tf.truncated_normal的用法

tf.truncated_normal(shape, mean, stddev) :shape表示生成张量的维度，mean是均值，stddev是标准差。这个函数产生正太分布，均值和标准差自己设定。这是一个截断的产生正太分布的函数，就是说产生正太分布的值如果与均值的差值大于两倍的标准差，那就重新生成。和一般的正太分布的产生随机数据比起来，这个函数产生的随机数与均值的差距不会超过两倍的标准差，但是一般的别的函数是可能的。

例如：

import tensorflow as tf;
import numpy as np;
import matplotlib.pyplot as plt;  
 
c = tf.truncated_normal(shape=[10,10], mean=0, stddev=1)  
 
with tf.Session() as sess:
    print sess.run(c)  

 

输出：

[[ 1.95758033 -0.68666345 -1.83860338  0.78213859 -1.08119416 -1.44530308
   0.38035342  0.57904619 -0.57145643 -1.22899497]
 [-0.75853795  0.48202974  1.03464043  1.19210851 -0.15739718  0.8506189
   1.18259966 -0.99061841 -0.51968449  1.38996458]
 [ 1.05636907 -0.02668529  0.64182931  0.4110294  -0.4978295  -0.64912242
   1.27779591 -0.01533993  0.47417602 -1.28639436]
 [-1.65927458 -0.364887   -0.45535028  0.078814   -0.30295736  1.91779387
  -0.66928798 -0.14847915  0.91875714  0.61889237]
 [-0.01308221 -0.38468206  1.34700036  0.64531708  1.15899456  1.09932268
   1.22457981 -1.1610316   0.59036094 -1.97302651]
 [-0.24886213  0.82857937  0.09046989  0.39251322  0.21155456 -0.27749416
   0.18883201  0.08812679 -0.32917103  0.20547724]
 [ 0.05388507  0.45474565  0.23398806  1.32670367 -0.01957406  0.52013856
  -1.13907862 -1.71957874  0.75772947 -1.01719368]
 [ 0.27155915  0.05900437  0.81448066 -0.37997526 -0.62020499 -0.88820189
   1.53407145 -0.01600445 -0.4236775  -1.68852305]
 [ 0.78942037 -1.32458341 -0.91667277 -0.00963761  0.76824385 -0.5405798
  -0.73307443 -1.19854116 -0.66179073  0.26329204]
 [ 0.59473759 -0.37507254 -1.21623695 -1.30528259  1.18013096 -1.32077384
  -0.59241474 -0.28063133  0.12341146  0.48480138]]