What is TensorFlow? Architecture, Algorithms, Applications And More

Overview

Machine learning is a technical discipline. But it is difficult to incorporate a model of machine learning than it used to be. In the Machine learning frameworks like google that eases the process of retrieving data, training model, refining future results and surfing prediction.

In Computer Vision, developing accurate machine learning models capable of finding and recognizing multiple objects in a single image remains a key challenge. The TensorFlow Object Detection API is an open-source framework simplifying the deployment and creation of training and object detection models.

What is TensorFlow?

The Google Brain team developed TensorFlow for internal use on Google, during 9 November 2015 under the Apache License 2.0

TensorFlow gives TensorFlow an edge over other competitors is the fact that it is open source and has huge community support, that not only provides researchers to build a new model but also a platform to interact with others that face some issues.

TensorFlow is the standard way of representing data in deep learning. Tensor is a vector or matrics of n dimension that represents datatype or operation connected in a graph and the graph is said computation that takes successively.

Companies using TensorFlow 

• Airbnb: Firstly, Airbnb engineers use machine learning using TensorFlow to classify images and to detect object and scale helping to improve the guest experience
• GEHealthcare:  When it comes to the health care industry using TensorFlow GE Healthcare is training a neural network to identify specific anatomy during the brain emirates to improve speed and reliability.
• PayPal: Moreover, PayPal is using TensorFlow to cut the edge of fraud detection. Using it generates modeling and able to recognize complex fraud patterns for increasing accuracy by the increasing experience of users through an increase in precision in identification.
• ChinaMobile: While China Mobile built a deep learning framework using TensorFlow which can automatically predict a time period, verify log operations and detect anomalies in the network Features of Tensor flow allows multiple levels of abstraction and choose the right one of your needs.

Many other companies also using TensorFlow.

Features of TensorFlow

• At first, Tensor flow allows multiple levels of abstraction and choose the right one of your needs.
• TensorFlow gives you flexibility and control with features like Keras API and models of classing API for creating complex logic.
• If we need more flexibility Eager execution allowed immediate iteration and debugging.
• It provides a direct path for protection even they are on servers or on the web.
• TensorFlow gives you flexibility and control with features like Keras API and models of classing API for creating complex logic.
• Tensor Flow supports the ecosystem and moreover they provide many experiments with libraries and models.

Architecture of TensorFlow

In Tensorflow Architecture, Serviceable terms are TensorFlow Types, TensorFlow Models, Loaders, Sources, Manager and Core.

The term and its functionality in TensorFlow architecture are below.

• TensorFlow Servable are the key unfinished units operating in TensorFlow and Servables are the objects the clients uses for computation.
• TensorFlow server is able to handle one or more versions of the servables over the lifetime of any single application event.
• Servable streams are a series of versions of any serviceable sorting using rising numbers.
• TensorFlow model contains one or more algorithms and the embedding tables.
• TensorFlow Loaders control the life cycle of a serviceable.
• Detection and serving of Sources in the architecture of TensorFlow.
• TensorFlow managers handle the entire service life cycle including Unloading Services
• TensorFlow managers handle the entire service life cycle including Unloading Services TensorFlow core manages the following: TensorFlow Lifecycle, Metrics, and representing core satisfaction servables and loaders as the opaque objects.
  

Technical TensorFlow Architecture

• Sources build loaders for Servable Versions, and then loaders are sent to the Manager as Aspired versions, who will load and service them on requests from clients.
• The Loader includes metadata, and the serviceable must be loaded.
• The source uses a callback to forward the Aspired version manager.
• To assess the next action, the Director applies the appropriate version policy.
• When the manager decides that it requires the loader to load a new version, customers ask the manager for the servable, and specifically define a version, or request the current version.

List of Prominent Algorithms that TensorFlow supports

• Linear regression: tf.estimator.LinearRegressor Classification: 
• Classification:tf.estimator. LinearClassifier 
• Deep learning classification: tf.estimator.DNNClassifier 
• Deep and deep learning wipe:tf.estimator.DNLinearCombinedClassifier 
• Booster tree regression:tf.estimator.BoostedTreesRegressor
• Boosted tree classification: tf.estimator.BoostedTreesClassifier

Object Detection using TensorFlow

In this paragraph will discuss Object Detection using TensorFlow. A set of images is given to the model and using the TensorFlow we trained our model.

Training of the model is done using deep learning and the main objective is to extract features and now these are visual features that are based on facial detection and many more. Features are being extracted and then created a model.

When these features are extracted and then these models are created to test the model we give test data which is again images and using this model we get final output in which object detected can be in the set of images.

And using this we get our final output in which our we have object detected in the image. The image converting in a NumPy array in a tensor flow object detection, which make easy understanding and computation.

What is Object Detection?

Object detection is a computer technology related to computer vision and image processing that deals with detecting a certain type of semi-conduct object.

Detection of objects allows multiple objects with an image for recognization, detection, and localization. As a result, it gives us a much better picture of an image as a whole.

The Object Detection algorithm uses extractive features and learning algorithm models to recognize instances of an object category. Besides, it adds to applications like image retrieval, security services, and advanced driver assistance systems

History Of Object Detection 

• Firstly, in 2001, the first efficient facial detection algorithm came out which was invented by Paul Viola &Michael Jones.
• Secondly, after 4 years, HOG for pedestrian detection had been a new discovery by Navneet Dalal and Bil Triggs. The goal of this algorithm is how dark is the current pixel similar to that of the surrounded pixel.
• Thirdly, in 2012, deep learning became a golden standard for image classification. We can take any classifier we can slide the classifier over every single part of the image, so we can parse through every single pixel or region and get a bunch of classification.
• Lastly, Convolution NeuralNetwork came after two years, where R-CNN (Region with CNN features ) creates bounding boxes or regions using a process called Selective Search.

Real life Example

Estimation of  Parking Time Using TensorFlow

TensorFlow Object plays a vital role in estimating the cost. The model detects the vehicle and saves the number plate using object detection.

When it finds the vehicle saves the number plate and records the time when it enters for parking. So when the car leaves from the parking spot it checks for the number plate and time which it has been saved when the car entered for parking and records the exit time.

By getting the entering time and exit time will get the parking time taken by the vehicle. As a result they can estimate the cost of parking. 

There are mainly 3 steps in Object Detection :

• Firstly, we had the trained data which input and created our model. Upon extracting the features we have a classification model created. For checking the model we use test data.
• Next step we use testing data and extract all the features and move to model to see which all features are matching our class in the model used.

Conclusion

To conclude, TensorFlow was a library that programmed the dataflow at its core. We’ll use the TensorFlow Python API, operating with Python 2.7 and Python 3.3 +.

Over time, TensorFlow has increased in popularity and developers now use deep learning methods for image recognition, video detection, word processing, such as sentiment analysis, and so on to solve problems.
Alternatively, using documentation, then helping the group to represent issues such as data graphs and solving them with TensorFlow would make machine learning smaller

Each graph node represents an instance of a mathematical operation, and each edge is a multi-dimensional data set each performs the operations on. In TensorFlow, all the computations are clear using data flow graphs.

Read Next: Reinforcement Learning