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Title: Understanding Why Random Seed Doesn't Guarantee Constant Results in Python
Introduction:
Randomness is often a crucial element in many programming scenarios, from generating synthetic datasets to training machine learning models. In Python, the random module is commonly used for such purposes. However, setting a random seed doesn't always guarantee constant results. In this tutorial, we'll explore the reasons behind this behavior and provide code examples to illustrate the concept.
A random seed is a starting point for the generation of pseudo-random numbers. It initializes the internal state of the random number generator (RNG), ensuring reproducibility. Setting a random seed is useful when you want to recreate the same random sequences, enabling others to reproduce your results.
While setting a random seed is a good practice for reproducibility, it doesn't guarantee constant results in all situations. The reason lies in the fact that Python's random module is not the only source of randomness in many applications. Libraries such as NumPy and TensorFlow have their own random number generators, and not all of them respect the global random seed.
Let's demonstrate this with a simple example involving both the random module and NumPy.
In this example, we set the random seed using random.seed() and np.random.seed(). However, the results will still differ because NumPy has its own random number generator.
To ensure constant results across different libraries, you must set the seed for each library individually. Modify the code as follows:
Now, the results from both the random module and NumPy will be consistent because both seeds are set.
Setting a random seed in Python is a good practice for reproducibility, but it's essential to understand that not all libraries respect the global seed. To ensure consistent results, set the seed for each library you're using. This knowledge is crucial when working with complex projects involving multiple libraries that leverage random number generation.
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