I've started learning Tensorflow about 4 years ago and found it overly complicated. I then worked mostly with Keras which was a really nice experience. After Keras got integrated into Tensorflow it was a pretty seamless experience. About one year ago I started to work more with PyTorch and it's definitely my favorite now. I find the code much more readable and I have an easier time to write reusable code. The reason I started using PyTorch more was that a lot of research papers provide implementations for it.
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Difference
Deep learning is a subset of Artificial Intelligence (AI), a field growing in popularity over the last several decades. Like any new concept, some questions and details need ironing out before employing it in real-world applications.
What is Deep Learning?
It’s common to hear the terms “deep learning,” “machine learning,” and “artificial intelligence” used interchangeably, and that leads to potential confusion. Deep learning and machine learning are part of the artificial intelligence family, though deep learning is also a subset of machine learning. Understanding the nuances of these concepts is essential for any discussion of Keras vs TensorFlow vs PyTorch.
What is Keras?
Keras is an effective high-level neural network Application Programming Interface (API) written in Python. This open-source neural network library is designed to provide fast experimentation with deep neural networks, and it can run on top of CNTK, TensorFlow, and Theano.
Keras focuses on being modular, user-friendly, and extensible. It doesn’t handle low-level computations; instead, it hands them off to another library called the Backend.
Keras was adopted and integrated into TensorFlow in mid-2017. Users can access it via the tf.keras module. However, the Keras library can still operate separately and independently.
What is PyTorch?
PyTorch is a relatively new deep learning framework based on Torch. Developed by Facebook’s AI research group and open-sourced on GitHub in 2017, it’s used for natural language processing applications. PyTorch has a reputation for simplicity, ease of use, flexibility, efficient memory usage, and dynamic computational graphs. It also feels native, making coding more manageable and increasing processing speed.
What is TensorFlow?
TensorFlow is an end-to-end open-source deep learning framework developed by Google and released in 2015. It is known for documentation and training support, scalable production and deployment options, multiple abstraction levels, and support for different platforms, such as Android.
TensorFlow is a symbolic math library used for neural networks and is best suited for dataflow programming across a range of tasks. It offers multiple abstraction levels for building and training models.
A promising and fast-growing entry in the world of deep learning, TensorFlow offers a flexible, comprehensive ecosystem of community resources, libraries, and tools that facilitate building and deploying machine learning apps. Also, as mentioned before, TensorFlow has adopted Keras, which makes comparing the two seem problematic. Nevertheless, we will still compare the two frameworks for the sake of completeness, especially since Keras users don’t necessarily have to use TensorFlow.
Don’t Forget Theano!
Although this article throws the spotlight on Keras vs TensorFlow vs PyTorch, we should take a moment to recognize Theano. Theano used to be one of the more popular deep learning libraries, an open-source project that lets programmers define, evaluate, and optimize mathematical expressions, including multi-dimensional arrays and matrix-valued expressions.
Theano was developed by the Universite de Montreal in 2007 and is a key foundational library used for deep learning in Python. It’s considered the grandfather of deep learning frameworks and has fallen out of favor by most researchers outside academia.
Now, let us explore the PyTorch vs TensorFlow differences.
PyTorch vs TensorFlow
Both TensorFlow and PyTorch offer useful abstractions that ease the development of models by reducing boilerplate code. They differ because PyTorch has a more "pythonic" approach and is object-oriented, while TensorFlow offers a variety of options.
PyTorch is used for many deep learning projects today, and its popularity is increasing among AI researchers, although of the three main frameworks, it is the least popular. Trends show that this may change soon.
When researchers want flexibility, debugging capabilities, and short training duration, they choose PyTorch. It runs on Linux, macOS, and Windows.
Thanks to its well-documented framework and abundance of trained models and tutorials, TensorFlow is the favorite tool of many industry professionals and researchers. TensorFlow offers better visualization, which allows developers to debug better and track the training process. PyTorch, however, provides only limited visualization.
TensorFlow also beats PyTorch in deploying trained models to production, thanks to the TensorFlow Serving framework. PyTorch offers no such framework, so developers need to use Django or Flask as a back-end server.
In the area of data parallelism, PyTorch gains optimal performance by relying on native support for asynchronous execution through Python. However, with TensorFlow, you must manually code and optimize every operation run on a specific device to allow distributed training. In summary, you can replicate everything from PyTorch in TensorFlow; you just need to work harder at it.
If you’re just starting to explore deep learning, you should learn PyTorch first due to its popularity in the research community. However, if you’re familiar with machine learning and deep learning and focused on getting a job in the industry as soon as possible, learn TensorFlow first.