Understanding the Differences between Tensorflow Keras Sequential Class and Model Class


Keras is a popular open-source deep learning library widely used for building and training neural networks. It provides two primary classes for constructing deep learning models: Sequential and Model. While both classes serve the purpose of creating neural networks, they have distinct characteristics and are suitable for different scenarios. In this article, we will delve into the key differences between the Keras Sequential class and Model class to help you understand when and how to use each effectively.


The Sequential Class

The Sequential class in Keras is a simple and straightforward way to build neural networks. It allows you to create a model by stacking layers on top of each other in a linear fashion. This class is well-suited for designing feedforward networks, where the data flows sequentially through the layers from input to output.

Key Features of the Sequential Class:

  • a) Ease of Use: The Sequential class is beginner-friendly and provides a user-friendly API for quickly defining and training models without the need for advanced configurations.
  • b) Single Input, Single Output: Sequential models support a single input tensor and a single output tensor, making them suitable for tasks that involve a single input source and output prediction.
  • c) Layer Stacking: Layers can be added one after another using the add() method, resulting in a simple and intuitive model architecture.


The Model Class

The Model class in Keras offers a more flexible and powerful approach for constructing neural networks. It allows you to define complex models with multiple inputs and outputs, as well as implement shared layers or model architectures with branching and merging capabilities. This class provides a functional API, enabling you to create sophisticated network topologies.

Key Features of the Model Class:

  • a) Multiple Inputs and Outputs: The Model class supports models with multiple input and output tensors, which is particularly useful for tasks such as multi-input/multi-output architectures, siamese networks, or models with auxiliary outputs.
  • b) Shared Layers: You can easily create models with shared layers using the functional API, where multiple inputs can be processed by different branches of the network and merged later.
  • c) Custom Architectures: The Model class allows you to define arbitrary network architectures by manipulating the network graph, enabling more complex configurations such as residual connections or skip connections.


Choosing the Right Class

Knowing the differences between the Sequential class and Model class will help you make informed decisions when building your neural network models.

Use Sequential Class When:

  • You are working with simple, single-input, single-output feedforward networks.
  • The network architecture can be represented as a linear stack of layers.
  • You are a beginner looking for a straightforward way to build and train models.

Use Model Class When:

  • You need to handle multiple inputs or outputs, such as multi-modal networks or multi-task learning scenarios.
  • The network architecture involves shared layers or complex topologies like skip connections or residual connections.
  • You require fine-grained control over the network’s functional components and connections.



In summary, the choice between the Keras Sequential class and Model class depends on the complexity and requirements of your deep learning model. The Sequential class is ideal for simple, single-input, single-output architectures, providing an easy-to-use interface. On the other hand, the Model class offers more flexibility and power, supporting complex network configurations with multiple inputs and outputs, shared layers, and custom architectures. By understanding the strengths and characteristics of each class, you can make informed decisions when designing and building your neural network models using Keras.