Source code for pyro.distributions.transforms.softplus

# Copyright Contributors to the Pyro project.
# SPDX-License-Identifier: Apache-2.0

from torch.distributions import constraints
from torch.distributions.transforms import Transform
from torch.nn.functional import softplus

def softplus_inv(y):
    return y + y.neg().expm1().neg().log()

# Backport of
[docs]class SoftplusTransform(Transform): r""" Transform via the mapping :math:`\text{Softplus}(x) = \log(1 + \exp(x))`. """ domain = constraints.real codomain = constraints.positive bijective = True sign = +1 def __eq__(self, other): return isinstance(other, SoftplusTransform) def _call(self, x): return softplus(x) def _inverse(self, y): return softplus_inv(y)
[docs] def log_abs_det_jacobian(self, x, y): return -softplus(-x)
[docs]class SoftplusLowerCholeskyTransform(Transform): """ Transform from unconstrained matrices to lower-triangular matrices with nonnegative diagonal entries. This is useful for parameterizing positive definite matrices in terms of their Cholesky factorization. """ domain = constraints.independent(constraints.real, 2) codomain = constraints.lower_cholesky def __eq__(self, other): return isinstance(other, SoftplusLowerCholeskyTransform) def _call(self, x): diag = softplus(x.diagonal(dim1=-2, dim2=-1)) return x.tril(-1) + diag.diag_embed() def _inverse(self, y): diag = softplus_inv(y.diagonal(dim1=-2, dim2=-1)) return y.tril(-1) + diag.diag_embed()
__all__ = [ "SoftplusTransform", "SoftplusLowerCholeskyTransform", ]