Keras documentation: Code examples (original) (raw)

► Code examples

Our code examples are short (less than 300 lines of code), focused demonstrations of vertical deep learning workflows.

All of our examples are written as Jupyter notebooks and can be run in one click in Google Colab, a hosted notebook environment that requires no setup and runs in the cloud. Google Colab includes GPU and TPU runtimes.

★

= Good starter example

V3

= Keras 3 example

Computer Vision

Image classification

★ V3 Image classification from scratch ★ V3 Simple MNIST convnet ★ V3 Image classification via fine-tuning with EfficientNet V3 Image classification with Vision Transformer V3 Classification using Attention-based Deep Multiple Instance Learning V3 Image classification with modern MLP models V3 A mobile-friendly Transformer-based model for image classification V3 Pneumonia Classification on TPU V3 Compact Convolutional Transformers V3 Image classification with ConvMixer V3 Image classification with EANet (External Attention Transformer) V3 Involutional neural networks V3 Image classification with Perceiver V3 Few-Shot learning with Reptile V3 Semi-supervised image classification using contrastive pretraining with SimCLR V3 Image classification with Swin Transformers V3 Train a Vision Transformer on small datasets V3 A Vision Transformer without Attention V3 Image Classification using Global Context Vision Transformer V3 When Recurrence meets Transformers V3 Image Classification using BigTransfer (BiT)

Image segmentation

★ V3 Image segmentation with a U-Net-like architecture V3 Multiclass semantic segmentation using DeepLabV3+ V2 Highly accurate boundaries segmentation using BASNet V3 Image Segmentation using Composable Fully-Convolutional Networks

Object detection

V2 Object Detection with RetinaNet V3 Keypoint Detection with Transfer Learning V3 Object detection with Vision Transformers

3D

V3 3D image classification from CT scans V3 Monocular depth estimation ★ V3 3D volumetric rendering with NeRF V3 Point cloud segmentation with PointNet V3 Point cloud classification

OCR

V3 OCR model for reading Captchas V3 Handwriting recognition

Image enhancement

V3 Convolutional autoencoder for image denoising V3 Low-light image enhancement using MIRNet V3 Image Super-Resolution using an Efficient Sub-Pixel CNN V3 Enhanced Deep Residual Networks for single-image super-resolution V3 Zero-DCE for low-light image enhancement

Data augmentation

V3 CutMix data augmentation for image classification V3 MixUp augmentation for image classification V3 RandAugment for Image Classification for Improved Robustness

Image & Text

★ V3 Image captioning V2 Natural language image search with a Dual Encoder

Vision models interpretability

V3 Visualizing what convnets learn V3 Model interpretability with Integrated Gradients V3 Investigating Vision Transformer representations V3 Grad-CAM class activation visualization

Image similarity search

V2 Near-duplicate image search V3 Semantic Image Clustering V3 Image similarity estimation using a Siamese Network with a contrastive loss V3 Image similarity estimation using a Siamese Network with a triplet loss V3 Metric learning for image similarity search V2 Metric learning for image similarity search using TensorFlow Similarity V3 Self-supervised contrastive learning with NNCLR

Video

V3 Video Classification with a CNN-RNN Architecture V3 Next-Frame Video Prediction with Convolutional LSTMs V3 Video Classification with Transformers V3 Video Vision Transformer

Performance recipes

V3 Gradient Centralization for Better Training Performance V3 Learning to tokenize in Vision Transformers V3 Knowledge Distillation V3 FixRes: Fixing train-test resolution discrepancy V3 Class Attention Image Transformers with LayerScale V3 Augmenting convnets with aggregated attention V3 Learning to Resize

Other

V2 Semi-supervision and domain adaptation with AdaMatch V2 Barlow Twins for Contrastive SSL V2 Consistency training with supervision V2 Distilling Vision Transformers V2 Focal Modulation: A replacement for Self-Attention V2 Using the Forward-Forward Algorithm for Image Classification V2 Masked image modeling with Autoencoders V2 Segment Anything Model with 🤗Transformers V2 Semantic segmentation with SegFormer and Hugging Face Transformers V2 Self-supervised contrastive learning with SimSiam V2 Supervised Contrastive Learning V2 Efficient Object Detection with YOLOV8 and KerasCV

Natural Language Processing

Text classification

★ V3 Text classification from scratch V3 Review Classification using Active Learning V3 Text Classification using FNet V2 Large-scale multi-label text classification V3 Text classification with Transformer V3 Text classification with Switch Transformer V2 Text classification using Decision Forests and pretrained embeddings V3 Using pre-trained word embeddings V3 Bidirectional LSTM on IMDB V3 Data Parallel Training with KerasHub and tf.distribute

Machine translation

V3 English-to-Spanish translation with KerasHub ★ V3 English-to-Spanish translation with a sequence-to-sequence Transformer V3 Character-level recurrent sequence-to-sequence model

Entailment prediction

V2 Multimodal entailment

Named entity recognition

V3 Named Entity Recognition using Transformers

Sequence-to-sequence

V2 Text Extraction with BERT V3 Sequence to sequence learning for performing number addition

Text similarity search

V3 Semantic Similarity with KerasHub V3 Semantic Similarity with BERT V3 Sentence embeddings using Siamese RoBERTa-networks

Language modeling

V3 End-to-end Masked Language Modeling with BERT V3 Abstractive Text Summarization with BART V2 Pretraining BERT with Hugging Face Transformers

Parameter efficient fine-tuning

V3 Parameter-efficient fine-tuning of GPT-2 with LoRA

Other

V2 MultipleChoice Task with Transfer Learning V2 Question Answering with Hugging Face Transformers V2 Abstractive Summarization with Hugging Face Transformers

Structured Data

Structured data classification

★ V3 Structured data classification with FeatureSpace ★ V3 FeatureSpace advanced use cases ★ V3 Imbalanced classification: credit card fraud detection V3 Structured data classification from scratch V3 Structured data learning with Wide, Deep, and Cross networks V3 Classification with Gated Residual and Variable Selection Networks V2 Classification with TensorFlow Decision Forests V3 Classification with Neural Decision Forests V3 Structured data learning with TabTransformer

Structured data regression

V3 Deep Learning for Customer Lifetime Value

Recommendation

V3 Collaborative Filtering for Movie Recommendations V3 A Transformer-based recommendation system

Other

V2 Classification with Gated Residual and Variable Selection Networks with HyperParameters tuning

Timeseries

Timeseries classification

★ V3 Timeseries classification from scratch V3 Timeseries classification with a Transformer model V3 Electroencephalogram Signal Classification for action identification V3 Event classification for payment card fraud detection

Anomaly detection

V3 Timeseries anomaly detection using an Autoencoder

Timeseries forecasting

V3 Traffic forecasting using graph neural networks and LSTM V3 Timeseries forecasting for weather prediction

Other

V2 Electroencephalogram Signal Classification for Brain-Computer Interface

Generative Deep Learning

Image generation

★ V3 Denoising Diffusion Implicit Models ★ V3 A walk through latent space with Stable Diffusion 3 V2 DreamBooth V2 Denoising Diffusion Probabilistic Models V2 Teach StableDiffusion new concepts via Textual Inversion V2 Fine-tuning Stable Diffusion V3 Variational AutoEncoder V3 GAN overriding Model.train_step V3 WGAN-GP overriding Model.train_step V3 Conditional GAN V3 CycleGAN V3 Data-efficient GANs with Adaptive Discriminator Augmentation V3 Deep Dream V3 GauGAN for conditional image generation V3 PixelCNN V2 Face image generation with StyleGAN V2 Vector-Quantized Variational Autoencoders V3 A walk through latent space with Stable Diffusion

Style transfer

V3 Neural style transfer V2 Neural Style Transfer with AdaIN

Text generation

★ V3 GPT2 Text Generation with KerasHub V3 GPT text generation from scratch with KerasHub V3 Text generation with a miniature GPT V3 Character-level text generation with LSTM V2 Text Generation using FNet

Audio generation

V3 Music Generation with Transformer Models

Graph generation

V3 Drug Molecule Generation with VAE V2 WGAN-GP with R-GCN for the generation of small molecular graphs

Other

V2 Density estimation using Real NVP

Audio Data

Vocal track separation

V3 Vocal Track Separation with Encoder-Decoder Architecture

Speech recognition

V3 Automatic Speech Recognition with Transformer

Other

V2 Automatic Speech Recognition using CTC V2 MelGAN-based spectrogram inversion using feature matching V2 Speaker Recognition V2 Audio Classification with the STFTSpectrogram layer V2 English speaker accent recognition using Transfer Learning V2 Audio Classification with Hugging Face Transformers

Reinforcement Learning

Actor Critic Method Proximal Policy Optimization Deep Q-Learning for Atari Breakout Deep Deterministic Policy Gradient (DDPG)

Graph Data

Graph attention network (GAT) for node classification Node Classification with Graph Neural Networks Message-passing neural network (MPNN) for molecular property prediction Graph representation learning with node2vec

Quick Keras Recipes

Keras usage tips

V3 Parameter-efficient fine-tuning of Gemma with LoRA and QLoRA V3 Float8 training and inference with a simple Transformer model V3 Keras debugging tips V3 Customizing the convolution operation of a Conv2D layer V3 Trainer pattern V3 Endpoint layer pattern V3 Reproducibility in Keras Models V3 Writing Keras Models With TensorFlow NumPy V3 Simple custom layer example: Antirectifier V3 Packaging Keras models for wide distribution using Functional Subclassing

Serving

V3 Serving TensorFlow models with TFServing

ML best practices

V3 Estimating required sample size for model training V3 Memory-efficient embeddings for recommendation systems V3 Creating TFRecords

Other

V2 Approximating non-Function Mappings with Mixture Density Networks V2 Probabilistic Bayesian Neural Networks V2 Knowledge distillation recipes V2 Evaluating and exporting scikit-learn metrics in a Keras callback V2 How to train a Keras model on TFRecord files

Adding a new code example

We welcome new code examples! Here are our rules:

• They should be shorter than 300 lines of code (comments may be as long as you want).
• They should demonstrate modern Keras best practices.
• They should be substantially different in topic from all examples listed above.
• They should be extensively documented & commented.

New examples are added via Pull Requests to the keras.io repository. They must be submitted as a .py file that follows a specific format. They are usually generated from Jupyter notebooks. See the tutobooks documentation for more details.

If you would like to convert a Keras 2 example to Keras 3, please open a Pull Request to the keras.io repository.