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Add test-network option implementation

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augustin64 2022-11-30 10:21:56 +01:00
parent 7da3544d8b
commit e584dfc791
1 changed files with 84 additions and 1 deletions
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src/cnn/test_network.c
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@ -9,8 +9,91 @@
#include "include/free.h" #include "include/free.h"
#include "include/cnn.h" #include "include/cnn.h"
void test_network(int dataset_type, char* modele, char* images_file, char* labels_file, char* data_dir, bool preview_fails) {
void test_network_mnist(Network* network, char* images_file, char* labels_file, bool preview_fails) {
(void)preview_fails; // Inutilisé pour le moment
int width, height; // Dimensions des images
int nb_elem; // Nombre d'éléments
int maxi; // Catégorie reconnue
int accuracy = 0; // Nombre d'images reconnues
// Load image
int* mnist_parameters = read_mnist_images_parameters(images_file);
int*** images = read_mnist_images(images_file);
unsigned int* labels = read_mnist_labels(labels_file);
nb_elem = mnist_parameters[0];
width = mnist_parameters[1];
height = mnist_parameters[2];
free(mnist_parameters);
// Load image in the first layer of the Network
for (int i=0; i < nb_elem; i++) {
if(i %(nb_elem/100) == 0) {
printf("Avancement: %.0f%%\r", 100*i/(float)nb_elem);
fflush(stdout);
}
write_image_in_network_32(images[i], height, width, network->input[0][0]);
forward_propagation(network);
maxi = indice_max(network->input[network->size-1][0][0], 10);
if (maxi == (int)labels[i]) {
accuracy++;
}
for (int j=0; j < height; j++) {
free(images[i][j]);
}
free(images[i]);
}
free(images);
printf("%d Images. Taux de réussite: %.2f%%\n", nb_elem, 100*accuracy/(float)nb_elem);
}
void test_network_jpg(Network* network, char* data_dir, bool preview_fails) {
(void)preview_fails; // Inutilisé pour le moment
jpegDataset* dataset = loadJpegDataset(data_dir);
int accuracy = 0;
int maxi;
for (int i=0; i < (int)dataset->numImages; i++) {
if(i %(dataset->numImages/100) == 0) {
printf("Avancement: %.1f%%\r", 1000*i/(float)dataset->numImages);
fflush(stdout);
}
write_image_in_network_260(dataset->images[i], dataset->height, dataset->height, network->input[0]);
forward_propagation(network);
maxi = indice_max(network->input[network->size-1][0][0], 50);
if (maxi == (int)dataset->labels[i]) {
accuracy++;
}
free(dataset->images[i]);
}
printf("%d Images. Taux de réussite: %.2f%%\n", dataset->numImages, 100*accuracy/(float)dataset->numImages);
free(dataset->images);
free(dataset->labels);
free(dataset);
}
void test_network(int dataset_type, char* modele, char* images_file, char* labels_file, char* data_dir, bool preview_fails) {
Network* network = read_network(modele);
if (dataset_type == 0) {
test_network_mnist(network, images_file, labels_file, preview_fails);
} else {
test_network_jpg(network, data_dir, preview_fails);
}
free_network(network);
} }