Keras, TensorFlow and PyTorch are among the top three frameworks that are preferred by Data Scientists as well as beginners in the field of Deep Learning.This comparison on Keras vs TensorFlow vs PyTorch will provide you with a crisp knowledge about the top Deep Learning Frameworks and help you find out which one is suitable for you. In this post you will get a complete insight into the above three frameworks in the following sequence:

  • Introduction to Keras, TensorFlow & PyTorch
  • Comparison Factors
  • Final Verdict

Introduction to Keras, TensorFlow & PyTorch


Keras is an open source neural network library written in Python. It is capable of running on top of TensorFlow. It is designed to enable fast experimentation with deep neural networks


TensorFlow is an open-source software library for dataflow programming across a range of tasks. It is a symbolic math library that is used for Machine Learning applications like neural networks.


PyTorch is an open source machine learning library for Python, based on Torch. It is used for applications such as natural language processing and was developed by Facebook’s AI research group.

Comparison Factors

All the three frameworks are related to each other and also have certain basic differences that distinguishes them from one another.

So lets have a look at the parameters that distinguish them:

  • Level of API
  • Speed
  • Architecture
  • Debugging
  • Dataset
  • Popularity

Level of API

Keras is a high-level API capable of running on top of TensorFlow, CNTK and Theano. It has gained favor for its ease of use and syntactic simplicity, facilitating fast development.

TensorFlow is a framework that provides both high and low level APIs. Pytorch, on the other hand, is a lower-level API focused on direct work with array expressions. It has gained immense interest in the last year, becoming a preferred solution for academic research, and applications of deep learning requiring optimizing custom expressions.


The performance is comparatively slower in Keras whereas Tensorflow and PyTorch provide a similar pace which is fast and suitable for high performance.


Keras has a simple architecture. It is more readable and concise . Tensorflow on the other hand is not very easy to use even though it provides Keras as a framework that makes work easier. PyTorch has a complex architecture and the readability is less when compared to Keras.


In Keras, there is usually very less frequent need to debug simple networks. But in case of Tensorflow, it is quite difficult to perform debugging. Pytorch on the other hand has better debugging capabilities as compared to the other two.


Keras is usually used for small datasets as it is comparitively slower. On the other hand, TensorFlow and PyTorch are used for high performance models and largedatasets that require fast execution.


With the increasing demand in the field of Data Science, there has been an enormous growth of Deep learning technology in the industry. With this, all the three frameworks have gained quite a lot of popularity. Keras tops the list followed by TensorFlow and PyTorch. It has gained immense popularity due to its simplicity when compared to the other two.

These were the parameters that distinguish all the three frameworks but there is no absolute answer to which one is better. The choice ultimately comes down to 

  • Technical background
  • Requirements and
  • Ease of Use

Final Verdict

Now coming to the final verdict of Keras vs TensorFlow vs PyTorch let’s have a look at the situations that are most preferable for each one of these three Deep Learning Frameworks

Keras is most suitable for:

  • Rapid Prototyping
  • Small Dataset
  • Multiple back-end support

TensorFlow is most suitable for:

PyTorch is most suitable for:

  • Flexibility
  • Short Training Duration
  • Debugging capabilities

Now with this, we come to an end of this comparison on Keras vs TensorFlow vs PyTorch. I Hope you guys enjoyed this article and understood which Deep Learning Framework is most suitable for you.

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#machine-learning #tensorflow #python #deep-learning #data-science

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