# TensorFlow Probability TensorFlow 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 more here. 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 of `tf.linalg` in core TF. __Layer 1: Statistical Building Blocks__ * Distributions (`tfp.distributions`): A large collection of probability distributions and related statistics with batch and broadcasting semantics. See the Distributions Tutorial. * Bijectors (`tfp.bijectors`): Reversible and composable transformations of random variables. Bijectors provide a rich class of transformed distributions, from classical examples like the log-normal distribution to sophisticated deep learning models such as masked autoregressive flows. __Layer 2: Model Building__ * Joint Distributions (e.g., `tfp.distributions.JointDistributionSequential`): Joint distributions over one or more possibly-interdependent distributions. For an introduction to modeling with TFP's `JointDistribution`s, check out this colab * Probabilistic Layers (`tfp.layers`): Neural network layers with uncertainty over the functions they represent, extending TensorFlow Layers. __Layer 3: Probabilistic Inference__ * Markov chain Monte Carlo (`tfp.mcmc`): Algorithms for approximating integrals via sampling. Includes Hamiltonian 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. Includes Stochastic 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. ## Examples See `tensorflow_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 Autoencoder Disentangled 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. ## Installation For additional details on installing TensorFlow, guidance installing prerequisites, and (optionally) setting up virtual environments, see the TensorFlow installation guide. ### Stable Builds To install the latest stable version, run the following: ```shell # 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 >= 19.0 python -m pip install --upgrade --user pip python -m pip install --upgrade --user tensorflow tensorflow_probability ``` For CPU-only usage (and a smaller install), install with `tensorflow-cpu`. To use a pre-2.0 version of TensorFlow, run: ```shell python -m pip install --upgrade --user "tensorflow<2" "tensorflow_probability<0.9" ``` Note: Since TensorFlow is *not* included as a dependency of the TensorFlow Probability package (in `setup.py`), you must explicitly install the TensorFlow package (`tensorflow` or `tensorflow-cpu`). This allows us to maintain one package instead of separate packages for CPU and GPU-enabled TensorFlow. See the TFP release notes for more details about dependencies between TensorFlow and TensorFlow Probability. ### Nightly Builds There are also nightly builds of TensorFlow Probability under the pip package `tfp-nightly`, which depends on one of `tf-nightly` or `tf-nightly-cpu`. Nightly builds include newer features, but may be less stable than the versioned releases. Both stable and nightly docs are available here. ```shell python -m pip install --upgrade --user tf-nightly tfp-nightly ``` ### Installing from Source You can also install from source. This requires the [Bazel]( https://bazel.build/) build system. It is highly recommended that you install the nightly build of TensorFlow (`tf-nightly`) before trying to build TensorFlow Probability from source. The most recent version of Bazel that TFP currently supports is 6.4.0; support for 7.0.0+ is WIP. ```shell # sudo apt-get install bazel git python-pip # Ubuntu; others, see above links. python -m pip install --upgrade --user tf-nightly git clone https://github.com/tensorflow/probability.git cd probability bazel build --copt=-O3 --copt=-march=native :pip_pkg PKGDIR=$(mktemp -d) ./bazel-bin/pip_pkg $PKGDIR python -m pip install --upgrade --user $PKGDIR/*.whl ``` ## Community As 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 the TensorFlow Community page for more details. Check out our latest publicity here: + [Coffee with a Googler: Probabilistic Machine Learning in TensorFlow]( https://www.youtube.com/watch?v=BjUkL8DFH5Q) + [Introducing TensorFlow Probability]( https://medium.com/tensorflow/introducing-tensorflow-probability-dca4c304e245) ## Contributing We're eager to collaborate with you! See `CONTRIBUTING.md` for a guide on how to contribute. This project adheres to TensorFlow's code of conduct. By participating, you are expected to uphold this code. ## References If 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.)