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FeedForward_Individual_MNIST_PROGMEM.ino
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FeedForward_Individual_MNIST_PROGMEM.ino
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#define NumberOf(arg) ((unsigned int) (sizeof (arg) / sizeof (arg [0]))) // calculates the number of layers (in this case 4)
#define _1_OPTIMIZE 0B11010110 // https://github.com/GiorgosXou/NeuralNetworks#define-macro-properties
#include <NeuralNetwork.h>
const unsigned int layers[] = {784, 7, 9, 1};
float *output; // 4th layer's output(s)
// It is 1 for each layer [Pretrained Biases ]
const PROGMEM float biases[] = {1,1,0.6364130};
// It is 784*7 + 7*9 + 9*1 [Pretrained weights] (81% Accuracy) | Possibly overtrained too lol [...]
const PROGMEM float weights[] = {
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0.6000720,-0.2282100,-0.1238880,-0.1362900,-0.4290700,-1.5995760,-0.6017880,-0.4979690,-1.1096460, 0.1683600,-0.1847800, 0.2280150,-0.2497960,-0.2908360,-0.9975660,-0.1764870,-1.3395060,-0.1104940,-0.9822070, 0.3132000, 0.6256180, 0.0647670,-0.2250610,-0.4012020,-1.0414200,-0.3469350,-0.3870300,-0.2066900,-0.0836940, 0.1876730,-0.4213240,-0.2941900, 0.6745000,-0.1006720, 0.7369710, 0.4373850,-0.6374820, 0.5617710,-0.0357530, 0.6901350, 0.2118500, 0.6395380,-0.9701490,-0.0095470,-0.2695340, 0.1592820,-1.0480950, 0.6878960,-0.3057640, 0.6587660, 0.3445170,-0.0169380,-0.1502580,-0.1608760,-0.4126000,-0.3279060, 0.4478840,-0.3895880,-0.1898550,-0.7910040, 0.4860170, 1.2217960, 0.9316950,-0.7593040, 0.2233950, 0.2213610, 0.8712190,-0.7009730, 0.2588080, 0.3059320,-0.2144780,-0.8309070,-0.0330590, 0.2720800,-0.1916480, 0.7117110,-0.1870230, 0.4008590,-0.3914270,-0.2435070,-0.0698120, 0.0077620, 1.1057130,-1.2839680,-0.2737540, 0.6325500,-0.8457980,-0.6590140, 0.3451000, 0.7158000,-0.0275910,-0.6367800,-0.9083220,-0.4444040, 0.3872660, 0.4156680,-0.5965100,-0.3955170,-0.4828740,-0.3970450, 0.5153850,-0.6920020,-0.7636200, 1.0067870,-0.5363390, 1.0032960, 0.1060720, 0.4461200,-0.0706970,-0.6260030,-0.3628670, 0.2043450, 0.8271590,-0.6074090,-0.1380660, 1.0578320,-0.2655650, 1.0500680, 1.4518070, 0.7553090,-0.7948050, 0.3930420, 0.7238000, 0.8440880, 0.0442490, 0.6092520, 0.9024900, 1.0066050, 1.1415010,-0.2453780, 0.9154930,-0.2306800,-0.1755620, 0.1790750,-0.4328780, 0.4160130, 1.4143380, 0.8342050, 0.5899690, 0.0419500, 0.4579280, 0.6711300,-0.1110540, 0.2873150, 0.4626300, 0.6708680, 0.9272890, 1.1671080, 0.1417840, 0.7081460,-0.5003910,-0.8218930,-0.1786460,-0.2337610, 0.3437360, 1.0119470, 0.2982180, 0.5551560, 1.3231400,-0.2626920, 0.8782060, 0.0878530, 0.7594230,-0.3266750,-0.4201190,-0.4937930, 0.4082920, 0.3147740, 0.2246740,-0.1773620, 0.8979280, 0.5185500, 0.8876330, 1.6000700, 1.1655700, 0.2014990, 0.7273140,-0.2555470,-0.7328690, 0.2732410,-0.4443870, 0.5819240, 2.1660690, 1.0660660, 0.6286660,-0.6957740, 1.2275250, 0.9471920,-0.5816560, 0.1642130, 0.7733960, 0.8496980, 1.6896450, 0.9781160, 0.4840490,-0.4551940, 0.6290900, 0.4007460,-0.1814520, 1.0836590, 1.0906800, 1.2700270, 0.0761560,-0.1805540,-0.5297870,-0.7855720, 0.5013000, 0.2151840,-0.5432210, 1.0369710,-0.1169690,-0.6061000, 0.2189920, 0.9315660, 0.1271960, 0.1246850, 1.0669220, 1.0974440, 0.7742050, 0.8128270, 0.6857260, 1.1797580, 1.4176100, 0.9264520, 0.7083550, 1.1321180, 0.1970760, 0.6419600, 0.9166890, 1.6667560, 0.2105670,-0.1233820,-0.5539560,-0.3272970,-0.7081040,-0.2952710, 0.2037350, 0.2918270, 0.1735890, 0.6846510,-0.3881390,-0.1647600,-0.5558170,-0.3163370, 0.3779410,-0.4874560, 0.5823020, 0.6575070,-0.3203220,-0.1374360, 0.1255830,-1.4142210, 1.2175580, 0.6817180, 0.2113400, 0.7993130, 0.0345180, 0.4908190, 0.4259050, 0.4449500, 0.3593410, 0.7209230, 0.5114560,-0.1917870, 0.4266200, 0.3110420, 0.2994030, 0.2082250,-0.2189880, 0.1791580,-1.5978410, 0.5071950,-0.6098100,-1.1355430,-0.5795770,-1.0139920,-0.0583240,-0.8610110,-0.1910870, 1.2420440,-0.9256410, 0.2731410,-0.0952210, 0.4422660,-0.0558470,-0.3725150, 0.5712200, 0.7629450, 0.0040070,-0.6172520,-0.3852000,-0.3423000, 0.8585480,-0.5990140,-0.4871340,-0.0550480,-0.0534140, 0.2432450, 0.7307050,-0.0201240, 0.3095730,-0.2161260,-0.0556270,-0.9222740,-0.5686190,-0.3470720,-1.1396020,-1.2591270,-0.3620520,-0.2081620,-0.3777670,-0.2053110, 0.2391080,-0.9208520, 0.2398430,-0.4664240,-0.5230740,-0.6355000,-0.3881000, 0.0844000, 0.8204060,-0.6607770, 0.2734200, 0.2558370,-0.9272130,-0.9595830,-1.1147960,-0.1385350,-0.3169760, 0.2640380,-0.1099220,-0.6177710,-0.0403390,-0.4108210, 0.5944920, 0.4592710,-0.8048720,-0.5438120,-1.1228290,-0.2767080,-1.5562470,-0.4861430, 0.6504680, 1.0910890,-0.1847540, 0.3871000,-0.0378000, 0.4960000,-0.0328010,-0.7270430,-0.1022250,-0.0584960,-0.1351490,-0.2762090,-1.8792510,-1.4316300,-1.4358400,-2.5010570,-1.8770820,-2.5742390,-1.6836890,-2.0075260,-1.9194700,-2.4416160,-0.5985490,-0.4984650,-1.2894850,-0.2745680,-0.2918980, 0.0804010, 0.3036000, 0.5195110, 0.4558240, 0.0734000,-0.2296000,-0.4041000,-0.6586000, 0.1981510,-0.3463570, 0.6327040,-0.6487690,-0.3137180,-0.8520690,-0.1464300,-0.9495970,-0.3777310,-1.7677340,-1.2960040,-0.8597520,-0.2359030,-0.7407360, 0.8416350, 0.1983510,-0.9912800, 0.0812890,-1.1119810, 0.0244140, 0.7551050, 0.8247510,-0.0227030,-0.6534000, 0.3830000,-0.7355000, 0.2294000,-0.2626000, 0.4259000, 0.8483840,-0.3930500, 0.1135470,-0.5501090, 0.5077940,-0.7178550,-0.8781830, 0.3462040,-0.5927810, 0.0866240, 0.2842700,-0.6238550, 0.3182420,-0.8388430, 0.1341220,-0.0055800,-0.6091300,-0.7991490, 0.3673180, 0.4914340, 0.7461000, 0.2364000, 0.6804000,-0.2834000,
1.6607560, 0.3172630,-1.6163330, 6.3892740, 0.7968500,-0.0623880,-0.2220940,
1.8403950, 0.4346420,-2.7185990, 8.4255860, 0.8026380,-0.5122110,-0.7568110,
2.9952470, 0.7933410, 2.3511700,-0.2726000,-1.4991860,-0.3859660,-3.2672000,
-0.7207520,-0.8591020, 1.6693450, 1.7486960, 1.1425950, 1.6178900,-0.0774000,
6.4178520, 0.0583360, 3.1722030,-1.0006130,-2.7730540, 0.1041180, 5.2981770,
1.0184650,-2.3350870,-2.2548340,-1.6518840, 1.6927980,-0.0879640, 1.4802400,
1.4859870,-4.4175790,-1.3996840,-2.9189890,-1.7992430, 3.7585340,-2.3671950,
2.3549730, 4.2584400, 2.2620930, 3.4490580,-0.0425240,-4.6678380,-3.8839620,
1.9594780, 2.8785290, 2.8678540,-0.1955130, 0.2964510,-4.0302990,-0.4547760,
2.2179170, 4.3427660,-1.0707080,-4.3444080,-2.7079850,-1.4452590,-2.9488280, 3.1879660,-2.2935330
}; // 22240 bytes = 72% of Arduino-UNO's programmable memory (?)
// Images of handwritten Numbers in byte form.
const PROGMEM byte NumbersInPixels[2][784] = {
{
0,0,0,0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,0,0,0,
0,0,0,0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,0,0,0,
0,0,0,0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,0,0,0,
0,0,0,0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,0,0,0,
0,0,0,0, 0, 0, 0, 67,128,188,254,254,255,254,254,181,123, 38, 3, 0, 0, 0, 0, 0, 0,0,0,0,
0,0,0,0, 0, 0, 94,249,251,202,136, 88, 89,136,136,160,230,253,186, 21, 0, 0, 0, 0, 0,0,0,0,
0,0,0,0, 0, 22,235,193, 60, 0, 0, 0, 0, 0, 0, 0, 15, 99,214,205, 0, 0, 0, 0, 0,0,0,0,
0,0,0,0, 0, 88,235, 19, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 65,253, 49, 0, 0, 0, 0,0,0,0,
0,0,0,0, 0, 25,121, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5,236, 79, 0, 0, 0, 0,0,0,0,
0,0,0,0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,107,253, 43, 0, 0, 0, 0,0,0,0,
0,0,0,0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,118,247,174, 0, 0, 0, 0, 0,0,0,0,
0,0,0,0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 49,211,249,144, 8, 0, 0, 0, 0, 0,0,0,0,
0,0,0,0, 0, 0, 0, 0, 2, 20, 62, 98,152,175,242,254,211, 85, 0, 0, 0, 0, 0, 0, 0,0,0,0,
0,0,0,0, 0, 0, 0, 0, 20,253,253,253,254,253,253,253,207, 57, 6, 0, 0, 0, 0, 0, 0,0,0,0,
0,0,0,0, 0, 0, 0, 0, 9,117,117,117, 63, 51,117,140,236,253,220, 95, 0, 0, 0, 0, 0,0,0,0,
0,0,0,0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 14, 92,241,253,229, 60, 0, 0, 0,0,0,0,
0,0,0,0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,121,248,230, 51, 0, 0,0,0,0,
0,0,0,0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 53,244,203, 3, 0,0,0,0,
0,0,0,0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,153,253, 19, 0,0,0,0,
0,0,0,0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,141,253, 19, 0,0,0,0,
0,0,0,0, 0,13, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 44,134,254,189, 0, 0,0,0,0,
0,0,0,0,16,231,112, 40, 25, 0, 0, 0, 0, 0, 0, 25, 40,100,197,253,208,125, 27, 0, 0,0,0,0,
0,0,0,0, 3,168,253,253,239,215,172,136,191,215,215,238,254,253,253,150, 0, 0, 0, 0, 0,0,0,0,
0,0,0,0, 0, 3, 62,122,175,187,229,253,254,193,175,175, 98, 97, 31, 2, 0, 0, 0, 0, 0,0,0,0,
0,0,0,0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,0,0,0,
0,0,0,0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,0,0,0,
0,0,0,0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,0,0,0,
0,0,0,0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,0,0,0,
},
{
0,0,0,0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,0,0,0,
0,0,0,0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,0,0,0,
0,0,0,0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,0,0,0,
0,0,0,0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,0,0,0,
0,0,0,0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,0,0,0,
0,0,0,0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,0,0,0,
0,0,0,0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,0,0,0,
0,0,0,0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,0,0,0,
0,0,0,0, 0, 0, 0, 0, 0, 0, 38,113,113,113,114,238,253,253,253,255,206, 88, 0, 0, 0,0,0,0,
0,0,0,0, 0, 0, 0, 57, 85,147,234,252,252,252,253,252,252,252,252,253,252,246, 75, 0, 0,0,0,0,
0,0,0,0, 0, 67,197,234,252,253,252,252,252,252,196,195,195,195,195,253,252,252, 84, 0, 0,0,0,0,
0,0,0,0, 0, 85,252,252,252,225,223,114, 84, 84, 0, 0, 0, 0, 16,253,252,214, 28, 0, 0,0,0,0,
0,0,0,0, 0, 38,221,157,112, 0, 0, 0, 0, 0, 0, 0, 0, 16,203,253,252,118, 0, 0, 0,0,0,0,
0,0,0,0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 26,207,253,255,168, 0, 0, 0, 0,0,0,0,
0,0,0,0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,104,252,252,215, 33, 0, 0, 0, 0,0,0,0,
0,0,0,0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 67,240,252,220, 31, 0, 0, 0, 0, 0,0,0,0,
0,0,0,0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 29,181,252,217, 37, 0, 0, 0, 0, 0, 0,0,0,0,
0,0,0,0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,253,252,252, 84, 0, 0, 0, 0, 0, 0, 0,0,0,0,
0,0,0,0, 0, 0, 0, 0, 0, 0, 0, 0, 76,191,255,215,110, 0, 0, 0, 0, 0, 0, 0, 0,0,0,0,
0,0,0,0, 0, 0, 0, 0, 0, 0, 0, 19,196,252,253,121, 0, 0, 0, 0, 0, 0, 0, 0, 0,0,0,0,
0,0,0,0, 0, 0, 0, 0, 0, 0, 67,209,252,252, 56, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0,0,0,0,
0,0,0,0, 0, 0, 0, 0, 0, 29,181,252,217, 84, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,0,0,0,
0,0,0,0, 0, 0, 0, 0, 0,253,252,252, 84, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,0,0,0,
0,0,0,0, 0, 0, 0, 13,191,255,215, 31, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,0,0,0,
0,0,0,0, 0, 0, 0,154,252,253,121, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,0,0,0,
0,0,0,0, 0, 0, 13,187,252,133, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,0,0,0,
0,0,0,0, 0, 0, 57,252,252, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,0,0,0,
0,0,0,0, 0, 0, 57,252,252, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,0,0,0,
}};// there was no more memory for another Number(s)InPixels for Arduino-UNO (unless you flash it without a bootloader)
unsigned int timestamp;
void setup()
{
Serial.begin(9600);
NeuralNetwork NN(layers, weights, biases, NumberOf(layers)); // Creating a NeuralNetwork with pretrained Weights and Biases
//Goes through each image
for (unsigned int i = 0; i < 2; i++)
{
timestamp = millis();
// FeedForwards each Individual Pixel by first normalizing it bettween 1 and 0
for (unsigned int j = 0; j < 784; j++) // and then it returns the predicted output(s).
output = NN.FeedForward_Individual(pgm_read_byte(&NumbersInPixels[i][j])/255.0);
Serial.println((String)"Predicted [" + (round(output[0]* 9)) + "] - Under " + (millis() - timestamp) + " milliseconds."); // (outputs[0]* 9.0) = denormalizing/converting prediction back to decimical (i think i should have used 10 instead of 9 when i was training but nvm...)
}
}
void loop(){}