Keras is a deep learning API designed for human beings, not machines. Keras focuses on debugging speed, code elegance & conciseness, maintainability, and deployability.

Structured data preprocessing utilities Tensor utilities Python & NumPy utilities Scikit-Learn API wrappers Keras configuration utilities Keras 3 API documentation Models API Layers API …

Our code examples are short (less than 300 lines of code), focused demonstrations of vertical deep learning workflows. All of our examples are written as Jupyter notebooks and can be run …

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.

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 …

We recommend a clean python environment for each backend to avoid CUDA version mismatches. As an example, here is how to create a JAX GPU environment with Conda:

Enjoy the library! We're excited for you to try out the new Keras and improve your workflows by leveraging multi-framework ML. Let us know how it goes: issues, points of friction, feature …

Apr 27, 2020 · Introduction This example shows how to do image classification from scratch, starting from JPEG image files on disk, without leveraging pre-trained weights or a pre-made …

Apr 12, 2020 · Here are two common transfer learning blueprint involving Sequential models. First, let's say that you have a Sequential model, and you want to freeze all layers except the …

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.