layer_autoregressive_transform {tfprobability} | R Documentation |
layer_autoregressive
.Following Papamakarios et al. (2017), given
an autoregressive model p(x)
with conditional distributions in the location-scale
family, we can construct a normalizing flow for p(x)
.
layer_autoregressive_transform(object, made, ...)
object |
What to compose the new
|
made |
A |
... |
Additional parameters passed to Keras Layer. |
Specifically, suppose made is a [layer_autoregressive()]
– a layer implementing
a Masked Autoencoder for Distribution Estimation (MADE) – that computes location
and log-scale parameters made(x)[i]
for each input x[i]
. Then we can represent
the autoregressive model p(x)
as x = f(u)
where u
is drawn
from from some base distribution and where f
is an invertible and
differentiable function (i.e., a Bijector) and f^{-1}(x)
is defined by:
library(tensorflow) library(zeallot) f_inverse <- function(x) { c(shift, log_scale) %<-% tf$unstack(made(x), 2, axis = -1L) (x - shift) * tf$math$exp(-log_scale) }
Given a layer_autoregressive()
made, a layer_autoregressive_transform()
transforms an input tfd_*
p(u)
to an output tfd_*
p(x)
where
x = f(u)
.
a Keras layer
tfb_masked_autoregressive_flow()
and layer_autoregressive()