If you’ve been following the tech scene closely (or even remotely, for that matter), you must have heard the term “Deep Learning”. It’s a much widely talked about the term – and rightly so.
Deep learning has revolutionized artificial intelligence by helping us build machines and systems that were only dreamt of in the past. In true essence, Deep Learning is a sub-sect of Machine Learning that uses deep artificial neural networks (at this point, if you’re confused by what Neural Networks are, do check out our article on the same) to tackle the problems of Machine Learning.
A Deep Neural Network is just a Neural Network with many layers stacked on top of each other – greater the number of layers, deeper the network. The growing need for Deep Learning, and, consequently, training of Deep Neural Networks gave rise to a number of libraries and frameworks dedicated to Deep Learning.
In this blog post, we are going to talk about two of such Deep Learning frameworks. By the end of this blog post, you’ll have a much clearer understanding of what is Keras, what is TensorFlow, how the two differ, and are the two similar in any aspect?
But before that, we should briefly discuss the two, so that you know what you’re in for. Tensorflow is the most used library used in development of Deep Learning models. The community of TensorFlow is extremely vast and supportive, especially because it’s an open-sourced platform. The number of commits and forks on the GitHub repository of TensorFlow are enough to let you understand the widespread popularity of the framework. However, it is not that easy to work with.
Keras, on the other end, is a high-level API that is built on top of TensorFlow. It is extremely user-friendly and comparatively easier than TensorFlow.
Reading the above section might raise a few questions:
- If Keras is built on top of TF, what’s the difference between the two then?
- If Keras is more user-friendly, why should I ever use TF for building deep learning models?
Through this article, let’s walk you through the intricacies of both frameworks and help you answer the questions.
In this article, we’ll be talking about two of the many libraries and frameworks – TensorFlow and Keras.
What is TensorFlow?
TensorFlow is Google’s gift to the developers involved in Machine Learning. It makes the power of Deep Learning accessible to the people in pursuit. Google has a beginner as well as an advanced tutorial which introduce you to both ML and TF concurrently to solve a multi-feature problem — character recognition. Further, if you want to dive into even more technical aspects of it, we suggest you check out our courses on the same!
TensorFlow is an open-sourced library that’s available on GitHub. It is one of the more famous libraries when it comes to dealing with Deep Neural Networks. The primary reason behind the popularity of TensorFlow is the sheer ease of building and deploying applications using TensorFlow. The sample projects provided in the GitHub repository are not only powerful but also written in a beginner-friendly way.
So, what is TensorFlow used for?
TensorFlow excels at numerical computing, which is critical for deep learning. It provides APIs in most major languages and environments needed for deep learning projects: Python, C, C++, Rust, Haskell, Go, Java, Android, IoS, Mac OS, Windows, Linux, and Raspberry Pi.
Moreover, TensorFlow was created keeping the processing power limitations in mind. Implying, we can run this library on all kinds of computers, irrespective of their processing powers. It can even be run on a smartphone (yes, even that overpriced thing you’re holding with a bitten apple on it).
TensorFlow is currently in v1.3 and runs on almost all major platforms used today, from mobiles to desktops, to embedded devices, to specialized workstations, to distributed clusters of servers on the cloud or on-premise. This pervasiveness, openness, and large community have pushed TensorFlow into the enterprise for solving real-world applications such as analyzing images, generating data, natural language processing, intelligent chatbots, robotics, and more.
Interestingly, TensorFlow is being used by a wide array of coders to implement language translation and even early detection of skin cancer among other cases. It is truly changing the way developers are interacting with machine learning technology.
When it comes to Deep Learning, TensorFlow has gained much more momentum that its competitors – Caffe, Theano, Torch, and other well-known frameworks. TensorFlow is extensively used in voice recognition, text-based applications like Google Translate, image recognition, and Video Detection.
Interestingly enough, NASA is developing a predictive model of Near Earth Objects with TensorFlow and Deep Learning. According to the people at NASA, TensorFlow can help design a multilayer model that will be able to recognize and classify the potential of NEOs. TensorFlow is used by some of the biggest data companies in the world – the likes of Airbnb, Airbus, Dropbox, Snapchat, and Uber.
Some of the major applications of TensorFlow are:
Natural Language Processing
- Tensorflow has been successfully implemented in DeepDream – the automated image captioning software – uses TensorFlow.
- Google’s RankBrain, backed by TensorFlow, handles a substantial number of queries every minute and has effectively replaced the traditional static algorithm-based search.
- If you’ve used the Allo application, you must’ve seen a feature similar to Google’s Inbox – you can reply to the last message from a few customized options. All thanks to Machine Learning with TensorFlow. Another feature analyses the images sent to you in order to suggest a relevant response.
What is Keras?
Keras is a high-level library that’s built on top of Theano or TensorFlow. It provides a scikit-learn type API (written in Python) for building Neural Networks. Developers can use Keras to quickly build neural networks without worrying about the mathematical aspects of tensor algebra, numerical techniques, and optimization methods.
The key idea behind the development of Keras is to facilitate experimentations by fast prototyping. The ability to go from an idea to result with the least possible delay is key to good research.
This offers a huge advantage for scientists and beginner developers alike because they can dive right into Deep Learning without getting their hands dirty with low-level computations. The rise in the demand for Deep Learning has resulted in the rise in demand for people skilled in Deep Learning.
Every organization is trying to incorporate Deep Learning in one way or another, and Keras offers a very easy to use as well as intuitive enough to understand API which essentially helps you test and build Deep Learning applications with least considerable efforts. This is good because Deep Learning research is such a hot topic right now and scientists need a tool to try out their ideas without wasting time on putting together a Neural Network model.
Salient Features of Keras
- Keras is a high-level interface and uses Theano or Tensorflow for its backend.
- It runs smoothly on both CPU and GPU.
- Keras supports almost all the models of a neural network – fully connected, convolutional, pooling, recurrent, embedding, etc. Furthermore, these models can be combined to build more complex models.
- Keras, being modular in nature, is incredibly expressive, flexible, and apt for innovative research.
- Keras is a completely Python-based framework, which makes it easy to debug and explore.
Keras vs TensorFlow: How do they compare?
Keras is a neural networks library written in Python that is high-level in nature – which makes it extremely simple and intuitive to use. It works as a wrapper to low-level libraries like TensorFlow or Theano high-level neural networks library, written in Python that works as a wrapper to TensorFlow or Theano. In that sense, the comparison doesn’t make much sense because Keras itself uses TensorFlow for the back-end.
But, if we must, we must.
Keras is very simple to understand and implement – using Keras is much like dealing with Lego blocks. It was built to help developers perform quick tests, POC’s, and experiments before going full scale. Keras allows you to use TensorFlow in the backend – eliminating the need to learn it.
Keras was developed with the objective of allowing people to write their own scripts without having to learn the backend in detail. After all, most of the users wouldn’t bother about the performance of scripts and the details of the algorithms.
However, one size does not fit all when it comes to Machine Learning applications – the proper difference between Keras and TensorFlow is that Keras won’t work if you need to make low-level changes to your model. For that, you need TensorFlow. Although difficult to understand, once you get a hold of the syntax, you’ll be building your models in no time.
So, like everything, it all boils down to your requirements at hand. If you’re looking to fiddle around with Deep Neural Networks or just want to build a prototype – Keras is your calling. However, if you’re the one that likes to dive deep and get control of the low-level functionalities, you should spend some time exploring TensorFlow.
The world is swiftly moving towards automation with Deep Learning taking control of everything. There’s no denying the fact that in the days to come, the use of Deep Neural Networks will only grow, and with that, the need for skilled people will grow, too. So, if you think Deep Learning is your calling, start by exploring either Keras or TensorFlow as soon as possible!
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