View source on GitHub
|
Compute Y = g(X) = transpose_rightmost_dims(X, rightmost_perm).
Inherits From: Bijector
oryx.bijectors.Transpose(
perm=None, rightmost_transposed_ndims=None, validate_args=False,
name='transpose'
)
This bijector is semantically similar to tf.transpose except that it
transposes only the rightmost "event" dimensions. That is, unlike
tf.transpose the perm argument is itself a permutation of
tf.range(rightmost_transposed_ndims) rather than tf.range(tf.rank(x)),
i.e., users specify the (rightmost) dimensions to permute, not all dimensions.
The actual (forward) transformation is:
def forward(x, perm):
sample_batch_ndims = tf.rank(x) - tf.size(perm)
perm = tf.concat([
tf.range(sample_batch_ndims),
sample_batch_ndims + perm,
], axis=0)
return tf.transpose(x, perm)
Examples
tfp.bijectors.Transpose(perm=[1, 0]).forward(
[
[[1, 2],
[3, 4]],
[[5, 6],
[7, 8]],
])
# ==>
# [
# [[1, 3],
# [2, 4]],
# [[5, 7],
# [6, 8]],
# ]
# Using `rightmost_transposed_ndims=2` means this bijector has the same
# semantics as `tf.matrix_transpose`.
tfp.bijectors.Transpose(rightmost_transposed_ndims=2).inverse(
[
[[1, 3],
[2, 4]],
[[5, 7],
[6, 8]],
])
# ==>
# [
# [[1, 2],
# [3, 4]],
# [[5, 6],
# [7, 8]],
# ]
Args | |
|---|---|
perm
|
Positive int32 vector-shaped Tensor representing permutation of
rightmost dims (for forward transformation). Note that the 0th index
represents the first of the rightmost dims and the largest value must be
rightmost_transposed_ndims - 1 and corresponds to tf.rank(x) - 1.
Only one of perm and rightmost_transposed_ndims can (and must) be
specified. The number of elements in a permutation must have a value
that can be determined statically.
Default value:
tf.range(start=rightmost_transposed_ndims, limit=-1, delta=-1).
|
rightmost_transposed_ndims
|
Positive int32 scalar-shaped Tensor
representing the number of rightmost dimensions to permute.
Only one of perm and rightmost_transposed_ndims can (and must) be
specified. If rightmost_transposed_ndims is specified, the rightmost
dims are reversed. This argument must have a value that can be
determined statically.
Default value: tf.size(perm).
|
validate_args
|
Python bool indicating whether arguments should be
checked for correctness.
|
name
|
Python str name given to ops managed by this object.
|
Raises | |
|---|---|
ValueError
|
if both or neither perm and rightmost_transposed_ndims are
specified.
|
NotImplementedError
|
if rightmost_transposed_ndims is not known prior to
graph execution.
|
Attributes | |
|---|---|
dtype
|
|
forward_min_event_ndims
|
Returns the minimal number of dimensions bijector.forward operates on.
Multipart bijectors return structured |
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 |
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.
|
perm
|
|
rightmost_transposed_ndims
|
|
trainable_variables
|
|
validate_args
|
Returns True if Tensor arguments will be validated. |
variables
|
|
Methods
copy
copy(
**override_parameters_kwargs
)
Creates a copy of the bijector.
| Args | |
|---|---|
**override_parameters_kwargs
|
String/value dictionary of initialization arguments to override with new values. |
| Returns | |
|---|---|
bijector
|
A new instance of type(self) initialized from the union
of self.parameters and override_parameters_kwargs, i.e.,
dict(self.parameters, **override_parameters_kwargs).
|
experimental_batch_shape
experimental_batch_shape(
x_event_ndims=None, y_event_ndims=None
)
Returns the batch shape of this bijector for inputs of the given rank.
The batch shape of a bijector decribes the set of distinct
transformations it represents on events of a given size. For example: the
bijector tfb.Scale([1., 2.]) has batch shape [2] for scalar events
(event_ndims = 0), because applying it to a scalar event produces
two scalar outputs, the result of two different scaling transformations.
The same bijector has batch shape [] for vector events, because applying
it to a vector produces (via elementwise multiplication) a single vector
output.
Bijectors that operate independently on multiple state parts, such as
tfb.JointMap, must broadcast to a coherent batch shape. Some events may
not be valid: for example, the bijector
tfd.JointMap([tfb.Scale([1., 2.]), tfb.Scale([1., 2., 3.])]) does not
produce a valid batch shape when event_ndims = [0, 0], since the batch
shapes of the two parts are inconsistent. The same bijector
does define valid batch shapes of [], [2], and [3] if event_ndims
is [1, 1], [0, 1], or [1, 0], respectively.
Since transforming a single event produces a scalar log-det-Jacobian, the
batch shape of a bijector with non-constant Jacobian is expected to equal
the shape of forward_log_det_jacobian(x, event_ndims=x_event_ndims)
or inverse_log_det_jacobian(y, event_ndims=y_event_ndims), for x
or y of the specified ndims.
| Args | |
|---|---|
x_event_ndims
|
Optional Python int (structure) number of dimensions in
a probabilistic event passed to forward; this must be greater than
or equal to self.forward_min_event_ndims. If None, defaults to
self.forward_min_event_ndims. Mutually exclusive with y_event_ndims.
Default value: None.
|
y_event_ndims
|
Optional Python int (structure) number of dimensions in
a probabilistic event passed to inverse; this must be greater than
or equal to self.inverse_min_event_ndims. Mutually exclusive with
x_event_ndims.
Default value: None.
|
| Returns | |
|---|---|
batch_shape
|
TensorShape batch shape of this bijector for a
value with the given event rank. May be unknown or partially defined.
|
experimental_batch_shape_tensor
experimental_batch_shape_tensor(
x_event_ndims=None, y_event_ndims=None
)
Returns the batch shape of this bijector for inputs of the given rank.
The batch shape of a bijector decribes the set of distinct
transformations it represents on events of a given size. For example: the
bijector tfb.Scale([1., 2.]) has batch shape [2] for scalar events
(event_ndims = 0), because applying it to a scalar event produces
two scalar outputs, the result of two different scaling transformations.
The same bijector has batch shape [] for vector events, because applying
it to a vector produces (via elementwise multiplication) a single vector
output.
Bijectors that operate independently on multiple state parts, such as
tfb.JointMap, must broadcast to a coherent batch shape. Some events may
not be valid: for example, the bijector
tfd.JointMap([tfb.Scale([1., 2.]), tfb.Scale([1., 2., 3.])]) does not
produce a valid batch shape when event_ndims = [0, 0], since the batch
shapes of the two parts are inconsistent. The same bijector
does define valid batch shapes of [], [2], and [3] if event_ndims
is [1, 1], [0, 1], or [1, 0], respectively.
Since transforming a single event produces a scalar log-det-Jacobian, the
batch shape of a bijector with non-constant Jacobian is expected to equal
the shape of forward_log_det_jacobian(x, event_ndims=x_event_ndims)
or inverse_log_det_jacobian(y, event_ndims=y_event_ndims), for x
or y of the specified ndims.
| Args | |
|---|---|
x_event_ndims
|
Optional Python int (structure) number of dimensions in
a probabilistic event passed to forward; this must be greater than
or equal to self.forward_min_event_ndims. If None, defaults to
self.forward_min_event_ndims. Mutually exclusive with y_event_ndims.
Default value: None.
|
y_event_ndims
|
Optional Python int (structure) number of dimensions in
a probabilistic event passed to inverse; this must be greater than
or equal to self.inverse_min_event_ndims. Mutually exclusive with
x_event_ndims.
Default value: None.
|
| Returns | |
|---|---|
batch_shape_tensor
|
integer Tensor batch shape of this bijector for a
value with the given event rank.
|
forward
forward(
x, name='forward', **kwargs
)
Returns the forward Bijector evaluation, i.e., X = g(Y).
| Args | |
|---|---|
x
|
Tensor (structure). The input to the 'forward' evaluation.
|
name
|
The name to give this op. |
**kwargs
|
Named arguments forwarded to subclass implementation. |
| Returns | |
|---|---|
Tensor (structure).
|
| Raises | |
|---|---|
TypeError
|
if self.dtype is specified and x.dtype is not
self.dtype.
|
NotImplementedError
|
if _forward is not implemented.
|
forward_dtype
forward_dtype(
dtype=UNSPECIFIED, name='forward_dtype', **kwargs
)
Returns the dtype returned by forward for the provided input.
forward_event_ndims
forward_event_ndims(
event_ndims, **kwargs
)
Returns the number of event dimensions produced by forward.
forward_event_shape
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 (structure) indicating event-portion shape
passed into forward function.
|
| Returns | |
|---|---|
forward_event_shape_tensor
|
TensorShape (structure) indicating
event-portion shape after applying forward. Possibly unknown.
|
forward_event_shape_tensor
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 (structure) indicating event-portion
shape passed into forward function.
|
name
|
name to give to the op |
| Returns | |
|---|---|
forward_event_shape_tensor
|
Tensor, int32 vector (structure)
indicating event-portion shape after applying forward.
|
forward_log_det_jacobian
forward_log_det_jacobian(
x, event_ndims=None, name='forward_log_det_jacobian', **kwargs
)
Returns both the forward_log_det_jacobian.
| Args | |
|---|---|
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.
|
inverse
inverse(
y, name='inverse', **kwargs
)
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.
|
NotImplementedError
|
if _inverse is not implemented.
|
inverse_dtype
inverse_dtype(
dtype=UNSPECIFIED, name='inverse_dtype', **kwargs
)
Returns the dtype returned by inverse for the provided input.
inverse_event_ndims
inverse_event_ndims(
event_ndims, **kwargs
)
Returns the number of event dimensions produced by inverse.
inverse_event_shape
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 (structure) indicating event-portion shape
passed into inverse function.
|
| Returns | |
|---|---|
inverse_event_shape_tensor
|
TensorShape (structure) indicating
event-portion shape after applying inverse. Possibly unknown.
|
inverse_event_shape_tensor
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 (structure) indicating
event-portion shape passed into inverse function.
|
name
|
name to give to the op |
| Returns | |
|---|---|
inverse_event_shape_tensor
|
Tensor, int32 vector (structure)
indicating event-portion shape after applying inverse.
|
inverse_log_det_jacobian
inverse_log_det_jacobian(
y, event_ndims=None, name='inverse_log_det_jacobian', **kwargs
)
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 (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.
|
parameter_properties
@classmethodparameter_properties( dtype=tf.float32 )
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.
|
__call__
__call__(
value, name=None, **kwargs
)
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.Distributioninstance, returntfd.TransformedDistribution(distribution=input, bijector=self). - If the input is a
tfb.Bijectorinstance, returntfb.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.
|
Examples
sigmoid = tfb.Reciprocal()(
tfb.Shift(shift=1.)(
tfb.Exp()(
tfb.Scale(scale=-1.))))
# ==> `tfb.Chain([
# tfb.Reciprocal(),
# tfb.Shift(shift=1.),
# tfb.Exp(),
# tfb.Scale(scale=-1.),
# ])` # ie, `tfb.Sigmoid()`
log_normal = tfb.Exp()(tfd.Normal(0, 1))
# ==> `tfd.TransformedDistribution(tfd.Normal(0, 1), tfb.Exp())`
tfb.Exp()([-1., 0., 1.])
# ==> tf.exp([-1., 0., 1.])
__eq__
__eq__(
other
)
Return self==value.