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class paddle.distribution. ChainTransform ( transforms: Sequence[Transform] ) [source]

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Composes multiple transforms in a chain.

Parameters

transforms (Sequence[Transform]) – A sequence of transformations.

Examples

>>> import paddle


>>> x = paddle.to_tensor([0., 1., 2., 3.])

>>> chain = paddle.distribution.ChainTransform((
...     paddle.distribution.AffineTransform(
...         paddle.to_tensor(0.), paddle.to_tensor(1.)),
...     paddle.distribution.ExpTransform()
>>> ))
>>> print(chain.forward(x))
Tensor(shape=[4], dtype=float32, place=Place(cpu), stop_gradient=True,
        [1.         , 2.71828175 , 7.38905621 , 20.08553696])
>>> print(chain.inverse(chain.forward(x)))
Tensor(shape=[4], dtype=float32, place=Place(cpu), stop_gradient=True,
        [0., 1., 2., 3.])
>>> print(chain.forward_log_det_jacobian(x))
Tensor(shape=[4], dtype=float32, place=Place(cpu), stop_gradient=True,
        [0., 1., 2., 3.])
>>> print(chain.inverse_log_det_jacobian(chain.forward(x)))
Tensor(shape=[4], dtype=float32, place=Place(cpu), stop_gradient=True,
        [ 0., -1., -2., -3.])

forward ( x: Tensor ) → Tensor

forward?

Forward transformation with mapping \(y = f(x)\).

Useful for turning one random outcome into another.

Parameters

x (Tensor) – Input parameter, generally is a sample generated from Distribution.

Returns

Outcome of forward transformation.

Return type

Tensor

forward_log_det_jacobian ( x: Tensor ) → Tensor

forward_log_det_jacobian?

The log of the absolute value of the determinant of the matrix of all first-order partial derivatives of the inverse function.

Parameters

x (Tensor) – Input tensor, generally is a sample generated from Distribution

Returns

The log of the absolute value of Jacobian determinant.

Return type

Tensor

forward_shape ( shape: Sequence[int] ) → Sequence[int]

forward_shape?

Infer the shape of forward transformation.

Parameters

shape (Sequence[int]) – The input shape.

Returns

The output shape.

Return type

Sequence[int]

inverse ( y: Tensor ) → Tensor

inverse?

Inverse transformation \(x = f^{-1}(y)\). It’s useful for “reversing” a transformation to compute one probability in terms of another.

Parameters

y (Tensor) – Input parameter for inverse transformation.

Returns

Outcome of inverse transform.

Return type

Tensor

inverse_log_det_jacobian ( y: Tensor ) → Tensor

inverse_log_det_jacobian?

Compute \(log|det J_{f^{-1}}(y)|\). Note that forward_log_det_jacobian is the negative of this function, evaluated at \(f^{-1}(y)\).

Parameters

y (Tensor) – The input to the inverse Jacobian determinant evaluation.

Returns

The value of \(log|det J_{f^{-1}}(y)|\).

Return type

Tensor

inverse_shape ( shape: Sequence[int] ) → Sequence[int]

inverse_shape?

Infer the shape of inverse transformation.

Parameters

shape (Sequence[int]) – The input shape of inverse transformation.

Returns

The output shape of inverse transformation.

Return type

Sequence[int]