Package: tensorflow-probability
TensorFlow ProbabilityTensorFlow Probability is a library for probabilistic reasoning and statistical
analysis in TensorFlow. As part of the TensorFlow ecosystem, TensorFlow
Probability provides integration of probabilistic methods with deep networks,
gradient-based inference via automatic differentiation, and scalability to
large datasets and models via hardware acceleration (e.g., GPUs) and distributed
computation.TFP also works as "Tensor-friendly Probability" in pure JAX!:from tensorflow_probability.substrates import jax as tfp--
Learn morehere.Our probabilistic machine learning tools are structured as follows.Layer 0: TensorFlow.Numerical operations. In particular, the LinearOperator
class enables matrix-free implementations that can exploit special structure
(diagonal, low-rank, etc.) for efficient computation. It is built and maintained
by the TensorFlow Probability team and is now part oftf.linalgin core TF.Layer 1: Statistical Building BlocksDistributions (tfp.distributions):
A large collection of probability
distributions and related statistics with batch andbroadcastingsemantics. See theDistributions Tutorial.Bijectors (tfp.bijectors):
Reversible and composable transformations of random variables. Bijectors
provide a rich class of transformed distributions, from classical examples
like thelog-normal distributionto sophisticated deep learning models such asmasked autoregressive flows.Layer 2: Model BuildingJoint Distributions (e.g.,tfp.distributions.JointDistributionSequential):
Joint distributions over one or more possibly-interdependent distributions.
For an introduction to modeling with TFP'sJointDistributions, check outthis colabProbabilistic Layers (tfp.layers):
Neural network layers with uncertainty over the functions they represent,
extending TensorFlow Layers.Layer 3: Probabilistic InferenceMarkov chain Monte Carlo (tfp.mcmc):
Algorithms for approximating integrals via sampling. IncludesHamiltonian Monte Carlo,
random-walk Metropolis-Hastings, and the ability to build custom transition
kernels.Variational Inference (tfp.vi):
Algorithms for approximating integrals via optimization.Optimizers (tfp.optimizer):
Stochastic optimization methods, extending TensorFlow Optimizers. IncludesStochastic Gradient Langevin Dynamics.Monte Carlo (tfp.monte_carlo):
Tools for computing Monte Carlo expectations.TensorFlow Probability is under active development. Interfaces may change at any
time.ExamplesSeetensorflow_probability/examples/for end-to-end examples. It includes tutorial notebooks such as:Linear Mixed Effects Models.
A hierarchical linear model for sharing statistical strength across examples.Eight Schools.
A hierarchical normal model for exchangeable treatment effects.Hierarchical Linear Models.
Hierarchical linear models compared among TensorFlow Probability, R, and Stan.Bayesian Gaussian Mixture Models.
Clustering with a probabilistic generative model.Probabilistic Principal Components Analysis.
Dimensionality reduction with latent variables.Gaussian Copulas.
Probability distributions for capturing dependence across random variables.TensorFlow Distributions: A Gentle Introduction.
Introduction to TensorFlow Distributions.Understanding TensorFlow Distributions Shapes.
How to distinguish between samples, batches, and events for arbitrarily shaped
probabilistic computations.TensorFlow Probability Case Study: Covariance Estimation.
A user's case study in applying TensorFlow Probability to estimate covariances.It also includes example scripts such as:Representation learning with a latent code and variational inference.Vector-Quantized Autoencoder.
Discrete representation learning with vector quantization.Disentangled Sequential Variational AutoencoderDisentangled representation learning over sequences with variational inference.Bayesian Neural Networks.
Neural networks with uncertainty over their weights.Bayesian Logistic Regression.
Bayesian inference for binary classification.InstallationFor additional details on installing TensorFlow, guidance installing
prerequisites, and (optionally) setting up virtual environments, see theTensorFlow installation guide.Stable BuildsTo install the latest stable version, run the following:# Notes:# - The `--upgrade` flag ensures you'll get the latest version.# - The `--user` flag ensures the packages are installed to your user directory#   rather than the system directory.# - TensorFlow 2 packages require a pip >= 19.0python-mpipinstall--upgrade--userpip
python-mpipinstall--upgrade--usertensorflowtensorflow_probabilityFor CPU-only usage (and a smaller install), install withtensorflow-cpu.To use a pre-2.0 version of TensorFlow, run:python-mpipinstall--upgrade--user"tensorflow<2""tensorflow_probability<0.9"Note: SinceTensorFlowisnotincluded
as a dependency of the TensorFlow Probability package (insetup.py), you must
explicitly install the TensorFlow package (tensorflowortensorflow-cpu).
This allows us to maintain one package instead of separate packages for CPU and
GPU-enabled TensorFlow. See theTFP release notesfor more
details about dependencies between TensorFlow and TensorFlow Probability.Nightly BuildsThere are also nightly builds of TensorFlow Probability under the pip packagetfp-nightly, which depends on one oftf-nightlyortf-nightly-cpu.
Nightly builds include newer features, but may be less stable than the
versioned releases. Both stable and nightly docs are availablehere.python-mpipinstall--upgrade--usertf-nightlytfp-nightlyInstalling from SourceYou can also install from source. This requires theBazelbuild system. It is highly recommended that you install
the nightly build of TensorFlow (tf-nightly) before trying to build
TensorFlow Probability from source.# sudo apt-get install bazel git python-pip  # Ubuntu; others, see above links.python-mpipinstall--upgrade--usertf-nightly
gitclonehttps://github.com/tensorflow/probability.gitcdprobability
bazelbuild--copt=-O3--copt=-march=native:pip_pkgPKGDIR=$(mktemp-d)./bazel-bin/pip_pkg$PKGDIRpython-mpipinstall--upgrade--user$PKGDIR/*.whlCommunityAs part of TensorFlow, we're committed to fostering an open and welcoming
environment.Stack Overflow: Ask
or answer technical questions.GitHub: Report bugs or
make feature requests.TensorFlow Blog: Stay up to date on content
from the TensorFlow team and best articles from the community.Youtube Channel: Follow TensorFlow shows.tfprobability@tensorflow.org:
Open mailing list for discussion and questions.See theTensorFlow Communitypage for
more details. Check out our latest publicity here:Coffee with a Googler: Probabilistic Machine Learning in TensorFlowIntroducing TensorFlow ProbabilityContributingWe're eager to collaborate with you! SeeCONTRIBUTING.mdfor a guide on how to contribute. This project adheres to TensorFlow'scode of conduct. By participating, you are expected to
uphold this code.ReferencesIf you use TensorFlow Probability in a paper, please cite:TensorFlow Distributions.Joshua V. Dillon, Ian Langmore, Dustin Tran,
Eugene Brevdo, Srinivas Vasudevan, Dave Moore, Brian Patton, Alex Alemi, Matt
Hoffman, Rif A. Saurous.arXiv preprint arXiv:1711.10604, 2017.(We're aware there's a lot more to TensorFlow Probability than Distributions, but the Distributions paper lays out our vision and is a fine thing to cite for now.)