minor changes

Ci testing

See merge request jastornig/c-net!12

.gitlab-ci.yml

@@ -48,7 +48,7 @@ release:
 
     assets:
       links:
-        - name: c_net
+        - name: c_net linux download (precompiled)
           url: '${CI_PROJECT_URL}/-/jobs/${BUILD_JOB_ID}/artifacts/file/c_net'
 
   script: echo "Define your deployment script!"

image.c

@@ -14,23 +14,27 @@ void big_endian_to_c_uint(const char * bytes, void * target, int size) {
 
 void read_until_space_or_newline(char * buff, int maxCount, FILE * fptr){
     int bufferOffset = 0;
-    char c = -1;
+    char c;
+    int counter = 0;
     do{
         c = (char)getc(fptr);
         buff[bufferOffset++] = c;
 
-    }while(!feof(fptr) && c != 0 && c != ' ' && c !='\n');
+    }while(!feof(fptr) && c != 0 && c != ' ' && c !='\n' && counter++ < maxCount);
     buff[bufferOffset-1] = 0;
 }
 
 Image * load_pgm_image(char * image_file_string){
     FILE * fptr = fopen(image_file_string, "r");
+    if(!fptr){
+        printf("could not open image file. exit\n");
+        exit(1);
+    }
     Image *image = malloc(sizeof(Image));
     image->label = -1;
 
 
-    char buffer[100];
+    char buffer[2048];
-    int magic_number = 0;
     fgets(buffer, 4, fptr);
     if(buffer[0] != 'P' || buffer[1] != '5'){
         printf("Wrong file Format");
@@ -40,17 +44,16 @@ Image * load_pgm_image(char * image_file_string){
         fgets(buffer, 1024, fptr);
     }
 
-    int image_width, image_height, image_length, image_white ;
+    int image_width, image_height, image_white ;
     read_until_space_or_newline(buffer, 10, fptr);
-    image_width = strtol(buffer, NULL, 10);
+    image_width = (int)strtol(buffer, NULL, 10);
 
     read_until_space_or_newline(buffer, 10, fptr);
-    image_height = strtol(buffer, NULL, 10);
+    image_height = (int)strtol(buffer, NULL, 10);
 
     read_until_space_or_newline(buffer, 10, fptr);
-    image_white = strtol(buffer, NULL, 10);
+    image_white = (int)strtol(buffer, NULL, 10);
 
-    image_length = image_width * image_height;
 
     image->pixel_values = matrix_create(image_height, image_width);
     for(int i = 0; i < image_height; i++){

main.c

@@ -8,7 +8,7 @@
 #include "util.h"
 
 void parsingErrorPrintHelp(){
-    printf("Syntax: c_net [train | detect]\n");
+    printf("Syntax: c_net [train | predict]\n");
     printf("commands:\n");
     printf("train\t train the network\n");
     printf("predict\t load a pgm image and predict_demo the number\n");
@@ -23,7 +23,7 @@ void parsingErrorTrain(){
 
 void parsingErrorDetect(){
     printf("invalid syntax\n");
-    printf("Syntax: c_net predict_demo [path_to_network] [image_file]");
+    printf("Syntax: c_net predict_demo [path_to_network] [image_file]\n");
 }
 
 void predict_demo(int argc, char** arguments){
@@ -58,19 +58,18 @@ void train(int argc, char** arguments) {
     }
     char *save_path = arguments[6];
     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);
     randomize_network(nn, 1);
     printf("training_network\n");
     for(int epoch = 1; epoch <= epochs; 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) {
                 updateBar(i * 100 / imported);
             }
@@ -78,7 +77,7 @@ void train(int argc, char** arguments) {
         }
         updateBar(100);
         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");
     save_network(nn, save_path);

matrix.c

@@ -216,21 +216,6 @@ Matrix* scale(Matrix* matrix, double value) {
     return result_matrix;
 }
 
-Matrix* addScalar(Matrix* matrix, double value) {
-
-    // create a copy of the original matrix
-    Matrix* result_matrix = matrix_copy(matrix);
-
-    // iterate over all numbers in the matrix and add the scalar value
-    for (int i = 0; i < result_matrix->rows; i++) {
-        for (int j = 0; j < result_matrix->columns; j++) {
-            result_matrix->numbers[i][j] += value;
-        }
-    }
-
-    // return the copy
-    return result_matrix;
-}
 
 Matrix* transpose(Matrix* matrix) {
 
@@ -249,16 +234,6 @@ Matrix* transpose(Matrix* matrix) {
 
 }
 
-double matrix_sum(Matrix* matrix) {
-    double sum = 0;
-    for (int i = 0; i < matrix->rows; i++) {
-        for (int j = 0; j < matrix->columns; j++) {
-            sum += matrix->numbers[i][j];
-        }
-    }
-    return sum;
-}
-
 void matrix_save(Matrix* matrix, char* file_string){
 
     // open the file in append mode

matrix.h

@@ -36,6 +36,4 @@ Matrix* subtract(Matrix* matrix1, Matrix* matrix2);
 Matrix* dot(Matrix* matrix1, Matrix* matrix2);
 Matrix* apply(double (*function)(double), Matrix* matrix);
 Matrix* scale(Matrix* matrix, double value);
-Matrix* addScalar(Matrix* matrix, double value);
 Matrix* transpose(Matrix* matrix);
-double matrix_sum(Matrix* matrix);

neuronal_network.c

@@ -117,7 +117,8 @@ double measure_network_accuracy(Neural_Network* network, Image** images, int amo
     int num_correct = 0;
 
     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);
         Matrix* prediction = predict_image(network, images[i]);