormal
random variable Y
may be generated via
Y = SoftmaxCentered(X),
X = Normal(normal_loc, normal_scale)
Args:
normal_loc
:float
-likeTensor
with shape[b1, ..., bB, K-1]
, B>=0. The location parameter of the Normal used to construct the SoftmaxNormal.normal_scale
:float
-likeTensor
. Broadcastable withnormal_loc
. The scale parameter of the Normal used to construct the SoftmaxNormal.quadrature_size
: Pythonint
scalar representing the number of quadrature points.validate_args
: Pythonbool
, defaultFalse
. WhenTrue
distribution parameters are checked for validity despite possibly degrading runtime performance. WhenFalse
invalid inputs may silently render incorrect outputs.name
: Pythonstr
name prefixed to Ops created by this class.
Returns:
grid
: Shape[b1, ..., bB, K, quadrature_size]
Tensor
representing the convex combination of affine parameters forK
components.grid[..., :, n]
is then
-th grid point, living in theK - 1
simplex.probs
: Shape[b1, ..., bB, K, quadrature_size]
Tensor
representing the associated with each grid point.