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.

How to Install Keras?

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

pip install tensorflow

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

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

import tensorflow as tf
print('TensorFlow Version: ', tf.__version__)
print('Keras Version ', tf.keras.__version__)

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. Sequential API
2. Functional API

The Sequential API are easy to work with models with a single input and output and a linear stack of layers. Whereas, the Functional API can be used for models that require multiple inputs and outputs or layers have multiple inputs or outputs.

Building Model using Sequential API

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.

from keras.models import Sequential
from keras.layers import Dense, Activation

model = Sequential()
model.add(Dense(units=64, input_dim=100))
model.add(Activation('relu'))
model.add(Dense(units=10))
model.add(Activation('softmax'))

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.

from keras.layers import Input, Dense, concatenate
from keras.models import Model

input1 = Input(shape=(100,))
input2 = Input(shape=(50,))
hidden1 = Dense(64, activation='relu')(input1)
hidden2 = Dense(32, activation='relu')(input2)
merged = concatenate([hidden1, hidden2])
output = Dense(10, activation='softmax')(merged)

model = Model(inputs=[input1, input2], outputs=output)

Applications of Keras

1. Image and Video Processing: Keras facilitates tasks like image classification, object detection and video analysis through convolutional neural networks (CNNs). This makes it ideal for applications from medical imaging diagnostics to automated manufacturing quality control.

2. Natural Language Processing (NLP): In NLP, Keras aids in building models for sentiment analysis and machine translation. Its support for sequential data processing is essential for systems capable of summarizing texts.

3. Time Series Forecasting: Keras models equipped with LSTM or GRU layers are perfect for predicting time series data, which is used in fields like finance for stock price predictions or meteorology for weather forecasting.

4. Autonomous Systems: Keras helps process real-time data from sensors in robotics and autonomous vehicles, It eases complex decision-making processes necessary for task performance without human input.

5. Game Development and Reinforcement Learning: Keras can be used in AI development for video games and simulations, employing reinforcement learning to create adaptable and engaging gameplay experiences.

Keras provides a streamlined and efficient interface for deep learning, making it easier to build and train neural networks. It supports efficient execution on both CPU and GPU.

For a better understanding on keras, explore the tutorial page

• Keras Tutorial