TanhTransform¶

class paddle.distribution. TanhTransform [source]

Tanh transformation with mapping \(y = \tanh(x)\).

Examples

           >>> import paddle

>>> tanh = paddle.distribution.TanhTransform()

>>> x = paddle.to_tensor([[1., 2., 3.], [4., 5., 6.]])

>>> 
>>> print(tanh.forward(x))
Tensor(shape=[2, 3], dtype=float32, place=Place(cpu), stop_gradient=True,
    [[0.76159418, 0.96402758, 0.99505472],
        [0.99932921, 0.99990916, 0.99998784]])
>>> print(tanh.inverse(tanh.forward(x)))
Tensor(shape=[2, 3], dtype=float32, place=Place(cpu), stop_gradient=True,
    [[1.        , 2.        , 2.99999666],
        [3.99993253, 4.99977016, 6.00527668]])
>>> print(tanh.forward_log_det_jacobian(x))
Tensor(shape=[2, 3], dtype=float32, place=Place(cpu), stop_gradient=True,
        [[-0.86756170 , -2.65000558 , -4.61865711 ],
         [-6.61437654 , -8.61379623 , -10.61371803]])
>>> print(tanh.inverse_log_det_jacobian(tanh.forward(x)))
Tensor(shape=[2, 3], dtype=float32, place=Place(cpu), stop_gradient=True,
        [[0.86756176 , 2.65000558 , 4.61866283 ],
         [6.61441946 , 8.61399269 , 10.61451530]])
>>> 

          

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]