tf.contrib.distributions.bijectors.ScaleTriL

Transforms unconstrained vectors to TriL matrices with positive diagonal.

Inherits From: Chain

tf.contrib.distributions.bijectors.ScaleTriL(
    diag_bijector=None, diag_shift=1e-05, validate_args=False, name='scale_tril'
)

This is implemented as a simple tfb.Chain of tfb.FillTriangular followed by tfb.TransformDiagonal, and provided mostly as a convenience. The default setup is somewhat opinionated, using a Softplus transformation followed by a small shift (1e-5) which attempts to avoid numerical issues from zeros on the diagonal.

Examples

import tensorflow_probability as tfp
tfd = tfp.distributions
tfb = tfp.bijectors

b = tfb.ScaleTriL(
     diag_bijector=tfb.Exp(),
     diag_shift=None)
b.forward(x=[0., 0., 0.])
# Result: [[1., 0.],
#          [0., 1.]]
b.inverse(y=[[1., 0],
             [.5, 2]])
# Result: [log(2), .5, log(1)]

# Define a distribution over PSD matrices of shape `[3, 3]`,
# with `1 + 2 + 3 = 6` degrees of freedom.
dist = tfd.TransformedDistribution(
        tfd.Normal(tf.zeros(6), tf.ones(6)),
        tfb.Chain([tfb.CholeskyOuterProduct(), tfb.ScaleTriL()]))

# Using an identity transformation, ScaleTriL is equivalent to
# tfb.FillTriangular.
b = tfb.ScaleTriL(
     diag_bijector=tfb.Identity(),
     diag_shift=None)

# For greater control over initialization, one can manually encode
# pre- and post- shifts inside of `diag_bijector`.
b = tfb.ScaleTriL(
     diag_bijector=tfb.Chain([
       tfb.AffineScalar(shift=1e-3),
       tfb.Softplus(),
       tfb.AffineScalar(shift=0.5413)]),  # softplus_inverse(1.)
                                          #  = log(expm1(1.)) = 0.5413
     diag_shift=None)

Args
diag_bijector	Bijector instance, used to transform the output diagonal to be positive. Default value: None (i.e., tfb.Softplus()).
diag_shift	Float value broadcastable and added to all diagonal entries after applying the diag_bijector. Setting a positive value forces the output diagonal entries to be positive, but prevents inverting the transformation for matrices with diagonal entries less than this value. Default value: 1e-5 (i.e., no shift is applied).
validate_args	Python bool indicating whether arguments should be checked for correctness. Default value: False (i.e., arguments are not validated).
name	Python str name given to ops managed by this object. Default value: scale_tril.

Attributes
bijectors
dtype	dtype of Tensors transformable by this distribution.
forward_min_event_ndims	Returns the minimal number of dimensions bijector.forward operates on.
graph_parents	Returns this Bijector's graph_parents as a Python list.
inverse_min_event_ndims	Returns the minimal number of dimensions bijector.inverse operates on.
is_constant_jacobian	Returns true iff the Jacobian matrix is not a function of x. Note: Jacobian matrix is either constant for both forward and inverse or neither.
name	Returns the string name of this Bijector.
validate_args	Returns True if Tensor arguments will be validated.

Methods

forward

View source

forward(
    x, name='forward'
)

Returns the forward Bijector evaluation, i.e., X = g(Y).

Args
x	Tensor. The input to the "forward" evaluation.
name	The name to give this op.

Returns
Tensor.

Raises
TypeError	if self.dtype is specified and x.dtype is not self.dtype.
NotImplementedError	if _forward is not implemented.

forward_event_shape

View source

forward_event_shape(
    input_shape
)

Shape of a single sample from a single batch as a TensorShape.

Same meaning as forward_event_shape_tensor. May be only partially defined.

Args
input_shape	TensorShape indicating event-portion shape passed into forward function.

Returns
forward_event_shape_tensor	TensorShape indicating event-portion shape after applying forward. Possibly unknown.

forward_event_shape_tensor

View source

forward_event_shape_tensor(
    input_shape, name='forward_event_shape_tensor'
)

Shape of a single sample from a single batch as an int32 1D Tensor.

Args
input_shape	Tensor, int32 vector indicating event-portion shape passed into forward function.
name	name to give to the op

Returns
forward_event_shape_tensor	Tensor, int32 vector indicating event-portion shape after applying forward.

forward_log_det_jacobian

View source

forward_log_det_jacobian(
    x, event_ndims, name='forward_log_det_jacobian'
)

Returns both the forward_log_det_jacobian.

Args
x	Tensor. The input to the "forward" Jacobian determinant evaluation.
event_ndims	Number of dimensions in the probabilistic events being transformed. Must be greater than or equal to self.forward_min_event_ndims. The result is summed over the final dimensions to produce a scalar Jacobian determinant for each event, i.e. it has shape x.shape.ndims - event_ndims dimensions.
name	The name to give this op.

Returns
Tensor, if this bijector is injective. If not injective this is not implemented.

Raises
TypeError	if self.dtype is specified and y.dtype is not self.dtype.
NotImplementedError	if neither _forward_log_det_jacobian nor {_inverse, _inverse_log_det_jacobian} are implemented, or this is a non-injective bijector.

inverse

View source

inverse(
    y, name='inverse'
)

Returns the inverse Bijector evaluation, i.e., X = g^{-1}(Y).

Args
y	Tensor. The input to the "inverse" evaluation.
name	The name to give this op.

Returns
Tensor, if this bijector is injective. If not injective, returns the k-tuple containing the unique k points (x1, ..., xk) such that g(xi) = y.

Raises
TypeError	if self.dtype is specified and y.dtype is not self.dtype.
NotImplementedError	if _inverse is not implemented.

inverse_event_shape

View source

inverse_event_shape(
    output_shape
)

Shape of a single sample from a single batch as a TensorShape.

Same meaning as inverse_event_shape_tensor. May be only partially defined.

Args
output_shape	TensorShape indicating event-portion shape passed into inverse function.

Returns
inverse_event_shape_tensor	TensorShape indicating event-portion shape after applying inverse. Possibly unknown.

inverse_event_shape_tensor

View source

inverse_event_shape_tensor(
    output_shape, name='inverse_event_shape_tensor'
)

Shape of a single sample from a single batch as an int32 1D Tensor.

Args
output_shape	Tensor, int32 vector indicating event-portion shape passed into inverse function.
name	name to give to the op

Returns
inverse_event_shape_tensor	Tensor, int32 vector indicating event-portion shape after applying inverse.

inverse_log_det_jacobian

View source

inverse_log_det_jacobian(
    y, event_ndims, name='inverse_log_det_jacobian'
)

Returns the (log o det o Jacobian o inverse)(y).

Mathematically, returns: log(det(dX/dY))(Y). (Recall that: X=g^{-1}(Y).)

Note that forward_log_det_jacobian is the negative of this function, evaluated at g^{-1}(y).

Args
y	Tensor. The input to the "inverse" Jacobian determinant evaluation.
event_ndims	Number of dimensions in the probabilistic events being transformed. Must be greater than or equal to self.inverse_min_event_ndims. The result is summed over the final dimensions to produce a scalar Jacobian determinant for each event, i.e. it has shape y.shape.ndims - event_ndims dimensions.
name	The name to give this op.

Returns
Tensor, if this bijector is injective. If not injective, returns the tuple of local log det Jacobians, log(det(Dg_i^{-1}(y))), where g_i is the restriction of g to the ith partition Di.

Raises
TypeError	if self.dtype is specified and y.dtype is not self.dtype.
NotImplementedError	if _inverse_log_det_jacobian is not implemented.