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"Keras has something for every user: easy customisability for the academic; out-of-the-box, performant models and pipelines for use by the industry, and readable, modular code for the student. Keras has made it very simple to quickly iterate over experiments without worrying about low-level details." Abheesht Sharma Research Scientist - Amazon
Keras is an open-source library that provides a Python interface for artificial neural networks. Keras was first independent software, then integrated into the TensorFlow library, and later supporting more. "Keras 3 is a full rewrite of Keras [and can be used] as a low-level cross-framework language to develop custom components such as layers ...
The core data structures of Keras are layers and models. A layer is a simple input/output transformation, and a model is a directed acyclic graph (DAG) of layers. Layers. The tf.keras.layers.Layer class is the fundamental abstraction in Keras. A Layer encapsulates a state (weights) and some computation (defined in the tf.keras.layers.Layer.call ...
Keras is an open-source deep-learning framework that gained attention due to its user-friendly interface. Keras offers ease of use, flexibility, and the ability to run seamlessly on top of TensorFlow. In this article, we are going to provide a comprehensive overview of Keras.
That version of Keras is then available via both import keras and from tensorflow import keras (the tf.keras namespace). Starting with TensorFlow 2.16, doing pip install tensorflow will install Keras 3. When you have TensorFlow >= 2.16 and Keras 3, then by default from tensorflow import keras (tf.keras) will be Keras 3.
Keras 3: Deep Learning for Humans. Keras 3 is a multi-backend deep learning framework, with support for JAX, TensorFlow, and PyTorch. Effortlessly build and train models for computer vision, natural language processing, audio processing, timeseries forecasting, recommender systems, etc.
The full Keras API, available for JAX, TensorFlow, and PyTorch. 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 intended to work as a drop-in replacement for tf.keras (when using the TensorFlow backend). Just take your existing tf.keras code, make sure that your calls to model.save() are using the up-to-date .keras format, and you're done.. If your tf.keras model does not include custom components, you can start running it on top of JAX or PyTorch immediately.
Keras is a platform that simplifies the complexities associated with deep neural networks. Based on principles of user-friendliness, compatibility with Python, and an ability to use across various devices and platforms, Keras excels in faster creation of models and robust support for deployment and adoption.
Learn how to use Keras 3, a deep learning framework that works with TensorFlow, JAX, and PyTorch, to train a convnet on MNIST digits. See examples of custom layers, models, metrics, and more.