CODEWITHSUNDEEP
tensorflow
Alex Deft
Alex Deft

Reputation: 2777

tf.random.shuffle not giving reproducible results even when seed is specified

There's an argument in that function seed=something. Even when I set its value, the shuffle give random results. I want the same results.

tf.random.suffle(tf.range(5), seed=5)

Upvotes: 2

Views: 1939

Answers (2)

tepsijash
tepsijash

Reputation: 400

Came across this question. It seems like there is now a tf.random.experimental.stateless_shuffle that should give reproducible results.

To get random seeds, you can use tf.random.Generator.

Upvotes: 0

bluesummers
bluesummers

Reputation: 12607

If you want to reproduce shuffle results, use (on TF 2.0 beta) the following

tf.random.set_seed(5)
tf.random.shuffle(tf.range(5))
<tf.Tensor: id=35, shape=(5,), dtype=int32, numpy=array([0, 4, 1, 3, 2], dtype=int32)>
tf.random.set_seed(5)
tf.random.shuffle(tf.range(5))
<tf.Tensor: id=41, shape=(5,), dtype=int32, numpy=array([0, 4, 1, 3, 2], dtype=int32)>
tf.random.set_seed(5)
tf.random.shuffle(tf.range(5))
<tf.Tensor: id=47, shape=(5,), dtype=int32, numpy=array([0, 4, 1, 3, 2], dtype=int32)>

About the seed you have used, it indeed fails to reproduce results, tested in TF 2.0 beta

In TF 1.x I believe the right functions is tf.random.set_random_seed

From the docs, I can see there are op level seed, and graph level seed. You are setting the op level, which is not enough - setting the graph level seed with the function in the code above solves this behavior.

Upvotes: 3

Related Questions