sets all random seeds python numpy and backend framework e g tf you can use this utility to make almost any keras program fully deterministic some limitations apply in cases where network communications are involved e g parameter server distribution which creates additional sources of randomness or when certain nondeterministic cudnn ops are involved calling this utility is equivalent to the following python import random import numpy as np from keras utils moduleutils import tensorflow as tf random seedseed np random seedseed tf random setseedseed note that the tensorflow seed is set even if you re not using tensorflow as your backend framework since many workflows leverage tf data pipelines which feature random shuffling likewise many workflows might leverage numpy apis arguments seed integer the random seed to use sets all random seeds python numpy and backend framework e g tf you can use this utility to make almost any keras program fully deterministic some limitations apply in cases where network communications are involved e g parameter server distribution which creates additional sources of randomness or when certain non deterministic cudnn ops are involved calling this utility is equivalent to the following python 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 note that the tensorflow seed is set even if you re not using tensorflow as your backend framework since many workflows leverage tf data pipelines which feature random shuffling likewise many workflows might leverage numpy apis arguments seed integer the random seed to use
import random

import numpy as np

from keras import backend
from keras.api_export import keras_export
from keras.utils.module_utils import tensorflow as tf


@keras_export("keras.utils.set_random_seed")
def set_random_seed(seed):
    
    if not isinstance(seed, int):
        raise ValueError(
            "Expected `seed` argument to be an integer. "
            f"Received: seed={seed} (of type {type(seed)})"
        )
    random.seed(seed)
    np.random.seed(seed)
    if tf.available:
        tf.random.set_seed(seed)
    if backend.backend() == "torch":
        import torch

        torch.manual_seed(seed)