From 628729096b223fbe41812936151580a94f1d272c Mon Sep 17 00:00:00 2001
From: haemmerlre98889 <haemmerlre98889@th-nuernberg.de>
Date: Wed, 26 Nov 2025 10:17:35 +0100
Subject: [PATCH] inputDim aus for-Schleife gezogen

---
 neuralNetworkTests.c | 39 +++++++++++++++++++--------------------
 1 file changed, 19 insertions(+), 20 deletions(-)

diff --git a/neuralNetworkTests.c b/neuralNetworkTests.c
index 26551a9..f87cee9 100644
--- a/neuralNetworkTests.c
+++ b/neuralNetworkTests.c
@@ -5,7 +5,6 @@
 #include "unity.h"
 #include "neuralNetwork.h"
 
-#define FILE_HEADER_STRING "__info2_neural_network_file_format__"
 
 //Testdatei schreiben
 static void prepareNeuralNetworkFile(const char *path, const NeuralNetwork nn)
@@ -15,30 +14,30 @@ FILE *file = fopen(path, "wb");
 if (file == NULL)
 return;
 
-// Header inklusive Nullterminator schreiben  
-fwrite(FILE_HEADER_STRING, sizeof(char), strlen(FILE_HEADER_STRING), file);  
+// Header schreiben  
+const char *fileTag = "__info2_neural_network_file_format__";
+fwrite(fileTag, 1, strlen(fileTag), file);
 
-for (unsigned int i = 0; i < nn.numberOfLayers; i++)  
-{  
-    const Layer *layer = &nn.layers[i];  
+// input Dimension schreiben
+int inputDim = nn.layers[0].weights.cols;
+fwrite(&inputDim, sizeof(int), 1, file);
 
-      
-    int inputDim = layer->weights.cols;  // Spalten
-    int outputDim = layer->weights.rows;  // Zeilen
+// 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);
 
-    // Dimensionen schreiben  
-    fwrite(&inputDim, sizeof(int), 1, file);  
-    fwrite(&outputDim, sizeof(int), 1, file);  
+    int weightCount = nn.layers[i].weights.rows * nn.layers[i].weights.cols;
+    fwrite(nn.layers[i].weights.buffer, sizeof(MatrixType), weightCount, file);
 
-    // Gewichtswerte schreiben  
-    fwrite(layer->weights.buffer, sizeof(MatrixType), layer->weights.rows * layer->weights.cols, file);  
+    int biasesCount = nn.layers[i].biases.rows * nn.layers[i].biases.cols;
+    fwrite(nn.layers[i].biases.buffer, sizeof(MatrixType), biasesCount, file);
+}
 
-    // Biaswerte schreiben
-    fwrite(layer->biases.buffer, sizeof(MatrixType), layer->biases.rows * layer->biases.cols, file);  
-}  
- 
-int zero = 0;  
-fwrite(&zero, sizeof(int), 1, file);   
+// Ende: loadModel liest 0 ein 
+int fileEnd = 0;  
+fwrite(&fileEnd, sizeof(int), 1, file);   
 
 // Datei schließen
 fclose(file);