Keras provides a complete framework to create any type of neural networks. Keras is innovative as well as very easy to learn. It supports simple neural network to very large and complex neural network model. Let us understand the architecture of Keras framework and how Keras helps in deep learning in this blog.

Architecture of Keras

Keras API can be divided into three main categories −

Model
Layer
Core Modules

In Keras, every ANN is represented by Keras Models. In turn, every Keras Model is composition of Keras Layers and represents ANN layers like input, hidden layer, output layers, convolution layer, pooling layer, etc., Keras model and layer access Keras modules for activation function, loss function, regularization function, etc., Using Keras model, Keras Layer, and Keras modules, any ANN algorithm (CNN, RNN, etc.,) can be represented in a simple and efficient manner.

The following diagram depicts the relationship between model, layer and core modules −

keras 6

Let us see the overview of Keras models, Keras layers and Keras modules.

Model

Keras Models are of two types as mentioned below −

Sequential Model − Sequential model is basically a linear composition of Keras Layers. Sequential model is easy, minimal as well as has the ability to represent nearly all available neural networks.

A simple sequential model is as follows −

from keras.models import Sequential
from keras.layers import Dense, Activation
model = Sequential()
model.add(Dense(512, activation = 'relu', input_shape = (784,)))

Where,

Line 1 imports Sequential model from Keras models
Line 2 imports Dense layer and Activation module
Line 4 create a new sequential model using Sequential API
Line 5 adds a dense layer (Dense API) with relu activation (using Activation module) function.

Sequential model exposes Model class to create customized models as well. We can use sub-classing concept to create our own complex model.

Functional API − Functional API is basically used to create complex models.

Layer

Each Keras layer in the Keras model represent the corresponding layer (input layer, hidden layer and output layer) in the actual proposed neural network model. Keras provides a lot of pre-build layers so that any complex neural network can be easily created. Some of the important Keras layers are specified below,

Core Layers
Convolution Layers
Pooling Layers
Recurrent Layers

A simple python code to represent a neural network model using sequential model is as follows −

from keras.models import Sequential
from keras.layers import Dense, Activation, Dropout model = Sequential()
model.add(Dense(512, activation = 'relu', input_shape = (784,)))
model.add(Dropout(0.2))
model.add(Dense(512, activation = 'relu')) model.add(Dropout(0.2))
model.add(Dense(num_classes, activation = 'softmax'))

Where,

Line 1 imports Sequential model from Keras models
Line 2 imports Dense layer and Activation module
Line 4 create a new sequential model using Sequential API
Line 5 adds a dense layer (Dense API) with relu activation (using Activation module) function.
Line 6 adds a dropout layer (Dropout API) to handle over-fitting.
Line 7 adds another dense layer (Dense API) with relu activation (using Activation module) function.
Line 8 adds another dropout layer (Dropout API) to handle over-fitting.
Line 9 adds final dense layer (Dense API) with softmax activation (using Activation module) function.

Keras also provides options to create our own customized layers. Customized layer can be created by sub-classing the Keras.Layer class and it is similar to sub-classing Keras models.

Core Modules

Keras also provides a lot of built-in neural network related functions to properly create the Keras model and Keras layers. Some of the function are as follows −

Activations module − Activation function is an important concept in ANN and activation modules provides many activation function like softmax, relu, etc.,
Loss module − Loss module provides loss functions like mean_squared_error, mean_absolute_error, poisson, etc.,
Optimizer module − Optimizer module provides optimizer function like adam, sgd, etc.,
Regularizers − Regularizer module provides functions like L1 regularizer, L2 regularizer, etc.,

So, this brings us to the end of blog. This Tecklearn ‘How Keras help in Deep Learning and Architecture of Keras Library’ blog helps you with commonly asked questions if you are looking out for a job in Artificial Intelligence. If you wish to learn Artificial Intelligence and build a career in AI or Machine Learning domain, then check out our interactive, Artificial Intelligence and Deep Learning with TensorFlow Training, that comes with 24*7 support to guide you throughout your learning period. Please find the link for course details:

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What you will Learn in this Course?

Introduction to Deep Learning and AI

What is Deep Learning?
Advantage of Deep Learning over Machine learning
Real-Life use cases of Deep Learning
Review of Machine Learning: Regression, Classification, Clustering, Reinforcement Learning, Underfitting and Overfitting, Optimization
Pre-requisites for AI & DL
Python Programming Language
Installation & IDE

Environment Set Up and Essentials

Installation
Python – NumPy
Python for Data Science and AI
Python Language Essentials
Python Libraries – Numpy and Pandas
Numpy for Mathematical Computing

More Prerequisites for Deep Learning and AI

Pandas for Data Analysis
Machine Learning Basic Concepts
Normalization
Data Set
Machine Learning Concepts
Regression
Logistic Regression
SVM – Support Vector Machines
Decision Trees
Python Libraries for Data Science and AI

Introduction to Neural Networks

Creating Module
Neural Network Equation
Sigmoid Function
Multi-layered perception
Weights, Biases
Activation Functions
Gradient Decent or Error function
Epoch, Forward & backword propagation
What is TensorFlow?
TensorFlow code-basics
Graph Visualization
Constants, Placeholders, Variables

Multi-layered Neural Networks

Error Back propagation issues
Drop outs

Regularization techniques in Deep Learning

Deep Learning Libraries

Tensorflow
Keras
OpenCV
SkImage
PIL

Building of Simple Neural Network from Scratch from Simple Equation

Training the model

Dual Equation Neural Network

TensorFlow
Predicting Algorithm

Introduction to Keras API

Define Keras
How to compose Models in Keras
Sequential Composition
Functional Composition
Predefined Neural Network Layers
What is Batch Normalization
Saving and loading a model with Keras
Customizing the Training Process
Using TensorBoard with Keras
Use-Case Implementation with Keras

GPU in Deep Learning

Introduction to GPUs and how they differ from CPUs
Importance of GPUs in training Deep Learning Networks
The GPU constituent with simpler core and concurrent hardware
Keras Model Saving and Reusing
Deploying Keras with TensorBoard

Keras Cat Vs Dog Modelling

Activation Functions in Neural Network

Optimization Techniques

Some Examples for Neural Network

Convolutional Neural Networks (CNN)

Introduction to CNNs
CNNs Application
Architecture of a CNN
Convolution and Pooling layers in a CNN
Understanding and Visualizing a CNN

RNN: Recurrent Neural Networks

Introduction to RNN Model
Application use cases of RNN
Modelling sequences
Training RNNs with Backpropagation
Long Short-Term memory (LSTM)
Recursive Neural Tensor Network Theory
Recurrent Neural Network Model

Application of Deep Learning in image recognition, NLP and more

Real world projects in recommender systems and others

Got a question for us? Please mention it in the comments section and we will get back to you.

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