tfp.distributions.kl_divergence | TensorFlow Probability

Install Learn

Introduction

New to TensorFlow?
TensorFlow

The core open source ML library
For JavaScript

TensorFlow.js for ML using JavaScript
For Mobile & Edge

TensorFlow Lite for mobile and edge devices
For Production

TensorFlow Extended for end-to-end ML components

API

TensorFlow (v2.12.0)
Versions…

TensorFlow.js
TensorFlow Lite
TFX

Resources

Models & datasets

Pre-trained models and datasets built by Google and the community
Tools

Ecosystem of tools to help you use TensorFlow
Libraries & extensions

Libraries and extensions built on TensorFlow
TensorFlow Certificate program

Differentiate yourself by demonstrating your ML proficiency
Learn ML

Educational resources to learn the fundamentals of ML with TensorFlow
Responsible AI

Resources and tools to integrate Responsible AI practices into your ML workflow

Community

Join

Explore ways to get involved
Blog

Stay up to date with all things TensorFlow
Forum ↗

Discussion platform for the TensorFlow community
Groups

User groups, interest groups and mailing lists
Contribute

Guide for contributing to code and documentation

Why TensorFlow

About
Case studies

English
中文 – 简体

GitHub

Probability

Overview Guide & Tutorials API

Install
Learn
- More
API
- More
Resources
- More
Community
- More
Why TensorFlow
- More
GitHub

tfp
- Overview
tfp.bijectors
tfp.debugging
- Overview
- benchmarking
tfp.distributions
tfp.experimental
tfp.glm
tfp.layers
- Overview
- AutoregressiveTransform
- BlockwiseInitializer
- CategoricalMixtureOfOneHotCategorical
- Convolution1DFlipout
- Convolution1DReparameterization
- Convolution2DFlipout
- Convolution2DReparameterization
- Convolution3DFlipout
- Convolution3DReparameterization
- DenseFlipout
- DenseLocalReparameterization
- DenseReparameterization
- DenseVariational
- DistributionLambda
- IndependentBernoulli
- IndependentLogistic
- IndependentNormal
- IndependentPoisson
- KLDivergenceAddLoss
- KLDivergenceRegularizer
- MixtureLogistic
- MixtureNormal
- MixtureSameFamily
- MultivariateNormalTriL
- OneHotCategorical
- VariableLayer
- VariationalGaussianProcess
- default_loc_scale_fn
- default_mean_field_normal_fn
- default_multivariate_normal_fn
- conv_variational
  - Overview
- dense_variational
  - Overview
- dense_variational_v2
  - Overview
  - kullback_leibler
    Overview
    augment_kl_xent_docs
- distribution_layer
  - Overview
- initializers
  - Overview
- masked_autoregressive
  - Overview
- util
- variable_input
  - Overview
- weight_norm
  - Overview
  - WeightNorm
tfp.math
tfp.mcmc
tfp.monte_carlo
- Overview
- expectation
tfp.optimizer
tfp.random
tfp.stats
tfp.sts
tfp.substrates