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Gustav Louw 2018-03-30 13:04:37 -07:00
parent 82ea55d512
commit 171bd195e9
3 changed files with 141 additions and 122 deletions
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136
Tinn.c
View File

@ -4,110 +4,114 @@
#include <math.h>
#include <time.h>
static double error(Tinn t, double* tg)
// Error function.
static double err(double a, double b)
{
double error = 0.0;
int i;
for(i = 0; i < t.nops; i++)
error += 0.5 * pow(tg[i] - t.o[i], 2.0);
return error;
return 0.5 * pow(a - b, 2.0);
}
static void backwards(Tinn t, double* in, double* tg, double rate)
// Partial derivative of error function.
static double pderr(double a, double b)
{
double* x = t.w + t.nhid * t.nips;
int i;
for(i = 0; i < t.nhid; i++)
{
double sum = 0.0;
int j;
/* Calculate total error change with respect to output */
for(j = 0; j < t.nops; j++)
{
double a = t.o[j] - tg[j];
double b = t.o[j] * (1 - t.o[j]);
double c = x[j * t.nhid + i];
sum += a * b * c;
}
/* Correct weights in input to hidden layer */
for(j = 0; j < t.nips; j++)
{
double a = sum;
double b = t.h[i] * (1 - t.h[i]);
double c = in[j];
t.w[i * t.nips + j] -= rate * a * b * c;
}
/* Correct weights in hidden to output layer */
for(j = 0; j < t.nops; j++)
{
double a = t.o[j] - tg[j];
double b = t.o[j] * (1 - t.o[j]);
double c = t.h[i];
x[j * t.nhid + i] -= rate * a * b * c;
}
}
return a - b;
}
static double act(double net)
// Total error.
static double terr(const double* tg, const double* o, int size)
{
return 1.0 / (1.0 + exp(-net));
double sum = 0.0;
for(int i = 0; i < size; i++)
sum += err(tg[i], o[i]);
return sum;
}
static double frand(void)
// Activation function.
static double act(double a)
{
return 1.0 / (1.0 + exp(-a));
}
// Partial derivative of activation function.
static double pdact(double a)
{
return a * (1.0 - a);
}
// Floating point random from 0.0 - 1.0.
static double frand()
{
return rand() / (double) RAND_MAX;
}
static void forewards(Tinn t, double* in)
// Back propagation.
static void backwards(const Tinn t, const double* in, const double* tg, double rate)
{
double* x = t.w + t.nhid * t.nips;
int i;
/* Calculate hidden layer neuron values */
for(i = 0; i < t.nhid; i++)
for(int i = 0; i < t.nhid; i++)
{
double sum = 0.0;
int j;
for(j = 0; j < t.nips; j++)
// Calculate total error change with respect to output.
for(int j = 0; j < t.nops; j++)
{
double a = in[j];
double b = t.w[i * t.nips + j];
sum += a * b;
double a = pderr(t.o[j], tg[j]);
double b = pdact(t.o[j]);
sum += a * b * x[j * t.nhid + i];
// Correct weights in hidden to output layer.
x[j * t.nhid + i] -= rate * a * b * t.h[i];
}
// Correct weights in input to hidden layer.
for(int j = 0; j < t.nips; j++)
t.w[i * t.nips + j] -= rate * sum * pdact(t.h[i]) * in[j];
}
}
// Forward propagation.
static void forewards(const Tinn t, const double* in)
{
double* x = t.w + t.nhid * t.nips;
// Calculate hidden layer neuron values.
for(int i = 0; i < t.nhid; i++)
{
double sum = 0.0;
for(int j = 0; j < t.nips; j++)
sum += in[j] * t.w[i * t.nips + j];
t.h[i] = act(sum + t.b[0]);
}
/* Calculate output layer neuron values */
for(i = 0; i < t.nops; i++)
// Calculate output layer neuron values.
for(int i = 0; i < t.nops; i++)
{
double sum = 0.0;
int j;
for(j = 0; j < t.nhid; j++)
{
double a = t.h[j];
double b = x[i * t.nhid + j];
sum += a * b;
}
for(int j = 0; j < t.nhid; j++)
sum += t.h[j] * x[i * t.nhid + j];
t.o[i] = act(sum + t.b[1]);
}
}
static void twrand(Tinn t)
// Randomizes weights and biases.
static void twrand(const Tinn t)
{
int wgts = t.nhid * (t.nips + t.nops);
int i;
for(i = 0; i < wgts; i++) t.w[i] = frand();
for(i = 0; i < t.nb; i++) t.b[i] = frand();
for(int i = 0; i < wgts; i++) t.w[i] = frand() - 0.5;
for(int i = 0; i < t.nb; i++) t.b[i] = frand() - 0.5;
}
double xttrain(Tinn t, double* in, double* tg, double rate)
double* xpredict(const Tinn t, const double* in)
{
forewards(t, in);
return t.o;
}
double xttrain(const Tinn t, const double* in, const double* tg, double rate)
{
forewards(t, in);
backwards(t, in, tg, rate);
return error(t, tg);
return terr(tg, t.o, t.nops);
}
Tinn xtbuild(int nips, int nhid, int nops)
{
Tinn t;
// Tinn only supports one hidden layer so there are two biases.
t.nb = 2;
t.w = (double*) calloc(nhid * (nips + nops), sizeof(*t.w));
t.b = (double*) calloc(t.nb, sizeof(*t.b));
@ -121,7 +125,7 @@ Tinn xtbuild(int nips, int nhid, int nops)
return t;
}
void xtfree(Tinn t)
void xtfree(const Tinn t)
{
free(t.w);
free(t.h);

28
Tinn.h
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@ -1,23 +1,25 @@
#ifndef _TINN_H_
#define _TINN_H_
#pragma once
typedef struct
{
double* w;
double* b;
double* h;
double* o;
double* w; // Weights.
double* b; // Biases.
double* h; // Hidden layer.
double* o; // Output layer.
// Number of biases - always two - Tinn only supports a single hidden layer.
int nb;
int nips;
int nhid;
int nops;
int nips; // Number of inputs.
int nhid; // Number of hidden neurons.
int nops; // Number of outputs.
}
Tinn;
extern double xttrain(Tinn, double* in, double* tg, double rate);
double xttrain(const Tinn, const double* in, const double* tg, double rate);
extern Tinn xtbuild(int nips, int nhid, int nops);
Tinn xtbuild(int nips, int nhid, int nops);
extern void xtfree(Tinn);
void xtfree(Tinn);
#endif
double* xpredict(const Tinn, const double* in);

99
test.c
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@ -1,20 +1,14 @@
#include "Tinn.h"
#include <stdio.h>
#include <string.h>
#include <stdlib.h>
#include <time.h>
#define toss(t, n) ((t*) malloc((n) * sizeof(t)))
#define retoss(ptr, t, n) (ptr = (t*) realloc((ptr), (n) * sizeof(t)))
typedef struct
{
double** id;
double** od;
int icols;
int ocols;
double** in;
double** tg;
int nips;
int nops;
int rows;
}
Data;
@ -41,12 +35,12 @@ static char* readln(FILE* const file)
int ch = EOF;
int reads = 0;
int size = 128;
char* line = toss(char, size);
char* line = ((char*) malloc((size) * sizeof(char)));
while((ch = getc(file)) != '\n' && ch != EOF)
{
line[reads++] = ch;
if(reads + 1 == size)
retoss(line, char, size *= 2);
line = (char*) realloc((line), (size *= 2) * sizeof(char));
}
line[reads] = '\0';
return line;
@ -54,30 +48,30 @@ static char* readln(FILE* const file)
static double** new2d(const int rows, const int cols)
{
double** row = toss(double*, rows);
double** row = (double**) malloc((rows) * sizeof(double*));
for(int r = 0; r < rows; r++)
row[r] = toss(double, cols);
row[r] = (double*) malloc((cols) * sizeof(double));
return row;
}
static Data ndata(const int icols, const int ocols, const int rows)
static Data ndata(const int nips, const int nops, const int rows)
{
const Data data = {
new2d(rows, icols), new2d(rows, ocols), icols, ocols, rows
new2d(rows, nips), new2d(rows, nops), nips, nops, rows
};
return data;
}
static void parse(const Data data, char* line, const int row)
{
const int cols = data.icols + data.ocols;
const int cols = data.nips + data.nops;
for(int col = 0; col < cols; col++)
{
const float val = atof(strtok(col == 0 ? line : NULL, " "));
if(col < data.icols)
data.id[row][col] = val;
const double val = atof(strtok(col == 0 ? line : NULL, " "));
if(col < data.nips)
data.in[row][col] = val;
else
data.od[row][col - data.icols] = val;
data.tg[row][col - data.nips] = val;
}
}
@ -85,11 +79,11 @@ static void dfree(const Data d)
{
for(int row = 0; row < d.rows; row++)
{
free(d.id[row]);
free(d.od[row]);
free(d.in[row]);
free(d.tg[row]);
}
free(d.id);
free(d.od);
free(d.in);
free(d.tg);
}
static void shuffle(const Data d)
@ -97,28 +91,29 @@ static void shuffle(const Data d)
for(int a = 0; a < d.rows; a++)
{
const int b = rand() % d.rows;
double* ot = d.od[a];
double* it = d.id[a];
double* ot = d.tg[a];
double* it = d.in[a];
// Swap output.
d.od[a] = d.od[b];
d.od[b] = ot;
d.tg[a] = d.tg[b];
d.tg[b] = ot;
// Swap input.
d.id[a] = d.id[b];
d.id[b] = it;
d.in[a] = d.in[b];
d.in[b] = it;
}
}
static Data build(const char* path, const int icols, const int ocols)
static Data build(const char* path, const int nips, const int nops)
{
FILE* file = fopen(path, "r");
if(file == NULL)
{
printf("Could not open %s\n", path);
printf("Get the training data: \n");
printf("Get it from the machine learning database: ");
printf("wget http://archive.ics.uci.edu/ml/machine-learning-databases/semeion/semeion.data\n");
exit(1);
}
const int rows = lns(file);
Data data = ndata(icols, ocols, rows);
Data data = ndata(nips, nops, rows);
for(int row = 0; row < rows; row++)
{
char* line = readln(file);
@ -129,22 +124,40 @@ static Data build(const char* path, const int icols, const int ocols)
return data;
}
int main(void)
int main()
{
const Data data = build("semeion.data", 256, 10);
shuffle(data);
const Tinn tinn = xtbuild(data.icols, 64, data.ocols);
for(int i = 0; i < 10000; i++)
// Input and output size is harded coded here,
// so make sure the training data sizes match.
const int nips = 256;
const int nops = 10;
// Hyper Parameters.
// Learning rate is annealed and thus not constant.
const int nhid = 32;
double rate = 0.5;
// Load the training set.
const Data data = build("semeion.data", nips, nops);
// Rock and roll.
const Tinn tinn = xtbuild(nips, nhid, nops);
for(int i = 0; i < 100; i++)
{
shuffle(data);
double error = 0.0;
for(int j = 0; j < data.rows; j++)
{
double* in = data.id[j];
double* tg = data.od[j];
//error += xttrain(tinn, in, tg, 0.5);
const double* const in = data.in[j];
const double* const tg = data.tg[j];
error += xttrain(tinn, in, tg, rate);
}
printf("%.12f\n", error);
printf("error %.12f :: rate %f\n", error / data.rows, rate);
rate *= 0.99;
}
// Ideally, you would load a testing set for predictions,
// but for the sake of brevity the training set is reused.
const double* const in = data.in[0];
const double* const tg = data.tg[0];
const double* const pd = xpredict(tinn, in);
for(int i = 0; i < data.nops; i++) { printf("%f ", tg[i]); } printf("\n");
for(int i = 0; i < data.nops; i++) { printf("%f ", pd[i]); } printf("\n");
xtfree(tinn);
dfree(data);
return 0;