ChainTransform¶

class paddle.distribution. ChainTransform ( transforms ) [source]

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 ) 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 ) 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 ) 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 ) 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 ) 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 ) 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]