Module: tfp.experimental.bijectors
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TensorFlow Probability experimental bijectors package.
Classes
class HighwayFlow
: Implements an Highway Flow bijector [1].
class ScalarFunctionWithInferredInverse
: Bijector to associate a numeric inverse with any invertible function.
class Sharded
: A meta-bijector meant for use in an SPMD distributed context.
Functions
build_trainable_highway_flow(...)
: Builds a HighwayFlow parameterized by trainable variables.
forward_log_det_jacobian_ratio(...)
: Computes p.fldj(x, ndims) - q.fdlj(y, ndims)
, numerically stably.
inverse_log_det_jacobian_ratio(...)
: Computes p.ildj(x, ndims) - q.idlj(y, ndims)
, numerically stably.
make_distribution_bijector(...)
: Builds a bijector to approximately transform N(0, 1)
into distribution
.
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Last updated 2023-11-21 UTC.
[null,null,["Last updated 2023-11-21 UTC."],[],[],null,["# Module: tfp.experimental.bijectors\n\n\u003cbr /\u003e\n\n|--------------------------------------------------------------------------------------------------------------------------------------------------|\n| [View source on GitHub](https://github.com/tensorflow/probability/blob/v0.23.0/tensorflow_probability/python/experimental/bijectors/__init__.py) |\n\nTensorFlow Probability experimental bijectors package.\n\nClasses\n-------\n\n[`class HighwayFlow`](../../tfp/experimental/bijectors/HighwayFlow): Implements an Highway Flow bijector \\[1\\].\n\n[`class ScalarFunctionWithInferredInverse`](../../tfp/experimental/bijectors/ScalarFunctionWithInferredInverse): Bijector to associate a numeric inverse with any invertible function.\n\n[`class Sharded`](../../tfp/experimental/bijectors/Sharded): A meta-bijector meant for use in an SPMD distributed context.\n\nFunctions\n---------\n\n[`build_trainable_highway_flow(...)`](../../tfp/experimental/bijectors/build_trainable_highway_flow): Builds a HighwayFlow parameterized by trainable variables.\n\n[`forward_log_det_jacobian_ratio(...)`](../../tfp/experimental/bijectors/forward_log_det_jacobian_ratio): Computes `p.fldj(x, ndims) - q.fdlj(y, ndims)`, numerically stably.\n\n[`inverse_log_det_jacobian_ratio(...)`](../../tfp/experimental/bijectors/inverse_log_det_jacobian_ratio): Computes `p.ildj(x, ndims) - q.idlj(y, ndims)`, numerically stably.\n\n[`make_distribution_bijector(...)`](../../tfp/experimental/bijectors/make_distribution_bijector): Builds a bijector to approximately transform `N(0, 1)` into `distribution`."]]