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README.md
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README.md
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Tinn (Tiny Neural Network) is a 200 line dependency free neural network library written in C99.
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```c
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#include "Tinn.h"
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#include <stdio.h>
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#define SETS 4
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#define NIPS 2
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#define NHID 8
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#define NOPS 1
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#define ITER 2000
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#define RATE 1.0f
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int main()
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{
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// This example learns XOR.
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float in[SETS][NIPS] = {
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{ 0, 0 },
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{ 0, 1 },
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{ 1, 0 },
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{ 1, 1 },
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};
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float tg[SETS][NOPS] = {
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{ 0 },
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{ 1 },
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{ 1 },
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{ 0 },
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};
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// Build.
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const Tinn tinn = xtbuild(NIPS, NHID, NOPS);
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// Train.
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for(int i = 0; i < ITER; i++)
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{
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float error = 0.0f;
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for(int j = 0; j < SETS; j++)
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error += xttrain(tinn, in[j], tg[j], RATE);
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printf("%.12f\n", error / SETS);
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}
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// Predict.
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for(int i = 0; i < SETS; i++)
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{
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const float* pd = xtpredict(tinn, in[i]);
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printf("%f :: %f\n", tg[i][0], (double) pd[0]);
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}
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// Cleanup.
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xtfree(tinn);
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return 0;
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}
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```
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For a more complicated demo on how to learn hand written digits, get some training data:
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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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@ -62,10 +14,6 @@ If you're on Windows it's:
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mingw32-make & tinn.exe
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For the layman not accustomed to makefiles the makefile devolves into:
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gcc test.c Tinn.c -lm
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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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