Overview
Syllabus
Intro
The Keras architecture
What does a Layer do?
What does a Layer not do?
The most basic layer
A canonical lazy layer (build(), add_weight())
Nested layers
Basic usage of a layer
Defining losses on the fly and collecting them at the end
Making your layers serializable
Special call argument: training
Basic Model
A Model handles top-level functionality
Eager & graph execution for fit(), evaluate()
The Functional API is a way to create DAGs of layers
A Functional Model behaves like any other Layer/Model, but it has several methods autogenerated (call, build, get_config)
Anatomy of a Functional Model
keras history is the coordinates of the tensor in a 3D construction grid
Static input compatibility checks
Whole-model saving / serialization and reinstantiation across platforms
Automatic masking: a first example
Automatic masking: details
In-depth: what happens when you call a layer on symbolic inputs
Using dynamic layers
Taught by
TensorFlow