tf.contrib.distributions.quadrature_scheme_softmaxnormal_quantiles

Use SoftmaxNormal quantiles to form quadrature on K - 1 simplex. (deprecated)

tf.contrib.distributions.quadrature_scheme_softmaxnormal_quantiles(
    normal_loc, normal_scale, quadrature_size, validate_args=False, name=None
)

A SoftmaxNormal random variable Y may be generated via

Y = SoftmaxCentered(X),
X = Normal(normal_loc, normal_scale)

Args
`normal_loc`	`float`-like `Tensor` with shape `[b1, ..., bB, K-1]`, B>=0. The location parameter of the Normal used to construct the SoftmaxNormal.
`normal_scale`	`float`-like `Tensor`. Broadcastable with `normal_loc`. The scale parameter of the Normal used to construct the SoftmaxNormal.
`quadrature_size`	Python `int` scalar representing the number of quadrature points.
`validate_args`	Python `bool`, default `False`. When `True` distribution parameters are checked for validity despite possibly degrading runtime performance. When `False` invalid inputs may silently render incorrect outputs.
`name`	Python `str` name prefixed to Ops created by this class.

Returns
`grid`	Shape `[b1, ..., bB, K, quadrature_size]` `Tensor` representing the convex combination of affine parameters for `K` components. `grid[..., :, n]` is the `n`-th grid point, living in the `K - 1` simplex.
`probs`	Shape `[b1, ..., bB, K, quadrature_size]` `Tensor` representing the associated with each grid point.

Args