Techniques to Build Models in Keras

Build deep learning model in Keras using Sequential, Functional Keras API, and Model Subclassing

If you are creating machine learning models using Keras, know all the available techniques to build a model, their strengths, and when to apply them along with the code.

Keras Sequential Model

Sequential is the most common and simple technique to build models in Keras.

Sequential groups a linear stack of layers into a Keras Model. The Sequential model is built by passing a list of layers to the Sequential constructor

When to build Sequential models in Keras?

Sequential Models are appropriate for a plain stack of layers with exactly one input tensor and one output tensor like VGGNet.

When to avoid building a Sequential model?

You cannot use the Sequential model when

• The model has multiple inputs or multiple outputs. Use Functional API or Model Subclassing to create models with multiple inputs or multiple outputs.
• The intermediate layers in the model have multiple inputs or outputs like in ResNet.
• There are share layers within the model.
• The sequential model builds only linear topology, hence not…