TensorFlow 2.0 introduced several improvements over its previous versions, and among them was better support for randomness and reproducibility. However, some users face issues with `tf.random.set_seed`, experiencing non-deterministic results despite setting a seed. This article delves into the function, the root causes of inconsistent behavior, and solutions to common pitfalls.

Understanding `tf.random.set_seed`

TensorFlow's `tf.random.set_seed` is designed to ensure reproducibility by setting a global seed for random operations within a TensorFlow program. This is crucial for debugging and ensuring that experiments are repeatable.

Basic Usage

• TensorFlow operations are stateful. If you call random operations in various orders or include other TensorFlow functions unexpectedly, results can vary.
• Some operations are inherently non-deterministic due to multi-threading or GPU computations. This can lead to variations in outputs, even if the random seed is set.
• Using other libraries, such as NumPy, that also generate random numbers might affect outcomes. Each library's random state must be set independently.
• In neural networks and other complex models, operations such as weight initialization may depend on global states not adequately controlled by just `tf.random.set_seed`.
• Ensure you set seeds for TensorFlow, NumPy, and any other libraries you might be using.
• If possible, use TensorFlow operations with determined outputs, avoiding non-deterministic backend implementations.
• Limit or control the number of parallel threads or operations, especially if relying on GPU or distributed resources.
• Keep the order of operations consistent by encapsulating randomness into controlled functions or modules.
• Ensure you are using a version of TensorFlow where these bugs are identified and fixed, or consider upgrading to a newer version if available.
• If using more than TensorFlow, verify that randomness settings are aligned, potentially providing additional seed settings for other frameworks like PyTorch or JAX.
• For extensive models, include logging or version control strategies that track random states and outputs for different operations.