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TensorFlow Probability 0.9
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@jburnim jburnim released this 15 Jan 05:30
v0.9.0
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Release notes

This is the 0.9 release of TensorFlow Probability. It is tested and stable against TensorFlow version 2.1.0.

NOTE: The 0.9 releases of TensorFlow Probability will be the last to support Python 2. Future versions of TensorFlow Probability will require Python 3.5 or later.

Change notes

  • Distributions

    • Add Pixel CNN++ distribution.
    • Breaking change: Remove deprecated behavior of Poisson.rate and Poisson.log_rate.
    • Breaking change: Remove deprecated behavior of logits, probs properties.
    • Add _default_event_space_bijector to distributions.
    • Add validation that samples are within the support of the distribution.
    • Support positional and keyword args to JointDistribution.prob and JointDistribution.log_prob.
    • Support OrderedDict dtype in JointDistributionNamed.
    • tfd.BatchReshape is tape-safe
    • More accurate survival function and CDF for the generalized Pareto distribution.
    • Added Plackett-Luce distribution over permutations.
    • Fix long-standing bug with cdf, survival_function, and quantile for TransformedDistributions having decreasing bijectors.
    • Export the DoubleMaxwell distribution.
    • Add method for analytic Bayesian linear regression with LinearOperators.

  • Bijectors

    • Breaking change: Scalar bijectors must implement _is_increasing if using cdf/survival_function/quantile on TransformedDistribution. This supports resolution of a long-standing bug, e.g. tfb.Scale(scale=-1.)(tfd.HalfNormal(0,1)).cdf was incorrect.
    • Deprecate tfb.masked_autoregressive_default_template.
    • Fixed inverse numerical stability bug in tfb.Softfloor
    • Tape-safe Reshape bijector.

  • MCMC

    • Optimize tfp.mcmc.ReplicaExchangeMonteCarlo by replacing TF control flow and
    • ReplicaExchangeMC now can trace exchange proposals/acceptances.
    • Correct implementation of log_accept_ratio in NUTS
    • Return non-cumulated leapfrogs_taken in nuts kernel_result.
    • Make unrolled NUTS reproducible.
    • Bug fix of Generalized U-turn in NUTS.
    • Reduce NUTS test flakiness.
    • Fix convergence test for NUTS.
    • Switch back to original U turn criteria in Hoffman & Gelman 2014.
    • Make autobatched NUTS reproducible.

  • STS

    • Update example "Structural Time Series Modeling Case Studies" to TF2.0 API.
    • Add fast path for sampling STS LocalLevel models.
    • Support posterior sampling in linear Gaussian state space models.
    • Add a fast path for Kalman smoothing with scalar latents.
    • Add option to disallow drift in STS Seasonal models.

  • Breaking change: Removed a number of functions, methods, and classes that were deprecated in TensorFlow Probability 0.8.0 or earlier.

    • Remove deprecated trainable_distributions_lib.
    • Remove deprecated property Dirichlet.total_concentration.
    • Remove deprecated tfb.AutoregressiveLayer -- use tfb.AutoregressiveNetwork.
    • Remove deprecated tfp.distributions.* methods.
    • Remove deprecated tfp.distributions.moving_mean_variance.
    • Remove two deprecated tfp.vi functions.
    • Remove deprecated tfp.distributions.SeedStream -- use tfp.util.SeedStream.
    • Remove deprecated properties of tfd.Categorical.

  • Other

    • Add make_rank_polymorphic utility, which lifts a callable to a vectorized callable.
    • Dormand-Prince solver supports nested structures. Implemented adjoint sensitivity method for Dormand-Prince solver gradients.
    • Run Travis tests against latest tf-estimator-nightly.
    • Supporting gast 0.3 +
    • Add tfp.vi.build_factored_surrogate_posterior utility for automatic black-box variational inference.

Huge thanks to all the contributors to this release!

  • Aditya Grover
  • Alexey Radul
  • Anudhyan Boral
  • Arthur Lui
  • Billy Lamberta
  • Brian Patton
  • Christopher Suter
  • Colemak
  • Dan Moldovan
  • Dave Moore
  • Dmitrii Kochkov
  • Edward Loper
  • Emily Fertig
  • Ian Langmore
  • Jacob Burnim
  • Joshua V. Dillon
  • Junpeng Lao
  • Katherine Wu
  • Kibeom Kim
  • Kristian Hartikainen
  • Mark Daoust
  • Pavel Sountsov
  • Peter Hawkins
  • refraction-ray
  • RJ Skerry-Ryan
  • Sanket Kamthe
  • Sergei Lebedev
  • Sharad Vikram
  • Srinivas Vasudevan
  • Yanhua Sun
  • Yash Katariya
  • Zachary Nado
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