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updated readme
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README.md
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README.md
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@ -1,24 +1,48 @@
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![](img/logo.PNG)
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Tinn (Tiny Neural Network) is a 200 line dependency free neural network library written in C99.
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Tinn can be compiled with any C++ compiler as well.
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#include "Tinn.h"
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#include <stdio.h>
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#define len(a) ((int) (sizeof(a) / sizeof(*a)))
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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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float in[] = { 0.05, 0.10 };
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float tg[] = { 0.01, 0.99 };
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/* Two hidden neurons */
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const Tinn tinn = xtbuild(len(in), 2, len(tg));
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for(int i = 0; i < 1000; i++)
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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 = xttrain(tinn, in, tg, 0.5);
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printf("%.12f\n", error);
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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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@ -34,3 +58,21 @@ And if you're on Linux / MacOS just build and run:
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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 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 1% accuracy for the other digits.
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