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example1.c
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115 lines (109 loc) · 4.13 KB
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#include <example1.c>
#include <genann.h>
#include <stdio.h>
#include <stdlib.h>
#include <time.h>
struct genann;
typedef double (*genann_actfun)(const struct genann *ann, double a);
typedef struct genann
{
int inputs;
int hidden_layers;
int hidden;
int outputs;
genann_actfun activation_hidden;
genann_actfun activation_output;
int total_weights;
int total_neurons;
double *weight;
double *output;
double *delta;
} genann;
genann *genann_init(int inputs, int hidden_layers, int hidden, int outputs);
genann *genann_read(FILE *in);
void genann_randomize(genann *ann);
genann *genann_copy(const genann *ann);
void genann_free(genann *ann);
const double *genann_run(const genann *ann, const double *inputs);
void genann_train(const genann *ann, const double *inputs, const double *desired_outputs, double learning_rate);
void genann_write(const genann *ann, FILE *out);
void genann_init_sigmoid_lookup(const genann *ann);
double genann_act_sigmoid(const genann *ann, double a);
double genann_act_sigmoid_cached(const genann *ann, double a);
double genann_act_threshold(const genann *ann, double a);
double genann_act_linear(const genann *ann, double a);
int main(int argc, char *argv[])
{
printf("GENANN example 1.\n");
printf("Train a small ANN to the XOR function using backpropagation.\n");
srand(time(0));
const double input[4][2] = {{0, 0}, {0, 1}, {1, 0}, {1, 1}};
const double output[4] = {0, 1, 1, 0};
int i;
genann *ann = genann_init(2, 1, 2, 1);
for (i = 0; i < 500; i += 1)
{
genann_train(ann, input[0], output + 0, 3);
genann_train(ann, input[1], output + 1, 3);
genann_train(ann, input[2], output + 2, 3);
genann_train(ann, input[3], output + 3, 3);
}
printf("Output for [%1.f, %1.f] is %1.f.\n", input[0][0], input[0][1], *genann_run(ann, input[0]));
printf("Output for [%1.f, %1.f] is %1.f.\n", input[1][0], input[1][1], *genann_run(ann, input[1]));
printf("Output for [%1.f, %1.f] is %1.f.\n", input[2][0], input[2][1], *genann_run(ann, input[2]));
printf("Output for [%1.f, %1.f] is %1.f.\n", input[3][0], input[3][1], *genann_run(ann, input[3]));
genann_free(ann);
return 0;
}
struct genann;
typedef double (*genann_actfun)(const struct genann *ann, double a);
typedef struct genann
{
int inputs;
int hidden_layers;
int hidden;
int outputs;
genann_actfun activation_hidden;
genann_actfun activation_output;
int total_weights;
int total_neurons;
double *weight;
double *output;
double *delta;
} genann;
genann *genann_init(int inputs, int hidden_layers, int hidden, int outputs);
genann *genann_read(FILE *in);
void genann_randomize(genann *ann);
genann *genann_copy(const genann *ann);
void genann_free(genann *ann);
const double *genann_run(const genann *ann, const double *inputs);
void genann_train(const genann *ann, const double *inputs, const double *desired_outputs, double learning_rate);
void genann_write(const genann *ann, FILE *out);
void genann_init_sigmoid_lookup(const genann *ann);
double genann_act_sigmoid(const genann *ann, double a);
double genann_act_sigmoid_cached(const genann *ann, double a);
double genann_act_threshold(const genann *ann, double a);
double genann_act_linear(const genann *ann, double a);
int main(int argc, char *argv[])
{
printf("GENANN example 1.\n");
printf("Train a small ANN to the XOR function using backpropagation.\n");
srand(time(0));
const double input[4][2] = {{0, 0}, {0, 1}, {1, 0}, {1, 1}};
const double output[4] = {0, 1, 1, 0};
int i;
genann *ann = genann_init(2, 1, 2, 1);
for (i = 0; i < 500; i += 1)
{
genann_train(ann, input[0], output + 0, 3);
genann_train(ann, input[1], output + 1, 3);
genann_train(ann, input[2], output + 2, 3);
genann_train(ann, input[3], output + 3, 3);
}
printf("Output for [%1.f, %1.f] is %1.f.\n", input[0][0], input[0][1], *genann_run(ann, input[0]));
printf("Output for [%1.f, %1.f] is %1.f.\n", input[1][0], input[1][1], *genann_run(ann, input[1]));
printf("Output for [%1.f, %1.f] is %1.f.\n", input[2][0], input[2][1], *genann_run(ann, input[2]));
printf("Output for [%1.f, %1.f] is %1.f.\n", input[3][0], input[3][1], *genann_run(ann, input[3]));
genann_free(ann);
return 0;
}