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Keras vs TensorFlow: What are the differences?

# Introduction

1. **Architecture**: Keras is a high-level neural networks API, written in Python and capable of running on top of TensorFlow, CNTK, or Theano. TensorFlow, on the other hand, is an open-source deep learning framework developed by Google. While Keras offers a user-friendly interface for building neural networks, TensorFlow provides more flexibility in terms of architecture customization.
2. **Backend Support**: Keras allows for easy switching between different backend engines such as TensorFlow, Theano, and CNTK. In contrast, TensorFlow has its own backend engine, which is optimized for performance and provides robust support for distributed computing across multiple devices. This makes TensorFlow more suitable for large-scale machine learning projects.
3. **Ease of Use**: Keras focuses on simplicity and ease of use, with intuitive APIs that allow developers to quickly prototype deep learning models. On the other hand, TensorFlow requires more code for the same tasks, making it less user-friendly for beginners. However, TensorFlow's low-level APIs offer greater control and flexibility for advanced users.
4. **Community Support**: TensorFlow has a larger and more active community compared to Keras, resulting in more resources, tutorials, and third-party contributions. This makes it easier to find solutions to common problems and stay up-to-date with the latest developments in the field of deep learning.
5. **Deployment**: Keras models are easier to deploy due to their lightweight nature and higher level of abstraction. TensorFlow, being a more complex framework, requires additional steps for deployment and productionization. This makes Keras a better choice for rapid prototyping and quick deployment in production environments.
6. **Extensibility**: TensorFlow offers more extensive support for customized operations and extensions through its low-level APIs, allowing for more advanced research and development. Keras, while more beginner-friendly, may be limited in terms of extending and customizing neural network architectures beyond what is provided in its high-level APIs.

In Summary, Keras and TensorFlow differ in architecture, backend support, ease of use, community support, deployment, and extensibility, making them suitable for different use cases based on the specific requirements of a project. 
Decisions about Keras and TensorFlow

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 · 51.4K 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 · 93.9K 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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Pros of Keras
Pros of TensorFlow
  • 8
    Quality Documentation
  • 7
    Supports Tensorflow and Theano backends
  • 7
    Easy and fast NN prototyping
  • 32
    High Performance
  • 19
    Connect Research and Production
  • 16
    Deep Flexibility
  • 12
    Auto-Differentiation
  • 11
    True Portability
  • 6
    Easy to use
  • 5
    High level abstraction
  • 5
    Powerful

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Cons of Keras
Cons of TensorFlow
  • 4
    Hard to debug
  • 9
    Hard
  • 6
    Hard to debug
  • 2
    Documentation not very helpful

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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 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.

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What companies use Keras?
What companies use TensorFlow?
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What tools integrate with Keras?
What tools integrate with TensorFlow?

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What are some alternatives to Keras and TensorFlow?
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.
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.
JavaScript
JavaScript is most known as the scripting language for Web pages, but used in many non-browser environments as well such as node.js or Apache CouchDB. It is a prototype-based, multi-paradigm scripting language that is dynamic,and supports object-oriented, imperative, and functional programming styles.
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
GitHub is the best place to share code with friends, co-workers, classmates, and complete strangers. Over three million people use GitHub to build amazing things together.
See all alternatives