In which Keras API do we have to provide the input shape for the model?

The Keras Functional API is designed to allow for the creation of complex neural network architectures in a more flexible way than the Sequential API. In the Functional API, you typically define the input layer by specifying the input shape, which provides the model with the dimensions of the input data it will process. This explicit definition is crucial as it sets the groundwork for how the rest of the layers interact with the input data.

When using the Functional API, the model is built by defining inputs and outputs programmatically, which allows for advanced features like sharing layers and creating non-linear connectivity between layers. By specifying the input shape at the beginning, you facilitate the model's understanding of the data flow through the architecture.

In contrast, while the Sequential API also allows you to build models layer by layer, it infers the input shape from the first layer added to the model, meaning you do not explicitly define the input shape. The Keras Model API generally refers to models built using either the Sequential or Functional APIs for training and evaluation, and the Subclassing API is a way to create models by extending the Model class and defining input shapes within that custom architecture.

Hence, the necessity of providing the input shape is specific to the functional approach in the Keras framework