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TensorFlow Probability 0.7
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@brianwa84 brianwa84 released this 20 Jun 18:10
· 3 commits to r0.7 since this release
v0.7
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Learn about vigilant mode.

Release notes

This is the 0.7 release of TensorFlow Probability. It is tested and stable against TensorFlow version 1.14.0.

Change notes

  • Internal optimizations to HMC leapfrog integrator.
  • Add FeatureTransformed, FeatureScaled, and KumaraswamyTransformed PSD kernels
  • Added tfp.debugging.benchmarking.benchmark_tf_function.
  • Added optional masking of observations for hidden_markov_model methods posterior_marginals and posterior_mode.
  • Fixed evaluation order of distributions within JointDistributionNamed
  • Rename tfb.AutoregressiveLayer to tfb.AutoregressiveNetwork.
  • Support kernel and bias constraints/regularizers/initializers in tfb.AutoregressiveLayer.
  • Created Backward Difference Formula (BDF) solver for stiff ODEs.
  • Update Cumsum bijector.
  • Add distribution layer for masked autoregressive flow in Keras.
  • Shorten repr, str Distribution strings by using "?" instead of "<unknown>" to represent None.
  • Implement FiniteDiscrete distribution
  • Add Cumsum bijector.
  • Make Seasonal STS more flexible to handle none constant num_steps_per_season for each season.
  • In tfb.BatchNormalization, use keras layer over compat.v1 layer.
  • Forward kwargs in MaskedAutoregressiveFlow.
  • Added tfp.math.pivoted_cholesky for low rank preconditioning.
  • Add tfp.distributions.JointDistributionCoroutine for specifying simple directed graphical models via Python generators.
  • Complete the example notebook demonstrating multilevel modeling using TFP.
  • Remove default None initializations for Beta and LogNormal parameters.
  • Bug fix in init method of Rational quadratic kernel
  • Add Binomial.sample method.
  • Add SparseLinearRegression structural time series component.
  • Remove TFP support of KL Divergence calculation of tf.compat.v1.distributions which have been deprecated for 6 months.
  • Added tfp.math.cholesky_concat (adds columns to a cholesky decomposition)
  • Introduce SchurComplement PSD Kernel
  • Add EllipticalSliceSampler as an experimental MCMC kernel.
  • Remove intercepting/reuse of variables created within DistributionLambda.
  • Support missing observations in structural time series models.
  • Add Keras layer for masked autoregressive flows.
  • Add code block to show recommended style of using JointDistribution.
  • Added example notebook demonstrating multilevel modeling.
  • Correctly decorate the training block in the VI part of the JointDistribution example notebook.
  • Add tfp.distributions.Sample for specifying plates in tfd.JointDistribution*.
  • Enable save/load of Keras models with DistributionLambda layers.
  • Add example notebook to show how to use joint distribution sequential for small-median Bayesian graphical model.
  • Add NaN propagation to tfp.stats.percentile.
  • Add tfp.distributions.JointDistributionSequential for specifying simple directed graphical models.
  • Enable save/load of models with IndependentX or MixtureX layers.
  • Extend monte_carlo_csiszar_f_divergence so it also work with JointDistribution.
  • Fix typo in value_and_gradient docstring.
  • Add SimpleStepSizeAdaptation, deprecate step_size_adaptation_fn.
  • batch_interp_regular_nd_grid added to tfp.math
  • Adds IteratedSigmoidCentered bijector to unconstrain unit simplex.
  • Add option to constrain seasonal effects to zero-sum in STS models, and enable by default.
  • Add two-sample multivariate equality in distribution.
  • Fix broadcasting errors when forecasting STS models with batch shape.
  • Adds batch slicing support to most distributions in tfp.distributions.
  • Add tfp.layers.VariationalGaussianProcess.
  • Added posterior_mode to HiddenMarkovModel
  • Add VariationalGaussianProcess distribution.
  • Adds slicing of distributions batch axes as dist[..., :2, tf.newaxis, 3]
  • Add tfp.layers.VariableLayer for making a Keras model which ignores inputs.
  • tfp.math.matrix_rank.
  • Add KL divergence between two blockwise distributions.
  • tf.function decorate tfp.bijectors.
  • Add Blockwise distribution for concatenating different distribution families.
  • Add and begin using a utility for varying random seeds in tests when desired.
  • Add two-sample calibrated statistical test for equality of CDFs, incl. support for duplicate samples.
  • Deprecating obsolete moving_mean_variance. Use assign_moving_mean_variance and manage the variables explicitly.
  • Migrate Variational SGD Optimizer to TF 2.0
  • Migrate SGLD Optimizer to TF 2.0
  • TF2 migration
  • Make all test in MCMC TF2 compatible.
  • Expose HMC parameters via kernel results.
  • Implement a new version of sample_chain with optional tracing.
  • Make MCMC diagnostic tests Eager/TF2 compatible.
  • Implement Categorical to Discrete Values bijector, which maps integer x (0<=x<K) to values[x], where values is a predefined 1D tensor with size K.
  • Run dense, conv variational layer tests in eager mode.
  • Add Empirical distribution to Edward2 (already exists as a TFP distribution).
  • Ensure Gumbel distribution does not produce inf samples.
  • Hid tensor shapes from operators in HMM tests
  • Added Empirical distribution
  • Add the Blockwise bijector.
  • Add MixtureNormal and MixtureLogistic distribution layers.
  • Experimental support for implicit reparameterization gradients in MixtureSameFamily
  • Fix parameter broadcasting in DirichletMultinomial.
  • Add tfp.math.clip_by_value_preserve_gradient.
  • Rename InverseGamma rate parameter to scale, to match its semantics.
  • Added option 'input_output_cholesky' to LKJ distribution.
  • Add a semi-local linear trend STS model component.
  • Added Proximal Hessian Sparse Optimizer (a variant of Newton-Raphson).
  • find_bins(x, edges, ...) added to tfp.stats.
  • Disable explicit caching in masked_autoregressive in eager mode.
  • Add a local level STS model component.
  • Docfix: Fix constraint on valid range of reinterpreted_batch_dims for Independent.

Huge thanks to all the contributors to this release!

  • Alexey Radul
  • Anudhyan Boral
  • axch
  • Brian Patton
  • cclauss
  • Chikanaga Tomoyuki
  • Christopher Suter
  • Clive Chan
  • Dave Moore
  • Gaurav Jain
  • harrismirza
  • Harris Mirza
  • Ian Langmore
  • Jacob Burnim
  • Janosh Riebesell
  • Jeff Pollock
  • Jiri Simsa
  • joeyhaohao
  • johndebugger
  • Joshua V. Dillon
  • Juan A. Navarro P?rez
  • Junpeng Lao
  • Matej Rizman
  • Matthew O'Kelly
  • MG92
  • Nicola De Cao
  • Parsiad Azimzadeh
  • Pavel Sountsov
  • Philip Pham
  • PJ Trainor
  • Rif A. Saurous
  • Sergei Lebedev
  • Sigrid Keydana
  • Sophia Gu
  • Srinivas Vasudevan
  • ykkawana
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