TensorFlow Probability 0.12.0-rc2

This is RC2 of the TensorFlow Probability 0.12 release. It is tested against TensorFlow 2.4.0-rc2.

TensorFlow Probability 0.12.0-rc1

This is RC1 of the TensorFlow Probability 0.12 release. It is tested against TensorFlow 2.4.0-rc1.

TensorFlow Probability 0.12.0-rc0

This is RC0 of the TensorFlow Probability 0.12 release. It is tested against TensorFlow 2.4.0-rc0.

TensorFlow Probability 0.11.1

This is a patch release for compatibility with CloudPickle >= 1.3. It is tested and stable against TensorFlow version 2.3.0.

TensorFlow Probability 0.11.0

Release notes

This is the 0.11 release of TensorFlow Probability. It is tested and stable against TensorFlow version 2.3.0.

Change notes

Links point to examples in the TFP 0.11.0 release Colab.

• Distributions

  • Support automatic vectorization in JointDistribution*AutoBatched instances.
  • Reproducible sampling, even in Eager.
  • Add Weibull distribution.
  • Add TruncatedCauchy distribution.
  • Add SphericalUniform distribution.
  • Add PowerSpherical distribution.
  • Add LogLogistic distribution.
  • Add Bates distribution.
  • Add GeneralizedNormal distribution.
  • Add JohnsonSU distribution.
  • Add ContinuousBernoulli distribution.
  • Simplify MultivariateNormalDiagPlusLowRank and make it tape-safer; remove deprecation.
  • Added KL(PowerSpherical || VonMisesFisher)
  • Adds KL(PowerSpherical || UniformSpherical), PowerSpherical.entropy and SphericalUniform.entropy
  • Fix gradient for Gamma samples with respect to rate parameter.
  • Increase accuracy of default Distribution.{log_}survival_function if log_cdf is implemented but cdf is not.
  • More accurate log_probs and entropies across many Distributions that were subtracting lgammas under the hood.
  • Fix Multinomial log_prob when classes have zero probability.
  • Improve performance of Multinomial sampler when total_count is high.
  • More accurate Binomial sampling and log_prob for large counts and small probabilities.
  • Binomial will no longer emit samples below 0 or above total_count.
  • Add nan handling for Bates log_prob and cdf.
  • Allow named arguments in JointDistribution*.sample().

• Bijectors:

  • Add the Split bijector.
  • Add GompertzCDF and ShiftedGompertzCDF bijectors
  • Add Sinh bijector.
  • Scale bijector can take in log_scale parameter.
  • Blockwise now supports size changing bijectors.
  • Allow using conditioning inputs in AutoregressiveNetwork.
  • Move bijector caching logic to its own library.

• MCMC:

  • tfp.mcmc now supports stateless sampling. tfp.mcmc.sample_chain(..., seed=(1,2)) is expected to always return the same results (within a release), and is deterministic (provided the underlying kernel is deterministic).
  • Better static shape inference for Metropolis-Hastings kernels with partially-specified shapes.
  • TransformedTransitionKernel nests properly with itself and other wrapper kernels.
  • Pretty-printing MCMC kernel results.

• Structured time series:

  • Automatically constrain STS inference when weights have constrained support.

• Math:

  • Add tfp.math.bessel_iv_ratio for ratios of modified bessel functions of the first kind.
  • round_exponential_bump_function added to tfp.math.
  • Support dynamic num_steps and custom convergence_criteria in tfp.math.minimize.
  • Add tfp.math.log_cosh.
  • Define more accurate lbeta and log_gamma_difference.

• Jax/Numpy substrates:

  • TFP runs on JAX!
  • Expose MaskedAutogregressiveFlow to Numpy and JAX.

• Experimental:

  • Add experimental Sequential Monte Carlo sample driver.
  • Add experimental tools for estimating parameters of sequential models using iterated filtering.
  • Use Distributions as CompositeTensors.
  • Inference Gym: Add logistic regression.
  • Add support for convergence criteria in tfp.vi.fit_surrogate_posterior.

• Other:

  • Added tfp.random.split_seed for stateless sampling. Moved tfp.math.random_{rademacher,rayleigh} to tfp.random.{rademacher,rayleigh}.
  • Possibly breaking change: SeedStream seed argument may not be a Tensor.

Huge thanks to all the contributors to this release!

• Alexey Radul
• anatoly
• Anudhyan Boral
• Ben Lee
• Brian Patton
• Christopher Suter
• Colin Carroll
• Cristi Cobzarenco
• Dan Moldovan
• Dave Moore
• David Kao
• Emily Fertig
• erdembanak
• Eugene Brevdo
• Fearghus Robert Keeble
• Frank Dellaert
• Gabriel Loaiza
• Gregory Flamich
• Ian Langmore
• Iqrar Agalosi Nureyza
• Jacob Burnim
• jeffpollock9
• jekbradbury
• Jimmy Yao
• johannespitz
• Joshua V. Dillon
• Junpeng Lao
• Kate Lin
• Ken Franko
• luke199629
• Mark Daoust
• Markus Kaiser
• Martin Jul
• Matthew Feickert
• Maxim Polunin
• Nicolas
• npfp
• Pavel Sountsov
• Peng YU
• Rebecca Chen
• Rif A. Saurous
• Ru Pei
• Sayam753
• Sharad Vikram
• Srinivas Vasudevan
• summeryue
• Tom Charnock
• Tres Popp
• Wataru Hashimoto
• Yash Katariya
• Zichun Ye

TensorFlow Probability 0.11.0-rc1

This is RC1 of the TensorFlow Probability 0.11 release. It is tested against TensorFlow 2.3.0-rc2.

TensorFlow Probability 0.11.0-rc0

This is RC0 of the TensorFlow Probability 0.11 release. It is tested against TensorFlow 2.3.0-rc1.

TensorFlow Probability 0.10.1

This is a patch release to pin the CloudPickle version to 1.3 to address #991 . It is tested and stable against TensorFlow version 2.2.0.

Tensorflow Probability 0.10.0

Release notes

This is the 0.10 release of TensorFlow Probability. It is tested and stable against TensorFlow version 2.2.0.

Change notes

• Distributions

  • Beta-Binomial distribution.
  • Add new AutoBatched joint distribution variants that treat a joint sample as a single probabilistic event.
  • XLA-able Python TF Gamma sampler.
  • XLA-able binomial sampler. Replaces the existing sampler, which implements binomial using one-hot categoricals via multinomial, with a batched rejection sampler. The new sampler is 4-6 times slower for very small problems, but an unbounded amount faster on large problems, since it removes a linear dependency on total_count. Additionally, since the previous solver required memory proportional to total_count*num_samples, many problems which OOM'd before are now feasible.
  • Enable use of joint bijectors in TransformedDistribution.
  • Remove unused get_logits_and_probs from internal/distribution_util.
  • Batched rejection sampling utilities.
  • Update batched_rejection_sampler to use prefer_static.shape to handle possibly-dynamic shape.

• Bijectors

  • Add Lambert W transform bijectors.

• MCMC

  • EllipticalSliceSampler in tfp.experimental.mcmc
  • Add cross-chain ESS, following Vehtari et al. 2019.

• Optimizer

  • Add convergence criteria for optimizations.

• Stats

  • Add tfp.stats.expected_calibration_error_quantiles.

• Math

  • Add a 'special' module to tfp.math - a TF version of scipy.special.
  • Add scan_associative function, implementing parallel prefix scan of tensors with a user-provided binary operation.

• Breaking change: Removed a number of functions, methods, and classes that were deprecated in TensorFlow Probability 0.9.0 or earlier.

  • Removed deprecated tfb.Weibull -- use tfb.WeibullCDF.
  • Remove VectorLaplaceLinearOperator
  • Remove deprecated method tfp.sts.build_factored_variational_loss.
  • Remove deprecated tfb.Kumaraswamy -- use tfb.Invert(tfb.KumaraswamyCDF).
  • Remove deprecated tfd.VectorSinhArcsinhDiag, tfd.VectorLaplaceDiag.
  • Remove deprecated tfb.Gumbel -- use tfb.GumbelCDF.

• Other

  • Python 3.8 compatibility.
  • TensorFlow now requires gast version 0.3.2 and is no longer compatible with 0.2.2.
  • Moving TF Session C++ to Python code and functionality from swig to pybind11.
  • Update TFP examples to Python 3.

Huge thanks to all the contributors to this release!

• Alexander Ivanov
• Alexey Radul
• Amanda
• Amelio Vazquez-Reina
• Amit Patankar
• Anudhyan Boral
• Artem Belevich
• Brian Patton
• Christopher Suter
• Colin Carroll
• Dan Moldovan
• Dave Moore
• Demetri Pananos
• Dmitrii Kochkov
• Emily Fertig
• gameshamilton
• Georg M. Goerg
• Ian Langmore
• Jacob Burnim
• jeffpollock9
• Joshua V. Dillon
• Junpeng Lao
• kovak1
• Kristian Hartikainen
• Liam
• Martin Jul
• Matt Hoffman
• nbro
• Olli Huotari
• Pavel Sountsov
• Pyrsos
• Rif A. Saurous
• Rushabh Vasani
• Sayam753
• Sharad Vikram
• Spyros
• Srinivas Vasudevan
• Taylor Robie
• Xiaojing Wang
• Zichun Ye

Tensorflow Probability 0.10.0-rc1

This is RC1 of the TensorFlow Probability 0.10 release. It is tested against TensorFlow 2.2.0-rc4.