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Bijective transformations.
Classes
class AbsoluteValue: Computes Y = g(X) = Abs(X), element-wise.
class Ascending: Maps unconstrained R^n to R^n in ascending order.
class AutoCompositeTensorBijector: Base for CompositeTensor bijectors with auto-generated TypeSpecs.
class AutoregressiveNetwork: Masked Autoencoder for Distribution Estimation [Germain et al. (2015)][1].
class BatchNormalization: Compute Y = g(X) s.t. X = g^-1(Y) = (Y - mean(Y)) / std(Y).
class Bijector: Interface for transformations of a Distribution sample.
class Blockwise: Bijector which applies a list of bijectors to blocks of a Tensor.
class Chain: Bijector which applies a composition of bijectors.
class CholeskyOuterProduct: Compute g(X) = X @ X.T; X is lower-triangular, positive-diagonal matrix.
class CholeskyToInvCholesky: Maps the Cholesky factor of M to the Cholesky factor of M^{-1}.
class Composition: Base class for Composition bijectors (Chain, JointMap).
class CorrelationCholesky: Maps unconstrained reals to Cholesky-space correlation matrices.
class Cumsum: Computes the cumulative sum of a tensor along a specified axis.
class DiscreteCosineTransform: Compute Y = g(X) = DCT(X), where DCT type is indicated by the type arg.
class Exp: Compute Y = g(X) = exp(X).
class Expm1: Compute Y = g(X) = exp(X) - 1.
class FFJORD: Implements a continuous normalizing flow X->Y defined via an ODE.
class FillScaleTriL: Transforms unconstrained vectors to TriL matrices with positive diagonal.
class FillTriangular: Transforms vectors to triangular.
class FrechetCDF: The Frechet cumulative density function.
class GeneralizedExtremeValueCDF: Compute the GeneralizedExtremeValue CDF.
class GeneralizedPareto: Bijector mapping R**n to non-negative reals.
class Glow: Implements the Glow Bijector from Kingma & Dhariwal (2018)[1].
class GlowDefaultExitNetwork: Default network for the glow exit bijector.
class GlowDefaultNetwork: Default network for the glow bijector.
class GompertzCDF: Compute Y = g(X) = 1 - exp(-c * (exp(rate * X) - 1), the Gompertz CDF.
class GumbelCDF: Compute Y = g(X) = exp(-exp(-(X - loc) / scale)), the Gumbel CDF.
class Householder: Compute the reflection of a vector across a hyperplane.
class Identity: Compute Y = g(X) = X.
class Inline: Bijector constructed from custom callables.
class Invert: Bijector which inverts another Bijector.
class IteratedSigmoidCentered: Bijector which applies a Stick Breaking procedure.
class JointMap: Bijector which applies a structure of bijectors in parallel.
class KumaraswamyCDF: Compute Y = g(X) = (1 - X**a)**b, X in [0, 1].
class LambertWTail: LambertWTail transformation for heavy-tail Lambert W x F random variables.
class Log: Compute Y = log(X). This is Invert(Exp()).
class Log1p: Compute Y = log1p(X). This is Invert(Expm1()).
class MaskedAutoregressiveFlow: Affine MaskedAutoregressiveFlow bijector.
class MatrixInverseTriL: Computes g(L) = inv(L), where L is a lower-triangular matrix.
class MatvecLU: Matrix-vector multiply using LU decomposition.
class MoyalCDF: Compute Y = g(X) = erfc(exp(- 1/2 * (X - loc) / scale) / sqrt(2)).
class NormalCDF: Compute Y = g(X) = NormalCDF(x).
class Pad: Pads a value to the event_shape of a Tensor.
class Permute: Permutes the rightmost dimension of a Tensor.
class Power: Compute g(X) = X ** power; where X is a non-negative real number.
class PowerTransform: Compute Y = g(X) = (1 + X * c)**(1 / c), X >= -1 / c.
class RationalQuadraticSpline: A piecewise rational quadratic spline, as developed in [1].
class RayleighCDF: Compute Y = g(X) = 1 - exp( -(X/scale)**2 / 2 ), X >= 0.
class RealNVP: RealNVP 'affine coupling layer' for vector-valued events.
class Reciprocal: A Bijector that computes the reciprocal b(x) = 1. / x entrywise.
class Reshape: Reshapes the event_shape of a Tensor.
class Restructure: Converts between nested structures of Tensors.
class Scale: Compute Y = g(X; scale) = scale * X.
class ScaleMatvecDiag: Compute Y = g(X; scale) = scale @ X.
class ScaleMatvecLU: Matrix-vector multiply using LU decomposition.
class ScaleMatvecLinearOperator: Compute Y = g(X; scale) = scale @ X.
class ScaleMatvecLinearOperatorBlock: Compute Y = g(X; scale) = scale @ X for blockwise X and scale.
class ScaleMatvecTriL: Compute Y = g(X; scale) = scale @ X.
class Shift: Compute Y = g(X; shift) = X + shift.
class ShiftedGompertzCDF: Compute Y = g(X) = (1 - exp(-rate * X)) * exp(-c * exp(-rate * X)).
class Sigmoid: Bijector that computes the logistic sigmoid function.
class Sinh: Bijector that computes Y = sinh(X).
class SinhArcsinh: Y = g(X) = Sinh( (Arcsinh(X) + skewness) * tailweight ) * multiplier.
class SoftClip: Bijector that approximates clipping as a continuous, differentiable map.
class Softfloor: Compute a differentiable approximation to tf.math.floor.
class SoftmaxCentered: Bijector which computes Y = g(X) = exp([X 0]) / sum(exp([X 0])).
class Softplus: Bijector which computes Y = g(X) = Log[1 + exp(X)].
class Softsign: Bijector which computes Y = g(X) = X / (1 + |X|).
class Split: Split a Tensor event along an axis into a list of Tensors.
class Square: Compute g(X) = X^2; X is a positive real number.
class Tanh: Bijector that computes Y = tanh(X), therefore Y in (-1, 1).
class TransformDiagonal: Applies a Bijector to the diagonal of a matrix.
class Transpose: Compute Y = g(X) = transpose_rightmost_dims(X, rightmost_perm).
class UnitVector: Bijector mapping vectors onto the unit sphere.
class WeibullCDF: Compute Y = g(X) = 1 - exp( -( X / scale) ** concentration), X >= 0.
Functions
masked_autoregressive_default_template(...): Build the Masked Autoregressive Density Estimator (Germain et al., 2015). (deprecated)
masked_dense(...): A autoregressively masked dense layer. Analogous to tf.layers.dense.
pack_sequence_as(...): Returns a Bijector variant of tf.nest.pack_sequence_as.
real_nvp_default_template(...): Build a scale-and-shift function using a multi-layer neural network.
tree_flatten(...): Returns a Bijector variant of tf.nest.flatten.