fix: fixed get_network_success_rate problem

src/utils/AI/network_main.c

@@ -3,6 +3,7 @@
 #include <err.h>
 #include <string.h>
 #include "../Application/ApplicationUtils.h"
+#include "../Image/ImageUtils.h"
 #include "neural_utils.h"
 
 void network_train(neural_network* network, char* training_data_dir, char* save_path, size_t iteration, size_t warmup, size_t warmup_iteration, double learning_rate, size_t AdaFactor)
@@ -23,16 +24,32 @@ void network_train(neural_network* network, char* training_data_dir, char* save_
     save_neural_network(network, save_path);
 }
 
-void network_use(neural_network* network, char** inputs)
+void network_use(neural_network* network, double* inputs)
 {
     for (size_t i = 0; i < network->nb_input; i++)
     {
-        network->inputs[i].activation = atof(inputs[i]);
+        network->inputs[i].activation = inputs[i];
     }
 
     process_network(network);
 
-    printf("Predicted character: %c", get_network_char_prediction(network, network->nb_output / 26));
+    printf("Predicted character: %c\n", get_network_char_prediction(network, network->nb_output / 26));
+}
+
+void network_test(char* network_path, char* data_path)
+{
+    neural_network network;
+
+    load_neural_network(&network, read_file(network_path));
+
+    size_t AdaFactor = network.nb_output / 26;
+
+    size_t nb_data;
+    training_data* datas = load_dataset(data_path, AdaFactor, &nb_data);
+
+    printf("Network total cost: %f\n", get_network_average_cost(&network, datas, nb_data));
+    size_t suc = get_network_success_rate(&network, datas, nb_data, AdaFactor);
+    printf("Network success rate: %li/%li: %.2f%%\n", suc, nb_data, (((double)suc / (double)nb_data) * 100.0));
 }
 
 void network_main(int argc, char* argv[])
@@ -41,7 +58,7 @@ void network_main(int argc, char* argv[])
     char* network_application_directory = path_get_directory(argv[0]);
 
     if (argc < 2)
-        errx(EXIT_FAILURE, "missing arguments, usage: ./network <train or use>");
+        errx(EXIT_FAILURE, "missing arguments, usage: ./network <train, use, test or help>");
 
     char* action = argv[1];
 
@@ -69,28 +86,47 @@ void network_main(int argc, char* argv[])
 
         network_train(&network, data_dir, network_path, iterations, warmup, warmup_iterations, learning_rate, AdaFactor);
     }
-    else if (strcmp(action, "use") == 0)    //use network: ./network use <network.csv> input1,input2,...,inputx
+    else if (strcmp(action, "use") == 0)    //use network: ./network use <network.csv> <image>
     {
         if (argc < 3)
-            errx(EXIT_FAILURE, "missing arguments, usage: ./network use <network.csv> input1,input2,...,inputx");
+            errx(EXIT_FAILURE, "missing arguments, usage: ./network use <network.csv> <image>");
 
         char* network_path = combine_path(network_application_directory, argv[2]);
 
-        char* input_str = argv[3];
+        char* input_path = combine_path(network_application_directory, argv[3]);
 
-        size_t nb_input = 0;
+        size_t len = 0;
 
-        char** input_array = string_split(input_str, ',', &nb_input);
+        double* input_array = image_to_bool_array(input_path, &len);
 
         neural_network network;
 
         load_neural_network(&network, read_file(network_path));
 
-        if (nb_input != network.nb_input)
+        if (len != network.nb_input)
             errx(EXIT_FAILURE, "inputs are not valid");
 
         network_use(&network, input_array);
     }
+    else if (strcmp(action, "test") == 0)       //test network: ./network test <network.csv> <data directory>
+    {
+        if (argc < 3)
+            errx(EXIT_FAILURE, "missing arguments, usage: ./network test <network.csv> <data directory>");
+
+        char* network_path = combine_path(network_application_directory, argv[2]);
+
+        char* data_path = combine_path(network_application_directory, argv[3]);
+
+        network_test(network_path, data_path);
+    }
+    else if (strcmp(action, "help") == 0)
+    {
+        printf("Network usage:\ntrain network:  ./network train <network.csv> <data directory> <iterations> <warmup> <warmup iterations> [learning_rate] [AdaFactor]\nuse network:    ./network use <network.csv> <image>\ntest network:   ./network test <network.csv> <data directory>");
+    }
+    else
+    {
+        errx(EXIT_FAILURE, "invalid arguments!");
+    }
 
     /*printf("Succes rate: %i\n", (int)(get_network_success_rate(&network, training_datas, data_len, 1) * 100));
 

src/utils/AI/neural_utils.c

@@ -553,6 +553,11 @@ double get_network_total_cost(neural_network* network, training_data* datas, siz
     return cost;
 }
 
+double get_network_average_cost(neural_network* network, training_data* datas, size_t data_len)
+{
+    return get_network_total_cost(network, datas, data_len) / (double) data_len;
+}
+
 char get_data_char_prediction(training_data data, size_t nb_output)
 {
     char res = '\0';
@@ -672,9 +677,9 @@ void train_network(neural_network* network, training_data* datas, size_t data_le
     }
 }
 
-double get_network_success_rate(neural_network* network, training_data* datas, size_t data_len, size_t AdaFactor)
+size_t get_network_success_rate(neural_network* network, training_data* datas, size_t data_len, size_t AdaFactor)
 {
-    int success = 0;
+    size_t success = 0;
 
     for (size_t i = 0; i < data_len; i++)
     {
@@ -682,16 +687,13 @@ double get_network_success_rate(neural_network* network, training_data* datas, s
 
         process_network(network);
 
-        for (size_t o = 0; o < data_len; o++)
-        {
-            if (get_network_char_prediction(network, AdaFactor) != get_data_char_prediction(datas[i], network->nb_output))
-                continue;
-        }
+        if (get_network_char_prediction(network, AdaFactor) != get_data_char_prediction(datas[i], network->nb_output))
+            continue;
 
         success++;
     }
 
-    return (double)success / (double)data_len;
+    return success;
 }
 
 training_data* load_dataset(const char* directory, size_t AdaFactor, size_t* nb_data)
@@ -764,7 +766,7 @@ training_data* load_dataset(const char* directory, size_t AdaFactor, size_t* nb_
             char letter = dir->d_name[0];
             char letter_num = tolower(letter) - 'a';
 
-            datas[data_id].inputs = image_to_bool_array(file);
+            datas[data_id].inputs = image_to_bool_array(file, NULL);
 
             double* out = calloc(26*AdaFactor, sizeof(double));
 

src/utils/AI/neural_utils.h

@@ -58,9 +58,11 @@ void network_process_epoche(neural_network* network, training_data* data, size_t
 
 double get_network_total_cost(neural_network* network, training_data* datas, size_t data_len);
 
+double get_network_average_cost(neural_network* network, training_data* datas, size_t data_len);
+
 void train_network(neural_network* network, training_data* datas, size_t data_len, float learning_rate, size_t warmup, size_t warmup_iterations, size_t iterations);
 
-double get_network_success_rate(neural_network* network, training_data* datas, size_t data_len, size_t AdaFactor);
+size_t get_network_success_rate(neural_network* network, training_data* datas, size_t data_len, size_t AdaFactor);
 
 training_data* load_dataset(const char* directory, size_t AdaFactor, size_t* nb_data);
 

src/utils/Image/ImageUtils.c

@@ -262,7 +262,7 @@ SDL_Surface* rotate_pixels(SDL_Surface* surface, double angle) {
     return rotated_surface;
 }
 
-double* image_to_bool_array(const char* file)
+double* image_to_bool_array(const char* file, size_t* image_len)
 {
 	SDL_Surface* surface = IMG_Load(file);
 
@@ -271,6 +271,9 @@ double* image_to_bool_array(const char* file)
 
 	double* res = calloc(surface->w * surface->h , sizeof(double));
 
+	if (image_len != NULL)
+		*image_len = surface->w * surface->h;
+
 	for (int x = 0; x < surface->w; x++)
 	{
 		for (int y = 0; y < surface->h; y++)

src/utils/Image/ImageUtils.h

@@ -36,7 +36,7 @@ SDL_Surface* create_sub_surface(SDL_Surface* surface, SDL_Rect rect);
 
 SDL_Surface* rotate_pixels(SDL_Surface* surface, double angle);
 
-double* image_to_bool_array(const char* file);
+double* image_to_bool_array(const char* file, size_t* image_len);
 
 SDL_Surface* resize_image(SDL_Surface* source, int new_width, int new_height);