tf.keras is TensorFlow's high-level API for building and training deep
learning models. It's used for fast prototyping, state-of-the-art research,
and production, with three key advantages:
Keras has a simple, consistent interface optimized for common use cases. It provides clear and actionable feedback for user errors.
- Modular and composable
Keras models are made by connecting configurable building blocks together, with few restrictions.
- Easy to extend
Write custom building blocks to express new ideas for research. Create new layers, metrics, loss functions, and develop state-of-the-art models.
The guide Keras: A Quick Overview will help you get started.
To dive more into the API, see the following set of guides that cover what you need to know as a TensorFlow Keras power user:
- Guide to the Keras functional API
- Guide to training and evaluation
- Guide to writing layers and models from scratch with subclassing
- Guide to the recurrent neural network API
- Guide to masking and padding
- Guide to saving and serializing models
- Guide to writing custom callbacks
Watch Inside TensorFlow on YouTube for a Keras internals deep dive: