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Chainer

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Torch

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Chainer vs Torch: What are the differences?

What is Chainer? 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.

What is Torch? An open-source machine learning library and a script language based on the Lua programming language. It is easy to use and efficient, thanks to an easy and fast scripting language, LuaJIT, and an underlying C/CUDA implementation.

Chainer and Torch can be primarily classified as "Machine Learning" tools.

Some of the features offered by Chainer are:

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

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

  • A powerful N-dimensional array
  • Lots of routines for indexing, slicing, transposing
  • Amazing interface to C, via LuaJIT

Chainer is an open source tool with 5.03K GitHub stars and 1.33K GitHub forks. Here's a link to Chainer's open source repository on GitHub.

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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 Torch?

It is easy to use and efficient, thanks to an easy and fast scripting language, LuaJIT, and an underlying C/CUDA implementation.

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What are some alternatives to Chainer and Torch?
Keras
Deep Learning library for Python. Convnets, recurrent neural networks, and more. Runs on TensorFlow or Theano. https://keras.io/
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).
Caffe
It is a deep learning framework made with expression, speed, and modularity in mind.
See all alternatives