Calling these functions with the same arguments (which include the seed) and on the same device will always produce the same results. Through the purely-functional stateless random functions like tf.random.stateless_uniform.
Each such object maintains a state (in tf.Variable) that will be changed after each number generation. Through the explicit use of tf.random.Generator objects. TensorFlow provides two approaches for controlling the random number generation process: Note: The random numbers are not guaranteed to be consistent across TensorFlow versions. This document describes how you can control the random number generators, and how these generators interact with other tensorflow sub-systems. "Dynamic Creation of Pseudorandom Number Generators." In Proceedings of the Third International Conference on Monte Carlo and Quasi-Monte Carlo Methods in Scientific Computing: Monte Carlo and Quasi ‐Monte Carlo Methods 1998, 56 –69, 2000.TensorFlow provides a set of pseudo-random number generators (RNG), in the tf.random module. "Computer Generation of Poisson Deviates from Modified Normal Distributions." ACM Transactions on Mathematical Software 8, no. "Computer Generation of Hypergeometric Random Variates." Journal of Statistical Computation and Simulation 22, no. "Binomial Random Variate Generation." Communications of the ACM 31, no. "Polar Generation of Random Variates with the t-Distribution." Mathematics of Computation 62, no. "A Family of Switching Algorithms for the Computer Generation of Beta Random Variables." Biometrika 66, no. "Generating Beta Variables with Nonintegral Shape Parameters." Communications of the ACM 21, no. "Erzeugung von Betaverteilten und Gammaverteilten Zufallszahlen." Metrika 8 (1964): 5 –15.
"Some Simple Gamma Variate Generators." Applied Statistics 28, no. "Algorithm AS 53: Wishart Variate Generator." Applied Statistics 21, no. Continuous Univariate Distributions, Volume 2, 2nd ed. Random Number Generation and Monte Carlo Methods, 2nd ed. "Cryptographic Secure Pseudo-Random Bits Generation: The Blum –Blum –Shub Generator." August 1999. "Tables of 64-Bit Mersenne Twisters." ACM Transactions on Modeling and Computer Simulation 10, no. "Mersenne Twister: A 623-Dimensionally Equidistributed Uniform Pseudorandom Number Generator." ACM Transactions on Modeling and Computer Simulation 8, no. "Explaining the Gibbs Sampler." The American Statistician 46, no. "Stochastic Relaxation, Gibbs Distributions, and the Bayesian Restoration of Images." IEEE Transactions on Pattern Analysis and Machine Intelligence 6, no.
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