tfp.bijectors.Invert  |  TensorFlow Probability (original) (raw)

Bijector which inverts another Bijector.

Inherits From: AutoCompositeTensorBijector, Bijector, AutoCompositeTensor

tfp.bijectors.Invert(
    bijector, validate_args=False, parameters=None, name=None
)

Example Use: ExpGammaDistribution (see Background & Context)models Y=log(X) where X ~ Gamma.

exp_gamma_distribution = TransformedDistribution(
  distribution=Gamma(concentration=1., rate=2.),
  bijector=bijector.Invert(bijector.Exp())

/nWhen an Invert bijector is constructed, if its bijector arg is not a CompositeTensor instance, an _Invert instance is returned instead. Bijectors subclasses that inherit from Invert will also inherit from CompositeTensor.

Args
bijector Bijector instance.
validate_args Python bool indicating whether arguments should be checked for correctness.
parameters Locals dict captured by subclass constructor, to be used for copy/slice re-instantiation operators.
name Python str, name given to ops managed by this object.
Attributes
bijector
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.
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.
name_scope Returns a tf.name_scope instance for this class.
non_trainable_variables Sequence of non-trainable variables owned by this module and its submodules.
parameters Dictionary of parameters used to instantiate this Bijector.
submodules Sequence of all sub-modules.Submodules are modules which are properties of this module, or found as properties of modules which are properties of this module (and so on). a = tf.Module() b = tf.Module() c = tf.Module() a.b = b b.c = c list(a.submodules) == [b, c] True list(b.submodules) == [c] True list(c.submodules) == [] True
trainable_variables Sequence of trainable variables owned by this module and its submodules.
validate_args Returns True if Tensor arguments will be validated.
variables Sequence of variables owned by this module and its submodules.

Methods

copy

View source

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

View source

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 astfb.JointMap, must broadcast to a coherent batch shape. Some events may not be valid: for example, the bijectortfd.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_ndimsis [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 xor 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 toself.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 withx_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

View source

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 astfb.JointMap, must broadcast to a coherent batch shape. Some events may not be valid: for example, the bijectortfd.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_ndimsis [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 xor 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 toself.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 withx_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

View source

experimental_compute_density_correction(
    x, tangent_space, backward_compat=False, **kwargs
)

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.
**kwargs Optional keyword arguments forwarded to tangent space methods.
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 ofTangentSpace has been called explicitly.

forward

View source

forward(
    x, **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 notself.dtype.
NotImplementedError if _forward is not implemented.

forward_dtype

View source

forward_dtype(
    dtype=bijector_lib.UNSPECIFIED, **kwargs
)

Returns the dtype returned by forward for the provided input.

forward_event_ndims

View source

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 Nonevalues. The structure should match that ofself.forward_min_event_ndims, and all non-None values must be greater than or equal to the corresponding value inself.forward_min_event_ndims.
**kwargs Optional keyword arguments forwarded to nested bijectors.
Returns
forward_event_ndims Structure of integers and/or None values matchingself.inverse_min_event_ndims. These are computed using 'prefer static' semantics: if any inputs are None, some or all of the outputs may beNone, 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

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 (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

View source

forward_event_shape_tensor(
    input_shape
)

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

View source

forward_log_det_jacobian(
    x, event_ndims=None, **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 toself.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 thatevent_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_jacobiannor {_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

View source

inverse(
    y, **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 uniquek 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

View source

inverse_dtype(
    dtype=bijector_lib.UNSPECIFIED, **kwargs
)

Returns the dtype returned by inverse for the provided input.

inverse_event_ndims

View source

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 Nonevalues. The structure should match that ofself.inverse_min_event_ndims, and all non-None values must be greater than or equal to the corresponding value inself.inverse_min_event_ndims.
**kwargs Optional keyword arguments forwarded to nested bijectors.
Returns
inverse_event_ndims Structure of integers and/or None values matchingself.forward_min_event_ndims. These are computed using 'prefer static' semantics: if any inputs are None, some or all of the outputs may beNone, 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

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 (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
)

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, **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 toself.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 thatevent_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'sTensor-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 Astr ->tfp.python.internal.parameter_properties.ParameterPropertiesdict mapping constructor argument names toParameterProperties` instances.

with_name_scope

@classmethod with_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:

mod = MyModule() mod(tf.ones([1, 2])) <tf.Tensor: shape=(1, 3), dtype=float32, numpy=..., dtype=float32)> mod.w <tf.Variable 'my_module/Variable:0' shape=(2, 3) dtype=float32, numpy=..., dtype=float32)>

Args
method The method to wrap.
Returns
The original method wrapped such that it enters the module's name scope.

__call__

View source

__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, returntfd.TransformedDistribution(distribution=input, bijector=self).
  2. If the input is a tfb.Bijector instance, returntfb.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 createdtfd.TransformedDistribution, tfb.Bijector, or self.forward.
Returns
composition A tfd.TransformedDistribution if the input was atfd.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__

View source

__eq__(
    other
)

Return self==value.

__getitem__

View source

__getitem__(
    slices
)

__iter__

View source

__iter__()