This is useful when constructing non-trivial chains of multipart bijectors.
It partitions inputs into different logical "blocks", which may be fed as
arguments to downstream multipart bijectors.
Example Usage:
# Pack a 3-element list of tensors into a dict. The output structure,
# `structure_1`, is defined as a dict in which the values are list
# indices.
structure_1 = {'a': 1, 'b': 2, 'c': 0}
list_to_dict = Restructure(output_structure=structure_1)
input_list = [0.01, 0.02, 0.03]
assert list_to_dict.forward(input_list) == {
'a': 0.02, 'b': 0.03, 'c': 0.01}
# Now assume that, instead of a list/tuple (the default), the input
# structure is another dict. The output structure is the same as
# defined above, and consecutive integers are again used to associate
# components of the input and output structures.
structure_2 = {'c': 2, 'd': 1, 'e': 0}
dict_to_dict = Restructure(
structure_1, input_structure=structure_2)
input_dict = {'c': -3.5, 'd': 96.0, 'e': 12.0}
assert dict_to_dict.forward(input_dict) == {
'a': 96.0, 'b': -3.5, 'c': 12.0}
# Restructure a dict to a namedtuple.
Example = collections.namedtuple('Example', ['x', 'y', 'z'])
structure_3 = Example(2, 0, 1)
namedtuple_to_dict = Restructure(structure_3, input_structure=structure_2)
assert namedtuple_to_dict(input_dict) == Example(x=-3.5, y=12.0, z=96.0)
assert namedtuple_to_dict.inverse(Example(x=0.01, y=0.02, z=0.03)) == {
'c': 0.01, 'd': 0.03, 'e': 0.02}
# Restructure can be applied to structures of mixed type and arbitrary
# depth:
restructure = Restructure({
'foo': [0, 1],
'bar': [3, 2],
'baz': [4, 5, 6]
})
# Note that x is a *python-list* of tensors.
# To permute elements of an individual Tensor, see `tfb.Permute`.
x = [1, 2, 4, 8, 16, 32, 64]
assert restructure.forward(x) == {
'foo': [1, 2],
'bar': [8, 4],
'baz': [16, 32, 64]
}
# Where Restructure is useful:
complex_bijector = Chain([
# Apply different transformations to each block.
JointMap({
'foo': ScaleMatVecLinearOperator(...), # Operates on the full block
'bar': ScaleMatVecLinearOperator(...), # Operates on the full block
'baz': [Exp(), Scale(10.), Shift(-1.)] # Different bijectors for each
}),
# Group the tensor into logical blocks.
Restructure({
'foo': [0, 1],
'bar': [3, 2],
'baz': [4, 5, 6],
}),
# Split an input tensor into 7 chunks.
Split([2, 4, 6, 8, 10, 12, 14])
])
Args
output_structure
A tf.nest-compatible structure of tokens describing the
output of forward (equivalently, the input of inverse).
input_structure
A tf.nest-compatible structure of tokens describing the
input to forward. If unspecified, a default structure is inferred from
output_structure. The default structure expects a list if tokens are
integers, or a dict if the tokens are strings.
name
Name of this bijector.
Raises
ValueError
If tokens are duplicated, or a required default structure
cannot be inferred.
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. (deprecated)
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.
Tensor (structure). The input to the 'forward' Jacobian determinant
evaluation.
event_ndims
Optional number of dimensions in the probabilistic events
being transformed; this must be greater than or equal to
self.forward_min_event_ndims. If event_ndims is specified, the
log Jacobian determinant is summed to produce a
scalar log-determinant for each event. Otherwise
(if event_ndims is None), no reduction is performed.
Multipart bijectors require structured event_ndims, such that the
batch rank rank(y[i]) - event_ndims[i] is the same for all
elements i of the structured input. In most cases (with the
exception of tfb.JointMap) they further require that
event_ndims[i] - self.inverse_min_event_ndims[i] is the same for
all elements i of the structured input.
Default value: None (equivalent to self.forward_min_event_ndims).
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.
ValueError
if the value of event_ndims is not valid for this 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
Optional number of dimensions in the probabilistic events
being transformed; this must be greater than or equal to
self.inverse_min_event_ndims. If event_ndims is specified, the
log Jacobian determinant is summed to produce a
scalar log-determinant for each event. Otherwise
(if event_ndims is None), no reduction is performed.
Multipart bijectors require structured event_ndims, such that the
batch rank rank(y[i]) - event_ndims[i] is the same for all
elements i of the structured input. In most cases (with the
exception of tfb.JointMap) they further require that
event_ndims[i] - self.inverse_min_event_ndims[i] is the same for
all elements i of the structured input.
Default value: None (equivalent to self.inverse_min_event_ndims).
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.
NotImplementedError
if _inverse_log_det_jacobian is not implemented.
ValueError
if the value of event_ndims is not valid for this bijector.
Returns a dict mapping constructor arg names to property annotations.
This dict should include an entry for each of the bijector's
Tensor-valued constructor arguments.
Args
dtype
Optional float dtype to assume for continuous-valued parameters.
Some constraining bijectors require advance knowledge of the dtype
because certain constants (e.g., tfb.Softplus.low) must be
instantiated with the same dtype as the values to be transformed.
Returns
parameter_properties
A
str ->tfp.python.internal.parameter_properties.ParameterPropertiesdict mapping constructor argument names toParameterProperties`
instances.
with_name_scope
@classmethodwith_name_scope(
method
)
Decorator to automatically enter the module name scope.
class MyModule(tf.Module): @tf.Module.with_name_scope def __call__(self, x): if not hasattr(self, 'w'): self.w = tf.Variable(tf.random.normal([x.shape[1], 3])) return tf.matmul(x, self.w)
Using the above module would produce tf.Variables and tf.Tensors whose
names included the module name:
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.
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