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Bijector Ops.
Use tfp.bijectors instead.
Classes
class AbsoluteValue: Computes Y = g(X) = Abs(X), element-wise.
class Affine: Compute Y = g(X; shift, scale) = scale @ X + shift.
class AffineLinearOperator: Compute Y = g(X; shift, scale) = scale @ X + shift.
class AffineScalar: Compute Y = g(X; shift, scale) = scale * X + shift.
class BatchNormalization: Compute `Y = g(X) s.t.
class Bijector: Interface for transformations of a Distribution sample.
class Chain: Bijector which applies a sequence of bijectors.
class CholeskyOuterProduct: Compute g(X) = X @ X.T; X is lower-triangular, positive-diagonal matrix.
class ConditionalBijector: Conditional Bijector is a Bijector that allows intrinsic conditioning.
class Exp: Compute Y = g(X) = exp(X).
class FillTriangular: Transforms vectors to triangular.
class Gumbel: Compute Y = g(X) = exp(-exp(-(X - loc) / scale)).
class Identity: Compute Y = g(X) = X.
class Inline: Bijector constructed from custom callables.
class Invert: Bijector which inverts another Bijector.
class Kumaraswamy: Compute Y = g(X) = (1 - (1 - X)**(1 / b))**(1 / a), X in [0, 1].
class MaskedAutoregressiveFlow: Affine MaskedAutoregressiveFlow bijector for vector-valued events.
class MatrixInverseTriL: Computes g(L) = inv(L), where L is a lower-triangular matrix.
class Ordered: Bijector which maps a tensor x_k that has increasing elements in the last
class Permute: Permutes the rightmost dimension of a Tensor.
class PowerTransform: Compute Y = g(X) = (1 + X * c)**(1 / c), X >= -1 / c.
class RealNVP: RealNVP "affine coupling layer" for vector-valued events.
class Reshape: Reshapes the event_shape of a Tensor.
class ScaleTriL: Transforms unconstrained vectors to TriL matrices with positive diagonal.
class Sigmoid: Bijector which computes Y = g(X) = 1 / (1 + exp(-X)).
class SinhArcsinh: Compute Y = g(X) = Sinh( (Arcsinh(X) + skewness) * tailweight ).
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 Square: Compute g(X) = X^2; X is a positive real number.
class TransformDiagonal: Applies a Bijector to the diagonal of a matrix.
Functions
masked_autoregressive_default_template(...): Build the Masked Autoregressive Density Estimator (Germain et al., 2015). (deprecated)
masked_dense(...): A autoregressively masked dense layer. (deprecated)
real_nvp_default_template(...): Build a scale-and-shift function using a multi-layer neural network. (deprecated)