Tensorflow概率采样需要很长时间

问题描述

我正在尝试使用tfp进行采样。从beta分布中抽取样本，并将结果作为概率输入来馈送以从二项式分布中抽取样本。花了永远的时间。

我应该以此方式运行还是有最佳方式？

'''

import tensorflow_probability as tfp
tfd = tfp.distributions

m = 100000 # sample size

### first sample from Beta distribution 
### and Feed the result as probability to the binomial distribution sampling
s = tfd.Sample(
    tfd.Beta(2,2),sample_shape = m
)
phi = s.sample()

### Second sample from Binominal distribution 
### !!! it took forever to run...
s2 = tfd.Sample(
    tfd.Binomial(total_count=10,probs=phi),sample_shape = m
)

y = s2.sample() # not working well


### scipy code which works well:
from scipy import stats
m = 100000 # sample size
phi = stats.beta.rvs(2,2,size = m)
y = stats.binom.rvs(10,phi,size = m)

'''

解决方法

TFP发行版支持一个称为“批量形状”的概念。在这里，通过给probs=phi加上phi.shape = [100000]，可以有效地创建100k个二项式的“批量”。然后，您将从其中采样100k次，这正试图创建1e10个样本，这将需要一段时间！相反，请尝试以下操作：

m = 100000
s = tfd.Sample(
    tfd.Beta(2,2),sample_shape = m
)
phi = s.sample()

### Second sample from Binominal distribution 
s2 = tfd.Binomial(total_count=10,probs=phi)

y = s2.sample()

或者，使用tfd.BetaBinomial！

bb = tfd.BetaBinomial(total_count=10,concentration1=2,concentration0=2)
bb.sample(100000)

但最重要的是，看一看示例笔记本如何通过TFP的形状语义：https://www.tensorflow.org/probability/examples/Understanding_TensorFlow_Distributions_Shapes

干杯！