keras.utils.set_random_seed does not set seeds

[markodjordjic]

OS: Windows 11
Python == 3.11.0 64 bit
Keras == 3.0.5

keras.utils.set_random_seed() does not set the reproducibility for (at least) Numpy operations.

import numpy as np
import keras

seed = 0

keras.utils.set_random_seed(seed=seed)
random_number_generator = np.random.default_rng()

gamma =
random_number_generator.gamma(shape=2, size=(100, 1))

Multiple runs will produce different random values.

With random_number_generator = np.random.default_rng(seed=seed) multiple runs will produce same random values.

[markodjordjic]

This did work: np.random.gamma(shape=2, size=(100, 1)) But it should be more clear in the documentation that this is the limitation on how to use the setting of the seed for numpy via Keras.

[fchollet]

The set_random_seed() docstring says:

Calling this utility is equivalent to the following:

import random
import numpy as np
from keras.utils.module_utils import tensorflow as tf
random.seed(seed)
np.random.seed(seed)
tf.random.set_seed(seed)

Calling np.random.seed(seed) does not affect np.random.default_rng() because by definition default_rng() is standalone and locally-seeded. It does depend on the global seed state. This is well documented by NumPy.

[markodjordjic]

Many, thanks! @SuryanarayanaY and @fchollet