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TensorFlow vs Keras [Which One Should You Choose]

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14th Jul, 2020
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TensorFlow vs Keras [Which One Should You Choose]

Keras VS TensorFlow is easily one of the most popular topics among ML enthusiasts. Both of these libraries are prevalent among machine learning and deep learning professionals. Many times, people get confused as to which one they should choose for a particular project. 

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However, it would be best if you didn’t worry because in this article we’ll find out the difference between Keras and TensorFlow in detail. Let’s dive in:

What is Keras?

Keras is a Python-based API for deep neural networks. It simplifies building neural network models and is a high-level API. Keras also supports numerous back-end engines for neural network computations. 

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The focus of Keras is to follow best practices to reduce cognitive load. With Keras, you can create new models by combining multiple standalone modules such as optimizers, activation functions, neural layers, regularization schemes as well as cost functions. 

It runs on top of CNTK, Theano, and TensorFlow, which allows it to offer multiple advantages to developers. 

Advantages of Keras

Keras offers the following benefits to its users:

User-focused:

Learning Keras is straightforward because of its simple syntax, and Apart from that, it has simplified model building, so you don’t have to put much effort in that regard. Its interface is very user-friendly so learning its operation becomes very easy as well. 

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Easy Extension:

You can create custom building blocks for your ongoing projects by using Keras, which is another prominent advantage of this library. 

Composable and Modular:

To build a Keras model, you have to connect different building blocks. This concept simplifies working with the Keras much more uncomplicated and makes it more composable and modular. You get to work with enhanced efficiency and fewer restrictions. 

Simple:

It has multiple consistent APIs which reduce the necessary user actions for fundamental use cases. Keras has APIs to offer much-needed feedback to the user too, which alerts you if you make an error. This makes debugging the code much more comfortable and faster while reducing the possibility of technical errors substantially. 

What is TensorFlow?

TensorFlow is an open-source library for machine learning. It allows you to work on machine learning with more speed and efficiency. It’s a product of the Google Brain Team which had created it primarily to accelerate research and prototyping. However, since its inception, TensorFlow has become a crucial tool to enhance research prototypes and deploy machine learning productions faster. 

It provides an accessible front-end API by using Python so you can build applications quickly. To deliver high performance, it uses C++ to execute those applications. TensorFlow can train and run neural networks for word embeddings, digit classification, RNNs (recurrent neural networks), image recognition, NLP (natural language processing), and other prominent ML applications.

Advantages of TensorFlow

TensorFlow offers the following benefits:

Robust Experimentation:

TensorFlow has multiple features and functionalities for robust experimentation, which you would need to perform during research prototyping. The availability of different APIs such as Model Subclassing API and the Keras Functional API add more power to its experimentation capabilities. 

Simplified Model Building:

As TensorFlow provides you with various abstraction levels to create and train models, these tasks become much easier and uncomplicated. You don’t have to focus on the specific details of implementing an ML algorithm while working with TensorFlow, and it will take care of all that. 

Enhanced Accessibility:

TensorFlow allows you to train and deploy your machine learning model on any platform while using any programming language. You can choose from Java, Python, R, and many prominent programming languages, which make it more accessible for ML programmers. 

Easier Deployment:

Google has added multiple features to TensorFlow to enhance its deployment. For example, TensorFlow has an online hub where people can share models that they created with TensorFlow. It has mobile-friendly and in-browser versions as well, so you can use it through different devices. 

Keras, on the other hand, is limited to Python. 

Keras VS TensorFlow: Which one should you choose?

Choosing one of these two is challenging. However, you should note that since the release of TensorFlow 2.0, Keras has become a part of TensorFlow. So, the issue of choosing one is no longer that prominent as it used to before 2017. 

This also means that Keras can provide you with the advantages of using TensorFlow along with its original ones. The same is the case with TensorFlow. 

However, the primary difference between the two is their focus. TensorFlow focuses on machine learning tasks, whereas Keras focuses primarily on neural networks. Keras has an advantage over TensorFlow because it’s based in Python. Python makes Keras much user-friendly as we’ve discussed previously. 

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A common advantage of both of these libraries is accessibility. You can use Keras (or TensorFlow) and deploy your model on-premise, in the cloud, or through your web browser. 

Know more: The What’s What of Keras and TensorFlow

Final Thoughts

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We’ve reached the end of our discussion on Keras VS TensorFlow. Choosing one among these two can be challenging in some cases, while in others, it might not even be necessary. It would be best if you always chose a library according to your project requirements. Both Keras and TensorFlow offer a ton of advantages to their users, so you must have a general understanding of which benefits you require for a particular task.

If you want to learn more about TensorFlow, here are the most popular TensorFlow projects

If you’re interested to learn more about machine learning, check out IIIT-B & upGrad’s PG Diploma in Machine Learning & AI which 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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Pavan Vadapalli

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Director of Engineering @ upGrad. Motivated to leverage technology to solve problems. Seasoned leader for startups and fast moving orgs. Working on solving problems of scale and long term technology strategy.
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