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Difference between TensorFlow and Keras

TensorFlow vs Keras

TensorFlow and Keras both are the top frameworks that are preferred by Data Scientists and beginners in the field of Deep Learning. This comparison of TensorFlow and PyTorch will provide us with a crisp knowledge about the top Deep Learning Frameworks and help us find out what is suitable for us.

TensorFlow is an open-source software library used for dataflow programming beyond a range of tasks. It is a math library that is used for machine learning applications like neural networks.

Keras is an open-source neural network library written in Python. It can run on top of TensorFlow. It is defined to enable fast experimentation with deep neural networks.

Comparison b/w both frameworks

All three frameworks are internally related to each other and have some fundamental differences that distinguish them from one another.

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

Origin

TensorFlow library is developed by the Google brain team and free software library. And this library is open source in nature. And Keras is a minimalist Python library for deep learning which can run on top of Theano or TensorFlow and developed by Francois Chollet, a Google engineer using four guidelines principles: Modularity, Minimalism, Extensibility, and Python.

Speed

The performance is approximately lower in Keras, whereas TensorFlow and Pytorch provide a similar pace, which is fast and suitable for high performance.

TensorFlow vs Keras

Level of API

Keras is a high-level API able to run on the top of TensorFlow, CNTK, and Theano. It has gained support for its ease of use and syntactic simplicity, facilitating fast development.

TensorFlow is a framework that provides both high and low-level APIs. But Pytorch, on the other hand, is a lower-level API focused on direct work with array expressions.

TensorFlow vs Keras

Architecture

Keras has pure architecture. It is more readable and concise. TensorFlow, on the other hand, is not easy to use, although it provides Keras as a framework that makes work easier.

TensorFlow vs Keras

Debugging

There is usually very little need to debug simple networks in Keras. But in the case of TensorFlow, it is tricky to perform the debugging. PyTorch has better debugging capabilities as compared to the other two.

TensorFlow vs Keras

Dataset

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

TensorFlow vs Keras

Popularity

With the ascending demand in the field of Data Science, there has been a big growth of Deep learning in the industry. With this, all three frameworks have gained a lot of popularity. Keras is top in the list, followed by TensorFlow and PyTorch. It had gained immense popularity due to its simplicity when compared to the other two.

TensorFlow vs Keras

APIs

Keras library has a very high-level API, which could run on CNTK and Theano, but the TensorFlow library has both low-level and high-level APIs.

Keras is most suitable for:

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

TensorFlow is most suitable for:

  • Large Dataset
  • High Performance
  • Functionality
  • Object detection





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