What is Keras?

Keras is a deep learning API that simplifies the process of building deep neural networks. Initially it was developed as an independent library, Keras is now tightly integrated into TensorFlow as its official high-level API. It supports multiple backend engines like TensorFlow, Theano and Microsoft Cognitive Toolkit (CNTK). Keras makes it easier to train and evaluate deep learning models without requiring extensive knowledge of low-level operations.

Installing Keras

1. Since Keras is now part of TensorFlow, it can be installed easily using pip:

This command installs TensorFlow 2.x, which includes Keras.

2. To check the installation, open Python and run:

Output:

TensorFlow Version: 2.18.0
Keras Version 3.8.0

History of Keras

Keras was developed by Google engineer named François Chollet. It was developed as part of research project called ONEIROS (Open-ended Neuro-Electronic Intelligent Robot Operating System) and it was released in March 2015.

The goal of Keras was to enable experimentation with deep neural networks. Later, Keras was tagged into TensorFlow as 'tf.keras', which made it an official high-level API of TensorFlow while having its own version that could interface with other computational backends like Theano or CNTK.

Key Features of Keras Library

How to Build a Model in Keras?

Keras provides two main ways to build models:

1. Building Model using Sequential API

The Sequential API are easy to work with models with a single input and output and a linear stack of layers

Here’s how you can define a Sequential model:

• We create a Sequential model.
• Add a fully connected (Dense) layer with 64 units and ReLU activation.
• Add another Dense layer with 10 units (for classification) and a Softmax activation.

2. Building Model using Functional API

Functional API allows more flexibility in creating complex architectures. You can create models with shared layers, multiple inputs/outputs and skip connections.

For example:

• We define two input layers (input1 and input2).
• Create separate hidden layers for each input.
• Merge the hidden layers using the concatenate function.
• Finally, add an output layer with SoftMax activation.

Applications

• Image and Video Processing: Supports image classification, object detection, and video analysis using CNNs for applications like medical imaging and quality control.
• Natural Language Processing (NLP): Helps build models for tasks such as sentiment analysis, translation, and text summarization.
• Time Series Forecasting: Uses LSTM and GRU models to predict data in areas like finance and weather forecasting.
• Autonomous Systems: Processes sensor data for robotics and autonomous vehicles to support real-time decision making.