Uniform¶
均匀分布
概率密度函数（pdf）为：
\[ \begin{align}\begin{aligned}pdf(x; a, b) = \frac{1}{Z}, a <=x < b\\Z = b - a\end{aligned}\end{align} \]
上面的数学公式中：
\(low = a\) 。 \(high = b\) 。 \(Z\)：正态分布常量。
参数low和high的维度必须能够支持广播。
参数¶
low (float|list|numpy.ndarray|Variable) - 均匀分布的下边界。数据类型为float32。
high (float|list|numpy.ndarray|Variable) - 均匀分布的上边界。数据类型为float32。
代码示例¶
import numpy as np
from paddle.fluid import layers
from paddle.fluid.layers import Uniform
# 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]])
# With broadcasting:
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([2])
# a random tensor created by uniform distribution with shape: [2, 1]
entropy = uniform.entropy()
# [0.6931472] with shape: [1]
lp = uniform.log_prob(value_tensor)
# [-0.6931472] with shape: [1]
参数¶
shape (list) - 1维列表，指定生成样本的维度。数据类型为int32。
seed (int) - 长整型数。
返回¶
预先设计好维度的张量，数据类型为float32
返回¶
均匀分布的信息熵，数据类型为float32
参数¶
value (Variable) - 输入张量。数据类型为float32或float64。
返回¶
对数概率，数据类型与value相同
返回类型¶
Variable