tf.keras.utils.set_random_seed
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Sets all random seeds for the program (Python, NumPy, and TensorFlow).
tf.keras.utils.set_random_seed(
seed
)
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:
import random
import numpy as np
import tensorflow as tf
random.seed(seed)
np.random.seed(seed)
tf.random.set_seed(seed)
Arguments |
seed
|
Integer, the random seed to use.
|
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Last updated 2022-10-27 UTC.
[null,null,["Last updated 2022-10-27 UTC."],[],[],null,["# tf.keras.utils.set_random_seed\n\n\u003cbr /\u003e\n\n|----------------------------------------------------------------------------------------------------------|\n| [View source on GitHub](https://github.com/keras-team/keras/tree/v2.9.0/keras/utils/tf_utils.py#L35-L65) |\n\nSets all random seeds for the program (Python, NumPy, and TensorFlow). \n\n tf.keras.utils.set_random_seed(\n seed\n )\n\nYou can use this utility to make almost any Keras program fully deterministic.\nSome limitations apply in cases where network communications are involved\n(e.g. parameter server distribution), which creates additional sources of\nrandomness, or when certain non-deterministic cuDNN ops are involved.\n\nCalling this utility is equivalent to the following: \n\n import random\n import numpy as np\n import tensorflow as tf\n random.seed(seed)\n np.random.seed(seed)\n tf.random.set_seed(seed)\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n| Arguments --------- ||\n|--------|----------------------------------|\n| `seed` | Integer, the random seed to use. |\n\n\u003cbr /\u003e"]]