neuralNetwork Test bis auf den ersten test passts

neuralNetwork.c

@@ -170,7 +170,12 @@ NeuralNetwork loadModel(const char *path)
 
 static Matrix imageBatchToMatrixOfImageVectors(const GrayScaleImage images[], unsigned int count)
 {
-    Matrix matrix = {NULL, 0, 0};
+    //Matrix matrix = {NULL, 0, 0};
+    // Explizite Initialisierung verwenden, um die Feldreihenfolge in matrix.h zu umgehen:
+    Matrix matrix;
+    matrix.buffer = NULL;
+    matrix.rows = 0;
+    matrix.cols = 0;
 
     if(count > 0 && images != NULL)
     {

neuralNetworkTests.c

@@ -5,10 +5,52 @@
 #include "unity.h"
 #include "neuralNetwork.h"
 
+// Define the file header string as used in neuralNetwork.c
+#define FILE_HEADER_STRING "__info2_neural_network_file_format__"
 
 static void prepareNeuralNetworkFile(const char *path, const NeuralNetwork nn)
 {
     // TODO
+    FILE *file = fopen(path, "wb"); // Binärmodus zum Schreiben öffnen
+
+    if (file != NULL)
+    {
+        // 1. Identifikationstag schreiben
+        const char *fileTag = FILE_HEADER_STRING;
+        fwrite(fileTag, sizeof(char), strlen(fileTag), file);
+
+        // 2. Schichten (Layer) sequenziell schreiben
+        for (unsigned int i = 0; i < nn.numberOfLayers; i++)
+        {
+            const Layer currentLayer = nn.layers[i];
+
+            unsigned int inputDimension = currentLayer.weights.cols;
+            unsigned int outputDimension = currentLayer.weights.rows;
+
+            // Schreibe Input Dimension. Wichtig: Nutze sizeof(int)
+            // da readDimension in neuralNetwork.c in einen int liest.
+            fwrite(&inputDimension, sizeof(int), 1, file);
+
+            // Schreibe Output Dimension.
+            fwrite(&outputDimension, sizeof(int), 1, file);
+
+            // Schreibe Gewichtsmatrix (Weights) Daten
+            size_t numWeights = currentLayer.weights.rows * currentLayer.weights.cols;
+            fwrite(currentLayer.weights.buffer, sizeof(MatrixType), numWeights, file);
+
+            // Schreibe Biasmatrix (Biases) Daten
+            size_t numBiases = currentLayer.biases.rows * currentLayer.biases.cols;
+            fwrite(currentLayer.biases.buffer, sizeof(MatrixType), numBiases, file);
+        }
+
+        // 3. Ende des Modells signalisieren
+        unsigned int zero = 0;
+        // Wichtig: Auch hier sizeof(int) verwenden
+        fwrite(&zero, sizeof(int), 1, file); // Input Dimension = 0
+        fwrite(&zero, sizeof(int), 1, file); // Output Dimension = 0
+
+        fclose(file);
+    }
 }
 
 void test_loadModelReturnsCorrectNumberOfLayers(void)