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neuralNetwork.c

@@ -170,7 +170,7 @@ NeuralNetwork loadModel(const char *path)
 
 static Matrix imageBatchToMatrixOfImageVectors(const GrayScaleImage images[], unsigned int count)
 {
-    Matrix matrix = {NULL, 0, 0};
+    Matrix matrix = {0, 0, NULL};
 
     if(count > 0 && images != NULL)
     {

neuralNetworkTests.c

@@ -9,45 +9,51 @@
 
 static void writeWeights(Layer layer, FILE *file)
 {
-    fwrite(&layer.weights.rows, sizeof(unsigned int), 1, file);
-    fwrite(&layer.weights.cols, sizeof(unsigned int), 1, file);
-    fwrite(layer.weights.buffer, sizeof(float ), layer.weights.rows * layer.weights.cols, file);
+    unsigned int n = (unsigned int)layer.weights.rows * layer.weights.cols; //col und row müssen nicht extra eingelesen werden, da loadModel die Dimensionen selbst aus der Datei liest
+    fwrite(layer.weights.buffer, sizeof(MatrixType), n, file);
 }
 
 static void writeBiases(Layer layer, FILE *file)
 {
-    fwrite(&layer.biases.rows, sizeof(unsigned int), 1, file);
-    fwrite(&layer.biases.cols, sizeof(unsigned int), 1, file);
-    fwrite(layer.biases.buffer, sizeof(float ), layer.biases.rows * layer.biases.cols, file);
+    unsigned int n = (unsigned int)layer.biases.rows * layer.biases.cols;
+    fwrite(layer.biases.buffer, sizeof(MatrixType), n, file);
 }
 
 static void prepareNeuralNetworkFile(const char *path, const NeuralNetwork nn)
 {
-//file erstellen und zum binärschreiben öffnen
+    //file erstellen und zum Binärschreiben öffnen
     FILE *file = fopen(path, "wb");
-    if(!file)
+    if(file == NULL)
         return;
        
     //header reinschreiben
     const char *header = IDENT_TAG;
     fwrite(header, sizeof(char), strlen(header), file);
 
-    //einfachheitshalber ein layer erstellen
+    //Schließen der Datei, falls kein Layer vorhanden
+    if (nn.numberOfLayers == 0 || nn.layers == NULL)
+    {
+        fclose(file);
+        return;
+    }
 
-    fwrite(&nn.numberOfLayers, sizeof(unsigned int), 1, file);
+    //Erste Eingangsdimension: Spalten der ersten Gewichtsmatrix
+    unsigned int inputDim = (unsigned int)nn.layers[0].weights.cols;
+    fwrite(&inputDim, sizeof(unsigned int), 1, file);
+
+    //für jede Schicht: Dimension, Gewichte und Biases schreiben
     for (unsigned int i = 0; i < nn.numberOfLayers; i++)
     {
         Layer layer = nn.layers[i];
    
-        //activationType initialisieren (formt ergebnis der matritzenmultiplikation um, damit es in einem neuronalen Netzwerk sinnvoll weiterverwendet werden kann.)
-        unsigned int activationType = 1; //Aktivirungstyp id (zb 1 für ReLU)
-        fwrite(&activationType, sizeof(unsigned int), 1, file);
+        unsigned int outputDim = (unsigned int)layer.weights.rows;
+        fwrite(&outputDim, sizeof(unsigned int), 1, file);
    
-        //dimensionen festlegen(weights)  
+        //Weight-Matrixwerte schreiben
         writeWeights(layer, file);
     
 
-        //dimension festlegen(bias)
+        //Bias-Vektorwerte schreiben
         writeBiases(layer, file);