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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

Description:

Explore an in-depth technical overview of tf.Keras in this 54-minute video presentation by Francois Chollet, the creator of Keras. Dive into the internal architecture of Keras, understanding the role and functionality of layers, models, and the Functional API. Learn about lazy layer building, nested layers, loss collection, serialization, and the differences between eager and graph execution. Discover advanced topics such as automatic masking, symbolic inputs, and dynamic layers. Gain insider knowledge from the TensorFlow team's internal training session, offering valuable insights for both beginners and experienced developers working with TensorFlow and Keras.

Inside TensorFlow - tf.Keras - Part 1

TensorFlow

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#Computer Science #Machine Learning #TensorFlow #Artificial Intelligence #Neural Networks #Deep Learning #Keras