Functional API

As explained in Evolutionary Deep Learning: Genetic algorithms and neural networks

The Keras Functional API is an interface for defining neural-network models as directed graphs of layer operations and tensors.

How Evolutionary Deep Learning: Genetic algorithms and neural networks covers it

How deep it goes

Demonstrates executable model construction with branching and concatenation, but does not develop the API beyond this application.

Examples

Uses Dense layers, parallel branches, concatenation, environment-specific inputs and outputs, and an instinct-selection list.

What it assumes

Assumes familiarity with DQN or deep reinforcement learning while demonstrating the functional construction incrementally.

What you can do after

Builds and compiles branched Keras models from supplied layer functions.

How it connects

Connects layers, tensors, neural-network models, deep reinforcement learning, DQN, and evolutionary optimization.

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Also explained in

• 

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  Deep Learning with R, Third EditionThe Keras Functional API is an interface for constructing models by connecting layer operations as a directed computation graph.
• 

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  Deep Learning with Python, Third EditionThe Keras model-building interface that expresses a model as a directed acyclic graph of layers with explicit connectivity.

Compare how 3 books explain Functional API →

Related concepts

• Sequential modelalternative to · A sequential model is a neural-network model comprising a linear, ordered stack of layers through which data flows from input to output.
• deep learningused by · Deep learning is a machine-learning approach that uses neural networks with multiple computational layers to learn representations from data.
• model subclassingalternative to · Model subclassing is a technique for defining a neural-network model by creating a class that extends a model base class and specifies its computation.
• directed acyclic graphuses · A directed acyclic graph is a graph with directed edges and no directed cycles.
• graphuses · A graph is a mathematical structure consisting of vertices connected by edges that represent relationships or connections.

This explainer was generated by Manning's knowledge graph from the text of Evolutionary Deep Learning: Genetic algorithms and neural networks. The section links point to the parts of the book it draws on.