In this tutorial, you will learn about Keras framework or API. It is used to develop and define Deep Learning Models. It is implemented on the freeware machine libraries such as Theano, TensorFlow, etc. It provides a simple method to Develop Deep Learning Models.

What is Keras?

Keras is a freeware deep learning framework of Python. It is developed by an artificial intelligence researcher whose name is “Francois Chollet”. It is a top-level neural network API developed in python. It supports both recurrent and convolutional networks and amalgamation of both. Many Top companies, like Netflix, Google, Square, are presently using Keras tools. It is used to develop Deep Learning Models. It uses libraries of different programming languages like Python, C++, C, etc.

Keras Features

The features of Keras are as follows:

  • Simple, extensible and constant API.
  • It supports backends and different platforms.
  • Due to its Customizable framework, it can work on both GPU and CPU.
  • Calculations are extremely scalable.

Advantages of Keras

The Advantages of Keras are as follows:

  • It is tested easily.
  • The neural networks of Keras are developed in Python, which is easy to understand.
  • It endorses both recurrent and convolutional networks.
  • It has great community support.
  • As the models of deep learning are discrete components, they can be merged.

Keras Backend

In Keras, with the help of TensorFlow Libraries, the backend carries out all the bottom level calculations. The backend engine carries out the development of the models. In Keras, we will use TensorFlow as the default backend engine.

TensorFlow

It is a freeware machine learning library utilized for arithmetical calculations. Google develops it. Many researchers have helped in developing this framework for the purpose of deep learning.

Keras is a framework of Deep Learning, so, let us study about Deep Learning.

Deep Learning

Deep Learning is considered a subdomain of Machine Learning. The main job of deep learning is studying the input layer by layer. Some of the fundamentals of Deep Learning are as follows:

1. Artificial Neural Networks

The main method of deep learning is Artificial Neural networks. They are motivated by the human brain model. In the human brain, we will have interconnected neurons, in the same way in ANNs(Artificial Neural Networks), we will have interconnected nodes in the structure of hidden layers.   

The nodes present in the inner layers will study the given input, and the input traverses through other hidden layers, and the output layer forecasts the output. The output layer may give the required output.

2. Multi-Layer Perceptron

It is the easiest form of ANNs. It contains one input layer, multiple hidden layers, and lastly an output layer. In Multi-layer Perceptron, one hidden layer will process some part of the input, and it will transmit that to the other hidden layer. Each hidden layer contains single or multiple neurons. The final hidden layer transmits the data to the output layer. The output layer gives the required output.

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3. Convolution Neural Network

It is a well-known Artificial Neural network. It is hugely used for the purpose of video and image recognition. It is founded on a mathematical concept called “convolution”. It is identical to the Multilayer Perceptron. The main layers of Convolution Neural Network are as follows:

  • Convolution Layer: It is a basic element and carries out arithmetic operations according to the convolution function.
  • Pooling Layer: It is organized beside the convolution layer. It is used to reduce the scale of the input by eliminating redundant information. 
  • Fully Connected Layer: It is organized beside the pooling and convolution layer and categorizes data into different categories.

4. Recurrent Neural Network

It is used to detect the defects present in the other ANN Models. The main job of RNN is to save the past data and decisions. This method is mainly used in image categorization. The RNN bidirectional is useful to anticipate the future according to the past.

Modules in Keras

The Existing Models of Keras are as follows:

  • Constraints: This module is used to set various functions to limit the layer. Some constraint Functions are: 1) NonVeg 2) UnitNorm 3) MaxNorm.
  • Callback: It gives a callback function list. It is used at the time of the training process to print the intermediate data, according to the given condition.
  • Utilities: In deep learning, it is helpful in providing many utility functions.
  • Text Processing: It gives functions for converting text into Numpy arrays, and this is useful for machine learning.
  •  Image Processing: It works the same as how Text Processing. Text Processing       Function is used in the Machine Learning’s Preparation Phase.
  • Sequence Processing: It is used to create time-based data from the input data.
  • Initialiser: To assign the initial weight to the given data, we use the initializer model.
  • Regularizer: Regularizer Module is helpful in the phase of optimization. The main job of a Regularizer is to fix the penalties on the layer.

Keras Architecture

The API of Keras is distributed into three primary categories:

  • Model
  • Layer
  • Core Modules

In Keras, each ANN is indicated using Keras Models.

Keras Architecture

The above diagram represents the Keras Architecture.

Keras Models are of two types, they are:

1. Sequential Model

    It is a sequential arrangement of Keras Layers. The sequential model can depict all the existing neural networks.

2. Functional API

    It helps to create complex models.

Layers

Each Keras layer present in the Keras model depicts the respective layer present in the real neural network model. The essential Keras Layers are as follows:

  • Pooling layers
  • Convolution Layers
  • Core Layers
  • Recurrent Layers.

Core Modules

Keras Provides some neural network functions; they are as follows:

  • Activations Module: It gives many activation functions like relu, softmax, etc.
  • Optimizer Module: It gives optimizer functions like sgd, adm, etc.
  • Regularizers: L 1 Regularizer and L 2 Regularizer functions are provided by Regularizers.
  • Loss Module: It gives loss functions like Poisson, mean_absolute_error, etc.

Time Sequence Prediction through LSTM RNN

The sequence is a collection of values, and every value represents a specific instance of time. For example, if we are reading a sentence, we have to understand every word and the meaning of the word in a given perspective. In this scenario, values represent the words, the first value represents the first word, the second value represents the second word, and this order continues until the last word.

LSTM

The features of the above model are as follows:

  • The input layer of this model contains 128 features
  • The first layer is dense and contains 128 units with recurrent dropout and normal dropout fixed to 0.2.
  • The output layer is also dense and contains one unit and an activation function called ‘Sigmoid”.
  • Adam is used as an optimizer.
  • Accuracy is used as a metric.
  • 32 is used as a batch size.
  • 15 is used as epochs.

Convolution Neural Network

The Convolution Neural Network is depicted as follows:

Convolution Neural Network

The important features of this model are as follows:

  • Input Layer Contains (1,8,28) values.
  • First Layer Contains “relu” activation functions and 32 filters.
  • The second layer contains 64 filters and “relu” activation functions.
  • Third Layer is Known as MaxPooling.
  • The fifth layer is used to flatten the complete input data into one dimension.
  • The sixth Layer is Known as “Dense” and contains “relu” activation function and 128 neurons.
  • The seventh Layer is Known as “Dropout”, and its value is “0.5.”
  • Last and Eight Layer Contains “softmax” neuron function and ten neurons.
  • Accuracy is used for metrics.
  • Adadelta() is used as an optimizer.

Model Evolution and Model Prediction

Model Evolution

At the time of development of the model, Evolution is a process to check the model is suitable for the problem and the respective data. To do the evolution process, Keras has a function. The three arguments of the function are as follows:

  • Test data Label
  • Verbose- false or true
  • Test data

Model Prediction

The final step in the Keras Model Development is “ Model Prediction”. To make Model Prediction, Keras gives a method called “predict” and it predicts the trained model.

Keras VS TensorFlow

Criteria Keras TensorFlow
Objective It is used for developing conventional Layers It is used for developing model layers or calculation tasks.
Tools It will use API tools like TFDBG It will use Tensorboard Visualization tools
Difficulty If you have knowledge of Python, we can use Python easily For using TensorFlow, we need to learn the syntax of some TensorFlow Functions.
Type It is High-Level Wrapper It is Low-level API
Community It has many active communities It has many active communities

Keras Applications

The Keras Applications is used for developing pre-trained models for the purpose of deep neural networks. Keras Models are used for fine-tuning, prediction, and feature extraction.

Pre-trained Models

Trained Models contains two modules: Model weights and Model architecture. Model weights are big files. Therefore you have to download it, and the feature should be 

Extracted from the ImageNet database. The famous Pre-trained Models are as follows:

  • InceptionV3
  • VGG16
  • ResNet
  • MobileNet

Conclusion

Keras is considered as a framework used in deep learning to analyze the given input and develop the Deep Learning Models. It is built on libraries like Theano, Caffe, TensorFlow, Caffe, etc. It is more helpful in the image and video recognition process. As the requirement of machine learning is increasing, the demand for Keras framework and Deep Learning is also increasing. So, the professionals who are working with Machine learning must have knowledge on Keras framework.