done?

neuralNetworkTests.c

@@ -9,41 +9,60 @@
 static void prepareNeuralNetworkFile(const char *path, const NeuralNetwork nn)
 {
     FILE *file = fopen(path, "wb");
-    if (file == NULL) {
+    if (!file) {
         perror("Fehler beim Erstellen der Testdatei");
         exit(EXIT_FAILURE);
     }
 
-    // Dateikopf speichern
+    // File header
-    const char *fileTag = "info2_neural_network_file_format";
+    const char *fileTag = "__info2_neural_network_file_format__";
-    fwrite(fileTag, sizeof(char), strlen(fileTag), file);
+    fwrite(fileTag, strlen(fileTag), 1, file);
 
-    // Dimensionen der Eingabe und Ausgabe für den ersten Layer speichern
-    unsigned int inputDimension = nn.layers[0].weights.rows;  // Eingabedimension ist die Anzahl der Eingabeneuronen im ersten Layer
-    unsigned int outputDimension = nn.layers[0].weights.cols; // Ausgabedimension ist die Anzahl der Ausgabeneuronen im ersten Layer
-    fwrite(&inputDimension, sizeof(unsigned int), 1, file);
-    fwrite(&outputDimension, sizeof(unsigned int), 1, file);
 
-    // Alle Layer speichern
+    if (nn.numberOfLayers == 0)
-    for (unsigned int i = 0; i < nn.numberOfLayers; i++) {
+    {
-        // Layer-Dimensionen speichern
+        unsigned int zero = 0;
-        inputDimension = nn.layers[i].weights.rows;
+        fwrite(&zero, sizeof(unsigned int), 1, file);
-        outputDimension = nn.layers[i].weights.cols;
+        fclose(file);
-        
+        return;
-        fwrite(&inputDimension, sizeof(unsigned int), 1, file);
-        fwrite(&outputDimension, sizeof(unsigned int), 1, file);
-
-        // Gewichte speichern
-        fwrite(&nn.layers[i].weights.rows, sizeof(unsigned int), 1, file);
-        fwrite(&nn.layers[i].weights.cols, sizeof(unsigned int), 1, file);
-        fwrite(nn.layers[i].weights.buffer, sizeof(MatrixType), nn.layers[i].weights.rows * nn.layers[i].weights.cols, file);
-
-        // Biases speichern
-        fwrite(&nn.layers[i].biases.rows, sizeof(unsigned int), 1, file);
-        fwrite(&nn.layers[i].biases.cols, sizeof(unsigned int), 1, file);
-        fwrite(nn.layers[i].biases.buffer, sizeof(MatrixType), nn.layers[i].biases.rows * nn.layers[i].biases.cols, file);
     }
 
+    // first layer dimension
+    unsigned int in  = nn.layers[0].weights.cols;
+    unsigned int out = nn.layers[0].weights.rows;
+
+    fwrite(&in,  sizeof(unsigned int), 1, file);
+    fwrite(&out, sizeof(unsigned int), 1, file);
+
+    // do all layers
+    for (unsigned int i = 0; i < nn.numberOfLayers; i++)
+    {
+        const Layer *L = &nn.layers[i];
+
+        // Write weights matrix
+        fwrite(L->weights.buffer,
+               sizeof(MatrixType),
+               L->weights.rows * L->weights.cols,
+               file);
+
+        // Write biases matrix
+        fwrite(L->biases.buffer,
+               sizeof(MatrixType),
+               L->biases.rows * L->biases.cols,
+               file);
+
+        // After layer i, write dimension of next layer
+        if (i + 1 < nn.numberOfLayers)
+        {
+            unsigned int nextOut = nn.layers[i+1].weights.rows;
+            fwrite(&nextOut, sizeof(unsigned int), 1, file);
+        }
+    }
+
+    // --- 5. Write terminating zero ---
+    unsigned int zero = 0;
+    fwrite(&zero, sizeof(unsigned int), 1, file);
+
     fclose(file);
 }