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Keras vs Chainer: What are the differences?

Developers describe Keras as "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/. On the other hand, Chainer is detailed as "A Powerful, Flexible, and Intuitive Framework for Neural Networks". It is an open source deep learning framework written purely in Python on top of Numpy and CuPy Python libraries aiming at flexibility. It supports CUDA computation. It only requires a few lines of code to leverage a GPU. It also runs on multiple GPUs with little effort.

Keras and Chainer belong to "Machine Learning Tools" category of the tech stack.

Some of the features offered by Keras are:

  • neural networks API
  • Allows for easy and fast prototyping
  • Convolutional networks support

On the other hand, Chainer provides the following key features:

  • Supports CUDA computation
  • Runs on multiple GPUs
  • Supports various network architectures

Keras and Chainer are both open source tools. It seems that Keras with 43.5K GitHub stars and 16.5K forks on GitHub has more adoption than Chainer with 4.98K GitHub stars and 1.32K GitHub forks.

What is Chainer?

It is an open source deep learning framework written purely in Python on top of Numpy and CuPy Python libraries aiming at flexibility. It supports CUDA computation. It only requires a few lines of code to leverage a GPU. It also runs on multiple GPUs with little effort.

What is Keras?

Deep Learning library for Python. Convnets, recurrent neural networks, and more. Runs on TensorFlow or Theano. https://keras.io/
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      What are some alternatives to Chainer and Keras?
      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.
      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.
      Theano
      Theano is a Python library that lets you to define, optimize, and evaluate mathematical expressions, especially ones with multi-dimensional arrays (numpy.ndarray). Using Theano it is possible to attain speeds rivaling hand-crafted C impleme
      Torch
      It is easy to use and efficient, thanks to an easy and fast scripting language, LuaJIT, and an underlying C/CUDA implementation.
      Caffe
      It is a deep learning framework made with expression, speed, and modularity in mind.
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      Decisions about Chainer and Keras
      Conor Myhrvold
      Conor Myhrvold
      Tech Brand Mgr, Office of CTO at Uber | 6 upvotes 549.4K views
      atUber TechnologiesUber Technologies
      TensorFlow
      TensorFlow
      Keras
      Keras
      PyTorch
      PyTorch

      Why we built an open source, distributed training framework for TensorFlow , Keras , and PyTorch:

      At Uber, we apply deep learning across our business; from self-driving research to trip forecasting and fraud prevention, deep learning enables our engineers and data scientists to create better experiences for our users.

      TensorFlow has become a preferred deep learning library at Uber for a variety of reasons. To start, the framework is one of the most widely used open source frameworks for deep learning, which makes it easy to onboard new users. It also combines high performance with an ability to tinker with low-level model details鈥攆or instance, we can use both high-level APIs, such as Keras, and implement our own custom operators using NVIDIA鈥檚 CUDA toolkit.

      Uber has introduced Michelangelo (https://eng.uber.com/michelangelo/), an internal ML-as-a-service platform that democratizes machine learning and makes it easy to build and deploy these systems at scale. In this article, we pull back the curtain on Horovod, an open source component of Michelangelo鈥檚 deep learning toolkit which makes it easier to start鈥攁nd speed up鈥攄istributed deep learning projects with TensorFlow:

      https://eng.uber.com/horovod/

      (Direct GitHub repo: https://github.com/uber/horovod)

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