SoftplusTransform#

class pytorch_forecasting.data.encoders.SoftplusTransform(cache_size=0)[source]#

Bases: Transform

Transform via the mapping \(\text{Softplus}(x) = \log(1 + \exp(x))\). The implementation reverts to the linear function when \(x > 20\).

Methods

`forward_shape`(shape)	Infers the shape of the forward computation, given the input shape.
`inverse_shape`(shape)	Infers the shapes of the inverse computation, given the output shape.
`log_abs_det_jacobian`(x, y)	Computes the log det jacobian log \|dy/dx\| given input and output.
`with_cache`([cache_size])

Attributes

`bijective`
`codomain`
`domain`
`event_dim`
`inv`	Returns the inverse `Transform` of this transform.
`sign`

log_abs_det_jacobian(x, y)[source]#: Computes the log det jacobian log |dy/dx| given input and output.