Module: tfp.experimental.distributions | TensorFlow Probability

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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