Chainer – A flexible framework of neural networks¶
Chainer is a powerful, flexible and intuitive deep learning framework.
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.
Chainer supports various network architectures including feed-forward nets, convnets, recurrent nets and recursive nets. It also supports per-batch architectures.
Forward computation can include any control flow statements of Python without lacking the ability of backpropagation. It makes code intuitive and easy to debug.
- Chainer at a Glance
- Concepts Walkthrough
- Neural Net Examples
- Colab Notebook Examples
- Awesome Chainer
- API Reference
- Variable and Parameter
- Link and Chains
- Probability Distributions
- Weight Initializers
- Training Tools
- Backends and Devices
- Configuring Chainer
- Debug Mode
- Visualization of Computational Graph
- Static Subgraph Optimizations: Usage
- Static Subgraph Optimizations: Design Notes
- Caffe Model Support
- Assertion and Testing
- ChainerX Documentation
- Distributed Deep Learning with ChainerMN
- Export Chainer to ONNX