![]() G = tf.random.get_global_generator().split(1) For example: with tf.device("cpu"): # change "cpu" to the device you want Spawning new generators is also useful when you want to make sure the generator you use is on the same device as other computations, to avoid the overhead of cross-device copy. In addition to being independent of each other, the new generators ( new_gs) are also guaranteed to be independent of the old one ( g). Split will change the state of the generator on which it is called ( g in the above example), similar to an RNG method such as normal. This is achieved by using Generator.split to create multiple generators that are guaranteed to be independent of each other (i.e. In many applications one needs multiple independent random-number streams, independent in the sense that they won't overlap and won't have any statistically detectable correlations. g = tf._seed(1)Ĭreating independent random-number streams A better way to reset the global generator is to use one of the "reset" functions such as Generator.reset_from_seed, which won't create new generator objects. This function should be used with caution though, because the old global generator may have been captured by a tf.function (as a weak reference), and replacing it will cause it to be garbage collected, breaking the tf.function. There is also a function tf.t_global_generator for replacing the global generator with another generator object. So, for example, if the first site you call tf.random.get_global_generator is within a tf.device("gpu") scope, the global generator will be placed on the GPU, and using the global generator later on from the CPU will incur a GPU-to-CPU copy. The global generator is created (from a non-deterministic state) at the first time tf.random.get_global_generator is called, and placed on the default device at that call. When using tf.random.get_global_generator to get the global generator, you need to be careful about device placement. There are yet other ways to create generators, such as from explicit states, which are not covered by this guide. A generator created this way will start from a non-deterministic state, depending on e.g. See the Algorithms section below for more information about it.Īnother way to create a generator is with om_non_deterministic_state. from_seed also takes an optional argument alg which is the RNG algorithm that will be used by this generator: g1 = tf._seed(1, alg='philox') The easiest is om_seed, as shown above, that creates a generator from a seed. ![]() There are multiple ways to create a generator object. ![]() You can get a tf.random.Generator by manually creating an object of the class or call tf.random.get_global_generator() to get the default global generator: g1 = tf._seed(1) Because the state is managed by tf.Variable, it enjoys all facilities provided by tf.Variable such as easy checkpointing, automatic control-dependency and thread safety. It maintains an internal state (managed by a tf.Variable object) which will be updated every time random numbers are generated. The tf.random.Generator class is used in cases where you want each RNG call to produce different results. Physical_devices = tf.config.list_physical_devices("CPU") # Creates some virtual devices (cpu:0, cpu:1, etc.) for using distribution strategy Warning: The old RNGs from TF 1.x such as tf.random.uniform and tf.random.normal are not yet deprecated but strongly discouraged. ![]() 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. TensorFlow provides a set of pseudo-random number generators (RNG), in the tf.random module.
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