Introduction

When working with TensorFlow, especially in tasks involving neural networks like autoencoders, reproducibility is often a crucial aspect. However, users frequently encounter issues with the set_seed() function not rendering the expected deterministic behavior. This article delves into the recurring issues around set_seed in TensorFlow with a focus on autoencoder scenarios.

Background

Autoencoders are a class of neural networks used to learn efficient representations of data, typically for dimensionality reduction or, in some cases, generation. Ensuring the same output for the same input, across different runs, is vital, especially during development and debugging stages. TensorFlow attempts to facilitate reproducibility via the set_seed() function.

Understanding set_seed()

The core function in TensorFlow for setting seeds is tf.random.set_seed() . This function aims to control the randomness by setting up a seed for all random operations following it. However, its behavior can be non-intuitive due to several factors:

1. Global and Operation Seeds: TensorFlow's randomness relies on two seed values—global and operation-specific. If you solely rely on tf.random.set_seed() , you might encounter unexpected randomness unless both global and operation seeds are controlled.
2. Interaction with Other Libraries: Many workflows combine TensorFlow with NumPy or other libraries, each with their own random state. A change in one library might not reflect the same randomness in another unless explicitly set.
3. Session-Level Randomness: In TensorFlow 1.x, sessions introduce another layer of complexity where each session might have its randomness state.

Sample Code with Potential Error

• Hardware Variations: Certain operations might produce different results across different hardware (like GPUs vs CPUs), even with identical seeds.
• Model Architecture: Some layers or methods, especially those leveraging non-deterministic algorithms for performance, might inherently induce randomness.
• Environment: Execution environments, such as utilizing different OS or different Python setups, might introduce variations, even with seeds set.