contrib.distributions.bijectors.ConditionalBijector
tf.contrib.distributions.bijectors.ConditionalBijector
class tf.contrib.distributions.bijectors.ConditionalBijector
Defined in tensorflow/contrib/distributions/python/ops/bijectors/conditional_bijector_impl.py
.
Conditional Bijector is a Bijector that allows intrinsic conditioning.
Properties
dtype
dtype of Tensor
s 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.
Note: Jacobian is either constant for both forward and inverse or neither.
Returns:
-
is_constant_jacobian
: Pythonbool
.
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 thisBijector
. -
is_constant_jacobian
: Pythonbool
indicating that the Jacobian is not a function of the input. -
validate_args
: Pythonbool
, defaultFalse
. Whether to validate input with asserts. Ifvalidate_args
isFalse
, and the inputs are invalid, correct behavior is not guaranteed. -
dtype
:tf.dtype
supported by thisBijector
.None
means dtype is not enforced. -
name
: The name to give Ops created by the initializer.
forward
forward( *args, **kwargs )
kwargs
:
-
**condition_kwargs
: Named arguments forwarded to subclass implementation.
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 intoforward
function.
Returns:
-
forward_event_shape_tensor
:TensorShape
indicating event-portion shape after applyingforward
. 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 intoforward
function. -
name
: name to give to the op
Returns:
-
forward_event_shape_tensor
:Tensor
,int32
vector indicating event-portion shape after applyingforward
.
forward_log_det_jacobian
forward_log_det_jacobian( *args, **kwargs )
kwargs
:
-
**condition_kwargs
: Named arguments forwarded to subclass implementation.
inverse
inverse( *args, **kwargs )
kwargs
:
-
**condition_kwargs
: Named arguments forwarded to subclass implementation.
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 intoinverse
function.
Returns:
-
inverse_event_shape_tensor
:TensorShape
indicating event-portion shape after applyinginverse
. 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 intoinverse
function. -
name
: name to give to the op
Returns:
-
inverse_event_shape_tensor
:Tensor
,int32
vector indicating event-portion shape after applyinginverse
.
inverse_log_det_jacobian
inverse_log_det_jacobian( *args, **kwargs )
kwargs
:
-
**condition_kwargs
: Named arguments forwarded to subclass implementation.
© 2017 The TensorFlow Authors. All rights reserved.
Licensed under the Creative Commons Attribution License 3.0.
Code samples licensed under the Apache 2.0 License.
https://www.tensorflow.org/api_docs/python/tf/contrib/distributions/bijectors/ConditionalBijector