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.
As announced, Chainer is under the maintenance phase and further development will be limited to bug-fixes and maintenance only.
- 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