Thread by @gusthema

Sometimes you need to build a Machine Learning model that cannot be expressed with the Sequential API

For these moments, when you need a more complex model, with multiple inputs and outputs or with residual connections, that's when you need the Functional API!

[2.46 min]

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The Functional API is more flexible than the Sequential API.

The easiest way to understand is to visualize the same model created using the Sequential and Functional API

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You can think of the Functional API as a way to create a Directed Acyclic Graph (DAG) of layers while the Sequential API can only create a stack of layers.

Functional is also known as Symbolic or Declarative API

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Thinking with the Graph creation in mind, given the layers A and B are "vertices", when you call:

B(A)

You're are creating an edge between them like: A ➡️ B

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Benefits of the Functional API:

• Plotting the model and model.summary work as expected to give a good visualization
• Debugging will happen during model definition since layers can be "type-checked"

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The Functional API has the limitation of enable only directed acyclic graphs, if you need something like a dynamic network or recursive networks, than you won't be able to build with this API

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This blog post by @random_forests has an even better explanation of the Functional API

blog.tensorflow.org/2019/01/what-a…

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This guide have more information and playing with it can give you more insights about the Functional API:

tensorflow.org/guide/keras/fu…

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One good example of Functional API usage is on this tutorial: tensorflow.org/hub/tutorials/…

YAMNet has 3 outputs. To use it to extract audio embedding, the model has to be able to deal with multiple outputs! It's perfect for a Functional API.

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