Practical overview
When it fits
Use Kann when a project calls for a compact, CPU-based neural-network library in C, or when you want to study and experiment with model construction and training without a large framework dependency.
Who it is for
- C and C++ developers building or experimenting with modest neural networks.
- Developers who want to include neural-network library source files in an application.
- Learners exploring neural-network construction, training and prediction through C APIs.
Documented uses
- Construct models from computational-graph operators and layers.
- Train feedforward or recurrent networks on prepared or generated data.
- Save a trained model and apply it to input data.
- Compile and run command-line neural-network examples.
Requirements & limits
The library source can be included directly in a project; the README says no separate installation is needed. The repository documents compiling examples with make.
- Kann uses C and depends only on the standard C library by default.
- The library supports graph-based reverse-mode automatic differentiation and feedforward, convolutional and recurrent networks.
- The documented optional CBLAS setting uses an OpenBLAS path for faster matrix multiplication.
- It is CPU-only, is not intended for huge networks, and lacks some common operators such as batch normalization.
Check current requirements in the official documentation. Source-based guidance does not mean OpenViST has run or independently tested this project.
Compile the examples and train an addition model
Build Kann’s examples, train the documented recurrent addition model on generated examples, and apply the saved model.
Before you start
- A shell environment with git, make, seq and awk available.
- An optional OpenBLAS installation if using the documented CBLAS setting.
Steps
Clone the Kann repository and change into its directory.
Run
make, or use the command below for the documented optional matrix-multiplication setting.make CBLAS=/path/to/openblasPipe generated addition pairs into
./examples/rnn-bit -m7 -o add.kan -.Pass an input pair to
./examples/rnn-bit -Ai add.kan -to apply the saved model.
Expected outcome
A saved `add.kan` model trained on generated unsigned-addition examples, which the example program can then apply to input pairs.
Guide limitations
- The example generates numbers within 10000 and uses 30000 training samples.
- The library is CPU-only and is not intended for training huge neural networks.
Sources & review
Source record details
- Source availability
- Active
- Sources checked
- 7 November 2026
- Original website title
- attractivechaos/kann · Official homepage title · retained in its source language