While standard Keras APIs provide efficient ways to build many models, certain research goals or complex applications demand greater flexibility. This chapter introduces techniques for extending TensorFlow's core functionalities to suit specific needs.

You will learn how to gain finer control over model architecture and training by:

• Subclassing tf.keras.Model to create fully customized model structures.
• Developing unique processing blocks by subclassing tf.keras.layers.Layer.
• Implementing custom loss functions and evaluation metrics tailored to specific objectives.
• Writing custom training loops using tf.GradientTape for precise control over gradient computation and weight updates.
• Working with specialized data representations like tf.RaggedTensor for variable-length inputs and tf.SparseTensor for data with many zero values.
• Utilizing TensorFlow Addons for access to additional community-contributed components.

Mastering these techniques allows you to move beyond pre-defined structures and implement novel ideas or highly specialized machine learning systems.