diff --git a/neuralNetworkTests.c b/neuralNetworkTests.c
index 9be45e2..6a5004e 100644
--- a/neuralNetworkTests.c
+++ b/neuralNetworkTests.c
@@ -4,44 +4,43 @@
 #include <math.h>
 #include "unity.h"
 #include "neuralNetwork.h"
-//Dateischichten sind in neuralNetwork.h definiert
-// Dateiname: __info2_neural_network_file_format__
 
+
+//Testdatei schreiben
 static void prepareNeuralNetworkFile(const char *path, const NeuralNetwork nn)
 {
-    FILE *file = fopen(path, "wb");
-    if (!file) return;
+// Datei öffnen
+FILE *file = fopen(path, "wb");
+if (file == NULL)
+return;
 
-    // Header schreiben
-    fwrite(FILE_HEADER_STRING, 1, strlen(FILE_HEADER_STRING), file);
+// Header schreiben
+const char *fileTag = "__info2_neural_network_file_format__";
+fwrite(fileTag, 1, strlen(fileTag), file);
 
-    // Anzahl Layer schreiben
-    uint32_t nLayers = nn.numberOfLayers;
-    fwrite(&nLayers, sizeof(uint32_t), 1, file);
+// input Dimension schreiben
+int inputDim = nn.layers[0].weights.cols;
+fwrite(&inputDim, sizeof(int), 1, file);
 
-    for (uint32_t i = 0; i < nLayers; i++) {
-        Layer layer = nn.layers[i];
+// für weiter Layer nur output Dimension schreiben
+for (unsigned int i = 0; i < nn.numberOfLayers; i++)
+{
+    int outputDim = nn.layers[i].weights.rows;
+    fwrite(&outputDim, sizeof(int), 1, file);
 
-        // Weights-Dimensionen
-        uint32_t rW = layer.weights.rows;
-        uint32_t cW = layer.weights.cols;
-        fwrite(&rW, sizeof(uint32_t), 1, file);
-        fwrite(&cW, sizeof(uint32_t), 1, file);
+    int weightCount = nn.layers[i].weights.rows * nn.layers[i].weights.cols;
+    fwrite(nn.layers[i].weights.buffer, sizeof(MatrixType), weightCount, file);
 
-        // Weights-Werte
-        fwrite(layer.weights.buffer, sizeof(MatrixType), rW * cW, file);
+    int biasesCount = nn.layers[i].biases.rows * nn.layers[i].biases.cols;
+    fwrite(nn.layers[i].biases.buffer, sizeof(MatrixType), biasesCount, file);
+}
 
-        // Bias-Dimensionen
-        uint32_t rB = layer.biases.rows;
-        uint32_t cB = layer.biases.cols;
-        fwrite(&rB, sizeof(uint32_t), 1, file);
-        fwrite(&cB, sizeof(uint32_t), 1, file);
+// Ende: loadModel liest 0 ein
+int fileEnd = 0;
+fwrite(&fileEnd, sizeof(int), 1, file);
 
-        // Bias-Werte
-        fwrite(layer.biases.buffer, sizeof(MatrixType), rB * cB, file);
-    }
-
-    fclose(file);
+// Datei schließen
+fclose(file);
 }