update neuralNetwork

.vscode/settings.json

@@ -1,5 +0,0 @@
-{
-    "files.associations": {
-        "unity.h": "c"
-    }
-}

imageInput.c

@@ -33,6 +33,7 @@ GrayScaleImageSeries *readImages(const char *path)
         return NULL;
     }
     //liest die Anzahl der Bilder aus
+    series->count = 0;
     fread(&series->count, sizeof(unsigned short),1, data);
     series->images = malloc(series->count * sizeof(GrayScaleImage));
     if (series->images == NULL){

imageInputTests.c

@@ -123,7 +123,8 @@ void setUp(void) {
     // Falls notwendig, kann hier Vorbereitungsarbeit gemacht werden
 }
 
-void tearDown(void) {
+void tearDown(void) 
+{
     // Hier kann Bereinigungsarbeit nach jedem Test durchgeführt werden
 }
 

neuralNetworkTests.c

@@ -5,27 +5,22 @@
 #include "unity.h"
 #include "neuralNetwork.h"
 
-
 static void prepareNeuralNetworkFile(const char *path, const NeuralNetwork nn)
 {
     FILE *file = fopen(path, "wb");
     if (!file)
-    {
         return;
-    }
 
-    // Schreibe den Datei-Tag
+    const char *tag = "__info2_neural_network_file_format__";
-    const char *tag = "info2_neural_network_file_format";
     fwrite(tag, 1, strlen(tag), file);
 
-    // Überprüfe, ob es Layer gibt
+    // Überprüfung, ob es Layer gibt
     if (nn.numberOfLayers == 0)
     {
         fclose(file);
         return;
     }
 
-    
     // Schreibe die Eingabe- und Ausgabegrößen des Netzwerks
     int input = nn.layers[0].weights.cols;
     int output = nn.layers[0].weights.rows;
@@ -40,10 +35,8 @@ static void prepareNeuralNetworkFile(const char *path, const NeuralNetwork nn)
         int out = layer->weights.rows;
         int in = layer->weights.cols;
 
-
         fwrite(layer->weights.buffer, sizeof(MatrixType), out * in, file);
 
-
         fwrite(layer->biases.buffer, sizeof(MatrixType), out * 1, file);
 
         if (i + 1 < nn.numberOfLayers)
@@ -52,8 +45,9 @@ static void prepareNeuralNetworkFile(const char *path, const NeuralNetwork nn)
             fwrite(&nextOut, sizeof(int), 1, file);
         }
     }
-
     fclose(file);
+
+   
 }
 
 void test_loadModelReturnsCorrectNumberOfLayers(void)
@@ -250,8 +244,8 @@ void test_predictReturnsCorrectLabels(void)
     Matrix biases1 = {.buffer = biasBuffer1, .rows = 2, .cols = 1};
     Matrix biases2 = {.buffer = biasBuffer2, .rows = 3, .cols = 1};
     Matrix biases3 = {.buffer = biasBuffer3, .rows = 5, .cols = 1};
-    Layer layers[] = {{.weights=weights1, .biases=biases1, .activation=someActivation}, \
+    Layer layers[] = {{.weights = weights1, .biases = biases1, .activation = someActivation},
-                      {.weights=weights2, .biases=biases2, .activation=someActivation}, \
+                      {.weights = weights2, .biases = biases2, .activation = someActivation},
                       {.weights = weights3, .biases = biases3, .activation = someActivation}};
     NeuralNetwork netUnderTest = {.layers = layers, .numberOfLayers = 3};
     unsigned char *predictedLabels = predict(netUnderTest, inputImages, 2);
@@ -261,11 +255,13 @@ void test_predictReturnsCorrectLabels(void)
     free(predictedLabels);
 }
 
-void setUp(void) {
+void setUp(void)
+{
     // Falls notwendig, kann hier Vorbereitungsarbeit gemacht werden
 }
 
-void tearDown(void) {
+void tearDown(void)
+{
     // Hier kann Bereinigungsarbeit nach jedem Test durchgeführt werden
 }