Keras vs PyTorch: What are the differences?
What is Keras? Deep Learning library for Theano and TensorFlow. Deep Learning library for Python. Convnets, recurrent neural networks, and more. Runs on TensorFlow or Theano. https://keras.io/.
What is PyTorch? A deep learning framework that puts Python first. PyTorch is not a Python binding into a monolothic C++ framework. It is built to be deeply integrated into Python. You can use it naturally like you would use numpy / scipy / scikit-learn etc.
Keras and PyTorch can be primarily classified as "Machine Learning" tools.
"Easy and fast NN prototyping" is the primary reason why developers consider Keras over the competitors, whereas "Developer Friendly" was stated as the key factor in picking PyTorch.
Keras and PyTorch are both open source tools. It seems that Keras with 42.5K GitHub stars and 16.2K forks on GitHub has more adoption than PyTorch with 29.6K GitHub stars and 7.18K GitHub forks.
StyleShare Inc., Home61, and Suggestic are some of the popular companies that use Keras, whereas PyTorch is used by Suggestic, cotobox, and Depop. Keras has a broader approval, being mentioned in 52 company stacks & 50 developers stacks; compared to PyTorch, which is listed in 21 company stacks and 46 developer stacks.
Pytorch is a famous tool in the realm of machine learning and it has already set up its own ecosystem. Tutorial documentation is really detailed on the official website. It can help us to create our deep learning model and allowed us to use GPU as the hardware support.
I have plenty of projects based on Pytorch and I am familiar with building deep learning models with this tool. I have used TensorFlow too but it is not dynamic. Tensorflow works on a static graph concept that means the user first has to define the computation graph of the model and then run the ML model, whereas PyTorch believes in a dynamic graph that allows defining/manipulating the graph on the go. PyTorch offers an advantage with its dynamic nature of creating graphs.
For my company, we may need to classify image data. Keras provides a high-level Machine Learning framework to achieve this. Specifically, CNN models can be compactly created with little code. Furthermore, already well-proven classifiers are available in Keras, which could be used as Transfer Learning for our use case.
We chose Keras over PyTorch, another Machine Learning framework, as our preliminary research showed that Keras is more compatible with .js. You can also convert a PyTorch model into TensorFlow.js, but it seems that Keras needs to be a middle step in between, which makes Keras a better choice.
For data analysis, we choose a Python-based framework because of Python's simplicity as well as its large community and available supporting tools. We choose PyTorch over TensorFlow for our machine learning library because it has a flatter learning curve and it is easy to debug, in addition to the fact that our team has some existing experience with PyTorch. Numpy is used for data processing because of its user-friendliness, efficiency, and integration with other tools we have chosen. Finally, we decide to include Anaconda in our dev process because of its simple setup process to provide sufficient data science environment for our purposes. The trained model then gets deployed to the back end as a pickle.
Sign up to add or upvote prosMake informed product decisions
Sign up to add or upvote consMake informed product decisions
What is Keras?
What is PyTorch?
Need advice about which tool to choose?Ask the StackShare community!
Sign up to get full access to all the companiesMake informed product decisions
Sign up to get full access to all the tool integrationsMake informed product decisions
Red Hat, Inc.