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Keras

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description Keras Overview

Keras offers a high-level Python API for building neural networks from reusable components. Layers, models and training routines let developers express an experiment without writing every low-level operation themselves. Its modular approach supports changing parts of a network while keeping much of the surrounding workflow familiar. This makes it useful for learning deep learning and for experienced practitioners who want to test model ideas with less setup code.

Keras 3 supports TensorFlow, JAX and PyTorch backends, so its interface is not tied to a single computation framework. Backend choice still affects the surrounding environment and any framework-specific code. The project did not begin simply as a TensorFlow wrapper; earlier Keras releases also supported multiple backends. Its defining role is to make neural network development more approachable while leaving room for custom components when an experiment needs more detailed control.

help Keras FAQ

What is Keras used for?

Keras is a high-level Python API for building, training, evaluating, and saving neural networks. Its familiar workflow includes layers, models, optimizers, losses, and methods such as fit and evaluate.

Which backends does Keras 3 support?

Keras 3 supports TensorFlow, JAX, and PyTorch as primary backends. The same high-level Keras model code can therefore be used with different machine-learning ecosystems, subject to backend-specific limitations.

Is Keras the same thing as TensorFlow?

No. Keras is an API, while TensorFlow is one possible backend for executing Keras models. Keras 3 is deliberately multi-backend, so a project can use PyTorch or JAX instead of TensorFlow.

What must I install before using Keras 3?

You need Keras 3 plus at least one backend such as TensorFlow, JAX, or PyTorch. The backend is selected through Keras configuration, so installing Keras alone does not provide the numerical engine needed to train a model.

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