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Keras vs PyTorch: What are the differences?

  1. Ease of Use: Keras is known for its simplicity and ease of use, as it provides a high-level API that allows for quick prototyping and experimentation. On the other hand, PyTorch offers more flexibility and control to the users, making it suitable for researchers and practitioners who require fine-grained control over their models.

  2. Computational Graph Definition: In Keras, the computational graph is defined statically before the model is run, which can limit flexibility in certain scenarios. PyTorch, on the other hand, utilizes dynamic computation graphs, allowing for more flexibility as the graph is built on-the-fly during execution.

  3. Deployment and Production: Keras models can be more easily deployed in production environments due to its simplified API and integration with tools like TensorFlow Serving. PyTorch, while providing flexibility, may require more effort for deployment in production systems.

  4. Community Support and Documentation: Keras has a larger community and more comprehensive documentation compared to PyTorch, making it easier for beginners to find help and resources. PyTorch, on the other hand, is favored by many researchers and academic institutions, leading to a more research-heavy community.

  5. Graphical User Interface: Keras provides tools like TensorBoard for visualization and monitoring of models, whereas PyTorch lacks a built-in graphical interface, although third-party tools can be integrated for similar functionality.

  6. Integration with Other Frameworks: Keras has seamless integration with TensorFlow, allowing users to leverage the capabilities of both frameworks. PyTorch, while compatible with other libraries, may require more manual intervention for integrating with different frameworks like TensorFlow.

In Summary, the key differences between Keras and PyTorch lie in ease of use, computational graph definition, deployment, community support, graphical user interface, and integration with other frameworks.

Decisions about Keras and PyTorch

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.

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Fabian Ulmer
Software Developer at Hestia · | 3 upvotes · 51.6K views

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.

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Xi Huang
Developer at University of Toronto · | 8 upvotes · 94.2K 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
  • 8
    Quality Documentation
  • 7
    Supports Tensorflow and Theano backends
  • 7
    Easy and fast NN prototyping
  • 15
    Easy to use
  • 11
    Developer Friendly
  • 10
    Easy to debug
  • 7
    Sometimes faster than TensorFlow

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Cons of Keras
Cons of PyTorch
  • 4
    Hard to debug
  • 3
    Lots of code
  • 1
    It eats poop

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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.

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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.
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Git
Git is a free and open source distributed version control system designed to handle everything from small to very large projects with speed and efficiency.
GitHub
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