mindspore.nn.probability.bijector.ScalarAffine

class mindspore.nn.probability.bijector.ScalarAffine(scale=1.0, shift=0.0, name='ScalarAffine')[source]

Scalar Affine Bijector. This Bijector performs the operation:

\[Y = a * X + b\]

where a is the scale factor and b is the shift factor.

Parameters
Inputs and Outputs of APIs:

The accessible APIs of the Scalar affine bijector is defined in the base class, including:

  • forward

  • inverse

  • forward_log_jacobian

  • backward_log_jacobian

It should be notice that the inputs to APIs of the Scalar affine bijector should be always a tensor, with a shape that can be broadcasted to that of shift and scale. For more details of all APIs, including the inputs and outputs of APIs of the scalar affine bijector, please refer to mindspore.nn.probability.bijector.Bijector, and examples below.

Supported Platforms:

Ascend GPU

Note

The dtype of shift and scale must be float. If shift, scale are passed in as numpy.ndarray or tensor, they have to have the same dtype otherwise an error will be raised.

Raises

TypeError – When the dtype of shift or scale is not float, and when the dtype of shift and scale is not same.

Examples

>>> import mindspore
>>> import mindspore.nn as nn
>>> from mindspore import Tensor
>>>
>>> # To initialize a ScalarAffine bijector of scale 1.0 and shift 2.
>>> scalaraffine = nn.probability.bijector.ScalarAffine(1.0, 2.0)
>>> value = Tensor([1, 2, 3], dtype=mindspore.float32)
>>> ans1 = scalaraffine.forward(value)
>>> print(ans1.shape)
(3,)
>>> ans2 = scalaraffine.inverse(value)
>>> print(ans2.shape)
(3,)
>>> ans3 = scalaraffine.forward_log_jacobian(value)
>>> print(ans3.shape)
()
>>> ans4 = scalaraffine.inverse_log_jacobian(value)
>>> print(ans4.shape)
()
extend_repr()[source]

Display instance object as string.

property scale

Return the scale parameter of the bijector.

Output:

Tensor, the scale parameter of the bijector.

property shift

Return the shift parameter of the bijector.

Output:

Tensor, the shift parameter of the bijector.