Keras is a deep learning API designed for human beings, not machines. Keras focuses on debugging speed, code elegance & conciseness, maintainability, and deployability. When you choose Keras, your codebase is smaller, more readable, easier to iterate on.

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.

Keras 3 implements the full Keras API and makes it available with TensorFlow, JAX, and PyTorch — over a hundred layers, dozens of metrics, loss functions, optimizers, and callbacks, the Keras training and evaluation loops, and the Keras saving & serialization infrastructure.

Getting started Developer guides Code examples 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 Utilities Keras 2 API documentation KerasTuner: Hyperparam Tuning KerasHub: Pretrained Models

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.

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 ...

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.

The new Keras 2 API is our first long-term-support API: codebases written in Keras 2 next month should still run many years from now, on up-to-date software. To make this possible, we have extensively redesigned the API with this release, preempting most future issues.

Keras enables you to write custom Layers, Models, Metrics, Losses, and Optimizers that work across TensorFlow, JAX, and PyTorch with the same codebase. Let's take a look at custom layers first. The keras.ops namespace contains: An implementation of the NumPy API, e.g. keras.ops.stack or keras.ops.matmul.

Keras documentation. None Getting started Developer guides Code examples Computer Vision Natural Language Processing Text classification from scratch Review Classification using Active Learning Text Classification using FNet Large-scale multi-label ...