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Split pendigits into training and testing

This commit is contained in:
rjp 2018-04-12 14:04:00 +01:00
parent a674bc9592
commit 76616462da
2 changed files with 245 additions and 45 deletions
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102
test-pendig.c
View File

@ -14,6 +14,17 @@ typedef struct
}
Data;
typedef struct {
int k;
float tg;
float pd;
} pos;
void output_svg(int j, Data data, int realnum, float pcage, int goodbad)
{
printf("ln -s %05d-%d.svg %s/\n", j, realnum, goodbad ? "good" : "bad");
}
static int lns(FILE* const file)
{
int ch = EOF;
@ -68,12 +79,8 @@ static void parse(const Data data, char* line, const int row)
for(int col = 0; col < data.nips; col++)
{
const float val = atof(strtok(col == 0 ? line : NULL, ", "));
/* Input values are 0-100 pixel coordinates; scale to 0.0-1.0 */
data.in[row][col] = val / 100.0;
data.in[row][col] = val/100.0;
}
/* Last value is a 0-9 numeral which we need to convert
* into a size 10 vector of {0.00, 1.00}
*/
const float val = atof(strtok(NULL, ", "));
for(int col = 0; col < data.nops; col++) {
data.tg[row][col] = 0.0;
@ -115,7 +122,7 @@ static Data build(const char* path, const int nips, const int nops)
{
printf("Could not open %s\n", path);
printf("Get it from the machine learning database: ");
printf("wget http://archive.ics.uci.edu/ml/machine-learning-databases/pendigits/pendigits.tra\n");
printf("wget http://archive.ics.uci.edu/ml/machine-learning-databases/semeion/semeion.data\n");
exit(1);
}
const int rows = lns(file);
@ -130,6 +137,14 @@ static Data build(const char* path, const int nips, const int nops)
return data;
}
int sort_by_pd(const void *a, const void *b) {
pos x = *(pos*)a;
pos y = *(pos*)b;
if (x.pd > y.pd) { return -1; }
if (x.pd < y.pd) { return +1; }
return 0;
}
int main()
{
// Tinn does not seed the random number generator.
@ -138,49 +153,46 @@ int main()
// repositories usually don't include the input and output size in the data itself.
const int nips = 16;
const int nops = 10;
// Hyper Parameters.
// Learning rate is annealed and thus not constant.
// It can be fine tuned along with the number of hidden layers.
// Feel free to modify the anneal rate.
// The number of iterations can be changed for stronger training.
float rate = 1.0f;
const int nhid = 28;
const float anneal = 0.99f;
const int iterations = 128;
// Load the training set.
const Data data = build("pendigits.tra", nips, nops);
// Train, baby, train.
const Tinn tinn = xtbuild(nips, nhid, nops);
for(int i = 0; i < iterations; i++)
{
shuffle(data);
float error = 0.0f;
for(int j = 0; j < data.rows; j++)
{
const float* const in = data.in[j];
const float* const tg = data.tg[j];
error += xttrain(tinn, in, tg, rate);
}
printf("error %.12f :: learning rate %f\n",
(double) error / data.rows,
(double) rate);
rate *= anneal;
}
// This is how you save the neural network to disk.
xtsave(tinn, "saved.tinn");
xtfree(tinn);
const Data data = build("pendigits.tes", nips, nops);
// This is how you load the neural network from disk.
const Tinn loaded = xtload("saved.tinn");
pos check[nops];
int correct = 0;
// Now we do a prediction with the neural network we loaded from disk.
// Ideally, we would also load a testing set to make the prediction with,
// but for the sake of brevity here we just reuse the training set from earlier.
// One data set is picked at random.
const int pick = rand() % data.rows;
const float* const in = data.in[pick];
const float* const tg = data.tg[pick];
const float* const pd = xtpredict(loaded, in);
xtprint(tg, data.nops);
xtprint(pd, data.nops);
for (int j = 0; j < data.rows; j++) {
const float* const in = data.in[j];
const float* const tg = data.tg[j];
const float* const pd = xtpredict(loaded, in);
// To find the "best match", we need to sort by probability (`pd`)
// whilst keeping the target (`tg`) aligned. Copying them into
// our struct and then `qsort`ing on `pd` satisfies this.
for(int i = 0; i < data.nops; i++) {
check[i].k = i;
check[i].tg = tg[i];
check[i].pd = pd[i];
}
qsort(check, data.nops, sizeof(pos), sort_by_pd);
// If the highest probability guess is the correct one, success.
if (check[0].tg == 1) {
correct++;
}
// Otherwise we print out our best guess and the correct answer.
else {
int realnum = -1;
printf("%05d %d %.5f | ", j, check[0].k, (double) check[0].pd);
for (int i=1; i < data.nops; i++) {
if (check[i].tg == 1) {
printf("%d %.5f", check[i].k, (double) check[i].pd);
realnum = i;
}
}
printf("\n");
}
}
//
printf("%d correct out of %d rows\n", correct, data.rows);
// All done. Let's clean up.
xtfree(loaded);
dfree(data);

188
train-pendig.c Normal file
View File

@ -0,0 +1,188 @@
#include "Tinn.h"
#include <stdio.h>
#include <time.h>
#include <string.h>
#include <stdlib.h>
typedef struct
{
float** in;
float** tg;
int nips;
int nops;
int rows;
}
Data;
static int lns(FILE* const file)
{
int ch = EOF;
int lines = 0;
int pc = '\n';
while((ch = getc(file)) != EOF)
{
if(ch == '\n')
lines++;
pc = ch;
}
if(pc != '\n')
lines++;
rewind(file);
return lines;
}
static char* readln(FILE* const file)
{
int ch = EOF;
int reads = 0;
int size = 128;
char* line = (char*) malloc((size) * sizeof(char));
while((ch = getc(file)) != '\n' && ch != EOF)
{
line[reads++] = ch;
if(reads + 1 == size)
line = (char*) realloc((line), (size *= 2) * sizeof(char));
}
line[reads] = '\0';
return line;
}
static float** new2d(const int rows, const int cols)
{
float** row = (float**) malloc((rows) * sizeof(float*));
for(int r = 0; r < rows; r++)
row[r] = (float*) malloc((cols) * sizeof(float));
return row;
}
static Data ndata(const int nips, const int nops, const int rows)
{
const Data data = {
new2d(rows, nips), new2d(rows, nops), nips, nops, rows
};
return data;
}
static void parse(const Data data, char* line, const int row)
{
for(int col = 0; col < data.nips; col++)
{
const float val = atof(strtok(col == 0 ? line : NULL, ", "));
/* Input values are 0-100 pixel coordinates; scale to 0.0-1.0 */
data.in[row][col] = val / 100.0;
}
/* Last value is a 0-9 numeral which we need to convert
* into a size 10 vector of {0.00, 1.00}
*/
const float val = atof(strtok(NULL, ", "));
for(int col = 0; col < data.nops; col++) {
data.tg[row][col] = 0.0;
}
data.tg[row][(int)val] = 1.0;
}
static void dfree(const Data d)
{
for(int row = 0; row < d.rows; row++)
{
free(d.in[row]);
free(d.tg[row]);
}
free(d.in);
free(d.tg);
}
static void shuffle(const Data d)
{
for(int a = 0; a < d.rows; a++)
{
const int b = rand() % d.rows;
float* ot = d.tg[a];
float* it = d.in[a];
// Swap output.
d.tg[a] = d.tg[b];
d.tg[b] = ot;
// Swap input.
d.in[a] = d.in[b];
d.in[b] = it;
}
}
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 it from the machine learning database: ");
printf("wget http://archive.ics.uci.edu/ml/machine-learning-databases/pendigits/pendigits.tra\n");
exit(1);
}
const int rows = lns(file);
Data data = ndata(nips, nops, rows);
for(int row = 0; row < rows; row++)
{
char* line = readln(file);
parse(data, line, row);
free(line);
}
fclose(file);
return data;
}
int main()
{
// Tinn does not seed the random number generator.
srand(time(0));
// Input and output size is harded coded here as machine learning
// repositories usually don't include the input and output size in the data itself.
const int nips = 16;
const int nops = 10;
// Hyper Parameters.
// Learning rate is annealed and thus not constant.
// It can be fine tuned along with the number of hidden layers.
// Feel free to modify the anneal rate.
// The number of iterations can be changed for stronger training.
float rate = 1.0f;
const int nhid = 28;
const float anneal = 0.99f;
const int iterations = 128;
// Load the training set.
const Data data = build("pendigits.tra", nips, nops);
// Train, baby, train.
const Tinn tinn = xtbuild(nips, nhid, nops);
for(int i = 0; i < iterations; i++)
{
shuffle(data);
float error = 0.0f;
for(int j = 0; j < data.rows; j++)
{
const float* const in = data.in[j];
const float* const tg = data.tg[j];
error += xttrain(tinn, in, tg, rate);
}
printf("error %.12f :: learning rate %f\n",
(double) error / data.rows,
(double) rate);
rate *= anneal;
}
// This is how you save the neural network to disk.
xtsave(tinn, "saved.tinn");
xtfree(tinn);
// This is how you load the neural network from disk.
const Tinn loaded = xtload("saved.tinn");
// Now we do a prediction with the neural network we loaded from disk.
// Ideally, we would also load a testing set to make the prediction with,
// but for the sake of brevity here we just reuse the training set from earlier.
// One data set is picked at random.
const int pick = rand() % data.rows;
const float* const in = data.in[pick];
const float* const tg = data.tg[pick];
const float* const pd = xtpredict(loaded, in);
xtprint(tg, data.nops);
xtprint(pd, data.nops);
// All done. Let's clean up.
xtfree(loaded);
dfree(data);
return 0;
}