SoftmaxTransform

class tensorplay.distributions.SoftmaxTransform(cache_size: int = 0)[source]

Transform from unconstrained space to the simplex via $y = \exp(x)$ then normalizing.

This is not bijective and cannot be used for HMC. However this acts mostly coordinate-wise (except for the final normalization), and thus is appropriate for coordinate-wise optimization algorithms.

property inv: Transform

Returns the inverse Transform of this transform. This should satisfy t.inv.inv is t.

log_abs_det_jacobian(x, y)

Computes the log det jacobian log |dy/dx| given input and output.

property sign: int

Returns the sign of the determinant of the Jacobian, if applicable. In general this only makes sense for bijective transforms.