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.
