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Keras

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PyTorch

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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.

Decisions about Keras and PyTorch
Xi Huang
Developer at University of Toronto · | 8 upvotes · 27.7K views

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.

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A large part of our product is training and using a machine learning model. As such, we chose one of the best coding languages, Python, for machine learning. This coding language has many packages which help build and integrate ML models. For the main portion of the machine learning, we chose PyTorch as it is one of the highest quality ML packages for Python. PyTorch allows for extreme creativity with your models while not being too complex. Also, we chose to include scikit-learn as it contains many useful functions and models which can be quickly deployed. Scikit-learn is perfect for testing models, but it does not have as much flexibility as PyTorch. We also include NumPy and Pandas as these are wonderful Python packages for data manipulation. Also for testing models and depicting data, we have chosen to use Matplotlib and seaborn, a package which creates very good looking plots. Matplotlib is the standard for displaying data in Python and ML. Whereas, seaborn is a package built on top of Matplotlib which creates very visually pleasing plots.

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Pros of Keras
Pros of PyTorch
  • 5
    Quality Documentation
  • 4
    Easy and fast NN prototyping
  • 3
    Supports Tensorflow and Theano backends
  • 8
    Easy to use
  • 7
    Developer Friendly
  • 5
    Easy to debug
  • 3
    Sometimes faster than TensorFlow

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Cons of Keras
Cons of PyTorch
  • 3
    Hard to debug
  • 2
    Lots of code

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What is Keras?

Deep Learning library for Python. Convnets, recurrent neural networks, and more. Runs on TensorFlow or Theano. https://keras.io/

What is PyTorch?

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.

Need advice about which tool to choose?Ask the StackShare community!

What companies use Keras?
What companies use PyTorch?

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What are some alternatives to Keras and PyTorch?
TensorFlow
TensorFlow is an open source software library for numerical computation using data flow graphs. Nodes in the graph represent mathematical operations, while the graph edges represent the multidimensional data arrays (tensors) communicated between them. The flexible architecture allows you to deploy computation to one or more CPUs or GPUs in a desktop, server, or mobile device with a single API.
MXNet
A deep learning framework designed for both efficiency and flexibility. It allows you to mix symbolic and imperative programming to maximize efficiency and productivity. At its core, it contains a dynamic dependency scheduler that automatically parallelizes both symbolic and imperative operations on the fly.
scikit-learn
scikit-learn is a Python module for machine learning built on top of SciPy and distributed under the 3-Clause BSD license.
CUDA
A parallel computing platform and application programming interface model,it enables developers to speed up compute-intensive applications by harnessing the power of GPUs for the parallelizable part of the computation.
ML Kit
ML Kit brings Google’s machine learning expertise to mobile developers in a powerful and easy-to-use package.
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