Keras
Product information, use cases, and access for Keras.
Pricing information
No verified public pricing is available yet.
DevPrice organizes public information and does not sell Keras subscriptions. Prices and availability are determined by Keras.
What is Keras?
Keras is a deep learning API designed for people who build and study machine learning models. It emphasizes readable code, concise model definitions, debugging speed, maintainability, and deployability, making it useful for developers who need to move between experimentation and production-oriented work. The official site combines getting-started material, developer guides, code examples, and API documentation so users can choose the level of detail they need.
With Keras 3, the same Keras approach can work across JAX, TensorFlow, and PyTorch backends, giving teams a way to use different ecosystems while keeping a consistent modeling interface. Models can be organized with Sequential or Functional APIs, or customized by creating new layers and models through subclassing. KerasHub adds implementations of popular architectures and pretrained checkpoints for training and inference, while the broader examples cover computer vision, natural language processing, and generative deep learning.
Key features of Keras
Model building and training
Keras provides Sequential and Functional APIs for composing models, while subclassing lets developers define custom layers and model behavior. Its built-in compile, fit, evaluation, and prediction methods support the core workflow from an initial experiment through testing and inference.
Multi-backend development
Keras 3 supports JAX, TensorFlow, and PyTorch backends, allowing developers to keep a consistent Keras programming style while working with different framework ecosystems. This is useful when a project needs to evaluate backends, reuse model code, or work alongside existing machine learning tooling.
Pretrained model access
KerasHub provides Keras 3 implementations of popular model architectures together with pretrained checkpoints that can be used for training and inference. The official examples include language models, text-to-image models, and other generative systems, giving developers a starting point for model-based exploration.
Broad deep learning tasks
Official examples and guides cover computer vision, natural language processing, and generative deep learning. Developers can use them to explore areas such as image classification, object detection, video processing, text classification, machine translation, language modeling, and diffusion-based generation.