Tinn 一个微小的神经网络库

Tinn 一个微小的神经网络库

Tinn (Tiny Neural Network) is a 200 line dependency free neural network library written in C99.

For a demo on how to learn hand written digits, get some training data:

wget http://archive.ics.uci.edu/ml/machine-learning-databases/semeion/semeion.datamake; ./test

The training data consists of hand written digits written both slowly and quickly. Each line in the data set corresponds to one handwritten digit. Each digit is 16x16 pixels in size giving 256 inputs to the neural network.

At the end of the line 10 digits signify the hand written digit:

0: 1 0 0 0 0 0 0 0 0 01: 0 1 0 0 0 0 0 0 0 02: 0 0 1 0 0 0 0 0 0 03: 0 0 0 1 0 0 0 0 0 04: 0 0 0 0 1 0 0 0 0 0...9: 0 0 0 0 0 0 0 0 0 1

This gives 10 outputs to the neural network. The test program will output the accuracy for each digit. Expect above 99% accuracy for the correct digit, and less that 0.1% accuracy for the other digits.

Features

Portable - Runs where a C99 or C++98 compiler is present. Sigmoidal activation. One hidden layer.

Tips

Tinn will never use more than the C standard library. Tinn is great for embedded systems. Train a model on your powerful desktop and load it onto a microcontroller and use the analog to digital converter to predict real time events. The Tinn source code will always be less than 200 lines. Functions externed in the Tinn header are protected with the xt namespace standing for externed tinn. Tinn can easily be multi-threaded with a bit of ingenuity but the master branch will remain single threaded to aid development for embedded systems. Tinn does not seed the random number generator. Do not forget to do so yourself. Always shuffle your input data. Shuffle again after every training iteration. Get greater training accuracy by annealing your learning rate. For instance, multiply your learning rate by 0.99 every training iteration. This will zero in on a good learning minima.

Disclaimer

Tinn is a practice in minimalism.

Tinn is not a fully featured neural network C library like Kann, or Genann:

https://github.com/attractivechaos/kannhttps://github.com/codeplea/genann

Ports

Rust: https://github.com/dvdplm/rustinn

Other

A Tutorial using Tinn NN and CTypes

Tiny Neural Network Library in 200 Lines of Code

版权声明：本文内容由网络用户投稿，版权归原作者所有，本站不拥有其著作权，亦不承担相应法律责任。如果您发现本站中有涉嫌抄袭或描述失实的内容，请联系我们jiasou666@gmail.com 处理，核实后本网站将在24小时内删除侵权内容。