Keras' Sequential model is typically used for simple stack-based layers architectures. However, dealing with multiple inputs requires a more nuanced approach, usually in conjunction with the Functional API due to its increased flexibility. This article explores how to implement a Sequential model with multiple inputs, examining the technical reasons behind using this approach, examples, and additional considerations.

Introduction to Keras Sequential Model

The Sequential model is Keras' most straightforward model. It consists of a linear stack of layers, which are added to the model using the `add()` method. However, when dealing with multiple inputs, the Sequential model may not suffice due to its inherently linear nature, as it only processes inputs sequentially. The Keras Functional API is typically used in these scenarios, allowing for more complex architectures, including models that can handle multiple inputs and outputs, shared layers, and even directed acyclic graphs of layers.

Using Multiple Inputs with Keras

While a pure Sequential model is not designed to handle multiple inputs, we can implement a hybrid structure using Sequential components within the Functional API. This allows us to create sub-models using the Sequential approach and integrate them into a more complex architecture.

Understanding the Functional API

Before diving into examples, it’s crucial to understand how the Functional API works. Unlike the Sequential model, the Functional API lets you connect graphs of layers. This capability enables the creation of models with non-linear topology, such as multi-input models, multi-output models, and layer sharing. The fundamental difference lies in how input and output are defined:

• Inputs: Defined using `keras.layers.Input()`, specifying the shape of the data.
• Outputs: Defined by applying a stack of layers to the input tensor.

Let's explore a model with two input features processed independently before being combined in the final layer.

Implementing a Model with Multiple Inputs

• Flexibility: The Functional API is versatile, accommodating various input processing strategies.
• Complex Architectures: Capability to design intricate models beyond simple layered stacks.
• Computational Overhead: More complex models might require more computational resources.
• Debugging Complexity: Debugging is typically more challenging due to intricate architecture.