diff --git a/neuralNetworkTests.c b/neuralNetworkTests.c
index fce7b48..83742e1 100644
--- a/neuralNetworkTests.c
+++ b/neuralNetworkTests.c
@@ -4,11 +4,55 @@
 #include <math.h>
 #include "unity.h"
 #include "neuralNetwork.h"
+#define IDENT_TAG "__info2_neural_network_file_format__"
 
 
+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);
+}
+
+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);
+}
+
 static void prepareNeuralNetworkFile(const char *path, const NeuralNetwork nn)
 {
-//TODO       
+//file erstellen und zum binärschreiben öffnen
+    FILE *file = fopen(path, "wb");
+    if(!file)
+        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);
+    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);
+   
+        //dimensionen festlegen(weights)  
+        writeWeights(layer, file);
+    
+
+        //dimension festlegen(bias)
+        writeBiases(layer, file);
+
+    
+    }
+    fclose(file);
 }
 
 void test_loadModelReturnsCorrectNumberOfLayers(void)