ReshapeTransform¶

class paddle.distribution. ReshapeTransform ( in_event_shape, out_event_shape ) [source]

Reshape the event shape of a tensor.

Note that in_event_shape and out_event_shape must have the same number of elements.

Parameters

• in_event_shape (Sequence[int]) – The input event shape.

• out_event_shape (Sequence[int]) – The output event shape.

Examples

import paddle

x = paddle.ones((1,2,3))
reshape_transform = paddle.distribution.ReshapeTransform((2, 3), (3, 2))
print(reshape_transform.forward_shape((1,2,3)))
# (5, 2, 6)
print(reshape_transform.forward(x))
# Tensor(shape=[1, 3, 2], dtype=float32, place=Place(gpu:0), stop_gradient=True,
#        [[[1., 1.],
#          [1., 1.],
#          [1., 1.]]])
print(reshape_transform.inverse(reshape_transform.forward(x)))
# Tensor(shape=[1, 2, 3], dtype=float32, place=Place(gpu:0), stop_gradient=True,
#        [[[1., 1., 1.],
#          [1., 1., 1.]]])
print(reshape_transform.forward_log_det_jacobian(x))
# Tensor(shape=[1], dtype=float32, place=Place(gpu:0), stop_gradient=True,
#        [0.])

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]