Keras: Deep Learning for humans Keras is a deep learning API designed for human beings, not machines Keras focuses on debugging speed, code elegance conciseness, maintainability, and deployability
Getting started with Keras Getting started with Keras Learning resources Are you a machine learning engineer looking for a Keras introduction one-pager? Read our guide Introduction to Keras for engineers Want to learn more about Keras 3 and its capabilities? See the Keras 3 launch announcement Are you looking for detailed guides covering in-depth usage of different parts of the Keras API? Read our Keras developer guides Are you looking for tutorials showing Keras in action across a wide range of use cases? See the
Keras: Deep Learning for humans Keras 3 is a full rewrite of Keras that enables you to run your Keras workflows on top of either JAX, TensorFlow, PyTorch, or OpenVINO (for inference-only), and that unlocks brand new large-scale model training and deployment capabilities You can pick the framework that suits you best, and switch from one to another based on your current goals
Developer guides - Keras Keras documentation: 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
Code examples - Keras Keras documentation: Code examples Our code examples are short (less than 300 lines of code), focused demonstrations of vertical deep learning workflows
About Keras 3 About Keras 3 Keras is a deep learning API written in Python and capable of running on top of either JAX, TensorFlow, or PyTorch Keras is: Simple – but not simplistic Keras reduces developer cognitive load to free you to focus on the parts of the problem that really matter Flexible – Keras adopts the principle of progressive disclosure of complexity: simple workflows should be quick and easy, while arbitrarily advanced workflows should be possible via a clear path that builds upon
Keras 3 API documentation Keras Applications Xception EfficientNet B0 to B7 EfficientNetV2 B0 to B3 and S, M, L ConvNeXt Tiny, Small, Base, Large, XLarge VGG16 and VGG19 ResNet and ResNetV2 MobileNet, MobileNetV2, and MobileNetV3 DenseNet NasNetLarge and NasNetMobile InceptionV3 InceptionResNetV2
Keras Applications Keras documentation: Keras Applications Keras Applications Keras Applications are deep learning models that are made available alongside pre-trained weights These models can be used for prediction, feature extraction, and fine-tuning Weights are downloaded automatically when instantiating a model They are stored at ~ keras models Upon instantiation, the models will be built according to the image data format set in your Keras configuration file at ~ keras keras json For instance, if
Optimizers - Keras Keras documentation: Optimizers Abstract optimizer base class If you intend to create your own optimization algorithm, please inherit from this class and override the following methods: build: Create your optimizer-related variables, such as momentum variables in the SGD optimizer update_step: Implement your optimizer's variable updating logic get_config: serialization of the optimizer Example
Natural Language Processing - Keras Keras documentation: Natural Language Processing English-to-Spanish translation with a sequence-to-sequence Transformer