This gallery of examples uses Luz to train a validate a range of common deep learning architectures as well as demonstrates basic and advanced usage of Luz.

Binary classification
basic

Demonstrates using pre-trained models to build a binary classification model.

See code
Autoencoder
basic

Builds an autoencoder for the MNIST dataset. Demonstrates overwriting the predict method

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Virtual batch size
advanced

Showcases how to create a custom fully customized training step

See code
Simple CNN
basic

Trains an AlexNet like CNN to classify digits of the MNIST dataset

See code
DCGAN
intermediate

Deep Convolutional Generative Adversarial Network to generate images.

See code
Triplet loss
intermediate

Train an embedding model to minimized the triplet loss.

See code
UNET implementation
intermediate

Implements a UNET model to separate the background of images of cats and dogs.

See code