Call for code example contributions

This is a constantly-updated list of code examples that we're currently interested in.

If you're not sure whether your idea would make a good code example, please ask us first!

Structured data examples featuring Keras Preprocessing Layers (KPL)

E.g. feature hashing, feature indexing with handling of missing values, mixing numerical, categorical, and text features, doing feature engineering with KPL, etc.

Transformer model for MIDI music generation

Reference TF/Keras implementation

Text-to-image

A text-to-image diffusion model in the style of Imagen, using a frozen BERT encoder from KerasHub and a multi-stage diffusion model.

Text-to-speech

Example TF2/Keras implementation

Learning to rank

Reference Kaggle competition

DETR: End-to-End Object Detection with Transformers

Reference implementation
TF/Keras implementation

3D image segmentation

Question answering from structured knowledge base and freeform documents

Instance segmentation

Tensorflow-YOLACT
Additional references

EEG & MEG signal classification

Text summarization

Audio track separation

Audio style transfer

Timeseries imputation

Customer lifetime value prediction

Keras reproducibility recipes

Standalone Mixture-of-Experts (MoE) layer

MoE layers provide a flexible way to scale deep models to train on larger datasets. The aim of this example should be to show how replace the regular layers (such as Dense, Conv2D) with compatible MoE layers.

References:

A relevant paper on MoE: https://arxiv.org/abs/1701.06538
Switch Transformers on keras.io
Keras implementation of Dense and Conv2D MoE layers

Guide to report the efficiency of a Keras model

It's often important to report the efficiency of a model. But what factors should be included when reporting the efficiency of a deep learning model? The Efficiency Misnomer paper discusses this thoroughly and provides guidelines for practitioners on how to properly report model efficiency.

The objectives of this guide will include the following:

What factors to consider when reporting model efficiency?
How to calculate certain metrics like FLOPS, number of examples a model can process per second (both in training and inference mode), etc?

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