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TensorFlow vs Keras vs PyTorch

  • by Mr. Bharani Kumar
  • July 20, 2020
  • 637

If you are a beginner choosing a framework for a deep learning model can be challenging. Many scientists, AI experts/researchers use TensorFlow as their first choice. But over the past few years, the two libraries have gained popularity due to some advantages over TensorFlow.

Let’s understand in detail about all these three libraries in different aspects. Before moving into the comparisons let's understand each library.

  • TensorFlow:

    It is an Open-source software library for programming different tasks. This is a symbolic math library used to solve machine learning and deep learning problems. TensorFlow is called a low-level programming API. It was created by google on 9th Nov 2015.

  • Keras:

    It is again an open-source library which is written in python. It is capable of running on top of Theano, CNTK, and TensorFlow. This is majorly used to perform fast experimentation with deep learning. Keras is called a high-level programming API. It was developed by Francois Chollet in 27th March 2015

  • PyTorch:

    It is an open-source machine learning library written in python which is based on the torch library. It was developed by Facebook’s research group in Oct 2016. In terms of high level vs low level, this falls somewhere in-between TensorFlow and Keras.

Here are some of the key comparisons:

The architecture of Keras is very simple and its readability is easy. Whereas the architecture of TensorFlow and PyTorch is a bit complex and the readability is poor.

  • Architecture

    Training a huge and complex model is the most time consuming and slow, so more weightage is given to the processing time and the fastness.

    TensorFlow and PyTorch win this race as they are low-level frameworks and are fast in terms of time and speed. So, it becomes really difficult to choose between these two. Whereas Keras is a high-level API, lags in these two features.

  • Speed

    Keras supports python with an R interface. TensorFlow supports python, JavaScript, C++, Go, Java, Swift, and PyTorch supports Python, C++, and Java.

  • Supported Languages

    Keras is very easy to understand and program. It was built for easy experimentation and prototyping of deep learning models. Whereas TensorFlow and PyTorch are nor beginner-friendly as they are not as easy as Keras to understand and write the code. Click here to learn Artificial Intelligence Course

  • Beginner Friendly

    It is easy to work with Keras but difficult to debug as it has several levels of abstraction and often difficult to debug whereas in TensorFlow it is even more difficult than Keras. Tensorflow has a debugging module that can be used to debug the errors. Pytorch is as simple as debugging errors in python. Any standard python debuggers can be used to solve the errors.

  • Debugging

    Since Keras is not good at speed and its time consuming, opting Keras for larger datasets can be time-consuming and slow. So PyTorch or TensorFlow can be a better option to pick if we have a huge dataset to handle.

  • Dataset Size

    Github has gained its popularity from the era of AI, Data Science, and Python. We can analyze the repository popularity by checking the stars, contributors, forks, and watchers. By looking at these graphs we can analyze that TensorFlow top’s in all the four scenarios followed by PyTorch followed by Keras.

  • GitHub Popularity

    GitHub Popularity

    Using Google trends we can analyze the popularity of the three different libraries. As we can see Keras is worldwide popular from past 5 years followed by TensorFlow and PyTorch

  • Google Trends Popularity

    Google Trends Popularity

    Keras is most popular in companies like Nvidia, Uber, Google, Amazon, Apple, and Netflix Tensorflow is also used in Google, Linkedin, Snapchat, AMD, Bloomberg, Paypal, and Qualcomm. Pytorch is majorly used by Facebook, Wells Fargo, Salesforce, Genentech, Microsoft, and JPMorgan Chase.

  • Corporate Popularity

    It has been observed that TensorFlow was more popular among articles submission in Medium followed by Keras and there were very few articles on Pytorch.

  • Articles Popularity in Medium

    Articles Popularity in Medium

    It has been observed that TensorFlow was more popular among articles submission in Medium followed by Keras and there were very few articles on Pytorch.

So to Summarize,

  TensorFlow Keras PyTorch
Architecture Complex Simple Complex
Processing Speed Fast Slow Fast
Supported Languages python, JavaScript, C++, Go, Java, Swift Python, R Python, C++, and Java
Beginner Friendly Complex Simple Complex
Debugging Difficult Difficult Easy
Dataset Size Fast with huge Datasets Slow with huge Datasets Fast with huge Datasets
Popular on GitHub Popular Least Popular Medium Popular
Popular on Google Trends Medium Popular Popular Least Popular
Popular on Medium Blog Popular Medium Popular Least Popular

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