Probabilistic reasoning and statistical analysis in TensorFlow

Last update: Jan 05, 2023

Overview

TensorFlow Probability

TensorFlow Probability is a library for probabilistic reasoning and statistical analysis in TensorFlow. As part of the TensorFlow ecosystem, TensorFlow Probability provides integration of probabilistic methods with deep networks, gradient-based inference via automatic differentiation, and scalability to large datasets and models via hardware acceleration (e.g., GPUs) and distributed computation.

Our probabilistic machine learning tools are structured as follows.

Layer 0: TensorFlow. Numerical operations. In particular, the LinearOperator class enables matrix-free implementations that can exploit special structure (diagonal, low-rank, etc.) for efficient computation. It is built and maintained by the TensorFlow Probability team and is now part of tf.linalg in core TF.

Layer 1: Statistical Building Blocks

Distributions (tfp.distributions): A large collection of probability distributions and related statistics with batch and broadcasting semantics. See the Distributions Tutorial.
Bijectors (tfp.bijectors): Reversible and composable transformations of random variables. Bijectors provide a rich class of transformed distributions, from classical examples like the log-normal distribution to sophisticated deep learning models such as masked autoregressive flows.

Layer 2: Model Building

Joint Distributions (e.g., tfp.distributions.JointDistributionSequential): Joint distributions over one or more possibly-interdependent distributions. For an introduction to modeling with TFP's JointDistributions, check out this colab
Probabilistic Layers (tfp.layers): Neural network layers with uncertainty over the functions they represent, extending TensorFlow Layers.

Layer 3: Probabilistic Inference

Markov chain Monte Carlo (tfp.mcmc): Algorithms for approximating integrals via sampling. Includes Hamiltonian Monte Carlo, random-walk Metropolis-Hastings, and the ability to build custom transition kernels.
Variational Inference (tfp.vi): Algorithms for approximating integrals via optimization.
Optimizers (tfp.optimizer): Stochastic optimization methods, extending TensorFlow Optimizers. Includes Stochastic Gradient Langevin Dynamics.
Monte Carlo (tfp.monte_carlo): Tools for computing Monte Carlo expectations.

TensorFlow Probability is under active development. Interfaces may change at any time.

Examples

See tensorflow_probability/examples/ for end-to-end examples. It includes tutorial notebooks such as:

Linear Mixed Effects Models. A hierarchical linear model for sharing statistical strength across examples.
Eight Schools. A hierarchical normal model for exchangeable treatment effects.
Hierarchical Linear Models. Hierarchical linear models compared among TensorFlow Probability, R, and Stan.
Bayesian Gaussian Mixture Models. Clustering with a probabilistic generative model.
Probabilistic Principal Components Analysis. Dimensionality reduction with latent variables.
Gaussian Copulas. Probability distributions for capturing dependence across random variables.
TensorFlow Distributions: A Gentle Introduction. Introduction to TensorFlow Distributions.
Understanding TensorFlow Distributions Shapes. How to distinguish between samples, batches, and events for arbitrarily shaped probabilistic computations.
TensorFlow Probability Case Study: Covariance Estimation. A user's case study in applying TensorFlow Probability to estimate covariances.

It also includes example scripts such as:

Variational Autoencoders. Representation learning with a latent code and variational inference.
Vector-Quantized Autoencoder. Discrete representation learning with vector quantization.
Disentangled Sequential Variational Autoencoder Disentangled representation learning over sequences with variational inference.
Latent Dirichlet Allocation (Distributions version, Mixed membership modeling for capturing topics in a document.
Bayesian Neural Networks. Neural networks with uncertainty over their weights.
Bayesian Logistic Regression. Bayesian inference for binary classification.

Installation

For additional details on installing TensorFlow, guidance installing prerequisites, and (optionally) setting up virtual environments, see the TensorFlow installation guide.

Stable Builds

To install the latest stable version, run the following:

# Notes:

# - The `--upgrade` flag ensures you'll get the latest version.
# - The `--user` flag ensures the packages are installed to your user directory
#   rather than the system directory.
# - TensorFlow 2 packages require a pip >= 19.0
python -m pip install --upgrade --user pip
python -m pip install --upgrade --user tensorflow tensorflow_probability

For CPU-only usage (and a smaller install), install with tensorflow-cpu.

To use a pre-2.0 version of TensorFlow, run:

python -m pip install --upgrade --user "tensorflow<2" "tensorflow_probability<0.9"

Note: Since TensorFlow is not included as a dependency of the TensorFlow Probability package (in setup.py), you must explicitly install the TensorFlow package (tensorflow or tensorflow-cpu). This allows us to maintain one package instead of separate packages for CPU and GPU-enabled TensorFlow. See the TFP release notes for more details about dependencies between TensorFlow and TensorFlow Probability.

Nightly Builds

There are also nightly builds of TensorFlow Probability under the pip package tfp-nightly, which depends on one of tf-nightly or tf-nightly-cpu. Nightly builds include newer features, but may be less stable than the versioned releases. Both stable and nightly docs are available here.

python -m pip install --upgrade --user tf-nightly tfp-nightly

Installing from Source

You can also install from source. This requires the Bazel build system. It is highly recommended that you install the nightly build of TensorFlow (tf-nightly) before trying to build TensorFlow Probability from source.

# sudo apt-get install bazel git python-pip  # Ubuntu; others, see above links.
python -m pip install --upgrade --user tf-nightly
git clone https://github.com/tensorflow/probability.git
cd probability
bazel build --copt=-O3 --copt=-march=native :pip_pkg
PKGDIR=$(mktemp -d)
./bazel-bin/pip_pkg $PKGDIR
python -m pip install --upgrade --user $PKGDIR/*.whl

Community

As part of TensorFlow, we're committed to fostering an open and welcoming environment.

Stack Overflow: Ask or answer technical questions.
GitHub: Report bugs or make feature requests.
TensorFlow Blog: Stay up to date on content from the TensorFlow team and best articles from the community.
Youtube Channel: Follow TensorFlow shows.
[email protected]: Open mailing list for discussion and questions.

See the TensorFlow Community page for more details. Check out our latest publicity here:

Contributing

We're eager to collaborate with you! See CONTRIBUTING.md for a guide on how to contribute. This project adheres to TensorFlow's code of conduct. By participating, you are expected to uphold this code.

References

If you use TensorFlow Probability in a paper, please cite:

TensorFlow Distributions. Joshua V. Dillon, Ian Langmore, Dustin Tran, Eugene Brevdo, Srinivas Vasudevan, Dave Moore, Brian Patton, Alex Alemi, Matt Hoffman, Rif A. Saurous. arXiv preprint arXiv:1711.10604, 2017.

(We're aware there's a lot more to TensorFlow Probability than Distributions, but the Distributions paper lays out our vision and is a fine thing to cite for now.)

Comments

Discrepancy between TFP and Stan's CholeskyLKJ log prob
I'm trying to understand a discrepancy in the log_prob computation between Stan and TFP. I don't know if this is bug, and if it is, where; I'm reporting this here in case anyone thinks this may be of interest.

I generated Cholesky factors of LKJ matrices using the following Stan code:

code <- " data { int K; } parameters { cholesky_factor_corr[K] L; } model { target += lkj_corr_cholesky_lpdf(L|2.0); }

I extracted samples and log_prob:

library(rstan) data = list(K=5) fit <- stan(model_code = code, data=data) e <- as.data.frame(extract(fit))

Finally, I computed the log prob of these matrices using TFP:

m = tfd.CholeskyLKJ(5,2) b = tfb.CorrelationCholesky() for datum in data[:k,:-1]: L = datum.reshape(5,5).T tfp_lps.append(m.log_prob(L).numpy())

When I compare tfp_lps to Stan's lp__, I get a high correlation, but they're not the same. For comparison, running this check with something like a normal distribution passes np.allclose. I've also suspected that this might be related to different handling of log_det_jacobian, but subtracting tfb.CorrelationCholesky().inverse_log_det_jacobian(L,2) didn't help.

Is there something wrong in the way I'm running the comparison, or in the expectation that they would be the same in the first place?

Thanks in advance!
opened by adamhaber 53
Correlation matrix [cholesky] bijector

As discussed on https://groups.google.com/a/tensorflow.org/d/msg/tfprobability/JYNa3_g33qo/asqjrRs0BAAJ it would be nice to have a bijector to go from unconstrained vectors to LKJ distributed correlation matrices. Some guessing that our existing LKJ might already have the right forward transformation implemented, but would need to add the inverse and the log det of deformation.
good first issue

opened by brianwa84 46

feature request: KL divergence for Gaussian Mixture Model

Hi all,

Would it be possible to add KL between two Mixture of gaussians distirbutions or even between one multivariate gaussian and a mixture of gaussian? Example:

A =tfd.Normal( loc=[1., -1],scale=[1, 2.])
B =tfd.Normal( loc=[1., -1],scale=[1, 2.])
C = tfd.MixtureSameFamily(
    mixture_distribution=tfd.Categorical(
        probs=[0.3, 0.7]),
    components_distribution=tfd.Normal(
      loc=[-1., 1],       # One for each component.
      scale=[0.1, 0.5]))  # And same here.
D = tfd.MixtureSameFamily(
    mixture_distribution=tfd.Categorical(
        probs=[0.1, 0.9]),
    components_distribution=tfd.Normal(
      loc=[-1., 1],       # One for each component.
      scale=[0.1, 0.5]))  # And same here.

Then

tf.distributions.kl_divergence(A, B)

works fine and returns <tf.Tensor 'KullbackLeibler/kl_normal_normal/add:0' shape=(2,) dtype=float32> Yet, tf.distributions.kl_divergence(A, D)and tf.distributions.kl_divergence(C, D) returns

No KL(distribution_a || distribution_b) registered for distribution_a type Normal and distribution_b type MixtureSameFamily

This would be of great value since more and more KL of this type are being calulated (or at least approximated) while training Neural Nets.

Thanks Belhal

opened by BelhalK 37

Implicit reparameterization gradients
The idea is simple to implement and well-scoped as part of TF Distributions. I personally like the idea of even having it be the default for Gamma, Beta, and others. The gradient implementations may be tricky. Maybe @mfigurnov has an implementation we can build on? (if open source or google-internal)

https://arxiv.org/abs/1805.08498

https://twitter.com/mfigurnov/status/999198094905499648

https://twitter.com/ericjang11/status/999183266237198336
opened by dustinvtran 33
Tensor is unhashable if Tensor equality is enabled. Instead, use tensor.experimental_ref() as the key
https://github.com/tensorflow/tensorflow/issues/32139

Error occurs: tf-gpu 2.0.0-rc0 with tfp 0.7

Code to reproduce:

import tensorflow_probability as tfp tfp.distributions.MultivariateNormalDiag([0.], [1.]).sample()

Error returned:

Traceback (most recent call last): File "/home/pycharm_project/VAE/save_issue_reproduction.py", line 3, in tfp.distributions.MultivariateNormalDiag([0.], [1.]).sample() File "/usr/local/lib/python3.5/dist-packages/tensorflow_probability/python/distributions/distribution.py", line 840, in sample return self._call_sample_n(sample_shape, seed, name, **kwargs) File "/usr/local/lib/python3.5/dist-packages/tensorflow_probability/python/distributions/transformed_distribution.py", line 391, in _call_sample_n y = self.bijector.forward(x, **bijector_kwargs) File "/usr/local/lib/python3.5/dist-packages/tensorflow_probability/python/bijectors/bijector.py", line 933, in forward return self._call_forward(x, name, **kwargs) File "/usr/local/lib/python3.5/dist-packages/tensorflow_probability/python/bijectors/bijector.py", line 904, in _call_forward mapping = self._lookup(x=x, kwargs=kwargs) File "/usr/local/lib/python3.5/dist-packages/tensorflow_probability/python/bijectors/bijector.py", line 1343, in _lookup mapping = self._from_x[x].get(subkey, mapping).merge(x=x) File "/usr/local/lib/python3.5/dist-packages/tensorflow_probability/python/bijectors/bijector.py", line 151, in getitem return super(WeakKeyDefaultDict, self).getitem(weak_key) File "/usr/local/lib/python3.5/dist-packages/tensorflow_probability/python/bijectors/bijector.py", line 181, in hash return hash(x) File "/usr/local/lib/python3.5/dist-packages/tensorflow_core/python/framework/ops.py", line 713, in hash raise TypeError("Tensor is unhashable if Tensor equality is enabled. " TypeError: Tensor is unhashable if Tensor equality is enabled. Instead, use tensor.experimental_ref() as the key.
opened by kristofgiber 27

TFP and TF2.0 incompatibility

After using TFP with TF2.0, I've realized that I'm spending a lot of time on understanding why it doesn't work rather than making any progress. The TFP relies too much on TF1.0, even not realizing it. Some conceptions of the TF2.0 and the TFP are just incompatible, e.g. tf.Module.

class Model(tf.Module):
    def __init__(self):
        pass  # Create variables, set the prior and etc.

data = ... setup data
model = Model()

def run_chain():
    def posterior_log_prob_fn(*parameters):
        model.assign_parameters(*parameters)
        log_prob = -model.neg_log_marginal_likelihood(data)
        return log_prob
    
    # state_gradients_are_stopped=True
    variables = model.trainable_variables
    hmc = mcmc.HamiltonianMonteCarlo(target_log_prob_fn=posterior_log_prob_fn,
                                     num_leapfrog_steps=2,
                                     step_size=1.0,
                                     state_gradients_are_stopped=True)
    adaptive_hmc = mcmc.SimpleStepSizeAdaptation(hmc, num_adaptation_steps=2)
    return mcmc.sample_chain(num_results=2,
                             num_burnin_steps=0,
                             current_state=variables,
                             kernel=adaptive_hmc)

The HMC will not be able to compute the gradients as there are no links in a graph between states and the model (with or without tf.function).

Other examples you can find here: https://github.com/tensorflow/probability/issues/333, https://github.com/tensorflow/probability/issues/348, https://github.com/tensorflow/probability/issues/47

There is no easy fix for that and hope I'm wrong), as the roots of these problems are in TF2.0 itself. I've created another github issue at TF2.0: https://github.com/tensorflow/tensorflow/issues/29367, describing how and what makes TFP framework less effective.

opened by awav 27

Implemented BNN for MNIST with LeNet5

Hi Dustin and Bayesflow team,

I've added my implementation for MNIST with Bayesian neural networks for the LeNet5 strucure. The performance reaches about 97% after 6000 steps. Let me know if there are other changes needed.

Best, Jiaming
cla: yes

opened by jmzeng 26

`ImportError: cannot import name 'abs'` when importing TFP in Python 3 (and in Python 2)

So I used the following code to import my dependencies:

from __future__ import absolute_import
from __future__ import division
from __future__ import print_function

import numpy as np
import matplotlib.pyplot as plt
from matplotlib.patches import Ellipse
import seaborn as sns
import tensorflow as tf                            # importing Tensorflow
import tensorflow_probability as tfp               # and Tensorflow probability
from tensorflow_probability import edward2 as ed   # Edwardlib extension

tfd = tfp.distributions             # Basic probability distribution toolkit
tfb = tfp.distributions.bijectors   # and their modifiers

# Eager Execution
# tfe = tf.contrib.eager
# tfe.enable_eager_execution()

%matplotlib inline
plt.style.use("fivethirtyeight")        # Styling plots like FiveThirtyEight

import warnings
warnings.filterwarnings('ignore')
%config InlineBackend.figure_format="retina" # improves resolution of plots

But I get this error when trying to import tensorflow_probability

---------------------------------------------------------------------------
ImportError                               Traceback (most recent call last)
<ipython-input-3-47fdbecb20a4> in <module>()
      7 from matplotlib.patches import Ellipse
      8 import seaborn as sns
----> 9 import tensorflow as tf                            # importing Tensorflow
     10 import tensorflow_probability as tfp               # and Tensorflow probability
     11 from tensorflow_probability import edward2 as ed   # Edwardlib extension

/usr/local/lib/python3.6/dist-packages/tensorflow/__init__.py in <module>()
     22 
     23 # pylint: disable=g-bad-import-order
---> 24 from tensorflow.python import pywrap_tensorflow  # pylint: disable=unused-import
     25 # pylint: disable=wildcard-import
     26 from tensorflow.tools.api.generator.api import *  # pylint: disable=redefined-builtin

/usr/local/lib/python3.6/dist-packages/tensorflow/python/__init__.py in <module>()
     79 # Bring in subpackages.
     80 from tensorflow.python import data
---> 81 from tensorflow.python import keras
     82 from tensorflow.python.estimator import estimator_lib as estimator
     83 from tensorflow.python.feature_column import feature_column_lib as feature_column

/usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/__init__.py in <module>()
     22 from __future__ import print_function
     23 
---> 24 from tensorflow.python.keras import activations
     25 from tensorflow.python.keras import applications
     26 from tensorflow.python.keras import backend

/usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/activations/__init__.py in <module>()
     20 
     21 # Activation functions.
---> 22 from tensorflow.python.keras._impl.keras.activations import elu
     23 from tensorflow.python.keras._impl.keras.activations import hard_sigmoid
     24 from tensorflow.python.keras._impl.keras.activations import linear

/usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/_impl/keras/__init__.py in <module>()
     19 from __future__ import print_function
     20 
---> 21 from tensorflow.python.keras._impl.keras import activations
     22 from tensorflow.python.keras._impl.keras import applications
     23 from tensorflow.python.keras._impl.keras import backend

/usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/_impl/keras/activations.py in <module>()
     21 import six
     22 
---> 23 from tensorflow.python.keras._impl.keras import backend as K
     24 from tensorflow.python.keras._impl.keras.utils.generic_utils import deserialize_keras_object
     25 from tensorflow.python.layers.base import Layer

/usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/_impl/keras/backend.py in <module>()
     35 from tensorflow.python.framework import ops
     36 from tensorflow.python.framework import sparse_tensor
---> 37 from tensorflow.python.layers import base as tf_base_layers
     38 from tensorflow.python.ops import array_ops
     39 from tensorflow.python.ops import clip_ops

/usr/local/lib/python3.6/dist-packages/tensorflow/python/layers/base.py in <module>()
     23 from tensorflow.python.framework import dtypes
     24 from tensorflow.python.framework import ops
---> 25 from tensorflow.python.keras.engine import base_layer
     26 from tensorflow.python.ops import variable_scope as vs
     27 from tensorflow.python.ops import variables as tf_variables

/usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/engine/__init__.py in <module>()
     19 from __future__ import print_function
     20 
---> 21 from tensorflow.python.keras.engine.base_layer import InputSpec
     22 from tensorflow.python.keras.engine.base_layer import Layer
     23 from tensorflow.python.keras.engine.input_layer import Input

/usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/engine/base_layer.py in <module>()
     30 from tensorflow.python.framework import tensor_shape
     31 from tensorflow.python.framework import tensor_util
---> 32 from tensorflow.python.keras import backend
     33 from tensorflow.python.keras import constraints
     34 from tensorflow.python.keras import initializers

/usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/backend/__init__.py in <module>()
     20 
     21 # pylint: disable=redefined-builtin
---> 22 from tensorflow.python.keras._impl.keras.backend import abs
     23 from tensorflow.python.keras._impl.keras.backend import all
     24 from tensorflow.python.keras._impl.keras.backend import any

ImportError: cannot import name 'abs'

---------------------------------------------------------------------------
NOTE: If your import is failing due to a missing package, you can
manually install dependencies using either !pip or !apt.

To view examples of installing some common dependencies, click the
"Open Examples" button below.
---------------------------------------------------------------------------

This only just started happening after I entered pip install --upgrade tfp-nightly for into the terminal, which leads me to conclude that something within the combination of tb-nightly-1.9.0a20180519, tf-nightly-1.9.0.dev20180519, and tfp-nightly-0.0.1.dev20180519 is not working.

It was working previously but is not any longer. This error applies to both Python 3 AND python 2, so it's not as simple as just fixing the runtime type.

opened by matthew-mcateer 26

Example for CIFAR-10 with ResNet18 and VGG16

This is an example for CIFAR-10 with ResNet18 and VGG16. With the current setup, the ResNet18 hits about 80% validation accuracy and 90% training accuracy.

The visualization for MNIST has been removed because the plots did not really look as I expected. The validation accuracy would be pretty high but the distributions plotted are still like a random dist. I'm unsure as to why that is happening so I removed it for now. If you would like it for debugging, I can include it.

Please let me know if there are style changes needed.
cla: yes

opened by jmzeng 25
Priors definition

In bayesian_neural_network.py example, where are the priors on the weights defined? Are they Gaussians?

How should I proceed if I would like to use mixture of gaussians as priors for my BNN?

Thanks a lot for your time Belhal

opened by BelhalK 23

Incompatibility with cloudpickle==1.5.0

Hi all,

Due to a new update, it is not possible to import tensorflow_probability anymore. Using cloudpickle <= 1.4.1 fixed the issue

>>> import tensorflow_probability as tfp
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
  File "/usr/local/lib/python3.6/dist-packages/tensorflow_probability/__init__.py", line 76, in <module>
    from tensorflow_probability.python import *  # pylint: disable=wildcard-import
  File "/usr/local/lib/python3.6/dist-packages/tensorflow_probability/python/__init__.py", line 23, in <module>
    from tensorflow_probability.python import distributions
  File "/usr/local/lib/python3.6/dist-packages/tensorflow_probability/python/distributions/__init__.py", line 88, in <module>
    from tensorflow_probability.python.distributions.pixel_cnn import PixelCNN
  File "/usr/local/lib/python3.6/dist-packages/tensorflow_probability/python/distributions/pixel_cnn.py", line 37, in <module>
    from tensorflow_probability.python.layers import weight_norm
  File "/usr/local/lib/python3.6/dist-packages/tensorflow_probability/python/layers/__init__.py", line 31, in <module>
    from tensorflow_probability.python.layers.distribution_layer import CategoricalMixtureOfOneHotCategorical
  File "/usr/local/lib/python3.6/dist-packages/tensorflow_probability/python/layers/distribution_layer.py", line 28, in <module>
    from cloudpickle.cloudpickle import CloudPickler
ImportError: cannot import name 'CloudPickler'

opened by ltetrel 22

Cant slice along batch for MixtureSameDistribution produced by Keras layer

Not sure if this is Keras bug or tfp bug.

I'm trying to make some dense layers that output the parameters of Gaussian mixture (a mixture density network). I want to run a batch of data through the network (for speed), get out a batch of distributions, and then slice to work with only some elements of the batch at a time. If I were doing this with just tfp, I call:

import tensorflow_probability as tfp
tfd = tfp.distributions

num_mixture_components = 12
batch_size = 4

probs = np.random.rand(batch_size, num_mixture_components)
loc = np.random.rand(batch_size, num_mixture_components)
scale = np.random.rand(batch_size, num_mixture_components)

gm = tfd.MixtureSameFamily(
      mixture_distribution=tfd.Categorical(probs=probs),
      components_distribution=tfd.Normal(loc=loc, scale=scale))  

print(gm)
# slice along batch
print(gm[1])

This works as expected giving

tfp.distributions.MixtureSameFamily("MixtureSameFamily", batch_shape=[4], event_shape=[], dtype=float64)
tfp.distributions.MixtureSameFamily("MixtureSameFamily", batch_shape=[], event_shape=[], dtype=float64)

However when I try the same thing with a mixture density network in Keras I get an error

import tensorflow.keras.layers as tfkl
import tensorflow.keras as tfk

num_mixture_components = 12

l = tfkl.Input(shape=(100))

# Make a fully connected network that outputs parameters of Gaussian mixture
mu = tfkl.Dense(units=num_mixture_components, activation=None)(l)
sigma = tfkl.Dense(units=num_mixture_components, activation='softplus')(l)
alpha = tfkl.Dense(units=num_mixture_components, activation='softmax')(l)
stacked = tfkl.Concatenate()([mu, sigma, alpha])

mixture = tfp.layers.MixtureNormal(num_mixture_components, 
                                        event_shape=[], name="test")(stacked)
model = tf.keras.Model(inputs=l, outputs=mixture)



out = model(np.random.rand(4, 100))

gm = out.tensor_distribution;


print(gm)
# slice along batch
print(gm[1])

Gives me this cryptic error:

tfp.distributions._MixtureSameFamily("MixtureSameFamily", batch_shape=[4], event_shape=[], dtype=float32)

---------------------------------------------------------------------------
TypeError                                 Traceback (most recent call last)
Cell In [72], line 27
     25 print(gm)
     26 # slice along batch
---> 27 print(gm[1])

File ~/mambaforge/envs/phenotypes/lib/python3.10/site-packages/tensorflow_probability/python/distributions/distribution.py:852, in Distribution.__getitem__(self, slices)
    825 def __getitem__(self, slices):
    826   """Slices the batch axes of this distribution, returning a new instance.
    827 
    828   ```python
   (...)
    850     dist: A new `tfd.Distribution` instance with sliced parameters.
    851   """
--> 852   return slicing.batch_slice(self, {}, slices)

File ~/mambaforge/envs/phenotypes/lib/python3.10/site-packages/tensorflow_probability/python/internal/slicing.py:220, in batch_slice(batch_object, params_overrides, slices, bijector_x_event_ndims)
    217 slice_overrides_seq = slice_overrides_seq + [(slices, params_overrides)]
    218 # Re-doing the full sequence of slice+copy override work here enables
    219 # gradients all the way back to the original batch_objectribution's arguments.
--> 220 batch_object = _apply_slice_sequence(
    221     orig_batch_object,
    222     slice_overrides_seq,
    223     bijector_x_event_ndims=bijector_x_event_ndims)
    224 setattr(batch_object,
    225         PROVENANCE_ATTR,
    226         batch_object._no_dependency((orig_batch_object, slice_overrides_seq)))  # pylint: disable=protected-access
    227 return batch_object

File ~/mambaforge/envs/phenotypes/lib/python3.10/site-packages/tensorflow_probability/python/internal/slicing.py:179, in _apply_slice_sequence(batch_object, slice_overrides_seq, bijector_x_event_ndims)
    177 """Applies a sequence of slice or copy-with-overrides operations to `batch_object`."""
    178 for slices, overrides in slice_overrides_seq:
--> 179   batch_object = _apply_single_step(
    180       batch_object,
    181       slices,
    182       overrides,
    183       bijector_x_event_ndims=bijector_x_event_ndims)
    184 return batch_object

File ~/mambaforge/envs/phenotypes/lib/python3.10/site-packages/tensorflow_probability/python/internal/slicing.py:168, in _apply_single_step(batch_object, slices, params_overrides, bijector_x_event_ndims)
    166   override_dict = {}
    167 else:
--> 168   override_dict = _slice_params_to_dict(
    169       batch_object, slices, bijector_x_event_ndims=bijector_x_event_ndims)
    170 override_dict.update(params_overrides)
    171 parameters = dict(batch_object.parameters, **override_dict)

File ~/mambaforge/envs/phenotypes/lib/python3.10/site-packages/tensorflow_probability/python/internal/slicing.py:153, in _slice_params_to_dict(batch_object, slices, bijector_x_event_ndims)
    150 else:
    151   batch_shape = batch_object.experimental_batch_shape_tensor(
    152       x_event_ndims=bijector_x_event_ndims)
--> 153 return batch_shape_lib.map_fn_over_parameters_with_event_ndims(
    154     batch_object,
    155     functools.partial(_slice_single_param,
    156                       slices=slices,
    157                       batch_shape=batch_shape),
    158     bijector_x_event_ndims=bijector_x_event_ndims)

File ~/mambaforge/envs/phenotypes/lib/python3.10/site-packages/tensorflow_probability/python/internal/batch_shape_lib.py:367, in map_fn_over_parameters_with_event_ndims(batch_object, fn, bijector_x_event_ndims, require_static, **parameter_kwargs)
    361     elif (properties.is_tensor
    362           and not tf.is_tensor(param)
    363           and not tf.nest.is_nested(param_event_ndims)):
    364       # As a last resort, try an explicit conversion.
    365       param = tensor_util.convert_nonref_to_tensor(param, name=param_name)
--> 367   results[param_name] = nest.map_structure_up_to(
    368       param, fn, param, param_event_ndims)
    369 return results

File ~/mambaforge/envs/phenotypes/lib/python3.10/site-packages/tensorflow/python/util/nest.py:1435, in map_structure_up_to(shallow_tree, func, *inputs, **kwargs)
   1361 @tf_export("__internal__.nest.map_structure_up_to", v1=[])
   1362 def map_structure_up_to(shallow_tree, func, *inputs, **kwargs):
   1363   """Applies a function or op to a number of partially flattened inputs.
   1364 
   1365   The `inputs` are flattened up to `shallow_tree` before being mapped.
   (...)
   1433     `shallow_tree`.
   1434   """
-> 1435   return map_structure_with_tuple_paths_up_to(
   1436       shallow_tree,
   1437       lambda _, *values: func(*values),  # Discards the path arg.
   1438       *inputs,
   1439       **kwargs)

File ~/mambaforge/envs/phenotypes/lib/python3.10/site-packages/tensorflow/python/util/nest.py:1535, in map_structure_with_tuple_paths_up_to(shallow_tree, func, *inputs, **kwargs)
   1526 flat_value_gen = (
   1527     flatten_up_to(  # pylint: disable=g-complex-comprehension
   1528         shallow_tree,
   1529         input_tree,
   1530         check_types,
   1531         expand_composites=expand_composites) for input_tree in inputs)
   1532 flat_path_gen = (
   1533     path
   1534     for path, _ in _yield_flat_up_to(shallow_tree, inputs[0], is_nested_fn))
-> 1535 results = [
   1536     func(*args, **kwargs) for args in zip(flat_path_gen, *flat_value_gen)
   1537 ]
   1538 return pack_sequence_as(structure=shallow_tree, flat_sequence=results,
   1539                         expand_composites=expand_composites)

File ~/mambaforge/envs/phenotypes/lib/python3.10/site-packages/tensorflow/python/util/nest.py:1536, in <listcomp>(.0)
   1526 flat_value_gen = (
   1527     flatten_up_to(  # pylint: disable=g-complex-comprehension
   1528         shallow_tree,
   1529         input_tree,
   1530         check_types,
   1531         expand_composites=expand_composites) for input_tree in inputs)
   1532 flat_path_gen = (
   1533     path
   1534     for path, _ in _yield_flat_up_to(shallow_tree, inputs[0], is_nested_fn))
   1535 results = [
-> 1536     func(*args, **kwargs) for args in zip(flat_path_gen, *flat_value_gen)
   1537 ]
   1538 return pack_sequence_as(structure=shallow_tree, flat_sequence=results,
   1539                         expand_composites=expand_composites)

File ~/mambaforge/envs/phenotypes/lib/python3.10/site-packages/tensorflow/python/util/nest.py:1437, in map_structure_up_to.<locals>.<lambda>(_, *values)
   1361 @tf_export("__internal__.nest.map_structure_up_to", v1=[])
   1362 def map_structure_up_to(shallow_tree, func, *inputs, **kwargs):
   1363   """Applies a function or op to a number of partially flattened inputs.
   1364 
   1365   The `inputs` are flattened up to `shallow_tree` before being mapped.
   (...)
   1433     `shallow_tree`.
   1434   """
   1435   return map_structure_with_tuple_paths_up_to(
   1436       shallow_tree,
-> 1437       lambda _, *values: func(*values),  # Discards the path arg.
   1438       *inputs,
   1439       **kwargs)

File ~/mambaforge/envs/phenotypes/lib/python3.10/site-packages/tensorflow_probability/python/internal/slicing.py:101, in _slice_single_param(param, param_event_ndims, slices, batch_shape)
     85 """Slices into the batch shape of a single parameter.
     86 
     87 Args:
   (...)
     98     `slices`.
     99 """
    100 # Broadcast the parmameter to have full batch rank.
--> 101 param = batch_shape_lib.broadcast_parameter_with_batch_shape(
    102     param, param_event_ndims, ps.ones_like(batch_shape))
    103 param_batch_shape = batch_shape_lib.get_batch_shape_tensor_part(
    104     param, param_event_ndims)
    105 # At this point the param should have full batch rank, *unless* it's an
    106 # atomic object like `tfb.Identity()` incapable of having any batch rank.

File ~/mambaforge/envs/phenotypes/lib/python3.10/site-packages/tensorflow_probability/python/internal/batch_shape_lib.py:274, in broadcast_parameter_with_batch_shape(param, param_event_ndims, batch_shape)
    270 base_shape = ps.concat([batch_shape,
    271                         ps.ones([param_event_ndims], dtype=np.int32)],
    272                        axis=0)
    273 if hasattr(param, '_broadcast_parameters_with_batch_shape'):
--> 274   return param._broadcast_parameters_with_batch_shape(base_shape)  # pylint: disable=protected-access
    275 elif hasattr(param, 'matmul'):
    276   # TODO(davmre): support broadcasting LinearOperator parameters.
    277   return param

File ~/mambaforge/envs/phenotypes/lib/python3.10/site-packages/tensorflow_probability/python/distributions/distribution.py:952, in Distribution._broadcast_parameters_with_batch_shape(self, batch_shape)
    926 def _broadcast_parameters_with_batch_shape(self, batch_shape):
    927   """Broadcasts each parameter's batch shape with the given `batch_shape`.
    928 
    929   This is semantically equivalent to wrapping with the `BatchBroadcast`
   (...)
    950       the given `batch_shape`.
    951   """
--> 952   return self.copy(
    953       **batch_shape_lib.broadcast_parameters_with_batch_shape(
    954           self, batch_shape))

File ~/mambaforge/envs/phenotypes/lib/python3.10/site-packages/tensorflow_probability/python/distributions/distribution.py:915, in Distribution.copy(self, **override_parameters_kwargs)
    897 """Creates a deep copy of the distribution.
    898 
    899 Note: the copy distribution may continue to depend on the original
   (...)
    909     `dict(self.parameters, **override_parameters_kwargs)`.
    910 """
    911 try:
    912   # We want track provenance from origin variables, so we use batch_slice
    913   # if this distribution supports slicing. See the comment on
    914   # PROVENANCE_ATTR in batch_slicing.py
--> 915   return slicing.batch_slice(self, override_parameters_kwargs, Ellipsis)
    916 except NotImplementedError:
    917   pass

File ~/mambaforge/envs/phenotypes/lib/python3.10/site-packages/tensorflow_probability/python/internal/slicing.py:220, in batch_slice(batch_object, params_overrides, slices, bijector_x_event_ndims)
    217 slice_overrides_seq = slice_overrides_seq + [(slices, params_overrides)]
    218 # Re-doing the full sequence of slice+copy override work here enables
    219 # gradients all the way back to the original batch_objectribution's arguments.
--> 220 batch_object = _apply_slice_sequence(
    221     orig_batch_object,
    222     slice_overrides_seq,
    223     bijector_x_event_ndims=bijector_x_event_ndims)
    224 setattr(batch_object,
    225         PROVENANCE_ATTR,
    226         batch_object._no_dependency((orig_batch_object, slice_overrides_seq)))  # pylint: disable=protected-access
    227 return batch_object

File ~/mambaforge/envs/phenotypes/lib/python3.10/site-packages/tensorflow_probability/python/internal/slicing.py:179, in _apply_slice_sequence(batch_object, slice_overrides_seq, bijector_x_event_ndims)
    177 """Applies a sequence of slice or copy-with-overrides operations to `batch_object`."""
    178 for slices, overrides in slice_overrides_seq:
--> 179   batch_object = _apply_single_step(
    180       batch_object,
    181       slices,
    182       overrides,
    183       bijector_x_event_ndims=bijector_x_event_ndims)
    184 return batch_object

File ~/mambaforge/envs/phenotypes/lib/python3.10/site-packages/tensorflow_probability/python/internal/slicing.py:172, in _apply_single_step(batch_object, slices, params_overrides, bijector_x_event_ndims)
    170 override_dict.update(params_overrides)
    171 parameters = dict(batch_object.parameters, **override_dict)
--> 172 return type(batch_object)(**parameters)

File ~/mambaforge/envs/phenotypes/lib/python3.10/site-packages/decorator.py:231, in decorate.<locals>.fun(*args, **kw)
    229 def fun(*args, **kw):
    230     if not kwsyntax:
--> 231         args, kw = fix(args, kw, sig)
    232     return caller(func, *(extras + args), **kw)

File ~/mambaforge/envs/phenotypes/lib/python3.10/site-packages/decorator.py:203, in fix(args, kwargs, sig)
    199 def fix(args, kwargs, sig):
    200     """
    201     Fix args and kwargs to be consistent with the signature
    202     """
--> 203     ba = sig.bind(*args, **kwargs)
    204     ba.apply_defaults()  # needed for test_dan_schult
    205     return ba.args, ba.kwargs

File ~/mambaforge/envs/phenotypes/lib/python3.10/inspect.py:3179, in Signature.bind(self, *args, **kwargs)
   3174 def bind(self, /, *args, **kwargs):
   3175     """Get a BoundArguments object, that maps the passed `args`
   3176     and `kwargs` to the function's signature.  Raises `TypeError`
   3177     if the passed arguments can not be bound.
   3178     """
-> 3179     return self._bind(args, kwargs)

File ~/mambaforge/envs/phenotypes/lib/python3.10/inspect.py:3168, in Signature._bind(self, args, kwargs, partial)
   3166         arguments[kwargs_param.name] = kwargs
   3167     else:
-> 3168         raise TypeError(
   3169             'got an unexpected keyword argument {arg!r}'.format(
   3170                 arg=next(iter(kwargs))))
   3172 return self._bound_arguments_cls(self, arguments)

TypeError: got an unexpected keyword argument 'reinterpreted_batch_ndims'

Using tensorflow 2.11.0 tensorflow-probability 0.19.0

Any ideas why this is happening and how to fix?

opened by henrypinkard 0

MultivariateNormalFullCovariance gives false log_prob with JAX backend, GPU and fp64

Code

from jax.config import config
config.update("jax_enable_x64", True)
import os
#os.environ['XLA_PYTHON_CLIENT_ALLOCATOR']='platform'
import jax
import jax.numpy as jnp
from tensorflow_probability.substrates import jax as tfp

tfd = tfp.distributions

Y=jnp.ones((4032,258),dtype=jnp.float64)
distribution = tfd.MultivariateNormalFullCovariance(loc = jnp.zeros((Y.shape[1])),covariance_matrix = jnp.eye(Y.shape[1],dtype = jnp.float64))
distribution.log_prob(Y)

Problem

When I enables fp64 for jax, with GPU, I can only calculate the log_prob of Y with size smaller than (4032,258). For example, size of (4096,258) would give false result:

DeviceArray([-366.08614157, -366.08614157, -366.08614157, ...,
                       nan,           nan,           nan], dtype=float64)

nan should be a false result. However with CPU everything works fine. I suppose this is the error in #1666

When i uncommented the os.environ['XLA_PYTHON_CLIENT_ALLOCATOR']='platform', I got an error:

---------------------------------------------------------------------------
UnfilteredStackTrace                      Traceback (most recent call last)
[<ipython-input-1-6651013d38f8>](https://ajr892vy7y4-496ff2e9c6d22116-0-colab.googleusercontent.com/outputframe.html?vrz=colab-20230104-060047-RC00_499454183#) in <module>
     12 distribution = tfd.MultivariateNormalFullCovariance(loc = jnp.zeros((Y.shape[1])),covariance_matrix = jnp.eye(Y.shape[1],dtype = jnp.float64))
---> 13 distribution.log_prob(Y)
     14 #jnp.where(log_prob<0)

59 frames
UnfilteredStackTrace: jaxlib.xla_extension.XlaRuntimeError: INTERNAL: Failed to complete all kernels launched on stream 0x89279a0: Could not synchronize CUDA stream: CUDA_ERROR_ILLEGAL_ADDRESS: an illegal memory access was encountered

The stack trace below excludes JAX-internal frames.
The preceding is the original exception that occurred, unmodified.

--------------------

The above exception was the direct cause of the following exception:

XlaRuntimeError                           Traceback (most recent call last)
[/usr/local/lib/python3.8/dist-packages/jax/_src/scipy/linalg.py](https://ajr892vy7y4-496ff2e9c6d22116-0-colab.googleusercontent.com/outputframe.html?vrz=colab-20230104-060047-RC00_499454183#) in solve_triangular(***failed resolving arguments***)
    404                      debug: Any = None, check_finite: bool = True) -> Array:
    405   del overwrite_b, debug, check_finite  # unused
--> 406   return _solve_triangular(a, b, trans, lower, unit_diagonal)
    407 
    408 

XlaRuntimeError: INTERNAL: Failed to complete all kernels launched on stream 0x89279a0: Could not synchronize CUDA stream: CUDA_ERROR_ILLEGAL_ADDRESS: an illegal memory access was encountered

Material

A colab can be found here which reproduces the error.

opened by dkn16 0

distributions.mvn_diag using deprecated scale_identity_multiplier kwarg

TFP 0.19.0 is using a deprecated TFP kwarg scale_identity_multiplier which results in the following log spam:

From site-packages/tensorflow_probability/python/distributions/distribution.py:342: calling MultivariateNormalDiag.__init__ (from tensorflow_probability.python.distributions.mvn_diag) with scale_identity_multiplier is deprecated and will be removed after 2020-01-01.
Instructions for updating:
`scale_identity_multiplier` is deprecated; please combine it into `scale_diag` directly instead.

Related https://github.com/tensorflow/tensorflow/issues/55190

opened by stewartmiles 1

RelaxedOneHotCategorical has incorrect output for logits under 1 when logits are type of float32
Hi, I was trying out the RelaxedOneHotCategorical function on tensorfloa_probability version 0.19.0. The following code gives me the incorrect distribution.

from tensorflow_probability.substrates import jax as tfp temperature = 0.5 p = [0.1, 0.5, 0.4] dist = tfp.distributions.RelaxedOneHotCategorical(temperature, logits=p) from jax import random dist.sample(seed=random.PRNGkey(0))

The expected behavior is "the 2nd class is the most likely be the largest component in samples". However, I got the reverse probability instead.

Array([0.9872344, 0.00204739, 0.01071812], dtype=float32)

This behavior disappears when the logits are > 1, or if we cast p to be float16.

Is this expected?
opened by umyta 0
Model stuck when calling .fit(x, y) using negative binomial in DistributionLambda Layer
Hi all,

I have a simple BNN that I just tried to change to have a negative binomial distribution as output:

def get_model(input_shape, loss, optimizer, metrics, kl_weight, output_shape): inputs = Input(shape=(input_shape)) x = BatchNormalization()(inputs) x = tfpl.DenseVariational(units=128, activation='tanh', make_posterior_fn=get_posterior, make_prior_fn=get_prior, kl_weight=kl_weight)(x) count = Dense(1)(x) logits = Dense(output_shape, activation = 'sigmoid')(x) neg_binom = tfp.layers.DistributionLambda( lambda t: tfd.NegativeBinomial(total_count=t[..., 0:1], probs = t[..., 1:])) cat = Concatenate(axis=-1)([count, logits]) outputs = neg_binom(cat) model = Model(inputs, outputs) model.compile(optimizer=optimizer, loss=loss, metrics=metrics) return model

I do not get an error, it compiles and when I call model.fit(x,y) I just get:

Epoch 1/500

and it's stuck here forever (about 20 minutes I waited for the longest).

When I use a Poisson Layer, which I did before it starts fitting instantly, an epoch runs about 1s.

What could be the cause of this? Is there something wrong with my code above? I was hoping to call param_size but distribution lambda seems not to support this (just in case I am missing something).

If I use a single Dense Layer without concatenate and just linear activation I get the same behavior.

Many thanks for your insights and tips on things to try and debug this behavior.
opened by aegonwolf 1
Error when running shared HMC on distributed GPUs with big dataset

I'm currently running a distributed HMC on 4 Tesla V100 cards, and my codes are like:

import functools import collections import contextlib from jax.config import config config.update("jax_enable_x64", True) import jax import jax.numpy as jnp from jax import lax from jax import random import tensorflow as tf from tensorflow_probability.substrates import jax as tfp

tfd = tfp.distributions tfb = tfp.bijectors tfm = tfp.mcmc tfed = tfp.experimental.distribute tfde = tfp.experimental.distributions tfem = tfp.experimental.mcmc

Root = tfed.JointDistributionCoroutine.Root

def shard_value(x): x = x.reshape((jax.device_count(), -1, *x.shape[1:])) return jax.pmap(lambda x: x)(x) # pmap will physically place values on devices

shard = functools.partial(jax.tree_map, shard_value) Y=jnp.ones((256*256,258),dtype=jnp.float64)

dtype = jnp.float64 @functools.partial(jax.pmap, axis_name='data', in_axes=(None, None,0), out_axes=(None,None)) def run(seed, X, data): #data = Y # a sharded dataset num_examples, dim = data.shape # this is our model def model_fn(): k= yield Root(tfd.Sample(tfd.HalfNormal(dtype(2.)), 1)) k = k*jnp.eye(dim) yield tfed.Sharded(tfd.Independent(tfd.MultivariateNormalFullCovariance(loc = jnp.zeros((num_examples,dim),dtype=jnp.float64),covariance_matrix = k),1), shard_axis_name='data') model = tfed.JointDistributionCoroutine(model_fn) init_seed, sample_seed = random.split(seed) initial_state = model.sample(seed=init_seed)[:-1] def target_log_prob(*state): return model.log_prob((*state, data)) kernel = tfm.NoUTurnSampler(target_log_prob, 1e-2,max_tree_depth=8) kernel = tfm.DualAveragingStepSizeAdaptation(kernel,800,target_accept_prob=0.8) def trace_fn(state, pkr): log_prob = target_log_prob(*state) return ( log_prob, pkr.inner_results.has_divergence, 10**pkr.inner_results.log_accept_ratio, #accuracy(*state), pkr.new_step_size, ) states,traces = tfm.sample_chain( num_results=20, #num_burnin_steps=1000, current_state=initial_state, kernel=kernel, trace_fn=trace_fn, seed=sample_seed, ) return states, traces

The code works fine when the second axis of Y is shorter than 256, however, when Y.shape[1]>256, it reports:

2022-12-23 12:12:25.279516: E external/org_tensorflow/tensorflow/compiler/xla/stream_executor/cuda/cuda_driver.cc:752] failed to free device memory at 0x14c238201200; result: CUDA_ERROR_ILLEGAL_ADDRESS: an illegal memory access was encountered 2022-12-23 12:12:25.279535: E external/org_tensorflow/tensorflow/compiler/xla/stream_executor/cuda/cuda_driver.cc:752] failed to free device memory at 0x14c238200c00; result: CUDA_ERROR_ILLEGAL_ADDRESS: an illegal memory access was encountered 2022-12-23 12:12:25.279539: E external/org_tensorflow/tensorflow/compiler/xla/stream_executor/cuda/cuda_driver.cc:752] failed to free device memory at 0x14c238201400; result: CUDA_ERROR_ILLEGAL_ADDRESS: an illegal memory access was encountered 2022-12-23 12:12:25.279541: E external/org_tensorflow/tensorflow/compiler/xla/stream_executor/cuda/cuda_driver.cc:752] failed to free device memory at 0x14c238200e00; result: CUDA_ERROR_ILLEGAL_ADDRESS: an illegal memory access was encountered 2022-12-23 12:12:25.279544: E external/org_tensorflow/tensorflow/compiler/xla/stream_executor/cuda/cuda_driver.cc:752] failed to free device memory at 0x14c238201000; result: CUDA_ERROR_ILLEGAL_ADDRESS: an illegal memory access was encountered 2022-12-23 12:12:25.279551: E external/org_tensorflow/tensorflow/compiler/xla/stream_executor/cuda/cuda_driver.cc:752] failed to free device memory at 0x14c2862a3c00; result: CUDA_ERROR_ILLEGAL_ADDRESS: an illegal memory access was encountered 2022-12-23 12:12:25.279562: E external/org_tensorflow/tensorflow/compiler/xla/stream_executor/cuda/cuda_driver.cc:752] failed to free device memory at 0x14c2862a3600; result: CUDA_ERROR_ILLEGAL_ADDRESS: an illegal memory access was encountered 2022-12-23 12:12:25.279565: E external/org_tensorflow/tensorflow/compiler/xla/pjrt/pjrt_stream_executor_client.cc:2153] Execution of replica 1 failed: INTERNAL: Unable to launch triangular solve 2022-12-23 12:12:25.279572: E external/org_tensorflow/tensorflow/compiler/xla/stream_executor/cuda/cuda_driver.cc:752] failed to free device memory at 0x14c2862a3e00; result: CUDA_ERROR_ILLEGAL_ADDRESS: an illegal memory access was encountered 2022-12-23 12:12:25.279576: E external/org_tensorflow/tensorflow/compiler/xla/stream_executor/cuda/cuda_driver.cc:752] failed to free device memory at 0x14c2862a3800; result: CUDA_ERROR_ILLEGAL_ADDRESS: an illegal memory access was encountered 2022-12-23 12:12:25.279599: E external/org_tensorflow/tensorflow/compiler/xla/stream_executor/cuda/cuda_driver.cc:752] failed to free device memory at 0x14c2862a3a00; result: CUDA_ERROR_ILLEGAL_ADDRESS: an illegal memory access was encountered 2022-12-23 12:12:25.279614: E external/org_tensorflow/tensorflow/compiler/xla/pjrt/pjrt_stream_executor_client.cc:2153] Execution of replica 0 failed: INTERNAL: cublas error 2022-12-23 12:12:25.279668: E external/org_tensorflow/tensorflow/compiler/xla/stream_executor/cuda/cuda_driver.cc:752] failed to free device memory at 0x14c238401200; result: CUDA_ERROR_ILLEGAL_ADDRESS: an illegal memory access was encountered 2022-12-23 12:12:25.279679: E external/org_tensorflow/tensorflow/compiler/xla/stream_executor/cuda/cuda_driver.cc:752] failed to free device memory at 0x14c238400c00; result: CUDA_ERROR_ILLEGAL_ADDRESS: an illegal memory access was encountered 2022-12-23 12:12:25.279682: E external/org_tensorflow/tensorflow/compiler/xla/stream_executor/cuda/cuda_driver.cc:752] failed to free device memory at 0x14c238401400; result: CUDA_ERROR_ILLEGAL_ADDRESS: an illegal memory access was encountered 2022-12-23 12:12:25.279685: E external/org_tensorflow/tensorflow/compiler/xla/stream_executor/cuda/cuda_driver.cc:752] failed to free device memory at 0x14c238400e00; result: CUDA_ERROR_ILLEGAL_ADDRESS: an illegal memory access was encountered 2022-12-23 12:12:25.279687: E external/org_tensorflow/tensorflow/compiler/xla/stream_executor/cuda/cuda_driver.cc:752] failed to free device memory at 0x14c238401000; result: CUDA_ERROR_ILLEGAL_ADDRESS: an illegal memory access was encountered 2022-12-23 12:12:25.279697: E external/org_tensorflow/tensorflow/compiler/xla/pjrt/pjrt_stream_executor_client.cc:2153] Execution of replica 2 failed: INTERNAL: cublas error 2022-12-23 12:12:25.280175: E external/org_tensorflow/tensorflow/compiler/xla/stream_executor/cuda/cuda_blas.cc:219] failed to create cublas handle: cublas error 2022-12-23 12:12:25.280206: W external/org_tensorflow/tensorflow/compiler/xla/stream_executor/stream.cc:1088] attempting to perform BLAS operation using StreamExecutor without BLAS support 2022-12-23 12:12:25.280218: E external/org_tensorflow/tensorflow/compiler/xla/stream_executor/cuda/cuda_driver.cc:752] failed to free device memory at 0x14c238601200; result: CUDA_ERROR_ILLEGAL_ADDRESS: an illegal memory access was encountered 2022-12-23 12:12:25.280221: E external/org_tensorflow/tensorflow/compiler/xla/stream_executor/cuda/cuda_driver.cc:752] failed to free device memory at 0x14c238600c00; result: CUDA_ERROR_ILLEGAL_ADDRESS: an illegal memory access was encountered 2022-12-23 12:12:25.280224: E external/org_tensorflow/tensorflow/compiler/xla/stream_executor/cuda/cuda_driver.cc:752] failed to free device memory at 0x14c238601400; result: CUDA_ERROR_ILLEGAL_ADDRESS: an illegal memory access was encountered 2022-12-23 12:12:25.280227: E external/org_tensorflow/tensorflow/compiler/xla/stream_executor/cuda/cuda_driver.cc:752] failed to free device memory at 0x14c238600e00; result: CUDA_ERROR_ILLEGAL_ADDRESS: an illegal memory access was encountered 2022-12-23 12:12:25.280230: E external/org_tensorflow/tensorflow/compiler/xla/stream_executor/cuda/cuda_driver.cc:752] failed to free device memory at 0x14c238601000; result: CUDA_ERROR_ILLEGAL_ADDRESS: an illegal memory access was encountered 2022-12-23 12:12:25.280241: E external/org_tensorflow/tensorflow/compiler/xla/pjrt/pjrt_stream_executor_client.cc:2153] Execution of replica 3 failed: INTERNAL: Unable to launch triangular solve

I checked that it probably is not the OOM problem, because when Y.shape[0]=256, it only took half of the GPU memory.

My environment is jax=0.3.23,CUDA=11.8,jaxlib=0.3.22+cuda11.cudnn82,tensorflow_probability=0.19,tensorflow = 2.11

Does anyone have the same problem as me?

opened by dkn16 5

Releases(v0.19.0)

v0.19.0(Dec 6, 2022)
Release notes

This is the 0.19.0 release of TensorFlow Probability. It is tested and stable against TensorFlow version 2.11 and JAX 0.3.25 .

Change notes

Bijectors

Added UnitVector bijector to map to the unit sphere.

Distributions

Added noncentral Chi2 distribution to TFP.

Added differentiable quantile and cdf function approximation to NC2 distribution.

Added quantiles to Student-T, Beta and SigmoidBeta, with efficient implementations for Student-T quantile/cdf.

Allow structured index points to GaussianProcess* classes.

Improved efficiency of GaussianProcess* gradients through custom gradients on log_prob.

Linear Algebra

Added functions (with custom gradients) to handle Hermitian Symmetric Positive-definite matrices:

tfp.math.hspd_logdet

tfp.math.hpsd_quadratic_form_solve and tfp.math.hpsd_quadratic_form_solvevec

tfp.math.hpsd_solve and tfp.math.hpsd_solvevec

Optimizer

BUGFIX: Prevent Hager-Zhang linesearch from terminating early.

PSD Kernels

Added support for structured inputs in PSD Kernel.

STS

Added seasonality support to STS Gibbs Sampler.

Other

BUGFIX: Allow jnp.bfloat16 arrays to be correctly recognized as floats.

Huge thanks to all the contributors to this release!

Brian Patton

Chen Qian

Christopher Suter

Colin Carrol

Emily Fertig

Francois Chollet

Ian Langmore

Jacob Burnim

Jonas Eschle

Kyle Loveless

Leandro Campos

Du Phan

Pavel Sountsov

Sebastian Nowozin

Srinivas Vasudevan

Thomas Colthurst

Umer Javed

Urs Koster

Yash Katariya

Source code(tar.gz)
Source code(zip)
v0.18.0(Sep 12, 2022)

Release notes

This is the 0.18.0 release of TensorFlow Probability. It is tested and stable against TensorFlow version 2.10 and JAX 0.3.17 .

Change notes

[coming soon]

Huge thanks to all the contributors to this release!

[coming soon]
Source code(tar.gz)
Source code(zip)
v0.17.0(Jun 7, 2022)
Release notes

This is the 0.17.0 release of TensorFlow Probability. It is tested and stable against TensorFlow version 2.9.1 and JAX 0.3.13 .

Change notes

Distributions

Discrete distributions transform correctly when a bijector is applied.

Fix bug in Taylor approximation of log-normalizing constant for the ContinuousBernoulli.

Add TwoPieceNormal distribution and reparameterize it's samples.

Make IncrementLogProb a proper tfd.Distribution.

Add quantiles to Empirical distribution.

Add tfp.experimental.distributions.MultiTaskGaussianProcessRegressionModel

Improve efficiency of MultiTaskGaussian Processes in the presence of observation noise: Reduce complexity from O((NT)^3) to O(N^3 + T^3) where N is the number of data points and T is the number of tasks.

Improve efficiency of VariationalGaussianProcess.

Add tfd.LognNormal.experimental_from_mean_variance.

Bijectors

Fix Softfloor bijector to act as the identity at high temperature, and floor at low temperature.

Remove tfb.Ordered bijector and finite_nondiscrete flags in Distributions.

Math

Add tfp.math.betainc and gradients with respect to all parameters.

STS

Several bug fixes and performance improvements to tfp.experimental.sts_gibbs for Gibbs sampling Bayesian structural time series models with sparse linear regression.

Enable tfp.experimental.sts_gibbs under JAX

Experimental

Ensemble Kalman filter is now efficient in the case of ensemble size << observation size and an "easy to invert" modeled observation covariance.

Add a perturbed_observations option to ensemble_kalman_filter_log_marginal_likelihood.

Add Experimental support for custom JAX PRNGs.

Other

Add assertAllMeansClose to tfp.TestCase for testing sampling code.

Huge thanks to all the contributors to this release!

Adam Sorrenti

Alexey Radul

Christopher Suter

Colin Carroll

Du Phan

Emily Fertig

Fabien Hertschuh

Faizan Muhammad

Francois Chollet

Ian Langmore

Jacob Burnim

Jake VanderPlas

Kathy Wu

Kristian Hartikainen

Kyle Loveless

Leandro Campos

Xinle Sheila Liu

ltsaprounis

Matt Hoffman

Manas Mohanty

Max Jiang

Pavel Sountsov

Peter Hawkins

Praveen Narayan

Renu Patel

Ryan Russell

Scott Zhu

Sergey Lebedev

Sharad Vikram

Srinivas Vasudevan

tagoma

Urs Koster

Vaidotas Simkus

Vishnuvardhan Janapati

Yilei Yang

Source code(tar.gz)
Source code(zip)
v0.16.0(Feb 14, 2022)
Release notes

This is the 0.16.0 release of TensorFlow Probability. It is tested and stable against TensorFlow version 2.8.0 and JAX 0.3.0 .

Change notes

[coming soon]

Huge thanks to all the contributors to this release!

Alexey Radul

Ben Lee

Billy Lamberta

Brian Patton

Chansoo Lee

Christopher Suter

Colin Carroll

Dave Moore

Du Phan

Emily Fertig

François Chollet

Gianluigi Silvestri

Jacob Burnim

Jake Taylor

Junpeng Lao

Matthew Johnson

Michael Weiss

Pavel Sountsov

Peter Hawkins

Rebecca Chen

Sharad Vikram

Soo Sung

Srinivas Vasudevan

Urs Köster

Source code(tar.gz)
Source code(zip)
v0.15.0(Nov 18, 2021)
Release notes

This is the 0.15 release of TensorFlow Probability. It is tested and stable against TensorFlow version 2.7.0.

Change notes

Distributions

Add tfd.StudentTProcessRegressionModel.

Distributions' statistics now all have batch shape matching the Distribution itself.

JointDistributionCoroutine no longer requires Root when sample_shape==().

Support sample_distributions from autobatched joint distributions.

Expose mask argument to support missing observations in HMM log probs.

BetaBinomial.log_prob is more accurate when all trials succeed.

Support broadcast batch shapes in MixtureSameFamily.

Add cholesky_fn argument to GaussianProcess, GaussianProcessRegressionModel, and SchurComplement.

Add staticmethod for precomputing GPRM for more efficient inference in TensorFlow.

Add GaussianProcess.posterior_predictive.

Bijectors

Bijectors parameterized by distinct tf.Variables no longer register as ==.

BREAKING CHANGE: Remove deprecated AffineScalar bijector. Please use tfb.Shift(shift)(tfb.Scale(scale)) instead.

BREAKING CHANGE: Remove deprecated Affine and AffineLinearOperator bijectors.

PSD kernels

Add tfp.math.psd_kernels.ChangePoint.

Add slicing support for PositiveSemidefiniteKernel.

Add inverse_length_scale parameter to kernels.

Add parameter_properties to PSDKernel along with automated batch shape inference.

VI

Add support for importance-weighted variational objectives.

Support arbitrary distribution types in tfp.experimental.vi.build_factored_surrogate_posterior.

STS

Support + syntax for summing StructuralTimeSeries models.

Math

Enable JAX/NumPy backends for tfp.math.ode.

Allow returning auxiliary information from tfp.math.value_and_gradient.

Experimental

Speedup to experimental.mcmc windowed samplers.

Support unbiased gradients through particle filtering via stop-gradient resampling.

ensemble_kalman_filter_log_marginal_likelihood (log evidence) computation added to tfe.sequential.

Add experimental joint-distribution layers library.

Delete tfp.experimental.distributions.JointDensityCoroutine.

Add experimental special functions for high-precision computation on a TPU.

Add custom log-prob ratio for IncrementLogProb.

Use foldl in no_pivot_ldl instead of while_loop.

Other

TFP should now support numpy 1.20+.

BREAKING CHANGE: Stock unpacking seeds when splitting in JAX.

Huge thanks to all the contributors to this release!

8bitmp3

adriencorenflos

Alexey Radul

Allen Lavoie

Ben Lee

Billy Lamberta

Brian Patton

Christopher Suter

Colin Carroll

Dave Moore

Du Phan

Emily Fertig

Faizan Muhammad

George Necula

George Tucker

Grace Luo

Ian Langmore

Jacob Burnim

Jake VanderPlas

Jeremiah Liu

Junpeng Lao

Kaan

Luke Wood

Max Jiang

Mihai Maruseac

Neil Girdhar

Paul Chiang

Pavel Izmailov

Pavel Sountsov

Peter Hawkins

Rebecca Chen

Richard Song

Rif A. Saurous

Ron Shapiro

Roy Frostig

Sharad Vikram

Srinivas Vasudevan

Tomohiro Endo

Urs Köster

William C Grisaitis

Yilei Yang

Source code(tar.gz)
Source code(zip)
v0.14.1(Sep 30, 2021)
Release notes

This is the 0.14.1 release of TensorFlow Probability. It is tested and stable against TensorFlow version 2.6.0 and JAX 0.2.21.

Change notes

[coming soon]

Huge thanks to all the contributors to this release!

8bitmp3

adriencorenflos

allenl

axch

bjp

blamb

csuter

colcarroll

davmre

derifatives

emilyaf

europeanplaice

Frightera

fmuham

gcluo

GianluigiSilvestri

gisilvs

gjt

grisaitis

harahu

jburnim

langmore

leben

lukewood

mihaimaruseac

NeilGirdhar

phandu

phawkins

rechen

ronshapiro

scottzhu

sharadmv

siege

srvasude

ursk

vanderplas

xingyousong

yileiyang

Source code(tar.gz)
Source code(zip)
v0.14.0(Sep 21, 2021)
Release notes

This is the 0.14 release of TensorFlow Probability. It is tested and stable against TensorFlow version 2.6.0 and JAX 0.2.20.

Change notes

Please see the release notes for TFP 0.14.1 at https://github.com/tensorflow/probability/releases/v0.14.1 .

Huge thanks to all the contributors to this release!

8bitmp3

adriencorenflos

allenl

axch

bjp

blamb

csuter

colcarroll

davmre

derifatives

emilyaf

europeanplaice

Frightera

fmuham

gcluo

GianluigiSilvestri

gisilvs

gjt

grisaitis

harahu

jburnim

langmore

leben

lukewood

mihaimaruseac

NeilGirdhar

phandu

phawkins

rechen

ronshapiro

scottzhu

sharadmv

siege

srvasude

ursk

vanderplas

xingyousong

yileiyang

Source code(tar.gz)
Source code(zip)
v0.13.0(Jun 18, 2021)
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

Source code(tar.gz)
Source code(zip)
0.13.0-rc0(May 24, 2021)

This is the RC0 release candidate of the TensorFlow Probability 0.13 release.

It is tested against TensorFlow 2.5.0.
Source code(tar.gz)
Source code(zip)
v0.12.2(Apr 19, 2021)

This is the 0.12.2 release of TensorFlow Probability, a patch release to cap the JAX dependency to a compatible version. It is tested and stable against TensorFlow version 2.4.0.

For detailed change notes, please see the 0.12.1 release at https://github.com/tensorflow/probability/releases/tag/v0.12.1 .
Source code(tar.gz)
Source code(zip)
v0.12.1(Dec 29, 2020)
Release notes

This is the 0.12.1 release of TensorFlow Probability. It is tested and stable against TensorFlow version 2.4.0.

Change notes

NOTE: Links point to examples in the TFP 0.12.1 release Colab.

Bijectors:

Add implementation of GLOW at tfp.bijectors.Glow.

Add RayleighCDF bijector.

Add Ascending bijector and deprecate Ordered.

Add optional low parameter to the Softplus bijector.

Enable ScaleMatvecLinearOperator bijector to wrap blockwise LinearOperators to form a multipart bijectors.

Allow passing kwargs to Blockwise.

Bijectors now share a global cache, keyed by the bijector parameters and the value being transformed.

Distributions:

BREAKING: Remove deprecated HiddenMarkovModel.num_states property.

BREAKING: Change the naming scheme of un-named variables in JointDistributions.

BREAKING: Remove deprecated batch_shape and event_shape arguments of TransformedDistribution.

Add Skellam distribution.

JointDistributionCoroutine{AutoBatched} now uses namedtuples as the sample dtype.

von-Mises Fisher distribution now works for dimensions > 5 and implements VonMisesFisher.entropy.

Add ExpGamma and ExpInverseGamma distributions.

JointDistribution*AutoBatched now support (reproducible) tensor seeds.

Add KL(VonMisesFisher || SphericalUniform).

Added Distribution.parameter_properties method.

experimental_default_event_space_bijector now accepts additional arguments to pin some distribution parts.

Add JointDistribution.experimental_pin and JointDistributionPinned.

Add NegativeBinomial.experimental_from_mean_dispersion method.

Add tfp.experimental.distribute, with DistributionStrategy-aware distributions that support cross-device likelihood computations.

HiddenMarkovModel can now accept time varying observation distributions if time_varying_observation_distribution is set.

Beta, Binomial, and NegativeBinomial CDF no longer returns nan outside the support.

Remove the "dynamic graph" code path from the Mixture sampler. (Mixture now ignores the use_static_graph parameter.)

Mixture now computes standard deviations more accurately and robustly.

Fix incorrect nan samples generated by several distributions.

Fix KL divergence between Categorical distributions when logits contain -inf.

Implement Bernoulli.cdf.

Add a log_rate parameter to tfd.Gamma.

Add option for parallel filtering and sampling to LinearGaussianStateSpaceModel.

MCMC:

Add tfp.experimental.mcmc.ProgressBarReducer.

Update experimental.mcmc.sample_sequential_monte_carlo to use new MCMC stateless kernel API.

Add an experimental streaming MCMC framework that supports computing statistics over a (batch of) Markov chain(s) without materializing the samples. Statistics supported (mostly on arbitrary functions of the model variables): mean, (co)variance, central moments of arbitrary rank, and the potential scale reduction factor (R-hat). Also support selectively tracing history of some but not all statistics or model variables. Add algorithms for running mean, variance, covariance, arbitrary higher central moments, and potential scale reduction factor (R-hat) totfp.experimental.stats.

untempered_log_prob_fn added as init kwarg to ReplicaExchangeMC Kernel.

Add experimental support for mass matrix preconditioning in Hamiltonian Monte Carlo.

Add ability to temper part of the log prob in ReplicaExchangeMC.

tfp.experimental.mcmc.{sample_fold,sample_chain} support warm restart.

even_odd_swap exchange function added to replica_exchange_mc.

Samples from ReplicaExchangeMC can now have a per-replica initial state.

Add omitted n/(n-1) term to tfp.mcmc.potential_scale_reduction_factor.

Add KernelBuilder and KernelOutputs to experimental.

Allow tfp.mcmc.SimpleStepSizeAdaptation and DualAveragingStepSizeAdaptation to take a custom reduction function.

Replace make_innermost_getter et al. with tfp.experimental.unnest utilities.

VI:

Add automated construction of ASVI surrogate posteriors.

Math + Stats:

Add tfp.math.bessel_ive, tfp.math.bessel_kve, tfp.math.log_bessel_ive.

Add optional weights to tfp.stats.histogram.

Add tfp.math.erfcinv.

Add tfp.math.reduce_log_harmonic_mean_exp.

Other:

Add tfp.math.psd_kernels.GeneralizedMaternKernel (generalizes MaternOneHalf, MaternThreeHalves and MaternFiveHalves).

Add tfp.math.psd_kernels.Parabolic.

Add tfp.experimental.unnest utilities for accessing nested attributes.

Enable pytree flattening for TFP distributions in JAX

More careful handling of nan and +-inf in {L-,}BFGS.

Remove Edward2 from TFP. Edward2 is now in its own repo at https://github.com/google/edward2 .

Support vector-valued offsets in sts.Sum.

Make DeferredTensor actually defer computation under JAX/NumPy backends.

Huge thanks to all the contributors to this release!

Adrian Buzea

Alexey Radul

Ben Lee

Ben Poole

Brian Patton

Christopher Suter

Colin Carroll

Cyril Chimisov

Dave Moore

Du Phan

Emily Fertig

Eugene Brevdo

Federico Tomasi

François Chollet

George Karpenkov

Giovanni Palla

Ian Langmore

Jacob Burnim

Jacob Valdez

Jake VanderPlas

Jason Zavaglia

Jean-Baptiste Lespiau

Jeff Pollock

Joan Puigcerver

Jonas Eschle

Josh Darrieulat

Joshua V. Dillon

Junpeng Lao

Kapil Sachdeva

Kate Lin

Kibeom Kim

Luke Metz

Mark Daoust

Matteo Hessel

Michal Brys

Oren Bochman

Padarn Wilson

Pavel Sountsov

Peter Hawkins

Rif A. Saurous

Ru Pei

ST John

Sharad Vikram

Simeon Carstens

Srinivas Vasudevan

Tom O'Malley

Tomer Kaftan

Urs Köster

Yash Katariya

Yilei Yang

Source code(tar.gz)
Source code(zip)
v0.12.0(Dec 29, 2020)

This is the 0.12.0 release of TensorFlow Probability. It is tested and stable against TensorFlow version 2.4.0.

For detailed change notes, please see the 0.12.1 release at https://github.com/tensorflow/probability/releases/tag/v0.12.1 .
Source code(tar.gz)
Source code(zip)
v0.12.0-rc4(Dec 9, 2020)

This is RC4 of the TensorFlow Probability 0.12 release. It is tested against TensorFlow 2.4.0-rc4.
Source code(tar.gz)
Source code(zip)
v0.12.0-rc2(Nov 21, 2020)

This is RC2 of the TensorFlow Probability 0.12 release. It is tested against TensorFlow 2.4.0-rc2.
Source code(tar.gz)
Source code(zip)
v0.12.0-rc1(Nov 11, 2020)

This is RC1 of the TensorFlow Probability 0.12 release. It is tested against TensorFlow 2.4.0-rc1.
Source code(tar.gz)
Source code(zip)
v0.12.0-rc0(Nov 10, 2020)

This is RC0 of the TensorFlow Probability 0.12 release. It is tested against TensorFlow 2.4.0-rc0.
Source code(tar.gz)
Source code(zip)
v0.11.1(Oct 9, 2020)

This is a patch release for compatibility with CloudPickle >= 1.3. It is tested and stable against TensorFlow version 2.3.0.
Source code(tar.gz)
Source code(zip)
v0.11.0(Jul 28, 2020)
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

Source code(tar.gz)
Source code(zip)
v0.11.0-rc1(Jul 20, 2020)

This is RC1 of the TensorFlow Probability 0.11 release. It is tested against TensorFlow 2.3.0-rc2.
Source code(tar.gz)
Source code(zip)
v0.11.0-rc0(Jul 16, 2020)

This is RC0 of the TensorFlow Probability 0.11 release. It is tested against TensorFlow 2.3.0-rc1.
Source code(tar.gz)
Source code(zip)
v0.10.1(Jul 6, 2020)

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.
Source code(tar.gz)
Source code(zip)
v0.10.0(May 14, 2020)
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

Source code(tar.gz)
Source code(zip)
v0.10.0-rc1(Apr 30, 2020)

This is RC1 of the TensorFlow Probability 0.10 release. It is tested against TensorFlow 2.2.0-rc4.
Source code(tar.gz)
Source code(zip)
v0.10.0-rc0(Apr 15, 2020)

This is the RC0 release candidate of the Tensorflow Probability 0.10 release. It is tested against Tensorflow 2.2.0-rc3.
Source code(tar.gz)
Source code(zip)
v0.9.0(Jan 15, 2020)
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

Source code(tar.gz)
Source code(zip)
0.8.0(Oct 1, 2019)
Release notes

This is the 0.8 release of TensorFlow Probability. It is tested and stable against TensorFlow version 2.0.0 and 1.15.0rc1.

Change notes

GPU-friendly "unrolled" NUTS: tfp.mcmc.NoUTurnSampler

Open-source the unrolled implementation of the No U-Turn Sampler.

Switch back to original U turn criteria in Hoffman & Gelman 2014.

Bug fix in Unrolled NUTS to make sure it does not lose shape for event_shape=1.

Bug fix of U turn check in Unrolled NUTS at the tree extension.

Refactor U turn check in Unrolled NUTS.

Fix dynamic shape bug in Unrolled NUTS.

Move NUTS unrolled into mcmc, with additional clean up.

Make sure the unrolled NUTS sampler handle scalar target_log_probs correctly.

Change implementation of check U turn to using a tf.while_loop in unrolled NUTS.

Implement multinomial sampling across tree (instead of Slice sampling) in unrolled NUTS.

Expose additional diagnostics in previous_kernel_results in unrolled NUTS so that it works with *_step_size_adaptation.

MCMC

Modify the shape handling in DualAveragingStepSizeAdaptation so that it works with non-scalar event_shape.

support structured samples in tfp.monte_carlo.expectation.

Minor fix for docstring example in leapfrog_integrator

VI

Add utilities for fitting variational distributions.

Improve Csiszar divergence support for joint variational distributions.

ensure that joint distributions are correctly recognized as reparameterizable by monte_carlo_csiszar_f_divergence.

Rename monte_carlo_csiszar_f_divergence to monte_carlo_variational_loss.

Refactor tfp.vi.csiszar_vimco_helper to expose useful leave-one-out statistical tools.

Distributions

Added tfp.distributions.GeneralizedPareto

Multinomial and DirichletMultinomial samplers are now reproducible.

HMM samples are now reproducible.

Cleaning up unneeded conversion to tensor in quantile().

Added support for dynamic num_steps in HiddenMarkovModel

Added implementation of quantile() for exponential distributions.

Fix entropy of Categorical distribution when logits contains -inf.

Annotate float-valued Deterministic distributions as reparameterized.

Establish patterns which ensure that TFP objects are "GradientTape Safe."

"GradientTape-safe" distributions: FiniteDiscrete, VonMises, Binomial, Dirichlet, Multinomial, DirichletMultinomial, Categorical, Deterministic

Add tfp.util.DeferredTensor to delay Tensor operations on tf.Variables (also works for tf.Tensors).

Add probs_parameter, logits_parameter member functions to Categorical-like distributions. In the future users should use these new functions rather than probs/logits properties because the properties might be None if that's how the distribution was parameterized.

Bijectors

Add log_scale parameter to AffineScalar bijector.

Added tfp.bijectors.RationalQuadraticSpline.

Add SoftFloor bijector. (Note: Known inverse bug WIP.)

Allow using an arbitrary bijector in RealNVP for the coupling.

Allow using an arbitrary bijector in MaskedAutoregressiveFlow for the coupling.

Experimental auto-batching system: tfp.experimental.auto_batching

Open-source the program-counter-based auto-batching system.

Added tfp.experimental.auto_batching, an experimental system to recover batch parallelism across recursive function invocations.

Autobatched NUTS supports batching across consecutive trajectories.

Add support for field references to autobatching.

Increase the amount of Python syntax that "just works" in autobatched functions.

pop-push fusion optimization in the autobatching system (also recently did tail-call optimization but forgot to add a relnote).

Open-source the auto-batched implementation of the No U-Turn Sampler.

STS

Support TF2/Eager-mode fitting of STS models, and deprecate build_factored_variational_loss.

Use dual averaging step size adaptation for STS HMC fitting.

Add support for imputing missing values in structural time series models.

Standardize parameter scales during STS inference.

Layers

Add WeightNorm layer wrapper.

Fix gradients flowing through variables in the old style variational layers.

tf.keras.model.save_model and model.save now defaults to saving a TensorFlow SavedModel.

Stats/Math

Add calibration metrics to tfp.stats.

Add output_gradients argument to value_and_gradient.

Add Geyer initial positive sequence truncation criterion to tfp.mcmc.effective_sample_size.

Resolve shape inconsistencies in PSDKernels API.

Support dynamic-shaped results in tfp.math.minimize.

ODE: Implement the Adjoint Method for gradients with respect to the initial state.

Huge thanks to all the contributors to this release!

Alexey Radul

Anudhyan Boral

Arthur Lui

Brian Patton

Christopher Suter

Colin Carroll

Dan Moldovan

Dave Moore

Edward Loper

Emily Fertig

Gaurav Jain

Ian Langmore

Igor Ganichev

Jacob Burnim

Jeff Pollock

Joshua V. Dillon

Junpeng Lao

Katherine Wu

Mark Daoust

Matthieu Coquet

Parsiad Azimzadeh

Pavel Sountsov

Pavithra Vijay

PJ Trainor

prabhu prakash kagitha

prakashkagitha

Reed Wanderman-Milne

refraction-ray

Rif A. Saurous

RJ Skerry-Ryan

Saurabh Saxena

Sharad Vikram

Sigrid Keydana

skeydan

Srinivas Vasudevan

Yash Katariya

Zachary Nado

Source code(tar.gz)
Source code(zip)
0.8.0-rc0(Aug 30, 2019)

This is the RC0 release candidate of the TensorFlow Probability 0.8 release.

It is tested against TensorFlow 2.0.0-rc0
Source code(tar.gz)
Source code(zip)
v0.7(Jun 20, 2019)
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

Source code(tar.gz)
Source code(zip)
v0.7.0-rc0(May 30, 2019)

This is the 0.7.0-rc0 release of TensorFlow Probability. It is tested and stable against TensorFlow version 1.14-rc0 and 2.0.0-alpha
Source code(tar.gz)
Source code(zip)
v0.6.0(Feb 27, 2019)
Release notes

This is the 0.6 release of TensorFlow Probability. It is tested and stable against TensorFlow version 1.13.1.

Change notes

Adds tfp.positive_semidefinite_kernels.RationalQuadratic

Support float64 in tfpl.MultivariateNormalTriL.

Add IndependentLogistic and IndependentPoisson distribution layers.

Add make_value_setter interceptor to set values of Edward2 random variables.

Implementation of Kalman Smoother, as a member function of LinearGaussianStateSpaceModel.

Bijector caching is enabled only in one direction when executing in eager mode. May cause some performance regression in eager mode if repeatedly computing forward(x) or inverse(y) with the same x or y value.

Handle rank-0/empty event_shape in tfpl.Independent{Bernoulli,Normal}.

Run additional tests in eager mode.

quantiles(x, n, ...) added to tfp.stats.

Makes tensorflow_probability compatible with Tensorflow 2.0 TensorShape indexing.

Use scipy.special functions when testing KL divergence for Chi, Chi2.

Add methods to create forecasts from STS models.

Add a MixtureSameFamily distribution layer.

Add Chi distribution.

Fix doc typo tfp.Distribution -> tfd.Distribution.

Add Gumbel-Gumbel KL divergence.

Add HalfNormal-HalfNormal KL divergence.

Add Chi2-Chi2 KL divergence unit tests.

Add Exponential-Exponential KL divergence unit tests.

Add sampling test for Normal-Normal KL divergence.

Add an IndependentNormal distribution layer.

Added posterior_marginals to HiddenMarkovModel

Add Pareto-Pareto KL divergence.

Add LinearRegression component for structural time series models.

Add dataset ops to the graph (or create kernels in Eager execution) during the python Dataset object creation instead doing it during Iterator creation time.

Text messages HMC benchmark.

Add example notebook encoding a switching Poisson process as an HMM for multiple changepoint detection.

Require num_adaptation_steps argument to make_simple_step_size_update_policy.

s/eight_hmc_schools/eight_schools_hmc/ in printed benchmark string.

Add tfp.layers.DistributionLambda to enable plumbing tfd.Distribution instances through Keras models.

Adding tfp.math.batch_interp_regular_1d_grid.

Update description of fill_triangular to include an in-depth example.

Enable bijector/distribution composition, eg, tfb.Exp(tfd.Normal(0,1)).

linear and midpoint interpolation added to tfp.stats.percentile.

Make distributions include only the bijectors they use.

tfp.math.interp_regular_1d_grid added

tfp.stats.correlation added (Pearson correlation).

Update list of edward2 RVs to include recently added Distributions.

Density of continuous Uniform distribution includes the upper endpoint.

Add support for batched inputs in tfp.glm.fit_sparse.

interp_regular_1d_grid added to tfp.math.

Added HiddenMarkovModel distribution.

Add Student's T Process.

Optimize LinearGaussianStateSpaceModel by avoiding matrix ops when the observations are statically known to be scalar.

stddev, cholesky added to tfp.stats.

Add methods to fit structual time series models to data with variational inference and HMC.

Add Expm1 bijector (Y = Exp(X) - 1).

New stats namespace. covariance and variance added to tfp.stats

Make all available MCMC kernels compatible with TransformedTransitionKernel.

Huge thanks to all the contributors to this release!

Adam Wood

Alexey Radul

Anudhyan Boral

Ashish Saxena

Billy Lamberta

Brian Patton

Christopher Suter

Cyril Chimisov

Dave Moore

Eugene Zhulenev

Griffin Tabor

Ian Langmore

Jacob Burnim

Jakub Arnold

Jiahao Yao

Jihun

Jiming Ye

Joshua V. Dillon

Juan A. Navarro Pérez

Julius Kunze

Julius Plenz

Kristian Hartikainen

Kyle Beauchamp

Matej Rizman

Pavel Sountsov

Peter Roelants

Rif A. Saurous

Rohan Jain

Roman Ring

Rui Zhao

Sergio Guadarrama

Shuhei Iitsuka

Shuming Hu

Srinivas Vasudevan

Tabor473

ValentinMouret

Youngwook Kim

Yuki Nagae

Source code(tar.gz)
Source code(zip)

Probabilistic reasoning and statistical analysis in TensorFlow

Related tags

Overview

TensorFlow Probability

Examples

Installation

Stable Builds

Nightly Builds

Installing from Source

Community

Contributing

References

Comments

Code

Problem

Material

Releases(v0.19.0)

v0.19.0(Dec 6, 2022)

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Huge thanks to all the contributors to this release!

v0.18.0(Sep 12, 2022)

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Huge thanks to all the contributors to this release!

v0.17.0(Jun 7, 2022)

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Change notes

Huge thanks to all the contributors to this release!

v0.16.0(Feb 14, 2022)

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Huge thanks to all the contributors to this release!

v0.15.0(Nov 18, 2021)

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Huge thanks to all the contributors to this release!

v0.14.1(Sep 30, 2021)

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Huge thanks to all the contributors to this release!

v0.14.0(Sep 21, 2021)

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Huge thanks to all the contributors to this release!

v0.13.0(Jun 18, 2021)

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Huge thanks to all the contributors to this release!

0.13.0-rc0(May 24, 2021)

v0.12.2(Apr 19, 2021)

v0.12.1(Dec 29, 2020)

Release notes

Change notes

Huge thanks to all the contributors to this release!

v0.12.0(Dec 29, 2020)

v0.12.0-rc4(Dec 9, 2020)

v0.12.0-rc2(Nov 21, 2020)

v0.12.0-rc1(Nov 11, 2020)

v0.12.0-rc0(Nov 10, 2020)

v0.11.1(Oct 9, 2020)

v0.11.0(Jul 28, 2020)

Release notes

Change notes

Huge thanks to all the contributors to this release!

v0.11.0-rc1(Jul 20, 2020)

v0.11.0-rc0(Jul 16, 2020)

v0.10.1(Jul 6, 2020)

v0.10.0(May 14, 2020)

Release notes

Change notes

Huge thanks to all the contributors to this release!

v0.10.0-rc1(Apr 30, 2020)

v0.10.0-rc0(Apr 15, 2020)

v0.9.0(Jan 15, 2020)

Release notes

Change notes

Huge thanks to all the contributors to this release!

0.8.0(Oct 1, 2019)

Release notes