Category: 04. Facts

With TensorFlow 2.x, Keras integrates tightly with TensorFlow’s tf.function decorator, allowing for improved performance through graph compilation and execution optimizations.

While Keras provides high-level APIs for training models (fit function), it also supports custom training loops, giving advanced users flexibility and control over the training process.

Keras seamlessly integrates with GPUs and TPUs, enabling accelerated training and inference for deep learning models.

Keras models can be saved in the HDF5 format, allowing easy serialization and distribution. They can also be exported to TensorFlow’s SavedModel format for deployment and serving using TensorFlow Serving or other serving solutions.

Keras models can be easily exported to TensorFlow Hub for sharing and reuse, and they can also be converted to run in web browsers using TensorFlow.js.

Keras has a strong community with active contributors and extensive documentation, including tutorials and examples that help users get started with deep learning quickly.

Keras provides utilities for data preprocessing and data augmentation, which are essential for training deep learning models on image, text, and sequence data.

Keras is widely adopted in both industry and academia due to its simplicity, flexibility, and powerful capabilities.

New layers, loss functions, and metrics can be easily added to Keras, either by creating subclasses of existing classes or by writing them from scratch as functions.

Keras models are made up of building blocks called layers, which can be easily connected together to create complex neural network architectures.