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Last Updated: Mar 27, 2024

TensorFlow vs. PyTorch vs. Keras

Author Rajkeshav
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Master Python: Predicting weather forecasts
Ashwin Goyal
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Machine learning is a set of computer algorithms that learns the pattern from the data and evolves on its own as time passes. Deep learning is popular due to its reliability in terms of accuracy. Whether we want to start applying it to the business based on the next side project or gain marketable skills, picking the proper deep learning framework to learn is an essential step towards reaching the goal. The three most popular frameworks for deep learning are Keras, TensorFlow, and PyTorch. 

Also Read, Resnet 50 Architecture




  1. Keras is an open-source neural network library written in Python. Keras can run on top of the TensorFlow Microsoft cognitive toolkit or Theano. It enables fast experimentation with deep neural networks. It also focuses on being user-friendly, modular, and extensible.
  2. Keras has a high-level API capable of running on top of TensorFlow and Theano. It has gained favor for its syntactic simplicity facilitating fast development.
  3. The Speed of Keras is slower as compared to TensorFlow and PyTorch. 
  4. Kears has simple architecture. It is more readable and concise; It is simple and easy to use, so most beginners prefer to use Keras compared to the other two.
  5. There is a single line of code for implementing it, which makes it a preferable framework for programmers.
  6. In Keras, there is significantly less frequent need to debug simple networks, and it offers a more direct unconvoluted debugging experience regardless of model complexity.


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  1. Tensorflow is an open-source software library for dataflow programming across various tasks. It is also a symbolic Math library used for Machine learning applications such as neural networks. 
  2. TensorFlow provides both high and low-level API. 
  3. The speed of TensorFlow is high as compared to Keras.
  4. TensorFlow is straightforward and has a complex architecture that might not be very helpful for beginners.
  5. TensorFlow provides a reduced size model along with high accuracy.
  6.  In Tensorflow, it isn't easy to perform debugging.





  1. PyTorch is an open-source Machine learning library for Python based on Torch and is used for applications such as natural language processing. Facebook's AI research group primarily develops it, and also, uber Spyro software for probabilistic programming is built on it.
  2. PyTorch provides a low-level API that is focused on direct work with Eddy expressions.  
  3. PyTorch speed is equivalent to that of TensorFlow. PyTorch has a very complex architecture, and also the readability is less when compared to Keras. 
  4. PyTorch consists of more lines in the code, and it is not so simple compared to the other two.
  5. PyTorch has better debugging capabilities than the other two. It has fewer opportunities to go wrong, but it is challenging to ping down the exact line that causes the trouble if something goes wrong.


Frequently Asked Questions

1) What is the suitable situation for using Keras?

=> Keras is ideal in the case of-

  • Rapid Prototyping 
  • Small-sized datasets
  • Best for newbies as it is simple and easy to understand


2)  What is the suitable situation for using TensorFlow?

=> TensorFlow is ideal in the case of-

  • Large datasets
  • High performance


3)  What is the suitable situation for using PyTorch?

=> PyTorch is ideal in the case of-

  • Flexibility
  • Training duration
  • Debugging capabilities


4) Discuss the community support of all three frameworks?

  • Keras has got minor community support when it comes to troubleshooting any error.
  • TensorFlow has got strong community support.
  • PyTorch has strong community support.


5) List out some of the pre-trained models in Keras?

=>Some available pre-trained models are:

  • VGG16
  • VGG19
  • InceptionV3
  • MobileNet
  • Resnet50, etc.


Key Takeaways

The blog taught some significant differences between all three deep learning frameworks. Must visit (TensorFlowKerasPyTorch) for more complex applications.

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