The article explores Keras as a high-level API that operates on top of tf.Module, highlighting its advanced features such as optional losses, metric support, and mechanisms for training and inference. Keras models and layers enable complex configurations through subclassing, which is beneficial for constructing custom models like GANs and VAEs. The article emphasizes the ease of training, evaluating, and saving models, conveying how Keras layers streamline model management, making them an essential tool in the machine learning toolkit.
Keras offers a high-level API built on top of tf.Module, enhancing model complexity through optional losses, metrics, and configurable saving options, fostering seamless training.
Keras layers simplify the model building process, allowing customization through subclassing and integrated support for training, evaluation, and persistence in machine learning workflows.
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