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)?
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)