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The purpose of Keras is to give an unfair advantage to any developer looking to ship Machine Learning-powered apps. Keras focuses on debugging speed, code elegance & conciseness, maintainability, and deployability. When you choose Keras, your codebase is smaller, more readable, easier to iterate on.
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 training and evaluation loops, and the Keras saving & serialization infrastructure.
Keras 3 is a multi-framework deep learning API. As a multi-framework API, Keras can be used to develop modular components that are compatible with any framework – JAX, TensorFlow, or PyTorch. This approach has several key benefits: Always get the best performance for your models.
Keras documentation. About Keras Getting started Developer guides Keras 3 API documentation Models API Layers API Callbacks API Ops API Optimizers Metrics Losses Data loading Built-in small datasets Keras Applications Mixed precision Multi-device distribution RNG API Utilities KerasTuner KerasCV KerasNLP KerasHub Keras 2 API documentation Code examples KerasTuner: ...
Meanwhile, the legacy Keras 2 package is still being released regularly and is available on PyPI as tf_keras (or equivalently tf-keras – note that -and _ are equivalent in PyPI package names). To use it, you can install it via pip install tf_keras then import it via import tf_keras as keras.
Jan 30, 2016 · Building your Keras REST API. Our Keras REST API is self-contained in a single file named run_keras_server.py. We kept the installation in a single file as a manner of simplicity — the implementation can be easily modularized as well. Inside run_keras_server.py you'll find three functions, namely:
Our code examples are short (less than 300 lines of code), focused demonstrations of vertical deep learning workflows. All of our examples are written as Jupyter notebooks and can be run in one click in Google Colab, a hosted notebook environment that requires no setup and runs in the cloud. Google Colab includes GPU and TPU runtimes.
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.
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. Weights are downloaded automatically when instantiating a model. They are stored at ~/.keras/models/.
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 this possible, we have extensively redesigned the API with this release, preempting most future issues.
