Keras

What is Keras?

Keras is a widely popular, high-level API designed for building neural networks. Developed in Python, it offers compatibility with various neural network computation engines, making it versatile and accessible for developers.

Adopted by the TensorFlow team as the high-level API for TensorFlow 2.0, Keras is recognized for its user-friendliness, modularity, and extensibility. It allows seamless integration of different components, such as neural layers and activation functions, enabling developers to create custom models effortlessly.

Why Use Keras?

Keras stands out for its simplicity and ease of learning. It supports multiple back-end engines and offers robust GPU and distributed training options. Companies like Netflix, Uber, and Square have leveraged Keras for developing commercial applications.

The framework is known for its rapid prototyping capabilities, helping users implement ideas swiftly. It presents a consistent API that simplifies model development and minimizes user errors.

Keras Features

• Compatibility with both CPU and GPU.
• Support for nearly all neural network models.
• Expressive and adaptable, ideal for research.

Building Models with Keras

Create a Network: Define layers and their connections using sequential or functional models.
Assemble a Network: Compile the model with a loss function, optimizer, and metrics.
Adapt to the Network: Train the model on your data.
Evaluate the Network: Assess model accuracy and make predictions with Model.predict().

Applications of Keras

Keras is utilized for developing deep models operational on smartphones and for distributed deep learning. It is also favored in deep learning competitions for its ability to quickly develop and deploy models.