mirror of
https://github.com/glouw/tinn
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72 lines
2.3 KiB
Markdown
72 lines
2.3 KiB
Markdown
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Tinn (Tiny Neural Network) is a 200 line dependency free neural network library written in C99.
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For a demo on how to learn hand written digits, get some training data:
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wget http://archive.ics.uci.edu/ml/machine-learning-databases/semeion/semeion.data
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And if you're on Linux / MacOS just build and run Tinn with the test file:
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make; ./tinn
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If you're on Windows it's:
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mingw32-make & tinn.exe
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The training data consists of hand written digits written both slowly and quickly.
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Each line in the data set corresponds to one handwritten digit. Each digit is 16x16 pixels in size
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giving 256 inputs to the neural network.
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At the end of the line 10 digits signify the hand written digit:
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0: 1 0 0 0 0 0 0 0 0 0
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1: 0 1 0 0 0 0 0 0 0 0
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2: 0 0 1 0 0 0 0 0 0 0
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3: 0 0 0 1 0 0 0 0 0 0
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4: 0 0 0 0 1 0 0 0 0 0
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...
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9: 0 0 0 0 0 0 0 0 0 1
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This gives 10 outputs to the neural network. The test program will output the
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accuracy for each digit. Expect above 99% accuracy for the correct digit, and
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less that 0.1% accuracy for the other digits.
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# Features
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* Portable - Runs on Windows, MacOS, Linux, and embedded chips like ARM, AVR, and Microchip
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* Sigmoidal activation.
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* One hidden layer.
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# Tips
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* Tinn will never use more than the C standard library.
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* Tinn is great for embedded systems. Train a model on your powerful desktop and load
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it onto a microcontroller and use the analog to digital converter to predict real time events.
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* The Tinn source code will always be less than 200 lines. Functions externed in the Tinn header
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are protected with the _xt_ namespace standing for _externed tinn_.
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* Tinn can easily be multi-threaded with a bit of ingenuity but the master branch will remain
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single threaded to aid development for embedded systems.
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* Tinn does not seed the random number generator. Do not forget to do so yourself.
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* Always shuffle your input data. Shuffle again after every training iteration.
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* Get greater training accuracy by annealing your learning rate. For instance, multiply
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your learning rate by 0.99 every training iteration. This will zero in on a good learning minima.
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# Disclaimer
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Tinn is a practice in minimalism.
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Tinn is not a fully featured neural network C library like Kann, or Genann:
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https://github.com/attractivechaos/kann
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https://github.com/codeplea/genann
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