tf.random.Generator Class
TensorFlow provides us with tf.random.Generator which is a powerful tool for managing random number generation. It allows us to create unique random numbers, each of them having their own internal state. This internal state is updated each time a random number is generated. It provides a level of control and reproducibility.
Creating a Generator
A generator can be created by using various methods like Generator.from_seed or Generator.from_non_deterministic_state. You can check the code given below.
The tf.random.Generator class in TensorFlow is used to create random number generators with customizable properties and behaviors. Instances of this class represent individual random number generators that can be used to produce random numbers according to various distributions and configurations.
In the following code, we are using TensorFlow’s ‘tf.random.Generator’ to create a random number generator with specific seed. Then, we are going generate random numbers from a normal (Gaussian) distribution using this generator.
- we first create a random number generator with a specified seed value, the practise of using a fixed seed ensures that the generated random number will be the same every time we run the code.
- then, we generate random numbers from a normal distribution using the generator.normal() method.
Python3
import tensorflow as tf# Create a generator with a specific seedgenerator = tf.random.Generator.from_seed(42)# Generate random numbers using the generatorrandom_numbers = generator.normal(shape=(4, 2))print(random_numbers) |
Output:
tf.Tensor(
[[-0.7565803 -0.06854702]
[ 0.07595026 -1.2573844 ]
[-0.23193763 -1.8107855 ]
[ 0.09988727 -0.50998646]], shape=(4, 2), dtype=float32)
The output contains random numbers drawn from normal distribution with mean 0 and standard deviation 1.
Device Placement
In TensorFlow, Device placement refers to the assignment of computational operations to specific devices, such as CPUs or GPUs, for execution. In this case device placement is relevent, when we are obtaining the global generator using tf.random.get_global_generator().
The tf.random.get_global_generator() function in TensorFlow is used to obtain the global random number generator instance.
It is important to consider device placement when you are working with generators. When you invoke tf.random.get_global_generator() the first time, the global generator is constructed and set up on the default device.
Python3
# Obtain the global generatorglobal_generator = tf.random.get_global_generator()# Generate random numbers using the global generatorrandom_numbers_global = global_generator.normal(shape=())print(random_numbers_global) |
Output:
tf.Tensor(-0.1616769, shape=(), dtype=float32)
As an output we get a single random number. Take a note that the global generator is set up on the default device.
Switching Global Generator
It is possible to switch the global generator with a different one. This is done using tf.random.set_global_generator. However, you must use this function cautiously. A change in the global generator can impact tf.function.
Python3
# Create a new generatornew_generator = tf.random.Generator.from_seed(123)# Switch out the global generatortf.random.set_global_generator(new_generator)# Generate random numbers using the new global generatorrandom_numbers_new_global = tf.random.normal(shape=())print(random_numbers_new_global) |
Output:
tf.Tensor(-0.054140557, shape=(), dtype=float32)
In the above code, we have created a new generator and set it as global generator. Then, by using this new global generator we have generated a random number.
Random number generation using TensorFlow
In the field of Machine Learning, Random numbers generation plays an important role by providing stochasticity essential for model training, initialization, and augmentation. We have TensorFlow, a powerful open-source machine learning library, that contains tf.random module. This module helps us for implementing reproducible random number generation.
In this article, we will understand how we can generate random numbers by using the tools provided by TensorFlow. Two major approaches that we will discuss are:
- tf.random.Generator Class
- Stateless Random Functions
Let us discuss each of these approaches in detail.
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