How does the structure of a model in the Keras Functional API differ from that in the Sequential API?

In the Keras Functional API, the ability to create complex architectures with shared layers and multiple outputs is a key distinguishing feature. This flexibility allows developers to design models where layers can be reused across different parts of the model or where a single model can produce several outputs. For example, one might create a model that processes different types of input data and uses the same processing layer for all of them, or a model that predicts both classification and regression outputs from the same base of features.

This contrasts with the Sequential API, which is designed for building simple, stack-like models, where layers are added one after another in a linear manner. The Sequential API does not support shared layers, meaning layers cannot be reused at different points in the architecture, nor does it facilitate the creation of models with multiple outputs.

While the Functional API does require more specification in terms of how inputs are handled (which includes defining the input shapes), it is primarily the model's capability for shared layers and producing multiple outputs that highlights its more advanced structure and versatility. Other options such as not allowing multiple layers or only permitting convolutional layers misrepresent the capability of both APIs, as both can employ various layer types and handle multiple layers, albeit in different architectures.