All about TensorFlow


The word TensorFlow is derived from Tensor which means multidimensional arrays of data. Its computation is represented in the form of data flow graphs. It's an open-source and freely available artificial intelligence ( AI) based collection of information also called a library. It is constructed with the help of data flow graphs to build models. Specifically, its a mathematical function library. The main use of it is in the research and production area. It helps in machine learning and allied domains like neural networking. Tensorflow is accessible on mobiles may it be IOS or Android, on 64 bit Linux Windows and Mac OS systems too.

Tensorflow was launched under Apache licence 2.0 on November 9, 2015, by the Google brain team with the purpose of internal Google usage. Outstanding quality of TensorFlow being it can work on multiple CPUs and GPUs with CUDA and Sycl extensions. Kubeflow is a technology developed in 2017 by Google, Cisco, Caicloud and Redhat together to perform operations and employee TensorFlow on platform Kubernetes. Many more of such collaborations took place which helped in the growth and enlargement of TensorFlow. Some of them being machine learning in JavaScript, TensorFlow graphics involving deep learning, tensor processing unit, etc.

TPU is an application-specific integrated circuit which helps in assessing machine learning. It's an AI-based accelerator which can be programmed and used for providing High arithmetic outputs of less accuracy (for example 8 bits) and originates to run models and use them instead of training them. This leads to an optimisation in the performance of machine learning. The second-generation TPU performs up to 180 teraflops to 11.5 petaflops. While 3rd generation TPU delivers 420 teraflops with a high bandwidth memory (HBM) of 128 GB.

Tensorflow is a base to technological advancements like automated image captioning software like a deep dream. Tensorflow supports python thus making it easier to operate the mathematical functions. Machine learning is a complicated concept but its application and use are easier because of TensorFlow framework. It uses python for developing the front end & API to develop application Framework. Tensorflow also instructs and trains neural network with image recognition, handwritten number categorisation, embedding words, Natural language processing NLP, mathematical functions, predicting scales, etc related simulations.

When C++ and python are frontends. The API layers which are an interface between layers of deep learning performs its internal work. There are two types of layer: a lower level and higher-level layer. The higher-level layers are present over the former one. Keras and Estimator API are an illustration of such form of models.

TensorFlow Execution

Graphs: Tensorflow enables us to program code which is used to make graphs. These graphs make a data structure that explains all the functions and operations. This execution model of the graph can be run on multiple platforms.

Eager execution: This feature allows executing the program with the help of the kernel immediately, eliminating the prior need of building graph before execution. After ensuring the accuracy and correctness of the code graph can be automatically constructed later.

Tensorflow debugger which enables us to see the internal structure and working of graphs which helps in debugging codes and solve bugs in the model.

How TensorFlow works

Tensorflow permits to design data flow graph that explains the movement of data in a graph or between interconnected nodes, wherein each connection between them is a multidimensional array of data, also called as a tensor. Python language is responsible for providing all these to programmers. Node and tensor are objects of python.

Applications of TensorFlow are in a way applications of python but mathematical operations are not executed by python. Python only does the work of directing the traffic between nodes and availing high-level programming abstraction to bring together both python and C++. Tensorflow provides transformation library which is written in the form of C++ binary. Its subsidiary applications can easily be run on any target platform such as cluster, a machine, mobile phones etc. A new model that is TensorFlow lite helps to make models run on any platform.

Benefits of using TensorFlow

Abstraction in machine learning: Tensorflow ensures the quality of dealing with ideas and logic rather than focusing on details of the event like algorithm figuring. Thus, it helps in controlling all unseen parameters.

Tensorflow is a boon to programmers for debugging ( removing bugs) and sells analysis. A mode called as eager execution helps in evaluation and modification of graphs. Because of it, the need for constructing the whole graph as a single objective and then evaluating is eliminated instead each part of the graph can be evaluated and modified separately. Tensorboard visualisation performs the work of inspection and then figure out the manner in which the graph runs with the help of a web dashboard.

Challenges faced by TensorFlow

The model training is difficult to be provided for every data type and set, there are cases where a model trained on one system gives a different output when worked upon another system, even when they are provided with exactly same data. This is what called as slippery.

There are various machine learning frameworks and models emerging in the market like PyTorch, CNTK, etc that perform similar work as that of TensorFlow. Hence, an increase in competition.

Some of the latest models launched by Tensorflow are The object detection API, tf- seq 2 seq, Parsley Sauras, MultiStyle Pastiche Generator.

Future Aspects of TensorFlow

Tensorflow is working in the direction of making mobile-friendly apps by quantization and decreasing code print. Some of the other project being worked on includes Mobile Net (to accurately define speed), TF detects and many more. Tensorflow needs to curb the challenges being faced by it and continue to evolve with time and fulfil requirements of end-users.