I am trying to generate a random variable and use it twice. However, when I use it the second time, the generator creates a second random variable that is not identical to the f
Your question has the same issue as this question, in that if you call random_uniform
twice you will get two results, and as such you need to set your second variable to the value of the first. That means that, assuming you are not changing rand_var_1
later, you can do this:
rand_var_1 = tf.random_uniform([5],0,10, dtype = tf.int32, seed = 0)
rand_var_2 = rand_var_1
But, that said, if you want z1
and z2
to be equal, why have separate variables at all? Why not do:
import numpy as np
import tensorflow as tf
# A random variable
rand_var = tf.random_uniform([5],0,10, dtype = tf.int32, seed = 0)
op = tf.add(rand_var,rand_var)
init = tf.initialize_all_variables()
with tf.Session() as sess:
sess.run(init)
z1_op = sess.run(op)
z2_op = sess.run(op)
print(z1_op,z2_op)