► Developer guides
Developer guides
Our developer guides are deep-dives into specific topics such as layer subclassing, fine-tuning, or model saving. They're one of the best ways to become a Keras expert.
Most of our guides 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.
Available guides
- The Functional API
- The Sequential model
- Making new layers & models via subclassing
- Training & evaluation with the built-in methods
- Customizing
fit()with JAX - Customizing
fit()with TensorFlow - Customizing
fit()with PyTorch - Writing a custom training loop in JAX
- Writing a custom training loop in TensorFlow
- Writing a custom training loop in PyTorch
- Serialization & saving
- Customizing saving & serialization
- Writing your own callbacks
- Transfer learning & fine-tuning
- Distributed training with JAX
- Distributed training with TensorFlow
- Distributed training with PyTorch
- Distributed training with Keras 3
- Migrating Keras 2 code to Keras 3
Hyperparameter Tuning
- Getting started with KerasTuner
- Distributed hyperparameter tuning with KerasTuner
- Tune hyperparameters in your custom training loop
- Visualize the hyperparameter tuning process
- Handling failed trials in KerasTuner
- Tailor the search space
KerasCV
- Use KerasCV to assemble object detection pipelines
- Use KerasCV to train powerful image classifiers.
- CutMix, MixUp, and RandAugment image augmentation with KerasCV
- High-performance image generation using Stable Diffusion in KerasCV
- Custom Image Augmentations with BaseImageAugmentationLayer
- Semantic Segmentation with KerasCV
- Segment Anything in KerasCV
KerasNLP
- Getting Started with KerasNLP
- Pretraining a Transformer from scratch with KerasNLP
- Uploading Models with KerasNLP