neuralNetwork.c

neuralNetworkTests.c
check

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}; //hier evtl Null auf int casten?
+    Matrix matrix = {0, 0, NULL}; //hier evtl Null auf int casten?
 
     if(count > 0 && images != NULL)
     {

neuralNetworkTests.c

@@ -8,7 +8,42 @@
 
 static void prepareNeuralNetworkFile(const char *path, const NeuralNetwork nn)
 {
-    // TODO
+    FILE *file = fopen(path, "wb");
+
+    if (file == NULL) {
+        return;
+    }
+
+    // 1. Header schreiben
+    const char *fileTag = "__info2_neural_network_file_format__";
+    fwrite(fileTag, sizeof(char), strlen(fileTag), file);
+
+    // 2. Alle Schichten schreiben
+    for (unsigned int i = 0; i < nn.numberOfLayers; i++) {
+        // NUR bei der ERSTEN Schicht: Input-Dimension schreiben
+        if (i == 0) {
+            int inputDim = nn.layers[i].weights.cols;
+            fwrite(&inputDim, sizeof(int), 1, file);
+        }
+
+        // Output-Dimension (= Anzahl Zeilen der Gewichtsmatrix)
+        int outputDim = nn.layers[i].weights.rows;
+        fwrite(&outputDim, sizeof(int), 1, file);
+
+        // Gewichtsmatrix schreiben (alle Werte)
+        int weightCount = nn.layers[i].weights.rows * nn.layers[i].weights.cols;
+        fwrite(nn.layers[i].weights.buffer, sizeof(MatrixType), weightCount, file);
+
+        // Bias-Matrix schreiben (alle Werte)
+        int biasCount = nn.layers[i].biases.rows * nn.layers[i].biases.cols;
+        fwrite(nn.layers[i].biases.buffer, sizeof(MatrixType), biasCount, file);
+    }
+
+    // 3. Terminator schreiben (outputDimension = 0 zum Stoppen)
+    int terminator = 0;
+    fwrite(&terminator, sizeof(int), 1, file);
+
+    fclose(file);
 }
 
 void test_loadModelReturnsCorrectNumberOfLayers(void)