tf_random_normal.py

import tensorflow as tf
import numpy as np

tf.set_random_seed(100)
stddev = 0.5
numOfValues = 100000
normal = tf.Variable(tf.random_normal([1,numOfValues], stddev = stddev))
init_op = tf.global_variables_initializer()

with tf.Session() as sess:
  sess.run(init_op)
  normalArr = normal.eval()

oneSTDDEV = 0.0
twoSTDDEV = 0.0
threeSTDDEV = 0.0
fourSTDDEV = 0.0
fiveSTDDEV = 0.0
for x in np.nditer(normalArr):
  if x < stddev and x > -stddev:
    oneSTDDEV += 1
  elif x < 2*stddev and x > 2*-stddev:
    twoSTDDEV += 1
  elif x < 3*stddev and x > 3*-stddev:
    threeSTDDEV += 1
  elif x < 4*stddev and x > 4*-stddev:
    fourSTDDEV += 1
  elif x < 5*stddev and x > 5*-stddev:
    fiveSTDDEV += 1
  else:
    print("Wow, above 5 standard deviations")

print("1 std: {0}, {1}".format(oneSTDDEV, oneSTDDEV/numOfValues))
print("2 std: {0}, {1}".format(twoSTDDEV, twoSTDDEV/numOfValues))
print("3 std: {0}, {1}".format(threeSTDDEV, threeSTDDEV/numOfValues))
print("4 std: {0}, {1}".format(fourSTDDEV, fourSTDDEV/numOfValues))
print("5 std: {0}, {1}".format(fiveSTDDEV, fiveSTDDEV/numOfValues))