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TensorFlow Probability API-unstable package.
This package contains potentially useful code which is under active development
with the intention eventually migrate to TFP proper. All code in
tfp.experimental
should be of production quality, i.e., idiomatically
consistent, well tested, and extensively documented. tfp.experimental
code
relaxes the TFP non-experimental contract in two regards:
tfp.experimental
has no API stability guarantee. The public footprint oftfp.experimental
code may change without notice or warning.- Code outside
tfp.experimental
cannot depend on code withintfp.experimental
.
You are welcome to try any of this out (and tell us how well it works for you!).
Modules
auto_batching
module: TensorFlow Probability auto-batching package.
bijectors
module: TensorFlow Probability experimental bijectors package.
distribute
module: Experimental module for doing distributed log prob calculations.
distributions
module: TensorFlow Probability experimental distributions package.
lazybones
module: Graphify anything and everything.
linalg
module: Experimental tools for linear algebra.
marginalize
module: Marginalizable probability distributions.
mcmc
module: TensorFlow Probability experimental MCMC package.
nn
module: Tools for building neural networks.
parallel_filter
module: TensorFlow Probability experimental parallel filtering package.
sequential
module: TensorFlow Probability experimental sequential estimation package.
stats
module: Statistical functions.
substrates
module: TensorFlow Probability alternative substrates.
util
module: TensorFlow Probability experimental python utilities.
vi
module: Experimental methods and objectives for variational inference.
Classes
class AutoCompositeTensor
: Recommended base class for @auto_composite_tensor
-ified classes.
Functions
as_composite(...)
: Returns a CompositeTensor
equivalent to the given object.
auto_composite_tensor(...)
: Automagically generate CompositeTensor
behavior for cls
.
register_composite(...)
: A decorator that registers a TFP object as composite-friendly.