contrib.distributions.bijectors.PowerTransform
tf.contrib.distributions.bijectors.PowerTransform
class tf.contrib.distributions.bijectors.PowerTransform
Defined in tensorflow/contrib/distributions/python/ops/bijectors/power_transform_impl.py
.
See the guide: Random variable transformations (contrib) > Bijectors
Compute Y = g(X) = (1 + X * c)**(1 / c), X >= -1 / c
.
The power transform maps inputs from [0, inf]
to [-1/c, inf]
; this is equivalent to the inverse
of this bijector.
This bijector is equivalent to the Exp
bijector when c=0
.
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
.
power
The c
in: Y = g(X) = (1 + X * c)**(1 / c)
.
validate_args
Returns True if Tensor arguments will be validated.
Methods
__init__
__init__( power=0.0, event_ndims=0, validate_args=False, name='power_transform' )
Instantiates the PowerTransform
bijector.
Args:
-
power
: Pythonfloat
scalar indicating the transform power, i.e.,Y = g(X) = (1 + X * c)**(1 / c)
wherec
is thepower
. -
event_ndims
: Python scalar indicating the number of dimensions associated with a particular draw from the distribution. -
validate_args
: Pythonbool
indicating whether arguments should be checked for correctness. -
name
: Pythonstr
name given to ops managed by this object.
Raises:
-
ValueError
: ifpower < 0
or is not known statically.
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
: ifself.dtype
is specified andx.dtype
is notself.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 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( 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
.
Raises:
-
TypeError
: ifself.dtype
is specified andy.dtype
is notself.dtype
. -
NotImplementedError
: if neither_forward_log_det_jacobian
nor {_inverse
,_inverse_log_det_jacobian
} are implemented.
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
.
Raises:
-
TypeError
: ifself.dtype
is specified andy.dtype
is notself.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 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( 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.
Args:
-
y
:Tensor
. The input to the "inverse" Jacobian evaluation. -
name
: The name to give this op.
Returns:
Tensor
.
Raises:
-
TypeError
: ifself.dtype
is specified andy.dtype
is notself.dtype
. -
NotImplementedError
: if_inverse_log_det_jacobian
is not implemented.
© 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/PowerTransform