What is Keras, and how does it relate to TensorFlow?

Keras is a high-level API designed for building and training neural networks. It provides a user-friendly interface that simplifies the process of creating machine learning models, especially for developers who prioritize ease of use and rapid prototyping. TensorFlow, on the other hand, is an open-source machine learning framework developed by Google that handles low-level operations like tensor computations, GPU acceleration, and distributed training. Keras is tightly integrated with TensorFlow as its official high-level API (accessible via tf.keras), allowing developers to leverage Keras’ simplicity while retaining access to TensorFlow’s powerful backend.

Keras abstracts much of TensorFlow’s complexity by offering pre-built components like layers, optimizers, and loss functions. For example, building a neural network in Keras typically involves stacking layers (e.g., Dense, Conv2D) using the Sequential or Functional API, compiling the model with an optimizer (e.g., Adam) and a loss function, then training it with model.fit(). Under the hood, TensorFlow handles the computation graph, automatic differentiation, and hardware acceleration. This integration means developers can focus on model architecture and experimentation without writing low-level code. Additionally, Keras models can be customized with TensorFlow operations if needed, blending high-level convenience with low-level control.

Before TensorFlow 2.0, Keras was a standalone library that could use multiple backends (e.g., Theano, CNTK). However, TensorFlow later adopted Keras as its official high-level API, merging its strengths into the TensorFlow ecosystem. Today, tf.keras is the recommended way to use Keras with TensorFlow, ensuring compatibility and performance optimizations. For instance, TensorFlow-specific features like distributed training or TensorFlow Lite deployment work seamlessly with Keras models. While standalone Keras still exists, most developers use tf.keras to avoid compatibility issues and access TensorFlow’s full capabilities. This integration has made Keras a central tool for TensorFlow users, balancing simplicity and flexibility.