TransformedDistribution

class paddle.distribution. TransformedDistribution ( base, transforms ) [source]

Applies a sequence of Transforms to a base distribution.

Parameters

  • base (Distribution) – The base distribution.

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

Examples

import paddle
from paddle.distribution import transformed_distribution

d = transformed_distribution.TransformedDistribution(
    paddle.distribution.Normal(0., 1.),
    [paddle.distribution.AffineTransform(paddle.to_tensor(1.), paddle.to_tensor(2.))]
)

print(d.sample([10]))
# Tensor(shape=[10], dtype=float32, place=Place(gpu:0), stop_gradient=True,
#        [-0.10697651,  3.33609009, -0.86234951,  5.07457638,  0.75925219,
#         -4.17087793,  2.22579336, -0.93845034,  0.66054249,  1.50957513])
print(d.log_prob(paddle.to_tensor(0.5)))
# Tensor(shape=[1], dtype=float32, place=Place(gpu:0), stop_gradient=True,
#        [-1.64333570])
sample ( shape=() )

sample

Sample from TransformedDistribution.

Parameters

shape (tuple, optional) – The sample shape. Defaults to ().

Returns

The sample result.

Return type

[Tensor]

log_prob ( value )

log_prob

The log probability evaluated at value.

Parameters

value (Tensor) – The value to be evaluated.

Returns

The log probability.

Return type

Tensor

property batch_shape

Returns batch shape of distribution

Returns

batch shape

Return type

Sequence[int]

entropy ( )

entropy

The entropy of the distribution.

property event_shape

Returns event shape of distribution

Returns

event shape

Return type

Sequence[int]

kl_divergence ( other ) [source]

kl_divergence

The KL-divergence between self distributions and other.

property mean

Mean of distribution

prob ( value )

prob

Probability density/mass function evaluated at value.

Parameters

value (Tensor) – value which will be evaluated

probs ( value )

probs

Probability density/mass function.

Note

This method will be deprecated in the future, please use prob instead.

rsample ( shape=() )

rsample

reparameterized sample

property variance

Variance of distribution