Technology
Custom training loop
Take full control of the gradient descent process by manually managing forward passes, loss calculations, and weight updates.
Custom training loops bypass high-level abstractions like Keras .fit() to provide granular control over the execution of machine learning models. By utilizing a GradientTape context manager, developers can implement complex logic such as multiple optimizers, non-standard loss weighting, or specialized data augmentation mid-batch. This approach is essential for research-heavy architectures like GANs or Reinforcement Learning agents where standard loops lack the flexibility to handle non-linear training steps (e.g., updating a discriminator twice for every generator update). It ensures maximum transparency in the training pipeline and simplifies debugging of the underlying mathematical operations.
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