import tensorflow as tf
import numpy as np
a = tf.random_normal([1])
with tf.Session() as sess2:
tf.set_random_seed(1234)
print(sess2.run(a))
print(sess2.run(a))
with tf.Session() as sess2:
tf.set_random_seed(1234)
print(sess2.run(a))
print(sess2.run(a))
#[1.2800848]
#[0.19427519]
#[0.97334963]
#[1.4822826]
#[-1.2332402]
#[-0.4761824]
#[-0.58630925]
#[-1.9449654]
# tf.set_random_seed 要放在sess.run的外面 构建图的时候
for i in range(0,5,1):
np.random.seed(5)
for i in range(0,5,1):
print(np.random.random())
#0.22199317109
#0.22199317109
#0.22199317109
#0.22199317109
#0.22199317109
#0.22199317109
#0.870732306177
#0.206719155339
#0.918610907938
import numpy as np
a = tf.random_normal([1])
with tf.Session() as sess2:
tf.set_random_seed(1234)
print(sess2.run(a))
print(sess2.run(a))
with tf.Session() as sess2:
tf.set_random_seed(1234)
print(sess2.run(a))
print(sess2.run(a))
#[1.2800848]
#[0.19427519]
#[0.97334963]
#[1.4822826]
#[-1.2332402]
#[-0.4761824]
#[-0.58630925]
#[-1.9449654]
# tf.set_random_seed 要放在sess.run的外面 构建图的时候
for i in range(0,5,1):
np.random.seed(5)
print(np.random.random())
np.random.seed(5)for i in range(0,5,1):
print(np.random.random())
#0.22199317109
#0.22199317109
#0.22199317109
#0.22199317109
#0.22199317109
#0.22199317109
#0.870732306177
#0.206719155339
#0.918610907938
#0.488411188795
#理解:
#random_seed生成了序列 生成random的时候按顺序读取
#如有问题欢迎指正