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71 lines
1.9 KiB
Markdown
71 lines
1.9 KiB
Markdown
# Theano 随机数流变量
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In [1]:
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```py
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import theano
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import theano.tensor as T
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import numpy as np
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```
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```py
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Using gpu device 1: Tesla C2075 (CNMeM is disabled)
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```
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`Theano` 的随机数变量由 `theano.sandbox.rng_mrg` 中的 `MRG_RandomStreams` 实现（`sandbox` 表示是实验代码）：
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In [2]:
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```py
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from theano.sandbox.rng_mrg import MRG_RandomStreams
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```
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新建一个 `MRG_RandomStreams(seed=12345, use_cuda=None)` 实例：
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In [3]:
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```py
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srng = MRG_RandomStreams()
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```
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它支持以下方法：
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* `normal(size, avg=0.0, std=1.0, ndim=None, dtype=None, nstreams=None)`
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* 产生指定形状的、服从正态分布 $N(avg, std)$ 的随机数变量，默认为标准正态分布
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* `uniform(size, low=0.0, high=1.0, ndim=None, dtype=None, nstreams=None)`
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* 产生指定形状的、服从均匀分布 $U(low, high)$ 的随机数变量，默认为 0-1 之间的均匀分布
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* `binomial(size=None, n=1, p=0.5, ndim=None, dtype='int64', nstreams=None)`
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* 产生指定形状的、服从二项分布 $B(n,p)$ 的随机数变量
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* `multinomial(size=None, n=1, pvals=None, ndim=None, dtype='int64', nstreams=None)`
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* 产生指定形状的、服从多项分布的随机数变量
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与 np.random.random 不同，它产生的是随机数变量，而不是随机数数组，因此可以将 `size` 作为参数传给它：
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In [4]:
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```py
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rand_size = T.vector(dtype="int64")
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rand_normal = srng.normal(rand_size.shape)
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rand_uniform = srng.uniform(rand_size.shape)
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rand_binomial = srng.binomial(rand_size.shape)
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f_rand = theano.function(inputs = [rand_size],
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outputs = [rand_normal, rand_uniform, rand_binomial])
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print f_rand(range(5))[0]
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print f_rand(range(5))[1]
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print f_rand(range(5))[2]
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```
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```py
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[ 0.10108768 -1.64354193 0.71042836 -0.77760422 0.06291872]
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[ 0.23193923 0.71880513 0.03122572 0.97318739 0.99260223]
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[0 1 0 1 1]
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``` |