diff --git a/neuralNetworkTests.c b/neuralNetworkTests.c
index 83742e1..9fab030 100644
--- a/neuralNetworkTests.c
+++ b/neuralNetworkTests.c
@@ -9,39 +9,45 @@
 
 static void writeWeights(Layer layer, FILE *file)
 {
-    fwrite(&layer.weights.rows, sizeof(unsigned int), 1, file);
-    fwrite(&layer.weights.cols, sizeof(unsigned int), 1, file);
-    fwrite(layer.weights.buffer, sizeof(float ), layer.weights.rows * layer.weights.cols, file);
+    unsigned int n = (unsigned int)layer.weights.rows * layer.weights.cols; //col und row müssen nicht extra eingelesen werden, da loadModel die Dimensionen aus der Fkt loadModel() selbst liest
+    fwrite(layer.weights.buffer, sizeof(MatrixType), n, file);
 }
 
 static void writeBiases(Layer layer, FILE *file)
 {
-    fwrite(&layer.biases.rows, sizeof(unsigned int), 1, file);
-    fwrite(&layer.biases.cols, sizeof(unsigned int), 1, file);
-    fwrite(layer.biases.buffer, sizeof(float ), layer.biases.rows * layer.biases.cols, file);
+    unsigned int n = (unsigned int)layer.weights.rows * layer.weights.cols;
+    fwrite(layer.biases.buffer, sizeof(float ), n, file);
 }
 
 static void prepareNeuralNetworkFile(const char *path, const NeuralNetwork nn)
 {
 //file erstellen und zum binärschreiben öffnen
     FILE *file = fopen(path, "wb");
-    if(!file)
+    if(file == NULL)
         return;
        
     //header reinschreiben
     const char *header = IDENT_TAG;
     fwrite(header, sizeof(char), strlen(header), file);
-   
-    //einfachheitshalber ein layer erstellen
-    
-    fwrite(&nn.numberOfLayers, sizeof(unsigned int), 1, file);
+
+    //Schließen der Datei, falls kein Layer vorhanden
+    if (nn.numberOfLayers == 0 || nn.layers == NULL)
+    {
+        fclose(file);
+        return;
+    }
+
+    //Erste Eingangsdimension: Spalten der ersten Gewichtsmatrix
+    unsigned int inputDim = (unsigned int)nn.layers[0].weights.cols;
+    fwrite(&inputDim, sizeof(unsigned int), 1, file);
+
+    //für jede Schicht: Dimension, Gewichte und Biases einlesen
     for (unsigned int i = 0; i < nn.numberOfLayers; i++)
     {
         Layer layer = nn.layers[i];
    
-        //activationType initialisieren (formt ergebnis der matritzenmultiplikation um, damit es in einem neuronalen Netzwerk sinnvoll weiterverwendet werden kann.)
-        unsigned int activationType = 1; //Aktivirungstyp id (zb 1 für ReLU)
-        fwrite(&activationType, sizeof(unsigned int), 1, file);
+        int outputDim = (unsigned int)layer.weights.rows;
+        fwrite(&outputDim, sizeof(unsigned int), 1, file);
    
         //dimensionen festlegen(weights)  
         writeWeights(layer, file);