TensorFlow features pseudo-random number generators within the tf.random module, which includes tf.random.Generator objects and stateless functions. The tf.random.Generator maintains its state in a tf.Variable, altering its state after generating each random number. On the other hand, stateless random functions like tf.random.stateless_uniform ensure deterministic outcomes when provided with the same arguments on identical devices. Despite the availability of old random number functions from TensorFlow 1.x, their use is discouraged in favor of the newer generators. Compatibility across TensorFlow versions may vary, affecting random number consistency.
TensorFlow provides a set of pseudo-random number generators in the tf.random module, allowing for control of random number generation with tf.random.Generator objects and stateless functions.
Each tf.random.Generator object is stateful and maintains its state in a tf.Variable, changing its state after each number generation for controlled randomness.
Stateless random functions like tf.random.stateless_uniform guarantee the same results when called with the same arguments on the same device, ensuring reproducibility.
The old RNGs from TF 1.x are not deprecated but discouraged, with users encouraged to utilize tf.random.Generator for better practices in randomness.
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