rawalcher/c-net
Archived
1
0
Fork 0
Code Issues 5 Pull requests Projects Releases 2 Packages Wiki Activity Actions

(fix) evaluation

Browse source 
trained with 50000 images
evaluated with 10000
This commit is contained in:
Jakob Stornig 2023-09-24 22:29:41 +02:00
parent 38fafa672c
commit 1cfdf93f7c
2 changed files with 9 additions and 9 deletions
Download patch file Download diff file Expand all files Collapse all files

15
main.c
View file

@ -8,7 +8,7 @@
#include "util.h" #include "util.h"
void parsingErrorPrintHelp(){ void parsingErrorPrintHelp(){
printf("Syntax: c_net [train | detect]\n"); printf("Syntax: c_net [train | predict]\n");
printf("commands:\n"); printf("commands:\n");
printf("train\t train the network\n"); printf("train\t train the network\n");
printf("predict\t load a pgm image and predict_demo the number\n"); printf("predict\t load a pgm image and predict_demo the number\n");
@ -58,19 +58,18 @@ void train(int argc, char** arguments) {
} }
char *save_path = arguments[6]; char *save_path = arguments[6];
int imported = 0; int imported = 0;
Image **images = import_images(image_file, label_file, &imported, 50000); Image ** images = import_images(image_file, label_file, &imported, 60000);
Image ** evaluation_images = images+50000;
// for(int i = 0; i < imported; i++){ int training_image_count = 50000;
// matrix_save(images[i]->pixel_values, "images.txt"); int testing_image_count = 10000;
// }
// exit(1);
Neural_Network *nn = new_network(28 * 28, neurons_per_layer, hidden_count, 10, learning_rate); Neural_Network *nn = new_network(28 * 28, neurons_per_layer, hidden_count, 10, learning_rate);
randomize_network(nn, 1); randomize_network(nn, 1);
printf("training_network\n"); printf("training_network\n");
for(int epoch = 1; epoch <= epochs; epoch++){ for(int epoch = 1; epoch <= epochs; epoch++){
printf("epoch %d\n", epoch); printf("epoch %d\n", epoch);
for (int i = 0; i < imported; i++) { for (int i = 0; i < training_image_count; i++) {
if (i % 1000 == 0) { if (i % 1000 == 0) {
updateBar(i * 100 / imported); updateBar(i * 100 / imported);
} }
@ -78,7 +77,7 @@ void train(int argc, char** arguments) {
} }
updateBar(100); updateBar(100);
printf("\n"); printf("\n");
printf("accuracy %lf\n", measure_network_accuracy(nn, images, 10000)); printf("accuracy %lf\n", measure_network_accuracy(nn, evaluation_images, testing_image_count));
} }
printf("done training!\n"); printf("done training!\n");
save_network(nn, save_path); save_network(nn, save_path);

3
neuronal_network.c
View file

@ -117,7 +117,8 @@ double measure_network_accuracy(Neural_Network* network, Image** images, int amo
int num_correct = 0; int num_correct = 0;
printf("evaluating network\n"); printf("evaluating network\n");
for (int i = 50001; i < amount; i++) { if(amount > 10000) amount = 10000;
for (int i = 0; i < amount; i++) {
updateBar(i*100/amount); updateBar(i*100/amount);
Matrix* prediction = predict_image(network, images[i]); Matrix* prediction = predict_image(network, images[i]);