ExpTransform¶

class paddle.distribution. ExpTransform [source]

Exponent transformation with mapping \(y = \exp(x)\).

Examples

import paddle

exp = paddle.distribution.ExpTransform()
print(exp.forward(paddle.to_tensor([1., 2., 3.])))
# Tensor(shape=[3], dtype=float32, place=Place(gpu:0), stop_gradient=True,
#        [2.71828175 , 7.38905621 , 20.08553696])

print(exp.inverse(paddle.to_tensor([1., 2., 3.])))
# Tensor(shape=[3], dtype=float32, place=Place(gpu:0), stop_gradient=True,
#        [0.        , 0.69314718, 1.09861231])

print(exp.forward_log_det_jacobian(paddle.to_tensor([1., 2., 3.])))
# Tensor(shape=[3], dtype=float32, place=Place(gpu:0), stop_gradient=True,
#        [1., 2., 3.])

print(exp.inverse_log_det_jacobian(paddle.to_tensor([1., 2., 3.])))
# Tensor(shape=[3], dtype=float32, place=Place(gpu:0), stop_gradient=True,
#        [ 0.        , -0.69314718, -1.09861231])

forward ( x )

forward¶

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

Useful for turning one random outcome into another.

Parameters

x (Tensos) – 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]