diff --git a/info2praktikum-neuronalesnetz/neuralNetworkTests.c b/info2praktikum-neuronalesnetz/neuralNetworkTests.c
index 75740d5..783127b 100644
--- a/info2praktikum-neuronalesnetz/neuralNetworkTests.c
+++ b/info2praktikum-neuronalesnetz/neuralNetworkTests.c
@@ -22,12 +22,19 @@ static void prepareNeuralNetworkFile(const char *path, const NeuralNetwork nn)
     for (unsigned int i = 0; i < nn.numberOfLayers; i++) {
         Layer layer = nn.layers[i];
 
-        unsigned int inputDim = layer.weights.cols;   // Anzahl Eingänge
-        unsigned int outputDim = layer.weights.rows;  // Anzahl Ausgänge
 
-        // Dimensionen schreiben
-        fwrite(&inputDim, sizeof(unsigned int), 1, file);
-        fwrite(&outputDim, sizeof(unsigned int), 1, file);
+        if(i == 0){
+            // beim ersten Layer inputDim und outputDim schreiben
+            unsigned int inputDim = layer.weights.cols;   // Anzahl Eingänge
+            fwrite(&inputDim, sizeof(unsigned int), 1, file);
+            unsigned int outputDim = layer.weights.rows;  // Anzahl Ausgänge
+            fwrite(&outputDim, sizeof(unsigned int), 1, file);
+        }
+        else{
+            // bei allen anderen Layern nur outputDim schreiben
+            unsigned int outputDim = layer.weights.rows;  // Anzahl Ausgänge
+            fwrite(&outputDim, sizeof(unsigned int), 1, file);
+        }
 
         // Gewichte schreiben
         size_t weightCount = layer.weights.rows * layer.weights.cols;
@@ -38,8 +45,7 @@ static void prepareNeuralNetworkFile(const char *path, const NeuralNetwork nn)
         fwrite(layer.biases.buffer, sizeof(MatrixType), biasCount, file);
     }
 
-
-    // End-Marker (inputDim = 0)
+    // End-Marker (outputDim = 0)
     unsigned int zero = 0;
     fwrite(&zero, sizeof(unsigned int), 1, file);