mindspore.nn.probability.bijector.Invert

class mindspore.nn.probability.bijector.Invert(bijector, name='')

Invert Bijector. Compute the inverse function of the input bijector. If the function of the forward mapping, namely the input of bijector below, is \(Y = g(X)\), then the function of corresponding inverse mapping Bijector is \(Y = h(X) = g^{-1}(X)\).

Parameters:

• bijector (Bijector) – Base Bijector.

• name (str) – The name of the Bijector. Default: "" . When name is set to "", it is actually 'Invert' + Bijector.name.

Supported Platforms:

Ascend GPU CPU

Examples

>>> import numpy as np
>>> import mindspore
>>> import mindspore.nn as nn
>>> import mindspore.nn.probability.bijector as msb
>>> from mindspore import Tensor
>>> class Net(nn.Cell):
...     def __init__(self):
...         super().__init__()
...         self.origin = msb.ScalarAffine(scale=2.0, shift=1.0)
...         self.invert = msb.Invert(self.origin)
...
...     def construct(self, x_):
...         return self.invert.forward(x_)
>>> forward = Net()
>>> x = np.array([2.0, 3.0, 4.0, 5.0]).astype(np.float32)
>>> ans = forward(Tensor(x, dtype=mindspore.float32))
>>> print(ans.shape)
(4,)

property bijector

Return base bijector.

forward(x)

Compute the inverse mapping of underlying Bijector, namely \(Y = h(X) = g^{-1}(X)\) .

Parameters

• x (Tensor) - the value of output after mapping by the underlying bijector.

Returns

Tensor, the inverse mapping of underlying Bijector.

forward_log_jacobian(x)

Compute the log value of the inverse mapping of underlying Bijector \(\log dg^{-1}(x) / dx\).

Parameters

• x (Tensor) - the value of output after mapping by the underlying bijector.

Returns

Tensor, the log value of the inverse mapping of underlying Bijector.

inverse(y)

Compute the forward mapping of underlying Bijector, namely \(Y = g(X)\).

Parameters

• y (Tensor) - the value to compute by the underlying bijector.

Returns

Tensor, the forward mapping of underlying Bijector.

inverse_log_jacobian(y)

Compute the log value of the forward mapping of underlying Bijector, namely \(Y = \log dg(x) / dx\).

Parameters

• y (Tensor) - the value to compute by the underlying bijector.

Returns

Tensor, the log value of forward mapping of underlying Bijector.

property bijector

Return base bijector.

forward(x)

Perform the forward transformation of the inverse bijector, namely the inverse transformation of the underlying bijector.

Parameters:

x (Tensor) – the value of the input random variable.

Output:

Tensor, the value of the transformed random variable.

forward_log_jacobian(x)

Logarithm of the derivative of the forward transformation of the inverse bijector, namely logarithm of the derivative of the inverse transformation of the underlying bijector.

Parameters:

x (Tensor) – the value of the input random variable.

Output:

Tensor, logarithm of the derivative of the forward transformation of the inverse bijector.

inverse(y)

Perform the inverse transformation of the inverse bijector, namely the forward transformation of the underlying bijector.

Parameters:

y (Tensor) – the value of the transformed random variable.

Output:

Tensor, the value of the input random variable.

inverse_log_jacobian(y)

Logarithm of the derivative of the inverse transformation of the inverse bijector, namely logarithm of the derivative of the forward transformation of the underlying bijector.

Parameters:

y (Tensor) – the value of the transformed random variable.

Output:

Tensor, logarithm of the derivative of the inverse transformation of the inverse bijector.