# Uniform¶

class paddle.fluid.layers.Uniform(low, high)[source]

Uniform distribution with low and high parameters.

Mathematical Details

The probability density function (pdf) is,

$pdf(x; a, b) = \frac{1}{Z}, \ a <=x <b$
$Z = b - a$

In the above equation:

• $$low = a$$,

• $$high = b$$,

• $$Z$$: is the normalizing constant.

The parameters low and high must be shaped in a way that supports broadcasting (e.g., high - low is a valid operation).

Parameters
• low (float|list|numpy.ndarray|Variable) – The lower boundary of uniform distribution.The data type is float32

• high (float|list|numpy.ndarray|Variable) – The higher boundary of uniform distribution.The data type is float32

Examples

import numpy as np

# Without broadcasting, a single uniform distribution [3, 4]:
u1 = Uniform(low=3.0, high=4.0)
# 2 distributions [1, 3], [2, 4]
u2 = Uniform(low=[1.0, 2.0],
high=[3.0, 4.0])
# 4 distributions
u3 = Uniform(low=[[1.0, 2.0],
[3.0, 4.0]],
high=[[1.5, 2.5],
[3.5, 4.5]])

u4 = Uniform(low=3.0, high=[5.0, 6.0, 7.0])

# Complete example
value_npdata = np.array([0.8], dtype="float32")
value_tensor = layers.create_tensor(dtype="float32")
layers.assign(value_npdata, value_tensor)

uniform = Uniform([0.], [2.])

sample = uniform.sample()
# a random tensor created by uniform distribution with shape: [2, 1]
entropy = uniform.entropy()
# [0.6931472] with shape: 
lp = uniform.log_prob(value_tensor)
# [-0.6931472] with shape: 

sample(shape, seed=0)

Generate samples of the specified shape.

Parameters
• shape (list) – 1D int32. Shape of the generated samples.

• seed (int) – Python integer number.

Returns

A tensor with prepended dimensions shape.The data type is float32.

Return type

Variable

log_prob(value)

Log probability density/mass function.

Parameters

value (Variable) – The input tensor.

Returns

log probability.The data type is same with value.

Return type

Variable

entropy()

Shannon entropy in nats.

Returns

Shannon entropy of uniform distribution.The data type is float32.

Return type

Variable

kl_divergence(other)

The KL-divergence between self distributions and other.