What is TensorFlow, exactly?
If you’re interested in machine learning and deep learning, then you’ve come to the right place! In this article, we’ll explore what TensorFlow is, how it works, and what it’s made of. Read on to learn it all!
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TensorFlow Explained
TensorFlow is a machine learning framework and a product of Google. It simplifies the tasks of model training, data acquisition, result refinement, and serving of predictions. It is an open-source deep-learning library, and Google uses it to empower their numerous technologies. TensorFlow makes many neural networking models and machine learning algorithms useful for computations and applications.
For its front-end API, it uses Python, and you can use it to build applications. To execute the same apps, you can use the popular C++ language. TensorFlow is capable of training and running deep learning technologies to develop word embedding, digit classification, RNNs (recurrent neural networks), and many others.
Google uses TensorFlow to enhance user experience and enhance its search feature. A great example of this is the autofill in the search bar of Google. Google has a vast dataset, and by using machine learning, they can enhance their users’ experience.
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TensorFlow Components
TensorFlow contains two primary components:
- Tensor
- Graphs
Tensor
Tensor is the main framework of Tensorflow. A tensor is a matrix or vector that has n-dimensions and represents all kinds of data. All the values present in a tensor possess identical data types with a shape, which is the dimensionality of the array or matrix.
A tensor could originate from input or as a result of some computation. And all the operations in TensorFlow take place in a graph. The name of these operations is op node. A tensor is made up of an edge and a node. While the node has the mathematical process, the edge explains the relationship between the nodes.
Graphs
TensorFlow is based on the graph framework. Its graph collects and explains the computations the system performs during the training session. The graph is quite portable and enables the preservation of calculations so you can use them whenever you require them. You can save the chart for future use as well.
The computations in a graph take place through connecting the tensors. Graphs can run on different GPUs and CPUs. You can run them on mobile systems as well.
Why is TensorFlow so Popular?
The creators of TensorFlow had created it to scale. Its accessibility is phenomenal because anyone can access it. Its library has various API that you can use to construct complex architectures such as Recurrent Neural Networks.
It uses graphs to visualize the development of neural networks for the developer. It does so through Tensorboard and helps the developer in debugging the program, deploying at scale, and building robust solutions. It is the most popular deep learning framework library on GitHub. So you’ve understood by now as to why it’s so popular.
How does TensorFlow Work?
You can build dataflow graphs by using TensorFlow. These graphs show how data goes through a series of nodes present in a graph. As we’ve mentioned earlier, these nodes represent mathematical operations, and the connections between these nodes are tensors.
You can use the Python language to use all of these facilities. Python is quite easy to learn and gives you simple methods to convey complex abstractions. The nodes and tensors of TensorFlow are Python objects, and its applications are Python applications as well.
But while you can use Python to work with nodes and tensors, you can’t use the same to perform mathematical operations. For that purpose, you’ll have to use C++. Python only handles the traffic between TensorFlow’s pieces. The transformations’ libraries in TensorFlow are C++ binaries, that’s why you’ll need to use C++ to work with them.
Running TensorFlow applications is easy and convenient. You can run them on the cloud, a local machine, or even on a smartphone. You can use Google’s TensorFlow Processing Unit, too, if you’re using it on the cloud. Recently, TensorFlow 2.0 entered the market, and it has simplified the user experience further by incorporating the latest solutions (such as the Keras API).
Supported Platforms
Let’s now focus on which platforms you would use to perform different operations with TensorFlow. We can classify its requirements into two sections:
Development
This is the section when you train a model. For this phase, you should use a laptop or desktop computer.
Inference
In this phase, you run TensorFlow on a platform. You can choose from various platforms such as mobile devices (Android or iOS), desktop PCs (Windows, Linux, or macOS), or on the cloud.
After training a model, you have the option to use it on a different machine. TensorFlow allows training on GPUs as well. Stanford’s researchers discovered in 2010 that GPUs are excellent for algebra and matrix operations. Since that discovery, GPU has become a tool for those functions as well. And TensorFlow is compatible with GPUs.
Read more: The What’s What of Keras and TensorFlow
Advantages of TensorFlow
Without mentioning the advantages of TensorFlow, we can’t answer the question, “What is TensorFlow?” properly.
Abstraction
Abstraction is the biggest advantage of TensorFlow. It allows you to solve your problem by focusing on the logic of the application. It removes the requirement of focusing on the minute details of an algorithm, or the method of producing an output from a particular input.
Convenient
TensorFlow has a very easy to use interface, which makes it a convenient tool for any developer. Particularly, it allows developers to debug their TensorFlow apps much easier. It has the eager execution mode through which you can check and edit every graph function transparently and separately. Otherwise, you would’ve needed to modify the entire graph as a single unit to modify or evaluate it properly.
Another excellent example of TensorFlow’s convenience is TensorBoard. It’s the visualization tool of TensorFlow that lets you inspect graphs through a web-based and interactive dashboard.
Google’s Backing
TensorFlow is a product of Google’s Brain Team, and that’s a major advantage in itself. Not only did that help TensorFlow in developing faster, but it also made it easier to deploy and use. Google’s TensorFlow Processing Unit silicon boosts its performance substantially and has provided it many capabilities.
One Drawback
TensorFlow surely has multiple features, but it also has a drawback. Its implementation makes the job of getting deterministic training results rather difficult. In some cases, a model you trained on one system would behave differently on another system even if you feed them the same data.
TensorFlow vs. Others
TensorFlow isn’t alone in the industry of machine learning frameworks. You also have PyTorch, Apache MXNet, and CNTK.
Pytorch is based on Python and is quite similar to TensorFlow. The former is excellent for small projects, whereas the latter is better suited for long projects. CNTK is the Microsoft Cognitive Toolkit and is ahead of TensorFlow in many areas (faster handling of neural networks, graph structures, etc.). Still, it is much more difficult to learn in comparison to TensorFlow.
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MXNet supports multiple language APIs, including Scala, Python, Perl, JavaScript, Go, and R. It is Amazon’s primary deep learning framework for AWS. The native APIs of MXNet is quite unpleasant to use when you compare them with the APIs of TensorFlow.
Also read: Tensorflow vs Pytorch – Comparison, Features & Applications
Final Thoughts
So, what is TensorFlow? We hope you found the answer in this article. If you’re interested in learning more about TensorFlow and its capabilities, then you can check out our blog. Here are some interesting TensorFlow project ideas to get you started.
You can also take a look at our machine learning courses and start your learning journey there. Our comprehensive courses will help you get rid of all your doubts and develop the skills necessary to become a machine learning expert. IIIT-B & upGrad’s PG Diploma in Machine Learning & AI is designed for working professionals and offers 450+ hours of rigorous training, 30+ case studies & assignments, IIIT-B Alumni status, 5+ practical hands-on capstone projects & job assistance with top firms.
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