TF 2.0 is out! Get hands-on practice at TF World, Oct 28-31. Use code TF20 for 20% off select passes. Register now


View source on GitHub

Create a random variable for SinhArcsinh.



See SinhArcsinh for more details.



Original Docstring for Distribution

Construct SinhArcsinh distribution on (-inf, inf).

Arguments (loc, scale, skewness, tailweight) must have broadcastable shape (indexing batch dimensions). They must all have the same dtype.


  • loc: Floating-point Tensor.
  • scale: Tensor of same dtype as loc.
  • skewness: Skewness parameter. Default is 0.0 (no skew).
  • tailweight: Tailweight parameter. Default is 1.0 (unchanged tailweight)
  • distribution: tf.Distribution-like instance. Distribution that is transformed to produce this distribution. Default is tfd.Normal(0., 1.). Must be a scalar-batch, scalar-event distribution. Typically distribution.reparameterization_type = FULLY_REPARAMETERIZED or it is a function of non-trainable parameters. WARNING: If you backprop through a SinhArcsinh sample and distribution is not FULLY_REPARAMETERIZED yet is a function of trainable variables, then the gradient will be incorrect!
  • 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.
  • allow_nan_stats: Python bool, default True. When True, statistics (e.g., mean, mode, variance) use the value "NaN" to indicate the result is undefined. When False, an exception is raised if one or more of the statistic's batch members are undefined.
  • name: Python str name prefixed to Ops created by this class.