# tf.distributions.bijectors.Bijector

## Class Bijector

### Aliases:

• Class tf.contrib.distributions.bijectors.Bijector
• Class tf.distributions.bijectors.Bijector

See the guide: Random variable transformations (contrib) > Bijectors

Interface for transformations of a Distribution sample.

Bijectors can be used to represent any differentiable and injective (one to one) function defined on an open subset of R^n. Some non-injective transformations are also supported (see "Non Injective Transforms" below).

#### Mathematical Details

A Bijector implements a smooth covering map, i.e., a local diffeomorphism such that every point in the target has a neighborhood evenly covered by a map (see also). A Bijector is used by TransformedDistribution but can be generally used for transforming a Distribution generated Tensor. A Bijector is characterized by three operations:

1. Forward
Useful for turning one random outcome into another random outcome from a different distribution.
2. Inverse
Useful for "reversing" a transformation to compute one probability in terms of another.
3. (log o det o Jacobian o inverse)(x)
"The log of the determinant of the matrix of all first-order partial derivatives of the inverse function."
Useful for inverting a transformation to compute one probability in terms of another. Geometrically, the det(Jacobian) is the volume of the transformation and is used to scale the probability.

By convention, transformations of random variables are named in terms of the forward transformation. The forward transformation creates samples, the inverse is useful for computing probabilities.

#### Example Uses

• Basic properties:
x = ...  # A tensor.
# Evaluate forward transformation.
fwd_x = my_bijector.forward(x)
x == my_bijector.inverse(fwd_x)
x != my_bijector.forward(fwd_x)  # Not equal because x != g(g(x)).

• Computing a log-likelihood:
def transformed_log_prob(bijector, log_prob, x):
return (bijector.inverse_log_det_jacobian(x) +
log_prob(bijector.inverse(x)))

• Transforming a random outcome:
def transformed_sample(bijector, x):
return bijector.forward(x)


#### Example Bijectors

• "Exponential"
Y = g(X) = exp(X)
X ~ Normal(0, 1)  # Univariate.


Implies:

  g^{-1}(Y) = log(Y)
|Jacobian(g^{-1})(y)| = 1 / y
Y ~ LogNormal(0, 1), i.e.,
prob(Y=y) = |Jacobian(g^{-1})(y)| * prob(X=g^{-1}(y))
= (1 / y) Normal(log(y); 0, 1)


Here is an example of how one might implement the Exp bijector:

  class Exp(Bijector):

def __init__(self, event_ndims=0, validate_args=False, name="exp"):
super(Exp, self).__init__(
event_ndims=event_ndims, validate_args=validate_args, name=name)

def _forward(self, x):
return math_ops.exp(x)

def _inverse(self, y):
return math_ops.log(y)

def _inverse_log_det_jacobian(self, y):
return -self._forward_log_det_jacobian(self._inverse(y))

def _forward_log_det_jacobian(self, x):
if self.event_ndims is None:
raise ValueError("Jacobian requires known event_ndims.")
event_dims = array_ops.shape(x)[-self.event_ndims:]
return math_ops.reduce_sum(x, axis=event_dims)


"Affine"


Y = g(X) = sqrtSigma * X + mu X ~ MultivariateNormal(0, I_d)

Implies:

g^{-1}(Y) = inv(sqrtSigma) * (Y - mu)
|Jacobian(g^{-1})(y)| = det(inv(sqrtSigma))
Y ~ MultivariateNormal(mu, sqrtSigma) , i.e.,
prob(Y=y) = |Jacobian(g^{-1})(y)| * prob(X=g^{-1}(y))
= det(sqrtSigma)^(-d) *
MultivariateNormal(inv(sqrtSigma) * (y - mu); 0, I_d)



#### Jacobian

The Jacobian is a reduction over event dims. To see this, consider the Exp Bijector applied to a Tensor which has sample, batch, and event (S, B, E) shape semantics. Suppose the Tensor's partitioned-shape is (S=[4], B=[2], E=[3, 3]). The shape of the Tensor returned by forward and inverse is unchanged, i.e., [4, 2, 3, 3]. However the shape returned by inverse_log_det_jacobian is [4, 2] because the Jacobian is a reduction over the event dimensions.

It is sometimes useful to implement the inverse Jacobian as the negative forward Jacobian. For example,

def _inverse_log_det_jacobian(self, y):
return -self._forward_log_det_jac(self._inverse(y))  # Note negation.


The correctness of this approach can be seen from the following claim.

• Claim:

Assume Y = g(X) is a bijection whose derivative exists and is nonzero for its domain, i.e., dY/dX = d/dX g(X) != 0. Then:

(log o det o jacobian o g^{-1})(Y) = -(log o det o jacobian o g)(X)

• Proof:

From the bijective, nonzero differentiability of g, the inverse function theorem implies g^{-1} is differentiable in the image of g. Applying the chain rule to y = g(x) = g(g^{-1}(y)) yields I = g'(g^{-1}(y))*g^{-1}'(y). The same theorem also implies g^{-1}' is non-singular therefore: inv[ g'(g^{-1}(y)) ] = g^{-1}'(y). The claim follows from properties of determinant.

Generally its preferable to directly implement the inverse Jacobian. This should have superior numerical stability and will often share subgraphs with the _inverse implementation.

#### Subclass Requirements

• Subclasses typically implement:

• _forward,
• _inverse,
• _inverse_log_det_jacobian,
• _forward_log_det_jacobian (optional).

The _forward_log_det_jacobian is called when the bijector is inverted via the Invert bijector. If undefined, a slightly less efficiently calculation, -1 * _inverse_log_det_jacobian, is used.

If the bijector changes the shape of the input, you must also implement:

• _forward_event_shape_tensor,
• _forward_event_shape (optional),
• _inverse_event_shape_tensor,
• _inverse_event_shape (optional).

By default the event-shape is assumed unchanged from input.

• If the Bijector's use is limited to TransformedDistribution (or friends like QuantizedDistribution) then depending on your use, you may not need to implement all of _forward and _inverse functions.

Examples:

1. Sampling (e.g., sample) only requires _forward.
2. Probability functions (e.g., prob, cdf, survival) only require _inverse (and related).
3. Only calling probability functions on the output of sample means _inverse can be implemented as a cache lookup.

See "Example Uses" [above] which shows how these functions are used to transform a distribution. (Note: _forward could theoretically be implemented as a cache lookup but this would require controlling the underlying sample generation mechanism.)

#### Non Injective Transforms

WARNING Handing of non-injective transforms is subject to change.

Non injective maps g are supported, provided their domain D can be partitioned into k disjoint subsets, Union{D1, ..., Dk}, such that, ignoring sets of measure zero, the restriction of g to each subset is a differentiable bijection onto g(D). In particular, this imples that for y in g(D), the set inverse, i.e. g^{-1}(y) = {x in D : g(x) = y}, always contains exactly k distinct points.

The property, _is_injective is set to False to indicate that the bijector is not injective, yet satisfies the above condition.

The usual bijector API is modified in the case _is_injective is False (see method docstrings for specifics). Here we show by example the AbsoluteValue bijector. In this case, the domain D = (-inf, inf), can be partitioned into D1 = (-inf, 0), D2 = {0}, and D3 = (0, inf). Let gi be the restriction of g to Di, then both g1 and g3 are bijections onto (0, inf), with g1^{-1}(y) = -y, and g3^{-1}(y) = y. We will use g1 and g3 to define bijector methods over D1 and D3. D2 = {0} is an oddball in that g2 is one to one, and the derivative is not well defined. Fortunately, when considering transformations of probability densities (e.g. in TransformedDistribution), sets of measure zero have no effect in theory, and only a small effect in 32 or 64 bit precision. For that reason, we define inverse(0) and inverse_log_det_jacobian(0) both as [0, 0], which is convenient and results in a left-semicontinuous pdf.

abs = tf.contrib.distributions.bijectors.AbsoluteValue()

abs.forward(-1.)
==> 1.

abs.forward(1.)
==> 1.

abs.inverse(1.)
==> (-1., 1.)

# The |dX/dY| is constant, == 1.  So Log|dX/dY| == 0.
abs.inverse_log_det_jacobian(1.)
==> (0., 0.)

# Special case handling of 0.
abs.inverse(0.)
==> (0., 0.)

abs.inverse_log_det_jacobian(0.)
==> (0., 0.)


## Properties

### dtype

dtype of Tensors transformable by this distribution.

### event_ndims

Returns then number of event dimensions this bijector operates on.

### graph_parents

Returns this Bijector's graph_parents as a Python list.

### is_constant_jacobian

Returns true iff the Jacobian is not a function of x.

#### Returns:

• is_constant_jacobian: Python bool.

### name

Returns the string name of this Bijector.

### validate_args

Returns True if Tensor arguments will be validated.

## Methods

### __init__

__init__(
event_ndims=None,
graph_parents=None,
is_constant_jacobian=False,
validate_args=False,
dtype=None,
name=None
)


Constructs Bijector.

A Bijector transforms random variables into new random variables.

Examples:

# Create the Y = g(X) = X transform which operates on vector events.
identity = Identity(event_ndims=1)

# Create the Y = g(X) = exp(X) transform which operates on matrices.
exp = Exp(event_ndims=2)


See Bijector subclass docstring for more details and specific examples.

#### Args:

• event_ndims: number of dimensions associated with event coordinates.
• graph_parents: Python list of graph prerequisites of this Bijector.
• is_constant_jacobian: Python bool indicating that the Jacobian 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.
• name: The name to give Ops created by the initializer.

#### Raises:

• ValueError: If a member of graph_parents is not a Tensor.

### forward

forward(
x,
name='forward'
)


Returns the forward Bijector evaluation, i.e., X = g(Y).

#### Args:

• x: Tensor. The input to the "forward" evaluation.
• name: The name to give this op.

#### Returns:

Tensor.

#### Raises:

• TypeError: if self.dtype is specified and x.dtype is not self.dtype.
• NotImplementedError: if _forward is not implemented.

### 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 indicating event-portion shape passed into forward function.

#### Returns:

• forward_event_shape_tensor: TensorShape 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 indicating event-portion shape passed into forward function.
• name: name to give to the op

#### Returns:

• forward_event_shape_tensor: Tensor, int32 vector indicating event-portion shape after applying forward.

### forward_log_det_jacobian

forward_log_det_jacobian(
x,
name='forward_log_det_jacobian'
)


Returns both the forward_log_det_jacobian.

#### Args:

• x: Tensor. The input to the "forward" Jacobian evaluation.
• name: The name to give this op.

#### Returns:

Tensor, if this bijector is injective. If not injective this is not implemented.

#### Raises:

• TypeError: if self.dtype is specified and y.dtype is not self.dtype.
• NotImplementedError: if neither _forward_log_det_jacobian nor {_inverse, _inverse_log_det_jacobian} are implemented, or this is a non-injective bijector.

### inverse

inverse(
y,
name='inverse'
)


Returns the inverse Bijector evaluation, i.e., X = g^{-1}(Y).

#### Args:

• y: Tensor. The input to the "inverse" evaluation.
• name: The name to give this op.

#### Returns:

Tensor, 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 self.dtype is specified and y.dtype is not self.dtype.
• NotImplementedError: if _inverse is not implemented.

### 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 indicating event-portion shape passed into inverse function.

#### Returns:

• inverse_event_shape_tensor: TensorShape 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 indicating event-portion shape passed into inverse function.
• name: name to give to the op

#### Returns:

• inverse_event_shape_tensor: Tensor, int32 vector indicating event-portion shape after applying inverse.

### inverse_log_det_jacobian

inverse_log_det_jacobian(
y,
name='inverse_log_det_jacobian'
)


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. The input to the "inverse" Jacobian evaluation.
• name: The name to give this op.

#### Returns:

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 self.dtype is specified and y.dtype is not self.dtype.
• NotImplementedError: if _inverse_log_det_jacobian is not implemented.