Python list of graph prerequisites of this Bijector.
is_constant_jacobian
Python bool indicating that the Jacobian matrix is
not a function of the input.
validate_args
Python bool, default False. Whether to validate input
with asserts. If validate_args is False, and the inputs are invalid,
correct behavior is not guaranteed.
dtype
tf.dtype supported by this Bijector. None means dtype is not
enforced. For multipart bijectors, this value is expected to be the
same for all elements of the input and output structures.
forward_min_event_ndims
Python integer (structure) indicating the
minimum number of dimensions on which forward operates.
inverse_min_event_ndims
Python integer (structure) indicating the
minimum number of dimensions on which inverse operates. Will be set to
forward_min_event_ndims by default, if no value is provided.
parameters
Python dict of parameters used to instantiate this
Bijector. Bijector instances with identical types, names, and
parameters share an input/output cache. parameters dicts are
keyed by strings and are identical if their keys are identical and if
corresponding values have identical hashes (or object ids, for
unhashable objects).
name
The name to give Ops created by the initializer.
Raises
ValueError
If neither forward_min_event_ndims and
inverse_min_event_ndims are specified, or if either of them is
negative.
ValueError
If a member of graph_parents is not a Tensor.
Attributes
dtype
forward_min_event_ndims
Returns the minimal number of dimensions bijector.forward operates on.
Multipart bijectors return structured ndims, which indicates the
expected structure of their inputs. Some multipart bijectors, notably
Composites, may return structures of None.
graph_parents
Returns this Bijector's graph_parents as a Python list.
has_static_min_event_ndims
Returns True if the bijector has statically-known min_event_ndims.
inverse_min_event_ndims
Returns the minimal number of dimensions bijector.inverse operates on.
Multipart bijectors return structured event_ndims, which indicates the
expected structure of their outputs. Some multipart bijectors, notably
Composites, may return structures of None.
is_constant_jacobian
Returns true iff the Jacobian matrix is not a function of x.
name
Returns the string name of this Bijector.
parameters
Dictionary of parameters used to instantiate this Bijector.
trainable_variables
validate_args
Returns True if Tensor arguments will be validated.
Tensor (structure). 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 rank(x) - event_ndims dimensions.
Multipart bijectors require structured event_ndims, such that
rank(y[i]) - rank(event_ndims[i]) is the same for all elements i of
the structured input. Furthermore, the first event_ndims[i] of each
x[i].shape must be the same for all i (broadcasting is not allowed).
name
The name to give this op.
**kwargs
Named arguments forwarded to subclass implementation.
Returns
Tensor (structure), if this bijector is injective.
If not injective this is not implemented.
Raises
TypeError
if y's dtype is incompatible with the expected output dtype.
NotImplementedError
if neither _forward_log_det_jacobian
nor {_inverse, _inverse_log_det_jacobian} are implemented, or
this is a non-injective bijector.
Returns the inverse Bijector evaluation, i.e., X = g^{-1}(Y).
Args
y
Tensor (structure). The input to the 'inverse' evaluation.
name
The name to give this op.
**kwargs
Named arguments forwarded to subclass implementation.
Returns
Tensor (structure), 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 y's structured dtype is incompatible with the expected
output dtype.
Note that forward_log_det_jacobian is the negative of this function,
evaluated at g^{-1}(y).
Args
y
Tensor (structure). 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 rank(y) - event_ndims dimensions.
Multipart bijectors require structured event_ndims, such that
rank(y[i]) - rank(event_ndims[i]) is the same for all elements i of
the structured input. Furthermore, the first event_ndims[i] of each
x[i].shape must be the same for all i (broadcasting is not allowed).
name
The name to give this op.
**kwargs
Named arguments forwarded to subclass implementation.
Returns
ildj
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 x's dtype is incompatible with the expected inverse-dtype.
Applies or composes the Bijector, depending on input type.
This is a convenience function which applies the Bijector instance in
three different ways, depending on the input:
If the input is a tfd.Distribution instance, return
tfd.TransformedDistribution(distribution=input, bijector=self).
If the input is a tfb.Bijector instance, return
tfb.Chain([self, input]).
Otherwise, return self.forward(input)
Args
value
A tfd.Distribution, tfb.Bijector, or a (structure of) Tensor.
name
Python str name given to ops created by this function.
**kwargs
Additional keyword arguments passed into the created
tfd.TransformedDistribution, tfb.Bijector, or self.forward.
Returns
composition
A tfd.TransformedDistribution if the input was a
tfd.Distribution, a tfb.Chain if the input was a tfb.Bijector, or
a (structure of) Tensor computed by self.forward.