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