tfp.substrates.numpy.bijectors.Composition

Base class for Composition bijectors (Chain, JointMap).

Inherits From: Bijector

View aliases

Main aliases

tfp.experimental.substrates.numpy.bijectors.Composition

tfp.substrates.numpy.bijectors.Composition(
    bijectors, name, parameters, forward_min_event_ndims=None,
    inverse_min_event_ndims=None, validate_event_size=False, **kwargs
)

A Composition represents a partially ordered set of invertible transformations. These transformations may happen in series (Chain), in parallel (JointMap), or they could be an arbitrary DAG. Composition handles the common machinery of such transformations, delegating graph-traversal to _walk_forward and _walk_inverse (which must be overridden by subclasses).

The _walk_{direction} methods take a step_fn, a single (structured) argument (representing zipped *args), and arbitrary **kwargs. They are responsible for invoking step_fn(bij, bij_inputs, **bij_kwds) for each nested bijector. See Chain and JointMap for examples.

These methods are typically invoked using _call_walk_{direction}, which wraps step_fn and converts structured *args into a single structure of tuples, allowing users to provide a step_fn with multiple positional arguments (e.g., foward_log_det_jacobian).

In practice, Bijector methods are defined in the base-class, and users should not need to invoke walk methods directly.

Args
bijectors	A nest-compatible structure of bijector instances or a Composition bijector. If bijectors is a nested structure, then _walk_forward and _walk_inverse must be implemented. If bijectors is a Composition bijector, _walk_forward and _walk_inverse call its corresponding methods.
name	Name of this bijector.
parameters	Dictionary of parameters used to initialize this bijector. These must be the exact values passed to __init__.
forward_min_event_ndims	A (structure of) integers or None values. If all values are integers, then these specify the mininimum rank of each event part; their structure must match that of inputs to forward. If the minimum ranks are not known, inference may be triggered by passing a structure of None values instead. This structure must be such that calling self._walk_forward(step_fn, forward_min_event_ndims) passes exactly one None value to each toplevel invocation of step_fn---that is, it should correspond to the structure of bijectors that act directly on a user-passed input (excluding bijectors that act on downstream quantities), which may in general be shallower than the structure of inputs to forward. For example, the first step of a Chain applies a single bijector, so Chain would pass a single None (even though the initial bijector might itself accept a multipart input). On the other hand, the bijectors in a JointMap are all applied directly to user-passed input, so the appropriate structure would be that of self.bijectors (even if some of those bijectors might themselves accept multipart inputs).
inverse_min_event_ndims	A (structure of) integers and/or None values, with semantics analogous to forward_min_event_ndims.
validate_event_size	Checks that bijectors are not applied to inputs with incomplete support. For example, the following LDJ would be incorrect: Chain([Scale(), SoftmaxCentered()]).forward_log_det_jacobian([1], [1]) The jacobian contribution from Scale applies to a 2-dimensional input, but the output from SoftMaxCentered is a 1-dimensional input embedded in a 2-dimensional space. Setting validate_event_size=True (default) prints warnings in these cases. When validate_args is also True, the warning is promoted to an exception.
**kwargs	Additional parameters forwarded to the bijector base-class.

Attributes
bijectors
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. Note: Jacobian matrix is either constant for both forward and inverse or neither.
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.
validate_event_size
variables

Methods

copy

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copy(
    **override_parameters_kwargs
)

Creates a copy of the bijector.

Note: the copy bijector may continue to depend on the original initialization arguments.

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

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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

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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.

experimental_compute_density_correction

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experimental_compute_density_correction(
    x, tangent_space, backward_compat=False
)

Density correction for this transformation wrt the tangent space, at x.

Subclasses of Bijector may call the most specific applicable method of TangentSpace, based on whether the transformation is dimension-preserving, coordinate-wise, a projection, or something more general. The backward-compatible assumption is that the transformation is dimension-preserving (goes from R^n to R^n).

Args
x	Tensor (structure). The point at which to calculate the density.
tangent_space	TangentSpace or one of its subclasses. The tangent to the support manifold at x.
backward_compat	bool specifying whether to assume that the Bijector is dimension-preserving.

Returns
density_correction	Tensor representing the density correction---in log space---under the transformation that this Bijector denotes.

Raises
TypeError if backward_compat is False but no method of TangentSpace has been called explicitly.

forward

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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

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forward_dtype(
    dtype=UNSPECIFIED, name='forward_dtype', **kwargs
)

Returns the dtype returned by forward for the provided input.

forward_event_ndims

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forward_event_ndims(
    event_ndims, **kwargs
)

Returns the number of event dimensions produced by forward.

Args
event_ndims	Structure of Python and/or Tensor ints, and/or None values. The structure should match that of self.forward_min_event_ndims, and all non-None values must be greater than or equal to the corresponding value in self.forward_min_event_ndims.
**kwargs	Optional keyword arguments forwarded to nested bijectors.

Returns

forward_event_ndims

Structure of integers and/or None values matching self.inverse_min_event_ndims. These are computed using 'prefer static' semantics: if any inputs are None, some or all of the outputs may be None, indicating that the output dimension could not be inferred (conversely, if all inputs are non-None, all outputs will be non-None). If all input event_ndims are Python ints, all of the (non-None) outputs will be Python ints; otherwise, some or all of the outputs may be Tensor ints.

forward_event_shape

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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

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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

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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

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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

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inverse_dtype(
    dtype=UNSPECIFIED, name='inverse_dtype', **kwargs
)

Returns the dtype returned by inverse for the provided input.

inverse_event_ndims

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inverse_event_ndims(
    event_ndims, **kwargs
)

Returns the number of event dimensions produced by inverse.

Args
event_ndims	Structure of Python and/or Tensor ints, and/or None values. The structure should match that of self.inverse_min_event_ndims, and all non-None values must be greater than or equal to the corresponding value in self.inverse_min_event_ndims.
**kwargs	Optional keyword arguments forwarded to nested bijectors.

Returns

inverse_event_ndims

Structure of integers and/or None values matching self.forward_min_event_ndims. These are computed using 'prefer static' semantics: if any inputs are None, some or all of the outputs may be None, indicating that the output dimension could not be inferred (conversely, if all inputs are non-None, all outputs will be non-None). If all input event_ndims are Python ints, all of the (non-None) outputs will be Python ints; otherwise, some or all of the outputs may be Tensor ints.

inverse_event_shape

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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

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 (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

View source

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

View source

@classmethod
parameter_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__

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__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:

1. If the input is a tfd.Distribution instance, return tfd.TransformedDistribution(distribution=input, bijector=self).
2. If the input is a tfb.Bijector instance, return tfb.Chain([self, input]).
3. 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__

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__eq__(
    other
)

Return self==value.

__getitem__

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__getitem__(
    slices
)