Chainer: A flexible framework for neural networks

Important Chainer is under the maintenance phase. Read the blog.

Bridge the gap between algorithms and implementations of deep learning

Powerful

Chainer supports CUDA computation. It only requires a few lines of code to leverage a GPU. It also runs on multiple GPUs with little effort.

Flexible

Chainer supports various network architectures including feed-forward nets, convnets, recurrent nets and recursive nets. It also supports per-batch architectures.

Intuitive

Forward computation can include any control flow statements of Python without lacking the ability of backpropagation. It makes code intuitive and easy to debug.

Quick Start

Install Chainer:

pip install chainer

Run the MNIST example:

wget https://github.com/chainer/chainer/archive/v7.8.1.tar.gz
tar xzf v7.8.1.tar.gz
python chainer-7.8.1/examples/mnist/train_mnist.py

Learn more from the official documentation.

Extension Libraries

A library that implements various state-of-the-art deep reinforcement algorithms.

A collection of tools to train and run neural networks for computer vision tasks.

Slides

Introduction to Chainer