The article outlines how to create custom training and evaluation loops in TensorFlow Keras, offering an alternative to its built-in methods like fit() and evaluate(). By implementing the train_step() method in a subclassed model, users can achieve granular control over the learning algorithm. The use of GradientTape allows for optimal weight updates based on loss gradients, with a focus on detailed control that enables effective training of complex models like GANs. Practical examples and code snippets make the guide beginner-friendly yet informative for advanced users.
Keras allows customization of training through subclassing Model and implementing train_step(), enabling more precise control over model training and evaluation.
To achieve low-level control over training, writing custom training and evaluation loops is essential, allowing for advanced manipulation of training processes.
Utilizing GradientTape for gradient retrieval during training facilitates direct manipulation of model weights based on loss, optimizing the training process.
For beginners, Keras provides built-in methods like fit() and evaluate(), while advanced users can implement bespoke training operations using the low-level API.
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