Tensorflow `set_random_seed` not working [duplicate]

Question

Calling tf.set_random_seed(SEED) has no effect that I can tell...

For example, running the code below several times inside an IPython notebook produces different output each time:

import tensorflow as tf
tf.set_random_seed(42)
sess = tf.InteractiveSession()
a = tf.constant([1, 2, 3, 4, 5])
tf.initialize_all_variables().run()
a_shuf = tf.random_shuffle(a)
print(a.eval())
print(a_shuf.eval())
sess.close()

If I set the seed explicitly: a_shuf = tf.random_shuffle(a, seed=42), the output is the same after each run. But why do I need to set the seed if I already call tf.set_random_seed(42)?

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The equivalent code using numpy just works:

import numpy as np
np.random.seed(42)
a = [1,2,3,4,5]
np.random.shuffle(a)
print(a)

Answer 1

That only sets the graph-level random seed. If you execute this snippet several times in a row, the graph will change, and two shuffle statements will get different operation-level seeds. The details are described in the doc string for set_random_seed

To get deterministic a_shuf you can either

1. Call tf.reset_default_graph() between invocations or
2. Set operation-level seed for shuffle: a_shuf = tf.random_shuffle(a, seed=42)