We can’t take away the importance and usefulness of frameworks to data scientists.

Frameworks, on the other hand, are defined as sets of packages and libraries that play a crucial role in making easy the overall programming experience to develop a certain type of application.

Frameworks like Keras and Tensorflow are key in data science just the way they are for deep machine learning.

In this article, Keras vs Tensorflow we will open your mind to top Deep Learning Frameworks and assist you in discovering the best for you.

We have pointed out some very few important points here to help you out as you select.

Keras vs Tensorflow

Keras is known as a high-level neural network that is known to be run on TensorFlow, CNTK, and Theano.


An interesting thing about Keras is that you are able to quickly and efficiently use it to prototype in deep learning. Python language is what they used in writing Keras.

Some key features of Keras are explained below:

  • Extendability: Keras enables you to write custom building blocks for new ideas and researches.
  • Usability: APIs under Keras is more or less constant and simple – This APIs help in reducing user actions needed for common use cases. Based on user error, it offers understandable and actionable feedback.
  • User-Friendly: A simple, consistent interface of Keras improves your productivity with it.
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Keras vs Tensorflow

Tensorflow, on the other hand, is an end-to-end open-source platform for machine learning. It contains tools, libraries and other resources that make workflows possible with high-level APIs.

For developing and deploying machine learning models, Tensorflow provides users with many levels of concepts for them to choose the one they need.

Some key features of TensorFlow are explained below:

  • Powerful Experimentation For Research: With Tensorflow, you enjoy flexibility and control on certain features like the Keras Functional API and Model Subclassing API to develop advanced topologies.
  • Relatively easy Model building: You are provided with different levels of abstraction by TensorFlow for developing and training models.
  • Rich Machine Learning Everywhere: Tensorflow is not sensitive about the programming language or platform you plan using on, it allows you to develop and deploy your model with comfort without stress.
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We will leave our comparison exclusively between Keras and TensorFlow alone in this article. Both learning frameworks will be compared based on:

Speed

The performance in Keras is relatively slower compared to that of TensorFlow. TensorFlow moves at a fast pace that supports high performance.

Architecture

The obvious simple architecture in Keras makes users always find it very readable and succinct. It’s a fact that users find TensorFlow a little bit difficult to use even though it offers Keras as a framework which we all know makes work simple and faster.

Debugging

You don’t need to always debug simple networks in Keras. Users most certainly find debugging in TensorFlow not easy.

Popularity

Data Science is day by day growing and becoming popular among users in the world today. This popularity has led to an enormous growth of Deep machine learning technology. The two frameworks are increasingly becoming widely common but Keras has always been more popular than TensorFlow recently.

Level of API

TensorFlow offers both high and low APIs while Keras, on the other hand, is just a high API that’s run on TensorFlow. Keras’ increasing development, simple syntax, and easy usability make lots of users prefer it.

DataSet

Users don’t use Keras for large datasets because it’s relatively slower than TensorFlow.

TensorFlow, on the other hand, is only considered for high datasets that need hasten execution and also for high-performance models.

Conclusion

As stated in the article, Keras is known as a wrapper to the TensorFlow framework. Both got advantages and disadvantages as a careful study of both of them shows this. Now that we all know their differences it’s left to a researcher to select the framework that best suits what he/she wants to do.