Skip to content

TensorFlow Probability 0.13.0

Choose a tag to compare

View all tags
@brianwa84 brianwa84 released this 18 Jun 21:06
v0.13.0
9e50fa3
This commit was created on GitHub.com and signed with GitHub’s verified signature. The key has expired.
GPG key ID: 4AEE18F83AFDEB23
Expired
Verified
Learn about vigilant mode.

Release notes

This is the 0.13 release of TensorFlow Probability. It is
tested and stable against TensorFlow version 2.5.0.

See the visual release notebook in colab.

Change notes

  • Distributions

    • Adds tfd.BetaQuotient
    • Adds tfd.DeterminantalPointProcess
    • Adds tfd.ExponentiallyModifiedGaussian
    • Adds tfd.MatrixNormal and tfd.MatrixT
    • Adds tfd.NormalInverseGaussian
    • Adds tfd.SigmoidBeta
    • Adds tfp.experimental.distribute.Sharded
    • Adds tfd.BatchBroadcast
    • Adds tfd.Masked
    • Adds JAX support for tfd.Zipf
    • Adds Implicit Reparameterization Gradients to tfd.InverseGaussian.
    • Adds quantiles for tfd.{Chi2,ExpGamma,Gamma,GeneralizedNormal,InverseGamma}
    • Derive Distribution batch shapes automatically from parameter annotations.
    • Ensuring Exponential.cdf(x) is always 0 for x < 0.
    • VectorExponentialLinearOperator and VectorExponentialDiag distributions now return variance, covariance, and standard deviation of the correct shape.
    • Bates distribution now returns mean of the correct shape.
    • GeneralizedPareto now returns variance of the correct shape.
    • Deterministic distribution now returns mean, mode, and variance of the correct shape.
    • Ensure that JointDistributionPinned's support bijectors respect autobatching.
    • Now systematically testing log_probs of most distributions for numerical accuracy.
    • InverseGaussian no longer emits negative samples for large loc / concentration
    • GammaGamma, GeneralizedExtremeValue, LogLogistic, LogNormal, ProbitBernoulli should no longer compute nan log_probs on their own samples. VonMisesFisher, Pareto, and GeneralizedExtremeValue should no longer emit samples numerically outside their support.
    • Improve numerical stability of tfd.ContinuousBernoulli and deprecate lims parameter.

  • Bijectors

    • Add bijectors to mimic tf.nest.flatten (tfb.tree_flatten) and tf.nest.pack_sequence_as (tfb.pack_sequence_as).
    • Adds tfp.experimental.bijectors.Sharded
    • Remove deprecated tfb.ScaleTrilL. Use tfb.FillScaleTriL instead.
    • Adds cls.parameter_properties() annotations for Bijectors.
    • Extend range tfb.Power to all reals for odd integer powers.
    • Infer the log-deg-jacobian of scalar bijectors using autodiff, if not otherwise specified.

  • MCMC

    • MCMC diagnostics support arbitrary structures of states, not just lists.
    • remc_thermodynamic_integrals added to tfp.experimental.mcmc
    • Adds tfp.experimental.mcmc.windowed_adaptive_hmc
    • Adds an experimental API for initializing a Markov chain from a near-zero uniform distribution in unconstrained space. tfp.experimental.mcmc.init_near_unconstrained_zero
    • Adds an experimental utility for retrying Markov Chain initialization until an acceptable point is found. tfp.experimental.mcmc.retry_init
    • Shuffling experimental streaming MCMC API to slot into tfp.mcmc with a minimum of disruption.
    • Adds ThinningKernel to experimental.mcmc.
    • Adds experimental.mcmc.run_kernel driver as a candidate streaming-based replacement to mcmc.sample_chain

  • VI

    • Adds build_split_flow_surrogate_posterior to tfp.experimental.vi to build structured VI surrogate posteriors from normalizing flows.
    • Adds build_affine_surrogate_posterior to tfp.experimental.vi for construction of ADVI surrogate posteriors from an event shape.
    • Adds build_affine_surrogate_posterior_from_base_distribution to tfp.experimental.vi to enable construction of ADVI surrogate posteriors with correlation structures induced by affine transformations.

  • MAP/MLE

    • Added convenience method tfp.experimental.util.make_trainable(cls) to create trainable instances of distributions and bijectors.

  • Math/linalg

    • Add trapezoidal rule to tfp.math.
    • Add tfp.math.log_bessel_kve.
    • Add no_pivot_ldl to experimental.linalg.
    • Add marginal_fn argument to GaussianProcess (see no_pivot_ldl).
    • Added tfp.math.atan_difference(x, y)
    • Add tfp.math.erfcx, tfp.math.logerfc and tfp.math.logerfcx
    • Add tfp.math.dawsn for Dawson's Integral.
    • Add tfp.math.igammaincinv, tfp.math.igammacinv.
    • Add tfp.math.sqrt1pm1.
    • Add LogitNormal.stddev_approx and LogitNormal.variance_approx
    • Add tfp.math.owens_t for the Owen's T function.
    • Add bracket_root method to automatically initialize bounds for a root search.
    • Add Chandrupatla's method for finding roots of scalar functions.

  • Stats

    • tfp.stats.windowed_mean efficiently computes windowed means.
    • tfp.stats.windowed_variance efficiently and accurately computes windowed variances.
    • tfp.stats.cumulative_variance efficiently and accurately computes cumulative variances.
    • RunningCovariance and friends can now be initialized from an example Tensor, not just from explicit shape and dtype.
    • Cleaner API for RunningCentralMoments, RunningMean, RunningPotentialScaleReduction.

  • STS

    • Speed up STS forecasting and decomposition using internal tf.function wrapping.
    • Add option to speed up filtering in LinearGaussianSSM when only the final step's results are required.
    • Variational Inference with Multipart Bijectors: example notebook with the Radon model.
    • Add experimental support for transforming any distribution into a preconditioning bijector.

  • Other

    • Distributed inference example notebook
    • sanitize_seed is now available in the tfp.random namespace.
    • Add tfp.random.spherical_uniform.

Huge thanks to all the contributors to this release!

  • Abhinav Upadhyay
  • axch
  • Brian Patton
  • Chris Jewell
  • Christopher Suter
  • colcarroll
  • Dave Moore
  • ebrevdo
  • Emily Fertig
  • Harald Husum
  • Ivan Ukhov
  • jballe
  • jburnim
  • Jeff Pollock
  • Jensun Ravichandran
  • JulianWgs
  • junpenglao
  • jvdillon
  • j-wilson
  • kateslin
  • Kristian Hartikainen
  • ksachdeva
  • langmore
  • leben
  • mattjj
  • Nicola De Cao
  • Pavel Sountsov
  • paweller
  • phawkins
  • Prasanth Shyamsundar
  • Rene Jean Corneille
  • Samuel Marks
  • scottzhu
  • sharadmv
  • siege
  • Simon Dirmeier
  • Srinivas Vasudevan
  • Thomas Markovich
  • ursk
  • Uzair
  • vanderplas
  • yileiyang
  • ZeldaMariet
  • Zichun Ye
Assets 2
Loading