User:Alexander Roidl/nn: Difference between revisions
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Reflecting on: | Reflecting on: | ||
* the resources that machine learning algorithms need | |||
* the history and fundamental basics of deep learning | |||
Next step: | Next step: | ||
* find a useful input for the network as it is random now | |||
* how can I use this setup to comment on the neural network algorithms itself? | |||
* more experiments untangling neural networks | |||
* installing the neural network on arbitrary (obsolete) devices | |||
Latest revision as of 19:03, 16 November 2018
Neural Network in C
I followed a tutorial and created a very basic neural network in C.
/*******************************************************************************/
#include <stdio.h>
#include <stdlib.h>
#include <time.h>
#include <math.h>
#include <fcntl.h>
#define NUMPAT 4
#define NUMIN 2
#define NUMHID 2
#define NUMOUT 1
#define rando() ((double)rand()/((double)RAND_MAX+1))
main() {
int i, j, k, p, np, op, ranpat[NUMPAT+1], epoch;
int NumPattern = NUMPAT, NumInput = NUMIN, NumHidden = NUMHID, NumOutput = NUMOUT;
double Input[NUMPAT+1][NUMIN+1] = { 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 1, 1 };
double Target[NUMPAT+1][NUMOUT+1] = { 0, 0, 0, 0, 0, 1, 0, 1, 0, 0 };
double SumH[NUMPAT+1][NUMHID+1], WeightIH[NUMIN+1][NUMHID+1], Hidden[NUMPAT+1][NUMHID+1];
double SumO[NUMPAT+1][NUMOUT+1], WeightHO[NUMHID+1][NUMOUT+1], Output[NUMPAT+1][NUMOUT+1];
double DeltaO[NUMOUT+1], SumDOW[NUMHID+1], DeltaH[NUMHID+1];
double DeltaWeightIH[NUMIN+1][NUMHID+1], DeltaWeightHO[NUMHID+1][NUMOUT+1];
double Error, eta = 0.5, alpha = 0.9, smallwt = 0.5;
/*random values for the networks weights*/
for( j = 1 ; j <= NumHidden ; j++ ) { /* initialize WeightIH and DeltaWeightIH */
for( i = 0 ; i <= NumInput ; i++ ) {
DeltaWeightIH[i][j] = 0.0 ;
WeightIH[i][j] = 2.0 * ( rando() - 0.5 ) * smallwt ;
}
}
for( k = 1 ; k <= NumOutput ; k ++ ) { /* initialize WeightHO and DeltaWeightHO */
for( j = 0 ; j <= NumHidden ; j++ ) {
DeltaWeightHO[j][k] = 0.0 ;
WeightHO[j][k] = 2.0 * ( rando() - 0.5 ) * smallwt ;
}
}
for( epoch = 0 ; epoch < 10000000 ; epoch++) { /* iterate weight updates */
for( p = 1 ; p <= NumPattern ; p++ ) { /* randomize order of individuals */
ranpat[p] = p ;
}
for( p = 1 ; p <= NumPattern ; p++) {
np = p + rando() * ( NumPattern + 1 - p ) ;
op = ranpat[p] ; ranpat[p] = ranpat[np] ; ranpat[np] = op ;
}
Error = 0.0 ;
for( np = 1 ; np <= NumPattern ; np++ ) { /* repeat for all the training patterns */
p = ranpat[np];
for( j = 1 ; j <= NumHidden ; j++ ) { /* compute hidden unit activations */
SumH[p][j] = WeightIH[0][j] ;
for( i = 1 ; i <= NumInput ; i++ ) {
SumH[p][j] += Input[p][i] * WeightIH[i][j] ;
}
/*Thats the sigmoid function*/
Hidden[p][j] = 1.0/(1.0 + exp(-SumH[p][j])) ;
}
for( k = 1 ; k <= NumOutput ; k++ ) { /* compute output unit activations and errors */
SumO[p][k] = WeightHO[0][k] ;
for( j = 1 ; j <= NumHidden ; j++ ) {
SumO[p][k] += Hidden[p][j] * WeightHO[j][k] ;
}
Output[p][k] = 1.0/(1.0 + exp(-SumO[p][k])) ; /* Sigmoidal Outputs */
/* Output[p][k] = SumO[p][k]; Linear Outputs */
Error += 0.5 * (Target[p][k] - Output[p][k]) * (Target[p][k] - Output[p][k]) ; /* SSE */
/* Error -= ( Target[p][k] * log( Output[p][k] ) + ( 1.0 - Target[p][k] ) * log( 1.0 - Output[p][k] ) ) ; Cross-Entropy Error */
DeltaO[k] = (Target[p][k] - Output[p][k]) * Output[p][k] * (1.0 - Output[p][k]) ; /* Sigmoidal Outputs, SSE */
/* DeltaO[k] = Target[p][k] - Output[p][k]; Sigmoidal Outputs, Cross-Entropy Error */
/* DeltaO[k] = Target[p][k] - Output[p][k]; Linear Outputs, SSE */
}
for( j = 1 ; j <= NumHidden ; j++ ) { /* 'back-propagate' errors to hidden layer */
SumDOW[j] = 0.0 ;
for( k = 1 ; k <= NumOutput ; k++ ) {
SumDOW[j] += WeightHO[j][k] * DeltaO[k] ;
}
DeltaH[j] = SumDOW[j] * Hidden[p][j] * (1.0 - Hidden[p][j]) ;
}
for( j = 1 ; j <= NumHidden ; j++ ) { /* update weights WeightIH */
DeltaWeightIH[0][j] = eta * DeltaH[j] + alpha * DeltaWeightIH[0][j] ;
WeightIH[0][j] += DeltaWeightIH[0][j] ;
for( i = 1 ; i <= NumInput ; i++ ) {
DeltaWeightIH[i][j] = eta * Input[p][i] * DeltaH[j] + alpha * DeltaWeightIH[i][j];
WeightIH[i][j] += DeltaWeightIH[i][j] ;
}
}
for( k = 1 ; k <= NumOutput ; k ++ ) { /* update weights WeightHO */
DeltaWeightHO[0][k] = eta * DeltaO[k] + alpha * DeltaWeightHO[0][k] ;
WeightHO[0][k] += DeltaWeightHO[0][k] ;
for( j = 1 ; j <= NumHidden ; j++ ) {
DeltaWeightHO[j][k] = eta * Hidden[p][j] * DeltaO[k] + alpha * DeltaWeightHO[j][k] ;
WeightHO[j][k] += DeltaWeightHO[j][k] ;
}
}
}
if( epoch%100 == 0 ) fprintf(stdout, "\nEpoch %-5d : Error = %f", epoch, Error) ;
if( Error < 0.0004 ) break ; /* stop learning when 'near enough' */
}
fprintf(stdout, "\n\nNETWORK DATA - EPOCH %d\n\nPat\t", epoch) ; /* print network outputs */
for( i = 1 ; i <= NumInput ; i++ ) {
fprintf(stdout, "Input%-4d\t", i) ;
}
for( k = 1 ; k <= NumOutput ; k++ ) {
fprintf(stdout, "Target%-4d\tOutput%-4d\t", k, k) ;
}
for( p = 1 ; p <= NumPattern ; p++ ) {
fprintf(stdout, "\n%d\t", p) ;
for( i = 1 ; i <= NumInput ; i++ ) {
fprintf(stdout, "%f\t", Input[p][i]) ;
}
for( k = 1 ; k <= NumOutput ; k++ ) {
fprintf(stdout, "%f\t%f\t", Target[p][k], Output[p][k]) ;
}
}
fprintf(stdout, "\n\nDONE\n\n") ;
return 1 ;
}
/*******************************************************************************/
Neural Network on an iPod
I patched my old classic iPod with rockbox and compiled the neural network for it. So I got a neural network running on the iPod.
Reflecting on:
- the resources that machine learning algorithms need
- the history and fundamental basics of deep learning
Next step:
- find a useful input for the network as it is random now
- how can I use this setup to comment on the neural network algorithms itself?
- more experiments untangling neural networks
- installing the neural network on arbitrary (obsolete) devices