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
scale (float, list, numpy.ndarray, Tensor) – The scale factor. Default:
1.0
.shift (float, list, numpy.ndarray, Tensor) – The shift factor. Default:
0.0
.name (str) – The name of the bijector. Default:
'ScalarAffine'
.
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.
- Supported Platforms:
Ascend
GPU
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) ()
- property shift
Return the shift parameter of the bijector.
- Returns
Tensor, the shift parameter of the bijector.
- property scale
Return the scale parameter of the bijector.
- Returns
Tensor, the scale parameter of the bijector.
- forward(value)
forward mapping, compute the value after mapping.
- Parameters
value (Tensor) - the value to compute.
- Returns
Tensor, the value to compute.
- forward_log_jacobian(value)
compute the log value after mapping.
- Parameters
value (Tensor) - the value to compute.
- Returns
Tensor, the log value of forward mapping.
- inverse(value)
Inverse mapping, compute the value after inverse mapping.
- Parameters
value (Tensor) - the value of output after mapping.
- Returns
Tensor, the value of output after mapping.
- inverse_log_jacobian(value)
Compute the log value of the inverse mapping.
- Parameters
value (Tensor) - the value of output after mapping.
- Returns
Tensor, the log value of the inverse mapping.