Keras is a deep learning API designed for human beings, not machines. Keras focuses on debugging speed, code elegance & conciseness, maintainability, and deployability.
Read our Keras developer guides. Are you looking for tutorials showing Keras in action across a wide range of use cases? See the Keras code examples: over 150 well-explained notebooks demonstrating Keras best practices in computer vision, natural language processing, and generative AI.
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.
Keras follows the principle of progressive disclosure of complexity: it makes it easy to get started, yet it makes it possible to handle arbitrarily advanced use cases, only requiring incremental learning at each step.
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.
Structured data preprocessing utilities Tensor utilities Python & NumPy utilities Scikit-Learn API wrappers Keras configuration utilities Keras 3 API documentation Models API Layers API Callbacks API Ops API Optimizers Metrics Losses Data loading Built-in small datasets Keras Applications Mixed precision Multi-device distribution RNG API ...
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.
Introduction Keras 3 is a deep learning framework works with TensorFlow, JAX, and PyTorch interchangeably. This notebook will walk you through key Keras 3 workflows.
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.
outputs: The output (s) of the model: a tensor that originated from keras.Input objects or a combination of such tensors in a dict, list or tuple. See Functional API example below.
