Merge branch 'main' of https://git.efi.th-nuernberg.de/gitea/turtschinba100320/info2Praktikum-NeuronalesNetzBastiBjoern

matrix.c

@@ -8,19 +8,20 @@
 Matrix createMatrix(unsigned int rows, unsigned int cols)
 {
     Matrix matrix;
-    matrix.rows = rows;
-    matrix.cols = cols;
-
-    if (rows == 0 || cols == 0) {
     matrix.buffer = NULL;
-        return matrix;
-    }
-
-    matrix.buffer = (MatrixType *)malloc(rows * cols * sizeof(MatrixType));
-    if (matrix.buffer == NULL)
-    {
     matrix.rows = 0;
     matrix.cols = 0;
+    
+
+    // Wenn die Dimensionen gültig sind, Speicher reservieren
+    if (rows > 0 && cols > 0) 
+    {
+        matrix.buffer = (MatrixType *)malloc(rows * cols * sizeof(MatrixType));
+        if (matrix.buffer != NULL)
+        {
+            matrix.rows = rows;
+            matrix.cols = cols;
+        }
     }
     return matrix;
 }

matrix.h

@@ -8,9 +8,10 @@ typedef float MatrixType;
 // TODO Matrixtyp definieren
 typedef struct 
 {
+    MatrixType *buffer;
     unsigned int rows;
     unsigned int cols;
-    MatrixType *buffer;
+    
 } Matrix; 
 
 

neuralNetworkTests.c

@@ -8,7 +8,58 @@
 
 static void prepareNeuralNetworkFile(const char *path, const NeuralNetwork nn)
 {
-    // TODO
+    // TODO : Fehlerbehandlung
+    // Öffne die Datei zum Schreiben im Binärmodus
+  FILE *file = fopen(path, "wb");
+    if (!file) return;
+
+    // Schreibe den Datei-Tag
+    const char *tag = "__info2_neural_network_file_format__";
+    fwrite(tag, 1, strlen(tag), file);
+
+    // Schreibe die Anzahl der Layer
+    if (nn.numberOfLayers == 0) {
+        fclose(file);
+        return;
+    }  
+
+    // Schreibe die Eingabe- und Ausgabegrößen des Netzwerks
+    int input  = nn.layers[0].weights.cols;
+    int output = nn.layers[0].weights.rows;
+
+    fwrite(&input,  sizeof(int), 1, file);
+    fwrite(&output, sizeof(int), 1, file);
+
+    // Schreibe die Layer-Daten
+    for (int i = 0; i < nn.numberOfLayers; i++)
+    {
+        const Layer *layer = &nn.layers[i];
+        int out = layer->weights.rows;
+        int in  = layer->weights.cols;
+
+        
+        fwrite(layer->weights.buffer, sizeof(MatrixType), out * in, file);
+
+        
+        fwrite(layer->biases.buffer, sizeof(MatrixType), out * 1, file);
+
+        if (i + 1 < nn.numberOfLayers)
+        {
+            int nextOut = nn.layers[i + 1].weights.rows;
+            fwrite(&nextOut, sizeof(int), 1, file);
+        }
+    }
+
+    fclose(file);
+
+    // Debuging-Ausgabe
+    printf("prepareNeuralNetworkFile: Datei '%s' erstellt mit %u Layer(n)\n", path, nn.numberOfLayers);
+    for (unsigned int i = 0; i < nn.numberOfLayers; i++) {
+        Layer layer = nn.layers[i];
+        printf("Layer %u: weights (%u x %u), biases (%u x %u)\n", 
+               i, layer.weights.rows, layer.weights.cols, layer.biases.rows, layer.biases.cols);
+    }
+
 }
 
 void test_loadModelReturnsCorrectNumberOfLayers(void)