Tensorflow's random.truncated_normal returns different results with the same seed-CodePudding

The following lines are supposed to get the same result:

print (tf.random.truncated_normal(shape=[2],seed=1234))
print (tf.random.truncated_normal(shape=[2],seed=1234))

But I got:

tf.Tensor([-0.12297685 -0.76935077], shape=(2,), dtype=float32)
tf.Tensor([0.37034193 1.3367208 ], shape=(2,), dtype=float32)

Why?

CodePudding user response：

This seems to be intentional, see the docs here. Specifically the "Examples" section.

What you need is stateless_truncated_normal:

print(tf.random.stateless_truncated_normal(shape=[2],seed=[1234, 1]))
print(tf.random.stateless_truncated_normal(shape=[2],seed=[1234, 1]))

Gives me

tf.Tensor([1.0721238  0.10303579], shape=(2,), dtype=float32)
tf.Tensor([1.0721238  0.10303579], shape=(2,), dtype=float32)

Note: The seed needs to be two numbers here, I honestly don't know why (the docs don't say).

CodePudding user response：

Tensorflow has two types of seeds the global and the operational - this is also why you need to pass two numbers stateless_truncated_normal as xdurch0 describes in his answer. Tensorflow combines these two seeds to generate a new one.

tf.random.truncated_normal(shape=[2],seed=1234) # global seed #1 & operational 1234 -> Seed A
tf.random.truncated_normal(shape=[2],seed=1234) # global seed #2 & operational 1234 -> Seed B

There are multiple ways to tackle your problem. Set the global seed as well beforehand twice. Work inside @tf.functions or use stateless_truncated_normal as written in the other answer.