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

Clean up (1)

Browse source
This commit is contained in:
Thomas 2023-09-24 12:22:28 +02:00
parent f836c53711
commit cf8b0a8b94
8 changed files with 312 additions and 302 deletions
Download patch file Download diff file Expand all files Collapse all files

61
main.c
View file

@ -4,26 +4,61 @@
#include "neuronal_network.h"
int main() {
Image** images = import_images("../data/train-images.idx3-ubyte", "../data/train-labels.idx1-ubyte", NULL, 60000);
const int amount_of_images_to_load = 60000;
const int amount_of_images_used_to_train = 30000;
const int amount_of_images_used_to_test = 1000;
const int input_size = 28*28;
const int hidden_layer_size = 50;
const int hidden_layer_count = 3;
const double learning_rate = 0.1;
/*
* Loading Images from Dataset
*/
Image** images = import_images("../data/train-images.idx3-ubyte", "../data/train-labels.idx1-ubyte", NULL, amount_of_images_to_load);
// img_visualize(images[0]);
// img_visualize(images[1]);
// img_print(images[0]);
// matrix_print(images[0]->pixel_values);
// matrix_print(images[1]->pixel_values);
/*
* Create a new network and randomize the weights
*/
Neural_Network* nn = new_network(28*28, 50, 3, 10, 0.1);
randomize_network(nn, 1);
// Neural_Network* nn = load_network("../networks/newest_network.txt");
Neural_Network* network = new_network(input_size, hidden_layer_size, hidden_layer_count, 10, learning_rate);
randomize_network(network, 1);
for (int i = 0; i < 60000; ++i) {
train_network(nn, images[i], images[i]->label);
/*
* Training
*/
for (int i = 0; i < amount_of_images_used_to_train; i++) {
train_network(network, images[i], images[i]->label);
}
// batch_train(nn, images, 30000, 2);
// Batch training works if you change the train_network method, but the results are not that good (needs further testing)
// batch_train(nn, images, 30000, 2);
printf("Trinaing Done!\n");
// save_network(nn);
/*
* Saving and Loading
*/
printf("%lf\n", measure_network_accuracy(nn, images, 10000));
// save_network(network);
// Neural_Network* network = load_network("../networks/newest_network.txt");
}
/*
* Measure Accuracy & predict single images
*/
printf("Accuracy: %lf\n", measure_network_accuracy(network, images, amount_of_images_used_to_test));
// matrix_print(predict_image(network, images[0]));
images_free(images, amount_of_images_to_load);
free_network(network);
return 0;
}