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90% accuracy

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This commit is contained in:
Gustav Louw 2018-03-27 00:16:37 -07:00
parent 39352c3809
commit ab10629287
3 changed files with 54 additions and 84 deletions
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128
Genann.h
View File

@ -30,7 +30,6 @@
#pragma once
#include <stdio.h>
#include <assert.h>
#include <errno.h>
#include <math.h>
@ -76,7 +75,7 @@ static inline double genann_act_sigmoid_cached(double a)
// delete this entire function and replace references
// of genann_act_sigmoid_cached to genann_act_sigmoid.
const double min = -15.0;
const double max = 15.0;
const double max = +15.0;
static double interval;
static int initialized = 0;
static double lookup[4096];
@ -137,7 +136,7 @@ static inline Genann *genann_init(int inputs, int hidden_layers, int hidden, int
const int total_neurons = inputs + hidden * hidden_layers + outputs;
// Allocate extra size for weights, outputs, and deltas.
const int size = sizeof(Genann) + sizeof(double) * (total_weights + total_neurons + (total_neurons - inputs));
Genann *ret = malloc(size);
Genann* ret = (Genann*) malloc(size);
if(!ret)
return 0;
ret->inputs = inputs;
@ -147,7 +146,7 @@ static inline Genann *genann_init(int inputs, int hidden_layers, int hidden, int
ret->total_weights = total_weights;
ret->total_neurons = total_neurons;
// Set pointers.
ret->weight = (double*)((char*)ret + sizeof(Genann));
ret->weight = (double*) ((char*) ret + sizeof(Genann));
ret->output = ret->weight + ret->total_weights;
ret->delta = ret->output + ret->total_neurons;
genann_randomize(ret);
@ -162,35 +161,10 @@ static inline void genann_free(Genann *ann)
free(ann);
}
static inline Genann *genann_read(FILE *in)
{
int inputs, hidden_layers, hidden, outputs;
errno = 0;
int rc = fscanf(in, "%d %d %d %d", &inputs, &hidden_layers, &hidden, &outputs);
if(rc < 4 || errno != 0)
{
perror("fscanf");
return NULL;
}
Genann *ann = genann_init(inputs, hidden_layers, hidden, outputs);
for(int i = 0; i < ann->total_weights; ++i)
{
errno = 0;
rc = fscanf(in, " %le", ann->weight + i);
if(rc < 1 || errno != 0)
{
perror("fscanf");
genann_free(ann);
return NULL;
}
}
return ann;
}
static inline Genann *genann_copy(Genann const *ann)
{
const int size = sizeof(Genann) + sizeof(double) * (ann->total_weights + ann->total_neurons + (ann->total_neurons - ann->inputs));
Genann *ret = malloc(size);
Genann* ret = (Genann*) malloc(size);
if(!ret)
return 0;
memcpy(ret, ann, size);
@ -218,9 +192,7 @@ static inline double const *genann_run(Genann const *ann, double const *inputs)
{
double sum = *w++ * -1.0;
for(int k = 0; k < (h == 0 ? ann->inputs : ann->hidden); ++k)
{
sum += *w++ * i[k];
}
*o++ = act(sum);
}
i += (h == 0 ? ann->inputs : ann->hidden);
@ -253,20 +225,14 @@ static inline void genann_train(Genann const *ann, double const *inputs, double
double const *t = desired_outputs;
// Set output layer deltas.
if(ann->activation_output == genann_act_linear)
{
for(int j = 0; j < ann->outputs; ++j)
{
*d++ = *t++ - *o++;
}
}
else
{
for(int j = 0; j < ann->outputs; ++j)
{
*d++ = (*t - *o) * *o * (1.0 - *o);
++o; ++t;
}
}
}
// Set hidden layer deltas, start on last layer and work backwards.
// Note that loop is skipped in the case of hidden_layers == 0.
@ -276,13 +242,13 @@ static inline void genann_train(Genann const *ann, double const *inputs, double
double const *o = ann->output + ann->inputs + (h * ann->hidden);
double *d = ann->delta + (h * ann->hidden);
// Find first delta in following layer (which may be hidden or output).
double const * const dd = ann->delta + ((h+1) * ann->hidden);
double const * const dd = ann->delta + ((h + 1) * ann->hidden);
// Find first weight in following layer (which may be hidden or output).
double const * const ww = ann->weight + ((ann->inputs+1) * ann->hidden) + ((ann->hidden+1) * ann->hidden * (h));
double const * const ww = ann->weight + ((ann->inputs + 1) * ann->hidden) + ((ann->hidden+1) * ann->hidden * (h));
for(int j = 0; j < ann->hidden; ++j)
{
double delta = 0;
for(int k = 0; k < (h == ann->hidden_layers-1 ? ann->outputs : ann->hidden); ++k)
for(int k = 0; k < (h == ann->hidden_layers - 1 ? ann->outputs : ann->hidden); ++k)
{
const double forward_delta = dd[k];
const int windex = k * (ann->hidden + 1) + (j + 1);
@ -290,34 +256,23 @@ static inline void genann_train(Genann const *ann, double const *inputs, double
delta += forward_delta * forward_weight;
}
*d = *o * (1.0-*o) * delta;
++d; ++o;
++d;
++o;
}
}
// Train the outputs.
{
// Find first output delta.
// First output delta.
double const *d = ann->delta + ann->hidden * ann->hidden_layers;
// Find first output delta. First output delta.
const double * d = ann->delta + ann->hidden * ann->hidden_layers;
// Find first weight to first output delta.
double *w = ann->weight + (ann->hidden_layers
? ((ann->inputs+1) * ann->hidden + (ann->hidden+1) * ann->hidden * (ann->hidden_layers-1))
: (0));
double* w = ann->weight + (ann->hidden_layers ? ((ann->inputs + 1) * ann->hidden + (ann->hidden + 1) * ann->hidden * (ann->hidden_layers - 1)) : 0);
// Find first output in previous layer.
double const * const i = ann->output + (ann->hidden_layers
? (ann->inputs + (ann->hidden) * (ann->hidden_layers-1))
: 0);
const double* const i = ann->output + (ann->hidden_layers ? (ann->inputs + ann->hidden * (ann->hidden_layers - 1)) : 0);
// Set output layer weights.
for(int j = 0; j < ann->outputs; ++j) {
for(int k = 0; k < (ann->hidden_layers ? ann->hidden : ann->inputs) + 1; ++k) {
if(k == 0)
{
*w++ += *d * learning_rate * -1.0;
}
else
{
*w++ += *d * learning_rate * i[k-1];
}
}
for(int j = 0; j < ann->outputs; ++j)
{
for(int k = 0; k < (ann->hidden_layers ? ann->hidden : ann->inputs) + 1; ++k)
*w++ += (k == 0) ? (*d * learning_rate * -1.0) : (*d * learning_rate * i[k-1]);
++d;
}
assert(w - ann->weight == ann->total_weights);
@ -326,28 +281,15 @@ static inline void genann_train(Genann const *ann, double const *inputs, double
for(int h = ann->hidden_layers - 1; h >= 0; --h)
{
// Find first delta in this layer.
double const *d = ann->delta + (h * ann->hidden);
const double* d = ann->delta + (h * ann->hidden);
// Find first input to this layer.
double const *i = ann->output + (h
? (ann->inputs + ann->hidden * (h-1))
: 0);
const double* i = ann->output + (h ? (ann->inputs + ann->hidden * (h - 1)) : 0);
// Find first weight to this layer.
double *w = ann->weight + (h
? ((ann->inputs+1) * ann->hidden + (ann->hidden+1) * (ann->hidden) * (h-1))
: 0);
double* w = ann->weight + (h ? ((ann->inputs + 1) * ann->hidden + (ann->hidden + 1) * (ann->hidden) * (h - 1)) : 0);
for(int j = 0; j < ann->hidden; ++j)
{
for(int k = 0; k < (h == 0 ? ann->inputs : ann->hidden) + 1; ++k)
{
if (k == 0)
{
*w++ += *d * learning_rate * -1.0;
}
else
{
*w++ += *d * learning_rate * i[k-1];
}
}
*w++ += (k == 0) ? (*d * learning_rate * -1.0) : (*d * learning_rate * i[k - 1]);
++d;
}
}
@ -359,3 +301,31 @@ static inline void genann_write(Genann const *ann, FILE *out)
for(int i = 0; i < ann->total_weights; ++i)
fprintf(out, " %.20e", ann->weight[i]);
}
static inline Genann *genann_read(FILE *in)
{
int inputs;
int hidden_layers;
int hidden;
int outputs;
errno = 0;
int rc = fscanf(in, "%d %d %d %d", &inputs, &hidden_layers, &hidden, &outputs);
if(rc < 4 || errno != 0)
{
perror("fscanf");
return NULL;
}
Genann *ann = genann_init(inputs, hidden_layers, hidden, outputs);
for(int i = 0; i < ann->total_weights; ++i)
{
errno = 0;
rc = fscanf(in, " %le", ann->weight + i);
if(rc < 1 || errno != 0)
{
perror("fscanf");
genann_free(ann);
return NULL;
}
}
return ann;
}

2
Makefile
View File

@ -1,4 +1,4 @@
CC = gcc
CC = g++
NAME = shaper

8
main.c
View File

@ -3,13 +3,13 @@
// Get it from the machine learning database:
// wget http://archive.ics.uci.edu/ml/machine-learning-databases/semeion/semeion.data
#include "Genann.h"
#include <stdio.h>
#include <string.h>
#include <stdlib.h>
#include <time.h>
#include "Genann.h"
#define toss(t, n) ((t*) malloc((n) * sizeof(t)))
#define retoss(ptr, t, n) (ptr = (t*) realloc((ptr), (n) * sizeof(t)))
@ -69,7 +69,7 @@ static double** new2d(const int rows, const int cols)
static Data ndata(const int icols, const int ocols, const int rows, const double percentage)
{
const Data data = {
new2d(rows, icols), new2d(rows, ocols), icols, ocols, rows, rows * percentage
new2d(rows, icols), new2d(rows, ocols), icols, ocols, rows, (int) (rows * percentage)
};
return data;
}
@ -150,7 +150,7 @@ static void predict(Genann* ann, const Data d)
printf("%f\n", (double) matches / (d.rows - d.split));
}
static Data build(char* path, const int icols, const int ocols, const double percentage)
static Data build(const char* path, const int icols, const int ocols, const double percentage)
{
FILE* file = fopen(path, "r");
const int rows = lns(file);