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 …

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 …

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 documentation. None Getting started Developer guides Code examples Keras 3 API documentation Models API Layers API Callbacks API Ops API Optimizers Metrics Losses …

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 …

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.

Mar 14, 2017 · 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 …

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, …

Jan 30, 2016 · How to (and how not to) load a Keras model into memory so it can be efficiently used for inference; How to use the Flask web framework to create an endpoint for our API; …

There are three ways to create Keras models: The Sequential model, which is very straightforward (a simple list of layers), but is limited to single-input, single-output stacks of …