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About Keras Getting started Developer guides The Functional API The Sequential model Making new layers & models via subclassing Training & evaluation with the built-in methods Customizing what happens in `fit()` Writing a training loop from scratch Serialization & saving Writing your own callbacks Working with preprocessing layers Working with recurrent neural networks Understanding masking & padding Multi-GPU & distributed training Transfer learning & fine-tuning Hyperparameter Tuning KerasCV KerasNLP Keras API reference Code examples Why choose Keras? Community & governance Contributing to Keras KerasTuner KerasCV KerasNLP
» 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 what happens in fit()
  • Writing a training loop from scratch
  • Serialization & saving
  • Writing your own callbacks
  • Working with preprocessing layers
  • Working with recurrent neural networks
  • Understanding masking & padding
  • Multi-GPU & distributed training
  • Transfer learning & fine-tuning

Hyperparameter Tuning

  • Getting started with KerasTuner
  • Distributed hyperparameter tuning with KerasTuner
  • Tune hyperparameters in your custom training loop
  • Visualize the hyperparameter tuning process
  • Tailor the search space

KerasCV

  • CutMix, MixUp, and RandAugment image augmentation with KerasCV
  • Custom Image Augmentations with BaseImageAugmentationLayer
  • Using KerasCV COCO Metrics

KerasNLP

  • Pretraining a Transformer from scratch with KerasNLP
Developer guides
▻ Available guides
Hyperparameter Tuning
KerasCV
KerasNLP