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TensorFlow vs Keras vs PyTorch

  • July 12, 2023
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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 examine each of these three libraries in depth from various angles. Let's first examine each library before going on to the comparisons.

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  • 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:

    This time, the library is open-source and developed in Python. It can function while atop Theano, CNTK, and TensorFlow. This is mostly employed for quick deep learning experiments. As a high-level programming API, Keras is referred to. Francois Chollet created it on March 27, 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:

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The readability and architecture of Keras are both fairly straightforward. While TensorFlow and PyTorch have a more complicated and difficult-to-read design.

  • 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

    While working with Keras is simple, debugging it may be challenging because to its several levels of abstraction. TensorFlow makes debugging much more challenging than Keras. The faults may be investigated using Tensorflow's debugging module. Debugging Python problems is as simple as using Pytorch. To fix the issues, any common Python debuggers can be used.

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  • 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 grown in popularity as a result of the prominence of Python, AI, and data science. By looking at the stars, contributors, forks, and observers, we can assess the popularity of the repository. We can deduce from these graphs that TensorFlow comes out on top in all four instances, followed by PyTorch and 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

  • 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

    TensorFlow and Keras were found to be more frequently used in articles submitted to Medium, whereas Pytorch articles were few.

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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