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

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.

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 …

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 …

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 …

Keras requires that the output of such iterator-likes be unambiguous. The iterator should return a tuple of length 1, 2, or 3, where the optional second and third elements will be used for y and …

Basically, the SELU activation function multiplies scale (> 1) with the output of the keras.activations.elu function to ensure a slope larger than one for positive inputs.

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 …

Usage with compile() & fit() An optimizer is one of the two arguments required for compiling a Keras model:

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