Difference Between Stateless RNGs and tf.random.Generator
A major difference between stateless RNGs and tf.random.Generator class is that stateless RNGs do not maintain any internal state. Here, the randomness is solely determined by the input seed. Thus, these functions are useful in scenarios where you want to control randomness deterministically. Let’s explore more differences:
|
Feature |
Stateful RNGs (e.g., |
Stateless RNGs (e.g., |
|---|---|---|
| Internal State | Maintains internal state that evolves as random numbers are generated | Does not maintain any internal state; random numbers are solely determined by input seeds |
| Reproducibility | Sequence of random numbers may vary depending on internal state, but can be seeded for reproducibility | Produces deterministic and reproducible random numbers based solely on input seeds |
| Parallelism and Independence | May introduce dependencies among parallel random number sequences | Generates independent random number sequences from the same seed values |
| Performance | Optimized for generating large numbers of random samples | Slightly higher computational overhead due to hash function calculations for each random number |
| Use Cases | General-purpose random number generation where exact reproducibility is not required | Scenarios requiring deterministic reproducibility and parallelism, such as distributed computing or model training |
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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